Journal Publications

Journal Articles Related to MRMS QPE

Osborne, A. P., J. Zhang, M. J. Simpson, K. W. Howard, and S. B. Cocks, 2023: Application of machine learning techniques to improve Multi-Radar Multi-Sensor (MRMS) precipitation estimates in the western United States. Artif. Intell. Earth Syst., 2, 220053, doi: 10.1175/AIES-D-22-0053.1.

Martinaitis, S. M., S. Lincoln, D. Scholtzhauer, S. B. Cocks, and J. Zhang, 2023: A temporal gauge quality control algorithm as a method for identifying potential instrumentation malfunctions. J. Atmos. Oceanic Technol.40, 265–283, doi: 10.1175/JTECH-D-22-0038.1.

Hanft, W., J. Zhang, and M. Simpson, 2023: Dual-pol VPR corrections for improved operational radar QPE in MRMS. J. Hydrometeor., 24, 353–371, doi: 10.1175/JHM-D-22-0010.1.

Ryzhkov, A., P. Zhang, P. Bukovcic, J. Zhang, and S. Cocks, 2022: Polarimetric radar quantitative precipitation estimation. Remote Sensing, 14, 1695, doi: 10.3390/rs14071695.

Gerard, A., S. M. Martinaitis, J. J. Gourley, K. W. Howard, and J. Zhang, 2021: An overview of the performance and operational applications of the MRMS and FLASH systems in recent significant urban flash flood events. Bull. Amer. Meteor. Soc., 102, E2165–E2176, doi:10.1175/BAMS-D-19-0273.1.

Martinaitis, S. M., S. B. Cocks, M. J. Simpson, A. P. Osborne, S. S. Harkema, H. M. Grams, J. Zhang, K. W. Howard, 2021: Advancements and Characteristics of Gauge Ingest and Quality Control within the Multi-Radar Multi-Sensor System. J. Hydrometeor., 22, 2455–2474, doi:10.1175/JHM-D-20-0234.1.

Martinaitis, S. M., S. B. Cocks, A. P. Osborne, M. J. Simpson, L. Tang, J. Zhang, and K. W. Howard, 2021: The historic rainfalls of Hurricanes Harvey and Florence: A perspective from the Multi-Radar Multi-Sensor system. J. Hydrometeor.22, 721–738. doi:10.1175/JHM-D-20-0199.1.

Zhang, J., L. Tang, S. Cocks, P. Zhang, A. Ryzhkov, K. Howard, C. Langston, and B. Kaney, 2020: A dual-polarization radar synthetic QPE for operations. J. Hydrometeor.21, 2507–2521. doi:10.1175/JHM-D-19-0194.1.

Tang, L., J. Zhang, M. Simpson, A. Arthur, H. Grams, Y. Wang, and C. Langston, 2020: Updates on the radar data quality control in the MRMS quantitative precipitation estimation system. J. Atmos. Oceanic Technol., 37, 1521–1537. doi:10.1175/JTECH-D-19-0165.1.

Martinaitis, S. M., A. P. Osborne, M. J. Simpson, J. Zhang, K. W. Howard, S. B. Cocks, A. Arthur, C. Langston, and B. T. Kaney, 2020: A physically based multisensor quantitative precipitation estimation approach for gap-filling radar coverage. J. Hydrometeor., 21, 1485–1511. doi:10.1175/JHM-D-19-0264.1.

Cocks, S. B.L. TangP. ZhangA. RyzhkovB. KaneyK. ElmoreY. WangJ. Zhang, and K. Howard2019A prototype quantitative precipitation estimation algorithm for operational S-band polarimetric radar utilizing specific attenuation and specific differential phase. Part II: Performance verification and case study analysis. J. Hydrometeor.20, 999–1014. doi:10.1175/JHM-D-18-0070.1.

