A climate version of the regional atmospheric modeling system · linear interactions between the...

Department of Atmospheric Science, Colorado State University, Fort Collins, Colorado, USA

A climate version of the regional atmospheric modeling system�

G. E. Liston and R. A. Pielke

With 12 Figures

Received September 27, 1999Revised December 11, 1999

Summary

The Regional Atmospheric Modeling System (RAMS) hasbeen widely used to simulate relatively short-term atmo-spheric processes. To perform full-year to multi-year modelintegrations, a climate version of RAMS (ClimRAMS) hasbeen developed, and is used to simulate diurnal, seasonal,and annual cycles of atmospheric and hydrologic variablesand interactions within the central United States during 1989.The model simulation uses a 200-km grid covering theconterminous United States, and a nested, 50-km gridcovering the Great Plains and Rocky Mountain states ofKansas, Nebraska, South Dakota, Wyoming, and Colorado.The model's lateral boundary conditions are forced by six-hourly NCEP reanalysis products. ClimRAMS includessimpli®ed precipitation and radiation sub-models, andrepresentations that describe the seasonal evolution ofvegetation-related parameters. In addition, ClimRAMS canuse all of the general RAMS capabilities, like its morecomplex radiation sub-models, and explicit cloud andprecipitation microphysics schemes. Thus, together with itsnonhydrostatic and fully-interactive telescoping-grid cap-abilities, ClimRAMS can be applied to a wide variety ofproblems. Because of non-linear interactions between theland surface and atmosphere, simulating the observedclimate requires simulating the observed diurnal, synoptic,and seasonal cycles. While previous regional climatemodeling studies have demonstrated their ability to simulatethe seasonal cycles through comparison with observedmonthly-mean temperature and precipitation data sets, thisstudy demonstrates that a regional climate model can also

capture observed diurnal and synoptic variability. Observedvalues of daily precipitation and maximum and minimumscreen-height air temperature are used to demonstrate thisability.

1. Introduction

Current global-scale, general circulation models(GCMs) used to simulate weather and climate donot operate at ®ne enough grid resolutions toresolve many observed regional weather andclimate features. To simulate these meteorologicalfeatures, regional or limited-area atmosphericmodels have been used. These models are run athigher resolution than the GCMs and are thus ableto better represent mesoscale dynamics andthermodynamics, including processes resultingfrom ®ner-scale topographic and land-surfacefeatures. Typically the regional atmosphericmodel is run while receiving lateral boundary-condition inputs from a relatively-coarse resolu-tion atmospheric analysis model or from theoutput of a GCM. The model simulationsperformed as part of the Project to IntercompareRegional Climate Simulations (PIRCS) (Takleet al., 1999) are an example of these kinds ofsimulations. Additional discussions of regionalclimate modeling efforts can be found in Giorgi(1995), Christensen et al. (1997), McGregor(1997) and Beniston (1998).

Typically, full-year regional climate modelintegrations have been validated against monthlymean temperature and precipitation observations

Theor. Appl. Climatol. 68, 155±173 (2001)

� This article has been published for the ®rst time in Theoreticaland Applied Climatology 66: 29±47 (2000). Due to the extremelypoor quality of the ®gures caused by the printer we decided toreprint the article again in this issue to guarantee a proper pre-sentation of the paper.The Publisher

(e.g., Giorgi et al., 1993; Marinucci et al., 1995;L�uthi et al., 1996; Christensen et al., 1997;Christensen et al., 1998). Such studies have beenable to demonstrate the model's ability tosimulate the seasonal cycles associated with theparticular domain of interest. Because of the non-linear interactions between the land surface andatmosphere, a realistic climate model must alsobe able to simulate the diurnal and synopticcycles that average to make up the observedmonthly climatologies. Mearns et al. (1995), forexample, has compared regional climate modeloutputs with daily observations. As a speci®cillustrative example of why comparison withdaily observations is important, consider anobserved monthly precipitation total of 30 mmof water; the behavior of the land-atmospheresystem is expected to be quite sensitive towhether this precipitation falls as a one-day,30 mm precipitation event, or if it falls for 30 daysat 1 mm dayÿ1. These differences are expected toin¯uence a wide range of land-surface andatmospheric processes and interactions, includ-ing: soil-moisture characteristics, vegetationresponse, runoff, and surface energy and mois-ture ¯uxes. Consequently, efforts to validate theperformance of regional climate models shouldinclude an assessment of the model's ability tosimulate general atmospheric variables at a rangeof temporal scales. Speci®cally, a regionalclimate model should be able to reasonablysimulate diurnal, synoptic, and seasonal cycles.An additional measure of a regional climatemodel's performance, is its competence insimulating interannual variability; something thatis not addressed in this paper. The analysespresented herein assesses a regional climatemodel's performance on diurnal, synoptic, andseasonal time scales.

The Regional Atmospheric Modeling System(RAMS) is a general-purpose, atmospheric-simulation model that includes the equations ofmotion, heat, moisture, and continuity in aterrain-following coordinate system. Thisincludes a land-surface sub-model that representsthe storage and exchange of heat and moistureassociated with the atmosphere-terrestrial inter-face, and cloud, precipitation, and radiationsub-models representing physical processesassociated with atmospheric energy and moistureinteractions. RAMS has been widely used to

simulate relatively short-term (hours to severaldays) atmospheric and land-surface processes,and the interactions between the two (Pielkeet al., 1992; Nicholls et al., 1995). To performfull-year to multi-year regional atmosphericmodel integrations, a climate version of RAMS(ClimRAMS) has been developed. It containsadditional features required to satisfy bothcomputational constraints and time-evolvingboundary conditions and land-surface featureslike vegetation parameters and seasonal snow-cover. The model is used to perform historicalsimulations where atmospheric analyses data areavailable to de®ne the lateral boundary-conditionforcing. The model could also be con®gured touse GCM outputs for the lateral boundaryconditions. A necessary (although not suf®cient)condition for using GCM outputs as lateralboundary conditions, is that realistic regionalclimate model simulations should result when theanalyses lateral boundary conditions are used.

A primary purpose of this ClimRAMS devel-opment and validation effort is to provide aversion of RAMS that can be used to supportvegetation- and snow-related evolution andatmospheric interaction studies. These types ofprojects require a model capable of simulatingthe full annual cycle. For example, ClimRAMShas been coupled to the CENTURY ecosystemmodel (Lu et al., 2001) and the GEMTMecosystem model (Eastman, 1999). A currentde®ciency in most regional and global climatemodel land-surface parameterizations is that theyuse only simple climatological approaches, basedon time-of-year, to de®ne the model vegetationparameters (e.g., leaf area or albedo de®nedaccording to Julian date). What is lacking inthese schemes is a realistic representation ofvegetation response, or changes in live biomass,to atmospheric and hydrologic in¯uences. Theclimatological approach is incapable of realisti-cally responding to deviations from climatology,such as wetter and drier than average seasons, orto changes in climate. CENTURY (Parton et al.,1996), when coupled with ClimRAMS, providesthat biospheric response and allows an analysisof the interactions and feedbacks between theatmosphere and vegetated surface. In addition,ClimRAMS has been used to develop subgrid-scale snow-distribution representations for appli-cation in regional and global climate models

156 G. E. Liston and R. A. Pielke

(Liston et al., 1999). Both of these researchefforts require an atmospheric model capable ofperforming realistic diurnal, synoptic, and sea-sonal cycles.

