NWS / SPoRT Coordination Call

August 19, 2010Topics: LIS, SST Composite, Technical Issues

19 Aug 2010Jonathan Case and Robby James

transitioning unique NASA data and research technologies to operations

Summertime Convective Initiation Diagnosis using Data from the Land

Information System

Introduction

• Background on Land Information System (LIS)

• Summer intern student presentation

• SPoRT Greenness Vegetation Product

• Summary

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

Topography,Soils

Land Cover, Vegetation Properties

Meteorology(Atmospheric

Forcing)

Snow Soil MoistureTemperature

Land Surface Models

(e.g. Noah, VIC, SIB, SHEELS)

Data Assimilation Modules

Soil Moisture &

Temp

Evaporation

Runoff

SnowpackProperties

Inputs OutputsPhysics Applications

Weather/Climate

Water Resources

HomelandSecurity

Military Ops

Natural Hazards

Land Surface Modeling with LIS

Topography,Soils

Land Cover, Vegetation Properties

Meteorology(Atmospheric

Forcing)

Snow Soil MoistureTemperature

Land Surface Models

(e.g. Noah, VIC, SIB, SHEELS)

Data Assimilation Modules

Soil Moisture &

Temp

Evaporation

Runoff

SnowpackProperties

Inputs OutputsPhysics Applications

Weather/Climate

Water Resources

HomelandSecurity

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

Topography,Soils

Land Cover, Vegetation Properties

Meteorology(Atmospheric

Forcing)

Snow Soil MoistureTemperature

Land Surface Models

(e.g. Noah, VIC, SIB, SHEELS)

Data Assimilation Modules

Soil Moisture &

Temp

Evaporation

Runoff

SnowpackProperties

Inputs OutputsPhysics Applications

Weather/Climate

Water Resources

HomelandSecurity

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

Topography,Soils

Land Cover, Vegetation Properties

Meteorology(Atmospheric

Forcing)

Snow Soil MoistureTemperature

Land Surface Models

(e.g. Noah, VIC, SIB, SHEELS)

Data Assimilation Modules

Soil Moisture &

Temp

Evaporation

Runoff

SnowpackProperties

Inputs OutputsPhysics Applications

Weather/Climate

Water Resources

HomelandSecurity

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)

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)

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

Soil and Vegetation Types

1 June 2009

• “Random” convection found at 2015 UTC over Birmingham, AL

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)

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

7 July 2009

7 July 2009

L

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

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)

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”

Latent Heat Flux at 1800 UTC

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

MODIS GVF Composites, cont.

Latent Heat Flux: 18z 27 Jun 2010

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

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)

Potential ApplicationWarm season:

• Identify gradients where BL conv/div or changes may influence precipitation

• Tropical cyclone influence and impacts (link to blog post)

Cool season:• Sea breeze strength• Coastal fog and low clouds w/

possible advection issues (link to blog post)

All seasons: • NWP initialization of surface boundary• Use in GFE

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