Spatial variability of daily rainfall over Orissa, India, during the southwest summer monsoon season

Mohapatra, M.; Mohanty, U. C.; Behera, Swadhin K.

doi:10.1002/joc.974

Cited by 31 publications

(13 citation statements)

References 27 publications

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“…These six stations represent six homogeneous regions of daily precipitation over Orissa during the summer monsoon season (Mohapatra et al, 2003). The empirical orthogonal function (EOF) analysis of daily rainfall over 31 uniformly distributed stations of Orissa during the summer monsoon season over the period of 1980-1999 by Mohapatra et al (2003) has resulted in five significant EOFs. It is found by Mohapatra et al (2003) that the rotation in temporal mode yields better regionalization.…”

Section: Methodsmentioning

confidence: 99%

“…The empirical orthogonal function (EOF) analysis of daily rainfall over 31 uniformly distributed stations of Orissa during the summer monsoon season over the period of 1980-1999 by Mohapatra et al (2003) has resulted in five significant EOFs. It is found by Mohapatra et al (2003) that the rotation in temporal mode yields better regionalization. Hence, considering the rotated score coefficients of these significant EOFs above a threshold value, six homogeneous regions, as shown in Figure 2, are obtained by them.…”

Section: Methodsmentioning

confidence: 99%

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Prediction of occurrence and quantity of daily summer monsoon precipitation over Orissa (India)

Mohanty¹,

Mohapatra²

2007

Meteorological Applications

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ABSTRACT:The precipitation over Orissa State, a meteorological subdivision on the east coast of India, shows large-scale spatio-temporal variation caused by the interaction of the basic monsoon flow with the monsoon disturbances over the Bay of Bengal and the orography owing to the Eastern Ghats and other hill peaks in Orissa and its neighbourhood. Hence, it is difficult to predict daily precipitation over Orissa. The objective of this study is to predict the occurrence and quantity of precipitation 24 h ahead, over specific locations of Orissa during the summer monsoon season (June-September). For this purpose, a probability of precipitation (PoP) model has been developed by applying stepwise regression with the available surface and upper air parameters from synoptic and radiosonde and radio wind stations in and around Orissa as potential predictors. The parameters selected through stepwise regression for the PoP model have been used to develop a probabilistic model for a Quantitative Precipitation Forecast (QPF) in different ranges, such as 0.1-10, 11-25, 26-50, 51-100 and >100 mm, using Multiple Discriminant Analysis (MDA). Both the PoP and QPF models have been developed based on data from 1980 to 1994 and verified with the data from 1995 to 1998.Considering six representative stations for six homogeneous regions in Orissa, the PoP model performs very well with percentages of correct forecast for occurrence/non-occurrence of precipitation being about 73 and 65% respectively for developmental and independent data. However, the skill score of the MDA model for categorical forecast is poor, especially for higher values of precipitation.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Prediction of occurrence and quantity of daily summer monsoon precipitation over Orissa (India)

Mohanty¹,

Mohapatra²

2007

Meteorological Applications

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“…A PoP model has been developed to predict occurrence/non-occurrence of precipitation and a probabilistic QPF model has been developed for prediction of precipitation in different ranges over six rain gauge stations ( Fig. 2a) representing six homogeneous regions of Orissa as obtained by MOHAPATRA et al (2003).…”

Section: Methodsmentioning

confidence: 99%

An Objective Approach for Prediction of Daily Summer Monsoon Rainfall over Orissa (India) due to Interaction of Mesoscale and Large-scale Synoptic Systems

Mohapatra¹,

Mohanty

2007

Pure appl. geophys.

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Orissa State, a meteorological subdivision of India, lies on the east coast of India close to north Bay of Bengal and to the south of the normal position of the monsoon trough. The monsoon disturbances such as depressions and cyclonic storms mostly develop to the north of 15°N over the Bay of Bengal and move along the monsoon trough. As Orissa lies in the southwest sector of such disturbances, it experiences very heavy rainfall due to the interaction of these systems with mesoscale convection sometimes leading to flood. The orography due to the Eastern Ghat and other hill peaks in Orissa and environs play a significant role in this interaction. The objective of this study is to develop an objective statistical model to predict the occurrence and quantity of precipitation during the next 24 hours over specific locations of Orissa, due to monsoon disturbances over north Bay and adjoining west central Bay of Bengal based on observations to up 0300 UTC of the day. A probability of precipitation (PoP) model has been developed by applying forward stepwise regression with available surface and upper air meteorological parameters observed in and around Orissa in association with monsoon disturbances during the summer monsoon season (June-September). The PoP forecast has been converted into the deterministic occurrence/nonoccurrence of precipitation forecast using the critical value of PoP. The parameters selected through stepwise regression have been considered to develop quantitative precipitation forecast (QPF) model using multiple discriminant analysis (MDA) for categorical prediction of precipitation in different ranges such as 0.1-10, 11-25, 26-50, 51-100 and >100 mm if the occurrence of precipitation is predicted by PoP model. All the above models have been developed based on data of summer monsoon seasons of 1980-1994, and data during 1995-1998 have been used for testing the skill of the models.Considering six representative stations for six homogeneous regions in Orissa, the PoP model performs very well with percentages of correct forecast for occurrence/non-occurrence of precipitation being about 96% and 88%, respectively for developmental and independent data. The skill of the QPF model, though relatively less, is reasonable for lower ranges of precipitation. The skill of the model is limited for higher ranges of precipitation.

