NASA/TM–2012-104606/Vol 29

June 2012

Michele M. Rienecker, Dick Dee, Jack Woollen, Gilbert P. Compo, Kazutoshi Onogi, Ron Gelaro, 

Michael G. Bosilovich, Arlindo da Silva, Steven Pawson, Siegfried Schubert, Max Suarez, Dale Barker, 

Hirotaka Kamahori, Robert Kistler, and Suranjana Saha

National Aeronautics and 

Space Administration 

Goddard Space Flight Center

Greenbelt, Maryland 20771

Technical Report Series on Global Modeling and Data Assimilation, 

Volume 29

Max J. Suarez, Editor 

Atmospheric Reanalyses—Recent Progress and Prospects 

for the Future. 

A Report from a Technical Workshop, April 2010
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NASA/TM–2012-104606/Vol 29

June 2012

Michele M. Rienecker

Goddard Space Flight Center, Greenbelt, Maryland

Dick Dee

European Centre for Medium-Range Weather 

Forecasts, Reading, United Kingdom

Jack Woollen

I.M. Systems Group, Inc., Rockville, Maryland 

Gilbert P. Compo 

University of Colorado, Boulder, Colorado

Kazutoshi Onogi

Japan Meteorological Agency, Tokyo, Japan

Ron Gelaro

Goddard Space Flight Center, Greenbelt, Maryland

Michael G. Bosilovich

Goddard Space Flight Center, Greenbelt, Maryland

Arlindo da Silva

Goddard Space Flight Center, Greenbelt, Maryland

Steven Pawson

Goddard Space Flight Center, Greenbelt, Maryland

Siegfried Schubert

Goddard Space Flight Center, Greenbelt, Maryland

Max Suarez

Goddard Space Flight Center, Greenbelt, Maryland 

Dale Barker

Met Office, Exeter, United Kingdom 

Hirotaka Kamahori

Meteorological Research Institute

Japan Meteorological Agency, Tsukuba, Japan

Robert Kistler 

NOAA National Centers for Environmental Prediction 

Camp Springs, Maryland

Suranjana Saha 

NOAA National Centers for Environmental Prediction 

Camp Springs, Maryland 

Technical Report Series on Global Modeling and Data Assimilation, 

Volume 29

Max J. Suarez, Editor 

Atmospheric Reanalyses—Recent Progress and Prospects 

for the Future. 

A Report from a Technical Workshop, April 2010
Available from: 

NASA Center for AeroSpace Information 

7115 Standard Drive 

Hanover, MD 21076-1320

National Technical Information Service 

5285 Port Royal Road 

Springfield, VA 22161 Price Code: A17

Level of Review: This material has been technically reviewed by technical management

Trade names and trademarks are used in this report for identification only. Their usage does not 

constitute an official endorsement, either expressed or implied, by the National Aeronautics and 

Space Administration.

Notice for Copyrighted Information

This manuscript has been authored by employees of the European Centre for Medium-Range Weather 

Forecasts, Reading, United Kingdom; I.M. Systems Group, Inc. in Rockville, Maryland; 

The University of Colorado; the Japan Meteorological Agency; Met Office, Exeter, United 

Kingdom; and the NOAA National Centers for Environmental Prediction with the National 

Aeronautics and Space Administration. The United States Government has a non-exclusive, 

irrevocable, worldwide license to prepare derivative works, publish, or reproduce this manuscript, and allow others to do so, for United States Government purposes. Any publisher 

accepting this manuscript for publication acknowledges that the United States Government 

retains such a license in any published form of this manuscript. All other rights are retained 

by the copyright owner.
iii

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In April 2010, developers representing each of the major reanalysis centers met at Goddard 

Space Flight Center to discuss technical issues – system advances and lessons learned –

associated with recent and ongoing atmospheric reanalyses and plans for the future. The meeting

included overviews of each center’s development efforts, a discussion of the issues in 

observations, models and data assimilation, and, finally, identification of priorities for future 

directions and potential areas of collaboration. This report summarizes the deliberations and 

recommendations from the meeting as well as some advances since the workshop.
iv
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Figure 1: Tropical averages (20°S-20°N) of 12-hourly variational bias estimates (K) for HIRS channel 11 

radiance data from NOAA-11 and NOAA-12 in ERA-Interim..............................................3

Figure 2: Global mean 12-hourly variational bias estimates (K) for MSU channel 2 radiance data from 

NOAA-10, NOAA-11, NOAA-12, and NOAA-14. The upper panel is from ERA-Interim (from Dee 

and Uppala, 2009) and the lower panel is from MERRA. The latter uses MSU data from 

NOAA/NESDIS that has been intercalibrated using the simultaneous nadir overpass (SNO) method 

(Zou et al. 2006).....................................................................................................3

Figure 3a: Extratropical anomaly correlations for 3-, 5-, and 7-day forecasts of 500 hPa height based on 

the ECMWF operational forecast system (top) and the ERA-Interim and ERA-40 reanalyses (bottom), 

after Dee and Uppala (2009).......................................................................................5

Figure 3b: Yearly averaged (left) Southern Hemisphere and (right) Northern Hemisphere 00 UTC 120-h 

forecast anomaly correlations for CFSR (black triangles), operational GFS (red circles), CFSR-Lite 

(green squares), CFS R2 (purple diamonds), and CDAS R1 (blue stars)....................................6

Figure 4: (a) The time series of the spatial correlation of annual mean precipitation averaged over the 

tropics (15°S-15°N) from several reanalyses and CMAP (black curve) with that from GPCP. (b) The 

annual mean spatial standard deviation of precipitation (mm day-1

) over the tropics. The black dashed 

line denotes GPCP...................................................................................................7

Figure 5a: The precipitation bias (mm day-1

) in several reanalyses relative to GPCP, January 1990-2002..7

Figure 5b: Taylor diagrams of annual mean precipitation from reanalyses using GPCP as a reference and 

CMAP as an additional observational reference. Each panel shows the statistics for different regions: 

(a) globe, (b) tropics, (c) global land, and (c) global oceans. The red and blue lines show limits of 

expected high and low correlation as determined by comparing GPCP and CMAP observations. See 

Bosilovich et al. (2008) for details. ...............................................................................8

Figure 6: MERRA’s estimate of the vertically integrated water vapor budget for January 2004, in kg m-2

day-1

...................................................................................................................9

Figure 7: The joint frequency distribution of the top-of-atmosphere long-wave (abscissa) and short-wave 

(ordinate) fluxes for 2004 from MERRA, NCEP-DOE R2, and JRA-25. For comparison, the results 

are shown from CERES. The solid line contours from CERES overlay the shaded contours in all 

panels..................................................................................................................9

Figure 8: The global energy budget (W m-2

). The numbers in black and white are those from satellite data 

climatologies and other observations for the period 2000-2005 from Trenberth et al. (2009). The 

colored values are from reanalyses for the 2002-2008 period (except ERA40): MERRA (red), NCEPNCAR R1 (green), ERA40 (brown), CFSR (orange), JRA-25 (blue), NCEP-DOE R2 (purple), ERAInterim (cyan), and 20CR (green)...............................................................................10

Figure 9: The lag correlation of precipitation and SST for winter over the Western Pacific. The 

correlation for CFSR is compared with that from observations, NCEP-NCAR R1, and NCEP-DOE 

R2. Positive lags denote precipitation is leading SST........................................................11

Figure 10: The eddy height field at 300 hPa for January 1995 (upper row) and January 1998 (middle row) 

from MERRA (first column) and ERA-Interim (second column). The differences between MERRA 

and ERA-Interim are shown in the third column while the fourth column compares MERRA with 

ERA-40. The bottom panels show the differences between the two years................................11
vii

Figure 11: Correlations of MERRA with other estimates of selected monthly-mean quantities in January. 

The comparisons with ERA-Interim (300 hPa eddy height, vertical velocity at 500 hPa, u-wind at 850 

hPa and specific humidity (q) at 850 hPa) are for 1990-2008. The comparison with SSM/I (TPW) is 

for 1993-2002, and the comparison with GPCP v2.1 (precipitation) is for 1979-2008..................12

Figure 12: Zonal mean values of the correlation between MERRA and ERA-Interim, and between 

MERRA and selected observational data sets, for various monthly-mean quantities during January 

(left-hand panels) and July (right-hand panels) for the period 1990 to 2008. Comparisons with GPCP 

precipitation and from NOAA’s OLR product are also included............................................13

Figure 13: Time series of anomaly pattern correlations between monthly mean anomaly fields of northern 

hemisphere extratropical 300 hPa geopotential height from two upper-air based reanalyses and 20CR 

for the months of December (cool colors) and June (warm colors). Correlations with ERA-40 are 

shown by the blue and red curves. The black curves show the expected anomaly pattern correlations 

when only the observed SST fields are available for the months of (thick) December and (thin) June 

using the ECHAM4.5 atmospheric model forced with observed SSTs (courtesy of the International 

Research Institute). The thin black lines show the mean value of these expected correlations, with the 

thicker line corresponding to December and the thinner corresponding to June..........................14

Figure 14: Wavenumber-frequency diagram for precipitation, following Wheeler and Kiladis (1999). ER 

is equatorial Rossby wave; EIG is eastward inertio-gravity wave, MRG is mixed Rossby-gravity 

wave. The calculations are based on daily precipitation for 1989-2008 averaged between 15°S and 

15°N. For each product, the left-hand panel is the symmetric component and the right-hand panel is 

the anti-symmetric component...................................................................................15

Figure 15: Tropical cyclone detection rate from CFSR............................................................16

Figure 16: Maximum intensity of tropical storms in the Atlantic detected in MERRA over the period 

1998-2005, compared with observations from 1997 to 2008................................................16

Figure 17: Diurnal variation in precipitation (mm day-1

) over the United States for July 2004. The July 

mean has been removed. Results are shown as 6-hour averages for TRMM observations, and for the 

reanalyses: MERRA, ERA-Interim, CFSR, JRA-25, NCEP-DOE R2, and NCEP-NCAR R1.........17

Figure 18: Map of the local anomaly correlation between four-times daily anomalies of 300hPa 

geopotential from ERA-40 and 20CR for the period 1979 – 2001. The thick black line contour 

indicates the region where ERA-40 and NCEP-NCAR R1 correlate highly (0.975) in this quantity 

during this period...................................................................................................18

