Radars use radio waves to detect location, movement, and other properties of targets. Doppler radars can additionally measure target velocity by comparing transmitted and received signal frequencies. The Indian Meteorological Department operates a network of 40 radars for weather monitoring and forecasting, including cyclone detection radars, storm detection radars, and multi-purpose meteorological radars. Doppler weather radars produce various products like reflectivity, velocity, and derived products that provide practical weather information.
Identifying and Overcoming Noise in Data AcquisitionYokogawa1
Have you ever captured noisy data to your PC, having to then use software to "massage" the data to try and yield useful results?
Join us for this 1-hour, complimentary webcast and learn the basic sources of electrical noise in data-acquisition systems including power line, aliasing, common mode, and quantization noise. You will learn how to easily identify, isolate, and remove these noise sources to ensure measurement data is accurate and reliable.
From these webinar slides, you will learn how to easily identify and isolate these noise sources and the basic steps to remove them. These noise sources include:
Quantization noise
A/D internal noise
Power line noise
Aliasing noise
Common mode noise
Radiated noise (EMI)
Identifying and Overcoming Noise in Data AcquisitionYokogawa1
Have you ever captured noisy data to your PC, having to then use software to "massage" the data to try and yield useful results?
Join us for this 1-hour, complimentary webcast and learn the basic sources of electrical noise in data-acquisition systems including power line, aliasing, common mode, and quantization noise. You will learn how to easily identify, isolate, and remove these noise sources to ensure measurement data is accurate and reliable.
From these webinar slides, you will learn how to easily identify and isolate these noise sources and the basic steps to remove them. These noise sources include:
Quantization noise
A/D internal noise
Power line noise
Aliasing noise
Common mode noise
Radiated noise (EMI)
This presentation covers:
Different types of antennas used in satellite communication
Role of an antenna
Antenna temperature
Cassegrain feed Antenna
Parabolic antenna
Radars are very complex electronic and electromagnetic systems. Often they are
complex mechanical systems as well. Radar systems are composed of many different
subsystems, which themselves are composed of many different components. There is a great
diversity in the design of radar systems based on purpose, but the fundamental operation and
main set of subsystems is the same.
Side Lobe Level (SLL) Reduction Methods in AntennaDarshan Bhatt
Side Lobe levels are the important aspects in RADAR and navigation engineering and many other real time transmission systems. It is nothing but wastage of transmitted power in undesired direction. So, for reduction of SLL different methods are used for different types of antennas. In this presentation SLL reduction is discussed for Antenna arrays and for microstrip patch antenna arrays.
This presentation covers:
Different types of antennas used in satellite communication
Role of an antenna
Antenna temperature
Cassegrain feed Antenna
Parabolic antenna
Radars are very complex electronic and electromagnetic systems. Often they are
complex mechanical systems as well. Radar systems are composed of many different
subsystems, which themselves are composed of many different components. There is a great
diversity in the design of radar systems based on purpose, but the fundamental operation and
main set of subsystems is the same.
Side Lobe Level (SLL) Reduction Methods in AntennaDarshan Bhatt
Side Lobe levels are the important aspects in RADAR and navigation engineering and many other real time transmission systems. It is nothing but wastage of transmitted power in undesired direction. So, for reduction of SLL different methods are used for different types of antennas. In this presentation SLL reduction is discussed for Antenna arrays and for microstrip patch antenna arrays.
Ijeee 20-23-target parameter estimation for pulsed doppler radar applicationsKumar Goud
Target Parameter Estimation for Pulsed Doppler Radar Applications
Pratibha Jha1 S.Swetha2 D.Kavitha3
M.Tech Scholar (ECE), Dept of ECE Senior Assistant Professor & Associate Professor, Dept of ECE
Aurora’s Scientific Technological &
Research Academy Aurora’s Scientific Technological &
Research Academy, JNTUH Aurora’s Scientific Technological &
Research Academy, JNTUH
Bandlaguda, Hyderabad, TS, India Bandlaguda, Hyderabad, TS, India Bandlaguda, Hyderabad, TS, India
pratibhajha1001@yahoo.co.in swetha.sirisin@gmail.com kavitadevireddy@gmail.com
Abstract- Conventional monostatic single-input single-output (SISO) radar transmits an electro-magnetic (EM) wave from the transmitter. The properties of this wave are altered while reflecting from the surfaces of the targets towards the receiver. The altered properties of the wave enable estimation of unknown target parameters like range, Doppler, and attenuation. However, such systems offer limited degrees of freedom. Multiple-input and multiple-output (MIMO) radar systems use arrays of transmitting and receiving antennas like phased array radars but while a phased array transmits highly correlated signals which form a beam, MIMO antennas transmit signals from a diverse set and independence between the signals is exploited
Keywords: radar, OTA, MIMO, FHSS, DSSS, MISO
In the modern age, High-resolution radar images can be achieved by employing SAR technique. It is well
known that SAR can provide several times better image resolution than conventional radars. The exploration for efficient
image denoising methods still remains a valid challenge for researchers. Despite the difficulty of the recently proposed
methods, mostly of the algorithms have not yet attained a pleasing level of applicability; each algorithm has its
assumptions, advantages, and limitations. This paper presents a review of synthetic aperture radar. Behind a brief
introduction in our work we are especially targeting the noise called backscattered noise in SAR terminology which
causes the appearance of speckle Potential future work in the area of air flight navigation, mapping Weather Monitoring
& during natural disaster like earth quake. The SAR having the capability, to make human visibility beyond optical
vision, is also discussed.
Countering the Growing Ballistic Missile Threat
The AN/TPY-2 is a missile defense radar that can detect, track and discriminate ballistic missiles.
It operates in the X-band of the electromagnetic spectrum. This enables it to see targets more clearly and distinguish between an actual menace and non-threats, like launch debris.
Two Modes, One Steady Defense
AN/TPY-2 can operate in two modes: Forward-based mode and Terminal mode.
In Forward-based mode, the radar detects ballistic missiles after they are launched.
In Terminal mode, the radar helps guide interceptors toward a descending missile to defeat the threat. Most notably when operating in Terminal mode, AN/TPY-2 leads the Terminal High Altitude Area Defense ballistic missile defense system by guiding the THAAD missile.
Proven Performance Against Ballistic Missiles
AN/TPY-2 has a record of flawless performance against all classes of ballistic missiles.
SAR is a type of radar which works with antenna and receiver using radio waves which can create two dimension or three dimension of the objects . A synthetic-aperture radar is an imaging radar mounted on a moving platform. SAR gives high resolution data and works 24*7.
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
Connector Corner: Automate dynamic content and events by pushing a buttonDianaGray10
Here is something new! In our next Connector Corner webinar, we will demonstrate how you can use a single workflow to:
Create a campaign using Mailchimp with merge tags/fields
Send an interactive Slack channel message (using buttons)
Have the message received by managers and peers along with a test email for review
But there’s more:
In a second workflow supporting the same use case, you’ll see:
Your campaign sent to target colleagues for approval
If the “Approve” button is clicked, a Jira/Zendesk ticket is created for the marketing design team
But—if the “Reject” button is pushed, colleagues will be alerted via Slack message
Join us to learn more about this new, human-in-the-loop capability, brought to you by Integration Service connectors.
And...
Speakers:
Akshay Agnihotri, Product Manager
Charlie Greenberg, Host
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
UiPath Test Automation using UiPath Test Suite series, part 3DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 3. In this session, we will cover desktop automation along with UI automation.
Topics covered:
UI automation Introduction,
UI automation Sample
Desktop automation flow
Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
JMeter webinar - integration with InfluxDB and GrafanaRTTS
Watch this recorded webinar about real-time monitoring of application performance. See how to integrate Apache JMeter, the open-source leader in performance testing, with InfluxDB, the open-source time-series database, and Grafana, the open-source analytics and visualization application.
In this webinar, we will review the benefits of leveraging InfluxDB and Grafana when executing load tests and demonstrate how these tools are used to visualize performance metrics.
Length: 30 minutes
Session Overview
-------------------------------------------
During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana:
- What out-of-the-box solutions are available for real-time monitoring JMeter tests?
- What are the benefits of integrating InfluxDB and Grafana into the load testing stack?
- Which features are provided by Grafana?
- Demonstration of InfluxDB and Grafana using a practice web application
To view the webinar recording, go to:
https://www.rttsweb.com/jmeter-integration-webinar
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
UiPath Test Automation using UiPath Test Suite series, part 4
Frequently asken interview questions about radars and their answers
1. Frequently asked questions on radars and their answers
Q: What is a radar?
A: Radar is acronym for Radio Detection and Ranging. It uses electro-magnetic waves in microwave region to detect
location (range & direction), height (altitude), intensity (in case of weather systems) and movement of moving and
non-moving targets.
Q: What is the working principle of radars?
A: Radars are used for detection of aircrafts, ships, weather systems and a variety of other applications. Our discussion
is restricted to weather radars only. Radar transmitter transmits electro-magnetic waves through a directional
antenna in any given direction in a focused manner. A part of the transmitted energy is absorbed by the
atmosphere. Some of the energy travels further through the atmosphere and a fraction of it is scattered backward
by the targets and is received by the radar receiver. The amount of received power depends upon radar parameters
like transmitted power, radar wavelength, horizontal and vertical beam widths, scattering cross section of the target
atmospheric characteristics etc., In case of weather echoes like clouds it depends also on physical state (raindrops,
snow, hail etc.) and drop size distribution hydro meteors. The amount of return power provides information about
the intensity of weather systems and azimuth & elevation of the antenna gives the location and height of the cloud
systems. The time taken in to and fro journey of the electromagnet waves gives the range (or distance from radar)
of the targets. Modern day radars, viz., Doppler Weather Radars, employ Doppler principle to provide information
about the speed and direction of the moving targets.
