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  • Author or Editor: Ramesh C. Srivastava x
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Xiaowen Li
and
Ramesh C. Srivastava

Abstract

An analytical solution for the evaporation of a single raindrop is derived in this paper. Based on this solution, a parameter D* is defined as the diameter of the raindrop that just evaporates completely after falling through a certain distance in a prescribed environment. The parameter D* is then used for studying the modification of raindrop size distribution by evaporation in a steady, still atmosphere. The results for the Marshall–Palmer distribution are used to discuss errors caused by rain evaporation in radar rainfall measurements. Quantitative estimation of these errors, or as an equivalent, estimation of the rain evaporation along the falling path, using both radar reflectivity Z and radar differential reflectivity ZDR techniques, is studied. The results show that, for the detection of rain evaporation, reflectivity is more sensitive than differential reflectivity, whereas for the estimation of rainfall rate R, an empirical ZDR–ZR formula is more robust and accurate than a ZR formula.

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Thomas Matejka
and
Ramesh C. Srivastava

Abstract

Extended velocity-azimuth display (EVAD) analysis is useful for obtaining vertical profiles of horizontal divergence, vertical air velocity, vertical hydrometer velocity, and hydrometeor terminal fall speed in widespread precipitation. The technique uses a volume of velocity data collected with a single Doppler radar.

Several improvements to the previously reported EVAD technique are discussed. They include the weighting of Fourier series coefficients to reflect their estimated error, a correction for heteroscedasticity (the systematic variation of residuals) in the regression analysis, and the weighting of data from different elevation angles to compensate for the finite thicknesses of the layers in which each analysis is performed. Vertical air velocity is obtained through a variational procedure. Procedures for dealiasing the velocity data and for rejecting outliers from the dataset are summarized. Recommendations for collecting radar data for use in EVAD analysis are made.

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Richard E. Passarelli Jr.
and
Ramesh C. Srivastava

Abstract

Unlike raindrops, ice particles of the same mass can have different fallspeeds, due to variations in the particle shape and bulk density. (This is an extension of the popular axiom that no two snowflakes are alike.) This is an additional source of variance for vertical incidence Doppler (VID) spectra taken in snow that has been neglected in previous studies which assume a one-to-one fallspeed-mass relationship. The total VID spectral variance due to the dispersion of ice particle fallspeeds can be broken down into two components, i.e., that due to the mean fallspeed-mass relationship and that due to fluctuations about the mean. Existing data on ice particle fallspeeds are not adequate for a thorough evaluation, but do indicate that these two sources of fallspeed variance can be of the same order. These results suggest that the task of deducing snow-size spectra from VID measurements is more difficult than has been recognized.

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Richard E. Passarelli Jr.
and
Ramesh C. Srivastava

Abstract

A new theoretical approach to snowflake aggregation is presented which accounts for the fact that snowflakes of the same mass can have a spectrum of fallspeeds. The essence of the approach is to define a modified kernel which can be used in the stochastic collection equation exactly as one would use a standard kernel computed by assuming that snowflakes of the same mass have a unique fallspeed. The modified kernel approach predicts more rapid aggregation than the standard kernel approach, the degree of enhancement being critically dependent on the width of the fallspeed spectrum. For the case of aggregates of dendrites, measurements made by a number of investigators suggest that the width of the fallspeed spectrum is of the same order as the variation in the mean fallspeed over the typical range of snowflake sites. Thus the effect of including the fallspeed spectrum in calculations of aggregation is large, and may even dominate the aggregation process.

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Lin Tian
,
Gerald M. Heymsfield
,
Lihua Li
,
Andrew J. Heymsfield
,
Aaron Bansemer
,
Cynthia H. Twohy
, and
Ramesh C. Srivastava

Abstract

An analysis of two days of in situ observations of ice particle size spectra, in convectively generated cirrus, obtained during NASA’s Tropical Composition, Cloud, and Climate Coupling (TC4) mission is presented. The observed spectra are examined for their fit to the exponential, gamma, and lognormal function distributions. Characteristic particle size and concentration density scales are determined using two (for the exponential) or three (for the gamma and lognormal functions) moments of the spectra. It is shown that transformed exponential, gamma, and lognormal distributions should collapse onto standard curves. An examination of the transformed spectra, and of deviations of the transformed spectra from the standard curves, shows that the lognormal function provides a better fit to the observed spectra.

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Suryachandra A. Rao
,
B. N. Goswami
,
A. K. Sahai
,
E. N. Rajagopal
,
P. Mukhopadhyay
,
M. Rajeevan
,
S. Nayak
,
L. S. Rathore
,
S. S. C. Shenoi
,
K. J. Ramesh
,
R. S. Nanjundiah
,
M. Ravichandran
,
A. K. Mitra
,
D. S. Pai
,
S. K. R. Bhowmik
,
A. Hazra
,
S. Mahapatra
,
S. K. Saha
,
H. S. Chaudhari
,
S. Joseph
,
P. Sreenivas
,
S. Pokhrel
,
P. A. Pillai
,
R. Chattopadhyay
,
M. Deshpande
,
R. P. M. Krishna
,
Renu S. Das
,
V. S. Prasad
,
S. Abhilash
,
S. Panickal
,
R. Krishnan
,
S. Kumar
,
D. A. Ramu
,
S. S. Reddy
,
A. Arora
,
T. Goswami
,
A. Rai
,
A. Srivastava
,
M. Pradhan
,
S. Tirkey
,
M. Ganai
,
R. Mandal
,
A. Dey
,
S. Sarkar
,
S. Malviya
,
A. Dhakate
,
K. Salunke
, and
Parvinder Maini

Abstract

In spite of the summer monsoon’s importance in determining the life and economy of an agriculture-dependent country like India, committed efforts toward improving its prediction and simulation have been limited. Hence, a focused mission mode program Monsoon Mission (MM) was founded in 2012 to spur progress in this direction. This article explains the efforts made by the Earth System Science Organization (ESSO), Ministry of Earth Sciences (MoES), Government of India, in implementing MM to develop a dynamical prediction framework to improve monsoon prediction. Climate Forecast System, version 2 (CFSv2), and the Met Office Unified Model (UM) were chosen as the base models. The efforts in this program have resulted in 1) unparalleled skill of 0.63 for seasonal prediction of the Indian monsoon (for the period 1981–2010) in a high-resolution (∼38 km) seasonal prediction system, relative to present-generation seasonal prediction models; 2) extended-range predictions by a CFS-based grand multimodel ensemble (MME) prediction system; and 3) a gain of 2-day lead time from very high-resolution (12.5 km) Global Forecast System (GFS)-based short-range predictions up to 10 days. These prediction skills are on par with other global leading weather and climate centers, and are better in some areas. Several developmental activities like coupled data assimilation, changes in convective parameterization, cloud microphysics schemes, and parameterization of land surface processes (including snow and sea ice) led to the improvements such as reducing the strong model biases in the Indian summer monsoon simulation and elsewhere in the tropics.

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