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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
2022-12-09 02:35 19.3 73.9 12.4 6.0 249 6.8 247 24.924 0.2 0.00
2022-12-09 02:30 19.3 70.7 11.4 6.0 255 7.1 250 24.924 0.1 0.00
2022-12-09 02:25 18.5 73.0 11.3 5.8 255 6.5 246 24.924 0.2 0.00
2022-12-09 02:20 18.5 70.6 10.6 3.5 239 5.1 239 24.923 0.1 0.00
2022-12-09 02:15 19.9 69.2 11.4 3.0 244 3.5 242 24.923 0.1 0.00
2022-12-09 02:10 20.1 67.8 11.2 3.2 252 5.1 296 24.925 0.2 0.00
2022-12-09 02:05 19.3 68.0 10.5 4.1 298 5.9 315 24.926 0.1 0.00
2022-12-09 02:00 19.8 63.3 9.3 2.8 314 3.2 285 24.927 0.1 0.00
2022-12-09 01:55 20.1 68.4 11.3 1.9 286 3.1 289 24.929 0.2 0.00
2022-12-09 01:50 21.0 65.8 11.4 1.7 287 2.1 279 24.933 0.2 0.00
2022-12-09 01:45 20.9 65.5 11.2 1.7 260 2.3 259 24.930 0.2 0.00
2022-12-09 01:40 20.4 66.8 11.1 1.5 246 2.3 227 24.933 0.1 0.00
2022-12-09 01:35 20.6 66.2 11.1 0.4 250 1.8 250 24.934 0.2 0.00
2022-12-09 01:30 20.1 67.5 11.0 1.0 355 1.9 349 24.932 0.2 0.00
2022-12-09 01:25 20.4 65.8 10.7 1.8 349 2.4 49 24.928 0.2 0.00
2022-12-09 01:20 20.2 66.0 10.7 2.9 69 3.3 78 24.931 0.2 0.00
2022-12-09 01:15 20.2 67.5 11.2 3.2 61 4.4 70 24.930 0.1 0.00
2022-12-09 01:10 22.1 63.0 11.4 2.2 62 2.8 61 24.927 0.1 0.00
2022-12-09 01:05 22.2 63.0 11.5 2.0 179 2.8 183 24.920 0.2 0.00
2022-12-09 01:00 22.7 61.9 11.6 2.6 192 4.2 193 24.915 0.2 0.00
2022-12-09 00:55 22.2 64.1 11.9 3.3 187 4.3 195 24.915 0.2 0.00
2022-12-09 00:50 21.2 67.2 12.0 2.2 204 3.2 204 24.917 0.2 0.00
2022-12-09 00:45 20.3 67.6 11.3 0.8 230 2.2 235 24.919 0.2 0.00
2022-12-09 00:40 18.2 69.7 10.0 1.6 234 3.0 222 24.926 0.2 0.00
2022-12-09 00:35 17.0 70.9 9.2 2.2 199 4.0 188 24.928 0.2 0.00
2022-12-09 00:30 18.4 68.4 9.7 3.0 254 4.4 297 24.925 0.2 0.00
2022-12-09 00:25 21.3 64.0 11.0 2.2 294 3.1 256 24.925 0.2 0.00
2022-12-09 00:20 24.3 56.9 11.2 1.6 190 2.3 199 24.927 0.2 0.00
2022-12-09 00:15 24.1 54.6 10.1 2.2 187 4.3 174 24.930 0.2 0.00
2022-12-09 00:10 23.3 57.8 10.6 3.6 178 5.0 183 24.935 0.2 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
2022-12-09 02:35 -7.0 73.9 -10.9 2.7 249 3.1 247 844.02 0.2 0.00
2022-12-09 02:30 -7.0 70.7 -11.5 2.7 255 3.2 250 844.02 0.1 0.00
2022-12-09 02:25 -7.5 73.0 -11.5 2.6 255 2.9 246 844.03 0.2 0.00
2022-12-09 02:20 -7.5 70.6 -11.9 1.5 239 2.3 239 844.00 0.1 0.00
2022-12-09 02:15 -6.7 69.2 -11.5 1.3 244 1.6 242 844.00 0.1 0.00
2022-12-09 02:10 -6.6 67.8 -11.5 1.4 252 2.3 296 844.04 0.2 0.00
2022-12-09 02:05 -7.1 68.0 -12.0 1.8 298 2.6 315 844.11 0.1 0.00
2022-12-09 02:00 -6.8 63.3 -12.6 1.2 314 1.4 285 844.13 0.1 0.00
2022-12-09 01:55 -6.6 68.4 -11.5 0.8 286 1.4 289 844.20 0.2 0.00
2022-12-09 01:50 -6.1 65.8 -11.4 0.7 287 0.9 279 844.33 0.2 0.00
2022-12-09 01:45 -6.1 65.5 -11.6 0.8 260 1.0 259 844.22 0.2 0.00
2022-12-09 01:40 -6.4 66.8 -11.6 0.7 246 1.0 227 844.32 0.1 0.00
2022-12-09 01:35 -6.4 66.2 -11.6 0.2 250 0.8 250 844.35 0.2 0.00
2022-12-09 01:30 -6.6 67.5 -11.6 0.5 355 0.9 349 844.30 0.2 0.00
2022-12-09 01:25 -6.5 65.8 -11.8 0.8 349 1.1 49 844.17 0.2 0.00
2022-12-09 01:20 -6.5 66.0 -11.8 1.3 69 1.5 78 844.25 0.2 0.00
2022-12-09 01:15 -6.6 67.5 -11.6 1.4 61 2.0 70 844.24 0.1 0.00
2022-12-09 01:10 -5.5 63.0 -11.4 1.0 62 1.3 61 844.11 0.1 0.00
2022-12-09 01:05 -5.5 63.0 -11.4 0.9 179 1.3 183 843.88 0.2 0.00
2022-12-09 01:00 -5.2 61.9 -11.4 1.2 192 1.9 193 843.73 0.2 0.00
2022-12-09 00:55 -5.4 64.1 -11.2 1.5 187 1.9 195 843.71 0.2 0.00
2022-12-09 00:50 -6.0 67.2 -11.1 1.0 204 1.4 204 843.78 0.2 0.00
2022-12-09 00:45 -6.5 67.6 -11.5 0.3 230 1.0 235 843.85 0.2 0.00
2022-12-09 00:40 -7.6 69.7 -12.2 0.7 234 1.3 222 844.08 0.2 0.00
2022-12-09 00:35 -8.3 70.9 -12.7 1.0 199 1.8 188 844.16 0.2 0.00
2022-12-09 00:30 -7.5 68.4 -12.4 1.3 254 2.0 297 844.05 0.2 0.00
2022-12-09 00:25 -6.0 64.0 -11.7 1.0 294 1.4 256 844.06 0.2 0.00
2022-12-09 00:20 -4.3 56.9 -11.6 0.7 190 1.0 199 844.11 0.2 0.00
2022-12-09 00:15 -4.4 54.6 -12.2 1.0 187 1.9 174 844.23 0.2 0.00
2022-12-09 00:10 -4.8 57.8 -11.9 1.6 178 2.2 183 844.38 0.2 0.00
CIRA

