August 2006

10

CEOP’s Future Role in Water Resource Risk Management
Ann Henderson-Sellers, Director of the World Climate Research Programme (WCRP).

We are so vulnerable to fluctuations inwater supply that the availability of water
is predicted to be a major trigger of future
global conflicts. Current pressure on potable
and agricultural supplies worldwide will
be exacerbated, particularly in developing
nations, by climate fluctuations, whatever
their cause, because response times of
meteorological, hydrological, engineering,
Ann Henderson-Sellers social and political systems differ. A key
commitment from the Johannesburg 2002 World Summit on
Sustainable Development was to ‘develop integrated water resourcesmanagement and water efficiency plans by 2005’.
CEOP, established in 2001 by WCRP's Global Energy and Water
Cycle Experiment (GEWEX), was motivated by international efforts
focused on measuring, understanding and modeling of the waterand energy cycles within the climate system. CEOP has gained theinterest of other international organizations, including being selected
as a transitional element of the first stage of GEOSS and as the firstelement of the Integrated Global Water Cycle Observations (IGWCO)
theme within the framework of the Integrated Global Observing
Strategy Partnership (IGOS-P). These are remarkable achievements,
which deserve praise. With this heritage, it is straightforward for
CEOP to take fully into account the new WCRP strategic frameworkin planning the assembly of coordinated data sets and developing
the scientific goals of its Phase 2.
CEOP has been implemented with the support of a large
number of field scientists, numerical weather prediction centres
and space agencies. Here, I take this opportunity to raise anotherarea of coordinated enhanced observation relevant to CEOP andof importance for future water resource risk management. Stablewater isotopes have an innovative contribution to make to the goalof reducing uncertainty in water availability predictions, which is justbecoming attainable. Two rare but naturally occurring isotopes ofwater, 1H218O and 1H2H16O, have practical applications in diagnosisof climate and Earth Systems and hence in water resource risk
mitigation. The potential of these stable isotopes as tracers andvalidation tools in hydrological systems derives from the systematicand different (from each other and from the most abundant nuclide:1 1 16
H H O) paths and residence times.
It seems to me that CEOP Phase 2 could be very readily extended
to allow and encourage testing of novel hypotheses such as:
Observation and analysis of the diurnal fluxes of 1H218O and 1H2H16O

between the soil, plants and atmosphere can accurately determine
the partitioning of precipitation into transpiration, evaporation andtotal runoff (surface plus soil drainage). If validated, by CEOP, thistype of hypothesis can contribute (i) to improving the accuracy withwhich land-surface schemes partition net available surface energy
into latent and sensible heat fluxes and thus (ii) to decreasing
the uncertainty in hydro-climate modelling and water resource
vulnerability predictions. Two examples from large river basins
already involved in CEOP illustrate the potential benefits of including
stable water isotopes into CEOP Phase 2.
All land surfaces recycle precipitation, the most effective systembeing the Amazon Basin, which recycles about half its rainfall witha water recycling time of about 5.5 days. Field measurementsof water isotopes in the Amazon in the 1970s demonstrated theimportance of the soil and vegetation by differentiating moisturesources and mapping the fate of water intercepted by the vegetation.
In a complete simulation of the Amazon’s forest hydrology, theland surface must correctly partition the moisture fluxes between
water evaporation (fractionating), transpiration (non-fractionating),
re-evaporated canopy-intercepted rainfall (non-fractionating if
complete) and runoff. Statistically significant temporal changes(1965-1990) in water isotopic signatures detected in the Amazon
are more consistent with the effects of greenhouse warming,
possibly combined with land-use change, than with the effects ofdeforestation alone. However, very recent isotopic data “fingerprint”
the impact of deforestation: vegetation removal prompting lessrecycling and less re-insertion of heavy isotopes into the basin
system.
The Murray Darling basin poses a complementary but equallysignificant challenge for water risk management. Specifically,
global and regional models exhibit large sensitivity to land-covercharacterization and to land-use changes. A GEWEX inter-modelcomparison reveals a lack of quantitative skill in the Murray DarlingContents:• CEOP’s Future Role in Water Resource Risk Management •1,2
• Initial Comparisons of Model Analyses during NAME • • • • • 2,3• Inter-comparisons of Prediction Skill of Operational
GCMs and a Land Data Assimilation System • • • • • • • • • • • • 3,4,5• GLDAS Output Supports CEOP Studies • • • • • • • • • • • • • • • • • • 5,6• Comparing the Diurnal Behaviour of Model Output with
In-Situ Data in the MDB • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • 6,7• Notice of the AIRS data • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • 7• Summary of the Fifth International
CEOP meeting in Paris, France • • • • • • • • • • • • • • • • • • • • • • • • • • • 8

