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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

CIRA Machine Learning (ML) home


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 moistureArtif. 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 DataMonthly Weather Review149(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.