Multi-model Analysis For CEOP (MAC): A model data product supporting CEOP
science objectives and the GEWEX Roadmap
Michael G. Bosilovich, David M. Mocko [1]John Roads[2], Lawrie Rikus[3]Other authors listed here
All model contributors and any contributors to the plan will be added with concurrence,
including reviews/contributions from GRP and GMPP

A plan to synthesize the gridded model contributions to the CEOP data archives
to facilitate their use in GEWEX science and operational objectives.

[1] NASA Goddard Space Flight Center – Global Modeling and Assimilation Office (GMAO)

1. Background
Early in the formulation of the Coordinated Enhanced Observing Period (CEOP), the need for model data
to support science objectives became apparent. Additionally, the observations being developed for CEOP
would be very useful to the validation of model analyses and forecasts. Invitations were sent to the majorinternational Numerical Weather Prediction and data assimilation Centers (NWPCs). Ten centers
responded favorably, and by the end of 2007, seven centers provided 27 months’ worth of data for the
CEOP EOP-3 and -4 (Oct 2002 – Dec 2004). Two separate model contributions from ECPC gave a totalof 8 (eight) analyses. NASA/GSFC GMAO’s GEOS-5 reanalysis data is currently under development,
and is not included within these 8 analyses. The EOP-3/4 period is an excellent timeframe for testing themodel data, as tremendous amounts of global remotely-sensed data are also available for verification.
Comparing the analyses from the NWPCs has primarily been through the single-point Model Output
Location Time Series (MOLTS) co-located with CEOP reference sites (Yang et al. 2007; Chou et al.
2007; Rikus 2007 and Bosilovich et al. 2007). To get at the comparison of global grids, an ensemble of
the analyses is proposed. This ensemble serves several purposes. First, the variance of the analyses canprovide a measure of uncertainty in analyses. It also provides a range of the state-of-the-art analyses.
Second, this ensemble may make a better benchmark for comparing individual analyses than simply
differencing one against another. The veracity of the ensemble can be tested against global independent
observations (e.g., GPCP, ISCCP, SRB, etc.). Lastly, it is hoped that these datasets will demonstrate thebenefit of such a multi-model analysis for global atmospheric data assimilation systems, for future longerterm studies. The GSWP-2 multi-model analysis provides encouraging results showing that the ensemble
average can be a high-quality data product (Dirmeyer et al, 2006).
The purpose of this paper is to outline the methods and procedure for developing a Multi-model Analysis
for CEOP (MAC). Input from the GEWEX community (GMPP, GRP, and others) is crucial to develop adataset useful beyond the interest of the modeling groups. This paper will contain descriptions of theregridding method, common grid, variables to be ensembled, and any special considerations. An
accompanying spreadsheet (see Appendix) will provide additional details of the CEOP MAC data. This
document contains the description of the first version of the MAC. Subsequent versions may include
different variables, methods or new data.

2. Data
The data contributed to CEOP consists of analysis and forecast cycles. Few requirements were placed on
the contributing NWPCs; thus, the length of the forecasts, frequency of output, variables and units wereleft to the individual center (see Tables 1 and 2 in the Appendix). While the archive of data is perfectly
useable, these issues need careful consideration for users intending on applying these data to a validation
exercise or scientific evaluation.
The CEOP MAC data will synthesize a subset of high-priority variables (see Appendix), ensure that units
are consistent, and grids are made common. Each NWPC’s system has its own topography andland/water mask. All centers did not provide a mask and topography for their system, so these will notfactor into this initial pilot study of ensembling analyses. Also, the dataset does not synthesize data
beyond the first 6-hourly forecast period available past the analysis time. Most of the centers do notprovide data beyond the 6-hour forecast, and thus the number of members drops at longer forecast times.
However, CPTEC data is only available daily beyond a 12-hour forecast; thus, the daily CPTEC data isfrom a 12-hour to 36-hour forecast. Also, MSC data is only provided from a single daily run at 12Z; thus,
the daily data is from 0-hour to 24-hour forecast. The 6-hourly analysis and forecast cycles will beaveraged into monthly and daily averages, as these will be well-posed frequencies in the individual timeseries.
The centers that have provided global gridded analyses to the project are:
•
•
•
•
•
•
•

Bureau of Meteorology Research Centre (BMRC)
Centro de Previsão de Tempo e Estudos Climáticos (CPTEC, The Center for Weather Forecasts
and Climate Studies)
Experimental Climate Prediction Center (ECPC)Japan Meteorological Agency (JMA)
Meteorological Services of Canada (MSC)
National Centers for Environmental Prediction (NCEP)
United Kingdom Meteorological Office (UKMO)

The raw model data from the NWPCs participating in CEOP is stored (in Grib1 format) and archived by
the Model & Data group at the Max Planck Institute (MPI) for Meteorology in Hamburg, Germany. Only
those data files containing the subset of high-priority variables and the minimum-available forecast times
described above were downloaded to a workstation at NASA/GSFC GMAO. Because these files
typically contained additional variables and forecasts beyond what was desired, often at very highresolution, the amount of data downloaded to data has to date exceeded 1.13 Tb. A significant portion ofthis data was transferred over a high-speed optical network that was configured during the development of
the CEOP MAC. This optical path used end-to-end communication over the University of IllinoisChicago's TeraFlow Testbed network, which included support from NASA/GSFC’s Software Integration
and Visualization Office.

3. Methods and Plan
The procedure for generating the Multi-model Analysis for CEOP (MAC) is as follows:a) Generate a 6-hourly dataset for all centers, using consistent units and time-framesb) Regrid the 6-hourly data from each center to a common grid (1.25 deg. by 1.25 deg.)c) Create an ensemble mean of the 6-hourly data; also create a 6-hourly standard deviation (Figure 1)d) Create daily-averages and monthly-averages from the 6-hourly ensemble mean (Figures 2 and 3)e) Create daily- and monthly-averages of the individual centers (Figures 2 and 3)f) Create daily and monthly standard deviations between the individual centers averagesg) Write the re-gridded data for all centers, the mean, and the standard deviation at the 6-hourly,
daily, and monthly times in the final formats of NetCDF and grib1 with consistent units andvariable names.
The steps listed above provide a broad outline of the procedure. Many decisions and problems needed to
be overcome along the way, and are discussed in further detail below.

