GOAL: To achieve better understanding and estimation of space-time precipitation structure over the Mississippi River Basin, including improvements in atmospheric model representation of precipitation to support improved coupled modeling.

The hypothesis behind GCIP precipitation research is that improved high-resolution precipitation prediction from atmospheric models is expected to result (via coupled land- atmosphere modeling) in better predictions of other hydrologic variables (e.g., soil moisture and runoff) at the small basin scale and over storm to daily to seasonal time scales. Such improved predictions will be useful for water management decisions and basin interpretations of climatic changes.

Objective: Study the statistical structure of precipitation variability at a range of space-time scales and develop subgrid scale precipitation downscaling algorithms (from large to small scales) to be used in atmospheric models.

Activities to support this objective are:

• A comprehensive study of the statistical subgrid scale rainfall variability over the Mississippi river basin (MRB) as function of storm type (e.g., cold vs. warm season precipitation, statisfrom vs. concective) and other relevant meteorological parameters of the storm environment (e.g., convective available potential energy -- CAPE). Scale- invariant relationships are especially useful as they provide efficient parameterizations over a large range of space-time scales.

• Development of precipitation downscaling algorithms that can recreate the subgrid scale statistical variability of rainfall given its large scale average and other physical characteristics of the storm environment e.g., represented through certain atmospheric instability indices such as CAPE. The reconstruction of the fraction of area covered by rainfall as a function of scale (grid box) is especially desirable in these downscaling algorithms.

• Characterization of the time evolution of the subgrid scale precipitation variability, e.g., at the build-up, maturity and dissipation stage of a storm system, for the purpose of continuous-time rainfall downscaling. For this task it is particularly useful to connect statistical subgrid scale parameterizations to observables which can be computed from observed meteorological variables or can be predicted by atmospheric models as the storm evolves.

6.1.2. Atmospheric Precipitation Processes

Objective: Understand the physics of precipitating clouds and their relation to the storm environment and the produced precipitation fields.

Activities to support this objective are:

• Understand the 3D structure of precipitation fields and its variation in time, especially in relation to extreme surface precipitation and flooding and to the interaction of storms with the vertical distribution of water vapor in the large-scale storm environment.

• Understand the impact of relative amounts and patterns of stratiform and convective precipitation on: (a) the mesoscale organization of the weather system producing the precipitation, (b) the nature of precipitation mechanisms producing the precipitation, (c) the water budgets of individual storms, and (d) the vertical distribution of heating associated with the precipitation process.

• Develop a radar-based climatology of storms over the Mississippi river basin including algorithms for convective vs. stratiform separation of precipitation from radar echoes and a method for estimating vertical distribution of heating.

• Develop and test convective precipitation parameterizations.

• Understand the effects of orographic influences on the spatial structure of the produced precipitation structures, develop methods of parameterization, and test the performance of available orographic precipitation models for the Mississippi River basin.

6.1.3. Precipitation Predictability

Objective: Assess the limits of predictability of atmospheric model precipitation as a function of scale.

Activities to support this objective are:

• Understand the effects of relative patterns of convective/stratiform rainfall and of subgrid scale spatial rainfall variability on rainfall prediction at the atmospheric model grid scale and temporal scales of hours to days.

• Understand how parameterizations of cloud microphysical processes affect precipitation prediction at the atmospheric model grid scale and temporal scales of hours to days.

• Understand how the resolution of orography affects precipitation predictions,which affect hydrologic balances and flooding over the Mississippi river basin.

• Under the premise that two-way coupling (atmosphere to land to the atmospherefeedbacks) will not only improve hydrologic predictions but also precipitation predictions themselves, investigate the sensitivity of the precipitation predictions to the two-way coupling processes (parameterization and resolution) in coupled models.

• Develop methods of atmospheric and coupled model verification, paying special attention to proper ways of integrating atmospheric and hydrologic information at different scales, i.e., point observations, model output products, and larger scale measurements.

6.1.4. Data for GCIP Precipitation Research

Objective: Improve the availability and quality of data that are neeeded to support the research activities descibed above.

Activities to support this objective are:

• Improve the availability and quality of WSR-88D and concurrent atmospheric observations and develop better algorithms for using these data for atmospheric model verification and analysis of space-time rainfall structures. Other atmospheric observations include GOES satellite data, soundings, runoff, fluxes, as well as more frequent observations of standard surface meteorological variables.

• Evaluate if current gridded precipitation products (e.g., hourly 4x4 km composites) meet the requirements for atmospheric model verification studies and for analysis of space-time precipitation structures.

• Develop methods for better use of WRS-88D scans over complex terrain, especially use of information obtained in higher elevation scans and possibly modifying the scans over complex topography to take advantage of this information.

6.1.5 Snow

Objective: Produce gridded snow water equivalent data sets for the upper Mississippi River basin by integrating ground-based, airborne and satellite snow cover data sets.

Activities to support this objective are:

• Develop algorithms for cloud detection for GOES data over land and AVHRR scenes over land.

• Develop prototype ice versus sea ice algorithms.

• Obtain GOES-8 (and GOES-9) data in the appropriate GVAR format and adopt the GOES-7 processing to handle GVAR data.

• Identify suitable Landsat data, contemporaneous with available AVHRR data for validation/modification of the prototype snow detection algorithm.

• Enhance implementation to ingest and process ground-based snow data from Regional Forecast Centers (RFC) for incorporation into the Snow Estimation and Upadating System (SEUS) for the Upper Midwest.

6.2 Soil moisture

Overall Objective: Improve understanding and estimation of the space-time structure of soil moisture, the relationship between model estimates of soil moisture and observations of soil moisture, and to produce soil moisture fields for the GCIP area to be used as diagnostic and input data for modeling.

Objective: Produce the best possible estimates of soil moisture at four depths over the entire GCIP study area with the initial emphasis over the LSA-SW.

Activities that are needed or support this objective are:

A validated soil moisture product is needed for the Mississippi River Basin at a spatial scale of about 40 km and a daily temporal scale for four depths corresponding to the Eta and MAPS model output. This assimilated product must be produced from a variety of data sources, including output from hydrologic models driven by measured meteorological data, instu soil moisture observations, and remote sensing. The challenge will be to combine the various data forms to produce the "best" possible gridded product and to develop a way to validate the product with in-situ data, preferably data not used in the assimilation process.

Initially, a subset of the soil moisture product is to be developed for the LSA- SW because this is the area where the most in situ data are available and the region where remote sensing can provide the best information because of the relativly less dense vegetation cover.

