scholarly journals Forecasting Tropical Cyclone Formation in the Fiji Region: A Probit Regression Approach Using Bayesian Fitting

2011 ◽  
Vol 26 (2) ◽  
pp. 150-165 ◽  
Author(s):  
Savin S. Chand ◽  
Kevin J. E. Walsh
Keyword(s):  
Tropical Cyclone ◽  
Large Scale ◽  
Environmental Parameters ◽  
Model Parameters ◽  
Probit Regression ◽  
Relative Operating Characteristic ◽  
Verification Methods ◽  
Skill Scores ◽  
Regression Approach ◽  
Objective Methodology

Abstract An objective methodology for forecasting the probability of tropical cyclone (TC) formation in the Fiji, Samoa, and Tonga regions (collectively the FST region) using antecedent large-scale environmental conditions is investigated. Three separate probabilistic forecast schemes are developed using a probit regression approach where model parameters are determined via Bayesian fitting. These schemes provide forecasts of TC formation from an existing system (i) within the next 24 h (W24h), (ii) within the next 48 h (W48h), and (iii) within the next 72 h (W72h). To assess the performance of the three forecast schemes in practice, verification methods such as the posterior expected error, Brier skill scores, and relative operating characteristic skill scores are applied. Results suggest that the W24h scheme, which is formulated using large-scale environmental parameters, on average, performs better than that formulated using climatology and persistence (CLIPER) variables. In contrast, the W48h (W72h) scheme formulated using large-scale environmental parameters performs similar to (poorer than) that formulated using CLIPER variables. Therefore, large-scale environmental parameters (CLIPER variables) are preferred as predictors when forecasting TC formation in the FST region within 24 h (at least 48 h) using models formulated in the present investigation.

scholarly journals Modeling Seasonal Tropical Cyclone Activity in the Fiji Region as a Binary Classification Problem

2012 ◽  
Vol 25 (14) ◽  
pp. 5057-5071 ◽  
Author(s):  
Savin S. Chand ◽  
Kevin J. E. Walsh
Keyword(s):  
Tropical Cyclone ◽  
Large Scale ◽  
Southern Oscillation ◽  
Binary Classification ◽  
Probability Model ◽  
Classification Problem ◽  
Relative Vorticity ◽  
Classification Model ◽  
Model Parameters ◽  
Probit Regression

Abstract This study presents a binary classification model for the prediction of tropical cyclone (TC) activity in the Fiji, Samoa, and Tonga regions (the FST region) using the accumulated cyclone energy (ACE) as a proxy of TC activity. A probit regression model, which is a suitable probability model for describing binary response data, is developed to determine at least a few months in advance (by July in this case) the probability that an upcoming TC season may have for high or low TC activity. Years of “high TC activity” are defined as those years when ACE values exceeded the sample climatology (i.e., the 1985–2008 mean value). Model parameters are determined using the Bayesian method. Various combinations of the El Niño–Southern Oscillation (ENSO) indices and large-scale environmental conditions that are known to affect TCs in the FST region are examined as potential predictors. It was found that a set of predictors comprising low-level relative vorticity, upper-level divergence, and midtropspheric relative humidity provided the best skill in terms of minimum hindcast error. Results based on hindcast verification clearly suggest that the model predicts TC activity in the FST region with substantial skill up to the May–July preseason for all years considered in the analysis, in particular for ENSO-neutral years when TC activity is known to show large variations.

Response of Tropical Cyclone Potential Intensity to a Global Warming Scenario in the IPCC AR4 CGCMs

2010 ◽  
Vol 23 (6) ◽  
pp. 1354-1373 ◽  
Author(s):  
Jinhua Yu ◽  
Yuqing Wang ◽  
Kevin Hamilton
Keyword(s):  
Global Warming ◽  
Tropical Cyclone ◽  
Large Scale ◽  
Linear Trend ◽  
Relative Effect ◽  
Vertical Shear ◽  
Model Parameters ◽  
Climate Projections ◽  
South Indian ◽  
Projected Changes

