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J8.3 AN EVALUATION OF USING REAL-TIME, SATELLITE-DERIVEDVEGETATION FRACTION IN THE ETA MODEL

David J. Stensrud and Nicole P. KurkowskiNOAA/National Severe Storms Laboratory, Norman, OK

andMichael E. Baldwin

Cooperative Institute for Mesoscale Meteorological Studies, Norman, OK

1.0 INTRODUCTION1

Vegetation plays an important role in land-atmosphere interactions by helping to determinethe partitioning of the surface sensible and latentheat flux. Ookouchi et al. (1984) show thatsolenoidal circulations are able to developbetween patches of moist and dry soil.Similarly, areas of very dense vegetation next tobare soil surfaces under favorable environmentalconditions promote sea breeze-type circulations(Segal et al. 1986). Observations indicate thatharvesting winter wheat over Oklahoma altersthe surface sensible heat flux, such thatafternoon cumulus clouds develop overharvested fields before they form over adjacentareas with an active green canopy (Rabin et al.1990). Markowski and Stensrud (1998) furthershow that the harvesting of winter wheat overOklahoma and Kansas plays a major role on thespatial distribution of monthly mean diurnalcycles of conserved variables in the surfacelayer.

Schwartz and Karl (1990) find there arestatistically and practically significantrelationships between the timing of the onset ofvegetation and surface daily maximumtemperature. Their study demonstrates at least a3.5oC reduction in surface daily maximumtemperature at an agricultural inland area overany two-week period subsequent to first leafcompared to a two-week period prior to firstleaf. Stations generally near major bodies ofwater show a smaller (1.5oC) reduction.

1 Corresponding author address: David Stensrud,NOAA/NSSL, 1313 Halley Circle, Norman, OK73069. Email: David.Stensrud@nssl.noaa.gov

As a result of the important role thatvegetation plays in land-surface and land-atmosphere interactions, it needs to berepresented adequately in numerical weatherprediction models. Thus, it is essential that thevegetation conditions be gathered in an accurateand timely manner. Remote sensing devicessuch as the National Oceanic and AtmosphericAdministration (NOAA) Advanced Very HighResolution Radiometer (AVHRR) represent thepreferred method for gathering vegetation data.

There are several advantages to utilizing the1-km AVHRR data over that of the 30-m landremote sensing satellite system (LANDSAT) or10-m Systeme Probatoire d’Observation de laTerre (SPOT). First, the revisit periods ofLANDSAT and SPOT are near two weeks.Since cloud-free images are not always possiblewhen the satellite passes over a given location(Crawford et al. 2001), a month or longer maypass before the land surface can be observedclearly. Second, AVHRR data have nominalcost, whereas high-resolution data fromLANDSAT and SPOT are costly and cover onlylimited regions of the globe episodically(Gutman and Ignatov 1995). Other notableadvantages include the availability of spectralinformation for vegetation studies, globalcoverage, and the long-term continuousobservational period.

It is possible to compute both the vegetationfraction and leaf area index (LAI) from theAVHRR through calculations based upon theNormalized Difference Vegetation Index(NDVI) (see Crawford et al. 2001). However,according to Gutman and Ignatov (1998), it isless difficult to compute the horizontal density,or vegetation fraction, than the LAI from theNDVI. Thus, for example, the LAI is assigned aconstant value of 1 in the Eta model and only the

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vegetation fraction is allowed to vary in timeand space. Other land surface models allowboth parameters to vary.

Currently, the NCEP Eta model uses a 0.144degree (approximately 14 km) resolutionmonthly database for vegetation fraction, basedupon a five year climatology (Black et al. 1997)developed at the National EnvironmentalSatellite Data and Information Service(NESDIS). The monthly vegetation fractionvalues apply to the 15th of every month and thesedata are interpolated for daily values. Theseestimations are based upon observations ofNDVI, which are obtained from NOAA’s polar-orbiting satellites that have the AVHRR.

Vegetation characteristics change from year-to-year, however, and are influenced bycircumstances such as fires, irrigation,deforestation, desertification, crop harvesting,drought-affected vegetation, hailstorms, and theearly onset of spring vegetation (Crawford et al.2001). Using near real-time satellite-derivedvalues of vegetation fraction and LAI improvesthe 2-m temperature simulations of the PennState- National Center for AtmosphericResearch Mesoscale Model version 5 (MM5)when using the Parameterization for Land-Atmosphere-Cloud Exchange (PLACE; Wetzeland Boone 1995) for the land surface (Crawfordet al. 2001). Thus, it is hypothesized that animproved initialization of vegetation fraction inthe Eta model can lead to smaller surfacetemperature forecast errors, that likely result inpart from the use of climatological land useinformation (Mitchell et al. 2000). Comparisonsof numerical forecasts of the Eta model aremade, using both climatological and satellite-derived values of fractional vegetation coveragefrom the NOAA AVHRR data, in order toexamine the potential importance of real-timevegetation fraction information to forecasts of 2-meter temperatures and dewpoint temperaturesfrom 0 to 48 h. The cases selected span thegrowing season of 2001.

