Deterministic Skill of Subseasonal Precipitation Forecasts for the East Africa‐West Asia Sector from September to May

Abstract

The East Africa-West Asia (EA-WA) sector is a region where the skill in forecasting rainfall is unusually high in the subseasonal-to-seasonal (S2S) time range and where ENSO and MJO signals have significant impacts. Much of regional rainfall intra-seasonal variability is related to tropical-temperate interactions on synoptic time scales, and we examine whether the skill of dynamical S2S forecasts for the region exceeds the predictability associated with ENSO and the MJO. Deterministic skill (Spearman's rank correlation) of multimodel ensemble forecasts of weekly and Week 3-4 precipitation averages is examined for starts in September-April, based on three ensemble prediction systems (EPS) from the S2S prediction project over the common 1999-2010 reforecasts period. The skill of weekly forecasts decreases with increasing lead but remains statistically significant out to Week 3 and Week 4, a level of skill that has not been previously reported at such leads. The skill of Week 3-4 forecasts is higher than that of Week 3 or Week 4 forecasts. The prediction skill of weekly and 2-week precipitation averages from ENSO and the MJO is estimated using a multiple linear regression and found to be less than that achieved by the dynamical forecasts for Week 3, Week 4, and Week 3-4 targets. This is evidence for achievable forecast skill that extends beyond typical weather-scale lead times and that is not simply related to ENSO or MJO signals. The opportunity for skillful predictions is illustrated through successful forecasts up to 4 weeks in advance of extreme rainfall over the Arabian peninsula related to strong tropical-temperate interactions during weak ENSO and MJO conditions.

Subseasonal Precipitation Prediction for Africa: Forecast Evaluation and Sources of Predictability

“…This paper investigated single models when developing a weekly precipitation forecast verification framework for Africa. Combining forecasts from a multimodel perspective may help to improve resolution and discrimination of predictions (e.g., Vigaud et al 2018). Different calibration methods, such as model output statistic (e.g., Doss-Gollin et al 2018), should be explored to identify which is the best one practice to be employed for delivering more reliable forecasts to operational centers and applications communities.…”

“…For example, de Andrade et al (2019) evaluated weekly precipitation hindcasts from all models participating in the S2S project, finding best agreement with precipitation observations during the first two weeks lead and worst quality in subsequent weeks, especially over extratropical regions. Weekly precipitation predictions were also verified over summer monsoon regions of the Northern Hemisphere and the East Africa-West Asia sector (Vigaud et al 2017b(Vigaud et al , 2018, both showing worst quality for longer lead times (i.e., beyond two weeks lead).…”

“…Moreover, de Andrade et al (2019) found a reduction in forecast quality after removing ENSO-and MJOrelated precipitation patterns from weekly forecasts. Other drivers could also affect the quality of subseasonal precipitation predictions, for instance, tropical-extratropical interactions (Vigaud et al 2019) and stratosphere-troposphere coupling (Domeisen et al 2020). For a more comprehensive description of relevant drivers of subseasonal predictability, the reader is referred to Mariotti et al (2020).…”

“…Among the many efforts to improve understanding of subseasonal forecast skill over the past years, one important aspect is forecast verification (Coelho et al 2019). Several studies have identified areas where precipitation forecast quality of S2S models could be refined (e.g., Vigaud et al 2018;de Andrade et al 2019). However, only few of those studies have employed detailed verifications to analyze the attributes of forecast quality defined in Murphy (1993).…”

Drivers of Subseasonal Forecast Errors of the East African Short Rains

“…10.1029/2021GL093292 2 of 10 and 12°N (see Figure 1a), following Vigaud et al (2019) -as   i i F w f , where F stands for "Forecast", the sum is performed for all land points, the weight of each grid point value is   cos / cos i i j w  , where i  is the latitude of land point i and the sum is performed for all land points, and i f is the standardized forecast anomaly in i. Similarly, we calculate the area-averaged CHIRPS rainfall anomalies as   i i O w o (O for "Observations").…”