 CEOP Newsletter No.10

demanding the imposition of additional constraints on the surface
water and energy budget. New river and groundwater isotope
measurements from Australia substantiate the identification of the
2003-2004 El Niño drought as evaporatively most extreme.
Since its establishment, CEOP has developed comprehensive
composited data sets that have stimulated a number of research

projects, which are continuing to advance our understanding ofprocesses important for future water management. I very muchapplaud CEOP’s progress to date and encourage the developmentof CEOP Phase 2 as broadly as possible, for example to include
stable water isotopes, so that its endeavours maximize the benefits
delivered to end-users.

Initial Comparisons of Model Analyses during the North American Monsoon Experiment(NAME)
Michael Bosilovich, David Mocko and William K.-M. Lau NASA Goddard Space Flight Center
Alex Ruane and John Roads Scripps Institute for Ocenography

CEOP collections of data assimilation products from operationaland research numerical weather prediction centers are a link
between the implementation of global observations and model
development. As many as 11 international centers are contributing
data and guidance on analysis data. Presently, four centers havecontributed data for the full EOP3 and EOP4 periods (namely NCEP,
UKMO, JMA and ECPC/RII). These contributions are being evaluatedand compared to observations with an overall objective to bettercharacterize the uncertainty of analysis data. In addition, the CEOP
collection of both model and observed data can provide a central
baseline for long term development at individual centers.
A key consideration in analyses is the precipitation field. Whenmoisture is assimilated, there is a significant impact on the simulated
physical processes. Analysis products tend to provide precipitationfrom the forward integration of the model (e.g. 6 hours forecast fromthe analysis). As such, the model influence on precipitation data
is substantial. In principle, comparisons of these data can provide
information on the modeled processes. In an initial investigation,
we evaluated the four primary contributions to the CEOP modelarchives during the North American Monsoon Experiment (NAME,
Higgins et al., 2006) period (June – September 2004). We will follow
some comparisons included in the North American Monsoon Model
Assessment Project (NAMAP2, e.g. Gutzler et al. 2005) in orderto facilitate comparisons with the regional and global simulations.
In addition to the contributions to CEOP, we also include the North
American Regional Reanalysis (NARR, Mesinger et al. 2006), whichassimilates rain rate (gauge over land, CMAP for oceans). We use
the merged Tropical Rainfall Measurement Mission (TRMM) rainfall
product for a baseline (Huffman et al. 2006, http://precip.gsfc.nasa.
gov/ ).
Figure 1 shows the difference between July and June 2004
monthly mean precipitation in the North American monsoon region
for TRMM and each of the analysis systems. Generally speaking,the analyses show the increase of precipitation in the monsoon
region including Arizona, and the decrease of precipitation in thesouth central United States. The global analyses favor increasing
tropical convection in the southwest corner of this figure, similar
to TRMM, but the NARR increases precipitation farther north. The
NARR captures the maximum precipitation in Northern Mexico. This
comparison is generally reasonable at large scales, though somedifferences with TRMM are apparent. The TRMM precipitation showsa wide swath where there is little change in the precipitation, so that
New Mexico is not increasing from June to July. All of the analyses
show increases in New Mexico.
Figure 2 shows the time series for the accumulation of
precipitation throughout the 2004 season for Arizona-New Mexico(AZNM, lon 115-108W, lat 32-35N) and the core monsoon region(CORE, lon 112-106W, lat 24-30N). The occurrences of major rainfall
events in the TRMM data are generally represented well by thereanalyses. The major differences are the magnitude of the events. A
noticeable feature is that the JMA rainfall is nearly double the otherdata (scaled on the right axis). It appears to be a regional bias in thegeneration of convective precipitation. The ECPC RII system starts
raining late in the season, but eventually catches up with strong lateseason events. Also, there is a tremendous range of variations in theanalysis systems.
We will evaluate the processes contributing to precipitation in thesystems (e.g. Betts 2006). However it should be noted that not all
the analyses provided the same diagnostic variables. Additionally,
the models have different spatial resolutions. The NCEP and ECPC
systems are approximately 2 degrees while UKMO, JMA and NARR
are more fine scale. Subsequent work in this region should consider
carefully the spatial resolution of the analysis systems (Berbery andFox-Rabinovitz, 2003).
Lastly, it is worthwhile to mention that the comparisons produced
here were done remotely using a Grads Data Server (GDS) as thesource for the data. In an effort to show the utility of this approach
the scripts and instructions to use them are made available fordownload at ftp://gmaoftp.gsfc.nasa.gov/pub/data/mikeb/CEOP/
NAME_example.zip. While some experience with Grads will help
running these scripts, the data is also accessible with Ferret, MatLab
and IDL. Only a portion of the model data is currently available
online.
References
Betts, A.K., 2006: Radiative scaling of the nocturnal boundary layer and thediurnal temperature range. J. Geophys. Res., 111, doi:10.1029/2005JD006560(in press).
Berbery, E. H. and M. S. Fox-Rabinovitz, 2003: Multiscale diagnosis of theNorth American monsoon system using a variable-resolution GCM. J. Clim., 16,
1929-1947.

