Association of thunderstorm frequency with rainfall occurrences over an Indian urban metropolis

2014 ◽  
Vol 138 ◽  
pp. 240-252 ◽  
Author(s):  
U. Saha ◽  
A. Maitra ◽  
S.K. Midya ◽  
G.K. Das
Keyword(s):  
1987 ◽  
Vol 23 (5) ◽  
pp. 875-884 ◽  
Author(s):  
Efi Foufoula-Georgiou ◽  
Dennis P. Lettenmaier

1984 ◽  
Vol 16 (8-9) ◽  
pp. 147-153 ◽  
Author(s):  
Van-Thanh-Van Nguyen

The present study, a continuation of a previous work by the author, suggests a new theoretical approach to the characterization of the temporal pattern of storms. A storm is defined as a continuous run of non-zero one-hour rainfall depths. A general stochastic model is developed to determine the probability distributions of cumulative storm rainfall amounts at successive time intervals after the storm began. The previous model for characterizing storm temporal patterns was based on the assumption that hourly rainfall depths were independent and identically exponentially distributed random variables, while sequences of wet hours were modeled by a first-order stationary Markov chain. Hence, the model did only introduce dependence of wet hour occurences into the rainfall process through the first-order Markov chain. The present paper proposes a more general model that can take into account both the persistence in hourly rainfall occurrences and the dependence between successive hourly rainfall depths. Results of an illustrative example show that by accounting for the correlation structure of consecutive rainfall depths the present model gives a better fit to the observations than the previous one.


2018 ◽  
Author(s):  
Rohit Chakraborty ◽  
Madineni Venkat Ratnam ◽  
Ghouse Basha

Abstract. Long-term trends of the parameters related to convection and instability obtained from 27 radiosonde stations across 6 sub-divisions over Indian region during the period 1980–2016 is presented. A total of 16 parcel and instability parameters along with moisture content, wind shear, and thunderstorm and rainfall frequencies have been utilized for this purpose. Robust fit regression analysis is employed on the regional average time series to calculate the long-term trends on both seasonal and yearly basis. The Level of Free Convection (LFC) and Equilibrium Level (EL) height is found to ascend significantly in all Indian sub-divisions. Consequently, the coastal regions (particularly the western coasts) experience strengthening in Severe Thunderstorm (TSS) and Severe Rainfall Frequencies (SRF) in the pre-monsoon while the inland regions (especially central India) experience an increase in Ordinary Thunderstorm (TSO) and Weak Rain Frequency (WRF) during the monsoon and post-monsoon. The 16–20 year periodicity is found to dominate the long-term trends significantly compared to other periodicities and the increase in TSS, SRF and CAPE is found more severe after the year 1999. The enhancement in moisture transport and associated cooling at 100 hPa along with dispersion of boundary layer pollutants is found to be the main cause for the increase in Convective Available Potential Energy (CAPE) which leads to more convective severity in the coastal regions. However, in inland regions moisture-laden winds are absent and the presence of strong capping effect of pollutants on instability in the lower troposphere has resulted in more Convective Inhibition Energy (CINE). Hence, TSO and weak rainfall occurrences have increased particularly in these regions.


1985 ◽  
Vol 12 (4) ◽  
pp. 886-898 ◽  
Author(s):  
J. D. Bonser ◽  
T. E. Unny ◽  
K. Singhal

A mathematical description of summer rainfall occurrences in southern Ontario is developed in this paper. The theory of Poisson point processes with specific application to rainfall modelling is presented with a critical review of previous literature on Poisson rainfall models. A marked Poisson process model of summer storms is formulated, using the marks to represent the random duration and intensity of the events. Model parameters are estimated for four locations in southern Ontario using a total of 48 seasons of hourly rainfall data. The model is applied to calculate the seasonal return period of extreme storms and the probability distribution of total seasonal rainfall volume. These two examples demonstrate the accuracy and usefulness of the model. Key words: rainfall, storm duration, storm intensity, temporal storm pattern, probabilistic model, Poisson process, exponential distribution, gamma distribution, Weibull distribution.


Weather ◽  
1991 ◽  
Vol 46 (7) ◽  
pp. 196-200
Author(s):  
P. Wemyss
Keyword(s):  

2009 ◽  
Vol 13 (12) ◽  
pp. 2299-2314 ◽  
Author(s):  
A. Bárdossy ◽  
G. G. S. Pegram

Abstract. From the point of view of multisite stochastic daily rainfall modelling, there are two new ideas introduced in this paper. The first is the use of asymmetrical copulas to model the spatial interdependence structure of the rainfall amounts together with the rainfall occurrences in one relationship. The second is in the evaluation of the (necessary but often ignored) congregating behaviour of the higher values of simulated rainfall; this evaluation is performed by calculating the entropy of the observations at all the near equilateral triangles that can be formed from the sequences at the gauge sites, as a function of their mutual separation distance. It turns out that the model captures the qualities desired and offers a fresh approach to a relatively mature problem in hydrometeorology.


2007 ◽  
Vol 22 (5) ◽  
pp. 981-1002 ◽  
Author(s):  
Ching-Sen Chen ◽  
Yi-Leng Chen ◽  
Che-Ling Liu ◽  
Pay-Liam Lin ◽  
Wan-Chin Chen

Abstract The seasonal variations of heavy rainfall days over Taiwan are analyzed using 6-yr (1997–2002) hourly rainfall data from about 360 rainfall stations, including high-spatial-resolution Automatic Rainfall and Meteorological Telemetry System stations and 25 conventional stations. The seasonal variations and spatial variations of nontyphoon and typhoon heavy rainfall occurrences (i.e., the number of rainfall stations with rainfall rate >15 mm h−1 and daily accumulation >50 mm) are also analyzed. From mid-May to early October, with abundant moisture, potential instability, and the presence of mountainous terrain, nontyphoon heavy rainfall days are frequent (>60%), but only a few stations recorded extremely heavy rainfall (>130 mm day−1) during the passage of synoptic disturbances or the drifting of mesoscale convective systems inland. During the mei-yu season, especially in early June, these events are more widespread than in other seasons. The orographic effects are important in determining the spatial distribution of heavy rainfall occurrences with a pronounced afternoon maximum, especially during the summer months under the southwesterly monsoon flow. After the summer–autumn transition, heavy rainfall days are most frequent over northeastern Taiwan under the northeasterly monsoon flow. Extremely heavy rainfall events (>130 mm day−1) are infrequent during the winter months because of stable atmospheric stratification with a low moisture content. Typhoon heavy rainfall events start in early May and become more frequent in late summer and early autumn. During the analysis period, heavy rainfall occurrences are widespread and dominated by extremely heavy rainfall events (>130 mm day−1) on the windward slopes of the storm circulations. The spatial distribution of typhoon heavy rainfall occurrences depends on the typhoon track with very little diurnal variation.


MAUSAM ◽  
2021 ◽  
Vol 49 (4) ◽  
pp. 453-460
Author(s):  
ASOI LAL ◽  
R. S. SUNDAR

Whenever a vortex or system of low or depression forms over head bay during Monsoon months, the west coast experiences heavy rainfall. These heavy rainfall occurrences are usually higher than the normal rainfall. An attempt has been made in this study to visualise the easterly wave model during monsoon months with the help of satellite imageries. The rain is expected heavy and wide spread over Madhya Maharashtra and South Gujarat when third sector of the wave covers these areas, as visualised in satellite wave and depression or vortex lies in the 5th or 6th sector of the wave.


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