WESP Major Activities
June 15, 2002
Roads, John (co-chairman), Jose Marengo (co-chairman), Sam Benedict, Hugo Berbery, Alan
Betts, Michael Bosilovich, Mike Fiorino, Carl Fortelius, Paul Houser, Toshio Koike, Rick
Lawford, Terri Lebel, Nobutaka Mannouji, Kooiti Masuda, Ken Mitchell, Kamal Puri, Burkhardt
Rockel, Matthew Rodell, Ron Stewart, Pedro Viterbo
1. BackgroundThe scientific objective for CEOP WESP is --- To use enhanced observations to diagnose,
simulate and predict water and energy fluxes and reservoirs over land on diurnal to annual
temporal scales as well as apply these predictions for water resource applications. WESP
studies are designed to understand what components of the global water and energy cycles canbe measured, simulated, and predicted at regional and global scales? In particular: (1) what are
the gaps in our measurements? (2) What are the deficiencies in our models? (3) What is our skill
in predicting hydroclimatological water and energy budgets?
Starting from the current efforts to close simplified vertically integrated water and energy
budgets with observations and analyses, and beginning efforts to simulate these budgets
regionally, CEOP will begin the effort to transfer this knowledge to global scales, include morewater and energy cycle processes, and begin to examine the vertical structure in the atmosphereand land. Specific tasks for the WESP working group during CEOP include:• Summarizing component and coupled system modelling studies currently underway.
• Articulating scientific issues that need to be addressed in light of advances in each CSE.
• Defining guidelines for commonality and standards in the background fields and measureof progress.
• Devising the detailed nature of the experimental periods.
Below, we describe the initial WEBS (sec. 2) and transferability projects (sec. 3) that currently
have a direct link to CEOP, recognizing that additional projects will eventually become entrained
within the CEOP WESP framework. Land data assimilation projects are discussed in sec. 4. We
also discuss the nature of the data being requested from the NWP centers (sec. 5). Our eventual
goal is to identify the processes and state variables that can be compared to in situ and satellite
measurements and to then develop community intercomparison projects that can help to define
and quantify measured and modelled processes.
2. WEBS
During the past several years, the Global Energy and Water Cycle Experiment (GEWEX)
continental-scale experiments (CSEs) have started to develop regional hydroclimatologicaldatasets and Water and Energy Balance (WEBS) studies and will be reporting on these studies at
a special WEBS session at the 2003 AGU/EGS spring meeting in Nice, France and a special
WEBS session at the 2003 IUGG summer meeting in Saporo, Japan. These studies will besummarized in Sept. at a WEBS workshop at GISS in the fall of 2002, where GHP will movetoward developing a global synthesis as a prototype for future CEOP WESP syntheses.

