Analyses of Global Monthly Precipitation Using Gauge Observations, Satellite Estimates, and Numerical Model Predictions

Abstract An algorithm is developed to construct global gridded fields of monthly 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 satellite estimates [the infrared-based GOES Precipitation Index, the microwave (MW) scattering-based Grody, and the MW emission-based Chang estimates], and predictions produced by the operational forecast model of the European Centre for Medium-Range Weather Forecasts. A two-step strategy is used to: 1) reduce the random error found in the individual sources and 2) reduce the bias of the combined analysis. First, the three satellite-based estimates and the model predictions are combined linearly based on a maximum likelihood estimate, in which the weighting coefficients are inversely proportional to the squares of the individual random errors determined by comparison with gauge observations and subjective assumptions. This c...