scholarly journals Estimation of precipitation induced by tropical cyclones based on machine‐learning‐enhanced analogue identification of numerical prediction

2021 ◽  
Vol 28 (2) ◽  
Author(s):  
Yuan‐Yuan Liu ◽  
Lei Li ◽  
Ye‐Sen Liu ◽  
Pak‐Wai Chan ◽  
Wen‐Hai Zhang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Tropical Cyclones ◽  
Numerical Prediction

scholarly journals Numerical prediction of tropical cyclones : A review of research at Andhra University

MAUSAM ◽  
2021 ◽  
Vol 70 (2) ◽  
pp. 195-214
Author(s):  
DODLA VENKATA BHASKAR RAO
Keyword(s):  
Tropical Cyclones ◽  
Numerical Prediction

Machine learning cloud top height detection based on GNSS radio occultations: a step ahead towards an operational use

2021 ◽  
Author(s):  
Riccardo Biondi ◽  
Pierre-Yves Tournigand ◽  
Mohammed Hammouti
Keyword(s):  
Machine Learning ◽  
Tropical Cyclones ◽  
Early Warning Systems ◽  
Extreme Weather Events ◽  
Time Range ◽  
Vertical Resolution ◽  
Bending Angle ◽  
Upper Troposphere ◽  
Detection Techniques ◽  
Volcanic Clouds

<p>The Global Navigation Satellite Systems (GNSS) Radio Occultation (RO) technique allows the sounding of the atmosphere with a vertical resolution of about 100 m in the upper troposphere. It has already been demonstrated that the RO bending angle, by showing clear anomalies at the cloud top heights, is an efficient parameter to highlight the presence of dense clouds in the atmosphere. The objective of this work is to use the bending angle anomaly technique to systematically detect the presence of dense clouds in the atmosphere as well as their altitude and type. Several studies demonstrated the detection efficiency of the bending angle on tropical cyclones, severe convection and volcanic clouds altitude with high accuracy. However, the clouds type differentiation remains a challenge. One of the main issue on this regard, is the lack of volcanic cloud case studies, due to the low number of eruptions in comparisons to the extreme weather events, and to the large uncertainties on volcanic clouds detection techniques.</p><p>In this work we collected all the RO collocate in a short time range with tropical cyclones and volcanic clouds, and we collocate them with the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) backscatter. The bending angle anomaly profile is given in input to a machine learning algorithm to retrieve the presence of the cloud and its height. The CALIOP backscatter has 30-meter vertical resolution in the troposphere and 60-meter in the upper troposphere/lower stratosphere. We manually constrain the cloud edges, compute the cloud top height from each cloud and use this value as target for the algorithm output. To get a balanced training of the algorithm, we add to the dataset an equal number of clear sky samples.</p><p>The algorithm aims at quickly providing the cloud top height to be used for aviation and nowcast issues and to be included in early warning systems.</p>

scholarly journals Impact of Madden - Julian Oscillation on North Indian Ocean Cyclone Intensity

2021 ◽  
Author(s):  
M M Ali ◽  
Uppalapati Naga Tanusha ◽  
C. Purna Chand ◽  
B Himasri ◽  
Mark A. Bourassa ◽  
...  
Keyword(s):  
Machine Learning ◽  
Indian Ocean ◽  
Tropical Cyclones ◽  
Learning Algorithm ◽  
North Indian Ocean ◽  
Machine Learning Algorithm ◽  
Madden Julian Oscillation ◽  
Cyclone Intensity ◽  
The North ◽  
Cyclonic Storms

The influence of the Madden - Julian Oscillation (MJO) on the intensity of the Tropical Cyclones in the North Indian Ocean is investigated through a machine learning algorithm using cyclone data from the Joint Typhoon Warning Centre and MJO from the Bureau of Meteorology, Australia during 1974–2019. The scatter index varies from 0.45 for depressions to 0.03 for the super cyclonic storms indicating that the MJO index is another parameter that should be investigated in cyclone studies.

Use of Machine Learning algorithms in evaluating the WRF model parameter sensitivity for the simulation of tropical cyclones

2021 ◽  
Author(s):  
Baki Harish ◽  
Sandeep Chinta ◽  
Chakravarthy Balaji ◽  
Balaji Srinivasan
Keyword(s):  
Machine Learning ◽  
Sensitivity Analysis ◽  
Tropical Cyclones ◽  
Surrogate Model ◽  
Learning Algorithms ◽  
Wrf Model ◽  
Machine Learning Algorithms ◽  
Model Parameters ◽  
Model Parameter ◽  
Parameter Values

