CCN characteristics over a tropical coastal station during south-west monsoon: observations and closure studies

2019 ◽

Vol 8 (3) ◽

pp. 4460-4465

Keyword(s):

Weather Prediction ◽

Rule Mining ◽

Local Data ◽

Coastal Station ◽

North East ◽

South West ◽

South West Monsoon ◽

Using Data ◽

West Monsoon ◽

Regional Forecast

There is a growing demand for spot specific forecast. Presently this has to be extracted from the regional forecast based on synoptic models. Synoptic models require input from various observatories of regions or the country and the central analysis centre is required for generating the synoptic charts. But recently the authors have established the potential of local data alone as a continuous time scale for use in effective local forecast using data mining techniques. Following the same association rule mining and classifier approach is tried for the forecast of wet and dog days on North East Monsoon and South West Monsoon months for the Chennai region with Latitude 13°11' N and Longitude 80°11' E, a coastal station over Bay of Bengal in South India and results are presented.

Download Full-text

Forecasting of Summer Monsoon Rainfall over Gangetic West Bengal, India Utilising Intrinsic Mode Functions, Linear and Neural Regression

Journal of Modeling and Optimization ◽

10.32732/jmo.2020.12.1.60 ◽

2020 ◽

Vol 12 (1) ◽

pp. 60-69 ◽

Cited By ~ 1

Author(s):

Pijush Basak

Keyword(s):

Neural Network ◽

West Bengal ◽

Monsoon Rainfall ◽

Linear Part ◽

Intrinsic Mode Functions ◽

Quasi Biennial Oscillation ◽

Nonlinear Part ◽

South West ◽

South West Monsoon ◽

West Monsoon

The South West Monsoon rainfall data of the meteorological subdivision number 6 of India enclosing Gangetic West Bengal is shown to be decomposable into eight empirical time series, namely Intrinsic Mode Functions. This leads one to identify the first empirical mode as a nonlinear part and the remaining modes as the linear part of the data. The nonlinear part is modeled with the technique Neural Network based Generalized Regression Neural Network model technique whereas the linear part is sensibly modeled through simple regression method. The different Intrinsic modes as verified are well connected with relevant atmospheric features, namely, El Nino, Quasi-biennial Oscillation, Sunspot cycle and others. It is observed that the proposed model explains around 75% of inter annual variability (IAV) of the rainfall series of Gangetic West Bengal. The model is efficient in statistical forecasting of South West Monsoon rainfall in the region as verified from independent part of the real data. The statistical forecasts of SWM rainfall for GWB for the years 2012 and 2013 are108.71 cm and 126.21 cm respectively, where as corresponding to the actual rainfall of 93.19 cm 115.20 cm respectively which are within one standard deviation of mean rainfall.

Download Full-text

Recurrent landslides in Southern Western Ghats, India: A changing environmental perspective

10.5194/egusphere-egu21-12601 ◽

2021 ◽

Author(s):

Asokan Laila Achu ◽

Girish Gopinath

Keyword(s):

Western Ghats ◽

Hot Spot ◽

Extreme Rainfall ◽

Vital Role ◽

Monsoon Period ◽

Extreme Rainfall Events ◽

South West ◽

South West Monsoon ◽

West Monsoon ◽

The Western Ghats

<p>The Western Ghats (WG), an elevated passive continental margin along the southwestern coast of India, is the most widely populated biodiversity hot spot in the world. Monsoon climate is prevalent throughout the length of the Western Ghats. The WG region is prone to the occurrence of various hydro-climatic disasters such as extreme rainfall-driven floods and landslides. During the past 100 years, landslides and floods caused by extreme rainfall events in the WG have occurred in 1924 and 1979; but the most disastrous event, in terms of area of impact, loss of life and economic impact, occurred in August 2018. Generally, the south-west monsoon (Indian summer monsoon) occurs in the first week of June and extends up to September and the Indian Meteorological Department (IMD) predicted above-normal rainfall of 13% during the month of August 2018. But the State received an excess of 96% during the period from 1st to 30th August 2018, and 33% during the entire monsoon period till the end of August. The unprecedented heavy rains, storms, floods and associated thousands of landslides have caused exorbitant losses including 400 life losses, over 2.20 lakh people were displaced, and 20000 homes and 80 dams were damaged or destructed. This study aimed to elucidate the reasons behind the thousands of landslides caused in WG using observed and field evidences. Changes in south-west monsoon pattern and rainfall intensity played a vital role in the occurrence of landslides in WG. Further, the extensive causalities are the result of anthropogenic disturbances including landscape alterations and improper landuse practices in the hilly tracks of WG. The major causative factors for series of landslides in various segments of WG is due to hindrance of lower order streams/springs, vertical cutting, intensive quarrying, unscientific rain pits & man-made structures together with erratic rainfall triggered major and minor landslides in various segments of WG. The present investigation concludes that a scientific landuse policy and geoscientific awareness is essential to mitigate the environment.</p>

Download Full-text