H141
New Approaches to Characterize, Model, and Detect Precipitation Variability: Scientific and Practical Applications III Posters

Monday, 14 December 2020: 04:00-20:59
Poster
Primary Convener:  Giuseppe Mascaro, Arizona State University, Tempe, AZ, United States
Conveners:  Shruti Ashok Upadhyaya, Cooperative Institute for Mesoscale Meteorological Studies, Norman, OK, United States, Alain Mailhot, Institut National de la Recherche Scientifique, Eau-Terre-Environnement, Québec, QC, Canada and Veljko Petković, Colorado State University, Dept. of Atmospheric Science, Fort Collins, CO, United States
Primary Liaison:  Conrad Wasko, University of New South Wales, Sydney, Australia
Chairs:  Veljko Petković, Colorado State University, Dept. of Atmospheric Science, Fort Collins, CO, United States and Giuseppe Mascaro, Arizona State University, Tempe, AZ, United States
OSPA Liaison:  Veljko Petković, Colorado State University, Dept. of Atmospheric Science, Fort Collins, CO, United States
 
A Machine Learning Approach to Estimating Rainfall Rate Based on Simulated Polarimetric Radar Variables (712644)
Kyuhee Shin1,2, Joon Jin Song3, Wonbae Bang2 and Gyuwon Lee1,2, (1)Kyungpook National University, Department of Astronomy and Atmospheric Sciences, Daegu, South Korea, (2)Kyungpook National University, Center for Atmospheric REmote sensing (CARE), Daegu, South Korea, (3)Baylor University, Department of Statistical Science, Waco, United States
 
Reconstruction of a Blended Monthly Precipitation Dataset for the Presatellite Era (757847)
Vincent Cheng, Environment and Climate Change Canada, Climate Research Division, Toronto, ON, Canada, Xiaolan L Wang, Climate Research Division, Toronto, ON, Canada and Achan Lin, Environment and Climate Change Canada, Toronto, Canada
 
A New Framework for Incorporating Nonstationarity in Intensity-Duration-Frequency Curves Under a Changing Climate (674653)
Slobodan P. Simonovic, The University of Western Ontario, Department of Civil and Environmental Engineering and the Institute for Catastrophic Loss Reduction, London, ON, Canada and Daniele Feitoza Silva, Federal do Rio Grande do Sul, Instituto de Pesquisas Hidráulicas, Porto Alegre, Brazil
 
A review of climate change impacts on design rainfalls (661650)
Conrad Wasko, University of Melbourne, Parkville, VIC, Australia, Seth Westra, University of Adelaide, Adelaide, SA, Australia, Rory Nathan, University of Melbourne, Parkville, Australia, Harriet G Orr, Environment Agency, Bristol, United Kingdom, Gabriele Villarini, University of Iowa, Civil and Environmental Engineering, Iowa City, IA, United States, Roberto Villalobos Herrera, Newcastle University, School of Engineering, Newcastle, United Kingdom and Hayley J Fowler, Newcastle University, School of Engineering, Tyne and Wear, United Kingdom
 
CC or super-CC? A critical appraisal of temperature-precipitation scaling rates at sub-hourly timescales. (692068)
Marie-Claire Ten Veldhuis1, David Teruel1, Ruud van der Ent2 and Marc Schleiss3, (1)Delft University of Technology, Water Management, Delft, Netherlands, (2)Delft University of Technology, Delft, Netherlands, (3)Delft University of Technology, Geosciences and Remote Sensing, Delft, Netherlands
 
Characteristics of precipitation and discharge using a pseudo global warming experiment result of the typhoon No.12, 2011 in the River Shingu,JAPAN (668960)
Kohji Tanaka, Osaka Institute of Technology, Civil Engineering and Urban Design, Hirakata, Japan, Takuma Kobayashi, C.T.I. Engineering, Co., Ltd., River planning, Osaka, Japan and Tetsuya Takemi, Disaster Prevention Research Institute, Kyoto University, Kyoto, Japan
 
Does stochastic modelling using instrumental data capture pre-instrumental variability? A validation study using ice core data (687407)
Matthew Armstrong1,2, Anthony Kiem2, George A. Kuczera3 and Tessa Vance4, (1)University of Newcastle, Callaghan, Australia, (2)Centre for Water, Climate and Land (CWCL), University of Newcastle, Callaghan, NSW, Australia, (3)The University of Newcastle, Centre for Water Security and Environmental Sustainability and School of Engineering, Callaghan, NSW, Australia, (4)University of Tasmania, Hobart, TAS, Australia
 
Emulating Tropical Marine Radar Reflectivity by Applying a Convolutional Neural Network to Geostationary Satellite Radiances (682338)
Sean Heslin1, Scott W Powell1 and Marko Orescanin2, (1)Naval Postgraduate School, Department of Meteorology, Monterey, CA, United States, (2)Naval Postgraduate School, Department of Computer Science, Monterey, CA, United States
 
