A presentation made at Open Data Camp, Bangalore, on March 24, 2012.
A part of the Know Your Climate initiative (knowyourclimate.org ) of Public Affairs Centre.
Publication and long term archival of observational data in the field of environmental sciences is a challenging topic of today's eScience research. The amount of effort that goes into technical and scientific quality assurance prior to publication is considerable and might well turn out to be a barrier to data publication. Our project's goal is to lower the amount of manual effort and, at the same time, increase data quality in the process of submitting observational data for publication – in this case meteorological observational data. This goal is divided into the following subgoals:
Establish a standard procedure for the publication of observational data in the area of meteorology including quality information.
Develop a workflow system for the automatisation of the publication process.
Make the procedure usable for environmental sciences in general.
Integration of the procedure into an existing central data repository for meteorology (CERA data base at the World Data Center for Climate).
This talk is about the current state of the project from an eResearch and technical point of view.
ICLR Friday Forum: Climate data in Ontario (November 13, 2015)glennmcgillivray
Preparing for the anticipated effects of climate change intuitively suggests that we need the best possible understanding and quantification of those effects. However, it can be challenging for practitioners to navigate the world of climate data. Not only can it be difficult to obtain information about our past and future climate and to discern which of the many sources should be used, but it is also a challenge to understand the limitations of the data and how it can be used appropriately and defensibly. The presentation described the current state and availability of climate data in Ontario, which is an illustrative case typical of most Canadian jurisdictions. The many sources of historic and future climate data were described, along with a discussion about the pitfalls of pursuing precision in climate data when its application is inherently uncertain.
Ryan Ness is Senior Manager of Research and Development at the Toronto and Region Conservation Authority. In this role, he is responsible for developing policy and technical solutions that allow the Authority to fulfill its water resources and ecological conservation mandate in the face of new and emerging challenges, including climate change. Ryan is a professional engineer with 16 years experience, 12 of which have been with the TRCA. He is currently pursuing a PhD in sustainability studies at the University of Waterloo.
Climate Data Sharing for Urban Resilience - OGC Testbed 11George Percivall
OGC Testbed 11:
Delivering on our commitment to the Climate Data Initiative
In December 2014 the US White House Office of Science and Technology (OSTP) released a Policy Fact Sheet titled "Harnessing Climate Data to Boost Ecosystem & Water Resilience." The Fact Sheet includes OGC’s commitment to increase open access to climate change information using open standards. Testbed 11 results are now available delivering on that commitment.
The results of this major interoperability testbed contribute to development and refinement of international standards that are critical for the communication and integration of geospatial information. http://www.opengeospatial.org/projects/initiatives/testbed11
• Nine sponsors provided requirements and funding for Testbed 11.
• Thirty organizations participated in Testbed 11 by contributing prototypes, engineering
reports and participating in a scenario driven demonstration of the technical advances Technical results of Testbed 11 relevant to the Climate Data Initiative include:
• Analysis and prediction based on open climate data accessed using open standards
• Making predictive models more accessible with OGC Web Processing Service (WPS)
• Verifying model predictions using mobile operations, in-situ gauges and social media.
Climate adaptation, resilience and security planning based on technology from OGC Testbed 11:
• Estimating geographic extend of coastal inundation in dynamic weather conditions
• Assessing social unrest with displaced population due to climate change
• Integrating spatial and non-spatial models of human geography and resilience
• Predictive models and verifications to support planning and response phases
Interpreting Climate Data - Analysing climate vulnerability- online training ...Vestlandsforsking WRNI
Interpreting Climate Data
This module provides an introduction to climate data and how to effectively use it. The following will be covered:
How regionalised climate data is produced
How to understand and interpret regionalised climate data
How to identify and communicate uncertainties
Publication and long term archival of observational data in the field of environmental sciences is a challenging topic of today's eScience research. The amount of effort that goes into technical and scientific quality assurance prior to publication is considerable and might well turn out to be a barrier to data publication. Our project's goal is to lower the amount of manual effort and, at the same time, increase data quality in the process of submitting observational data for publication – in this case meteorological observational data. This goal is divided into the following subgoals:
Establish a standard procedure for the publication of observational data in the area of meteorology including quality information.
Develop a workflow system for the automatisation of the publication process.
Make the procedure usable for environmental sciences in general.