a. 6-hourly dataset (Step 1)
For each NWPC dataset, a Grib table was used to identify and locate the subset of high-priority variables
listed in the Appendix. The minimum forecast time available for each variable of the center was thenpulled from the raw model data using “wgrib”. The minimum forecast time available typically was theanalysis (0-hour forecast) for the instantaneous variables, and the 0-6 hourly forecast for the
average/accumulation (ave/acc) variables. Some major exceptions include the CPTEC data, the MSC
data, and some variables from the ECPC data. The CPTEC data at 00Z is a 12-hourly forecast, at 06Z an
18-hr forecast, at 12Z a 24-hr forecast, and at 18Z a 36-hr forecast. Similarly, the ave/acc variables from
00Z to 06Z are a 12-18 hourly forecast, and so on. The MSC instantaneous surface variable data at 12Z isan analysis/0-hr forecast, at 18Z a 6-hr forecast, at 00Z a 12-hr forecast, and at 06Z an 18-hr forecast. The
upper air data, however, was not available at 06Z and 18Z; the 12Z data is an analysis/0-hr forecast andthe 00Z data is a 12-hr forecast. The MSC ave/acc variables from 12Z to 18Z are a 0-6 hourly forecast,and so on. Several ECPC RII and SFM instantaneous surface variables are a 6-hr forecast rather than an
analysis/0-hr forecast. Further details, including descriptions of any missing times and variables, areavailable in the spreadsheet and the Appendix (see Tables 1 and 2).

b. Regridding (Step 2)
In order to produce an ensemble, a common grid must be defined. Since most operational analyses arenear or going to ~100km spatial scales, a grid on the order of 1 degrees latitude and longitude wasdesirable. Also, many data products (GPCP and the reanalyses data) use a regular latitude-longitude
coarse grid (2.5 degrees). Thus, a regular latitude-longitude grid that is near the spatial scale of theobservational analyses, but also can be related easily to the reanalyses coarse grid, was chosen. The
resolution is 1.25º longitude by 1.25º latitude (288×144 gridpoints), with the 1,1 center point located at
179.375W, 89.375S.
The native grid from each of the NWPCs supplied to CEOP was interpolated to the common grid using
the OpenGrADS re() command (http://opengrads.org/). In the cases where the native grid is finer than
1.25ºx1.25º, box averaging was used. In the cases where the native grid is coarser than 1.25ºx1.25º,bilinear interpolation was be used. No other filtering or screening of the gridded data was applied (except
for some below-ground heights – details in Sections 3c-f). At the end of this step, the data from each
NWPC was on the common grid at a 6-hourly timestep, with common variable names and units. A list of
the available variables for each center can be found in Table 1.

c. Ensemble Average (Step 3)
The ensemble average is the straight average of all of the available variables from each NWPC at each 6hourly timestep. As all centers did not provide all variables, the ensemble averaging was done with thosecenters that did provide the given variable. If any data was missing from one or more of the NWPCs at agiven time, the ensemble average was the average of the remaining data available. For the upper-air data
at 850hPa and 700hPa, a masking to the MAC ensemble was applied for areas where the surface pressure
at the given time was less than the pressure of the level (less than 850hPa or than 700hPa). This masking
was also performed for the BMRC 6-hourly data, but not for the other individual NWPCs. The flowchartdecisions used for each variable during the creation of the MAC ensemble at each of the 3292 6-hourly
times is shown in Figure 1.

Figure 1: Flowchart of the creation of the 6-hourly ensemble mean and standard deviation.

The individual center’s regridded variable is also provided with the MAC, so that it will be apparent whendata is included in the ensemble average or not. Also, a separate dataset is provided that enumerates thenumber of ensemble members for each variable for each time. Similarly, the standard deviation at each 6hourly timestep was computed from the available data that made up the ensemble average. The ensemble
mean and standard deviation are provided as separate datasets on the same grid and same format as theindividual NWPCs described at the end of Section 3b (Step 2).

d-f. Daily- and Monthly-Averages (Steps 4, 5, and 6)
The daily average of the ensemble mean was the simple average of the 00Z, 06Z, 12Z, and 18Z data on
the given date. For the individual NWPCs, the daily average was the same, except that if an individual
variable was missing or unavailable for at least one time during the date, that variable was considered to
be undefined for that center on that day. The one exception to this is the MSC upper-air data, which was
only available at 00Z and 12Z, and the daily average is just the average of these two times. Also, for eachdataset, if at least one of the four times of the day had a point masked out because the surface pressure
was less than the pressure of the upper-air level, then that point was also masked out for the entire day.
The flowchart for the daily averages is shown in Figure 2. The daily standard deviation was thencalculated between the centers that had valid daily averages for each variable. Note that the daily
ensemble mean may include more data/centers than the daily ensemble standard deviation. An exampleof this is to suppose the 500 hPa heights was missing for 12Z only for one center. The 6-hourly ensemblemeans will include the 00Z, 06Z, and 18Z times for this center, and thus the daily ensemble mean willproportionally include this data. However, the daily mean for this center/variable will be considered
undefined, and will not be included in the daily ensemble mean.
The monthly average of the ensemble mean was the simple average of all times in the month. For theindividual NWPCs, the monthly average was calculated differently. First, all the 00Z times during themonth were averaged, then the 06Z times, the 12Z times, and then the 18Z times. Next, these four times
were summed and divided by four (4). This method was done to minimize the effect of an individual
missing time on the monthly average. For example, if a 06Z time was missing for a variable such as
downward surface radiation on a single date, this missing time would have a noticeable effect on themonthly average. If the similar times were averaged first, this problem is reduced, but does give a little
extra weight to the other dates where the variable was available. No more than 6 times during the month
were allowed to be undefined (out of a typical 120 or 124 6-hourly periods). If more than 6 times were
undefined, the variable for that month was undefined. Similarly, if a given point had more than 6 times
masked because the surface pressure was less than the pressure of the upper-air level, the point was also
masked. The exceptions to this were for the UKMO data (numerous missing times, see Appendix), for
the CPTEC data (only for May 2003, due to missing data), and for the MSC data (only 00Z and 12Z data
available). The flowchart for the monthly averages is shown in Figure 3. The monthly-average standard
deviation was then calculated between the individual centers’ monthly averages. Again, because of thedifferent methods of the monthly-average calculations, the monthly-average standard deviation will not be
exactly centered about the ensemble mean monthly-average.

Figure 2: Flowchart of the creation of the daily average for ensemble mean and individual models.