Q: What is Doppler Principle?
A: When the source for signals and the observer are in relative motion, there is change in frequency (wavelength)
observed by the observer. In case the source and observer are moving closer, frequency increases and vice versa.
The principle was first discovered by Austrian physicist Christian Doppler, hence named after him as Doppler
Principle.
Q. How do Doppler Radars measure target velocity?
A: Doppler Radars compares the frequency of transmitted and received signals and compute the difference in frequency.
The (positive or negative) Change in frequency is directly proportional to the velocity of the target towards or away
from the radar. Thus target velocity is calculated from the change in frequency observed by the Doppler radars.
Q: How many different types of radar are being operated currently in IMD’s Radar network?
A IMD is currently operating a network of 40 radars. These can be classified on the basis of their use as
follows :
2. (a) Cyclone Detection Radars (CDRs) –S-band (10 cms. Wave length)
Eleven numbers of S – Band high power radars are located along east and west coasts of India and are used
primarily for detection of cyclones approaching the Indian Coast. 5 of these radars are sate of art DWRs 4 of which
were procured from M/s Gematronik, Germany and are installed at Chennai, Kolkata, Machilipatnam and
Visakhapatnam. One DWR installed at Sriharikota, Andhra Pradesh has been developed under ISRO- IMD
collaboration. During periods other than cyclone season, some of these radars are also used for detection of storms
and other severe weather phenomenon for use in local forecasting. The effective range of these radars is 400 Km. as
shown in Fig.1.
4. Ten numbers of medium power X - Band radars are located mostly near airports; they detect localized
weather phenomenon like thunderstorm, squalls etc. for aviation use. The effective range of X - Band radars is 250
Km. In addition, two more S-band radars (10 cms.) have been installed at Jaisalmer and Sriganganagar for storm
detection.
(c) Multi-Met Radar ( MMR ) – X-band (3 Cms. wavelength)
17 Numbers of multi-purpose Meteorological radars operating in the IMD’s network are X - Band wind
finding – cum – weather radars. Location of Storm Detection and Multi-Met radars is shown in Fig.2. IMD had
conceptualized the idea of integrated upper air sounding system built around X-Band wind finding cum weather
radar, along with receiver equipment for Radio sonde observations (using 401 MHz Radio Sonde transmitter).
These radars are operated for upper wind observations at 00 & 12 UTC hours. During day time and when required
these are operated in weather mode for local forecast & aviation use.
Following Table gives details of locations of existing radars along with dates of their installations and use.
(Date of radar commissioning shown in brackets against each station)
CYCLONE DETECTION RADAR STORM DETECTION
RADAR
MULTIMET RADAR
Conventional DWR
1. PARADIP
(01.05.86)
2. BHUJ
(24.01.87)
3. KOCHI
(28.09.87)
4. MUMBAI
(04.05.89)
5. GOA
(15.05.2002)
6 KARAIKAL
(21.10.89)
1. CHENNAI
(21.02.2002)
2. KOLKATA
(29.01.2003)
3.MACHILI PATNAM
(08.12.2004)
4.VISAKHAPATNAM
(26.07.2006)
5. SRIHARIKOTA
(09.04.2004)
1. MUMBAI
(24.03.86)
2. SRIGANGANAGAR
(31.03.88)
3. LUCKNOW
(14.12.89)
4. AGARTALA
(09.03.90)
5.NAGPUR
(31.05.90)
6.NEW DELHI
(09.09.92)
1. MOHANBARI
(23.04.79)
2. PATNA
(24.09.83)
3.BHOPAL
(06.10.83)
4. PATIALA
(22.11.84)
5.SRINAGAR
(28.10.85)
6.MACHILIPATNAM
(12.05.86)
7. Q: What are the meteorological products available from DWRs?
A: The base parameters available from Doppler Weather Radars are Reflectivity (Z), radial velocity (V) and spectral width
(ω). Based on standard algorithms and assumptions, various products of practical utility for issuing forecasts and
warnings are generated from these base parameters; they are published in the IMD web site for easy access. Some
of the displays and derived products below:
i. PPI - Plan Position Indicator
This product is same as available from conventional radars. A constant elevation surface data is presented as a cloud
image around the radar station. The data displayed is on the slant range depending on the elevation angle (generally
0.5 degree); thus the PPI is quite similar to a classical radar display.
ii. RHI - Range Height Indicator
This product is same as available in conventional radars. A display is generated with the range on the X-axis and the
height of the cloud targets on the Y-axis. A Cartesian grid is displayed as an overlay to facilitate reading height of
clouds. This grid is bending along the X-axis to show the effect of earth curvature correction.
iii. MAX - Maximum (reflectivity) Display
The Maximum Product uses a polar volume raw data set, converts it to a Cartesian volume, generates three partial
images and combines them to the displayed image. The height and the distance between two Cartesian layers are
user definable. The partial images are:
A top view of the highest measured (reflectivity) values in Z-direction. This image shows the highest
measured value for each vertical column, seen from the top of the Cartesian volume.
A north-south view of the highest measured values in Y –direction. This image is appended above the top
view and shows the highest measured value for each horizontal line seen from north to south.
An east-west view of the highest measured values in X-direction. This image is appended to the right of the
top view and shows the highest measured value for each horizontal line seen from east to west.
This single product provides distribution parameters measured by DWR in three dimensional spaces.
iv. CAPPI - Constant Altitude PPI
The CAPPI (Constant Altitude Plan Position Indicator) product uses a volume data set of the selected data type – Z, V,
W - as input. The CAPPI algorithm generates an image of the selected data type in a user-definable height (layer)
above ground.
v. PCAPPI - Pseudo CAPPI
8. The Pseudo CAPPI product takes a volume data set of the selected type - Z, R, V, W -as input. The Pseudo CAPPI
algorithm generates an image of the selected data type in user-selectable height above ground. The generation
scheme is nearly the same as for the standard CAPPI product. Additionally, the possible "no data" areas of the
standard CAPPI close to the Radar site and at lager ranges are filled with data of the corresponding elevation: at short
ranges the data are taken from the highest elevation until this beam crosses the defined height, and for large ranges,
where the lowest beam is higher than the defined height, the data accumulation follows the lowest beam.
vi. VCUT - Vertical Cut
The VCUT displays a vertical cut through a polar volume raw data set. The position of the vertical plane is defined by
two points A and B) chosen by user interactively on DWR product. The display shows height over distance with point A
at 0 km. The main advantage of VCUT from RHI is, that the positions of A and B can be defined interactively. A new
product image is generated, as soon as the newly defined positions for A and B are saved.
vii. ETOP - Echo Top
The echo top algorithm uses a polar volume raw data set. The display shows the uppermost height where the
measured value is within a user-defined range. Minimum and maximum height of the volume to be searched is also
user definable.
viii. EBASE - Echo Base
The echo base algorithm uses a polar volume raw data set. The display shows the lowermost height where the
measured value is within a user-defined range. The maximum height of the volume to be searched is user definable.
ETOP and EBASE displays are similar to PPI display and provide heights of cloud top and base respectively in kilometre.
ix. VAD - Velocity Azimuth Display
The VAD displays the radial velocity versus the azimuth angle for a fixed elevation and a fixed slant range. The
elevation and the displayed range (= slant range) are user selectable. The range of the velocity axis is always from -1.0
to 1.0. This range represents the measured range. The real value of the measured range is displayed in the legend.
For a uniform wind field, VAD is a true sinusoidal curve. The minima represent the direction of approaching wind
and maxima give the direction for receding wind. The speed can be calculated by multiplying amplitude of the sin
curve with maximum unambiguous velocity of the observation.
x. VVP2 - Volume Velocity Processing (2)
The VVP(2) displays the horizontal wind velocity and the wind direction in a vertical column above the radar site.
These quantities are derived from a volume raw data set with velocity data. A linear wind field model is used to derive
the additional information from the measured radial velocity data. The algorithm calculates velocity and wind
direction for a set of equidistant layers. The user can choose one of two ways to display the results:
9. a) Vertical profile diagram of speed and direction
Speed and direction are displayed in separate diagrams. The first diagram shows height over wind direction, the
second diagram shows height over wind speed.
b) Wind barbs
This version displays speed and direction in a height over time diagram. Wind barbs are used to indicate wind speed
'and wind direction. A column of wind barbs shows velocity and direction for a time step, subsequent columns show
the wind profile for subsequent VVP(2) product generations.
xi. Uniform Wind Technique
This product shows horizontal wind vectors at user defined grid points in any top projection image as overlay. The
algorithm calculates tangential component using Uniform Wind Technique. Several validation procedures are
conducted before estimating the horizontal wind. In this techniques wind field is assumed to be uniform in selected
grid boxes and tries to restore tangential component. Horizontal wind is calculated by using the formula
V = (Vr
2
+ Vt
2
)
½
The horizontal wind vectors are displayed in any top projection image as overlay.
xii. VIL - Vertical Integrated Liquid
The aim of the VIL product is to give an instantaneous estimate of the water content residing in a user-defined
atmospheric layer in the atmosphere. For this reason, the VIL product will need volume scan reflectivity data. These
reflectivity data are converted into liquid water content data using following relationship:
Z = C * M
D
Where Z – reflectivity [mm6
/m3
], M – Liquid water content and C and D are constants. The values of C and D depend
on the type hydrometeors.
