Variational merged of hourly gauge‐satellite precipitation in China: Preliminary results

The article describes a variational scheme for the analysis of high-resolution hourly precipitation from China Meteorological Administration gauges and NOAA CMORPH satellite products in China and tests their impact on data-sparse regions and the heavy rainfall occurrences during the summer season (June–August 2009). In the variational scheme, a cost function is defined to measure the distance between analyzed precipitation field and observed rainfall quantity. A recursive filter is incorporated into the cost function which helps spread the observations to nearby grid points. Then a quasi-Newton method is used to solve the optimal estimation problem by minimizing the cost function. The adjoint technique is used to derive the gradient of cost function with respect to analysis precipitation. A series of experiments are performed to intercompare the variational analysis with the original CMORPH satellite products (CMP) and the bias-adjusted satellite products (Adj-CMP) against the observations. The best overall performance is from the variational analysis especially rainfall intensity by more than 10 mm h−1 with a prevailing mean relative spatial bias nearly reduction zero, and the correlation coefficient is almost around 0.5 in convection active areas. Ground cross-validation experiments in which each affected station is withdrawn at once indicated that the variational analysis can particularly be beneficial and subsequent investigation of heavy rainfall events. It also reveals that the precipitation analysis field has the ability to improve the accuracy of rainfall estimation and capture the spatial precipitation pattern agreements in relatively data-sparse regions.

[1]  Dong-Jun Seo,et al.  Real-time estimation of mean field bias in radar rainfall data , 1999 .

[2]  Wanqiu Wang,et al.  A New High-Resolution Blended Real-Time Global Sea Surface Temperature Analysis , 2003 .

[3]  Ralph Ferraro,et al.  Special sensor microwave imager derived global rainfall estimates for climatological applications , 1997 .

[4]  P. Jones,et al.  REPRESENTING TWENTIETH CENTURY SPACE-TIME CLIMATE VARIABILITY. , 1998 .

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

[6]  Jay P. Breidenbach,et al.  Real-time adjustment of range-dependent biases in WSR-88D rainfall estimates due to nonuniform vertical profile of reflectivity , 2000 .

[7]  Soroosh Sorooshian,et al.  Evaluation of PERSIANN-CCS rainfall measurement using the NAME event rain gauge network , 2007 .

[8]  J. Derber.  Variational Four-dimensional Analysis Using Quasi-Geostrophic Constraints , 1987 .

[9]  R. Errico.  What is an adjoint model , 1997 .

[10]  Peter Bauer,et al.  “1D+4DVAR” Assimilation of NCEP Stage-IV Radar and Gauge Hourly Precipitation Data at ECMWF , 2007 .

[11]  Dong-Jun Seo,et al.  Real-time estimation of rainfall fields using rain gage data under fractional coverage conditions , 1998 .

[12]  P. Jones,et al.  Representing Twentieth-Century Space-Time Climate Variability. Part II: Development of 1901-96 Monthly Grids of Terrestrial Surface Climate , 2000 .

[13]  G. Villarini,et al.  Empirically-based modeling of spatial sampling uncertainties associated with rainfall measurements by rain gauges , 2008 .

[14]  S. Sorooshian,et al.  Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks , 1997 .

[15]  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 .

[16]  Jose Maliekal,et al.  The Uncertainty of Simple Spatial Averages Using Rain Gauge Networks , 1995 .

[17]  V. Kousky,et al.  Assessing objective techniques for gauge‐based analyses of global daily precipitation , 2008 .

[18]  P. Xie,et al.  Performance of high‐resolution satellite precipitation products over China , 2010 .

[19]  A. Lorenc.  A Global Three-Dimensional Multivariate Statistical Interpolation Scheme , 1981 .

[20]  Robin T. Clarke,et al.  A comparison of extreme rainfall characteristics in the Brazilian Amazon derived from two gridded data sets and a national rain gauge network , 2010 .

[21]  J. Janowiak,et al.  The Global Precipitation Climatology Project (GPCP) combined precipitation dataset , 1997 .

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

[23]  Bong-Chul Seo,et al.  Towards Better Utilization of NEXRAD Data in Hydrology: An Overview of Hydro-NEXRAD , 2005 .

[24]  W. Krajewski,et al.  Estimation of Rainfall Interstation Correlation , 2001 .

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

[26]  P. Courtier,et al.  The ECMWF implementation of three‐dimensional variational assimilation (3D‐Var). I: Formulation , 1998 .