Figure 3: Flowchart of the creation of the monthly average for ensemble mean and individual models.

g. Write the gridded data out for the MAC (Step 7)
Data were written to binary output, then were converted to the NetCDF and Grib1 formats (using the
public GrADS script LATS4d) for release to the contributors and community. The resulting binary (orNetCDF) output size is roughly 284Gb (about 134Gb in Grib1). Each file contains each variable listed inTable 1 (with the common naming convention). Common utilities, ncdump and wgrib, can be used to
identify the vital information needed to access the data. A grib table common to all processed centers andthe MAC is also provided. The data will be sent to the NASA Goddard Data Information Services Center(DISC) and the MPI Model and Data Center (others can be included if interested).

4. Summary and next steps
The global gridded analyses contributed to CEOP have been modified to fit a common framework tofacilitate comparisons among the analyses and make the access to the data easier for science efforts. The
eventual goal is to quantify the uncertainty among operational analyses. Preliminary results andevaluation of the MAC ensemble have been favorable, so the data should be returned to the contributing
centers, and made available to the scientific community. These results were presented at the AMS annual
meeting (http://ams.confex.com/ams/88Annual/techprogram/paper_131182.htm) and the WCRP 3rd
International Reanalysis Conference (http://jra.kishou.go.jp/3rac_en.html).
Two major tasks are nearing completion. First, make the data accessible to the contributing centers and
CEOP science efforts. Possibly, some review of the data and results could be available by the annual
CEOP meeting in September 2008. Second, we would like to invite the data contributors to collaborate
on an overview paper of the data set. The eventual manuscript would go to peer review, but perhaps amore broad distribution, for example, Bulletin of the AMS. Lastly, the documentation on each of thesystem is quite variable. It would be useful to researchers/users to have some basic information andcitations for the systems’ model and data assimilation methods.
Acknowledgments
We would like to express sincerest appreciation for the numerical prediction centers contributing time andmodel data to the CEOP archives, without which this study could not have been easily accomplished. In
addition, MPI Model and Data Center’s effort in hosting and serving the data provided a critical resource.
The NASA/GSFC SIVO branch is thanked for the development of a high-speed optical path for quicker
downloads between GSFC and MPI. The effort to compile the CEOP model data was funded by theNASA Model, Analysis, and Prediction program.

5. Appendix 1: Summary of available model data used in the MAC
Centers
Description
Surface Pressure
Mean Sea Level Pressure
Surface Air Temperature
Surface Skin Temperature
Surface Air Moisture
Surface Eastward Wind
Surface Northward Wind
Precipitation
Convective Precipitation
Surface Runoff
Liquid equivalent snow depth
Latent Heat Flux
Sensible Heat Flux
Surface Incoming Shortwave
Surface Incoming Longwave
Surface Reflected Shortwave
Surface Outgoing Longwave
TOA Longwave Outgoing
TOA Shortwave Incoming
TOA Shortwave Outgoing
Total Cloud CoverTotal Column Water Vapor
Total Column Condensed Water
Q850
T850
U850
V850
H850
Q700
T700
U700
V700
H700
Q500
T500
U500
V500
H500
Q300
T300
U300
V300
H300
Q200
T200
U200
V200
H200

Units
Pa
Pa
K
K
kg kg-1
m s-1-1ms
kg m-2 s-1
kg m-2 s-1
kg m-2
-2
kg m
-2
Wm
W m-2
W m-2
-2
Wm
W m-2
W m-2
W m-2
-2
Wm
W m-2
(0-1)
kg m-2kg m-2
-1
kg kg
K
m s-1
m s-1
m
kg kg-1
K
m s-1
m s-1
m
kg kg-1
K
-1
ms
m s-1
m
kg kg-1
K
-1
ms
m s-1
m
kg kg-1
K
-1
ms
m s-1
m

BMRC
CPTEC
SURFPsfc
PRESsfc
MSLPsfc
TMP2m
TMP2m
SURFTsfc SURFTsfc
SPFH2m
RH2m
UGRD10m UGRD10m
VGRD10m VGRD10m
APCPsfc
APCPsfc
WATRsfc
SNODsfc
LHTFLsfc
SHTFLsfc
DSWRFsfc
DLWRFsfc
USWRFsfc
ULWRFsfc

