Related papers: A simple model for predicting tropical cyclone min…
The radius of maximum wind ($R_{max}$) in a hurricane governs the footprint of hazards, particularly damaging wind and rainfall. However, $R_{max}$ is noisy to observe directly and is poorly resolved in reanalyses and climate models. In…
The impacts of a tropical cyclone after landfall depend not only on storm intensity but also on the size and structure of the wind field. Hence, a simple predictive model for the wind field after landfall has significant potential value.…
Short-term forecasting is an important tool in understanding environmental processes. In this paper, we incorporate machine learning algorithms into a conditional distribution estimator for the purposes of forecasting tropical cyclone…
Tropical cyclones are important drivers of coastal flooding which have severe negative public safety and economic consequences. Due to the rare occurrence of such events, high spatial and temporal resolution historical storm precipitation…
Tropical cyclones cause significant inland hazards, including wind damage and freshwater flooding, that depend strongly on how storm intensity evolves at and after landfall. Existing theoretical predictions for the time-dependent and…
A presumed impact of global climate change is the increase in frequency and intensity of tropical cyclones. Due to the possible destruction that occurs when tropical cyclones make landfall, understanding their formation should be of mass…
Intensification of tropical storms measured as the central pressure tendency represents a subtle imbalance, of the order of $10^{-3}$, between the inflow and outflow of air in the storm core. Factors driving this imbalance, especially in…
The prediction of the intensity, location and time of the landfall of a tropical cyclone well advance in time and with high accuracy can reduce human and material loss immensely. In this article, we develop a Long Short-Term memory based…
Global deep-learning weather prediction models have recently been shown to produce forecasts that rival those from physics-based models run at operational centers. It is unclear whether these models have encoded atmospheric dynamics, or…
Tropical Cyclones (TCs) have devastating effects on several coastal regions worldwide. Precautionary knowledge about TC characteristics such as wind direction, wind speed, epicenter position, condensed vapor pressure measure, and radius of…
The concept of Maximum Potential Intensity (MPI) is widely used in tropical cyclone research to estimate the minimum central pressure and the maximum velocity of tropical storms from environmental parameters. The MPI pressure derives from…
We propose a short-term wind forecasting framework for predicting real-time variations in atmospheric turbulence based on nacelle-mounted anemometer and ground-level air-pressure measurements. Our approach combines linear stochastic…
Anthropogenic influences have been linked to tropical cyclone (TC) poleward migration, TC extreme precipitation, and an increased proportion of major hurricanes [1, 2, 3, 4]. Understanding past TC trends and variability is critical for…
Tropical cyclones can be of varied intensity and cause a huge loss of lives and property if the intensity is high enough. Therefore, the prediction of the intensity of tropical cyclones advance in time is of utmost importance. We propose a…
A group of algorithms for estimating the current intensity (CI) of tropical cyclones (TCs), which use infrared and microwave sensor-based images as the input of the algorithm because it is more skilled than each algorithm separately, are…
Annual North Atlantic tropical cyclone (TC) counts are frequently modeled as a Poisson process with a state-dependent rate. We provide a lower bound on the forecasting error of this class of models. Remarkably we find that this bound is…
TCBench is a benchmark for evaluating global, short to medium-range (1-5 days) forecasts of tropical cyclone (TC) track and intensity. To allow a fair and model-agnostic comparison, TCBench builds on the IBTrACS observational dataset and…
A hidden Markov model is developed to simulate tropical cyclone intensity evolution dependent on the surrounding large-scale environment. The model considers three unobserved (hidden) discrete states of intensification and associates each…
We present a statistical model for the unconditional mean tracks of hurricanes. Our model is a semi-parametric scheme that averages together observed hurricane displacements. It has a single parameter that defines the averaging length…
Numerical Weather Prediction (NWP) models that integrate coupled physical equations forward in time are the traditional tools for simulating atmospheric processes and forecasting weather. With recent advancements in deep learning, AI-based…