Descripción del proyecto
Algoritmos para mejorar las predicciones de precipitación
La región mediterránea es un foco del cambio climático antropogénico. Para garantizar una planificación eficaz a corto plazo, los responsables de la toma de decisiones en los sectores que dependen de las condiciones meteorológicas se atienen a pronósticos certeros de las precipitaciones a escalas de tiempo subestacional a estacional (S2S, por sus siglas en inglés). Con todo, algunos problemas básicos impiden pronósticos fiables más allá de aproximadamente diez días. En el proyecto CausalBoost, financiado con fondos europeos, se empleará un método innovador para mejorar los pronósticos S2S de las precipitaciones en la región mediterránea. El método combina algoritmos innovadores de descubrimiento causal a partir del aprendizaje automático y modelos de pronóstico operativo. El trabajo del proyecto permitirá definir los factores S2S que determinan las precipitaciones en la región mediterránea, evaluarlos sistemáticamente con modelos de predicción y realizar correcciones de sesgo basadas en procesos.
Objetivo
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.
Ámbito científico
Programa(s)
Régimen de financiación
MSCA-IF-EF-ST - Standard EFCoordinador
RG6 6AH Reading
Reino Unido