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.
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.
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…
One key requirement for effective supply chain management is the quality of its inventory management. Various inventory management methods are typically employed for different types of products based on their demand patterns, product attributes, and supply network. In this paper, our goal is to develop robust demand pr…
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…
Model forecasts hourly electricity demand influenced by weather, socio-economic, and political factors.
problem Accurate hourly electricity demand forecasting in the face of multifaceted uncertainties.
method Interpretable probabilistic mid-term forecasting model using Generalized Additive Models (GAMs).
result Highlights vulnerability of countries to extreme weather scenarios under electric heating adoption.
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.
Probabilistic NDVI forecasting from sparse satellite data.
problem Challenges in short-term NDVI forecasting due to sparse and irregular satellite data.
method Probabilistic forecasting framework using historical NDVI and meteorological observations, with temporal-distance weighted quantile loss and extreme-weather feature engineering.
result The proposed method outperforms baselines on pointwise and probabilistic evaluation metrics.
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.
Graph conformal prediction predicts future power outages with high confidence.
problem Accurately predicting future power outages to enable rapid recovery.
method Developed a graph conformal prediction method for quarter-hourly outage data.
result Graph conformal prediction method delivers accurate prediction regions for future outage numbers.
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 …
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
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 …
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.
Framework for Granger causality in extreme events.
problem Identifying causal links from extreme events in time series.
method Causal tail coefficient and novel inference method.
result Framework outperforms state-of-the-art methods in detecting Granger causality in extremes.
PBC improves AI and dynamical subseasonal forecasts by reducing biases.
problem Subseasonal forecast accuracy drops due to model biases and compounding errors.
method Probabilistic bias correction (PBC) using machine learning to correct historical forecasts.
result PBC doubles AI Forecasting System's subseasonal skill and improves dynamical model skill.
Develops a new stochastic volatility model for temperature derivatives.
problem Assessing risk related to temperature volatility.
method Conditional Least Squares and Fourier transform techniques.
result Better assessment of temperature volatility risk.
Machine learning predicts Atlantic blocking using limited data.
problem Underestimation of blocking event duration in climate models.
method Transfer Learning and Explainable AI (SHAP analysis)
result High-pressure anomalies in specific regions contribute to blocking events.
Deep learning predicts lightning strikes with high accuracy.
problem Predicting thunderstorms and lightning strikes accurately.
method Convolutional neural network architecture inspired by UNet++ and ResNet.
result Probability of detection of more than 94% for lightning strikes within 15 minutes.
Weather balloons deploy sensors to collect stratospheric data.
problem Limited data collection in the stratosphere.
method Modeling forecast deviation as a Gaussian process to determine sensor release times; novel hardware system for optimal sensor release.
result Data engineering framework effectively collects stratospheric data through real flights and simulations.
Study improves PM concentration forecasting using MCCR loss.
problem Forecasting particulate matter concentration in South Korea.
method Used MCCR loss for regression analysis of air pollution and weather data.
result MCCR loss is more effective for extreme value forecasting.
Joint scene understanding and segmentation for automotive applications is a challenging problem in two key aspects:- (1) classifying every pixel in the entire scene and (2) performing this task under unstable weather and illumination changes (e.g. foggy weather), which results in poor outdoor scene visibility. This poo…
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.
ML weather forecasts lack physical consistency, but add value.
problem Lack of physical consistency in ML weather forecasts.
method Examined Pangu-Weather forecasts for fidelity and physical consistency.
result ML forecasts lack physical consistency and accuracy can be partly due to this.
FCNv2 robustness tested under noise and random initial conditions.
problem Assessing AI weather forecasting model robustness to input noise.
method Two experiments with varying noise levels and random initial conditions.
result FCNv2 preserves hurricane features under low to moderate noise, but underestimates intensity and persistence.
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.
