A Deep Learning Method for Real-time Bias Correction of Wind Field
Forecasts in the Western North Pacific
- URL: http://arxiv.org/abs/2212.14160v1
- Date: Thu, 29 Dec 2022 02:58:12 GMT
- Title: A Deep Learning Method for Real-time Bias Correction of Wind Field
Forecasts in the Western North Pacific
- Authors: Wei Zhang, Yueyue Jiang, Junyu Dong, Xiaojiang Song, Renbo Pang, Boyu
Guoan and Hui Yu
- Abstract summary: 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.
- Score: 24.287588853356972
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Forecasts by the European Centre for Medium-Range Weather Forecasts (ECMWF;
EC for short) can provide a basis for the establishment of maritime-disaster
warning systems, but they contain some systematic biases.The fifth-generation
EC atmospheric reanalysis (ERA5) data have high accuracy, but are delayed by
about 5 days. To overcome this issue, a spatiotemporal deep-learning method
could be used for nonlinear mapping between EC and ERA5 data, which would
improve the quality of EC wind forecast data in real time. In this study, we
developed the Multi-Task-Double Encoder Trajectory Gated Recurrent Unit
(MT-DETrajGRU) model, which uses an improved double-encoder forecaster
architecture to model the spatiotemporal sequence of the U and V components of
the wind field; we designed a multi-task learning loss function to correct wind
speed and wind direction simultaneously using only one model. The study area
was the western North Pacific (WNP), and real-time rolling bias corrections
were made for 10-day wind-field forecasts released by the EC between December
2020 and November 2021, divided into four seasons. Compared with the original
EC forecasts, after correction using the MT-DETrajGRU model the wind speed and
wind direction biases in the four seasons were reduced by 8-11% and 9-14%,
respectively. In addition, the proposed method modelled the data uniformly
under different weather conditions. The correction performance under normal and
typhoon conditions was comparable, indicating that the data-driven mode
constructed here is robust and generalizable.
Related papers
- Wind Speed Forecasting Based on Data Decomposition and Deep Learning Models: A Case Study of a Wind Farm in Saudi Arabia [0.0]
Wind power generation is always accompanied by uncertainty due to the wind speed's volatility.
Wind speed forecasting (WSF) is essential for power grids' dispatch, stability, and controllability.
This study proposes a novel WSF framework for stationary data based on a hybrid decomposition method.
arXiv Detail & Related papers (2024-12-17T22:04:46Z) - Improving sub-seasonal wind-speed forecasts in Europe with a non-linear model [0.0]
This study explores the potential of leveraging non-linear relationships between 500 hPa geopotential height (Z500) and surface wind speed to improve subs-seasonal wind speed forecasting skills in Europe.
Our proposed framework uses a Multiple Linear Regression (MLR) or a Convolutional Neural Network (CNN) to regress surface wind speed from Z500.
arXiv Detail & Related papers (2024-11-28T11:53:59Z) - FengWu-W2S: A deep learning model for seamless weather-to-subseasonal forecast of global atmosphere [53.22497376154084]
We propose FengWu-Weather to Subseasonal (FengWu-W2S), which builds on the FengWu global weather forecast model and incorporates an ocean-atmosphere-land coupling structure along with a diverse perturbation strategy.
Our hindcast results demonstrate that FengWu-W2S reliably predicts atmospheric conditions out to 3-6 weeks ahead, enhancing predictive capabilities for global surface air temperature, precipitation, geopotential height and intraseasonal signals such as the Madden-Julian Oscillation (MJO) and North Atlantic Oscillation (NAO)
Our ablation experiments on forecast error growth from daily to seasonal timescales reveal potential
arXiv Detail & Related papers (2024-11-15T13:44:37Z) - 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) - ExtremeCast: Boosting Extreme Value Prediction for Global Weather Forecast [57.6987191099507]
We introduce Exloss, a novel loss function that performs asymmetric optimization and highlights extreme values to obtain accurate extreme weather forecast.
We also introduce ExBooster, which captures the uncertainty in prediction outcomes by employing multiple random samples.
Our solution can achieve state-of-the-art performance in extreme weather prediction, while maintaining the overall forecast accuracy comparable to the top medium-range forecast models.
arXiv Detail & Related papers (2024-02-02T10:34:13Z) - 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) - 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) - WF-UNet: Weather Fusion UNet for Precipitation Nowcasting [4.213427823201119]
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.
arXiv Detail & Related papers (2023-02-08T14:50:52Z) - 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) - 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.