Abstract This paper reports on an analysis of the tropical cyclone (TC) potential intensity (PI) and its control parameters in transient global warming simulations. Specifically, the TC PI is calculated for phase 3 of the Coupled Model Intercomparison Project (CMIP3) integrations during the first 70 yr of a transient run forced by a 1% yr−1 CO2 increase. The linear trend over the period is used to project a 70-yr change in relevant model parameters. The results for a 15-model ensemble-mean climate projection show that the thermodynamic potential intensity (THPI) increases on average by 1.0% to ∼3.1% over various TC basins, which is mainly attributed to changes in the disequilibrium in enthalpy between the ocean and atmosphere in the transient response to increasing CO2 concentrations. This modest projected increase in THPI is consistent with that found in other recent studies. In this paper the effects of evolving large-scale dynamical factors on the projected TC PI are also quantified, using an empirical formation that takes into account the effects of vertical shear and translational speed based on a statistical analysis of present-day observations. Including the dynamical efficiency in the formulation of PI leads to larger projected changes in PI relative to that obtained using just THPI in some basins and smaller projected changes in others. The inclusion of the dynamical efficiency has the largest relative effect in the main development region (MDR) of the North Atlantic, where it leads to a 50% reduction in the projected PI change. Results are also presented for the basin-averaged changes in PI for the climate projections from each of the 15 individual models. There is considerable variation among the results for individual model projections, and for some models the projected increase in PI in the eastern Pacific and south Indian Ocean regions exceeds 10%.

A Parameter for Forecasting Tornadoes Associated with Landfalling Tropical Cyclones

2014 ◽  
Vol 29 (5) ◽  
pp. 1238-1255 ◽  
Author(s):  
Matthew J. Onderlinde ◽  
Henry E. Fuelberg
Keyword(s):  
Tropical Cyclone ◽  
Tropical Cyclones ◽  
Stepwise Regression ◽  
Atlantic Coast ◽  
Convective Available Potential Energy ◽  
Environmental Parameters ◽  
Time Of Day ◽  
Skill Scores ◽  
Landfalling Tropical Cyclones ◽  
Center Time

Abstract The authors develop a statistical guidance product, the tropical cyclone tornado parameter (TCTP), for forecasting the probability of one or more tornadoes during a 6-h period that are associated with landfalling tropical cyclones affecting the coastal Gulf of Mexico and the southern Atlantic coast. TCTP is designed to aid forecasters in a time-limited environment. TCTP provides a “quick look” at regions where forecasters can then conduct detailed analyses. The pool of potential predictors included tornado reports and tropical cyclone data between 2000 and 2008, as well as storm environmental parameters. The original pool of 28 potential predictors is reduced to six using stepwise regression and logistic regression. These six predictors are 0–3-km wind shear, 0–3-km storm relative helicity, azimuth angle of the tornado report from the tropical cyclone, distance from the cyclone’s center, time of day, and 950–1000-hPa convective available potential energy. Mean Brier scores and Brier skill scores are computed for the entire TCTP-dependent dataset and for corresponding forecasts produced by the Storm Prediction Center (SPC). TCTP then is applied to four individual cyclone cases to qualitatively and quantitatively assess the parameter and compare its performance with SPC forecasts. Results show that TCTP has skill at identifying regions of tornado potential. However, tornadoes in some tropical systems are overpredicted, but underpredicted in others. TCTP 6-h forecast periods provide slightly poorer statistical performance than the 1-day tornado probability forecasts from SPC, probably because the SPC product includes forecaster guidance and because their forecasts are valid for longer periods (24 h).

scholarly journals Seasonal Forecasts of Tropical Cyclone Activity in a High-Atmospheric-Resolution Coupled Prediction System*

2016 ◽  
Vol 29 (3) ◽  
pp. 1179-1200 ◽  
Author(s):  
Julia V. Manganello ◽  
Kevin I. Hodges ◽  
Benjamin A. Cash ◽  
James L. Kinter ◽  
Eric L. Altshuler ◽  
...  
Keyword(s):  
Tropical Cyclone ◽  
North Pacific ◽  
Large Scale ◽  
Coupled Model ◽  
Prediction System ◽  
Seasonal Forecasts ◽  
The North ◽  
Skill Scores ◽  
Activity Measures ◽  
The Tropical Pacific

Abstract Seasonal forecast skill of the basinwide and regional tropical cyclone (TC) activity in an experimental coupled prediction system based on the ECMWF System 4 is assessed. As part of a collaboration between the Center for Ocean–Land–Atmosphere Studies (COLA) and the ECMWF called Project Minerva, the system is integrated at the atmospheric horizontal spectral resolutions of T319, T639, and T1279. Seven-month hindcasts starting from 1 May for the years 1980–2011 are produced at all three resolutions with at least 15 ensemble members. The Minerva system demonstrates statistically significant skill for retrospective forecasts of TC frequency and accumulated cyclone energy (ACE) in the North Atlantic (NA), eastern North Pacific (EP), and western North Pacific. While the highest scores overall are achieved in the North Pacific, the skill in the NA appears to be limited by an overly strong influence of the tropical Pacific variability. Higher model resolution improves skill scores for the ACE and, to a lesser extent, the TC frequency, even though the influence of large-scale climate variations on these TC activity measures is largely independent of resolution changes. The biggest gain occurs in transition from T319 to T639. Significant skill in regional TC forecasts is achieved over broad areas of the Northern Hemisphere. The highest-resolution hindcasts exhibit additional locations with skill in the NA and EP, including land-adjacent areas. The feasibility of regional intensity forecasts is assessed. In the presence of the coupled model biases, the benefits of high resolution for seasonal TC forecasting may be underestimated.

