Rainfall Contribution of Tropical Cyclones in the Bay of Bengal between 1998 and 2016 using TRMM Satellite Data

In the Bay of Bengal (BoB) area, landfalling Tropical Cyclones (TCs) often produce heavy rainfall that results in coastal flooding and causes enormous loss of life and property. However, the rainfall contribution of TCs in this area has not yet been systematically investigated. To fulfil this objective, firstly, this paper used TC best track data from the Indian Meteorological Department (IMD) to analyze TC activity in this area from 1998 to 2016 (January–December). It showed that on average there were 2.47 TCs per year generated in BoB. In 1998, 1999, 2000, 2005, 2008, 2009, 2010, 2013, and 2016 there were 3 or more TCs; while in 2001, 2004, 2011, 2012, and 2015, there was only 1 TC. On a monthly basis, the maximum TC activity was in May, October, and November, and the lowest TC activity was from January to April and in July. Rainfall data from the Tropical Rainfall Measurement Mission (TRMM) were used to estimate TC rainfall contribution (i.e., how much TC contributed to the total rainfall) on an interannual and monthly scale. The result showed that TCs accounted for around 8% of total overland rainfall during 1998–2016, and with a minimum of 1% in 2011 and a maximum of 34% in 1999. On the monthly basis, TCs’ limited rainfall contribution overland was found from January to April and in July (less than 14%), whereas the maximum TC rainfall contribution overland was in November and December (16%), May (15%), and October (14%). The probability density functions showed that, in a stronger TC, heavier rainfall accounted for more percentages. However, there was little correlation between TC rainfall contribution and TC intensity, because the TC rainfall contribution was also influenced by the TC rainfall area and frequency, and as well the occurrence of other rainfall systems.

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Impact of vortex size and Initialization on prediction of landfalling tropical cyclones over Bay of Bengal

Atmospheric Research ◽

10.1016/j.atmosres.2019.03.014 ◽

2019 ◽

Vol 224 ◽

pp. 18-29 ◽

Cited By ~ 5

Author(s):

N.K.R. Busireddy ◽

Raghu Nadimpalli ◽

Krishna K. Osuri ◽

Kumar. Ankur ◽

U.C. Mohanty ◽

...

Keyword(s):

Tropical Cyclones ◽

Bay Of Bengal ◽

Vortex Size ◽

Landfalling Tropical Cyclones

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Streamflow In The Sapucaí River Watershed, Brazil: Probabilistic Modeling, Reference Streamflow, And Regionalization

10.22541/au.162134118.84540536/v1 ◽

2021 ◽

Author(s):

Marcel Abreu ◽

Micael Fraga ◽

Laura Almeida ◽

Felipe Silva ◽

Roberto Cecílio ◽

...

Keyword(s):

Probability Density ◽

Probabilistic Modeling ◽

Probability Density Functions ◽

Drainage Area ◽

Predictor Variables ◽

Density Functions ◽

Total Rainfall ◽

River Watershed ◽

Non Linear ◽

Linear Regressions

This work aims to study the streamflow statistic patterns in the Sapucaí River watershed, state of Minas Gerais, Brazil. This study embraces the streamflow probabilistic modeling to determine the reference streamflow and, later, the streamflow regionalization to improve the water resources management. A 26-year-data series (1989 - 2014) of maximum, average, and minimum streamflow were used. Probability density functions were applied to the maximum and minimum daily streamflow to determine the recurrence periods. Long-term average annual and monthly streamflow were also calculated. Linear and non-linear regressions were adjusted for the streamflow regionalization. The drainage area and the streamflow equivalent to the total rainfall (with and without abstractions) were used as predictor variables. The probability density functions that best adjusted the maximum streamflow data set were the Generalized Extreme Values, and for the minimum streamflow was the normal distribution. Linear and non-linear regressions were efficient (R²> 0.90 and d Willmott> 0.97) in the regionalization process regardless of the predictor variables. However, a small statistical advantage was found for the adjustment of non-linear regressions that used the predictor variables drainage area and the streamflow equivalent to the total rainfall (without abstractions).

