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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 22:05 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
2023-01-31 20:05 7.2 83.1 3.1 4.5 264 5.5 260 24.888 0.0 0.00
2023-01-31 20:00 6.8 81.0 2.2 4.6 249 5.7 256 24.887 0.0 0.00
2023-01-31 19:55 6.5 83.1 2.5 4.5 257 5.3 244 24.890 0.0 0.00
2023-01-31 19:50 7.5 79.7 2.6 2.3 229 3.1 228 24.889 0.0 0.00
2023-01-31 19:45 7.4 82.0 3.0 2.1 212 2.6 171 24.889 0.1 0.00
2023-01-31 19:40 7.2 80.6 2.5 2.4 171 2.8 220 24.891 0.1 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 22:05 -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
2023-01-31 20:05 -13.8 83.1 -16.0 2.0 264 2.5 260 842.81 0.0 0.00
2023-01-31 20:00 -14.0 81.0 -16.5 2.1 249 2.5 256 842.78 0.0 0.00
2023-01-31 19:55 -14.2 83.1 -16.4 2.0 257 2.4 244 842.87 0.0 0.00
2023-01-31 19:50 -13.6 79.7 -16.4 1.0 229 1.4 228 842.83 0.0 0.00
2023-01-31 19:45 -13.7 82.0 -16.1 0.9 212 1.2 171 842.85 0.1 0.00
2023-01-31 19:40 -13.8 80.6 -16.4 1.1 171 1.3 220 842.91 0.1 0.00
CIRA

Cooperative Institute for Research in the Atmosphere

Publications

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  • Haynes, J.M., Noh, Y.J., Miller, S.D., Haynes, K.D., Ebert-Uphoff, I. and Heidinger, A., Low Cloud Detection in Multilayer Scenes using Satellite Imagery with Machine Learning Methods, Journal of Atmospheric and Oceanic Technology, early online release Dec 15, 2021, https://doi.org/10.1175/JTECH-D-21-0084.1.
  • Roebber, P.J., 2021. Toward an Adaptive Artificial Neural Network–Based Postprocessor. Monthly Weather Review149(12), pp.4045-4055, Dec 2021, https://doi.org/10.1175/MWR-D-21-0089.1.
  • Lagerquist, R., Turner, D., Ebert-Uphoff, I., Stewart, J. and Hagerty, V., Using Deep Learning to Emulate and Accelerate a Radiative Transfer Model. Journal of Atmospheric and Oceanic Technology, 38(10), pp.1673-1696, Oct 2021, https://doi.org/10.1175/MWR-D-21-0096.1.
  • Lagerquist, R., Stewart, J.Q., Ebert-Uphoff, I. and Kumler, C., Using Deep Learning to Nowcast the Spatial Coverage of Convection from Himawari-8 Satellite Data. Monthly Weather Review149(12), pp.3897-3921, Oct 2021, https://doi.org/10.1175/JTECH-D-21-0007.1.
  • Lee, Y., Kummerow, C. D., Ebert-Uphoff, I.  Applying machine learning methods to detect convection using GOES-16 ABI data, Atmospheric Measurement Techniques, April 2021, https://doi.org/10.5194/amt-2020-420.
  • Samarasinghe, S.M., Barnes, E.A., Connolly, C., Ebert-Uphoff, I., Sun, L.  Strengthened causal connections between the MJO and the North Atlantic with climate warming, Geophysical Research Letters, Feb 1 2021, https://doi.org/10.1029/2020GL091168.
    Featured as research highlight in Nature Climate Change: B. Langenbrunner, The Madden–Julian oscillation strengthens its reach. Nature Climate Change, 11, 183, 3 March 2021. https://doi.org/10.1038/s41558-021-01008-7
  • McGovern, Amy; Bostrom, Ann; Ebert-Uphoff, Imme; He, Ruoying; Thorncroft, Chris; Tissot, Philippe; Boukabara, Sid; Demuth, Julie; Gagne II, David John; Hickey, Jason; Williams, John K. (2020) Weathering Environmental Change Through Advances in AI. EOS, Volume 101, https://doi.org/10.1029/2020EO147065, July 2020.
  • Barnes, E. A., Toms, B., Hurrell, J. W., Ebert-Uphoff, I., Anderson, C., Anderson, D. Indicator patterns of forced change learned by an artificial neural network. Journal of Advances in Modeling Earth Systems (JAMES), https://doi.org/10.1029/2020MS002195, Aug 2020.  (arXiv preprint from May 2020: here).
  • Ebert-Uphoff, I., Hilburn, K. A. Evaluation, Tuning and Interpretation of Neural Networks for Meteorological Applications, Bulletin of the American Meteorological Society (BAMS), https://doi.org/10.1175/BAMS-D-20-0097.1, Aug 2020.  (arXiv preprint from May 2020 here).
  • Hilburn, K. A., Ebert-Uphoff, I., and Miller, S. D., Development and Interpretation of a Neural Network-Based Synthetic Radar Reflectivity Estimator Using GOES-R Satellite Observations. Journal of Applied Meteorology and Climatology, https://doi.org/10.1175/JAMC-D-20-0084.1, Jan 2021. (arXiv preprint from April 2020: here)
  • Roebber, P.J., and Crockett, J.,, Using a coevolutionary postprocessor to improve skill for both forecasts of surface temperature and nowcasts of convection occurrence. Monthly Weather Review147(11), pp.4241-4259, https://doi.org/10.1175/MWR-D-19-0063.1, Nov 2019.

