Precipitation Nowcasting Using Physics Informed Discriminator Generative Models
- URL: http://arxiv.org/abs/2406.10108v1
- Date: Fri, 14 Jun 2024 15:12:53 GMT
- Title: Precipitation Nowcasting Using Physics Informed Discriminator Generative Models
- Authors: Junzhe Yin, Cristian Meo, Ankush Roy, Zeineh Bou Cher, Yanbo Wang, Ruben Imhoff, Remko Uijlenhoet, Justin Dauwels,
- Abstract summary: State-of-the-art models, including PySTEPS, encounter difficulties in accurately forecasting extreme weather events because of their unpredictable distribution patterns.
We design a physics-informed neural network to perform precipitation nowcasting using the precipitation and meteorological data from the Royal Netherlands Meteorological Institute.
Our findings demonstrate that the PID-GAN model outperforms numerical and SOTA deep generative models in terms of precipitation nowcasting downstream metrics.
- Score: 9.497627628556875
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Nowcasting leverages real-time atmospheric conditions to forecast weather over short periods. State-of-the-art models, including PySTEPS, encounter difficulties in accurately forecasting extreme weather events because of their unpredictable distribution patterns. In this study, we design a physics-informed neural network to perform precipitation nowcasting using the precipitation and meteorological data from the Royal Netherlands Meteorological Institute (KNMI). This model draws inspiration from the novel Physics-Informed Discriminator GAN (PID-GAN) formulation, directly integrating physics-based supervision within the adversarial learning framework. The proposed model adopts a GAN structure, featuring a Vector Quantization Generative Adversarial Network (VQ-GAN) and a Transformer as the generator, with a temporal discriminator serving as the discriminator. Our findings demonstrate that the PID-GAN model outperforms numerical and SOTA deep generative models in terms of precipitation nowcasting downstream metrics.
Related papers
- GPTCast: a weather language model for precipitation nowcasting [0.0]
GPTCast is a generative deep-learning method for ensemble nowcast of radar-based precipitation.
We employ a GPT model as a forecaster to learn precipitation dynamics using tokenized radar images.
arXiv Detail & Related papers (2024-07-02T09:25:58Z) - Generalizing Weather Forecast to Fine-grained Temporal Scales via Physics-AI Hybrid Modeling [55.13352174687475]
This paper proposes a physics-AI hybrid model (i.e., WeatherGFT) which Generalizes weather forecasts to Finer-grained Temporal scales.
Specifically, we employ a carefully designed PDE kernel to simulate physical evolution on a small time scale.
We introduce a lead time-aware training framework to promote the generalization of the model at different lead times.
arXiv Detail & Related papers (2024-05-22T16:21:02Z) - ClimODE: Climate and Weather Forecasting with Physics-informed Neural ODEs [14.095897879222676]
We present ClimODE, a continuous-time process that implements key principle of statistical mechanics.
ClimODE models precise weather evolution with value-conserving dynamics, learning global weather transport as a neural flow.
Our approach outperforms existing data-driven methods in global, regional forecasting with an order of magnitude smaller parameterization.
arXiv Detail & Related papers (2024-04-15T06:38:21Z) - Extreme Precipitation Nowcasting using Transformer-based Generative
Models [9.497627628556875]
This paper presents an innovative approach to extreme precipitation nowcasting by employing Transformer-based generative models, namely NowcastingGPT with Extreme Value Loss (EVL) regularization.
We introduce a novel method for computing EVL without assuming fixed extreme representations, addressing the limitations of current models in capturing extreme weather events.
We present both qualitative and quantitative analyses, demonstrating the superior performance of the proposed NowcastingGPT-EVL in generating accurate precipitation forecasts.
arXiv Detail & Related papers (2024-03-06T18:39:41Z) - Weather Prediction with Diffusion Guided by Realistic Forecast Processes [49.07556359513563]
We introduce a novel method that applies diffusion models (DM) for weather forecasting.
Our method can achieve both direct and iterative forecasting with the same modeling framework.
The flexibility and controllability of our model empowers a more trustworthy DL system for the general weather community.
arXiv Detail & Related papers (2024-02-06T21:28:42Z) - FengWu-4DVar: Coupling the Data-driven Weather Forecasting Model with 4D Variational Assimilation [67.20588721130623]
We develop an AI-based cyclic weather forecasting system, FengWu-4DVar.
FengWu-4DVar can incorporate observational data into the data-driven weather forecasting model.
Experiments on the simulated observational dataset demonstrate that FengWu-4DVar is capable of generating reasonable analysis fields.
arXiv Detail & Related papers (2023-12-16T02:07:56Z) - Learning Robust Precipitation Forecaster by Temporal Frame Interpolation [65.5045412005064]
We develop a robust precipitation forecasting model that demonstrates resilience against spatial-temporal discrepancies.
Our approach has led to significant improvements in forecasting precision, culminating in our model securing textit1st place in the transfer learning leaderboard of the textitWeather4cast'23 competition.
arXiv Detail & Related papers (2023-11-30T08:22:08Z) - An advanced spatio-temporal convolutional recurrent neural network for
storm surge predictions [73.4962254843935]
We study the capability of artificial neural network models to emulate storm surge based on the storm track/size/intensity history.
This study presents a neural network model that can predict storm surge, informed by a database of synthetic storm simulations.
arXiv Detail & Related papers (2022-04-18T23:42:18Z) - 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) - SmaAt-UNet: Precipitation Nowcasting using a Small Attention-UNet
Architecture [5.28539620288341]
We show that it is possible to produce an accurate precipitation nowcast using a data-driven neural network approach.
We evaluate our approaches on a real-life datasets using precipitation maps from the region of the Netherlands and binary images of cloud coverage of France.
arXiv Detail & Related papers (2020-07-08T20:33:10Z)
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.