2. Study area and background

For the model simulation discussed herein, themodel domain and grid con®gurations are givenin Fig. 1, where a 200-km grid covers theconterminous United States, and a 50-km nestedgrid covers Kansas, Nebraska, South Dakota,Wyoming, Colorado, and parts of the regionssurrounding those states. The ®ne-grid domainhas been chosen to include portions of both theGreat Plains and the Rocky Mountains, thusincluding the different in¯uences of topographyand vegetation on atmospheric process found tooccur in those regions.

The winter weather over the ®ne-grid area ofFig. 1 is dominated by frequent migratory highand low pressure systems that are associated withthe polar jet stream and its associated polar front.If the jet stream ¯ow is basically zonal, the airmasses associated with the front are Paci®c inorigin, producing substantial snows in themountains, and relatively mild air just to thelee of these barriers. Further east, the weather inthe Great Plains is cooler, with some air fromCanada entrained south into the region west ofthe low pressure systems. Occasionally, Arctichigh pressure systems travel southward over theregion producing the area's coldest weather ofthe year. These intrusions of Arctic air occurwhen the polar jet stream travels far north into

Alaska and northwest Canada, before plungingsouth over the Great Plains. Upslope snowsfrequently occur in the western High Plainsduring these cold outbreaks.

Spring is a transition season when the highersun angles can produce warmer days, yet cold airmasses still occasionally travel southward bring-ing heavy snows to the Plains. In the westernhigh Plains, March and April are the snowiestmonths of the year. At this time in the easternhigh Plains, thunderstorms become common.Thunderstorms can often be quite intense at thistime of the year as a result of strong solar surfaceinsolation and relatively cold air aloft. Thetornado season in that region peaks in April,May, and June, as a still vigorous polar jet streamprovides large changes in wind speed anddirection with altitude. This large wind shearprovides the initial horizontal wind circulationfor tornadic thunderstorms, when the wind shearis tilted on its side by intense thunderstormupdrafts and downdrafts. A tornado can subse-quently be produced when this horizontal windcirculation is concentrated into a small area byintense updrafts. By summer, the polar jet hastypically migrated far to the north. Rainfallbecomes dominated by topographically-heatedupslope ¯ows, and weak migratory low pressuresystems. During this period, the weather-patternchanges are relatively slow. Organized clusters ofthunderstorms, called mesoscale convective sys-tems (MCSs), can develop and are associatedwith weak frontal boundaries or higher terrain.These MCSs usually move eastward in responseto the weak westerly winds in the middle andupper troposphere found during this time of theyear. A dryline boundary usually forms in thewestern Plains, separating humid air comingfrom the Gulf of Mexico from dry air originatingin the desert Southwest and northern Mexico.During summer, thunderstorms frequently formin this dryline region.

In late summer, the Mexican monsoon starts toaffect the western portion of the region. Sub-stantial rains often occur as moisture originatingin tropical Paci®c Ocean is advected northwardinto the southern Plains and southern RockyMountains. This monsoon ¯ow regime weakensin late August, and relatively dry weather,dominated by persistent and often nearly sta-tionary high pressure systems, begins to dom-

Fig. 1. ClimRAMS simulation domain and grid con®gura-tion. The coarse and ®ne grid intervals are 200 km and50 km, respectively

A climate version of the regional atmospheric modeling system 157

inate the fall months. Occasionally, snowstormsoccur as early as September in the northern andcentral Great Plains and Rocky Mountains, whenthe polar jet stream migrates southward in early fall.

In general, the climate of the Great Plains andRocky Mountains of the United States displays astrong seasonal cycle. Each season is character-ized by signi®cantly different meteorologicalconditions that are described by weather eventssuch as the passage of frontal systems and theproduction of convective storms. These weather

events operate on time scales ranging fromseveral hours to a few days. ClimRAMS hasbeen designed to represent these individualweather events and their seasonal evolution.

Model validation is performed using NationalClimatic Data Center (NCDC) Summary-of-the-Day (SOD) meteorological-station precipitationand maximum and minimum air temperaturedata. These SOD observations are availablethroughout the year and have a daily temporalcoverage that includes approximately 3800 sta-tions distributed across the United States (Fig. 2).Also shown in Fig. 2 is the outer-boundary of the50-km grid from Fig. 1. The SOD station data aregridded to the 50-km ClimRAMS grid using anobjective analysis scheme (Cressman, 1959), andthen compared with the model outputs. Beforethis comparison is made, the model-producedmaximum and minimum temperature ®elds areadjusted to account for the difference betweenthe model and station elevations. This is done bygridding the station elevations to the model grid,and then applying a spatially- and temporally-constant lapse rate of ÿ 6.5 �C kmÿ1 to thedifference between the station and model topog-raphy. The resulting temperature correction(Fig. 3) is then added to the model temperatures.We have chosen to adjust the model temperatures

Fig. 2. Locations of National Climatic Data Center(NCDC) Summary-of-the-Day (SOD) meteorological sta-tions. The inset box de®nes the ®ne-grid boundary shown inFig. 1

Fig. 3. Temperature correction (�C) added tothe model temperatures to account for differ-ences between station and model elevations; inthe western area of the ®ne-grid domain themodel topography is generally higher than thestation elevations


to the station elevations to avoid any modi®ca-tion of the observed data. We also recognize thatthe observations are biased towards lower eleva-tions, and that, in some sense, the modeledtemperatures may be more representative of thetrue grid-cell averages. The observational datasets are known to include other errors, such asurban heat island effects on temperature (Karlet al., 1988; Gallo et al., 1996) and wind andwetting effects on precipitation (Groisman andLegates, l994), that have not been taken intoaccount in our model validation.

Copeland et al. (1996) used a preliminaryversion of ClimRAMS to perform two month-long simulations for July 1989. These simula-tions were used to compare the in¯uence ofchanging the vegetation distribution over theUnited States from its pre-settlement, naturalvegetation distribution (from, say, 200 yearsago), to the current vegetation distribution. Tobe compatible with the Copeland et al. (1996)model simulations, 1989 was chosen for themodel simulations presented herein. To highlighthow representative 1989 is of the area's generalclimatology, 15 years spanning the period 1982through 1996, of monthly mean, daily precipita-tion and screen-height average maximum andminimum air temperature observations weregridded to the 50-km grid in Fig. 1 and averagedover the domain represented by that grid. Thesemonthly data were used to compute yearlyaverages. Over the 15-year period the meantemperature was 8.52 �C, with a standard devia-tion of 0.65 �C, and a 1989 mean of 8.29 �C. Themean precipitation over the period was 1.37 mmdayÿ1, with a standard deviation of 0.16 mmdayÿ1, and a 1989 mean of 1.13 mm dayÿ1.