Cooperative Institute for Research in the Atmosphere

ML Overview

ML activities related to satellites


CIRA has a long history of developing satellite products.  Machine learning (ML), also often called Artificial Intelligence (AI), has emerged as a powerful tool to process and extract information from satellites.  Thus many of CIRA’s machine learning activities are related to satellites.  Topics include:

  • Detecting convection from satellite imagery (for NWP data assimilation),
  • Nowcasting convection from satellite imagery (for severe weather forecasting)
  • Using satellite imagery to infer cloud properties (for aviation)
  • Using satellite imagery to improve estimates of vertical profiles (for aviation),
  • Image-to-image translation:
    • Generating simulated radar imagery from geostationary satellite imagery (for severe weather forecasting)
    • Generating simulated passive microwave imagery from geostationary satellite imagery (for tropical cyclones)
  • Anticipating rapid intensification of tropical cyclones

To learn more about these activities, visit ML4Clouds and ML4TC

 

CIRA’s long history of working with satellites

Satellite Earth Station

With NOAA support, CIRA has operated a Geostationary Operational Environmental Satellite (GOES) Earth Station since 1980.  Today our collection capability handles three simultaneous GOES transmissions, NOAA polar data and European MSG.  CIRA also plays an important role in each new GOES GVAR satellite as one of the primary test sites for initial transmissions and sensor verification.

(On right:  Satellite dishes at CIRA headquarters, Fort Collins, providing real-time satellite data feeds.)

Satellite products

Many of CIRA’s satellite products are publicly available here:
Check out SLIDER at https://rammb-slider.cira.colostate.edu/
(For starters, switch “sector” from “Full disk” to “Conus”, then choose a “Product” to view different satellite products, e.g., from GOES-16, in real time.)

 

Tropical cyclones

CIRA and the Atmospheric Science department at CSU also have a long and strong history of research on tropical cyclones which we leverage for some of the satellite activities described above.

