An efficient method to account for microphysical inhomogeneity in mesoscale models by using one-dimensional variability factor.

Author(s):  
Yefim Kogan

<p>Neglecting subgrid-scale (SGS) variability can lead to substantial bias in calculations of microphysical process rates. The solution to the SGS variability bias problem lies in representing the variability using two-dimensional joint probability distribution functions (JPDFs) for the pairs of different microphysical variables. The JPDFs have also been shown to vary in the vertical: as a result their implementation in mesoscale models presents a challenging task.</p><p>We developed a more efficient formulation of cloud inhomogeneity by using a concept of “generic” JPDF. Using Large Eddy Simulation (LES) studies of shallow cumulus and cumulus congestus clouds we showed that JPDFs calculated based on datasets representing “typical” cloud types (“generic” JPDFs) provide good approximation of microphysical process rates. The generic JPDF, therefore, represent the cloud type in general, i.e. they do not depend on changing ambient conditions. The advantage of generic JPDFs is that they can be a-priory integrated and yield a one-dimensional variability factor (<strong><em>V-facto</em></strong>r) specific for each cloud type. A quite accurate approximation of V-factors by an analytical function in the form of a 3<sup>rd</sup> order polynomial was obtained and can be easily implemented in mesoscale models.</p><p><strong>How big is the effect of cloud inhomogeneity on precipitation</strong>? To answer this question we evaluated the effect of accounting for cloud inhomogeneity on precipitation in sensitivity simulations. In the shallow Cu case over the 24 hr simulation the surface precipitation increased by about 40% when inhomogeneity was accounted. In the congestus Cu case the increase in precipitation was even more significant: by more than 75% over only 8 hours since rain first appeared at the surface. The sensitivity experiments also revealed that most of the increase resulted from the augmented autoconversion process. The effect of modified by the <em><strong>V-factor</strong></em> accretion rates was much less significant, primarily, because of the nearly linear dependence of accretion on its parameters.  This shows importance of the most accurate formulation of the autoconversion process.</p><p> </p><p> </p>

2018 ◽  
Vol 75 (8) ◽  
pp. 2549-2561
Author(s):  
Yefim Kogan

Abstract Different formulations of the joint probability distribution function (JPDF) based on large-eddy simulation (LES) studies of shallow cumulus and cumulus congestus clouds were evaluated. It was shown that inhomogeneity in both cloud types can be quantified by their respective JPDFs calculated using datasets from the entire simulation time period (“generic” JPDFs). The generic JPDF can be a priori integrated and yield a one-dimensional variability factor (V factor) specific for each cloud type. A quite accurate approximation of V factors by an analytical function in the form of a third-order polynomial was obtained and can be easily implemented in mesoscale models. The effect on precipitation of conversion rates modified by V factors was also evaluated in LES sensitivity studies of shallow cumulus (Cu) and congestus Cu clouds. The surface precipitation increased significantly when V factors were taken into account. The sensitivity experiments revealed that most of the increase resulted from the modified autoconversion process. The effect of accretion rates modified by V factors was much less significant, primarily because of the nearly linear dependence of accretion on its parameters. This fact shows the importance of the most accurate formulation of the autoconversion process.


2017 ◽  
Vol 14 ◽  
pp. 103-107 ◽  
Author(s):  
Yefim Kogan

Abstract. Subgrid-scale (SGS) variability of cloud microphysical variables over the mesoscale numerical weather prediction (NWP) model has been evaluated by means of joint probability distribution functions (JPDFs). The latter were obtained using dynamically balanced Large Eddy Simulation (LES) model dataset from a case of marine trade cumulus initialized with soundings from Rain in Cumulus Over the Ocean (RICO) field project. Bias in autoconversion and accretion rates from different formulations of the JPDFs was analyzed. Approximating the 2-D PDF using a generic (fixed-in-time), but variable-in-height JPDFs give an acceptable level of accuracy, whereas neglecting the SGS variability altogether results in a substantial underestimate of the grid-mean total conversion rate and producing negative bias in rain water. Nevertheless the total effect on rain formation may be uncertain in the long run due to the fact that the negative bias in rain water may be counterbalanced by the positive bias in cloud water. Consequently, the overall effect of SGS neglect needs to be investigated in direct simulations with a NWP model.


