This document discusses concepts related to atmospheric moisture, evaporation, and condensation. It begins by reviewing key prior knowledge, including the second law of thermodynamics and how systems move towards equilibrium. It then explains that evaporation and condensation rates depend on how far a system is from saturation. Water is a polar molecule that forms hydrogen bonds in liquid and solid states. Evaporation occurs when kinetic energy exceeds bond energy. Temperature measures molecular kinetic energy. Saturation vapor pressure represents the maximum water vapor amount for a given temperature. Relative humidity compares actual to saturation vapor pressures. Dew point temperature indicates the temperature at which air becomes saturated. Worked examples apply these concepts to calculate vapor pressures, relative humidity, and dew point.
Osmosis is the spontaneous net movement or diffusion of solvent molecules through a selectively permeable membrane from a region of high water potential to a region of low water potential, in the direction that tends to equalize the solute concentrations on the two sides.
States of Matter and properties of matter: State of matter, changes in the state of matter, latent heats, vapour pressure, sublimation critical point, eutectic mixtures, gases, aerosols – inhalers, relative humidity, liquid complexes, liquid crystals, glassy states, solid- crystalline, amorphous & polymorphism.
Physicochemical properties of drug molecules: Refractive index, optical rotation, dielectric constant, dipole moment, dissociation constant, determinations and applications
Osmosis is the spontaneous net movement or diffusion of solvent molecules through a selectively permeable membrane from a region of high water potential to a region of low water potential, in the direction that tends to equalize the solute concentrations on the two sides.
States of Matter and properties of matter: State of matter, changes in the state of matter, latent heats, vapour pressure, sublimation critical point, eutectic mixtures, gases, aerosols – inhalers, relative humidity, liquid complexes, liquid crystals, glassy states, solid- crystalline, amorphous & polymorphism.
Physicochemical properties of drug molecules: Refractive index, optical rotation, dielectric constant, dipole moment, dissociation constant, determinations and applications
Climate feedbacksWe talked briefly about the positivWilheminaRossi174
Climate �feedbacks�
We talked briefly about the positive feedback processes of climate
change in previous lectures. What is “feedback”?
Feedback is a concept that explains the interaction of the climate
system that alters changes in climate. When the rate of climate change
is amplified (either by warming or cooling), the process is called
“positive feedback”. The upper figure demonstrates the basic way that
these feedbacks operate.
On the other hand, when the rate of climate change is suppressed, then
the process is called “negative feedback” (lower figure).
Primary Climate System Feedbacks
• Radiation feedback (hotter planet radiates
more energy out to space, E=sT4)
• Snow/ice-albedo feedback
• Water Vapor feedback
• Cloud feedback (high versus low clouds)
So, climate feedbacks are a loop of cause and effect; positive (amplifier) and
negative feedbacks (stabilizer). Some feedback processes are more
complicated than others. Here are a few important feedbacks that affect our
climate system.
Temperatureà radiation feedback
Energy emitted = σT4
éTemperature
éradiation to
space
éCO2
êTemperature
The temperature of the Earth is increasing due to a rise in greenhouse gases in
the atmosphere. Thus, how will the climate feedback system change with this
temperature increase?
First, increases in temperature will alter radiation feedback because the energy
emitted from a blackbody is proportionate to its temperature to the fourth (σT4).
Feedback process: Increasing CO2 concentration in the atmosphere – increasing
temperature – increasing associated energy radiation to space – decreasing
temperature
Thus, increasing CO2 is a negative feedback process in the long term. However,
this feedback process in the climate system is far more complex. This is not the
only feedback loop that we know of.
Snow/sea ice albedo feedback
Melting of snow/sea ice directly affects the
albedo of the Earth (less ice = decrease in albedo)
Measuring Earth’s Albedo
https://earthobservatory.nasa.gov/IOTD/view.php
?id=84499
https://earthobservatory.nasa.gov/IOTD/view.php?id=84499
Also, we have seen how
recent warming has
been impacting the
arctic sea ice (see the
following two slides)
Polar amplification!
Global temperature departures from average
during January through May 2020, compared
with a 1951-1980 average. (Berkeley Earth).
Greater climate change observed near the pole responds to changes in the
radiation balance (e.g. intensified greenhouse effect). This phenomenon is
known as “polar amplification”.
