LIS

P2B.7 Initializing the Land Surface* with the NASA Land Information System to Improve

WRF Predictions of Summertime ConvectionJonathan L. Case1, Sujay V. Kumar2, Jayanthi Srikishen3, and Gary J. Jedlovec4

1ENSCO Inc./Short-term Prediction Research and Transition (SPoRT) Center; 2SAIC/NASA Goddard Space Flight Center, Greenbelt, MD;3Universities Space Research Association/SPoRT Center, Huntsville, AL; 4NASA Marshall Space Flight Center/SPoRT Center, Huntsville, AL

Tropical Storm Fay: 18-26 AUG 2008

Hypothesis and Objectives

Hypothesis: High-resolution land and water datasets from

NASA utilities can lead to improvements in simulated

summertime pulse-type convection over the S.E. U.S.

Experiment objectives

Use NASA Land Information System (LIS) to provide high-

resolution land surface initializations

Incorporate SPoRT MODIS composites for detailed

representation of sea surface temperatures (SSTs)

Demonstrate proof of concept in using these datasets in

local model applications with the Weather Research and

Forecasting (WRF) model

Quantify possible improvements to WRF simulations

*NASA/SPoRT MODIS SST Initialization

Moderate Resolution Imaging Spectroradiometer

(MODIS) SSTs provide superior resolution

Ocean surface initialized with SPoRT/MODIS SSTs

Quality check with the latency product

Current weakness is high latency in areas with

persistent cloud cover

JPL collaboration to improve product with AMSR-E

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 OutputsPhysics Applications

Weather/

Climate

Water

Resources

Homeland

Security

Military

Ops

Natural

Hazards

Land Surface Modeling with LIS

NAM

10 June 2008 Example:

MODE Precip Objects and Verification

Control LISMOD

Fcst

hour

Grid Area

Match

Grid Area

Un-match

Grid Area

Match

Grid Area

Un-match

15 0 492 0 474

16 0 802 232 587

17 388 544 606 653

18 419 1039 470 711

19 108 1122 186 916

20 318 680 271 674

21 394 301 382 646

22 0 596 110 424

23 28 632 30 501

24 0 328 0 417

(a) Control (RTG) (b) MODIS

(c) MODIS RTG

Use of MET/MODE for Precip Verification

Stage IV analyses used as validation for

traditional precip stats and MODE

Traditional grid point verification

Bias, Threat Score, Heidke Skill Score (HSS)

1-h / 3-h accumulation intervals

5, 10, and 25 mm thresholds

Neighborhood precipitation verification

Occurrence of precipitation threshold in a “box”

surrounding a grid point

Relaxes stringency and determines model skill at

distance thresholds

MODE object classification

Resolves objects through convolution

thresholding:

Filter function applied to raw data using a tunable

radius of influence

Filtered field thresholded (tunable parameter) to create

mask field

Raw data restored to objects where mask

meets/exceeds threshold

Attributes computed for “matched” objects

Methodology and Data

LIS/Noah 4-km LSM run: 1/1/2004 to 9/1/2008

Same soil and vegetation parameters as in WRF

Atmospheric forcing from GDAS + Stage IV analyses

Run long enough for soil to reach equilibrium state

Output GRIB files initialize WRF land surface variables

Bring LIS data into WRF initial conditions

Modifications to WRF Preprocessing System (WPS):

Created Vtable.LIS ; added LIS fields into METGRID.TBL file

Soil moisture/temp, skin temp, snow-water, land-sea mask

LIS data over-write NAM land surface data

MODIS SSTs over-write NAM / RTG SSTs in WPS

Run parallel WRF simulations

Once daily 27-h simulations, initialized at 0300 UTC

81 total forecasts (Jun – Aug 2008)

11 missing dates due to missing/corrupted MODIS SSTs

Control: ICs/BCs from NCEP 12-km NAM model

LISMOD: Same as Control, except:

Replace land surface data with LIS output fields

Replace SSTs with SPoRT MODIS composites

Evaluation and Verification

Graphical and subjective comparisons

Meteorological Evaluation Tools (MET) package

Standard point / grid verification statistics

Method for Object-Based Diagnostic Evaluation (MODE):

object-oriented, non-traditional verification method

WRF Model Configuration

Model domain over Southeastern U.S.

