2. COUPLED MODEL DEVELOPMENT AND EVALUATION

Goal: Develop a coupled hydrologic/atmospheric model with an initial validation focus on the Mississippi River basin at a time scale of days to seasons increasing to an interannual time scale.

In the context of the GCIP, a coupled atmospheric-hydrologic model is defined to be a model or combination of models which simultaneously represents both atmospheric and hydrological processes, which can operate in predictive mode without the need to specify variables or exchanges at the interface between the two model components, and which can benefit from the assimilation of data to specify that interface.

2.1 General Approach

The implementation of model development in GCIP has followed two paths as described in the GCIP Implementation Plan (IGPO, 1993) and was shown in Figure 1-3. On the "research" path are the longer term modeling and analysis activities needed to achieve the GCIP coupled modeling objective:

Develop and evaluate coupled hydrologic/atmospheric models at resolutions appropriate to large scale continental basins.

Research is focusing on determining , understanding and modeling those processes which are demonstrably important in coupling atmospheric and hydrological systems, rather than those processes which are separately important within these two systems. A GCIP Coupled Modeling Workshop held in May 1996 resulted in a number of recommendations which are incorporated in this and other sections of the Major Activities Plan for 1997, 1998 and Outlook for 1999.

An "operational" path was started in 1993 during in the GCIP Buildup Phase to develop and implement the improvements needed in the operational analysis and prediction schemes to produce the model assimilated and forecast output products for GCIP research, especially for energy and water budget studies. The regional mesoscale models also serve to test components of a regional climate model and can provide output for the evaluation of a coupled hydrologic/atmospheric model during the assimilation and early prediction time periods as a precursor to developing and testing a coupled hydrologic/atmospheric climate model. The output from three different regional mesoscale models is routinely compiled as part of the GCIP data set as described in section 11 of this plan.

The activities for each of these paths are described in the remainder of this section.

2.2 Coupled Modeling Research

The GCIP coupled modeling research is predicated on the hypothesis that the creation of regional-scale coupled models which simultaneously represent both relevant atmospheric and the land-surface processes, and the validation of these models against observations from GCIP, will improve our ability to: In accordance with this hypothesis, GCIP is focusing on those research activities which create, calibrate, and apply coupled models of the atmospheric and hydrologic systems with priority given to research to improve climate prediction and to improve hydrological interpretation of meteorological predictions at the above time scales. The GCIP coupled modeling research is focusing on three program elements that address the three scientific questions and priority needs given in Table 2-1. These issues and planned research activities are described further in the following paragraphs.

Table 2-1: Scientific Agenda Recommended by the GCIP Coupled Modeling Workshop ________________________________________________________________________________________________________

1. "To what extent is meterological prediction at daily to seasonal time scales sensitive to hydrologic-atmospheric coupling processes?" - the priority research issues to be addressed by GCIP are:

2. "To what extent can meteorological predictions be given hydrological interpretation?" - the priority needs in GCIP are for: 3. "How can models of relevant hydrologic-atmospheric coupling processes be improved to enhance meteorological and hydrological prediction?" - the priority needs for GCIP are:

2.2.1 Atmospheric/Hydrologic Coupling Sensitivity

Progress in the representation of land-atmosphere interactions over the last two decades has been sufficient to motivate several operational modeling centers (for example, the National Center for Environmental Prediction, the European Centre for Medium Range Forecasting, and the Japanese Meteorological Center) to implement and benefit from modern-era, multi-layer soil-vegetation-atmosphere transfer schemes. Planetary, continental, and regional atmospheric circulation patterns in such assimilation systems are constrained near truth by the assimilation of atmospheric observations. Nonetheless, the implementation of improved representation of hydrologic-atmospheric interactions has undoubtedly improved the quality of the precipitation and low-level temperature analysis products provided by data assimilation systems.