For each vertical column the liquid water content is integrated within the user defined boundaries of the atmospheric
layer. The resultant vertically integrated liquid water VIL in [mm] is displayed in a PPI type image. This product is an
excellent tool to indicate the rainfall potential of a severe storm.
xiii. SRI - Surface Rainfall Intensity
The SRI generates an image of the rainfall intensity in a user selectable surface layer with constant height above
ground. A user definable topographical map is used to find the co-ordinates of this surface layer relative to the
position of the radar. This map is also used to check for regions, where the user selected surface layer is not accessible
to the radar. These parts of the image will be filled with the NO DATA value. The product provides instantaneous
values of rainfall intensity. The estimated values of reflectivity are converted to SRI by using Z=AR
b
relationship
10. (Marshall et al. (1947) where R is the rainfall intensity and constants A and b are constants. The value of A & b varies
from season to season and place to place.
xiv. PAC - Precipitation Accumulation
The PAC product is a second level product. It takes SRI products of the same type as input and accumulates the rainfall
rates a user-definable time period (look back time). Every time a new SRI product is generated, the PAC is updated by
regeneration of the product. The display shows the colour coded rainfall amount in [mm] for the defined time period.
The display looks similar to SRI product.
xv. HHW - Hail Warning
The input for the HHW product is a volume data set with reflectivity values. A Cartesian layer is searched for bins that
match the hail threshold. A pixel in the product image is set if any of the Cartesian bins above this pixel has a
reflectivity value that is higher than or equal to the hail threshold. Furthermore the layer should be above 1.4 Km from
the freezing level. The areas of probable and very probable are marked in different colours.
xvi. GUF - Gust Front Detection
The surge of gusty wind on or near the ground from the mesoscale high formed by descending cold air with down
drafts is called gust front. The spreading cold air undercuts the warm air prevalent in the atmosphere deflected
upward by ground friction and forms “precipitation roll”. As may be seen, wind direction near the ground and above
is not same. Further, the air near the ground moves more slowly due to ground friction than the air above it. This leads
to increase in spectral width. The radars also receive weak returns (echoes) from gust front due to in refractive index
gradients. The primary cause for this region of small scale fluctuation is turbulence. Doppler Weather Radar can
detect gust fronts provided it is not moving tangentially with respect to radar.
The Gust Front algorithm takes a polar or single elevation set of velocity data and searches for regions that match the
Gust Front conditions. A parabola is matched to each of these regions by a least square fit. These parabolas are used
as an overlay to the image of another product that is specified by the user.
Q: How can one get these DWR products?
A: Six products namely PPI(Z), PPI(V), Max(Z), VVP2, SRI and PAC, are uploaded on departmental website. Other
products can be obtained by individual radar stations on demand .Demand of products can also be placed with Dy.
Director General of Meteorology (Upper Air Instruments) , India Meteorological Department, Lodi Road, New Delhi –
110 003. The product will be supplied as per data supply policy of the department.
Q: How often the DWR products are updated on IMD's website?
A: At present six products are updated at 10 minutes interval.
11. Q: What are plans for modernisation of the radar network of IMD?
A: Most of the radars in the network are very old and are based on old technology. IMD has already taken up
modernisation of the network in phased manner. Four numbers of cyclone detection radars have already been
replaced with state of art Doppler weather radars which are operational at Chennai, Kolkata, Machilpatnam and
Visakhapatnam. One Doppler weather Radar indigenously developed by ISRO is also functional at Sriharikota (Andhra
Pradesh).
In the first phase of modernisation 14 DWRs will be installed at Patiala, Lucknow, Delhi, Patna, Hyderabad ,
Nagpur, Mumbai, Goa, Karaikal, Agartala, Mohanbari, Paradip, Bhuj and Kochi. In phase II and III all remaining
radars will be replaced with DWRs. DWRs also will be installed at few new locations to fill data gaps. Two C-band
Polarimetric DWRs will be procured and installed at Jaipur and Delhi.
On completion of modernisation program, IMD will have 55 DWRs in its observational network.
Q: What is frequency of operation of weather radars?
A: The weather radars operate in X, C and S band frequency. Radars operating in Ku band are used for studying cloud
physics and atmospheric research.
Q: How do radars help in predicting the movement of weather systems?
A: Radars provide instantaneous location of the weather systems which are plotted weather charts. Future location
of the system is predicted by extrapolation of the past track.
Q: What is the range of radar observations?
A: The observational range of radar is limited by the attenuation of radar waves by rain and the earth curvature effect.
Weather radar can detect systems up to a range of 600 Km but for velocity measurements, the effective range is 250
Km.
Q: Why do S-band radars are preferred at coastal stations?
A: Electromagnetic waves suffer attenuation due to absorption of energy by raindrops. Attenuation of these waves
increases with increase in frequency of radiation. In S-band frequency, the attenuation is very less. Coastal regions
are battered by cyclones which produce heavy rainfall. If radars of higher operating frequencies are installed in
coastal regions, the radar rays will be obscured by heavy precipitation and hence effective coverage range of radar
will be reduced.
Q: Why Klystron Transmitters are preferred over magnetron Transmitters in DWRs?
A: Comparative study for klystron and magnetron transmitters is tabulated in following table which indicates that
performance of klystron transmitter is better:
Feature Klystron Magnetron
12. Design
High power, high gain amplifier which allows
coherent operation, linear beam design beam dump is
not part of the signal interaction structure.
Advantages:
Full control of the transmitted phase
Higher average power capability
High power microwave oscillator. For
Doppler operation the phase of each
transmitted pulse must be sampled,
as coherent -on-receive reference.
Crossed-filed designed, beam energy
is absorbed by anode which is also
part of the microwave resonator.
Phase stability Mainly depending on inter pulse stability' of gun voltage
,typical system phase stability is 50-55 dB.
Advantages:
10-15 dB more clutter cancellation
capability;
Higher accuracy of velocity estimation
Depending on inter pulse frequency
stability, oscillation onset jitter and
phase stability of the transmitting
pulse sampling circuit. Typical system
phase stability is 38- 40 dB.
Frequency
agility
Easy to achieve
Advantages:
Complete decorrelation of weather return
signals
Reduces variance of reflectivity estimates
Smaller numbers of samples required ,increases
scanning speed
Very difficult, requires expensive
special tubes
Phase
agility(Phase
coding , Pulse to
pulse phase
shifting)
Easy
Advantages:
velocity dealiasing
Recovery of second trip Echoes
Support for future dealiasing techniques which
require controlled phase
The phase of each pulse is random
with respect to previous pulse.
Random ~ phase modulation is an
inherent feature of magnetron
transmitter
Pulse
compression
Possible
Advantages:
Suited for future upgrade of sophisticated pulse
compression techniques
Not possible
Occupied Band
width
Spectrum can be controlled by shaping the amplified
signal' ,no spurious only harmonic emissions.
Advantages:;
Cleaner spectrum
Harmonics and strong spurious
emissions. Filters might be required
Q: What are advantages Doppler weather Radars:
A: Advantages of Doppler Weather Radar:
13. One of the significant applications of radars over the last 40 years has been in real time weather
monitoring and for research in Meteorology. Most of our current knowledge about the structure of thunderstorms,
cyclones and other precipitating cloud systems comes from radar observation. Depending upon the radar being used,
it is also possible to estimate or measure the number of droplets present, their sizes and whether they are in the form
of water or ice within the cloud.
Radars using Doppler techniques (i.e. Doppler Weather Radar) not only detect and measure the power
received from a target (Reflectivity), but also measure the speed of the target towards or away from the radar (the
Radial Velocity of the target) and Spectrum Width (indicator of wind sheer and turbulence in the atmosphere)
The design of meteorological Doppler Radars has now been standardized and due to its additional capability
of wind measurement, it is now replacing old conventional radars in most countries.
A Doppler radar gives a number of derived products from the three base products viz. Reflectivity (which is a
way to measure possible rainfall amount), Velocity and Spectrum Width Products based on reflectivity data are
available from conventional radars as well. However since Doppler radar measures radial velocity of the echoes in
addition to reflectivity it is easy to eliminate ground clutter (resulting from unwanted structure like buildings, hills
etc.) and anomalous propagation echoes (arises from birds, insects or any other special atmospheric condition). Thus
the accuracy of reflectivity estimates are better (generally by one order) from Doppler radar particularly in regions
where ground clutter is a serious problem
Q: What are the limitations of Doppler Weather radars in rainfall measurements?
A: Radar observations are very effective tool for detecting tracking and monitoring their growth, decay and movement
of weather system and issuing reliable forecast and warnings for severe weather events which can cause huge loss of
life and property. These observations also provide rainfall distribution due the weather systems within effective range
around the radar. However, the use radar observations shall be used with much care as these are influenced by
propagation through the atmosphere, earth curvature, blockage of radar beam by permanent structures, bright band
occurrence, radar resolution etc. which may lead to erroneous conclusions. Some of the factors are explained
below:
(a) Propagation effect
The atmosphere around the earth is non-uniform. There is gradual decrease in refractivity with height which causes
the radar waves to bend down wards. The bending depends on gradient refractivity instead of its absolute value.
When the bending of radar waves due decrease in refractivity is equal to the earth curvature, the wave will travel
parallel to the earth’s surface. Such an atmosphere is called standard atmosphere and the bending is called normal.
The standard atmospheric conditions are not always present. Therefore, the bending depending on refractivity
gradient is some times grater or less than the normal.
When the downward banding of the radar waves is stronger than normal this condition is called supper refraction.
Under supper refraction conditions radar receives returns from ground which gives false indication of precipitation
echoes. It also causes over estimation of cloud heights.