Integration of the procedure into an existing central data repository for meteorology (CERA data base at the World Data Center for Climate).
This talk is about the current state of the project from an eResearch and technical point of view.
ICLR Friday Forum: Climate data in Ontario (November 13, 2015)glennmcgillivray
Preparing for the anticipated effects of climate change intuitively suggests that we need the best possible understanding and quantification of those effects. However, it can be challenging for practitioners to navigate the world of climate data. Not only can it be difficult to obtain information about our past and future climate and to discern which of the many sources should be used, but it is also a challenge to understand the limitations of the data and how it can be used appropriately and defensibly. The presentation described the current state and availability of climate data in Ontario, which is an illustrative case typical of most Canadian jurisdictions. The many sources of historic and future climate data were described, along with a discussion about the pitfalls of pursuing precision in climate data when its application is inherently uncertain.
Ryan Ness is Senior Manager of Research and Development at the Toronto and Region Conservation Authority. In this role, he is responsible for developing policy and technical solutions that allow the Authority to fulfill its water resources and ecological conservation mandate in the face of new and emerging challenges, including climate change. Ryan is a professional engineer with 16 years experience, 12 of which have been with the TRCA. He is currently pursuing a PhD in sustainability studies at the University of Waterloo.
Climate Data Sharing for Urban Resilience - OGC Testbed 11George Percivall
OGC Testbed 11:
Delivering on our commitment to the Climate Data Initiative
In December 2014 the US White House Office of Science and Technology (OSTP) released a Policy Fact Sheet titled "Harnessing Climate Data to Boost Ecosystem & Water Resilience." The Fact Sheet includes OGC’s commitment to increase open access to climate change information using open standards. Testbed 11 results are now available delivering on that commitment.
The results of this major interoperability testbed contribute to development and refinement of international standards that are critical for the communication and integration of geospatial information. http://www.opengeospatial.org/projects/initiatives/testbed11
• Nine sponsors provided requirements and funding for Testbed 11.
• Thirty organizations participated in Testbed 11 by contributing prototypes, engineering
reports and participating in a scenario driven demonstration of the technical advances Technical results of Testbed 11 relevant to the Climate Data Initiative include:
• Analysis and prediction based on open climate data accessed using open standards
• Making predictive models more accessible with OGC Web Processing Service (WPS)
• Verifying model predictions using mobile operations, in-situ gauges and social media.
Climate adaptation, resilience and security planning based on technology from OGC Testbed 11:
• Estimating geographic extend of coastal inundation in dynamic weather conditions
• Assessing social unrest with displaced population due to climate change
• Integrating spatial and non-spatial models of human geography and resilience
• Predictive models and verifications to support planning and response phases
Interpreting Climate Data - Analysing climate vulnerability- online training ...Vestlandsforsking WRNI
Interpreting Climate Data
This module provides an introduction to climate data and how to effectively use it. The following will be covered:
How regionalised climate data is produced
How to understand and interpret regionalised climate data
How to identify and communicate uncertainties
Presentation by Jacob van Etten.
CCAFS workshop titled "Using Climate Scenarios and Analogues for Designing Adaptation Strategies in Agriculture," 19-23 September in Kathmandu, Nepal.
Edwards climate data detectives - yale 2-2015Paul Edwards
Talk to the Yale History of Science/History of Medicine group on 9 Feb 2015. Discusses evolving knowledge infrastructures, the history of weather and climate data, and several recent controversies over climate data. "Data detectives" refers to the forensic work involved in adjusting data for changes in instrument characteristics, location, etc.
ESRI User Conference 2014 - A Location Aware Mobile Tool for Direct and Indir...Francisco Ramos
Access to GIS data from mobile platforms continues to be a challenge and there is a wide range of fields where it is extremely useful. In this work, we combined three key aspects: climate data sensors, mobile platforms and spatial proximity operations. We published and made use of a web 2.0 network of climate data, where content is user-collected, by means of their meteorological stations, and exposed as available information for the virtual community. Moreover, we enriched this data by giving the users the opportu- nity to directly inform the system with different climate measures. In general, management of this type of information from a mobile application could result in an important decision tool, as it enables us to provide climate-related data according to a context and a geographical location. Therefore, we imple- mented a native mobile application for iPhone and iPad platforms by using ArcGIS SDK for iOS and by integrating a series of ArcGIS webmaps, which allows us to perform geospatial queries based on the user’s location, offering, at the same time, access to all the data provided by the climate data sensor network and from direct users.