2.2.1.1 Seasonal Predictability Evidence and Mechanisms

GCIP provides an excellent rationale and data source for investigating the hypothesis, in the context of North America, that (globally determined) soil moisture anomalies at the beginning of the warm season influence the regional precipitation during the subsequent months. Atmospheric general circulation model runs are required with improved representation of interactive moist processes to test this hypothesis and to determine the conditions and limitations under which it might apply. Diagnostic studies are also needed. These would involve comprehensive analyses to explore the lagged correlation, both locally and perhaps downwind, between all the relevant data (on rainfall, evaporation, temperature, clouds, radiation, vegetative state, etc.) now available within GCIP.

2.2.1.2 Coupling importance over an annual cycle

The strength and influence of the hydrologic-atmospheric coupling varies between cool and warm seasons, which leads to seasonal differences in the importance of land-atmosphere coupling relative to other regional-scale and global-scale influences. Understanding this seasonal variation will aid in defining the relative complexity required in the representation for different hydrologic-atmospheric coupling processes when used for meteorological prediction. Land-atmosphere coupling processes which are important in seasons when local controls are more important likely need more precise representation than those which are important in seasons when global-scale influences dominate.

The required studies will involve a combination of measurement and modeling activities. Observations would likely include atmospheric profiles of moisture, temperature, and wind during both warm and cool seasons and during the transition from cold to warm season, together with simultaneous measurements of the surface fluxes of water and energy. Modeling studies could include sensitivity studies using validated coupled models applied in different seasons and at different spatial scales.

2.2.1.3 Significance of diurnal variations in surface energy fluxes

Based on the results from a coupled land-atmosphere model, Koster and Suarez (1995) suggested that large scale circulation is affected by short-term variability in the surface energy balance. Hence a land surface scheme that realistically reproduces the mean diurnal cycle of the surface energy balance may nonetheless be inadequate for coupled modeling purposes. The scheme might also need to reproduce the short-term variations in the balance of energy.

The extent to which short-term variations in surface energy balance require representation in predictive models when applied at seasonal-to-interannual time scales merits more detailed investigation. Modeling experiments are required to explore this limit on the complexity of the representation of hydrologic-atmospheric processes.

2.2.2 Hydrological interpretation of meteorological predictions

The nature of the meteorological predictions calculated by global-scale models of the ocean-atmosphere-land system is likely to be profoundly different from actual meteorological observations in terms of their spatial and temporal precision and accuracy, even when those predictions have been down-scaled through mesoscale, regional models. Existing hydrological models are designed to work from observations, and their form and function reflect the nature of these observations. Research is required to determine what type of hydrological prediction is possible from seasonal-to-interannual meteorological predictions and at what spatial and temporal scales hydrological interpretation can have worthwhile credibility and utility. Handling uncertainty in meteorological predictions is not a resolved issue in hydrological models, even for short-term forecasts, and reservoir management practice will always need to be incorporated into the hydrological interpretation for North American water resource issues.

There is opportunity to improve communication between atmospheric scientists and hydrologists on this issue, because neither of these two groups have hitherto had opportunity to fully appreciate the relevant capabilities of the other. Hydrologists do not yet appreciate what the nature and form of seasonal-to-interannual meteorological predictions might be, and there is some lack of clarity on this issue. Equally, meteorologists do not yet have an appreciation of what type of seasonal-to-interannual prediction might have practical value to hydrologists. At this time, therefore, the need is to provide better definition of these issues in order to establish a means of interaction between the two communities.

2.2.2.1 Exploratory seasonal-to-interannual predictions

Although there are unresolved scientific issues regarding the optimum representation of physical processes in coupled hydrologic-atmospheric models, the viability of the land-surface schemes presently being used at operational forecast centers is such that it is now time to undertake experimental, free-running seasonal-to-interannual simulations with coupled models of the land-atmosphere-ocean system to give global and regional forecasts. Realistically, early expectations of skill should be limited to capturing modest indications of regional-scale monthly or seasonal anomalies in precipitation and temperature.

