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Transitioning Land Surface Skin Temperature and Snow Improvement to Operation at NCEP/EMC. Xubin Zeng (University of Arizona, Tucson) Zhuo Wang (NESDIS) Mike Barlage and Fei Chen (NCAR) Helin Wei, Weizhong Zheng , Mike Ek (NCEP/EMC) 11 October 2012
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Transitioning Land Surface Skin Temperature and Snow Improvement to Operation at NCEP/EMC XubinZeng (University of Arizona, Tucson) Zhuo Wang (NESDIS) Mike Barlage and Fei Chen (NCAR) Helin Wei, WeizhongZheng, Mike Ek (NCEP/EMC) 11 October 2012 Xubin@atmo.arizona.edu
Q: For our JCSDA project, why do we focus on the improvement of NCEP land model (Noah)? Less data assimilated over land Even less over arid regions in daytime ..partly because of Noah deficiencies Assimilated NOAA-18 AMSU-A channel 15 (89 GHz) data in operational GDAS in July 2007 (Zheng et al. 2012)
Q: For our JCSDA project, why do we focus on the improvement of NCEP land model (Noah)? Snow data are assimilated, but the initial information is lost too fast. ..partly because of Noah deficiencies GFS/Noah 7-day forecasting of snow over one grid cell in western U.S. in April 2010
1. Skin temperature over semi-arid regions July 2007 Ts Diff GVF 2 Zheng and Mitchell (2008) Zheng et al. (2012)
CLM has a similar problem Obs 2 Zeng et al. (2012)
Question Can we develop unified formulations to improve both Noah and CLM in the simulation of the Ts diurnal cycle over arid regions? Yes (Zeng et al. 2012; Zheng et al. 2012). Main ideas: • To improve the formulations of roughness lengths • for momentum and heat; • To improve the treatment of stable turbulence in • the atmospheric boundary layer through the • interplay between sensible and ground heat fluxes 2
Results: daytime and nighttime improvements New-10 layers New CON Noah CLM Desert Rock Gaize 3
Mean absolute deviation (K) Desert Rock Gaize Noah (Con) 2.8 5.8 Noah (New) 0.5 1.6 CLM (Con) 1.9 4.6 CLM (New) 0.7 1.8 11
Con New Tb bias in satellite pixels used in GFS GSI (NOAA-17 HIRS-3 Ch. 8) (Zheng et al. 2012)
Tb from NOAA-18 AMSU-A Ch. 15 Case: 18Z, 20070702 CON NEW W CONUS: 243 Veg_7: 66 W CONUS: 483 Veg_7: 274
Tb simulation for NOAA-18 AMSU-A Ch. 1 GFS CON CON + improved surface emissivity CON + improved surface emissivity +improved Noah
2. Major snow deficiency of Noah over forest areas Snow melts too early, too fast GFS/Noah 7-day forecasting Offline Noah Simulations of SWE over Niwot Ridge, CO Obs SWENoah This deficiency has been known for a few years for Noah and some other land models 12
Our solution (Wang et al. 2010): Main ideas : Vegetation shading effect; Snow density adjustment; Under-canopy resistance; Revised Z0m under snow condition Using existing Noah model structure (for easy operational implementation)
Niwot Ridge forest (40.03N, 105.55W, 3050 meters) July 2006 – June 2007 Results are also improved over grassland 14
Improvement Snapshot 20110317 120h Forecast(Wei et al. 2012) Obs Con New
Time series over 100-120W, 50-60N Obs New Operational GFS 3/17 3/25
Q: Can better snowpack forecast lead to better prediction of other fields? 1-month (11 Mar – 10 Apr 2011)
1-month (20110311-20110410) Obs New Impacts on other forecast scores (e.g., 500 hPa height, precipitation) are small
Impact of Noah improvements in WRF v3.4.1 Evergreen Needleleaf WRF Snow in an idealized 6-mon simulation Our Noah improvements (UA) perform as well as the explicit canopy model (MP) at maintaining snowpack in spring. They will be released in next WRF release in Spring 2013 Barlage et al. (2012) CON MPNEW
Summary Improvements in Noah daytime Ts were implemented in GFS in May 2011 - a successful R2O transition; they also increase the number of GSI-assimilated satellite Tb data from surface-sensitive IR and MW channels; Nighttime Ts improvements are ready for testing in GFS; Diurnal Ts improvements have been tested in CLM and are ready for implementation. Noah snow improvements have been tested in GFS and are ready for implementation; They have also been tested in WRF and will be available to the community in the next WRF release in Spring 2013. 2
Critical Issues • Noah Ts improvements don’t necessarily improve T2m or troposphere temperature • Better forecast of snowpack doesn’t necessarily result in better surface temperature forecast, • because GFS atmospheric model had been tuned to partially compensate for Noah land model biases. • We have to improve both land and atmospheric models together (so that GFS GSI could assimilate more satellite data over land and have a positive impact on NCEP forecasting).