Western Disturbances – Structure

Dimri, A. P.; Chevuturi, Amulya

doi:10.1007/978-3-319-26737-1_1

Cited by 8 publications

(4 citation statements)

References 75 publications

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“…The MGP records average annual rainfall on the order of 100-120 cm, three-quarters of which is downpoured within 4 months long monsoon season (Trivedi et al, 2019). The influence of western disturbances (WDs) on Indian monsoonal rainfall is well-documented in the form of sporadic rains and hailstorms during the southward migration of intertropical convergence in winter months (Dimri and Chevuturi, 2016). The seasonal variability in the Ganga River discharge has led hydrologists to term river discharge of Indian River Network systems associated with monsoon systems such as monsoonal discharge, post-monsoonal discharge, summer or winter monsoon discharge (Gupta, 1984).…”

Section: Study Areamentioning

confidence: 99%

Flood Susceptibility Modeling in a Subtropical Humid Low-Relief Alluvial Plain Environment: Application of Novel Ensemble Machine Learning Approach

et al. 2021

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This study has developed a new ensemble model and tested another ensemble model for flood susceptibility mapping in the Middle Ganga Plain (MGP). The results of these two models have been quantitatively compared for performance analysis in zoning flood susceptible areas of low altitudinal range, humid subtropical fluvial floodplain environment of the Middle Ganga Plain (MGP). This part of the MGP, which is in the central Ganga River Basin (GRB), is experiencing worse floods in the changing climatic scenario causing an increased level of loss of life and property. The MGP experiencing monsoonal subtropical humid climate, active tectonics induced ground subsidence, increasing population, and shifting landuse/landcover trends and pattern, is the best natural laboratory to test all the susceptibility prediction genre of models to achieve the choice of best performing model with the constant number of input parameters for this type of topoclimatic environmental setting. This will help in achieving the goal of model universality, i.e., finding out the best performing susceptibility prediction model for this type of topoclimatic setting with the similar number and type of input variables. Based on the highly accurate flood inventory and using 12 flood predictors (FPs) (selected using field experience of the study area and literature survey), two machine learning (ML) ensemble models developed by bagging frequency ratio (FR) and evidential belief function (EBF) with classification and regression tree (CART), CART-FR and CART-EBF, were applied for flood susceptibility zonation mapping. Flood and non-flood points randomly generated using flood inventory have been apportioned in 70:30 ratio for training and validation of the ensembles. Based on the evaluation performance using threshold-independent evaluation statistic, area under receiver operating characteristic (AUROC) curve, 14 threshold-dependent evaluation metrices, and seed cell area index (SCAI) meant for assessing different aspects of ensembles, the study suggests that CART-EBF (AUCSR = 0.843; AUCPR = 0.819) was a better performant than CART-FR (AUCSR = 0.828; AUCPR = 0.802). The variability in performances of these novel-advanced ensembles and their comparison with results of other published models espouse the need of testing these as well as other genres of susceptibility models in other topoclimatic environments also. Results of this study are important for natural hazard managers and can be used to compute the damages through risk analysis.

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Section: Study Areamentioning

confidence: 99%

Flood Susceptibility Modeling in a Subtropical Humid Low-Relief Alluvial Plain Environment: Application of Novel Ensemble Machine Learning Approach

et al. 2021

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“…The regions of the Indo‐Gangetic Plains, the world's most densely populated area, have a typical geographical structure, ample agricultural lands, and substantial anthropogenic emissions, which contribute to fog formation (Badarinath et al ., 2007; Goswami & Tyagi, 2007). Several researchers (Dimri & Chevuturi, 2016; Gunturu & Kumar, 2021; Parde et al ., 2022a; Pithani et al ., 2019, 2020) have shown that stability, moisture availability, low‐level inversions, soil states, and other synoptic and mesoscale features strongly affect fog formation. Despite significant progress in understanding fog, it remains a grand challenge (Pithani et al ., 2020; Román‐Cascón et al ., 2016; Steeneveld et al ., 2014; Wilkinson et al ., 2013) to predict fog using numerical weather prediction (NWP) models.…”

Section: Introductionmentioning

confidence: 99%

Is a more physical representation of aerosol chemistry needed for fog forecasting?