Not only is GCIP in a strong position to foster experimental seasonal-to-interannual forecasts focused on the North American continent, it is also uniquely able to provide effective validation of such forecasts by virtue of the existing and new data that are being collected for the U.S. in general, and for the Mississippi River basin in particular. However, some redefinition of GCIP data products will be required. Specifically, once the form, nature, and spatial and temporal scale of seasonal-to-interannual prediction products are defined, it will be necessary to synthesize equivalent observational products from GCIP's precipitation and temperature measuring networks. Future westerly extension of the GCIP study area also seems essential if there is to be a better match between areas in the U.S., where seasonal-to-interannual prediction is most feasible, and areas in which data collection within GCIP has priority. Arguably, the single-most challenging technical problem will be providing a credible regional measurement of cold-season precipitation for the purposes of comparison with seasonal-to-interannual predictions.

2.2.2.2 Definition of the predictive products required by hydrologists

Resource managers within the hydrological community might be able to make use of a range of predicted outputs from coupled land-atmosphere-ocean models, but hitherto they have tended to rely on traditional meteorological and hydrological measurements applied to conventional hydrological models for streamflow predictions. Although hydrologists have a good capability for using statistical forecast information, so far the coupled modeling community has not given priority to providing this type of information. However, research into the possible hydrological interpretation of these predictions cannot begin until the nature and form of such predictions are better defined.

There is a need to develop better understanding of the requirements of the hydrological community so that any predictive meteorological products provided at the seasonal-to-interannual time scale can be tailored more precisely and the opportunities for timely application of GCIP research within hydrology thus enhanced.

2.2.3 Improved coupling processes - issues and actions

Accepting the hypothesis that better representation of processes in coupled atmospheric-hydrologic models will yield improved meteorological prediction at all time scales, research is required to determine, understand, and model such coupling processes. The focus of research into several coupling processes might evolve in response to better specification. However, initially, research will address improved representation of precipitation, soil moisture and biospheric processes.

2.2.3.1 Precipitation processes

Clouds and their associated precipitation are important in the global water and energy cycle and their accurate representation in atmospheric models is crucial. However, incorporating moisture processes is difficult because cloud and precipitation physics is poorly understood, and because the horizontal resolution of large-scale models is much larger than the scales at which clouds are formed -- hence cloud-precipitation processes are subgrid-scale mechanisms which must be parameterized.

(1) Improved parameterization of convective precipitation in atmospheric models

Focused smaller-scale modeling studies are needed to investigate how to improve the parameterization of convective precipitation within regional-scale atmospheric models. To have credibility, such studies require experimental validation. Such experiments would involve simultaneous measurements in the atmosphere and at the surface, and would need to be framed in a proper regional context by specification of the atmospheric flow fields through the study area. GCIP has already begun planning the provision of some of the required observations, in the form of a Near-Surface Observation Data Set described in Section 12. GCIP is also fostering opportunities to validate regional models of precipitation within the Mississippi River basin through collaboration with other observational programs such as ARM, the US Weather Research Program, and the GEWEX Cloud System Study as described in Section 8.

(2) Statistical analyses of sub-grid scale precipitation

Studies are needed to characterize the true variability of precipitation in space and time and its relation with the state of the overlying atmosphere. Understanding the relationship between actual continental precipitation and that predicted by atmospheric models is a very high priority for GCIP. Such studies are especially important at hourly to daily time scales and at spatial scales up to the area covered by a few grid intervals in mesoscale and large-scale atmospheric models.

The accuracy with which precipitation can be measured (by gauges, radar, or both) is likely to be an issue in such studies. Recognizing this last point, the LSA-SW would be the appropriate initial focus for such studies since the stage 3, gauge-calibrated radar precipitation products provided by the Arkansas-Red Basin River Forecast Center (ABRFC) now have established value for comparison against modeled estimates using the Eta, MAPS and RFE regional NWP models.

