El Cajon, CA, United States
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Jameson A.R.,RJH Scientific Inc. | Larsen M.L.,College of Charleston
Journal of Geophysical Research: Atmospheres | Year: 2016

Distributions of drop sizes can be expressed as DSD = Nt × PSD, where Nt is the total number of drops in a sample and PSD is the frequency distribution of drop diameters (D). Their discovery permitted remote sensing techniques for rainfall estimation using radars and satellites measuring over large domains of several kilometers. Because these techniques depend heavily on higher moments of the PSD, there has been a bias toward attributing the variability of the intrinsic rainfall rates R over areas (σR) to the variability of the PSDs. While this variability does increase up to a point with increasing domain dimension L, the variability of the rainfall rate R also depends upon the variability in the total number of drops Nt. We show that while the importance of PSDs looms large for small domains used in past studies, it is the variability of Nt that dominates the variability of R as L increases to 1 km and beyond. The PSDs contribute to the variability of R through the relative dispersion of χ = D3Vt, where Vt is the terminal fall speed of drops of diameter D. However, the variability of χ is inherently limited because drop sizes and fall speeds are physically limited. In contrast, it is shown that the variance of Nt continuously increases as the domain expands for physical reasons explained below. Over domains larger than around 1 km, it is shown that Nt dominates the variance of the rainfall rate with increasing L regardless of the PSD. © 2015. American Geophysical Union. All Rights Reserved.


Larsen M.L.,College of Charleston | Kostinski A.B.,Michigan Technological University | Jameson A.R.,RJH Scientific Inc.
Geophysical Research Letters | Year: 2014

A network of optical disdrometers (including laser precipitation monitors and a two-dimensional video disdrometer) was utilized to determine whether the recent reports of "superterminal" raindrops were spurious results of drop breakup occurring on instrumentation. Results unequivocally show that superterminal raindrops at small (less than 1 mm) sizes are ubiquitous, are measurable over an extended area, and appear in every rain event investigated. No evidence was found to suggest that superterminal drops are the result of drop breakup due to impact with the measurement instrument; thus, if the superterminal drops are the result of drop fragmentation, this fragmentation happens in the ambient atmosphere during all rain events measured in this study. The ubiquity of superterminal drops at small drop sizes raises natural questions regarding rain accumulation estimations, estimates of drop size distributions, and erosion characterization. Key Points Superterminal drops are realSuperterminal drops are not the result of instrument splashingA large fraction of drops less than 1 mm in diameter appears to be superterminal ©2014. American Geophysical Union. All Rights Reserved.


Jameson A.R.,RJH Scientific Inc. | Heymsfield A.J.,U.S. National Center for Atmospheric Research
Journal of Applied Meteorology and Climatology | Year: 2013

This study addresses the issue of how to upscale cloud-sized in situ measurements of ice to yield realistic simulations of ice clouds for a variety of modeling studies. Aircraft measurements of ice particle counts along a 79-km zigzag path were collected in a Costa Rican cloud formed in the upper-level outflow from convection. These are then used to explore the applicability of Bayesian statistics to the problems of upscaling and downscaling. Using the 10-m particle counts, the analyses using Bayesian statistics provide estimates of the probability distribution function of all possible mean values corresponding to these counts. The statistical method of copulas is used to produce an extensive ensemble of estimates of these mean values, which are then combined to derive the probability density function (pdf) of mean values at 1-km resolution. These are found to compare very well to the observed 1-km particle counts when spatial correlation is included. The profiles of the observed and simulated mean counts along the flight path show similar features and have very similar statistical characteristics. However, because the observed and the simulated counts are both the results of stochastic processes, there is no way to upscale exactly to the observed profile. Each simulation is a unique realization of the stochastic processes, as are the observations themselves. These different realizations over all the different sizes can then be used to upscale particle size distributions over large areas. © 2013 American Meteorological Society.


Jameson A.R.,RJH Scientific Inc. | Heymsfield A.J.,U.S. National Center for Atmospheric Research
Meteorology and Atmospheric Physics | Year: 2014

What is new in this manuscript is a method of using aircraft observations from a long horizontal path through an ice cloud to produce properly correlated 2D fields of particle counts consistent with the observations, including all null values, at several different sizes for use in algorithm development and in scientific studies. A Bayesian approach is used to define the distributions of average counts, P(C), at every size. These are, in turn, used to expand the number of values at each particle size by a factor of 50. These data, then fill a square 2D grid of 20 × 20 km at 100-m resolution. At each grid point, the number concentrations corresponding to each particle size define the particle size distributions (PSD). A method for assuring the proper correlation of counts at each size over the entire grid is devised and discussed. These PSD can then be integrated to yield a number of different quantities over the entire grid such as radar reflectivities and ice water contents. From this perspective, one can then consider the set of observations as just one realization from a much larger ensemble of possible realizations by giving fuller expression to all of the information contained within the observed correlation functions and P(C)s. The in situ observations, however, remain crucial since this method does not 'make-up' new meteorology but simply gives wider expression to the meteorology contained within the observations. © 2013 The Author(s).