LHTFLsfc
SHTFLsfc
DSWRFsfc
DLWRFsfc
USWRFsfc
ULWRFsfc
ULWRFtoa

USWRFtoa
TCDCclm
PWATclm PWATclm
SPFHprs
TMPprsUGRDprs
VGRDprs
HGTprsSPFHprs
TMPprsUGRDprs
VGRDprs
HGTprsSPFHprs
TMPprsUGRDprs
VGRDprs
HGTprsSPFHprs
TMPprsUGRDprs
VGRDprs
HGTprsSPFHprs
TMPprsUGRDprs
VGRDprs
HGTprsSPFHprs
TMPprsUGRDprs
VGRDprs
HGTprsSPFHprs
TMPprsUGRDprs
VGRDprs
HGTprsSPFHprs
TMPprsUGRDprs
VGRDprs
HGTprsSPFHprs
TMPprsUGRDprs
VGRDprs
HGTprsSPFHprs
TMPprsUGRDprs
VGRDprs
HGTprsECPCRII ECPCSFM
JMA
MSC
NCEP
UKMO
PRESsfc
PRESsfc
PRESsfc
SURFPsfc
PRESsfc
SURFPsfc
PRMSLmsl PRESmsl
PRMSLmsl
TMP2m
TMP2m
TMP2m
TTSUsfc
TMP2m
LOWT2m
TMPsfcTMPsfcSURFTsfc
TMPsfcSURFTsfc
SPFH2mSPFH2mSPFHhbl
HUSUsfc
SPFH2m LOWSH2m
UGRD10m UGRD10m UGRD10m UUSUsfc UGRD10m TENUS10m
VGRD10m VGRD10m VGRD10m VVSUsfc VGRD10m TENVS10m
PRATEsfc PRATEsfc PRATEsfc
PRsfc
PRATEsfc APCPsfc
CPRATsfc CPRATsfc
ACPCPsfc
WATRsfc WATRsfc
N0sfc
WATRsfc WATRsfc
WEASDsfc WEASDsfc
I5sfc
WEASDsfc
LHTFLsfc LHTFLsfc LHTFLsfc
AVsfc
LHTFLsfc LHTFLsfc
SHTFLsfc SHTFLsfc SHTFLsfc
AHsfc
SHTFLsfc SHTFLsfc
DSWRFsfc DSWRFsfc DSWRFsfc
N4sfc
DSWRFsfc TDSWSsfc
DLWRFsfc DLWRFsfc DLWRFsfc
ADsfc
DLWRFsfc TDLWSsfc
USWRFsfc USWRFsfc USWRFsfc N4sfc-ASsfc USWRFsfc TUSWSsfc
ULWRFsfc ULWRFsfc ULWRFsfc ADsfc-AIsfc ULWRFsfc TULWSsfc
ULWRFtoa ULWRFtoa ULWRFtoa
ARsfc
ULWRFtoa TULWTtoa
DSWRFtoa DSWRFtoa DSWRFtoa
ABsfc
TDSWTtoa
USWRFtoa USWRFtoa USWRFtoa
AUsfc
USWRFtoa TUSWTtoa
TCDCclm TCDCclm TCDCsfc
TCDCsfc
TCDCclm TCDCsfc
PWATclm PWATclm
IHsfc
PWATclm
CWATprs
IEsfc
CWATclm
SPFHprs
SPFHprs
SPFHprs
RHprs
RHprs
TMPprsTMPprsTMPprsTMPprsTMPprsTMPprsUGRDprs UGRDprs UGRDprs UGRDprs UGRDprs UGRDprs
VGRDprs VGRDprs VGRDprs VGRDprs VGRDprs VGRDprs
HGTprs
HGTprsGPprs
HGTprs
HGTprsGPprs
SPFHprs
SPFHprs
SPFHprs
RHprs
RHprs
TMPprsTMPprsTMPprsTMPprsTMPprsTMPprsUGRDprs UGRDprs UGRDprs UGRDprs UGRDprs UGRDprs
VGRDprs VGRDprs VGRDprs VGRDprs VGRDprs VGRDprs
HGTprs
HGTprsGPprs
HGTprs
HGTprsGPprs
SPFHprs
SPFHprs
SPFHprs
RHprs
RHprs
TMPprsTMPprsTMPprsTMPprsTMPprsTMPprsUGRDprs UGRDprs UGRDprs UGRDprs UGRDprs UGRDprs
VGRDprs VGRDprs VGRDprs VGRDprs VGRDprs VGRDprs
HGTprs
HGTprsGPprs
HGTprs
HGTprsGPprs
SPFHprs
SPFHprs
SPFHprs
RHprs
RHprs
TMPprsTMPprsTMPprsTMPprsTMPprsTMPprsUGRDprs UGRDprs UGRDprs UGRDprs UGRDprs UGRDprs
VGRDprs VGRDprs VGRDprs VGRDprs VGRDprs VGRDprs
HGTprs
HGTprsGPprs
HGTprs
HGTprsGPprs
SPFHprs
SPFHprs
RHprs
RHprs
TMPprsTMPprsTMPprsTMPprsTMPprsUGRDprs UGRDprs UGRDprs
UGRDprs UGRDprs
VGRDprs VGRDprs VGRDprs
VGRDprs VGRDprs
HGTprs
HGTprsGPprs
HGTprs
GPprs

Table 1: List of variables, descriptions/names, and availability by center of data used in the MAC.

Description
Surface Pressure
Mean Sea Level Pressure
Surface Air Temperature
Surface Skin Temperature
Surface Air Moisture
Surface Eastward Wind
Surface Northward Wind
Precipitation
Convective Precipitation
Surface Runoff
Liquid equivalent snow depth
Latent Heat Flux
Sensible Heat Flux
Surface Incoming Shortwave
Surface Incoming Longwave
Surface Reflected Shortwave
Surface Outgoing Longwave
TOA Longwave Outgoing
TOA Shortwave Incoming
TOA Shortwave Outgoing
Total Cloud CoverTotal Column Water Vapor
Total Column Condensed Water
Q850
T850
U850
V850
H850
Q700
T700
U700
V700
H700
Q500
T500
U500
V500
H500
Q300
T300
U300
V300
H300
Q200
T200
U200
V200
H200

Units
Pa
Pa
K
K
-1
kg kg
-1
ms
-1
ms
-2 -1
kg m s-2 -1
kg m s
-2
kg m
-2
kg m
-2
Wm
-2
Wm
-2
Wm
-2
Wm
-2
Wm
-2
Wm
-2
Wm
-2
Wm
-2
Wm
(0-1)
-2
kg m
-2
kg m
-1
kg kg
K
-1
ms
-1
ms
m
-1
kg kg
K
-1
ms
-1
ms
m
-1
kg kg
K
-1
ms
-1
ms
m
-1
kg kg
K
-1
ms
-1
ms
m
-1
kg kg
K
-1
ms
-1
ms
m

BMRC
analysisanalysisanalysisanalysisanalysisanalysisanalysis0-6hr ave

CPTEC
12hr fcst+

analysis12hr fcst+
12hr fcst+
12hr fcst+

ECPCRII ECPCSFM
6hr fcst
6hr fcst
analysisanalysis
6hr fcst
6hr fcst
6hr fcst
6hr fcst
6hr fcst
6hr fcst
6hr fcst
6hr fcst
6hr fcst
6hr fcst
0-6hr ave
0-6hr ave
0-6hr ave
0-6hr ave
0-6hr ave
0-6hr ave
6hr fcst
6hr fcst
0-6hr ave
0-6hr ave
0-6hr ave
0-6hr ave
0-6hr ave
0-6hr ave
0-6hr ave
0-6hr ave
0-6hr ave
0-6hr ave
0-6hr ave
0-6hr ave
0-6hr ave
0-6hr ave
0-6hr ave
0-6hr ave
0-6hr ave
0-6hr ave
0-1hr ave
0-1hr ave
6hr fcst
6hr fcst

analysisanalysisanalysisanalysisanalysisanalysisanalysisanalysisanalysisanalysisanalysisanalysisanalysisanalysisanalysisanalysisanalysisanalysisanalysisanalysisanalysisanalysisanalysisanalysisanalysis12hr fcst+
12hr fcst+
12hr fcst+
12hr fcst+
12hr fcst+
12hr fcst+
12hr fcst+
12hr fcst+
12hr fcst+
12hr fcst+
12hr fcst+
12hr fcst+
12hr fcst+
12hr fcst+
12hr fcst+
12hr fcst+
12hr fcst+
12hr fcst+
12hr fcst+
12hr fcst+
12hr fcst+
12hr fcst+
12hr fcst+
12hr fcst+
12hr fcst+

analysisanalysisanalysisanalysisanalysisanalysisanalysisanalysisanalysisanalysisanalysisanalysisanalysisanalysisanalysisanalysisanalysisanalysisanalysisanalysisanalysisanalysisanalysisanalysisanalysis12hr fcst+
12hr fcst+
12hr fcst+
12hr fcst+
12hr fcst+
12hr fcst+
12hr fcst+

analysis0-6hr ave
0-6hr ave
0-6hr ave
0-6hr ave
0-6hr ave
0-6hr ave

12hr fcst+
12hr fcst+
12hr fcst+
12hr fcst+
12hr fcst+
12hr fcst+
12hr fcst+

JMA
analysisMSC
anl/6hr fcst+

analysisanl/6hr fcst+ 6hr fcst
anl/6hr fcst+ 6hr fcst
anl/6hr fcst+ 6hr fcst
anl/6hr fcst+ 6hr fcst
anl/6hr fcst+ 6hr fcst
3hr fcst+
0-6hr aveanalysisanalysisanalysis
6hr fcst