Contextualizing the Visualization of Climate DataRaquel Alegre
EGU 2014, 27th April - 2nd May 2014, Vienna (Austria)
Session: Techniques and tools for effective visualization and sonification in the geosciences
Category: Earth & Space Science Informatics (ESSI)
Collaborate 2012: Environmental Accounting and ReportingAngela Miller
Presentation on the implementation of Oracle's Environmental Accounting and Reporting (EAR) module in JD Edwards. As more entities desire to self-report to The Climate Registry and the Global Reporting Initiative, tools like EAR will become imperative for their organizations to automate the data collection.
These visuals were prepared to support a string quartet performance and panel on climate change at Northwestern University in February 2106.
A well-designed graphic can help audiences to quickly understand the main message embedded within a complex set of climate data and to retain those ideas longer than they would have if they were conveyed by words alone. But the visual aids used regularly by climate scientists also have their limitations: they are most easily understood by people who are already fluent in technical illustrations; they're usually static and sometimes do not tell an obvious story; and for many, they don't elicit a strong emotional response.
Music, by contrast, is inherently narrative and is known to exert a powerful influence on human emotions. Because of this, sonification — the transformation of data into acoustic signals — may have considerable promise as a tool to enhance the communication of climate science.
Daniel Crawford and Scott St. George report on a collaboration between scientists and artists that uses music to transmit the evidence of climate change in an engaging and visceral way.
Big Data and the Climate/Environment domain (vis-a-vis the respective H2020 Societal Challenge) - Opportunities, Challenges and Requirements. As presented and discussed in the public launch of the BigDataEurope project.
The Role of DAta for Climate Monitoring and PredictionNAP Events
Presentation by: Stefan Rösner
4.1 Climate services in support of NAPs
This event will bring together experts involved in the provision of climate services and testimony from countries of how climate services are being used to support decision-making and effective adaptation. The event will start with brief statements, and will be followed by a panel discussion, where participants from the floor will have the opportunity to engage the panelists with questions or comments. The panel will demonstrate the practical benefits of climate services in support of climate risk management and adaptation to climate variability and change. It will also provide lessons learned through various activities being implemented at regional and national level.
Presentation on groundwater management in Saudi Arabia by Dr. Ali Saad Al-Tokhais at the International Annual UN-Water Zaragoza Conference 2012/2013. Preparing for the 2013 International Year. Water Cooperation: Making it Happen! 8-10 January 2013.
On the performance analysis of rainfall prediction using mutual information...IJECEIAES
Monsoon rainfall prediction over a small geographic region is indeed a challenging task. This paper uses monthly means of climate variables, namely air temperature (AT), sea surface temperature (SST), and sea level pressure (SLP) over the globe, to predict monthly and seasonal summer monsoon rainfall over the state of Maharashtra, India. Mutual information correlates the temperature and pressure from a grid of 10 longitude X 10 latitude with Maharashtra’s monthly rainfall time series. Based on the correlations, selected features over the respective latitude and longitudes are given as inputs to an artificial neural network. It was observed that AT and SLP could predict monthly monsoon rainfall with excellent accuracy. The performance of the test dataset was evaluated through mean absolute error; root mean square error, correlation coefficient, Nash Sutcliffe model efficiency coefficient, and maximum rainfall prediction capability of the network. The individual climate variable model for AT performed better in all evaluation parameters except maximum rainfall capability, where the combined model 2 with AT, SLP and SST as predictors outperformed. The SLP-only model’s performance was comparable to the AT-only model. The combined model 1 with AT and SLP as predictors was found better than the combined model 2.
Presentation by Jacob van Etten.
CCAFS workshop titled "Using Climate Scenarios and Analogues for Designing Adaptation Strategies in Agriculture," 19-23 September in Kathmandu, Nepal.
Edwards climate data detectives - yale 2-2015Paul Edwards
Talk to the Yale History of Science/History of Medicine group on 9 Feb 2015. Discusses evolving knowledge infrastructures, the history of weather and climate data, and several recent controversies over climate data. "Data detectives" refers to the forensic work involved in adjusting data for changes in instrument characteristics, location, etc.