(3) Research into cold season precipitation issues

Snow is an important component of precipitation, particularly so in the northern and western regions of the U.S., where it provides an important component of the available surface-water resource. Many of the basic atmospheric parameterization issues are similar for warm and cold season precipitation, though parameter values are likely to change between seasons. However, there are additional important research issues related to quantifying cold season precipitation and its partition into runoff or soil moisture which must be addressed as a priority in GCIP. Such questions will be priority issues in the scientific agenda for GCIP studies in the LSA-NC.

The central question is how to develop precipitation volumes that give an accurate measure of the temporal and spatial distribution of snowfall. Associated with this question is the need to determine how representative are rain gauge measurements of snowfall and how to combine surface observations of snow depth and with remote-sensing estimates from aircraft and satellites. These questions of snowfall measurements are discussed further in Section 6.1. To assess the amount and timing of water resources and the soil moisture available for subsequent evaporation, it is also necessary to document, understand and model how the water is partitioned into runoff and infiltration when snow and ice melts.

(4) Improved understanding of topographic influences on precipitation

Water is a critical resource in the western U.S. It occurs mainly through the winter season and to a great extent depends on the total water vapor flux across the mountains and, hence, on large-scale circulation in the atmosphere in winter. However, it is strongly influenced by orography, and GCIP has the potential to make an important contribution to the improved seasonal-to-interannual prediction of water resources in the western U.S. by improving the predictability of orographic precipitation. Accurate forecasting of water resources requires better definition of the location of precipitation than is possible with current weather forecast models. The optimal spatial scale for these forecasts is around 2-3 km, but to achieve this would require a nested modeling approach as an extension of presently available systems. Exploratory research is required to evaluate the value of successively nested forecast models as a possible mechanism for applying seasonal-to-interannual forecasts to water resource issues.

2.2.3.2 Soil moisture processes

Within the climate system, near-surface soil moisture possesses a memory due to its seasonal evolution, and is determined as the residual between precipitation on the one hand and evaporation and surface and subsurface runoff on the other. Many of the modeling studies which have provided evidence that seasonal predictions show sensitivity to hydrologic-atmospheric coupling have in fact been framed in terms of sensitivity to modeled or prescribed soil moisture. There is, therefore, a clear understanding of the importance of soil moisture for climate prediction at the seasonal time scale.

Heterogeneity in the spatial distribution of soil moisture is an inevitable consequence of uneven precipitation, and this can be exacerbated by the subsequent flow of surface and subsurface water across uneven topography. Preliminary modeling investigations (e.g. Avissar and Liu, 1996) indicate that naturally occurring soil moisture heterogeneity (acting through land-atmosphere coupling processes) significantly influences the behavior of the overlying atmosphere. Progress in understanding the effect of area-average soil moisture, understanding the effect of heterogeneity in soil moisture fields, and in validating models which describe the seasonal evolution of soil moisture in space and time have all been curtailed by the historic (and still current) lack of soil moisture measurements.

(1) Improved and extended soil moisture measurement

The growing deployment within GCIP of arrays of field systems capable of routine measurement of soil moisture and the prospect of future deployment of aircraft- and space-borne sensors capable of providing indirect measurements of near-surface soil wetness promise relief from observational limits on understanding for soil moisture processes in coupled models.

Exploratory installation of automatic soil moisture sensors within the ARM-CART array is underway, and plans are being made to extend deployment to the Oklahoma Mesonet and similar distributed data collection networks elsewhere in the Mississippi River basin. GCIP has an interest in deploying a set of soil moisture (and temperature) profile measurements along a north-south transect in the North-Central study area to make observations over the annual cycle, but with emphasis on documenting freezing and thawing episodes during the cold season. Meanwhile, there is investigation of the value of installing soil moisture measurements along a transect from the Little Washita watershed in Oklahoma to the Shingobee watershed in northern Minnesota. Pending these new data sources, the Illinois state water survey soil moisture data (Hollinger and Icard, 1994) remain a valuable data resource for GCIP. The distribution of soil moisture data from these new arrays of soil moisture sensors to the GCIP coupled modeling community is a high priority, as is their synthesis into regional products for model initiation and calibration purposes. A more detailed description of the soil moisture measurement and analysis is given in Section 6.2.

