Study improves accuracy of weather data for real-time building simulations.
problem Anomalous and missing weather data affect real-time building energy simulations.
method Introduces a framework for quality control of measured weather data using anomaly detection and neural network infilling.
result Neural Networks enhance the accuracy of data imputation compared to traditional methods.
Modern weather forecast models perform uncertainty quantification using ensemble prediction systems, which collect nonparametric statistics based on multiple perturbed simulations. To provide accurate estimation, dozens of such computationally intensive simulations must be run. We show that deep neural networks can be …
This study tackles basis risk in weather parametric insurance using Monte Carlo simulations.
problem Mismatch between actual loss and payout in weather parametric insurance leads to loss without payout or payout without loss.
method Empirical research using Monte Carlo simulations to test diversification and hedging strategies.
result Portfolio basis risk and volatility decrease with more contracts, and spatial relationships significantly impact basis risk.
We price weather-contingent options by use of Monte Carlo simulations. After calibrating the models to fit quoted prices, we analyze bid-ask spreads in terms of correlations across markets. Results are presented for a double-trigger Weather vs. Natural Gas call option.
The ability to analyze and forecast stratospheric weather conditions is fundamental to addressing climate change. However, our capacity to collect data in the stratosphere is limited by sparsely deployed weather balloons. We propose a framework to collect stratospheric data by releasing a contrail of tiny sensor device…
Nowadays, with the unprecedented penetration of renewable distributed energy resources (DERs), the necessity of an efficient energy forecasting model is more demanding than before. Generally, forecasting models are trained using observed weather data while the trained models are applied for energy forecasting using for…
Deep neural networks improve ensemble weather forecasts.
problem Improving accuracy and efficiency of ensemble weather forecasts.
method Mixed model combining subset of trajectories with deep neural networks for post-processing.
result Achieved over 14% relative improvement in ensemble forecast skill.
New method predicts wind farm power and wakes using weather patterns.
problem Inefficient and computationally intensive wind energy resource assessment.
method Unsupervised clustering of ERA5 data on wind velocity, WRF simulations at cluster centers, and post-processing.
result Accurate long-term predictions of power and wakes with reduced computational time.
A deep learning method for probabilistic weather forecasting.
problem Probabilistic forecasting of weather.
method Two chained machine-learning steps: dimension reduction and density estimation using normalizing flows.
result The method produces accurate conditional forecast distributions for weather.
Deep learning improves weather modeling for electricity load forecasting.
problem Accurate load and renewable energy forecasting requires complex spatio-temporal weather modeling.
method Automated spatio-temporal feature extraction using deep neural networks.
result Deep learning outperforms traditional methods in French national load forecasting.
Generative model emulates climate model for 100-year forecasts.
problem Challenges in accurately simulating long-term climate data.
method Integrates DYffusion with SFNO for stable, accurate climate simulations.
result Achieves near gold-standard performance for climate model emulation.
Then detection and identification of extreme weather events in large-scale climate simulations is an important problem for risk management, informing governmental policy decisions and advancing our basic understanding of the climate system. Recent work has shown that fully supervised convolutional neural networks (CNNs…
PhysicsFormer improves TSF models for GSWF with WEATHER-5K dataset.
problem Lack of comprehensive datasets for GSWF.
method PhysicsFormer combines dynamic core and Transformer, enforcing physical consistency.
result PhysicsFormer outperforms TSF models in operational forecasting.
ClimART dataset benchmarks ML emulators for atmospheric RT in climate models.
problem Lack of a comprehensive dataset and standardized practices for ML benchmarking in climate models.
method Builds ClimART, a large dataset with over 10 million samples, and presents novel baselines.
result Indicates shortcomings of prior datasets and network architectures.
Enhances weather detection by learning from auxiliary information.
problem Mispredictions in unsupervised severe weather detection.
method Learning joint representations of textual and weather data.
result Improved decision boundaries for severe weather detection.
New methods quantify uncertainties in AI weather forecasts.
problem Uncertainty in AI weather predictions.
method Comparing ensemble and post-hoc uncertainty quantification methods.
result Probabilistic forecasts improve over ensemble physics-based models.
As renewable distributed energy resources (DERs) penetrate the power grid at an accelerating speed, it is essential for operators to have accurate solar photovoltaic (PV) energy forecasting for efficient operations and planning. Generally, observed weather data are applied in the solar PV generation forecasting model w…
The paper models quanto weather and energy derivatives using Ornstein-Uhlenbeck processes and develops methods to hedge them.
problem Valuation and hedging of quanto derivatives on temperature and electricity.
method Developed a coupled model using Ornstein-Uhlenbeck processes and Conditional Least Square method for parameter estimation.
result Explicit and semi-explicit formulas for quanto options and hedging strategies are derived.