Figure 19a: Time series of the monthly mean zonal velocity at Singapore from (top) rawinsonde 

observations, (center) NCEP-NCAR R1, and (bottom) ERA-15. The contour interval is 10 ms-1

, the 

15 ms-1 and 25 ms-1 are also included (dashed) and positive values (i.e. westerlies) are shaded........19

Figure 19b: The QBO and SAO from the zonal mean zonal wind component from MERRA and ERAInterim, averaged between 10°S and 10°N.....................................................................20

Figure 20a: Global mean precipitation from the most recent set of reanalyses compared against two 

observational data sets, GPCP and CMAP. The times of introduction of AMSU-A on NOAA-15 and 

NOAA-16 are shown, along with the timing of the removal of AMSU-A on NOAA-16 because of 

instrument failure...................................................................................................21

Figure 20b: Global mean analysis increment of moisture inferred from the imbalance between 

evaporation and precipitation, from ERA-Interim (red), JRA-25 (orange), MERRA (green), and 

CFSR (purple)......................................................................................................21

Figure 21: The annual cycle of the global mean precipitation, evaporation, and their difference, from 

MERRA. The pre-AMSU era is shown in green and the post-AMSU era in cyan.......................22
viii

Figure 22a: Temperature anomalies at 2m (K) calculated relative to the 1989-1998 mean. The time series 

from ERA-40 and ERA-Interim are compared against the CRUTEM3 observations (Brohan et al., 

2006). The topmost panels are the time series of 12-month running global average anomalies. The top 

left-hand panel is the time series from anomalies sampled according to availability of CRUTEM3 

data (see lower set of panels); the top right-hand panel is from the raw time series......................22

Figure 22b: The annual global mean 2-m temperature over land in NCEP-NCAR R1 (green), CFSR (red), 

and GHCN-CAMS (blue) over the period of 1979–2009. Units: K. Least squares linear fits of the 

three time series against time (thin lines). The linear trends are 0.66, 1.02, and 0.94 K (31 yr)–1 for R1, 

CFSR, and GHCN-CAMS, respectively. (Note that straight lines may not portray climate change 

trends accurately)...................................................................................................23

Figure 23: Global mean temperature anomalies (K) in the middle stratosphere from ERA-Interim (blue) 

and MERRA (red). Data are shown for 5hPa (top) and 10hPa (bottom). The anomalies are computed 

from the mean annual cycle in 2000-2010 for each individual analysis....................................23

Figure 24: Global mean precipitation (mm day-1

) for the available reanalyses compared against two 

observational data sets, GPCP and CMAP.....................................................................24

Figure 25: Time series of the 6-hourly first guess (blue curves) root-mean-square (RMS) difference from 

pressure observations and (red curves) expected RMS difference calculated over separate years from 

1870 to 2008 for the (a) northern hemisphere (20°N-90°N) and (b) southern hemisphere (20°S-90°S). 

The square root is calculated on the annual mean square values. The thin black curve shows the 

average number of pressure observations for each analysis in the indicated year (note the logarithmic 

scale).................................................................................................................27

Figure 26: Globally averaged bias estimates (K) for AMSU-A channel 6 radiances from NOAA-15, -16, -

17 and Aqua based on ERA-Interim (from Dee and Uppala (2009), top) and MERRA (bottom). The 

bias estimates based on a 12-hourly assimilation cycle in ERA-Interim and a 6-hourly assimilation 

cycle in MERRA...................................................................................................28

Figure 27: Globally analysis departures (K) and observation counts for radiosonde observations at 200 

hPa based on MERRA (top) and ERA-Interim (from Dee and Uppala (2009), bottom). Observation 

counts represent 12-hour totals in ERA-Interim and 6-hour totals in MERRA...........................29

 
1

56 7/$%,84'$1,/

Between early 2009 and early 2010, four new global atmospheric reanalyses became available 

for scientific research: ECMWF’s ERA-Interim reanalysis (Dee et al., 2011a, b), NASA’s 

Modern-Era Retrospective analysis for Research and Applications (MERRA, Rienecker et al.,

2011), NCEP’s Climate Forecast System Reanalysis (CFSR, Saha et al., 2010) and the NOAACIRES Twentieth Century Reanalysis (20CR, Compo et al., 2011). In addition, the JMA began

production of a new global reanalysis (JRA-55, Ebita et al., 2011). With so much recent 

experience being focused on the methodologies and technologies of reanalyses, it seemed an 

opportune time to gather the primary developers for a meeting to review the system advances 

and lessons learned from the evaluations of the reanalyses. Thus, in April 2010, developers 

representing each of the major reanalysis centers met at NASA Goddard Space Flight Center. 

The meeting included overviews of each center’s development efforts, a discussion of the issues 

in observations, models and data assimilation, and, finally, identification of priorities for future 

directions and potential areas of collaboration. This report summarizes the deliberations and 

recommendations from the meeting as well as some advances since the workshop.

96 :&;,%(<=#$+>(!8?&/'+#

The earlier generations of reanalyses from NCEP and ECMWF have proven to be extremely 

valuable scientific tools, enabling climate and weather research not otherwise possible. They 

continue to be used, even with their known flaws, because of the information content and form of 

the products: regular, gridded meteorological fields based on observations. The community of 

users has a broad array of needs, but those needs seem to be well met by time series of such 

gridded fields that are of long duration and that are kept current.

Individual centers have their own objectives, depending on their mission (see Appendix A), in 

preparing these newest reanalyses. However, the primary rationale is the availability of new 

systems or updates to older systems that make some significant progress in addressing 

deficiencies in NCEP-NCAR R1 and ERA-40, especially in terms of the hydrological cycle and 

treatment of biases in satellite observations. Here we briefly summarize advances made in 

models, analysis/assimilation systems, and the treatment of observations.

965 :,8+*

The models used in the latest reanalyses are not very different from those used, for example, for 

ERA-40 or NCEP-DOE R2. Of course there have been updates in the tuning of 

parameterizations as new satellite observations, especially from the NASA EOS series, have 

provided new information and insight on cloud properties and moisture distributions. Perhaps the 

most significant development has been the implementation of prognostic cloud schemes, which 

have also facilitated updates to the use of moisture observations during assimilation. The 

inclusion of prognostic ozone has also allowed the assimilation of ozone retrievals; in MERRA 

the ozone analysis also has a radiative impact. Additionally, the higher horizontal and vertical 

resolution and extension of the vertical domain have been important for better representation of 

transports.
2

969 !/&*=#1#(&/8(!##1>1*&$1,/

The analysis schemes used in the current generation of reanalyses have been improved in several 

ways compared with their first-generation predecessors. ERA-Interim (see A.2) and JRA-55 (see 

A.5) use four-dimensional variational (4D-Var) algorithms, which take the time dimension into 

account explicitly as observations are used during the assimilation window. This involves not 

only computing the observation-minus-background state departures at the “correct” time of the 

observations, but also using the forecast model to propagate the influence of the observations 

through time in a dynamically consistent manner. This is likely to be of increased importance 

during the satellite era in which large numbers of observations are available on a near-continuous 

basis. 

MERRA (see A.1) and CFSR (see A.3) use a 3D-Var algorithm based on the Grid-point 

Statistical Interpolation (GSI) scheme jointly developed by NCEP and GMAO, which also 

includes a number of advancements over 3D-Var algorithms used previously. In particular, the 

observation-minus-background departures are computed with increased temporal accuracy, and a 

dynamic constraint on noise is employed to improve the balance properties of the analysis 

solution. In MERRA, an incremental analysis update (IAU) procedure is also used in which the 

analysis correction is applied incrementally to the forecast model through an additional tendency 

term in the model equations. This has ameliorated the spin-up problem with precipitation during 

the very early stages of the forecast and greatly improved aspects of the stratospheric circulation.

The 20CR (see A.6) is unique in its use of an ensemble Kalman filter (EnKF) to reanalyze 

observations of surface pressure from 1871 to the present time. A well-known advantage of the 

EnKF is that the background error covariances evolve dynamically from one assimilation time to 

the next. The technique is especially well suited for problems involving a sparse observing 

network (in which accurate extrapolation of the available observational information to 

unobserved locations becomes critical), or an observing network that changes substantially 

during the course of the reanalysis (so that the background errors reflect the associated changes 

in the accuracy of the background forecast). Both aspects are of primary importance in 20CR.

Except for 20CR, the current reanalyses make extensive use of satellite radiance information, 

including the hyper-spectral data from AIRS. Successful use of these data requires careful 

quality control and bias correction procedures that are channel-specific. The bias in a given 

satellite channel can vary significantly in space and time depending on the atmospheric 

conditions, systematic errors in the radiative transfer model, and quality and age of the 

instrument. In most data assimilation schemes, the bias in each satellite radiance measurement is 

represented by a linear predictor model in which a relatively small number (~10) of parameters is 

used to describe these and other related dependencies. In the first-generation reanalyses that 

used satellite radiances, including ERA-40 and JRA-25 (see A.4), these parameters were 

estimated separately for each channel using an offline procedure based on a reference data set.

In the current reanalyses, bias estimation is performed automatically during the data assimilation 

procedure using a variational bias correction (VarBC) scheme, which was originally developed 

for numerical weather prediction at NCEP (Derber and Wu, 1998). The bias parameters are 

updated each analysis cycle by including them in the control vector used to minimize the 

analysis cost function. This ensures that the bias estimates are continuously adjusted to maintain 

consistency between the bias-corrected radiances and all other information used in the analysis, 
3

including conventional observations and the model background state (Dee and Uppala, 2009, 

henceforth DU09). An important technical advantage of this approach is that it removes the need 

for manual tuning and other interventions as the satellite observing system changes over time. 

The bias estimates also adapt in response to natural phenomena that can severely affect the 

radiance measurements, such as the Mt. Pinatubo eruption in 1991 (e.g., Figure 1, from DU09). 

The use of variational bias correction thus represents one of the most important advancements in 

the assimilation methodology of the current generation of reanalyses.