ESRI User Conference 2014 - A Location Aware Mobile Tool for Direct and Indir...Francisco Ramos
Access to GIS data from mobile platforms continues to be a challenge and there is a wide range of fields where it is extremely useful. In this work, we combined three key aspects: climate data sensors, mobile platforms and spatial proximity operations. We published and made use of a web 2.0 network of climate data, where content is user-collected, by means of their meteorological stations, and exposed as available information for the virtual community. Moreover, we enriched this data by giving the users the opportu- nity to directly inform the system with different climate measures. In general, management of this type of information from a mobile application could result in an important decision tool, as it enables us to provide climate-related data according to a context and a geographical location. Therefore, we imple- mented a native mobile application for iPhone and iPad platforms by using ArcGIS SDK for iOS and by integrating a series of ArcGIS webmaps, which allows us to perform geospatial queries based on the user’s location, offering, at the same time, access to all the data provided by the climate data sensor network and from direct users.
Contextualizing the Visualization of Climate DataRaquel Alegre
EGU 2014, 27th April - 2nd May 2014, Vienna (Austria)
Session: Techniques and tools for effective visualization and sonification in the geosciences
Category: Earth & Space Science Informatics (ESSI)
Collaborate 2012: Environmental Accounting and ReportingAngela Miller
Presentation on the implementation of Oracle's Environmental Accounting and Reporting (EAR) module in JD Edwards. As more entities desire to self-report to The Climate Registry and the Global Reporting Initiative, tools like EAR will become imperative for their organizations to automate the data collection.
These visuals were prepared to support a string quartet performance and panel on climate change at Northwestern University in February 2106.
A well-designed graphic can help audiences to quickly understand the main message embedded within a complex set of climate data and to retain those ideas longer than they would have if they were conveyed by words alone. But the visual aids used regularly by climate scientists also have their limitations: they are most easily understood by people who are already fluent in technical illustrations; they're usually static and sometimes do not tell an obvious story; and for many, they don't elicit a strong emotional response.
Music, by contrast, is inherently narrative and is known to exert a powerful influence on human emotions. Because of this, sonification — the transformation of data into acoustic signals — may have considerable promise as a tool to enhance the communication of climate science.
Daniel Crawford and Scott St. George report on a collaboration between scientists and artists that uses music to transmit the evidence of climate change in an engaging and visceral way.
Big Data and the Climate/Environment domain (vis-a-vis the respective H2020 Societal Challenge) - Opportunities, Challenges and Requirements. As presented and discussed in the public launch of the BigDataEurope project.
The Role of DAta for Climate Monitoring and PredictionNAP Events
Presentation by: Stefan Rösner
4.1 Climate services in support of NAPs
This event will bring together experts involved in the provision of climate services and testimony from countries of how climate services are being used to support decision-making and effective adaptation. The event will start with brief statements, and will be followed by a panel discussion, where participants from the floor will have the opportunity to engage the panelists with questions or comments. The panel will demonstrate the practical benefits of climate services in support of climate risk management and adaptation to climate variability and change. It will also provide lessons learned through various activities being implemented at regional and national level.
Presentation on groundwater management in Saudi Arabia by Dr. Ali Saad Al-Tokhais at the International Annual UN-Water Zaragoza Conference 2012/2013. Preparing for the 2013 International Year. Water Cooperation: Making it Happen! 8-10 January 2013.
On the performance analysis of rainfall prediction using mutual information...IJECEIAES
Monsoon rainfall prediction over a small geographic region is indeed a challenging task. This paper uses monthly means of climate variables, namely air temperature (AT), sea surface temperature (SST), and sea level pressure (SLP) over the globe, to predict monthly and seasonal summer monsoon rainfall over the state of Maharashtra, India. Mutual information correlates the temperature and pressure from a grid of 10 longitude X 10 latitude with Maharashtra’s monthly rainfall time series. Based on the correlations, selected features over the respective latitude and longitudes are given as inputs to an artificial neural network. It was observed that AT and SLP could predict monthly monsoon rainfall with excellent accuracy. The performance of the test dataset was evaluated through mean absolute error; root mean square error, correlation coefficient, Nash Sutcliffe model efficiency coefficient, and maximum rainfall prediction capability of the network. The individual climate variable model for AT performed better in all evaluation parameters except maximum rainfall capability, where the combined model 2 with AT, SLP and SST as predictors outperformed. The SLP-only model’s performance was comparable to the AT-only model. The combined model 1 with AT and SLP as predictors was found better than the combined model 2.
