One of the principal functions of the regional mesoscale models, as was noted in Section 2, is to produce the model assimilated and forecast output products for GCIP research, especially for energy and water budget studies. The production of such data sets was an important part of the GCIP Implementation Plan (IGPO, 1993). A major thrust area for the production of such data sets from three different regional models was initiated in 1995 with the following objectives:
(i) To produce model assimilated and forecast data products for GCIP investigators with an emphasis on those variables needed to produce energy and water budgets over a continental scale with detailed emphasis in 1997 on the LSA-SW and the LSA-NC and beginning the application of such detailed emphasis capability to the LSA-E during 1998, and to the LSA-NW during 2000.
(ii) To produce a quantitative assessment of the accuracy and reliability of the model assimilated and forecast data products for applications to energy and water budgets.
(iii) To conduct the research needed to improve the time and space distribution along with the accuracy and reliability of the model assimilated and forecast data products.
The details of the regional model output to satisfy the first objective above is given in the remainder of this Appendix.

B.1 Regional Mesoscale Model Output

The list of model output fields needed by GCIP researchers was given in Table 3, Volume I of the GCIP Implementation Plan (IGPO,1993). From the beginning of GCIP, it has been the intent to acquire model output from several different models of varying resolution, physics and data assimilation systems. The large volume of data produced by the current generation of atmospheric models has forced a number of compromises in order to achieve a tractable data handling solution for model output data. The data volume is further enlarged by the GCIP need to enhance the traditional model output to include additional fields needed by researchers to perform meaningful studies of the water and energy cycles. The near-term GCIP needs for model output data will be met by concentrating on three regional mesoscale models: The model output is divided into three types:
(1) One-dimensional vertical profile and surface time series at selected locations referred to as Model Location Time Series (MOLTS)

(2) Gridded two-dimensional fields, especially ground surface state fields, ground surface flux fields, top-of-the-atmosphere (TOA) flux fields, and atmospheric fields referred to as Model Output Reduced Data Sets (MORDS)

(3) Gridded three-dimensional atmospheric fields containing all of the atmospheric variables produced by the models.

Each model output type is described in the following sections.

B.2 Model Location Time Series

Results from the GCIP Integrated Systems Test (GIST) in 1994 and ESOP-95 demonstrated that the vertical and surface time series at selected points is a very useful type of output for a number of applications. Indeed, some energy and water budget computations are making use of this type of model output data. GCIP labels this type of model output as Model Location Time Series (MOLTS) which is produced as an enhanced output containing a complete set of the "surface" type of state and flux data needed by GCIP in addition to the basic atmospheric data which operational centers produce for normal monitoring use and other applications.

The output variables for the MOLTS are listed in Table B-1. The variables listed under 2) Surface Variables and 3) Atmospheric Variables are considered a"fundamental" list. The MOLTS list from a specific model may add other variables depending on choice of physics package or other non-GCIP user requirements. Some examples for the surface variables could include turbulent kinetic energy and other diabatic heating and moistening rates, such as those due to vertical and horizontal diffusion. Some examples of the non-profile variables could include canopy water content, boundary layer depth, convective storm stability indices, precipitation type (frozen?), etc.

An assessment of the MOLTS requirements for GCIP, MAGS and other investigators indicates that a maximum number of 300 locations will satisfy these requirements during the EOP. The specific number could be less than this maximum number depending on resources available to the data producers and the changes in requirements for GCIP during the Enhanced Seasonal Observing Periods and outside of these periods. GCIP will provide inputs to the requirements as part of its annual update of the GCIP Major Activities Plan. The distribution of 300 MOLTS locations is shown in Figure B-1.

B.3 Model Output Reduced Data Set

An analysis of the different GCIP requirements for the gridded two- and three-dimensional fields indicates that most of the requirements can be met by a selected set of two-dimensional gridded fields. [NOTE: Some of the requirements for three-dimensional fields can be met with the MOLTS , e.g. by placing the locations around the boundaries of a river basin to do budget studies.] Some of the other 3-D field requirements can be met by a vertical integration through the atmosphere, e.g. vertically integrated atmospheric moisture divergence needed to calculated water budgets. GCIP will make use of this concentration of requirements to further the tractability of the model output data handling problem. A Model Output Reduced Data Set (MORDS) will continue to be produced as two-dimensional fields with the expectation that the MORDS can meet most of the GCIP requirements at a significantly reduced data volume over that needed to provide the information as three-dimensional fields. GCIP is proposing a total of 60 output variables for MORDS separated into the following four components:

A. Near-surface fields which will include all the sub-surface and surface land characteristics and hydrology variables plus the surface meteorological variables including wind components at 10 meters.

B. Lowest-level atmospheric fields which includes the lowest model level and the mean value in a 30 hpa layer above the surface.

C. Upper atmosphere fields at a few standard levels plus the tropopause height and the top-of-atmosphere radiation as a time average.

