Performance evaluation of radar and satellite rainfalls for Typhoon Morakot over Taiwan: Are remote-sensing products ready for gauge denial scenario of extreme events?

summary This study evaluated rainfall estimates from ground radar network and four satellite algorithms with a relatively dense rain gauge network over Taiwan Island for the 2009 extreme Typhoon Morakot at various spatiotemporal scales (from 0.04 to 0.25 and hourly to event total accumulation). The results show that all the remote-sensing products underestimate the rainfall as compared to the rain gauge measurements, in an order of radar (� 18%), 3B42RT (� 19%), PERSIANN-CCS (28%), 3B42V6 (� 36%), and CMORPH (� 61%). The ground radar estimates are also most correlated with gauge measurements, having a correlation coefficient (CC) of 0.81 (0.82) at 0.04 (0.25) spatial resolution. For satellite products, CMORPH has the best spatial correlation (0.70) but largely underestimates the total rainfall accumulation. Compared to microwave ingested algorithms, the IR-dominant algorithms provide a better estimation of the total rainfall accumulation but poorly resolve the temporal evolution of the warm cloud typhoon, especially for a large overestimation at the early storm stage. This study suggests that the best performance comes from the ground radar estimates that could be used as an alternative in case of the gauge denial. However, the current satellite rainfall products still have limitations in terms of resolution and accuracy, especially for this type of extreme typhoon.

[1]  Faisal Hossain,et al.  Investigating the Optimal Configuration of Conceptual Hydrologic Models for Satellite-Rainfall-Based Flood Prediction , 2008, IEEE Geoscience and Remote Sensing Letters.

[2]  Yoshiyuki Seya,et al.  307 Evaluation of High Resolution MR Angiography , 1995 .

[3]  Stanley A. Morain,et al.  Comparison of TRMM and water district rain rates over New Mexico , 2005 .

[4]  Alexis Berne,et al.  Toward an error model for radar quantitative precipitation estimation in the Cevennes-Vivarais region, France ERAD 2006 , 2010 .

[5]  Zhao Ren-jun,et al.  The Xinanjiang model applied in China , 1992 .

[6]  Yang Hong,et al.  An Experimental Global Prediction System for Rainfall-Triggered Landslides Using Satellite Remote Sensing and Geospatial Datasets , 2006, IEEE Transactions on Geoscience and Remote Sensing.

[7]  Y. Hong,et al.  Hydrologic Evaluation of Rainfall Estimates from Radar, Satellite, Gauge, and Combinations on Ft. Cobb Basin, Oklahoma , 2011 .

[8]  Y. Hong,et al.  Precipitation Estimation from Remotely Sensed Imagery Using an Artificial Neural Network Cloud Classification System , 2004 .

[9]  Xinxuan Zhang Using NWP Analysis in Satellite Rainfall Estimation of Heavy Precipitation Events over Complex Terrain , 2012 .

[10]  M. Hoerling,et al.  ENSO variability, teleconnections and climate change , 2001 .

[11]  F. J. Turk,et al.  Toward improved characterization of remotely sensed precipitation regimes with MODIS/AMSR-E blended data techniques , 2005, IEEE Transactions on Geoscience and Remote Sensing.

[12]  Eyal Amitai,et al.  Multiplatform Comparisons of Rain Intensity for Extreme Precipitation Events , 2012, IEEE Transactions on Geoscience and Remote Sensing.

[13]  Jonathan D. Hall,et al.  High-Resolution Modeling of Typhoon Morakot (2009): Vortex Rossby Waves and Their Role in Extreme Precipitation over Taiwan , 2013 .

[14]  J. Janowiak,et al.  CMORPH: A Method that Produces Global Precipitation Estimates from Passive Microwave and Infrared Data at High Spatial and Temporal Resolution , 2004 .

[15]  Robert F. Adler,et al.  An intercomparison of three satellite infrared rainfall techniques over Japan and surrounding waters , 1993 .