14. In case the radar waves are not bent down ward as much as usual (normal reflection), under extreme conditions may
bent upward. This condition is called sub refraction. This decreases the radar detection range. Under such
condition radar under estimates the echo heights.
(b) Radar resolution problem
The radar beam width and thus the sampling volume increases with range. Therefore, radar resolution at long
distances is poorer. This leads to distorted echoes at long ranges non-detection of multi trip echoes, tornado
signatures. Two approaching echoes may appear as one and the receding echoes may appear to merge in one.
(c) Attenuation due to rain
The attenuation of radar waves (especially for short wavelengths 5 cm or less) by rain at close distances may obscure
precipitating echo at long ranges. The stronger echoes at long distances may appear weak. Also, the precipitation
area observed by radar may be less than actual.
(d) Partial beam filling
In case the radar beam is not completely filled by hydrometeors, the echo will be displayed as if it is from entire beam.
As such the values displayed will not be true representative of the sampling volume.
(e) Bright band
In clouds, the precipitation particles are in the form of ice/snow above the freezing level and liquid water below it.
When ice / snow particles fall through the freezing level, they start melting gradually and get coated with water but
retain their large surface area. Thus, to the radar melting snow will look like large drops. As the water coated ice
particles fall further and melt, their size decreases. Further, the reflectivity from ice is less than that from water for
particles of the same size because the dielectric constant for ice is less than the water. Therefore, the radar observes
slightly higher reflectivity below freezing level. Differential fall velocity of solid and liquid particle, aggregation and
coalescence of particles play role in increase of reflectivity in this layer.
The identification is of practical importance in rainfall estimation. Attenuation and reflectivity values from a bright
band are high. Radar estimates of precipitation are required to be corrected if the radar beam cuts the bright band.
(f) Beam blockage
If the beam is obstructed by man made or natural objects (building, trees, hills etc), the radar will not be able to probe
beyond the range of obstruction. If the beam is partially blocked, observations will not true representative of the
area. As we probe with distance, the bin volume increases with range. A small obstruction which completely blocks a
range bin very close to the radar causes no data beyond that obstruction for the rest of the range in that particular
elevation. Therefore, the data in respect of these bins is needs correction before processing it for computation of
rainfall estimates.
Q: What is the principle of working of polarimetric radar?
A: Dual-polarization weather radars transmit vertically and horizontally polarized electromagnetic waves alternately and
receive and process both type of polarized backscattered signals. The backscattering characteristics of a single
precipitation particle are described in terms of the backscattering matrix. By comparing these backscatter signals in
different ways (ratios, correlation etc.), we can obtain information on the size, shape, class and precipitation rate etc.
15. Q: What do Polarimetric Radars measure?
A: Some of the fundamental variable measured by polarimetric radars and their brief description is given below:
(a) Differential Reflectivity (ZDR): The differential reflectivity is a ratio of the reflected horizontal and vertical power
returns. Amongst other things, it is a good indicator of drop shape. In turn, the shape is a good estimate of average
drop size.
(b) Correlation Coefficient (ρHV): The correlation coefficient is a correlation between the reflected horizontal and vertical
power returns. It is a good indicator of regions where there is a mixture of precipitation types, such as rain and snow.
(c) Linear Depolarization Ratio (LDR): The linear depolarization ratio is a ratio of a vertical power return from a
horizontal pulse or a horizontal power return from a vertical pulse. It too is a good indicator of regions where a
mixture of precipitation types occurs.
(d) Specific Differential Phase (KDP): The specific differential phase is a comparison of the returned phase difference
between the horizontal and vertical pulses. This phase difference is caused by the difference in the number of wave
cycles (or wavelengths) along the propagation path for horizontal and vertically polarized waves. It should not to be
confused with the Doppler frequency shift, which is caused by the motion of the cloud and precipitation particles.
Q: How do the Polarimetric Radars help in rainfall estimates?
A: Specific Differential Phase (KDP), is related to precipitation rate (R) by power law relations of the form R= A KDP
b
Following R- KDP relations are considered to appropriate for rainfall rate estimations:
R = 44 KDP
0.822
for λ = 11 cm
R = 25.1 KDP
0.0.777
for λ = 5.45 cm
R = 19.9 KDP
0.803
for λ = 3.2 cm
Q: Why are the rainfall estimates from Polarimetric radars are more accurate?
A: Normal radars estimate rainfall intensity using Z=AR
b
relationship where Z is the Z radar measured reflectivity, R the
Rain rate A and b are constants. Values A and b depend on Drop Size Distribution which is not the same for all places
and seasons and type of rain. As the values of these constants are to be determined for different seasons, places and
types of rain. Wrong values of A and b constants lead to erroneous rainfall rates. Also, rainfall estimates are
influenced by bright band, partial beam filling, Beam blockage, errors in radar calibration, attenuation of radar beam
by rain etc. KDP is calculated from relative phase measurements and therefore immune to:
o Errors in Radar calibrations
o Partial beam blockage or partial beam filling
o Attenuation of electromagnetic waves due precipitation.
In view of above rainfall estimates obtained from Polarimetric radars are more accurate.
16. Q: What are the advantages of Polarimetric Radars?
A: Following are the main advantages of polarimetric Radars:
1. Rainfall estimates from Polarimetric radars are more accurate.
2. Polarimetric parameters observed by these radars are used for hydrometeor classification.
3. Polarimetric parameters are used for applying correction for attenuation of the radar beams due to rain etc
4. Polarimetric radars are capable of identifying and mitigating the effects of ground clutter, anomalous
propagation and non-meteorological scatterers, thereby providing more accurate weather radar data.
.
17. Subject: Frequently asked questions on radars and their answers.
Q: What is a radar?
A: Radar is acronym for Radio Detection and Ranging. It uses electro-magnetic waves in microwave region to detect
location (range & direction), height (altitude), intensity (in case of weather systems) and movement of moving and
non-moving targets.
Q: What is the working principle of radars?
A: Radars are used for detection of aircrafts, ships, weather systems and a variety of other applications. Our discussion
is restricted to weather radars only. Radar transmitter transmits electro-magnetic waves through a directional
antenna in any given direction in a focused manner. A part of the transmitted energy is absorbed by the
atmosphere. Some of the energy travels further through the atmosphere and a fraction of it is scattered backward
by the targets and is received by the radar receiver. The amount of received power depends upon radar parameters
like transmitted power, radar wavelength, horizontal and vertical beam widths, scattering cross section of the target
atmospheric characteristics etc., In case of weather echoes like clouds it depends also on physical state (raindrops,
snow, hail etc.) and drop size distribution hydro meteors. The amount of return power provides information about
the intensity of weather systems and azimuth & elevation of the antenna gives the location and height of the cloud
systems. The time taken in to and fro journey of the electromagnet waves gives the range (or distance from radar)
of the targets. Modern day radars, viz., Doppler Weather Radars, employ Doppler principle to provide information
about the speed and direction of the moving targets.
Q: What is Doppler Principle?
A: When the source for signals and the observer are in relative motion, there is change in frequency (wavelength)
observed by the observer. In case the source and observer are moving closer, frequency increases and vice versa.
The principle was first discovered by Austrian physicist Christian Doppler, hence named after him as Doppler
Principle.
Q. How do Doppler Radars measure target velocity?
A: Doppler Radars compares the frequency of transmitted and received signals and compute the difference in frequency.
The (positive or negative) Change in frequency is directly proportional to the velocity of the target towards or away
from the radar. Thus target velocity is calculated from the change in frequency observed by the Doppler radars.
Q: How many different types of radar are being operated currently in IMD’s Radar network?
A IMD is currently operating a network of 40 radars. These can be classified on the basis of their use as
follows :
18. (d) Cyclone Detection Radars (CDRs) –S-band (10 cms. Wave length)
Eleven numbers of S – Band high power radars are located along east and west coasts of India and are used
primarily for detection of cyclones approaching the Indian Coast. 5 of these radars are sate of art DWRs 4 of which
were procured from M/s Gematronik, Germany and are installed at Chennai, Kolkata, Machilipatnam and
Visakhapatnam. One DWR installed at Sriharikota, Andhra Pradesh has been developed under ISRO- IMD
collaboration. During periods other than cyclone season, some of these radars are also used for detection of storms
and other severe weather phenomenon for use in local forecasting. The effective range of these radars is 400 Km. as
shown in Fig.1.
20. Ten numbers of medium power X - Band radars are located mostly near airports; they detect localized
weather phenomenon like thunderstorm, squalls etc. for aviation use. The effective range of X - Band radars is 250
Km. In addition, two more S-band radars (10 cms.) have been installed at Jaisalmer and Sriganganagar for storm
detection.
(f) Multi-Met Radar ( MMR ) – X-band (3 Cms. wavelength)
17 Numbers of multi-purpose Meteorological radars operating in the IMD’s network are X - Band wind
finding – cum – weather radars. Location of Storm Detection and Multi-Met radars is shown in Fig.2. IMD had
conceptualized the idea of integrated upper air sounding system built around X-Band wind finding cum weather
radar, along with receiver equipment for Radio sonde observations (using 401 MHz Radio Sonde transmitter).
These radars are operated for upper wind observations at 00 & 12 UTC hours. During day time and when required
these are operated in weather mode for local forecast & aviation use.
Following Table gives details of locations of existing radars along with dates of their installations and use.