The effects of weather on agriculture in recent years have become a major global concern. Hence, the need for an effective weather risk management tool (i.e., weather derivatives) that can hedge crop yields against weather uncertainties. However, most smallholder farmers and agricultural stakeholders are unwilling to p…
Signature kernel scoring rule improves weather forecasting by capturing temporal and spatial dependencies.
problem Lack of suitable scoring rules for probabilistic weather forecasting.
method Reframe weather variables as continuous paths using iterated integrals (signature kernels) to capture temporal and spatial dependencies.
result Signature kernel scoring rule outperforms conventional methods in weather forecasting, especially for long-term forecasts.
WeatherFormer learns robust weather features from small datasets.
problem Modeling complex weather dynamics from limited data.
method Pretrained transformer encoder on large satellite dataset, with spatiotemporal encoding.
result State-of-the-art performance in county-level soybean yield prediction and influenza forecasting.
Delta Variances efficiently estimate epistemic uncertainty in neural networks.
problem Mitigating uncertainty in neural networks with limited data.
method Delta Variances, a computationally efficient algorithm for epistemic uncertainty quantification.
result Empirically competitive results with a single gradient computation.
This study analyzes how weather impacts bike sharing usage in Washington D.C.
problem Understanding how weather affects bike sharing usage patterns.
method Gathered bike usage and weather data, used k-means clustering algorithm to identify clusters.
result Weather significantly impacts bike usage, with temperature and precipitation being the most influential factors.
Dynamical weather and climate prediction models underpin many studies of the Earth system and hold the promise of being able to make robust projections of future climate change based on physical laws. However, simulations from these models still show many differences compared with observations. Machine learning has bee…
Paper models spatio-temporal extremes using conditional variational autoencoders.
problem Modeling co-occurrence of extreme weather events under changing climate conditions.
method Conditional Variational Autoencoder (cXVAE) with CNN integration.
result Accurately emulates spatial fields and recovers extremal dependence with low computational cost.
A framework assesses the quality of crowdsourced weather data.
problem Quality control and assessment of crowdsourced weather data from third-party stations.
method Proposes a simple, scalable, and interpretable AI/Stats/ML framework to assess TPAWS data.
result Demonstrates the performance of the framework using synthetic and real data.
Paper compares neural networks and time-series models for weather derivative pricing.
problem Pricing accuracy and regime adaptation for temperature and precipitation weather derivatives.
method Benchmarked harmonic-regression/ARMA vs. feed-forward neural network for temperature. Used CNN for precipitation, adapting to seasonal heterogeneity.
result CNN yields more accurate pricing, especially for regime-adapted seasonal data.
Graph-EFM models weather uncertainty with graph-based ensembles.
problem Accurately capturing forecast uncertainty in chaotic weather.
method Flexible latent-variable formulation with hierarchical graph construction.
result Graph-EFM ensembles achieve equivalent or lower errors than deterministic models.
We present the results of study of a possible relationship between the space weather and terrestrial markets of agricultural products. It is shown that to implement the possible effect of space weather on the terrestrial harvests and prices, a simultaneous fulfillment of three conditions is required: 1) sensitivity of …
AI boosts study of rare weather extremes with lower costs.
problem Difficulty in studying rare weather events due to limited data and models.
method Coupling AI forecasts with physics models using rare-event algorithms.
result Efficiently characterizes very rare events like once-per-millennium heatwaves.
Machine learning improves measuring climate adaptation impacts.
problem Measuring adaptation to climate change using weather damage elasticities.
method Debiased machine learning approach in panel data settings.
result Long-run impacts of damaging heat exposure significantly offset short-run impacts.
Improved weather forecasting with gridded pseudo-token TNPs.
problem Handling large-scale, unstructured spatio-temporal data in weather forecasting.
method Introducing gridded pseudo-token transformer neural processes (TNPs) with efficient attention mechanisms.
result Consistently outperforms baselines on various synthetic and real-world regression tasks involving large-scale data.
Neural network predicts weather probabilities with added uncertainty.
problem Inferring uncertainty from weather predictions.
method Data-driven neural network trained on WeatherBench dataset, combining outputs from subsets of variables.
result More accurate than some numerical models, provides probabilistic information.