Activities that are needed or support this objective are:

• Comparison of the model representation of the soil volume to the potential sources of measured soil moisture data This activity involves examining how the model represents the profile (i.e., number of layers) as well as how the model represents the horizontal variability. It also involves comparing the model sensitivity to changes in soil moisture to the precision capabilities of point and remote measurements. Current models will likely have to be modified to use measured soil moisture as input data or as a state variable. These studies link directly to PILPS phases l(c) and 2(b) (see Figure 8) and will draw upon PILPS intercomparison results.

• Comparisons of actual model estimates of soil moisture (spatially and temporally) with measured values. The measured values may come from the index stations, existing data collection programs (Little Washita, Illinois State Water Survey, FIFE, etc.), or from airborne remote sensing campaigns. The objective of these comparisons is to evaluate which models may be able to use measured data and what data might be used. A subsidiary task is to modify existing models to use measured data.

• Investigation of the interannual variability of soil moisture and the duration needed to experience a wide range of soil moisture anomalies. This investigation will have to be done by modeling. Using models developed or tested as described in the two above activities, the interannual variability of soil moisture can be simulated by using historical weather records that include wet and drought years.

• Analysis of selected one-dimensional land-atmospheric models and three-dimensional (3-D) hydrological models to document how they represent and use soil moisture. Comparison with measured surface and profile data such as that from the Little Washita or other well-instrumented watersheds.

• Performance of model sensitivity tests.

• Use of selected data sets from field campaigns to compare model derived soil moisture with measured soil moisture using various means and modification of models if necessary.

• Selection of a suitable model or models to force with some long-term data sets of precipitation and potential evapotranspiration.

6.2.3 Local Variability of Soil Moisture

Activities that are needed or support his objective are:

• Improved understanding of soil moisture dynamics using the local measurements of soil moisture and available water and energy forcing from comprehensive field experiments such as FIFE, HAPEX-Sahel, Multi-sensor Aircraft Campaign (MAC)-Hydro 90, Monsoon 90, and Washita. The issues to be addressed here are the control exhibited by soil physical properties, vegetation, and topography on the interstorm changes in surface and profile soil moisture. Although sufficient data may not be available to address these questions, the attempt should be made with the existing data and hillslope models such as Topography-based (TOP) model and with other work done in the partial area runoff field.

• Development of an improved strategy for using local soil moisture observations in GCIP to develop an improved soil moisture sampling plan. The strategy here should be to establish index soil moisture measuring locations that are supported by coexisting hydrologic and atmospheric data collection programs. No attempt will be made to address the horizontal spatial variability with these index stations. Instead, these stations should focus on monitoring the temporal changes with depth. Their locations should be chosen geographically to represent the major soil-vegetation-climate regions within the GCIP region. A major objective of these index stations will be to identify the timing of deep seepage (ground water recharge) in the relatively humid areas and the depth and duration of a zero flux boundary in the more arid regions.

• Development of a procedure for extrapolating or assimilating these point or small area measurements to represent the soil moisture distribution on a basin and a regional basis. This procedure should use static data such as soil properties and topography and atmospheric forcing in the form of WSR-88D radar rainfall products and evaporation estimates from mesoscale atmospheric and hydrological models. Estimates of the accuracy of these procedures should be carried out with short but intense field sampling programs.

• Organization and assembly of data sets with remote sensing data, soil moisture measurements, and concomitant hydrological and flux data, DEMs, soils, and land cover maps, etc.

• Inventories to determine which of the Natural Resources Conservation Service soil moisture sampling locations are suitable for GCIP, collection of historic data, and determinations if any USDA Natural Resources Conservation Service stations need upgrading.

• Development of the criteria for establishing the location, number, depths, etc., for establishing in situ soil moisture index stations within the GCIP area.

• Prioritization of the locations and installation of the instrumentation.

• Examination of the possibility of using SAR data from ERS-2 and RADARSAT to extend the in situ data to larger areas.

• Examination of the possibility of using hydrological models forced by measured inputs to extend the point samples of measured soil moisture to larger areas.

6.2.4 Remote Sensing of Soil Moisture

Activities that are needed or support this objective are:

• Conducting a large scale ( ~ 10,000 sq km) aircraft remote sensing data collection campaign to provide a relatively long term data set approaching the type of data one would get with satellite remote sening. Selecting and carrying out a series of imbedded experiments that address issues of model derived soil moisture, scaling and uncertainties.

• Development and validation of algorithms to estimate soil moisture from both active and passive microwave sensors. The issues to be addressed are the effects of roughness, vegetation, and topography.

• Studies to understand the relationship between soil moisture in the top ~ 5 to10 cm and total profile soil water to depths accessible to plants. Modeling approaches need to be pursued that consider the plant species and information about rooting depths and seasonal growth curves. Direct statistical techniques also need to be pursued for the relationship between microwave response and measured soil moisture at certain index measuring stations.

• Studies to investigate any relationship between direct measurements of the surface (the composite of soil and various vegetation types) with microwave data and surface wetness. Direct microwave measurements include the effects of soil moisture as well as the biomass (and other factors such as roughness). These issues are difficult to separate in algorithms and difficult to measure characteristics of the surface and canopy. The possibility exists that the microwave measurement is "seeing" something that correlates well with what the atmosphere sees. That is, might the microwave measurement provide an empirical measure of some surface wetness function that could be used directly to describe the moisture available to the atmosphere (i.e., a combination of soil moisture and vegetation condition)?

• Tests of various algorithms with existing data sets.

• Evaluation of in situ data from bare and vegetated soils to determine the conditions under which the surface soil moisture is decoupled from the remainder of the soil moisture profile.

• Development of simple statistical models relating soil moisture profile to the surface layer.

• Initiation of studies to compare existing remote sensing data sets with output from mesoscale models.

• Studies to use ERS-2 and RADARSAT data over specific target areas (i.e., Little Washita) for comparisons of soil moisture or wetness product with Eta and MAPS model output.

6.3 Land Surface Characteristics

6.3.1 Land Surface Characteristics Research

Multiresolution land characterization research in the near-term will be directed toward meeting the minimum requirements of GCIP Principal Research Areas for land cover, soils, and topographic data, including associated characteristics and properties of each, at four regional scales. The initial project regions and their associated gridding intervals include the CSA and LSA-SW (40-km grids), ARM/CART as the initial ISA (10-km grid), and Little Washita as the initial SSA (4-km grid). The primary land surface data sets that are currently available throughout the conterminous United States to potentially meet some of GCIP's immediate requirements for land data within these four regions include various 1-km and coarser spatial resolution, advanced very-high-resolution radiometer (AVHRR) data products from NOAA's polar-orbiting satellites; the 1:250,000-scale USDA/Natural Resources Conservation Service State Soil Geographic Database (STATSGO); and DEMs of 0.5-km and approximately 100-m grid cell resolutions, respectively, available from the USGS. Land characterization research will focus on the adaptation and use of these primary data sets as the basis to develop, test, and evaluate key derivative land surface characteristics data sets for use by GCIP modelers.

