scholarly journals Exceptional heavy rainfall over Ajoy, Mayurakshi and Kansabati catchments and QPF verification during flood season of September 2009

MAUSAM ◽  
2022 ◽  
Vol 63 (3) ◽  
pp. 479-488
Author(s):  
SOUMENDU SENGUPTA ◽  
B.K. MANDAL ◽  
D. PRADHAN
Keyword(s):  
Heavy Rainfall ◽  
West Bengal ◽  
Precipitation Forecast ◽  
Flood Season ◽  
Quantitative Precipitation Forecast ◽  
Damodar Valley ◽  
Average Precipitation ◽  
Synoptic Features ◽  
River Catchments

Ajoy, Mayurakshi, Kansabati are three important river catchments of West Bengal and Jharkhand state, received very heavy rainfall during two consecutive days of flood season in the month of September 2009. The contribution of heavy rainfall & combined discharges from Damodar Valley Corporation (DVC) reservoirs during the period of heavy rainspells over these catchments enhanced flood situation in some districts of West Bengal. The synoptic features based on weather charts, cloud imageries of satellite and radar pictures have been taken to analyse. The realized areal average precipitation (AAP) as per rainfall recorded at 0300 UTC of next day have also been taken to verify the quantitative precipitation forecast (QPF) of 6&7 September 2009.

scholarly journals A Synoptic analogue model to issue QPF over Gangetic West Bengal and adjoining Jharkhand

MAUSAM ◽  
2021 ◽  
Vol 69 (2) ◽  
pp. 297-308
Author(s):  
S. CHATTOPADHYAY ◽  
S. SENGUPTA
Keyword(s):  
West Bengal ◽  
Monsoon Season ◽  
Low Pressure ◽  
Precipitation Forecast ◽  
Analogue Model ◽  
Cyclonic Storm ◽  
Damodar Valley ◽  
Synoptic Features ◽  
Pressure Area ◽  
River Catchments

 In this study the Areal Average Precipitation (AAP) data for each day over each of the six catchments of Gangetic West Bengal (GWB) and adjoining Jharkhand namely river catchments of Mayurakhshi, Ajoy, Kansabati, Damodar, Barakar and Lower Valley of Damodar Valley Corporation during monsoon season for  25  years from  1990 to 2014 have been analyzed by grouping the AAP in three different ranges (11-25 mm, 26-50 mm, 51-100 mm and more), excluding Mainly Dry and 01-10 mm. The associated main synoptic features viz., trough at mean sea level, low pressure area, well marked low pressure area, cyclonic storm and cyclonic circulation for each day and their location with respect to the river catchments, viz., over the catchment, neighbourhood of the catchment (within 200 km South or North) and outside the catchment (more than 200 km South or North) have also been studied. The association of AAP ranges over six catchments with different categories of synoptic features has been examined. The distribution of percentage frequency of AAPs associated with the category of synoptic feature for the period 1990 to 2014 has led to development of a Synoptic Analogue Model (SAM) for issue of Quantitative Precipitation Forecast (QPF). The results obtained from SAM have been verified for rainfall data and calculated AAPs of monsoon season of 2015 over all the catchments and different skills scores also presented in this study.  

scholarly journals The Development of a Quantitative Precipitation Forecast Correction Technique Based on Machine Learning for Hydrological Applications

Atmosphere ◽  
2020 ◽  
Vol 11 (1) ◽  
pp. 111 ◽  
Author(s):  
Chul-Min Ko ◽  
Yeong Yun Jeong ◽  
Young-Mi Lee ◽  
Byung-Sik Kim
Keyword(s):  
Machine Learning ◽  
Heavy Rainfall ◽  
Extreme Rainfall ◽  
Machine Learning Techniques ◽  
Precipitation Forecast ◽  
Machine Learning Technique ◽  
Rainfall Forecast ◽  
Quantitative Precipitation Forecast ◽  
Correction Technique ◽  
Learning Technique

This study aimed to enhance the accuracy of extreme rainfall forecast, using a machine learning technique for forecasting hydrological impact. In this study, machine learning with XGBoost technique was applied for correcting the quantitative precipitation forecast (QPF) provided by the Korea Meteorological Administration (KMA) to develop a hydrological quantitative precipitation forecast (HQPF) for flood inundation modeling. The performance of machine learning techniques for HQPF production was evaluated with a focus on two cases: one for heavy rainfall events in Seoul and the other for heavy rainfall accompanied by Typhoon Kong-rey (1825). This study calculated the well-known statistical metrics to compare the error derived from QPF-based rainfall and HQPF-based rainfall against the observational data from the four sites. For the heavy rainfall case in Seoul, the mean absolute errors (MAE) of the four sites, i.e., Nowon, Jungnang, Dobong, and Gangnam, were 18.6 mm/3 h, 19.4 mm/3 h, 48.7 mm/3 h, and 19.1 mm/3 h for QPF and 13.6 mm/3 h, 14.2 mm/3 h, 33.3 mm/3 h, and 12.0 mm/3 h for HQPF, respectively. These results clearly indicate that the machine learning technique is able to improve the forecasting performance for localized rainfall. In addition, the HQPF-based rainfall shows better performance in capturing the peak rainfall amount and spatial pattern. Therefore, it is considered that the HQPF can be helpful to improve the accuracy of intense rainfall forecast, which is subsequently beneficial for forecasting floods and their hydrological impacts.