2.0 METHODS

a. Eta Model Description

The workstation version of the Eta model(Black 1994) is used in this study. This model

uses a domain that is approximately 20% of theoperational Eta domain, although it has the samehorizontal grid spacing of 22 km and the same50 vertical layers that were used operationally inthe Eta model during most of 2001. The initialand boundary conditions are obtained directlyfrom a subset of the operational Eta modelfields. However, there are two major differencesbetween the operational Eta model and theversion run for this study. This workstationversion uses the Kain-Fritsch convectiveparameterization scheme (Kain and Fritsch1993) and uses fourth-order horizontal diffusioninstead of second-order horizontal diffusion.Neither of these changes is expected to influencesignificantly the results of the present studyregarding the importance of vegetation fractionon the model forecasts.

The land-surface model (LSM) used withinthe Eta model is a multi-layer soil-vegetation-snowpack model. It was originally developed atOregon State University (Mahrt and Pan 1984;Pan and Mahrt 1987) and was then modified atNCEP for use in the Eta model (Chen et al 1996;Black et al. 1997). This LSM has one canopylayer and eight prognostic variables: soilmoisture and temperature in the three soil layers,water stored on the canopy, and snow stored onthe ground (Chen et.al.1996). Vegetationfraction influences the surface latent heat fluxcalculations directly by weighting the relativecontributions of bare soil and canopytranspiration. Thus, a larger value of vegetationfraction indicates that the vegetated surfacecontributes a greater portion of the total latentheat flux than a smaller value of vegetationfraction. This also has a large influence on themodel predictions of 2-m dewpoint temperature.In addition, since the vegetation fractioninfluences the latent heat flux, it also influencesthe sensible heat flux through the surface energybalance. Thus, vegetation fraction has apotentially large influence on both the 2-mtemperature and dewpoint temperature, and thusaffects the development of the planetaryboundary layer in the model forecasts.

b. Calculation of vegetation fraction

Since 1989, United States GeologicalSurvey’s Earth Resources Observing Systems

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(EROS) Data Center (EDC) has used the 1-kmAVHRR daily observations to produce biweeklymaximum NDVI composites over theconterminous United States (Gutman andIgnatov 1995). Since clouds are associated withlow values of NDVI, biweekly maximum NDVIcomposites tend to have minimal cloudcontamination. Biweekly composites of NDVIfrom USGS/EROS for 2001 are available on theweb approximately 4 months after the date ofinterest. These data are downloaded andnavigated using a geographic informationsystem. The biweekly period that contains theforecast day of interest is selected for use inorder to mimic the data that could be available ifdaily updates of this information were providedroutinely.

The values for vegetation fraction (fveg) arederived from NDVI using the followingequation (Chang and Wetzel 1991):

ÓÌÏ

>-

£-=

547.0,08.1)(2.3

547.0),1.0(5.1

NDVINDVI

NDVINDVIfveg

where the values of vegetation fraction areconstrained to lie between 0 and 1. Since theEta model has a spatial resolution of 22 km,while the NDVI data resolution is 1 km, anobjective analysis is necessary to determine arepresentative value for the vegetation fractionon the Eta grid. Data points for the 1-kmvegetation fraction that lie within a 10 km radiusof each Eta grid point are averaged and thenapplied to that Eta grid point. While moresophisticated analyses are possible, thisapproach is reasonable for an initial study intothe importance of vegetation fraction in the Etamodel. Locations outside of the region of thebiweekly composite data use the Eta modelclimatology for the vegetation fraction, whichcan produce large gradients in vegetationfraction in southern Canada.

c. Running the Eta model

Two different runs of the Eta model areperformed: one with the climatological valuefor vegetation fraction (Eta) and the other withthe NDVI-derived vegetation fraction (VegEta).The model grid, physical process schemes, and

initial and boundary conditions are identical inthese two runs except for the vegetation fractiondefined in the surface parameter input file. Themodel provides output every 3 h, beginning atthe 1200 UTC initialization time and ending at48 h.

A total of 12 case studies are examined forthe months of April to August of 2001 (Table 1).Two forecasts are completed for each of thesemonths, except for April, which has fourforecasts. Each day is chosen because amajority of the U.S. for that day is free of deepconvective clouds, thereby minimizing theinfluence of the convective parameterizations.This case selection maximizes the potential forthe effects of vegetation to be seen anddiagnosed in the model forecasts, as theinfluences of many of the other model physicalprocess schemes are minimized.