<p>The Indian subcontinent is prone to tropical cyclones that originate in the Indian Ocean and cause widespread destruction to life and property. Accurate prediction of cyclone track, landfall, wind, and precipitation are critical in minimizing damage. The Weather Research and Forecast (WRF) model is widely used to predict tropical cyclones. The accuracy of the model prediction depends on initial conditions, physics schemes, and model parameters. The parameter values are selected empirically by scheme developers using the trial and error method, implying that the parameter values are sensitive to climatological conditions and regions. The number of tunable parameters in the WRF model is about several hundred, and calibrating all of them is highly impossible since it requires thousands of simulations. Therefore, sensitivity analysis is critical to screen out the parameters that significantly impact the meteorological variables. The Sobol’ sensitivity analysis method is used to identify the sensitive WRF model parameters. As this method requires a considerable amount of samples to evaluate the sensitivity adequately, machine learning algorithms are used to construct surrogate models trained using a limited number of samples. They could help generate a vast number of required pseudo-samples. Five machine learning algorithms, namely, Gaussian Process Regression (GPR), Support Vector Machine, Regression Tree, Random Forest, and K-Nearest Neighbor, are considered in this study. Ten-fold cross-validation is used to evaluate the surrogate models constructed using the five algorithms and identify the robust surrogate model among them. The samples generated from this surrogate model are then used by the Sobol’ method to evaluate the WRF model parameter sensitivity.</p>

Supervised Machine Learning Techniques to Assess Tropical Cyclones in Bay of Bengal and Bangladesh

2021 ◽  
Author(s):  
Mariam Hussain ◽  
Seon Ki Park
Keyword(s):  
Machine Learning ◽  
Tropical Cyclone ◽  
Linear Regression ◽  
Tropical Cyclones ◽  
Bay Of Bengal ◽  
Early Warning Systems ◽  
Supervised Machine Learning ◽  
Extreme Weather Events ◽  
Economic Losses ◽  
Coastal Regions

<p>Bangladesh experiences extreme weather events such as heavy rainfall due to monsoon, tropical cyclones, and thunderstorms resulting in floods every year. Regular flood events significantly affect in agricultural industries and human lives for economic losses. One of the reasons for these weather phenomena to sustain is latent heat release from Bay of Bengal (BoB) and Southeast Tropical Indian Ocean (SETIO). As the country has limited observations from stations and oceans, modeling for numerical weather prediction (NWP) are challenging for local operations. For operational NWP, computational resources and time are also concerns for a developing country like Bangladesh. Besides, recent machine learning (ML) techniques are widely applied to study various meteorological events with efficient results. Therefore, this research aims to estimate predictability and accuracy of supervised ML for tropical cyclones by assessing air temperature at 2 meter (AT) and sea surface temperature (SST). For AT and SST, the study utilizes monthly data at 0.25 × 0.25<sup>o</sup> horizontal resolution provided by the ECMWF reanalysis (ERA5). The gridded data is downscaled to area of interests such as coastal regions, BoB and SEITO with a study period of 40 years from 1979 to 2018. Furthermore, Bangladesh Meteorological Department (BMD) provides AT for 36 years from 1979 to 2015. The experiments segregate into two sections: (1) data normalizations via linear regression (LR) and multi-linear regression (MLR) and (2) supervised ML techniques applications in Matlab 2018b. The pre-processed data for LR show that AT from coastal regions such as Chittagong (CG), Barishal (BR), and Khulna (KL) divisions have stronger correlations (R) to SST in BOB with R = 0.910, 0.850, and 0.846 respectively than SEITO (R = 0.698, 0.675 and 0.678 respectively). Moreover, for these three regions, the correlation of MLR is 0.916 and 0.745 for BoB and SEITO with residual standard error (RSE) 1.312 and 1.218 respectively. For supervised ML applications, coarse decision tree (CDT) predict SST based on AT with train (80%) and test (20%) of the ERA5 data. Finally, the results from CDT model indicate that SST predictions are possible with 98.5% accuracy based on coastal stations. The trained CDT also validated model prediction utilizing observed AT (BMD observations) to forecast monthly SST and found 85% accuracy for monthly time series. In conclusions, CDT can predict SST from station data and assess if there is any possibility for tropical cyclone formation. The future works include further assessment for various categories of tropical cyclone and predict their intensity based on SSTs. This research aims to contribute in disaster mitigation by improving early warning systems. The possibility of cyclone formations will help for preparedness in saving property damages in Bangladesh.</p>

Modeling the economic impact of incoming tropical cyclones using machine learning

2021 ◽  
Author(s):  
Vera Wendler-Bosco ◽  
Charles Nicholson
Keyword(s):  
Machine Learning ◽  
Economic Impact ◽  
Tropical Cyclones

scholarly journals Machine Learning in Tropical Cyclone Forecast Modeling: A Review

Atmosphere ◽  
2020 ◽  
Vol 11 (7) ◽  
pp. 676
Author(s):  
Rui Chen ◽  
Weimin Zhang ◽  
Xiang Wang
Keyword(s):  
Machine Learning ◽  
Tropical Cyclone ◽  
Tropical Cyclones ◽  
Numerical Models ◽  
Weather Forecasts ◽  
Dynamic Mechanisms ◽  
Forecast Models ◽  
Strong Winds ◽  
The Many ◽  
Axisymmetric Structures

