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Matthew C. Mahalik, Brandon R. Smith, Kimberly L. Elmore, Darrel M. Kingfield, Kiel L. Ortega, and Travis M. Smith

Abstract

The local, linear, least squares derivative (LLSD) approach to radar analysis is a method of quantifying gradients in radar data by fitting a least squares plane to a neighborhood of range bins and finding its slope. When applied to radial velocity fields, for example, LLSD yields part of the azimuthal (rotational) and radial (divergent) components of horizontal shear, which, under certain geometric assumptions, estimate one-half of the two-dimensional vertical vorticity and horizontal divergence equations, respectively. Recent advances in computational capacity as well as increased usage of LLSD products by the meteorological community have motivated an overhaul of the LLSD methodology’s application to radar data. This paper documents the mathematical foundation of the updated LLSD approach, including a complete derivation of its equation set, discussion of its limitations, and considerations for other types of implementation. In addition, updated azimuthal shear calculations are validated against theoretical vorticity using simulated circulations. Applications to nontraditional radar data and new applications to nonvelocity radar data including reflectivity at horizontal polarization, spectrum width, and polarimetric moments are also explored. These LLSD gradient calculations may be leveraged to identify and interrogate a wide variety of severe weather phenomena, either directly by operational forecasters or indirectly as part of future automated algorithms.

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Brandon R. Smith, Thea Sandmæl, Matthew C. Mahalik, Kimberly L. Elmore, Darrel M. Kingfield, Kiel L. Ortega, and Travis M. Smith
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Steven M. Martinaitis, Jonathan J. Gourley, Zachary L. Flamig, Elizabeth M. Argyle, Robert A. Clark III, Ami Arthur, Brandon R. Smith, Jessica M. Erlingis, Sarah Perfater, and Benjamin Albright

Abstract

There are numerous challenges with the forecasting and detection of flash floods, one of the deadliest weather phenomena in the United States. Statistical metrics of flash flood warnings over recent years depict a generally stagnant warning performance, while regional flash flood guidance utilized in warning operations was shown to have low skill scores. The Hydrometeorological Testbed—Hydrology (HMT-Hydro) experiment was created to allow operational forecasters to assess emerging products and techniques designed to improve the prediction and warning of flash flooding. Scientific goals of the HMT-Hydro experiment included the evaluation of gridded products from the Multi-Radar Multi-Sensor (MRMS) and Flooded Locations and Simulated Hydrographs (FLASH) product suites, including the experimental Coupled Routing and Excess Storage (CREST) model, the application of user-defined probabilistic forecasts in experimental flash flood watches and warnings, and the utility of the Hazard Services software interface with flash flood recommenders in real-time experimental warning operations. The HMT-Hydro experiment ran in collaboration with the Flash Flood and Intense Rainfall (FFaIR) experiment at the Weather Prediction Center to simulate the real-time workflow between a national center and a local forecast office, as well as to facilitate discussions on the challenges of short-term flash flood forecasting. Results from the HMT-Hydro experiment highlighted the utility of MRMS and FLASH products in identifying the spatial coverage and magnitude of flash flooding, while evaluating the perception and reliability of probabilistic forecasts in flash flood watches and warnings.

NSSL scientists and NWS forecasters evaluate new tools and techniques through real-time test bed operations for the improvement of flash flood detection and warning operations.