Figure 1: Tropical averages (20°S-20°N) of 12-hourly variational bias estimates (K) for HIRS channel 

11 radiance data from NOAA-11 and NOAA-12 in ERA-Interim. [From Dee and Uppala (2009)]

Figure 2: Global mean 12-hourly variational bias estimates (K) for MSU channel 2 radiance data from 

NOAA-10, NOAA-11, NOAA-12, and NOAA-14. The upper panel is from ERA-Interim (from Dee 

and Uppala, 2009); the lower panel is from MERRA. The latter uses MSU data from NOAA/NESDIS

that has been intercalibrated using the simultaneous nadir overpass (SNO) method (Zou et al. 2006).

a

b
4

96@ A"#+%?&$1,/#

Just as variational bias correction has provided significant benefit to the assimilation of satellite 

radiances, so have efforts, by data providers and others, to calibrate or reprocess certain 

observational data sets improved their usefulness in the current reanalyses. In terms of satellite 

observations, the Microwave Sounding Unit (MSU) on board NOAA-10, -11, -12 and -14 

provides one of the longest records of remotely sensed atmospheric temperature from a single 

sensor type, extending from 1978 – 2007 with overlapping lifetimes of up to three years between 

satellites. In the original data sets, the global mean bias estimates for the same MSU channel on 

different satellites differ by up to a degree or more (Figure 2a), limiting the usefulness of these 

data for climate-change research and possibly having a negative effect in the variational bias 

correction scheme. NOAA/NESDIS has begun recalibrating observations from MSU and other 

instruments using a simultaneous nadir overpass (SNO, e.g., Zou et al., 2006) method. The 

recalibrated radiances for MSU channels 1 – 3 have been assimilated in MERRA, and exhibit 

near-uniform biases with a discernible upward trend over the length of the data record (Figure 

2b).

Another important observational item worthy of mention is the treatment of radiosonde data 

since these data continue to have a major impact on global analyses. In addition to the radiation 

bias correction that all reanalyses apply to radiosonde temperatures to account for changing solar 

effects on the thermistor, MERRA and ERA-Interim applied some additional pre-processing 

corrections. Corrections for MERRA include the removal of large time-mean temperature 

differences in radiosonde observations collected at 00 and 12 UTC with the Vaisala RS-80 

instrument. The differences occur as a result of a coding error in the post-processing software at 

the observing stations, and primarily affect observations in the stratosphere (Redder et al., 2004). 

The homogenization scheme of Haimberger (2007) was then applied to radiosonde observations

(until 2005), with updated values consistent with the Vaisala RS-80 corrections described above. 

ERA-Interim and JRA-55 also applied Haimberger’s homogenization correction, but using the 

correction developed without the prior correction of Vaisala RS-80 data. Finally, since those preprocessing corrections were made for MERRA, the radiation bias correction applied to

radiosonde temperature observations was modified so that it could still account for seasonal 

changes in the solar elevation angle that affect the thermistor even in the presence of the other 

corrections.

@6 B4&*1$=(,-($C+ D+&/&*=#+#

In this section, we provide an assessment of the extent to which the system advances noted above

have led to measureable improvements in the reanalysis products. We briefly look at several, 

mostly climate-oriented, metrics that can be used to evaluate the quality of the reanalyses. The 

view includes both improvements over the previous generation of analyses and remaining 

deficiencies. For the former, we highlight major improvements regardless of whether or not they 

occur in all the reanalyses; for the latter, we attempt to highlight the problems that are common 

to these products. In this way we hope to avoid focusing on problems that are specific to any one 

system, while emphasizing the capabilities and promises that current reanalysis technologies can 

offer the climate community. 
5

A basic question is how to measure the quality of reanalysis products. The discussion below is 

organized around four types of metrics: (1) the quality of forecasts made from the analyzed 

states, which itself requires appropriate metrics; (2) climate-related diagnostics with comparisons 

against observation-only based products; (3) the magnitude and nature of analysis increments, 

particularly systematic corrections that are required to keep the assimilation close to the observed 

trajectory and affect the energy and water budgets of the atmosphere; and (4) the sensitivity of 

the product to changes in the observing system, which is particularly important if the reanalyses 

are to be useful in assessing climate change.

@65 E?&*4&$1,/#($C%,43C(F+&$C+%(-,%+'&#$(#G1**

Figure 3a: Extratropical anomaly correlations for 3-, 5-, and 7-day forecasts of 500 hPa height based 

on the ECMWF operational forecast system (top) and the ERA-Interim and ERA-40 reanalyses 

(bottom), after Dee and Uppala (2009).

The generation of new retrospective analyses of historical meteorological data has been driven 

by the steady improvements of the data assimilation systems used for operational weather 

predictions. Modern systems are capable of extracting much more information from earlier data 

than the systems that were operational at the time the observations were taken. This effect is 

dramatically illustrated in Figure 3a (from DU09), which shows how the forecast skill of the 

operational ECWMF system has improved over time (upper panel), as compared with the 

forecast skill when more recent ECMWF systems are used to re-analyze the historical 

observation (lower panel). Since both operational and reanalysis forecasts are based on 
6

practically1 identical observation streams, the difference between the two can be attributed 

almost solely to improvements in the data assimilation system – better models and better analysis 

techniques. Conversely, it is interesting to note (lower panel) the role of the improving observing

system in allowing more accurate forecasts, especially during the last decade. 

Improving the accuracy of an analysis is a central concern of the numerical weather prediction 

community as a means of achieving improved forecast skill. In the case of climate reanalyses, it 

can be debated whether improving accuracy (and therefore forecast skill) should receive priority 

over other improvements such as temporal consistency (see section 3.3). It is clear, however, that 

accuracy is nevertheless an important property of reanalyses, impacting the quality of the first 

guess fields, as well as the physical consistency of the products (in the sense of having smaller 

analysis increments – discussed further below). Figure 3a shows how the ERA-Interim product 

has benefitted from the recent improvements to the operational model and analysis system, 

showing essentially modern-day skill levels extending back to the late 1980s where they are

substantially above the operational skill levels of that time. Figure 3b shows the same for NCEP 

reanalyses versus operations. Such improvements highlight the benefits of reanalyzing earlier 

periods with a fixed modern-day data assimilation system (including improved observations) to

address, at least in part, consistency of the products in time in the face of a changing observing 

system.

Figure 3b: Yearly averaged (left) Southern Hemisphere and (right) Northern Hemisphere 00 UTC 120-h forecast 

anomaly correlations for CFSR (black triangles), operational GFS (red circles), CFSR-Lite (green squares), CFS R2 

(purple diamonds), and CDAS R1 (blue stars).

 1 Two caveats are that (1) the observations used for reanalysis are often “cleaned-up” versions of the real-time 

streams and may include some additional data, and (2) as time progresses, an aging reanalysis system may be 

technically unprepared to assimilate new observing systems.
7

@69 .*1>&$+H%+*&$+8(I1&3/,#$1'#

@6965 J&$+%(&/8(E/+%3=(.='*+#

Current models have improved considerably in the representation of the hydrological cycle, and 

reanalyses have benefited from this. There are still outstanding issues, especially in the 

representation of precipitation and clouds; however, the quality, measured in terms of bias and 

spatial structure, of the precipitation has improved substantially in the more recent reanalyses. In 

fact, the time series of the spatial correlations in Figure 4 show improvements in the earlier years 

(compare MERRA and CFSR with ERA-40) that further illustrate the benefits of reanalyzing 

historical observations with an improved data assimilation system. The spatial maps of the bias 

in the January mean precipitation from the various reanalyses, as estimated with the observationonly Global Precipitation Climatology Project (GPCP, Figure 5a), and the summary Taylor plots 

(Figure 5b) show that much of the improvement has occurred over the tropical oceans. 

Figure 4: (a) The time series of the spatial correlation of annual mean precipitation averaged over the tropics 

(15°S-15°N) from several reanalyses and CMAP (black curve) with that from GPCP. (b) The annual mean 

spatial standard deviation of precipitation (mm day-1

) over the tropics. The black dashed line denotes GPCP. 

 Figure 5a: The precipitation bias (mm day-1) in several reanalyses relative to GPCP, January 1990-2002.

(a) (b)
8

 

 

Figure 5b: Taylor diagrams of annual mean precipitation from reanalyses using GPCP as a reference 

and CMAP as an additional observational reference. Each panel shows the statistics for different 

regions: (a) globe, (b) tropics, (c) global land, and (c) global oceans. The red and blue lines show 

limits of expected high and low correlation as determined by comparing GPCP and CMAP 

observations. See Bosilovich et al. (2008) for details. 

Earlier reanalyses were limited in their ability to support water and energy budget analyses, 

having large biases in the various physical forcing terms (e.g., precipitation, heating rates). 

“Forcing” fields could only be estimated as a residual of the terms that involved quantities that 

were strongly constrained by the analysis (e.g., rotational wind) and were less dependent on the 

model physical parameterizations. In conducting MERRA, emphasis was placed on providing a 

complete and internally consistent budget including any unphysical terms associated with the 

analysis increments and other non-physical adjustments (e.g., filtering). For example, Figure 6 

shows the various terms in the vertically integrated moisture budget from MERRA, indicating 

that while the bias (e.g., in precipitation) has been reduced compared to previous reanalyses, the 

analysis increments nevertheless still contribute substantially to the budget.

Improvements in the representation of clouds remain a challenge that affects the quality of both 

the water and energy cycles. For example, cloud-related deficiencies are apparent in the joint 

frequency distribution of long-wave and short-wave fluxes at the top of the atmosphere shown in 

Figure 7. The joint frequency distribution of these fluxes may be viewed as a two-dimensional 

histogram that provides an evaluation of the processes that relate temperature, humidity and 

cloud fields. The latest reanalyses (MERRA and ERA-Interim) appear to have patterns closer to 

observed (CERES) than the earlier reanalyses (NCEP-DOE R2 and JRA-25), demonstrating the 

advances made in a general sense regarding the representation of the water and energy cycles.

(a)

(d)

(c)

(b)
9

Figure 6: MERRA’s estimate of the vertically integrated water vapor budget for January 2004, in kg 

m-2 day-1

.

Figure 7: The joint frequency distribution of the top-of-atmosphere long-wave (abscissa) and shortwave (ordinate) fluxes for 2004 from MERRA, NCEP-DOE R2, and JRA-25. For comparison, the 

results are shown from CERES. The solid line contours from CERES overlay the shaded contours in 

all panels. [Figure courtesy of Junye Chen]
10

A summary of the annual and global mean energy flow and balances, estimated from recent 

reanalyses and compared with an observationally based estimate from Trenberth et al. (2011), is 

shown in Figure 8. Here, consistency and improvements appear to be greatest at the top of the 

atmosphere where the net fluxes from the recent reanalyses are all small, while substantial 

imbalances occur at the surface. The lack of balance at the surface is one of the outstanding 

problems that continue to limit the use of reanalyses for driving ocean and land models. The 

primary causes of imbalances are related to the representation of clouds and surface boundary 

layer processes. Such deficiencies hinder efforts to develop coupled data assimilation systems. 