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Tropical cyclone risk assessment with special reference to Bangladesh

MAUSAM ◽

10.54302/mausam.v48i2.4013 ◽

2021 ◽

Vol 48 (2) ◽

pp. 305-322

Author(s):

ANWAR ALI ◽

JAHIR UDDIN CHOWDHURY

Keyword(s):

Risk Assessment ◽

Special Reference ◽

Tropical Cyclones ◽

Bay Of Bengal ◽

Storm Surges ◽

Vulnerability Analysis ◽

Loss Of Life ◽

Strong Winds ◽

Greenhouse Effects ◽

Future Loss

Tropical cyclones are regarded as the most deadly among all natural disasters. They bring catastrophic ravages to life and property as well as to environment. Among all the areas in the world affected by tropical cyclones, the countries along the rim of the Bay of Bengal suffer most and Bangladesh is the worst sufferer. In order to minimise the future loss of life and property, proper cyclone disaster management action is an absolute necessity. This, in turn, requires a better assessment of risks associated with a cyclone. The present paper discusses the major components of risk assessment, viz., (i) inventory of cyclones with associated causes of hazards, (ii) analysis of damages and inventory of elements at risk and (iii) vulnerability analysis with special reference to Bangladesh. Inventory of cyclones deals with the cyclone climatology in the Bay of Bengal region over the period 1881-1990. Discussions on causes of hazards cover strong winds. storm surges, rainfall. socio-economic factors, greenhouse effects, etc. An idea about the degree of cyclone damages and the elements at risks in Bangladesh is given. Some discussions on vulnerability analysis and risk reduction/mitigation with a few case studies in Bangladesh are made. Finally few recommendations are put forward.

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Forecasting High-Impact Weather in Landfalling Tropical Cyclones Using a Warn-on-Forecast System

Bulletin of the American Meteorological Society ◽

10.1175/bams-d-18-0203.1 ◽

2019 ◽

Vol 100 (8) ◽

pp. 1405-1417 ◽

Cited By ~ 4

Author(s):

Thomas Jones ◽

Patrick Skinner ◽

Nusrat Yussouf ◽

Kent Knopfmeier ◽

Anthony Reinhart ◽

...

Keyword(s):

United States ◽

Tropical Cyclones ◽

Rapid Evolution ◽

The United States ◽

Significant Loss ◽

Loss Of Life ◽

Convective Storms ◽

The Public ◽

High Impact Weather ◽

Landfalling Tropical Cyclones

AbstractLandfalling tropical cyclones (TCs) are among the greatest natural threats to life and property in the United States, since they can produce multiple hazards associated with convective storms over a wide region. Of these hazards, tornadoes within TC rainbands pose a particularly difficult forecast problem owing to their rapid evolution and their frequent occurrence coincident with additional hazards, such as flash flooding and damaging winds. During the 2017 Atlantic hurricane season, Hurricanes Harvey and Irma impacted the continental United States, causing significant loss of life and billions of dollars in property damage. Application of the Warn-on-Forecast (WoF) concept of short-term, probabilistic guidance of convective hazards (Stensrud et al. 2009, 2013), including the potential for tornadoes within TCs, offers the ability to provide forecasters with valuable tools for prioritizing the relative risk from multiple convective threats and effectively communicating them to the public.

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Mie scattering measurements of scalar probability density functions in compressible mixing layers

10.2514/6.1991-1686 ◽

1991 ◽

Cited By ~ 3

Author(s):

N. MESSERSMITH ◽

J. DUTTON ◽

H. KRIER

Keyword(s):

Probability Density ◽

Mie Scattering ◽

Probability Density Functions ◽

Mixing Layers ◽

Density Functions ◽

Scattering Measurements

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Summary Statistics of Implied Probability Density Functions and their Properties

SSRN Electronic Journal ◽

10.2139/ssrn.314392 ◽

2002 ◽

Cited By ~ 1

Author(s):

Damien P.G. Lynch ◽

Nikolaos Panigirtzoglou

Keyword(s):

Probability Density ◽

Probability Density Functions ◽

Density Functions ◽

Summary Statistics

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Modeling Diameter Distributions with Six Probability Density Functions in Pinus halepensis Mill. Plantations Using Low-Density Airborne Laser Scanning Data in Aragón (Northeast Spain)

Remote Sensing ◽

10.3390/rs13122307 ◽

2021 ◽

Vol 13 (12) ◽

pp. 2307

Author(s):

J. Javier Gorgoso-Varela ◽

Rafael Alonso Ponce ◽

Francisco Rodríguez-Puerta

Keyword(s):