Trend analysis of temporal variations in evi with respect to rainfall of jaip...eSAT Journals
Abstract
For the protection and restoration of vegetation, it is imperative to comprehend and map current states of its distribution.
Vegetation mapping and its quantification helps in understanding and correlating the factors that contribute to its spread cover
over different regions. ‘Jaipur city’, also called Pink city is the capital of the state of Rajasthan lying in the semi-arid zone.
Irregular and intermittent rainfall and drastic changes in land use and land cover types highly influence vegetation cover in the
region. In order to study influence of erratic patterns of rainfall on vegetation, this study analyses the trend followed by the Post-
Monsoonal vegetation pattern of Jaipur with respect to the monsoonal rainfall in the years 2003 to 2010 with help of remote
sensing data. Freely available remotely sensed data of Enhanced Vegetation Index (EVI) from MODIS and rainfall data from
water resources department are processed here to exhibit the dependence of natural vegetation distribution in arid and semi-arid
regions on rainfall. It is concluded that the EVI value rises with rise in rainfall. Coefficient of correlation value of 0.138 suggests
that other factors may also be responsible for variations in EVI than rainfall. It is also concluded that MODIS EVI gives good
estimation of seasonal patterns.
Key Words: Enhanced Vegetation Index (EVI), Rainfall, MODIS, Remote Sensing, Geographical Information Systems
Complexity Neural Networks for Estimating Flood Process in Internet-of-Things...Dr. Amarjeet Singh
With the advancement of the Internet of Things (IoT)-based water conservation computerization, hydrological data is increasingly enriched. Considering the ability of deep learning on complex features extraction, we proposed a flood process forecastin gmodel based on Convolution Neural Network(CNN) with two-dimension(2D) convolutional operation. At first, we imported the spatial-temporal rainfall features of the Xixian basin. Subsequently, extensive experiments were carried out to determine the optimal hyperparameters of the proposed CNN flood forecasting model.
Tracking emerging diseases from space: Geoinformatics for human healthMarkus Neteler
European and other countries are at increasing risk for new or re-emerging vector-borne diseases. Among the top ten vector-borne diseases with greatest potential to affect European citizens are Dengue fever, Chikungunya, Hantavirus, and Crimean-Congo hemorrhagic fever. Despite the risk of disease transmission, many vectors like the Asian tiger mosquito or ticks are also a nuisance in daily life. The examination of disease vector spread and a better understanding of spatio-temporal patterns in disease transmission and diffusion is greatly facilitated by Geoinformatics. New methods including the use of high resolution time series from space in spatial models enable us to predict species invasion and survival, and to assess potential health risks. Geoinformatics is able to address the increasing challenge for human and veterinary public health not only in Europe, but across the globe, assisting decision makers and public health authorities to develop surveillance plans and vector control.
Christian P. MORTGAT1, Pane STOJANOVSKI2, Auguste C. BOISSONNADE2, Alex BERNHARDT3
1Risk Management Solutions, Inc.; 2Asia Risk Centre, Inc.; 3Guy Carpenter & Company, LLC
Assessment of two Methods to study Precipitation PredictionAI Publications
Presipitation analysis plays an important role in hydrological studies. In this study, using 50 years of rainfall data and ARIMA model, critical areas of Iran were determined. For this purpose, annual rainfall data of 112 different synoptic stations in Iran were gathered. To summarize, it could be concluded that: ARIMA model was an appropriate tool to forecast annual rainfall. According to obtained results from relative error, five stations were in critical condition. At 45 stations accrued rainfalls with amounts of less than half of average in the 50-year period. Therefore, in these 45 areas, chance of drought is more than other areas of Iran.