In what follows, a summary of the mainClimRAMS components, initial and boundaryconditions, and general model con®guration arepresented. Detailed comparisons of the modelresults and the observational data sets arepresented in Section 4. Section 5 summarizesour general ®ndings and conclusions.

3. Model description

3.1 ClimRAMS

RAMS was developed at Colorado State Uni-versity primarily to facilitate research into

mesoscale and regional, cloud and land-surfaceatmospheric phenomena and interactions (Pielke,1974; Tripoli and Cotton, 1982; Tremback et al.,1985; Pielke et al., 1992; Walko et al., 1995a).The model is fully three-dimensional and non-hydrostatic (Tripoli and Cotton, 1980). TheRAMS horizontal grid uses an oblique (orrotated) polar-stereographic projection, wherethe pole of the projection is rotated to an areanear the center of the simulation domain, thusminimizing the projection distortion in the mainarea of interest. The grid's vertical structureuses a �z terrain-following coordinate system(Gal-Chen and Somerville, 1975; Clark, 1977;Tripoli and Cotton, 1982), where the top of themodel is ¯at and the bottom follows the terrain.An Arakawa-C grid con®guration is used in themodel, where the velocity components u, v, andw are de®ned at locations staggered one-halfa grid length in the x, y, and z directions,respectively, from the thermodynamic, moisture,and pressure variables (Arakawa and Lamb,1977). Grid nesting is available to provide highspatial resolution in selected areas, while cover-ing a larger domain at lower resolution. The grid-nesting technique follows Clark and Farley(1984) and Clark and Hall (1991), with ageneralization for stretched grids and a spa-tially-variable nesting ratio described by Walkoet al. (1995b). This implementation includes atwo-way communication of all prognostic vari-ables between any nested grid and its parent grid,and the methodology conserves mass, momen-tum, and internal thermodynamic energy.

ClimRAMS contains all of the above features,with the addition of several changes designed toallow full-year to multi-year integrations. Tomeet the requirements of a regional modelrunning at both weather and climate time scales,several modi®cations to the base modelingsystem were made, including: (1) sea-surfacetemperatures and vegetation parameters areupdated daily; (2) a collection of routines thatsimulate grid-scale snow accumulation, snowmelt, and their effects on surface hydrology andsurface energy exchanges were added; (3) amoisture- and precipitation-physics scheme forlong model runs was implemented; and (4)simpli®ed incoming shortwave and longwaveradiation schemes were introduced. Details of theClimRAMS model implementation and physics


representations are provided in the followingparagraphs.

In ClimRAMS, the Mahrer and Pielke (1977)shortwave and longwave radiation model is usedin conjunction with the scheme presented byThompson (1993) to account for the presence ofclouds. In this scheme a threshold relativehumidity is used to de®ne the presence of clouds,and Neiburger's (1949) curve is used to relatecloud depth to cloud albedo for shortwaveradiation. For longwave radiation, these de®nedclouds modify the downward radiative ¯uxesfollowing Hurley and Boers (1996). Other, morecomplex, shortwave and longwave radiationschemes are also available in ClimRAMS/RAMS(Chen and Cotton, 1983, 1987; Harrington,1997).

In addition to the standard explicit cloud andprecipitation microphysics representation gener-ally available in RAMS (Meyers et al., 1992;Meyers, 1995; Walko et al., 1995a), two addi-tional precipitation schemes have been imple-mented in ClimRAMS. The most simpleprecipitation scheme is based on the `̀ dump-bucket'' parameterization used in the RAMSforecast model (Cotton et al., 1995). In thisparameterization, water vapor in excess ofsaturation is assumed to condensate, and thenthe precipitation amount is computed andremoved from the saturated layer by applying aprecipitation ef®ciency that is a function of thesaturated-layer temperature (Rhea, 1978). Theresulting precipitation is assumed to reach theground without any further interactions with theatmosphere. This method does not distinguishbetween the various types of precipitation (e.g.,rain, snow, hail), and thus additional informationis required to identify whether liquid (rain) orice-phase (snow) precipitation reaches theground. This is accomplished by assuming thatwhen the air temperature in the lowest atmo-spheric model layer is less than 2.0 �C, snowreaches the ground; all other conditions lead torain (Auer, 1974). The explicit cloud physicsparameterization of Schultz (1995) has also beenimplemented in ClimRAMS. The complexity ofthis precipitation scheme lies between the fullmicrophysics and the `̀ dump-bucket'' options.Schultz (1995) includes ®ve categories of con-densate in this precipitation sub-model: cloudliquid, assumed to have a zero fall velocity;

pristine cloud-ice crystals; rain; snow; andprecipitating ice (includes graupel, sleet, andhail). Condensation, evaporation, collection,freezing, and melting are all accounted for aspart of the precipitation evolution and interac-tions with the surrounding atmosphere. Both the`̀ dump-bucket'' and Schultz schemes reduce thecomputational requirements considerably overthe full microphysics option, with apparentlyonly minimal loss in precipitation skill score forthe 50-km grid increment considered in thisstudy. For the case of the `̀ dump-bucket'' scheme,the ClimRAMS computation time is approxi-mately reduced by half when compared to thefull microphysics scheme. In addition to simulat-ing large-scale precipitation, ClimRAMS uses amodi®cation of the generalized form (Molinari,1985) of the Kuo (1974) convective parameter-ization (Tremback, 1990) to account for convec-tion-produced precipitation.

Each surface grid cell in RAMS is divided intothree different surface-type classes: water, baresoil, and vegetated. Prognostic temperature andmoisture variables are carried for the bare soiland vegetated surfaces. A vegetated surface isassumed to have bare ground under the vegeta-tion canopy. For bare soil, the McCumber andPielke (1981) and Tremback and Kessler (1985)multi-level prognostic soil model is used. Thesurface temperature is determined from thesurface energy balance, accounting for incomingand outgoing shortwave and longwave radiation,latent and sensible heat ¯ux, and the soilconductive ¯ux. The soil model includes equa-tions for the diffusion of heat and moisturefollowing Philip and DeVries (1957) and Clappand Hornberger (1978). The soil thermal proper-ties are temporally-evolving as a function of soilmoisture. The soil-moisture boundary conditionat the deepest soil level is held constant in timeand equal to the initial value. Plant transpirationextracts soil moisture through the roots by themethod outlined in Avissar and Mahrer (1982).The vertical root pro®le is de®ned to be the sameas that used in the Biosphere-AtmosphereTransfer Scheme (BATS) (Dickinson et al.,1986). The vegetation classi®cation and param-eter descriptions also follow those used inBATS, where there are 18 vegetation classi®ca-tions. Also following BATS, the surface char-acteristics of albedo, emissivity, roughness


length, displacement height, canopy transmissiv-ity to solar radiation, fractional vegetation cover-age, and leaf area index (LAI) are de®ned basedon climatological temperatures and vegetationtype. The seasonal variation of vegetation canopytransmissivity, fractional coverage, and LAI areprede®ned by assigning a cosine function to theclimatological temperatures used to computethese parameters. Example seasonal evolutionsof these parameters are provided in Fig. 4. Thesoil-texture-class spatial distribution is de®nedaccording to the United States Department ofAgriculture, STATSGO soils database (Millerand White, 1998). The soil-texture distributionfor the ®ne grid in Fig. 1 is given in Fig. 5. Thetemperature of the bottom soil layer variesfollowing the deep-soil temperature model ofDeardorff (1978). The model implementationpresented herein uses 10 soil layers, with thebottom of each layer at the following levels (m):0.05, 0.125, 0.2, 0.3, 0.45, 0.65, 0.95, 1.3, 1.65and 2.0.