As GCIP evolves, land characterization research will focus on meeting changing land data requirements, developing data sets for new regions, and facilitating the use of geographic information systems (GIS) technology and other appropriate tools for land surface data analysis. For example, model sensitivity analysis from GCIP and PILPS investigators will help to more clearly identify requirements for specific types of land surface characteristics including accuracy specifications. Detailed analysis of multiresolution satellite data for the ARM/CART region will lead to remote sensing algorithms that can be applied within the LSA- and CSA-scale regions. GCIP would also significantly benefit from remote sensing algorithms developed and tested under the proposed ISLSCP Initiative No. 2 project.

Additional regions will be defined and higher resolution land data sources will be investigated and developed, as required. Candidate regions include the Upper Walnut River watershed located within the ARM/CART as related to the proposed Cooperative Atmosphere-Surface Exchange Study (CASES); the LSA-NC; and SSAs within the LSA EAST, for example, River subbasins within the Tennessee River Drainage basin and the Goodwin Creek watershed (part of the Yazoo River basin) a USDA/ARS experimental watershed located in north central Mississippi. Organization of the LSA-NC and LSA-East is underway.

Some of the key secondary land data sources will include various types of 30-m LANDSAT thematic mapper (TM) data products for land cover characterization within the ISA- and SSA-scale regions, selected county-level digital USDA/Natural Resources Conservation Service Soil Survey Geographic Database (SSURGO) (as available), USGS digital 60-m DEMs for the ARM/CART, and USGS 30-m DEMs available in a 7.5-minute quad format for selected locations within the GCIP domain. The land data sets developed for the Upper Mississippi region by the Scientific Assessment and Strategy Team (SAST) concerning flood plain management following the 1993 floods potentially represent a significant contribution to the land surface characterization requirements for the LSA-NC (see the World Wide Web at the URL address: http://edcwww.cr.usgs.gov/sast-home.html).

Additionally, the identification and facilitation of the use of appropriate data analysis tools, such as GISs and digital image processing systems, will be needed to tailor land surface characteristics from primary data sets and to integrate and analyze disparate data sets of interest to land process modelers. Both standard and new image processing techniques will be necessary for analysis of multitemporal land cover characteristics data, frequently available from satellite remote sensing systems with different spatial resolutions. Moreover, the application of appropriate geostatistical techniques, such as measures of dispersion or aggregation of landscape patterns, will be investigated to assist in understanding the spatial linkages extant between land surface characteristics and the hydrometeorological conditions within the GCIP study area.

This land surface characterization research strategy will be accomplished through objectives and associated research activities involving land cover characteristics, soils and geology, and topographic information. The research activities under each objective are listed according to priority for accomplishment. To meet the requirements of GCIP's other Principal Research Areas, the highest priority activities within this land surface characterization plan was initiated during 1996. Tailoring available multiresolution AVHHR land cover characteristics data for multiresolution model test and evaluation, preparing preliminary soils data sets from STATSGO for the Mississippi River basin, and evaluating high resolution land cover classifications and obtaining Landsat TM images for the ARM/CART and Little Washita regions are the top priorities for 1996 and early-1997.

Some of the sources for vegetation/land cover characteristics data include the global one- degree latitude-longitude modeling data sets recently published on CD-ROM by NASA/GSFC under GEWEX/ISLSCP Initiative No. 1 and various AVHRR data sets produced by NOAA/NESDIS and USGS. For example, NASA's ISLSCP CD-ROM includes monthly one- degree by one-degree calibrated, continental NDVI data (1982 to 1990); enhanced NDVI fields; Fraction of Absorbed Photosynthetically Active Radiation (FPAR) fields derived from enhanced-NDVI data; LAI and canopy greenness resistance fraction calculated from the derived FPAR fields; surface albedo and roughness length fields derived from land process models; and canopy photosynthesis and canopy conductance fields estimated by inverting the Simple Biosphere (SiB) Model 2 land surface parameterization (LSP) with FPAR as the key model input. The CD-ROM also includes a one-degree global land cover data set.

Although these ISLSCP Initiative No. 1 CD-ROM data are of direct interest to GCM and coarse grid cell resolution mesoscale modeling, the remote sensing algorithms and approaches for inverting an LSP to derive the land cover characteristics will guide efforts to similar use of higher resolution AVHRR and LANDSAT TM data. NASA/GSFC is currently planning ISLSCP Initiative No. 2 which would focus on enhanced global land cover characteristics data sets at a 1/2-degree latitude-longitude grid. The ISLSCP No. 2 data are planned for release during late 1997.

The NOAA/NESDIS has developed AVHRR global vegetation index (GVI) data sets. These data sets include weekly satellite image composites consisting of five AVHRR channels, solar zenith and azimuth angles, and the GVI for 1985 to the present. These data are calibrated for sensor drift and intersensor variability, and are available in a 1/6-degree resolution latitude-longitude product. Recently, NOAA/NESDIS produced a five-year climatology of the GVI data, and is now working to derive vegetation fraction from the GVI. The NOAA/NESDIS is also working with NASA/GSFC on the AVHRR Global Area Coverage (GAC) Pathfinder project to develop calibrated 8-km AVHRR data with a period of record beginning in 1981.

The USGS EROS Data Center (EDC) has developed 1-km AVHRR databases for the conterminous United States and is now processing global 1-km AVHRR data for land areas. The databases for the conterminous United States include biweekly AVHRR time-series image composites on CD-ROM (1990-1995) and a prototype land cover characteristics database for 1990 on CD-ROM. This 1990 land cover characteristics database is currently undergoing validation based on field survey data. Ongoing USGS activities also include the preliminary development of experimental, temporally smoothed 1-km seasonal NDVI greenness statistics for test and evaluation. These statistics consist of 12 seasonal characteristics that are associated with each 1-km NDVI seasonal profile for each year during the period 1989 to 1993, as well as the five-year means throughout the conterminous United States. Under the auspices of the International Geosphere Biosphere Project (IGBP)-led 1-km AVHRR global landcover database development activity, the USGS is currently processing global, 10-day AVHRR image composites for land areas. A proto-type 1-km AVHRR land cover data for the North American continent was recently made available for test and evaluation. These land cover data (for example, the BATS, Sib2, IGBP, and other land cover classification schemes), ten-day global AVHRR data, and a 1-km digital elevation model (DEM) for North America can be obtained via ftp through the EDC Distributed Active Archive (DAAC) Homepage (http://edcwww.cr.usgs.gov/landdaac/). Several global climate change research modelers, including some GCIP investigators, are currently testing and evaluating these USGS data sets.

