Methods to Evaluate Subcolumn Profiles Based on Two-Point Diagnostics

doi:10.1029/2024JD040926

Methods to Evaluate Subcolumn Profiles Based on Two-Point Diagnostics

2024-10-19

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Benjamin A. Stephens *

DOI：10.1029/2024JD040926

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In atmospheric models, stochastic generation of subgrid-scale profiles or subcolumns has been used for a variety of purposes. Such subcolumns can be generated from subgrid probability density functions (PDFs) at different vertical levels, when such PDFs are available. To do so, the generator needs to decide how strongly points should be correlated in the vertical, that is, how much the values should be overlapped. This is sometimes called PDF overlap. To assess vertical correlation in a simplified, observable setting, here the vertical correlation of vertical velocity in subcloud layers is examined. Doppler lidar is used to evaluate the vertical profiles of vertical velocity produced by a large-eddy simulation (LES) model and the Subgrid Importance Latin Hypercube Sampler (SILHS) subcolumn generator. In order to diagnose unrealistic features in subcolumn profiles, various statistical diagnostics are examined here, including the bivariate PDF of vertical velocity at two separated points (i.e., altitudes), the two-point velocity correlation, the integral correlation length, the PDF of two-point velocity differences, and the skewness and kurtosis of two-point velocity differences. The profiles produced by LES match lidar well, except that they are too smooth at small scales. The profiles produced by SILHS exhibit sharp jumps from updraft to downdraft that are not observed in the lidar data. To reduce the generation of these unrealistically sharp jumps, the SILHS sampling method is revised. The diagnostics confirm that the revised sampling method reduces the overprediction of sharp jumps. In numerical models of the atmosphere, it proves useful for a variety of applications to model the vertical coherence of various fields. It is difficult, however, to characterize and quantify the degree of vertical coherence in a simple and general way. Several statistical methods to characterize vertical coherence are assessed here for their ability to capture visual impressions of selected vertical profiles. These statistical methods are used to evaluate and revise a model of vertical coherence. Lidar is used to evaluate profiles of vertical velocity from a large-eddy simulation and a stochastic subcolumn generator Statistical diagnostics are developed for the purpose of evaluation, and they detect spurious sharp jumps in the subcolumn profiles The over-prevalence of sharp jumps can be mitigated by the use of a revised sampling strategy

Keywords:

Doppler lidar

atmospheric boundary layer

large-eddy simulation

subcolumn generation

subgrid sampling

atmospheric diagnostics

Methods to Evaluate Subcolumn Profiles Based on Two-Point Diagnostics

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