(Date of radar commissioning shown in brackets against each station)
CYCLONE DETECTION RADAR STORM DETECTION
RADAR
MULTIMET RADAR
Conventional DWR
1. PARADIP
(01.05.86)
2. BHUJ
(24.01.87)
3. KOCHI
(28.09.87)
4. MUMBAI
(04.05.89)
5. GOA
(15.05.2002)
6 KARAIKAL
(21.10.89)
1. CHENNAI
(21.02.2002)
2. KOLKATA
(29.01.2003)
3.MACHILI PATNAM
(08.12.2004)
4.VISAKHAPATNAM
(26.07.2006)
5. SRIHARIKOTA
(09.04.2004)
1. MUMBAI
(24.03.86)
2. SRIGANGANAGAR
(31.03.88)
3. LUCKNOW
(14.12.89)
4. AGARTALA
(09.03.90)
5.NAGPUR
(31.05.90)
6.NEW DELHI
(09.09.92)
1. MOHANBARI
(23.04.79)
2. PATNA
(24.09.83)
3.BHOPAL
(06.10.83)
4. PATIALA
(22.11.84)
5.SRINAGAR
(28.10.85)
6.MACHILIPATNAM
(12.05.86)
23. Q: What are the meteorological products available from DWRs?
A: The base parameters available from Doppler Weather Radars are Reflectivity (Z), radial velocity (V) and spectral width
(ω). Based on standard algorithms and assumptions, various products of practical utility for issuing forecasts and
warnings are generated from these base parameters; they are published in the IMD web site for easy access. Some
of the displays and derived products below:
v. PPI - Plan Position Indicator
This product is same as available from conventional radars. A constant elevation surface data is presented as a cloud
image around the radar station. The data displayed is on the slant range depending on the elevation angle (generally
0.5 degree); thus the PPI is quite similar to a classical radar display.
vi. RHI - Range Height Indicator
This product is same as available in conventional radars. A display is generated with the range on the X-axis and the
height of the cloud targets on the Y-axis. A Cartesian grid is displayed as an overlay to facilitate reading height of
clouds. This grid is bending along the X-axis to show the effect of earth curvature correction.
vii. MAX - Maximum (reflectivity) Display
The Maximum Product uses a polar volume raw data set, converts it to a Cartesian volume, generates three partial
images and combines them to the displayed image. The height and the distance between two Cartesian layers are
user definable. The partial images are:
A top view of the highest measured (reflectivity) values in Z-direction. This image shows the highest
measured value for each vertical column, seen from the top of the Cartesian volume.
A north-south view of the highest measured values in Y –direction. This image is appended above the top
view and shows the highest measured value for each horizontal line seen from north to south.
An east-west view of the highest measured values in X-direction. This image is appended to the right of the
top view and shows the highest measured value for each horizontal line seen from east to west.
This single product provides distribution parameters measured by DWR in three dimensional spaces.
viii. CAPPI - Constant Altitude PPI
The CAPPI (Constant Altitude Plan Position Indicator) product uses a volume data set of the selected data type – Z, V,
W - as input. The CAPPI algorithm generates an image of the selected data type in a user-definable height (layer)
above ground.
xi. PCAPPI - Pseudo CAPPI
24. The Pseudo CAPPI product takes a volume data set of the selected type - Z, R, V, W -as input. The Pseudo CAPPI
algorithm generates an image of the selected data type in user-selectable height above ground. The generation
scheme is nearly the same as for the standard CAPPI product. Additionally, the possible "no data" areas of the
standard CAPPI close to the Radar site and at lager ranges are filled with data of the corresponding elevation: at short
ranges the data are taken from the highest elevation until this beam crosses the defined height, and for large ranges,
where the lowest beam is higher than the defined height, the data accumulation follows the lowest beam.
xii. VCUT - Vertical Cut
The VCUT displays a vertical cut through a polar volume raw data set. The position of the vertical plane is defined by
two points A and B) chosen by user interactively on DWR product. The display shows height over distance with point A
at 0 km. The main advantage of VCUT from RHI is, that the positions of A and B can be defined interactively. A new
product image is generated, as soon as the newly defined positions for A and B are saved.
xiii. ETOP - Echo Top
The echo top algorithm uses a polar volume raw data set. The display shows the uppermost height where the
measured value is within a user-defined range. Minimum and maximum height of the volume to be searched is also
user definable.
xiv. EBASE - Echo Base
The echo base algorithm uses a polar volume raw data set. The display shows the lowermost height where the
measured value is within a user-defined range. The maximum height of the volume to be searched is user definable.
ETOP and EBASE displays are similar to PPI display and provide heights of cloud top and base respectively in kilometre.
xv. VAD - Velocity Azimuth Display
The VAD displays the radial velocity versus the azimuth angle for a fixed elevation and a fixed slant range. The
elevation and the displayed range (= slant range) are user selectable. The range of the velocity axis is always from -1.0
to 1.0. This range represents the measured range. The real value of the measured range is displayed in the legend.
For a uniform wind field, VAD is a true sinusoidal curve. The minima represent the direction of approaching wind
and maxima give the direction for receding wind. The speed can be calculated by multiplying amplitude of the sin
curve with maximum unambiguous velocity of the observation.
xvi. VVP2 - Volume Velocity Processing (2)
The VVP(2) displays the horizontal wind velocity and the wind direction in a vertical column above the radar site.
These quantities are derived from a volume raw data set with velocity data. A linear wind field model is used to derive
the additional information from the measured radial velocity data. The algorithm calculates velocity and wind
direction for a set of equidistant layers. The user can choose one of two ways to display the results:
25. c) Vertical profile diagram of speed and direction
Speed and direction are displayed in separate diagrams. The first diagram shows height over wind direction, the
second diagram shows height over wind speed.
d) Wind barbs
This version displays speed and direction in a height over time diagram. Wind barbs are used to indicate wind speed
'and wind direction. A column of wind barbs shows velocity and direction for a time step, subsequent columns show
the wind profile for subsequent VVP(2) product generations.
xi. Uniform Wind Technique
This product shows horizontal wind vectors at user defined grid points in any top projection image as overlay. The
algorithm calculates tangential component using Uniform Wind Technique. Several validation procedures are
conducted before estimating the horizontal wind. In this techniques wind field is assumed to be uniform in selected
grid boxes and tries to restore tangential component. Horizontal wind is calculated by using the formula
V = (Vr
2
+ Vt
2
)
½
The horizontal wind vectors are displayed in any top projection image as overlay.
xii. VIL - Vertical Integrated Liquid
The aim of the VIL product is to give an instantaneous estimate of the water content residing in a user-defined
atmospheric layer in the atmosphere. For this reason, the VIL product will need volume scan reflectivity data. These
reflectivity data are converted into liquid water content data using following relationship:
Z = C * M
D
Where Z – reflectivity [mm6
/m3
], M – Liquid water content and C and D are constants. The values of C and D depend
on the type hydrometeors.
For each vertical column the liquid water content is integrated within the user defined boundaries of the atmospheric
layer. The resultant vertically integrated liquid water VIL in [mm] is displayed in a PPI type image. This product is an
excellent tool to indicate the rainfall potential of a severe storm.
xvii. SRI - Surface Rainfall Intensity
The SRI generates an image of the rainfall intensity in a user selectable surface layer with constant height above
ground. A user definable topographical map is used to find the co-ordinates of this surface layer relative to the
position of the radar. This map is also used to check for regions, where the user selected surface layer is not accessible
to the radar. These parts of the image will be filled with the NO DATA value. The product provides instantaneous
values of rainfall intensity. The estimated values of reflectivity are converted to SRI by using Z=AR
b
relationship
26. (Marshall et al. (1947) where R is the rainfall intensity and constants A and b are constants. The value of A & b varies
from season to season and place to place.
xviii. PAC - Precipitation Accumulation
The PAC product is a second level product. It takes SRI products of the same type as input and accumulates the rainfall
rates a user-definable time period (look back time). Every time a new SRI product is generated, the PAC is updated by
regeneration of the product. The display shows the colour coded rainfall amount in [mm] for the defined time period.
The display looks similar to SRI product.
xix. HHW - Hail Warning
The input for the HHW product is a volume data set with reflectivity values. A Cartesian layer is searched for bins that
match the hail threshold. A pixel in the product image is set if any of the Cartesian bins above this pixel has a
reflectivity value that is higher than or equal to the hail threshold. Furthermore the layer should be above 1.4 Km from
the freezing level. The areas of probable and very probable are marked in different colours.
xx. GUF - Gust Front Detection
The surge of gusty wind on or near the ground from the mesoscale high formed by descending cold air with down
drafts is called gust front. The spreading cold air undercuts the warm air prevalent in the atmosphere deflected
upward by ground friction and forms “precipitation roll”. As may be seen, wind direction near the ground and above
is not same. Further, the air near the ground moves more slowly due to ground friction than the air above it. This leads
to increase in spectral width. The radars also receive weak returns (echoes) from gust front due to in refractive index
gradients. The primary cause for this region of small scale fluctuation is turbulence. Doppler Weather Radar can
detect gust fronts provided it is not moving tangentially with respect to radar.
The Gust Front algorithm takes a polar or single elevation set of velocity data and searches for regions that match the
Gust Front conditions. A parabola is matched to each of these regions by a least square fit. These parabolas are used
as an overlay to the image of another product that is specified by the user.
Q: How can one get these DWR products?
A: Six products namely PPI(Z), PPI(V), Max(Z), VVP2, SRI and PAC, are uploaded on departmental website. Other
products can be obtained by individual radar stations on demand .Demand of products can also be placed with Dy.
Director General of Meteorology (Upper Air Instruments) , India Meteorological Department, Lodi Road, New Delhi –
110 003. The product will be supplied as per data supply policy of the department.
Q: How often the DWR products are updated on IMD's website?
A: At present six products are updated at 10 minutes interval.