CEOP Newsletter No.10 

Gutzler, D. S., H.-K. Kim, R. W. Higgins, H.-M. H. Juang, M. Kanamitsu, K.
Mitchell, K. Mo, P. Pegion, E. Ritchie, J.-K. Schemm, S. Schubert, Y. Song and
R. Yang. 2005: The North American Monsoon Model Assessment Project:
Integrating Numerical Modeling into a Field-based Process Study. Bull. Amer.
Met. Soc., 86, 1423–1429.

Higgins, W. and co-authors, 2006: The NAME 2004 Field Campaign andModeling Strategy. Bull. Amer. Met. Soc., 87, 79–94.
Huffman, G.J., R.F. Adler, D.T. Bolvin, G. Gu, E.J. Nelkin, K.P. Bowman, E.F.
Stocker, D.B. Wolff, 2006: The TRMM Multi-satellite Precipitation Analysis:
Quasi-Global, Multi-Year, Combined-Sensor Precipitation Estimates at Fine
Scale. J. Hydrometeor., in revision.
Mesinger, F. and co-authors 2006: North American Regional Reanalysis. Bull.
Amer. Met. Soc., 87, 343–360.

Figure 1: Differences of monthly mean precipitation (July-June, 2004) showing theextent of the North American monsoon onset in the TRMM observations, NARR, andfour of the contributed analyses to the CEOP archives. Units are mm day -1.

Figure 2: Time series of accumulated precipitation for the five analyses and TRMM
observations. JMA precipitation is scaled by the right axis. Units are mm.

Inter-comparisons of Prediction Skill of Operational GCMs and a Land Data Assimilation
SystemK. Yang, M. Rasmy, S. Rauniyar, and T. Koike, Department of Civil Engineering, University of Tokyo, Japan

1. Introduction
Diagnosing model errors by comparing model output withobservations is the basis for improving representations of key
physical processes. CEOP archives model output and in-situ datawith a global coverage and provides a unique platform to detect
model deficiencies in global and regional scales. Model skill is being
evaluated at individual NWP centers using CEOP data (See reference
papers submitted to a CEOP special issue), but it is necessary tohave model-cross comparisons (Bosilovich, 2005). In this article,
we briefly report the results of an inter-comparison study within theframework of a full annual cycle, multi-site, multi-parameter, andmulti-model output. Details can be found in Yang et al. (2006).
2. Data
Observations at 27 reference sites and model output location
time series (MOLTS) from JMA and BMRC (hourly), NCEP, UKMO,
ECPC, and GLDAS (3-hourly) for Oct. 2002 ~ Sep. 2003 (EOP3)
were compared. We present the results for the first 6-hr forecast of