Wang, Y.S. B. CocksL. TangA. RyzhkovP. ZhangJ. Zhang, and K. Howard2019A prototype quantitative precipitation estimation algorithm for operational S-band polarimetric radar utilizing specific attenuation and specific differential phase. Part I: Algorithm description. J. Hydrometeor.20, 985–997. doi:10.1175/JHM-D-18-0071.1.

Rosenow, A. A., K. Howard, and J. Meitin, 2018: Gap-filling mobile radar observations of a snow squall in the San Luis Valley. Mon. Wea. Rev., 146, 2469–2481. doi:10.1175/MWR-D-17-0323.1.

Martinaitis, S. M., H. M. Grams, C. Langston, J. Zhang, and K. Howard, 2018: A real-time evaporation correction scheme for radar-derived mosaicked precipitation estimations. J. Hydrometeor., 19, 87–111. doi:10.1175/JHM-D-17-0093.1.

Qi, Y, and J. Zhang, 2017: A physically-based two-dimensional seamless reflectivity mosaic for radar QPE in the MRMS system. J. Hydrometeor., 18, 1327–1340. doi:10.1175/JHM-D-16-0197.1.

Cocks, S. B., J. Zhang, S. M. Martinaitis, Y. Qi, B. Kaney, and K. Howard, 2017: MRMS QPE performance east of the Rockies during the 2014 warm season. J. Hydrometeor., 18, 761–775. doi:10.1175/JHM-D-16-0179.1.

Martinaitis, S. M., and Coauthors, 2017: The HMT Multi-Radar Multi-Sensor Hydro Experiment. Bull. Amer. Meteor. Soc., 98, 347–359. doi:10.1175/BAMS-D-15-00283.1.

Grams, H. M., P.-E. Kirstetter, and J. J. Gourley, 2016: Naïve Bayesian precipitation type retrieval from satellite using a cloud-top and ground-radar matched climatology. J. Hydrometeor.17, 2649–2665. doi:10.1175/JHM-D-16-0058.1.

Qi, Y., S. Martinaitis, J. Zhang, and S. Cocks, 2016: A real-time automated quality control of hourly rain gauge data based on multiple sensors in MRMS system. J. Hydrometeor.17, 1675–1691. doi:10.1175/JHM-D-15-0188.1.

Zhang, J., and Coauthors, 2016: Multi-Radar Multi-Sensor (MRMS) quantitative precipitation estimation: Initial operating capabilities. Bull. Amer. Meteor. Soc., 97, 621–637. doi:10.1175/BAMS-D-00174.1.

Cocks, S. B., S. M. Martinaitis, B. Kaney, J. Zhang, and K. Howard, 2016: MRMS QPE performance during the 2013-14 cool season. J. Hydrometeor., 17, 791–810. doi:10.1175/JHM-D-15-0095.1.

Martinaitis, S. M., S. B. Cocks, Y. Qi., B. T. Kaney, J. Zhang, and K. Howard, 2015: Understanding winter precipitation impacts on automated gauge observations within a real-time system. J. Hydrometeor., 16, 2345–2363. doi:10.1175/JHM-D-15-0020.1.

Zhang, J., Y. Qi., C. Langston, B. Kaney, and K. Howard, 2014: A real-time algorithm for merging radar QPEs with rain gauge observations and orographic precipitation climatology. J. Hydrometeor15, 1794–1809. doi:10.1175/JHM-D-13-0163.1.

Tang, L., J. Zhang, C. Langston, J. Krause, K. Howard, and V. Lakshmanan, 2014: A physically based precipitation–nonprecipitation radar echo classifier using polarimetric and environmental data in a real-time national system. Wea. Forecasting29, 1106-1119. doi:10.1175/WAF-D-13-00072.1.

Qi, Y., J. Zhang, B. Kaney, C. Langston, and K. Howard, 2014: Improving WSR-88D radar QPE for orographic precipitation using profiler observations. J. Hydrometeor.15, 1135–1151. doi:10.1175/JHM-D-13-0131.1.