In mid-1998, the Earth Observing System (EOS) AM1 platform is scheduled for launch as part of NASA's Mission to Planet Earth (MTPE). A wide variety of land cover characteristics data will be produced from data collected by the MODIS, MISR, ASTER, and CERES sensors on board the AM1 Platform. These data will be subsequently available for GCIP research through the EOS Data and Information System. For example, atmospherically- corrected reflectance and vegetation index data will be potentially available. In addition, current NASA plans also call for the 1998-launch of Landsat 7, which will be in near- synchronous orbit with the AM1 package. Land surface research will benefit from concurrent overlapping Landsat 7 and EOS AM1 products.

Objective: Improve the quantitative understanding of the relationships between land cover characteristics and the land surface parameterizations and land process components of atmospheric and hydrological models, and meet the requirements of the GCIP modeling and research activities for multiresolution land cover characteristics data.

Activities to support this objective in order of priority follow:

a. Definition of the requirements of GCIP modelers for multiresolution land cover characteristics data, documentation of available data sources, and assessment of the adequacy of available data for GCIP Principal Research Areas.

As a first priority, the requirements of GCIP's scientific investigators for multiresolution land cover characteristics data must be determined, specifically, requirements for land cover classes, agricultural crop and land use categories, and seasonally variable biophysical properties (i.e., vegetation attributes or characteristics). The potential sources for land cover and land use data will be identified and documented. This effort includes published data on vegetation characteristics and biophysical attributes such as those prescribed for Biosphere-Atmosphere Transfer Scheme (BATS), Simple Biosphere Model 2 (SiB2), Land-Ecosystem-Atmosphere Feedback (LEAF), and other land cover classification schemes. The adequacy of available data sources for GCIP modeling, especially in terms of detailed agricultural land use classes and attributes, will be assessed.

Ongoing feedback is needed from GCIP, PILPS, and ISLCSP activities concerning requirements for land cover characteristics data and the results of model sensitivity analysis concerning land cover characteristics. (Note: The requirements for multiresolution land surface characteristics data on land cover, soil, and topography identified under this and subsequent objectives will be used to prepare a matrix gridding plan showing regions versus land surface characteristics data requirements.)

b. Tests and evaluation of AVHRR-derived land cover data in GCIP models and assessment of data accuracy limitations.

Available 1-km and coarser resolution AVHRR-derived land cover data sets (i.e., the BATS, SiB2, USGS Anderson Level II-modified, Olson, and similar vegetation and land use classifications) will be tailored for test and evaluation in the land surface parameterization component of atmospheric GCMs, nested mesoscale meteorological models, and multiscale watershed hydrological models. The sensitivity of GCIP models to potential data accuracy limitations will be assessed.

c. Facilitation of the use of GCIP model output and data assimilation products by remote sensing data centers to improve remote sensing processing techniques, especially approaches for making atmospheric corrections to satellite reflectance data for atmospheric water vapor content and aerosol concentrations.

d. Facilitation of the adaptation, development, and use of biophysical remote sensing algorithms to estimate seasonally variable land cover characteristics data needed to parameterize, initialize, and validate GCIP's models; validation of the remote sensing algorithms and tests to evaluate the biophysical data in GCIP's atmospheric and hydrological models.

This effort is focused on facilitating the use of multitemporal AVHRR channel reflectance, NDVI greenness, and GVI data to develop and evaluate seasonally variable, time-dependent biophysical land cover characteristics data that are required by GCIP investigators for development, initialization, test and evaluation, and validation of their land process models and parameterizations. For example, NOAA/NESDIS has developed 5-year climatologies of the GVI including derived estimates of vegetation fraction from the GVI. Another potential data resource is the experimental 1-km resolution vegetation seasonality characteristics data set now under development by the USGS from biweekly, 1-km AVHRR NDVI greenness temporal profile data (1989-1994). Following the application of temporal smoothing algorithms, calculated seasonality data for each 1-km pixel of each year and the five-year averages include the NDVI Julian dates and associated numerical values for onset, peak, and end of greenness; rates of NDVI change; duration of greenness season; greenness curve modality; and seasonally integrated total NDVI.

Some of the biophysical characteristics to be potentially developed and tested from these types of seasonality data include estimated LAI, ratio of vegetation to bare soil (i.e., vegetation fraction), greenleaf fraction, vegetation height, and FPAR. Research on the interannual variability of satellite vegetation indexes, for example, due to the effects of the atmospheric water vapor and aerosols or due to year-to-year weather variations or other factors is a key requirement to ensure the proper use of seasonality data. These atmospheric corrections are also essential to the use of channel reflectance data to estimate land surface albedo and other derived parameters. This biophysical remote sensing research is impeded by the lack of operational algorithms to correct satellite reflectance data for the effects of atmospheric water vapor and aerosols.

e. Development of high-resolution land cover classifications (i.e., vegetation type and land use) including preliminary land cover characteristics data sets for the ARM/CART site and selected SSAs, for example, based on 30-m resolution LANDSAT TM data.

Facilitating the use of relatively inexpensive LANDSAT TM data by Federal agencies and state university remote sensing centers to develop digital land cover classification maps is one possible strategy to meet this need. Comparisons with AVHRR-derived land cover characteristics data are needed.

The DOE ARM program has identified available high-resolution land cover and land use data sets for the ARM/CART, while the USDA/ARS is presently completing a GIS for the Little Washita including recent Landsat TM-derived land cover classifications, historical Landsat MSS products back to 1972, and other land surface characteristics data sets.

f. Exploration of multiresolution relationships among land cover/vegetation, soil, and topographic characteristics data, as well as relationships with microclimatic and hydrometeorological data, ranging from the landscape and watershed scales up to the ISA and LSA regions.

One prime reason for this research is to ensure that the land surface characteristics data sets on land cover, vegetation attributes, soil properties, and topography are appropriately and consistently tailored within model grid cells or watershed polygons for model applications. In addition to model sensitivity studies concerning accuracy issues for individual data layers, error propagation analysis will also be conducted to assess the net impact of effectively "overlaying" land cover, soil, and topographic data sets in the model, where these data are characterized by differing levels of accuracies, precision, uncertainties, and other data limitations.

g. Conductance of advanced land cover characterization research within SSAs and ISAs to meet GCIP's requirements for more detailed land cover characteristics data and information.