Bhowmik,

Hazra,

Ghude

et al. 2024

Quart J Royal Meteoro Soc

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With the changing climate, the study of fog formation is essential due to the impact of the complexity of natural and anthropogenic aerosols. The evolution of the droplet size distribution in the presence of different aerosol species remains poorly understood. To make progress towards reducing the uncertainty of fog forecasts, the Eulerian–Lagrangian particle‐based small‐scale model for the diffusional growth of droplets is used to better understand the droplet activation and growth. The small‐scale model simulations are performed using observed data from the Winter Fog Experiment study over Indira Gandhi International Airport, New Delhi. The microphysical properties, such as droplet number concentrations (Nd) and liquid water content (LWC), important for fog simulation, are evaluated to gain more insights. The small‐scale simulations have shown the droplet microphysical properties at different evolutionary stages. The Nd and effective radius change with variations in LWC for different aerosol chemistries (i.e., organics, mix, and inorganic). The calculated visibility at small scale is also shown with the variation of Nd and LWC. This study compared visibility from an existing parametrization with parcel–direct numerical simulation calculation. The hygroscopicity , which is highly related to the activation of aerosols to condensation nuclei, is taken into account to demonstrate the contribution of aerosol chemistry to fog droplet formation. The results highlight that hygroscopicity is essential in the numerical model for fog and visibility prediction as the microphysical properties of fog are regulated by aerosol species.

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“…Both radiation and advection fog frequently occur over relatively flat areas like Bangladesh. Radiation fog also forms in the rear sector of a western disturbance while advection fog develops in the forward sector of the western disturbance (Dimir et al, 2015). The persistence and intensification of foggy conditions are also driven by the high concentration of pollutants and abundant moisture supply.…”

Section: Introductionmentioning

confidence: 99%

Simulation of Radiation Fog Events over Dhaka, Bangladesh Using WRF Model and Validation with METAR and Radiosonde Data

Mehrin

Khan²,

Mannan³

et al. 2021

Dhaka Univ J Earth Env Sci

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Fog causes severe hazards in the fields of aviation, transportation, agriculture and public health over Dhaka, Bangladesh during the winter season every year. The characterization of fog occurrences, its onset, duration and dissipation time over Hazrat Shahjalal International Airport, Dhaka are the topics of interest in the present study. Attempts have therefore been made to investigate the climatological perspectives of fog over Dhaka, Bangladesh by conducting two selected dense fog events occurred during 24-25 December 2019 and 14-15 January 2020 using WRF-ARW model. The model performance is evaluated by analyzing different meteorological parameters namely visibility, relative humidity, temperature, and wind. The model outputs have been compared with METAR data from Dhaka Airport, Sounding data and INSAT 3D satellite images for validation purpose. Considering RMSE, the model underestimates of relative humidity. Model simulations are good for other meteorological parameters. Thermodynamic analysis reveals that calm wind persists at surface level during fog formation, southwesterly dry wind was over Dhaka and inversion layer is found to persist in the lower troposphere over Dhaka during the event dates. It is observed in the satellite images that fog/low-level cloud was present over Dhaka during the fog events. The Dhaka University Journal of Earth and Environmental Sciences, Vol. 9(1), 2020, P 39-47

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Western Disturbances – Structure

Cited by 8 publications

References 75 publications

Flood Susceptibility Modeling in a Subtropical Humid Low-Relief Alluvial Plain Environment: Application of Novel Ensemble Machine Learning Approach

Flood Susceptibility Modeling in a Subtropical Humid Low-Relief Alluvial Plain Environment: Application of Novel Ensemble Machine Learning Approach

Is a more physical representation of aerosol chemistry needed for fog forecasting?

Simulation of Radiation Fog Events over Dhaka, Bangladesh Using WRF Model and Validation with METAR and Radiosonde Data

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