AMS Annual meeting – Jan 2022 – 21st Conference on Artificial Intelligence for Environmental Science (AMS AI)

CIRA presentations include:

  • C. Slocum, Y. D. Rao, R. Redmon, and E. Kihn, Promoting NOAA Workforce Proficiency on Artificial Intelligence through Open Science and Partnership.  AMS AI presentation
  • C. Slocum and J. Knaff, Improving the Feature Selection Process for Tropical Cyclone Rapid Intensification Guidance.  AMS AI presentation
  • R. Lagerquist, I. Ebert-Uphoff, J. Q. Stewart, and C. Kumler, Nowcasting Convection with Deep Learning and Custom Spatially Aware Loss Functions. AMS AI presentation
  • R. A. Lagerquist, D. D. Turner, I. Ebert-Uphoff, J. Q. Stewart, and V. Hagerty, Grid-Agnostic Deep Learning for Parameterizing Radiative Transfer. AMS AI presentation
  • I. Ebert-Uphoff, R. Lagerquist, K. A. Hilburn, Y. Lee, K. Haynes, J. Stock, C. Kumler, and J. Q. Stewart, How to Develop Custom Loss Functions for Neural Networks in Meteorology. AMS AI presentation
  • R. Lagerquist, I. Ebert-Uphoff, Exploring the Benefits of Integrating Fourier and Wavelet Transforms into Neural Networks for Meteorological Applications. AMS AI presentation
  • K. Haynes, C. Slocum, J. A. Knaff, K. Musgrave, and I. Ebert-Uphoff, Simulating 89-GHz Imagery from Operational Geostationary Satellites Using Machine Learning. AMS AI presentation
  • M.G. Cains, C. D. Wirz, J. L. Demuth, A. Bostrom, A. McGovern, I. Ebert-Uphoff, D. J. Gagne II, A. Burke, and R. A. Sobash, NWS Forecasters’ Perceptions and Potential Uses of Trustworthy AI/ML for Hazardous Weather Risks. AMS AI presentation
  • A. McGovern, I. Ebert-Uphoff, A. Bostrom, and D. J. Gagne II, Ethical and Responsible AI and Trust for Weather and Climate. AMS AI presentation
  • A. Mamalakis, I. Ebert-Uphoff and E. Barnes, Explainable Artificial Intelligence for Environmental Science: Introducing Objectivity into the Assessment of Neural Network Attribution Methods. AMS AI presentation

(Workshop agenda: https://2021noaaaiworkshop.sched.com/)

CIRA and RAMMB researchers and CSU collaborators presented 17 oral talks during the 3rd NOAA AI Workshop, including:

  • K. Haynes presented a talk titled “Using Machine Learning to Simulate 89-GHz Imagery from Geostationary Satellites”.  The talk discussed the ability of random forests and neural networks to predict microwave imagery from the GOES ABI, using tropical cyclone data from the TC PRIMED dataset.  Co-authors: C. Slocum, J. Knaff, K. Musgrave, and I. Ebert-Uphoff.  (POC: K. Haynes, Katherine.Haynes@colostate.edu; Funding: NOAA GOES-R)
  • J. Haynes presented a talk titled “Low Cloud Detection in Multilayer Scenes from GOES ABI using Machine Learning Methods”. The talk discussed how machine learning is being used to improve low cloud detection from the GOES ABI, with an eye toward operational implementation in cooperation with our Aviation Weather Center partners. Co-authors: Y. Noh, S. Miller, K. Haynes, I. Ebert-Uphoff, and A. Heidinger. (POC: J. Haynes, John.Haynes@colostate.edu; Funding: NOAA GOES-R Program Office)
  • C. Slocum presented a talk titled “Beyond Dvorak: creating feature-based labels in geostationary imagery for tropical cyclones.” The talk featured SALT (Satellite Analyst Labeling Toolkit) that provides better application specific tools for labeling NOAA satellite data in an AI-ready and analysis-ready manner. The talk used tropical cyclones for the application. (POC: C. Slocum, Christopher.Slocum@noaa.gov; Funding: PDRA)
  • K. Hilburn presented a talk titled “Improving GREMLIN: A Case Study in AI Application Development”. The talk discussed recent improvements to the GREMLIN model, including use of multi-task learning and uncertainty estimates for each pixel. The talk highlighted the value of GLM lightning area for improving GREMLIN estimates of synthetic radar reflectivity and convective and stratiform fraction. Co-authors: Y. Lee and I. Ebert-Uphoff. (POC: K. Hilburn, Kyle.Hilburn@noaa.gov; Funding: GOES-R).
  • I. Ebert-Uphoff presented a highlight talk entitled “Guide to Custom Loss Functions for Neural Networks in Environmental Science”.  The talk highlighted community resources developed by CIRA to better customize neural networks to the special requirements of environmental science applications. Co-authors: R. Lagerquist, K. Hilburn, Y. Lee, K. Haynes, J. Stock, C. Kumler, J. Stewart. (POC: I. Ebert-Uphoff, iebert@colostate.edu; Funding: NSF, NOAA GOES-R, NOAA RDHPCS).
  • Y. Lee presented a talk titled “Exploring ways to effectively use temporal satellite images in detecting convection from GOES-16”. Results using convolutional 3D layers highly improved results from using convolutional 2D layers, and future work includes using optical flow developed by Jason Apke during preprocessing of the input data. Co-authors: K. Hilburn and I. Ebert-Uphoff. (POC: Y. Lee, Yoonjin.Lee@colostate.edu; Funding: GOES-R).
  • J. Stock presented a talk titled “Using Machine Learning to Improve Vertical Profiles of Temperature and Moisture for Severe Weather Forecasting”.  Co-authors: J. Dandy, I. Ebert-Uphoff, J. Dostalek, L. Grasso.  (POC: J. Dostalek, Jack.Dostalek@colostate.edu, L. Grasso, Lewis.Grasso@colostate.edu, I. Ebert-Uphoff, iebert@colostate.edu; Funding: NOAA)
  • L. Ver Hoef presented a talk titled “An Introduction to Topological Data Analysis for Remote Sensing”.  Co-authors: Y. Lee, K. Hilburn, H. Adams, E. King, I. Ebert-Uphoff.  (POC:  I. Ebert-Uphoff, iebert@colostate.edu; Funding: CIRA, NSF)
  • A. Mamalakis presented a talk titled “Explainable Artificial Intelligence for Environmental Sciences: A benchmark to assess and compare neural network attribution methods”.  Co-authors: I. Ebert-Uphoff,  E. Barnes.  (POC: I. Ebert-Uphoff, iebert@colostate.edu; Funding: NSF)
  • R. Lagerquist presented a talk titled “U-net++ for emulation and acceleration of a radiative-transfer model”.  Co-authors: D. Turner, I. Ebert-Uphoff, J. Stewart, V. Hagerty (POC: R. Lagerquist, Ryan.Lagerquist@colostate.edu; Funding: CIRA)
  • R. Lagerquist presented a poster titled “U-nets for nowcasting the timing and location of thunderstorms based on satellite data”.  Co-authors:  Jebb Stewart, Imme Ebert-Uphoff, Christina Kumler.  (POC: R. Lagerquist, Ryan.Lagerquist@colostate.edu; Funding: CIRA)