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Christman Field Latest Observations
Select Units
Date Time
MST		 Temp
°F		 RH
%		 DewPt
°F		 Wind
mph		 Dir
°		 Gust
mph		 Dir
°		 Press
in Hg		 Solar
W/m^2		 Prec
in
2023-06-09 03:45 54.9 85.1 50.5 2.6 310 3.3 314 24.862 0.0 0.00
2023-06-09 03:40 54.6 86.6 50.7 2.5 269 3.3 276 24.863 0.0 0.00
2023-06-09 03:35 54.9 85.9 50.8 3.3 273 3.6 308 24.861 0.0 0.00
2023-06-09 03:30 55.5 84.1 50.8 2.6 314 3.3 319 24.860 0.0 0.00
2023-06-09 03:25 55.6 82.6 50.4 3.2 335 4.0 319 24.860 0.0 0.00
2023-06-09 03:20 55.4 83.4 50.4 3.6 314 4.3 324 24.861 0.0 0.00
2023-06-09 03:15 55.0 83.1 50.0 3.8 323 4.8 334 24.861 0.0 0.00
2023-06-09 03:10 55.5 83.3 50.6 1.4 298 4.8 323 24.862 0.0 0.00
2023-06-09 03:05 55.9 82.8 50.8 1.3 168 2.7 156 24.862 0.0 0.00
2023-06-09 03:00 56.3 81.5 50.7 1.8 156 2.6 156 24.864 0.0 0.00
2023-06-09 02:55 56.3 80.4 50.3 1.4 113 2.1 65 24.864 0.0 0.00
2023-06-09 02:50 55.9 79.1 49.6 2.0 64 2.4 43 24.866 0.0 0.00
2023-06-09 02:45 55.8 81.0 50.1 2.6 66 3.2 12 24.867 0.0 0.00
2023-06-09 02:40 56.0 81.1 50.3 3.1 1 4.7 310 24.870 0.0 0.00
2023-06-09 02:35 56.1 81.2 50.4 3.0 307 4.4 305 24.867 0.0 0.00
2023-06-09 02:30 55.9 82.4 50.6 0.8 354 1.7 355 24.864 0.0 0.00
2023-06-09 02:25 56.7 82.1 51.3 0.0 179 0.1 162 24.864 0.0 0.00
2023-06-09 02:20 56.9 78.2 50.2 0.6 43 1.7 43 24.865 0.0 0.00
2023-06-09 02:15 56.9 79.5 50.7 0.9 43 1.8 55 24.865 0.0 0.00
2023-06-09 02:10 56.8 79.5 50.5 0.8 54 1.8 65 24.864 0.0 0.00
2023-06-09 02:05 56.8 81.4 51.1 2.1 122 2.5 163 24.866 0.0 0.00
2023-06-09 02:00 57.1 80.1 51.0 1.9 211 2.8 284 24.864 0.0 0.00
2023-06-09 01:55 57.1 80.2 51.0 1.6 294 2.4 308 24.864 0.0 0.00
2023-06-09 01:50 57.4 80.0 51.3 2.9 283 3.9 315 24.863 0.0 0.00
2023-06-09 01:45 57.8 78.0 51.0 3.6 328 4.7 325 24.865 0.0 0.00
2023-06-09 01:40 57.1 78.0 50.3 3.1 329 5.2 315 24.868 0.0 0.00
2023-06-09 01:35 56.6 82.4 51.3 0.1 230 1.0 243 24.872 0.1 0.00
2023-06-09 01:30 56.4 83.6 51.5 0.1 44 1.1 263 24.873 0.0 0.00
2023-06-09 01:25 57.1 83.2 52.1 3.1 260 5.2 243 24.873 0.0 0.00
2023-06-09 01:20 57.4 79.1 51.0 1.8 334 3.5 335 24.874 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
2023-06-09 03:45 12.7 85.1 10.3 1.2 310 1.5 314 841.94 0.0 0.00
2023-06-09 03:40 12.6 86.6 10.4 1.1 269 1.5 276 841.97 0.0 0.00
2023-06-09 03:35 12.7 85.9 10.4 1.5 273 1.6 308 841.87 0.0 0.00
2023-06-09 03:30 13.1 84.1 10.5 1.2 314 1.5 319 841.85 0.0 0.00
2023-06-09 03:25 13.1 82.6 10.2 1.4 335 1.8 319 841.85 0.0 0.00
2023-06-09 03:20 13.0 83.4 10.2 1.6 314 1.9 324 841.88 0.0 0.00
2023-06-09 03:15 12.8 83.1 10.0 1.7 323 2.2 334 841.89 0.0 0.00
2023-06-09 03:10 13.1 83.3 10.3 0.6 298 2.2 323 841.94 0.0 0.00
2023-06-09 03:05 13.3 82.8 10.4 0.6 168 1.2 156 841.93 0.0 0.00
2023-06-09 03:00 13.5 81.5 10.4 0.8 156 1.2 156 841.98 0.0 0.00
2023-06-09 02:55 13.5 80.4 10.2 0.6 113 0.9 65 841.99 0.0 0.00
2023-06-09 02:50 13.3 79.1 9.8 0.9 64 1.1 43 842.04 0.0 0.00
2023-06-09 02:45 13.2 81.0 10.0 1.1 66 1.4 12 842.09 0.0 0.00
2023-06-09 02:40 13.3 81.1 10.2 1.4 1 2.1 310 842.19 0.0 0.00
2023-06-09 02:35 13.4 81.2 10.2 1.3 307 2.0 305 842.10 0.0 0.00
2023-06-09 02:30 13.3 82.4 10.3 0.4 354 0.7 355 841.99 0.0 0.00
2023-06-09 02:25 13.7 82.1 10.7 0.0 179 0.0 162 841.99 0.0 0.00
2023-06-09 02:20 13.8 78.2 10.1 0.3 43 0.7 43 842.02 0.0 0.00
2023-06-09 02:15 13.8 79.5 10.4 0.4 43 0.8 55 842.03 0.0 0.00
2023-06-09 02:10 13.8 79.5 10.3 0.4 54 0.8 65 841.98 0.0 0.00
2023-06-09 02:05 13.8 81.4 10.6 0.9 122 1.1 163 842.05 0.0 0.00
2023-06-09 02:00 13.9 80.1 10.5 0.8 211 1.3 284 841.98 0.0 0.00
2023-06-09 01:55 13.9 80.2 10.6 0.7 294 1.1 308 841.98 0.0 0.00
2023-06-09 01:50 14.1 80.0 10.7 1.3 283 1.7 315 841.96 0.0 0.00
2023-06-09 01:45 14.3 78.0 10.5 1.6 328 2.1 325 842.04 0.0 0.00
2023-06-09 01:40 14.0 78.0 10.2 1.4 329 2.3 315 842.11 0.0 0.00
2023-06-09 01:35 13.7 82.4 10.7 0.0 230 0.4 243 842.28 0.1 0.00
2023-06-09 01:30 13.6 83.6 10.8 0.0 44 0.5 263 842.30 0.0 0.00
2023-06-09 01:25 14.0 83.2 11.2 1.4 260 2.3 243 842.31 0.0 0.00
2023-06-09 01:20 14.1 79.1 10.5 0.8 334 1.6 335 842.33 0.0 0.00
CIRA