Comparative Study of Machine Learning Algorithms for Rainfall Predictionijtsrd
Majority of Indian framers depend on rainfall for agriculture. Thus, in an agricultural country like India, rainfall prediction becomes very important. Rainfall causes natural disasters like flood and drought, which are encountered by people across the globe every year. Rainfall prediction over drought regions has a great importance for countries like India whose economy is largely dependent on agriculture. A sufficient data length can play an important role in a proper estimation drought, leading to a better appraisal for drought risk reduction. Due to dynamic nature of atmosphere statistical techniques fail to provide good accuracy for rainfall prediction. So, we are going to use Machine Learning algorithms like Multiple Linear Regression, Random Forest Regressor and AdaBoost Regressor, where different models are going to be trained using training data set and tested using testing data set. The dataset which we have collected has the rainfall data from 1901 2015, where across the various drought affected states. Nonlinearity of rainfall data makes Machine Learning algorithms a better technique. Comparison of different approaches and algorithms will increase an accuracy rate of predicting rainfall over drought regions. We are going to use Python to code for algorithms. Intention of this project is to say, which algorithm can be used to predict rainfall, in order to increase the countries socioeconomic status. Mylapalle Yeshwanth | Palla Ratna Sai Kumar | Dr. G. Mathivanan M.E., Ph.D ""Comparative Study of Machine Learning Algorithms for Rainfall Prediction"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-3 , April 2019, URL: https://www.ijtsrd.com/papers/ijtsrd22961.pdf
Paper URL: https://www.ijtsrd.com/computer-science/data-miining/22961/comparative-study-of-machine-learning-algorithms-for-rainfall-prediction/mylapalle-yeshwanth
Village agriculture is very important in Bangladesh. In emerging nations like our own, agriculture has a significant impact on national GDP. Basically, because of our current circumstances, the monsoons, which are agriculture's primary source of water, are insufficient. The irrigation system is used in agriculture as a solution to this issue. In this technique, the agricultural field will receive water depending on the type of soil. In agriculture, there are two factors to consider: the soil's moisture content and its fertility. There are already a variety of irrigation options available to lessen the demand for rain. An electrical power on/off schedule controls this kind of method. The use of IOT to create a smart irrigation system is covered in this article. Our method uses hydropumps to regulate multiple pumps at once, which saves time and energy. This system will have a significant impact on the national economy if we implement it.
THREDDS Data Server and Solar Insolation Prediction using Machine Learning Te...Chaitali Patel
THREDDS Data Server and Solar Insolation Prediction using Machine Learning Techniques for Gandhinagar SRRA station using ground station data and satellite data
Binary classification of rainfall time-series using machine learning algorithmsIJECEIAES
Summer monsoon rainfall contributes more than 75% of the annual rainfall in India. For the state of Maharashtra, India, this is more than 80% for almost all regions of the state. The high variability of rainfall during this period necessitates the classification of rainy and non-rainy days. While there are various approaches to rainfall classification, this paper proposes rainfall classification based on weather variables. This paper explores the use of support vector machine (SVM) and artificial neural network (ANN) algorithms for the binary classification of summer monsoon rainfall using common weather variables such as relative humidity, temperature, pressure. The daily data, for the summer monsoon months, for nineteen years, was collected for the Shivajinagar station of Pune in the state of Maharashtra, India. Classification accuracy of 82.1 and 82.8%, respectively, was achieved with SVM and ANN algorithms, for an imbalanced dataset. While performance parameters such as misclassification rate, F1 score indicate that better results were achieved with ANN, model parameter selection for SVM was less involved than ANN. Domain adaptation technique was used for rainfall classification at the other two stations of Maharashtra with the network trained for the Shivajinagar station. Satisfactory results for these two stations were obtained only after changing the training method for SVM and ANN.
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1. Climate Data in India:
Open and Closed
- Pavan Srinath
Environmental Governance Group, Public Affairs Centre
pavan.srinath@pacindia.org | @zeusisdead
2. Introduction
Public Affairs Centre is a non-profit organisation in Bangalore promoting good governance since 1994
through research, advocacy and action.
In 2010, we formed a new group called the Environmental Governance Group, focusing on trying to develop
governance solutions to environmental issues in India, including climate change adaptation.
In the struggle for assessing climate change impacts, exploring robust adaptation options, and trying to
work with both community knowledge & climate science, we thought that it was worthwhile to take a step
back, and try to learn more about our local climate first.
This started an initiative called Know Your Climate.
www.pacindia.org | www.knowyourclimate.org
3. Climate Data in India
• Indian Meteorological Department, the Hegemon
– Lots of data vs. No data available in the public domain.
The conundrum is that India has one of the oldest meteorological institutions in the world,
with a rich instrumental record, but very little data is available for the public to use!
• IMD Stations & Everybody else’s Rain Gauges
While IMD maintains weather stations across India, state economics departments, disaster
management cells and others maintain a far larger number of rain gauges across the country.