BMRC, ECPC/SFM6, and JMA, the first 24-hr forecast of UKMO and
NCEP.
3. First results of GCM inter-comparisons
Figure 1 shows all-sites mean bias error (MBE) and root meansquare error (RMSE) in monthly-mean values. The symbol µ denotes
the measured monthly-mean values.
Radiation budget. Downward shortwave radiation (SWD) wasover-predicted by all of the models. Downward longwave radiation(LWD) was under-predicted by NCEP, JMA and ECPC but reasonably
predicted by UKMO (MBE ~ 0). The latter was based on the Edwards
and Slingo (1996) scheme. Compensation of errors in SWD and
LWD results in smaller biases in SWD+LWD. Errors in net radiation
are similar to that in SWD+LWD. The SWD over-estimation and LWD
under-estimation by GCMs have been found in many early studies(e.g. Cess et al. 1995; Garratt and Prata 1996). This might be due
to radiation schemes' errors and/or due to under-prediction ofcloudiness (Milton and Earnshaw, 2006).

 CEOP Newsletter No.10

Surface energy budget. The RMSE values of surface heat
fluxes (> 20 Wm-2 ) are comparable to the observed heat
fluxes themselves, indicating that surface energy budget was
not well modeled. Based on flux data at seven sites, JMA and
UKMO produced better results, ECPC and BMRC generally
over-predicted H (sensible heat flux) in the summer, and NCEP
generally over-predicted lE (latent heat flux). The accuracy ofnear-surface variables can be directly related to the modelingaccuracy of surface energy budget. Figure 2 is an example,
showing that over-prediction of H and under-prediction of lE
results in warm and dry biases at the LBA/Santarem site during
Oct-Dec. In NCEP, a strong coupling between E (evaporation)
and P (precipitation) was found, and thus the model's overprediction of precipitation (see Fig.1 (a1)) may be related toits over-prediction of E. NCEP has updated its LSM since May
2005 and improved their prediction of precipitation (Mitchell etFigure 1: All-site mean bias error (a1, b1) and root mean square error (a2, b2) in monthly-mean al. 2005), which supports our results.
values. SWD-Downward Shortwave Radiation, LWD-Downward Longwave Radiation, Rn-Net
Diurnal cycle of precipitation. The diurnal cycle of rainfall
Radiation, H-Sensible heat flux, lE-Latent heat flux.
from CEOP in-situ data is comparable to reports of earlystudies (not shown). Figure 3 shows the observed and modeled
diurnal cycle of precipitation intensity that was normalized bymean precipitation amount at each site and then averaged
over 16 sites. All the models generally produced an afternoon
peak, but its intensity is different among models. No model
was able to reproduce the minimum in the early evening(around 18 LST). Using a method (Goldenburg et al. 1990) to
separate convective and stratiform precipitation, we found thatthe observed minimum was caused by a rapid decrease of
convective rainfall in the early evening and start of stratiform
precipitation afterwards (not shown). JMA, NCEP, and UKMO
yielded the diurnal cycle better than other models. There is noremarkable difference in precipitation diurnal cycle between
forecast ('F') and analysis ('A') for both UKMO and BMRC,
Figure 2: Observed and modeled monthly mean values (a and b) and monthly mean diurnal
implying that the diurnal cycle is mainly determined by thevariation (c and d) at the LBA-Santarem site for CEOP/EOP3. NCEP data are not available during
nature of each model rather than the initial conditions. The
Oct-Nov 2002.
diurnal cycle in ECPC/SFM is sensitive to spinup period (seethe comparisons between 6-hr and 36-hr forecasts).
4. GLDAS Evaluation
GLDAS embeds three land models: Mosaic, Noah LSM, andCLM. In general, Noah LSM and CLM simulated Tsfc better
than GCMs and Mosaic. Mosaic yielded obvious cold biases fornighttime (not shown). However, the three LSMs produced quite
different heat fluxes, as shown in Figure 4 for GAPP/Bondvilleand BALTEX/ Cabauw. Both model ~ observation and model ~model differences are large for the summer season.
5. Recommendation for Phase 2 activities
This study is our first step to evaluate the prediction skillof CEOP-participating models and it provides some clues forfurther identification of model deficiencies. The second stepshould focus on inter-comparisons and improvements ofphysical schemes, for which NWP centers and data analysis
Figure 3: Composite diurnal cycle of precipitation intensity of observations and model output
groups have to collaborate. It is crucial to develop an inter(F-forecast, A-analysis) at 16 sites in rainy seasons (three months with maximum precipitation
amount). Only inland data except CAMP/Tongyu site were used for ECPC.
comparison platform, for setting priority of target processes,

