Wind speed super-resolution and validation: from ERA5 to CERRA via
diffusion models
- URL: http://arxiv.org/abs/2401.15469v2
- Date: Wed, 31 Jan 2024 10:17:28 GMT
- Title: Wind speed super-resolution and validation: from ERA5 to CERRA via
diffusion models
- Authors: Fabio Merizzi, Andrea Asperti, Stefano Colamonaco
- Abstract summary: This paper introduces a novel method using diffusion models to approximate CERRA downscaling in a data-driven manner.
We focus on wind speed around Italy, our model, trained on existing CERRA data, shows promising results.
- Score: 0.0
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: The Copernicus Regional Reanalysis for Europe, CERRA, is a high-resolution
regional reanalysis dataset for the European domain. In recent years it has
shown significant utility across various climate-related tasks, ranging from
forecasting and climate change research to renewable energy prediction,
resource management, air quality risk assessment, and the forecasting of rare
events, among others. Unfortunately, the availability of CERRA is lagging two
years behind the current date, due to constraints in acquiring the requisite
external data and the intensive computational demands inherent in its
generation. As a solution, this paper introduces a novel method using diffusion
models to approximate CERRA downscaling in a data-driven manner, without
additional informations. By leveraging the lower resolution ERA5 dataset, which
provides boundary conditions for CERRA, we approach this as a super-resolution
task. Focusing on wind speed around Italy, our model, trained on existing CERRA
data, shows promising results, closely mirroring original CERRA data.
Validation with in-situ observations further confirms the model's accuracy in
approximating ground measurements.
Related papers
- Efficient Localized Adaptation of Neural Weather Forecasting: A Case Study in the MENA Region [62.09891513612252]
We focus on limited-area modeling and train our model specifically for localized region-level downstream tasks.
We consider the MENA region due to its unique climatic challenges, where accurate localized weather forecasting is crucial for managing water resources, agriculture and mitigating the impacts of extreme weather events.
Our study aims to validate the effectiveness of integrating parameter-efficient fine-tuning (PEFT) methodologies, specifically Low-Rank Adaptation (LoRA) and its variants, to enhance forecast accuracy, as well as training speed, computational resource utilization, and memory efficiency in weather and climate modeling for specific regions.
arXiv Detail & Related papers (2024-09-11T19:31:56Z) - A Novel Fusion of Optical and Radar Satellite Data for Crop Phenology Estimation using Machine Learning and Cloud Computing [0.0]
In the era of big Earth observation data ubiquity, attempts have been made to accurately predict crop phenology based on Remote Sensing data.
Here, we estimate phenological developments for eight major crops and 13 phenological stages across Germany at 30m scale using a novel framework.
arXiv Detail & Related papers (2024-08-16T13:44:35Z) - DUNE: A Machine Learning Deep UNet++ based Ensemble Approach to Monthly, Seasonal and Annual Climate Forecasting [0.0]
A novel Deep UNet++-based Ensemble (DUNE) neural architecture is introduced.
It produces the first AI-based global monthly, seasonal, or annual mean forecast of 2-meter temperatures (T2m) and sea surface temperatures (SST)
These forecasts outperform persistence, climatology, and multiple linear regression for all domains.
arXiv Detail & Related papers (2024-08-12T16:22:30Z) - Super Resolution for Renewable Energy Resource Data With Wind From Reanalysis Data (Sup3rWind) and Application to Ukraine [0.0]
There is an expanding global need for historically accurate high-resolution wind data.
In this work, we present a novel deep learning-based downscaling method, using adversarial networks.
We achieve results comparable in historical accuracy and variability to conventional downscaling.
arXiv Detail & Related papers (2024-07-26T21:07:17Z) - Advancing Data-driven Weather Forecasting: Time-Sliding Data
Augmentation of ERA5 [3.3748750222488657]
We introduce a novel strategy that deviates from the common dependence on high-resolution data.
This paper improves on conventional approaches by adding more variables and a novel approach to data augmentation and processing.
Our findings reveal that despite the lower resolution, the proposed approach demonstrates considerable accuracy in predicting atmospheric conditions.
arXiv Detail & Related papers (2024-02-13T03:01:22Z) - FengWu-GHR: Learning the Kilometer-scale Medium-range Global Weather
Forecasting [56.73502043159699]
This work presents FengWu-GHR, the first data-driven global weather forecasting model running at the 0.09$circ$ horizontal resolution.
It introduces a novel approach that opens the door for operating ML-based high-resolution forecasts by inheriting prior knowledge from a low-resolution model.
The hindcast of weather prediction in 2022 indicates that FengWu-GHR is superior to the IFS-HRES.
arXiv Detail & Related papers (2024-01-28T13:23:25Z) - Observation-Guided Meteorological Field Downscaling at Station Scale: A
Benchmark and a New Method [66.80344502790231]
We extend meteorological downscaling to arbitrary scattered station scales and establish a new benchmark and dataset.
Inspired by data assimilation techniques, we integrate observational data into the downscaling process, providing multi-scale observational priors.
Our proposed method outperforms other specially designed baseline models on multiple surface variables.
arXiv Detail & Related papers (2024-01-22T14:02:56Z) - Residual Corrective Diffusion Modeling for Km-scale Atmospheric Downscaling [58.456404022536425]
State of the art for physical hazard prediction from weather and climate requires expensive km-scale numerical simulations driven by coarser resolution global inputs.
Here, a generative diffusion architecture is explored for downscaling such global inputs to km-scale, as a cost-effective machine learning alternative.
The model is trained to predict 2km data from a regional weather model over Taiwan, conditioned on a 25km global reanalysis.
arXiv Detail & Related papers (2023-09-24T19:57:22Z) - Long-term drought prediction using deep neural networks based on geospatial weather data [75.38539438000072]
High-quality drought forecasting up to a year in advance is critical for agriculture planning and insurance.
We tackle drought data by introducing an end-to-end approach that adopts a systematic end-to-end approach.
Key findings are the exceptional performance of a Transformer model, EarthFormer, in making accurate short-term (up to six months) forecasts.
arXiv Detail & Related papers (2023-09-12T13:28:06Z) - A generative adversarial network approach to (ensemble) weather
prediction [91.3755431537592]
We use a conditional deep convolutional generative adversarial network to predict the geopotential height of the 500 hPa pressure level, the two-meter temperature and the total precipitation for the next 24 hours over Europe.
The proposed models are trained on 4 years of ERA5 reanalysis data from 2015-2018 with the goal to predict the associated meteorological fields in 2019.
arXiv Detail & Related papers (2020-06-13T20:53:17Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.