1. Application of Satellite Land Surface Observations in Improving NCEP Numerical Weather Prediction Weizhong Zheng 1,2 and Mike Ek 1 1 NOAA/NCEP/Environmental Modeling Center, College Park, MD 2 IMSG @NOAA/NCEP/EMC, College Park, MD Cooperators: J. Meng, H. Wei, J. Dong, Y. Wu, R. Sun, J. Han and Global Branch (NCEP/EMC); GSI/CRTM teams; X. Zhan's team, F. Weng's team and M. Vargas's team (NESDIS); X. Zeng (UA); C. Peters-Lidard (NASA); J. Eylander (Air Force); etc. The 13th JCSDA Technical Review Meeting & Science Workshop, May 13-15, 2015

2. Climate CFS Motivation ➢ Objective: To improve satellite data utilization over land in NCEP data assimilation system and then improve the numerical weather prediction (NWP). ➢ Land satellite data assimilation: – Utilization of satellite data sets in the models (e.g., GVF, snow, burning area, albedo, radiation, vegetation and soil type) – Assimilation of satellite products (e.g, Soil moisture: AMSR2 data; snow); – Direct radiance assimilation (Tb) (sfc parameters, sfc emissivity and sub-grid scale land surface) ➢ Land-Atmosphere Interaction – I dentify and understand the interaction and feedback between land and atmosphere, and then improve NWP and DA. 2

3. Status of Weekly Real-Time VIIRS GVF ➢ Objective: Use weekly real-time VIIRS GVF data set in the NCEP models to replace the old 5-year mean monthly climatology from AVHRR. ➢ Last year: Summer case had been tested in GFS. ➢ Current status: More cases and impacts are continuously investigated, included in NAM. ➢ Main achievements from last year. – New GVF showed a good improvement for NWP. 3

4. CPC Soil Moisture Anomaly Courtesy CPC Monthly Climate Review GVF: 15 May 2014 Drought in Spring Clim VIIRS 4

5. Surface temperature and its RMSE CONUS East Reduced cold bias(~0.5 °C) and RMSE (~0.25 °C) afternoon and nighttime 5

6. Surface temperature and its RMSE CONUS West Reduced cold bias(~1 °C) and RMSE (~0.25 °C) afternoon and nighttime, but increase a little daytime RMSE. 6

7. Surface dew point temp and its RMSE CONUS East Reduced wet bias and RMSE afternoon and nighttime (~0.5 ° C) 7

8. ++ 0+ ++ 0+ ++ -- Forecast Verification Statistics (FVS) regions +: improve; -: degrade; 0: neutral 8

9. Precipitation Skill Scores over CONUS: f60-f84 Improved scores for light and medium precipitation. 9

10. GVF Testing in NAM Climatology VIIRS-Snow VIRRS-No-Snow Clim a VIIRS-Snow VIIRS-No Snow ➢ 5 year climatology of GVF currently used in NAM ➢ Near real time VIIRS GVF corrected with phenology prediction and snow contamination ➢ Near real time VIIRS GVF corrected with phenology prediction and without snow contamination ➢ Near real time VIIRS GVF from Marco’s Group ➢ NAM will be run for 4 different seasons in 2014 to compare the effect of different GVF product in NAM forecast Courtesy Yihua Wu 10

11. ➢ Surface physical characteristics in NAM are updated based burning products derived from satellite ➢ Two burned files are read in by the surface update program of NAM: -- 30 day accumulation of burned area – 2 day accumulation of burned area which is included in the 30 day accumulation ➢ Vegetation cover, roughness and albedo are updated based on the 30 day accumulation while the surface temperature, soil temperature and moisture of the top soil layer are updated based on the 2 day accumulation ➢ Two Fire cases (the Wallow Fire in 2011 and the Carlton Complex fire in 2014) were tested. Courtesy Yihua Wu To Incorporate Fire Effect in NAM GVF ALB Zo 11

12. Status of SMOPS Soil Moisture Assimilation ➢ Objective: Assimilate SMOPS SM products and improve NWP. ➢ Last year: Some cases tested; SMOPS re-scaled with GDAS. ➢ Current status: More validation using more in situ SM measurements; New SMOPS SM product test. ➢ Main achievements from last year. – Validation indicated that SMOPS blended SM quality is impacted by low quality SM retrievals from WindSat. – Soil moisture retrievals from AMSR2 is expected to improve SMOPS blended soil moisture when NESDIS-GCOM-W system becomes operational.. 12

