Scientific Theme: Advanced Modeling and Observing Systems

Theme report: Advanced Modeling and Observing Systems
running the model backwards in time, as is required for the DFI, so considerable development effort has been
necessary. Such a capability was, in fact, developed in 2005 for WRF v2.1. Work to extend this capability to WRF
v2.2 has turned out to be decidedly nontrivial, but it is progressing.
MILESTONE GSD01.2:
Incorporate enhancements to Gridpoint Statistical Interpolation (GSI) code to improve use of
surface weather observations (METARs) and introduce GSD-developed procedures into GSI to
incorporate three-dimensional, high-frequency National Weather Service WSR-88D radar data in
the initialization of cloud and precipitation hydrometeors.
ACCOMPLISHMENTS FOR GSD01.2:
Work on GSI at GSD was begun in 2005. After some very basic code adjustments, work started to concentrate on
features important for RR applications, particularly those required for one-hour cycling and assimilation of
hydrometeors. (GSI has heretofore been used only with models cycling every six hours and without assimilation of
hydrometeor information.) The two most important of these features are usage of surface observations, including
ceiling height and present weather, and analysis of hydrometeors.
One of the strengths of the present Rapid Update Cycle (RUC) is the handling of surface data in the RUC analysis.
The difference between the surface observation of virtual potential temperature, water-vapor mixing ratio and
horizontal wind components and the one-hour background forecast of these quantities is spread vertically through
the mixed-layer (but only if there is such a mixed-layer) in the one-hour background forecast by generating ―pseudoobservations‖
within the mixed-layer, but not above. For the time being, a different approach is taken in the GSI,
using anisotropic background error covariance information in an adaptive way. The premise is to apply the PBL
depth of the background forecast as computed in the present RUC and use this to constrain the scale of vertical
correlation in the background error. This approach is combined with a model for anisotropic background error
developed and implemented in GSI by NCEP. The present code employs the background virtual potential
temperature when computing the vertical correlation of background error. Present work is directed toward
introduction and intense testing of this approach in the real-time one-hour RR cycle soon to begin on the new ESRL
supercomputer (see Milestone GSD01.1).
The existing operational RUC cloud analysis is done outside the variational framework of the RUC 3dVAR. It uses
NESDIS cloud products (cloud top pressure and temperature) and METARs to decide the location of cloud top and
clear area and then adds a thin layer of cloud under the cloud top and clears the column above cloud top. (The whole
column is cleared of hydrometeors if the column is identified as being clear by the RUC cloud analysis.)
Versions of GSI released by NCEP have yet to incorporate any cloud analysis procedures. Therefore, an enhanced
version of the existing RUC cloud-analysis procedure within the GSI software framework was incorporated, but as
with RUC, outside the variational parts of the code. A systematic approach toward introducing the RUC cloud
analysis into GSI has been pursued, including a) building onto the GSI framework to accommodate the cloud
analysis, b) ingesting cloud observations, c) building the cloud analysis driver, and d) incorporating RUC cloud
analysis into GSI. All the programming work has been finished including parallelization of the cloud-analysis code.
Preliminary results show successful performance. The code will be further tested in the real time RR cycle.
A new procedure for assimilation of three-dimensional high-frequency National Weather Service WSR-88D radar
data is being tested in real-time RUC cycles at GSD. Briefly summarized, this procedure uses the 1-km Q2 Mosaic
radar-reflectivity product developed at the National Severe Storms Lab of NOAA, plus some additional quality
control, to identify volumes of the atmosphere having radar reflectivity due to hydrometeors. In areas where radar
data are absent, lightning is used to identify volumes likely to possess hydrometeors. The procedure also identifies
areas where the one-hour background forecast has hydrometeors that do not exist in reality. In order to render the
initial mass and momentum fields more consistent with these implied hydrometeor fields, the diabatic digital filter
initialization is employed. After the initial adiabatic backward stage of the DFI, there follows the diabatic forward
step. During this forward step, the potential temperature tendencies from the microphysics and convection
parameterization schemes are replaced by tendencies specified based on the mixing ratios of the hydrometeors. In
this way an initial divergent component of the wind field is introduced that has some measure of consistency with
the hydrometeor fields and allows a much improved forecast of precipitation through the first three to six hours of
the model forecast. Introduction of this procedure into the RR cycling later this year is planned, once a version of
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