Melting sea ice in the Arctic decreases the Earth’s albedo. Changes in albedo are
likely contributing to significant temperature increases in the northern
hemisphere. The increase in surface temperature is observed mainly in the
higher latitude in the northern hemisphere, where most sea ice is, and where
there is a greater continental distribution (more continent is located in the
northern hemisph ...
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
Software Delivery At the Speed of AI: Inflectra Invests In AI-Powered QualityInflectra
In this insightful webinar, Inflectra explores how artificial intelligence (AI) is transforming software development and testing. Discover how AI-powered tools are revolutionizing every stage of the software development lifecycle (SDLC), from design and prototyping to testing, deployment, and monitoring.
Learn about:
• The Future of Testing: How AI is shifting testing towards verification, analysis, and higher-level skills, while reducing repetitive tasks.
• Test Automation: How AI-powered test case generation, optimization, and self-healing tests are making testing more efficient and effective.
• Visual Testing: Explore the emerging capabilities of AI in visual testing and how it's set to revolutionize UI verification.
• Inflectra's AI Solutions: See demonstrations of Inflectra's cutting-edge AI tools like the ChatGPT plugin and Azure Open AI platform, designed to streamline your testing process.
Whether you're a developer, tester, or QA professional, this webinar will give you valuable insights into how AI is shaping the future of software delivery.
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/
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.
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
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024Tobias Schneck
As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
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
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on the notifications, alerts, and approval requests using Slack for Bonterra Impact Management. The solutions covered in this webinar can also be deployed for Microsoft Teams.
Interested in deploying notification automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
Essentials of Automations: Optimizing FME Workflows with ParametersSafe Software
Are you looking to streamline your workflows and boost your projects’ efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, you’re in the right place.
Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
Here’s what you’ll gain:
- Essentials of FME Parameters: Understand the pivotal role of parameters, including Reader/Writer, Transformer, User, and FME Flow categories. Discover how they are the key to unlocking automation and optimization within your workflows.
- Practical Applications in FME Form: Delve into key user parameter types including choice, connections, and file URLs. Allow users to control how a workflow runs, making your workflows more reusable. Learn to import values and deliver the best user experience for your workflows while enhancing accuracy.
- Optimization Strategies in FME Flow: Explore the creation and strategic deployment of parameters in FME Flow, including the use of deployment and geometry parameters, to maximize workflow efficiency.
- Pro Tips for Success: Gain insights on parameterizing connections and leveraging new features like Conditional Visibility for clarity and simplicity.
We’ll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic.
Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.
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.
Generating a custom Ruby SDK for your web service or Rails API using Smithyg2nightmarescribd
Have you ever wanted a Ruby client API to communicate with your web service? Smithy is a protocol-agnostic language for defining services and SDKs. Smithy Ruby is an implementation of Smithy that generates a Ruby SDK using a Smithy model. In this talk, we will explore Smithy and Smithy Ruby to learn how to generate custom feature-rich SDKs that can communicate with any web service, such as a Rails JSON API.
Leading Change strategies and insights for effective change management pdf 1.pdf
Enst202 atmospheric moisture
1. Atmospheric moisture: Evaporation and
condensation
●
The goal of this section is to use previously learned
concepts to develop an understanding of relative
humidity and thus the processes that determine
relative rates of evaporation and condensation in
the atmosphere.
2. Expected prior knowledge
●
Environmental processes
●
Equilibrium and rate of processes
●
Atmospheric pressure
●
Evaporation and condensation
●
Why water is a polar molecule
●
Hydrogen bonds and potential energy
●
Thermodynamics (e.g., TKE, temperature)
3. The rate of many environmental processes is
dependent on how far a system is from equilibrium.
Consider the diagram below. Ball A has more
gravitational potential energy than Ball B because
the difference in height from the top to bottom of
the ramp is greater. Ball A will therefore be moving
at a greater velocity than Ball B along the ramp.
>
4. Previously, we learned that the second law of
thermodynamics dictates that thermal energy will
only spontaneously travel from warmer to colder
objects (increasing entropy). The rate of energy
transfer is dependent on the difference in
temperature between the two objects. This is why
an ice cube feels colder in our hand than a glass of
liquid water.
>
5. The second law has also been applied to the
spontaneous expansion of gases. Gas molecules will
move from areas of high concentration (high
density) to areas of low concentration (low density).
The rate of transfer is dependent on the difference in
density. In the example below, the rate of transfer is
greater between the left hand boxes because the
difference in density is greater.
