Inter-product biases in global precipitation extremes

Biases in climatological and extreme precipitation estimates are assessed for 11 global observational datasets constructed with merged satellite measurements and/or rain gauge networks. Specifically, the biases in extreme precipitation are contrasted with mean-state biases. Extreme precipitation is defined by a 99th percentile threshold (R99p) on a daily, 1°×1° grid for 50°S-50°N. The spatial pattern of extreme precipitation lacks distinct features such as the ITCZ that is evident in the global climatological map, and the climatology and extremes share little in common in terms of the spatial characteristics of inter-product biases. The time series of different datasets also exhibit a larger spread in the extremes than in the climatology. Further, when analysed from 2001 to 2013, they show a relatively consistent decadal stability in the climatology over ocean while the dispersion is larger for the extremes over ocean. This contrast is not observed over land. Overall, the results suggest that the inter-product biases apparent in the climatology are a poor predictor of the extreme-precipitation biases even in a qualitative sense.

[1]  U. Schneider,et al.  GPCC Full Data Daily Version 1.0: Daily Land-Surface Precipitation from Rain Gauges built on GTS based and Historic Data , 2016 .

[2]  Robert F. Adler,et al.  Global Precipitation: Means, Variations and Trends During the Satellite Era (1979–2014) , 2017, Surveys in Geophysics.

[3]  S. Sorooshian,et al.  A Review of Global Precipitation Data Sets: Data Sources, Estimation, and Intercomparisons , 2018 .

[4]  R. Roca.  Estimation of extreme daily precipitation thermodynamic scaling using gridded satellite precipitation products over tropical land , 2019, Environmental Research Letters.

[5]  W. Press,et al.  Numerical Recipes in C++: The Art of Scientific Computing (2nd edn)1 Numerical Recipes Example Book (C++) (2nd edn)2 Numerical Recipes Multi-Language Code CD ROM with LINUX or UNIX Single-Screen License Revised Version3 , 2003 .

[6]  M. Bosilovich,et al.  FROGS: a daily 1°  ×  1° gridded precipitation database of rain gauge, satellite and reanalysis products , 2019, Earth System Science Data.

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

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

[9]  Ali Behrangi,et al.  Large uncertainties in observed daily precipitation extremes over land , 2017 .

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

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

[12]  Manuel Merino,et al.  Evaluation of the Performance of Three Satellite Precipitation Products over Africa , 2016, Remote. Sens..

[13]  U. Schneider,et al.  A description of the global land-surface precipitation data products of the Global Precipitation Climatology Centre with sample applications including centennial (trend) analysis from 1901–present , 2012 .

[14]  R. Lin,et al.  Reprocessed, Bias-Corrected CMORPH Global High-Resolution Precipitation Estimates from 1998 , 2017 .

[15]  B. Soden,et al.  Atmospheric Warming and the Amplification of Precipitation Extremes , 2008, Science.

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

[17]  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 .

[18]  Lei Shi,et al.  The GEWEX Water Vapor Assessment: Overview and Introduction to Results and Recommendations , 2019, Remote. Sens..

[19]  G. Hegerl,et al.  Indices for monitoring changes in extremes based on daily temperature and precipitation data , 2011 .

[20]  U. Schneider,et al.  The Global Precipitation Climatology Project (GPCP) Monthly Analysis (New Version 2.3) and a Review of 2017 Global Precipitation. , 2018, Atmosphere.

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

[22]  J. Michaelsen,et al.  A global satellite-assisted precipitation climatology , 2015 .

[23]  William H. Press,et al.  Numerical recipes in C (2nd ed.): the art of scientific computing , 1992 .

[24]  H. Masunaga,et al.  Origins of Heavy Precipitation Biases in the TRMM PR and TMI Products Assessed with CloudSat and Reanalysis Data , 2019, Journal of Applied Meteorology and Climatology.

[25]  J. Susskind,et al.  Global Precipitation at One-Degree Daily Resolution from Multisatellite Observations , 2001 .

[26]  Robert F. Adler,et al.  Tropical Rainfall Variability on Interannual-to-Interdecadal and Longer Time Scales Derived from the GPCP Monthly Product , 2007 .

[27]  Marielle Gosset,et al.  Quantifying the contribution of the Megha‐Tropiques mission to the estimation of daily accumulated rainfall in the Tropics , 2018, Quarterly Journal of the Royal Meteorological Society.

[28]  Thomas M. Hamill,et al.  Comparison of Global Precipitation Estimates across a Range of Temporal and Spatial Scales , 2015 .

[29]  J. Schulz,et al.  Comparison of decadal global water vapor changes derived from independent satellite time series , 2014 .

[30]  M. Webb,et al.  Regional Intensification of the Tropical Hydrological Cycle During ENSO , 2018 .

[31]  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.

[32]  Axel Andersson,et al.  The Hamburg Ocean Atmosphere Parameters and Fluxes from Satellite Data – HOAPS-3 , 2010 .

[33]  S. Sorooshian,et al.  Evaluation of satellite-retrieved extreme precipitation rates across the central United States , 2011 .