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Christman Field Latest Observations
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Date Time
MST
Temp
°F
RH
%
DewPt
°F
Wind
mph
Dir
°
Gust
mph
Dir
°
Press
in Hg
Solar
W/m^2
Prec
in
2023-01-31 22:35 8.8 78.1 3.4 4.6 1 5.6 345 24.901 0.0 0.00
2023-01-31 22:30 10.8 76.4 4.8 2.3 288 3.2 288 24.900 0.0 0.00
2023-01-31 22:25 11.1 76.2 5.1 0.5 260 1.9 260 24.901 0.0 0.00
2023-01-31 22:20 11.5 75.5 5.2 2.1 217 3.1 261 24.898 0.0 0.00
2023-01-31 22:15 10.1 78.5 4.8 2.4 266 4.2 259 24.899 0.0 0.00
2023-01-31 22:10 10.6 78.0 5.1 2.2 216 4.9 244 24.900 0.0 0.00
2023-01-31 22:05 10.3 78.3 4.9 3.1 247 3.8 248 24.899 0.0 0.00
2023-01-31 22:00 9.5 80.2 4.6 4.5 219 5.5 255 24.897 0.0 0.00
2023-01-31 21:55 11.1 76.3 5.1 4.2 262 8.2 255 24.896 0.0 0.00
2023-01-31 21:50 9.0 86.9 5.9 5.5 251 8.9 253 24.898 0.0 0.00
2023-01-31 21:45 5.8 84.0 2.0 3.7 265 5.0 247 24.899 0.0 0.00
2023-01-31 21:40 6.2 83.5 2.2 3.5 259 4.4 247 24.897 0.0 0.00
2023-01-31 21:35 5.7 85.8 2.4 2.7 317 3.5 316 24.896 0.0 0.00
2023-01-31 21:30 5.3 84.5 1.7 3.1 299 4.3 261 24.899 0.1 0.00
2023-01-31 21:25 5.2 84.3 1.5 2.7 248 4.3 237 24.901 0.0 0.00
2023-01-31 21:20 5.3 82.4 1.1 1.8 249 3.2 249 24.899 0.0 0.00
2023-01-31 21:15 4.7 83.7 0.9 0.8 286 2.1 286 24.897 0.1 0.00
2023-01-31 21:10 4.9 82.6 0.7 1.8 343 2.4 351 24.898 0.1 0.00
2023-01-31 21:05 4.9 82.2 0.7 3.0 351 3.7 310 24.900 0.0 0.00
2023-01-31 21:00 5.1 80.8 0.5 3.2 310 3.9 326 24.900 0.0 0.00
2023-01-31 20:55 6.6 79.4 1.6 3.4 326 4.5 345 24.900 0.0 0.00
2023-01-31 20:50 7.8 78.0 2.3 3.6 1 4.2 4 24.895 0.0 0.00
2023-01-31 20:45 7.3 82.0 3.0 2.6 350 3.0 351 24.895 0.0 0.00
2023-01-31 20:40 5.6 83.8 1.8 2.1 288 2.8 299 24.897 0.0 0.00
2023-01-31 20:35 5.8 78.4 0.5 2.5 231 4.7 337 24.895 0.0 0.00
2023-01-31 20:30 6.4 82.6 2.3 4.9 337 5.8 349 24.894 0.0 0.00
2023-01-31 20:25 5.7 82.4 1.5 3.5 11 4.8 11 24.892 0.0 0.00
2023-01-31 20:20 5.8 81.1 1.3 2.3 356 3.8 12 24.889 0.0 0.00
2023-01-31 20:15 6.4 79.5 1.4 2.1 66 3.4 252 24.889 0.0 0.00
2023-01-31 20:10 7.0 82.2 2.7 2.9 252 3.6 263 24.885 0.0 0.00
Date Time
MST
Temp
°C
RH
%
DewPt
°C
Wind
m/s
Dir
°
Gust
m/s
Dir
°
Press
hPa
Solar
W/m^2
Prec
mm
2023-01-31 22:35 -12.9 78.1 -15.9 2.1 1 2.5 345 843.24 0.0 0.00
2023-01-31 22:30 -11.8 76.4 -15.1 1.0 288 1.4 288 843.19 0.0 0.00
2023-01-31 22:25 -11.6 76.2 -15.0 0.2 260 0.9 260 843.24 0.0 0.00
2023-01-31 22:20 -11.4 75.5 -14.9 1.0 217 1.4 261 843.16 0.0 0.00
2023-01-31 22:15 -12.2 78.5 -15.1 1.1 266 1.9 259 843.17 0.0 0.00
2023-01-31 22:10 -11.9 78.0 -15.0 1.0 216 2.2 244 843.20 0.0 0.00
2023-01-31 22:05 -12.0 78.3 -15.0 1.4 247 1.7 248 843.19 0.0 0.00
2023-01-31 22:00 -12.5 80.2 -15.2 2.0 219 2.5 255 843.12 0.0 0.00
2023-01-31 21:55 -11.6 76.3 -14.9 1.9 262 3.7 255 843.06 0.0 0.00
2023-01-31 21:50 -12.8 86.9 -14.5 2.5 251 4.0 253 843.13 0.0 0.00
2023-01-31 21:45 -14.6 84.0 -16.7 1.7 265 2.2 247 843.17 0.0 0.00
2023-01-31 21:40 -14.4 83.5 -16.5 1.5 259 2.0 247 843.12 0.0 0.00
2023-01-31 21:35 -14.6 85.8 -16.5 1.2 317 1.5 316 843.06 0.0 0.00
2023-01-31 21:30 -14.8 84.5 -16.8 1.4 299 1.9 261 843.18 0.1 0.00
2023-01-31 21:25 -14.9 84.3 -16.9 1.2 248 1.9 237 843.23 0.0 0.00
2023-01-31 21:20 -14.8 82.4 -17.2 0.8 249 1.4 249 843.16 0.0 0.00
2023-01-31 21:15 -15.2 83.7 -17.3 0.4 286 0.9 286 843.11 0.1 0.00
2023-01-31 21:10 -15.1 82.6 -17.4 0.8 343 1.1 351 843.16 0.1 0.00
2023-01-31 21:05 -15.1 82.2 -17.4 1.3 351 1.6 310 843.23 0.0 0.00
2023-01-31 21:00 -14.9 80.8 -17.5 1.4 310 1.7 326 843.21 0.0 0.00
2023-01-31 20:55 -14.1 79.4 -16.9 1.5 326 2.0 345 843.21 0.0 0.00
2023-01-31 20:50 -13.5 78.0 -16.5 1.6 1 1.9 4 843.03 0.0 0.00
2023-01-31 20:45 -13.7 82.0 -16.1 1.2 350 1.3 351 843.05 0.0 0.00
2023-01-31 20:40 -14.7 83.8 -16.8 1.0 288 1.3 299 843.11 0.0 0.00
2023-01-31 20:35 -14.6 78.4 -17.5 1.1 231 2.1 337 843.04 0.0 0.00
2023-01-31 20:30 -14.2 82.6 -16.5 2.2 337 2.6 349 843.00 0.0 0.00
2023-01-31 20:25 -14.6 82.4 -16.9 1.5 11 2.2 11 842.94 0.0 0.00
2023-01-31 20:20 -14.5 81.1 -17.1 1.0 356 1.7 12 842.83 0.0 0.00
2023-01-31 20:15 -14.2 79.5 -17.0 0.9 66 1.5 252 842.84 0.0 0.00
2023-01-31 20:10 -13.9 82.2 -16.3 1.3 252 1.6 263 842.70 0.0 0.00
CIRA

