1. NWS / SPoRT Coordination Call August 19, 2010 Topics: LIS, SST Composite, Technical Issues

2. 19 Aug 2010 Jonathan Case and Robby James transitioning unique NASA data and research technologies to operations Summertime Convective Initiation Diagnosis using Data from the Land Information System

3. Introduction Background on Land Information System (LIS) Summer intern student presentation SPoRT Greenness Vegetation Product Summary

4. NASA Land Information System (LIS) High-performance land surface modeling and data assimilation system – Runs a variety of Land Surface Models (LSMs) – Combines satellite, ground, and reanalysis data to integrate LSM in offline mode – Can run coupled to Advanced Research WRF – Data assimilation capability (EnKF) built-in – Framework enables substitution of datasets SPoRT experience with LIS – Positive impacts to WRF forecast of sea breeze over FL – Modest improvement to forecasts of air-mass convection in the Southeast U.S. using object-based verification – Providing LIS output to BMX WFO as a diagnostic tool for CI

5. Topography, Soils Land Cover, Vegetation Properties Meteorology (Atmospheric Forcing) Snow Soil Moisture Temperature Land Surface Models (e.g. Noah, VIC, SIB, SHEELS) Data Assimilation Modules Soil Moisture & Temp Evaporation Runoff Snowpack Properties Inputs Outputs Physics Applications Weather/ Climate Water Resources Homeland Security Military Ops Natural Hazards Land Surface Modeling with LIS

6. Topography, Soils Land Cover, Vegetation Properties Meteorology (Atmospheric Forcing) Snow Soil Moisture Temperature Land Surface Models (e.g. Noah, VIC, SIB, SHEELS) Data Assimilation Modules Soil Moisture & Temp Evaporation Runoff Snowpack Properties Inputs Outputs Physics Applications Weather/ Climate Water Resources Homeland Security Military Ops Natural Hazards Land Surface Modeling with LIS  1-km topography, averaged for coarser resolution grids  Bottom soil temperature: 6-yr climo  Soil type:  1-km, 19-class State Soil Geographic database  Dominant soil type used for grid spacing > 1 km

7. Topography, Soils Land Cover, Vegetation Properties Meteorology (Atmospheric Forcing) Snow Soil Moisture Temperature Land Surface Models (e.g. Noah, VIC, SIB, SHEELS) Data Assimilation Modules Soil Moisture & Temp Evaporation Runoff Snowpack Properties Inputs Outputs Physics Applications Weather/ Climate Water Resources Homeland Security Military Ops Natural Hazards Land Surface Modeling with LIS  Vegetation/land cover:  1-km, 24-class USGS  Monthly green veg fraction (0.15°)  Derived from 1992-93 AVHRR data  1-km Land mask  Determined off of vegetation type  Quarterly and max snow (MODIS) albedo

8. Topography, Soils Land Cover, Vegetation Properties Meteorology (Atmospheric Forcing) Snow Soil Moisture Temperature Land Surface Models (e.g. Noah, VIC, SIB, SHEELS) Data Assimilation Modules Soil Moisture & Temp Evaporation Runoff Snowpack Properties Inputs Outputs Physics Applications Weather/ Climate Water Resources Homeland Security Military Ops Natural Hazards Land Surface Modeling with LIS  Datasets driving LSM physics:  Input variables: 2-m T, q, sfc pressure, 10-m wind, downward short/longwave radiation, precipitation  Forcing sources used by SPoRT/LIS: Global Data Assimilation System (GDAS, GFS assimilation cycle) North American Land Data Assimilation System (NLDAS) Stage IV precipitation analyses GFS forecasts (for same-day predictions)

9. Summer Intern Project Outline Focus on the hours of 0900 to 2100 UTC – Pre-dawn conditions – Diurnal heating Locate convective initiation – Find land correlations and soil features of interest LIS products being used – Soil moisture (0-10 & 40-100 cm) – Sensible/Latent Heat Flux – Soil/Vegetation Type – Surface Skin Temperature – Relative Soil Moisture (analog to RH in atmosphere)