CEOP Newsletter No.10 providing offline source code of each scheme and auxiliary
input data, collecting benchmark data set, and implementing
comparisons.
References
Bosilovich, M.G., 2005: CEOP Newsletter 7, 4-5.
Cess, R.D. et al., 1995: Science, 267(5197), 496-499.
Chou, S. et al., 2006: CEOP special issue of JMSJ, submitted.
Garratt, J.R. and A.J. Prata, 1996: J. Climate, 9, 646-655.
Goldenburg et al., 1990: JMSJ, 68, 37 - 63.
Hirai, M. et al., 2006: CEOP special issue of JMSJ, submitted.
Kato, H. et al., 2006: CEOP special issue of JMSJ, submitted.
Mitchell, K. et al., 2005: GEWEX Newsletter, Nov. 2005, 8-9.
Meinke, I. et al., 2006: CEOP special issue of JMSJ, submitted
Milton, S. and P. Earnshaw, 2006: CEOP special issue of JMSJ,
submitted.
Rikus, L., 2006: CEOP special issue of JMSJ, submitted.
Ruane, A., and J. Roads, 2006: CEOP special issue of JMSJ,
submitted.
Yang, K. et al., 2006: CEOP special issue of JMSJ, submitted.

Figure 4: Comparison of surface heat fluxes between observation and three GLDAS
products (Mosaic, CLM, Noah) at Cabauw (a1, a2) and Bondville (b1, b2).

GLDAS Output Supports CEOP Studies
Matt Rodell1 and Hiroko Kato1,2
1
Hydrological Sciences Branch, NASA Goddard Space Flight Center
2
Earth System Science Interdisciplinary Center, University of Maryland

The Global Land Data Assimilation System (GLDAS; Rodell et al.,2004) has entered a new phase, led by scientists in the Hydrological
Sciences Branch at NASA's Goddard Space Flight Center. NASA's
Energy and Water Cycle Study (NEWS) program now supports the
project, and NASA's Land Information System (LIS; Kumar et al.,2006) has improved the software infrastructure. In this phase we
seek to integrate the best water and energy cycle observations
LSM

Resolution

Grids

MOLTS

Noah

0.25





Noah

0.25





Noah

1.0



CLM2

1.0



Mosaic1.0



Options
MODIS snow cover assimilation

Table 1. GLDAS output datasets currently available through the CEOP archive,
including global grids and model location time series (MOLTS).

as data for parameterizing, forcing, constraining, and evaluating
sophisticated land surface models (LSMs). Advanced techniques,
including relatively mature data assimilation schemes, are being
incorporated and tested. These will augment the ability of GLDAS tosynthesize data from multiple ground and space based observation
systems in a physically coherent manner.
During the period of the project (2005-10), we will develop many
output datasets and make them available to the CEOP community.
These will include results from multiple LSMs, which have been run
at different resolutions (typically 1.0° and 0.25°) using different
parameters, forcings, and assimilation options. Table 1 lists the
GLDAS output datasets that have been delivered to the CEOP
archive to date for EOP1-4. Each simulation was parameterized
and forced using a standard set of inputs, including meteorologicalanalyses from NOAA's Global Data Assimilation System (GDAS) andobservation-based precipitation and solar radiation fields. The output

Figure 1. Rate of evapotranspiration [mm/day] on 1 May 2006 from the 0.25° GLDAS/Noah snow assimilation simulation.

