Comprehensive Evaluation of the IFloodS Radar Rainfall Products for Hydrologic Applications

This study describes the generation and testing of a reference rainfall product created from field campaign datasets collected during the NASA Global Precipitation Measurement (GPM) mission Ground Validation Iowa Flood Studies (IFloodS) experiment. The study evaluates ground-based radar rainfall (RR) products acquired during IFloodS in the context of building the reference rainfall product. The purpose of IFloodS was not only to attain a high-quality ground-based reference for the validation of satellite rainfall estimates but also to enhance understanding of flood-related rainfall processes and the predictability of flood forecasting. We assessed the six RR estimates (IFC, Q2, CSU-DP, NWS-DP, Stage IV, and Q2-Corrected) using data from rain gauge and disdrometer networks that were located in the broader field campaign area of central and northeastern Iowa. We performed the analyses with respect to time scales ranging from 1 h to the entire campaign period in order to compare the capabilities of each RR product and to characterize the error structure at scales that are frequently used in hydrologic applications. The evaluation results show that the Stage IV estimates perform superior to other estimates, demonstrating the need for gauge-based bias corrections of radar-only products. This correction should account for each product’s algorithm-dependent error structure that can be used to build unbiased rainfall products for the campaign reference. We characterized the statistical error structures (e.g., systematic and random components) of each RR estimate and used them for the generation of a campaign reference rainfall product. To assess the hydrologic utility of the reference product, we performed hydrologic simulations driven by the reference product over the Turkey River basin. The comparison of hydrologic simulation results demonstrates that the campaign reference product performs better than Stage IV in streamflow generation.

[1]  Bong-Chul Seo,et al.  Radar-rainfall estimation algorithms of Hydro-NEXRAD , 2011 .

[2]  Bong-Chul Seo,et al.  Statistical model of the range-dependent error in radar-rainfall estimates due to the vertical profile of reflectivity , 2011 .

[3]  Eyal Amitai,et al.  Classification of Rain Regimes by the Three-Dimensional Properties of Reflectivity Fields , 1995 .

[4]  Robert A. Houze,et al.  Comparison of Radar Data from the TRMM Satellite and Kwajalein Oceanic Validation Site , 2000 .

[5]  D. Kitzmiller,et al.  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 , 2012 .

[6]  A. H. Murphy,et al.  Economic value of weather and climate forecasts , 1997 .

[7]  Dong-Jun Seo,et al.  Characteristics of Reprocessed Hydrometeorological Automated Data System (HADS) Hourly Precipitation Data , 2009 .

[8]  A. Hou,et al.  The Global Precipitation Measurement Mission , 2014 .

[9]  Jian Zhang,et al.  Constructing Three-Dimensional Multiple-Radar Reflectivity Mosaics: Examples of Convective Storms and Stratiform Rain Echoes , 2005 .

[10]  Bong-Chul Seo,et al.  Real-Time Flood Forecasting and Information System for the State of Iowa , 2017 .

[11]  Jay P. Breidenbach,et al.  Real-Time Correction of Spatially Nonuniform Bias in Radar Rainfall Data Using Rain Gauge Measurements , 2002 .

[12]  E. Anagnostou.  A convective/stratiform precipitation classification algorithm for volume scanning weather radar observations , 2004 .

[13]  K. Droegemeier,et al.  PROJECT CRAFT A Real-Time Delivery System for Nexrad Level II Data Via The Internet , 2007 .

[14]  Witold F. Krajewski,et al.  New paradigm for statistical validation of satellite precipitation estimates: Application to a large sample of the TMPA 0.25° 3‐hourly estimates over Oklahoma , 2009 .

[15]  C. Daly,et al.  Physiographically sensitive mapping of climatological temperature and precipitation across the conterminous United States , 2008 .

[16]  J. Prueger,et al.  Soil Moisture Model Calibration and Validation: An ARS Watershed on the South Fork Iowa River , 2015 .

[17]  Alexander V. Ryzhkov,et al.  The Hydrometeor Classification Algorithm for the Polarimetric WSR-88D: Description and Application to an MCS , 2009 .

[18]  Chengqi He,et al.  Effects of Tai Chi on balance and fall prevention in Parkinson’s disease: a randomized controlled trial , 2014, Clinical rehabilitation.

[19]  Witold F. Krajewski,et al.  Extreme Flood Response: The June 2008 Flooding in Iowa , 2013 .

[20]  D. Maidment,et al.  Coordinate Transformations for Using NEXRAD Data in GIS-Based Hydrologic Modeling , 1999 .

[21]  J. L. Pippitt.  Data and Operational Processing for NASA's GPM Ground Validation Program , 2015 .

[22]  Mathew R. Schwaller,et al.  A Ground Validation Network for the Global Precipitation Measurement Mission , 2011 .

[23]  Jian Zhang,et al.  Evaluation and Uncertainty Estimation of NOAA/NSSL Next-Generation National Mosaic Quantitative Precipitation Estimation Product (Q2) over the Continental United States , 2013 .

[24]  Matthias Steiner,et al.  Effect of bias adjustment and rain gauge data quality control on radar rainfall estimation , 1999 .

[25]  D. Seo,et al.  Overall distributed model intercomparison project results , 2004 .