Climatological Variations in North Atlantic Tropical Cyclone Tracks

2012 ◽  
Vol 25 (2) ◽  
pp. 657-673 ◽  
Author(s):  
Angela J. Colbert ◽  
Brian J. Soden
Keyword(s):  
Tropical Cyclone ◽  
El Niño ◽  
Large Scale ◽  
El Nino ◽  
Environmental Parameters ◽  
Steering Flow ◽  
Systematic Shift ◽  
Meridional Mode ◽  
The Relationship ◽  
Atlantic Meridional Mode

Abstract This study investigates the relationship between tropical cyclone (TC) tracks and climatological variations in large-scale environmental parameters associated with the TC steering flow. By using the Atlantic Ocean hurricane database for 1950–2010, TCs that form in the main development region (MDR) are categorized into one of three track types: straight moving, recurving landfall, or recurving ocean. As expected, the straight-moving storms are associated with a westward extension and strengthening of the subtropical high, whereas the recurving ocean storms are associated with a weakening of the high. The presence of El Niño conditions in the tropical Pacific Ocean is shown to be associated with a weakening of the high, an increase in the percentage of recurving ocean TCs, and a decrease in the percentage of recurving landfall TCs. Positive phases of the Atlantic Meridional Mode are associated with an increase in the percentage of recurving ocean TCs and a decrease in the percentage of straight-moving TCs. Synthetic tracks are simulated for each storm using a beta and advection model. Sensitivity experiments using both observed and uniformly seeded genesis locations indicate that the path of straight-moving TCs is largely a reflection of their tendency to form in the southwestern portion of the MDR rather than of differences in steering flow. These experiments also suggest that the shift in TC tracks associated with El Niño/La Niña conditions is largely attributable to changes in the steering flow, whereas the track changes associated with variations in the Atlantic Meridional Mode are due to a systematic shift in genesis location.

Bayesian Forecasting of Seasonal Typhoon Activity: A Track-Pattern-Oriented Categorization Approach

2010 ◽  
Vol 23 (24) ◽  
pp. 6654-6668 ◽  
Author(s):  
Pao-Shin Chu ◽  
Xin Zhao ◽  
Chang-Hoi Ho ◽  
Hyeong-Seog Kim ◽  
Mong-Ming Lu ◽  
...  
Keyword(s):  
Regression Model ◽  
Large Scale ◽  
Vertical Wind Shear ◽  
Precipitable Water ◽  
Model Parameters ◽  
Probit Regression ◽  
Posterior Predictive Distribution ◽  
Dominant Type ◽  
Probabilistic Forecasts ◽  
Fuzzy Clustering Method

Abstract A new approach to forecasting regional and seasonal tropical cyclone (TC) frequency in the western North Pacific using the antecedent large-scale environmental conditions is proposed. This approach, based on TC track types, yields probabilistic forecasts and its utility to a smaller region in the western Pacific is demonstrated. Environmental variables used include the monthly mean of sea surface temperatures, sea level pressures, low-level relative vorticity, vertical wind shear, and precipitable water of the preceding May. The region considered is the vicinity of Taiwan, and typhoon season runs from June through October. Specifically, historical TC tracks are categorized through a fuzzy clustering method into seven distinct types. For each cluster, a Poisson or probit regression model cast in the Bayesian framework is applied individually to forecast the seasonal TC activity. With a noninformative prior assumption for the model parameters, and following Chu and Zhao for the Poisson regression model, a Bayesian inference for the probit regression model is derived. A Gibbs sampler based on the Markov chain Monte Carlo method is designed to integrate the posterior predictive distribution. Because cluster 5 is the most dominant type affecting Taiwan, a leave-one-out cross-validation procedure is applied to predict seasonal TC frequency for this type for the period of 1979–2006, and the correlation skill is found to be 0.76.

scholarly journals Deep Reinforcement Learning Based Weather Monitoring Systemusing Arduino for Smart Environment

2019 ◽  
Vol 8 (4) ◽  
pp. 4346-4350
Keyword(s):  
Large Scale ◽  
Weather Forecasting ◽  
Learning Algorithm ◽  
Meteorological Data ◽  
Hybrid Approach ◽  
Environmental Parameters ◽  
Base Station ◽  
Model Parameters ◽  
Smart Environment ◽  
Inference Procedure

