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CausalBoost SIGNED

Using causal discovery algorithms to boost subseasonal to seasonal forecast skill of Mediterranean rainfall

Total Cost €


EC-Contrib. €






 CausalBoost project word cloud

Explore the words cloud of the CausalBoost project. It provides you a very rough idea of what is the project "CausalBoost" about.

sources    marginal    s2s    limitations    puts    atmospheric    drivers    vulnerability    bias    relevance    techniques    mediterranean    progress    forecasts    desertification    background    makers    fall    time    combines    outcomes    weeks    droughts    dynamics    wild    conventional    created    models    position    statistical    boost    underlying    algorithms    heatwaves    anthropogenic    climatic    fundamental    timescales    shortages    losses    warming    impacts    persistent    interdisciplinary    modelled    decision    predictability    drying    limited    skill    approximately    prediction    robustly    teleconnection    seasonal    inference    corrections    discovery    innovative    felt    effort    science    systematically    predictions    days    me    failures    hotspot    weather    subseasonal    water    reducing    derive    rainfall    fires    causal    economic    urgent    forecast    crop    climate    times    probably    gap    overcome    season    putting    causing    risk    region    med    ahead    led   

Project "CausalBoost" data sheet

The following table provides information about the project.


Organization address
postcode: RG6 6AH

contact info
title: n.a.
name: n.a.
surname: n.a.
function: n.a.
email: n.a.
telephone: n.a.
fax: n.a.

 Coordinator Country United Kingdom [UK]
 Total cost 212˙933 €
 EC max contribution 212˙933 € (100%)
 Programme 1. H2020-EU.1.3.2. (Nurturing excellence by means of cross-border and cross-sector mobility)
 Code Call H2020-MSCA-IF-2018
 Funding Scheme MSCA-IF-EF-ST
 Starting year 2020
 Duration (year-month-day) from 2020-03-01   to  2022-02-28


Take a look of project's partnership.

# participants  country  role  EC contrib. [€] 
1    THE UNIVERSITY OF READING UK (READING) coordinator 212˙933.00


 Project objective

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.

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The information about "CAUSALBOOST" are provided by the European Opendata Portal: CORDIS opendata.

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