Case Days04.18.01 06.17.0104.19.01 06.18.0104.26.01 07.05.0104.27.01 07.07.0105.13.01 08.04.0105.16.01 08.07.01

Table 1. List of all days used in this study.

Once the forecasts and post-processing arecomplete, the model 2-m temperature anddewpoint temperature are bilinearly interpolatedto National Weather Service (NWS) surfaceobserving sites. Since the model domain coversthe contiguous 48 states, there are over 1600NWS surface stations that provide hourly reportsand can be used to compare the performance ofthe two forecasts at each output time. Thenumber of NWS stations actually used in theanalyses is somewhat less, varying fromapproximately 1350 to 1600. While the hourlysurface observations are examined for obviousproblems, errors may exist. However, any errorsin the observational data influence the results ofboth of the forecasts in the same manner andshould not influence the subsequentcomparisons. The bias, root mean-square-error(rmse), and Pearson (ordinary) correlation (seeWilks 1995 for documentation on all three ofthese measures) are computed both for both theoperational forecasts (Eta) and the NDVI-

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derived vegetation fraction forecasts (VegEta),and the results are compared. We now turn tothe forecast comparisons.

3.0 RESULTS

a. Description of vegetation fraction differences

Comparisons between the vegetationfraction from the Eta and VegEta model initialconditions (Figs. 1, 2 and 3) indicate that theVegEta vegetation fraction contains morehorizontal structure than the values from the Eta.Thus, the real-time satellite data are capturingthe temporal and horizontal variations invegetation growth that likely are masked by theuse of a 5-year monthly climatology. Early inthe growing season the largest differences invegetation fraction are over the southeast U.S.(Fig. 1c) with the values from the VegEta beinglarger than those from the Eta, but thesedifferences decrease as the growing seasonprogresses. By mid-June (Fig. 2c) the largestdifferences in vegetation fraction are over theplains states, stretching from Oklahoma to NorthDakota and in southeastern Canada. The regionof largest differences shifts farther northwardinto the northern plains during July (Fig. 2f) andAugust (Fig. 3c) and a more localized differenceis also seen in western Mexico associated withvegetation green-up during the North Americanmonsoon. Average absolute differences invegetation fraction calculated across the entiremodel grid from the Eta and VegEta are slightlymore than 0.10 for the 12 days examined.

Since the method used by Gutman andIgnatov (1998) to calculate vegetation fractionfor the Eta model produces slightly larger valuesof vegetation fraction for a given NDVI valuethan the method used in this study, it issomewhat surprising that the VegEta often haslarger vegetation fractions than the Eta.However, vegetation fractions can increase by0.30 or more over a one-month period,suggesting that one explanation for the largervegetation fractions in the VegEta is theimproved temporal and spatial resolution of thedata. Crop moisture estimations during 2001indicate that much of the eastern U.S. had moistto abnormally moist soil conditions, and surfacetemperatures during April and May over the

eastern U.S. exceeded normal values by 1 to3°C, with the largest temperature anomalies inApril centered over Illinois, suggesting that anearly start to the growing season is likely andwould explain many of these differences invegetation fraction. This assessment reinforcesthe conclusion of Crawford et al. (2001) on theneed for real-time vegetation information.

b. Comparisons between Eta and VegEta

Strong relationships exist between thespatial distribution of the vegetation fractiondifferences between Eta and VegEta and thecorresponding differences in surface fluxes, 2-mtemperature, and 2-m dewpoint temperature.For example, recall that for mid-May 2001 theVegEta contains broad regions with vegetationfractions larger than the Eta, with differencesapproaching 40% in the southeast and stretchingnorthward along the east coast into Canada (seeFig. 1f). In addition, areas in the far northwestalso contain larger values of vegetation fractionsin VegEta. Differences in the instantaneous fluxvalues between the two models show large,coherent regions in which both the VegEta latentand sensible heat fluxes are different from thoseof the Eta at 9 and 33 h (not shown), near thetime of daytime maximum heating duringforecast days 1 and 2. Many of these coherentareas of flux difference correspond well withareas of vegetation fraction difference betweenthe two forecasts.

To explore how the vegetation fractioninformation influences the forecasts, both the 2-m temperature and dewpoint temperature errorsat 9-h and 33-h are binned with respect to thevalue of the VegEta vegetation fraction. Resultsindicate that both the Eta and VegEta providethe worst 2-m temperature forecasts for bare soilconditions (Fig. 4). Both runs, however, havebetter 2-m temperature forecasts as thevegetation fraction increases, while bothforecasts also have worsening dewpointtemperature forecasts as the vegetation fractionincreases from 0 to 60%.