Probability Density ◽

Linear Models ◽

Pinus Halepensis ◽

Beta Function ◽

Probability Density Functions ◽

Lidar Data ◽

Density Functions ◽

Minimum Diameter ◽

Location Parameters ◽

Diameter Distributions

The diameter distributions of trees in 50 temporary sample plots (TSPs) established in Pinus halepensis Mill. stands were recovered from LiDAR metrics by using six probability density functions (PDFs): the Weibull (2P and 3P), Johnson’s SB, beta, generalized beta and gamma-2P functions. The parameters were recovered from the first and the second moments of the distributions (mean and variance, respectively) by using parameter recovery models (PRM). Linear models were used to predict both moments from LiDAR data. In recovering the functions, the location parameters of the distributions were predetermined as the minimum diameter inventoried, and scale parameters were established as the maximum diameters predicted from LiDAR metrics. The Kolmogorov–Smirnov (KS) statistic (Dn), number of acceptances by the KS test, the Cramér von Misses (W2) statistic, bias and mean square error (MSE) were used to evaluate the goodness of fits. The fits for the six recovered functions were compared with the fits to all measured data from 58 TSPs (LiDAR metrics could only be extracted from 50 of the plots). In the fitting phase, the location parameters were fixed at a suitable value determined according to the forestry literature (0.75·dmin). The linear models used to recover the two moments of the distributions and the maximum diameters determined from LiDAR data were accurate, with R2 values of 0.750, 0.724 and 0.873 for dg, dmed and dmax. Reasonable results were obtained with all six recovered functions. The goodness-of-fit statistics indicated that the beta function was the most accurate, followed by the generalized beta function. The Weibull-3P function provided the poorest fits and the Weibull-2P and Johnson’s SB also yielded poor fits to the data.

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A synergy of the velocity gradients technique and the probability density functions for identifying gravitational collapse in self-absorbing media

Monthly Notices of the Royal Astronomical Society ◽

10.1093/mnras/stab087 ◽

2021 ◽

Vol 502 (2) ◽

pp. 1768-1784

Author(s):

Yue Hu ◽

A Lazarian

Keyword(s):

Magnetic Fields ◽

Probability Density ◽

Column Density ◽

Mass Density ◽

Probability Density Functions ◽

Emission Lines ◽

Density Functions ◽

Star Forming ◽

Velocity Gradients ◽

Self Gravity

ABSTRACT The velocity gradients technique (VGT) and the probability density functions (PDFs) of mass density are tools to study turbulence, magnetic fields, and self-gravity in molecular clouds. However, self-absorption can significantly make the observed intensity different from the column density structures. In this work, we study the effects of self-absorption on the VGT and the intensity PDFs utilizing three synthetic emission lines of CO isotopologues 12CO (1–0), 13CO (1–0), and C18O (1–0). We confirm that the performance of VGT is insensitive to the radiative transfer effect. We numerically show the possibility of constructing 3D magnetic fields tomography through VGT. We find that the intensity PDFs change their shape from the pure lognormal to a distribution that exhibits a power-law tail depending on the optical depth for supersonic turbulence. We conclude the change of CO isotopologues’ intensity PDFs can be independent of self-gravity, which makes the intensity PDFs less reliable in identifying gravitational collapsing regions. We compute the intensity PDFs for a star-forming region NGC 1333 and find the change of intensity PDFs in observation agrees with our numerical results. The synergy of VGT and the column density PDFs confirms that the self-gravitating gas occupies a large volume in NGC 1333.

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Modelling with star-shaped distributions

Dependence Modeling ◽

10.1515/demo-2020-0003 ◽

2020 ◽

Vol 8 (1) ◽

pp. 45-69

Author(s):

Eckhard Liebscher ◽

Wolf-Dieter Richter

Keyword(s):

Probabilistic Models ◽

Arbitrary Dimension ◽

Probability Density Functions ◽

Density Functions ◽

Wide Range ◽

Gaussian Density ◽

Spherical Distributions ◽

Data Points ◽

Non Gaussian ◽

General Method

AbstractWe prove and describe in great detail a general method for constructing a wide range of multivariate probability density functions. We introduce probabilistic models for a large variety of clouds of multivariate data points. In the present paper, the focus is on star-shaped distributions of an arbitrary dimension, where in case of spherical distributions dependence is modeled by a non-Gaussian density generating function.

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