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Peter Bissolli, Catherine Ganter, Tim Li, Ademe Mekonnen, Ahira Sánchez-Lugo, Eric J. Alfaro, Lincoln M. Alves, Jorge A. Amador, B. Andrade, Francisco Argeñalso, P. Asgarzadeh, Julian Baez, Reuben Barakiza, M. Yu. Bardin, Mikhail Bardin, Oliver Bochníček, Brandon Bukunt, Blanca Calderón, Jayaka D. Campbell, Elise Chandler, Ladislaus Chang’a, Vincent Y. S. Cheng, Leonardo A. Clarke, Kris Correa, Catalina Cortés, Felipe Costa, A.P.M.A. Cunha, Mesut Demircan, K. R. Dhurmea, A. Diawara, Sarah Diouf, Dashkhuu Dulamsuren, M. ElKharrim, Jhan-Carlo Espinoza, A. Fazl-Kazem, Chris Fenimore, Steven Fuhrman, Karin Gleason, Charles “Chip” P. Guard, Samson Hagos, Mizuki Hanafusa, H. R. Hasannezhad, Richard R. Heim Jr., Hugo G. Hidalgo, J. A. Ijampy, Gyo Soon Im, Annie C. Joseph, G. Jumaux, K. R. Kabidi, P-H. Kamsu-Tamo, John Kennedy, Valentina Khan, Mai Van Khiem, Philemon King’uza, Natalia N. Korshunova, A. C. Kruger, Hoang Phuc Lam, Mark A. Lander, Waldo Lavado-Casimiro, Tsz-Cheung Lee, Kinson H. Y. Leung, Gregor Macara, Jostein Mamen, José A. Marengo, Charlotte McBride, Noelia Misevicius, Aurel Moise, Jorge Molina-Carpio, Natali Mora, Awatif E. Mostafa, Habiba Mtongori, Charles Mutai, O. Ndiaye, Juan José Nieto, Latifa Nyembo, Patricia Nying’uro, Xiao Pan, Reynaldo Pascual Ramírez, David Phillips, Brad Pugh, Madhavan Rajeevan, M. L. Rakotonirina, Andrea M. Ramos, M. Robjhon, Camino Rodriguez, Guisado Rodriguez, Josyane Ronchail, Benjamin Rösner, Roberto Salinas, Hirotaka Sato, Hitoshi Sato, Amal Sayouri, Joseph Sebaziga, Serhat Sensoy, Sandra Spillane, Katja Trachte, Gerard van der Schrier, F. Sima, Adam Smith, Jacqueline M. Spence, O. P. Sreejith, A. K. Srivastava, José L. Stella, Kimberly A. Stephenson, Tannecia S. Stephenson, S. Supari, Sahar Tajbakhsh-Mosalman, Gerard Tamar, Michael A. Taylor, Asaminew Teshome, Wassila M. Thiaw, Skie Tobin, Adrian R. Trotman, Cedric J. Van Meerbeeck, A. Vazifeh, Shunya Wakamatsu, Wei Wang, Fei Xin, F. Zeng, Peiqun Zhang, and Zhiwei Zhu
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Tim Li, Abdallah Abida, Laura S. Aldeco, Eric J. Alfaro, Lincoln M. Alves, Jorge A. Amador, B. Andrade, Julian Baez, M. Yu. Bardin, Endalkachew Bekele, Eric Broedel, Brandon Bukunt, Blanca Calderón, Jayaka D. Campbell, Diego A. Campos Diaz, Gilma Carvajal, Elise Chandler, Vincent. Y. S. Cheng, Chulwoon Choi, Leonardo A. Clarke, Kris Correa, Felipe Costa, A. P. Cunha, Mesut Demircan, R. Dhurmea, Eliecer A. Díaz, M. ElKharrim, Bantwale D. Enyew, Jhan C. Espinoza, Amin Fazl-Kazem, Nava Fedaeff, Z. Feng, Chris Fenimore, S. D. Francis, Karin Gleason, Charles “Chip” P. Guard, Indra Gustari, S. Hagos, Richard R. Heim Jr., Rafael Hernández, Hugo G. Hidalgo, J. A. Ijampy, Annie C. Joseph, Guillaume Jumaux, Khadija Kabidi, Johannes W. Kaiser, Pierre-Honore Kamsu-Tamo, John Kennedy, Valentina Khan, Mai Van Khiem, Khatuna Kokosadze, Natalia N. Korshunova, Andries C. Kruger, Nato Kutaladze, L. Labbé, Mónika Lakatos, Hoang Phuc Lam, Mark A. Lander, Waldo Lavado-Casimiro, T. C. Lee, Kinson H. Y. Leung, Andrew D. Magee, Jostein Mamen, José A. Marengo, Dora Marín, Charlotte McBride, Lia Megrelidze, Noelia Misevicius, Y. Mochizuki, Aurel Moise, Jorge Molina-Carpio, Natali Mora, Awatif E. Mostafa, uan José Nieto, Lamjav Oyunjargal, Reynaldo Pascual Ramírez, Maria Asuncion Pastor Saavedra, Uwe Pfeifroth, David Phillips, Madhavan Rajeevan, Andrea M. Ramos, Jayashree V. Revadekar, Miliaritiana Robjhon, Ernesto Rodriguez Camino, Esteban Rodriguez Guisado, Josyane Ronchail, Benjamin Rösner, Roberto Salinas, Amal Sayouri, Carl J. Schreck III, Serhat Sensoy, A. Shimpo, Fatou Sima, Adam Smith, Jacqueline Spence, Sandra Spillane, Arne Spitzer, A. K. Srivastava, José L. Stella, Kimberly A. Stephenson, Tannecia S. Stephenson, Michael A. Taylor, Wassila Thiaw, Skie Tobin, Dennis Todey, Katja Trachte, Adrian R. Trotman, Gerard van der Schrier, Cedric J. Van Meerbeeck, Ahad Vazifeh, José Vicencio Veloso, Wei Wang, Fei Xin, Peiqun Zhang, Zhiwei Zhu, and Jonas Zucule
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