2.1 BALTEXThe BALTIMOS project (http://www.baltimos.de/) coordinated by Daniela Jacob, is aiming at:(1) development of a coupled model system for the Baltic Sea and its catchment area in order tounderstand and model exchange processes between atmosphere, sea, land surface, and lakes
including hydrology; (2) validation and improvement of the model system following a validation
strategy to be developed jointly by all partners; (3) investigation of the water and energy cycles
in the Baltic Sea area for present and changed climate conditions; (4) investigation of the waterand energy budgets during the BRIDGE phase and their relation to long time series; (5)development of a strategy to achieve the transferability of methods and results from the proposed
project to other GEWEX - continental scale experiments
Carl Fortelius is coordinating a study of the HIRLAM limited area NWP system with emphasison: (1) quantifying the components of the climatic energy and water cycles during one year of
the BALTEX BRIDGE period (Sept 1999-Sept 2000) in BALTEX region through delayed mode
data assimilation using a limited area NWP system. (2002); (2) validating model-generated
precipitation data using in situ and remote-sensing measurements (ongoing); (3) validatingboundary layer parameters by comparing MOLTS data with available observations, including
CEOP reference stations Cabauw, Lindenberg, and Sodankylä. This work is closely tied to thecommitments of FMI as a national weather service.
2.2 GCIP/GAPP
Roads et al. (2002a), Roads (2002) described vertically integrated global and regional water andenergy budgets from the National Centers for Environmental Prediction – Dept. of Energy(NCEP-DOE) reanalysis II (NCEPRII). Maintaining the NCEPRII close to observations requires
some nudging to the short-range model forecast and this nudging is an important component ofanalysis budgets. Still, to first order we could discern important hydroclimatological mechanisms
in the reanalysis. For example, during summer, atmospheric water vapor, precipitation andevaporation as well as surface and atmospheric radiative heating increase and the dry static
energy convergence decrease almost everywhere over the land regions. We can further
distinguish differences between hydrologic cycles in midlatitudes and monsoon regions. The
monsoon hydrologic cycle has increased moisture convergence, soil moisture, runoff, but
decreased sensible heating with increasing surface temperature. The midlatitude hydrologic cycle,
on the other hand, has decreased moisture convergence and surface water and increased sensible
heating.
Roads et al. (2002b,c) developed a water and energy budget synthesis for GCIP, which includes
the best available data and model simulations for the period 1996-1999. This WEBS includes a
general description of the Mississippi River Basin climate, physiographic characteristics,
available observations, representative types of models used for GCIP investigations, and acomparison of water and energy variables and budgets from models and observations. Besides asummary paper, a companion CD-ROM (with the CEOP logo) with more extensive discussion,
figures, tables, and raw data was also made available to the interested researcher. Briefly,
observations cannot adequately “close” budgets since too many fundamental processes are
missing. Models that properly represent the many complicated atmospheric and near-surface
interactions are required for overall descriptions of the budgets. Models will also be needed foreventual predictions of these water and energy processes. Therefore, different classes of modelshave also been compared with available observations. The comparison includes a representative
global general circulation model, regional climate model, and a macroscale hydrologic model.
2.3 LBA
Marengo et al (2002a) used a combination ob station rainfall and the NCEP re-analysis tocharacterize the annual cycle of critical water budget parameters and their variations for thenorthern and southern sections of the Amazon River Basin. In the entire Amazonia, precipitation
exceeds evaporation representing a sink of moisture (P>E). Our estimates of the Amazon region’s
water balance do not show a closure of the budget, with an average imbalance of almost 44%,
meaning that some of the moisture that converges in the Amazon region is not unaccounted for.
The uncertainties come basically from the estimation of the E and moisture convergence termusing the NCEP reanalyses, as well as the rainfall indices from observed precipitation across thebasin. Estimates of rainfall from other data sets such as those from CMAP or even the NCEP
rainfall show some systematic differences with the observed rainfall, and some studies ob climate
predictability and model skill suggest the better predictability of the water balance components inthe northern part of the basin (the wettest part) as compared to Southern Amazonia.
Analyses from observations from field experiments in the basin have been able to show some
diurnal and intraseasonal variability of rainfall and circulation in the Amazon basin, especially thedifferences on the timing of the diurnal maximum of rainfall associated to westerly or easterly
circulations affecting the basin (Marengo et al. 2002b), or to the moisture transport from theAmazon basin during summer-autumn linked to the so called Low Level Jet east of the Andes,
that features a moisture corridor that import atmospheric moisture from tropical to subtropicalregions to the East of the Andes, such as southern Brazil-Northern Argentina. This in fact
regulates rainfall in the Parana-La Plata River basin (Marengo et al. 2002c).
Model experiences have shown also some systematic errors in the modeling of the water balanceterms for the Amazon Basin. The model tends to underestimate rainfall in the northern
Amazonia, which also reflects in the low model runoff estimates, which in some cases are up to
60% lower than the observed runoff near the mouth of the Amazon in Obidos. The fact that themodel does not capture the observed timing of the peak of the runoff is due to the effect that themodel has not included a routing runoff scheme on its parameterization of land surface.
3. Transferability
Within GHP, there has been much discussion about the transferability of coupledatmosphere/soil regional models to different regions on the globe. As noted in the summary of
the 2001 GHP meeting, “The transferability of regional models between regions and/or thevalidation of global models over continental-scale experimental regions and other regions isbeing addressed on a case by case basis. A list of models being used in different regions
continues to be updated each year and made available through the GHP web site. There are four
possible types of model transferability studies that could be used by GHP:
1.
"Home-based" global model using CEOP validation data
2.
"Home-based" global model and embedded regional model -- comparative evaluation
with "home-based" regional model output (e.g. other models with Eta over GCIP region)plus CEOP validation data3.
4.