The surface-layer ¯uxes of heat, momentum,and water vapor are computed using the methodof Louis (1979, 1982), who ®tted analytic curvesto the ¯ux-pro®le relationships determined byBusinger et al. (1971). This scheme is applied toeach grid-cell fraction of water, bare-soil, andvegetated surfaces, and the resulting ¯uxes are

weighted according to the fractional area of eachsurface type. The surface roughness is speci®edover land according to vegetation type, and overwater it is a function of wind speed. Parameter-ization of the horizontal and vertical diffusioncoef®cients is done using K-theory based onSmagorinski (1963), where the mixing coef®-cients are related to the ¯uid deformation rate,and corrections are made to account for thein¯uence of Brunt-V�ais�al�a frequency (Hill, 1974)and Richardson number (Lilly, 1962).

In ClimRAMS, a simple snow sub-modelaccounts for key features of the snowcover andits atmospheric and hydrologic interactions andfeedbacks. The primary components of the snowmodel are: (1) precipitation is assumed to fall assnow if the temperature of the lowest atmo-spheric model level is < 2.0 �C (Auer, 1974); (2)the snowpack is represented by one layer ofconstant density and thermal properties; (3) thealbedo decreases linearly with snow depth,towards the vegetation albedo, when the snow-water-equivalent depth is less than 5 cm, and ismodi®ed depending on whether the snow is dry(albedo� 0.8) or melting (albedo� 0.5) (Maleand Gray, 1981); (4) the ground heat-¯uxcomputation is modi®ed as a function of snowdepth; (5) the surface roughness is modi®edwhen snow is present; (6) the snow surface

Fig. 4. Example seasonal evolu-tions of leaf area index, vegetationfraction, and canopy transmissivityto solar radiation, for shortgrassprairie, evergreen needleleaf tree,and semi-desert vegetation classi®-cations. The ®ne-grid spatial dis-tribution of these classes are givenin Fig. 7


temperature is constrained to be � 0 �C; (7) theavailable energy to melt snow is computed aspart of the surface energy balance; and (8) snowmeltwater is added to the soil-moisture store.Additional details of the ClimRAMS snow sub-model, and the model's ability to simulateobserved snow distributions, can be found inListon et al. (1999).

3.2 Boundary and initial conditions

The model topography is de®ned using a global,30 arc-second latitude-longitude (approximately1-km) topographic data set called GTOPO30(Gesch et al., 1999) and available through theUnited States Geological Survey's (USGS) EarthResources Observation Systems (EROS) DataCenter. In processing these data for the Clim-RAMS grids, each coarse grid cell is assigned anelevation equal to the mean of the coincidentcollection of ®ne grid cells, thus ensuringcompatibility of terrain heights between nestedmodel grids, while still allowing higher-resolu-tion terrain features to be included in the ®nergrids. The topographic distribution for the ®negrid in Fig. 1 is given in Fig. 6. The modelvegetation distribution is de®ned using the

International Geosphere-Biosphere Programme(IGBP) land-cover classi®cation, available throughthe USGS EROS Data Center. The dominantland-cover class coincident with each Clim-RAMS grid cell is used to de®ne the model'sland-cover distribution. The vegetation distribu-tion for the ®ne grid in Fig. 1 is given in Fig. 7.The water-surface temperatures are de®nedaccording to National Oceanic and AtmosphericAdministration (NOAA) sea-surface-temperatureanalyses produced weekly on a global one-degree grid (Reynolds and Smith, 1994), andinterpolated to the ClimRAMS grids.

The method used to generate snow-water-equivalent distribution initial conditions closelyfollows Liston et al. (1999), and are provided by®rst generating observed snow-water-equivalentdepth distributions for the conterminous UnitedStates. This is done by merging two data sets: theNational Operational Hydrologic Remote Sen-sing Center (NOHRSC) snow-water-equivalentdepth data, and the NCDC SOD meteorological-station snow-depth data. The NOHRSC datacover the western United Sates on a 30 arc-second latitude-longitude grid, and are derivedfrom a variety of remote sensing and ground-based observations (Carroll, 1997). They are

Fig. 5. The soil-texture-class spatial distribu-tion for the ®ne grid in Fig. 1, de®ned accordingto the United States Department of Agriculture,STATSGO soils database (Miller and White,1998). The numbers correspond to the followingClimRAMS soil classes: 1, sand; 2, loamy sand;3, sandy loam; 4, silt loam; 5, loam; 6, sandyclay loam; 7, silty clay loam; 8, clay loam; 10,silty clay; 11, clay. Soil class 9 (sandy peat) and12 (peat) are not included in this domain at thisresolution


available approximately twice per week duringthe middle-winter through early-summer months.The SOD snow-depth observations are availablethroughout the year and have a daily temporalcoverage that includes the stations given in Fig. 2.

The SOD station data are ®rst gridded to a5-km grid using an objective analysis scheme(Cressman, 1959). The resulting snow-depthdistributions are then converted to snow-water-equivalent distributions using the snow-

Fig. 6. The topographic distribution for the®ne grid in Fig. 1. Also shown are the city/gridlocations used in Figs. 11 and 12

Fig. 7. The vegetation distribution for the ®negrid in Fig. 1, de®ned according to theInternational Geosphere-Biosphere Programme(IGBP) land-cover classi®cation. The numberscorrespond to the following ClimRAMS vege-tation classes: 1, crop/mixed farming; 2, short-grass prairie; 3, evergreen needleleaf tree; 5,deciduous broadleaf tree; 7, tallgrass prairie;10, irrigated crop; 11, semi-desert; 18, mixedwoodland. Vegetation class 4 (deciduous nee-dleleaf tree), 6 (evergreen broadleaf tree), 8(desert), 9 (tundra), 12 (ice cap/glacier), 13(bog/marsh), 14 (inland water), 15 (ocean), 16(evergreen shrubland), and 17 (deciduousshrubland) are not included in this domain atthis resolution


classi®cation distribution of Sturm et al. (1995),where the snow density used for each of the snowclasses is given in Table 1. The NOHRSC dataare then gridded to the 5-km grid used for theSOD data, and the two data sets are merged toprovide spatially continuous, 5-km coverage overthe conterminous United States. Because of thebroader collection of data sources used in theNOHRSC data sets (including mountain-basedUnited States Natural Resources ConservationService SNOTEL (SNOw TELemetry) observa-tions), the NOHRSC data are used wherever bothdata sets are coincident. These 5-km data arethen regridded to the 200 and 50-km RAMSgrids (Fig. 1) for use in the model simulations.As part of the model simulations, any requiredconversion from snow depth to snow-water-equivalent depth, and back, is accomplishedusing the Sturm et al. (1995) snow-classi®cationdistribution and the density values provided inTable 1.

