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 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.
Deep learning model predicts tropical cyclone intensification using satellite images.
problem Accurately predicting rapid intensification of tropical cyclones.
method Attention-based deep learning model using satellite images.
result Deep learning models outperform traditional methods in RI prediction.
Develops algorithms to estimate trends in global temperature variability.
problem Estimating trends in cloud reflectance temperature variability.
method Two novel algorithms for dense, gridded observations over space and time.
result Evaluation of methods with simulated and real-world data.
MetNet forecasts precipitation up to 8 hours with high spatial and temporal resolution.
problem Precise weather forecasting for long lead times.
method Neural network architecture using axial self-attention for global context aggregation.
result MetNet outperforms Numerical Weather Prediction at forecasts of up to 8 hours.
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.
A model predicts solar irradiance without local data using satellite and weather forecasts.
problem Forecasting solar irradiance without local measurements for geographically dispersed solar generators.
method Uses satellite data and weather forecasts with a deep neural network trained on a subset of ground data.
result Proposed model performs as well or better than local models across 25 locations and prediction horizons.
Paper predicts GNSS phase scintillations with machine learning.
problem Predicting phase scintillations due to ionosphere disturbances.
method Proposes a novel machine learning architecture and loss function.
result Achieves state-of-the-art prediction of phase scintillations 1 hour in advance.
New model predicts particle precipitation from magnetosphere to ionosphere.
problem Improving prediction of electron particle precipitation from magnetosphere to ionosphere.
method Compilation of new database, use of machine learning (ML) tools, neural network (PrecipNet).
result PrecipNet achieves >50% reduction in errors and better captures dynamic changes.
New method predicts soil moisture with uncertainty estimates.
problem Accurately predict soil moisture using deep learning models.
method Used Monte Carlo dropout with long short-term memory models.
result Successfully captures predictive error and detects dissimilarity.
Machine learning speeds up CRTM model predictions for weather forecasting.
problem Insufficient computational efficiency of radiative transfer models.
method Probabilistic neural network emulator of CRTM.
result Emulator predicts brightness temperatures with RMSE < 0.1 K for clear sky conditions.
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.
Study creates open-access wildfire dataset for Russia.
problem Data scarcity for comprehensive Eurasian wildfire research.
method Machine learning for exploratory data analysis and predictive modeling.
result Identified key environmental factors influencing fire behavior.
Sparse point observations can provide useful local constraints, but their benefit for radar-like fields depends on the training loss, uncertainty representation, and how observation support is encoded in the model.
problem Improving dense radar-field forecasts with sparse point observations
method A multimodal graph neural network nowcasting system over the Nordic radar domain
result Each source improves a different part of the forecast problem
New parameterization tackles stochasticity in weather models.
problem Uncertainty in small-scale processes in weather models.
method Bayesian neural network with Hamiltonian Monte Carlo for uncertainty quantification and memory.
result Shows skillful forecasts and trustworthy uncertainty quantifications.
A framework converts spatial data into embeddings for insurance risk modelling.
problem Improving underwriting precision and risk management in insurance with spatial data.
method Multi-view contrastive learning framework for generating spatial embeddings.
result Spatial embeddings consistently improve predictive accuracy across various models.
AI helps forecasters understand TC convective evolution before intensification.
problem Challenges in extracting scientific insights from complex TC data.
method Combining AI prediction algorithms and classical statistical inference.
result Identifies patterns in TC convective structure leading to intensification.
CBGP boosts GP covariance to model spatiotemporal irregularities.
problem Overfitting and overconfident uncertainty in nonstationary GP models.
method Boosting covariance priors, partially-whitened observations, gradient descent-like procedure.
result Accurate and reliable SBAS ionospheric corrections in challenging space weather.
New satellite constructions create infinite Brunnian links.
problem Creating new Brunnian links from existing ones.
method Satellite sum and satellite tie constructions.
result Every Brunnian link has a unique tree-arrow structure.
Formula for τ-invariant of satellite knots derived from L-space satellite operators.
problem Calculating the τ-invariant of satellite knots using L-space satellite operators.
method Algorithm to compute knot Floer complexes and formula for τ-invariant.
result Formula recovers existing formulas and proves new properties of τ-invariant.
Satellite knots can be trivialized by a single band move.
problem Satellite knots and their trivialization.
method Infinite family of satellite knots and a single band move.
result No disjoint band unknotting exists for satellite knots.
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.
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.
This study analyzes satellite communication latency using a stochastic geometry model.
problem Latency analysis of LEO satellite relay communication systems.
method Stochastic geometry framework with spherical BPP models, suboptimal satellite relay selection strategy.
result Derives distance distributions and analytical expressions for transmission delays.
Satellite links of fully positive braids are characterized.
problem Characterizing satellite links of fully positive braids.
method Analyzing fully positive braids and their satellites.
result Satellite links of fully positive braids are characterized by specific conditions.
