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Nature of the Mesoscale Boundary Layer Height and Water Vapor Variability Observed 14 June 2002 during.. Couvreux, F.; Guichard, F.; Austin, Philip H.; Chen, F. 2009

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Nature of the Mesoscale Boundary Layer Height and Water Vapor VariabilityObserved 14 June 2002 during the IHOP_2002 CampaignF. COUVREUX AND F. GUICHARDGAME-Meteo-France/CNRS-CNRM/GMME, Toulouse, FranceP. H. AUSTINAtmospheric Science Programme, Department of Earth and Ocean Sciences, University of British Columbia, Vancouver,British Columbia, CanadaF. CHENNational Center for Atmospheric Research, Boulder, Colorado(Manuscript received 4 September 2007, in final form 23 June 2008)ABSTRACTMesoscale water vapor heterogeneities in the boundary layer are studied within the context of theInternational H2O Project (IHOP_2002). A significant portion of the water vapor variability in theIHOP_2002 occurs at the mesoscale, with the spatial pattern and the magnitude of the variability changingfrom day to day. On 14 June 2002, an atypical mesoscale gradient is observed, which is the reverse of theclimatological gradient over this area. The factors causing this water vapor variability are investigated usingcomplementary platforms (e.g., aircraft, satellite, and in situ) and models. The impact of surface fluxheterogeneities and atmospheric variability are evaluated separately using a 1D boundary layer model,which uses surface fluxes from the High-Resolution Land Data Assimilation System (HRLDAS) andearly-morning atmospheric temperature and moisture profiles from a mesoscale model. This methodology,based on the use of robust modeling components, allows the authors to tackle the question of the nature ofthe observed mesoscale variability. The impact of horizontal advection is inferred from a careful analysis ofavailable observations. By isolating the individual contributions to mesoscale water vapor variability, it isshown that the observed moisture variability cannot be explained by a single process, but rather involves acombination of different factors: the boundary layer height, which is strongly controlled by the surfacebuoyancy flux, the surface latent heat flux, the early-morning heterogeneity of the atmosphere, horizontaladvection, and the radiative impact of clouds.1. IntroductionWater vapor variability was the main focus of theInternational H2O Project (IHOP_2002), which tookplace in May–June 2002 over the southern Great Plainsof the United States (Weckwerth et al. 2004). This fieldproject gathered together most of the techniques formeasuring water vapor. We address water vapor vari-ability at the mesoscale (scales larger than thermals,ranging from tens to a few hundreds of kilometers).Comparatively few investigations have considered thisscale of variability, mainly because of the lack of ob-servations. Milford et al. (1979), using observationsfrom an instrumented glider, first underscored the vari-ability of water vapor at the mesoscale, which theyfound to be larger than the variability of either poten-tial temperature or vertical velocity. Mahrt (1991), ana-lyzing aircraft in situ measurements at 300 m aboveground level, found that the mesoscale variability ofwater vapor exceeded the submesoscale variability.Mesoscale water vapor variability has been stressedas an important condition for convection. Crook (1996),Wulfmeyer et al. (2006), and Stirling and Petch (2004)have shown that the initiation of convection is stronglytied to the accurate estimate of water vapor within theboundary layer (BL). In the latter study, the authorsdemonstrated that the existence of moisture fluctua-tions accelerates the initiation of deep convection by1–3 h, and that convective initiation was most sensitiveCorresponding author address: F. Couvreux, GAME-Meteo-France/CNRS-CNRM/GMME, 42 av. G. Coriolis, 31057, Tou-louse CEDEX 1, France.E-mail: fleur.couvreux@meteo.fr414 MONTHLY WEATHER REVIEW VOLUME 137DOI: 10.1175/2008MWR2367.1© 2009 American Meteorological SocietyMWR2367to BL moisture fluctuations. They also showed that thehorizontal scale of moisture fluctuations should begreater than 10 km in order to have the strongest im-pact on convective initiation.Heterogeneous surface characteristics have beenidentified as one potential cause of mesoscale variabil-ity. These heterogeneities have been partitioned intotwo categories: “fixed” heterogeneities that are relatedto surface characteristics such as elevation, soil texture,or land cover, and transient heterogeneities such as soilmoisture that are strongly modulated by precipitation(Chen et al. 2001; Trier et al. 2004; Holt et al. 2006). Ithas been difficult to assess the impact of either fixed ortransient surface heterogeneity because of the scarcityof measurements: local surface flux measurements cansuffer from a lack of representativeness that limits theiruse at the mesoscale (André et al. 1990). Although sat-ellites may provide some estimates at the large scale, ithas been more common to use idealized studies to ana-lyze the impact of surface flux heterogeneities onboundary layer characteristics and convective initiation(Anthes 1984; Avissar and Schmidt 1998; Rabin et al.1990; Pielke 2001 for a review). Avissar and Schmidt(1998) and André et al. (1990) have shown that onlyscales of heterogeneity greater than 5–10 km can gen-erate a coherent atmospheric response at the meso-scale. Recently, Kang et al. (2007) used variance de-composition and cospectra to document water vaporvariability at scales of 1–20 km from aircraft observa-tions during 5 days of IHOP_2002 and found that for 2of these days surface heterogeneity could generate me-soscale circulations in convective boundary layers.More generally, Mahrt (2000) analyzed differentscales of heterogeneity that can affect the convectiveboundary layer. He defined three length scales used atthe mesoscale: (i) a convective length scale LRaufirstproposed by Raupach and Finnigan (1995), whereLRauH11005 (CUzi)/w*with C H11005 0.8 (see Table 1 for sym-bols); (ii) a minimum horizontal length scale for theinfluence of surface heterogeneities derived from thethermal blending height, LwmH11005 Cwmz(UH9258o)/(wH11032H9258H11032sfc),with CwmH11005 3 H11003 10H110023; and (iii) a larger horizontal scale,LRau2, where LRau2H11005 TeU for which surface heteroge-neities influence the BL with Te the time of the bound-ary layer growth. In the present study, consideringU H11005 5msH110021, ziH11005 1000 m, w* H11005 1msH110021, H9258oH11005 300 K,Te H11005 5h,andwH11032H9258H11032sfcH11005 200 WmH110022(0.1645 K m sH110021),these three length scales equal LRauH11005 4 km, LwmH11005 45km, and LRau2H11005 90 km. Accordingly, 4 km is the finestresolution analyzed here.Atmospheric conditions compete with surface het-erogeneity to influence mesoscale water vapor variabil-ity. Findell and Eltahir (2003) underlined the impor-tance of the state of the atmosphere in determining