WF-UNet: Weather Fusion UNet for Precipitation Nowcasting
- URL: http://arxiv.org/abs/2302.04102v2
- Date: Thu, 9 Feb 2023 12:00:52 GMT
- Title: WF-UNet: Weather Fusion UNet for Precipitation Nowcasting
- Authors: Christos Kaparakis, Siamak Mehrkanoon
- Abstract summary: We investigate the use of a UNet core-model and its extension for precipitation nowcasting in western Europe for up to 3 hours ahead.
We have collected six years of precipitation and wind radar images from Jan 2016 to Dec 2021 of 14 European countries.
WF-UNet outperforms the other examined best-performing architectures by 22%, 8% and 6% lower MSE at a horizon of 1, 2 and 3 hours respectively.
- Score: 4.213427823201119
- License: http://creativecommons.org/licenses/by-sa/4.0/
- Abstract: Designing early warning systems for harsh weather and its effects, such as
urban flooding or landslides, requires accurate short-term forecasts (nowcasts)
of precipitation. Nowcasting is a significant task with several environmental
applications, such as agricultural management or increasing flight safety. In
this study, we investigate the use of a UNet core-model and its extension for
precipitation nowcasting in western Europe for up to 3 hours ahead. In
particular, we propose the Weather Fusion UNet (WF-UNet) model, which utilizes
the Core 3D-UNet model and integrates precipitation and wind speed variables as
input in the learning process and analyze its influences on the precipitation
target task. We have collected six years of precipitation and wind radar images
from Jan 2016 to Dec 2021 of 14 European countries, with 1-hour temporal
resolution and 31 square km spatial resolution based on the ERA5 dataset,
provided by Copernicus, the European Union's Earth observation programme. We
compare the proposed WF-UNet model to persistence model as well as other UNet
based architectures that are trained only using precipitation radar input data.
The obtained results show that WF-UNet outperforms the other examined
best-performing architectures by 22%, 8% and 6% lower MSE at a horizon of 1, 2
and 3 hours respectively.
Related papers
- An ensemble of data-driven weather prediction models for operational sub-seasonal forecasting [0.08106028186803123]
We present an operations-ready multi-model ensemble weather forecasting system.
It is possible to achieve near-state-of-the-art subseasonal-to-seasonal forecasts using a multi-model ensembling approach with data-driven weather prediction models.
arXiv Detail & Related papers (2024-03-22T20:01:53Z) - 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) - FuXi-S2S: A machine learning model that outperforms conventional global subseasonal forecast models [13.852128658186876]
FuXi Subseasonal-to-Seasonal (FuXi-S2S) is a machine learning model that provides global daily mean forecasts up to 42 days.
FuXi-S2S, trained on 72 years of daily statistics from ECMWF ERA5 reanalysis data, outperforms the ECMWF's state-of-the-art Subseasonal-to-Seasonal model.
arXiv Detail & Related papers (2023-12-15T16:31:44Z) - Residual Diffusion Modeling for Km-scale Atmospheric Downscaling [51.061954281398116]
A cost-effective downscaling model is trained from a high-resolution 2-km weather model over Taiwan.
textitCorrDiff exhibits skillful RMSE and CRPS and faithfully recovers spectra and distributions even for extremes.
Downscaling global forecasts successfully retains many of these benefits, foreshadowing the potential of end-to-end, global-to-km-scales machine learning weather predictions.
arXiv Detail & Related papers (2023-09-24T19:57:22Z) - FengWu: Pushing the Skillful Global Medium-range Weather Forecast beyond
10 Days Lead [93.67314652898547]
We present FengWu, an advanced data-driven global medium-range weather forecast system based on Artificial Intelligence (AI)
FengWu is able to accurately reproduce the atmospheric dynamics and predict the future land and atmosphere states at 37 vertical levels on a 0.25deg latitude-longitude resolution.
The results suggest that FengWu can significantly improve the forecast skill and extend the skillful global medium-range weather forecast out to 10.75 days lead.
arXiv Detail & Related papers (2023-04-06T09:16:39Z) - A Deep Learning Method for Real-time Bias Correction of Wind Field
Forecasts in the Western North Pacific [24.287588853356972]
Real-time rolling bias corrections were made for 10-day wind-field forecasts released by the EC between December 2020 and November 2021.
Wind speed and wind direction biases in the four seasons were reduced by 8-11% and 9-14%, respectively.
arXiv Detail & Related papers (2022-12-29T02:58:12Z) - Pangu-Weather: A 3D High-Resolution Model for Fast and Accurate Global
Weather Forecast [91.9372563527801]
We present Pangu-Weather, a deep learning based system for fast and accurate global weather forecast.
For the first time, an AI-based method outperforms state-of-the-art numerical weather prediction (NWP) methods in terms of accuracy.
Pangu-Weather supports a wide range of downstream forecast scenarios, including extreme weather forecast and large-member ensemble forecast in real-time.
arXiv Detail & Related papers (2022-11-03T17:19:43Z) - Nowcasting-Nets: Deep Neural Network Structures for Precipitation
Nowcasting Using IMERG [1.9860735109145415]
We use Recurrent and Convolutional deep neural network structures to address the challenge of precipitation nowcasting.
A total of five models are trained using Global Precipitation Measurement (GPM) Integrated Multi-satellitE Retrievals for GPM (IMERG) precipitation data over the Eastern Contiguous United States (CONUS)
The models were designed to provide forecasts with a lead time of up to 1.5 hours and, by using a feedback loop approach, the ability of the models to extend the forecast time to 4.5 hours was also investigated.
arXiv Detail & Related papers (2021-08-16T02:55:32Z) - 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) - Sub-seasonal forecasting with a large ensemble of deep-learning weather
prediction models [6.882042556551611]
We present an ensemble prediction system using a Deep Learning Weather Prediction (DLWP) model.
This model uses convolutional neural networks (CNNs) on a cubed sphere grid to produce global forecasts.
Ensemble spread is primarily produced by randomizing the CNN training process to create a set of 32 DLWP models.
arXiv Detail & Related papers (2021-02-09T20:14:43Z) - 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.