 CEOP Newsletter No.10

variables provided include soil moisture in multiple layers, snow
water equivalent (SWE), surface and air temperatures, near surface
specific humidity, and all the land surface water and energy fluxes(see the example in Figure 1). GLDAS output has already supported
many CEOP related investigations, including several to be describedin the upcoming special issue of the Journal of the MeteorologicalSociety of Japan.
The newest GLDAS output dataset is from a 0.25° simulationof the Noah LSM, which featured assimilation of snow cover data
derived from the Moderate Resolution Imaging Spectroradiometer(MODIS) aboard NASA's Terra satellite. Daily MODIS snow covermaps were upscaled to 0.25°, quality controlled, and then imposedon the modeled SWE using the procedure developed by Rodell andHouser (2004). Preliminary evaluation indicates that assimilationof MODIS snow cover into GLDAS/Noah improves modeled SWE.
Based on snow depth observations from the US National Weather
Service Cooperative Observer Program (Co-op), from which SWE
was estimated using a constant ratio of 10:1, GLDAS/Noah tended

to underestimate SWE in the control simulation. The assimilation
scheme often was able to increase modeled SWE towards measured
SWE (Figure 2). The scheme only has a direct effect where snow
cover is partial or ephemeral, i.e., at midlatitudes and particularly
at the start and end of the winter. However, it indirectly affects
subsequent snowfall accumulation and, via snow's control of albedo,
the surface energy balance.
References
Kumar, S. V., C. D. Peters-Lidard, Y. Tian, P. R. Houser, J. Geiger, S. Olden, L.
Lighty, J. L. Eastman, B. Doty, P. Dirmeyer, J. Adams, K. Mitchell, E. F. Wood, andJ. Sheffield, Land Information System - An interoperable framework for highresolution land surface modeling, Environ. Modelling and Software, in press,
2006.
Rodell, M., and P. R. Houser, Updating a land surface model with MODIS
derived snow cover, J. Hydromet., 5 (6), 1064-1075, 2004.
Rodell, M., P. R. Houser, U. Jambor, J. Gottschalck, K. Mitchell, C.-J. Meng,
K. Arsenault, B. Cosgrove, J. Radakovich, M. Bosilovich, J. K. Entin, J. P. Walker,
D. Lohmann, and D. Toll, The Global Land Data Assimilation System, Bull. Amer.
Meteor. Soc., 85(3), 381-394, 2004.

Figure 2. Time series of snow water equivalent [mm], averaged over the Midwestern United States, from Co-op ground based observations(black) and GLDAS/Noah control (light blue) and assimilation run (dark blue) outputs.

Comparing the Diurnal Behaviour of Model Output with In-Situ Data in the MDB
Lawrie Rikus, Bureau of Meteorology Research Centre, Melbourne, Victoria, Australia

The Australian Bureau of Meteorology is one of the operational
NWP centres that has contributed EOP3 MOLTS data to CEOP. The
data were generated by running forecasts based on the archived
six hourly operational analysis files to obtain the intermediate hourly
output which was not saved at the time and by saving the values of
the requested fields at the model grid columns closest to each of
the 41 designated CEOP reference sites. The model version which
was operational during EOP3 (Seaman et al 1995) was a spectral
T239L29 model which corresponds to a horizontal grid spacing ofabout 80km with 29 vertical levels.
The Murray-Darling Basin (MDB) dataset comprises data from10 stations which spans an area equivalent to about 32 model grid
points and therefore provides an opportunity to investigate the local
spatial variability of the fields near the model MOLTS point. Figure 1
shows the MDB region giving the locations of the stations from the
CEOP in-situ surface data set along with the adjacent model grid
points. Note that the MOLTS data is only written out for the heavily
emphasised grid point centred just south of Kyeamba.
One of the primary foci of the next phase of CEOP is the study

Figure 1: The Murray Darling Basin with the locations of the in-situ data stations andthe adjacent model grid point centres marked. The model's MOLTS point is the heavy
cross just south of Kyeamba.