 

ML activities related to Numerical Weather Prediction (NWP) models


CIRA’s ML activities related to NWP models fall into two categories:

  1. Extracting information from satellites for assimilation into NWP models.
    For more information, see activities outlined for satellite tasks above.
  2. Using ML to speed up NWP models by emulating those physics equations that are particularly slow, namely radiative transfer, by much faster ML approaches.
    For more information on this topic see ML4RT.

Our ML approaches and expertise


Using machine learning to inform life-and-death decision making, e.g., to make decisions on which regions to evacuate, is very different from using machine learning for standard computer science applications. In particular, in order for machine learning approaches to be accepted into operational use for weather forecasting, they must be as robust and interpretable as possible.

We believe the best way to achieve robustness and interpretability is to make ML models as simple as possible, using any of the following approaches:

  • Integrating as much expert knowledge as possible into the ML algorithms by
    • Extensive feature engineering from expert knowledge
    • Developing custom loss functions that better model what experts are truly interested in
  • Exploring tools from other fields to extract relevant additional features, such as
    • Optical Flow
    • Traditional image processing techniques (pre-defined filter banks, e.g., Gabor filters)
    • Harmonic analysis (Fourier or wavelet transforms in image space)
    • Topological data analysis (persistent homology)
  • Simplifying neural networks by
    • Performing ablation studies to test which model elements are truly important
    • Fitting simpler machine learning models to more complex ones
  • Applying Explainable AI (XAI) methods to learn about the strategies used by ML algorithms, such as
    • Permutation importance tests
    • Shapley values
    • Attribution maps for neural networks (e.g., Layer-wise Relevance Propagation, input*gradient)

Use of an attribution method, namely Layer-Wise Relevance Propagation (LRP), to analyze a neural network model that seeks to translate imagery from the Geostationary Operational Environmental Satellite into Multi-Radar Multi-Sensor (MRMS) imagery. LRP results (bottom row) show where in the input channels the neural network is focusing when predicting the MRMS value for a single (central) pixel.  See Ebert-Uphoff and Hilburn (2020) for details.

CIRA resources for the community


A) Short courses for Atmospheric Scientists to get started in Machine Learning

CIRA Short Course on Machine Learning for Weather and Climate:

    • Available material: recorded video lectures, GitHub, and Jupyter Notebooks
    • 6 sessions, 90 minutes each.   Dates: 16 September – 21 October 2020
    • Instructors:  Ryan Lagerquist (CIRA Boulder and NOAA GSL) and Imme Ebert-Uphoff (CIRA Fort Collins and Colorado State University)
    • Link: CIRA Short Course ML for W&C website.
    • Or go directly to Youtube playlist (this link only gets you to the videos, not the other course material)

CIRA/AI2ES Short Course on Explainable AI:

    • Available material: recorded video lectures, each accompanied by slides and a Colab notebook with open-source Python code.
    • All Python examples focus on weather prediction.
    • 6 sessions, 90 minutes each.   Dates: 10 May – 21 June 2021
    • Instructor: Ryan Lagerquist (with guest lecture by Imme Ebert-Uphoff)
    • Course Information and Recordings

The Roebber Lectures:

    • Instructor:  Paul Roebber
    • Organized by NOAA’s VLab
    • 4 sessions (Feb 9 & 15, April 14 & 22, 2022)
    • Description:  The National Weather Service (NWS) has a long history of leveraging available data in support of weather forecasting efforts. These efforts are ongoing and with the advent of more advanced techniques (e.g., machine learning), the NWS is in the process of determining where and how to apply them.  This series of four talks is designed to provide some background on these techniques. No assumptions will be made regarding the statistical background of participants. The following table contains information on each talk including a link to register for the presentation.
    • Course Information and Recordings

B) Papers / Reports on Foundational Topics:

CIRA Guide to Custom Loss Functions for Neural Networks in Environmental Sciences–Version 1.  (2021)
Ebert-Uphoff, I., Lagerquist, R., Hilburn, K., Lee, Y., Haynes, K., Stock, J., Kumler, C. and Stewart, J.Q.
https://arxiv.org/abs/2106.09757

Machine Learning for Clouds and Climate – Invited Chapter for the AGU Geophysical Monograph Series” Clouds and Climate (2021)
Beucler, T., Ebert-Uphoff, I., Rasp, S., Pritchard, M. and Gentine, P., 2021.
https://www.essoar.org/doi/abs/10.1002/essoar.10506925.1

The Need for Ethical, Responsible, and Trustworthy Artificial Intelligence for Environmental Sciences.  (2021)
McGovern, A., Ebert-Uphoff, I., Gagne II, D.J. and Bostrom, A., 2021.
https://arxiv.org/abs/2112.08453