Future pluvial flooding associated with projected extreme rainfall in North Carolina, USA (718138)
Anna Jalowska1, Tanya Spero2, Daniel E. Line3, Barbara Doll3, Jared Bowden4 and Jack Kurki-Fox3, (1)US Environmental Protection Agency Research Triangle Park, Environmental Futures Analysis Branch, Durham, NC, United States, (2)US Environmental Protection Agency, Research Triangle Park, NC, United States, (3)North Carolina State University Raleigh, Raleigh, NC, United States, (4)North Carolina State University, Applied Ecology, Raleigh, NC, United States
 
Identifying weather regimes for a regional-scale stochastic precipitation generator in California (717635)
Nasser Najibi, Sudarshana Mukhopadhyay and Scott Steinschneider, Cornell University, Ithaca, NY, United States
 
Impact of GOES-16 Multi-spectral Satellite Observations on the Identification of Precipitation Typology using the Multi-Radar/Multi-Sensor System (732694)
Shruti Ashok Upadhyaya, Cooperative Institute for Mesoscale Meteorological Studies, Norman, OK, United States, Pierre-Emmanuel Kirstetter, University of Oklahoma, School of Civil Engineering and Environmental Sciences and School of Meteorology, Norman, OK, United States; NOAA/National Severe StormsLaboratory, Norman, OK, United States, Jonathan J Gourley, National Severe Storms Lab, Oklahoma City, OK, United States, Heather Grams, NOAA/National Severe Storms Laboratory, Norman, OK, United States and Yagmur Derin, University of Oklahoma Norman Campus, Norman, OK, United States
 
Improvement of NOAA SFR algorithm through machine learning approach (710017)
Yongzhen Fan, University of Maryland College Park, College Park, MD, United States, Huan Meng, Natl Oceanic & Atmospheric Adm, College Park, MD, United States, Jun Dong, University of Maryland College Park, Earth System Science Interdisciplinary Center, College Park, United States and Cezar Kongoli, University of Maryland, College Park, Earth System Science Interdisciplinary Center, College Park, MD, United States
 
Improvements in Daymet Continental-Scale Gridded Daily Precipitation and Temperature Estimates (738873)
Michele Thornton1, Rupesh Shrestha1, Peter E Thornton2, Shih-Chieh Kao1, Yaxing Wei1 and Bruce E Wilson1, (1)Oak Ridge National Laboratory, Oak Ridge, TN, United States, (2)Oak Ridge National Laboratory, Climate Change Science Institute and Environmental Sciences Division, Oak Ridge, TN, United States
 
Improving the accuracy of reanalysis-based hourly precipitation estimates over CONUS (775837)
Stergios Emmanouil, University of Connecticut, Civil and Environmental Engineering, Groton, CT, United States, Andreas Langousis, University of Patras, Patras, Greece, Efthymios I Nikolopoulos, Florida Institute of Technology, Mechanical and Civil Engineering, Melbourne, United States and Emmanouil N Anagnostou, University of Connecticut, Civil and Environmental Engineering, Storrs, CT, United States
 
Investigating temporal trends and spatial patterns of extreme precipitation in Northern Virginia, USA (687632)
Ishrat Jahan Dollan, George Mason University Fairfax, Sid and Reva Dewberry Dept of Civil, Environmental & Infrastructure Engineering, Fairfax, VA, United States, Viviana Maggioni, George Mason University Fairfax, Sid and Reva Dewberry Department of Civil, Environmental, and Infrastructure Engineering, Fairfax, VA, United States, Tasnuva Rouf, George Mason University Fairfax, Sid and Reva Dewberry Dept. of Civil, Environmental & Infrastructure Engineering, Fairfax, VA, United States and Yiwen Mei, University of Michigan, School for Environment and Sustainability, Ann Arbor, MI, United States
 
Investigating trend detection capabilities on worldwide extreme rainfall occurrences (724880)
Stefano Farris1, Roberto Deidda1, Francesco Viola1 and Giuseppe Mascaro2, (1)University of Cagliari, Department of Civil, Environmental and Architectural Engineering, Cagliari, Italy, (2)Arizona State University, Tempe, AZ, United States
 
Non-stationary Influences of Large-scale Climate Drivers on Low-flow Characteristics of Streamflows in Southeast Australia. (737042)
Pallavi Goswami1, Arpita Mondal2, Christoph Rudiger3 and Tim J Peterson3, (1)IITB-Monash Research Academy, Powai, India, (2)Assistant Professor, Department of Civil Engineering and Interdisciplinary Program in Climate Studies, Indian Institute of Technology Bombay, Mumbai, India, (3)Monash University, Department of Civil Engineering, Melbourne, VIC, Australia
 
Nowcasting Rainfall from Meteosat Data for Africa (719819)
Alan M Blyth1,2, Ralph Burton2, Alexander James Roberts3, John Marsham1, Jennifer K Fletcher4, Douglas J Parker5, James Groves4,6, George Pankiewicz7 and Claire Bartholomew7, (1)University of Leeds, Leeds, LS2, United Kingdom, (2)National Centre for Atmospheric Science, Environment, Leeds, United Kingdom, (3)University of Leeds, School of Earth and Environment, Leeds, LS2, United Kingdom, (4)University of Leeds, Leeds, United Kingdom, (5)School of Earth and Environment, University of Leeds, ICAS, Leeds, United Kingdom, (6)National Centre for Atmospheric Science, Leeds, United Kingdom, (7)Met Office, Exeter, United Kingdom
 
