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WRF Pre-processing System Version 4.0
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@mgduda mgduda released this 08 Jun 23:06
· 268 commits to master since this release
v4.0
3ab4522

This release includes the following notable changes:

  • Introduction of a 'lowres' option for geog_data_res to select coarse resolution
    (typically, 5-arc-minutes) sources for all mandatory static fields. This is
    useful in educational contexts or when the size of the geographical datasets
    must be minimized, e.g., when teaching with WRF/WPS in a Docker container or
    in the cloud.

  • New GEOGRID.TBL entry for a pre-computed urban fraction field (FRC_URB2D) based
    on 30-meter NLCD 2011 data.

  • A change in the default source of data for the MAXSNOALB and ALBEDO12M fields.
    The new defaults are MODIS-based and higher resolution (ALBEDO12M is based on MODIS product MCD43C3, and MAXSNOALB, 0.05 MODIS BRDF product)

  • Two fixes for numerical inaccuracies in the polar stereographic projection.

  • A new namelist option for ungrib, pmin, to specify the minimum pressure to
    extract from isobaric datasets.

  • A combined Vtable for all CFSR/CFSv2 data, both pressure-level and surface
    fields (Vtable.CFSR).

  • Added support for NCAR RDA's ECMWF "ds113.1" dataset.

  • Added support for FV3GFS GRIB data.

  • Generally, in the v4.0 release of the WPS, there is a move towards a model
    for handling geographical static fields in which all static fields that are
    not required by the default WRF namelist are optional from the viewpoint of
    geogrid.

    • Datasets that were previously mandatory but that are now optional:
      VAR_SSO and LAKE_DEPTH.

Note: There are now three types of downloads for the static, geographical data:
(1) Download the "full-resolution" sources for all default, mandatory fields.
Use this download for all simulations where accuracy is important.

(2) Download the "low-resolution" sources for all mandatory fields.
Use this download for testing and educational purposes.

(3) Downloads for individual, optional geographical fields.
Obtain these datasets as needed by different model options.

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