D. Metadata fixed fields as one-time companion file to the MORDS.

Table B-1. Output Variables for the Model Location Time Series (MOLTS)

1) Identifiers

Location ID
Valid Date/Time
Forecast Length
Location Elevation (in model)
2) Surface Variables
Mean sea level pressure
Ground surface pressure
Total precipitation in past hour
Convective precipitation in past hour
U wind component at 10 m
V wind component at 10 m
2-meter specific humidity
2-meter temperature
Skin temperature
Soil temperature (all soil layers)
Soil moisture (all soil layers)
Latent heat flux (surface evaporation)
Sensible heat flux
Ground heat flux
Surface momentum flux
Snow phase-change heat flux
Snow depth (water equivalent)
Snow melt
Surface runoff
Sub-surface runoff
Surface downward short-wave radiation flux
Surface upward short-wave radiation flux (gives albedo)
Surface downward longwave radiation flux
Surface upward longwave radiation flux
Top-of-atmosphere net longwave radiative flux
Top-of-atmosphere net shortwave radiative flux
Top-of-atmosphere pressure for above fluxes
3) Atmospheric variables at each model vertical level
geopotential height
specific humidity
U wind component
V wind component
Omega (vertical motion -- Dp/Dt)
convective precipitation latent heating rate
stable precipitation latent heating rate
shortwave radiation latent heating rate
longwave radiation latent heating rate
cloud water and/or cloud fraction


Figure B-1  Distribution of 300  MOLTS Locations

The specific model output variables in each of the four components are listed in Table B-2. Output from the regional mesoscale models on the AWIPS 212 Lambert Conformal Map base at a 40 km resolution constitutes about 30 Kilobytes per field for each output step. The 55 fields from the list of variables shown in Table B-2 will produce about 1.5 Mb for a single forecast or analysis valid time. The MORDS output of analysis, assimilation, and forecast fields for both 0000 UT and 1200 UT cycles comes to a daily total of about 40 Mb per day from each of the regional mesoscale models or about 1.2 Gb per month. This is significantly less than the data volume generated from each of the regional models output in three-dimensional fields.

B.4 Gridded Three-Dimensional Fields

The descriptions given in Section B.2 on MOLTS and Section B.3 on MORDS are aimed primarily at reducing the need to handle the full three-dimensional output fields from each of the regional models. This should make the model output more readily accessible for the GCIP investigators. It is also, in part, needed due to the limitations in the data handling capacity for the full model output by the Model Output Data Source Module in the GCIP Data Management and Service System. These limitations mean it will be possible to collect the three-dimensional fields at this location for the Eta model only. GCIP encourages the producers of the three-dimensional fields for the other two regional models to store them locally to the extent possible.

The description given above on how GCIP plans to meet the model output data requirements within the data handling limitations experienced is applicable for the near-term requirements. It is expected that these requirements will evolve as the land physics packages of these models demonstrate their utility. GCIP will reevaluate this area on an annual basis as part of preparing updates to the GCIP Major Activities Plan.

Table B-2  Output Variables for the Model Output Reduced Data Set

A. Near-Surface Fields

1 - Mean sea level pressure
2 - Surface pressure at 2 meters
3 - Temperature at 2 meters
4 - Specific humidity at 2 meters
5 - U component wind speed at 10 meters
6 - V component wind speed at 10 meters
7 - Surface latent heat flux (time avg)
8 - Surface sensible heat flux (time avg)
9 - Ground heat flux (time avg)
10 - Snow phase change heat flux (time avg)
11 - Surface momentum flux (time avg)
12 - Vertically integrated moisture convergence (time avg)
13 - Vertically integrated energy convergence (time avg)
14 - Total precipitation (time accumulated)
15 - Convective precipitation (time accumulated)
16 - Surface runoff (time accumulated)
17 - Subsurface runoff (time accumulated)
18 - Snow melt (time accumulated)
19 - Snow depth (water equivalent)
20 - Total soil moisture (within total active soil column)
21 - Canopy water content (if part of surface physics)
22 - Surface skin temperature
23 - Soil temperature in top soil layer
24 - Surface downward shortwave radiation (time avg)
25 - Surface upward shortwave radiation (time avg)
26 - Surface downward longwave radiation (time avg)
27 - Surface upward longwave radiation (time avg)
28 - Total cloud fraction (time avg)
29 - Total column water vapor
30 - Convective Available Potential Energy (CAPE)
B. Lowest level Atmospheric Fields
31 - Temperature (lowest model level)
32 - Specific humidity (lowest model level)
33 - U component wind speed (lowest model level)
34 - V component wind speed (lowest model level)
35 - Pressure (lowest model level)
36 - Geopotential (lowest model level)
37 - Temperature (mean in 30 hpa layer above ground)
38 - Specific humidity (mean in 30 hpa layer above ground)
39 - U component wind speed (mean in 30 hpa layer above ground)
40 - V component wind speed (mean in 30 hpa layer above ground)
C. Upper Atmospheric Fields
41 - 1000 hpa height
42 - 700 hpa vertical motion (omega -- Dp/Dt)
43 - 850 hpa height
44 - 850 hpa temperature
45 - 850 hpa specific humidity
46 - 850 hpa U component wind speed
47 - 850 hpa V component wind speed
48 - 500 hpa height
49 - 500 hpa absolute vorticity
50 - 250 hpa height
51 - 250 hpa U component wind speed
52 - 250 hpa V component wind speed
53 - Tropopause height (or pressure)
54 - Top-of-atmosphere net longwave radiation (time avg)
55 - Top-of-atmosphere net shortwave radiation (time avg)
D. Meta Data Fixed Fields (as one-time companion file to MORDS)
a - model terrain height
b - model roughness length
c - model max soil moisture capacity
d - model soil type
e - model vegetation type