The Development and Verification of TIPS: An Expert System to Forecast Thunderstorm Occurrence

Lee, Robert R.; Passner, Jeffrey E.

doi:10.1175/1520-0434(1993)008<0271:tdavot>2.0.co;2

Cited by 39 publications

(19 citation statements)

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“…This method was widely adopted by several researchers (e.g. Doswell et al 1990;Lee and Passner 1993;Huntrieser et al 1997;Mukhopadhyay et al 2003;Haklander and Delden 2003;Dhawan et al 2008). Based on the contingency table, nine skill scores are derived, and the description of the skill scores is given in Table 2 Apart from the nine skill scores, two more skill scores, normalized skill score (NSS) and l (Haklander and Delden 2003), are used in the present study.…”

Section: Skill Scoresmentioning

confidence: 99%

Study of thermodynamic indices in forecasting pre-monsoon thunderstorms over Kolkata during STORM pilot phase 2006–2008

2010

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The pre-monsoon convective atmosphere over Kolkata (22.52°N, 88.37°E) during STORM field phase 2006-2008 is investigated using 12 UTC radiosonde data and thermodynamic indices. In the present study, an attempt has been made to assess the skill of various indices and parameters and to propose suitable threshold values in forecasting the occurrence of thunderstorm activity at Kolkata. The thermodynamic indices and parameters used in the present study are lifted index (LI), K index (KI), severe weather threat index (SWEAT), total totals index (TTI), convective available potential energy (CAPE), deep convection index (DCI), humidity index (HI), Boyden index (BI), dew point temperature at 850 hpa (DEW), relative humidity at 700 hpa (RH), and bulk Richardson number (BRN). Validation of the suggested threshold values of indices was conducted on the days of thunderstorm activity. It was found that one index alone cannot predict the occurrence of thunderstorm over Kolkata region. The present study suggests that the indices with highest skill for thunderstorm prediction are KI, DCI, SWEAT, DEW, HI, RH, LI, TTI, while the prediction efficiency is poor for CAPE, BRN, and BI. Observed values of these indices also reveal that scattered, multi-cellular thunderstorms are possible over Kolkata during pre-monsoon months.

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Section: Skill Scoresmentioning

confidence: 99%

Study of thermodynamic indices in forecasting pre-monsoon thunderstorms over Kolkata during STORM pilot phase 2006–2008

2010

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“…These methods are widely used for the verification of weather forecasts (Wilks, 1995), but in many studies also for the evaluation of thunderstorm indices, e.g. by Doswell et al (1990), Lee andPassner (1993), or Huntrieser et al (1997). Whereas the observations (predictands) match a type of binary scheme -like thunderstorm occurrence vs. no thunderstorm occurrence -, the various parameters as predictors may assume a wide range of values.…”

Section: Categorical Verification and Skill Scoresmentioning

confidence: 99%

“…These so-called convective parameters and indices reflect the potential for thunderstorm development according to the prevailing properties of the air mass. In many studies, the efficiency of the various indices derived from the observed vertical profiles for thunderstorm prediction was investigated, for example by Schulz (1989), Lee and Passner (1993), Fuelberg and Biggar (1994), Huntrieser et al (1997), Haklander and Van Delden (2003), and Manzato (2005). However, little attention has been paid so far to the index-based prediction of severe thunderstorms.…”

Section: Introductionmentioning

confidence: 99%

The skill of convective parameters and indices to predict isolated and severe thunderstorms

Kunz¹

2007

Nat. Hazards Earth Syst. Sci.

165

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Abstract. The preconvective environment on days with ordinary, widespread, and severe thunderstorms in Southwest Germany was investigated. Various thermodynamic and kinetic parameters calculated from radiosoundings at 12:00 UTC were verified against subsequent thunderstorm observations derived from SYNOP station data, radar data, and damage reports of a building insurance company. The skill of the convective parameters and indices to predict thunderstorms was evaluated by means of probability distribution functions, probabilities of thunderstorms according to an index threshold, and skill scores like the Heidke Skill Score (HSS) that are based on categorical verification.For the ordinary decision as to whether a thunderstorm day was expected or not, the best results were obtained with the original Lifted Index (80% prediction probability for LI≤−1.73; HSS=0.57 for LI≤1.76), the Showalter Index, and the modified K-Index. Considering days with isolated compared to widespread thunderstorms, the best performance is reached by the Deep Convective Index. For days with severe thunderstorms that caused damage due to hail, local storms or floods, the best prediction skill is found again for the Lifted Index and the Deep Convective Index, but also for the Potential Instability Index, the Delta-θ e Index, and a version of the CAPE, where the lifting profile is determined by averaging over the lowest 100 hPa.

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“…Specific AI techniques used to predict thunderstorms include the application of adaptive boosting (e.g. Perler and Marchand, 2009), artificial neural networks (ANNs) (McCann, 1992;Manzato, 2005;Chaudhuri, 2010), and expert systems (Colquhoun, 1987;Lee and Passner, 1993). These statistical and AI models have demonstrated skills or utility.…”

Section: Introductionmentioning

confidence: 99%

An artificial neural network model to predict thunderstorms within 400 km² South Texas domains

Collins

Tissot

2015

Met. Apps

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Artificial neural network (ANN) models were developed to predict thunderstorm occurrence within three separate 400 km 2 regions, 9, 12 and 15 h (±2 h) in advance. The predictors include output from deterministic Numerical Weather Prediction models and from sub-grid scale soil moisture magnitude and heterogeneity estimates. The feed-forward multi-layer perceptron ANN topology, with one hidden layer and one neuron in the output layer, was chosen. Two sets of nine ANN models each were developed; one set was developed after a filtering-based feature selection technique was used to determine the predictor subset from 43 potential predictors. The other models were developed based on all 43 predictors. For each of the 18 models, a wrapper technique was used to determine the optimal number of neurons in the hidden layer. Thunderstorm artificial neural network (TANN) model performance was compared to that of multi-linear regression (MLR) models, and to human forecasters (NDFD), based on a novel data set. Results reveal that for several of the nine box/prediction hour combinations with respect to at least one skill-based performance metric, the TANN model's performance exceeded that of the MLR models and NDFD. Yet, the performance of both the MLR models and NDFD were superior to that of the corresponding TANN models in several other cases. Results indicate that the TANN models can provide automated predictions with skills similar to that of operational forecasters. Comparisons of the two sets of TANN models reveal utility in the use of feature selection.

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The Development and Verification of TIPS: An Expert System to Forecast Thunderstorm Occurrence

Cited by 39 publications

References 0 publications

Study of thermodynamic indices in forecasting pre-monsoon thunderstorms over Kolkata during STORM pilot phase 2006–2008

Study of thermodynamic indices in forecasting pre-monsoon thunderstorms over Kolkata during STORM pilot phase 2006–2008

The skill of convective parameters and indices to predict isolated and severe thunderstorms

An artificial neural network model to predict thunderstorms within 400 km² South Texas domains

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The Development and Verification of TIPS: An Expert System to Forecast Thunderstorm Occurrence

Cited by 39 publications

References 0 publications

Study of thermodynamic indices in forecasting pre-monsoon thunderstorms over Kolkata during STORM pilot phase 2006–2008

Study of thermodynamic indices in forecasting pre-monsoon thunderstorms over Kolkata during STORM pilot phase 2006–2008

The skill of convective parameters and indices to predict isolated and severe thunderstorms

An artificial neural network model to predict thunderstorms within 400 km2 South Texas domains

Contact Info

Product

Resources

About

An artificial neural network model to predict thunderstorms within 400 km² South Texas domains