Prediction of the Diurnal Change Using a Multimodel Superensemble. Part I: Precipitation

Abstract

ABSTRACT Modeling,the geographical,distribution of the phase and amplitude,of the diurnal change,is a challenging problem. This paper addresses,the issues of modeling,the diurnal mode,of precipitation over the Tropics. Largely an early morning,precipitation maximum,over the oceans and an afternoon rainfall maximum,over land areas describe the first-order diurnal variability. However, large variability in phase and amplitude prevails even,within the land and oceanic,areas. This paper addresses,the importance,of a multimodel superensemble,for much,improved,prediction of the diurnal mode,as compared to what,is possible from individual models. To begin this exercise, the skills of the member models, the ensemble mean of the member models, a unified cloud model, and the superensemble for the prediction of total rain as well as its day versus night distribution were,examined. Here it is shown,that the distributions of total rain over the earth (tropical belt) and over certain geographical,regions are predicted reasonably,well (RMSE less than 18%) from the construction,of a multimodel,superensemble.,This dataset is well suited for addressing,the diurnal change. The large errors in phase of the diurnal modes,in individual models,usually stem,from numerous physical processes such as the cloud radiation, shallow and deep cumulus convection, and the physics of the planetary boundary,layer. The multimodel,superensemble,is designed,to reduce such sys- tematic,errors and,provide,meaningful,forecasts. That application for the diurnal mode,appears,very promising. This paper examines some of the regions such as the Tibetan Plateau, the eastern foothills of the Himalayas, and the Amazon region of South America that are traditionally difficult for modeling the diurnal change. In nearly all of these regions, errors in phase and amplitude of the diurnal mode of precipitation increase with the increased length of forecasts. Model forecast errors on the order of 6–12 h for phase and 50% for the amplitude,are often seen from the member,models. The multimodel,superen- semble,reduces these errors and provides a close match (RMSE 6 h) to the observed,phase. The percent of daily rain and,their phases,obtained,from,the multimodel,superensemble,at 3-hourly intervals for different regions of the Tropics showed,a closer match,(pattern correlation about 0.4) with the satellite estimates. This is another,area where,the individual member,models,conveyed a much,lower skill.