27. Q: What are plans for modernisation of the radar network of IMD?
A: Most of the radars in the network are very old and are based on old technology. IMD has already taken up
modernisation of the network in phased manner. Four numbers of cyclone detection radars have already been
replaced with state of art Doppler weather radars which are operational at Chennai, Kolkata, Machilpatnam and
Visakhapatnam. One Doppler weather Radar indigenously developed by ISRO is also functional at Sriharikota (Andhra
Pradesh).
In the first phase of modernisation 14 DWRs will be installed at Patiala, Lucknow, Delhi, Patna, Hyderabad ,
Nagpur, Mumbai, Goa, Karaikal, Agartala, Mohanbari, Paradip, Bhuj and Kochi. In phase II and III all remaining
radars will be replaced with DWRs. DWRs also will be installed at few new locations to fill data gaps. Two C-band
Polarimetric DWRs will be procured and installed at Jaipur and Delhi.
On completion of modernisation program, IMD will have 55 DWRs in its observational network.
Q: What is frequency of operation of weather radars?
A: The weather radars operate in X, C and S band frequency. Radars operating in Ku band are used for studying cloud
physics and atmospheric research.
Q: How do radars help in predicting the movement of weather systems?
A: Radars provide instantaneous location of the weather systems which are plotted weather charts. Future location
of the system is predicted by extrapolation of the past track.
Q: What is the range of radar observations?
A: The observational range of radar is limited by the attenuation of radar waves by rain and the earth curvature effect.
Weather radar can detect systems up to a range of 600 Km but for velocity measurements, the effective range is 250
Km.
Q: Why do S-band radars are preferred at coastal stations?
A: Electromagnetic waves suffer attenuation due to absorption of energy by raindrops. Attenuation of these waves
increases with increase in frequency of radiation. In S-band frequency, the attenuation is very less. Coastal regions
are battered by cyclones which produce heavy rainfall. If radars of higher operating frequencies are installed in
coastal regions, the radar rays will be obscured by heavy precipitation and hence effective coverage range of radar
will be reduced.
Q: Why Klystron Transmitters are preferred over magnetron Transmitters in DWRs?
A: Comparative study for klystron and magnetron transmitters is tabulated in following table which indicates that
performance of klystron transmitter is better:
Feature Klystron Magnetron
28. Design
High power, high gain amplifier which allows
coherent operation, linear beam design beam dump is
not part of the signal interaction structure.
Advantages:
Full control of the transmitted phase
Higher average power capability
High power microwave oscillator. For
Doppler operation the phase of each
transmitted pulse must be sampled,
as coherent -on-receive reference.
Crossed-filed designed, beam energy
is absorbed by anode which is also
part of the microwave resonator.
Phase stability Mainly depending on inter pulse stability' of gun voltage
,typical system phase stability is 50-55 dB.
Advantages:
10-15 dB more clutter cancellation
capability;
Higher accuracy of velocity estimation
Depending on inter pulse frequency
stability, oscillation onset jitter and
phase stability of the transmitting
pulse sampling circuit. Typical system
phase stability is 38- 40 dB.
Frequency
agility
Easy to achieve
Advantages:
Complete decorrelation of weather return
signals
Reduces variance of reflectivity estimates
Smaller numbers of samples required ,increases
scanning speed
Very difficult, requires expensive
special tubes
Phase
agility(Phase
coding , Pulse to
pulse phase
shifting)
Easy
Advantages:
velocity dealiasing
Recovery of second trip Echoes
Support for future dealiasing techniques which
require controlled phase
The phase of each pulse is random
with respect to previous pulse.
Random ~ phase modulation is an
inherent feature of magnetron
transmitter
Pulse
compression
Possible
Advantages:
Suited for future upgrade of sophisticated pulse
compression techniques
Not possible
Occupied Band
width
Spectrum can be controlled by shaping the amplified
signal' ,no spurious only harmonic emissions.
Advantages:;
Cleaner spectrum
Harmonics and strong spurious
emissions. Filters might be required
Q: What are advantages Doppler weather Radars:
A: Advantages of Doppler Weather Radar:
29. One of the significant applications of radars over the last 40 years has been in real time weather
monitoring and for research in Meteorology. Most of our current knowledge about the structure of thunderstorms,
cyclones and other precipitating cloud systems comes from radar observation. Depending upon the radar being used,
it is also possible to estimate or measure the number of droplets present, their sizes and whether they are in the form
of water or ice within the cloud.
Radars using Doppler techniques (i.e. Doppler Weather Radar) not only detect and measure the power
received from a target (Reflectivity), but also measure the speed of the target towards or away from the radar (the
Radial Velocity of the target) and Spectrum Width (indicator of wind sheer and turbulence in the atmosphere)
The design of meteorological Doppler Radars has now been standardized and due to its additional capability
of wind measurement, it is now replacing old conventional radars in most countries.
A Doppler radar gives a number of derived products from the three base products viz. Reflectivity (which is a
way to measure possible rainfall amount), Velocity and Spectrum Width Products based on reflectivity data are
available from conventional radars as well. However since Doppler radar measures radial velocity of the echoes in
addition to reflectivity it is easy to eliminate ground clutter (resulting from unwanted structure like buildings, hills
etc.) and anomalous propagation echoes (arises from birds, insects or any other special atmospheric condition). Thus
the accuracy of reflectivity estimates are better (generally by one order) from Doppler radar particularly in regions
where ground clutter is a serious problem
Q: What are the limitations of Doppler Weather radars in rainfall measurements?
A: Radar observations are very effective tool for detecting tracking and monitoring their growth, decay and movement
of weather system and issuing reliable forecast and warnings for severe weather events which can cause huge loss of
life and property. These observations also provide rainfall distribution due the weather systems within effective range
around the radar. However, the use radar observations shall be used with much care as these are influenced by
propagation through the atmosphere, earth curvature, blockage of radar beam by permanent structures, bright band
occurrence, radar resolution etc. which may lead to erroneous conclusions. Some of the factors are explained
below:
(b) Propagation effect
The atmosphere around the earth is non-uniform. There is gradual decrease in refractivity with height which causes
the radar waves to bend down wards. The bending depends on gradient refractivity instead of its absolute value.
When the bending of radar waves due decrease in refractivity is equal to the earth curvature, the wave will travel
parallel to the earth’s surface. Such an atmosphere is called standard atmosphere and the bending is called normal.
The standard atmospheric conditions are not always present. Therefore, the bending depending on refractivity
gradient is some times grater or less than the normal.
When the downward banding of the radar waves is stronger than normal this condition is called supper refraction.
Under supper refraction conditions radar receives returns from ground which gives false indication of precipitation
echoes. It also causes over estimation of cloud heights.
30. In case the radar waves are not bent down ward as much as usual (normal reflection), under extreme conditions may
bent upward. This condition is called sub refraction. This decreases the radar detection range. Under such
condition radar under estimates the echo heights.
(c) Radar resolution problem
The radar beam width and thus the sampling volume increases with range. Therefore, radar resolution at long
distances is poorer. This leads to distorted echoes at long ranges non-detection of multi trip echoes, tornado
signatures. Two approaching echoes may appear as one and the receding echoes may appear to merge in one.
(c) Attenuation due to rain
The attenuation of radar waves (especially for short wavelengths 5 cm or less) by rain at close distances may obscure
precipitating echo at long ranges. The stronger echoes at long distances may appear weak. Also, the precipitation
area observed by radar may be less than actual.
(d) Partial beam filling
In case the radar beam is not completely filled by hydrometeors, the echo will be displayed as if it is from entire beam.
As such the values displayed will not be true representative of the sampling volume.
(e) Bright band
In clouds, the precipitation particles are in the form of ice/snow above the freezing level and liquid water below it.
When ice / snow particles fall through the freezing level, they start melting gradually and get coated with water but
retain their large surface area. Thus, to the radar melting snow will look like large drops. As the water coated ice
particles fall further and melt, their size decreases. Further, the reflectivity from ice is less than that from water for
particles of the same size because the dielectric constant for ice is less than the water. Therefore, the radar observes
slightly higher reflectivity below freezing level. Differential fall velocity of solid and liquid particle, aggregation and
coalescence of particles play role in increase of reflectivity in this layer.
The identification is of practical importance in rainfall estimation. Attenuation and reflectivity values from a bright
band are high. Radar estimates of precipitation are required to be corrected if the radar beam cuts the bright band.
(f) Beam blockage
If the beam is obstructed by man made or natural objects (building, trees, hills etc), the radar will not be able to probe
beyond the range of obstruction. If the beam is partially blocked, observations will not true representative of the
area. As we probe with distance, the bin volume increases with range. A small obstruction which completely blocks a
range bin very close to the radar causes no data beyond that obstruction for the rest of the range in that particular
elevation. Therefore, the data in respect of these bins is needs correction before processing it for computation of
rainfall estimates.
Q: What is the principle of working of polarimetric radar?
A: Dual-polarization weather radars transmit vertically and horizontally polarized electromagnetic waves alternately and
receive and process both type of polarized backscattered signals. The backscattering characteristics of a single
precipitation particle are described in terms of the backscattering matrix. By comparing these backscatter signals in
different ways (ratios, correlation etc.), we can obtain information on the size, shape, class and precipitation rate etc.
31. Q: What do Polarimetric Radars measure?
A: Some of the fundamental variable measured by polarimetric radars and their brief description is given below:
(e) Differential Reflectivity (ZDR): The differential reflectivity is a ratio of the reflected horizontal and vertical power
returns. Amongst other things, it is a good indicator of drop shape. In turn, the shape is a good estimate of average
drop size.
(f) Correlation Coefficient (ρHV): The correlation coefficient is a correlation between the reflected horizontal and vertical
power returns. It is a good indicator of regions where there is a mixture of precipitation types, such as rain and snow.