Model transferability inter-comparison using a "neutral global model" (e.g. ECMWF or
NCEP/NCAR re-analyses)
Regional model embedded in different global models to evaluate the effects of initial andboundary conditions from different global models

Research priorities for transferability studies are:• Evaluate and improve the representations of the effects of seasonally varying land-use, soil
moisture, vegetation cover, and other soil characteristics forcing and their spatial
heterogeneity in regional coupled models.
• Determine and model the multiscale responses of complex terrain on the regionalhydroclimates at seasonal and diurnal time scales.
• Examine the model's surface energy budgets to evaluate the performance of theparameterizations in physical terms.
• Characterize and model the temporal and spatial distribution of different land surface
conditions, such as snow cover including its accumulation/melt and the impact of frozen
ground, on atmosphere/hydrology interactions.
Characteristics of possible coupled hydrometeorological model case studies:• A relative simple geographic region without major topographic complexities which has
sufficient observations for data assimilation as well as model evaluation studies, such as theMississippi River basin being studied by the GCIP CSE.
• A complex geographic region, such as the Baltic Sea and surrounding land areas now being
studied by the BALTEX CSE.
• A neutral geographic region, which has not been studied by any of the CEOP participants,
such as the region of the Niger river basin of west Africa, CATCH. “
3.1 BALTEX Bridge
The Max-Planck-Institute for Meteorology in Hamburg (MPIH) is hosting a BALTEX Bridge
study with support of the GKSS research centre as a possible prototype for making use of andcontributing to CEOP data. The area for this study is the Baltic Sea and its catchment area
(shown as magenta line in figure 1). The time period is August to October 1995. This is theperiod where the BALTEX pilot experiment PIDCAP (Pilot study of Intensive Data Collection
and Analysis of precipitation) took place. The description of the synoptic situation during thisperiod has been described by Isemer, 1996. Within BALTEX an inter-comparison study ofseveral European regional atmospheric models has already been carried out with data from the
PIDCAP period. The focus was on components of the energy and water budget over the Baltic
Sea and its catchment area. Precipitation, precipitable water, total cloud cover, surface radiation,
surface sensible heat flux, evaporation, run off, as well as 2m temperature and 10m winds wherecompared between the models and with measurements. For more detailed description see Jacobet al., 2001. The experience from this inter-comparison study will be the background for thetransferability study. This study may eventually be extended as a five-year simulation during
CEOP.
HIRLAM analysis data from the Danish Meteorological Institute (DMI) are available on arotated lat/lon grid with 24 vertical hybrid (sigma-pressure) levels as boundary data for the study.
Resolution: horizontal ~ 24 km, temporal 6 hourly. The models should run with a horizontal

