PCM-package: Probabilistic cloud and snow detection on AVHRR imagery
In PCM: Cloud and snow discrimination on AVHRR imagery

Description Details Author(s) References

This package consists of set of procedures used to: pre-process input ancillary data (land cover, DEM, surface skin temperature); generate probabilistic cloud & snow mask for AVHRR data; post-classify the results into a binary form; compute cloud shadow mask

Package:	PCM
Type:	Package
Version:	1.0
Date:	2013-03-19
License:	GNU >= 2.0

The data pre-processing is done in 3 steps. First the land cover data are remapped and interpolated (nearest neighbor) to desired projection and geographic extent using the lc_prepare function. Further (optionally) the Digital Elevation Model is remapped and interpolated (bilinear) to desired projection and geographic extent together with a temperature correction file which consists of a difference between high resolution DEM and DEM provided by the NWP model (geopotential at surface) multiply by the constant lapse rate of 0.6K/100m (dem_prepare function). Next (optionally) the NWP Skin Surface Temperature data is remapped and interpolated (bilinear) to desired projection and geographic extent and the temperature correction data are added (link{skt_prepare} function). Finally the cloud & snow detection is performed with optional DEM & SKT input and the classification probability is stored in the GeoTif file (PCM function). Optionally the post-classification can be performed which transforms the probability estimates to binary form and crude cloud shadow determination is added.

Jan Musial

Maintainer: Jan Musial <[email protected]>

Musial, J., Probabilistic approach to cloud and snow detection on AVHHR imagery, Atmos. Chem. Phys (in prep.)

PCM documentation built on May 31, 2017, 2:58 a.m.