(g) Linear Depolarization Ratio (LDR): The linear depolarization ratio is a ratio of a vertical power return from a
horizontal pulse or a horizontal power return from a vertical pulse. It too is a good indicator of regions where a
mixture of precipitation types occurs.
(h) Specific Differential Phase (KDP): The specific differential phase is a comparison of the returned phase difference
between the horizontal and vertical pulses. This phase difference is caused by the difference in the number of wave
cycles (or wavelengths) along the propagation path for horizontal and vertically polarized waves. It should not to be
confused with the Doppler frequency shift, which is caused by the motion of the cloud and precipitation particles.
Q: How do the Polarimetric Radars help in rainfall estimates?
A: Specific Differential Phase (KDP), is related to precipitation rate (R) by power law relations of the form R= A KDP
b
Following R- KDP relations are considered to appropriate for rainfall rate estimations:
R = 44 KDP
0.822
for λ = 11 cm
R = 25.1 KDP
0.0.777
for λ = 5.45 cm
R = 19.9 KDP
0.803
for λ = 3.2 cm
Q: Why are the rainfall estimates from Polarimetric radars are more accurate?
A: Normal radars estimate rainfall intensity using Z=AR
b
relationship where Z is the Z radar measured reflectivity, R the
Rain rate A and b are constants. Values A and b depend on Drop Size Distribution which is not the same for all places
and seasons and type of rain. As the values of these constants are to be determined for different seasons, places and
types of rain. Wrong values of A and b constants lead to erroneous rainfall rates. Also, rainfall estimates are
influenced by bright band, partial beam filling, Beam blockage, errors in radar calibration, attenuation of radar beam
by rain etc. KDP is calculated from relative phase measurements and therefore immune to:
o Errors in Radar calibrations
o Partial beam blockage or partial beam filling
o Attenuation of electromagnetic waves due precipitation.
In view of above rainfall estimates obtained from Polarimetric radars are more accurate.
32. Q: What are the advantages of Polarimetric Radars?
A: Following are the main advantages of polarimetric Radars:
5. Rainfall estimates from Polarimetric radars are more accurate.
6. Polarimetric parameters observed by these radars are used for hydrometeor classification.
7. Polarimetric parameters are used for applying correction for attenuation of the radar beams due to rain etc
8. Polarimetric radars are capable of identifying and mitigating the effects of ground clutter, anomalous
propagation and non-meteorological scatterers, thereby providing more accurate and clear weather radar
data.
.
33. Radar is acronym for Radio Detection and Ranging. It uses electro-magnetic waves in microwave
region to detect location (range & direction), height (altitude), intensity (in case of weather systems) and
movement of moving and non-moving targets.
IMD is currently operating a network of 40 radars. These can be classified on the basis of their use as follows :
Cyclone Detection Radars (CDRs) –S-band (10 cms. Wave length) : Eleven numbers of S – Band high power radars
are located along east and west coasts of India and are used primarily for detection of cyclones approaching the
Indian Coast. 5 of these radars are sate of art DWRs 4 of which were procured from M/s Gematronik, Germany
and are installed at Chennai, Kolkata, Machilipatnam and Visakhapatnam. One DWR installed at Sriharikota,
Andhra Pradesh has been developed under ISRO- IMD collaboration. During periods other than cyclone season,
some of these radars are also used for detection of storms and other severe weather phenomenon for use in local
forecasting. The effective range of these radars is 400 Km.
Storm Detection Radar (SDR) – X-band (3 Cms. Wave length): Ten numbers of medium power X - Band radars are
located mostly near airports; they detect localized weather phenomenon like thunderstorm, squalls etc. for
aviation use. The effective range of X - Band radars is 250 Km. In addition, two more S-band radars (10 cms.)
have been installed at Jaisalmer and Sriganganagar for storm detection.
Multi-Met Radar ( MMR ) – X-band (3 Cms. wavelength): 17 Numbers of multi-purpose Meteorological radars
operating in the IMD’s network are X - Band wind finding – cum – weather radars. Location of Storm Detection
and Multi-Met radars is shown in Fig.2. IMD had conceptualized the idea of integrated upper air sounding
system built around X-Band wind finding cum weather radar, along with receiver equipment for Radio sonde
observations (using 401 MHz Radio Sonde transmitter). These radars are operated for upper wind observations
at 00 & 12 UTC hours. During day time and when required these are operated in weather mode for local
forecast & aviation use.
In the first phase of modernisation 14 DWRs will be installed at Patiala, Lucknow, Delhi, Patna, Hyderabad ,
Nagpur, Mumbai, Goa, Karaikal, Agartala, Mohanbari, Paradip, Bhuj and Kochi. In phase II and III all remaining
radars will be replaced with DWRs. DWRs also will be installed at few new locations to fill data gaps. Two C-band
Polarimetric DWRs will be procured and installed at Jaipur and Delhi.
On completion of modernisation program, IMD will have 55 DWRs in its observational network.
34.
35.
36. Following Table gives details of locations of existing radars along with dates of their installations and use.
(Date of radar commissioning shown in brackets against each station)
CYCLONE DETECTION RADAR STORM DETECTION
RADAR
MULTIMET RADAR
Conventional DWR
1. PARADIP
(01.05.86)
2. BHUJ
(24.01.87)
3. KOCHI
(28.09.87)
4. MUMBAI
(04.05.89)
5. GOA
(15.05.2002)
6 KARAIKAL
(21.10.89)
1. CHENNAI
(21.02.2002)
2. KOLKATA
(29.01.2003)
3.MACHILI PATNAM
(08.12.2004)
4.VISAKHAPATNAM
(26.07.2006)
5. SRIHARIKOTA
(09.04.2004)
1. MUMBAI
(24.03.86)
2. SRIGANGANAGAR
(31.03.88)
3. LUCKNOW
(14.12.89)
4. AGARTALA
(09.03.90)
5.NAGPUR
(31.05.90)
6.NEW DELHI
(09.09.92)
7. JAISALMER
(19.04.93)
8. CHENNAI
(29.01.96)
1. MOHANBARI
(23.04.79)
2. PATNA
(24.09.83)
3.BHOPAL
(06.10.83)
4. PATIALA
(22.11.84)
5.SRINAGAR
(28.10.85)
6.MACHILIPATNAM
(12.05.86)
7. KARAIKAL
(06.11.86)
8.HYDERABAD
(14.10.90)
39. The base parameters available from Doppler Weather Radars are Reflectivity (Z), radial velocity (V) and spectral
width (ω). Based on standard algorithms and assumptions, various products of practical utility for issuing forecasts
and warnings are generated from these base parameters; they are published in the IMD web site for easy access.
Some of the displays and derived products below:
PPI - Plan Position Indicator
This product is same as available from conventional radars. A constant elevation surface data is presented as a cloud
image around the radar station. The data displayed is on the slant range depending on the elevation angle (generally
0.5 degree); thus the PPI is quite similar to a classical radar display.
RHI - Range Height Indicator
This product is same as available in conventional radars. A display is generated with the range on the X-axis and the
height of the cloud targets on the Y-axis. A Cartesian grid is displayed as an overlay to facilitate reading height of
clouds. This grid is bending along the X-axis to show the effect of earth curvature correction.
MAX - Maximum (reflectivity) Display
The Maximum Product uses a polar volume raw data set, converts it to a Cartesian volume, generates three partial
images and combines them to the displayed image. The height and the distance between two Cartesian layers are
user definable. The partial images are:
A top view of the highest measured (reflectivity) values in Z-direction. This image shows the highest
measured value for each vertical column, seen from the top of the Cartesian volume.
A north-south view of the highest measured values in Y –direction. This image is appended above the top
view and shows the highest measured value for each horizontal line seen from north to south.
An east-west view of the highest measured values in X-direction. This image is appended to the right of the
top view and shows the highest measured value for each horizontal line seen from east to west.
This single product provides distribution parameters measured by DWR in three dimensional spaces.
CAPPI - Constant Altitude PPI
The CAPPI (Constant Altitude Plan Position Indicator) product uses a volume data set of the selected data type – Z, V,
W - as input. The CAPPI algorithm generates an image of the selected data type in a user-definable height (layer)
above ground.
PCAPPI - Pseudo CAPPI
40. The Pseudo CAPPI product takes a volume data set of the selected type - Z, R, V, W -as input. The Pseudo CAPPI
algorithm generates an image of the selected data type in user-selectable height above ground. The generation
scheme is nearly the same as for the standard CAPPI product. Additionally, the possible "no data" areas of the
standard CAPPI close to the Radar site and at lager ranges are filled with data of the corresponding elevation: at short
ranges the data are taken from the highest elevation until this beam crosses the defined height, and for large ranges,
where the lowest beam is higher than the defined height, the data accumulation follows the lowest beam.
VCUT - Vertical Cut
The VCUT displays a vertical cut through a polar volume raw data set. The position of the vertical plane is defined by
two points A and B) chosen by user interactively on DWR product. The display shows height over distance with point A
at 0 km. The main advantage of VCUT from RHI is, that the positions of A and B can be defined interactively. A new
product image is generated, as soon as the newly defined positions for A and B are saved.
ETOP - Echo Top
The echo top algorithm uses a polar volume raw data set. The display shows the uppermost height where the
measured value is within a user-defined range. Minimum and maximum height of the volume to be searched is also
user definable.
EBASE - Echo Base
The echo base algorithm uses a polar volume raw data set. The display shows the lowermost height where the
measured value is within a user-defined range. The maximum height of the volume to be searched is user definable.
