Implementation of intuitionistic fuzzy logic to assess the predictability of severe thunderstorms

Thunderstorms are responsible for remarkable devastation when accompanied with lightning flashes, high wind gusts, torrential rain, hail and tornadoes. Weather hazards due to thunderstorms of such severe measure take place every year over Kolkata (22°32′N, 88°20′E), India during the pre-monsoon season (April–May). Prediction of severe thunderstorms is extremely important to cope with the devastations. However, forecasting severe thunderstorms is very difficult because the weather system is confined within a very small spatial-temporal scale. The network of observation systems is not adequate to detect such high frequency small scale weather. The purpose of the present study is to bring in the concept of Intuitionistic fuzzy logic as a decision — making technique to assess the predictability of severe thunderstorms over Kolkata in the premonsoon season. Different measures of entropies are used to extract the route of fuzziness. The intuitionistic fuzzy logic is implemented with ten years (1997–2006) observation of the occurrence/nonoccurrence of severe thunderstorms to assess the predictability. The result reveals that two consecutive severe thunderstorm days are highly probable after two consecutive non-thunderstorm days whereas the probability of severe thunderstorm is very less after three consecutive non-thunderstorm days during the pre-monsoon season over Kolkata. The result is compared with the box-and-whisker plot and validated with four years (2007–2010) observations of India Meteorological Department (IMD).     

A composite stability index for dichotomous forecast of thunderstorms

Thunderstorms are the perennial feature of Kolkata (22° 32???N, 88° 20???E), India during the premonsoon season (April?CMay). Precise forecast of these thunderstorms is essential to mitigate the associated catastrophe due to lightning flashes, strong wind gusts, torrential rain, and occasional hail and tornadoes. The present research provides a composite stability index for forecasting thunderstorms. The forecast quality detection parameters are computed with the available indices during the period from 1997 to 2006 to select the most relevant indices with threshold ranges for the prevalence of such thunderstorms. The analyses reveal that the lifted index (LI) within the range of ?5 to ?12?°C, convective inhibition energy (CIN) within the range of 0?C150?J/kg and convective available potential energy (CAPE) within the ranges of 2,000 to 7,000?J/kg are the most pertinent indices for the prevalence thunderstorms over Kolkata during the premonsoon season. A composite stability index, thunderstorm prediction index (TPI) is formulated with LI, CIN, and CAPE. The statistical skill score analyses show that the accuracy in forecasting such thunderstorms with TPI is 99.67?% with lead time less than 12?h during training the index whereas the accuracies are 89.64?% with LI, 60?% with CIN and 49.8?% with CAPE. The performance diagram supports that TPI has better forecast skill than its individual components. The forecast with TPI is validated with the observation of the India Meteorological Department during the period from 2007 to 2009. The real-time forecast of thunderstorms with TPI is provided for the year?2010.     

Evaluation of High-Resolution WRF Model Simulations of Surface Wind over the West Coast of India

This paper presents results from a statistical validation of the hindcasts of surface wind by a high-reso-ution-mesoscale atmospheric numerical model Advanced Research WRF （ARW3.3）, which is set up to force the operational coastal ocean forecast system at Indian Na- tional Centre for Ocean Information Services （INCOIS）. Evaluation is carried out based on comparisons of day-3 forecasts of surface wind with in situ and remote-sensing data. The results show that the model predicts the surface wind fields fairly accurately over the west coast of India, with high skill in predicting the surface wind during the pre-monsoon season. The model predicts the diurnal variability of the surface wind with reasonable accuracy. The model simulates the land-sea breeze cycle in the coastal region realistically, which is very clearly observed during the northeast monsoon and pre-monsoon season and is less prominent during the southwest monsoon season.     

