Evaluation of Precipitation Climatology Derived from TRMM Multi-Satellite Precipitation Analysis (TMPA) Monthly Product over Land with Two Gauge-Based Products

Zhong Liu

doi:10.3390/cli3040964

Abstract

The NASA/JAXA Tropical Rainfall Measuring Mission (TRMM) has been in operation for over 17 years since 1997. The length of TRMM is far shorter than those from ground observations, raising a question as to whether TRMM derived climatology products are good enough for research and applications. In this study, three climatologies derived from a blended product (the TRMM Multi-Satellite Precipitation Analysis (TMPA) monthly product or 3B43) and gauge-based ground observations (Global Precipitation Climatology Center (GPCC) and Willmott and Matsuura (WM)) are compared over land on a global scale (50° N–50° S) to assess the performance and weaknesses of the TMPA-derived climatology. Results show that the 3B43 climatology matches well with the two gauge-based climatologies in all seasons in terms of spatial distribution, zonal means as well as seasonal variations. However, large variations are found in light rain (<0.5 mm/day) regions such as the Sahara Desert. At high rain rates, large negative biases (3B43 < WM < GPCC) are found in orographically influenced regions such as windward sides of monsoon mountains in JJA, which is associated with underestimation of shallow orographic rain rates in microwave radiometer algorithms and sparse gauge data for bias correction. Nonetheless, biases in 3B43 exist and vary with geographic locations due to a number of factors such as gauge availability and algorithm issues.

Highlights

Precipitation is a very important physical variable in our daily lives [1]
Large negative biases (3B43 < Willmott and Matsuura (WM) < Global Precipitation Climatology Center (GPCC)) are found in orographically influenced regions such as windward sides of monsoon mountains in JJA, which is associated with underestimation of shallow orographic rain rates in microwave radiometer algorithms and sparse gauge data for bias correction
In addition to precipitation, derived anomaly information can be useful in drought detection, monitoring and management activities by providing additional information compared to the normal condition which is known as climatology

Summary

Introduction

Precipitation is a very important physical variable in our daily lives [1]. as a double-edged sword, droughts and floods can occur each year in different regions around the world [2,3,4], often causing considerable damage to crops, properties, human fatalities, etc. The TMPA monthly product (3B43) is picked for TCC and surface observations from the Global Precipitation Climatology Center (GPCC) were used for comparison. It needs to be emphasized that validation or verification of satellite-based precipitation products still heavily relies on individuals who often can obtain ground measurements with higher spatial and temporal resolutions than those from GPCC. For such reason, this study is not intended for ground validation for 3B43 and the task should be left to those individual. The Version 2011 monthly land-surface precipitation climatology [21] from GPCC was derived from rain-gauges built on WMO GTS (Global Telecommunication System)-based and historic data. All comparisons were done offline because not all the analytical functions are available in TOVAS

Results

Bias Assessment

Summary and Discussion