of diurnal cycles. Figure 2 shows the mean diurnal variation of
screen level temperature and specific humidity and 10m wind speed
obtained by sorting the series into daily time bins representing thediurnal cycle. This assumes that the non-diurnal behaviour is random

CEOP Newsletter No.10 and that the diurnal behaviour itself is invariant over the total timeof the series; an assumption that is certainly not correct for annual
series of variables where seasons play an important role. The binningtechnique is also sensitive to the presence of model spin-up andtemporal artefacts due to the use of constant radiation fields over the
3 hour radiation time step. The latter introduces a lag in the radiation
which is responsible for the lag in phase of the temperature. The
model largely misses the diurnal structure of the screen levelspecific humidity but (apart from the obvious discontinuities) gives afair representation of the variation in wind speed.
Currently a new model is undergoing development and testingat BMRC. It will incorporate a more comprehensive land surface
scheme with four soil levels, prognostic cloud and a more
efficient radiative transfer scheme. A new assimilation scheme
is also undergoing testing and the combination is expected to go
operational at the Bureau later this year. It is planned to repeat

EOP3 with the new system and to continue the cycle up to thepresent. The MOLTS output has been modified to include all physicalprocess tendencies as well as some extra variables to aid in budget
calculations. The 4 grid points surrounding each model MOLTS point
will also be archived as well as gridded data (albeit at a coarser
temporal resolution). The combination of hourly MOLTS and gridded
data together with the upgraded physics and extra variables should
provide a useful testbed for the comparison of model and in-situ data.
References
Siriwardena, L., Chiew, F., Richter, H. and Western, A., 2003: Preparationof a climate data set for the Murrumbidgee River Catchment for land surface
modelling experiments. Cooperative Research Centre for Catchment Hydrology
working document 03/1, Cooperative Research Centre for Catchment Hydrology,
Clayton, Victoria, Australia.
Seaman, R., Bourke, W., Steinle, P., Hart, T., Embery, G. and Naughton,M., 1995: Evolution of the Bureau of Meteorology's Global Assimilation andPrediction system. Part 1: Analysis and initialisation. Aust. Met. Mag. 44, 19-40.

Figure 2: The diurnal variation (relative to each mean) of screen level temperature (left) and specific humidity (centre) and 10m wind speed (right). The different MDB sites areplotted in grey with Kyeamba in black. The mean MDB results are in red and the model is green (the analysis cycle) and blue (the 12-36 hour forecast cycle).

Bulletin of the American Meteorological Society (BAMS), Volume 87, No. 7, July 2006
publishes the article by Chahine et al.: “AIRS: Observing the Global Atmosphere in 3-D”
presenting results from AIRS (Atmospheric Infrared Sounder) during its first years ofoperation.
In September 2002 the Atmospheric Infrared Sounder (AIRS)
instrument on NASA's Aqua spacecraft began providing a detailed,global, three-dimensional record of atmospheric conditions.
The image shows an AIRS snapshot of water vapor as a storm
approaches the California coast on January 1, 2003. A broad
minimum over the deserts of Baja California separates moist areasin the temperate north and tropical south. Blue denotes low water
vapor content; green medium and red areas signify high water
vapor content. The vertical grid ranges from 250 millibar pressure
at the top to 1000 millibar pressure at the bottom. This is one
scene of an animation available at http://svs.gsfc.nasa.gov/
search/Instrument/AIRS.html, and was created by the Scientific
Visualization Studio at NASA/Goddard Space Flight Center.
The AIRS data are also contributed to the CEOP integrated
database.