Convective energies in forecasting severe thunderstorms with one hidden layer neural net and variable learning rate back propagation algorithm

Thunderstorms are perennial features of India. However, the severe thunderstorms of pre — monsoon season (April–May) over Kolkata (22°32′N, 88°20′E) are of great concern for imparting devastating effect on life and property on the ground and aviation aloft. The study is thus, focused on developing one hidden layer neural network model with variable learning rate back propagation algorithm to forecast such thunderstorms. Convective available potential energy (CAPE) and convective inhibition energy (CIN) are selected as the input parameters of the model after the estimation of various skill scores like, Probability of Detection (POD), False Alarm Ratio (FAR), Heidke Skill Score (HSS) and Odds Ratio Skill Score (Yule’s Q) on different stability indices. During training the model, the squared error for forecasting severe thunderstorms is observed to be 0.0022 when the values of CIN within the range of 0 to 140 J kg^?1 is taken as the input whereas the error is observed to be 0.0114 while the values of CAPE within the range of 2000 to 7000 J kg^?1 is considered as the input. The values of CIN and CAPE at twelve to six hours prior to the occurrence of severe thunderstorms are considered in this study. During validation of the model, the percentage of prediction error with the values of CIN as input is observed to be 0.042% and that with CAPE as input is 0.162%. The values of CIN within the range of 0–140 J kg^?1 are observed to be more persistent in forecasting severe thunderstorms over Kolkata than the values of CAPE within the range of 2000–7000 J kg^?1.     

“CMA北京模式”中三种不同阵风诊断方案在北京地区大风预报中的评估

数值模式预报是阵风预报的重要途径之一,对“中国气象局北京快速更新循环数值预报系统（简称CMA北京模式）”中AFWA、UPP、IUM三种阵风诊断方案在北京地区大风预报中的性能进行了分析评估。两次大风过程的分析以及各季节大风预报的批量试验检验结果显示:三种方案的阵风预报存在明显差异,IUM方案的阵风预报能力优势明显。IUM方案对冷空气大风和雷暴大风预警都有较好的指示意义。其对2020年3月18日冷空气大风过程中大风起始时间、大风区位置和演变以及过程极大风速均有较好的预报效果,对2020年8月2日雷暴大风过程中大风区范围预报偏大且位置存在偏差,但对大风预警的指示意义最强。IUM方案的阵风风速预报整体偏强,但对各个季节达到或超过5级阵风的等级预报较为准确。总体而言,IUM方案对北京地区大风预报性能较好,基于该方案制作的阵风预报产品可为大风预报提供有力支撑。      

Meta-heuristic ant colony optimization technique to forecast the amount of summer monsoon rainfall: skill comparison with Markov chain model

Forecasting summer monsoon rainfall with precision becomes crucial for the farmers to plan for harvesting in a country like India where the national economy is mostly based on regional agriculture. The forecast of monsoon rainfall based on artificial neural network is a well-researched problem. In the present study, the meta-heuristic ant colony optimization (ACO) technique is implemented to forecast the amount of summer monsoon rainfall for the next day over Kolkata (22.6°N, 88.4°E), India. The ACO technique belongs to swarm intelligence and simulates the decision-making processes of ant colony similar to other adaptive learning techniques. ACO technique takes inspiration from the foraging behaviour of some ant species. The ants deposit pheromone on the ground in order to mark a favourable path that should be followed by other members of the colony. A range of rainfall amount replicating the pheromone concentration is evaluated during the summer monsoon season. The maximum amount of rainfall during summer monsoon season (June—September) is observed to be within the range of 7.5–35 mm during the period from 1998 to 2007, which is in the range 4 category set by the India Meteorological Department (IMD). The result reveals that the accuracy in forecasting the amount of rainfall for the next day during the summer monsoon season using ACO technique is 95 % where as the forecast accuracy is 83 % with Markov chain model (MCM). The forecast through ACO and MCM are compared with other existing models and validated with IMD observations from 2008 to 2012.     

Phenological observations made by the I. R. Bohemian Patriotic�CEconomic Society, 1828�C1847

Drought forecasting is a critical component of drought risk management. Identification of effective predictors is a major component of forecasting models. Sea surface temperature (SST) and sea level pressure (SLP) are relevant predictors for short- to long-term drought forecasts. However, these datasets are captured globally within a cell-wise network. This paper describes an approach to locate the most effective cells of the SST and SLP datasets using data mining. They are then applied as input to an adaptive neurofuzzy inference system (ANFIS) model to forecast possible droughts 3, 6, and 9?months in advance. Tehran plain was selected as the study area, and drought events are designated using the effective drought index (EDI). In another treatment, past values of the EDI time series were introduced to the ANFIS and the results compared with the previous findings. It was shown that R ² values were higher for all cases applying the SST/SLP datasets. Additionally, the performance of SST/SLP datasets and the ANFIS model was assessed according to ??drought?? or ??wet?? classification, and it was concluded that more than 90% of the time the ANFIS model detected the drought status correctly or with only a one class error.     