thepotential influence of the land surface on convectivetriggering. Alapaty et al. (1997) used a 1D soil–vege-tation–BL model to analyze the impact on BL (e.g.,turbulent fluxes, structure, and height) of varying sur-face characteristics (e.g., soil texture, soil humidity, sto-matal resistance, leaf area index, and vegetation cover).The greatest influence was found from the first threeparameters. Desai et al. (2006) also studied the impactof soil moisture on the boundary layer height variabilityusing a one-dimensional boundary layer model. Theyfound that soil moisture has a strong impact on buoy-ancy flux that in turn is the primary driver of boundarylayer height variability. Here we use a similar approach,focusing on the boundary layer water vapor variability.We begin by documenting the boundary layer heightand the water vapor variability at the mesoscale ob-served during IHOP_2002. During 14 June 2002, a sig-nificant mesoscale gradient of the BL water vapor wasobserved. A boundary layer bulk model using inputsfrom a high-resolution land data assimilation systemand a mesoscale simulation is used to investigate andquantify the role of early-morning atmosphere and sur-face fluxes heterogeneity on such variability.Section 2 presents the data and the methodology,followed by an evaluation of each component of theapproach. In section 3, the case study, which is charac-terized by the strongest mesoscale variability observedduring IHOP_2002 is presented. Section 4 evaluates theimpact of surface flux heterogeneities, early-morningatmospheric profiles, and advection as sources of me-soscale variability.2. MethodologyFully coupled surface–atmospheric mesoscale modelsare powerful tools for the study of land–atmosphereinteractions. Their representations of moist processes,cloud cover, and surface fluxes are however still incom-TABLE 1. List of symbols used in the definition of the different length scales, LRau, Lwm, and LRau2.Symbol Uziw*Te H9258owH11032H9258H11032sfcBoundary layerwind speedDepth of theconvectiveboundary layerDeardorffconvectivevelocity scaleTime of boundarylayer growthBoundary layerpotentialtemperatureSurface sensibleheat fluxJANUARY 2009 C O U V R E U X E T A L . 415plete (e.g., Betts 2004), and the nonlinear coupling be-tween surface and atmosphere makes it difficult to iso-late individual physical processes affecting boundarylayer heterogeneity. The alternative and complemen-tary approach employed in section 2b is to study theevolution of an observed BL with mesoscale variabilityusing a collection of 1D models. The BL models areinitialized with heterogeneous atmospheric fields pro-vided by a mesoscale model and with surface fluxescalculated by a soil–vegetation–atmosphere transfer(SVAT) model. This framework allows separating theeffect of heterogeneity of surface forcing and early-morning atmospheric conditions, permitting a directcomparison of their respective impacts on the develop-ment and maintenance of water vapor variability at themesoscale.a. DataWe focus primarily on the observations made duringthe boundary layer evolution IOP of 14 June 2002documented in the vicinity of Homestead, Oklahoma.We also make use of observations available for 12 and13 June 2002, in order to provide a better understand-ing of the synoptic situation. The focus area is indi-cated in Fig. 1 (which will be referred to as the 1D BLmodel domain): the domains used for the SVAT [theHigh-Resolution Land Data Assimilation System(HRLDAS) model described in section 2b)] and thefifth-generation Pennsylvania State University–Na-tional Center for Atmospheric Research MesoscaleModel (MM5) are also shown. The 1D BL model do-main is about 500 km H11003 200 km.Nine surface flux stations [referred to as the Inte-grated Surface Flux Facility (ISFF)] were installed dur-ing IHOP_2002 (LeMone et al. 2007); they are used toevaluate the land surface model. Oklahoma mesonetsurface stations, radiosondes from the NationalWeather Service network, from the Atmospheric Ra-diation Measurement Program (ARM) southern GreatPlains (SGP) network and the specific soundings de-ployed for IHOP_2002 are used to document the me-soscale variability. In addition, in situ aircraft measure-ments from the Naval Research Laboratory P-3 (P-3)and the University of Wyoming King Air (UWKA) thatflew from 0700 to 1400 Local Daylight Time (LDT) 14June 2002 (1200–1900 UTC; UTC H11005 LDT H11001 5) sam-pled the horizontal variability within the boundarylayer. Boundary layer heights are derived from reflec-tivity profiles measured by the lidar on board theDLR-Falcon, following Davis et al. (2000). The precipi-FIG. 1. Map of the domain of interest with the simulation domain for MM5 and HRLDAS and thelocation of soundings, surface stations, and flight tracks of the Navy P-3 for the northernmost track,noted “a”, and of the DLR Falcon for both tracks, noted “a” and “b.”416 MONTHLY WEATHER REVIEW VOLUME 137tation field is provided by the National Centers for En-vironmental Prediction (NCEP) stage IV rainfall prod-uct, at 4-km resolution, which combines Oklahoma Me-sonet rain gauge observations and hourly precipitationradar data.The Moderate Resolution Imaging Spectroradiom-eter (MODIS) installed on Terra, a sun-synchronouspolar-orbiting satellite provides measurement of theprecipitable water (PW), a vertically integrated quan-tity. The MODIS MOD07 PW products are calculatedfor both day and nighttime orbits at 5 H11003 5km2resolu-tion (Seemann et al. 2003; King et al. 2003). The use ofMODIS PW is motivated by the analysis presented asfollows, where we first establish the role of low-levelwater vapor in the variability of PW using radiosound-ings. Figure 2a shows the variation of the total PW andthe contribution of the low levels (H110212000 m) for all thesoundings measured from 12 to 14 June 2002. The low-level water vapor contributes significantly to PW; formore than 65% of the soundings, 70% of the PW islocated below 2000 m. In addition, PW fluctuations aredominated by the variability of the low levels. Bound-ary layer water vapor mixing ratio and PW are stronglylinked (Fig. 2b, r2H11005 0.74). Thus, PW appears to be agood tracer of BL water vapor variability.b. Models descriptionWe use a 1D boundary layer model with, as lowerboundary conditions, the surface fluxes, provided byHRLDAS and initial atmospheric profiles provided byMM5. This model predicts the development of the con-vective boundary layer from the early-morning profilesusing time-varying surface fluxes. As noted in section 1,mesoscale variability is not sensitive to horizontal struc-ture below the 4-km convective length scale LRau,which also matches the 4-km horizontal resolution ofHRLDAS. Model columns are treated independentlyon the 1D BL model domain, ignoring mesoscale cir-culations; analysis of the MM5 simulation performedfor this day indicates that the mesoscale circulations areweak.1) THE MIXED LAYER SLAB MODELThis model (henceforth the “BL model”) is a mixedlayer slab model, based on the zero-order jump ap-proximation, which characterizes the convective boun-dary layer by its mean depth h, the height-independentmean potential temperature H9258m, and the height-inde-pendent water vapor mixing ratio qm. The surfacefluxes are prescribed by HRLDAS. The closure in-volves a parameterized form of the entrainment buoy-ancy flux. Here, we assume that the entrainment flux isa constant fraction c of the surface flux with c H11005H110020.2(Stull 1988). The BL model details are given in theappendix.The BL model has been evaluated against nine dif-ferent cases (initial profiles shown in Fig. 3, top panels)covering the range of environmental conditions en-countered during the focus period via systematic com-parisons with large-eddy simulations of the same cases(Couvreux et al. 2005, hereafter C05; Couvreux et al.2007, hereafter C07; for information on the design ofthese LES). The nine cases are aimed at testing theability of the BL model to represent the properties ofthe convective BL during its daytime growth underFIG. 