[27]  A. Lorenc.  Iterative analysis using covariance functions and filters , 1992 .

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

[29]  J. Thepaut,et al.  The ERA‐Interim reanalysis: configuration and performance of the data assimilation system , 2011 .

[30]  U. Schneider,et al.  Global precipitation estimates based on a technique for combining satellite-based estimates, rain gauge analysis, and NWP model precipitation information , 1995 .

[31]  Thomas M. Smith,et al.  Improved Global Sea Surface Temperature Analyses Using Optimum Interpolation , 1994 .

[32]  J. Janowiak,et al.  GPCP Pentad Precipitation analyses: An experimental dataset based on gauge observations and satellite estimates , 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]  Witold F. Krajewski,et al.  Cokriging radar‐rainfall and rain gage data , 1987 .

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

[37]  Phillip A. Arkin,et al.  Analyses of Global Monthly Precipitation Using Gauge Observations, Satellite Estimates, and Numerical Model Predictions , 1996 .

[38]  Phillip A. Arkin,et al.  The Relationship between Fractional Coverage of High Cloud and Rainfall Accumulations during GATE over the B-Scale Array , 1979 .

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

[40]  P. Xie,et al.  Kalman Filter–Based CMORPH , 2011 .

[41]  F. Turk,et al.  Component analysis of errors in satellite-based precipitation estimates , 2009 .

[42]  P. Courtier,et al.  Variational Assimilation of Meteorological Observations With the Adjoint Vorticity Equation. I: Theory , 2007 .

[43]  L. Gandin.  Objective Analysis of Meteorological Fields , 1963 .

[44]  Wanqiu Wang,et al.  A Multiplatform-Merged (MPM) SST Analysis , 2007 .

[45]  Z. Kawasaki,et al.  A Kalman Filter Approach to the Global Satellite Mapping of Precipitation (GSMaP) from Combined Passive Microwave and Infrared Radiometric Data , 2009 .

[46]  R. James Purser,et al.  Recursive Filter Objective Analysis of Meteorological Fields: Applications to NESDIS Operational Processing , 1995 .

[47]  P. Xie,et al.  Global Precipitation: A 17-Year Monthly Analysis Based on Gauge Observations, Satellite Estimates, and Numerical Model Outputs , 1997 .

[48]  Jidong Gao,et al.  A Three-Dimensional Variational Data Analysis Method with Recursive Filter for Doppler Radars , 2004 .

[49]  S. Schubert,et al.  MERRA: NASA’s Modern-Era Retrospective Analysis for Research and Applications , 2011 .

[50]  J. McGinley,et al.  Improving QPE and Very Short Term QPF: An Initiative for a Community-Wide Integrated Approach , 2007 .

[51]  Kuolin Hsu,et al.  Bias Adjustment of Satellite Precipitation Estimation Using Ground-Based Measurement: A Case Study Evaluation over the Southwestern United States , 2009 .

[52]  Uang,et al.  The NCEP Climate Forecast System Reanalysis , 2010 .

[53]  Jorge Nocedal,et al.  On the limited memory BFGS method for large scale optimization , 1989, Math. Program..

[54]  Christian Kummerow,et al.  A simplified scheme for obtaining precipitation and vertical hydrometeor profiles from passive microwave sensors , 1996, IEEE Trans. Geosci. Remote. Sens..

[55]  N. Roberts,et al.  Numerical Aspects of the Application of Recursive Filters to Variational Statistical Analysis. Part II: Spatially Inhomogeneous and Anisotropic General Covariances , 2003 .

[56]  Juanzhen Sun,et al.  Real-Time Low-Level Wind and Temperature Analysis Using Single WSR-88D Data , 2001 .

[57]  Philippe Lopez,et al.  Direct 4D-Var Assimilation of NCEP Stage IV Radar and Gauge Precipitation Data at ECMWF , 2011 .

[58]  Eric F. Wood,et al.  Correction of Global Precipitation Products for Orographic Effects , 2006 .

[59]  Pingping Xie,et al.  A conceptual model for constructing high‐resolution gauge‐satellite merged precipitation analyses , 2011 .

[60]  P. Xie,et al.  A Gauge-Based Analysis of Daily Precipitation over East Asia , 2007 .

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

[62]  B. Rudolf,et al.  A New Monthly Precipitation Climatology for the Global Land Areas for the Period 1951 to 2000 , 2004 .

[63]  Soo-Hyun Yoo,et al.  An assessment of the surface climate in the NCEP climate forecast system reanalysis , 2011 .