Figure 8: The global energy budget (W m-2). The numbers in black and white are those from satellite

data climatologies and other observations for the period 2000-2005 from Trenberth et al. (2009). The 

colored values are from reanalyses for the 2002-2008 period (except ERA40): MERRA (red), NCEPNCAR R1 (green), ERA40 (brown), CFSR (orange), JRA-25 (blue), NCEP-DOE R2 (purple), ERAInterim (cyan), and 20CR (green). [From Trenberth et al. (2011)]

Ultimately, studies of water and energy cycle budgets and variability require consistent state 

estimates of the ocean and land surface. The use of specified SSTs impacts surface flux estimates 

and limits the consistency of the reanalyses in the surface boundary layer with state estimates 

elsewhere. Only CSFR, by producing the analysis first guess with their coupled model, allows 

some degree of interaction between the atmosphere and ocean. One benefit of that is greater 

consistency between the SST and precipitation fields (e.g., Figure 9).
11

Figure 9: The lag correlation of precipitation and SST for winter over the Western Pacific. The 

correlation for CFSR is compared with that from observations, NCEP-NCAR R1, and NCEP-DOE 

R2. Positive lags denote precipitation is leading SST. [From Saha et al. (2010)]

@6969 7/$+%&//4&*(K&%1&"1*1$=

Figure 10: The eddy height field at 300 hPa for January 1995 (upper row) and January 1998 (middle 

row) from MERRA (first column) and ERA-Interim (second column). The differences between 

MERRA and ERA-Interim are shown in the third column while the fourth column compares MERRA 

with ERA-40. The bottom panels show the differences between the two years.

One of the strengths of the most recent reanalyses is the representation of interannual variability; 

however, the quality is not uniform but depends on both the variable of interest and the location 

(with primarily vertical and latitudinal dependence). Figure 10 shows, for example, a very high 
12

degree of agreement between the MERRA and ERA-Interim climate anomalies for January eddy 

height field at 300 hPa as indicated by the difference between monthly-mean analyses for two 

different years (one a neutral year in terms of the El Niño-Southern Oscillation (ENSO) and one 

an El Niño year). The differences are much smaller than the amplitude of the El Niño climate 

signal itself. This agreement is an improvement upon what was already a high level of agreement 

between MERRA and the ERA-40. Perhaps more surprising is the agreement in higher order 

moments, such as large-scale atmospheric transports, or in some of the derived fields, such as 

vertical velocity (Rienecker et al., 2011). 

Figure 11 summarizes the correlations of MERRA with observational estimates or, if the latter 

are unavailable, with ERA-Interim for selected monthly-mean quantities for January. For the 

eddy height field at 300 hPa, the agreement between MERRA and ERA-Interim is quite high 

everywhere except for some tropical regions, consistent with the results shown in Figure 10. The 

global mean of the local correlations is 0.98. While there is also generally close agreement 

between the two reanalyses of u-wind at 850 hPa, the agreement is lower than that for the eddy 

field. Some of the lowest correlations occur over tropical land and surrounding regions, 

including much of the tropical Atlantic. The high correlations of total precipitable water (TPW)

in MERRA with the SSM/I estimates over the ocean are not surprising since MERRA 

assimilated SSM/I radiances. On the other hand, the lower agreement with the specific humidity

at 850 hPa from ERA-Interim reflects the large uncertainties in the vertical structure of the 

moisture field in reanalyses. This, in turn, reflects a lack of strong observational constraints 

combined with a general sensitivity of the vertical structure of the moisture to the model’s 

convection scheme. 

Figure 11: Correlations of MERRA with other estimates of selected monthly-mean quantities in January. The 

comparisons with ERA-Interim (300 hPa eddy height, vertical velocity at 500 hPa, u-wind at 850 hPa and 

specific humidity (q) at 850 hPa) are for 1990-2008. The comparison with SSM/I (TPW) is for 1993-2002, 

and the comparison with GPCP v2.1 (precipitation) is for 1979-2008.
13

The correlations drop further for quantities related to the divergent wind field (e.g., vertical 

velocity at 500 hPa) and the hydrological cycle (e.g., precipitation), although though one can find 

regions where the correlations are quite high, for example, over the tropical Pacific. It should be 

emphasized, however, that this overall level of agreement is a substantial improvement compared 

with previous reanalyses (see for example the Taylor plots in Figure 5b).

Figure 12 summarizes results in terms of the zonal mean values of the correlation (R2) between 

MERRA and ERA-Interim, and between MERRA and selected observational data sets, for 

various quantities during January and July. The higher correlations between MERRA and ERAInterim for precipitation (and OLR), compared with the correlations between MERRA and 

GPCP, demonstrate the fact that the reanalyses are still more like each other than they are like 

the observational estimates.

Figure 12: Zonal mean values of the correlation between MERRA and ERA-Interim, and between 

MERRA and selected observational data sets, for various monthly-mean quantities during January 

(left-hand panels) and July (right-hand panels) for the period 1990 to 2008. Comparisons with GPCP 

precipitation and from NOAA’s OLR product are also included. [From Rienecker et al. (2011)]

Compo et al. (2011) provide an assessment of the overall quality of the weather and climate 

aspects of 20CR. One might suspect that while synoptic and submonthly variability throughout 

the troposphere could be captured well using only surface observations, lower frequency 

variability might be more poorly represented (Kanamitsu and Hwang, 2006). However, Figure

13 shows that the patterns of northern hemisphere monthly anomalies of 300 hPa geopotential 
14

height for 20CR correspond well with the ERA-40 and NCEP-NCAR reanalyses. Shown are 

time series of the pattern correlation between monthly anomalies from 20CR and NCEP-NCAR

R1 for the month of December (cyan curve) and June (orange curve). All other months fall 

between these two extremes. ERA-40 results for December (dark blue curve) and June (red 

curve) are similar and almost obscure the other curves. The increase of June correlations from an 

average of 0.84 for the period 1958 – 1978 to an average of 0.89 for 1979 to 2001 most likely 

reflects the increasing use of satellite observations that reduces the random error in both 

reanalyses used for comparison. The pattern correlations between 20CR and the upper-air based 

reanalyses are considerably higher than expected from SST-forcing alone (black curves), 

suggesting that the surface observation-based reanalysis fields provide useful estimates even of 

the monthly mean upper-tropospheric fluctuations. 

Figure 13: Time series of anomaly pattern correlations between monthly mean anomaly fields of 

northern hemisphere extratropical 300 hPa geopotential height from two upper-air based reanalyses 

and 20CR for the months of December (cool colors) and June (warm colors). Correlations with ERA40 are shown by the blue and red curves. The black curves show the expected anomaly pattern 

correlations when only the observed SST fields are available for the months of (thick) December and 

(thin) June using the ECHAM4.5 atmospheric model forced with observed SSTs (courtesy of the 

International Research Institute). The thin black lines show the mean value of these expected 

correlations, with the thicker line corresponding to December and the thinner corresponding to June. 

[From Compo et al. (2011)]

@696@ <4"H#+&#,/&*(&/8(L13C+%(2%+M4+/'=(K&%1&"1*1$=

There have been significant improvements in the representation of intra-seasonal tropical 

variability, particularly in the representation of the Madden-Julian Oscillation (MJO) and other 

convectively coupled equatorial waves. For example, Figure 14 shows wavenumber-frequency 

diagrams (Wheeler and Kiladis, 1999) for precipitation based on GPCP observations and several 

reanalyses. When compared against GPCP, the most recent reanalyses (MERRA, ERA-Interim,

and CSFR) show clear improvements over the earlier NCEP-NCAR reanalysis in terms of the 

power associated with the MJO and the lower frequency Kelvin waves.
15

Figure 14: Wavenumber-frequency diagram for precipitation, following Wheeler and Kiladis (1999).

ER is equatorial Rossby wave; EIG is eastward inertio-gravity wave, MRG is mixed Rossby-gravity 

wave. The calculations are based on daily precipitation for 1989-2008 averaged between 15°S and 

15°N. For each product, the left-hand panel is the symmetric component and the right-hand panel is 

the anti-symmetric component.

Reanalyses have traditionally provided good representations of synoptic-scale mid-latitude 

weather systems (e.g., CCSP, 2008). The recent reanalyses have considerably higher resolution 

than previous reanalyses and are beginning to provide useful information about the occurrence of 

tropical storms. For example, Figure 15 shows that tropical storm detection rates in CFSR are 

quite high for all but the eastern Pacific. In addition to enhanced resolution, this result also likely 

reflects the application of a tropical storm relocation procedure prior to performing the analysis. 
16

On the other hand, the most intense storms (for example, category 4 and 5 hurricanes) are still 

not well represented (see, e.g., Figure 16). 

Figure 15: Tropical cyclone detection rate from CFSR. [From Saha et al. (2010)]

 

Figure 16: Maximum intensity of tropical storms in the Atlantic detected in MERRA over the period 

1998-2005, compared with observations from 1997 to 2008.

While there has been significant improvement in the representation of variability on time scales 

shorter than one season, there has been little or no improvement in the representation of the 

diurnal cycle. For example, while the global distribution of the amplitude of the diurnal cycle in 

precipitation is generally reasonable, all reanalyses suffer from incorrect phasing, especially 

during the warm season over land areas where local phenomena such as low level jets and 

mesoscale convective systems, together with complicated/high terrain, can play an important role 

in determining the timing of rainfall (Figure 17). Deficiencies in the diurnal cycle (e.g., the 

timing of precipitation and clouds) have important impacts on the quality of land hydrology, 

contributing to unrealistic soil moisture and evaporative fluxes. 
17

Figure 17: Diurnal variation in precipitation (mm day-1) over the United States for July 2004. The July 

mean has been removed. Results are shown as 6-hour averages for TRMM observations, and for the 

reanalyses: MERRA, ERA-Interim, CFSR, JRA-25, NCEP-DOE R2, and NCEP-NCAR R1. 

A global examination of the synoptic quality of the 20CR fields is made by comparing the 

geopotential height fields with available reanalyses that included upper-air and satellite 

observations, such as ERA-40 (Uppala et al., 2005). Even in the upper-troposphere, the quality 
18

of the surface-pressure based 20CR is high, as shown by the correlation of 300 hPa geopotential 

height anomalies from 20CR with the ERA-40 (Figure 18). 