Cooperative Institute for Research in the Atmosphere

AI Institute

Overview of AI Institute (aka AI2ES)


NSF AI Institute for Research on Trustworthy AI in Weather, Climate, and Coastal Oceanography (AI2ES).

  • AI2ES was funded by the NSF in 2020 with a $20M grant over 5 years.
  • AI2ES is headquartered at the University of Oklahoma, and CSU is one of its founding members.
  • The graphic below gives a basic overview of the primary research areas AI2ES seeks to address and how they are connected.  For a more detailed explanation of AI2ES activities and philosophy, see https://www.ai2es.org/research/synergistic research path and applications diagram

AI2ES activities at CSU


Within CSU the core departments involved in AI2ES are

 

AI2ES activities at CIRA


Several of our CIRA members are funded by this NSF grant and are working on topics that are of interest to both CIRA and AI2ES.

CIRA activities related to this AI institute include:

  • Tropical cyclones:
    • Develop ML methods that can simulate passive microwave imagery from geostationary satellite imagery.
    • Goal:  Generate imagery that reveal internal structure of tropical cyclones at high temporal resolution.
  • XAI methods for weather and climate:
    • Select and adjust methods for eXplainable Artificial Intelligence (XAI) specifically for use in environmental applications.
  • Incorporating forecasters’ feedback: 
    • Work with an interdisciplinary team of risk communication experts, atmospheric scientists and AI experts on soliciting feedback from forecasters on which kinds of AI tools, visualization and explanations are actually useful for them.
    • Take that feedback into account to develop AI tools for forecasters that they actually find useful.
  • Ethical consideration for development of AI tools for environmental science 
    • Many people think that because AI is a mathematical tool it is objective in all aspects.  That’s not true.  There are many ways in which bias can sneak into the algorithms, such as
      • Bias in data (e.g., unevenly distributed sensor network, measurements only possible under certain conditions, etc.)
      • Bias can also arise from algorithm choices, e.g., spatial resolution of predictions greatly influences whether underrepresented groups are “averaged out”

 

 

Primary contact