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Ddpm Mediterranean Reanalysis Tas

Developed by jpxkqx
This project downscales ERA5 global reanalysis data to establish a machine learning model capable of generating high-resolution regional reanalysis data
Downloads 19
Release Time : 10/19/2023

Model Overview

Utilizes deep learning techniques such as U-Net, conditional generative adversarial networks, and diffusion models to develop a data assimilation module that evaluates the potential benefits of incorporating CERRA pseudo-observation data as additional predictors

Model Features

Multi-scheduler Support
Supports three noise schedulers: DDPMScheduler, DDIM, and LMSDiscreteScheduler
Climate Data-specific Normalization
Tested various climate data-specific preprocessing schemes, including pixel-level, regional-level, and instance normalization
Rigorous Validation Framework
Strict evaluation through a multi-dimensional validation framework, including traditional error metrics and spatiotemporal correlation analysis

Model Capabilities

Reanalysis data downscaling
High-resolution meteorological data generation
Spatiotemporal pattern reconstruction

Use Cases

Meteorological Forecasting
Regional Meteorological Data Enhancement
Downscales low-resolution global reanalysis data into high-resolution regional data
Current performance does not meet expectations and is still inferior to the basic bicubic interpolation method
Climate Research
Historical Climate Data Reconstruction
Generates high-resolution historical climate datasets
Capable of generating high-resolution details but with significant errors
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