Figure 18: Map of the local anomaly correlation between four-times daily anomalies of 300hPa 

geopotential from ERA-40 and 20CR for the period 1979 – 2001. The thick black line contour 

indicates the region where ERA-40 and NCEP-NCAR R1 correlate highly (0.975) in this quantity 

during this period. [From Compo et al. (2011)]

@696N )C+(<$%&$,#OC+%+

The dominant components of the climate and variability in the lower stratosphere over the Arctic

were represented quite well in early analyses produced with low model tops (e.g., Pawson and 

Fiorino, 1998a) since the large-scale structure in this region is sampled by radiosondes. Even in 

the Antarctic, temperature retrievals from space-based data were adequate to constrain the polar 

vortex structure, but early analyses do not capture low temperatures characteristic of the polar 

regions. Increasing the height of the upper boundary led to substantial improvements in the 

analyzed structure of the middle stratosphere in ERA-40, ERA-Interim and GEOS-5 compared to 

the earlier products. These model improvements coupled with improved use of space-based 

radiance observations have led to consistent and accurate analyses of the middle and polar 

latitudes in both hemispheres, up to altitudes of 30-40 km. 

At higher levels, even the most recent analyses are less successful. Manney et al. (2008) 

demonstrated that structures in the upper stratosphere and mesosphere are not well captured in 

analyses performed using systems that assimilate only nadir-sounding radiance observations. It 

has also been shown that the application of variational bias correction does not work well in the 

upper stratosphere, where the only data sources are deep-layer radiances from AMSU-A Channel 

14 or SSU, and the underlying models typically have large and fast-growing biases (e.g., 

Rienecker et al., 2011). Successful application of variational bias correction relies on the 

availability of a range of near-independent measurements with different (random) biases and also 

factors in the model state: this fails in the stratopause region because of persistent systematic 

model errors and the absence of a range of observations. 

In the tropics, the quasi-biennial oscillation (QBO) was captured by earlier reanalyses, as in

Figure 19a, but with important departures from observations. The differences largely concern the

strength of the winds at the various levels and the timing of the transitions between regimes
19

(Pawson and Fiorino, 1998b). Reasons for this are not entirely clear, although Gaspari et al. 

(2006) showed that adequately long length scales are needed to spread wind information from 

sparse radiosondes around the globe, and that inadequate data selection can readily lead to good 

observations being rejected in favor of poor analyses in the tropical stratosphere. ERA-40 

analyses of the QBO are in excellent agreement with observations, to the extent that these are 

used as pseudo-observations in CFSR. ERA-Interim and MERRA show very realistic zonal 

wind variability associated with the QBO (Figure 19b). There is less agreement about tropical 

winds in the upper stratosphere, dominated by half-yearly wind oscillations, where no direct 

constraints are available and the issues associated with model bias and the availability of deeplayer radiance observations are major factors. 

Figure 19a: Time series of the monthly mean zonal velocity at Singapore from (top) rawinsonde observations, 

(center) NCEP-NCAR R1, and (bottom) ERA-15. The contour interval is 10 ms-1

, the 15 ms-1 and 25 ms-1 are also 

included (dashed) and positive values (i.e. westerlies) are shaded.

Stratospheric applications of reanalyses include computations of trace gas transport, which are 

strongly sensitive to the strength and structure of the Brewer-Dobson circulation. 

Meteorological analyses performed in the 1990s typically showed an over-strong BrewerDobson circulation, with excessive tropical ascent and high-latitude descent (e.g., Douglass et 

al., 1996), leading to unrealistically low (high) values of tropical (polar) ozone columns. In the 

early 2000s, analyses tended to transport trace gases in an over-dispersive manner (e.g., 

Schoeberl et al., 2003 or Tan et al., 2004), leading to excessively young mean age of air and 

unrealistic age spectra. In the middle 2000s, the introduction of time smoothing techniques led 

to more realistic ozone transport computations and mean age of air distributions (e.g., Pawson et 

al., 2007). Monge-Sanz et al. (2007) showed successive improvements in stratospheric transport 

computed from ECMWF analyses that arose from increasing vertical resolution in the 

stratosphere of the analysis system and ultimately the introduction of 4D-Var.
20

Figure 19b: The QBO and SAO from the zonal mean zonal wind component from MERRA and ERAInterim, averaged between 10°S and 10°N. 

Regardless of these improvements in the Brewer-Dobson circulation, the fact remains that 

stratospheric transport depends crucially on our ability to represent the slow divergent motion in 

a system where assimilation constraints are imposed at a frequency consistent with synoptic 

variations and which is optimized to constrain the rotational component of the flow. 

@6@ )%+/8#(&/8(0,/3H$+%>(K&%1&"1*1$=

All reanalyses are affected by changes in the observing system, especially quantities involving 

the hydrological cycle (e.g., precipitation and clouds). Such quantities are especially sensitive to 

changes in moisture data through their impacts on the model convection schemes. Major changes 

to the observing system – such as the introduction of SSM/I in 1987, AMSU in 1998 and AIRS 

in 2002 – can produce spurious trends and distort long-term variability. As an example, the time 

series of global mean precipitation, which is a particularly sensitive measure of changes in the 

hydrological cycle, shows a number of abrupt changes as well as trends that appear to be linked 

to changes in the satellite observing system (Figure 20a). The more recent reanalyses, while 

showing more realistic magnitudes, still suffer from this problem. Interestingly, the “responses” 

to the observing system changes are very different among the different reanalyses. After the 

introduction of AMSU-A on NOAA-16, ERA-Interim and MERRA have virtually the same 

global mean precipitation rate and similar annual cycles. However the time series do not stay 

synchronized, and, particularly after the demise of AMSU-A on NOAA-16, the two time series 

depart toward their different pre-NOAA-16 levels. The time series of the inferred analysis 

increment of moisture (Figure 20b) clearly shows the impact of the changing observing system. 

The increments in MERRA and CFSR are very highly correlated although their mean values 

differ.

Spurious shifts in global mean precipitation in ERA-Interim coincide with changes in the number 

of assimilated rain-affected radiances from SSM/I (Dee et al., 2011, Section 5.2.1). These data 

were assimilated using an early version of the 1D+4D-Var retrieval scheme, which introduced a 

MERRA

ERA-Interim
21

net drying effect into the reanalysis (Geer et al., 2008). On the other hand, there are no 

discernible changes in mean precipitation in ERA-Interim that can be related to the use of

AMSU or SSM/I clear-sky radiance data.

Figure 20a: Global mean precipitation from the most recent set of reanalyses compared against two 

observational data sets, GPCP and CMAP. The times of introduction of AMSU-A on NOAA-15 and 

NOAA-16 are shown, along with the timing of the removal of AMSU-A on NOAA-16 because of 

instrument failure. 

Figure 20b: Global mean analysis increment of moisture inferred from the imbalance between 

evaporation and precipitation, from ERA-Interim (red), JRA-25 (orange), MERRA (green), and CFSR 

(purple).

N15 N16

N16

AMSU-A
22

AMSU-A has a strong impact on the annual cycle in MERRA (Figure 21), much more so than in 

(and in the opposite direction to) CFSR (not shown). 20CR does not show this effect, providing 

additional evidence of the link to the satellite observing system (Zhang et al., 2012).

On the other hand, substantial progress has been made in representing trends in near-surface air 

temperature over land (e.g., Figures 22a and 22b), even though surface observations affect the 

upper-air reanalyses only indirectly via the land-surface boundary conditions used by the forecast 

model (Simmons et al., 2010).

Figure 21: The annual cycle of the global mean precipitation, evaporation, and their difference, from 

MERRA. The pre-AMSU era is shown in green and the post-AMSU era in cyan. 

Figure 22a: Temperature anomalies at 2m (K) calculated relative to the 1989-1998 mean. The time

series from ERA-40 and ERA-Interim are compared against the CRUTEM3 observations (Brohan et 

al., 2006). The topmost panels are the time series of 12-month running global average anomalies. The 

top left-hand panel is the time series from anomalies sampled according to availability of CRUTEM3 

data (see lower set of panels); the top right-hand panel is from the raw time series.
23

Figure 22b: The annual global mean 2-m temperature over land in NCEP-NCAR R1 (green), CFSR 

(red), and GHCN-CAMS (blue) over the period of 1979–2009. Units: K. Least squares linear fits of 

the three time series against time (thin lines). The linear trends are 0.66, 1.02, and 0.94 K (31 yr)–1 for 

R1, CFSR, and GHCN-CAMS, respectively. (Note that straight lines may not portray climate change 

trends accurately). [From Saha et al. (2010)]

 Figure 23: Global mean temperature anomalies (K) in the middle stratosphere from ERA-Interim

(blue) and MERRA (red). Data are shown for 5hPa (top) and 10hPa (bottom). The anomalies are 

computed from the mean annual cycle in 2000-2010 for each individual analysis. 
24

Figure 23 shows time series of the middle stratospheric monthly temperature anomalies in 

MERRA and ERA-Interim. Anomalies are calculated relative to the mean annual cycle in 2000-

2010, a period of relative stability in both time series. ERA-Interim shows a discontinuity at 5

hPa in 1998 with the introduction of AMSU-A. While MERRA does not show an obvious SSUAMSU transition in 1998, it does show evidence of data instability when the first versions of 

SSU data were available. In the 1980s when SSU was available on both AM and PM platforms 

with relatively short lifetimes, the time series has marked interannual variability. With the advent 

of NOAA-11 and NOAA-14, the time series is more stable, but with large annual cycles. This is 

a complex issue that needs to be addressed in more detail in future reanalyses: it depends on the 

presence of model biases, the details of the radiative transfer modeled used, the bias correction,

and the lack of information on vertical structure inherent in the nadir observations available for 

reanalysis. At 10 hPa, where the temperature is more strongly constrained by radiosonde and 

other data, the variations are more stable and the SSU-AMSU transition is not noticeable. 

However, the issues associated with SSU in MERRA still leave their imprint.

N6 P*&//1/3(24$4%+(!$>,#OC+%1'(D+&/&*=#+#

The first generations of reanalyses from NCEP and ECMWF have proven to be extremely 

valuable scientific tools, enabling climate and weather research not otherwise possible. Their 

utility stems from the availability of data at regular intervals in time and space, including many 

variables not directly observed. An important caveat for users, however, is that a reanalysis is not 

an observation data set, but rather a merged product of model fields constrained by observations. 

