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Data assimilation for next-generation weather and climate models

Grant number: 23/04579-5
Support Opportunities:Scholarships in Brazil - Post-Doctoral
Effective date (Start): July 01, 2023
Effective date (End): June 30, 2025
Field of knowledge:Physical Sciences and Mathematics - Mathematics - Applied Mathematics
Principal Investigator:Pedro da Silva Peixoto
Grantee:Guilherme Luiz Torres Mendonça
Host Institution: Instituto de Matemática e Estatística (IME). Universidade de São Paulo (USP). São Paulo , SP, Brazil
Associated research grant:21/06176-0 - Numerical methods for a new generation of weather and climate models, AP.PFPMCG.JP2

Abstract

Data assimilation is a key area for weather and climate modeling and prediction. With the development of modern weather and climate models at very high spatial resolutions, new problems in data assimilation are emerging. This proposal is motivated by two of these problems. The first problem is that although the increased resolution will allow to explicitly resolve important small-scale nonlinear processes such as convection, data assimilation methods are traditionally based on linear assumptions. To address this challenge, approaches that take into account the nonlinear dynamics of the system are being developed. In particular, the Assimilation in the Unstable Subspace (AUS) seeks to gain computational efficiency by confining the assimilation process to the subspace spanned by Lyapunov vectors associated with non-negative exponents. This approach, although in principle promising, suffers from the computational cost of calculating these vectors. In this proposal we will explore potential solutions to this problem through numerical analysis of AUS applied to simple models. The second problem motivating this proposal is the challenge of how to assimilate data from the increasingly diverse observational sources available today. In this line of research, we will examine in particular the robustness of assimilation methods already implemented in the Model for Prediction Across Scales (MPAS), which will be the atmospheric component of the Brazilian Earth system model MONAN (Model for Ocean-LaNd-Atmospheric PredictioNs). The goal here will be to evaluate the performance of these methods when applied to wind power tower data, with consequences not only for MONAN but also for another wind energy project in which our research group is involved.

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