Descrizione del progetto
Algoritmi per migliorare le previsioni sulle precipitazioni
La regione mediterranea è un punto caldo per quanto concerne i cambiamenti climatici antropogenici. Per garantire una pianificazione a breve termine efficace, i responsabili decisionali attivi nei settori influenzati dalle condizioni meteorologiche dipendono da previsioni delle precipitazioni corrette su scale temporali da sub-stagionali a stagionali (S2S, sub-seasonal to seasonal). Alcune difficoltà cruciali, tuttavia, impediscono di effettuare previsioni affidabili oltre un periodo di circa 10 giorni. Il progetto CausalBoost, finanziato dall’UE, applicherà un metodo innovativo per migliorare le previsioni S2S delle precipitazioni che si verificano nella regione mediterranea. L’approccio si basa su una combinazione di innovativi algoritmi di scoperta casuale nell’ambito dell’apprendimento automatico con modelli operativi di previsione. Il progetto individuerà i fattori centrali nelle previsioni S2S delle precipitazioni che avvengono nella regione mediterranea, li valuterà in modo sistematico con i modelli di previsione e produrrà correzioni degli errori basate sui processi.
Obiettivo
The Mediterranean region (MED) is a hotspot of anthropogenic climate change and impacts are probably already felt today; recent heatwaves and persistent droughts have led to crop failures, wild fires and water shortages, causing large economic losses. Climate models robustly project further warming and drying of the region, putting it at risk of desertification. The particular vulnerability of this water-limited region to climatic changes has created an urgent need for reliable forecasts of rainfall on subseasonal to seasonal (S2S) timescales, i.e. 2 weeks up to a season ahead. This S2S time-range is particularly crucial, as the prediction lead time is long enough to implement adaptation measures, and short enough to be of immediate relevance for decision makers. However, predictions on lead-times beyond approximately 10 days fall into the so-called “weather-climate prediction gap”, with operational forecast models only providing marginal skill. The reasons for this are a range of fundamental challenges, including a limited causal understanding of the underlying sources of predictability.
The proposed research effort aims to improve S2S forecasts of MED rainfall by taking an innovative, interdisciplinary approach that combines novel causal discovery algorithms from complex system science with operational forecast models. This will overcome current limitations of conventional statistical methods to identify relevant sources of predictability and to evaluate modelled teleconnection processes. The outcomes of this project will (i) identify key S2S drivers of MED rainfall, (ii) systematically evaluate them in forecast models, (iii) derive process-based bias corrections to (iv) boost forecast skill. My strong background in both causal inference techniques and atmospheric dynamics puts me in a unique position to lead this innovative effort and to achieve real progress in reducing the “weather-climate prediction gap” for the MED region.
Campo scientifico
Programma(i)
Argomento(i)
Meccanismo di finanziamento
MSCA-IF-EF-ST - Standard EFCoordinatore
RG6 6AH Reading
Regno Unito