Noah mp snow

[cenlinhe]

TYPE: enhancement, new feature

KEYWORDS: Noah-MP, snow, MPTABLE, parameter

SOURCE: Cenlin He (NCAR/RAL)

DESCRIPTION OF CHANGES:
Several Noah-MP snow-related aspects have been updated to improve snow simulations:

1. Add snow retention process at the snowpack bottom to improve streamflow modeling (also to be consistent
   with National Water Model updates)
2. Modify wind-canopy absorption coefficient (CWPVT) parameter values in MPTABLE to be vegetation dependent based on Goudriaan1977; previous values are incorrect.
3. Bring hard-coded snow emissivity of 1.0 to a global tunable MPTABLE parameter, and set to 0.95 to improve
   WRF surface temperature simulation
4. Bring hard-coded constant parameter (i.e., 2.5*z0, z0=0.002m) in snow cover formulation to a vegetation-
   dependent tunable SCFFAC parameter in MPTABLE; values are currently optimized to improve SWE and surface
   albedo modeling by comparing with SNOTEL and MODIS observations.
5. Update MFSNO in snow cover formulation with optimized vegetation-dependent MFSNO values based on
   SNOTEL SWE & MODIS SCF & surface albedo comparison
6. Add wet-bulb temperature snow-rain partitioning scheme (OPT_SNF=5) based on Wang et al. 2019 GRL
7. Fix an issue for bulk leaf boundary layer resistance (RB) value range (>100) in RAGRB; Now using more realistic
   range (5~50)

LIST OF MODIFIED FILES: list of changed files
M phys/module_sf_noahmpdrv.F
M phys/module_sf_noahmplsm.F
M run/MPTABLE.TBL

TESTS CONDUCTED:

1. Conducted compilation and official em_real simulation tests with successful results in Cheyenne.
2. Passed Jenkins tests.

RELEASE NOTE: These Noah-MP updates include several snow-relevant enhancements and new features to improve snow simulations based on NCAR Noah-MP LSM team tests. The publication is still under development (He et al., 2021, in preparation). Snow improvements by this code update include: (1) new snow retention process improves streamflow modeling (tested with Noah-MP in National Water Model); (2) optimized parameter values for snow cover parameters (SCFFAC & MFSNO) improve snow water equivalent (SWE), snow depth, and surface albedo simulation; (3) new snow emissivity value improves WRF surface temperature simulation; (4) new wet-bulb temperature snow-rain partitioning scheme (OPT_SNF=5) improves SWE simulation see Wang et al. 2019 GRL; (5) enhanced flexibility of tuning model parameters by bringing the above-mentioned parameters from hard-coded values to MPTABLE; (6) Fix the incorrect values for wind-canopy absorption coefficient (CWPVT) parameter and the unrealistic value range of bulk leaf boundary layer resistance (RB).

[cenlinhe]

This is a PR separated from the original PR #1345.

[cenlinhe]

@davegill @dudhia Hi folks, have you merged my updates in this PR to the to-be-released version already? We found a mistake in the parameter value in the MPTABLE table. It is just the table value (run/MPTABLE.TBL). There is no change to the code files. We will try to update the table value using the correct values in one or two days (before Feb 21, 2021) and update the MPTABLE file in this PR. Please let me know if this works for you.

[dudhia]

It was approved but you should still be able to change it because it is not merged.

…

On Fri, Feb 19, 2021 at 1:00 PM Cenlin_He ***@***.***> wrote: @davegill <https://github.com/davegill> @dudhia <https://github.com/dudhia> Hi folks, have you merged my updates in this PR to the to-be-released version already? We found a mistake in the parameter value in the MPTABLE table. It is just the table value (run/MPTABLE.TBL). There is no change to the code files. We will try to update the table value using the correct values in one or two days (before Feb 21, 2021) and update the MPTABLE file in this PR. Please let me know if this works for you. — You are receiving this because you were mentioned. Reply to this email directly, view it on GitHub <#1403 (comment)>, or unsubscribe <https://github.com/notifications/unsubscribe-auth/AEIZ77CKQDH42QMF5XXXQB3S727N7ANCNFSM4XVDI2IA> .