  We present an extended validation of semi-analytical, semi-empirical
covariance matrices for the two-point correlation function (2PCF) on simulated
catalogs representative of Luminous Red Galaxies (LRG) data collected during
the initial two months of operations of the Stage-IV ground-based Dark Energy
Spectroscopic Instrument (DESI). We run the pipeline on multiple effective
Zel'dovich (EZ) mock galaxy catalogs with the corresponding cuts applied and
compare the results with the mock sample covariance to assess the accuracy and
its fluctuations. We propose an extension of the previously developed formalism
for catalogs processed with standard reconstruction algorithms. We consider
methods for comparing covariance matrices in detail, highlighting their
interpretation and statistical properties caused by sample variance, in
particular, nontrivial expectation values of certain metrics even when the
external covariance estimate is perfect. With improved mocks and validation
techniques, we confirm a good agreement between our predictions and sample
covariance. This allows one to generate covariance matrices for comparable
datasets without the need to create numerous mock galaxy catalogs with matching
clustering, only requiring 2PCF measurements from the data itself. The code
used in this paper is publicly available at
https://github.com/oliverphilcox/RascalC.
