A new cloud detection scheme based on Particle Filter#887
A new cloud detection scheme based on Particle Filter#887liujake merged 7 commits intowrf-model:developfrom
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@xdmjolly |
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@xdmjolly |
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@xdmjolly |
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Will review this at later time. |
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'Makefile' is an auto-genetated file during compilation, should NOT be added. |
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The inconsistency between 13 vs 14 is caused by the var/da/da_radiance/da_cloud_detect_iasi.inc, |
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@xdmjolly @liujake @jjguerrette
Dongmei, Jake and JJ, |
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@xdmjolly Now you have three logical namelist variables use_clddet_mmr, use_clddet_pf, and use_clddet_ecmwf for three different cloud detection schemes for 4 different sensors (airs, iasi, goes-imager, ahi). I guess these three schemes will NOT be activated/used simultaneously. So I think it is better to define an integer namelist parameter 'clddet_scheme=0,1,2,3' for no-detection, mmr, pf, and ecmwf. And I do not see ecmwf cloud-detection scheme even though you have a namelist variable for it. You may make a separate PR for ecmwf scheme. Better also add published references in your PR for MMR scheme. |
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Do mmr and pf work with both CRTM and RTTOV, or just CRTM? |
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mmr and pf work with both CRTM and RTTOV. |
make particle filter the default scheme.
align if .. end if
TYPE: new feature
KEYWORDS: WRFDA, Infrared Radiance Data Assimilation, Cloud detection
SOURCE: Dongmei Xu (NUIST/NCAR), Thomas Auligné(NCAR, now at JCSDA)
DESCRIPTION OF CHANGES:
Add a new channel-based cloud detection scheme based on the Particle Filter method for Infrared sensors such as AIRS, IASI, GOES-Imager, and AHI.
Same as two other existing schemes (MMR and ECMWF) in WRFDA, this cloud-detection scheme can retain high-peaking channels not affected by cloud even though the pixel scene is covered by cloud. This scheme is computationally more efficient than MMR scheme.
Integer namelist parameter 'use_clddet' determines which scheme to use (=0,1,2,3 for no cloud detection, MMR, particle filter, and ECMWF scheme). The default is set to 2 for particle filter.
LIST OF MODIFIED FILES:
M Registry/registry.var
M var/build/depend.txt
A var/da/da_radiance/da_cloud_detect.inc
D var/da/da_radiance/da_cloud_detect_airs.inc
D var/da/da_radiance/da_cloud_detect_iasi.inc
M var/da/da_radiance/da_crtm.f90
M var/da/da_radiance/da_get_innov_vector_crtm.inc
M var/da/da_radiance/da_qc_ahi.inc
M var/da/da_radiance/da_qc_airs.inc
M var/da/da_radiance/da_qc_goesimg.inc
M var/da/da_radiance/da_qc_iasi.inc
M var/da/da_radiance/da_radiance1.f90
M var/da/da_radiance/da_rttov.f90
TESTS CONDUCTED: WRFDA regression tests ran successfully with gnu/ifort+mpt on Cheyenne.
RELEASE NOTE: A new channel-based cloud detection scheme for Infrared sensors is added based on the Particle Filter method.
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