Select Conference Presentations and Extended Abstracts Given in 2022
Howard, K., and J. Zhang, 2022: Multi-Radar Multi-Sensor System (MRMS). 11th European Conf. on Radar in Meteorology and Hydrology, MeteoSwiss, NET.P9.
Osborne, A., J. Zhang, M. Simpson, S. Cocks, and K. Howard, 2022: Development of Multi-Radar Multi-Sensor (MRMS) machine learning QPE for complex terrain. 11th European Conf. on Radar in Meteorology and Hydrology, MeteoSwiss, ORO.T5.
Zhang, J., and W. Hanft, 2022: A dual-pol VPR correction for operational radar QPE. 11th European Conf. on Radar in Meteorology and Hydrology, MeteoSwiss, QPE2.P11.
Zhang, P., A. Ryzhkov, and S. Cocks, 2022: Rainfall estimation using specific attenuation with a new alpha optimization method. 11th European Conf. on Radar in Meteorology and Hydrology, MeteoSwiss, QPE2.P13.
Kirstetter, P., M. Simpson, J. Zhang, J. J. Gourlye, S. Martinaitis, and N. Indik, 2022: Probabilistic quantitative precipitation estimation with ground- and space-based radars. 11th European Conf. on Radar in Meteorology and Hydrology, MeteoSwiss, QPE1.P8.
Cocks, S. B., L. Tang, W. Hanft, C. Langston, J. Zhang, and K. Howard, 2022: Real-time verification/ evaluation results for the MRMS experimental dual-pol QPE Product during the 2021 warm season. 36th Conf. on Hydrology, Amer. Meteor. Soc., 437. [Link]
Cocks, S. B., L. Tang, A. Osborne, J. Zhang, and K. Howard, 2022: Impacts of NEXRAD supplemental lower-elevation angles on MRMS quality control and QPE. 36th Conf. on Hydrology, Amer. Meteor. Soc., 14B.4. [Link]
Grams, H. M., J. Noel, V. Fortin, and L. Fry, 2022: Comparison of operational quantitative precipitation estimates from the U.S. National Weather Service and Environment Canada over the Great Lakes region. 36th Conf. on Hydrology, Amer. Meteor. Soc., 14B.5. [Link]
Martinaitis, S. M., S. B. Cocks, C. Langston, B. Kaney, J. Zhang, K. Howard, H. M. Grams, M. Hurwitz, and M. Mullusky, 2022: Designing the next-generation Multisensor Precipitation Estimator program for NWS River Forecast Centers. 38th Conf. on Environmental Information Processing Technologies, Amer. Meteor. Soc., 8B.2. [Link]
Martinaitis, S. M., S. B. Cocks, J. Zhang, S. Lincoln, and D. Schlotzhauer, 2022: A temporal quality control of gauges to improve precipitation processes for MRMS. 36th Conf. on Hydrology, Amer. Meteor. Soc., 16B.5. [Link]
Osborne, A. P., M. Simpson, J. Zhang, S. B. Cocks, and K. Howard, 2022: Evaluation and interpretation of an MRMS machine learning approach for precipitation estimation in complex terrain. 21st Conf. on Artificial Intelligence, Amer. Meteor. Soc., 7B.4. [Link]
Reinhart, A. E., H. M. Grams, J. Brogden, K. Cooper, R. Toomey, K. Howard, and A. Gerard, 2022: The Multi-Radar Multi-Sensor (MRMS) cloud computing update. Eighth Symposium on High Performance Computing for Weather, Water, and Climate, Amer. Meteor. Soc., 5.5. [Link]
Simpson, M., J. Zhang, and K. Howard, 2022: Deep learning radar-based quantitative precipitation estimates in complex terrain. 21st Conf. on Artificial Intelligence, Amer. Meteor. Soc., 5B.3. [Link]
Thedens, E., H. M. Grams, R. A. Clark III, S. M. Martinaitis, and M. Simpson; Characterizing errors in MRMS quantitative precipitation estimates over Alaska, Hawaii, and Puerto Rico. 36th Conf. on Hydrology, Amer. Meteor. Soc., 14B.3. [Link]
Zhang, J., L. Tang, W. Hanft, A. P. Osborne, S. B. Cocks, M. Simpson, S. M. Martinaitis, H. M. Grams, C. Langston, K. Cooper, M. Taylor, A. Arthur, J. Brogden, and K. Howard; MRMS radar QPE updates: 2020–21. 36th Conf. on Hydrology, Amer. Meteor. Soc., 16B.4. [Link]