Author(s):  
Carmelo Giacovazzo

The title of this chapter may seem a little strange; it relates Fourier syntheses, an algebraic method for calculating electron densities, to the joint probability distribution functions of structure factors, which are devoted to the probabilistic estimate of s.i.s and s.s.s. We will see that the two topics are strictly related, and that optimization of the Fourier syntheses requires previous knowledge and the use of joint probability distributions. The distributions used in Chapters 4 to 6 are able to estimate s.i. or s.s. by exploiting the information contained in the experimental diffraction moduli of the target structure (the structure one wants to phase). An important tool for such distributions are the theories of neighbourhoods and of representations, which allow us to arrange, for each invariant or seminvariant Φ, the set of amplitudes in a sequence of shells, each contained within the subsequent shell, with the property that any s.i. or s.s. may be estimated via the magnitudes constituting any shell. The resulting conditional distributions were of the type, . . . P(Φ| {R}), (7.1) . . . where {R} represents the chosen phasing shell for the observed magnitudes. The more information contained within the set of observed moduli {R}, the better will be the Φ estimate. By definition, conditional distributions (7.1) cannot change during the phasing process because prior information (i.e. the observed moduli) does not change; equation (7.1) maintains the same identical algebraic form. However, during any phasing process, various model structures progressively become available, with different degrees of correlation with the target structure. Such models are a source of supplementary information (e.g. the current model phases) which, in principle, can be exploited during the phasing procedure. If this observation is accepted, the method of joint probability distribution, as described so far, should be suitably modified. In a symbolic way, we should look for deriving conditional distributions . . . P (Φ| {R}, {Rp}) , (7.2) . . . rather than (7.1), where {Rp} represents a suitable subset of the amplitudes of the model structure factors. Such an approach modifies the traditional phasing strategy described in the preceding chapters; indeed, the set {Rp} will change during the phasing process in conjunction with the model changes, which will continuously modify the probabilities (7.2).


Author(s):  
C. Giacovazzo ◽  
M. Ladisa ◽  
D. Siliqi

AbstractThe method of the joint probability distribution functions has been recently applied to SIR-MIR, SAD-MAD and SIRAS-MIRAS cases. The capacity of the method to treat various forms of errors (i.e., errors in measurements, possible lack of isomorphism, errors in a substructure model when a model is


2007 ◽  
Vol 39 (04) ◽  
pp. 991-1019 ◽  
Author(s):  
Frosso S. Makri ◽  
Andreas N. Philippou ◽  
Zaharias M. Psillakis

Statistics denoting the numbers of success runs of length exactly equal and at least equal to a fixed length, as well as the sum of the lengths of success runs of length greater than or equal to a specific length, are considered. They are defined on both linearly and circularly ordered binary sequences, derived according to the Pólya-Eggenberger urn model. A waiting time associated with the sum of lengths statistic in linear sequences is also examined. Exact marginal and joint probability distribution functions are obtained in terms of binomial coefficients by a simple unified combinatorial approach. Mean values are also derived in closed form. Computationally tractable formulae for conditional distributions, given the number of successes in the sequence, useful in nonparametric tests of randomness, are provided. The distribution of the length of the longest success run and the reliability of certain consecutive systems are deduced using specific probabilities of the studied statistics. Numerical examples are given to illustrate the theoretical results.