ETOP and EBASE displays are similar to PPI display and provide heights of cloud top and base respectively in kilometre.
VAD - Velocity Azimuth Display
The VAD displays the radial velocity versus the azimuth angle for a fixed elevation and a fixed slant range. The
elevation and the displayed range (= slant range) are user selectable. The range of the velocity axis is always from -1.0
to 1.0. This range represents the measured range. The real value of the measured range is displayed in the legend.
For a uniform wind field, VAD is a true sinusoidal curve. The minima represent the direction of approaching wind
and maxima give the direction for receding wind. The speed can be calculated by multiplying amplitude of the sin
curve with maximum unambiguous velocity of the observation.
VVP2 - Volume Velocity Processing (2)
The VVP(2) displays the horizontal wind velocity and the wind direction in a vertical column above the radar site.
These quantities are derived from a volume raw data set with velocity data. A linear wind field model is used to derive
the additional information from the measured radial velocity data. The algorithm calculates velocity and wind
direction for a set of equidistant layers. The user can choose one of two ways to display the results:
41. e) Vertical profile diagram of speed and direction
Speed and direction are displayed in separate diagrams. The first diagram shows height over wind direction, the
second diagram shows height over wind speed.
f) Wind barbs
This version displays speed and direction in a height over time diagram. Wind barbs are used to indicate wind speed
'and wind direction. A column of wind barbs shows velocity and direction for a time step, subsequent columns show
the wind profile for subsequent VVP(2) product generations.
Uniform Wind Technique
This product shows horizontal wind vectors at user defined grid points in any top projection image as overlay. The
algorithm calculates tangential component using Uniform Wind Technique. Several validation procedures are
conducted before estimating the horizontal wind. In this techniques wind field is assumed to be uniform in selected
grid boxes and tries to restore tangential component. Horizontal wind is calculated by using the formula
V = (Vr
2
+ Vt
2
)
½
The horizontal wind vectors are displayed in any top projection image as overlay.
VIL - Vertical Integrated Liquid
The aim of the VIL product is to give an instantaneous estimate of the water content residing in a user-defined
atmospheric layer in the atmosphere. For this reason, the VIL product will need volume scan reflectivity data. These
reflectivity data are converted into liquid water content data using following relationship:
Z = C * M
D
Where Z – reflectivity [mm6
/m3
], M – Liquid water content and C and D are constants. The values of C and D depend
on the type hydrometeors.
For each vertical column the liquid water content is integrated within the user defined boundaries of the atmospheric
layer. The resultant vertically integrated liquid water VIL in [mm] is displayed in a PPI type image. This product is an
excellent tool to indicate the rainfall potential of a severe storm.
SRI - Surface Rainfall Intensity
The SRI generates an image of the rainfall intensity in a user selectable surface layer with constant height above
ground. A user definable topographical map is used to find the co-ordinates of this surface layer relative to the
position of the radar. This map is also used to check for regions, where the user selected surface layer is not accessible
to the radar. These parts of the image will be filled with the NO DATA value. The product provides instantaneous
values of rainfall intensity. The estimated values of reflectivity are converted to SRI by using Z=AR
b
relationship
42. (Marshall et al. (1947) where R is the rainfall intensity and constants A and b are constants. The value of A & b varies
from season to season and place to place.
PAC - Precipitation Accumulation
The PAC product is a second level product. It takes SRI products of the same type as input and accumulates the rainfall
rates a user-definable time period (look back time). Every time a new SRI product is generated, the PAC is updated by
regeneration of the product. The display shows the colour coded rainfall amount in [mm] for the defined time period.
The display looks similar to SRI product.
HHW - Hail Warning
The input for the HHW product is a volume data set with reflectivity values. A Cartesian layer is searched for bins that
match the hail threshold. A pixel in the product image is set if any of the Cartesian bins above this pixel has a
reflectivity value that is higher than or equal to the hail threshold. Furthermore the layer should be above 1.4 Km from
the freezing level. The areas of probable and very probable are marked in different colours.
GUF - Gust Front Detection
The surge of gusty wind on or near the ground from the mesoscale high formed by descending cold air with down
drafts is called gust front. The spreading cold air undercuts the warm air prevalent in the atmosphere deflected
upward by ground friction and forms “precipitation roll”. As may be seen, wind direction near the ground and above
is not same. Further, the air near the ground moves more slowly due to ground friction than the air above it. This leads
to increase in spectral width. The radars also receive weak returns (echoes) from gust front due to in refractive index
gradients. The primary cause for this region of small scale fluctuation is turbulence. Doppler Weather Radar can
detect gust fronts provided it is not moving tangentially with respect to radar.
The Gust Front algorithm takes a polar or single elevation set of velocity data and searches for regions that match the
Gust Front conditions. A parabola is matched to each of these regions by a least square fit. These parabolas are used
as an overlay to the image of another product that is specified by the user.
Q: What are advantages Doppler weather Radars:
A: Advantages of Doppler Weather Radar:
One of the significant applications of radars over the last 40 years has been in real time weather
monitoring and for research in Meteorology. Most of our current knowledge about the structure of thunderstorms,
cyclones and other precipitating cloud systems comes from radar observation. Depending upon the radar being used,
it is also possible to estimate or measure the number of droplets present, their sizes and whether they are in the form
of water or ice within the cloud.
43. Radars using Doppler techniques (i.e. Doppler Weather Radar) not only detect and measure the power
received from a target (Reflectivity), but also measure the speed of the target towards or away from the radar (the
Radial Velocity of the target) and Spectrum Width (indicator of wind sheer and turbulence in the atmosphere)
The design of meteorological Doppler Radars has now been standardized and due to its additional capability
of wind measurement, it is now replacing old conventional radars in most countries.
A Doppler radar gives a number of derived products from the three base products viz. Reflectivity (which is a
way to measure possible rainfall amount), Velocity and Spectrum Width Products based on reflectivity data are
available from conventional radars as well. However since Doppler radar measures radial velocity of the echoes in
addition to reflectivity it is easy to eliminate ground clutter (resulting from unwanted structure like buildings, hills
etc.) and anomalous propagation echoes (arises from birds, insects or any other special atmospheric condition). Thus
the accuracy of reflectivity estimates are better (generally by one order) from Doppler radar particularly in regions
where ground clutter is a serious problem
Q: What is the principle of working of polarimetric radar?
A: Dual-polarization weather radars transmit vertically and horizontally polarized electromagnetic waves alternately and
receive and process both type of polarized backscattered signals. The backscattering characteristics of a single
precipitation particle are described in terms of the backscattering matrix. By comparing these backscatter signals in
different ways (ratios, correlation etc.), we can obtain information on the size, shape, class and precipitation rate etc.
Q: What do Polarimetric Radars measure?
A: Some of the fundamental variable measured by polarimetric radars and their brief description is given below:
(i) Differential Reflectivity (ZDR): The differential reflectivity is a ratio of the reflected horizontal and vertical power
returns. Amongst other things, it is a good indicator of drop shape. In turn, the shape is a good estimate of average
drop size.
(j) Correlation Coefficient (ρHV): The correlation coefficient is a correlation between the reflected horizontal and vertical
power returns. It is a good indicator of regions where there is a mixture of precipitation types, such as rain and snow.
(k) Linear Depolarization Ratio (LDR): The linear depolarization ratio is a ratio of a vertical power return from a
horizontal pulse or a horizontal power return from a vertical pulse. It too is a good indicator of regions where a
mixture of precipitation types occurs.
(l) Specific Differential Phase (KDP): The specific differential phase is a comparison of the returned phase difference
between the horizontal and vertical pulses. This phase difference is caused by the difference in the number of wave
44. cycles (or wavelengths) along the propagation path for horizontal and vertically polarized waves. It should not to be
confused with the Doppler frequency shift, which is caused by the motion of the cloud and precipitation particles.
Q: How do the Polarimetric Radars help in rainfall estimates?
A: Specific Differential Phase (KDP), is related to precipitation rate (R) by power law relations of the form R= A KDP
b
Following R- KDP relations are considered to appropriate for rainfall rate estimations:
R = 44 KDP
0.822
for λ = 11 cm
R = 25.1 KDP
0.0.777
for λ = 5.45 cm
R = 19.9 KDP
0.803
for λ = 3.2 cm
Q: Why are the rainfall estimates from Polarimetric radars are more accurate?
A: Normal radars estimate rainfall intensity using Z=AR
b
relationship where Z is the Z radar measured reflectivity, R the
Rain rate A and b are constants. Values A and b depend on Drop Size Distribution which is not the same for all places
and seasons and type of rain. As the values of these constants are to be determined for different seasons, places and
types of rain. Wrong values of A and b constants lead to erroneous rainfall rates. Also, rainfall estimates are
influenced by bright band, partial beam filling, Beam blockage, errors in radar calibration, attenuation of radar beam
by rain etc. KDP is calculated from relative phase measurements and therefore immune to:
o Errors in Radar calibrations
o Partial beam blockage or partial beam filling
o Attenuation of electromagnetic waves due precipitation.
In view of above rainfall estimates obtained from Polarimetric radars are more accurate.
Q: What are the advantages of Polarimetric Radars?
A: Following are the main advantages of polarimetric Radars:
9. Rainfall estimates from Polarimetric radars are more accurate.
10. Polarimetric parameters observed by these radars are used for hydrometeor classification.
11. Polarimetric parameters are used for applying correction for attenuation of the radar beams due to rain etc
12. Polarimetric radars are capable of identifying and mitigating the effects of ground clutter, anomalous
propagation and non-meteorological scatterers, thereby providing more accurate and clear weather radar
data.
.