Any knot in a solid torus, called a pattern or satellite operator, acts on knots in the 3-sphere via the satellite construction. We introduce a generalization of satellite operators which form a group (unlike traditional satellite operators), modulo a generalization of concordance. This group has an action on the set o…
Study on weather forecasting errors for solar energy forecasting.
problem Uncertainty in weather forecasting for solar PV generation.
method Comparison of forecasted and observed weather data, statistical metrics, and sensitivity test.
result Identified influential weather variables improving solar PV generation forecasting.
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.
Weather derivatives help farmers hedge against crop yield risks.
problem High basis risks in weather derivatives pricing models.
method Machine learning ensemble technique to determine yield-weather relationships; mean-reverting model with local temperature dependence.
result Average temperature is the most significant weather variable affecting maize yield.
Satellite operations have infinite rank in smooth concordance group.
problem Understanding satellite operations in the smooth concordance group.
method Reduction to winding number zero satellites and use of SO(3) gauge theory. result Provides a criterion for satellite operations to generate infinite rank subgroups.
Satellite links with many twists have simpler companions.
problem Relationship between satellite and companion links' complexity.
method Constructing satellite links with multiple full twists and analyzing their companion links.
result Satellite links with many twists have simpler companion links.
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.
Develops a new framework for integrating satellite allocations in small portfolios.
problem Feasibility constraints in small portfolios, not return predictability, are the primary concerns.
method A four-layer feasibility framework: physical, economic, structural, and epistemic.
result Closed-form feasibility bounds on satellite size, turnover, and breadth without return forecasts.
Model predicts one-year NDVI for Four Corners region.
problem Long-term forecasting of vegetation conditions using climate attributes.
method Two-phase machine learning model using historical climate data.
result Open-source tools outperform alternative methods for NDVI forecasts.
New methods link Legendrian satellites to Lagrangian cobordisms.
problem Understanding relations between Legendrian and Lagrangian knots.
method Constructing Lagrangian concordances through satellite operations.
result Maximum Thurston-Bennequin number restricts Legendrian satellite Lagrangian sliceness.
FedSpace optimizes ML training on satellites and ground stations.
problem Training machine learning models on satellites with limited bandwidth.
method Federated Learning framework that dynamically schedules model aggregation based on satellite orbits.
result Reduces training time by 1.7 days over state-of-the-art algorithms.
New proof for minimizing tunnel systems in satellite chain links.
problem Minimizing the tunnel number of satellite chain links.
method Proving the tunnel number is minimized for links with a specific number of components and bridge number.
result The result is sharp for satellite chain links over a 2-bridge knot.
Study assesses forecasting models in uncertain weather data.
problem Performance of forecasting models in uncertain weather data.
method Selected influential predictors, analyzed uncertainty, trained model using observed data.
result Comparison of forecasting methods in solar PV generation.
The Gluck twist preserves the diffeomorphism type of certain satellite 2-knots.
problem Preserving the diffeomorphism type of satellite 2-knots under the Gluck twist.
method Using new descriptions of satellite 2-knots, the paper shows that the Gluck twist does not change the diffeomorphism type of certain satellite 2-knots in three ways.
result The Gluck twist preserves the diffeomorphism type of certain satellite 2-knots.
Researchers compute Khovanov polynomials for satellite knots.
problem Computing Khovanov polynomials for satellite knots.
method Explicit computation using a computer program for two families of satellite knots.
result Khovanov polynomials can be expressed as a linear combination of pattern and companion invariants, with a jump at a critical point.
Deep neural networks reduce weather forecast uncertainty estimation costs.
problem Accurate estimation of weather forecast uncertainty using ensemble prediction systems.
method Modified 3D U-Net architecture and models incorporating temporal data.
result Deep neural networks can estimate weather forecast uncertainty with fewer simulations.
New satellite knots counter a conjecture about Lorenz knots.
problem A conjecture about satellite knots and Lorenz knots was disproven.
method Constructed infinitely many counterexamples of satellite knots that are not cables.
result The conjecture was amended and shown to hold for many Lorenz knots.
Formula for satellite operators using knot Floer homology.
problem Computing knot Floer homology for satellite knots.
method Using Heegaard Floer Dehn surgery formulas and formal knot Floer complexes.
result Formulas to compute knot Floer homology for satellite knots.
Satellite formula connects knot concordance invariants to surgery.
problem Understanding knot concordance invariants.
method Excision theorem for real Floer homotopy types.
result Concordance invariants depend only on zero-framed surgery.
The study computes invariants of satellite knots using bordered Floer homology.
problem Computing invariants of satellite knots with specific patterns.
method Using bordered Floer homology and the immersed curve interpretation of the bordered pairing theorem.
result Satellites with thin fibered companions or specific patterns have thin knot Floer homology.
We give sufficient conditions for a satellite knot to admit an L-space surgery, and use this result to give new infinite families of patterns which produce satellite L-space knots.
Necessary and sufficient conditions are given for a satellite knot to be fibered. Any knot k~ embeds in an unknotted solid torus V~ with arbitrary winding number in such a way that no satellite knot with pattern (V~,k~) is fibered. In particular, there exist nonfibered satellite knots wit…
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.