2. (a) Variations, for the period of interest, of the totalwater path (kg mH110022) (black circles), contribution from the lowlevels (below 2000 m, gray circles), and contribution from theupper levels (above 2000 m, black stars); (b) scatterplot of meanq in low levels as a function of the total water path. The soundingsare indicated in Fig. 1 (except MOBI that corresponds to themobile soundings).JANUARY 2009 C O U V R E U X E T A L . 417varying environmental conditions, therefore the casesonly differ by their initial profiles, namely their lapserate and mixing ratio profiles. The runs lasted for 7 hstarting at 0700 LDT. Both h and qmare compared tovalues obtained by LES (Fig. 3, bottom panels). TheBL model satisfactorily predicts the time evolution ofthe mixed layer characteristics. Although a more com-plex boundary layer model could be used, this simplemodel represents the basic features of a developingconvective boundary layer with sufficient accuracy forour purpose.2) HRLDAS MODELHRLDAS (Chen et al. 2007) employs the Noah landsurface model and is run in uncoupled mode (notcoupled to an atmospheric model) on a 4-km grid for an18-month spinup period starting 1 Jan 2001, so that theFIG. 3. Initial profiles of (a) potential temperature and (b) water vapor mixing ratio for the ninesimulations used to evaluate the BL model. Evaluation of the model against LES for the nine 7-h-longsimulations (a symbol is drawn for each hour, from 0700 to 1400 LDT): (c) boundary layer height and(d) boundary layer water vapor mixing ratio.418 MONTHLY WEATHER REVIEW VOLUME 137soil profiles used in this June 2002 case are physicallyreasonable. The land surface initialization uses a vari-ety of observed and analyzed conditions including 1)NCEP stage IV rainfall data (discussed in section 2a)on a 4-km national grid; 2) 0.5° hourly downward solarradiation derived from the Geostationary OperationalEnvironmental Satellites (GOES-8 and GOES-9); 3)near-surface atmospheric temperature, humidity, wind,downward longwave radiation, and surface pressurefrom 3-hourly NCEP Environmental Data AssimilationSystem (EDAS) analyses; 4) 1-km horizontal resolutionU.S. Geological Survey 24-category land-use and 1-kmhorizontal resolution state soil geographic soil texturemaps; and 5) 0.15° monthly satellite-derived green veg-etation fraction. As shown in Drusch and Viterbo(2007), it is premature to use soil moisture as an inputfor land surface initialization.This soil initialization system (HRLDAS) was evalu-ated by Chen et al. (2007) using IHOP_2002 ISFF andOklahoma Mesonet data along the IHOP_2002 period.Here, we evaluate HRLDAS in more detail for theperiod from 12 to 14 June. The soil temperature andhumidity are consistent with the values observed at thesix ISFF stations located in the BL model domain. Theincrease of soil moisture following the 13 June rainevents is greater in the south than the north, consistentwith the gradient in rainfall amounts. HRLDAS soilmoisture has also been compared for these 3 days to thesoil moisture derived from the Tropical Rainfall Mea-suring Mission (TRMM) Microwave Imager (TMI)brightness temperatures (Gao et al. 2006). After ac-counting for the difference in resolution, HRLDAS andTMI soil moisture maps display qualitatively similarpatterns (Figs. 4b–d), with a dry anomaly in the north-west persisting over the 3 days and wet anomaliesstrongly linked to the spatial structure of the cumula-tive precipitation field for the precipitation events de-scribed below in section 3. The amplitude of the soilmoisture fluctuations is smaller in HRLDAS (0.09, 0.34m3mH110023) than in the TMI retrieval (0.04, 0.48 m3mH110023).The smaller range is at least partly explained by theshallow (0.5 cm) sampling depth of the TMI comparedto the 0–10-cm first soil layer of HRLDAS. Note par-ticularly that TMI records a higher and more confinedmaximum in the southwest corner than HRLDAS. Soiltexture patterns (Fig. 4f) also influence HRLDAS soilmoisture, as shown by the sandy soil footprint evidentin soil moisture structures.An evaluation of HRLDAS surface fluxes using ISFFmeasurements is not always straightforward simply be-cause ISFF and HRLDAS provide distinct fields: spe-cifically the ISFF yields a local flux measurementwhereas the HRLDAS represents a 4 H11003 4km2horizon-tal mean. Here, we focus on the consistency of bothdatasets at two stations, ISFF-2 and ISFF-4 (Fig. 1),located, respectively, within crop/grassland and grass-land zones. Figure 5 shows the time evolution of day-time H (left panels), LE (middle panels), and total sur-face heat flux (right panels) for these two stations. Bothstations recorded similar radiation but with a largersensible heat flux (H) for station 2 and a larger latentheat flux (LE) for station 4 (the rainfall accumulationduring the period of interest was 7 times larger atISFF-4 than at ISFF-2). HRLDAS is able to representthe main differences between these two stations. How-ever, LE differs more than H between HRLDAS andISFF (with smaller values for ISFF), even thoughRnet H11002 G H11002 H (where Rnet is the net radiation and Gthe ground heat flux) in ISFF and HRLDAS are incloser agreement. This apparent overestimation of LEby HRLDAS is however consistent with the frequentlyreported underestimation of surface fluxes by eddy-correlation methods (e.g., Brotzge and Crawford 2003;Chen et al. 2007). Indeed, there is better agreementbetween H H11001 LE in HRLDAS and Rnet H11002 G measuredby ISFF. Such an underestimation occurs more oftenduring days following important precipitation events(LeMone et al. 2007) like 14 June. Nevertheless, thereis a general agreement between the time evolution ofthe ISFF-measured heat fluxes and those predicted byHRLDAS. Moreover, HRLDAS is able to reproducethe Bowen ratio observed at these stations (not shown),indicating a good simulation of the partitioning of Rnetbetween H and LE.For this period, which experienced significant pre-cipitation, HRLDAS predictions benefited in particularfrom the strong constraint on cumulative precipitationprovided by the NCEP stage IV rainfall product. In factforcing a land surface scheme with such analysis re-strains the errors introduced by surface–atmospherecoupling in atmospheric models, which often have bi-ases in their prediction of precipitation and other fields(Betts and Jakob 2002).3) INITIAL ATMOSPHERIC PROFILES FROM MM5MM5 (Grell et al. 1995), version 3, has been used toinitialize the BL model at 0800 LDT by gridded early-morning atmospheric profiles. The simulation starts at0700 LDT 14 June 2002 from the NCEP Eta analysis.Note that HRLDAS was not used to initialize the me-soscale model. Given the very weak early-morning sur-face fluxes, using a different surface flux parameteriza-tion is not expected to impact much the calculation ofthe 0800 LDT atmosphere stratification. The MM5 do-main is shown in Fig. 1; it is simulated with a horizontalJANUARY 2009 C O U V R E U X E T A L . 