resolution of about 18kmx18km in either of the following modes: (a) Forecast mode, i.e. daily
forecast for each day. The forecast starts at 00 UTC and runs for 24h+spin up time (the spin up
time may differ for each model and will be typically 6 hours or 12 hours); (b) Continuous mode,
i.e. a 3 month simulation with initialization at the 1st of August 1995. The soil parameters(temperature and water content) should only be used in the initialization data of the 1st of August.
This applies also for the forecast mode. For the 2nd, 3rd of August and so forth for the
initialization of the soil parameters the values of the forecast of the day before should be used.
For the intercomparison the following data are available from measurements, except evaporation(see also Jacob et al., 2001). In the following BSC stands for “Baltic Sea Catchment” (magenta
outline in Fig. 3).
(i) Total Precipitation
Mean daily precipitation [mm/day] from 6h – 6h.
BSC area weighted value for (a) land points only (including lakes), (b) Baltic Sea only, (c) total
area. Total number of values: 3x92(ii) Runoff
Mean daily runoff [mm/day] from 6h – 6h. BSC area weighted value for (a) land points only.
Total number of values: 92(iii) Integrated Water Vapor (i.e. Precipitable Water)
Mean 6h-average of IWV [kg/m2] of selected GPS stations. Use of the simulated value for thenearest corresponding grid point of the station. Total number of values: 368 per Station(iv) Evaporation
Mean daily evaporation [mm/day] from 6h – 6h.
BSC area weighted value for (a) land points only (including lakes), (b) Baltic Sea only, (c) total
area. Total number of values: 3x92(v) Sensible Heat Flux
Mean daily heat flux [W/m2] from 6h –6h.
BSC area weighted value for (a) land points only (including lakes), (b) Baltic Sea only, (c) total
area. Total number of values: 3x92(vi) Radiation
Mean daily downwelling shortwave and longwave radiation terms [W/m2] at the surface forselected stations from 0h – 0h. Use of the simulated value for the nearest corresponding grid
point of the station.
(vii) Total Cloud Cover
Daily total cloud cover [0-1], instantaneous value between 12h and 15h BSC area weighted valuefor (a) land points only (including lakes), (b) Baltic Sea only, (c) total area. Total number ofvalues: 3x9(viii) Synop Data
Instantaneous values of 2-meter temperature [ºC], 2meter relative humidity [%], and 10-meterwind speed [m/s] 4 times a day for selected stations. Use the simulated value for the nearest
corresponding grid point of the station.
3.2 La Plata
The La Plata Basin is second in size only to the Amazon basin in South America, and plays acritical role in the economies of the region. It is a primary factor in energy production, water
resources, transportation, agriculture and livestock. For comparison, the annual mean riverdischarge of the La Plata River is about 25% larger than that of the Mississippi River, and has adistinctly different annual cycle (Berbery and Barros 2002). The amplitude of the annual cycle of
La Plata River discharge is small: it is slightly larger during late summer, but continues withlarge volumes even during winter. However, further analysis of the main rivers contributing to
La Plata reveals that each contributing river basin has a well defined annual cycle, but withdifferent phases that can be traced primarily to different precipitation regimes. The more
important ones are: (a) a summer monsoon regime affecting the northern area; (b) precipitation
originated in Mesoscale Convective Complexes (MCCs) toward the central area of the basin; and(c) winter synoptic activity, producing mostly liquid precipitation. The Low-level Jet east of theAndes that supplies moisture from tropical South America to La Plata Basin is present
throughout the year (Berbery and Barros 2002; Nogués-Paegle et al. 2002). This is an uncommon
feature not observed in other regions like the Great Plains of the United States, where the Lowlevel Jet develops only during the warm season. Thus, the La Plata Basin has a steady supply ofmoisture and heat from warmer regions at all times of the year, favoring precipitation during
both the warm and cold seasons.
The goal of this transferability experiment is to evaluate the annual cycle of the hydrologic cycle
components in various regional models, and when relevant, compare them to those of otherbasins. Regional models will be evaluated systematically using a special data set of daily
observed precipitation. The low-level jet east of the Andes is strongest over Bolivia, a region
where data are sparse. This might pose a problem in assessing how realistic the circulation is at
low levels, but the field program to be conducted with PACS support by M. Douglas (NSSL)
should be critical for further evaluation of the models. A network of pilot balloons has been
deployed and measurements, already under way, will expand during CEOP. CEOP could play a
critical role by ensuring that this data are distributed to the community in a timely manner.
The transferability experiment will include evaluations of regional models': (a) performance interms of precipitation and winds in the La Plata basin; (b) potential to reproduce the low-level jet
east of the Andes; and, lastly, (c) functioning over complex mountainous terrain. Initial resultswith the Eta model have been encouraging for all seasons, even in subtropical regions. Selected
information of this model is being provided to Steve Williams to distribute through UCAR/JOSS.
See also http://www.meto.umd.edu/~berbery/etasam.
4. Land Data Assimilation
Traditional coupled land-atmosphere 4-D data assimilation systems (4DDA) often yieldsignificant errors and drift in a) soil moisture and temperature and b) surface energy and water
fluxes owing to substantial biases in the surface forcing fields from the parent atmospheric model,
especially biases in precipitation and surface solar insolation. Hence, as an uncoupled landsurface alternative to coupled 4DDA, there are a number of regional as well as a global Land
Data Assimilation System projects.
4.1 GLDASA Global Land Data Assimilation System (GLDAS) that uses various new satellite and groundbased observation systems within a land data assimilation framework to produce optimal output
fields of land surface states and fluxes has been developed under the leadership of Drs. PaulHouser and Matthew Rodell at NASA’s Goddard Space Flight Center. GLDAS includes fourcomponents implemented globally at ¼ degree resolution (higher resolutions are planned) in nearreal time: land modeling, land surface observation, land surface data assimilation and calibrationand validation. The core advantage of GLDAS is its use of satellite-derived observations(including precipitation, solar radiation, snow cover, surface temperature, and soil moisture) torealistically constrain the system dynamics. This allows it to avoid the biases that exist in nearsurface atmosphere fields produced by atmospheric forecast models, minimize the impact ofsimplified land parameterizations, and to identify and mitigate errors satellite observations usedin data assimilation procedures. These value-added GLDAS data will improve land surface,
weather, and climate predictions by providing global fields of land surface energy and moisture
stores for initialization. Through its Japanese and European collaborations, CEOP will likely
benefit GLDAS by enabling access to remotely sensed data from international space agencies.
GLDAS is a natural and important tool for CEOP because in a globally consistent manner, it will
integrate the information from multiple models and observation platforms to provide the best
available assessment of the current state of the land surface. The international GEWEX andCEOP communities have recognized that GLDAS can be leveraged and further developed toprovide optimal integration of CEOP data. CEOP is specifically interested in the generation andapplication of GLDAS results in regional climate analysis, model initialization, and comparison
with results from field campaigns and modeling experiments. The use of model location timeseries (MOLTS), which are time series of land surface model output for points of interest, will be
one of the primary tools to enable this globally-consistent intercomparison. Each GLDAS
MOLTS will be particularly relevant because it will be generated based on a GLDAS subgrid“tile” with a vegetation class that matches that of the observation. Furthermore, MOLTS can beproduced using each of the land surface models that GLDAS drives (currently three; fiveplanned). These comparison exercises and the data produced by the continental scale
experiments also will provide much-needed validation for the GLDAS project.
CEOP has requested that NASA further develop GLDAS as a central “CEOP data integration
center”, which will include: a testbed for evaluating multiple land surface models, long term landmodel baseline experiments and intercomparisons, linking and inclusion of reference site
observations with globally consistent observation and modeling to enable GEWEX-CSE landtransferability studies, and initialization for seasonal-to-interannual coupled predictions,evaluation of NWP and climate predictions for land, integrating remote sensing land
observations in land/atmospheric modeling for use in CEOP and higher level understanding,
producing a quality control check on observations, producing 4DDA “value-added” GLDASCEOP datasets, producing GLDAS MOLTS, expanding GLDAS to include selected atmosphereand ocean observations, and developing a long-term archive.
The GLDAS contribution to CEOP is expected to have the following timeline:• Data Integration Period (2002-2005)– Compile the forcing data (obs and analyses) and assimilation data
• Including radiance observations (level 1), high-level satellite data
products, in-situ observations, and NWP land analyses.
– Develop long term archive (Goddard DAAC, NCAR, Japan)– Develop MLDAS (Molts LDAS)• Reconfigure GLDAS to run only MOLTS points

