1 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

2 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)? Less data assimilated over land Even less over arid regions in daytime..partly because of Noah deficiencies

3 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 Zheng and Mitchell (2008) Zheng et al. (2012) 2 Ts Diff GVF 4

CLM has a similar problem 2 5 Obs 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: 1.To improve the formulations of roughness lengths 2. for momentum and heat; 3.To improve the treatment of stable turbulence in 4. the atmospheric boundary layer through the 5. interplay between sensible and ground heat fluxes 2 6

Noah CLM 3 7 New CON Results: daytime and nighttime improvements Desert Rock Gaize New-10 layers

Mean absolute deviation (K) Desert Rock Gaize Noah (Con) Noah (New) CLM (Con) CLM (New)

9 T b bias in satellite pixels used in GFS GSI (NOAA-17 HIRS-3 Ch. 8) (Zheng et al. 2012) ConNew

Case: 18Z, W CONUS: 243 Veg_7: 66 W CONUS: 483 Veg_7: 274 CON NEW Tb from NOAA-18 AMSU-A Ch

Tb simulation for NOAA-18 AMSU-A Ch. 1 GFS CON CON + improved surface emissivity +improved Noah 11

12 2. Major snow deficiency of Noah over forest areas Snow melts too early, too fast Offline Noah Simulations of SWE over Niwot Ridge, CO Obs SWE Noah GFS/Noah 7-day forecasting This deficiency has been known for a few years for Noah and some other land models

13 Our solution (Wang et al. 2010): Main ideas : Vegetation shading effect; Snow density adjustment; Under-canopy resistance; Revised Z 0 m under snow condition Using existing Noah model structure (for easy operational implementation)

14 Niwot Ridge forest (40.03  N,  W, 3050 meters) July 2006 – June 2007 Results are also improved over grassland

Snapshot h Forecast (Wei et al. 2012) Improvement Con New Obs 15

Time series over W, 50-60N Operational GFS New Obs 3/17 3/25 16

1-month (11 Mar – 10 Apr 2011) Q: Can better snowpack forecast lead to better prediction of other fields? 17

1-month ( ) Obs New Impacts on other forecast scores (e.g., 500 hPa height, precipitation) are small 18

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) Evergreen Needleleaf WRF Snow in an idealized 6-mon simulation Impact of Noah improvements in WRF v3.4.1 CON MP NEW 19

Summary Improvements in Noah daytime Ts were implemented in GFS in May a successful R2O transition; they also increase the number of GSI-assimilated satellite T b 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

21 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). Critical Issues