Advanced Research WRF v3.0.1.1

4-km horizontal grid spacing

39 sigma-p levels from surface to 50 mb

Min. spacing near surface of 0.004 sigma

Max. spacing of 0.034 sigma

Positive definite advection of scalars

Model physics options

Radiation: Dudhia SW and RRTM LW

Microphysics: WSM6

Land Surface: Noah LSM (same as LIS)

PBL: MYJ scheme

Cumulus parameterization: None

ny =

311

nx = 309

(a) Control (NAM) (b) LIS

(c) LIS – NAM

SST Comparison:

10 June 2008

Soil Moisture Comparison:

10 June 2008 Fuzzy engine weights applied to object

attributes to compute “total interest” field

Object Attribute Weight

Centroid

Distance20%

Minimum Boundary

Distance40%

Orientation

Angle Difference10%

Ratio of

Object Areas10%

Intersection

Area Ratio20%

Control LISMOD

10-mm (1 h)-1 MODE Objects

Traditional Verification: Summer 2008 MODE Verification: Summer 2008

Quantity Control LISMODDifference

(LISMOD – Con)

%

Improved

Mean Matched

(per model run)2456 2562 106 4.3%

Mean

Unmatched

(per model run)

6798 6538 -260 3.8%

0

10000

20000

30000

40000

50000

60000

70000

12 13 14 15 16 17 18 19 20 21 22 23 24

# o

f gr

id p

oin

ts

Forecast Hour

MODE: 10-mm Precip (Un)Matched AreaControl MatchLISMOD MatchControl UnmatchLISMOD Unmatch

(a)

-20

-15

-10

-5

0

5

10

15

20

25

30

12 13 14 15 16 17 18 19 20 21 22 23 24

% C

ha

ng

e

Forecast Hour

MODE: 10-mm (Un)Matched % Change

Matched

Unmatched

(b)

10-mm (1 h)-1 objects, by forecast run,

12-24 h combined together

10-mm (1 h)-1 objects, by forecast hour

0.4

0.6

0.8

1

P10 P25 P50 P75 P90

Tota

l In

tere

st

Interest Function Distribution: 1-h accum precip

Control-5mm LISMOD-5mm

Control-10mm LISMOD-10mm

Control-25mm LISMOD-25mm

0

1

2

3

12 13 14 15 16 17 18 19 20 21 22 23 24

Bia

s S

core

Forecast Hour

1-hour Precipitation Bias

Control-5mm LISMOD-5mm

Control-10mm LISMOD-10mm

Control-25mm LISMOD-25mm

0

0.02

0.04

0.06

0.08

0.1

12 13 14 15 16 17 18 19 20 21 22 23 24

He

idk

e S

kil

l S

core

Forecast Hour

1-hour Precipitation HSS

0.5

1

1.5

2

2.5

12 13 14 15 16 17 18 19 20 21 22 23 24

Me

dia

n B

ias

Sc

ore

Forecast Hour

Neighborhood Precip Bias: 10 mm (1 h)-1

Control: 20 km box LISMOD: 20 km boxControl: 50 km box LISMOD: 50 km boxControl: 80 km box LISMOD: 80 km box

0

0.1

0.2

0.3

0.4

12 13 14 15 16 17 18 19 20 21 22 23 24

Me

dia

n H

SS

Forecast Hour

Neighborhood Precip HSS: 10 mm (1 h)-1

Control: 20 km box LISMOD: 20 km boxControl: 50 km box LISMOD: 50 km boxControl: 80 km box LISMOD: 80 km box