The Applicability of Bipartite Graph Model for Thunderstorms Forecast over Kolkata
TL;DR: In this article, a single spectrum bipartite graph connectivity model was developed to forecast thunderstorms over Kolkata during the premonsoon season (April-May) and the statistical distribution of normal probability was observed for temperature, relative humidity, convective available potential energy (CAPE), and convective inhibition energy (CIN) to quantify the threshold values of the parameters for the prevalence of thunderstorms.
Abstract: Single Spectrum Bipartite Graph (SSBG) model is developed to forecast thunderstorms over Kolkata during the premonsoon season (April-May). The statistical distribution of normal probability is observed for temperature, relative humidity, convective available potential energy (CAPE), and convective inhibition energy (CIN) to quantify the threshold values of the parameters for the prevalence of thunderstorms. Method of conditional probability is implemented to ascertain the possibilities of the occurrence of thunderstorms within the ranges of the threshold values. The single spectrum bipartite graph connectivity model developed in this study consists of two sets of vertices; one set includes two time vertices (00UTC, 12UTC) and the other includes four meteorological parameters: temperature, relative humidity, CAPE, and CIN. Three distinct ranges of maximal eigen values are obtained for the three categories of thunderstorms. Maximal eigenvalues for severe, ordinary, and no thunderstorm events are observed to be , , and , respectively. The ranges of the threshold values obtained using ten year data (1997–2006) are considered as the reference range and the result is validated with the IMD (India Meteorological Department) observation, Doppler Weather Radar (DWR) Products, and satellite images of 2007. The result reveals that the model provides 12- to 6-hour forecast (nowcasting) of thunderstorms with 96% to 98% accuracy.
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TL;DR: In this article, an adaptive neuro-fuzzy inference system (ANFIS) was developed to forecast the peak gust speed associated with thunderstorms during the pre-monsoon season (April-May) over Kolkata (22°32′N, 88°20′E), India.
Abstract: The aim of the present study is to develop an adaptive neuro-fuzzy inference system (ANFIS) to forecast the peak gust speed associated with thunderstorms during the pre-monsoon season (April–May) over Kolkata (22°32′N, 88°20′E), India. The pre-monsoon thunderstorms during 1997–2008 are considered in this study to train the model. The input parameters are selected from various stability indices using statistical skill score analysis. The most useful and relevant stability indices are taken to form the input matrix of the model. The forecast through the hybrid ANFIS model is compared with non-hybrid radial basis function network (RBFN), multi layer perceptron (MLP) and multiple linear regression (MLR) models. The forecast error analyses of the models in the test cases reveal that ANFIS provides the best forecast of the peak gust speed with 3.52% error, whereas the errors with RBFN, MLP, and MLR models are 10.48, 11.57, and 12.51%, respectively. During the validation with the 2009 observations of the India Meteorological Department (IMD), the ANFIS model confirms its superiority over other comparative models. The forecast error during the validation of the ANFIS model is observed to be 3.69%, with a lead time of <12 h, whereas the errors with RBFN, MLP, and MLR are 12.25, 13.19, and 14.86%, respectively. The ANFIS model may, therefore, be used as an operational model for forecasting the peak gust speed associated with thunderstorms over Kolkata during the pre-monsoon season.
35 citations
TL;DR: In this paper, the authors presented a variation of atmospheric thermodynamic structure parameters between days of thunderstorm occurrence and nonoccurrence based on data sets obtained during Severe Thunderstorm-Observations and Regional Modeling (STORM) experiments conducted over Kharagpur (22.3°N, 87.2°E) in pre-monsoon season of 2009 and 2010.
Abstract: Variation of atmospheric thermodynamical structure parameters between days of thunderstorm occurrence and non-occurrence is presented based on data sets obtained during Severe Thunderstorm-Observations and Regional Modeling (STORM) experiments conducted over Kharagpur (22.3°N, 87.2°E) in pre-monsoon season of 2009 and 2010. Potential instability (stable to neutral) is noticed in the lower layers and enhanced (suppressed) convection in the middle troposphere during thunderstorm (non-thunderstorm) days. Low-level jets are observed during all days of the experimental period but with higher intensity on thunderstorm days. Convective available potential energy (CAPE) builds up until thunderstorm occurrence and becomes dissipated soon after, whereas convective inhibition (CIN) is greatly decreased prior to the event on thunderstorm days. In contrast, higher CAPE and CIN are noticed on non-thunderstorm days. Analysis of thermodynamic indices showed that indices including moisture [humidity index (HI) and dew point temperature at 850 hPa (DPT850)] are useful in differentiating thunderstorm from non-thunderstorm days. The present study reveals that significant moisture availability in the lower troposphere in the presence of convective instability conditions results in thunderstorm occurrence at Kharagpur.
33 citations
Cites background from "The Applicability of Bipartite Grap..."