419resolution of 12 km. A sensitivity test with a 4-km hori-zontal resolution on a smaller domain (35.5°–37.6°N,H11002102.4° to H1100299.5°W) demonstrated that increasing thehorizontal resolution had no significant impact on theresulting atmospheric profiles. Figure 6 shows the com-parison to radiosoundings at five locations noted in Fig.1. The mesoscale model correctly simulates the early-morning general structure of the atmosphere. In par-ticular the lapse rate and the q profile are well repro-duced, as is the observed range of variability. MM5does not, however, capture the vertical stratification ofthe mixing ratio observed by radiosoundings.3. The 14 June case study: A nonclimatologicalhumidity gradienta. Mesoscale variability and climatologyThe choice of the case study was motivated by thestrongest observed mesoscale variability in IHOP_2002aircraft data and the atypical moisture gradient.FIG. 4. (top left) The Ef computed from cumulative surface flux from 0600 to 1800 LDT 14 Jun, (middle left) the cumulative Efdifference between 13 and 14 Jun, (bottom left) latent plus sensible heat flux averaged from 0600 to 1800 LDT 14 Jun, (top right) TMIsoil moisture for 14 Jun, (middle right) HRLDAS soil moisture (the isolines, every 0.03 m3mH110023with negative values in dashed,correspond to the anomalies retrieved at a similar resolution as TMI), and (bottom right) HRLDAS soil textures. HRLDAS fields(fluxes and soil moisture) are available at a 4-km resolution whereas TMI has a 12 km H11003 15 km resolution.420 MONTHLY WEATHER REVIEW VOLUME 1371) MESOSCALE VARIABILITY ALONG IHOP_2002INFERRED FROM AIRCRAFT IN SITU DATAWater vapor mixing ratio variability is investigatedusing a total of 35 h of the UWKA aircraft data dividedinto 148 legs with an average leg length of 50 km atdifferent vertical levels sampled over 14 days. “Bound-ary layer heterogeneity” flights consisted of successivelegs at different altitudes from 65 m above the surfaceto the upper boundary layer while during “boundarylayer evolution” flights the aircraft flew at one or twoconstant altitudes in the lower half of the BL (Weck-werth et al. 2004). Since we focus on the mesoscale wefilter the 1-Hz (90 m) aircraft measurements with a10-km running mean.For these flight tracks, the mesoscale fluctuationsvary from day to day while the submesoscale (scalessmaller than 10 km) variability, which is caused primar-ily by the boundary layer dynamics, remains of thesame order of magnitude on the different days (C07).The sampled mesoscale variability in the lower half ofthe BL is greatest on 14 June, with a standard deviationfor each leg varying from 0.5 to 0.8 g kgH110021. This is largerthan the submesoscale standard deviation on this day,which is about 0.2 g kgH110021. The single-leg standard de-viations vary from 0.3 to 0.6 g kgH110021for 29 and 30 Mayto less than 0.4 g kgH110021for the other days. Note thatnormalizing this standard deviation by the length of theleg slightly decreases the difference between 14 June(longest legs) and the other days, but 14 June stillrecords the largest mesoscale variability.2) COMPARISON TO CLIMATOLOGYThe water vapor climatology in the IHOP_2002 do-main is characterized by a horizontal gradient in watervapor with drier air to the west and moister air to theeast (Dodd 1965). A strong east–west gradient in rain-fall is also typical for this area (Weckwerth et al. 2004).During the IHOP_2002 field campaign, the cumulativerainfall from 10 May to 25 June was about 70 mm forFIG. 5. Comparison of daytime surface fluxes predicted by HRLDAS hourly (black) and observed at the ISFFstation semihourly (gray stars) for 12, 13, and 14 Jun 2002: (top) ISFF-2 and (bottom) ISFF-4, (left) sensible heatflux, (middle) latent heat flux, and (right) sensible plus latent heat flux. The dark gray diamonds correspond to thelatent heat flux computed as a residual Rnet H11002 G H11002 H on the middle panels and to Rnet H11002 G on the right panels.JANUARY 2009 C O U V R E U X E T A L . 421ISFF-3 (36.9°N, H11002100.6°W), 170 mm for ISFF-5(37.4°N, H1100298.2°W), and 300 mm for ISFF-8 (37.4°N,H1100296.8°W; LeMone et al. 2007). A gradient is also evi-dent in the mean PW for June 2002 computed from the40-yr European Centre for Medium-Range WeatherForecasts (ECMWF) Re-Analysis (ERA-40), with val-ues lower than 30 kg mH110022west of 101°W, and a maxi-mum located on the southeastern part of the domainwith values greater than 34 kg mH110022. A similar gradientcharacterizes the mean PW over the area in June from1992 to 2002 (Fig. 8c). There are, however, day-to-daydepartures from climatology. From 1992 to 2002, onaverage, 4 days in June were characterized by an in-verse (westward increasing) moisture gradient. Typi-cally this inverse gradient is accompanied by a smalldomain-averaged value of PW and high surface pres-sure. The 14 June case is an example of one of thoseatypical days. It shares the same features, low PW (Fig.2 from soundings and Fig. 8d from ERA-40) and largemean surface pressure exceeding 1020 hPa.b. Overview of the 12–14 June periodThis period follows 3 days dominated by southerlyadvection of moisture from 8 to 11 June. From 12 to 14June, a low pressure center moved from the borderbetween Saskatchewan, Canada, and Montana on 12June to Minnesota on 14 June. This circulation broughtdry and relatively cool air: inspection of ERA-40 re-analysis (PW and horizontal winds) indicates advectionof dry air on the northeastern part of the domain. Ad-vection is less important to the northwest due to ananticyclonic circulation present on 14 June over thisarea. By bringing dry air into the northeast of the do-main, this advection tends to strengthen the observedmesoscale gradient. On 14 June, no large-scale bound-aries (such as fronts or drylines) are evident.Three precipitation events occurred over the selectedarea from 11 to 13 June. The first was associated with aconvective system that originated in Colorado, andbrought precipitation over the