applications of the land information system (lis) fifth meeting of the science advisory committee...

Applications of the Land Information System (LIS)

Fifth Meeting of the Science Advisory Committee18-20 November, 2009

Jonathan Case

transitioning unique NASA data and research technologies to operations

National Space Science and Technology Center, Huntsville, AL


LIS: Relevance to NASA/SPoRT

• NASA asset developed by GSFC

• LIS benefit to SPoRT end-users– LSM fields for model initialization– Diagnostics for short-term forecasts of

temperatures and/or convective initiation

• LIS framework is capable of incorporating NASA EOS datasets– MODIS-derived land cover– Assimilation of AMSR-E soil moisture


Accomplishments since 2007 SAC Meeting

• 2007 SAC Recommendations:“…look at some non-quiescent cases….Move toward a systematic evaluation with satellite and radar”

• Verification study of daily WRF runs from summer 2008: Focus on precipitation

• Configured real-time 3-km LIS– Hourly output– Used to initialize NWS Miami WRF runs

• Publications and presentations– J. Hydrometeor. (2008)– Annual AMS meetings (2008, 2009)– WRF Users Workshops (2008, 2009)


High-Level Overview of LIS

LSM First Guess / Initial Conditions

WRF

Land Surface Models (LSMs)

Noah,VIC, SIB, SHEELS

Coupled orForecast Mode

Uncoupled or Analysis Mode

Global, RegionalForecasts and (Re-) Analyses

Station Data

Satellite Products

ESMF

Data Assimilation (v, LST, snow)


Approach and Methods

• Daily 27-h WRF simulations over SE U.S.– 4-km grid spacing, 03z initializations– 81 total forecasts from Jun – Aug 2008– Control: Initial / boundary conditions

from NCEP 12-km NAM model – Experiment: LIS LSM and MODIS SST

initialization data (LISMOD)

• Evaluation and Verification – Focus on (convective) precipitation verification– Meteorological Evaluation Tools (MET)– Method for Object-Based Diagnostic Evaluation (MODE)

• Case studies of severe convection with GSFC/NSSLny

= 3

11

nx = 309


LIS Spin-up Run and WRF Initialization

• Run LIS/Noah offline from Jan 2004 to Sep 2008– Same soil and vegetation parameters as in WRF– Same horizontal resolution, but different grid

• Simulates a realistic real-time setup– Atmospheric forcing used to drive LIS/Noah:

• 3-hourly Global Data Assimilation System analyses• Hourly Stage IV radar + gauge precipitation

– Run long enough for soil to reach equilibrium state

• Initialize WRF land surface with LIS output and MODIS SSTs


Validation Against SCAN Soil Moisture Obs

-12

-9

-6

-3

0

3

0 3 6 9 12 15 18 21 24 27

Erro

r (%

)

Forecast Hour

Mean SoilQ Errors: SCAN Points

Q0-10_CON Q0-10_LISQ10-40_CON Q10-40_LISQ40-100_CON Q40-100_LIS

4

8

12

16

0 3 6 9 12 15 18 21 24 27

Erro

r (%

)

Forecast Hour

SoilQ RMSE: SCAN Points

Q0-10_CON Q0-10_LISQ10-40_CON Q10-40_LISQ40-100_CON Q40-100_LIS

• LIS (solid lines w/ labels) consistently drier than Control/NAM– Reduced moist bias in top model layer (blue)– Reduced RMSE in top 2 model layers (red)– Increased dry bias in lower layer (green)

• Apples vs. Oranges comparison (obs level vs. model layer)


10 Jun 2008 Sensitivity Example

0-10 cm soil moisture SST Differences

Control(NAM) LIS

LIS – NAM

Control(RTG)

MODIS

MODIS – RTG


10 Jun 2008: 1224 hour forecasts

Sensible Heat Flux 1-hour Precipitation

CNTL LISMOD

DIFF Stage IV

1-h Traditional Precip Verification (1224 hours; JunAug 2008)

• WRF has an overall high bias

• LISMOD reduces bias, esp. mid-AM to early-PM (1218 h; 1521z)

• WRF generally has low skill (right)

• LISMOD incrementally improves skill

0

1

2

3

1 2 1 3 1 4 1 5 1 6 1 7 1 8 1 9 2 0 2 1 2 2 2 3 2 4

Bias

Scor

e

F o re cas t H o u r

1 -h o u r P r e c ip ita ti o n B ia s

C o n tro l- 5m m LIS M O D - 5m mC o n tro l- 10m m LIS M O D - 10m mC o n tro l- 25m m LIS M O D - 25m m 0

0 .0 2

0 .0 4

0 .0 6

0 .0 8

0 .1

1 2 1 3 1 4 1 5 1 6 1 7 1 8 1 9 2 0 2 1 2 2 2 3 2 4

Heid

ke Sk

ill S

core


1 -h o u r P r e c ip ita ti o n H SS


Traditional Precip Verification Problem[from Baldwin et al. (2001), NWP/WAF conf.]

