Evaluation of eight high spatial resolution gridded precipitation products in Adige Basin (Italy) at multiple temporal and spatial scales.

This study provides a comprehensive evaluation of eight high spatial resolution gridded precipitation products in Adige Basin located in Italy within 45-47.1°N. The Adige Basin is characterized by a complex topography, and independent ground data are available from a network of 101 rain gauges during 2000-2010. The eight products include the Version 7 TRMM (Tropical Rainfall Measuring Mission) Multi-satellite Precipitation Analysis 3B42 product, three products from CMORPH (the Climate Prediction Center MORPHing technique), i.e., CMORPH_RAW, CMORPH_CRT and CMORPH_BLD, PCDR (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record), PGF (Global Meteorological Forcing Dataset for land surface modelling developed by Princeton University), CHIRPS (Climate Hazards Group InfraRed Precipitation with Station data) and GSMaP_MVK (Global Satellite Mapping of Precipitation project Moving Vector with Kalman-filter product). All eight products are evaluated against interpolated rain gauge data at the common 0.25° spatial resolution, and additional evaluations at native finer spatial resolution are conducted for CHIRPS (0.05°) and GSMaP_MVK (0.10°). Evaluation is performed at multiple temporal (daily, monthly and annual) and spatial scales (grid and watershed). Evaluation results show that in terms of overall statistical metrics the CHIRPS, TRMM and CMORPH_BLD comparably rank as the top three best performing products, while the PGF performs worst. All eight products underestimate and overestimate the occurrence frequency of daily precipitation for some intensity ranges. All products tend to show higher error in the winter months (December-February) when precipitation is low. Very slight difference can be observed in the evaluation metrics and aspects between at the aggregated 0.25° spatial resolution and at the native finer resolutions (0.05°) for CHIRPS and (0.10°) for GSMaP_MVK products. This study has implications for precipitation product development and the global view of the performance of various precipitation products, and provides valuable guidance when choosing alternative precipitation data for local community.

[1]  S. Sorooshian,et al.  Evaluation and comparison of satellite precipitation estimates with reference to a local area in the Mediterranean Sea , 2014 .

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

[3]  W. Petersen,et al.  Global precipitation measurement: Methods, datasets and applications , 2012 .

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

[5]  D. S. Pai,et al.  Assessment of TRMM‐based TMPA‐3B42 and GSMaP precipitation products over India for the peak southwest monsoon season , 2016 .

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

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

[8]  J. Michaelsen,et al.  The climate hazards infrared precipitation with stations—a new environmental record for monitoring extremes , 2015, Scientific Data.

[9]  G. Huffman,et al.  TRMM and Other Data Precipitation Data Set Documentation , 2015 .

[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]  Arthur P. Cracknell,et al.  Evaluation of Six High-Resolution Satellite and Ground-Based Precipitation Products over Malaysia , 2015, Remote. Sens..

[12]  Y. Hong,et al.  Global View Of Real-Time Trmm Multisatellite Precipitation Analysis: Implications For Its Successor Global Precipitation Measurement Mission , 2015 .

[13]  U. Schneider,et al.  GPCC's new land surface precipitation climatology based on quality-controlled in situ data and its role in quantifying the global water cycle , 2013, Theoretical and Applied Climatology.

[14]  Chris Kidd,et al.  Global Precipitation Measurement , 2008 .

[15]  U. Schneider,et al.  GPCC Full Data Reanalysis Version 7.0: Monthly Land-Surface Precipitation from Rain Gauges built on GTS based and Historic Data , 2016 .

[16]  Tomoo Ushio,et al.  Evaluation of GSMaP Precipitation Estimates over the Contiguous United States , 2010 .

[17]  Pingping Xie.  A 15-Year High - Resolution Gauge – Satellite Merged Analysis of Precipitation , 2013 .

[18]  R. Reynolds,et al.  The NCEP/NCAR 40-Year Reanalysis Project , 1996, Renewable Energy.

[19]  E. Wood,et al.  Development of a 50-Year High-Resolution Global Dataset of Meteorological Forcings for Land Surface Modeling , 2006 .

[20]  S. Sorooshian,et al.  PERSIANN-CDR: Daily Precipitation Climate Data Record from Multisatellite Observations for Hydrological and Climate Studies , 2015 .

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

[22]  J. Dozier,et al.  Inroads of remote sensing into hydrologic science during the WRR era , 2015 .