Jameson A.R.,RJH Scientific Inc. | Kostinski A.B.,Michigan Technological University
Journal of the Atmospheric Sciences | Year: 2010

Classical radar theory only considers incoherent backscatter from precipitation. Can precipitation generate coherent scatter as well? Until now, the accepted answer has been no, because hydrometeors are distributed sparsely in space (relative to radar wavelength) so that the continuum assumption used to explain coherent scatter in clear air and clouds does not hold. In this work, a theory for a different mechanism is presented. The apparent existence of the proposed mechanism is then illustrated in both rain and snow. A new power spectrum Z(f), the Fourier transform of the time series of the radar backscattered reflectivities, reveals statistically significant frequencies f of periodic components that cannot be ascribed to incoherent scatter. It is shown that removing those significant fs fromZ(f) at lower frequencies greatly reduces the temporal coherency. These lower frequencies, then, are associated with the increased temporal coherency. It is also shown that these fs are also directly linked to the Doppler spectral peaks through integer multiples of one-half the radar wavelength, characteristic of Bragg scatter. Thus, the enhanced temporal coherency is directly related to the presence of coherent scatter in agreement with theory. Moreover, the normalized backscattered power spectrum Z(f) permits the estimation of the fractional coherent power contribution to the total power, even for an incoherent radar. Analyses of approximately 26 000 one-second Z(f) in both rain and snow reveal that the coherent scatter is pervasive in these data. These findings present a challenge to the usual assumption that the scatter of radar waves from precipitation is always incoherent and to interpretations of backscattered power based on this assumption. © 2010 American Meteorological Society.


Jameson A.R.,RJH Scientific Inc.
Journal of Applied Meteorology and Climatology | Year: 2010

Previous work showed that the magnitudes of the radar-backscattered amplitudes have statistically significant periodic components of frequencies (f) in excess of those arising from the Doppler velocity fluctuations of incoherent scatter. Analyses in both rain and snow in the earlier work revealed what is interpreted as pervasive coherent scatter. This coherency is thought to come from precipitation structures acting like gratings in resonance with the radar wavelength that, when they move with a velocity component transverse to the beam, induce the observed f. The purpose of this article is to characterize briefly the temporal structure of f and, thereby indirectly, the temporal character of the structures producing the radar coherent backscatter. It is found that these structures last considerably longer than the decorrelation times of a few to 10 milliseconds, characteristic of Doppler velocity fluctuations associated with incoherent scatter. For the data analyzed, though, most last no more than a significant fraction of 1 s. Hence, for the observed transverse velocity of 2 ms-1, the dimensions of the gratings producing the radar coherent backscatter are only on the order of tens of centimeters to a few meters. Therefore, the typically large sampling volumes of most radars will contain many of these grids at any given time. Consequently, during 1 s of observations, one can envision the coherent scatter as coming from many individual grids twinkling on and off, much like the transient spectral reflections off ice crystals falling in sunlight. © 2010 American Meteorological Society.


Jameson A.R.,RJH Scientific Inc. | Kostinski A.B.,Michigan Technological University
Journal of Applied Meteorology and Climatology | Year: 2010

In this work, the authors present observations of enhanced temporal coherency beyond that expected using the observations of the standard deviation of the Doppler velocities and the assumption of a family of exponentially decaying autocorrelation functions. The purpose of this paper is to interpret these observations by developing the complex amplitude autocorrelation function when both incoherent and coherent backscatter are present. Using this expression, it is then shown that when coherent scatter is present, the temporal coherency increases as observed. Data are analyzed in snow and in rain. The results agree with the theoretical expectations, and the authors interpret this agreement as an indication that coherent scatter is the likely explanation for the observed enhanced temporal coherency. This finding does not affect decorrelation times measured using time series. However, when the time series is not available (as in theoretical studies), the times to decorrelation are often computed based upon the assumptions that the autocorrelation function is a member of the family of exponentially decaying autocorrelation functions and that the signal decorrelation is due solely to the Doppler velocity fluctuations associated with incoherent scatter. Such an approach, at times, may significantly underestimate the true required times to decorrelation thus leading to overestimates of statistical reliability of parameters. © 2010 American Meteorological Society.


