scholarly journals Semi quantitative forecasts for Baghmati/Adhwara Group of rivers/Kamala Balan catchments by synoptic analogue technique

MAUSAM ◽  
2021 ◽  
Vol 61 (3) ◽  
pp. 337-348
Author(s):  
K. M. SINGH ◽  
M. C. PRASAD ◽  
G. PRASAD
Keyword(s):  
Precipitation Forecast ◽  
Steep Gradient ◽  
South West ◽  
Quantitative Precipitation Forecast ◽  
South West Monsoon ◽  
Local Topography ◽  
Quantitative Precipitation Forecasts ◽  
Main Factors ◽  
West Monsoon

An attempt has been made to issue semi-quantitative precipitation forecasts for Baghmati/Adhwara Group of rivers/Kamala-Balan catchments based upon 22 years data (1982-2003). The study reveals that it is possible to issue semi-quantitative precipitation forecast with confidence. Local topography and its steep gradient on Indo-Nepal Border are main factors that give birth to severe floods during south west monsoon and pose problems to Darbhanga City.

scholarly journals Quantitative Precipitation Forecast (QPF) for Teesta basin and heavy rainfall warning over Teesta basin & adjoining areas in north Bengal & Sikkim using synoptic analog method

MAUSAM ◽  
2021 ◽  
Vol 60 (4) ◽  
pp. 491-504
Author(s):  
G. N. RAHA ◽  
K. BHATTACHARJEE ◽  
A. JOARDAR ◽  
R. MALLIK ◽  
M. DUTTA ◽  
...  
Keyword(s):  
Heavy Rainfall ◽  
Monsoon Season ◽  
Precipitation Forecast ◽  
South West ◽  
Quantitative Precipitation Forecast ◽  
South West Monsoon ◽  
Areal Precipitation ◽  
West Monsoon ◽  
North Bengal ◽  
Good Agreement

This article presents the method to issue Quantitative Precipitation Forecast (QPF) for Teesta catchment. A synoptic analog model has been developed analyzing 10 years (1998-2007) data for Teesta catchment. The outcomes are then validated with the realized Average Areal Precipitation (AAP) for the corresponding synoptic situations during south-west monsoon season 2008 (1st June to 30th September) over Teesta basin and results revealed that there exists a good agreement between day-to-day QPF with corresponding realized AAP calculated over this basin next day. In addition, occurrence of heavy rainfall has also been studied in this paper.

scholarly journals Semi-quantitative precipitation forecasts for Kosi/Mahananda catchment by synoptic analogue method

MAUSAM ◽  
2021 ◽  
Vol 61 (2) ◽  
pp. 175-186
Author(s):  
K. M. SINGH ◽  
M. C. PRASAD ◽  
G. PRASAD ◽  
R. PRASAD ◽  
M. K. JHA
Keyword(s):  
Lower Troposphere ◽  
Moist Air ◽  
Nepal Himalaya ◽  
Air Masses ◽  
Orographic Effects ◽  
South West Monsoon ◽  
Analogue Method ◽  
Local Topography ◽  
Quantitative Precipitation Forecasts ◽  
West Monsoon

An attempt has been made to issue semi-quantitative precipitation forecasts for Kosi/Mahananda catchment by synoptic analogue method. Based upon 22 years of data (1982 - 2003) the study reveals that it is possible to issue semi­-quantitative forecasts with confidence. Local topography of the catchments and its steep gradient from Bhim nagar to Chatra / Brahkshetra in Kosi and hills in Darjeeling are favourable regions where moist air masses of the Bay of Bengal and the Arabian Sea during South West Monsoon in lower troposphere converge and trough at 500 hPa especially diffluent in rear creates divergence and moist air mass is pulled up resulting in heavy / very heavy rainfall in sub montane districts of Bihar and Nepal Himalaya in addition to orographic effects. This gives birth to severe floods and makes the life of densely populated districts of  Pumea / Katihar / Saharsa / Kisanganj / Madhepura miserable and badly affects the economy of the region.

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

2020 ◽  
Vol 12 (1) ◽  
pp. 60-69 ◽  
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.

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

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>

scholarly journals The Theory of the South-West Monsoon

Nature ◽  
1922 ◽  
Vol 109 (2726) ◽  
pp. 109-112
Author(s):  
L. C. W. BONACINA
Keyword(s):  
The South ◽  
South West ◽  
South West Monsoon ◽  
West Monsoon

scholarly journals Strong CO2emissions from the Arabian Sea during south-west monsoon

1997 ◽  
Vol 24 (14) ◽  
pp. 1763-1766 ◽  
Author(s):  
Arne Körtzinger ◽  
Jan C. Duinker ◽  
Ludger Mintrop
Keyword(s):  
Arabian Sea ◽  
South West ◽  
South West Monsoon ◽  
West Monsoon

scholarly journals Ensemble Typhoon Quantitative Precipitation Forecasts Model in Taiwan

2015 ◽  
Vol 30 (1) ◽  
pp. 217-237 ◽  
Author(s):  
Jing-Shan Hong ◽  
Chin-Tzu Fong ◽  
Ling-Feng Hsiao ◽  
Yi-Chiang Yu ◽  
Chian-You Tzeng
Keyword(s):  
Model Performance ◽  
Precipitation Forecast ◽  
Ensemble Prediction ◽  
Model Bias ◽  
Ensemble Prediction System ◽  
Rainfall Forecast ◽  
Quantitative Precipitation Forecast ◽  
Quantitative Precipitation Forecasts ◽  
Typhoon Rainfall ◽  
Typhoon Tracks

Abstract In this study, an ensemble typhoon quantitative precipitation forecast (ETQPF) model was developed to provide typhoon rainfall forecasts for Taiwan. The ETQPF rainfall forecast is obtained by averaging the pick-out cases, which are screened using certain criterion based on given typhoon tracks from an ensemble prediction system (EPS). Therefore, the ETQPF model resembles a climatology model. However, the ETQPF model uses the quantitative precipitation forecasts (QPFs) from an EPS instead of historical rainfall observations. Two typhoon cases, Fanapi (2010) and Megi (2010), are used to evaluate the ETQPF model performance. The results show that the rainfall forecast from the ETQPF model, which is qualitatively compared and quantitatively verified, provides reasonable typhoon rainfall forecasts and is valuable for real-time operational applications. By applying the forecast track to the ETQPF model, better track forecasts lead to better ETQPF rainfall forecasts. Moreover, the ETQPF model provides the “scenario” of the typhoon QPFs according to the uncertainty of the forecast tracks. Such a scenario analysis can provide valuable information for risk assessment and decision making in disaster prevention and reduction. Deficiencies of the ETQPF model are also presented, including that the average over the pick-out case usually offsets the extremes and reduces the maximum ETQPF rainfall, the underprediction is especially noticeable for weak phase-locked rainfall systems, and the ETQPF rainfall error is related to the model bias. Therefore, reducing model bias is an important issue in further improving the ETQPF model performance.

Sign in / Sign up

Export Citation Format

Share Document