成都双流机场“7.9”低空风切变天气过程分析

利用多普勒雷达对2010年7月9日发生在成都双流机场的两次低空风切变飞行事件进行分析,这两次低空风切变过程是由中尺度对流系统（MCS）产生的阵风锋和下沉气流造成的。利用实时的多普勒气象雷达和地面自动观测数据,确定阵风锋的传播方向和速度,估计阵风锋引起的风切变发生的时段和位置;多普勒反射率因子的形态及多普勒速度图像能有效判断下沉气流的区域,对下沉气流造成的风切变有很好的指示和预警作用。     

Fine mode aerosol chemistry over a tropical urban atmosphere: characterization of ionic and carbonaceous species

An extensive aerosol sampling program was conducted during January-December 2006 over Kolkata (22o33?? N and 88o20?? E), a mega-city in eastern India in order to understand the sources, distributions and properties of atmospheric fine mode aerosol (PM_2.5). The primary focus of this study is to determine the relative contribution of natural and anthropogenic as well as local and transported components to the total fine mode aerosol loading and their seasonal distributions over the metropolis. The average concentrations of fine mode aerosol was found to be 71.2?±?25.2???gm^-3 varying between 34.5???gm^-3 in monsoon and 112.6???gm^-3 in winter. The formation pathways of major secondary aerosol components like nitrate and sulphate in different seasons are discussed. A long range transport of dust aerosol from arid and semi-arid regions of western India and beyond was observed during pre-monsoon which significantly enriched the total aerosol concentration. Vehicular emissions, biomass burning and transported dust particles were the major sources of PM_2.5 from local and continental regions whereas sea-salt aerosol was the major source of PM_2.5 from marine source regions.     

Sharp depletion of atmospheric refractive index associated with Nor��wester over Gangetic West Bengal: a possible method of forecasting Nor��wester

The paper presents the nature of the variation of refractive index of atmospheric medium with time and altitude before, during and after the onset of thunderstorms over Gangetic West Bengal during the pre-monsoon period. A critical analysis shows that sharp depletion of the refractive index takes place before the onset of Nor??westers and possible explanations are also offered for the said occurrence.     

Simulation of location-specific severe thunderstorm events using high resolution land data assimilation

In this study, the impact of different land initial conditions on the simulation of thunderstorms and monsoon depressions is investigated using the Weather Research and Forecasting (WRF) model. A control run (CNTL) and a simulation with an improved land state (soil moisture and temperature) using the High Resolution Land Data Assimilation System (HRLDAS, experiment name: EHRLDAS) are compared for three different rainfall cases in order to examine the robustness of the assimilation system. The study comprises two thunderstorm cases (one in the pre-monsoon and one during the monsoon) and one monsoon depression case that occurred during the Interaction of Convective Organisation, Atmosphere, Surface and Sea (INCOMPASS) field campaign of the 2016 Indian monsoon. EHRLDAS is shown to yield improvements in the representation of location-specific rainfall, particularly over land. Further, it is found that surface fluxes as well as convective indices are better captured for the pre-monsoon thunderstorm case in EHRLDAS. By analysing components of the vorticity tendency equation, it is found that the vertical advection term is the major contributor towards the positive vorticity tendency in EHRLDAS compared to CNTL, hence improving localised convection and consequently facilitating rainfall. Significant improvements in the simulation of the pre-monsoon thunderstorm are noted, as seen using Automatic Weather Station (AWS) validation, whereas improvements in the monsoon depression are minimal. Further, it is found that vertical advection (moisture flux convergence) is the major driver modulating the convective circulation in localised thunderstorm (monsoon depression) cases and these dynamics are better represented by EHRLDAS compared to CNTL. These findings underline the importance of accurate and high resolution land-state conditions in model initial conditions for forecasting severe weather systems, particularly the simulation of localised thunderstorms over India.     