>
6. Each of the described systems is
moving in the direction of
equilibrium. Once equilibrium is
achieved, there will be no net
change to the system unless acted
upon by an outside force. For
example, thermal energy will be
exchanged between objects in the
above illustration but the
temperature of the objects will not
change. Likewise, molecules of air
may move between boxes in the
lower diagram, but there will be no
net change in density.
7. When considering atmospheric moisture, our primary
concern is the relative rates of evaporation and
condensation of water.
●
●
Evaporation is the process by which water undergoes a
state change from a liquid to a gaseous state (water
vapor)
Condensation is the process by which water undergoes a
state change from a gaseous to a liquid state.
Prior to exploring the drivers of each of these rates, we
need to review what we know about water ….
8. Water is a polar molecule with two Hydrogen atoms and one
Oxygen atom. These atoms share their valence shell electrons in a
covalent bond.
H
(-)
O
H
(+)
Because the Oxygen atom is more
electronegative than the hydrogen
atom, the valence shell electrons are
held closer to the Oxygen atom. This
give the Oxygen atom a net negative
charge and the Hydrogen atoms a net
positive charge.
9. Due to the unequal distribution of charge, intermolecular forces,
known as hydrogen bonds, hold together water molecules in liquid
or solid states. These bonds are a form of intermolecular potential
energy (recall that potential energy is energy related to an object’s
position).
H
(+)
(-)
O
H
Liquid
10. If the thermal kinetic energy (vibrational, rotational, and random
translational motion) of a molecule exceeds the potential energy of
the hydrogen bond that holds it in liquid state, the molecule will
move from a liquid state to gaseous state (water vapor).
Gas
Liquid
11. Recall that temperature is a measure of the average thermal kinetic
energy of a substance or system. Some of the molecules in the
system have relatively low TKE (slow-moving) and some relatively
high (fast-moving). The plot below shows the proportion of
molecules present within a system at a given TKE.
12. The diagram below displays a system containing liquid water (solid
area under the curve) and water vapor (textured area under the
curve). The temperature of the system is 20 degrees Celsius.
13. The dashed line represents a boundary. Below the boundary, the
potential energy of the hydrogen bonds exceeds the TKE, therefore
the water is in a liquid state. Above the boundary the TKE of the
molecules exceeds the intermolecular potential energy, therefore
the water is in a gaseous state.
14. As displayed, this system is at equilibrium – in meteorology, this
equilibrium is known as the saturation point. This is the point at
which the evaporation and condensation rates are equal.
15. At equilibrium, the shaded area represents the partial vapor
pressure (e, synonyms: actual vapor pressure, vapor pressure). This
is essentially a proxy measurement of the amount of water held in a
vapor state.
16. We can calculate partial pressure by multiplying the proportion of
water vapor in an air parcel by the total air pressure. This is
expressed by the following formula:
𝑃𝑎𝑟𝑡𝑖𝑎𝑙 𝑣𝑎𝑝𝑜𝑟 𝑝𝑟𝑒𝑠𝑠𝑢𝑟𝑒 𝑒 = 𝐴𝑡𝑚𝑜𝑠𝑝ℎ𝑒𝑟𝑖𝑐 𝑝𝑟𝑒𝑠𝑠𝑢𝑟𝑒
𝑚𝑏 × 0.01 × %𝐻2 𝑂 𝑣
For example, if the atmospheric pressure at a given location is
1000 mb and a parcel of air contains 4.0% water vapor:
𝑃𝑎𝑟𝑡𝑖𝑎𝑙 𝑣𝑎𝑝𝑜𝑟 𝑝𝑟𝑒𝑠𝑠𝑢𝑟𝑒 𝑒 = 1000 𝑚𝑏 × 4 × 0.01 = 40 𝑚𝑏
17. A simple example: A parcel of air has a partial vapor pressure of
34.0 mb at an atmospheric pressure of 1014 mb. Water vapor
makes up what percentage of molecules within the parcel?
18. A simple example: A parcel of air has a partial vapor pressure of
34.0 mb at an atmospheric pressure of 1014 mb. Water vapor
makes up what percentage of molecules within the parcel?
