Land Surface Modeling with LIS · Land Surface Modeling with LIS NAM 10 June 2008 Example: MODE...
1
LIS P2B.7 Initializing the Land Surface* with the NASA Land Information System to Improve WRF Predictions of Summertime Convection Jonathan L. Case 1 , Sujay V. Kumar 2 , Jayanthi Srikishen 3 , and Gary J. Jedlovec 4 1 ENSCO Inc./Short-term Prediction Research and Transition (SPoRT) Center; 2 SAIC/NASA Goddard Space Flight Center, Greenbelt, MD; 3 Universities Space Research Association/SPoRT Center, Huntsville, AL; 4 NASA 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 Outputs Physics 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 Distance 20% Minimum Boundary Distance 40% Orientation Angle Difference 10% Ratio of Object Areas 10% Intersection Area Ratio 20% Control LISMOD 10-mm (1 h) -1 MODE Objects Traditional Verification: Summer 2008 MODE Verification: Summer 2008 Quantity Control LISMOD Difference (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 # of grid points Forecast Hour MODE: 10-mm Precip (Un)Matched Area Control Match LISMOD Match Control Unmatch LISMOD 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 % Change 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 Total Interest 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 Bias Score 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 Heidke Skill Score 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 Median Bias Score Forecast Hour Neighborhood Precip Bias: 10 mm (1 h) -1 Control: 20 km box LISMOD: 20 km box Control: 50 km box LISMOD: 50 km box Control: 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 Median HSS Forecast Hour Neighborhood Precip HSS: 10 mm (1 h) -1 Control: 20 km box LISMOD: 20 km box Control: 50 km box LISMOD: 50 km box Control: 80 km box LISMOD: 80 km box
Transcript of Land Surface Modeling with LIS · Land Surface Modeling with LIS NAM 10 June 2008 Example: MODE...
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