scholarly journals Using Betts-Miller-Janjic convective parameterization scheme in H14-31 model to forecast heavy rainfall in Vietnam

2007 ◽  
Vol 29 (2) ◽  
pp. 83-97
Author(s):  
Vu Thanh Hang ◽  
Kieu Thi Xin
Keyword(s):  
Heavy Rainfall ◽  
Precipitation Forecast ◽  
Parameterization Scheme ◽  
Model Resolution ◽  
Convective Parameterization ◽  
Rainfall Forecast ◽  
Quantitative Precipitation Forecast ◽  
Convective Adjustment ◽  
Convective Parameterization Scheme ◽  
The Tropics

According to Krishnamurti, improvements of physical parameterizations will mainly affect simulations for the tropics [10]. The study of William A. Gallus Jr. showed that the higher the model resolution and more detailed convective parameterizations, the better the skill in quantitative precipitation forecast (QPF) in general [16]. The quality of precipitation forecast is so sensitive to convective parameterization scheme (CPS) used in the model as well as model resolution. The fact shows that for high resolution regional model like H14-31 CPS based on low-level moisture convergence as Tiedtke did not give good heavy rainfall forecast in Vietnam. In this paper we used the scheme of Betts-Miller-Janjic (BMJ) based on the convective adjustment toward tropical observationally structures in reality instead of Tiedtke in Hl4-31. Statistical verification results and verification using CRA method of Hl4-31 of two CPSs for seperated cases and for three rain seasons (2003-2005) shows that heavy rainfall forecast of Hl4-31/BMJ is better than one of H14-31/TK for Vietnam-South China Sea. CRA verification also shows that it is possible to say that heavy rainfall forecast skill of l-I14-31/BMJ in tropics is nearly similar to the skill of LAPS of Australia.

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 An Improved Weighting Method of Time-Lag-Ensemble Averaging for Hourly Precipitation Forecasts and Its Application in a Typhoon-Induced Heavy Rainfall Event

Atmosphere ◽  
2021 ◽  
Vol 12 (7) ◽  
pp. 875
Author(s):  
Li Zhou ◽  
Lin Xu ◽  
Mingcai Lan ◽  
Jingjing Chen
Keyword(s):  
Heavy Rainfall ◽  
Time Lag ◽  
Probability Of Detection ◽  
Precipitation Forecast ◽  
Ensemble Averaging ◽  
Weighting Method ◽  
Prediction Ability ◽  
Rainfall Events ◽  
Hourly Precipitation ◽  
Heavy Rainfall Events

Heavy rainfall events often cause great societal and economic impacts. The prediction ability of traditional extrapolation techniques decreases rapidly with the increase in the lead time. Moreover, deficiencies of high-resolution numerical models and high-frequency data assimilation will increase the prediction uncertainty. To address these shortcomings, based on the hourly precipitation prediction of Global/Regional Assimilation and Prediction System-Cycle of Hourly Assimilation and Forecast (GRAPES-CHAF) and Shanghai Meteorological Service-WRF ADAS Rapid Refresh System (SMS-WARR), we present an improved weighting method of time-lag-ensemble averaging for hourly precipitation forecast which gives more weight to heavy rainfall and can quickly select the optimal ensemble members for forecasting. In addition, by using the cross-magnitude weight (CMW) method, mean absolute error (MAE), root mean square error (RMSE) and correlation coefficient (CC), the verification results of hourly precipitation forecast for next six hours in Hunan Province during the 2019 typhoon Bailu case and heavy rainfall events from April to September in 2020 show that the revised forecast method can more accurately capture the characteristics of the hourly short-range precipitation forecast and improve the forecast accuracy and the probability of detection of heavy rainfall.

Operational Precipitation Forecast Over China Using the Weather Research and Forecasting (WRF) Model at a Gray-Zone Resolution: Impact of Convection Parameterization

2021 ◽  
Author(s):  
XU ZHANG ◽  
YUHUA YANG ◽  
BAODE CHEN ◽  
WEI HUANG
Keyword(s):  
Case Studies ◽  
Southern China ◽  
Wrf Model ◽  
Warm Season ◽  
Precipitation Forecast ◽  
Weather Research And Forecasting ◽  
Operational System ◽  
Quantitative Precipitation Forecast ◽  
Geographical Regions ◽  
Convection Parameterization

AbstractThe quantitative precipitation forecast in the 9 km operational modeling system (without the use of a convection parameterization scheme) at the Shanghai Meteorological Service (SMS) usually suffers from excessive precipitation at the grid scale and less-structured precipitation patterns. Two scale-aware convection parameterizations were tested in the operational system to mitigate these deficiencies. Their impacts on the warm-season precipitation forecast over China were analyzed in case studies and two-month retrospective forecasts. The results from case studies show that the importance of convection parameterization depends on geographical regions and weather regimes. Considering a proper magnitude of parameterized convection can produce more realistic precipitation distribution and reduce excessive grid-scale precipitation in southern China. In the northeast and southwest China, however, the convection parameterization plays an insignificant role in precipitation forecast because of strong synoptic-scale forcing. A statistical evaluation of the two-month retrospective forecasts indicates that the forecast skill for precipitation in the 9-km operational system is improved by choosing proper convection parameterization. This study suggests that improvement in contemporary convection parameterizations is needed for their usage for various meteorological conditions and reasonable partitioning between parameterized and resolved convection.

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