Analysis of multiple precipitation products
as part of the Global Land Data Assimilation System (GLDAS) project

Jon Gottschalck

University of Maryland, Baltimore County
Goddard Earth Sciences and Technology Center
Hydrological Sciences Branch
NASA / Goddard Space Flight Center

Abstract:

Precipitation is arguably the most important meteorological forcing variable in land surface modeling as it determines the amount of water in the soil system and so impacts the partitioning of energy between sensible and latent heat. Many types of precipitation datasets exist (with various pros and cons) and include those from atmospheric data assimilation systems, satellites, rain gauges, ground radar, and merged products. These datasets are being evaluated in order to choose the most suitable precipitation forcing for real-time and retrospective simulations of The Global Land Data Assimilation System (GLDAS). Results are presented of a comparison of multiple precipitation estimates for the period from March 2002 - February 2003.

A comparison of total accumulated precipitation for the CONtinental United States (CONUS) illustrates that the Climate Prediction Center (CPC) Merged Analysis of Precipitation (CMAP) has the closest agreement with a CPC rain gauge dataset for all seasons except winter. The European Centre Model for Weather Forecasting (ECMWF) performs the best of the precipitation estimates from modeling systems. The satellite-only products suffer from a few deficiencies - most notably an overestimation of summertime precipitation in the central United States (200-400 mm). CMAP precipitation estimates are the most closely correlated with daily rain gauge data for the spring, fall, and winter seasons while the satellite only estimates perform best in summer. GLDAS simulations show that the sensitivity of land surface states is substantial when using different precipitation forcing. For volumetric soil water content, the span of differences between the runs ranged from 30-45% and 20-30% of the typical total range of volumetric soil water content (~0.0-0.50 m3/m3) for the spring/summer and fall/winter months respectively. Moreover, the soil temperature spread between GLDAS runs was considerable and ranged up to ± 3.0 K throughout the simulations.