•
•

•

Access the global forcing for flexibility in point definition
Smaller computing requirements, easier turnaround time. Good for
R&D, land model intercomparison• Link to CSE reference sites
Reanalysis Period (2006-2007)– 1/8 degree resolution; global land, CEOP time domain– Land model products (NOAH, CLM, VIC, others?)– Data assimilated value-added analysis

4.2 US LDA
NCEP/EMC, NCEP/CPC, NASA/GSFC, NWS/OHD, NESDIS/ORA, Princeton University,
University of Washington, Rutgers University, University of Maryland, and University ofOklahoma; see http://ldas.gsfc.nasa.gov). have undertaken the development and demonstrationof a National (N-LDAS) -- a realtime, hourly, distributed, uncoupled, land-surface simulation
and assimilation system executing on a horizontal grid spanning the U.S. CONUS domain at
0.125 degree resolution. The N-LDAS project represents a major component of the GAPP/GCIP
continental-scale experiment, which is a primary sponsoring program of the N-LDAS project.
The N-LDAS project is applying four different LSMs. These four LSMs are: (1) NOAH (Koren
et al., 1999), (2) MOSAIC (Koster and Suarez, 1996), 3) VIC-3L (Liang et al., 1996), and (4)SAC-SWA (Sacramento Model; Burnash et al. 1973). One or two additional LSMs may be
added in the near future (e.g. SSiB, CLM). In the N-LDAS, these LSMs execute in parallel inreal time on a common grid, using common terrain heights and land mask, and all driven bycommon surface forcing -- the latter highlighted by model-independent, observation-based
precipitation and solar insolation fields. Also, a common streamflow routing model is applied toeach LSM's gridded runoff to provide parallel streamflow simulations over selected basins acrossthe CONUS.
Several of the collaborating partners are providing various sources of retrospective forcing that
focus on three different periods spanning 1) several years (1996-present), 2) roughly one decade(1987-1998), and 3) the 50-plus years of the NCEP/NCAR Global Reanalysis (1948-present). In
all three cases, model independent precipitation analyses are provided. Using these sets ofretrospective forcing, the N-LDAS partners are executing retrospective companions to therealtime N-LDAS. One of many applications of the retrospective runs will be to characterize thecurrent realtime land states of soil moisture and snowpack of a particular LSM in terms ofpercentile departures or anomalies from a multi-year climatology of that LSM.
The N-LDAS project will contribute to CEOP by providing both validation fields and the initialconditions for the land states for regional climate model predictions. In particular, N-LDAS
evaluates the utility of the new generation of experimental satellites in land area and hydrologicalresearch to improve NWP and climate predictions and prepares land data assimilation products.
The realtime execution of the N-LDAS began before the start of CEOP will continue at least
through the CEOP period (2004) and beyond.
Four central scientific questions are being addressed by N-LDAS: (1) What is the relative impactof land-surface boundary conditions versus sea-surface boundary conditions on seasonal-to-annual predictability of the continental water cycle in coupled regional land/atmosphere climate
models? Is the land-surface impact increased by utilizing initial land states from an uncoupled
versus coupled LDAS (such as the NCEP Regional Reanalysis)? Is the land-surface impact
increased by employing the same LSM in both the coupled climate prediction model and the
LDAS that generates the initial conditions for the climate model? (2) How can calibration
methods for LSMs be extended or relaxed from local point-wise or small-catchment
measurements to widespread satellite measurements over large spatial domains? (3) Does theassimilation of satellite data improve the simulated states and fluxes of an LDAS? What satellite
data types and assimilation methods are most effective and operationally feasible? (4) Can
distributed LSMs running at grid resolutions feasible over a national domain simulate streamflow
with accuracies on par with catchment-specific, calibrated lumped models?
5. WESP Data
Three spatial scales (local, regional, global) are of interest to the CEOP community. At local
scales, in situ data from several international tower sites, along with level II and level III satellite
data plus numerical model and 4DDA output for these same sites, will be consolidated into
useful data sets for studying water and energy budgets. At regional and global scales, regionaland global networks of more standard observations, as well as model and 4DDA output, are alsoneeded for closing regional and global water and energy budgets and understanding monsoon
interactions over land and ocean.
In the GEWEX community, the first type of output above is referred to as Model Output
Location Time Series or MOLTS. At some numerical weather prediction centers, this type of
output is also referred to as "meteograms" and is typically output in the World MeteorologicalOrganization (WMO) format standard known as BUFR. Our goal is to translate the BUFR output