13. SMOPS 0.39 ALEXI 0.44 WindSat 0.28 ECV 0.47 Comparison with in Situ Measurements (2007-2013) ECV: Essential Climate Variable data set ALEXI: Atmosphere-Land Exchange Inversion (ALEXI) model using thermal infrared satellite observations of LST. Courtesy X. Zhan et al 13

14. Test Result of SMOPS Soil Moisture including AMSR2 AMSR2 ASCAT SMOS SMOPS Courtesy X. Zhan et al 14

15. Satellite-based Land Data Assimilation Tests in NWS GFS/CFS Operational Systems PI: Michael Ek (NOAA/NCEP/EMC) Co-Is: Jiarui Dong and Weizhong Zheng (IMSG at NOAA/NCEP/EMC) Christa Peters-Lidard (NASA/GSFC) and Grey Nearing (SAIC at NASA/GSFC) We propose to enable the existing NASA Land Information System (LIS) to serve as a global Land Data Assimilation System (LDAS) for both GFS and CFS. LIS integrates NOAA/NCEP’s operational land surface model (NCEP’s Noah), high-resolution satellite and observational data, and land data assimilation (DA) tools. The LIS EnKF-based land Data Assimilation tool is used to assimilate soil moisture from the NESDIS global Soil Moisture Operational Product System (SMOPS), snow cover area (SCA) from operational NESDIS Interactive Multisensor Snow and Ice Mapping System (IMS) and AFWA snow depth (SNODEP) products. Courtesy Jiarui Dong 15

16. Status of Land-Atmosphere Interaction ➢ Objective: Identify and understand the interaction and feedback between land and atmosphere, and then improve NWP and DA. ➢ Last year: Continuously investigated the cause of surface temperature biases in the NCEP models which affect DA too. ➢ Current status: Together with field exp., improve physical schemes. ➢ Main achievements: – GFS late afternoon rapidly cooling identified and fixed – Improvement of GFS surface temperatures forecast. – Improvement of GFS precipitation forecast. 16

17. Why is important to study land-atmosphere interaction for data assimilation (DA) A.Direct radiance assimilation: Requiring a forward radiative transfer model (RTM) to calculate Tb with input of model atmospheric profiles and surface parameters. For surface-sensitive channels, Tb simulation largely depends on: (a) Sfc parameters such as LST and soil moisture; (b) Sfc emissivity (IR/MW) Thus, in order to improve radiance DA, we have to improve sfc emissivity calculation and sfc parameters simulation. Our efforts on land DA must include these two tasks. 17

18. Courtesy G. Manikin Comparison of T 2m (F): NAM, GFS and Obs, 00UTC, 2015-02-17 NAM GFS Obs CTL: Rapidly cooling up to 15 °C for 3h; EXP: Substantially improved GFS T2m: EXP-CTL GFS T2m (K) at Utica, NY CTL: Black; EXP: Red ; Obs: Blue 18

19. GFS Test: Comparison of T1, T 2m and T skin for CTL and EXP T1: Temperature at the lowest model level (Blue); T 2m : Red; T skin : Black GFS: CTL GFS: EXP Rapidly cooling: Decoupled Improvement CTL: Large difference between T1 and T 2m with a cessation of turbulent transport between the surface and the atmosphere, i.e., discontinuously as a function of external parameters or loss of predictability. EXP: Substantially improved not only T 2m, but also T skin and T1. 19

20. Summer: Surface temperature and its RMSE Northwest Reduced warm bias in the morning and cold bias in the afternoon (1.5 ° C); Reduced RMSE afternoon and nighttime up to 1.2 ° C. 20

21. Summer: Temperature fits to Obs: Bias and RMSE at fh48 NH Reduced temperature bias and RMSE near the surface 21

22. Reduced cold bias afternoon and nighttime (~ 1.2 ° C); Reduced RMSE afternoon and nighttime up to 1.0 ° C (~ 25% RMSE). Winter: Surface temperature and its RMSE Northwest 22

23. Precipitation Skill Scores over CONUS: f12-f36, f36-f60, f60-f84 Winter: Improved scores for light and medium precipitation. 23

24. 24 Summary ➢ Several satellite data sets developed recently (e.g., GVF, snow, burning area, albedo, radiation, soil and vegetation type) were tested in the NCEP models and the results show good improvements, compared with the current data sets; ➢ The SMOPS soil moisture data assimilation in GFS is continuously examined; Snow/burned data used in NAM.; ➢ Large cold bias of surface temperatures in GFS was identified and substantially reduced by the proposed solution; ➢ We have been continuing our efforts and working with many research teams to improve satellite data utilization over land in NCEP data assimilation system and then improve the GFS numerical weather prediction (NWP). 24