Horizontal wind components, relative humid-ity, air temperature, and geopotential height arerequired to be used as atmospheric lateralboundary conditions for the ClimRAMS simula-tions. These data sets are available from theNational Centers for Environmental Prediction(NCEP) in the form of six-hourly atmosphericreanalyses products, on pressure levels and aglobal 2.5� latitude by 2.5� longitude grid(Kalnay et al., 1996). The variables are inter-polated to the ClimRAMS coarsest grid, and thenlinearly interpolated in time to each model timestep. Lateral boundary-condition nudging isperformed on the two outer-boundary grid cellsof the 200-km grid following the ¯ow-relaxationscheme of Davies (1976, 1983); there is nonudging performed in the interior of the coarse

grid or over any of the ®ne grid. Initialatmospheric ®elds are also provided by theNCEP reanalyses.

Soil moisture initial distributions are generatedby ®rst de®ning a spatially-constant soil moisturecontent over the domain, and running the modelfor one year. The soil moisture distribution on thelast day of that simulation is then used as theinitial conditions for the next year's simulation.A spatially-constant soil moisture of 55% of thetotal water capacity was used to start the one-year soil-moisture `̀ spin-up'' simulation. Thesimulation started on 1 January, and the 31December soil moisture distribution was used asthe 1 January soil moisture initial conditions forthe simulations presented herein.

For the model simulations discussed in thispaper, ClimRAMS has been set up with thehorizontal grid con®guration summarized inFig. 1, and with 20 vertical levels having athickness of 119 meters at the surface andstretching to 2000 meters at the 23-km domaintop. The simulations span the period 1 Januarythrough 31 December 1989. The model uses a120-second time step for both the 200 and 50-kmgrids. The pole point for the oblique polar-stereographic projection is de®ned to be 40�Nlatitude and 100�W longitude. The land-surfacesub-model is called at every atmospheric-dynamics time step, and the simple `̀ dump-bucket'' large-scale precipitation scheme is used.Under the assumption that the convective andradiative tendencies evolve on a time scalegreater than that de®ned by the model dynamicstime step, the convective parameterization andradiation schemes are called every 7.5 and30 minutes, respectively.

4. Results

The model's ability to simulate domain-averageddaily maximum and minimum screen-height airtemperature and daily precipitation are shown inFig. 8, where these variables have been averagedover the 50-km grid given in Fig. 1. Theobservations and model results are shown, andthe difference between these two values (modelminus observed) are plotted. The difference plotsinclude the 30-day running mean of the dailyvalues. Also shown are the mean (mn) andstandard deviation (sd) for each panel and

Table 1. Snow densities used to convert snow depth tosnow-water-equivalent depth using to the snow-classi®ca-tion distribution of Sturm et al. (1995)

Snow Snow DensityClassi®cation (kg mÿ3)

Tundra 280Taiga 225Alpine 250Prairie 250Maritime 300Ephemeral 350


variable plotted in the ®gure. The model is foundto reproduce the annual cycle of these primaryclimate variables and it also closely reproducesthe synoptic cycle that varies on an approxi-mately one-week time scale. Over the year, the

model-simulated daily maximum and minimumscreen-height air temperatures average 0.23 �Chigh and 0.23 �C low, respectively. The deviationfrom observed values can vary approximately� 3 �C on any given day, but deviations outside

Fig. 8. Modeled and observed, domain-averaged daily maximum and minimum screen-height air temperature and dailyprecipitation, where these variables have been averaged over the 50-km grid given in Fig. 1. Also shown is the differencebetween the model and observations, and the 30-day running mean of the difference values. Included are the mean (mn) andstandard deviation (sd) for each panel and variable


this range are rare. Over the year, the model-simulated daily precipitation averages 0.18 mmdayÿ1 too low. The model captures the timing ofindividual precipitation events throughout theyear, and the magnitudes are well-simulatedduring the fall and winter months. During springand summer, the model produces both excess andinadequate precipitation amounts, with the great-est underestimates occurring during May±Juneand August±September. The individual dailyprecipitation peaks are rarely captured by themodel.

The winter (January through March averages)spatial patterns of maximum and minimum dailyscreen-height temperature and daily precipitationare given in Fig. 9. Shown are the modeled andobserved ®elds, and the differences betweenthem (model minus observed). The model is

found to generally capture the observed spatialpatterns, and produces spatially-averaged max-imum and minimum temperatures 0.28 �C and0.74 �C less than those observed, respectively, andspatially-averaged precipitation of 0.10 mm dayÿ1

less than that observed. The modeled maximumtemperature is too low by as much as 7 �C inportions of the western (mountainous) region ofthe domain, and too high by as much as 6 �C inportions of the eastern (prairie) region of thedomain. The precipitation produced over theTeton mountain range in north-western Wyomingis greater than that observed.

The summer (June through August averages)spatial patterns of maximum and minimum dailytemperature and daily precipitation are given inFig. 10. Shown are the modeled and observed®elds, and the differences between them (model

Fig. 9. The winter spatial patterns ofmaximum and minimum daily screen-height temperature and daily precipi-tation, averaged over January throughMarch. Shown are the modeled andobserved ®elds, and the differencesbetween the two. Also included arethe mean (mn) and standard deviation(sd) for each panel and variable


minus observed). The model is found togenerally capture the observed spatial patterns,and produces spatially-averaged maximum andminimum temperatures 1.18 �C greater and0.04 �C less than those observed, respectively,and spatially-averaged precipitation of 0.04 mmdayÿ1 more than that observed. The maximum-temperature winter cold bias present in thewestern part of the domain is not present in thesummer period. The observed precipitationmaximum in the south-east corner of the domainis simulated by the model, although there is somesuggestion that the model's coarse- and ®ne-gridinformation transfer in¯uences the precipitationproduced in this area, and that the convectiveparameterization has been unable to correctlyrepresent the transition from the ®ne to coarsegrid at the ®ne-grid out¯ow boundary. The

temperature ®elds do not display any similarmisrepresentation.