[16]  Jian Zhang,et al.  A real‐time automated convective and stratiform precipitation segregation algorithm in native radar coordinates , 2013 .

[17]  Jian Zhang,et al.  Three- and four-dimensional high-resolution national radar mosaic , 2004 .

[18]  S. Sorooshian,et al.  Evaluation of PERSIANN system satellite-based estimates of tropical rainfall , 2000 .

[19]  Yang Hong,et al.  Hydrologic evaluation of Multisatellite Precipitation Analysis standard precipitation products in basins beyond its inclined latitude band: A case study in Laohahe basin, China , 2010 .

[20]  Marielle Gosset,et al.  An error model for instantaneous satellite rainfall estimates: evaluation of BRAIN‐TMI over West Africa , 2013 .

[21]  L. Xu Two-Layer Variable Infiltration Capacity Land Surface Representation for General Circulation Models , 1994 .

[22]  Corinne Le Quéré,et al.  Climate Change 2013: The Physical Science Basis , 2013 .

[23]  John D. Tuttle,et al.  Comparison of Ground-Based Radar and Geosynchronous Satellite Climatologies of Warm-Season Precipitation over the United States , 2008 .

[24]  Robert F. Adler,et al.  Evaluation of TMPA satellite-based research and real-time rainfall estimates during six tropical-related heavy rainfall events over Louisiana, USA , 2009 .

[25]  F. Hirpa,et al.  Evaluation of High-Resolution Satellite Precipitation Products over Very Complex Terrain in Ethiopia , 2010 .

[26]  Wan-chin Chen,et al.  Statistics of Heavy Rainfall Occurrences in Taiwan , 2007 .

[27]  Pietro Ceccato,et al.  Comparison of CMORPH and TRMM-3B42 over Mountainous Regions of Africa and South America , 2010 .

[28]  J. Janowiak,et al.  The Version 2 Global Precipitation Climatology Project (GPCP) Monthly Precipitation Analysis (1979-Present) , 2003 .

[29]  Y. Kuo,et al.  The Impacts of Taiwan Topography on the Predictability of Typhoon Morakot’s Record-Breaking Rainfall: A High-Resolution Ensemble Simulation , 2010 .

[30]  Sadiq I. Khan,et al.  The coupled routing and excess storage (CREST) distributed hydrological model , 2011 .

[31]  Sharon E. Nicholson,et al.  Validation of TRMM and Other Rainfall Estimates with a High-Density Gauge Dataset for West Africa. Part II: Validation of TRMM Rainfall Products , 2003 .

[32]  Marco Borga,et al.  Radar hydrology modifies the monitoring of flash‐flood hazard , 2003 .

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

[34]  Yang Hong,et al.  Flood and landslide applications of near real-time satellite rainfall products , 2007 .

[35]  R. Scofield,et al.  The Operational GOES Infrared Rainfall Estimation Technique , 1998 .

[36]  Roongroj Chokngamwong,et al.  Thailand Daily Rainfall and Comparison with TRMM Products , 2008 .

[37]  Yang Hong,et al.  Intercomparison of Rainfall Estimates from Radar, Satellite, Gauge, and Combinations for a Season of Record Rainfall , 2010 .

[38]  Yang Hong,et al.  Toward a Framework for Systematic Error Modeling of Spaceborne Precipitation Radar with NOAA/NSSL Ground Radar–Based National Mosaic QPE , 2012 .

[39]  Stefano Schiavon,et al.  Climate Change 2007: The Physical Science Basis. , 2007 .

[40]  Y. Hong,et al.  The TRMM Multisatellite Precipitation Analysis (TMPA): Quasi-Global, Multiyear, Combined-Sensor Precipitation Estimates at Fine Scales , 2007 .

[41]  Mary Lynn Baeck,et al.  Hydrologic analysis of the Fort Collins, Colorado, flash flood of 1997 , 2000 .