Study on price fluctuations and persistence in European electricity spot markets.
problem Analyzing variability and persistence of electricity prices in European spot markets.
method Analysis of hourly, intraday, and 15-min intraday market prices; quantification of fluctuations, correlations, and extreme events; classification into circulation weather types.
result Different time scales in market dynamics; multifractal behavior below 12 hours; anti-correlation and mean reversion above 12 hours; long-term behavior influenced by four-day weather patterns; q-Gaussian distributions as best fit. 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.
Study examines stock price reactions to Texas winter storm power outages.
problem Impact of natural disasters on stock market values.
method Used four benchmark models to measure abnormal returns.
result Firms experienced significant stock price drops after the Texas winter storm.
DL improves precipitation nowcasting from radar images.
problem Precise short-term precipitation predictions for extreme weather adaptation.
method Image-to-image translation using UNET CNN, compared to optical flow, persistence, and HRRR.
result UNET-based DL outperforms traditional models in 1 km x 1 km, 1 hour precipitation nowcasting.
NDI aims to forecast future natural disasters risk for insurers.
problem Increasing intensity and frequency of natural disasters.
method Develops a Natural Disasters Index (NDI) based on NOAA data.
result NDI forecasts future natural disasters risk for insurers.
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.
Paper examines adversarial attacks on weather forecasting models, focusing on TC trajectory prediction.
problem Adversarial attacks can mislead downstream TC trajectory predictions in DLWF models.
method Proposes Cyc-Attack, a method using a surrogate model and skewness-aware loss function to generate adversarial TC paths.
result Cyc-Attack achieves higher true positive rates and lower false alarm rates compared to conventional methods.
Study optimizes climate adaptation strategies for NYC.
problem Catastrophic damages from extreme weather in NYC.
method Real options analysis and extreme value theory.
result Optimal adaptation pathways identified for NYC.
Weather forecasting is usually solved through numerical weather prediction (NWP), which can sometimes lead to unsatisfactory performance due to inappropriate setting of the initial states. In this paper, we design a data-driven method augmented by an effective information fusion mechanism to learn from historical data …
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.
Study compares two methods for predicting extreme atmospheric events.
problem Forecasting threshold exceedances of atmospheric variables like temperature and wind speed.
method Direct vs. full distribution probabilistic methods for rare events.
result Full distribution approach outperforms direct method for extreme events.
New diffusion models capture heavy-tailed distributions better.
problem Diffusion models struggle with rare or extreme events in heavy-tailed distributions.
method Repurposed diffusion framework using multivariate Student-t distributions, tailored perturbation kernel, and γ-divergence. result Our models generate rare and extreme events more effectively than standard diffusion models.
Nostradamus links climate and stock market performance.
problem Understanding the impact of climate on stock prices.
method Analyzing historical data, climate indicators, and natural disasters.
result Significant correlation between climate and stock price fluctuations.
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.
This study improves weather forecasting accuracy with spatiotemporal models.
problem Complexity and resource-intensive nature of weather forecasting.
method Spatiotemporal forecasting models integrating machine learning and deep neural networks.
result Spatiotemporal models reduce computational costs and improve accuracy.
Weather is a key production factor in agricultural crop production and at the same time the most significant and least controllable source of peril in agriculture. These effects of weather on agricultural crop production have triggered a widespread support for weather derivatives as a means of mitigating the risk assoc…
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.
Deep hedging strategies for Green PPAs in electricity markets reduce risk.
problem Risk management in Green Power Purchase Agreements (PPAs) due to price and weather risks.
method Utilizes machine learning to construct hedging strategies.
result Deep hedging strategies outperform static and dynamic benchmarks.
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.
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.
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…
Study compares different scoring rules for machine-learned weather forecasts, finding scale-awareness improves forecast realism.
problem Improving the accuracy of machine-learned probabilistic weather forecasts.
method Comparison of scoring rules (CRPS, fair global energy score, graph energy score) and analysis of their impact on forecast field spectra.
result Scale-awareness improves forecast realism, particularly in the tropics.