Benchmarking High-Resolution Global Satellite Rainfall Products to Radar and Rain-Gauge Rainfall Estimates

This paper presents an in-depth investigation of the error properties of two high-resolution global-scale satellite rain retrievals verified against rainfall fields derived from a moderate-resolution rain-gauge network (25-30-km intergage distances) covering a region in the midwestern U.S. (Oklahoma Mesonet). Evaluated satellite retrievals include the NASA Tropical Rainfall Measuring Mission multisatellite precipitation analysis and the National Oceanic and Atmospheric Administration Climate Prediction Center morphing technique. The two satellite products are contrasted against a rain-gauge-adjusted radar rainfall product from the WSR-88D network in continental U.S. This paper presents an error characterization of the Mesonet rainfall fields based on an independent small-scale, but very dense (100-m intergage distances), rain-gauge network (named Micronet). The Mesonet error analysis, although significantly lower than the corresponding error statistics derived for the satellite and radar products, demonstrates the need to benchmark reference data sources prior to their quantitative use in validating remote sensing retrievals. In terms of the remote sensing rainfall products, this paper provides quantitative comparisons between the two satellite estimates and the most definitive rain-gauge-adjusted radar rainfall estimates at corresponding spatial and temporal resolutions (25 km and 3 hourly). Error quantification presented herein includes zero- (rain detection probability and false alarm), first- (bias ratio), and second-order (root mean square error and correlation) statistics as well as an evaluation of the spatial structure of error at warm and cold seasons of 2004 and 2006.

[1]  F. Hossain,et al.  Investigating Error Metrics for Satellite Rainfall Data at Hydrologically Relevant Scales , 2008 .

[2]  Michael D. Eilts,et al.  The Oklahoma Mesonet: A Technical Overview , 1995 .

[3]  Dong-Jun Seo,et al.  The WSR-88D rainfall algorithm , 1998 .

[4]  Emmanouil N. Anagnostou,et al.  Regional Differences in Overland Rainfall Estimation from PR-Calibrated TMI Algorithm , 2005 .

[5]  Faisal Hossain,et al.  Assessment of current passive-microwave- and infrared-based satellite rainfall remote sensing for flood prediction , 2004 .

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

[7]  Witold F. Krajewski,et al.  Effect of Temporal Sampling on Inferred Rainfall Spatial Statistics , 2005 .

[8]  Y. Hong,et al.  Self‐organizing nonlinear output (SONO): A neural network suitable for cloud patch–based rainfall estimation at small scales , 2005 .

[9]  Dara Entekhabi,et al.  A remote sensing observatory for hydrologic sciences: A genesis for scaling to continental hydrology , 2006 .

[10]  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 .

[11]  Frank S. Marzano,et al.  Rain field and reflectivity vertical profile reconstruction from C-band Radar volumetric data , 2004, IEEE Transactions on Geoscience and Remote Sensing.

[12]  W. Krajewski,et al.  On the estimation of radar rainfall error variance , 1999 .

[13]  Faisal Hossain,et al.  Flood prediction in the future: Recognizing hydrologic issues in anticipation of the Global Precipitation Measurement mission , 2006 .

[14]  Grant W. Petty,et al.  Validation and Intercomparison of SSM/I Rain-Rate Retrieval Methods over the Continental United States , 1998 .

[15]  Eric A. Smith,et al.  Foundations for statistical-physical precipitation retrieval from passive microwave satellite measurements. I: Brightness-temperature properties of a time-dependent cloud-radiation model , 1992 .

[16]  Witold F. Krajewski,et al.  Evaluation of Biases of Satellite Rainfall Estimation Algorithms over the Continental United States , 2002 .

[17]  Robert F. Adler,et al.  On the Tropical Rainfall Measuring Mission (TRMM) , 1996 .

[18]  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.

[19]  Eric A. Smith,et al.  Foundations for statistical-physical precipitation retrieval from passive microwave satellite measurements. II: Emission-source and generalized weighting-function properties of a time-dependent cloud-radiation model , 1993 .