The GCIP community strongly supports the proposal to provide routine remotely sensed measurements of soil moisture using a satellite L-band microwave radiometer. The community understands that such observations can only provide indirect estimates of near-surface soil wetness for certain vegetation covers, but also recognizes that these data are most reliable for short-rooted and sparse vegetation where soil moisture control is most important. Routinely provided soil wetness estimates from satellite could be exploited for coupled model initiation and validation using four-dimensional data assimilation techniques to improve the prospect of better seasonal climate predictions for North America. Moreover, GCIP provides a unique opportunity to validate and calibrate remote-sensing soil moisture data because of the richness of other data fields, such as WSR-88D and gauged rainfall, runoff, and modeled evaporation, from which alternative area-average soil wetness estimates can be made. Calibration of remotely sensed soil wetness data within the GCIP region could thus be the basis for their application elsewhere in the world.

The potential availability of new sources of soil moisture data gives rise to the need to determine how these data can best be used to initiate and validate coupled models. Research is required to investigate how to use sample data from arrays of surface measurements and exploratory remote-sensing data from airborne radiometers. Some modeling studies have been done, but with very limited field validation. Properly conceived combined field and modeling studies would greatly illuminate this issue. The coupled modeling community is aware of and applauds upcoming NASA-sponsored field studies within the ARM-CART study area in the Mississippi River basin that fulfill many of these observational needs, and look forward to working with the data that will result (see next section).

(2) Coupled modeling of the effect of soil moisture heterogeneity on the atmosphere

The opportunity exists to run fine-scale, nested grid, microscale (large-eddy simulation) models that can resolve clouds and the resulting precipitation fields in the context of the upcoming observational studies just described. These model results (considered in a statistical sense) can be compared with the airborne sensor and ground soil moisture observations and with radar and gauged rainfall measurements to determine the quality of the model simulation.

An alternative approach to coupled modeling is to assume that precipitation and other atmosphere processes cannot be predicted deterministically and to conceive models that provide statistical representation of these processes. The challenge is then to develop complementary hydrological models that can be forced with statistical distributions of meteorological variables such as precipitation, solar radiation, etc., and to use these to calculate statistical estimates of the feedback to the atmosphere in the form of sensible-heat and latent-heat fluxes, etc. Statistical models of this type would also benefit from validation against the statistical distributions of precipitation and soil moisture observed in the upcoming observational studies discussed above.

An important aspect of coupled modeling research concerns the possible importance of soil moisture on the formation and evolution of mesoscale convective complexes (MCCs) and mesoscale convective systems (MCSs). Such large mesoscale systems are often initiated over mountainous terrain and move eastward, and they produce a significant portion of warm season precipitation in the Mississippi River basin. Current studies in the western Mississippi River basin need to take account of these mesoscale systems because they play a major role in the warm season hydrological cycle in the southeastern Mississippi River basin. Fine-scale modeling studies are required to ensure adequate simulation of MCCs and to investigate their relation to the underlying soil moisture fields in the regional NWP models. Again, these studies would be best linked to upcoming observational initiatives. After accurate trial simulation of MCCs is accomplished in these particular situations, model tests of the effect of MCCs on the regional hydrology can be made under varying soil moisture conditions.

