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CausalBoost SIGNED

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

Total Cost €

0

EC-Contrib. €

0

Partnership

0

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 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.

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

Project "CausalBoost" data sheet

The following table provides information about the project.

Coordinator
THE UNIVERSITY OF READING 

Organization address
address: WHITEKNIGHTS CAMPUS WHITEKNIGHTS HOUSE
city: READING
postcode: RG6 6AH
website: http://www.rdg.ac.uk

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

 Partnership

Take a look of project's partnership.

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

Map

 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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