northeastern part of thedomain up to 0900 LDT 12 June. The second one de-veloped in the late afternoon of 12 June: numerousconvective cells developed slightly south of the coldfront present over the area. Most of the precipitationfell on the central part of the domain. On 13 June, amesoscale convective system initiated over Coloradomoved over the area bringing precipitation mainly tothe south of the domain. According to Wilson and Rob-erts (2006), 12–13 June was a convectively active pe-riod. These events deposited precipitation over the areaheterogeneously. More rainfall was received in thesouthwest and less in the northwest of the domain. Thisis illustrated in the soil moisture content observed byTMI 14 June 2002 (Fig. 4b).Both LE and rainfall at the three western ISFF areshown in Fig. 7. On 13 June, LE is the highest (about370WmH110022as maximum around midday) at ISFF-1where more rainfall (35 mm) was received. There is anapproximately 80 W mH110022difference between the maxi-mum LE at ISFF-1 and ISFF-2 at noon. This differencediminishes progressively during the 13 June afternoonand 14 June, indicating that after a day the LE contri-bution due to the rainfall difference has decreased con-siderably. The evaporative fraction (Ef; ratio of LEover H H11001 LE, not shown) also reflects the differenceafter rain in the three ISFF with larger values (0.8 on 13June and 0.7 on 14 June) for ISFF-1, than ISFF-2 (0.65on 13 June and 0.6 on 14 June) and ISFF-3 (about 0.4for both days). The LE morning maximum for 13 Juneat ISFF-3 seems to be caused by an afternoon increaseof cloudiness.For the three days, Ef in HRLDAS shows patterns atFIG. 6. Vertical profile of H9258 (black) and q (gray) (a) observed by radiosoundings and (b)simulated by MM5 at Amarillo (full line), Homestead, ISS (dotted line), Vici (dashed line),and Dodge City (dot–dashed line) on early morning 14 Jun 2002.422 MONTHLY WEATHER REVIEW VOLUME 137the large scale (defined as scales larger than hundred ofkilometers) that do not vary with time. There is a maxi-mum (Fig. 4a) to the southeast corresponding to anarea with a different land use (shrub) and a minimum inthe northwest intensifying through the period as littleprecipitation is received. At smaller scales, the spatialheterogeneity of Ef (Figs. 4a–c) reflects both the tran-sient heterogeneity of soil moisture whose fluctuationsare linked to precipitation events (correlation betweenEf and soil moisture is 0.44, 0.52, 0.35, respectively, for12, 13, and 14 June) and the fixed heterogeneity of soiltextures (cf. Fig. 4f). The Ef maxima correspond toareas that received the most rainfall: Ef patterns arestrongly linked to the most recent precipitation pattern.Nevertheless, the heterogeneity in surface fluxes due tothe heterogeneity in soil moisture is reduced 1 day afterthe rainfall, consistently with observations at ISFF. Theimpact of distinct soil textures on Ef can be seen atsmall scales. In fact, on 13 June, sandy soils located inthe east received some rain and they correspond to arelative maximum of Ef, whereas in the domain’snorthwestern region, which did not receive rain, sandysoils correspond to a stronger Ef minimum. On 14 June,the soil texture (Fig. 4c) signatures are less marked inEf in the east, as sandy soils display the largest decreasein Ef from 13 to 14 June.c. The observed 14 June mesoscale water vaporgradient1) MODIS DATAFigures 8a,b shows the MODIS PW aggregated to agrid of 17.5 km H11003 22.5 km at 2340 LDT 11 June and at1305 LDT 14 June. A mesoscale gradient is visible onboth days. On 11 June the atmosphere is moister in thesoutheast (maximum value of 35.5 kg mH110022) and drier inthe northwest (minimum value of 14.1 kg mH110022). Thisgradient is a typical climatological feature (Fig. 8c). On14 June, a different mesoscale gradient is observed,with moister air in the southwest (maximum value of36.5 kg mH110022) and drier air on the northeast (minimumvalue of 13 kg mH110022). This mesoscale gradient is mostlyaccounted for by the low-level water vapor gradient asassessed by sounding and aircraft data presented inthe following sections. This is also consistent with theMODIS water vapor retrieval for 920 hPa, which isclosely correlated with variations in PW (r2H11005 0.92).2) SOUNDING DATAThirty-five soundings were launched during 14 Junein a 200 H11003 200 km2zone around the Homestead pro-filing site. The range of variability in the mixing ratio asFIG. 7. Temporal evolution (semihourly) of surface latent heat flux (symbols, the line correspond tothe evolution averaged with a running-mean of 2 h) and cumulative precipitation (dashed line) overISFF-1 (black lines and stars; 36.5°N, 100.6°W), ISFF-2 (dark gray lines and diamonds; 36.6°N, 100.6°W),and ISFF-3 (light gray lines and triangles; 36.9°N, 100.6°W) from 12 to 14 Jun 2002.JANUARY 2009 C O U V R E U X E T A L . 423measured by soundings launched between 1200 and1230 LDT, was 5.5 g kgH110021intheBLand4gkgH110021in thefree troposphere (C05, see their Fig. 3). The northeast-ernmost sounding [Dodge City, Kansas (DDC), in Fig.1] is the driest, with a mean water vapor mixing ratio of5.5gkgH110021(also lower PW value; Fig. 2), whereas thesouthwesternmost sounding [Amarillo, Texas (AMA)]is the moistest, with a mean water vapor mixing ratio of11gkgH110021(also larger PW in Fig. 2). This is consistentwith the horizontal gradient sampled by the aircraftflight described below. Note that this gradient is al-ready present in early-morning hours as assessed by therange of variability in the soundings (Fig. 6), about 4 gkgH110021in the low levels.3) P-3 AND FALCON FLIGHT TRACKSIn situ data from the P-3 confirm the existence of a qgradient in the PBL at this scale. This aircraft flew at aheight of around 350 m on successive legs, each ap-proximately 80 km long oriented west-southwest–east-northeast. The gradient is 2 g kgH110021over 80 km at 0700LDTandupto3gkgH110021at 1230 LDT (cf. C05, see theirFig. 