0-6hr ave
0-6hr ave
0-6hr ave
0-6hr ave
0-6hr ave
0-6hr ave
0-6hr ave
0-6hr ave
0-6hr aveanalysisanalysisanalysisanalysisanalysisanalysisanalysisanalysisanalysisanalysisanalysisanalysisanalysisanalysisanalysisanalysisanalysisanalysisanalysisanalysisanalysisanalysisanalysisanalysisanalysisanalysisanalysisanalysisanalysisanalysisanalysisanalysisanalysisanalysisanalysisanalysisanalysisanalysisanalysisanalysisanalysisanalysisanalysis

3hr fcst+
anl/6hr fcst+
3hr fcst+
3hr fcst+
3hr fcst+
3hr fcst+
3hr fcst+
3hr fcst+
3hr fcst+
3hr fcst+
3hr fcst+
anl/6hr fcst+
anl/6hr fcst+
anl/6hr fcst+
anl/12hr fcst
anl/12hr fcst
anl/12hr fcst
anl/12hr fcst
anl/12hr fcst
anl/12hr fcst
anl/12hr fcst
anl/12hr fcst
anl/12hr fcst
anl/12hr fcst
anl/12hr fcst
anl/12hr fcst
anl/12hr fcst
anl/12hr fcst
anl/12hr fcst
anl/12hr fcst
anl/12hr fcst
anl/12hr fcst
anl/12hr fcst
anl/12hr fcst

analysisanalysisanalysisanalysisTable 2: Location in the forecast cycle of each NWPC’s variables included in the MAC.

NCEP
analysis0-6hr acc
6hr fcst
0-6hr ave
0-6hr ave
0-6hr ave
0-6hr ave
0-6hr ave
0-6hr ave
0-6hr ave
0-6hr ave
0-6hr aveanalysisanalysisanalysisanalysisanalysisanalysisanalysisanalysisanalysisanalysisanalysisanalysisanalysisanalysisanalysisanalysisanalysisanalysisanalysisanalysisanalysisanalysisanalysisanalysisanalysisanalysisanalysis

UKMO
analysisanalysisanalysisanalysisanalysisanalysisanalysis0-6hr acc
0-6hr acc
0-6hr acc
0-6hr ave
0-6hr ave
0-6hr ave
0-6hr ave
0-6hr ave
0-6hr ave
0-6hr ave
0-6hr ave
0-6hr aveanalysisanalysisanalysisanalysisanalysisanalysisanalysisanalysisanalysisanalysisanalysisanalysisanalysisanalysisanalysisanalysisanalysisanalysisanalysisanalysisanalysisanalysisanalysisanalysisanalysisanalysis

CEOP GRID 6-hourly Datasets
Center Data Status

Dimensions

Experiment:
DatasetKey Variables

X by Y: 288 by 145
Lon/Lat: 1.25 by 1.25
Levels: 22 pressure levels

CEOP_JMA_GRID_RAW:
CEOP_JMA_GRID_1

T, U/V, ω, Φ (22 levels)
Cloud liquid water (12levels)Total cloud cover %(column)
2-m T, 10-m U/V

X by Y: 640 by 320
Lon/Lat: 0.5625 by ~0.5625
Levels: 1 surface level

CEOP_JMA_GRID_RAW:
CEOP_JMA_GRID_2

TOA/surface LW/SW ↑/↓radiation
Sensible/Latent heat flux
Precip/Pressure/Roughness/
TKE
Momentum & Water Vapor
flux

X by Y: 320 by 160
Lon/Lat: 1.125 by ~1.125
Levels: 40 eta levels

CEOP_JMA_GRID_RAW:
CEOP_JMA_GRID_3

T, U/V, P, q (40 levels)
Local time tendency of T
(40 levels)

X by Y: 288 by 145
Lon/Lat: 1.25 by 1.25
Levels: 1 surface level

CEOP_UKMO_GRID_RAW:
CEOP_UKMO_GRID_DA_all_2D

TOA/surface LW/SW ↑/↓radiation
Sensible/Latent heat flux
Precip w/ Convective/Largescale
Pressure/Roughness/PBL
heightTotal/High/Mid/Low cloud
cover %
Convective cloud base/top
Snow fall/depth/melt
Runoff/Soil moisture2-m T/q, 10-m U/V

X by Y: 288 by 145
Lon/Lat: 1.25 by 1.25
Levels: 18 pressure levels

CEOP_UKMO_GRID_RAW:
CEOP_UKMO_GRID_DA_all_3D

T, U/V, RH, Φ (18 levels)
KE/Enthalpy (18 levels)

X by Y: 480 by 240
Lon/Lat: 0.75 by ~0.75
Levels: 15 pressure levels

CEOP_BMRC_GRID
with corrections in:
CEOP_BMRC_GRID_18Z
CEOP_BMRC_GRID_NEWSFCCEOP_BMRC_GRID_SPHUM

T, U/V, ω, Φ (15 levels)
Surface LW/SW ↑/↓radiation
Sensible/Latent heat flux
Precip/Pressure
Precipitable waterSnow depth
Surface albedo
Soil moisture & temp
2-m T/q, 10-m U/V(should be 3292
6-hourly files)

JMA

UKMO

BMRC

No times missing

278 times missing:(by month)
2002: 4, 3, 70
2003: 27, 5, 4, 2, 10,
26, 4, 24, 8, 9, 11, 1
2004: 5, 5, 5, 0, 4, 9,
5, 9, 9, 4, 9, 8

2 times missing:
12Z2003Dec12
18Z2003Dec12(data obtained direct from BMRC)

NCEP

1 time missing:
12Z2003Aug26

X by Y: 360 by 181
Lon/Lat: 1.0 by 1.0
Levels: 26 pressure levels

CEOP_NCEP_GRID_RAW:
NCEP_GR_FORC_06__00H
NCEP_GR_FORC_06__06H
NCEP_GR_FORC_06__12H
NCEP_GR_FORC_06__18H

T, U/V, Φ (26 levels)
Cloud water, RH, ω (21levels)
TOA/surface LW/SW ↑/↓radiation(no TOA SW↓, however)Sensible/Latent/Groundheat flux
Precip/Pressure/PBL height
Total cloud cover %(column)
Precipitable & Cloud water(column)
Snow depth
Surface albedo
Runoff/Soil moisture &temps
2-m T/q, 10-m U/V