The Eta and VegEta mean bias curves arenearly identical for vegetation fractions below60% and separate for vegetation fractions

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Fig. 1. Vegetation fractions from 18 April 2001from (a) the Eta model climatological database,and (b) the NDVI-derived 1 km data set as usedin the VegEta. The difference (VegEta-Eta) inthe vegetation fractions is shown in (c).Similarly, the same fields from 16 May 2001 areshown in (d) through (f). Color bar on theupper right represents the vegetation fractionsin % at intervals of 10%. The bottom right colorbar is for the vegetation fraction differencefields.

above this value, with the VegEta biases smallerthan those from the Eta. For vegetation fractionsbetween 90 and 100%, the VegEta temperatureforecast bias is smaller by 0.5oC compared to thebias in the Eta forecasts and, similarly, theVegEta dewpoint temperature bias is reduced by1.0oC compared to bias in the Eta forecasts. Asthe growing season progresses, the influence ofthe real-time vegetation information decreases asillustrated by analyses from 18 April and 7August (Fig. 5).

In April, the VegEta bias in 2-m temperatureis 1oC smaller than those from the Eta, and for 2-m dewpoint temperature is 3oC smaller thanthose from the Eta, for vegetation fractionsabove 90%. However, by August thesedifferences are only a fraction of a degree. Thisdecrease in the forecast differences is expected;the average absolute differences in vegetation

Fig. 2. As in Fig. 2, but for (a) to (c) 17 June2001 and (d) through (f) 7 July 2001.

Fig. 3. As in Fig. 2, but for (a) to (c) 7 August2001.

fraction between the VegEta and Eta alsodecrease during the growing season, and largeregions showing only small differences in thevegetation fraction between climatology and thereal-time data become apparent by July (Fig. 3f).However, for most vegetation fractions the

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VegEta biases continue to be less than thosefrom the Eta.

Fig. 4. Mean error, or bias (forecast –observation), vs. VegEta vegetation fraction (asderived from the NDVI data) for all 12 cases for(a) 9 h (2100 UTC) and (b) 33 h (2100 UTC)forecast times. Solid lines are for the Etaforecasts and dashed lines for the VegEtaforecasts. T indicates the 2-m temperaturecurves, and Td the 2-m dewpoint temperaturecurves. Data are averaged over 10% vegetationfraction bins based upon the VegEta vegetationfraction values.

The Wilcoxon signed-rank test, anonparametric test for the significance of thedifference between the distributions of two non-independent samples involving matched pairs(Wilks, 1995), is performed on the 9- and 33-hforecast 2-m temperatures and dewpointtemperatures for each case day. Results showthat the differences in the temperatures betweenthe VegEta and Eta forecasts are significant atthe 99% level for the forecasts during April andMay. However, the significance level decreasesbelow 99% for the remaining half of the forecastdays, with the exception of the 9-h forecasts forJuly 7th and August 7th and the 33-h forecasts forJune 16th and July 5th. Differences in thedewpoint temperatures between the VegEta and

Eta forecasts are significant at the 99% level forall the cases from April to July, but are notsignificant for the August days. This analysissupports the conclusion that the real-timevegetation information is most important earlyin the growing season and has decreasedimportance later during the summer when thevegetation growth largely is complete.

Fig. 5. As in Fig. 4, but from the 9 h forecasttime only from (a) 18 April 2001 and (b) 7August 2001. Note how the added vegetationinformation in the VegEta has a greater impactin April than in August.

4.0 DISCUSSION

The main goal of this study is to investigatethe impact of using NDVI-derived vegetationfraction information in the Eta model. Sincemethods could be developed to provide thesedata in near real-time, it is important to evaluatewhat would happen if we could detect drought-affected vegetation, the early onset of springgreen-up, regions of burned vegetation fromforest fires, and crop harvesting, whichotherwise are missed by the climatologicalvegetation data used in the operational Eta.Thus, values of the biweekly NDVI-derived

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vegetation fraction as determined using satellitedata are inserted into the Eta model for 12 casesspanning most of the growing season during2001 and forecasts using these data compared tothose from both the operational version of themodel and observations.

Results show that the use of the satellite-derived vegetation fraction improves theforecasts of both the 2-m temperature anddewpoint temperature at the 99% significancelevel for much of the growing season. Thisresult clearly illustrates the value of satellite-derived vegetation information and the need fora system to get this type of information into theEta model. While this study focuses solely onthe influence of the implementation of near real-time NDVI-derived vegetation fraction innumerical models, the improved specification ofother land-surface parameters that influence theforecasts also are worth investigation. Soilmoisture specification, LAI, surface roughness,and surface albedo also are important inpartitioning the surface energy budget andmethods for improving their initialization, suchas the Land Data Assimilation System (Mitchellet al. 2000), should be vigorously pursued.However, until these data are incorporated morecompletely into the operational models, themodel flux predictions and low-leveltemperature forecasts may continue to beproblematic.

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