4).The DLR differential absorbing lidar (DLR-DIAL)data also indicates mesoscale variability of severalgrams per kilogram in q over hundred of kilometers for14 June 2002. Strong lidar reflectivities indicate shallowconvection in the southwest (over moister BL), butonly much later in the northeast (drier BL).Below we investigate the origin of the 14 June vari-ability using the methodology presented in section 2,focusing on the respective role of surface fluxes andheterogeneity of the atmosphere on the observed me-soscale gradient.4. Mesoscale variabilityThe mesoscale variability of boundary layer heightand water vapor predicted by the 1D BL model is in-vestigated first along the flight tracks and then over thebroader 1D BL model domain.FIG. 8. PW from MODIS (resolution of 17.5 km H11003 22.5 km) (a) at 2340 LDT 11 Jun 2002 and (b) at 1305 LDT14 Jun 2002 and from ERA40 analysis (resolution 0.5°), (c) for the climatological (1992–2002) June month, and (d)at 0700 LDT 14 Jun 2002. The squares in (c) and (d) correspond to the outlined domain in (a) and (b).424 MONTHLY WEATHER REVIEW VOLUME 137a. Along the flight tracks1) BOUNDARY LAYER HEIGHTThe boundary layer height derived from DLR-DIALreflectivity data (following Davis et al. 2000) displays asuperposition of scales of variability (Fig. 9). The vari-ability at the submesoscale is linked to organizedboundary layer structures, specifically thermals anddry tongues (C05; C07) and is outside the scope ofthis paper. Figure 9 shows the boundary layer heightpredicted by the BL model at 4-km resolution, hmcompared to the 4-km running mean (shown as theblack full line, with the maximum as dashed black)of the DLR-DIAL estimate along two tracks, indi-cated in Fig. 1, at 1230 and 1250 LDT. There is generalagreement between the predicted and observedmesoscale variation. Note that the boundary layerheight is overestimated west of H11002100.7°W on track“b.” The lidar cross section indicates the presence ofshallow cumulus in the western end of the domainwith cloud depths of about 200 m. This might ex-plain the disagreement because (i) the existence ofcumuli causes uncertainties in the derivation of bound-ary layer height from lidar reflectivities and (ii) ourmethodology takes into account clouds only throughtheir radiative impacts at the surface (i.e., not on BLdynamics). The BL model also neglects advection in-cluding subsidence, which introduces additional un-certainty. C05 estimated the maximum subsidence tobe about 1 cm sH110021, corresponding to an underestimateof about 200 m in the boundary layer height at 1230LDT.2) WATER VAPOR MIXING RATIOThe water vapor mixing ratio observed by the P-3 isalso compared to the boundary layer water vapor mix-ing ratio predicted by the BL model, qm, as shown inFig. 10, on a 75-km leg at 1230 LDT. The qmgradient isconsistent with the observed gradient albeit with asmaller value of about 1 g kgH110021(75 km)H110021instead of theobserved 2.5 g kgH110021(75 km)H110021. The observed variabilityis also larger, which is expected given that the slabmodel does not reproduce the submesoscale variabilityresulting from the BL structures. As discussed below,the underestimate of the mesoscale gradient is partlyexplained by the neglect of advection. Another possiblesource of difference is the soil moisture: in TMI soilmoisture (Fig. 4b), a decrease is observed eastwardalong this track whereas HRLDAS for this particulartrack shows large soil moisture fluctuations without aneastward decrease, except at the far eastern side of thetrack (see Fig. 11c). This leads to a different partition-ing of the energy inbetween latent and sensible heatflux. In this case, greater latent heat flux and smallersensible heat flux will induce a moister BL through alarger moisture influx in a less diluted boundary layer.To understand the causes of the observed mesoscalevariability, in Fig. 11 we show the variability of differentvariables along the flight tracks: the early-morningMM5 water vapor content qo(the initial profile aver-aged over the mean boundary layer height, h), thesimulated boundary layer height, and the HRLDASsurface heat flux and soil moisture. The model gradientof qmover the track is well correlated with the gradientFIG. 9. Comparison of the boundary layer height predicted by the model (dark gray diamonds, 4-km resolution) with boundary layerheights deduced from lidar reflectivity profiles at 1230 and 1250 LDT along the respective flight tracks “a” and “b” noted in Fig. 1(dotted gray, 75-m resolution; black line for 4-km smoothed signal; dashed black for the 4-km maximum).JANUARY 2009 C O U V R E U X E T A L . 425in qoespecially for the segment at 1230 LDT (Fig. 11a).Nevertheless, there is more variability in qmthan in qoas suggested by variability at scales of a few tens ofkilometers. At these scales, qmfluctuations are well cor-related with the inverse of hm(Figs. 11d,h; r2H11022 0.65),with higher values for smaller boundary layer heightsconsistent with less dilution. Both qmand LE (notshown) are not well correlated for the 1230 LDT track,with lower fluxes to the west and larger fluxes to theeast. This is mainly due to the gradient of the totalsurface fluxes (with lower values to the southwest andhigher values to the northeast; Fig. 4e). The evapora-tive fraction and the soil moisture fluctuations (Figs.11b,c,f,g) are better correlated with qmfluctuations.The variability at scales of a few tens of kilometers islinked to the boundary layer height and the variabilityof surface characteristics (Ef and soil moisture). This isin agreement with LES results of Avissar and Schmidt(1998) or Couvreux (2005) showing that the impact ofsurface flux heterogeneities on water vapor variabilitywas significant from scales of 10 km.The flight track analysis has underscored the roleplayed by the initial spatial heterogeneity in determin-ing the water vapor gradient. In addition, while theinitial heterogeneity of the atmosphere accounts for thelarge-scale q gradient, the smaller-scale fluctuations aregenerated by both BL characteristics and surface het-erogeneity (e.g., soil moisture and surface fluxes). Anexample of this is the good correlation between qmand the inverse of the boundary layer height and to alesser extent Ef and soil moisture. Observations alsoindicate a correlation between fluctuations in water va-por mixing ratio and lidar-derived boundary layerheight. This suggests that the water vapor variabilityresults from interactions between the early-morningheterogeneity of the atmosphere, the surface fluxes,and the boundary layer height. Small-scale variability(at a few tens of kilometers) develops with time in theBL model, but the mesoscale gradient does not increaseas observed.b. Role of advectionWe have shown above that the daytime convectivedevelopment from the early-morning atmospheric con-ditions leads to a mesoscale water vapor gradient in theBL. Nevertheless, its strength is underestimated com-pared to aircraft observations. ERA-40 reanalysis sug-gests strong advection of dry air from the northerlyflow. Here, we investigate the role of horizontal advec-tion as a possible cause for the increase of the gradient.C05 found that in the southwestern end of the “a” flightFIG. 10. Comparison of the boundary layer water vapor mixing ratio predicted by the model(dark gray diamonds, 4-km resolution) along the P-3 flight track with in situ aircraft measuredvalues (dashed gray, 130-m resolution and black line for the 4-km smoothed signal) at 1230LDT.426 MONTHLY WEATHER REVIEW VOLUME 137FIG. 11. Comparison of fluctuations along the two flight tracks of Fig. 8 for qm, the BL model water vapormixing ratio (black) and (dashed gray) (a), (e) qo, the initial water vapor mixing ratio integrated over themean boundary layer height; (b), (f) Ef; (c), (g) soil moisture; and (d), (h) the inverse of the BL height.JANUARY 2009 C O U V R E U X E T A L . 