Grams, H. M., J. Zhang, and K. L. Elmore, 2014: Automated identification of enhanced rainfall rates using the near-storm environment for radar precipitation estimates. J. Hydrometeor.15, 1238–1254. doi:10.1175/JHM-D-13-042.1.

Qi, Y., J. Zhang, and P. Zhang, 2013: A real-time automated convective and stratiform precipitation segregation algorithm in native radar coordinates. Q. J. R. Meteorol. Soc.139, 2233–2240. doi:10.1002/qj.2095.

Qi, Y., and J. Zhang, 2013: Correction of radar QPE errors associated with low and partially observed brightband layers. J. Hydrometeor.14, 1933–1943. doi:10.1175/JHM-D-13-040.1.

Chen, S., J. J. Gourley, Y. Hong, P. E. Kirstetter, J. Zhang, K. W. Howard, Z. L. Flamig, J. Hu, and Y. Qi, 2013: Evaluation and uncertainty estimation of NOAA/NSSL next generation national mosaic QPE (Q2) over the Continental United States. J. Hydrometeor.14, 1308–1322. doi:10.1175/JHM-D-12-0150.1.

Zhang, J., Y. Qi, K. Howard, C. Langston, and B. Kaney, 2012: Radar Quality Index (RQI) – A combined measure of beam blockage and VPR effects in a national network. Weather Radar and Hydrology, R. J. Moore, S. J. Cole, and A. J. Illingworth, Eds., International Association of Hydrological Science, 388–393. [Link]

Zhang, J., Y. Qi, D. Kingsmill, and K. Howard, 2012: Radar-based quantitative precipitation estimation for the cool season in complex terrain: Case studies from the NOAA Hydrometeorology Testbed. J. Hydrometeor.13, 1836–1854. doi:10.1175/JHM-D-11-0145.1.

Wu, W., D. Kitzmiller, and S. Wu, 2012: Evaluation of radar precipitation estimates from the National Mosaic and Multisensor Quantitative Precipitation Estimation System and the WSR-88D precipitation processing system over the Conterminous United States. J. Hydrometeor., 13, 1080–1093. doi:10.1175/JHM-D-11-064.1.

Zhang, J., and Coauthors, 2011: National Mosaic and Multi-Sensor QPE (NMQ) System: Description, results, and future plans. Bull. Amer. Meteor. Soc.92, 1321–1338. doi:10.1175/2011BAMS-D-11-00047.1.

KitzmillerD., and Coauthors, 2011Evolving multisensor precipitation estimation methods: Their impacts on flow prediction using a distributed hydrologic modelJ. Hydrometeor.1214141431. doi:10.1175/JHM-D-10-05038.1.

Zhang, J., and Y. Qi, 2010: A real-time algorithm for the correction of brightband effects in radar-derived QPE. J. Hydrometeor.11, 1157–1171. doi:10.1175/2010JHM1201.1.

Vasiloff, S. V., K. W. Howard, and J. Zhang, 2009: Difficulties with correcting radar rainfall estimates based on rain gauge data: A case study of severe weather in Montana on 16–17 June 2007. Wea. Forecasting24, 1334–1344. doi:10.1175/2009WAF2222154.1.

Zhang, J., C. Langston, and K. Howard, 2008: Brightband identification based on vertical profiles of reflectivity from the WSR-88D. J. Atmos. Oceanic Technol.25, 1859–1872. doi:10.1175/2008JTECHA1039.1.

Xu, X., K. Howard, and J. Zhang, 2008: An automated radar technique for the identification of tropical precipitation. J. Hydrometeor.9, 885–902. doi:10.1175/2007JHM954.1.

Vasiloff, S. V., and Coauthors, 2007: Improving QPE and very short term QPF: An initiative for a community-wide integrated approach. Bull. Amer. Meteor. Soc.88, 1899–1911. doi:10.1175/BAMS-88-12-1899.