Some states like Rajasthan have put rain gauge data in the public domain (HT: India Water
Portal), but others are yet to follow.
• Station data vs. Gridded products vs. Satellite products
Station data can be really expensive! So, data from individual stations are usually processed
to develop gridded data sets, both in India and elsewhere. The IMD has developed a few that
are inexpensive, but with restrictions on use. There’s currently one daily gridded dataset from
Japan that’s available for public use! (see next page)
Now satellites like NASA’s TRMM can directly measure a host of weather & climate
parameters including surface temperature, rainfall, sea winds and more!
• The Intrepid farmer!
In the heavily contested area of good quality & open climate data, where you can’t always
trust the numbers, sometimes it’s the intrepid farmer with a private rain gauge who comes to
your rescue!
4. Gridded data from Japan!
The only long-term (1951-2007) daily rainfall data set that’s currently open for non-commercial use:
http://www.chikyu.ac.jp/
Krishnamurthi et al (2009)
APHRODITE Rainfall data set, Research Institute for Humanity and Nature, Japan
5. Mr. Vimal Kumar, a coffee planter from Wayanad who started collecting
rainfall data daily from April 1983, and hasn’t stopped since!
6. Gridded data from Japan!
Can’t we go local?
Pan-Indian climate & monsoon analyses cannot
take into account India’s immense geographic and
climatic diversity. It’s imperative that we go ‘local’
with our understanding. Let’s ask the question
“What do we know about Bangalore’s climate?”
rather than “What do we know about India’s
climate?”
With gridded data sets, we can begin doing that
in an inexpensive manner.
http://www.chikyu.ac.jp/
Krishnamurthi et al (2009)
APHRODITE Rainfall data set, Research Institute for Humanity and Nature, Japan
7. Going Local with Climate Data
Zooming in on Bangalore
The next few slides will look
at the little grid cell that
focuses on Bangalore. The
two pink dots represent the
city & HAL airport IMD
stations, the likely source for
generating the gridded data.
APHRODITE Rainfall data set, Research Institute for Humanity and Nature, Japan
9. The Joy of Daily Rainfall Data
Monthly Rainfall Profile of Bangalore (1951-2007)
This is the most common picture that you
see, when somebody talks about the ‘rainfall
180 profile’ of a place.
160 What you can tell from this is that Bangalore has 153.8
two seasons of rainfall, one summer and one 149.2
monsoon.
140
120
101.9
Rainfall (mm)
100
89.1
82.0
80
62.1
60 50.1
40 36.7
20 15.2
9.7
1.6 4.4
0
Source: APHRODITE Rainfall data set, Research Institute for Humanity and Nature, Japan
10. The Joy of Daily Rainfall Data
Monthly Rainfall Profile of Bangalore (1951-2007)
If the last graph is converted into mm/day, you
end up with this graph, showing the monthly
averages of how much rain fell on a ‘per day’
basis.
10
Monthly Average
8
Rainfall (mm/day)
6
4
2
0
1
Source: APHRODITE Rainfall data set, Research Institute for Humanity and Nature, Japan
11. The Joy of Daily Rainfall Data
Daily Rainfall Profile of Bangalore (1951-2007)
The daily averages show a completely different picture!
And our seasons don’t respect monthly boundaries.
10
Monthly Average
Daily Rainfall
8
Rainfall (mm/day)
6
4
2
0
1
Source: APHRODITE Rainfall data set, Research Institute for Humanity and Nature, Japan
12. The Joy of Daily Rainfall Data
Daily Rainfall Profile of Bangalore (1951-2007)
10.0
Daily Rainfall
8.0
When Bangalore
rainfall really
Rainfall (mm/day)
picks up!
6.0
Onset of the
Summer rain monsoon
4.0
2.0
A recurring dry period in June!
0.0
1
Source: APHRODITE Rainfall data set, Research Institute for Humanity and Nature, Japan
13. The Joy of Daily Rainfall Data
Daily Rainfall Probability, Bangalore
100%
Chance of Rainfall on
any given day
80%
% Chance of Rainfall
60%
On any given day, assuming no other
knowledge, there’s never any certain rain in
Bangalore. Unlike some places on the coast and
40% elsewhere.