• Both forecasts have same bias• Using traditional measures, forecast #2 has

larger RMS error & lower threat score• Which forecast is “better”?• Need non-standard verification method!

obs Fcst #1

Fcst #2


MET/MODE Object Verification

Obs PrecipFcst Precip

80 km

• Precipitation “objects” identified based on several spatial attributes

• Forecast objects matched to obs objects (i.e. “hit”) based on– Distance between objects– Similarities in spatial attributes

• In our use of MODE, fcst object must be within 80 km of obs object– Ensures that convection on Florida’s

West Coast does not get matched with convection on East Coast


10 Jun: MODE 10-mm/(1 h) Precip Objects

Control LISMOD

MatchedForecastObjects(“hits”)

MatchedObservedObjects


10 Jun: MODE 10-mm/(1 h) Precip Objects

Control LISMOD

Un-matchedForecastObjects

(false alarms)

Un-matchedObservedObjects(misses)


10 Jun: MODE 10-mm/(1 h) Precip ObjectsControl LISMOD

Fcst hour

Grid Area

Match

Grid Area Un-

match

Grid Area

Match

Grid Area Un-

match

12 0 115 0 115

13 0 93 0 64

14 0 222 0 108

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

Control LISMOD


MODE 1-h Precip Object Verification:(Un-)Matched Differences by Model Run, 1224 h Forecasts

-4 0 0 0

-3 0 0 0

-2 0 0 0

-1 0 0 0

0

1 0 0 0

2 0 0 0

3 0 0 0

4 0 0 0

5 0 0 0

Area

(grid

squa

res)

M o d e l In iti alizati o n D ate

M O D E 1 0 -m m /1 -h o u r D iff in A r e a (U n -)M a tch e d b y fo r e ca s t r u n (L ISM O D - C o n tr o l)

M atc h e d D iff

U m atc h e d D iff Quantity # Forecasts Improved

# Forecasts Degraded

5-mm matched 39 41

5-mm unmatched 56 25

10-mm matched 37 39


25-mm matched 13 8


Quantity(mean # grid points

per model run)Control LISMOD Difference

(LISMOD – Control)%

Change

5-mm Matched 11,911 12,045 134 1.1%

5-mm Unmatched 17,750 17,175 -575 -3.2%

10-mm Matched 2,456 2,562 106 4.3%

10-mm Unmatched 6,798 6,538 -260 -3.8%

25-mm Matched 60 60 0 0%

25-mm Unmatched 549 505 -44 -8.0%


Case Studies of Severe ConvectionAMA

CNTRLNASA/

LIS

NASA/LIS:More robustconvection inTX Panhandle

• WRF runs using NASA assets– 28 March 2007 tornado outbreak– LIS + Goddard radiation physics

improved convective forecasts– Additional cases to be run using

NSSL/WRF operational domain

• NASA Unified WRF, coupling of:– Satellite data simulator unit– Land Information System– NASA/Goddard physics in WRF– Atmos. chemistry (GO-CART)– NASA GEOS-5 global model


Real-time LIS/Noah at SPoRT

• 3-km LIS over southeast U.S.– Spin-up run; restarts 4x per day– Hourly output posted to ftp site

• LIS option in WRF Environmental Modeling System (EMS), v3– LIS initializations at NWS Miami, FL

• LIS output for diagnostics– Readily displayable in AWIPS II– NWS BHM: Convective initiation– Other short-term forecasting issues

(low temps, fire weather, etc.)


Summary and Conclusions

• Simulation methodology using NASA data and tools– LIS land surface + MODIS SST composites– High-resolution representation of land/water surface, consistent with

local & regional model resolution– Precipitation verification using object matching techniques in MET– Improvements to 1-hour daytime precipitation– Decrease in over-prediction of precipitation

• Likely related to overall drier LIS soil moisture

• Implemented real-time LIS runs at SPoRT– Initialize LSM fields in WRF EMS– Possible diagnostics for short-term forecasting


Future Work

• Submit SE U.S. verification study to Wea. Forecasting• Incorporate MODIS vegetation fraction

– Test in offline LIS and LIS/WRF coupled runs

• Explore diagnostic utility of real-time LIS– Collaboration with NWS Birmingham, AL– Extend Koch and Ray (1997) convergence

zones study to include LSM boundaries

• Support NWS offices using real-time LIS– WRF EMS model initialization– Ingest into AWIPS II for diagnostics

