scholarly journals QPF model for Sabarmati basin based on Synoptic analogue method

MAUSAM ◽  
2021 ◽  
Vol 63 (4) ◽  
pp. 565-572
Author(s):  
KAMALJIT RAY ◽  
B.N. JOSHI ◽  
I.M. VASOYA ◽  
N.S. DARJI ◽  
L.A. GANDHI
Keyword(s):  
Southwest Monsoon ◽  
Precipitation Forecast ◽  
Two Stage ◽  
Monsoon Period ◽  
Analogue Model ◽  
Quantitative Precipitation Forecast ◽  
Analogue Method ◽  
Areal Precipitation ◽  
Air Circulation ◽  
Percentage Correct

The paper formulates a synoptic analogue model for issuing Quantitative Precipitation Forecast (QPF) for Sabarmati basin based on 10 years data (2000-2009) during southwest monsoon period. The model was verified with the actual Average Areal Precipitation (AAP) for the corresponding synoptic situations during 2010.The performance of the model were observed Percentage Correct (PC) up to 71%. The cases out by one or two stage were due to variation in the intensity of the system especially upper air circulation (S3) over the basin. The synoptic analogue model was able to generate accurate QPF 24 hrs in advance to facilitate flood forecasters of Central Water Commission.

scholarly journals QPF Model for lower Yamuna catchment, synoptic analogue method

MAUSAM ◽  
2021 ◽  
Vol 62 (1) ◽  
pp. 27-40
Author(s):  
MEHFOOZ ALI ◽  
U. P. SINGH ◽  
D. JOARDAR
Keyword(s):  
Monsoon Season ◽  
Skill Score ◽  
Southwest Monsoon ◽  
Precipitation Forecast ◽  
Synoptic Situation ◽  
Analogue Model ◽  
Quantitative Precipitation Forecast ◽  
South West Monsoon ◽  
Heidke Skill Score ◽  
Southwest Monsoon Season

The paper formulates a synoptic analogue model for issuing Quantitative Precipitation Forecast (QPF) for Lower Yamuna Catchment (LYC) based upon eleven years data (1998-2008) during southwest monsoon season. The results so derived were verified with realized Average Areal Precipitation (AAP) for the corresponding synoptic situation during 2009 southwest monsoon season. The performance of the model was observed Percentage Correct (PC) up to 86 % and for extreme events showed 100% correct with Heidke Skill Score (HSS) value 0.9. The experience during south west monsoon 2009 has shown that Synoptic analogue model can produce 24 hours advance QPF with accuracy and greater skill to facilitate the flood forecasters of Central Water Commission.

scholarly journals A synoptic analogue model for QPF of river Sabarmati basin

MAUSAM ◽  
2021 ◽  
Vol 49 (4) ◽  
pp. 499-502
Author(s):  
Dr. (Mrs.) KAMALJIT RAY ◽  
M. L. SAHU
Keyword(s):  
Precipitation Forecast ◽  
Analogue Model ◽  
Quantitative Precipitation Forecast ◽  
Analogue Method

An attempt has been made to prepare a model for issuing semi quantitative precipitation forecast for river Sabarmati by synoptic analogue method. The model is based on 10 years (1986- 95) of data. The QPF issued by the model is verified with the WAR of years 1995 and 1996. The performance of model was good. This model can be used confidently for issue of QPF for Sabarmati basin.

scholarly journals QUANTITATIVE PRECIPITATION FORECAST FOR MAHI BASIN BASED ON SYNOPTIC ANALOGUE METHOD

MAUSAM ◽  
2021 ◽  
Vol 65 (1) ◽  
pp. 118-123
Author(s):  
KAMALJIT RAY ◽  
B.N. JOSHI ◽  
I.M. VASOYA ◽  
J.R. CHICHOLIKAR
Keyword(s):  
Precipitation Forecast ◽  
Quantitative Precipitation Forecast ◽  
Analogue Method

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 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.

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