Since the observational constraint is only partial and, moreover, varies in space and time, biases 

in the model can result in spurious signals in the reanalyzed fields. As such, understanding how 

errors and uncertainties in both models and observations affect the quality of the reanalysis is 

crucial for gauging its utility as well as the uncertainty in the reanalysis itself. 

Figure 24: Global mean precipitation (mm day-1) for the available reanalyses compared against two 

observational data sets, GPCP and CMAP. 
25

Significant improvements have come from each generation of reanalyses. Both the 

improvements and many of the remaining deficiencies are apparent in the (extended) time series 

of global mean precipitation shown in Figure 24. Many improvements have come from the NWP 

imperative, for which the systems will continue to evolve, taking advantage of new data types 

and improved data assimilation methodologies. However, the question remains as to what needs 

to be done to improve reanalyses further, especially to address jumps and false trends associated 

with changes in the observing system.

While agreeing that addressing the issue of artificial trends associated with the changing 

observing system is a top priority, workshop participants concluded that continuing to update 

reanalyses is still useful:

• To include improvements in data, using previously unused data (cloudy radiances, 

etc.)

• To include improvements in models (e.g., reduced biases with resolution)

• As we develop the implementations to help deal with the changing observing system 

(e.g., changing the background error covariance, B, with the observing system)

• To provide fields needed for historical re-forecasts for short-term climate

• To make still further major improvements in the water and energy cycle budgets.

The main issues to be addressed for the next generation reanalyses were identified as:

• Improving the hydrological cycle

• Improving the quality of the reanalyses in the stratosphere

• Improving quality of the reanalyses over the polar regions

• Improving estimates of uncertainty

• Reducing spurious trends and jumps

• Extending the reanalysis record for as long as possible, to include the 1970s for 

reanalyses focused on the satellite era, and to go back at least to 1850 with those 

reanalyses using sparse observations.

N65 :,8+*

As seen in Figure 20, the “responses” to observing system changes are very different among the 

different reanalyses. These responses can depend on the analysis method and/or the model used 

for data assimilation. What is clear is that correcting model biases will be an important element 

of reducing both the size of analysis increments and also reducing spurious trends and jumps 

associated with changes in the input data streams.

N69 !##1>1*&$1,/(<=#$+>(

Accounting for changes in the observing system over time is a major challenge for data 

assimilation in the context of reanalysis, and one that is likely to increase in significance as 

reanalyses are extended over many decades, and even centuries. While improved models will 

likely reduce the (often deleterious) effects of observing system changes on reanalysis quality 

and consistency, data assimilation systems still must be adapted to reflect these changes in terms 

of the expected accuracy of the background forecast. For example, it is well known that the 

quality of an analysis produced with a sparse observing system is highly sensitive to the 
26

specification of the background error covariances. As noted in section 2, ensemble-based data 

assimilation systems provide a unique capability in this regard. However, since most centers 

involved in generating the current- (and, presumably, next-) generation reanalyses will likely 

continue running some form of variational scheme, strategies for adapting the background error 

covariances in these systems should be considered.

NCEP and GMAO (in collaboration with ESRL) and ECMWF are developing hybrid variational 

schemes in which the background error covariances are augmented or replaced by information 

produced by an ensemble system running in some tandem configuration. The potential benefits 

of such systems for NWP have already been demonstrated (e.g., Buehner et al., 2010a, b), but it 

remains to be seen whether such schemes are in regular use by the time the next-generation 

reanalysis are produced. In the meantime, the JMA has begun its JRA-55 reanalysis using a 

traditional 4D-Var scheme, but with a simple inflation factor applied to the background error 

variances during the pre-satellite era. Pre-production tests have shown modest, but noticeable, 

positive impacts with this technique in terms of fits-to-observations, upper-air analysis errors and 

forecast skill (Ebita et al., 2011).

The effects of model error can also be accounted for (to varying degrees) within the context of 

the data assimilation scheme. The GMAO has experimented with a variational model bias 

correction scheme run as part of the minimization—analogous to the variational observation bias 

correction scheme discussed in section 2—as well as with the application of time-averaged or 

slowly varying bias estimates (produced by this scheme beforehand) as a forcing term in the 

model equations (analogous to IAU). Neither approach has produced consistently beneficial 

results so far, but will likely be re-examined as part of the development effort for the follow-on 

to MERRA. 

Ultimately, a weak-constraint formulation, in which a model error term is added to the analysis 

cost function, must be employed to produce an optimal analysis. However, specification of the 

required model error covariance operator (required in the context of 4D-Var) remains a 

significant stumbling block. ECMWF has experimented with several weak-constraint 

configurations, representing various levels of approximation to the full four-dimensional 

problem. A version of weak constraint 4D-Var applied to the stratosphere (without model error 

cycling) has been running operationally at ECMWF since late 2009 (e.g., Trémolet, 2006) and it 

is expected that the next reanalysis produced by ECMWF will be run with some form of weakconstraint 4D-Var. The 4D-Var systems under development at GMAO and NCEP are also weakconstraint capable, but have undergone only limited testing to date.

Users of reanalysis data often request a characterization of the quality of and the uncertainty in 

the fields. While intercomparison with reference data sets is common practice for ascertaining 

quality, such comparisons are usually restricted to long-term climatological statistics and seldom 

provide state-dependent measures of the uncertainties involved. The ensemble nature of the 

20CR provides a new capability in atmospheric reanalysis: it is the first reanalysis dataset to 

estimate the uncertainty in the analysis fields at each analysis time, albeit according to the model 

ensemble used. To evaluate whether the ensemble uncertainty varies as expected with the timechanging observation network, Figure 25 compares the expected error (red curves) to the actual 

RMS differences of the increments: the first-guess ensemble mean minus the assimilated

observations (blue curves). The time variation of the actual and expected error is consistent with 
27

the variations in the number of observations used in the analysis (black curves; note the log 

scale).

Figure 25: Time series of the 6-hourly first guess (blue curves) root-mean-square (RMS) difference 

from pressure observations and (red curves) expected RMS difference calculated over separate years 

from 1870 to 2008 for the (a) northern hemisphere (20°N-90°N) and (b) southern hemisphere (20°S90°S). The square root is calculated on the annual mean square values. The thin black curve shows the 

average number of pressure observations for each analysis in the indicated year (note the logarithmic 

scale). [From Compo et al. (2011)]

Comparison of such estimates with the statistics of differences between the most recent 

reanalyses using the full observing suite would be a useful undertaking. The innovations and 

analysis increments provide additional information on the quality of the analyses, as well as on 

the consistency of the different observations and how they are represented in the analysis. In 

addition to sharing observations, it would be useful for reanalysis producers to share such 

information on system performance in order to guide future development. The sharing of these 

and other metrics as part of future reanalyses would benefit users as well.

N6@ A"#+%?&$1,/#

Just as source codes for models and data assimilation systems are version-controlled so that 

changes are traceable, so too is it important to track the quality and release history of 

observational data sets used for reanalysis. The need for this capability is likely to increase as the 

number and types of available observations increase and as existing data sets are reprocessed to 

improve their quality and information content. For example, the Global Space-Based InterCalibration System (GSICS) has been established in part to retrospectively re-calibrate a wide 

range of archived satellite data sets to increase their utility for reanalysis and climate studies. 

Currently, there is no central data steward for reanalysis input observations, but only some 

informal sharing of observations and quality information between reanalysis centers. All 

participants in the workshop agreed that some centralization would be beneficial to future 

reanalyses, and it was noted that the WCRP Observations and Analyses Panel (WOAP) has 

initiated a committee to address this issue. So as to maximize the cross-center collaborations, 

participants agreed that future reanalyses should avoid using proprietary observations or input 

data sets. In the meantime, the current reanalyses have helped identify biases and trends in 
28

currently available satellite and in situ data sets that should be addressed in preparing for future 

reanalyses.

 Figure 26: Globally averaged bias estimates (K) for AMSU-A channel 6 radiances from NOAA15, -16, -17 and Aqua based on ERA-Interim (from Dee and Uppala (2009), top) and MERRA 

(bottom). The bias estimates based on a 12-hourly assimilation cycle in ERA-Interim and a 6-

hourly assimilation cycle in MERRA.

DU09 identified a number of such issues by analyzing the performance of the variational bias 

correction scheme for satellite radiances used in ERA-Interim. Figure 26 shows results from that 

paper, indicating a long-term trend in the global bias estimates for AMSU-A channel 6 radiances 

from different satellites, as well as similar results based on MERRA. Both reanalyses show a 

downward trend as large as 0.5 K per decade, although the bias estimates themselves differ in 

each system as a result of the different background states and other observational information. 

Dee and Uppala attributed this trend to known instrument problems, but also noted that the 

trends may be over-exaggerated in these results due to the assimilation of increasing numbers of 

(warmly) biased aircraft observations in the upper troposphere. Evidence of the latter is 

illustrated in Figure 27, which, like Figure 26, compares results from DU09 for ERA-Interim 

with ones for MERRA, but in this case showing observation-minus-analysis departures and 

observation counts for radiosonde reports at 200 hPa. Comparison with the same statistic for 

aircraft temperatures (not shown) shows that the increase in the magnitude of the upper 

tropospheric bias with respect to radiosondes starting in the mid- to-late nineties coincides with 

an increase in aircraft observations, which have a warm bias (Cardinali et al., 2003; Ballish and 

Kumar, 2008; DU09). As pointed out by DU09, after 1999 the mean analyzed temperatures near 

these altitudes are increasingly determined by the more numerous aircraft data, especially in the 

Northern Hemisphere, even though the observation error specified for radiosondes tends to be 

slightly lower than that specified for aircraft (0.65 K for radiosondes and 0.8 K for most aircraft 

observations at 300 hPa in MERRA).

ERA-Interim

MERRA
29

Figure 27: Globally analysis departures (K) and observation counts for radiosonde observations at 

200 hPa based on MERRA (top) and ERA-Interim (from Dee and Uppala (2009), bottom). 

Observation counts represent 12-hour totals in ERA-Interim and 6-hour totals in MERRA.

As noted above, off-line bias corrections (Redder et al., 2004) and a “homogenization” procedure 

(Haimberger, 2007) have been applied to radiosonde observations in MERRA, and it has been 

proposed to apply bias corrections to aircraft temperature observations (Balish and Kumar, 

2008). There is some effort underway to develop bias adjustments for radiosonde winds and 

moisture. Bias corrections were made to surface pressure data as part of 20CR. Ultimately, bias 

corrections for conventional observations are likely best estimated within the data assimilation 

scheme so as to be adaptive and consistent with all other information used in the analysis.