Several types of advanced land cover characterization research activities are needed. For example, a strategy is needed for collecting essential ground-based data and field observations that will be used in combination with published research results to develop, adapt, test, and/or validate remote sensing algorithms at the ISA/SSA-scales. In addition to basic remote sensing algorithms for making atmospheric corrections or other image processing, applications could also involve remote sensing algorithms for making regional extrapolations of land surface processes such as seasonal evapotranspiration or net primary productivity, or the validation of satellite-derived land cover characteristics data such as LAI. Field-based data sets are also needed to develop land surface parameterizations or to properly use satellite remote sensing data, such as multitemporal NDVI greenness data, as part of special studies to investigate how vegetation controls evapotranspiration.

Research on state-of-the-art canopy reflectance modeling is needed, especially as related to the estimation of canopy characteristics for agricultural crops. Model-based approaches for estimating key canopy parameters need investigation, for example the inversion of a land surface parameterization such as was done by NASA/GSFC with SiB2 as part of the ISLSCP Initiative No. 1 CD-ROM development. Advanced remote sensing research is also needed to investigate the potential use of other remote sensing data in GCIP, for example, the use of Airborne Visible Infrared Imaging Spectrometer (AVIRIS), Thermal Infrared Multispectral Scanner (TIMS), ERS-1, or RADARSAT data.

Research is needed to investigate how the spatial heterogeneity of vegetation (i.e., landscape patchiness) affects model parameterization, especially as related to spatial aggregation of data within model grid cells and polygons, or scaling parameterizations. This land surface characterization research emphasizes the spatial component within the landscape, for example, concerning the arrangement, pattern, distribution, and composition of various land cover types within a region that influence or potentially affect land- atmosphere interactions and hydrometeorological relationships.

Elements of land characterization research will focus on the use of remote sensing algorithms and geostatistical analysis tools to investigate the estimation and analysis of land surface energy fluxes and scaling issues, especially as related to comparisons with tower flux site, SSA, ISA ARM/CART observations, and GCIP model outputs. Scaling research issues involve remote sensing, for example, concerning the use of the NDVI or the simple ratio in canopy conductance modeling based on LANDSAT TM and AVHRR data inputs. The use of satellite-based remote sensing technology to directly or indirectly estimate surface energy fluxes is still an open research topic. This land surface characterization research will also contribute to efforts by GCIP researchers to investigate how landscape spatial heterogeneity contributes to surface flux distribution and parameterizations.

These aspects of land characterization research will use digital image processing, GIS, and various geostatistical tools for spatial and temporal analysis. Examples of geostatistical tools include autocorrelation analysis, kriging, variograms, and potentially other types of spatial analysis which may be useful in characterizing the impact of landscape pattern, arrangement, type, and distribution as a forcing function in hydrometeorological processes within the Mississippi River basin.

The land-atmosphere interactions modeling community is interested in the movement of water within the soil, as well as the influence of vegetation in linking soil water with the atmosphere. Modeling approaches are typically based on the Richards equation which describes the flow of water through the soil as a function of soil water content and its vertical gradient. The texture and structure of the soil medium are the primary controls on water movement. These physical properties determine the hydraulic nature (water-holding capacity and conductivity) of the soil. Due to the extremely difficult and tedious nature of the procedures required to measure the water content and hydraulic conductivity of soils, research since the early 1950s has focused on developing empirical relationships between traditionally observed soil physical properties and hydraulic characteristics. This work has been referenced by the land- atmosphere interactions modeling community in an effort to parameterize soil moisture conditions over the typically large domains encountered in mesoscale modeling. Unfortunately, the lack of a soil database corresponding to these regional scales has confounded efforts to improve this portion of the parameterization dilemma. Clearly, the community of modelers working in this area requires reliable, quantitative information on soil physical properties and, where feasible, direct observations of the hydraulic nature of the soil for use in quantification and validation of the empirical approaches used over large areas to estimate these properties. A range of soil survey products and databases will be required by GCIP researchers for use in land surface parameterizations.

The USDA-Natural Resources Conservation Service, through the National Cooperative Soil Survey (NCSS), is developing soil geographic databases at three scales. The familiar county-level soil survey is being converted to a digital database for use primarily in local-level planning. This database is SSURGO. At the regional level, the State Soil Geographic Database (STATSGO) has just been developed for river basin, multistate, state, and multicounty resource planning. The compiled soil maps were created with the USGS 1:250,000-scale topographic quadrangles as base maps and comply with national map accuracy guidelines. The third soil map product being created is the National Soil Geographic Database (NATSGO). This product is being compiled at a scale of 1:7,500,000 and is not yet available.

The STATSGO database provides the most useful resource for characterizing the role of soil in mesoscale atmospheric and hydrological models. This database was developed by generalizing soil-survey maps, including published and unpublished detailed soil surveys, county general soil maps, state general soil maps, state major land resource area maps, and, where no soil survey information was available, LANDSAT imagery. Map-unit composition is determined by transects or sampling areas on the detailed soil surveys that are then used to develop a statistical basis for map-unit characterization. The STATSGO map units developed in this manner are a combination of associated phases of soil series.

The 100-m DEM is generally appropriate for hydrological modeling in large basins (e.g., greater than 1,000 km2 in area). However, topographic data for small basins down to watersheds are needed at two general hydrological scales: hillslope and stream network. The hillslope scale is the scale at which water moves laterally to the stream network. Available USGS 60 m DEMs derived from 2-arcsecond contour data are generally available for the ARM/CART region.

Hillslope flow distances vary and may be as great as 500 m to 1 km. Definition of hillslope flow paths and the statistics of hillslope characteristics require surface elevation data at about 30 m spatial resolution. Such data have been digitized by the USGS from 1:24,000 scale map sheets for part, but not all of the Mississippi River basin. Also, stream locations (but not drainage boundaries) are available in vector form for these map sheets. Because 30- m resolution data are not available globally nor in some parts of the Mississippi basin, research is needed to see how well hillslope statistics, that are important to some hydrological models, can be estimated from topographic properties of lower resolution terrain data. Research is also needed to determine how important hillslope information is to hydrological response of the land surface. Because 1:24,000 scale maps are not available globally, research is needed on how best to use remote sensing techniques as part of a sampling strategy to develop regionalized hillslope statistics (which may be mapped at an appropriately large scale).

An important application of topographic information is to define the hydrological connectivity of basic hydrological computational elements of a model. These elements may be hydrological subbasins or grid elements. The model domain may be a river basin or a set of atmospheric model grid elements. In any case, a set of methods is needed to merge digital terrain, stream location, and existing basin boundary data to establish additional drainage boundaries relative to key locations in the stream channel network and to establish the hydrological connectivity of model elements. The research need is not so much to develop new methods but rather to organize some of the existing methods into a robust and user-friendly system to satisfy many of the needs for basin boundary locations and for hydrological connectivity. (The USGS/WRD and NOAA/NWS are developing a project to address some of these watershed basin and stream network delineation issues, especially standardization of algorithms and data).