Backup Slides



Validation Against SCAN Soil Temperatures

-5

-4

-3

-2

-1

0

0 3 6 9 12 15 18 21 24 27

Erro

r (°C

)

Forecast Hour

Mean SoilT Errors: SCAN Points

T0-10_CON T0-10_LIST10-40_CON T10-40_LIST40-100_CON T40-100_LIS

0

1

2

3

4

5

6

0 3 6 9 12 15 18 21 24 27

Erro

r (m

s-1)

Forecast Hour

SoilT RMSE: SCAN Points

T0-10_CONT0-10_LIST10-40_CON


Obligatory Point Verification

• LISMOD is slightly warmer/drier than the Control during the day

• Marginally larger RMSE

• Little to no differences in wind errors and MSLP (not shown)

2-m/10-m Bias

0

0 .5

1

1 .5

2

2 .5

3

3 .5

0 3 6 9 1 2 1 5 1 8 2 1 2 4 2 7

Error

(°C

)

Fo r e c a st H o u r

T/Td R M SE : La n d P o in ts

T_ C o n tro lT_ LIS M O DTd _ C o n tro lTd _ LIS M O D

0

1

2

3

4

0 3 6 9 1 2 1 5 1 8 2 1 2 4 2 7

Error

(m s

-1)


W in d R M SE : La n d P o in ts

S p d _ C o n tro l S p d _ LIS M O Du _ C o n tro l u _ LIS M O Dv_ C o n tro l v_ LIS M O D

-2

-1

0

1

2

0 3 6 9 1 2 1 5 1 8 2 1 2 4 2 7

Error

(°C

)


M e a n T/Td E r r o r s : La n d P o in ts

T_ C o n tro lT_ LIS M O DTd _ C o n tro lTd _ LIS M O D

0

1

2

3

0 3 6 9 1 2 1 5 1 8 2 1 2 4 2 7

Error

(m s

-1)


M e a n W in d E r r o r s : La n d P o in ts

S p d _ C o n tro l S p d _ LIS M O Du _ C o n tro l u _ LIS M O Dv_ C o n tro l v_ LIS M O D

2-m/10-m RMSE

3-h Traditional Precip Verification: (327 hours; Jun-Aug 2008)

-8

-6

-4

-2

0

2

4

6

8

3 6 9 1 2 1 5 1 8 2 1 2 4 2 7

Bias

% Im

prov

emen

t


LISM O D 3 -h P r e c ip B ia s Im p r o ve m e n t

5 m m

1 0 m m

2 5 m m

-1 0

-5

0

5

1 0

1 5

2 0

2 5

3 6 9 1 2 1 5 1 8 2 1 2 4 2 7HSS %

Impr

ovem

ent


LISM O D 3 -h P r e c ip H SS Im p r o ve m e n t

5 m m1 0 m m2 5 m m

• WRF has an overall high bias

• LISMOD reduces bias some, esp. during day- light hours (12-24 h)

• WRF generally has low skill (Heidke SS, right)

• LISMOD incrementally improves skill

0

0 .5

1

1 .5

2

2 .5

3 6 9 1 2 1 5 1 8 2 1 2 4 2 7

Bias

Scor

e


3 -h o u r P r e c ip ita ti o n B ia s

C o n tro l-5m m LIS M O D - 5m mC o n tro l-10m m LIS M O D - 10m mC o n tro l-25m m LIS M O D - 25m m

0

0 .0 4

0 .0 8

0 .1 2

0 .1 6

0 .2

3 6 9 1 2 1 5 1 8 2 1 2 4 2 7

Heid

ke Sk

ill S

core

F o re c as t H o u r

3 -h o u r P r e c ip ita ti o n H SS


MODE 1-h Precip Object Verification:Area Matched vs. Area Un-matched: All forecasts

0

1 0 0 0 0

2 0 0 0 0

3 0 0 0 0

4 0 0 0 0

5 0 0 0 0

6 0 0 0 0

7 0 0 0 0

1 2 1 3 1 4 1 5 1 6 1 7 1 8 1 9 2 0 2 1 2 2 2 3 2 4

# of g

rid po

ints


M O D E : 1 0 -m m P r e c ip (U n )M a tch e d A r e aC o n tro l M atchLIS M O D M atchC o n tro l U n m atchLIS M O D U n m atch

-2 0-1 5-1 0

-505

1 01 52 02 53 0

1 2 1 3 1 4 1 5 1 6 1 7 1 8 1 9 2 0 2 1 2 2 2 3 2 4

% Ch

ange


M O D E : 1 0 -m m (U n )M a tch e d % C h a n ge

M atch e dU n m atch e d

applications of the land information system (lis) fifth meeting of the science advisory committee...

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research technologies

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lis output

e soil moisture

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soil moisture sst differences

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