10. Background Extension of BMX CI project in summer 2009 “Unknown” boundaries = 20% – Possibly due to land characteristics Examine “random” convection events – Nominal atmospheric forcing Case dates from summer 2009 – 1 June, 7 July, 14 August, 15 August

11. Soil and Vegetation Types

12. 1 June 2009 “Random” convection found at 2015 UTC over Birmingham, AL

13. 1 June 2009 Storm forms downwind of the “Urban Heat Island” effect Skin temperature (color shading, °C), 20-dBZ contours (white), and accumulated rainfall (> 1 mm h -1, black contours) valid at a) 1800, b) 1900, c) 2000, and d) 2100 UTC. a)b) c) d)

14. 7 July 2009 Stationary front found in extreme southern Alabama/Georgia at 2000 UTC. Central and Northern AL received ample solar heating throughout the day “Unknown” boundary found at 2000 UTC over Birmingham, AL. – Could be caused by regional gradients in soil moisture

15. 7 July 2009

16. L Relative Soil Moisture (0-10 cm Layer) 2000 UTC

17. Skin temperature (color shading, °C), 20-dBZ contours (white), and accumulated rainfall (> 1 mm h -1, black contours) valid at a) 1600, b) 1700, c) 1800, and d) 1900 UTC. Another example of “Urban Heat Island” effect High area of Sensible Heat Flux (400 – 450 W m -2 ), higher skin temperatures. 14 August 2009 a) b) c)d)

18. 15 August 2009 Similar setup to August 14th – Weak southeast flow “Black Belt” area of interest – Comprised of mainly clay soils – Little convective initiation – Ongoing convection dissipates upon entry Red line shows approximate area of “Black Belt”

19. Latent Heat Flux at 1800 UTC

89. SPoRT/MODIS NDVI Composites Normalized Difference Vegetation Index (NDVI) – NDVI = (NIR – RED)/(NIR + RED) – Updated daily using swath data from Univ. of WI – CONUS domain at 1-km resolution – SPoRT began creating in real time on 1 June 2010 Greenness Vegetation Fraction (GVF) – GVF computed from NDVI – Implemented as an option into NASA/LIS – More representative detail compared to climo

90. MODIS GVF Composites, cont.

91. Latent Heat Flux: 18z 27 Jun 2010

92. Summary LIS shows the effect of “Urban Heat Islands” on downwind convection – Pronounced skin temperature gradients Convection favored along LIS gradients as flow becomes perpendicular to gradient SPoRT developed NDVI/GVF to improve land- atmosphere interactions

93. transitioning unique NASA data and research technologies to operations Overview Bring in AMSR-E (microwave) SST data for coverage in persistent cloudy periods Reduced data latency, hence more up-to-date values used in composite Increased number of available satellite passes through L2P stream from JPL 14 days of data used to compute SST pixel value SST compositing algorithm changes – latency, error, and resolution weighted product Inverse latency formula (1 / # of days) gives more influence to recent data Weighting factor: MODIS = 1, AMSR-E = 0.2, GOES/POES = 0.2, OSTIA fills where no other exists GOES/POES used in near-coastal (< 120 km) where AMSR-E can not be used AMSR-E at lower weight due to resolution Coverage of Enhanced MODIS/AMSR-E SST Product Current SST Composite Product Use near real-time L2P data stream (JPL) for MODIS and AMSR-E allows more passes Only highest quality flagged data is used Bias correction made based on the retrieval method MODIS-only composite discontinued (instructions available)

94. Potential Application Warm season: Identify gradients where BL conv/div or changes may influence precipitation Tropical cyclone influence and impacts (link to blog post)link to blog post Cool season: Sea breeze strength Coastal fog and low clouds w/ possible advection issues (link to blog post)link to blog post All seasons: NWP initialization of surface boundary Use in GFE

95. For Tropical Season applications the color scale highlights SSTs above 80F in 2.5F increments to 90F and then reds above 90F.