SPoRT Real-time Vegetation Dataset and Impact on Land Surface and Numerical Models Sixth Meeting of the Science Advisory Committee 28 February to 1 March 2012 transitioning unique NASA data and research technologies to operations National Space Science and Technology Center, Huntsville, AL  Relevance / accomplishments since 2009 SAC  Importance of vegetation in models  SPoRT real-time vegetation product  Modeling sensitivity methodology  Case study results  Summary and future

transitioning unique NASA data and research technologies to operations Relevance to NASA/SPoRT Addresses NWS forecast challenge – Improved local model forecasts needed, especially for warm-season processes Use of NASA EOS datasets – MODIS Aqua and Terra Data can be incorporated into both Land Information System (LIS) and Weather Research and Forecasting (WRF) models – Improve current SPoRT modeling capabilities – Expand upon data denial experiments with SPoRT partner WFOs – Supports the NASA-Unified WRF (NU-WRF) project: A larger-scale NASA project hosted by GSFC

transitioning unique NASA data and research technologies to operations Accomplishments since 2009 SAC Meeting 2009 SAC Recommendation: “Broaden the use of this [data denial] or similar concepts to help quantify the impact of unique NASA data sets.” Developed vegetation dataset that can be used by SPoRT partners for real-time modeling – Wrote module to incorporate vegetation data into LIS and NU-WRF – Transitioned data for use by SPoRT partners in WRF model and WRF-EMS without any required source code changes – Instructions document for WFOs to use SPoRT vegetation in WRF-EMS Impact investigations – 2011 intern student Publications and presentations – Annual AMS meetings (2011, 2012) – SPoRT virtual workshop (31 Aug 2011)

Importance of Vegetation in Models Evapotranspiration from Healthy Vegetation – Significant contribution of moisture transport into boundary layer during warm season – Important to represent vegetation accurately in models Horizontal and Vertical Density of Vegetation – Greenness Vegetation Fraction (GVF), horizontal density – Leaf Area Index (LAI), vertical density – In Noah Land Surface Model (LSM), LAI is prescribed while GVF varies spatially by model grid cell Other parameters depend on vegetation (e.g. albedo) transitioning unique NASA data and research technologies to operations

SPoRT Daily Real-Time Vegetation Product Continental-U.S. NDVI/GVF grid – NDVI from real-time MODIS swaths, mapped to CONUS grid – Inverse time-weighted NDVI composites Query up to 6 NDVI values in previous 20 days at each pixel Similar to original MODIS SST algorithm – Daily composites generated since 1 June 2010 – Calculate GVF on 0.01° grid for use in LIS/Noah LSM Following Zeng et al. (2000) and Miller et al. (2006) Based on distributions of NDVI max as a function of land use class transitioning unique NASA data and research technologies to operations NDVI max,i NDVI min

GVF 1-year Diff on 15 July: 2010 to 2011 Big 1-yr diff in GVF –TX was very wet in early summer 2010 –Virtually no rain in TX/OK in 2011 –1-yr reduction in GVF, up to 40%+ –Shows how much GVF can change from year to year –Climo doesn’t cut it! 2011 MODIS GVF actually compares better to NCEP climo  Texas summers should be dry!

transitioning unique NASA data and research technologies to operations Approach and Methods Assess impacts of real-time MODIS vegetation data on LIS/Noah and model forecasts of severe weather – Document model sensitivity to real-time versus climatological greenness vegetation fraction (GVF) – Can incorporating real-time GVF improve simulations of severe weather events? Employ unique NASA assets in modeling study – NASA Unified-Weather Research and Forecasting (NU-WRF) – Combination of several NASA capabilities and physical parameterizations into a single modeling software package

transitioning unique NASA data and research technologies to operations Approach and Methods, cont. Offline LIS run:  Noah Land Surface Model  Atmospheric analyses from GFS Data Assim. System

Results: General Observations Substantial changes to sensible/latent heat fluxes, and 2-m temperatures/dewpoints in places However, most WRF simulations look similar to each other – Similar model errors in onset/timing of convection – Good/poor control simulations  good/poor experimental forecasts Greatest impact occurs with maximum surface heating – 17 July 2010: “Clean” case (little pre-existing clouds/precip) with convection firing late in the day transitioning unique NASA data and research technologies to operations

17 July 2010 WRF Case Study Urban areas can be resolved much better by the SPoRT GVF. Higher GVFs prevail from NE to ND. DSM OMA MSP

17 Jul 2010, WRF 21-h fcst: 2-m Temp Higher SPoRT GVFs in the western portion of the focus area correctly led to lower forecast 2-m temperatures transitioning unique NASA data and research technologies to operations