into column ASCII format, which can be easily utilized by the research community. In any case,this type of output refers to model output at individual sites in vertical model columns (including
the earth surface and subsurface) at 3-hourly or more frequent intervals. Hence, MOLTS
represents the model-output analog of "observing station" time series and such output is neededby researchers to augment in situ data and for studying local processes. The locations of the
proposed in situ MOLTS sites are provided in Table 1 and Fig. 1. There are now 34 identified
sites in a broad array of climatic regions. A P and T diagram for the CEOP MOLTS sites isprovided in Fig. 2.
Equally important, 3D and 2D gridded output processed as synoptic snapshots at a minimum of
six hourly intervals (three hourly intervals strongly preferred if possible). At NWP centers, such
gridded synoptic snapshots are often output in the WMO format standard known as GRIB. An
example of 2D gridded output is earth-surface specific states or fluxes (e.g. snow pack watercontent), which are not defined throughout the atmospheric or subsurface medium. The 3D
gridded output is typically provided as a set of 2D gridded fields spanning the vertical levels.
CEOP has requested 3-hourly (or more frequent) analysis values for several hydroclimatic
variables and processes (Table 2). Some of these values should be accumulated over theprevious 3 hours, although approximations to the accumulation, such as averages over theprevious 3 hours would be acceptable. These values will be provided on the model vertical grid,
and we will then provide both the native vertical grid as well as interpolated values to theresearch community. Interpolated values will be provided every 5 mb in the vertical. In addition,
1-day forecasts initialized at 0 UTC and output every 3 hours for the same variables are also
requested so that we can better understand how models are adjusting from the initial analysis.
As may be seen from Table 2, the total number of 2-d grids are (3+23+3+7). The total number of
3-d atmospheric grids are (26). The total number 3-d subsurface grids are (2). The total numberof values at a single MOLTS station for a single time will therefore be(3+23+3+7)*1+(26)*200+(2)*6=5248 or 10496 for both initial analysis and forecast. For asingle day (8 3 hour analysis times) this will be 83968 words. For the 2-year period this will be
60 x 106 words. At 4 bytes per word this is 240 mb per molts station. For 34 stations this will be
8 gb per model. There are likely to be about 10 models and thus the 200 level CEOP MOLTS
data set should be on the order of 80gb. For a global grid of 192x94 (T62) there will be about
18048 grid points but a decrease in number of levels, to say 40, or equivalently each model
gridded data set (at this resolution) will be 100 times the MOLTS. The model data will bearchived by the individual centers initially although MPI has agreed in principle to archive thegridded model output data.
A number of other NWP centers have been requested to provide these products, and NCEP has
already responded in part by indicating that many of these variables and processes can be derived
from the basic global and regional analyses. However, despite the availability of NWP data, it isclear that major efforts will need to be undertaken in order to fully realize WESP goals. Suchefforts are being offered as a challenge to the research community.
5.1 NCEP Contributions
NCEP global and regional reanalysis products will be available during CEOP. Data sets include:(1) Global Reanalysis I (L28T62 grid); Global Reanalysis II (L28T62 grid); Regional Reanalysis(32-km, 45-layer); operational Eta/EDAS forecasts and 4DDA (MOLTS point-wise time series,
and MORDS 40-km gridded fields); operational MRF/GDAS forecasts and 4DDA (committed
only to MOLTS at this time, and adding CEOP MOLTS cites thereto; weekly global SST (1-deg
resolution); daily sea-ice cover (N.H., nominally 50-km, provided via NESDIS). Also during
CEOP, NCEP will contribute analyses of precipitation, SST, snow cover and sea-ice, as follows:A) global, 2.5-degree, 5-day, gage/satellite precipitation analysis in real-time, and reanalysis to
January 1979; B) U.S., 0.25-degree, daily, gage-only precipitation analysis in real-time, andreanalysis to January 1948; C) U.S., 4-km, hourly, radar/gage precipitation analysis in real-time,with archive to July 1996; D) Mexico, 1.0-degree, daily, gage-only precipitation analysis in realtime, and reanalysis to January 1948; E) global, 1.0-degree, weekly, SST analysis in real-time,and reanalysis to November 1981; and F) N. Hemisphere, 25-km, daily, snow cover and sea ice
analysis in real-time, with archive to January 1997 (via NESDIS partner). Fig. 2 shows theannual mean (1988-1999) NCEP/NCAR precipitation and temperature associated with the
MOLTS sites given in Table 1.
5.2 NASA DAO contributions
The NASA Data Assimilation Office (DAO) will make available operational analyses for the
CEOP period. The data will include complete energy and water cycle budgets including
observational analysis terms. The operational analysis is still under development and will
eventually include precipitation and land temperature assimilation. The DAO is undertaking a

