- New capability for directly assimilating radar reflectivity using a new
observation operator and its TL/AD considering snow and graupel.
Wang, S. and Z. Liu, 2019: A radar reflectivity operator with ice-phase hydrometeors
for variational data assimilation (version 1.0) and its evaluation with real
radar data, Geosci. Model Dev., 12, 4031–4051.
- New capability for variational bias correction of TAMDAR T observations.
The bias correction is applied based upon aircraft's IDs and phases
(descent/ascent/cruise). To turn on this function, set "use_varbc_tamdar = true"
in namelist (\wrfvar4).
Two types of bias model are provided:
varbc_tamdar_bm=1: two predictors are a constant of 1.0 and vertical motion rates
of aircraft.
varbc_tamdar_bm=2: five predictors are a constant of 1.0, vertical motion rates of
aircraft, Mach number, temperature tendency, and temperature measured.
Gao, F., Z. Liu, J. Ma, A. N. Jacobs, P. P. Childs, and H. Wang, 2019:
Variational Bias Correction of TAMDAR Temperature Observations in the WRF
Data Assimilation System. Monthly Weather Review. 147. 10.1175/MWR-D-18-0025.1.
- A new channel-based cloud detection scheme for Infrared sensors based on the Particle Filter
This is now the default option for IR cloud detection (use_clddet=2).
Xu D., T. Auligné, G. Descombes, and C. Snyder, 2016: A method for retrieving
clouds with satellite infrared radiances using the particle filter.
Geosci. Model Dev., 9, 3919–3932.
- New capability for an IR-only cloud detection scheme for AHI radiance data assimilation
To turn this option on, set use_clddet_zz=true. It also needs an static input file in
working directory. It can be downloaded from
here.
Wu, Y., Z. Liu, and D. Li, 2020, Improving forecasts of a record-breaking rainstorm
in Guangzhou by assimilating every 10-min AHI radiances with WRF 4DVAR.
Atmospheric Research, 239, 104912.
Zhuge, X., Zou, X., 2016. Test of a modified infrared-only ABI cloud mask algorithm
for AHI radiance observations. J. Appl. Meteorol. Climatol. 55 (11), 2529–2546.
- New stand-alone gen_be_v3. See var/gen_be_v3/README.gen_be_v3 for more info.
This program generates ep (ensemble perturbation) and be (background error)
for WRFDA alphacv_method=2, alpha_hydrometeors=true, cv_options=7 and
cloud_cv_options=2 applications. The major advantage of gen_be_v3 is that
it is much more efficient than the existing gen_be package.
-
Improved analysis_type="RANDOMCV". Now multiple perturbed output can be
obtained with one WRFDA run, set new namelist "n_randomcv" to the desired
number of ensemble.
-
For EnVar DA applications, logical "alpha_vertloc" namelist switch is replaced
by new integer option "alpha_vertloc_opt". See var/README.namelist.
alpha_vertloc_opt=0, no vertical localization.
alpha_vertloc_opt=1, the behavior is the same as before.
alpha_vertloc_opt=2 (default, recommended), let WRFDA internally calculate logP-based
vertical localization. be.vertloc.dat will be written out.
- New DA ep_format option to read in ensemble perturbation (ep) generated by
existing gen_be_ep2 and new gen_be_v3 utilities.
ep_format=1: (default) original format, double precision, each ep file is for
one variable and one member, as the output from the current gen_be_ep2.
ep_format=11: same as ep_format=1 except data are in single precision
ep_format=2: single precision, each ep file is for one variable and all members
ep_format=3: single precision, each ep file is for one variable and all members
but on decomposed patch domain. This is recommended for large domain.
For instructions on compiling and running the latest version of WRFDA, please refer to the updated User's Guide