The annual cycle of daily maximum andminimum screen-height air temperature, at themodel grid cells corresponding to three citieswithin the ®ne-grid domain, is given in Fig. 11.These cities are identi®ed by the markers inFig. 6, and are: Salina, Kansas; Sioux Falls,South Dakota; and Casper, Wyoming. They eachexist in different temperature and precipitationregimes, and the model has been able to capturethis variability. Differences between the modeland observations are also plotted in Fig. 11,where a 7-day running mean has been applied tothe data to improve clarity. The synoptic cyclesare captured very well in both the maximum andminimum temperatures, although magnitudes ofthe modeled values can differ by several degrees

Fig. 10. Same as Fig. 9, but forsummer, averaged over June throughAugust


on speci®c days. For the cases of Salina andSioux Falls, the annual average differences areless than a couple of degrees. Casper has asimilar annual average difference for the max-imum temperature, but the modeled minimum

temperature is 3.9 �C low. In addition, themodeled summer maximum temperatures areconsistently high. In the more mountainousregions of the domain we expect it to be moredif®cult to model the local terrain forcing that

Fig. 11. The annual cycle of daily maximum and minimum screen-height temperature at the model grid cells correspondingto three cities identi®ed by the markers in Fig. 6. Also shown is the difference between the model and observations, plottedusing a 7-day running mean to improve clarity. Included are the mean (mn) and standard deviation (sd) for each panel andvariable; the statistics for the difference plots were computed using the original data, prior to applying the running mean


strongly in¯uences weather and climate featuresthere. Many of these forcings exist as scalesbelow those resolved by the 50-km grid used inthese simulations.

The annual cycle of daily precipitation isprovided in Fig. 12, for the same three cities used

in Fig. 11 (see Fig. 6). These stations are able tocapture some of the regional variation that existswithin this domain; Casper receives signi®cantlyless precipitation than the prairie cites. Again it isclear that the model does not generally capturethe peak magnitudes of the precipitation events,

Fig. 12. The annual cycle of daily precipitation at the model grid cells corresponding to three cities identi®ed by the markersin Fig. 6. Also shown is the difference between the model and observations. Included are the mean (mn) and standarddeviation (sd) for each panel and variable


as shown by the negative tails in the differenceplots, but the timing of individual events and thesynoptic and seasonal cycles are well simulated.

5. Discussion and conclusions

ClimRAMS, a climate version of the RegionalAtmospheric Modeling System (RAMS) hasbeen used to simulate atmospheric and land-surface processes during 1989, over a portion ofthe Great Plains and Rocky Mountains of theUnited States. The year 1989 was reasonablyclose to the climate averages for the region beingconsidered. This regional climate modeling studyis unique in that it has used daily observations tovalidate the model outputs. While the diurnalcycle is not explicitly analyzed with sub-dailyobservational data, it is implicitly addressedthrough the use of daily maximum and minimumair-temperature data sets. Application of thesedaily data, as opposed to monthly-mean datatypically used in the past, has allowed demon-stration of the model's ability to capture thesynoptic cycles that dominate middle-latitudeweather and climate characteristics. On even®ner temporal scales, the model has been foundto generally simulate daily maximum and mini-mum screen-height air temperatures and dailyprecipitation. Thus, ClimRAMS has been shownto simulate the daily- to synoptic-scale atmo-spheric forcing that collectively merges to de®nethe climate of a region. Using six-hourly NCEPreanalysis data to de®ne the atmospheric lateralboundary conditions, at approximately the outerboundaries of the conterminous United States,the model has successfully transferred thatinformation into the interior of the domain,roughly the central United States. The model'sanalysis domain includes portions of the GreatPlains and Rocky Mountains and, as such, withinthis domain there are both north-south and east-west gradients of important climatological vari-ables such as temperature and precipitation. Themodel has been able to simulate these spatialdistributions, as well as their temporal evolutionthroughout the year.

Prior to comparing the modeled and observedscreen-height air temperatures, the modeled datawas corrected to the observation-station eleva-tions by applying a constant lapse rate to theelevation differences between model and sta-

tions. In the natural system, the lapse rate variesin both time and space, and is a function of manyfactors, including atmospheric moisture andstability conditions. Implementing a moresophisticated elevation correction would likelychange the details of our results, but is notexpected to change any of our general conclu-sions regarding the model's ability to simulatethe observed weather and climate.

In spite of the general success of the model,errors in the simulation of the basic observedclimatological ®elds still suggest that signi®cantmodel improvements can and should be made.Modeled errors can be the result of severalfactors that are often interrelated. For example,changes to the precipitation scheme lead todifferences in cloudiness that affect shortwaveand longwave radiation reaching the ground.This, in turn, modi®es surface energy ¯uxes,temperatures, and soil moisture; all of whichin¯uence cloud and precipitation processes. Aspart of the model development and other studiesusing ClimRAMS (e.g., Lu et al., 2001),improvements to the model's de®nition of theseasonal evolution of vegetation parameters, likeLAI and albedo, have been suggested. In thefuture we anticipate being able to use remotely-sensed data sets to help de®ne these quantitiesand make them more realistic. The soil moisturedistribution and evolution is also something thatthe model simulates, and its values in¯uencesurface energy and moisture ¯uxes, and airtemperature and humidity. Unfortunately, thereare only limited soil moisture observations, andthe exact modeled biases are unclear. We alsoknow that the natural system displays land-surface variability at scales much smaller thanthat represented by the 50-km grid. To accountfor this heterogeneity, higher-resolution modelsimulations are required. In addition, increasedvertical resolution is expected to lead toimproved representation of the modeled bound-ary layer. Improvements to any of these modelcomponents should improve the model's physicalrealism, and hopefully the simulated climatolo-gies.

An important attribute of ClimRAMS is itsability to also use the other RAMS components.These include the more complex cloud andprecipitation microphysics schemes, and radia-tion sub-models. For this 50-km grid simulation,


the use of a simple precipitation scheme hasbeen found to provide acceptable results. Iden-tical simulations, but using the RAMS fullprecipitation-microphysics scheme (not shown),did not appreciably improve the model simula-tion. For higher-resolution simulations, the morecomplex precipitation scheme has been found toimprove results (e.g., Gaudet and Cotton, 1998).In addition, the model's nonhydrostatic andfully-interactive telescoping-grid capabilitiesallow ClimRAMS to be applied to a wide varietyof problems ranging in horizontal scale from afew hundred kilometers to less than one kilo-meter.

ClimRAMS has been able to realisticallysimulate the seasonal, synoptic, and diurnalcycles over an entire year. Generally the modelerrors become greater as the temporal cycle ofinterest becomes shorter. For example, a one-dayshift in a frontal passage will lead to precipitationfalling on the wrong day, and this will show up asan error in the model simulation. But, averagedover that synoptic cycle, the precipitation isgenerally quite realistic. Analyses of the differ-ences between model results and observationssuggests that the temperature and precipitation®elds are acceptable representations of theregion's spatial and temporal climatologies. Thegeneral success of the model simulations pre-sented herein suggest that ClimRAMS can beused in support of vegetation- and snow-relatedevolution and atmospheric interaction studies.These research efforts require an atmosphericmodel capable of performing realistic annualintegrations that include reasonable representa-tions of diurnal, synoptic, and seasonal cycles.

