The 183-WSL fast rain rate retrieval algorithm. Part II: Validation using ground radar measurements

Abstract The Water vapor Strong Lines at 183 GHz (183-WSL) algorithm is a method for the retrieval of rain rates and precipitation type classification (convective/stratiform). It exploits the water vapor absorption line observations centered at 183.31 GHz of the Advanced Microwave Sounding Unit module B (AMSU-B) and of the Microwave Humidity Sounder (MHS) flying on NOAA-15/-17 and NOAA-18-19/MetOp-A satellite series, respectively. The characteristics of this algorithm were described in Part I of this paper together with comparisons against analogous precipitation products. The focus of Part II is the analysis of the performance of the 183-WSL technique based on surface radar measurements. The “ground truth” dataset consists of 2 years and 7 months of rainfall intensity fields from the NIMROD radar network, which covers North-Western Europe. The investigation of the 183-WSL retrieval performance is based on a twofold approach: 1) the dichotomous statistic is used to evaluate the capabilities of the method to identify rain and no-rain clouds and 2) the accuracy statistic is applied to quantify the errors in the estimation of rain rates. The results reveal that the 183-WSL technique shows good skills in the detection of rain/no-rain areas and in the quantification of rain rate intensities. The categorical analysis shows annual values of the Probability Of Detection (POD), False Alarm Ratio (FAR) and Hanssen–Kuiper discriminant (HK) indices varying in the range 0.80–0.82, 0.33–0.36 and 0.39–0.46, respectively. The RMSE value is 2.8 mm h − 1 for the whole period despite an overestimation in the retrieved rain rates. Of note is the distribution of the 183-WSL monthly mean rain rate with respect to radar: the seasonal fluctuations of the average rainfalls measured by radar are reproduced by the 183-WSL. However, the retrieval method appears to suffer during winter seasonal conditions especially when the soil is partially frozen and the surface emissivity drastically changes. This is verified by the discrepancy distribution diagrams where the 183-WSL performs better during the warm months, while during the winter time the discrepancies with radar measurements tend to maximum values. The stable behavior of the 183-WSL algorithm is demonstrated over the whole study period by an overall overestimation for rain rate intensities less than 1 mm h − 1 . This threshold is especially crucial in wintertime when the classification of low-intensity precipitation regimes is difficult.

[1]  Henri Sauvageot,et al.  The Probability Density Function of Rain Rate and the Estimation of Rainfall by Area Integrals , 1994 .

[2]  Elizabeth E. Ebert,et al.  Methods for Verifying Satellite Precipitation Estimates , 2007 .

[3]  Pietro Ceccato,et al.  Validation and Intercomparison of Satellite Rainfall Estimates over Colombia , 2010 .

[4]  B. Golding Nimrod: a system for generating automated very short range forecasts , 1998 .

[5]  V. Levizzani,et al.  First validation of retrieved rain rates and snow cover mask of the 183-WSL retrieval method , 2012, 2012 12th Specialist Meeting on Microwave Radiometry and Remote Sensing of the Environment (MicroRad).

[6]  B. Golding Quantitative precipitation forecasting in the UK , 2000 .

[7]  F. Joseph Turk,et al.  Precipitation from Space: Advancing Earth System Science , 2013 .

[8]  A. Dai,et al.  Summer Precipitation Frequency, Intensity, and Diurnal Cycle over China: A Comparison of Satellite Data with Rain Gauge Observations , 2007 .

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

[10]  W. Briggs Statistical Methods in the Atmospheric Sciences , 2007 .

[11]  Dawn Harrison,et al.  Improving precipitation estimates from weather radar using quality control and correction techniques , 2000 .

[12]  A. Hou,et al.  Evaluation of Coincident Passive Microwave Rainfall Estimates Using TRMM PR and Ground Measurements as References , 2008 .

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

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

[15]  Arthur Y. Hou,et al.  Estimation of Rain Intensity Spectra over the Continental United States Using Ground Radar-Gauge Measurements , 2012 .

[16]  E. Anagnostou,et al.  Precipitation: Measurement, remote sensing, climatology and modeling , 2009 .

[17]  Viju O. John,et al.  Scan asymmetries in AMSU‐B data , 2005 .

[18]  Kuolin Hsu,et al.  Intercomparison of High-Resolution Precipitation Products over Northwest Europe , 2012 .

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

[20]  Gerrit Holl,et al.  Monitoring scan asymmetry of microwave humidity sounding channels using simultaneous all angle collocations (SAACs) , 2013 .

[21]  C. Kummerow,et al.  The Tropical Rainfall Measuring Mission (TRMM) Sensor Package , 1998 .

[22]  Daniel S. Wilks,et al.  Statistical Methods in the Atmospheric Sciences: An Introduction , 1995 .

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

[24]  Vincenzo Levizzani,et al.  The 183-WSL fast rain rate retrieval algorithm: Part I: Retrieval design , 2011 .