The resolution at which stream network data are needed varies depending on the application. Digital stream locations data are available for the entire United States at several resolutions ranging from 1:250,000 to 1:24,000 scale.

Objective: Develop strategies to use available topographic information for model development and model parameter estimation, and investigate approaches suitable to obtain required multiresolution topographic data on a global basis.

Activities to support this objective follow:

a. Definitions of overall GCIP modeler and scientific investigator requirements for multiresolution topographic data including derivative topographic characteristics, documentation of available data sources, and assessment of data adequacy for GCIP Principal Research Areas.

b. Organization of existing topographic data analysis tools and algorithms into a user-friendly software package that will facilitate the generation of basin boundary locations and hydrological networks from existing topographic data resources, as well as hydrological modeling research.

c. Facilitation of hydrological modeling research that is focused on determining which topographic properties, including appropriate horizontal and vertical DEM resolution and accuracies, are essential for properly modeling the effects of hillslope processes on the surface water budget and on the timing of hillslope runoff.

d. Determination of the adequacy of available multiresolution topographic data sets to meet model requirements based on research results in the preceding activity.

Streamflow is determined from measurements of stream stage at a stream-gauging station. Runoff is the spatially distributed supply of water to the stream network which cannot be measured directly. Both surface and sub-surface components are part of runoff. A delay is also inherent between runoff initiation and the time when the runoff reaches a stream-gauging station. This delay varies spatially depending on the distance to the gauge and on how much runoff is occurring.

This research area is concerned with relationships between runoff as computed by atmospheric models, which is distributed in space, and streamflow as measured at streamgauges. This area includes development of globally applicable routing methods to account for the time lags between occurrence of runoff and occurrence of streamflow. Such routing methods might be used in a model to translate runoff to streamflow or they may be used as part of an analysis system to infer runoff from streamflow. Streamflow data are needed to assist in model development, model parameter estimation, and model testing and validation. Although methods may already exist for making streamflow data useful for each of these purposes, additional studies are needed to improve these methods and make them more useful globally.

Two scales of time delay exist between the initiation of runoff and when the runoff reaches a downstream gauge. The first is the hillslope or landscape scale when runoff is moving above and below the surface into the stream channel network; the second is the stream network scale. Because the hydrological processes that occur at the hillslope scale influence both the amount and timing of runoff, this research area is also concerned with estimating both the amount and timing of runoff at the hillslope scale.

Activities to support this objective follow:

• Development of globally applicable routing models appropriate for the scale of atmospheric models.

  The runoff routing problem has two components. The first is to account for the time delay for water to flow over and through hillslopes and the ground water system to the stream network and to pass through the upper, highly disperse reaches of the stream network. This time lag is often accounted for in hydrology using a "unit hydrograph." Globally transferrable applicable synthetic unit hydrograph approaches or some mathematically equivalent alternatives must be developed and tested, including nonlinear alternatives.

  The second component of the routing problem is to account for the time that water flows from upstreamgauges to those downstream or from the runoff generated from the atmospheric model grid through intervening grids to a streamflow measuring point downstream in the river network. Although the equations describing the unsteady flow of water in river channels are well known, further work is needed on methods of estimating a priori parameter values to apply these equations to specific river reaches globally. This estimation could include developing and testing various simplified, globally applicable approaches to the solution of the full unsteady flow equations using geographic information systems to estimate channel slope and other hydraulic parameters. These approaches must handle leaky rivers" and account for the natural losses in rivers and marsh areas. While routing may not be critical for estimating water budgets over a month this may not be the case for extremes and routing effects cannot always be ignored.

• Identification of tributaries of the Mississippi River basin and/or periods where water management effects can be neglected, and the subsequent evaluation of runoff predictions from atmospheric models by routing to a streamgauge and comparing the data with observed streamflow.

• Improved understanding and description of the effects of hillslopes and stream channel nonlinearities on the amount and timing of downstream discharge for large continental catchments and major tributaries thereof (drainage areas typically greater than 5000 km2).

6.4.2 Estimation of Runoff from Streamflow and Climate Data

Activities to support this objective follow:

• Evaluation of the error in methods for estimating gridded runoff fields as a function of catchment area and aggregation period using streamflow measurements independent of those used to estimate the runoff grid.

  Gridded monthly runoff data (on a 30-minute grid) are needed to assess the coupled model validation and diagnostic aspects. The model representation of the surface water budget depends on both local and large-scale processes. To understand how to improve the limitations indicated in model variations at specific streamgauges, additional information on a larger spatial scale is fundamental. For an initial comparison of coupled model gridded runoff, reconstituted runoff that accounts for diversions and ignores reservoir operations would be the simplest approach to developing diagnostic contours of runoff. This approach would enable a more qualitative comparison of the spatial variability of actual and model runoff and would enable the researcher to look more clearly at the various parts of the water budget. Distribution functions could be developed to obtain a better space-time resolution of the water budget components. The emphasis would be on the distribution, not necessarily the actual numbers. If reservoir storage effects are significant, they should be taken into account in developing the reconstituted flows.

  The grid-mapping approach of river discharge was recently reviewed by Arnell (1995). Five methods are considered. These methods and other appropriate approaches need to be evaluated in relation to related activities of agencies in the Mississippi River basin. The Global Runoff Data Centre (GRDC) is coordinating the data for a German-funded project, "Transformation of measured flow data to grid points" as a contribution to World Climate Programme (WCP)-Water Project B.3. The pilot area under study covers the basins of the Rhine, Weser, Elbe, Oder, and Weichsel Rivers within Germany, Czechoslovakia, and Poland. The results of this project and further work with European data by the UK Institute of Hydrology will assist in planning the best approach for the Mississippi River basin.

• Development of algorithms to estimate the uncertainty in gridded runoff fields as a function of drainage network configuration, streamgauge location, and space-time scale for cases with minimal water management effects.

• Development of improved methods for better estimating gridded runoff by evaluating the relative contributions of space-time aggregation, channel network and gauge configuration, and water management effects on the error in gridded runoff fields.

• Extend the available historical data base for unregulated basins at the SSA and ISA scales (10 to 1000 km2) in the LSA-SW (Arkansas-Red River basin) by updating from 1988 the active streamflow stations on the Wallis-Lettenmaier-Wood CD-ROM and the USGS HCDN CD-ROM. Include additional from the archival record that have shorter periods of records than those on the existing data sets, e.g., by adding two additional categories of unregulated stations which have 10 and 20 years of data. The purpose would be to develop and demonstrate regionalization methods for the estimation of hydrologic model parameters. In addition to allowing the estimation of the land-surface model parameters these data are needed for the development of runoff routing parameters and gridding runoff. This work to quality control and fill in missing data is being undertaken by the University of Washington. Extension to the LSA-NC could be included as part of the NWS WARFS initiative and the work within the NWS/NESDIS Core Project to develop the required historical data bases.