Acknowledgements

Special thanks is extended to T. Kittel who was instru-mental in developing the original ideas and funding for thismodel development effort. The authors would also like tothank J. Copeland, E. Greene, L. Lu, and R. Walko for theirassistance in making this modeling effort a success. Theland-cover and topographic data used in the modelsimulations are distributed by the EROS Data CenterDistributed Active Archive Center (EDC DAAC), located atthe United States Geological Survey's EROS Data Center inSioux Falls, South Dakota. This work was supported byNOAA Grant NA67RJ0152, NASA Grants NAG5-4760,NAG8-1511, and NAG5-7560, EPA Grant R824993-01-0,NSF Contract OPP-9614632, and NPS Contracts CA 1268-2-9004 COLR-R92-0204 and CEGR-R92-0193.

References

Arakawa A, Lamb RV (1977) Computational design of thebasic dynamical processes of the UCLA general circula-tion model. Methods in Computational Physics 17: 174±265

Auer AH (1974) The rain versus snow threshold tempera-tures. Weatherwise 27: 67

Avissar R, Mahrer Y (1982) Veri®cation study of a numer-ical greenhouse microclimate model. Trans Amer SocAgric Eng 25: 1711±1720

Beniston M (1998) From turbulence to climate: numericalinvestigations of the atmosphere with a hierarchy ofmodels. New York: Springer, 328 pp

Businger JA, Wyngaard JC, Izumi Y, Bradley EF (1971)Flux-pro®le relationship in the atmospheric surface layer.J Atmos Sci 28: 181±189

Carroll TR (1997) Integrated observations and processing ofsnow cover data in the NWS hydrology program, Pro-ceedings of the 77th AMS Annual Meeting. Symposiumon Integrated Observing Systems, Long Beach, Califor-nia, pp 180±183

Chen C, Cotton WR (1983) A one-dimensional simulationof the stratocumulus-capped mixed layer. Bound-LayerMeteor 25: 289±321

Chen C, Cotton WR (1987) The physics of the marinestratocumulus-capped mixed layer. J Atmos Sci 44:2951±2977

Clapp R, Hornberger G (1978) Empirical equations forsome soil hydraulic properties. Water Resour Res 14:601±604

Clark TL (1977) A small-scale dynamic model using aterrain-following coordinate transformation. J Comp Phys24: 186±215

Clark TL, Farley RD (1984) Severe downslope windstormcalculations in two and three spatial dimensions usinganelastic interactive grid nesting: A possible mechanismfor gustiness. J Atmos Sci 41: 329±350

Clark TL, Hall WD (1991) Multi-domain simulations of thetime dependent Navier-Stokes equations: Benchmarkerror analysis of some nesting procedures. J Comput Phys92: 456±481

Copeland JH, Pielke RA, Kittel TGF (1996) Potentialclimatic impacts of vegetation change: A regional model-ing study. J Geophys Res 101(D3): 7409±7418

Cotton WR, Weaver JF, Beitler BA (1995) An unusualsummertime downslope wind event in Fort Collins,Colorado, on 3 July 1993. Wea and Forecasting 10:786±797

Cressman GP (1959) An operational objective analysissystem. Mon Wea Rev 87: 367±374

Christensen JH, Machenhauer B, Jones RG, Sch�ar C, RutiPM, Castro M, Visconti G (1997) Validation of present-day regional climate simulations over Europe: LAMsimulations with observed boundary conditions. ClimateDynamics 13: 489±506

Christensen OB, Christensen JH, Machenhauer B, Botzet M(1998) Very high-resolution regional climate simulationsover Scandinavia ± present climate. J Climate 11: 3204±3229


Davies HC (1976) A lateral boundary formulation for multi-level prediction models. Quart J Roy Meteor Soc 102:405±418

Davies HC (1983) Limitations of some common lateralboundary schemes used in regional NWP models. MonWea Rev 111: 1002±1012

Deardorff JW (1978) Ef®cient prediction of ground surfacetemperature and moisture, with inclusion of a layer ofvegetation. J Geophys Res 83(C4): 1889±1903

Dickinson RE, Henderson-Sellers A, Kennedy PJ, WilsonMF (1986) Biosphere-Atmosphere Transfer Scheme(BATS) for the NCAR Community Climate Model.NCAR Tech Note NCAR/ TN-275�STR, 69 pp

Eastman JL (1999) Analysis of the effects of CO2 andlandscape change using a coupled plant and meteorolog-ical model. PhD dissertation, Department of AtmosphericScience, Colorado State University, 148 pp

Gal-Chen T, Somerville RCJ (1975) On the use of coordinatetransformation for the solution of the Navier-Stokes equa-tions. J Comput Phys 17: 209±228

Gallo KP, Easterling DR, Peterson TC (1996) The in¯uenceof land use/and cover on climatological values of thediurnal temperature range. J Climate 9: 2941±2944

Gaudet B, Cotton WR (1998) Statistical characteristics of areal-time precipitation forecasting model. Wea Forecast-ing 13: 966±982

Gesch DB, Verdin KL, Greenlee SK (1999) New land sur-face digital elevation model covers the Earth. EOS Trans-actions, AGU, 80(6): 69±70

Giorgi F (1995) Perspectives for regional earth systemmodeling. Global and Planetary Change 10: 23±42

Giorgi F, Bates GT, Nieman SJ (1993) The multiyear surfaceclimatology of a regional atmospheric model over thewestern United States. J Climate 6: 75±95

Groisman PY, Legates DR (1994) The accuracy of UnitedStates precipitation data. Bull Amer Meteor Soc 75(3):215±227

Harrington JY (1997) The effects of radiative and micro-physical processes on simulated warm and transitionseason Arctic stratus. PhD dissertation, AtmosphericScience Paper No. 637, Department of AtmosphericScience, Colorado State University, 289 pp

Hill GE (1974) Factors controlling the size and spacing ofcumulus clouds as revealed by numerical experiments.J Atmos Sci 31: 646±673

Hurley P, Boers R (1996) The effect of cloud cover onsurface net radiation: simple formulae for use in meso-scale models. Bound-Layer Meteor 79: 191±199

Kalnay E, Kanamitsu M, Kistler R, Collins W, Deaven D,Derber J, Gandin L, Sara S, White G, Woollen J, Zhu Y,Chelliah M, Ebisuzaki W, Higgins W, Janowiak J, Mo KC,Ropelewski C, Wang J, Leetma A, Renolds R, Jenne R(1995) The NMC/NCAR reanalysis project. Bull AmerMeteor Soc 77: 437±471

Karl TR, Diaz HF, Kukla G (1988) Urbanization: Its detec-tion and effect in the United States climate record.J Climate 1: 1099±1123

Kuo HL (1974) Further studies of the parameterization ofthe in¯uence of cumulus convection on large-scale ¯ow.J Atmos Sci 31: 1232±1240

Lilly DK (1962) On the numerical simulation of buoyantconvection. Tellus 14: 148±172

Liston GE, Pielke Sr RA, Greene EM (1999) Improving ®rst-order snow-related de®ciencies in a regional climatemodel. J Geophys Res 104(D16): 19559±19567

Louis JL (1979) A parametric model of vertical eddy ¯ux inthe atmosphere. Bound-Layer Meteor 17: 187±202