[20]  Nobuhiro Takahashi,et al.  The global satellite mapping of precipitation (GSMaP) project , 2005, Proceedings. 2005 IEEE International Geoscience and Remote Sensing Symposium, 2005. IGARSS '05..

[21]  Bart Nijssen,et al.  Effect of precipitation sampling error on simulated hydrological fluxes and states: Anticipating the Global Precipitation Measurement satellites , 2004 .

[22]  Frank S. Marzano,et al.  A Neural Networks–Based Fusion Technique to Estimate Half-Hourly Rainfall Estimates at 0.1° Resolution from Satellite Passive Microwave and Infrared Data , 2004 .

[23]  L. Giglio,et al.  A passive microwave technique for estimating rainfall and vertical structure information from space. Part 2: Applications to SSM/I data , 1994 .

[24]  Dong-Bin Shin,et al.  The Evolution of the Goddard Profiling Algorithm (GPROF) for Rainfall Estimation from Passive Microwave Sensors , 2001 .

[25]  J. Janowiak,et al.  COMPARISON OF NEAR-REAL-TIME PRECIPITATION ESTIMATES FROM SATELLITE OBSERVATIONS AND NUMERICAL MODELS , 2007 .

[26]  Christian D. Kummerow,et al.  A Passive Microwave Technique for Estimating Rainfall and Vertical Structure Information from Space. Part I: Algorithm Description , 1994 .

[27]  Misako Kachi,et al.  Global Precipitation Map Using Satellite-Borne Microwave Radiometers by the GSMaP Project: Production and Validation , 2006, IEEE Transactions on Geoscience and Remote Sensing.

[28]  Emmanouil N. Anagnostou,et al.  Investigation of the nonlinear hydrologic response to precipitation forcing in physically based land surface modeling , 2004 .

[29]  Abdou Ali,et al.  Rainfall Estimation in the Sahel. Part I: Error Function , 2005 .

[30]  Witold F. Krajewski,et al.  Characterization of the temporal sampling error in space‐time‐averaged rainfall estimates from satellites , 2004 .

[31]  Emmanouil N. Anagnostou,et al.  Uncertainty Quantification of Mean-Areal Radar-Rainfall Estimates , 1999 .

[32]  W. Woodley,et al.  Rain Estimation from Geosynchronous Satellite Imagery—Visible and Infrared Studies , 1978 .

[33]  Ralph Ferraro,et al.  The Development of SSM/I Rain-Rate Retrieval Algorithms Using Ground-Based Radar Measurements , 1995 .

[34]  Matthew Rodell,et al.  Analysis of Multiple Precipitation Products and Preliminary Assessment of Their Impact on Global Land Data Assimilation System Land Surface States , 2005 .

[35]  Emmanouil N. Anagnostou,et al.  On the use of real‐time radar rainfall estimates for flood prediction in mountainous basins , 2000 .

[36]  Y. Hong,et al.  Uncertainty quantification of satellite precipitation estimation and Monte Carlo assessment of the error propagation into hydrologic response , 2004 .

[37]  Yudong Tian,et al.  Multitemporal Analysis of TRMM-Based Satellite Precipitation Products for Land Data Assimilation Applications , 2007 .

[38]  E. Anagnostou,et al.  Overland Precipitation Estimation from TRMM Passive Microwave Observations , 2001 .

[39]  Robert J. Joyce,et al.  The estimation of global monthly mean rainfall using infrared satellite data: The GOES precipitation index (GPI) , 1994 .

[40]  Riko Oki,et al.  International Global Precipitation Measurement (GPM) Program and Mission: An Overview , 2007 .

[41]  Daniel Rosenfeld,et al.  Cloud Top Microphysics as a Tool for Precipitation Measurements , 2007 .

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

[43]  Chris Kidd,et al.  Satellite Rainfall Estimation Using a Combined Pasive Microwave and Infrared Algorithm. , 2003 .