Multiseason evaluation of the MM5, COAMPS and WRF over southeast United States

Three models, MM5, COAMPS, and WRF, have been applied for the warm season in 2003 and the cool season in 2003?C2004 to evaluate their performances. All models run over the same domain area covering the north Gulf Mexico and southeastern United States (US) region with the same spatial resolution of 27?km. It was found that the temporal variations of the mean error distribution and strength at 24 and 36?h were rather weak for surface temperature, sea level pressure, and surface wind speed for all models. A warm bias in surface temperature forecasts dominated over land during the warm season, whereas a cool bias existed during the cool season. The MM5 and WRF produced negative biases of sea level pressure during the warm season and positive biases during the cool season while the COAMPS yielded a similar distribution of sea level pressure biases during both seasons. During both seasons, similar surface wind speed biases produced by each model included a high wind speed forecast over most areas by MM5 while the COAMPS and WRF yielded weak surface winds over the western Plains and stronger surface winds over the eastern Plains. Root-mean-squared errors revealed that the forecast of surface temperature, sea level pressure, and surface wind speed were degraded with the increase of forecast time. For rainfall evaluation, it was found that the MM5 underpredicted seasonal precipitation while the COAMPS and WRF overpredicted. The bias scores revealed that the MM5 yielded an underprediction of the coverage of precipitation areas, especially for heavier rainfall events. The MM5 presented the lower threat score at lighter rainfall events compared to the COAMPS and WRF. For moderate and heavier thresholds, all models lacked forecast accuracy. The WRF accuracy in predicting precipitation was heavily dependent upon the performance of the selected cumulus parameterization scheme. Use of the Grell?CDevenyi and Bette?CMiller?CJanjic schemes helps suppress precipitation overprediction.     

成都双流机场“7.9”低空风切变天气过程分析

利用多普勒雷达对2010年7月9日发生在成都双流机场的两次低空风切变飞行事件进行分析, 这两次低空风切变过程是由中尺度对流系统 (MCS) 产生的阵风锋和下沉气流造成的。利用实时的多普勒气象雷达和地面自动观测数据, 确定阵风锋的传播方向和速度, 估计阵风锋引起的风切变发生的时段和位置;多普勒反射率因子的形态及多普勒速度图像能有效判断下沉气流的区域, 对下沉气流造成的风切变有很好的指示和预警作用。      

北京及周边地区雷暴阵风锋特征统计分析

为全面和系统研究北京及周边地区阵风锋各方面特征,使用2006—2015年暖季（5—9月）北京多普勒雷达探测资料及北京、河北、天津自动气象站观测资料对北京及周边地区的阵风锋过程进行综合统计分析。结果表明,346次阵风锋过程有232次触发了对流,占总数的67%,表明阵风锋对雷暴具有较强的抬升触发能力。阵风锋在6—8月出现的日数占5—9月阵风锋总日数的85%;出现的时段主要是午后至傍晚（12—21时,北京时）,维持时间0.5—3 h;阵风锋在北京东南方向生成的数量最多,且触发对流的次数也最多;其次为偏东和东北方向;偏南和西南方向生成阵风锋数量居中,而偏北、偏西和西北地区阵风锋个例相对较少,触发对流的比例也相对较低。产生阵风锋的母风暴中48%为孤立雷暴（包括孤立多单体和超级单体风暴）,31%为雷暴群,21%为飑线;97%的母风暴最强回波在50 dBz以上,阵风锋的回波强度为10—25 dBz。91%的阵风锋移动速度集中在10—60 km/h,84%的阵风锋与母风暴的最大距离为1—60 km;在母风暴回波强度减弱到30 dBz以下时,80%的阵风锋能够继续维持的时间不超过2 h。阵风锋母风暴向东南方向移动的个例最多,从阵风锋和母风暴移动方向的关系来看,阵风锋与母风暴移向一致的情况占比最高,为32%,其次为母风暴无移动及阵风锋弧形扩散情况,各占17%;阵风锋与母风暴移向相反情况所占比例最低,只有3%。最后统计了阵风锋经过地面自动气象站时,自动观测量的变化情况。结果显示,阵风锋在经过地面自动气象站时会造成风速增大、温度降低、相对湿度增大、气压升高。      