[23]  Viviana Maggioni,et al.  A Review of Merged High-Resolution Satellite Precipitation Product Accuracy during the Tropical Rainfall Measuring Mission (TRMM) Era , 2016 .

[24]  Emmanouil N. Anagnostou,et al.  Using High-Resolution Satellite Rainfall Products to Simulate a Major Flash Flood Event in Northern Italy , 2013 .

[25]  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 .

[26]  Robinson I. Negrón Juárez,et al.  Comparison of Precipitation Datasets over the Tropical South American and African Continents , 2009 .

[27]  Dawen Yang,et al.  Multi-scale evaluation of six high-resolution satellite monthly rainfall estimates over a humid region in China with dense rain gauges , 2014 .

[28]  P. Bauer,et al.  Algorithms for the retrieval of rainfall from passive microwave measurements , 1994 .

[29]  Guido Zolezzi,et al.  Assessing hydrological alterations at multiple temporal scales: Adige River, Italy , 2009 .

[30]  C. Frei,et al.  Comparison of six methods for the interpolation of daily, European climate data , 2008 .

[31]  Alberto Bellin,et al.  Impact of climate change and water use policies on hydropower potential in the south-eastern Alpine region. , 2016, The Science of the total environment.

[32]  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 .

[33]  Yudong Tian,et al.  A new approach to satellite-based estimation of precipitation over snow cover , 2014 .

[34]  A. Yatagai,et al.  Comparing high-resolution gridded precipitation data with satellite rainfall estimates of TRMM_3B42 over Iran , 2009 .

[35]  Wim G.M. Bastiaanssen,et al.  First results from Version 7 TRMM 3B43 precipitation product in combination with a new downscaling–calibration procedure , 2013 .

[36]  Adrianos Retalis,et al.  Validation of a high-resolution precipitation database (CHIRPS) over Cyprus for a 30-year period , 2016 .

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

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

[39]  Michele Brunetti,et al.  Temperature and precipitation variability in Italy in the last two centuries from homogenised instrumental time series , 2006 .

[40]  X. Ji,et al.  Characterizing spatial patterns of precipitation based on corrected TRMM 3B43 data over the mid Tianshan Mountains of China , 2012, Journal of Mountain Science.

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

[42]  Yang Hong,et al.  Evaluation of Spatial Errors of Precipitation Rates and Types from TRMM Spaceborne Radar over the Southern CONUS , 2013 .

[43]  Z. Duan,et al.  Evaluation of precipitation input for SWAT modeling in Alpine catchment: A case study in the Adige river basin (Italy). , 2016, The Science of the total environment.

[44]  Georg Teutsch,et al.  Managing the effects of multiple stressors on aquatic ecosystems under water scarcity. The GLOBAQUA project , 2015, The Science of the total environment.

[45]  Amir AghaKouchak,et al.  Extended contingency table: Performance metrics for satellite observations and climate model simulations , 2013 .

[46]  E. Anagnostou,et al.  Error Analysis of Satellite Precipitation Products in Mountainous Basins , 2014 .

[47]  Zheng Duan,et al.  Monthly and annual validation of TRMM Mulitisatellite Precipitation Analysis (TMPA) products in the Caspian Sea Region for the period 1999–2003 , 2012, 2012 IEEE International Geoscience and Remote Sensing Symposium.

[48]  Zhihao Qin,et al.  An Improved Spatial Downscaling Procedure for TRMM 3B43 Precipitation Product Using Geographically Weighted Regression , 2015, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[49]  Xiufeng He,et al.  Uncertainties in remotely sensed precipitation data over Africa , 2016 .

[50]  F. Pappenberger,et al.  ERA-Interim/Land: a global land surface reanalysis data set , 2015 .

[51]  Alberto Bellin,et al.  A review of hydrological and chemical stressors in the Adige catchment and its ecological status. , 2016, The Science of the total environment.

[52]  B. N. Meisner,et al.  The Relationship between Large-Scale Convective Rainfall and Cold Cloud over the Western Hemisphere during 1982-84 , 1987 .

[53]  A-Xing Zhu,et al.  Evaluation of Three Satellite Precipitation Products TRMM 3B42, CMORPH, and PERSIANN over a Subtropical Watershed in China , 2015 .

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

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

[56]  Joseph L. Awange,et al.  An evaluation of high‐resolution gridded precipitation products over Bhutan (1998–2012) , 2016 .

[57]  P. Jones,et al.  Updated high‐resolution grids of monthly climatic observations – the CRU TS3.10 Dataset , 2014 .