Solution:
𝑃𝑎𝑟𝑡𝑖𝑎𝑙 𝑣𝑎𝑝𝑜𝑟 𝑝𝑟𝑒𝑠𝑠𝑢𝑟𝑒 𝑒 = 𝐴𝑡𝑚𝑜𝑠𝑝ℎ𝑒𝑟𝑖𝑐 𝑝𝑟𝑒𝑠𝑠𝑢𝑟𝑒
34 𝑚𝑏 = 1014 𝑚𝑏 × 0.01 × %𝐻2 𝑂 𝑣
34 𝑚𝑏
= %𝐻2 𝑂 𝑣
1014 𝑚𝑏 × 0.01
3.34 % = %𝐻2 𝑂 𝑣
𝑚𝑏 × 0.01 × %𝐻2 𝑂 𝑣
19. Saturation vapor pressure (SVP, es; synonym: equilibrium vapor
pressure) is a proxy measure of the amount of water that can be held
in vapor state as a function of a system's temperature. Because the
described system is at equilibrium, the shaded area under the curve
represents both the actual VP and SVP.
20. If the temperature of the system were to increase from 20 to 22 °C,
the capacity to hold water molecules in a vapor state (SVP) is
increased. If no additional water is added to the system, the system
will no longer be at equilibrium and the evaporation rate will exceed
the rate of condensation.
21. Likewise, if the system was saturated at a temperature of 22 °C and
you cooled the system to 20 °C, the rate of condensation would
(instantaneously) exceed evaporation and the excess water vapor
would condense into liquid water.
22. Notice how the proportion of molecules that can be held in a vapor
state (saturation vapor pressure) increases considerably with
temperature. This results in a positive exponential relationship
between temperature and saturation vapor pressure.
23. We can diagram the positive exponential relationship
between saturation vapor pressure and temperature
using the following plot. Notice that SVP is not
dependent on the amount of water vapor present in the
air but, rather, the temperature alone.
24. We can calculate the relationship between saturation
vapor pressure and the temperature of a parcel using the
following formula:
17.67 × 𝑇𝑒𝑚𝑝𝑒𝑟𝑎𝑡𝑢𝑟𝑒 (℃)
𝑒 𝑠 = 6.112 𝑚𝑏 × 𝐸𝑋𝑃
𝑇𝑒𝑚𝑝𝑒𝑟𝑎𝑡𝑢𝑟𝑒 ℃ + 243.5
Notice, once again, that the only variable in the formula is
the temperature of the air parcel!
25. A simple example: The temperature of a parcel of air is
21°C, what is the saturation vapor pressure?
26. A simple example: The temperature of a parcel of air is
21°C, what is the saturation vapor pressure?
Solution:
17.67 × 21 ℃
𝑒 𝑠 = 6.112 𝑚𝑏 × 𝐸𝑋𝑃
= 24.85 𝑚𝑏
21℃ + 243.5
27. The greater the difference between the actual vapor
pressure and the saturation vapor pressure, the further
the system is from equilibrium. Similar to the ball on
the ramp, the greater the distance from equilibrium,
the faster the process will occur. This leads to high rates
of evaporation relative to condensation.
28. A more complicated example: A parcel of air contains
3.20 % water vapor at an atmospheric pressure of 976
mb.
a) What is the partial pressure of the air parcel?
b) If the temperature of the parcel is 27.0 °C, what is the
saturation vapor pressure?
c) How would the rate of evaporation, relative to
condensation change if the temperature of the parcel
were to decrease?
29. A more complicated example: A parcel of air contains
3.20 % water vapor at an atmospheric pressure of 976
mb. Solution:
a) What is the partial pressure of the air parcel?
𝑒 = 976 𝑚𝑏 × 6 × 0.01 = 31.2 𝑚𝑏
b) If the temperature of the parcel is 27.0 °C, what is the
saturation vapor pressure?
17.67 × 27 ℃
𝑒 𝑠 = 6.112 𝑚𝑏 × 𝐸𝑋𝑃
= 35.6 𝑚𝑏
27 ℃ + 243.5
c) How would the rate of evaporation, relative to
condensation change if the temperature of the parcel
were to decrease? It would decrease.
30. We can summarize the relationship between the actual
and saturation vapor pressure of a parcel with relative
humidity. Relative humidity is the proportion of actual
water vapor (e) relative to the saturation vapor pressure
(es), expressed as a percentage. This can be thought of
more simply as the amount of water vapor in a parcel of
air relative to the amount of water that can be held in a
vapor state. Relative humidity is expressed with the
formula:
𝑒
𝑅𝑒𝑙𝑎𝑡𝑖𝑣𝑒 ℎ𝑢𝑚𝑖𝑑𝑖𝑡𝑦 = × 100
𝑒𝑠
31. A simple example: A parcel of air has a partial vapor pressure of 13
mb and a saturation vapor pressure of 17 mb.
a) What is the relative humidity of the parcel?
b) If the temperature of the parcel were increased, how would the
relative humidity be altered? Why?
c) If the temperature of the parcel were decreased, how would the
rate of evaporation be altered?