427track, q remains approximately constant whereas in thenortheastern end it decreases strongly with time (seetheir Fig. 4). C05 also showed that moisture advectionoccurs at scales greater than 10 km and was significantbelow 3000 m with a value of 0.1 to 0.15 g kgH110021hH110021.The Oklahoma Mesonet provides surface data closeto the flight track at Buffalo (BUFF: 36.83°N, H1100299.64°W)and Goodwell (GODD: 36.6°N, H11002101.6°W). As indi-cated in Fig. 12a, prior to 1000 LDT, the northerly winddirection is similar at both stations. After 1000 LDT,the wind veers at GODD, becoming southerly whereasthe wind direction at BUFF stays roughly constant (thewind speed at both stations is about 3 m sH110021). Thischange of wind direction, consistent with aircraft mea-surement, is related to the displacement of the highpressure center. Given the gradient described above orobtained from the Oklahoma mesonet (about 1.5 gkgH110021in 140 km) the horizontal advection will bringmoister air to GOOD and tend to dry the boundarylayer at BUFF. Even though the wind speeds are rela-tively small, the existence of a strong gradient and op-posing wind directions can produce horizontal advec-tion that can lead to an increase of this gradient due toa decreasing (increasing) of q in the northeastern(southwestern) end. This partly explains the increase inthe q difference between these two stations from 1000to 1300 LDT (Fig. 12b).c. Spatial structuresAfter examining the variability of the boundary layerheight and water vapor along one direction (the flighttracks) we now focus on the different causes of thespatial variability in the BL model domain.1) COMPARISON TO MODIS PRECIPITABLEWATERSpatial structures of qmand the MODIS PW are pre-sented in Figs. 13a,b. Both show the large-scale gradi-ent with higher values in the southwest and lower val-ues in the northeast. This gradient is primarily due tothe initial heterogeneity of the atmosphere as shown bythe map of qo(Fig. 13c) at 1300 LDT (at this time, themean boundary layer height is about 1100 m). How-ever, differences are evident between the qmand qoheterogeneities: for example, in the southeast, qmisrelatively higher due to strong LE (see Ef in Fig. 4a). Inaddition, the spatial structure of qmevolves from a fieldorganized along a preferred direction (Fig. 13c) to afield presenting more variability along both directionsand at smaller scales: for example, note the bulges at(37°N, 100°W) and (36.5°N, 97°–98°W) both in qmandPW, consistent with the small-scale variability devel-oped throughout the day along the flight track. Thus,although the early-morning heterogeneity explains themesoscale water vapor variability to first order at thelargest scales, other factors shape the variability atsmaller scales.In the following, we investigate how the initial me-soscale gradient is modified by the boundary layer de-velopment and the heterogeneity of surface fluxes.2) ROLE OF HETEROGENEOUS SURFACE FLUXESThe boundary layer height simulated by the BLmodel at 1300 LDT displays large variations from 650up to 1800 m with a mean value of 1100 m (Fig. 14a).These variations are driven primarily by the spatialvariation in surface buoyancy flux (r2H11005 0.6). This isFIG. 12. Evolution of the (a) wind direction and (b) water vapor mixing ratio for two Oklahoma Mesonet stations (5-minresolution) on each side of the aircraft transect, BUFF (black) to the northeast in black and GODD (gray) to the southwest.428 MONTHLY WEATHER REVIEW VOLUME 137particularly the case in the northwest (latitude H1102236.5°N and longitude H11021H11002100°W) where r2reaches 0.9.In the northwest, HRLDAS simulates very dry soilsand high buoyancy fluxes. However, there is little cor-relation over the whole domain between the time-integrated buoyancy fluxes and the surface soil mois-ture (r2H11005 0.23), at the 4-km scale considered, in con-trast to the strong correlation found in this area duringthe Southern Great Plains 1997 field study (Desai et al.2006). They found the highest correlation for scalesabout 100 km; we note that at the 40-km scale, thecorrelation is higher (r2H11005 0.39). The correlation be-tween Ef and soil moisture is slightly larger, (r2H11005 0.36at the 4-km scale). In fact, dividing the sensible heatflux by LE H11001 H essentially normalizes H by Rnet (therecould also be a change in flux into the soil, G, but ISFFdata suggest a ratio of about 1/5 between horizontalvariation of Rnet and G). This Rnet variation can alsoexplain the differences between the flux and soil mois-ture correlations in the two cases; Desai et al. (2006)studied clear-sky days for which there was relativelylittle variation in Rnet.We also considered variations in the early-morningatmospheric stratification through fluctuations in lapserate in the initial profiles, from 4.8 to 5.6 K kmH110021,asasource of variability. A similar range of lapse-rate fluc-tuations is observed throughout the soundings on 14June 2002. This variability is only moderately corre-lated with fluctuations in boundary layer height (r2H110050.35). Differences at 1300 LDT between hmand ho(boundary layer height computed from a spatially ho-mogeneous atmosphere) were smaller than 200 m westof H1100298.5°W (Fig. 14b), confirming that in the west thebuoyancy flux (and not the spatially varying stability) isthe primary driver of the boundary layer height (r2H110050.85 for the correlation of hmand buoyancy flux west ofH1100298.5°W compared with r2H11005 0.52 east of H1100298.5°W). Inthe east, in contrast, the atmospheric stratification playsa significant role in boundary layer fluctuations. Tosummarize, the early-morning stratification of the at-mosphere is the determinant for the boundary layerwater vapor variability but less so for the boundarylayer height.To investigate the role of surface fluxes on the watervapor variability, we focus on the difference betweenqmcomputed by the BL model and q0, defined as theinitial water vapor mixing ratio averaged over the meanboundary layer height. As illustrated for 1300 LDT byFigs. 14a,c,d, spatial heterogeneities in the deviationfrom the initial water vapor content of the atmosphereare driven both by variations in hm(r2H11005 0.48) andvariations in Ef (r2H11005 0.55). The correlation betweenqmH11002 qoand LE is less important (r2H11005 0.25), under-lining the role of Rnet variations. Nevertheless, Ef andhmare not independent (as hmis primarily driven by thesurface buoyancy fluxes). The increase of Ef can be dueto an increase in LE and/or a decrease in H. Both willcontribute to an increase of the boundary layer q due toa larger influx of moisture and/or to less dilution by theboundary layer growth.With time, the surface flux heterogeneities introducesmaller-scale variability to the BL q field, through Efand hm(Fig. 14c). This underscores the point that theFIG. 13. Maps of (a) qmpredicted by the 1D BL model, (b) PWmeasured by MODIS, and (c) qo, the early-morning water vapormixing ratio averaged over the mean boundary layer height at1300 LDT 14 Jun 2002 (all fields are presented in anomaly rela-tively to the mean over the domain). The black zones in (b) cor-respond to the area with no data.JANUARY 2009 C O U V R E U X E T A L . 