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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 02:25 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
2022-12-09 00:05 23.0 62.0 11.9 3.2 189 3.9 184 24.939 0.2 0.00
2022-12-09 00:00 23.5 58.9 11.2 3.7 178 4.3 147 24.940 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
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
2022-12-09 00:05 -5.0 62.0 -11.2 1.4 189 1.8 184 844.52 0.2 0.00
2022-12-09 00:00 -4.7 58.9 -11.6 1.6 178 1.9 147 844.58 0.1 0.00
CIRA

Cooperative Institute for Research in the Atmosphere

ML4DA


Machine Learning for Data Assimilation

Accurate and timely observations are critical for making good weather forecasts, and data assimilation is the process that brings observations into weather models. Machine learning provides new opportunities to improve and extend data assimilation capabilities. For example, convolutional neural networks can make use of spatial context and texture in satellite images, which is currently going unused by weather forecasts. CIRA is performing the research to bring ML capabilities to operational data assimilation. Figure 1 (adapted from Back et al. 2021) provides an example of this research. The Rapid Refresh Forecast System (RRFS) uses radar-based estimates of the latent heating from clouds and precipitation (Fig. 1A) to initialize convection in short-range forecasts (Fig. 1B). This provides much better forecasts than using no radar initialization (Fig. 1C). At CIRA, we developed the GREMLIN machine learning model (Hilburn et al. 2021) that translates GOES-R radiances and lightning into synthetic radar reflectivity fields. Estimates of latent heating from GREMLIN (Fig. 1D) were used to initialize forecasts (Fig. 1E) that produce convection better matching observations (Fig. 1F). Additional analysis from this one-week retrospective simulation showed that using GREMLIN to initialize convection produced more skillful forecasts out to lead times of 12 hours over the western U.S. where radar coverage is spotty due to terrain blockage. These promising results show that machine learning offers new opportunities to use satellite observations to improve weather forecasts of high-impact events.

Figure 1. RRFS retrospective simulation evaluating GREMLIN, results from Back et al. (2021). (A) MRMS radar-based latent heating temperature tendency currently used in RRFS, (B) 1-hour forecast composite radar reflectivity using the radar-based latent heating, (C) 1-hour forecast

Publications


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.

Back, A., Weygandt, S., Alexander, C., Benjamin, S., Hu, M., Ge, G., James, E., Kliewer, A., Mecikalski, J., Dowell, D., Bruning, E., Hilburn, K., and Sebok, A., Convection-indicating GOES-R products assimilated in the experimental UFS Rapid Refresh System. AGU Fall Meeting, Presentation A22B-02, 14 Dec 2021.

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