...From earlier studies over Kolkata, thresholds of CAPE C1,000 J kg-1 (TYAGI et al., 2011) and CIN B50 J kg-1 (CHAUDHURI and MIDDEY, 2009) have been found favourable for thunderstorm occurrence....
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TL;DR: The results show that applying graph theory to this problem allows for the identification of features from infrared satellite data and the seamlessly identification in a precipitation rate satellite-based dataset, while innately handling the inherent complexity and non-linearity of mesoscale convective systems.
Abstract: Mesoscale convective systems are high impact convectively driven weather systems that contribute large amounts to the precipitation daily and monthly totals at various locations globally. As such, an understanding of the lifecycle, characteristics, frequency and seasonality of these convective features is important for several sectors and studies in climate studies, agricultural and hydrological studies, and disaster management. This study explores the applicability of graph theory to creating a fully automated algorithm for identifying mesoscale convective systems and determining their precipitation characteristics from satellite datasets. Our results show that applying graph theory to this problem allows for the identification of features from infrared satellite data and the seamlessly identification in a precipitation rate satellite-based dataset, while innately handling the inherent complexity and non-linearity of mesoscale convective systems.
30 citations
Cites methods from "The Applicability of Bipartite Grap..."
...This implementation of graph theory considered the relationship between atmospheric variables at a given time (Chaudhuri and Middey 2009; Chaudhuri and Middey 2011), or the spatial-temporal analysis of cloud volumes through combinatorial optimization of graph theory (Mukherjee and Acton 2002; Dixon and Wiener 1993)....
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...This implementation of graph theory considered the relationship between atmospheric variables at a given time (Chaudhuri and Middey 2009; Chaudhuri and Middey 2011), or the spatial-temporal analysis of cloud volumes through combinatorial optimization of graph theory (Mukherjee and Acton 2002; Dixon…...
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TL;DR: In this article, the authors assess the impact of Doppler weather radar (DWR) data (reflectivity and radial wind) assimilation on the simulation of severe thunderstorms (STS) events over the Indian monsoon region.
Abstract: This study assesses the impact of Doppler weather radar (DWR) data (reflectivity and radial wind) assimilation on the simulation of severe thunderstorms (STS) events over the Indian monsoon region. Two different events that occurred during the Severe Thunderstorms Observations and Regional Modeling (STORM) pilot phase in 2009 were simulated. Numerical experiments—3DV (assimilation of DWR observations) and CNTL (without data assimilation)—were conducted using the three-dimensional variational data assimilation technique with the Advanced Research Weather Research and Forecasting model (WRF-ARW). The results show that consistent with prior studies the 3DV experiment, initialized by assimilation of DWR observations, performed better than the CNTL experiment over the Indian region. The enhanced performance was a result of improved representation and simulation of wind and moisture fields in the boundary layer at the initial time in the model. Assimilating DWR data caused higher moisture incursion and increased instability, which led to stronger convective activity in the simulations. Overall, the dynamic and thermodynamic features of the two thunderstorms were consistently better simulated after ingesting DWR data, as compared to the CNTL simulation. In the 3DV experiment, higher instability was observed in the analyses of thermodynamic indices and equivalent potential temperature (θ
e) fields. Maximum convergence during the mature stage was also noted, consistent with maximum vertical velocities in the assimilation experiment (3DV). In addition, simulated hydrometeor (water vapor mixing ratio, cloud water mixing ratio, and rain water mixing ratio) structures improved with the 3DV experiment, compared to that of CNTL. From the higher equitable threat scores, it is evident that the assimilation of DWR data enhanced the skill in rainfall prediction associated with the STS over the Indian monsoon region. These results add to the body of evidence now which provide consistent and notable improvements in the mesoscale model results over the Indian monsoon region after assimilating DWR fields.
28 citations
Cites background from "The Applicability of Bipartite Grap..."
...CIN R ztop zbottom g Ttparcel Ttenv Ttenv dz B150 (Chaudhuri and Middey 2009) 1414 Nat Hazards (2014) 74:1403–1427...
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TL;DR: In this article, the authors analyzed thermodynamic indices variation over three sites of eastern Indian region: Bhubaneswar, Kolkata and Ranchi, associated with pre-monsoon thunderstorms for 20-year period (1987-2006) for Bhubaneh and Kolkatha and 15-years (1996-2010) for Ranchi.
Abstract: The present study analyses thermodynamic indices variation over three sites of eastern Indian region: Bhubaneswar, Kolkata and Ranchi, associated with pre-monsoon thunderstorms for 20-year period (1987–2006) for Bhubaneswar and Kolkata and 15 years (1996–2010) for Ranchi. All three sites are showing a rise in humidity over the period, unveiling the climate change over the region. We evaluated the threshold values of various thermodynamic indices for periods of 5-year intervals at each site based on skill score analysis. The indices associated with potential, convective, latent instability and moisture are showing varying threshold values over all the sites, and some of the indices are showing a definite increase/decrease in these threshold values. All three sites are showing a decrease in thunderstorm frequency over the study period. The work identifies the thermodynamic indices, which tend to capture the global warming impact in the threshold values by either showing an increase or decrease with the time at each site. The results advocate that for a long-term analysis of thermodynamic indices, the threshold values may change from one period to another.