Louis JL, Tiedtke M, Geleyn JF (1982) A short history of thePBL parameterization at ECMWF. Proc 1981 Workshopon PBL Parameterization, Shin®eld Park, Reading, UK,ECMWF pp 59±71

Lu L, Pielke Sr RA, Liston GE, Parton WJ, Ojima D,Hartman M (2001) Implementation of a two-way inter-active atmospheric and ecological model and its applica-tion to the Central United States. J Climate 14: 900±919

L�uthi D, Cress A, Davies HC, Frei C, Sch�ar C (1996)Interannual variability and regional climate simulations.Theor Appl Climatol 53: 185±209

Mahrer Y, Pielke RA (1977) A numerical study of the air¯owover irregular terrain. Beitr�age zur Physik der AtmosphaÈre50: 98±113

Male DH, Gray DM (1981) Snowcover ablation and runoff.In: Gray DM, Male DH (eds) Handbook of Snow ±Principles, Processes, Management and Use. Toronto:Pergamon Press, pp 360±436

Marinucci MR, Giorgi F, Beniston M, Wild M, Tschuck P,Ohmura A, Bernasconi A (1995) High resolution simula-tions of January and July climate over the Western AlpineRegion with a nested regional climate modeling system.Theor Appl Climatol 51: 119±138

McCumber MC, Pielke RA (1981) Simulation of the effectsof surface ¯uxes of heat and moisture in a mesoscalenumerical model, Part I: Soil layer. J Geophys Res 86:9929±9938

McGregor JL (1997) Regional climate modelling. MeteorolAtmos Phys 63: 105±117

Mearns LO, Giorgi F, McDaniel L, Shields C (1995) Anal-ysis of variability and diurnal range of daily temperaturein a nested regional climate model: comparison withobservations and doubled CO2 results. Climate Dynamics11: 193±209

Meyers MP (1995) The impact of a two-moment cloudmodel on the microphysical structure of two precipitationevents. Ph D dissertation, Atmospheric Science Paper No.575, Department of Atmospheric Science, Colorado StateUniversity, 165 pp

Meyers MP, DeMott PJ, Cotton WR (1992) New primary icenucleation parameterizations in an explicit cloud model.J Appl Meteor 31: 708±721

Miller DA, White RA (1998) A conterminous United Statesmulti-layer soil characteristics data set for regional cli-mate and hydrology modeling. Earth Interactions 2(Available on-line at http:// EarthInteractions.org)

Molinari J (1985) A general form of Kuo's cumulus para-meterization. Mon Wea Rev 113: 1411±1416

Neiburger M (1949) Re¯ection, absorption, and transmis-sion of insolation by stratus cloud. J Appl Meteor 6:98±104.

Nicholls ME, Pielke RA, Eastman JL, Finley CA, LyonsWA, Tremback CJ, Walko RL, Cotton WR (1995) Appli-


cations of the RAMS numerical model to dispersion overurban areas. In: Cermak JE et al (eds) Wind Climate inCities. The Netherlands: Kluwer Academic Publishers,pp 703±732

Parton WJ, Ojima DS, Schimel DS (1996) Models toevaluate soil organic matter storage and dynamics. In:Carter MR, Stewart BA (eds) Structure and OrganicMatter Storage in Agricultural Soils. New York: CRCPress, pp 421±448

Philip JR, DeVries DA (1957) Moisture movement in porousmaterials under temperature gradients. Trans Amer Geo-phys Union 38: 222±232

Pielke RA (1974) A three-dimensional numerical model ofthe sea breezes over south Florida. Mon Wea Rev 102:115±139

Pielke RA, Cotton WR, Walko RL, Tremback CJ, Lyons WA,Grasso LD, Nicholls ME, Moran MD, Wesley DA, Lee TJ,Copeland JH (1992) A comprehensive meteorologicalmodeling system±RAMS. Meteorol Atmos Phys 49:69±91

Reynolds RW, Smith TM (1994) Improved global sea sur-face temperature analyses using optimum interpolation.J Climate 7: 929±948

Rhea JO (1978) Orographic precipitation model for hydro-meteorological use. Ph D dissertation, AtmosphericScience Paper No. 287, Department of AtmosphericScience, Colorado State University, 199 pp

Sturm M, Holmgren J, Liston GE (1995) A seasonal snowcover classi®cation system for local to global applications.J Climate 8: 1261±1283

Schultz P (1995) An explicit cloud physics parameterizationfor operational numerical weather prediction. Mon WeaRev 123: 3331±3343

Smagorinski J (1963) General circulation experiments withthe primitive equations. Part I, The basic experiment. MonWea Rev 91: 99±164

Takle ES, Gutowski Jr WJ, Arritt RW, Pan Z, Anderson CJ,de Silva RR, Caya D, Chen S-C, Giorgi F, Christensen JH,Hong S-Y, Juang H-M H, Katzfey J, Lapenta WM, LapriseR, Liston GE, Lopez P, McGregor J, Pielke Sr RA, Roads,JO (1999) Project to Intercompare Regional Climate

Simulations (PIRCS): Description and initial results. JGeophys Res 104(D16): 19443±19461

Thompson G (1993) Prototype real-time mesoscale predic-tion during 1991±92 winter season and statistical veri®ca-tion of model data. MS thesis, Dept of AtmosphericScience, Colorado State University, 105 pp

Tremback CJ (1990) Numerical simulation of a mesoscaleconvective complex: Model development and numericalresults. PhD dissertation, Atmospheric Science Paper No.465, Department of Atmospheric Science, Colorado StateUniversity, 247 pp

Tremback CJ, Kessler R (1985) A surface temperature andmoisture parameterization for use in mesoscale numericalmodels. Preprints, 7th Conference on Numerical WeatherPrediction, Montreal, Canada, AMS, 17±20 June, pp 355±358

Tremback CJ, Tripoli GJ, Cotton WR (1985) A regionalscale atmospheric numerical model including explicitmoist physics and a hydrostatic time-split scheme. Pre-prints, 7th Conference on Numerical Weather Prediction,Montreal, Quebec, AMS, 17±20 June, pp 433±434

Tripoli GJ, Cotton WR (1980) A numerical investigation ofseveral factors contributing to the observed variable inten-sity of deep convection over South Florida. J Appl Meteor19: 1037±1063

Tripoli GJ, Cotton WR (1982) The Colorado State Univer-sity three-dimensional cloud/mesoscale model±1982, PartI: General theoretical framework and sensitivity experi-ments. J de Rech Atmos 16: 185±220

Walko RL, Cotton WR, Meyers MP, Harrington JH (1995a)New RAMS cloud microphysics parameterization, Part I:the single-moment scheme. Atmos Res 38: 29±62.

Walko RL, Tremback CJ, Pielke RA, Cotton WR (1995b) Aninteractive nesting algorithm for stretched grids and vari-able nesting ratios. J Appl Meteor 34: 994±999

Authors' address: Glen E. Liston ([email protected]) and Roger A. Pielke, Sr., Department of Atmo-spheric Science, Colorado State University, Fort Collins, CO80523-1371, USA.


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