(3) Improved parameterization of hydrologic submodels

Model parameter estimation is closely tied to model development. Model parameters in the hydrologic part of coupled models vary spatially and may also vary seasonally. Local model parameters are estimated on the basis of information about vegetation, soils and geology, and gridded fields of soils and vegetation characteristics are needed at various scales to provide such estimates. Many such gridded maps have been developed for the GCIP study area, but this process needs to continue. Procedures for estimating model parameters from these gridded data have been developed and have been used to calculate distributed fields of model parameters for some schemes, but the parameter estimation procedures used are largely untested, and it is known that there are wide margins of uncertainty in the ensuing estimates of parameters, such as rooting depth and the hydraulic characteristics of soils. Because model performance is highly sensitive to the value of these parameters, validation and improvement of methods for parameter estimation are needed. Hydrologic schemes might be improved by selecting a model structure which minimizes the effect of parameter uncertainty on model output. There are already a wide range of models available, and the Project for Intercomparison of Land-surface Parameterization Schemes (PILPS) has shown that the available schemes can indeed produce a wide range of different results, given the same parameterization data. No doubt part of this difference is due to model structure differences, but part is due to the way model parameters are estimated from the same basic parameterization data.

One factor which limits tests of the credibility of model parameter estimation techniques is the fact that validation data are not readily available. However, streamflow data can be used together with observations of precipitation and estimates of meteorological forcing from surface observations to validate the performance of hydrologic models and, hence, the procedures used to select the parameters applied within them. As a possible GCIP initiative, historical data series of these variables which last at least 10 years (to sample interannual variability) could be organized for some river basins over a wide range of climatic settings, and these then could be made available to the scientific community for the purpose of model parameter validation. Such data could be used to test the parameter estimation schemes used in a selection of hydrologic models as a precursor to their possible inclusion in coupled models.

A general approach and overall strategy for parameter estimation and testing as a precursor to coupled model experiments is illustrated in Figure 2-1. This figure is a process diagram that illustrates how activities fit together to produce the primary outputs expected from the off-line experiments. Locations of the primary outputs are shown in Figure 2-1. These primary outputs are:

[coupled]

Figure 2-1 Strategy for Coupled Model Experiments.

The parameter estimation strategy will be to build on existing experience with a priori parameter estimation using available land surface characteristics information, previous investigations of "effective" parameters that account for sub-grid variability of the actual parameters, and parameter calibration techniques that have been developed by the hydrology modeling community. As illustrated in Figure 2-1, this strategy involves beginning with existing a priori parameter estimates, using them in test watersheds to see how well they function over many years. The primary variable for the long term tests will be runoff, but soil moisture and surface flux data are available for limited periods at a few places. Adjustments can be made in the a priori parameters to get improved model performance. The basis for these adjustments might include theoretical analyses of scaling relationships or analyses of parameter sensitivity and uncertainty. Then, new relationships (some may be empirical) between adjusted parameter values and climate, soils and vegetation characteristics that can be known globally will be developed. These will be applied to other test watersheds and evaluated.

2.2.3.3 Biospheric processes

Vegetation influences several aspects of the hydrological cycle. There is long-standing evidence that vegetation cover affects catchment runoff. Foliage is known to exert active biological control on transpiration by regulating the stomatal pores through which water vapor leaves the plant. The morphology of plant canopies also influences the absorption of solar energy and the generation of turbulence. These factors together influence the partitioning of available energy into sensible heat and latent heat, and the heat flow into and out of the soil. The sensitivity of stomata to soil moisture change is small at high soil moisture values, but it is ultimately a strongly limiting control on transpiration flux at low soil moistures.

The International Satellite Land Surface Climatology Project (ISLSCP) has proposed NASA-sponsored observations and modeling studies to define the coupling between the biosphere and atmosphere across the GCIP study area. Long-term and continuous measurements of mass and energy fluxes and planetary boundary-layer characteristics are proposed for selected sites in the GCIP domain. These continuous tower-based measurements will allow documentation of diurnal, seasonal, and interannual variations in surface energy fluxes and PBL growth and also capture unexpected but important meteorological events such as drought and storms. The proposed sites would be established over representative land surfaces in the GCIP domain, such as crops, rangeland, and broadleaf forests. They will provide information on meteorological and biological variables needed to test and parameterize the soil-vegetation-atmosphere models which will be the repository of understanding of biosphere-atmosphere coupling in this ISLSCP initiative. Flux measurements at individual tower sites measure surface fluxes only over a limited upwind area and would be augmented with short-term studies over a larger area to determine how representative the towers are. Experimental campaigns with instrumented aircraft are the proposed mechanism to assess the spatial statistics associated with surface characteristics and with surface energy fluxes.

