A comparative study of stochastic and deep generative models for multisite precipitation synthesis

• URL: http://arxiv.org/abs/2107.08074v1
• Date: Fri, 16 Jul 2021 18:35:24 GMT
• Title: A comparative study of stochastic and deep generative models for multisite precipitation synthesis
• Authors: Jorge Guevara, Dario Borges, Campbell Watson, Bianca Zadrozny
• Abstract summary: We compare two open-source weather generators: IBMWeathergen and RPrec, and two deep generative models: GAN and VAE, on a variety of metrics. Our preliminary results can serve as a guide for improving the design of deep learning architectures and algorithms for the multisite precipitation synthesis task.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Future climate change scenarios are usually hypothesized using simulations from weather generators. However, there only a few works comparing and evaluating promising deep learning models for weather generation against classical approaches. This study shows preliminary results making such evaluations for the multisite precipitation synthesis task. We compared two open-source weather generators: IBMWeathergen (an extension of the Weathergen library) and RGeneratePrec, and two deep generative models: GAN and VAE, on a variety of metrics. Our preliminary results can serve as a guide for improving the design of deep learning architectures and algorithms for the multisite precipitation synthesis task.

Related papers

• Multi-Source Temporal Attention Network for Precipitation Nowcasting [4.726419619132143]
  Precipitation nowcasting is crucial across various industries and plays a significant role in mitigating and adapting to climate change. We introduce an efficient deep learning model for precipitation nowcasting, capable of predicting rainfall up to 8 hours in advance with greater accuracy than existing operational models.
  arXiv  Detail & Related papers  (2024-10-11T09:09:07Z)
• Prithvi WxC: Foundation Model for Weather and Climate [2.9230020115516253]
  Prithvi WxC is a 2.3 billion parameter foundation model developed using 160 variables from the Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2). The model has been designed to accommodate large token counts to model weather phenomena in different topologies at fine resolutions. We test the model on a set of challenging downstream tasks namely: Autoregressive rollout forecasting, Downscaling, Gravity wave flux parameterization, and Extreme events estimation.
  arXiv  Detail & Related papers  (2024-09-20T15:53:17Z)
• WeatherReal: A Benchmark Based on In-Situ Observations for Evaluating Weather Models [11.016845506758841]
  We introduce WeatherReal, a novel benchmark dataset for weather forecasting derived from global near-surface in-situ observations. This paper details the sources and processing methodologies underlying the dataset, and illustrates the advantage of in-situ observations in capturing hyper-local and extreme weather. Our work aims to advance the AI-based weather forecasting research towards a more application-focused and operation-ready approach.
  arXiv  Detail & Related papers  (2024-09-14T08:53:46Z)
• TRG-Net: An Interpretable and Controllable Rain Generator [61.2760968459789]
  This study proposes a novel deep learning based rain generator, which fully takes the physical generation mechanism underlying rains into consideration. Its significance lies in that the generator not only elaborately design essential elements of the rain to simulate expected rains, but also finely adapt to complicated and diverse practical rainy images. Our unpaired generation experiments demonstrate that the rain generated by the proposed rain generator is not only of higher quality, but also more effective for deraining and downstream tasks.
  arXiv  Detail & Related papers  (2024-03-15T03:27:39Z)
• Precipitation nowcasting with generative diffusion models [0.0]
  We study the efficacy of diffusion models in handling the task of precipitation nowcasting. Our work is conducted in comparison to the performance of well-established U-Net models.
  arXiv  Detail & Related papers  (2023-08-13T09:51:16Z)
• An evaluation of deep learning models for predicting water depth evolution in urban floods [59.31940764426359]
  We compare different deep learning models for prediction of water depth at high spatial resolution. Deep learning models are trained to reproduce the data simulated by the CADDIES cellular-automata flood model. Our results show that the deep learning models present in general lower errors compared to the other methods.
  arXiv  Detail & Related papers  (2023-02-20T16:08:54Z)
• ClimaX: A foundation model for weather and climate [51.208269971019504]
  ClimaX is a deep learning model for weather and climate science. It can be pre-trained with a self-supervised learning objective on climate datasets. It can be fine-tuned to address a breadth of climate and weather tasks.
  arXiv  Detail & Related papers  (2023-01-24T23:19:01Z)
• Spatiotemporal modeling of European paleoclimate using doubly sparse Gaussian processes [61.31361524229248]
  We build on recent scale sparsetemporal GPs to reduce the computational burden. We successfully employ such a doubly sparse GP to construct a probabilistic model of paleoclimate.
  arXiv  Detail & Related papers  (2022-11-15T14:15:04Z)
• Semi-Supervised Video Deraining with Dynamic Rain Generator [59.71640025072209]
  This paper proposes a new semi-supervised video deraining method, in which a dynamic rain generator is employed to fit the rain layer. Specifically, such dynamic generator consists of one emission model and one transition model to simultaneously encode the spatially physical structure and temporally continuous changes of rain streaks. Various prior formats are designed for the labeled synthetic and unlabeled real data, so as to fully exploit the common knowledge underlying them.
  arXiv  Detail & Related papers  (2021-03-14T14:28:57Z)
• Comparison between ARIMA and Deep Learning Models for Temperature Forecasting [0.0]
  This paper compares ARIMA (Auto Regressive Integrated Moving Average) model and deep learning models to forecast temperature. According to the experimental results deep learning model was able to perform better than the traditional ARIMA methodology.
  arXiv  Detail & Related papers  (2020-11-09T14:21:46Z)
• From Rain Generation to Rain Removal [67.71728610434698]
  We build a full Bayesian generative model for rainy image where the rain layer is parameterized as a generator. We employ the variational inference framework to approximate the expected statistical distribution of rainy image. Comprehensive experiments substantiate that the proposed model can faithfully extract the complex rain distribution.
  arXiv  Detail & Related papers  (2020-08-08T18:56:51Z)

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.