CEOP Reanalysis Project in order to produce a consisitent global data analysis product for the
CEOP period.
References
Baetghen, W., V. Barros, E.. Berbery, R.Clarke, H. Cullen, C. Ereño, B. Grassi, D.P.
Lettenmaier, R. Mechoso, and P. Silva Dias, 2001: Climatology and hydrology of the
Plata Basin, WCRP/VAMOS, 2001.
Berbery, E. H., and V. R. Barros, 2002: The hydrological cycle of the La Plata basin in SouthAmerica. Journal of Hydrometeorology, accepted.
Burnash, R.J.C., R.L. Ferral, and R.A. McGuire, 1973, A generalized streamflow simulation
system: conceptual models for digital computers, Joint Fed.-State River Forecast
Center.,Sacramento, California
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Table I. CEOP reference and MOLTS sites
Site
CAMP
1
Eastern Siberian Tundra (Tiki)
2
Eastern Siberian Taiga (Yakutsk)
3
Mongolia
4
Tibet
5
Yangtze River
6
Inner Mongolia
7
Northern South China Sea-Southern Japan
8
Himalayas
9
Korean Jeju
10
Korean Peninsula
11
Chao-Phraya River
12
North-East Thailand
13
Western Pacific Ocean
14
Equatorial Island
15
Tropical Western Pacific (Manus)
LBA
16
Rondonia (Forest Site)
17
Manaus18
Santarem (Primary Forest)
19
Caxiuana
20
Pantanol21
Brasilia (Cerrado)
GAPP
22Southern Great Plains
23
Bondville
24
Fort Peck25
Oak Ridge
26
Mt. Bigelow
MAGS
27
BERMS (Old Aspen)
28
BERMS (Old Black Spruce)
BALTEX
29
Lindenberg

LatitudeLongitude

71.62
62.25
45.74
31.37
32.00
44.46
24.97
27.96
33.17
37.10
17.16
14.47
07.05
-00.02-02.06

128.75129.62106.2691.90
116.00122.12121.1886.81
126.10123.12
99.87
102.38134.27100.32147.43-10.08-02.61-03.02-01.71-19.56-15.93-61.93-60.21-54.97-51.51-57.01-47.92

36.61
40.01
48.31
35.96
32.42

-97.49-88.29-105.10-84.29-110.73

53.63
53.99

-106.20-105.12

52.20

14.12

30
31
32
33
34

Cabauw
Sodankyla
CATCH
Oueme
NiameyOTHER
North Slope of Alaska (Barrow)

