Hola,
¿eres nuevo aquí?

Regístrate gratis y conecta tu empresa con financiación pública, partners y proyectos.

Tengo cuenta

Regístrate

Ver video

USMILE

Financiado

Cerrado

Understanding and Modelling the Earth System with Machine Learning

Earth system models are fundamental to understand climate change. Although they have improved significantly, considerable biases and uncertainties in their projections remain. Process parameterisations limit the models’ ability to... ver más

31/08/2026

DLR

10M€

Presupuesto del proyecto: 10M€

Líder del proyecto

DEUTSCHES ZENTRUM FUR LUFT UND RAUMFAHRT EV No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Fecha límite participación Sin fecha límite de participación.

Ver 6 Participantes

Financiación concedida El organismo H2020 notifico la concesión del proyecto el día 2020-05-11

ERC-2019-SyG: ERC Synergy Grant

I+D

Cerrada hace 6 años

0% 100% 100%

Información adicional privada

No hay información privada compartida para este proyecto. Habla con el coordinador.

6 Participantes

DLR

2.98M€ | Lider

METRO DE MADRID

362.01K€ | Participante

MPG

2.70M€ | Participante

2.04M€ | Participante

Conecta tu I+D

¿Tienes un proyecto y buscas un partner? Gracias a nuestro motor inteligente podemos recomendarte los mejores socios y ponerte en contacto con ellos. Te lo explicamos en este video

Proyectos interesantes

ECO2016-78652-P NUEVOS METODOS ECONOMETRICOS PARA EL CAMBIO CLIMATICO Y BIG... 51K€ Cerrado

SEDAL Statistical Learning for Earth Observation Data Analysis. 2M€ Cerrado

EXPEDITE EXPloring opportunitiEs for developing a risk and resilience... 189K€ Cerrado

ARTIST ARTificial Intelligence for Seasonal forecast of Temperature... 173K€ Cerrado

IMPASTO Inverse Modeling of PArameterized physics STochastic uncerta... 140K€ Cerrado

COCLIMAT Fusion of Alternative Climate Models By Dynamical Synchroniz... 196K€ Cerrado

Duración del proyecto: 75 meses Fecha Inicio: 2020-05-11
Fecha Fin: 2026-08-31

Líder del proyecto

DEUTSCHES ZENTRUM FUR LUFT UND RAUMFAHRT EV No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Presupuesto del proyecto 10M€

Fecha límite de participación Sin fecha límite de participación.

Descripción del proyecto Earth system models are fundamental to understand climate change. Although they have improved significantly, considerable biases and uncertainties in their projections remain. Process parameterisations limit the models’ ability to simulate both global and regional Earth system responses, which are key for assessing climate change and its impacts on ecosystems and society. In recent years, the volume of data from high-resolution models and observations has substantially increased to petabyte scales. Concomitantly, the field of machine learning (ML) has quickly developed, promising breakthroughs in detecting and analysing non-linear relationships and patterns in large multivariate datasets. Yet, traditionally, physical modelling and ML have been often treated as two different worlds with opposite scientific paradigms (theory-driven versus data-driven). Thus, despite its great potential, ML has not yet been widely adopted for addressing the urgent need of improved understanding and modelling of the Earth system. USMILE will combine multi-disciplinary expertise in ML and process-based atmosphere and land modelling to completely rethink model development and evaluation. ML will further allow us to define novel observational constraints on Earth system feedbacks and climate projections. We will (1) develop ML algorithms to enhance Earth observation datasets accounting for spatio-temporal covariations, (2) deploy ML-based parameterisations and sub-models for clouds and land-surface processes that have hindered progress in climate modelling for decades, and (3) detect and understand modes of climate variability, multivariate extremes and uncover dynamical aspects of the Earth system with novel deep learning and causal inference techniques. USMILE will drive a paradigm shift in the current modelling of the Earth system towards a new data-driven physics-aware science and to an unprecedented reduction of uncertainties in projections.

Conecta tu I+D

Entra hoy

¿Olvidé mi contraseña?

Financiación

Empresas

CTIs/Universidades

Proyectos

Investigadores