17 July 2010, WRF 21-h fcst: 2-m Dewp/CAPE Higher GVF values generally led to higher 2-m dewpoints Net result was increase in CAPE up to 1000 J kg -1 transitioning unique NASA data and research technologies to operations

17 July 2010, WRF Fcst 1-h precip: 27/33 h Both model runs close on placement with initial development and movement Some intensity variations CNTL run moves convection southward through Iowa too quickly EXP run correctly re-develops convection along IA/NE border in region of higher residual CAPE CNTL EXP DIFF OBS transitioning unique NASA data and research technologies to operations

Summary and Conclusions Developed a CONUS GVF dataset – 1 June 2010 to date – Can be ingested into LIS, WRF, NU-WRF, and WRF-EMS Significant GVF changes from summer 2010 to 2011 – Anomalously green over Texas in July 2010 – Year-to-year variations cannot be represented by a climatology dataset Using SPoRT GVF showed some improvement on the 17 July 2010 severe case Real-time vegetation datasets have potential to enhance accuracy of forecast models

transitioning unique NASA data and research technologies to operations Future Work Short-term – Contribute results to COMET module – Additional case studies, verification statistics – Study impact of GVF differences over Texas related to 2011 drought – Improve albedo in LIS/WRF using new look-up table method based on input GVF Long-term – Collaborate with NCEP/EMC: NOAA Global Vegetation Index Enhanced climo (weekly vs. monthly) Initial development of real-time product documented in Jiang et al. (2010, JGR) Original seasonal Albedo Albedo: Look-up table

Backup Slides transitioning unique NASA data and research technologies to operations

Default GVF Dataset in WRF/LIS Five-Year Monthly Global Climatology – Derived from AVHRR Normalized Difference Vegetation Index (NDVI) data from 1985  1991 – 0.144° resolution, valid at mid-point of each month – Currently in Noah LSM within NCEP/NAM and WRF models – Operational at NCEP since 1997 (Ek et al. 2003) Cannot account for variations in GVF due to: – Weather/climate anomalies (e.g. drought, excessive rain) – Land-use changes since the early 1990s (e.g. urban sprawl) – Wildfires and prescribed burn regions transitioning unique NASA data and research technologies to operations

NASA-Unified WRF (NU-WRF) Demonstrate NASA model capabilities at satellite- resolved scales Based on Advanced Research WRF dynamical core Incorporates numerous capabilities & NASA assets – Physics schemes (Goddard radiation, microphysics) – Goddard satellite data simulator unit (GSDSU) – Land Information System (LIS) and LIS Verification Toolkit – Goddard Chemistry Aerosol Radiation and Transport (GOCART) – Model verification and numerous post-processing options – Coupling between WRF model, LIS/GOCART, GSDSU

GVF Comparison: 17 July 2010 Improved resolution – Ability to resolve vegetation variation in complex terrain Greener in Western U.S. – Nearly 20-40% over High Plains – High Plains rainfall well above average in previous 3 months NW NE SW SE

SPoRT GVFs: – Consistently higher in western U.S. – Have more day-to-day variability – NE: Slightly lower in mid-summer, then higher in early Fall – SE: Closest to NCEP – Warmer than normal temperatures out East in Fall 2010 GVF Quadrant Comparison: SPoRT vs. NCEP; 1 June to 31 October transitioning unique NASA data and research technologies to operations

Land Surface Model Stats: NW Quadrant transitioning unique NASA data and research technologies to operations Higher SPoRT GVFs lead to: – Higher latent heat fluxes due to increased evapo- transpiration – Lower sensible heat flux initially – More rapid drying of soil moisture By late Summer/Fall: – Both latent and sensible heat flux increase – This is due to drier soils that cause more rapid surface heating GVF Diff (SPoRT – NCEP) Latent Heat Flux Diff: Time of Peak Heating Sensible Heat Flux Diff: Time of Peak Heating Soil Moisture Diff

transitioning unique NASA data and research technologies to operations 17 July 2010: Verification Stats Bias Error Standard Deviation

22 May 2011: Joplin, MO tornado day

22 May 2011, WRF fcst 1-h precip: 23/27 h More intense 1-h rain rates in sportgvf run just prior to tornadic event Both runs have too much false alarm in AR After event, sportgvf run better handles squall line evolution into Arkansas Reduced false alarm in central Arkansas CNTLEXP DIFF OBS CNTLEXP DIFF OBS transitioning unique NASA data and research technologies to operations

Daily GVFs: 1 June to 30 Oct 2010 transitioning unique NASA data and research technologies to operations