Papers Precipitation Forecasting
“Precipitation Forecasting” 태그가 달린 논문 63편 · 필터 해제
SimCast-S2S: An Efficient Generative Model for Subseasonal Precipitation Forecasting via Transfer Learning from Climate Simulations
Subseasonal-to-seasonal (S2S) precipitation forecasting has substantial financial and societal impact, yet remains challenging because of weak predictive signals, high associated uncertainty, and the computational cost o…
Precipitation ForecastingTransfer LearningBenchmarking ConvLSTM for One-Day-Ahead IMDAA Rainfall-Field Prediction across Four Indian Cities
Convolutional long short-term memory networks (ConvLSTMs) are widely used for precipitation forecasting, but most evidence for their performance comes from dense, high-frequency radar sequences. This study tests whether …
Precipitation ForecastingPhysics-Based Deep Spatiotemporal Hyperlocal Radar Nowcasting with a Multi-Variable U-Net for High-Resolution Precipitation Forecasting
Precipitation nowcasting over the immediate 10-90 min period is important for flood management and real-time decision-making in urban regions. Conventional short-range forecasting with high-resolution numerical weather p…
Precipitation ForecastingEnhancing the Forecasting Capability of Multi-Model Blending Algorithms for Extreme Precipitation via Joint Use of Station and Gridded Observations
Accurate extreme precipitation forecasting is critical for disaster mitigation but remains challenging for numerical weather prediction (NWP) models due to systemic intensity underestimation and spatial displacement. Tra…
Precipitation ForecastingIntegrating GNSS-Derived Zenith Wet Delay into a Weather Foundation Model Improves Precipitation Forecasting
Global Navigation Satellite Systems (GNSS), best known for positioning, also serve weather science, as atmospheric water vapour delays their signals. This delay, the Zenith Wet Delay (ZWD), is a direct, all-weather measu…
Precipitation ForecastingRainODE: Continuous-Time Precipitation Forecasting with Latent Neural ODEs
In precipitation forecasting, not only accuracy but also temporal resolution is critical. However, increasing temporal resolution is constrained by observational limitations and the computational cost of dense discrete m…
Precipitation ForecastingSwAIther-Precip: Lead-Time-Aware Bias Correction Enables Kilometer-Scale Downscaling of Global AI Precipitation Forecasts over Switzerland
Skillful medium-range precipitation forecasting at kilometer scale remains challenging over complex terrain because precipitation arises from multiscale nonlinear processes that global models cannot explicitly resolve at…
Precipitation ForecastingImprovements to the post-processing of weather forecasts using machine learning and feature selection
This study aims to develop and improve machine learning-based post-processing models for precipitation, temperature, and wind speed predictions using the Mesoscale Model (MSM) dataset provided by the Japan Meteorological…
Precipitation ForecastingAccurate Precipitation Forecast by Efficiently Learning from Massive Atmospheric Variables and Unbalanced Distribution
Short-term (0-24 hours) precipitation forecasting is highly valuable to socioeconomic activities and public safety. However, the highly complex evolution patterns of precipitation events, the extreme imbalance between pr…
Precipitation ForecastingComputational EfficiencyMP-MoE: Matrix Profile-Guided Mixture of Experts for Precipitation Forecasting
Precipitation forecasting remains a persistent challenge in tropical regions like Vietnam, where complex topography and convective instability often limit the accuracy of Numerical Weather Prediction (NWP) models. While …
Precipitation ForecastingAssessing the Utility of Volumetric Motion Fields for Radar-based Precipitation Nowcasting with Physics-informed Deep Learning
Estimating motion from spatiotemporal geoscientific data is a fundamental component of many environmental modeling and forecasting tasks. In this work, we propose a physics-informed deep learning framework for estimating…
Precipitation ForecastingMAD-SmaAt-GNet: A Multimodal Advection-Guided Neural Network for Precipitation Nowcasting
Precipitation nowcasting (short-term forecasting) is still often performed using numerical solvers for physical equations, which are computationally expensive and make limited use of the large volumes of available weathe…
Precipitation ForecastingComputational EfficiencyStation2Radar: query conditioned gaussian splatting for precipitation field
Precipitation forecasting relies on heterogeneous data. Weather radar is accurate, but coverage is geographically limited and costly to maintain. Weather stations provide accurate but sparse point measurements, while sat…
Precipitation ForecastingDK-STN: A Domain Knowledge Embedded Spatio-Temporal Network Model for MJO Forecast
Understanding and predicting the Madden-Julian Oscillation (MJO) is fundamental for precipitation forecasting and disaster prevention. To date, long-term and accurate MJO prediction has remained a challenge for researche…
Precipitation ForecastingExploring Machine Learning, Deep Learning, and Explainable AI Methods for Seasonal Precipitation Prediction in South America
Forecasting meteorological variables is challenging due to the complexity of their processes, requiring advanced models for accuracy. Accurate precipitation forecasts are vital for society. Reliable predictions help comm…
Precipitation ForecastingA Diffusion-Based Framework for High-Resolution Precipitation Forecasting over CONUS
Accurate precipitation forecasting is essential for hydrometeorological risk management, especially for anticipating extreme rainfall that can lead to flash flooding and infrastructure damage. This study introduces a dif…
Precipitation ForecastingA Space-Time Transformer for Precipitation Nowcasting
Meteorological agencies around the world rely on real-time flood guidance to issue life-saving advisories and warnings. For decades traditional numerical weather prediction (NWP) models have been state-of-the-art for pre…
Precipitation ForecastingWeather ForecastingDeep Learning for Short-Term Precipitation Prediction in Four Major Indian Cities: A ConvLSTM Approach with Explainable AI
Deep learning models for precipitation forecasting often function as black boxes, limiting their adoption in real-world weather prediction. To enhance transparency while maintaining accuracy, we developed an interpretabl…
Precipitation ForecastingEnhancing Heavy Rain Nowcasting with Multimodal Data: Integrating Radar and Satellite Observations
The increasing frequency of heavy rainfall events, which are a major cause of urban flooding, underscores the urgent need for accurate precipitation forecasting - particularly in urban areas where localized events often …
Precipitation ForecastingA Review of Neural Networks in Precipitation Prediction
Precipitation prediction has undergone a profound transformation. A notable limitation of traditional NWP is the need for extensive statistical post-processing. To address this challenge, neural network-based approaches …
Precipitation Forecasting