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Application of Satellite Land Measurements in Improving NCEP Numerical Weather Prediction

Application of Satellite Land Measurements in Improving NCEP Numerical Weather Prediction. Weizhong Zheng, Michael Ek, Helin Wei, Jesse Meng, Jiarui Dong and John Derber. Environmental Modeling Center (EMC) NOAA/NWS/NCEP, USA. Xiwu Zhan and Jicheng Liu NOAA/NESDIS/STAR, USA.

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Application of Satellite Land Measurements in Improving NCEP Numerical Weather Prediction

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  1. Application of Satellite Land Measurements in Improving NCEP Numerical Weather Prediction Weizhong Zheng, Michael Ek, Helin Wei, Jesse Meng, Jiarui Dong and John Derber Environmental Modeling Center (EMC) NOAA/NWS/NCEP, USA Xiwu Zhan and Jicheng Liu NOAA/NESDIS/STAR, USA WWOSC, Montreal, Canada 16 – 21 August, 2014 1

  2. Outline • Requirements of satellite products for NCEP land model • Utilization of satellite data sets (e.g. GVF) • Assimilation of satellite products (e.g. Soil moisture) • Satellite radiance assimilation • Summary and discussion

  3. Forecast NCEP, NCAR, UT-Austin, U. Ariz., & others Uncoupled “NLDAS” (drought) NOAH Land Surface Model Noah Land Model Connections in NOAA’s NWS Model Production Suite Oceans HYCOM WaveWatch III Climate CFS 2-Way Coupled Hurricane GFDL HWRF MOM3 3.5B Obs/Day Satellites 99.9% radar? Dispersion ARL/HYSPLIT Regional NAM WRF NMM (including NARR) Global Forecast System Global Data Assimilation Severe Weather Regional Data Assimilation WRF NMM/ARW Workstation WRF Short-Range Ensemble Forecast North American Ensemble Forecast System WRF: ARW, NMM ETA, RSM Air Quality GFS, Canadian Global Model NAM/CMAQ Rapid Update for Aviation (ARW-based) 3

  4. NCEP-NCAR unified Noah land model Role of Noah Land Model • Close surface energy & water budgets, • Determine heat, moisture, and momentum exchange between surface & atmosphere, • Noah land model provides surface boundary conditions to parent atmospheric model, e.g. NAM, GFS, CFS. 4

  5. Land Model Requirements To provide these proper boundary conditions, land model must have: • Atmospheric forcing to drive land model, • Appropriate physics to represent land-surface processes, • Initial land states, e.g. soil moisture/ice and snow, analogous to initial atmospheric conditions, though land states may carry more “memory”, especially deep soil moisture, similar to SSTs, • Land data sets e.g. land use/land cover (vegetation type), soil type, surface albedo and emissivity, and associated parameters, e.g. surface roughness, soil and vegetation properties. 5

  6. Remotely-sensed Atmospheric Forcing Incoming Longwave Precipitation Incoming solar Air temperature Specific humidity Wind speed 6 + Atmospheric Pressure Example from 18 UTC, 12 Feb 2011

  7. CMAP Surface gauge GDAS Atmospheric Forcing: Precipitation • Global Land Data Assimilation System (GLDAS) used in the NCEP Climate Forecast System (CFS) relies on a “blended” precipitation product, a function of: • Satellite-estimated precipitation (CMAP), heaviest weight in tropics where gauges sparse. • Surface gauge network, heaviest in mid-latitudes. • High-latitudes: Model-estimated precipitation based on Global Data Assimil. System (GDAS). 7

  8. Initial land states: Snow Products 02 April 2012 24-km Snow Depth (daily integrated AFWA product) 4-km Snow Cover (daily integrated NIC IMS product) 8

  9. Initial land states: Soil Moisture ESA SMOS 04 March 2014 SMOPS Blended Daily Soil Moisture • Testing assimilation of SMOPS into NCEP global model. NASA SMAP (Oct 2014 launch) 9

  10. Land Data Sets (e.g. GFS T574 ~25-km) Vegetation Type (1-degree, SiB) Soil Type (1-degree, Zobler) Mid-July 2013 Mid-Jan Mid-July Mid-Jan 2013 Green Vegetation Fraction (Weekly real-time, 1/8-deg, NESDIS/VIIRS) Green Vegetation Fraction (Multi-year, 1/8-deg, NESDIS/AVHRR) • Fixed annual/monthly/weekly climatologies, or near real-time observations: Veg_Type, Soil_Type, GVF, Max.-Snow Albedo, Snow-Free Albedo. • Some quantities may be assimilated into Noah, e.g. soil moist., snow. 10

  11. Land Conditions: Wildfire Effects Wildfires affect weather/climate systems: • atmospheric circulations (2) aerosols and clouds (3) land surface states (green vegetation fraction, albedo and surface temperature etc.) --> impact on surface energy budget, boundary-layer evolution, clouds & convection. Wu & Ek: “A parameterization for land surface physical characteristics of burning areas for weather and climate models” . Aug.19: 11:40 - 12:00, Room 520 F 11

