CIRA - Cooperative Institute for Research in the Atmosphere

Date Time MST	Temp °F	RH %	DewPt °F	Wind mph	Dir °	Gust mph	Dir °	Press in Hg	Solar W/m^2
2023-01-31 22:05	10.3	78.3	4.9	3.1	247	3.8	248	24.899	0.0
2023-01-31 22:00	9.5	80.2	4.6	4.5	219	5.5	255	24.897	0.0
2023-01-31 21:55	11.1	76.3	5.1	4.2	262	8.2	255	24.896	0.0
2023-01-31 21:50	9.0	86.9	5.9	5.5	251	8.9	253	24.898	0.0
2023-01-31 21:45	5.8	84.0	2.0	3.7	265	5.0	247	24.899	0.0
2023-01-31 21:40	6.2	83.5	2.2	3.5	259	4.4	247	24.897	0.0
2023-01-31 21:35	5.7	85.8	2.4	2.7	317	3.5	316	24.896	0.0
2023-01-31 21:30	5.3	84.5	1.7	3.1	299	4.3	261	24.899	0.1
2023-01-31 21:25	5.2	84.3	1.5	2.7	248	4.3	237	24.901	0.0
2023-01-31 21:20	5.3	82.4	1.1	1.8	249	3.2	249	24.899	0.0
2023-01-31 21:15	4.7	83.7	0.9	0.8	286	2.1	286	24.897	0.1
2023-01-31 21:10	4.9	82.6	0.7	1.8	343	2.4	351	24.898	0.1
2023-01-31 21:05	4.9	82.2	0.7	3.0	351	3.7	310	24.900	0.0
2023-01-31 21:00	5.1	80.8	0.5	3.2	310	3.9	326	24.900	0.0
2023-01-31 20:55	6.6	79.4	1.6	3.4	326	4.5	345	24.900	0.0
2023-01-31 20:50	7.8	78.0	2.3	3.6	1	4.2	4	24.895	0.0
2023-01-31 20:45	7.3	82.0	3.0	2.6	350	3.0	351	24.895	0.0
2023-01-31 20:40	5.6	83.8	1.8	2.1	288	2.8	299	24.897	0.0
2023-01-31 20:35	5.8	78.4	0.5	2.5	231	4.7	337	24.895	0.0
2023-01-31 20:30	6.4	82.6	2.3	4.9	337	5.8	349	24.894	0.0
2023-01-31 20:25	5.7	82.4	1.5	3.5	11	4.8	11	24.892	0.0
2023-01-31 20:20	5.8	81.1	1.3	2.3	356	3.8	12	24.889	0.0
2023-01-31 20:15	6.4	79.5	1.4	2.1	66	3.4	252	24.889	0.0
2023-01-31 20:10	7.0	82.2	2.7	2.9	252	3.6	263	24.885	0.0
2023-01-31 20:05	7.2	83.1	3.1	4.5	264	5.5	260	24.888	0.0
2023-01-31 20:00	6.8	81.0	2.2	4.6	249	5.7	256	24.887	0.0
2023-01-31 19:55	6.5	83.1	2.5	4.5	257	5.3	244	24.890	0.0
2023-01-31 19:50	7.5	79.7	2.6	2.3	229	3.1	228	24.889	0.0
2023-01-31 19:45	7.4	82.0	3.0	2.1	212	2.6	171	24.889	0.1
2023-01-31 19:40	7.2	80.6	2.5	2.4	171	2.8	220	24.891	0.1

Date Time MST	Temp °C	RH %	DewPt °C	Wind m/s	Dir °	Gust m/s	Dir °	Press hPa	Solar W/m^2
2023-01-31 22:05	-12.0	78.3	-15.0	1.4	247	1.7	248	843.19	0.0
2023-01-31 22:00	-12.5	80.2	-15.2	2.0	219	2.5	255	843.12	0.0
2023-01-31 21:55	-11.6	76.3	-14.9	1.9	262	3.7	255	843.06	0.0
2023-01-31 21:50	-12.8	86.9	-14.5	2.5	251	4.0	253	843.13	0.0
2023-01-31 21:45	-14.6	84.0	-16.7	1.7	265	2.2	247	843.17	0.0
2023-01-31 21:40	-14.4	83.5	-16.5	1.5	259	2.0	247	843.12	0.0
2023-01-31 21:35	-14.6	85.8	-16.5	1.2	317	1.5	316	843.06	0.0
2023-01-31 21:30	-14.8	84.5	-16.8	1.4	299	1.9	261	843.18	0.1
2023-01-31 21:25	-14.9	84.3	-16.9	1.2	248	1.9	237	843.23	0.0
2023-01-31 21:20	-14.8	82.4	-17.2	0.8	249	1.4	249	843.16	0.0
2023-01-31 21:15	-15.2	83.7	-17.3	0.4	286	0.9	286	843.11	0.1
2023-01-31 21:10	-15.1	82.6	-17.4	0.8	343	1.1	351	843.16	0.1
2023-01-31 21:05	-15.1	82.2	-17.4	1.3	351	1.6	310	843.23	0.0
2023-01-31 21:00	-14.9	80.8	-17.5	1.4	310	1.7	326	843.21	0.0
2023-01-31 20:55	-14.1	79.4	-16.9	1.5	326	2.0	345	843.21	0.0
2023-01-31 20:50	-13.5	78.0	-16.5	1.6	1	1.9	4	843.03	0.0
2023-01-31 20:45	-13.7	82.0	-16.1	1.2	350	1.3	351	843.05	0.0
2023-01-31 20:40	-14.7	83.8	-16.8	1.0	288	1.3	299	843.11	0.0
2023-01-31 20:35	-14.6	78.4	-17.5	1.1	231	2.1	337	843.04	0.0
2023-01-31 20:30	-14.2	82.6	-16.5	2.2	337	2.6	349	843.00	0.0
2023-01-31 20:25	-14.6	82.4	-16.9	1.5	11	2.2	11	842.94	0.0
2023-01-31 20:20	-14.5	81.1	-17.1	1.0	356	1.7	12	842.83	0.0
2023-01-31 20:15	-14.2	79.5	-17.0	0.9	66	1.5	252	842.84	0.0
2023-01-31 20:10	-13.9	82.2	-16.3	1.3	252	1.6	263	842.70	0.0
2023-01-31 20:05	-13.8	83.1	-16.0	2.0	264	2.5	260	842.81	0.0
2023-01-31 20:00	-14.0	81.0	-16.5	2.1	249	2.5	256	842.78	0.0
2023-01-31 19:55	-14.2	83.1	-16.4	2.0	257	2.4	244	842.87	0.0
2023-01-31 19:50	-13.6	79.7	-16.4	1.0	229	1.4	228	842.83	0.0
2023-01-31 19:45	-13.7	82.0	-16.1	0.9	212	1.2	171	842.85	0.1
2023-01-31 19:40	-13.8	80.6	-16.4	1.1	171	1.3	220	842.91	0.1

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 Review, 149(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 Review, 149(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 Review, 147(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)