Weather forecasting is an essential predictive challenge that has depended primarily on model-based methods. Collection of data about the different weather parameters is needed a smart environment. Recent developments in machine learning (ML) made possible to collect the data. The data from input sensors is then read by Arduino, which acts as server. The sensors collect the data of various environmental parameters and provide it to Arduino, which act as a base station. It then transmits the data using WIFI and the processed data will be displayed on laptop through accessing the server that is on the receiver side. In this paper, new directions are explored with forecasting weather as a data intensive challenge that involves inferences across space and time. Machine Learningmakes predictions through a unique hybrid approach that combines discriminatively trained predictive models and a deep neural network. The Deep Learning algorithm utilized here is ValueBased – Temporal Difference Algorithm. This in turnmodels the joint statistics of a set of weather-related variables. It is shown that the base model can be enhanced with spatial interpolation that uses learned long-range spatial dependencies. An efficient learning and inference procedure is also devised, that allows for large scale optimization of the model parameters. The methods are evaluated with experiments on real-world meteorological data that highlight the promise of the approach.

scholarly journals A Technique for Verification of Convection-Permitting NWP Model Deterministic Forecasts of Lightning Activity

2017 ◽  
Vol 32 (1) ◽  
pp. 97-115 ◽  
Author(s):  
Jonathan M. Wilkinson
Keyword(s):  
Large Scale ◽  
Prediction Models ◽  
Single Case ◽  
Weather Prediction ◽  
Lightning Activity ◽  
Forecast Performance ◽  
Verification Methods ◽  
Skill Scores ◽  
Resolution Model ◽  
Nwp Model

Abstract This manuscript introduces a new technique for evaluating lightning forecasts from convection-permitting models. In recent years, numerical weather prediction models at the convection-permitting scales (horizontal grid resolutions of 1–5 km) have been able to produce realistic-looking forecasts of lightning activity when compared with observations. However, it is challenging to assess what value these forecasts add above standard large-scale indices. Examining this problem, it is found that existing skill scores and neighborhood verification methods are unable to cope with both the double-penalty effect and the model’s variable frequency bias. A displacement distance and a quasi-symmetric distance score are introduced based on the distance between the model and the observations, the latter showing any improvement the forecast has over a completely “hedged” forecast. This can be combined with a domain-improved contingency table and comparisons between modeled and observed lightning flashes to evaluate the forecast performance in three important dimensions: coverage, distance, and intensity. The verification metric is illustrated with a single case, which shows that the convective-scale U.K. variable resolution model (UKV) delivers improved forecasts compared with the large-scale indices in both coverage and distance. Additionally, a month-long analysis is performed, which reveals that the coverage of lightning is in good agreement with the observations; lightning is displaced by the model by a distance on the order of 50–75 km, but the model overpredicts the lightning intensity by at least a factor of 6 after observational detection efficiencies have been considered.

A Bayesian Regression Approach for Predicting Seasonal Tropical Cyclone Activity over the Central North Pacific

2007 ◽  
Vol 20 (15) ◽  
pp. 4002-4013 ◽  
Author(s):  
Pao-Shin Chu ◽  
Xin Zhao
Keyword(s):  
Tropical Cyclone ◽  
North Pacific ◽  
Critical Region ◽  
Large Scale ◽  
Pearson Correlation ◽  
Predictor Variable ◽  
Vertical Wind Shear ◽  
Bayesian Regression ◽  
Model Parameters ◽  
Central North Pacific

Abstract In this study, a Poisson generalized linear regression model cast in the Bayesian framework is applied to forecast the tropical cyclone (TC) activity in the central North Pacific (CNP) in the peak hurricane season (July–September) using large-scale environmental variables available up to the antecedent May and June. Specifically, five predictor variables are considered: sea surface temperatures, sea level pressures, vertical wind shear, relative vorticity, and precipitable water. The Pearson correlation between the seasonal TC frequency and each of the five potential predictors over the eastern and central North Pacific is computed. The critical region for which the local correlation is statistically significant at the 99% confidence level is determined. To keep the predictor selection process robust, a simple average of the predictor variable over the critical region is then computed. With a noninformative prior assumption for the model parameters, a Bayesian inference for this model is derived in detail. A Gibbs sampler based on the Markov chain Monte Carlo (MCMC) method is designed to integrate the desired posterior predictive distribution. The proposed hierarchical model is physically based and yields a probabilistic prediction for seasonal TC frequency, which would better facilitate decision making. A cross-validation procedure was applied to predict the seasonal TC counts within the period of 1966–2003 and satisfactory results were obtained.

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