It is also worth noting here that most centers make only limited use of surface-sensitive radiance 

data over land or snow- and ice-covered surfaces that might otherwise be important sources of 

information in a reanalysis context. In MERRA, for example, microwave radiances from AMSUA channels 1-6 and 15 were excluded over snow, ice and mixed surfaces, as were radiances from 

AMSU-B channels 1, 2 and 5, and MSU channels 1 and 2. Surface-sensitive infrared radiances 

were also excluded over land. Successful use of these data requires, amongst other things, 

improved modeling of surface emissivity and other relevant land surface characteristics.

A number of operational data suppliers have reprocessed some of their products for climate 

studies and reanalysis purposes. For example, ESA has produced a complete set of ERS-1 and 

ERS-2 ocean surface winds, 1991-2007. EUMETSAT has reprocessed METEOSAT winds from 

1982-1987, with plans to reprocess more. JMA has produced reprocessed GMS winds from 1987 

through 2003. The GSICS in conjunction with NESDIS is in the process of recalibrating a 

number of the historical TOVS radiance datasets. Projects such as these are very important for 

improving the quality of existing (and future) satellite data sets for reanalysis. 
30

To adequately address the preparation of conventional (non-satellite) data, the problem revolves 

around a much longer and more scattered archive of observations. For example, there is still the 

need for discovery and recovery of significant parts of the observed record of the 20th century.

Even the satellite era (1979-present) conventional datasets are still to some degree fragmented 

among national, military, university, and other sources, although overall the situation has been 

greatly improved under the auspices of preparation for reanalysis projects completed so far. Due 

in part to the ongoing success of reanalysis efforts, there has been growing movement within 

traditional climate data centers such as NCDC and NCAR to consolidate their disparate archives 

and produce standardized and combined “macro” datasets suitable for reanalysis input. This very 

welcome development constitutes a big step towards some centralization of the stewardship of 

reanalysis data. These centers have the mandate and wherewithal to document observations’ 

provenance across versions of the datasets, and possibly including records of observation 

performance in different reanalysis projects. Several independent initiatives to recover and 

modernize historical observation from diverse sources, such as the ACRE project (Allan et al.,

2011) and the International Surface Temperature Initiative (Thorne et al. 2011), are also very 

productive overlaps between strictly climate research interests and NWP/reanalysis attempts to 

recreate climate from observations. 

Another major observational issue is the lack of long-term in situ temperature observations of the 

upper stratosphere, which, coupled with the model biases and the deep weighting functions of the 

SSU and AMSU-A radiance channels, makes precise constraints on meteorological fields rather 

difficult to ascertain. It also makes stratospheric satellite bias corrections difficult because of the 

major contributions of model bias in variational bias correction techniques. Figure 23 illustrates 

long-term temperature fluctuations that exceed the magnitude of expected decadal trends. 

Obviously, future reanalyses will need to focus on improving models (eliminating model biases) 

and also better calibrations of the input radiance data and improvement in the way these interact 

with the background states to produce the analyses. 

Limb-sounding temperature data are available for certain periods, and may be used as “anchors” 

for limited numbers of years, but these datasets generally do not overlap, so issues related to 

cross-dataset bias have not been addressed in detail. High-quality temperature time series are 

available from occultation measurements, but the extremely low density of these data makes 

them less useful for assimilation: a more promising way of use in reanalyses might be as 

calibration datasets – an aspect that will require substantial developments. The CFSR used 

reprocessed CHAMP and COSMIC occultation data starting in 2001, but these data are still more 

or less experimental, and a work in progress.

Similar arguments apply to ozone analyses, for which the SBUV data provide generally adequate 

constraints at any time, but issues of cross-calibration of the sensors, due to orbital differences 

and instrument characteristics, need to be addressed. Here, too, the use of sparse occultation 

observations for calibration may be promising. Additionally, inclusion of time-dependent 

greenhouse gases and ozone chemistry in the underlying model will be needed to more 

realistically represent long-term trends in temperature and ozone in the middle atmosphere. 
31

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N6N65 QOO+%(,'+&/(&/8(*&/8(#4%-&'+(

Many of the questions to be addressed through reanalyses (budgets and exchanges, trends, 

attribution of major anomalies, etc.) would benefit from consistent analyses across components. 

Hence there is considerable interest in integrated Earth System analyses. The NCEP CFSR is the 

first atmospheric reanalysis conducted where the first guess was provided by the integration of a 

coupled ocean-atmosphere-sea-ice-land-surface model. An example of the benefit from coupling 

can be seen in the correlation between precipitation and SST, compared to earlier reanalyses, 

seen in Figure 9. It is expected that future developments will have some focus on coupling, 

whether weakly coupled as in the CSFR or strongly coupled when observations in one 

component will impact the state in another. 

N6N69 .&%",/R(&+%,#,*#R(,$C+%(',/#$1$4+/$#

The increased interest in “Earth System” reanalyses that include atmospheric composition is 

based partly on the widespread use of reanalyses for driving atmospheric aerosol, chemistry and 

carbon models, and also on the increasing availability of observations of atmospheric 

composition in the past decade. It is reasonable to work towards reanalyses that include 

atmospheric aerosols, carbon species, and reactive gases, at least for the post-2000 period, as 

future projects are formulated. Such Earth System components depend fundamentally on the 

adequacy of the underlying meteorological analyses to constrain aspects such as transport 

(winds), reactive chemistry (temperature), and emissions of numerous constituents that depend 

variously upon physical properties such as surface winds (e.g., sea salt, dust, oceanic carbon 

exchange), surface temperature (e.g., isoprene), soil moisture (CO2). Additional constraints from 

space-borne sensors can be included into these analyses, such as leaf-area index (e.g., MODIS) 

that helps constrain respiration and photosynthesis calculations, burned areas and fire radiant 

energy (e.g., MODIS) that helps constrain CO, CO2, black- and organic-carbon aerosol 

emissions, and additional chemical species, such as NO2 (e.g., from OMI and GOME), that help 

constrain air pollution. Inclusion of these additional observations to variously constrain 

emissions and chemical processes adds complexity to the underlying models used in reanalyses, 

transporting additional species and computing the interactions among them, and adds additional 

cost to the analyses (more variables in the state vectors). 

While assimilation of these constituents is in theory straightforward, there are presently many 

uncertainties in the details, such as how to best represent background error covariances (with 

potential cross-species components and co-variance with temperature and transport). It is also 

important to realize that the multi-decadal analyses that the community has come to expect for 

meteorology are highly unlikely to be performed for a broad range of aerosols and constituents 

because of the dearth of relevant observations over the past decades. 

S6 .%,##H.+/$+% .,,%81/&$1,/ -,%($C+(T+U$H3+/+%&$1,/(D+&/&*=#+#

The workshop participants agreed that to improve the representation of the climate record in 

reanalyses it will be necessary to understand some of the sensitivities and responses of the 

assimilation systems to the different input data as well as the impact of different input data (such 

as the use of cross-calibrated MSU vs. using the variational bias correction to account for MSU 

channel drifts).
32

An obvious area that needs investigation is the sensitivity of the different systems to the 

introduction (and removal) of the AMSU-A instruments on NOAA-15 and -16. Each system

responds differently, but there is an imprint of change in global precipitation in many of the 

systems (Figure 20). Differences may reflect differences in QC measures. For example, 

developers at NCEP and GMAO cited a potential deficiency in QC for the water vapor sensitive 

channels (1-5 and 15) on AMSU-A in the GSI used for MERRA as compared to ERA-Interim. 

Hence the suggestion followed to undertake an in-depth investigation of how these channels 

were used in the various reanalyses. Another difference between the systems is the assimilation 

of rain-affected radiances (1d+4d var) in ERA-Interim. This was implemented/modified in ERAInterim in 1992 and had a noticeable impact on the global precipitation (Figure 20a). This then 

may lessen the impact of new data on the global precipitation in the ERA-Interim system. 

Obviously judicious experimentation and comparison may provide insight into the variations that 

are of concern.

The groups agreed to start a joint project of sensitivity analysis:

• Examine data utilization, including QC decisions and innovation statistics

• Identify joint experiments to be conducted to elucidate common issues

• Share information on results from sensitivity experiments.

To set the stage for future coordination, reanalysis centers are encouraged to prepare and share 

lists of anomalous behavior or features to help identify how common such anomalies are across 

the various reanalyses.

S65 A"#+%?&$1,/#

Much of the hard work in developing reanalyses is the assembly and processing (including 

correction and QC) of the observations. Crucial new inputs, including updated versions of the

International Comprehensive Ocean Atmosphere Data Set (Worley et al., 2005) and the 

International Best Track Archive for Climate Stewardship (Knapp et al., 2010), millions of 

newly recovered surface observations (Yin et al., 2008), and improved satellite records (e.g., Zou 

et al., 2006), were important contributions to the improvement of the current reanalyses over the 

previous generation. A workshop on improving observations for reanalysis (Schubert et al., 

2006) recommended improvements to historical observations (including data mining), improved

quality control, and further cross-calibration and bias-correction of observations to help to reduce 

the impacts from changes in the observing system. As noted above, some centralization and 

coordination on input observations and ancillary data would be beneficial to future reanalyses. 

One recommendation was for ACRE to expand from contributing surface observations to just the 

20CR to contributing to all future reanalysis efforts, possibly acting as a data coordinator and 

provider of surface data for all future reanalyses in collaboration with working groups of GCOS 

and WCRP.

S69 7//,?&$1?+(I1&3/,#$1'#

Given the lack of definitive validation data and the vast array of possible comparisons, the 

workshop participants encouraged the development of innovative diagnostics. Some examples:
33

• A useful metric of both quality and uncertainty is the level of variance of one 

reanalysis explained by another. This should be a standard diagnostic for the various 

reanalyses.

• Summaries such as the joint frequency distribution of long-wave and short-wave

fluxes in Figure 7, developed by J. Chen, provide a new and efficient view of the 

quality of the radiative analyses.

• A contextual bias analysis, as developed by A. da Silva, helps to see if different types 

of observations are providing conflicting information to the analysis.

The workshop participants also proposed the identification of important metrics to assess 

reanalysis quality, beyond NWP skill.