429mesoscale variability of water vapor results from thecombination of variability in boundary layer height, la-tent heat flux, and early-morning heterogeneity of theatmosphere and variations of horizontal moisture ad-vection. Rnet fluctuates significantly over the domain(Fig. 4e): H H11001 LE spans the range (200–450WmH110022)when averaged over the day (from 0600 to 1800 LDT).These fluctuations are due to clouds present in thesouthwest as attested by lidar measurements and theMODIS cloud mask. These variations in Rnet inducelarger variations in the sensible heat flux and tend tostrengthen the initial q gradient along the flight trackthrough smaller BL height.5. ConclusionsMesoscale water vapor heterogeneities in the bound-ary layer on 14 June 2002 have been studied within thecontext of the IHOP_2002 field campaign. During thisday, a strong mesoscale water vapor gradient was ob-served by different instruments deployed in the bound-ary layer (e.g., aircraft, radiosondes, and DIAL).MODIS was shown to provide observations relevant tothe evaluation of the mesoscale variability of water va-por, since most of the satellite-observed PW fluctua-tions arose from low-level water vapor mixing ratiofluctuations. This northeast–southwest gradient stoodin contrast to the characteristic northwest–southeastgradient expected from climatology.We have investigated the respective roles of early-morning heterogeneity in the atmosphere and hetero-geneities in surface fluxes on the daytime evolution ofmesoscale variability of boundary layer height and wa-ter vapor. To do so, we used a 1D boundary layermodel (a mixed layer model) in conjunction with ahigh-resolution land data assimilation system that pro-vides a valuable estimate of surface fluxes at mesoscale.This enables us to distinguish the impact of spatial het-erogeneities in surface fluxes from the impact of het-erogeneities in the early-morning stratification. Spatialheterogeneities in boundary layer height are primarilydriven by variability in the surface buoyancy fluxes. On14 June, this is particularly true west of H1100298.5°W. Eastof H1100298.5°W, the stratification of the atmosphere alsoplays a significant role. For this day, the buoyancyfluxes were not strongly correlated to soil moisture (es-pecially at small scales) but were more directly con-trolled by net radiation. Daytime boundary layer waterFIG. 14. Spatial fluctuations of (a) BL height, (b) BL height with minus BL height without initial atmospheric heterogeneity, (c) BLwater vapor mixing ratio minus initial water vapor mixing ratio integrated over the mean boundary layer height, and (d) cumulativeEf from 0600 to 1300 LDT 14 Jun 2002.430 MONTHLY WEATHER REVIEW VOLUME 137vapor displays variability at different scales that resultsfrom interactions between surface latent heat flux,boundary layer height, and the early-morning hetero-geneity of the atmosphere, with a spatially varying com-bination of processes. Horizontal advection also playsa significant role. This complexity underscores thefact that the evolving spatial heterogeneity of day-time boundary layer water vapor is related not only tosurface characteristics, but also depends on the early-morning atmospheric profiles at synoptic scales, consis-tent with the findings of Findell and Eltahir (2003). On14 June, the initial heterogeneity in the atmospherelargely accounts for the water vapor gradient at largescales. This heterogeneity likely results from horizontaladvection and modification of the atmosphere by moistconvection occurring on previous days. Variability atsmaller scales is more closely related to boundary layerheight and evaporative fraction patterns and is there-fore related to the surface flux heterogeneities.We have shown through this case study that the useof a 1D model fed by inputs from a SVAT for surfacefluxes and a mesoscale analysis is a useful tool to helpin the interpretation of field campaign observations. Incomparison to a mesoscale model, which integrates theeffect of many processes, this approach has the abilityto separately evaluate the impact of individual pro-cesses. This methodology can also be used to qualita-tively evaluate a land data assimilation system, giventhat a more direct surface flux validation is very diffi-cult because of the lack of observations and represen-tativeness issues.Acknowledgments. The authors thank K. Craig forthe retrieval of boundary layer depths from lidar dataand B. Geerts for KA data. This research has beensupported by the GAME (CNRS-Météo-France). Weacknowledge the support from the National Center forAtmosperic Research (NCAR) Water Cycle AcrossScale Program at The Institute for Integrative and Mul-tidisciplinary Earth Studies (TIIMES), the (NationalScience Foundation) NSF/NCAR U.S. Weather Re-search Program (USWRP), NASA-THP (Dr. Jared EntinNNG06GH17G), the NASA-GWEC (NNG05GB41G),and the Canadian Foundation for Climate and Atmo-spheric Science. The authors also thank the reviewersfor their helpful comments that considerably improvedthe manuscript.APPENDIXThe Mixed Layer Slab ModelThe time evolution of the boundary layer height isgiven by Garratt’s (1994) Eq. (6.21):hH20849tH208502H11005 hH20849t H11002 1H208502H11001 2 H11003H208491 H11001 2cH20850H9253H20885tH110021twH11032H9258H11032H9271sfdtH11032. H20849A1H20850This formulation takes into account the impact of watervapor on the growth of the boundary layer through theuse of the surface buoyancy flux. The lapse rate (H9253)isthe mean lapse rate between the previous boundarylayer height at the previous time h(t H11002 1) and the newone h(t). In this model, subsidence and any contributionof mechanical turbulence are neglected. The latter as-sumption requires that winds are moderate and for thisreason this model should not be used just after sunrise,where shear plays a significant role in the developmentof the boundary layer. Here, we focus primarily on 1200LDT and on a day (14 June) with a wind speed less than5msH110021. We do however begin the model spinup at 0800LDT. The model is then run for 7 h.The time evolution of the boundary layer water va-por mixing ratio is obtained by a simple scaling formu-lation:qmH20849tH20850H110051hH20849tH20850H11003 H20851qmH20849t H11002 1H20850H11003 hH20849t H11002 1H20850H11001H20885hH20849tH110021H20850hH20849tH20850qiH20849zH20850 dzH11001H20885tH110021twH11032qH11032sfdtH11032H20852. H20849A2H20850This approaches uses the vertical structure of the watervapor mixing ratio profile, as opposed to an analyticalprofile (e.g., assuming an exponential decrease), whichwould smooth the impact of any early-morning atmo-spheric vertical heterogeneity.REFERENCESAlapaty, K., S. Raman, and D. S. Niyogi, 1997: Uncertainty in thespecification of surface characteristics: A study of predictionerrors in the boundary layer. Bound.-Layer Meteor., 82, 473–500.André, J. C., P. 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