A hierarchy of models, including the most advanced biosphere-atmosphere models currently available, would then be tested against these observations. These would be used as the basis for developing simpler, mechanistically-based models that can be implemented by forecast meteorologists, hydrologists, and climatologists. Scientific foci of this proposed ISLSCP study would be the better determination of seasonality in leaf cover and ensuing changes in biospheric parameters; the biospheric response to seasonal changes in atmospheric demand, with particular attention to changes in the response of vegetation in extreme conditions; and the extension of current understanding on biospheric processes to the dominant land covers within the GCIP domain. Such scientific issues are important aspects of the GCIP coupled modeling research agenda, and the recent Coupled Modeling Workshop strongly supported this NASA-sponsored initiative.

2.3 Improvements to regional mesoscale models

For the past four years there has been an extensive effort to acquire the model output from several operational/experimental centers from a range of operational models of varying resolution, physics and data assimilation systems. GCIP is concentrating on three regional mesoscale models (IGPO, 1995): The participation by the operational centers in providing regional model output for GCIP leads to a mutually beneficial relationship. The principal benefit to GCIP is to provide a measure of the inter-model variability of the outputs from the different regional models which can also be related to the global model output from the operational centers. GCIP can provide benefit to the operational centers by enabling them to make use of the enhanced data sets to calibrate and validate the model data assimilation and forecast systems.

The regional mesoscale models are supporting GCIP research in the following manner:

The regional models now running operationally (NCEP/Eta and CMC/RFE) will be upgraded with numerous improvements during the next several years. The GCIP investigators need to be aware of these plans for improvements and the schedule being followed to incorporate these improvements into the operational models. The experimental MAPS model will also be upgraded during the next several years. Projected improvements to each of the regional models is described in the remainder of this section.

2.3.1 The NCEP Mesoscale Eta Model and Eta Data Assimilation System (EDAS)

Since April 1, 1995, output from the NCEP Eta model (Black, 1994) and its associated Eta-based 4-D Data Assimilation System known as EDAS (Rogers, 1995) have been routinely archived for GCIP. In conjunction with this milestone, NCEP implemented for GCIP an extensive expansion of the routine ETA/EDAS output products, including a vast suite of surface and near-surface products that encompass all the surface energy and water fluxes, soil moisture and temperature, snowpack and snowmelt, and surface and subsurface runoff. These output products include a) 3-hourly analysis and 6-hourly forecast horizontal gridded fields (known in GCIP as MORDS) and b) hourly station time series output (known is GCIP as MOLTS) at nearly 300 sites. A number of GCIP investigators have completed and published initial Mississippi River Basin water budget studies based on these Eta model GCIP archive products (Berbery et al., 1996; Yarosh et al. 1996).

NCEP, over the last three years with GCIP support, has accelerated ETA/EDAS improvements in the following three key areas:

These three improvement areas have resulted in the following specific items implemented in the ETA/EDAS system: Following three years of NCEP/EMC focused effort as a key participant in the NOAA-sponsored, land-surface related, GCIP project (in collaboration with the NWS Office of Hydrology), EMC operationally implemented in January 1996 a new multi-layer soil/vegetation scheme in the Eta model (see Secs 3.1.1 and 3.1.2 of Chen et. al. 1996a, also Chen et. al. 1996b). This land-surface physics package includes two soil layers, time dependent soil moisture and temperature, spatially varying vegetation and soil types, a seasonal vegetation cycle, snowpack physics, and runoff. A related land-surface scheme was implemented in the NCEP/EMC global model and its continuous global data assimilation system, which includes continuous cycling of soil moisture and soil temperature. The Eta model soil moisture and soil temperature are initialized from the latter global data assimilation system with improvements planned for a continuous cycling as identified late in this section. The snowdepth is initialized from a daily 47-km Northern Hemisphere Air Force snowdepth analysis.

