The Applicability of Bipartite Graph Model for Thunderstorms Forecast over Kolkata

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.

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.

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.

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.

Abstract: The aim of the present study is to forecast thunderstorms over Kolkata (22°32′N, 88°20′E), India, during the pre-monsoon season (April–May) with graph spectral distance and entropy analysis. Graph vertices represent points connected by lines or edges, and lifting condensation level, convective condensation level, level of free convection, freezing level, level of neutral buoyancy and the surface level are taken as the input of the graph vertices. The variation in the most probable distance between the vertices is investigated. The result reveals a particular orientation of the vertex distances for thunderstorm days which is significantly different from the non-thunderstorm days. The reference graphs for thunderstorm and non-thunderstorm days are formed using the most probable vertex distances. The spectral distance between the reference graph and the graphs corresponding to thunderstorms are computed with the data collected during the period 1997–2009. The entropies, or the measure of disorderliness or uncertainty, are estimated for the graph distance matrices. The result shows that the thunderstorm days possess lower distance entropy than the non-thunderstorm days. This indicates that the reference graph that has been constructed for thunderstorms is more consistent. The result further depicts that the forecast accuracy through the present method is 98% with 1 h lead time, whereas the accuracy is 93% with 6 h lead time. The forecast is validated with the India Meteorological Department observations for the years 2007, 2008 and 2009. Copyright © 2011 Royal Meteorological Society

Abstract: Eigenvalues and the Laplacian of a graph Isoperimetric problems Diameters and eigenvalues Paths, flows, and routing Eigenvalues and quasi-randomness Expanders and explicit constructions Eigenvalues of symmetrical graphs Eigenvalues of subgraphs with boundary conditions Harnack inequalities Heat kernels Sobolev inequalities Advanced techniques for random walks on graphs Bibliography Index.

Abstract: (2007). Statistical Methods in the Atmospheric Sciences. Journal of the American Statistical Association: Vol. 102, No. 477, pp. 380-380.

Abstract: Gaph Teory Fourth Edition Th is standard textbook of modern graph theory, now in its fourth edition, combines the authority of a classic with the engaging freshness of style that is the hallmark of active mathematics. It covers the core material of the subject with concise yet reliably complete proofs, while offering glimpses of more advanced methods in each fi eld by one or two deeper results, again with proofs given in full detail.

Abstract: Artificial neural networks are appearing as useful alternatives to traditional statistical modelling techniques in many scientific disciplines. This paper presents a general introduction and discussion of recent applications of the multilayer perceptron, one type of artificial neural network, in the atmospheric sciences.

Abstract: The preconvective environment on thunderstorm days in Switzerland north of the Alps has been investigated during a 5-yr period (1985–89). Thermodynamic and kinematic parameters calculated from the radiosounding in Payerne (started at 0000 and 1200 UTC) were used to characterize the initiation of convection. The best parameters were evaluated by using three methods: 1) skill scores, 2) probability distributions, and 3) mean temperature soundings and hodographs. For the decision whether a thunderstorm day was expected or not, the best results were obtained at 0000 UTC with the original Showalter index and at 1200 UTC with the SWEAT index. In addition, to decide whether an isolated or widespread thunderstorm day was expected, the most successful parameter was the modified CAPECCL. Furthermore, the best thermodynamic and kinematic parameters were combined to create new thunderstorm indices, similar to the calculations of the SWEAT index in the United States. The new thunderstorm indices especially de...