• Develop naturalized streamflow records at key locations in the LSA-SW up to the current time to enable the validation of the atmospheric model predictions. Key locations would include the Red River at Shreveport and the Arkansas River at Little Rock, being the largest basins which can be feasibly considered. This will require agency interaction, particularly with the COE, and the updating of calculated flows by the COE, or the acquisition of the reservoir storage algorithms and the algorithms used in the reservoir operating rules. Subject to some funding to support a post-doctoral fellow this work could be undertaken at the University of Washington.

• Test a method for estimating gridded runoff data for the LSA-SW to enable the direct validation of atmospheric model runoff predictions. This activity will require research funds and may be supported by the NCGIA, working in conjunction with the USGS and the UK TIGER project supporting GCIP.

• As an alternative to naturalized flows, compute the regulated runoff from atmospheric models by using runoff routing and reservoir storage models. The model feasibility has already been demonstrated. Model parameters from the NWS ABRFC are already available, together with their conversion to the application of gridded or distributed models as part of the NWS/NESDIS core Project and the macro-scale model parameters developed over the Arkansas-Red River basin by the University of Washington, the models and parameters will be available in 1996.

6.5 Clouds And Radiation

Activities to support this objective follow:

6.5.2 Validation of Satellite Algorithms to Retrieve the Surface and Atmospheric Radiation Budget

This objective meets one of the central goals of GCIP namely, the improvement of global systems for the observation of the energy cycle by means of intensive studies in well- instrumented areas. This GCIP activity will:

A process has been established whereby the University of Maryland accesses the GCIP insolation products as generated at NESDIS, as well as the input files used at NESDIS to generate the product. The input files are used to run the model off-line, compare with the product produced at NESDIS, and to test various options in the model configuration. Of particular interest are possibilities to optimize the models operation and/or introduce simplifications. The model output will be validated against ground observations,to include, in the near future, observations from SURFRAD, BSRN and ARM/CART. Ground with data for PAR are also needed for validation of this component of the SRB. This process is essential for achieving the best possible accuracy from satellite products.

In addition to the NOAA GOES-8/9 and POES operationally based retrievals in GCIP, the NASA CERES is sponsoring a more limited domain program of research retrievals of the SARB (Charlock et al., 1994). Satellite-based cloud retrievals, meteorological data, and radiative transfer calculations will be used to retrieve the SARB over the ARM/CART site in Oklahoma. Computed fluxes and radiances will be compared with ARM-observed surface and unmanned aerospace vehicles (UAV) fluxes, as well as with other satellite data. Tuning algorithms will subsequently adjust atmospheric and surface input parameters, bringing the calculated SARB to closer agreement with observations. Results of the SARB retrievals will be compared with those of other groups and with data. The aim is to develop accurate retrievals of the SARB based on satellite data and to foster the development of such retrievals in the atmospheric sciences community. The first research data set in this CERES/ARM/GEWEX activity covers the April 1994 IOP. In a 3 x 3 matrix with 0.3° increments, daylight cloud retrievals every 30 minutes are provided from GOES-7 with the Minnis et al. (1993) cloud retrievals for cloud albedo, cloud center height, cloud amount, cloud center temperature, cloud thickness, cloud infrared (IR) emissivity, cloud reflectance, cloud optical depth, cloud top height, cloud IR optical depth, cloud mean IR temperature, and cloud top temperature. In a subsequent ARM IOP, Dr. Charles Whitlock plans to employ a helicopter to measure the spectral bidirectional reflectance of the surface. This measurement will permit a detailed study of the clear as well as cloudy sky effects of the surface and aerosols on the profile of radiative fluxes.

The SARB drives the hydrological cycle, the general circulation, and the global climate change. The SARB computed by GCMs is not regarded to be sufficiently reliable for accurate climate prediction. The state of numerical weather prediction (NWP) model simulations of the SARB limits medium-range weather prediction, too. We lack an adequate observational record of the SARB either in clear or cloudy skies. Cloud feedback is generally considered vital to climate but remains uncertain. More fundamentally, forcing occurs, as well as feedback uncertainties because of the radiative effects due to atmospheric aerosols and the Earth's surface.

An observational SARB record is needed for the validation of GCMs and for diagnostic investigations of low-frequency variability and secular climate change. The development of an observational record of the SARB is one objective of the CERES activity (Wielicki and Barkstrom, 1991) in the EOS and GEWEX. The array of instruments deployed by ARM over the CART site presents a unique opportunity for developing and validating satellite-based retrievals of the SARB. The ARM is well suited to observing the profile of atmospheric water vapor, the vertical and horizontal structure of clouds, and aerosols; these parameters, as well as the ARM surface and UAV measurements of radiometric fluxes, are critical for testing satellite-based retrievals of the SARB. Activities to support this objective include:

• Retrieval of surface and atmospheric radiation budgets from satellite and meteorological data over the ARM/CART site.

• Comparison of computed fluxes and radiances with ARM-observed fluxes and other satellite data, and NWP model outputs.

• Development of techniques to retrieve aerosol and land-surface radiative forcing withj satellite and ground-based measurements.

6.5.4 Analyses of Clouds and Radiation

Activities to support this objective are:

6.5.5 Research relating to the GEWEX Cloud Systems Study

One of the aims of GCIP is to improve the treatment of surface and hydrologic processes in NWP and climate models, but clouds have an important impact on these processes. GCSS involvement would contribute to the cloud component to GCIP, by way of cloud-resolving modeling and related activities. In turn, the GCIP data sets would be used to evaluate these models against observations.

Cloud Resolving Models

Cloud resolving models, identified by their ability to resolve cloud dynamics, are the approach of choice of the GCSS. These models derive from traditional nonhydrostatic cloud models but their scope is more ambitious. The effects of convection on the environment and the interaction among physical processes (boundary layer, surface layer, radiation, and microphysics) are the pacing issues, rather than individual processes per se. Since the time scales of some interactions (e.g., cloud--radiation) can be weeks, this is not only demanding on model design but also requires large computer resources.

When used to study precipitating convection (e.g., Grabowski et al. 1996a, b) or frontal cloud systems (Dudhia 1994) grid lengths of about 1km can be successfully employed to calculate bulk effects. Consequently, the domains of cloud resolving models span many NWP grid volumes. The time scales examined by 2D models is up to several weeks and these models are poised to address issues on intraseasonal time scales. An example is the effect of cloud-radiation interactions on the atmospheric and surface energy budgets (Wu et al. 1995b).