 CEOP Newsletter No.10

The Sixth CEOP International Implementation/Science Planning Meeting will be held in
Washington, D.C., USA, from Monday 12 – Wednesday 14 March 2007.
The Third IGWCO Workshop will follow up the CEOP event from 14 – 16 March with ajoint CEOP/IGWCO session on Wednesday 14 March 2007.
The Fifth International Implementation/Science Planning meeting for the CoordinatedEnhanced Observing Period (CEOP), 26-28 February, 2006, Paris, France
Sam Benedict, CEOP International Coordinator, Sand Diego, California, USA
Petra Koudelova, CEOP International Coordinator, University of Tokyo, JapanThe fifth international implementation/science planning meetingfor CEOP was held at the UNESCO facilities in Paris, France, 26-28
February. The agenda and all of the presentation material presented
at the meeting can be found through the CEOP Home Page at: http://
www.ceop.net. The second Integrated Global Observing Strategy
Partners (IGOS-P) Integrated Global Water Cycle Observations Theme(IGWCO) Workshop and Business meeting was held at the same venue
just following the CEOP meeting to allow maximum participation in both
events by the science community and relevant agency representatives.
The meeting included more than 100 scientists and representatives of
relevant agencies and organizations from 16 Countries.
The meeting was hosted by the World Climate Research
Programme (WCRP) Coordinated Observation and Prediction of the
Earth System (COPES) strategy, support unit located in Paris, France.
Connections to the 10-year Implementation Plan for the Global Earth
Observation System of Systems (GEOSS) were also highlighted at themeeting. CEOP will contribute to both COPES and GEOSS.
The participants addressed several important issues including(a) endorsement of a concept for finalizing the CEOP Phase 2
Implementation/Science Plan; (b) ideas for maximizing the scienceand technology benefits from both CEOP and IGWCO; and (c) specificthoughts related to the framework for oversight of the science,
implementation plans and results during the initial phase of IGWCO
and CEOP Phase 2.
In this context, the main themes of the CEOP Meetingwere assessments and plans. Two types of assessments wereaddressed, (i) An assessment that dealt with the degree to which
the commitments made by agencies and organizations to CEOP,
such as for the provision of coordinated in-situ, satellite and model
data; had been fulfilled, and (ii) An assessment that focused on the

degree to which CEOP has been able to apply the resources it has
been provided to meet its observational and science goals up to thepresent. To achieve these goals at the meeting over forty technicalpapers and a corresponding number of posters were presented.
In keeping with the intent of the Joint Scientific Committee (JSC) to
WCRP recommendations, each step identified in the CEOP planning
process will have to include specific implementation strategies thatwill ensure close and effective connections to other national andinternational activities concerned with research of the Earth’s waterand energy cycle including, especially the core projects of WCRP.
Of particular importance was the joint session of the CEOP
Science and Advisory and Oversight Committees. The outcomeof this session, which was Co-Chaired by Drs Akimasa Sumi
(JAXA) Jack Kaye (NASA), was an articulation of the highest
priority issues CEOP must address as it moves forward. The main
recommendations/actions included:(i) CEOP must take extra effort to underwrite its current levelof success in development of its in-situ, model, and satellite
datasets.
(ii) In the context of its data collection process CEOP must
continue its relationship with data archive Centers at the
Max-Planck Institute (Hamburg, Germany); National Center
for Atmospheric Research (Colorado, USA) and University ofTokyo (Tokyo, Japan).
(iii) Expansion of the scientific scope of CEOP seems reasonable
and constitutes a natural development. Such expansion
may require additional data types as reflected in the CEOP
Phase 2 Implementation/Science Plan, but should be viewed
as a process that can not be reconciled with work being
undertaken by other international initiatives.

The NCAR CEOP Data Management and Reference Site Data Archive web pages have been moved toanother location. The new URL is: http://www.eol.ucar.edu/projects/ceop/dm/

Newsletter Editorial Board: Robert SCHIFFER, Carlos R. MECHOSO, Paul D. TRY, Einar-Arne HERLAND,
Michael MANTON

Contact Address of International Coordination Body:
Sam BENEDICT (International Coordinator)
742 “H” Avenue
Coronado, CA, 92118, USA
Phone: +1-619-437-7904
Fax: +1-619-437-6814
E-mail: sam.benedict@gewex.org

Petra KOUDELOVA, Akiko GODA
Department of Civil Engineering, The University of Tokyo
7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656, Japan
Phone: +81-3-5841-6132
Fax: +81-3-5841-6130
E-mail: ceop@monsoon.t.u-tokyo.ac.jp

Supported by: Japan Aerospace Exploration Agency (JAXA), Japan Science and Technology Agency (JST)