The following is a list of ongoing GCIP-focused ETA/EDAS developments now underway with a projected implementation in the next 24 months or less:

A 4-dimensional Variational Assimilation (4-D VAR) System is in advanced development and testing, using the adjoint of the Eta model. The 4-D VAR methodology allows easier incorporation of non-traditional data sources such as direct use of satellite radiances, cloud cover, precipitation, radar reflectivities, profilers, WVSS, ACARS, and ASOS. The assimilation of one to three hourly precipitation fields and satellite estimates of atmospheric water vapor has shown significant promise in tests to date. The satellite water vapor estimates have included GOES 8/9, SSM/I, and SSM/T2.

The current ETA/EDAS system operates at a resolution of 48-km and 38 layers. The companion mesoscale Eta system operates on a 29-km resolution with 50 layers. Routine realtime testing of 10-km nested Eta grids (Eastern U.S.) was successfully accomplished for 4 months during May-Aug 96 in a special Olympic support effort. Once-a-day prototype 10-km nested Eta runs are expectedto continue at NCEP as a demonstration of concept to be evaluated by NWS field forecasters.

2.3.2 Regional Model Upgrade at CMC

It is expected that the quality of the Regional Finite Element (RFE) model outputs will improve significantly during the coming two or three years, especially in terms of the variables that are important for the water and energy budgets which are of prime interest to GCIP.

Surface temperature and wind forecasts should become slightly more accurate due to modifications to the surface layer formulation which will affect the free convection limit and the roughness length. More realistic surface temperature and dew point predictions should result from refined treatments of surface evaporation, snow melt, and soil humidity analysis over North America. A 35-km resolution version of the model with 28 vertical levels was implemented at the end of 1995. Improvements are being tested for its stratiform clouds (the Sundqvist scheme with explicit prediction of cloud fraction and cloud water) which are expected to have a significant impact on precipitation and three-dimensional humidity forecasts. Energy budget calculations are expected to benefit from more sophisticated solar and infrared radiation parameterizations.

Within the coming year it is expected that a meso-scale convective parameterization (perhaps Fritsch-Chappell) and a fully interactive radiation/cloud water scheme will become available. The recent installation of a NEC SX-4 supercomputer should permit a further upgrade of the model resolution to approximately 20 km, with a corresponding increase in the number of vertical levels.

By the end of the three-year period it is likely that the regional forecast model will have been converted to the non-hydrostatic variable-resolution global finite-element based model that was described in the GCIP Major Activities Plan for 1995, 1996 and Outlook for 1997 (IGPO, 1994c).

2.3.3 Improvements to MAPS

The gridded output from the Mesoscale Analysis and Prediction System (MAPS) will improve over the next several years of the GCIP EOP in different areas including model physics, data assimilation, and spatial resolution.

Some improvements related to GCIP have already been implemented, including access to daily lake-surface temperatures (from NOAA's Great Lakes Environmental Research Laboratory), snow and ice cover (from NCEP and the US Air Force). MAPS is currently using monthly climatological sea-surface temperature, to be replaced by daily information from NCEP in the near future.

The most important of the GCIP-related changes has been the implementation of a multi-level soil/vegetation model. This model, currently running with 5 soil levels, is described by Smirnova et al. (1997). High-resolution data sets for fixed or seasonally varying surface characteristics (soil type, vegetation indices, albedo) made available by NCEP are being used now in MAPS.

Full atmospheric radiation has also been added to the MAPS experimental 40-km model, substantially improving lower troposphere temperature forecasts.

Projected changes during the early part of WY'97 in the experimental 40-km MAPS, include:

Plans for FY97 - Plans for FY98 - Outlook for FY99.