Role of synoptic-scale forcing in cyclogenesis over the Bay of Bengal

The cyclone frequency distribution over the Bay of Bengal during 1990–2009 was distinctly bimodal, with a primary post-monsoon peak and a secondary pre-monsoon peak, despite the very high convective available potential energy (CAPE) during the pre-monsoon. The location of the monsoon trough over the bay is a primary factor in tropical cyclogenesis. Because the trough was in the northernmost bay during the pre-monsoon season, cyclogenesis was inactive in the southern bay, where a strong southwesterly wind shear was found. In this season, moreover, a hot, dry air mass extending vertically from 950 to 600 hPa was advected from northwestern India toward the bay. Moist, warm southwesterly winds penetrating below the deep, dry air mass caused a prominent dryline to form aloft on the northwestern side of the bay. The synoptic-scale hot, dry air forcing to the bay suppressed the active convection necessary for cyclogenesis. The strength of the stable environmental layer, represented by convective inhibition (CIN), was extremely large, and acted as a cap over the northern and northwestern bay. Conversely, during the post-monsoon, there were no horizontal temperature or moisture gradients, and CAPE and CIN were fairly modest. The entire bay was covered by a very deep, moist layer from the surface to 700 hPa transported from the east. The monsoon trough position and the environmental CIN in combination can explain the lower frequency of cyclogenesis during the pre-monsoon compared with the post-monsoon season.     

Daytime boundary layer behavior over eastern region (per-humid climate) and western regions (semi-arid climate) of India: a case study

The annual variation in planetary boundary layer (PBL) height is determined from the profiles of conserved thermodynamic variables, i.e., virtual potential temperature ?? _v and equivalent potential temperature ?? _e, using radiosonde data at per-humid climate region, Ranchi (23°42??N, 85°33??E, 610?m asl) and semi-arid region, Anand (23°35??N, 72°55??E, 45.1?m asl), India. Of all the variables, the ?? _v profile seems to provide the most reasonable estimate of the PBL height. This has been supplemented by T-Phi gram analysis for specific days. It has been found that in winter the height of boundary layer is very low due to subsidence and radiational cooling, while pre-monsoon months exhibit the most variable convection. It may be inferred that synoptic conditions accompanied by a variety of weather phenomena such as thunderstorms, onset and withdrawal of monsoons, etc. control the ABL over Ranchi, while daytime solar insolation and nighttime radiative cooling mainly control the ABL over Anand.     

Modeling daily soil temperature using data-driven models and spatial distribution

The objective of this study is to develop data-driven models, including multilayer perceptron (MLP) and adaptive neuro–fuzzy inference system (ANFIS), for estimating daily soil temperature at Champaign and Springfield stations in Illinois. The best input combinations (one, two, and three inputs) can be identified using MLP. The ANFIS is used to estimate daily soil temperature using the best input combinations (one, two, and three inputs). From the performance evaluation and scatter diagrams of MLP and ANFIS models, MLP 3 produces the best results for both stations at different depths (10 and 20 cm), and ANFIS 3 produces the best results for both stations at two different depths except for Champaign station at the 20 cm depth. Results of MLP are better than those of ANFIS for both stations at different depths. The MLP-based spatial distribution is used to estimate daily soil temperature using the best input combinations (one, two, and three inputs) at different depths below the ground. The MLP-based spatial distribution estimates daily soil temperature with high accuracy, but the results of MLP and ANFIS are better than those of the MLP-based spatial distribution for both stations at different depths. Data-driven models can estimate daily soil temperature successfully in this study.     

