A verification framework for South American sub-seasonal precipitation predictions

Abstract

This paper proposes a verification framework for South American sub-seasonal (weekly accumulated) precipitation predictions produced one to four weeks in advance. The framework assesses both hindcast and near real time forecast quality focusing on a selection of attributes (association, accuracy, discrimination, reliability and resolution). These attributes are measured using deterministic and probabilistic scores. Such an attribute-based framework allows the production of verification information in three levels according to the availability of sub-seasonal hindcasts and near real time forecasts samples. The framework is useful for supporting future routine sub-seasonal prediction practice by helping forecasters to identify model forecast merits and deficiencies and regions where to trust the model guidance information. The three information levels are defined according to the verification sampling strategy and are referred to as target week hindcast verification, all season hindcast verification, all season near real time forecast verification. The framework is illustrated using ECMWF sub-seasonal precipitation predictions. For the investigated period (austral autumn), reasonable accordance was identified between hindcasts and near real time forecast quality across the three levels. Sub-seasonal precipitation predictions produced one to two weeks in advance presented better performance than those produced three to four weeks in advance. The northeast region of Brazil presented favorable sub-seasonal precipitation prediction performance, particularly in terms of association, accuracy and discrimination attributes. This region was identified as a region where sub-seasonal precipitation predictions produced one to four weeks in advance are most likely to be successful in South America. When aggregating all predictions over the South American continent the probabilistic assessment showed modest discrimination ability, with predictions clearly requiring calibration for improving reliability and possibly combination with predictions produced by other models for improving resolution. The proposed framework is also useful for providing feedback to model developers in identifying strengths and weaknesses for future sub-seasonal predictions systems improvements.

Keywords

• sub-seasonal prediction
• verification precipitation
• South America