Jameson A.R.,RJH Scientific Inc.
IEEE Transactions on Geoscience and Remote Sensing | Year: 2015

A time or spatial series of drop counts is but one realization of a multiple stochastic process. In this paper, a method is presented that extracts more of the information contained in the time series of 1-min Joss - Waldvogel disdrometer counts in rain than a simple analysis of the magnitudes of the counts would provide. This is done by greatly increasing the size of a data set using a Bayesian analysis of drop count measurements in 17 size bins. Using the empirical copula statistical technique of probability density function transformations, a 1391-min time series of drop counts was expanded to the equivalent of 40 000 min. This dramatic increase in sample size permits a deeper characterization of the rain. Using this single disdrometer, it also allows one to translate these counts into a 200 × 200 grid filled at each point with drop size distributions of mean drop concentrations consistent with the observed statistical properties of the rain. Such a field can be used for remote sensing studies of the effect of partial beam filling and for algorithm development. Moreover, since there is nothing unique to this set of drop counts, this approach can be applied to any other set of count data, including snow and clouds. © 2014 IEEE.


Grant
Agency: NSF | Branch: Standard Grant | Program: | Phase: | Award Amount: 465.73K | Year: 2012

Characteristic scales and structures of precipitation particle distributions have generally been explored over dimensions considerably larger than 1 km, yet for several reasons it is important to characterize their variability on far smaller scales. These include the need to: (1) optimize interpretation of observations made by simple rain gages and disdrometers and the translation of such measurements to much larger scales, as needed for validation of numerical weather forecasts and flood warnings; (2) better understand drop interactions and evolution in a multi-dimensional context rather than just the classical time dependent treatment in the vertical for falling precipitation (as has often been considered adequate for representation of processes including raindrop collision, coalescence and break-up); (3) achieve a more complete description of how radar-emitted microwaves interact with rain; and (4) ultimate connection and perhaps translation of 1-D observations of drop size distributions to larger 2-D domains.

The intellectual merit of this project derives from quantification of small-scale precipitation structures via combined use of a 2-D video disdrometer in conjunction with a networked array of optical disdrometers, through which investigators will calculate pair-correlation functions in rain to derive improved measures of any given precipitation fields horizontal geometry. Disdrometer observations (whose horizontal separation may be readily adjusted) will also allow examination a wide range of distances for possible scaling, self-similarity and functional structures of drop size distributions so as to better connect with remote-sensor measurements of precipitation covering large areas. The over-arching objective of this work is to collect and analyze time-series observations of rainfall over horizontal scales ranging from centimeters (as observed using a 2-D video disdrometer) up to several tens to perhaps a hundred meters or more. Broader Impacts will come through potential contributions to improved flood prediction and monitoring as well as better processes relevant to soil erosion, through support of hands-on student involvement in, and through improved observational infrastructure at an undergraduate institution serving underrepresented groups.


Grant
Agency: NSF | Branch: Continuing grant | Program: | Phase: PHYSICAL & DYNAMIC METEOROLOGY | Award Amount: 287.99K | Year: 2015

This award provides funding for researchers to study the topic of raindrop clustering. As can be seen in nature, rain does not fall with equal spacing between the individual drops. Rather, the raindrops tend to cluster or bunch. This can make the interpretation of tools used to measure rainfall, such as simple rain gauges or advanced weather radar, more complicated. In this study, researchers from two institutions will expand a measuring site that includes a significant number of disdrometers, which are instruments that can provide images and information about individual raindrops as they fall. The additional data will help the researchers answer a variety of questions which are ultimately relevant to the interpretation of data from radar and the effect of rain on soil erosion. Undergraduate students would be directly involved in the collection and analysis of the data, providing opportunities for the next generation of scientists.

The research team will continue and expand upon their work making measurements of small scale variability in rainfall. In their prior research grant, the researchers set up an array of optical disdrometers and a video disdrometer within a small 100m x 100m area. This award will add a second video disdrometer and a newer type of optical disdrometer in order to collect data that would answer questions raised by the investigation of the original data. Specifically, the research plan is to: (1) expand the library of data to obtain better and more complete sets of observations in a wider variety of meteorological conditions, (2) achieve higher temporal resolution of some instruments to reduce advection smoothing, particularly for more detailed studies of the spatial pair correlation function, (3) characterize further the spatial correlation function for many more rain events beyond the current 100m, (4) focus on centimeter scale studies using 2DVD data yet to be explored with particular regard to scales relevant to radar Bragg scatter, (5) expand the study of the effect of domain size on drop size distribution and their integrated parameters to include more data sets under different meteorological conditions, (6) focus on calculating 2D spatial correlation in different meteorological settings and different temporal resolutions with the aim of developing useful parametric expressions for applications, and (7) combine 2DVD observations from two instruments for unique simultaneity studies similar to historic and prize winning photon work.

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