32. A simple example: A parcel of air has a partial vapor pressure of 13
mb and a saturation vapor pressure of 17 mb.
Solution:
a) What is the relative humidity of the parcel?
13 𝑚𝑏
𝑅𝑒𝑙𝑎𝑡𝑖𝑣𝑒 ℎ𝑢𝑚𝑖𝑑𝑖𝑡𝑦 =
× 100 = 76%
17 𝑚𝑏
b) If the temperature of the parcel were increased, how would the
relative humidity be altered? Why? Decrease, because the SVP
increases exponentially with increasing temperature.
c) If the temperature of the parcel were decreased, how would the
rate of evaporation be altered? Why? Decrease, because the
relative humidity is increased.
33. The temperature at which the rate of evaporation is
equal to condensation (i.e., equilibrium or the saturation
point) is the dew point temperature. At constant
atmospheric pressure, the dew point of a given air parcel
is ONLY determined by the actual vapor pressure.
The dew point temperature can be calculated as follows:
𝑒
243.5 × ln
6.112
𝑇𝑑 =
𝑒
17.67 − ln
6.112
Where e is the actual vapor pressure.
34. A simple example: A parcel of air has an actual vapor
pressure of 14 mb. What is the dew point of the parcel?
35. A simple example: A parcel of air has an actual vapor
pressure of 14 mb. What is the dew point of the parcel?
Solution:
14.0 𝑚𝑏
243.5 × ln
6.112
𝑇𝑑 =
= 12.0 ℃
14.0 𝑚𝑏
17.67 − ln
6.112
Recall that this means that, in order to reach equilibrium at
this vapor pressure, the parcel would have to cool to a
temperature of 12.0 oC.
36. The difference between the dew point temperature and
the air temperature can provide a qualitative measure
of relative evaporation and condensation rates. If the
air temperature is considerably higher than the dew
point, the relative humidity is low and the evaporation
rate, relative to the condensation rate, is high.
37. If the air temperature falls below the dew point, the rate of
condensation (instantaneously) exceeds evaporation and the excess
water vapor condenses into liquid water. Because the air now
contains a lower concentration of water vapor (e decreases), the
dew point decreases simultaneously (thus relative humidity will not
exceed 100%).
38. A “less-than-simple” example: You measure the relative
humidity and air temperature of a parcel of air using a
sling psychrometer (see lab manual, lab six) and the
atmospheric pressure with a barometer. The air
temperature is 17.0 °C, the relative humidity is 47.0% and
the atmospheric pressure is 1019 mb.
a) What is the saturation vapor pressure of the parcel?
b) What is the dew point temperature of the parcel?
c) What is the percent composition of water vapor in the
parcel?
39. Air temperature = 17 °C
Relative humidity = 47%
Atmospheric pressure = 1019 mb
a) What is the saturation vapor pressure of the parcel?
Solution:
17.67 × 17 ℃
𝑒 𝑠 = 6.112 𝑚𝑏 × 𝐸𝑋𝑃
= 19.3 𝑚𝑏
17 ℃ + 243.5
40. Air temperature = 17 °C
Relative humidity = 47%
Atmospheric pressure = 1019 mb
Saturation vapor pressure = 19.3 mb
b) What is the dew point temperature of the parcel?
Solution:
𝑒 (𝑚𝑏)
47 × 19.3𝑚𝑏
× 100 = 47 → 𝑒 =
= 9.07𝑚𝑏
19.3 𝑚𝑏
100
9.07 𝑚𝑏
243.5 × ln
6.112
𝑇𝑑 =
= 5.56 ℃
9.07 𝑚𝑏
17.67 − ln
6.112
41. Air temperature = 17 °C
Relative humidity = 47%
Atmospheric pressure = 1019 mb
Saturation vapor pressure = 19.3 mb
Partial vapor pressure = 9.07mb
Dew point = 5.56 °C
c) What is the percent composition of water vapor in the
parcel?
9.07𝑚𝑏
× 100 = 0.890%
1019 𝑚𝑏