1999 ◽  
Vol 26 (1) ◽  
pp. 1-12 ◽  
Author(s):  
Kamal El-Fashny ◽  
Luc E Chouinard ◽  
Ghyslaine McClure

This study presents a structural reliability analysis of a microwave tower subject to wind and freezing-rain hazards. The tower (name code CEBJ, owned by Hydro-Québec) is a 66 m tall, three-legged, steel lattice structure located in the James Bay area. The reliability analysis is performed conditionally with respect to wind speed and ice thickness accretion, and the results are integrated over the domain of wind and ice values using their joint probability distribution. This approach makes it possible to perform sensitivity analyses with respect to various assumptions on the joint probability distribution function of the climatological variable, without having to repeat the detailed coupled reliability - structural analysis of the tower. The probability distribution functions assumed for the wind speed and the ice thickness accretion on the tower members are both extreme-value type I (Gumbel) distributions. Adopting a weakest link model, the failure of the tower is assumed to occur when any of the members fails either in tension, compression, or global buckling. Without loss of generality, the proposed procedure can be applied with more refined probability distribution functions.Key words: reliability, telecommunication towers, wind, ice.


2015 ◽  
Vol 73 (1) ◽  
pp. 167-184 ◽  
Author(s):  
Yefim L. Kogan ◽  
David B. Mechem

Abstract Calculating unbiased microphysical process rates over mesoscale model grid volumes necessitates knowledge of the subgrid-scale (SGS) distribution of variables, typically represented as probability distribution functions (PDFs) of the prognostic variables. In the 2014 Journal of the Atmospheric Sciences paper by Kogan and Mechem, they employed large-eddy simulation of Rain in Cumulus over the Ocean (RICO) trade cumulus to develop PDFs and joint PDFs of cloud water, rainwater, and droplet concentration. In this paper, the approach of Kogan and Mechem is extended to deeper, precipitating cumulus congestus clouds as represented by a simulation based on conditions from the TOGA COARE field campaign. The fidelity of various PDF approximations was assessed by evaluating errors in estimating autoconversion and accretion rates. The dependence of the PDF shape on grid-mean variables is much stronger in congestus clouds than in shallow cumulus. The PDFs obtained from the TOGA COARE simulations for the calculation of accretion rates may be applied to both shallow and congestus cumulus clouds. However, applying the TOGA COARE PDFs to calculate autoconversion rates introduces unacceptably large errors in shallow cumulus clouds, thus precluding the use of a “universal” PDF formulation for both cloud types.


2014 ◽  
Vol 53 (5) ◽  
pp. 1282-1296 ◽  
Author(s):  
Christopher R. Williams ◽  
V. N. Bringi ◽  
Lawrence D. Carey ◽  
V. Chandrasekar ◽  
Patrick N. Gatlin ◽  
...  

AbstractRainfall retrieval algorithms often assume a gamma-shaped raindrop size distribution (DSD) with three mathematical parameters Nw, Dm, and μ. If only two independent measurements are available, as with the dual-frequency precipitation radar on the Global Precipitation Measurement (GPM) mission core satellite, then retrieval algorithms are underconstrained and require assumptions about DSD parameters. To reduce the number of free parameters, algorithms can assume that μ is either a constant or a function of Dm. Previous studies have suggested μ–Λ constraints [where Λ = (4 + μ)/Dm], but controversies exist over whether μ–Λ constraints result from physical processes or mathematical artifacts due to high correlations between gamma DSD parameters. This study avoids mathematical artifacts by developing joint probability distribution functions (joint PDFs) of statistically independent DSD attributes derived from the raindrop mass spectrum. These joint PDFs are then mapped into gamma-shaped DSD parameter joint PDFs that can be used in probabilistic rainfall retrieval algorithms as proposed for the GPM satellite program. Surface disdrometer data show a high correlation coefficient between the mass spectrum mean diameter Dm and mass spectrum standard deviation σm. To remove correlations between DSD attributes, a normalized mass spectrum standard deviation is constructed to be statistically independent of Dm, with representing the most likely value and std representing its dispersion. Joint PDFs of Dm and μ are created from Dm and . A simple algorithm shows that rain-rate estimates had smaller biases when assuming the DSD breadth of than when assuming a constant μ.


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