A quantitative precipitation forecast experiment for Puerto Rico

A quantitative precipitation forecast (QPF) experiment is conducted for the island of Puerto Rico. The experiment ranks the utility of six objective r ainfall models and an operational forecast issued by the National Weather S ervice Forecast Office, San Juan. This is believed to be the first experime nt to rank the utility of rainfall forecast schemes in the tropics. Using an analysis of variance tool called common factor analysis (CFA), the island of Puerto Rico is divided into convective rainfall regions. These r egions are statistically independent and represent the forecast domains for the experiment. All forecasts and realizations are area-averaged over each convective region. The QPF experiment is conducted in real-time over three 6-week periods in 1 998. The periods fall in three separate rainfall seasons. All seven forecas t schemes are configured to produce an area-averaged 24 h rainfall forecast . Forecasts are realized through a network of 114 data rain gauges, whose 2 4 h values are also area-averaged within convective region. We conduct this experiment in a Bayesian framework. Users may determine the ex ante value of forecast products through the Bayesian correlation score (BCS). Over eac h of the three seasons, the climatology forecast held the highest ex ante u tility for users. Although objective forecast utility scores for heavy rain events are low, they yield higher BCS values than operational forecasts. ( C) 2000 Elsevier Science B.V. All rights reserved.