[26]  H. Ramsay,et al.  Calibrating Ground-Based Radars against TRMM and GPM , 2017 .

[27]  Riko Oki,et al.  THE GLOBAL PRECIPITATION MEASUREMENT (GPM) MISSION FOR SCIENCE AND SOCIETY. , 2017, Bulletin of the American Meteorological Society.

[28]  Jeff W. Brogden,et al.  Multi-Radar Multi-Sensor (MRMS) Quantitative Precipitation Estimation: Initial Operating Capabilities , 2016 .

[29]  V. Chandrasekar,et al.  A New Dual-Polarization Radar Rainfall Algorithm: Application in Colorado Precipitation Events , 2011 .

[30]  Jian Zhang,et al.  National mosaic and multi-sensor QPE (NMQ) system description, results, and future plans , 2011 .

[31]  Hoshin Vijai Gupta,et al.  Decomposition of the mean squared error and NSE performance criteria: Implications for improving hydrological modelling , 2009 .

[32]  W. Krajewski,et al.  High-Resolution QPF Uncertainty and Its Implications for Flood Prediction: A Case Study for the Eastern Iowa Flood of 2016 , 2018, Journal of Hydrometeorology.

[33]  Bong-Chul Seo,et al.  Comparison of Single- and Dual-Polarization–Based Rainfall Estimates Using NEXRAD Data for the NASA Iowa Flood Studies Project , 2015 .

[34]  Bong-Chul Seo,et al.  A Spatial–Dynamical Framework for Evaluation of Satellite Rainfall Products for Flood Prediction , 2016 .

[35]  E. Nadaraya.  On Non-Parametric Estimates of Density Functions and Regression Curves , 1965 .

[36]  Elizabeth A. Barnes,et al.  Primary Modes of Global Drop Size Distributions , 2018 .

[37]  Status Update on the GPM Ground Validation Iowa Flood Studies (IFloodS) Field Experiment , 2013 .

[38]  Bong-Chul Seo,et al.  Using the new dual-polarimetric capability of WSR-88D to eliminate anomalous propagation and wind turbine effects in radar-rainfall , 2015 .

[39]  R. Mantilla,et al.  Impact of radar‐rainfall error structure on estimated flood magnitude across scales: An investigation based on a parsimonious distributed hydrological model , 2012 .

[40]  Dong-Jun Seo,et al.  The distributed model intercomparison project (DMIP): Motivation and experiment design , 2004 .

[41]  Witold F. Krajewski,et al.  Connecting the power-law scaling structure of peak-discharges to spatially variable rainfall and catchment physical properties , 2014 .

[42]  Witold F. Krajewski,et al.  Conditional Bias in Radar Rainfall Estimation. , 2000 .

[43]  Bong-Chul Seo,et al.  NEXRAD NWS Polarimetric Precipitation Product Evaluation for IFloodS , 2015 .

[44]  Bong-Chul Seo,et al.  Hydro-NEXRAD-2: real-time access to customized radar-rainfall for hydrologic applications , 2013 .

[45]  R. Moore,et al.  Rainfall and sampling uncertainties: A rain gauge perspective , 2008 .

[46]  V. Chandrasekar,et al.  Hydrometeor classification system using dual-polarization radar measurements: model improvements and in situ verification , 2005, IEEE Transactions on Geoscience and Remote Sensing.

[47]  Alexander V. Ryzhkov,et al.  Polarimetric Rainfall Estimation in the Presence of Anomalous Propagation , 1998 .

[48]  Witold F. Krajewski,et al.  An Early Performance Evaluation of the NEXRAD Dual-Polarization Radar Rainfall Estimates for Urban Flood Applications , 2013 .

[49]  Bong-Chul Seo,et al.  Investigation of the scale-dependent variability of radar-rainfall and rain gauge error covariance , 2011 .

[50]  M. Sivapalan.  Prediction in ungauged basins: a grand challenge for theoretical hydrology , 2003 .

[51]  V. Chandrasekar,et al.  An Improved Dual-Polarization Radar Rainfall Algorithm (DROPS2.0): Application in NASA IFloodS Field Campaign , 2017 .

[52]  G. Villarini,et al.  Product-Error-Driven Uncertainty Model for Probabilistic Quantitative Precipitation Estimation with NEXRAD Data , 2007 .

[53]  Eugenio Gorgucci,et al.  POTENTIAL ROLE OF DUAL- POLARIZATION RADAR IN THE VALIDATION OF SATELLITE PRECIPITATION MEASUREMENTS Rationale and Opportunities , 2008 .

[54]  Frédéric Fabry,et al.  The accuracy of rainfall estimates by radar as a function of range , 1992 .

[55]  Tracy Depue,et al.  The Community Collaborative Rain, Hail, and Snow Network Informal Education for Scientists and Citizens , 2005 .

[56]  W. Krajewski,et al.  Uncertainty in radar‐rainfall composite and its impact on hydrologic prediction for the eastern Iowa flood of 2008 , 2013 .

[57]  J. Salas,et al.  A COMPARATIVE ANALYSIS OF TECHNIQUES FOR SPATIAL INTERPOLATION OF PRECIPITATION , 1985 .

[58]  R. Mantilla,et al.  Generalizing a nonlinear geophysical flood theory to medium‐sized river networks , 2010 .