Disparity in the characteristic of thunderstorms and associated lightning activities over dissimilar terrains

Thunderstorms and associated lightning flash activities are studied over two different locations in India with different terrain features. Lightning imaging sensor (LIS) data from 1998 to 2008 are analyzed during the pre-monsoon months (March, April and May). The eastern sector is designated as Sector A that represents a 2° × 2° square area enclosing Kolkata (22.65°N, 88.45°E) at the centre and covering Gangetic West Bengal, parts of Bihar and Orissa whereas the north-eastern sector designated as Sector B that also represents a 2° × 2° square area encircling Guwahati (26.10°N, 91.58°E) at the centre and covering Assam and foot hills of Himalaya of India. The stations Kolkata and Guwahati are selected for the present study from Sector A and Sector B, respectively, as these are the only stations over the selected areas having Radiosonde observatory. The result of the present study reveals that the characteristics of thunderstorms over the two locations are remarkably different. Lightning frequency is observed to be higher in Sector B than Sector A. The result further reveals that though the lightning frequency is less in Sector A, but the associated radiance is higher in Sector A than Sector B. It is also observed that the radiance increases linearly with convective available potential energy (CAPE) and their high correlation reveals that the lightning intensity can be estimated through the CAPE values. The sensitivity of lightning activity to CAPE is higher at the elevated station Guwahati (elevation 54 m) than Kolkata (elevation 6 m). Moderate resolution imaging spectrometer (MODIS) data products are used to obtain aerosol optical depth and cloud top temperature and employed to find their responses on lightning radiance.     

Role of intra-seasonal oscillations in modulating Indian summer monsoon rainfall

The day-to-day behavior of Indian summer monsoon rainfall (IMR) is associated with a hierarchy of quasi-periods, namely 3?C7, 10?C20 and the 30?C60?days. These two periods, the 10?C20?days and the 30?C60?days have been related with the active and break cycles of the monsoon rainfall over the Indian sub-continent. The seasonal strength of Indian summer monsoon rainfall may depend on the frequency and duration of spells of break and active periods associated with the fluctuations of the above intra-seasonal oscillations (ISOs). Thus the predictability of the seasonal (June through September) mean Indian monsoon depends on the extent to which the intra-seasonal oscillations could be predicted. The primary objective of this study is to bring out the dynamic circulation features during the pre-monsoon/monsoon season associated with the extreme phases of these oscillations The intense (weak) phase of the 10?C20 (30?C60) days oscillation is associated with anti-cyclonic circulation over the Indian Ocean, easterly flow over the equatorial Pacific Ocean resembling the normal or cold phase (La Nina) of El Nino Southern Oscillation (ENSO) phenomenon, and weakening of the north Pacific Sub-tropical High. On the other hand the weak phase of 10?C20?days mode and the intense phase of 30?C60?days mode shows remarkable opposite flow patterns. The circulation features during pre-monsoon months show that there is a tendency for the flow patterns observed in pre-monsoon months to persist during the monsoon months. Hence some indications of the behavior of these modes during the monsoon season could be foreshadowed from the spring season patterns. The relationship between the intensity of these modes and some of the long-range forecasting parameters used operationally by the India Meteorological Department has also been examined.     

Surface ozone concentrations in Agra: links with the prevailing meteorological parameters

Measurements of surface ozone (O₃), nitric oxide (NO), nitrogen dioxide (NO₂), oxides of nitrogen (NO_x=NO+NO₂) and meteorological parameters have been made at Agra (North Central India, 27°10??N, 78°05??E) in post monsoon and winter season. The diurnal variation in O₃ concentration shows daytime in situ photochemical production with diurnal maximum in noon hours ranging from 51 to 54 ppb in post monsoon and from 76 to 82 ppb in winter, while minimum (16?C24 ppb) during nighttime and early morning hours. Average 8-h O₃ concentration varied from 12.4 to 83.9 ppb. The relationship between meteorological parameters (solar radiation intensity, temperature, relative humidity, wind speed and wind direction) and surface O₃ variability was studied using principal component analysis (PCA), multiple linear regression (MLR) and correlation analysis (CA). PCA and MLR of daily mean O₃ concentrations on meteorological parameters explain up to 80 % of day to day ozone variability. Correlation with meteorology is strongly emphasized on days having strong solar radiation intensity and longer sunshine time.