51.97
67.37

04.93
26.65

09.50
13.50

02.00
02.50

71.32

-156.62

Table II. MOLTS and Gridded Output Variables for the top of the atmosphere, the atmosphere,
surface and subsurface. A=accumulated over the previous 3 hours, I=instantaneous. S=in situ
measurement, R=satellite remotely sensed.
Top of Atmosphere
Units
A,I S R1
shortwave downward flux
W/m2
A
X
2
2
shortwave upward flux
W/m
A
X
3
longwave upward flux
W/m2
AX
Atmosphere1Temperature
K
I
X
2
Humidity
kg/kg
I
X
3
Cloud water
kg/kg
I
X
4
Kinetic Energy
W/m2
5
Zonal windm/s
I6
Meridional windm/s
I
7
Pressure velocity
Pa/sI
8geopotential (gZ)
m2/s2
I
9
convective latent heating rate
W/m2
A
10
stable latent heating rate
W/m2
A
2
11
convective moistening ratekg/(m s)A
12stable moistening ratekg/(m2s)A
213
diffusive moistening ratekg/(m s)A
14
diffusive heating rate
W/m2
A
15
short-wave heating rate
W/m2
A
16
long-wave heating rate
W/m2
A
17
water vapor zonal fluxkg/(ms)A
18
water vapor meridional fluxkg/(ms)A
19water vapor vertical fluxkg/(ms)A
20
water vapor flux divergencekg/(m2s)A
21energy (CpT+gZ+KE) zonal flux
W/mA
22
energy (CpT+gZ+KE) meridional flux
W/mA
23
energy (CpT+gZ+KE) vertical flux
W/m
A
2
24
dry static energy flux divergence
W/mA
25
Local time tendency of temperature
W/m2
A
X
2
26
Local time tendency of moisturekg/(m s)A
X
Surface Values
1
Local time tendency of surface pressure
Pa/sA2
surface pressure
Pa
I

3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
1
2
1
2
3
1
2
3
4
5
6
7

skin temperature
2-meter temperature
2-meter specific humidity
u-component at 10 m
v_component at 10 m
potential temperature at 10 m
specific humidity at 10 m
snow water equivalentvegetation water
shortwave downward flux (+:downward)
shortwave upward flux (+:downward)
longwave downward flux (+:downward)
longwave upward flux (+:downward)
sensible heating
latent heatingsnow phase change heat
meridional wind stresszonal wind stress
10 m turbulent kin energy in a layer
surface runoff
base flow runoff
Below Surface Values
soil moisturetemperature
Bottom of Subsurface
Soil Moisture
Temperature
Ground Heat Flux
Miscellaneous
Precipitation type 1rain or 2snow
station land/sea/ice mask 0(land)or1(sea)or(2)ice
Total cloud coverplanetary boundary layer heightsurface exchange coefficientroughness length
green vegetation cover

K
K
kg/kg
m/s
m/s
K
kg/kg
m
kg/m**2
W/m2
W/m2
W/m2
W/m2
W/m2
W/m2
W/m2
W/m2
W/m2
m2/s2
kg/(m2s)kg/(m2s)
%
K
%
K
W/m2

%
m
m/s
m
%

I
I
I
I
I
I
I
I
I
A
A
A
A
A
A
A
A
A
A
A
A

X
X

X
X X
X X
X
X X
X
X

X

I X X
I X
I X
I X
I
I X
I X X
I X X
I
I
I
I

90 N

0

60 N

0
10

10
30 N

0

-10

-20

-1
0

0

-1

0
10

20

21000

20

20

0

20

20

30 S

20

20

10

0
-10
-20

0
-10
-20

60 S

0
-3-40
120 W

90 S
180

10

10

0

1

-50

-30 -40

60 W

0
-10
-20 -30
-40

-5

0

2

60 E

3

4

0
120 E

5

180

6

Precip (mm/day)Fig. 1 CEOP in situ reference and MOLTS sites.

CEOP MOLTS Stns Annual Means
14
4

15

Precip (mm/day)

13
16
18
19

3

7

21
1112 20
32

9

2

1

1

2

8

4

25
23
522
30
29
10

31
6

28
27
3

34

17

33

24
26

0
-15

-10

-5

0

5

10

15

20

Temp (deg C)

Fig. 2 P and T diagram for MOLTS sites from NCEP/NCAR Reanalysis

25

30

35

Figure 3: The Baltic Sea catchment area (magenta outline) to be used for the BALTEX Bridgetransferability experiment.