ECPC
RII

No times missing

X by Y: 192 by 94
Lon/Lat: 1.875 by ~1.915
Levels: 1 surface level

CEOP_ECPCRII_GRID_RAW:
ECPCRII6_flx_ft00_MUL(analysis)
ECPCRII6_flx_ft03_MUL(00-03 hour forecast average)
ECPCRII6_flx_ft06_MUL(03-06 hour forecast average)

X by Y: 144 by 73
Lon/Lat: 2.5 by 2.5
Levels: 17 pressure levels

CEOP_ECPCRII_GRID_RAW:
ECPCRII6_pgb_ft00_MUL(analysis)

TOA/surface LW/SW ↑/↓radiation
Sensible/Latent/Groundheat flux
Precip w/ Convective
Pressure/Roughness/PBL
heightMomentum flux/Gravity
wave stress
Total/High/Mid/Low cloud
cover %
Cloud base/top/
temperature/CWF
Snow fall/depth/melt
Surface albedo
Runoff/Soil moisture &temps
2-m T/q, 10-m U/VT, U/V, RH/q, ω, Φ, η (17levels)Precipitable water/RH(column)
Lifted index
Max wind level T, U/V,
Pressure
Tropopause T, U/V,
Pressure, Shear

2 times missing:
12Z2003Aug22
12Z2003Aug25

X by Y: 192 by 94
Lon/Lat: 1.875 by ~1.915
Levels: 1 surface level

CEOP_ECPCSFM_GRID_RAW:ECPCSFM6_flx_ft00_MUL(analysis)
ECPCSFM6_flx_ft03_MUL(00-03 hour forecast average)
ECPCSFM6_flx_ft06_MUL(03-06 hour forecast average)

No times missing

X by Y: 144 by 73
Lon/Lat: 2.5 by 2.5
Levels: 17 pressure levels

CEOP_ECPCSFM_GRID_RAW:ECPCSFM6_pgb_ft00_MUL(analysis)

MSC

No times missing

X by Y: 800 by 600
Lon/Lat: 0.45 by 0.3
Levels: 1 surface level &4 upper-air levels(upper-air only 12-hourly)

CEOP_MSC_GRID_RAW:
CEOP_MSC_GRID_RAW_xxxx(xxxx = variable name)

CPTEC

35 times missing:(by month)
2002: 0, 1, 0
2003: 1, 1, 1, 0, 14,
2, 1, 0, 1, 0, 0, 3
2004: 0, 0, 0, 1, 4, 0,
0, 0, 1, 0, 4, 0

X by Y: 384 by 192
Lon/Lat: 0.9375 by ~0.9375
Levels: 15 pressure levels

CEOP_MSC_GRID_RAW:
CPTEC_GR_gposnmc_fct_12_MUL
CPTEC_GR_gposnmc_fct_18_MUL
CPTEC_GR_gposnmc_fct_24_MUL
CPTEC_GR_gposnmc_fct_30_MUL

ECPC
SFM

TOA/surface LW/SW ↑/↓radiation
Sensible/Latent/Groundheat flux
Precip w/ Convective
Pressure/Roughness/PBL
heightMomentum flux/Gravity
wave stress
Total/High/Mid/Low cloud
cover %
Cloud base/top/
temperature
Snow fall/depth/melt
Surface albedo
Runoff/Soil moisture &temps
2-m T/q, 10-m U/VT, U/V, RH/q, ω, Φ, η (17levels)Precipitable water (column)

T, U/V, q, Φ (4 levels)
TOA/surface LW/SW ↑/↓radiation
Sensible/Latent/Groundheat flux
Precip/Pressure
1.5-m T/q, 10-m U/V
Total cloud cover %(column)
Precipitable & Cloud water(column)
Snow depth/SWE
Surface albedo
Runoff/Soil moisture
T, U/V, q, ω, Φ, diverg. (15levels)
TOA/surface LW/SW ↑/↓radiation(no TOA SW↓, however)Sensible/Latent heat flux
Precip/Pressure/Roughness
Runoff/Soil moisture2-m T/RH, 10-m U/V
Total cloud cover %(column)
Precipitable water (column)

JMA
Experiment name at MPI:CEOP_JMA_GRID_RAW

Dataset name at MPI:
Spatial resolution:
Vertical resolution:
Temporal resolution:
Missing data:
Key variables:

CEOP_JMA_GRID_1
288x145 (1.25 lon. X 1.25 lat.)22 pressure levels
6-hourly (3292 total files)
None
T, U/V, ω, Φ (22 levels)
Cloud liquid water (12 levels)
Total cloud cover % (column)
2-m T, 10-m U/V

Dataset name at MPI:
Spatial resolution:
Vertical resolution:
Temporal resolution:
Missing data:
Key variables:

CEOP_JMA_GRID_2
640x320 (0.5625 lon. X ~0.5625 lat.)
1 surface level6-hourly (3292 total files)
None
TOA/surface LW/SW ↑/↓ radiation
Sensible/Latent heat flux
Precipitation/Pressure/Roughness/TKE
Momentum & Water Vapor flux
Land/Sea/Ice Mask, Deep soil T

Dataset name at MPI:
Spatial resolution:
Vertical resolution:
Temporal resolution:
Missing data:
Key variables:

CEOP_JMA_GRID_3
320x160 (1.125 lon. X ~1.125 lat.)
40 eta model levels
6-hourly (3292 total files)
None
T, U/V, P, q (40 levels)
Local time tendency of T (40 levels)

Pull binary data from grib:~/CEOP/JMA/pull_grid1.bash~/CEOP/JMA/pull_grid2.bash~/CEOP/JMA/pull_grid3.bash~/CEOP/JMA/mma_regrid.gs~/CEOP/MAC/daily_average.gs“lats4d” average of 00Z, 06Z, 12Z, and 18Z times~/CEOP/MAC/monthly_average.gs“lats4d” average of 00Z (day 1) to 18Z (last day of month)Re-grid 6-hourly binary:
Daily average creation:
Daily average description:
Monthly average creation:
Monthly average description:

UKMO
Experiment name at MPI:CEOP_UKMO_GRID_RAW

Dataset name at MPI:CEOP_UKMO_GRID_DA_all_2D

Spatial resolution:
Vertical resolution:
Temporal resolution:
Missing data:

Key variables:

Dataset name at MPI:
Spatial resolution:
Vertical resolution:
Temporal resolution:
Missing data:

Key variables:
KE/Enthalpy (18 levels)
Pull binary data from grib:
Re-grid 6-hourly binary:
Daily average creation:
Daily average description:
Monthly average creation:
Monthly average description:

288x145 (1.25 lon. X 1.25 lat.)
1 surface level6-hourly (3014 total files)
278 times missing (by month):
2002: 4, 3, 70
2003: 27, 5, 4, 2, 10, 26, 4, 24, 8, 9, 11, 1
2004: 5, 5, 5, 0, 4, 9, 5, 9, 9, 4, 9, 8
TOA/surface LW/SW ↑/↓ radiation
Sensible/Latent heat flux
Precip w/ Convective/Large-scale
Pressure/Roughness/PBL height
Total/High/Mid/Low cloud cover %Convective cloud base/top
Snow fall/depth/melt
Runoff/Soil moisture2-m T/q, 10-m U/V
CEOP_UKMO_GRID_DA_all_3D
288x145 (1.25 lon. X 1.25 lat.)18 pressure levels
6-hourly (3014 total files)
278 times missing (by month):
2002: 4, 3, 70
2003: 27, 5, 4, 2, 10, 26, 4, 24, 8, 9, 11, 1
2004: 5, 5, 5, 0, 4, 9, 5, 9, 9, 4, 9, 8
T, U/V, RH, Φ (18 levels)~/CEOP/UKMO/pull_da2d.bash~/CEOP/UKMO/pull_da3d.bash~/CEOP/UKMO/mma_regrid.gs~/CEOP/MAC/daily_average.gs“lats4d” average of 00Z, 06Z, 12Z, and 18Z times~/CEOP/MAC/monthly_average.gs“lats4d” average of 00Z (day 1) to 18Z (last day of month)

BMRC
Experiment name:
CEOP_BMRC_GRID(obtained directly from BMRC)
Dataset names:
Spatial resolution:
Vertical resolution:
Temporal resolution:
Missing data:
Key variables:

N/A
480x240 (0.75 lon. X ~0.75 lat.)15 pressure levels & 1 surface level6-hourly (3290 total files)
2 time missing: 12Z2003Dec12 & 18Z2003Dec12
T, U/V, ω, Φ (15 levels)

Surface LW/SW ↑/↓ radiation
Sensible/Latent heat flux
Precip/Pressure
Precipitable waterSnow depth
Surface albedo
Soil moisture & temp
2-m T/q, 10-m U/VPull binary data from grib:
Re-grid 6-hourly binary:
Daily average creation:
Daily average description:
Monthly average creation:
Monthly average description:~/CEOP/BMRC/pull_grid.bash~/CEOP/BMRC/mma_regrid.gs~/CEOP/MAC/daily_average.gs“lats4d” average of 00Z, 06Z, 12Z, and 18Z times~/CEOP/MAC/monthly_average.gs“lats4d” average of 00Z (day 1) to 18Z (last day of month)

NCEP
Experiment name at MPI:CEOP_NCEP_GRID_RAW

Dataset names at MPI:

NCEP_GR_FORC_06__00H
NCEP_GR_FORC_06__06H
NCEP_GR_FORC_06__12H
NCEP_GR_FORC_06__18H
360x181 (1.0 lon. X 1.0 lat.)26 pressure levels & 1 surface level6-hourly (3291 total files)
1 time missing: 12Z2003Aug26
T, U/V, Φ (26 levels)
Cloud water, RH, ω (21 levels)
TOA/surface LW/SW ↑/↓ radiation (no TOA SW↓, however)Sensible/Latent/Ground heat flux
Precip/Pressure/PBL height
Total cloud cover % (column)
Precipitable & Cloud water (column)
Snow depth
Surface albedo
Runoff/Soil moisture & temps
2-m T/q, 10-m U/V

Spatial resolution:
Vertical resolution:
Temporal resolution:
Missing data:
Key variables:

Pull binary data from grib:
Re-grid 6-hourly binary:
Daily average creation:
Daily average description:
Monthly average creation:
Monthly average description:~/CEOP/NCEP/pull_grid.bash~/CEOP/NCEP/mma_regrid.gs~/CEOP/MAC/daily_average.gs“lats4d” average of 00Z, 06Z, 12Z, and 18Z times~/CEOP/MAC/monthly_average.gs“lats4d” average of 00Z (day 1) to 18Z (last day of month)ECPC RII
Experiment name at MPI:CEOP_ECPCRII_GRID_RAW

Dataset names at MPI:ECPCRII6_flx_ft00_MUL (analysis)
ECPCRII6_flx_ft03_MUL (00-03 hour forecast average)
ECPCRII6_flx_ft06_MUL (03-06 hour forecast average)
192x94 (1.875 lon. X ~1.915 lat.)
1 surface level6-hourly, with forecast data 3-hourly (3292 total files)
None
TOA/surface LW/SW ↑/↓ radiation
Sensible/Latent/Ground heat flux
Precip w/ Convective
Pressure/Roughness/PBL height
Momentum flux/Gravity wave stress
Total/High/Mid/Low cloud cover %Cloud base/top/temperature/CWF
Snow fall/depth/melt
Surface albedo
Runoff/Soil moisture & temps
2-m T/q, 10-m U/V

Spatial resolution:
Vertical resolution:
Temporal resolution:
Missing data:
Key variables:

Dataset names at MPI:
Spatial resolution:
Vertical resolution:
Temporal resolution:
Missing data:
Key variables:

ECPCRII6_pgb_ft00_MUL (analysis)
144x73 (2.5 lon. X 2.5 lat.)17 pressure levels
6-hourly, with forecast data 3-hourly (3292 total files)
None
T, U/V, RH/q, ω, Φ, h (17 levels)
Precipitable water/RH (column)
Lifted index
Max wind level T, U/V, Pressure
Tropopause T, U/V, Pressure, Shear

Pull binary data from grib:

1) ~/CEOP/ECPC-RII/pull_analysis.bash2) ~/CEOP/ECPC-RII/pull_6hourly.bash3) ~/CEOP/ECPC-RII/combine_3hourly.gs~/CEOP/ECPC-RII/mma_regrid.gs~/CEOP/MAC/daily_average.gs“lats4d” average of 00Z, 06Z, 12Z, and 18Z times~/CEOP/MAC/monthly_average.gs“lats4d” average of 00Z (day 1) to 18Z (last day of month)Re-grid 6-hourly binary:
Daily average creation:
Daily average description:
Monthly average creation:
Monthly average description:

ECPC SFM
Experiment name at MPI:CEOP_ECPCSFM_GRID_RAWDataset names at MPI:

Spatial resolution:
Vertical resolution:
Temporal resolution:
Missing data:
Key variables:

ECPCSFM6_flx_ft00_MUL (analysis)
ECPCSFM6_flx_ft03_MUL (00-03 hour forecast average)
ECPCSFM6_flx_ft06_MUL (03-06 hour forecast average)
192x94 (1.875 lon. X ~1.915 lat.)
1 surface level (with 3 soil moisture/temperature levels)
6-hourly, with forecast data 3-hourly (3292 total files)
2 times missing: 12Z2003Aug22 & 12Z2003Aug25
TOA/surface LW/SW ↑/↓ radiation
Sensible/Latent/Ground heat flux
Precip w/ Convective
Pressure/Roughness/PBL height
Momentum flux/Gravity wave stress
Total/High/Mid/Low cloud cover %Cloud base/top/temperature/CWF
Snow fall/depth/melt
Surface albedo
Runoff/Soil moisture & temps
2-m T/q, 10-m U/V

Dataset names at MPI:
Spatial resolution:
Vertical resolution:
Temporal resolution:
Missing data:
Key variables:

ECPCSFM6_pgb_ft00_MUL (analysis)
144x73 (2.5 lon. X 2.5 lat.)17 pressure levels
6-hourly, with forecast data 3-hourly (3292 total files)
None
T, U/V, RH/q, ω, Φ, h (17 levels)
Precipitable water/RH (column)

Pull binary data from grib:

1) ~/CEOP/ECPC-SFM/pull_analysis.bash2) ~/CEOP/ECPC-SFM/pull_6hourly.bash3) ~/CEOP/ECPC-SFM/combine_3hourly.gs~/CEOP/ECPC-SFM/mma_regrid.gs~/CEOP/MAC/daily_average.gs“lats4d” average of 00Z, 06Z, 12Z, and 18Z times~/CEOP/MAC/monthly_average.gs“lats4d” average of 00Z (day 1) to 18Z (last day of month)Re-grid 6-hourly binary:
Daily average creation:
Daily average description:
Monthly average creation:
Monthly average description:

MSC
Experiment name at MPI:CEOP_MSC_GRID_RAW

Dataset names at MPI:CEOP_MSC_GRID_RAW_xxxx(xxxx = variable name)800x600 (0.45 lon. X 0.3 lat.)
1 surface level (with 4 upper-air levels used)
24-hourly, with forecast data 3-hourly (823 total files)
Upper-air forecast data 12-hourly only

Spatial resolution:
Vertical resolution:
Temporal resolution:

Missing data:
Key variables:

2 times missing: 00Z2002Oct01 & 06Z2002Oct01
T, U/V, q, Φ (4 levels)
TOA/surface LW/SW ↑/↓ radiation
Sensible/Latent/Ground heat flux
Precip/Pressure
1.5-m T/q, 10-m U/V
Total cloud cover % (column)
Precipitable & Cloud water (column)
Snow depth/SWE
Surface albedo
Runoff/Soil moisture

Pull binary data from grib:

1) ~/CEOP/MSC/pull_analysis.bash2) ~/CEOP/MSC/pull_3hourly.bash~/CEOP/MSC/mma_regrid.gs~/CEOP/MAC/daily_average.gs“lats4d” average of 00Z, 06Z, 12Z, and 18Z times~/CEOP/MAC/monthly_average.gs“lats4d” average of 00Z (day 1) to 18Z (last day of month)Re-grid 6-hourly binary:
Daily average creation:
Daily average description:
Monthly average creation:
Monthly average description:

CPTEC
Experiment name at MPI:CEOP_CPTEC_GRID_RAW

Dataset names at MPI:CPTEC_GR_gposnmc_fct_12_MUL
CPTEC_GR_gposnmc_fct_18_MUL
CPTEC_GR_gposnmc_fct_24_MUL
CPTEC_GR_gposnmc_fct_30_MUL
384x192 (0.9375 lon. X ~0.9375 lat.)15 pressure levels & 1 surface level6-hourly, with forecast data from 12- to 30-hourly (3293 total files)
35 times missing (by month):
2002: 0, 1, 0
2003: 1, 1, 1, 0, 14, 2, 1, 0, 1, 0, 0, 3
2004: 0, 0, 0, 1, 4, 0, 0, 0, 1, 0, 4, 0
T, U/V, q, ω, Φ, diverg. (15 levels)
TOA/surface LW/SW ↑/↓ radiation(no TOA SW↓, however)Sensible/Latent heat flux
Precip/Pressure/Roughness
Runoff/Soil moisture2-m T/RH, 10-m U/V
Total cloud cover % (column)
Precipitable water (column)

Spatial resolution:
Vertical resolution:
Temporal resolution:
Missing data:

Key variables:

Pull binary data from grib:
Re-grid 6-hourly binary:~/CEOP/CPTEC/pull_grid.bash~/CEOP/CPTEC/mma_regrid.gs

Daily average creation:
Daily average description:
Monthly average creation:
Monthly average description:~/CEOP/MAC/daily_average.gs“lats4d” average of 00Z, 06Z, 12Z, and 18Z times~/CEOP/MAC/monthly_average.gs“lats4d” average of 00Z (day 1) to 18Z (last day of month)6. References
Bosilovich, M. G., J. D. Radakovich, A. da Silva, R. Todling and F. Verter: Skin Temperature Analysis
and Bias Correction in a Coupled Land-Atmosphere Data Assimilation System. JMSJ, Vol. 85A, 205228.
Chou, S. C., J. A. Marengo, C. P. Dereczynski, P. V. Waldheim and A. O. Manzi, 2007: Comparison of
CPTEC GCM and Eta Model Results with Observational Data from the Rondonia LBA Reference Site,
Brazil”. JMSJ, Vol. 85A, 25-42.
Dirmeyer, P. A. and co-authors, 2006: Multi-model Analysis and Implications for our perception of theland surface, Bull. Amer. Met. Soc., 87, 1381-1395.
Rikus, L., 2007: Validating Basic Surface Variables in the Australian Bureau of Meteorology Model with
CEOP EOP3 In-situ Data. JMSJ, Vol. 85A, 73-97.
Yang, K. and co-authors, 2007: Initial CEOP-based Review of the Prediction Skill of Operational General
Circulation Models and Land Surface Models. JMSJ, Vol. 85A, 99-116.

7. Acronyms