Observation-Guided Meteorological Field Downscaling at Station Scale: A
Benchmark and a New Method
- URL: http://arxiv.org/abs/2401.11960v1
- Date: Mon, 22 Jan 2024 14:02:56 GMT
- Title: Observation-Guided Meteorological Field Downscaling at Station Scale: A
Benchmark and a New Method
- Authors: Zili Liu, Hao Chen, Lei Bai, Wenyuan Li, Keyan Chen, Zhengyi Wang,
Wanli Ouyang, Zhengxia Zou and Zhenwei Shi
- Abstract summary: 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.
- Score: 66.80344502790231
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Downscaling (DS) of meteorological variables involves obtaining
high-resolution states from low-resolution meteorological fields and is an
important task in weather forecasting. Previous methods based on deep learning
treat downscaling as a super-resolution task in computer vision and utilize
high-resolution gridded meteorological fields as supervision to improve
resolution at specific grid scales. However, this approach has struggled to
align with the continuous distribution characteristics of meteorological
fields, leading to an inherent systematic bias between the downscaled results
and the actual observations at meteorological stations. In this paper, we
extend meteorological downscaling to arbitrary scattered station scales,
establish a brand new benchmark and dataset, and retrieve meteorological states
at any given station location from a coarse-resolution meteorological field.
Inspired by data assimilation techniques, we integrate observational data into
the downscaling process, providing multi-scale observational priors. Building
on this foundation, we propose a new downscaling model based on hypernetwork
architecture, namely HyperDS, which efficiently integrates different
observational information into the model training, achieving continuous scale
modeling of the meteorological field. Through extensive experiments, our
proposed method outperforms other specially designed baseline models on
multiple surface variables. Notably, the mean squared error (MSE) for wind
speed and surface pressure improved by 67% and 19.5% compared to other methods.
We will release the dataset and code subsequently.
Related papers
- Generative Data Assimilation of Sparse Weather Station Observations at Kilometer Scales [5.453657018459705]
We demonstrate the viability of score-based data assimilation in the context of realistically complex km-scale weather.
By incorporating observations from 40 weather stations, 10% lower RMSEs on left-out stations are attained.
It is a ripe time to explore extensions that combine increasingly ambitious regional state generators with an increasing set of in situ, ground-based, and satellite remote sensing data streams.
arXiv Detail & Related papers (2024-06-19T10:28:11Z) - Enhancing Weather Predictions: Super-Resolution via Deep Diffusion Models [0.0]
This study investigates the application of deep-learning diffusion models for the super-resolution of weather data.
We present a methodology for transforming low-resolution weather data into high-resolution outputs.
arXiv Detail & Related papers (2024-06-06T14:15:12Z) - SFANet: Spatial-Frequency Attention Network for Weather Forecasting [54.470205739015434]
Weather forecasting plays a critical role in various sectors, driving decision-making and risk management.
Traditional methods often struggle to capture the complex dynamics of meteorological systems.
We propose a novel framework designed to address these challenges and enhance the accuracy of weather prediction.
arXiv Detail & Related papers (2024-05-29T08:00:15Z) - MetaSD: A Unified Framework for Scalable Downscaling of Meteorological Variables in Diverse Situations [8.71735078449217]
This paper proposes a unified downscaling approach leveraging meta-learning.
We trained variables consisted of temperature, wind, surface pressure and total precipitation from ERA5 and GFS.
The proposed method can be extended to downscale convective precipitation, potential, energy height, humidity CFS, S2S and CMIP6 at differenttemporal scales.
arXiv Detail & Related papers (2024-04-26T06:31:44Z) - 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) - Multi-Modal Learning-based Reconstruction of High-Resolution Spatial
Wind Speed Fields [46.72819846541652]
We propose a framework based on Vari Data Assimilation and Deep Learning concepts.
This framework is applied to recover rich-in-time, high-resolution information on sea surface wind speed.
arXiv Detail & Related papers (2023-12-14T13:40:39Z) - Prompt Federated Learning for Weather Forecasting: Toward Foundation
Models on Meteorological Data [37.549578998407675]
To tackle the global climate challenge, it urgently needs to develop a collaborative platform for comprehensive weather forecasting on large-scale meteorological data.
This paper develops a foundation model across regions of understanding complex meteorological data and providing weather forecasting.
A novel prompt learning mechanism has been adopted to satisfy low-resourced sensors' communication and computational constraints.
arXiv Detail & Related papers (2023-01-22T16:47:05Z) - A Generative Deep Learning Approach to Stochastic Downscaling of
Precipitation Forecasts [0.5906031288935515]
Generative adversarial networks (GANs) have been demonstrated by the computer vision community to be successful at super-resolution problems.
We show that GANs and VAE-GANs can match the statistical properties of state-of-the-art pointwise post-processing methods whilst creating high-resolution, spatially coherent precipitation maps.
arXiv Detail & Related papers (2022-04-05T07:19:42Z) - Forecasting large-scale circulation regimes using deformable
convolutional neural networks and global spatiotemporal climate data [86.1450118623908]
We investigate a supervised machine learning approach based on deformable convolutional neural networks (deCNNs)
We forecast the North Atlantic-European weather regimes during extended boreal winter for 1 to 15 days into the future.
Due to its wider field of view, we also observe deCNN achieving considerably better performance than regular convolutional neural networks at lead times beyond 5-6 days.
arXiv Detail & Related papers (2022-02-10T11:37:00Z) - Lidar Light Scattering Augmentation (LISA): Physics-based Simulation of
Adverse Weather Conditions for 3D Object Detection [60.89616629421904]
Lidar-based object detectors are critical parts of the 3D perception pipeline in autonomous navigation systems such as self-driving cars.
They are sensitive to adverse weather conditions such as rain, snow and fog due to reduced signal-to-noise ratio (SNR) and signal-to-background ratio (SBR)
arXiv Detail & Related papers (2021-07-14T21:10:47Z)
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.