Modeling spatial extremes with non-Gaussian fields using SAR models and CNNs.
problem Challenges in modeling spatial data with heavy-tailed distributions and missing cells.
method Spatial autoregressive models with Generalized Extreme Value innovations, combined with CNN for fast parameter estimation.
result Effective modeling of spatial extremes in non-Gaussian fields, demonstrated on precipitation data.
This paper reviews radar-based nowcasting techniques for short-term weather predictions.
problem Short-term weather predictions for adverse events like heavy rain and flooding.
method Analysis of existing radar-based nowcasting techniques and machine learning approaches.
result New partnerships between environmental science and machine learning can improve nowcasting.
Graph-based weather prediction adapted for local models.
problem Applying neural weather prediction to limited area modeling.
method Adapting graph-based Neural Weather Prediction approach to local models.
result Validation of multi-scale hierarchical model extension for Nordic region.
Study uses SVM to predict weather-induced home insurance claims and losses.
problem Assessing future weather-induced home insurance claims and losses for disaster preparedness.
method Support Vector Machine (SVM) regression for forecasting future claim dynamics.
result Illustrates SVM approach in forecasting weather-induced home insurance claims in a Canadian city.
The emerge of new technologies to synthesize and analyze big data with high-performance computing, has increased our capacity to more accurately predict crop yields. Recent research has shown that Machine learning (ML) can provide reasonable predictions, faster, and with higher flexibility compared to simulation crop m…
Deep learning model predicts European weather parameters.
problem Ensemble weather prediction using deep learning.
method Conditional deep convolutional generative adversarial network (GAN) and Monte-Carlo dropout.
result Forecast skill for geopotential height and two-meter temperature is good, but precipitation is challenging.
PeakWeather provides Swiss weather station data for machine learning.
problem Accurate weather forecasting for various activities and decision-making.
method High-quality dataset of surface weather observations from 8 years of Swiss stations.
result Dataset supports a wide range of spatiotemporal tasks.
Paper proposes a method for weather-informed probabilistic forecasting and scenario generation in power systems.
problem Challenges of integrating renewable energy sources into power grids due to their stochasticity and uncertainty.
method Combines probabilistic forecasting and Gaussian copula for day-ahead prediction and scenario generation of load, wind, and solar power.
result Demonstrates superior performance of the proposed weather-informed Temporal Fusion Transformer (WI-TFT) model.
Rigorous uncertainty quantification of probabilistic AI weather forecasts with conformal prediction
problem Calibrated uncertainty quantification in probabilistic weather forecasts
method Conformal prediction
result Calibrated uncertainty at no expense to other probabilistic metrics
Improved weather forecasting using deep CNN on cubed-sphere grid.
problem Global weather prediction accuracy and speed.
method Deep convolutional neural network (CNN) on cubed-sphere grid, offline mapping, loss minimization.
result Significantly improved weather forecasts, indefinitely stable, realistic patterns at long lead times.
The paper presents a machine learning framework to combine weather forecasts from multiple models.
problem Combining forecasts from different NWP models with varying biases and limitations.
method Three-stage framework using Quantile Regression Forests and quantile averaging.
result The framework generates well-calibrated probabilistic weather forecasts suitable for decision support.
Even though end-to-end supervised learning has shown promising results for sensorimotor control of self-driving cars, its performance is greatly affected by the weather conditions under which it was trained, showing poor generalization to unseen conditions. In this paper, we show how knowledge can be transferred using …
Paper uses Gaussian processes and neural nets to model sub-km wind accurately.
problem Accurately modeling sub-kilometer surface wind for optimal decision-making.
method Integrates Gaussian processes and neural networks to model wind gusts at sub-kilometer resolution.
result Modeling covariance structure improves prediction quality and calibration.
Stochastic model prices weather derivatives for Indian states, highlighting temperature volatility impacts.
problem Quantifying financial risk in Indian markets due to seasonal weather variations.
method Modified Ornstein-Uhlenbeck process with jumps for temperature dynamics, calibrated with historical data, Monte Carlo simulations for pricing.
result Volatility significantly impacts weather derivative pricing, with higher prices in colder states and lower in hotter states.
DAUM networks improve weather forecasting with attention and multistream features.
problem Improving weather forecasting accuracy using deep learning.
method Proposes DAUM networks combining tensorial unistream and multistream representations with attention mechanisms.
result Adding self-attention blocks enhances forecasting performance.