Cloud-resolving models also explicitly resolve convection-mean flow interactions that are impossible to accurately observe and since cloud-scale dynamics is explicitly simulated, one key uncertainty is minimized. Data sets from cloud resolving models can be used to evaluate single-column climate models - the testbeds for convective parameterization schemes. These data sets are also a key element in formulating new and more comprehensive approaches to parameterization.

Models need to be evaluated against atmospheric data sets. The GCIP region features several cloud system types, ranging from deep precipitating convection during the warm season, to frontal clouds dominated by ice processes in winter. GCIP will provide data sets for evaluating cloud resolving models, noting the relatively high density of routine observations over the U.S., not to say the special long-term observations available from the ARM CART site.

Two different types of evaluation are required. First, an evaluation of the physical parameterizations used in cloud-resolving models (e.g., microphysics, turbulence, surface processes and radiation) is needed. However, this requires detailed cloud-scale observations, as well as intensive observation periods involving airborne platforms. Neither is available from GCIP.

Second, the effect of clouds on the environment directly relates to convective parameterizations in GCMs and is, in principle, an area to which GCIP can contribute. It is, however, far from a simple matter to utilize data collected during the GCIP Enhanced Seasonal Observing Periods (ESOPs) to evaluate the models.

A basic issue is: what is the minimum observational detail required to evaluate cloud resolving models? An ultimate answer will involve data assimilation in both regional and global models to "fill in'' missing or data-void areas. However, present assimilation methods are neither a panacea nor even practicable on cloud resolving model grids. GCSS will therefore focus on basic problems such as the ensemble response of clouds (deep and shallow) to spatially-averaged, time-dependent forcing applied over scales comparable to or exceeding, climate model grid scales.

Strategy

The GCSS has a cloud-resolving model intercomparison component. Modeling workshops have been conducted by the Working Group on Boundary Layer Clouds. Non-precipitating stratocumulus clouds in idealized environments were examined using Large Eddy Simulation models (Moeng et al. 1995).

The GCSS Working Group on Precipitating Convective Cloud Systems has an ongoing model intercomparison based on convection over the tropical western Pacific. The data set used in the model evaluation is from the Tropical Ocean Global Atmosphere Coupled Ocean Atmosphere Response Experiment (TOGA COARE). To identify scientific and numerical issues as well as to minimize the complications and difficulties of modeling precipitating cloud systems, prototype numerical experiments were conducted (e.g., Grabowski et al. 1995a). This working group intends to move on to continental cloud systems in due course. The GCIP ESOP in 1996, that focused on the GCIP Large Scale Area-South West (LSA-SW) during the warm season, is an opportunity to study organized precipitating systems. A prototype experiment relating to GCIP could start as soon as adequate resources are available and the ESOP data have been analyzed. (Note GCSS looks to the GCIP Data Management and Service System to provide data sets in the desired form).

GCSS/GCIP Projects

The following are candidate projects. Additional projects may arise; for example, noting that the 1997 GCIP ESOP will concentrate on wintertime processes, a GCSS initiative on frontal clouds is a possibility (Ron Stewart, private communication).

Project 1: Investigate the coupling of surface and boundary layer processes with convection under the influence of evolving large-scale forcing.

Comprehensive modeling studies of convection over the tropical oceans have been performed. Grabowski et al. (1996a) and Xu and Randall (1996) demonstrated, in simulations of convection during the GARP Atlantic Tropical Experiment (GATE), that realistic life cycles and transports could be achieved using two-dimensional cloud resolving models. This has been extended to three dimensions by Grabowski et al. A 39- day simulation of TOGA COARE convection (Wu et al. 1996) is equally encouraging.

Since the convective life cycle over land is quite different from that over the ocean, 2D modeling should be undertaken over the GCIP region (e.g. a domain of ~900km in the horizontal by ~40km in the vertical) to examine the coupling of convection with the boundary layer and surface processes--- that is to add a precipitating cloud component to existing GCIP studies. A key issue will be the treatment in these coarse-grid models of the atmospheric boundary layer in convectively-disturbed conditions. This could involve two GCSS Working Groups (Boundary Layer and Precipitating Convective Cloud Systems). The precipitating convection study could progress to three-dimensional simulations (e.g. domain of ~400km in the horizontal by ~400km by ~40km in the vertical).

Project 2: Quantify uncertainties in NWP models associated with precipitating convective cloud systems.

An issue to be explored is the large-scale effect of organized cloud systems, which are ubiquitous over the U.S. Southern Great Plains. These systems are copious (but intermittent) producers of precipitation over a large-area because of their longevity and propagation. Consequently, they have a significant hydrologic impact; they affect the surface fluxes; and they are likely to be responsive to changes in the large-scale circulation (e.g., through the influence on convection of vertical shear which may change in response to variability, on various time scales, in the low-level nocturnal jet originating from the Gulf of Mexico).

These organized systems violate the scale-separation assumption underpinning present parameterization methods. Organized fluxes are not adequately treated in existing convective parameterization schemes. For example, it has been shown that large mesoscale systems in the tropical western Pacific cause uncertainties in a medium-range NWP model (Moncrieff and Klinker 1996), mainly because the part-resolution causes an over-prediction of the thermodynamic and momentum tendencies.

Project 3: Quantify the large-scale effects of organized convection

Cloud-resolving models have been successfully employed to determine the transport properties by organized convection in idealized tropical western Pacific environments (Wu and Moncrieff 1996). A modeling and analysis study over the continental U.S., recognizing the very different role of the boundary layer over continental land masses from over the ocean, would be a valuable addition to existing knowledge. Interactively- nested, three-dimensional models (e.g., Clark and Farley 1984), containing microphysical and surface flux parameterizations would be used to simulate organized convection over the GCIP/ARM domain.

The CSU Regional Area Modeling System (RAMS) is another interactively-nested model being used to devise parameterizations of mesoscale convective systems (MCSs). The mesoscale parameterization is tied to a version of the Arakawa-Schubert convective parameterization scheme which is modified to employ a prognostic closure. One of the two MCS case studies being used is from the central U.S. (Alexander and Cotton 1995).

Moncrieff (1992) addressed the poorly-understood issue of convective momentum transport at basic level by formulating a dynamical model of the mass and momentum fluxes, and also pointed the way to its parameterization in large-scale models. LeMone and Moncrieff (1994) evaluated the fluxes predicted from this model against observations. Liu and Moncrieff (1995) added the effects of shear and buoyancy to the archetypal model. As far as GCIP is concerned, a possible course of action is to evaluate how well these dynamical models represent the mass and momentum fluxes by squall line convection over the Southern Great Plains. This could be a stand-alone project but, preferably, should be conducted as part of the analysis of cloud-resolving model data sets.