20%
0%
1
Chance of rain >= 2.5mm
14. The Joy of Daily Rainfall Data
Daily Rainfall Probability, Bangalore
100%
But the certainty of some 7 day
rain within a week is far
higher. 1 day
80%
% Probabiliy of Rainfall
60%
40%
20%
0%
Jan
Axis Title
Chance of rain >= 2.5mm
15. The Joy of Daily Rainfall Data
Daily Rainfall Profile of Bangalore (1951-2007)
10.0
Daily Rainfall
8.0
Rainfall (mm/day)
6.0
4.0
2.0
0.0
1
16. The Joy of Daily Rainfall Data
How much rain do we get, when it does rain?
20
Amount of Rainfall
per rainy day
15
Rainfall (mm/day)
10
5
0
1
Days such as those in Feb/Mar seem to correspond well to the adage: When it rains, it pours!
17. The Joy of Daily Rainfall Data
What’s the most it can rain in a day?
120
Maximum recorded
rainfall on any date
100
80
Rainfall (mm/day)
60
40
20
0
1
We can get heavy rainfall in Bangalore almost any time between April and December. And Bangalore
gets ~1-2 heavy rainfall days almost every year. So why do they still catch us by surprise?
18. The Depressing part about Rainfall
Rainfall in Aug-Sept 2011, Bangalore
This is actual rainfall by day in 2011. On August
16 night, it rained very heavily, with the
Bangalore city weather station (near Maharani’s
college) recording over 100mm of rain.
http://www.imdaws.com/ViewAwsData.aspx
19. The Depressing part about Rainfall
What happens to Bangalore when it rains…
Boy drowns in a (Taken from news clippings)
drain Storm Drains
overflowing
Flooding of houses Trees uprooted
Gali Anjaneya
Severe Flooding in Hebbal
temple &
surroundings
damaged
2 die in mud cave-
in
----------------------------------------------------Traffic Disruptions-------------------------------------------------
http://www.imdaws.com/ViewAwsData.aspx
20. The Depressing part about Rainfall
What happens to Bangalore when it rains…
(Taken from news clippings)
Boy drowns in a Storm Drains
drain overflowing
Flooding of houses Trees uprooted
Gali Anjaneya
Severe Flooding in Hebbal
temple &
surroundings
damaged What has JnNURM done for us?
Water gushes
under the metro
Storm Water Drain irregularities
discovered.
BBMP starts fixing
More metro woes potholes 2 die in mud cave-
----------------------------------------------------Traffic Disruptions-------------------------------------------------
in
21. Revisiting traditional knowledge systems
The Malayalam calendar & Rainfall in Wayanad
If we can map rainfall patterns onto the traditional
Karkkidakam
calendar, it would go a long way in mainstreaming
traditional knowledge! This is just an example from what we
did in
Wayanad, Kerala.
Chinga masam
Edavam paadhi
Tulavarsham
Puthu mazha
Vishu Onam
Kumbha mazha
Harvest
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Kumar and Srinath, Climate Trends in Wayanad: Voices from the Community (2011)
22. A year’s rainfall is like a signature – each one unique.
Monthly Rainfall series in Wayanad, 2000-2011
1000
Muttil
800
Rainfall (mm)
600
400
200
0
2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012
Year
Just to throw a note of caution: the presentation discusses rainfall patterns and daily averages, but it’s good to remember
that they’re just that: Averages. Each year’s climate & rainfall pattern is still quite unique, as seen in the graph above.
23. Acknowledgements
All that you have seen is a part of the fledgling Know your climate initiative, where we
want people to understand their local climate using data that we help visualize. It’s not
yet operational, and we are looking for volunteers to help us out with web-designing
and visualization! Please spread the word!
People who’ve helped make this possible are Adarsh DK and Yashas MS (Btech 2nd
year, NIT Surathkal), who spent summer ’11 working with us; Danesh Kumar, RASTA, for
the work in Wayanad; my colleagues at Public Affairs Centre: Jangal Jayaram, Prarthana
Rao, Kuldip Gyaneswar and Director R. Suresh.
24. References
APHRODITE Daily Rainfall Data set at the Research Institute for Humanity and
Nature, Japan: http://www.chikyu.ac.jp/
“Climate Trends in Wayanad: Voices from the Community”, Conference paper
(2011): http://goo.gl/QIA7k
IMD Automater Weather Station website:
http://www.imdaws.com/ViewAwsData.aspx
Do keep an eye on blog.knowyourclimate.org!