Forecasting precipitation over Delhi during the south‐west monsoon season

Mohanty, U. C.; Ravi, N.; Madan, O. P.

doi:10.1017/s1350482701001025

Cited by 9 publications

(6 citation statements)

References 16 publications

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“…Kruzinga (1989) has compared forecasting of probability of precipitation (POP) over the Netherlands using an analogue technique and logistic regression: the regression technique performed better than the analogue technique. Mohanty et al (2001) developed an objective technique to forecast POP over Delhi and provide the quantitative precipitation forecast (QPF) using a classical multivariate regression technique. Mohanty and Mohapatra (2007) have developed a statistical model for POP based on forward stepwise regression for Orissa State.…”

Section: Introductionmentioning

confidence: 99%

Prediction of occurrence of daily summer monsoon precipitation over Karnataka

Agnihotri

Mohapatra

2011

Meteorological Applications

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Karnataka, a State in south peninsular India, receives 73% of its annual rainfall during the southwest monsoon season. Because of the complex physiographical features, the rainfall pattern over the State shows large spatial variation from 50 to 350 cm. The co-efficient of interannual variation of the monsoon rainfall is about 15% over coastal Karnataka (CK) and between 20 and 30% over interior Karnataka. It is, therefore, a difficult task to predict the location specific daily rainfall over Karnataka. In this study, an attempt has been made to develop an objective tool for forecasting the occurrence and non-occurrence of precipitation during a 24 h period for the 19 stations in Karnataka during the monsoon season. The probability of precipitation (POP) model is developed using forward stepwise regression with the available surface and upper air parameters from synoptic and radiosonde and radio wind stations in and around Karnataka as potential predictors. The POP model has been developed based on the data from 1981 to 1996 and verified with the data from 1997 to 2002. Different skill scores are computed using a yes/no contingency table. The POP model performs very well, with percentages of correct (PC) forecasts for occurrence/non-occurrence of precipitation being 57-91% for the independent data. Comparing the results of the POP model with that of the conventional method of forecast for Bangalore City, the PC forecasts improves from 44 to 56% with the use of the POP model.

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Section: Introductionmentioning

confidence: 99%

Prediction of occurrence of daily summer monsoon precipitation over Karnataka

Agnihotri

Mohapatra

2011

Meteorological Applications

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“…Les précipitations sont un élément majeur ayant une influence sur les diverses activités humaines et surtout sur l'agriculture (Mohanty, et al, 2001). Cela pose plus de problème que d'autres éléments météorologiques comme la température, autrement dit, afin d'étudier le concept de variabilité comme la manière dans laquelle les éléments climatiques varient dans un délai donné des séries (Cuadrat e Pita, 1997;IPCC, 1995;Sant'anna Neto, 2003), les pluies sont les modèles avec le moins de fonctionnalité.…”

Section: I F E R E N ç a S C L I M áT I C A S O B S E Rva D A S P Eunclassified

Différences climatiques observées pour l’étude des précipitations et de la production de soja dans deux régions au Brésil

Carmello¹,

Geografia²,

Neto³

2018

Estudos Cindínicos

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“…This is a synoptic-analogue method in which synoptic systems are classified according to the observed rainfall rates in the ranges 11-25, 26-50 and >50 mm. Mohanty et al (2001) developed objective methods to forecast PoP and QPF at Delhi using classical multivariate regression and discriminant analysis. Maini et al (2002) employed the perfect prognostic method for precipitation and temperature forecasts during monsoon season.…”

Section: Introductionmentioning

confidence: 99%

Precipitation forecast over western Himalayas using k‐nearest neighbour method

Dimri

Joshi

Ganju

2008

Intl Journal of Climatology

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Knowledge of precipitation over the western Himalayas is of utmost importance during winter season (December, January, February, and March -DJFM) due to extreme weather conditions. During winter, eastward moving synoptic weather systems called western disturbances (WDs) yield enormous amount of precipitation over this region. This amount of precipitation by a number of WDs keeps on accumulating and poses an avalanche threat to the habitat. Not only this, low temperature conditions along with precipitation form extremely hostile winter conditions to live with. In the present study, the nearest neighbour (NN) method is used to forecast probability of precipitation (PoP) occurrence/nonoccurrence and its quantity. The method of NN is introduced to visualize the results. The NN forecast technique attempts to compare similar situations in the past with current data and assumes that similar events are likely to occur under similar conditions. At present, only nine important weather variables are considered for generating a 3-day advance forecast of PoP occurrence/non-occurrence and quantity at eight representative observatories in Jammu and Kashmir (J&K), the northmost state of India. This state receives maximum amount of solid precipitation in the form of snow during winter. Past data of 8-14 years (between 1988 and 2003) is considered, from which the nearest days are looked for and tested with data of 2-4 years (between 2003 and 2005). PoP occurrence/non-occurrence is well predicted by k-Nearest Neighbour (k-NN) sampling method. It is evident from the results that the model is able to give the projections 3 days in advance, with an accuracy of 71-88%. Probability forecast of occurrence of precipitation is predicted well by the present model setup, but the quantitative precipitation forecast (QPF) is an important issue that still needs improvement, keeping in view the topographical and land-use heterogeneity of the area under study.

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Forecasting precipitation over Delhi during the south‐west monsoon season

Abstract: The south-west monsoon (June-September)

Cited by 9 publications

References 16 publications

Prediction of occurrence of daily summer monsoon precipitation over Karnataka

Prediction of occurrence of daily summer monsoon precipitation over Karnataka

Différences climatiques observées pour l’étude des précipitations et de la production de soja dans deux régions au Brésil

Precipitation forecast over western Himalayas using k‐nearest neighbour method

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