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“…In the past, several studies conducted to analyze the rainfall trends over different regions of India (Mohapatra et al 2003;Kumar et al 2010;Krishnakumar et al 2009;Jain et al 2013;Adarsh and Janga Reddy 2015a). Few studies also dealt with the characterization and forecasting of monsoon rainfall over India (Walker 1933;Shukla and Paolino 1983;Kripalani and Kulkarni 1997;Bhattcharya and Narasimha 2007).…”

Section: Related Workmentioning

confidence: 97%

“…The rainfall trends in Kerala and spectral homogeneity has been reported by few studies in the recent past (for e.g., Krishnakumar et al 2009;Azad et al 2010; Adarsh and Janga Reddy 2015a). One of the major reasons for the spatio-temporal variability of the monsoon rainfall in India is identified to be the interaction of synoptic disturbances developed over the Bay of Bengal and its movement towards a north-westerly direction along the monsoon trough (Mohapatra et al 2003). The Orissa subdivision (17°-22°N latitude and 81°-87°E longitude approximately) falls in the weather forming zone of Bay of Bengal (within 15°-25°N latitude belt), which makes it more vulnerable to the changes in climate.…”

Section: Study Area and Datasetsmentioning

confidence: 99%

Time–frequency characterization of sub-divisional scale seasonal rainfall in India using the Hilbert–Huang transform

Reddy

Adarsh

2015

Stoch Environ Res Risk Assess

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Time-frequency characterization is useful in understanding the nonlinear and non-stationary signals of the hydro-climatic time series. The traditional Fourier transform, and wavelet transform approaches have certain limitations in analyzing non-linear and non-stationary hydro-climatic series. This paper presents an effective approach based on the Hilbert-Huang transform to investigate time-frequency characteristics, and the changing patterns of sub-divisional rainfall series in India, and explored the possible association of monsoon seasonal rainfall with different global climate oscillations. The proposed approach integrates the complete ensemble empirical mode decomposition with adaptive noise algorithm and normalized Hilbert transform method for analyzing the spectral characteristics of two principal seasonal rainfall series over four meteorological subdivisions namely Assam-Meghalaya, Kerala, Orissa and Telangana subdivisions in India. The Hilbert spectral analysis revealed the dynamic nature of dominant time scales for two principal seasonal rainfall time series. From the trend analysis of instantaneous amplitudes of multiscale components called intrinsic mode functions (IMFs), it is found that both intra and inter decadal modes are responsible for the changes in seasonal rainfall series of different subdivisions and significant changes are noticed in the amplitudes of inter decadal modes of two seasonal rainfalls in the four subdivisions since 1970s. Further, the study investigated the links between monsoon rainfall with the global climate oscillations such as Quasi Bienniel Oscillation (QBO), El Nino Southern Oscillation (ENSO), Sunspot Number (SN), Atlantic Multidecadal Oscillation (AMO) etc. The study noticed that the multiscale components of rainfall series IMF1, IMF2, IMF3, IMF4 and IMF5 have similar periodic structure of QBO, ENSO, SN, tidal forcing and AMO respectively. As per the seasonal rainfall patterns is concerned, the results of the study indicated that for Assam-Meghalaya subdivision, there is a likelihood of extreme rare events at *0.2 cycles per year, and both monsoon and pre-monsoon rainfall series have decreasing trends; for Kerala subdivision, extreme events can be expected during monsoon season with shorter periodicity (*2.5 years), and monsoon rainfall has statistically significant decreasing trend and post-monsoon rainfall has a statistically significant increasing trend; and for Orissa subdivision, there are chances of extremes rainfall events in monsoon season and a relatively stable rainfall pattern during post-monsoon period, but both monsoon and post-monsoon rainfall series showed an overall decreasing trend; for Telangana subdivision, there is a likelihood of extreme events during monsoon season with a periodicity of *4 years, but both monsoon and post-monsoon rainfall series showed increasing trends. The results of correlation analysis of IMF components of monsoon rainfall and five climate indices indicated that the association is expressed well only for low frequency modes wit...

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Spatial variability of daily rainfall over Orissa, India, during the southwest summer monsoon season

Cited by 31 publications

References 27 publications

Prediction of occurrence and quantity of daily summer monsoon precipitation over Orissa (India)

Prediction of occurrence and quantity of daily summer monsoon precipitation over Orissa (India)

An Objective Approach for Prediction of Daily Summer Monsoon Rainfall over Orissa (India) due to Interaction of Mesoscale and Large-scale Synoptic Systems

Time–frequency characterization of sub-divisional scale seasonal rainfall in India using the Hilbert–Huang transform

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