On the dependence between extreme rainfall and preceding temperature conditions across major Canadian cities (768000)
Ali Nazemi and Samaneh Ashraf, Concordia University, Department of Building, Civil and Environmental Engineering, Montreal, QC, Canada
 
Precipitation forecasting using machine-learning-based ensemble aggregation with Wasserstein-guided weighting (711110)
Fearghal O'Donncha, IBM Ireland, Dublin, Ireland, Kelsey Dipietro, Sandia National Laboratories, Jill Hruby Postdoctoral Fellow, Albuquerque, NM, United States, Scott C James, Baylor University, Geosciences and Mechanical Engineering, Waco, TX, United States, Bruce Byars, Baylor University, Waco, TX, United States, Kristopher Lander, NOAA Fort Worth, Fort Worth, TX, United States and Jack Settelmaier, NOAA/NWS, Southern Region HQ, Fort Worth, TX, United States
 
Representativeness of observed rainfall data and its influence on estimated quantiles (759000)
Andre Ballarin1, Jamil A.A. Anache1 and Edson Wendland2, (1)USP University of Sao Paulo, São Carlos School of Engineering, Hydraulics and Sanitary Engineering, São Carlos, Brazil, (2)USP University of Sao Paulo, São Carlos School of Engineering, Hydraulics and Sanitary Engineering, São Paulo, Brazil
 
Sensitivity of rainfall characteristics to thermodynamic and dynamic vertical structure over Costa Rica (749758)
Rani Wiggins, Rutgers University New Brunswick, New Brunswick, NJ, United States, Ana Duran-Quesada, University of Costa Rica, San Jose, Costa Rica, Yolande L Serra, University of Washington, Joint Institute for the Study of the Atmosphere and Ocean, Seattle, WA, United States and Benjamin R Lintner, Rutgers, New Brunswick, NJ, United States
 
Space-Time Statistical Quality Control of Extreme Precipitation Observations (704214)
Abbas El Hachem, Institute for Modelling Hydraulics and Environmental Systems, Stuttgart University, Stuttgart, Germany, András Bárdossy, University of Stuttgart, Department of Hydrology and Geohydrology, Institute for Modelling Hydraulic and Environmental Systems, Stuttgart, Germany, Jochen Seidel, Institute of Modelling Hydraulics and Environmental Systems, Stuttgart University, Stuttgart, Germany, Golbarg Goshtasbpour, Institute of Hydrology and Water Resourcese Management, Leibniz University Hannover, Hannover, Germany and Uwe Haberlandt, Institute of Hydrology and Water Resources Management, Leibniz University of Hannover, Hannover, Germany
 
Spatio-Temporal Interpolation of Cloud Data (766113)
Shane Grigsby1,2, Facundo Sapienza3, Tasha Snow1,4, Alice Cima3, Lindsey Justine Heagy3, Matthew Siegfried2, Fernando Perez3 and Jonathan Taylor5, (1)Cooperative Institute for Research in Environmental Sciences, Boulder, CO, United States, (2)Colorado School of Mines, Geophysics, Golden, CO, United States, (3)University of California, Berkeley, Statistics, Berkeley, CA, United States, (4)University of Colorado Boulder, Boulder, CO, United States, (5)Stanford University, Stanford, United States
 
Trend analysis of annual maximum precipitation in Western Europe over the past 70 years (758178)
Bidroha Basu1, Arunima Sarkar Basu1, Francesco Pilla1, Srikanta Sannigrahi1, Aoife Ni Rathaille2, Glauco Gallotti3, Marco Antonio Santo4, Christos Spyrou5, Paolo Ruggieri3, Aura Salmivaara6 and Silvana Di Sabatino4, (1)University College Dublin, Dublin, Ireland, (2)Dublin City Council, Dublin, Ireland, (3)University of Bologna, DIFA, Bologna, Italy, (4)University of Bologna, Bologna, Italy, (5)Innovative Technologies Centre, Athens, Greece, (6)LUKE, Helsinki, Finland
 
Using a New Dataset of International Climate Parameters to Drive Soil Erosion Modeling (712326)
Andrew Fullhart, Southwest Watershed Research Center, Tucson, AZ, United States, Mark Nearing, USDA-ARS, Southwest Watershed Research Center, Tucson, AZ, United States, Gerardo Armendariz, USDA-ARS Southwest Watershed Research Center, Tucson, AZ, United States and Mark Weltz, USDA ARS, Reno, NV, United States
 
Using k-means cluster analysis to regionalize precipitation in the Tropical Andes (734822)
Mario Cordova, Johanna Orellana-Alvear and Rolando Célleri, Universidad de Cuenca, Departamento de Recursos Hídricos y Ciencias Ambientales, Cuenca, Ecuador
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