  12. Remotely-Sensed Land Data Sets We Need: • Highest temporal and spatial resolution available. • “Blended” LEO and GEO products; include in situ. • Global domain, consistent use between NCEP global and regional models. • Full consistency between all products, e.g. a “burned area product” also reflected in green vegetation fraction (GVF), albedo, emissivity, surface temperature, soil moisture, etc. • Future: account for lakes, wetlands, water bodies. • Land data assimilation in Noah land model using NASA Land Information System (LIS), i.e. snow, soil moisture, GVF (future Noah with dynamic/growing vegetation). 12

  13. Utilization of Satellite Data Sets in the Model Comparison of GVF between VIIRS and Climatology Climatology data:(Gutman & Ignatov, 1998) • 5 year mean monthly climatology from AVHRR; • Resolution: 0.144 degrees (~16km); • Period: April 1985 – March 1991 (1988 excluded) VIIRS weekly real-time data: By Marco Vargas, Zhangyan Jiang & Junchang Ju • Weekly real-time; • Resolution: 0.036 degrees (~4km, global) & 0.009 degrees (~1km, regional) Algorithm: Uses VIIRS red (I1), near-infrared (I2) and blue (M3) bands centered at 0.640 μm, 0.865 μm and 0.490 μm, respectively, to calculate the Enhanced Vegetation Index (EVI) and derive GVF from EVI.

  14. Analysis on Anomaly Correlation at PMSL: Day5 +0.007 ↑ Good improvement of the PMSL scores in NH.

  15. Temperature fits to RAOBS: f24 and f48 Bias reduced in the lower atmosphere; RMSE slightly reduced. Reduction of bias & rmse.

  16. Precipitation Skill Scores over CONUS: f60-f84 Improved scores for medium & heavy precipitation but slightly degraded biases.

  17. Assimilation of Satellite Products Snow Data Assimilation (temporal comp.) Snow Cover Fraction Comparison of snow cover fraction between the MODIS (blue circles), the open loop simulation (black line) and the assimilation simulation (green line). Snow Water Equivalent Obs Comparison of snow water equivalent between the open loop simulation (green), the assimilation simulation (red) and the in-situ measurement (black) averaged over all SNOTEL sites in the study region. DA No DA *Jiarui Dong (NCEP/EMC)

  18. Schematic representation of assimilating satellite soil moisture products from NESDIS/SMOPS into NCEP Global Forecast System (GFS) Cooperators: Xiwu Zhan & Jicheng Liu (NESDIS/STAR)

  19. SMOPS DA in GFS: Precipitation Skill Scores over CONUS 36-60hr and 60-84hr forecast for 2 April through 5 May 2012 Bias: Black: w/o SMOPS Red: w/ SMOPS Error reduced with SMOPS (<0). Threat Scores: Black: w/o SMOPS Red: w/ SMOPS Scores increased with SMOPS (>0). f60 - f84 f36 - f60 f36-f60: Improved slightly the scores and reduced the bias; f60-f84: Improved the scores for light and medium precipitation but not for heavy precipitation. Good improvement for the bias.

  20. Satellite Radiance Assimilation It requires a forward radiative transfer model (RTM) to calculate Tb with input of model atm profiles and sfc parameters. (GSI/CRTM) Surface-sensitive channels Tb simulation: (a)Sfc parameters such as LST and soil moisture; • GFS LST daytime cold bias: new Zot (Ops GFS in May, 2011 ) • DICE (Diurnal Land/Atmosphere Coupling Experiment ) (b) Sfc emissivity (IR/MW) • Improvement of MW land surface emissivity model (Implemented in the latest release CRTM v2.1). • Sub-grid scale land surface: sea, land, ice and snow.

  21. ▶ Problems in surface sensitive channels data assimilation: Much less satellite data (IR/MW) is assimilated over land than over ocean. e.g. in GFS/GSI, the large Tb bias can be seen over the CONUS from the GDAS radiance assimilation monitoring . ▻ West CONUS: (a) Substantial cold bias of land surface skin temperature (LST) in GFS, resulting in the large simulated Tb bias (IR/MW) in the GSI; (b) In desert area, errors of emissivity calculation for MW; ▻ East CONUS: errors of emissivity calculation for MW. ▶ Approaches of improvement: (a) Reduction of GFS LST bias with new formulations (Zom and Zot) (Zeng et al., U. of Arizona; Zeng et al. 2012; Zheng et al. 2012). (Operational GFS in May, 2011) (b) Improvement of MW emissivity calculation in the MW land emissivity model (Weng et al., 2001). (Implemented in the latest release CRTM v2.1).

  22. Diurnal variation of Tsfc and T2m Verified with SURFRAD Observation Network Large cold bias July 2010 Improved ! July 2011 http://www.srrb.noaa.gov/surfrad New zot and zom implemented in NCEP Ops GFS on May 9, 2011.

  23. Comparisons of Tb bias and rmse and the number of obs assimilated in GSI Red: CTL Blue: SEN Channel Channel Channel Channel

  24. Summary and Discussion • Several satellite data sets developed recently were tested in the NCEP models and the results show good improvements, compared with the current data sets; • The weekly real-time VIRRS GVF data shows reduction of errors of temperature, humidity and wind speed forecasts, and improvement of precipitation scores. • The SMOPS soil moisture product was assimilated in the GFS and it shows some positive impacts on NWP; • 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).

  25. THANK YOU! 25

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