Tropical cyclones have always been a concern of meteorologists, and there are many studies regarding the axisymmetric structures, dynamic mechanisms, and forecasting techniques from the past 100 years. This research demonstrates the ongoing progress as well as the many remaining problems. Machine learning, as a means of artificial intelligence, has been certified by many researchers as being able to provide a new way to solve the bottlenecks of tropical cyclone forecasts, whether using a pure data-driven model or improving numerical models by incorporating machine learning. Through summarizing and analyzing the challenges of tropical cyclone forecasts in recent years and successful cases of machine learning methods in these aspects, this review introduces progress based on machine learning in genesis forecasts, track forecasts, intensity forecasts, extreme weather forecasts associated with tropical cyclones (such as strong winds and rainstorms, and their disastrous impacts), and storm surge forecasts, as well as in improving numerical forecast models. All of these can be regarded as both an opportunity and a challenge. The opportunity is that at present, the potential of machine learning has not been completely exploited, and a large amount of multi-source data have also not been fully utilized to improve the accuracy of tropical cyclone forecasting. The challenge is that the predictable period and stability of tropical cyclone prediction can be difficult to guarantee, because tropical cyclones are different from normal weather phenomena and oceanographic processes and they have complex dynamic mechanisms and are easily influenced by many factors.

scholarly journals Unlocking GOES: A Statistical Framework for Quantifying the Evolution of Convective Structure in Tropical Cyclones

2020 ◽  
Vol 59 (10) ◽  
pp. 1671-1689
Author(s):  
Trey McNeely ◽  
Ann B. Lee ◽  
Kimberly M. Wood ◽  
Dorit Hammerling
Keyword(s):  
Machine Learning ◽  
Tropical Cyclones ◽  
Satellite Imagery ◽  
The United States ◽  
Intensity Change ◽  
Complex Data ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Convective Structure ◽  
Structure Patterns

AbstractTropical cyclones (TCs) rank among the most costly natural disasters in the United States, and accurate forecasts of track and intensity are critical for emergency response. Intensity guidance has improved steadily but slowly, as processes that drive intensity change are not fully understood. Because most TCs develop far from land-based observing networks, geostationary satellite imagery is critical to monitor these storms. However, these complex data can be challenging to analyze in real time, and off-the-shelf machine-learning algorithms have limited applicability on this front because of their “black box” structure. This study presents analytic tools that quantify convective structure patterns in infrared satellite imagery for overocean TCs, yielding lower-dimensional but rich representations that support analysis and visualization of how these patterns evolve during rapid intensity change. The proposed feature suite targets the global organization, radial structure, and bulk morphology (ORB) of TCs. By combining ORB and empirical orthogonal functions, we arrive at an interpretable and rich representation of convective structure patterns that serve as inputs to machine-learning methods. This study uses the logistic lasso, a penalized generalized linear model, to relate predictors to rapid intensity change. Using ORB alone, binary classifiers identifying the presence (vs absence) of such intensity-change events can achieve accuracy comparable to classifiers using environmental predictors alone, with a combined predictor set improving classification accuracy in some settings. More complex nonlinear machine-learning methods did not perform better than the linear logistic lasso model for current data.

scholarly journals Ocean Wave Height Series Prediction with Numerical Long Short-Term Memory

2021 ◽  
Vol 9 (5) ◽  
pp. 514
Author(s):  
Xiaoyu Zhang ◽  
Yongqing Li ◽  
Song Gao ◽  
Peng Ren
Keyword(s):  
Machine Learning ◽  
Wave Height ◽  
Short Term Memory ◽  
Gaussian Approximation ◽  
Numerical Prediction ◽  
Short Term ◽  
Term Memory ◽  
Numerical Predictions ◽  
Probabilistic Distribution ◽  
Long Short Term Memory

This paper investigates the possibility of using machine learning technology to correct wave height series numerical predictions. This is done by incorporating numerical predictions into long short-term memory (LSTM). Specifically, a novel ocean wave height series prediction framework, referred to as numerical long short-term memory (N-LSTM), is introduced. The N-LSTM takes a combined wave height representation, which is formed of a current wave height measurement and a subsequent Simulating Waves Nearshore (SWAN) numerical prediction, as the input and generates the corrected numerical prediction as the output. The correction is achieved by two modules in cascade, i.e., the LSTM module and the Gaussian approximation module. The LSTM module characterizes the correlation between measurement and numerical prediction. The Gaussian approximation module models the conditional probabilistic distribution of the wave height given the learned LSTM. The corrected numerical prediction is obtained by sampling the conditional probabilistic distribution and the corrected numerical prediction series is obtained by iterating the N-LSTM. Experimental results validate that our N-LSTM effectively lifts the accuracy of wave height numerical prediction from SWAN for the Bohai Sea and Xiaomaidao. Furthermore, compared with the state-of-the-art machine learning based prediction methods (e.g., residual learning), the N-LSTM achieves better prediction accuracy by 10% to 20% for the prediction time varying from 3 to 72 h.

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