ANALYSES OF GLOBAL MONTHLY PRECIPITATION USING GAUGE OBSERVATIONS, SATELLITE ESTIMATES, AND NUMERICAL-MODEL PREDICTIONS

An algorithm is developed to construct global gridded fields of monthl y precipitation by merging estimates from five sources of information with different characteristics, including gauge-based monthly analyses from the Global Precipitation Climatology Centre, three types of sate llite estimates [the infrared-based GOES Precipitation Index, the micr owave (MW) scattering-based Grody, and the MW emission-based Chang est imates], and predictions produced by the operational forecast model of the European Centre for Medium-Range Weather Forecasts. A two-step st rategy is used to: 1) reduce the random error found in the individual sources and 2) reduce the bias of the combined analysis. First, the th ree satellite-based estimates and the model predictions are combined l inearly based on a maximum likelihood estimate, in which the weighting coefficients are inversely proportional to the squares of the individ ual random errors determined by comparison with gauge observations and subjective assumptions. This combined analysis is then blended with a n analysis based on gauge observations using a method that presumes th at the bias of the gauge-based field is small where sufficient gauges are available and that the gradient of the precipitation field is best represented by the combination of satellite estimates and model predi ctions elsewhere. The algorithm is applied to produce monthly precipit ation analyses for an 18-month period from July 1987 to December 1988. Results showed substantial improvements of the merged analysis relati ve to the individual sources in describing the global precipitation fi eld. The large-scale spatial patterns, both in the Tropics and the ext ratropics, are well represented with reasonable amplitudes. Both the r andom error and the bias have been reduced compared to the individual data sources, and the merged analysis appears to be of reasonable qual ity everywhere. However, the actual quality of the merged analysis dep ends strongly on our uncertain and incomplete knowledge of the error s tructures of the individual data sources.