23 citations
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TL;DR: In this paper, a high-resolution data assimilation system was developed at the Naval Research Laboratory (NRL) to assimilate high resolution data, especially those from Doppler radars, into the U.S. Navy's Coupled Ocean-Atmosphere Mesoscale Prediction System to improve the model capability and accuracy in short-term (0-6 h) prediction of hazardous weather for nowcasting.
Abstract: A high-resolution data assimilation system is under development at the Naval Research Laboratory (NRL). The objective of this development is to assimilate high-resolution data, especially those from Doppler radars, into the U.S. Navy’s Coupled Ocean–Atmosphere Mesoscale Prediction System to improve the model’s capability and accuracy in short-term (0–6 h) prediction of hazardous weather for nowcasting. A variational approach is used in this system to assimilate the radar observations into the model. The system is upgraded in this study with new capabilities to assimilate not only the radar radial-wind data but also reflectivity data. Two storm cases are selected to test the upgraded system and to study the impact of radar data assimilation on model forecasts. Results from the data assimilation experiments show significant improvements in storm prediction especially when both radar radial-wind and reflectivity observations are assimilated and the analysis incremental fields are adequately constrai...
35 citations
TL;DR: In this paper, it is shown that landscape variability decreases the temperature in the surface layer when, through mesoscale flow, cool air intrudes over warm patches, lifting warm air and weakening the static stability of the upper part of the planetary boundary layer.
Abstract: It is shown that landscape variability decreases the temperature in the surface layer when, through mesoscale flow, cool air intrudes over warm patches, lifting warm air and weakening the static stability of the upper part of the planetary boundary layer. This mechanism generates regions of upward vertical motion and a sizable amount of available potential energy and can make the environment of the lower troposphere more favorable to cloud formation. This process is enhanced by light ambient wind through the generation of trapped propagating waves, which penetrate into the midtropospheric levels, transporting upward the thermal perturbations and weakening the static stability around the top of the boundary layer. At moderate ambient wind speeds, the presence of surface roughness changes strengthens the wave activity, further favoring the vertical transport of the thermal perturbations. When the intensity of the ambient wind is larger than 5 m s−1, the vertical velocities induced by the surface ro...
30 citations
TL;DR: In this paper, a variety of statistical procedures, including conditional probabilities, exposure-period probabilities, and systems of multiple-regression equations based on nonlinear predictors, have been used for the forecasting of thunderstorm activity and associated adverse weather phenomena.
Abstract: Outline of some of the more successful diagnostic tools which have been developed to aid in the forecasting of thunderstorm activity and associated adverse weather phenomena. The techniques involve a variety of statistical procedures, including conditional probabilities, exposure-period probabilities, and systems of multiple-regression equations based on nonlinear predictors. It is demonstrated that thunderstorm data can be processed in a variety of ways, each designed to present specific answers to specific forecast requirements.
28 citations
TL;DR: In this paper, the utility of pattern recognition in assisting the prediction of severe and major weather in the Middle Atlantic region is examined, where the analog technique is applied to forecast maps as a pattern-recognition tool rather than to analysis maps as forecast tool.
Abstract: Advances in numerical weather prediction have occurred on numerous fronts, from sophisticated physics packages in the latest mesoscale models to multimodel ensembles of medium-range predictions. Thus, the skill of numerical weather forecasts continues to increase. Statistical techniques have further increased the utility of these predictions. The availability of large atmospheric datasets and faster computers has made pattern recognition of major weather events a feasible means of statistically enhancing the value of numerical forecasts. This paper examines the utility of pattern recognition in assisting the prediction of severe and major weather in the Middle Atlantic region. An important innovation in this work is that the analog technique is applied to NWP forecast maps as a pattern-recognition tool rather than to analysis maps as a forecast tool. A technique is described that employs a new clustering algorithm to objectively identify the anomaly patterns or “fingerprints” associated with past...
28 citations
TL;DR: The study recommends that the formulation of numerical weather prediction models for forecasting the occurrence of this high frequency meso-scale convective system must take into account the intrinsic chaos.
Abstract: The purpose of the present study is to investigate the existence of deterministic chaos in the time series of occurrence or non-occurrence of severe thunderstorms of the pre-monsoon season over the Northeastern part of India. Results from the current study reveal the existence of chaos in the relevant time series. The corresponding predictabilities are also computed quantitatively. The study recommends that the formulation of numerical weather prediction models for forecasting the occurrence of this high frequency meso-scale convective system must take into account the intrinsic chaos.
27 citations