Cooperative Institute for Research in the Atmosphere

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CIRA Machine Learning (ML) home

CIRA ML Home
ML Overview
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ML4DA
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Explore the topics below to learn more about CIRA’s ML activities.

ML Overview

  • CIRA’s ML philosophy, key expertise, core activities and educational resources.

ML4CLouds

  • ML related to inferring cloud properties

ML4TC

  • Machine Learning for Tropical Cyclones

ML4DNB

  • Machine Learning related to VIIRS Day Night Band

ML4DA

  • Machine Learning for Data Assimilation

ML4RT

  • Machine Learning for Radiative Transfer

ML4Soundings

  • Machine Learning for Soundings (vertical profiles of temperature and dewpoint)

AI Institute

  • CIRA’s role in the NSF AI Institute for Research on Trustworthy AI in Weather, Climate, and Coastal Oceanography (AI2ES)

News and Announcements

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

CIRA is involved in many sessions.  Follow this link to find many of them in the AMS program.

Recent papers:

  • Haynes, K., R. Lagerquist, M. McGraw, K. Musgrave, K., and I. Ebert-Uphoff, 2023: Creating and evaluating uncertainty estimates with neural networks for environmental science applications.  Artif. Intell. Earth Syst., 2, 220061, https://doi.org/10.1175/AIES-D-22-0061.1.
  • Haynes, K., J. Stock, J. Dostalek, C. Anderson, and I. Ebert-Uphoff, 2023: Exploring the use of machine learning to improve vertical profiles of temperature and moisture.  Artif. Intell. Earth Syst., conditionally accepted.
  • Haynes, J. M., Y. J. Noh, S. D. Miller, K. D. Haynes, I. Ebert-Uphoff, and A, Heidinger, 2022: Low Cloud Detection in Multilayer Scenes using Satellite Imagery with Machine Learning Methods, Journal of Atmospheric and Oceanic Technology, 39(3), 319-334, https://doi.org/10.1175/JTECH-D-21-0084.1.
  • White, C. H., A. K. Heidinger, and S. A. Ackerman, 2022: Probing the Explainability of Neural Network Cloud-Top Pressure Models for LEO and GEO Imagers. Artificial Intelligence for the Earth Systems, 1, 210001, https://doi.org/10.1175/AIES-D-21-0001.1.
  • Lagerquist, R., D. Turner, I. Ebert-Uphoff, J. Stewart, and V. Hagerty, 2021: Using Deep Learning to Emulate and Accelerate a Radiative Transfer Model. Journal of Atmospheric and Oceanic Technology, 38(10), pp.1673-1696, https://doi.org/10.1175/MWR-D-21-0096.1.
  • Lagerquist, R., J. Q. Stewart, I. Ebert-Uphoff, and C. Kumler, 2021: Using Deep Learning to Nowcast the Spatial Coverage of Convection from Himawari-8 Satellite Data. Monthly Weather Review, 149(12), pp.3897-3921, https://doi.org/10.1175/JTECH-D-21-0007.1.

Recent Preprints:

  • Ebert-Uphoff, I., R. Lagerquist, K. Hilburn, R. Lee, K. Haynes, J. Stock, C. Kumler, and J. Q. Stewart, 2021: CIRA Guide to Custom Loss Functions for Neural Networks in Environmental Sciences–Version 1. https://arxiv.org/abs/2106.09757
  • McGovern, A., I. Ebert-Uphoff, D. J. Gagne II, and A. Bostrom, 2021: The Need for Ethical, Responsible, and Trustworthy Artificial Intelligence for Environmental Sciences. arXiv preprint, https://arxiv.org/pdf/2112.08453.pdf.