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7/?,*?+>+/$(

The participants agreed that a mechanism for the timely exchange of information about the 

quality of the reanalyses, results of experiments, and plans for future developments was needed. 

As a response to this recommendation, the Reanalysis Intercomparison and Observations 

collaborative wiki site at Reanalyses.org was formed with support from NOAA, GCOS, WCRP, 

and ACRE and the active participation of all of the centers generating the datasets. At NOAA’s

request, the site was expanded to include ocean reanalyses. The goal of Reanalyses.org is to 

facilitate comparison between reanalysis and observational datasets. Evaluative content provided 

by reanalysis developers, observationalists, and users; and links to detailed data descriptions, 

data access methods, analysis and plotting tools, and dataset references are available. One focus 

is on discussions of the recovery of observations to improve reanalyses. The wiki framework 

encourages scientific discussion between members of Reanalyses.org and other reanalysis users.

S6N .,,%81/&$1/3(!/'1**&%=(P%,84'$#W(!''+##1"*+(!##1>1*&$+8(A"#+%?&$1,/#(

&/8(<$&$1#$1'#(

Making progress in understanding the (possibly different) sensitivities of the different systems to 

changes in the observing system, or (for example) identifying any benefits from prior crosscalibration of different satellites, will take a concerted effort at comparing information in the 

ancillary “feedback data” generated through the process of assimilation: innovations, bias 

corrections, outcomes of data selection algorithms, cloud detection outcomes, etc. In addition, 

examination of gridded feedback files and contextual bias analyses should be helpful to 

understand how the data are impacting the system performance. Initial progress in making these 

feedback data available has been made. For the 20CR, the feedback data are available from the 

International Surface Pressure Databank (rda.ucar.edu/datasets/ds132.0). The gridded feedback 

files from MERRA are available online with the primary MERRA products at

http://disc.sci.gsfc.nasa.gov/mdisc/.

S6S :,?1/3(2,%F&%8

Workshop participants agreed to identify a small group to coordinate these efforts between the 

centers. Support or organization of such coordination at the international level would facilitate 

collaborations at the technical level and ensure advances for the next generation of reanalyses. 

Participants agreed that resources directed to operational and laboratory development of 
34

reanalyses was crucial for future success and continued improvement of these vital datasets. This 

is consistent with the action item C12 in the 2010 GCOS Implementation Plan (GCOS, 2010). 

University research will also be an important component of developing advances for the next 

generation of reanalyses. 

X6 <4>>&%=(,-(D+',>>+/8&$1,/#

Target areas for improvements for the next generation of atmospheric reanalyses include:

• The hydrological cycle

• The quality of the reanalyses in the stratosphere

• The quality of the reanalyses over the polar regions

• Representation of surface fluxes

• Observational bias corrections and/or cross-calibration across platforms

• Estimates of uncertainty in the analyses, and

• Reductions of spurious trends and jumps associated with the changing observing 

system.

Several recommendations were made regarding areas for coordination between reanalysis centers

in order to prepare for the next reanalyses:

• Preparation and sharing of lists of anomalous behavior or features to help identify how 

common anomalies are across the various reanalyses.

• Examination of data utilization, including QC decisions, innovation statistics, bias 

corrections, outcomes of data selection algorithms, cloud detection outcomes, etc.

• Identification of joint experiments to be conducted to elucidate issues found to be in 

common in different reanalyses. 

• Sharing of results from jointly designed sensitivity experiments.

• Coordination of input observations and ancillary data and centralization of the serving 

of these observations where possible. 

• Expansion of ACRE’s efforts for contributing surface observations to 20CR to 

contributing to all future reanalysis efforts, possibly acting as a data coordinator 

and provider of surface data for all future reanalyses in collaboration with working 

groups of GCOS and WCRP.

• Development of innovative diagnostics and metrics to help quantify observational 

issues, the quality and also agreement of the reanalyses.

The workshop recommended that a mechanism be established for the timely exchange of 

information about the quality of the reanalyses, results of experiments, and plans for future 

developments. This idea was quickly embraced with the establishment of Reanalysis.org. 

However, further progress is needed in the utilization of such a capability to enhance 

communications between reanalysis groups.

Finally, consistent with the Arkin et al. (2003) workshop report, the workshop participants 

recommended extending the reanalysis record for as long as possible, to include the 1970s for 
35

reanalyses focused on the satellite era, and to go back at least to 1850 with those reanalyses using 

sparse observations.

Y6 D+-+%+/'+#

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statistics. J. Climate, 20, 1377-1403.

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N.J. Livesey, 2007: Stratospheric Transport Using Six-Hour Averaged Winds from a Data 

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Simmons, A. J., K.M. Willett, P.D. Jones, P.W. Thorne, and D.P. Dee, 2010: Low-Frequency 

Variations in Surface Atmospheric Humidity, Temperature, and Precipitation: Inferences 

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39

!OO+/81U(!W(<4>>&%=(,-(D+'+/$(&/8(A/3,1/3(D+&/&*=#+#

A.1 NASA MERRA

The development of the Modern-Era Retrospective analysis for Research and Application 

(MERRA) began with two primary objectives. It was recognized that various aspects of the 

hydrologic cycle represented in previous generations of reanalyses were not sufficient for climate 

and weather study, and MERRA proposed to improve upon the water cycle as a contribution to 

the science community and reanalysis research. MERRA’s assimilation of the complete satellite 

era is intended to place observations from NASA’s Earth Observing System satellites in a 

climate context.

MERRA is based on the GEOS-5 atmospheric data assimilation system, version 5.2.0. The 

NCEP-GMAO Gridpoint Statistical Interpolation (GSI) analysis is integrated with the GEOS-5 

atmospheric model in a six-hour assimilation cycle. At !° latitude " 2/3° longitude with 72 

vertical levels to 0.01 hPa, the spatial resolution of MERRA is finer than previous generation 

atmospheric reanalyses. MERRA utilizes Incremental Analysis Updates (IAU) that allows for 

reduced “spin-down” effects by applying the analysis as a tendency term in the governing 

equations. The observational forcing on the data from the increments is tallied in the output 

budgets of the model (e.g. water and enthalpy). MERRA provides complete atmospheric budgets 

that balance, including the analysis terms. 

MERRA was processed in three separate streams, each spun-up in two stages: from a two-year 

analysis at 2° " 2.5° and then a 1-year analysis on the MERRA grid. Streams 1 and 2 were each 

extended to overlap the next stream to examine the nature of the spin-up as well as 

predictability/uncertainty. The final MERRA distribution is from the longer streams 1 and 2, so 

that the distributed streams 2 and 3 have been spun up for 5 and 4 years, respectively, at MERRA 

resolution.

The MERRA observational suite was based in good part on the observational processing by 

NCEP. Notable differences include the ERA surface winds, where an older version was used for 

MERRA, and the SSM/I input data, which came from Goddard (precipitation) and from RSS 

(radiances and surface winds). The radiosonde data were processed differently (more similar to 

ERA-Interim processing) and the ERA-40 blacklist was used.

MERRA product collections are available at the Goddard Earth Sciences Data and Information 

Services Center (GES DISC, http://disc.sci.gsfc.nasa.gov/mdisc/).

A.2 ECMWF ERA-Interim

ERA-Interim (Dee et al., 2011a) is the latest global atmospheric reanalysis produced by the 

European Centre for Medium-Range Weather Forecasts (ECMWF). The ERA-Interim project 

was conducted in part to prepare for a new atmospheric reanalysis to replace ERA-40, which will 

extend back to the early part of the twentieth century. Since the meeting took place, the ERAInterim reanalysis has been extended backward to include the years 1979 – 1988 (Dee et al.,

2011b), so the reanalysis extends from 1 January 1979 to the present, continuing forward in nearreal time. Production was accomplished in two streams, from 1979 to 1988 and 1989 onwards.

ERA-Interim uses a sequential 4D-Var data assimilation scheme, advancing forward in time 

using 12-hourly analysis cycles. It is based on IFS release Cy31r2, used for operational 
40

forecasting at ECMWF from 12 December 2006 until 5 June 2007. The model has a spectral 

T255 horizontal resolution and 60 model layers with the top of the atmosphere located at 0.1 

hPa. Since ERA-40, there have been major updates to the model’s moist physics components and 

the assimilation includes rain-affected SSM/I radiances as described in detail by Dee et al. 

(2011).

Gridded data products include a large variety of 3-hourly surface parameters, describing weather 

as well as ocean-wave and land-surface conditions, and 6-hourly upper-air parameters covering 

the troposphere and stratosphere. Vertical integrals of atmospheric fluxes, monthly averages for 

many of the parameters, and other derived fields have also been produced (Berrisford et al., 

2009).

A.3 NCEP CFSR

NCEP has now completed the CFSR for the 31-yr period of 1979–2009. It took almost 2 years to 

accomplish this feat. The primary improvements, compared to previous NCEP global reanalyses 

are i) the coupling to the ocean during the generation of the 6-h guess field, ii) an interactive sea 

ice model, and iii) the assimilation of satellite radiances for the entire period. In addition, the

much higher horizontal and vertical resolution (T382L64) of the atmosphere, model, and 

assimilation improvements over the last 10–15 years, and the use of prescribed CO2

concentrations as a function of time, make for substantial improvements over R1 and R2, which 

were run at T62L28 resolution.

Another major advance was the real-time monitoring that took place during the execution of the 

CFSR. Thousands of graphical plots were generated automatically at the end of each reanalyzed 

month and were displayed on the CFSR Web site in real time. Many scientists from both CPC 

and EMC monitored different aspects of the reanalysis during this 2-year process. There were 

many times that the reanalysis was halted to address concerns that something may have gone 

wrong, and many corrections, backups, and restarts were made to ensure that the process was 

done as correctly and homogeneously as possible. An extremely large atlas of plots depicting 

nearly all aspects of the CFSR is available online at http://cfs.ncep.noaa.gov/cfsr.

A companion reanalysis for CFSR, called CFSR-Lite, is now in preparation at NCEP, which will 

reanalyze the global atmosphere at resolution T126L64, from 1948 through 2010. It will be 

conducted in only two streams with the break between 1978 and 1979, in order to reduce 

discontinuities, which were problematic at the five CFSR stream boundaries. It is anticipated to 

complete this project sometime in 2012.

A.4 JMA JRA-25

The Japan Meteorological Agency (JMA) and the Central Research Institute of Electric Power 

Industry conducted the Japanese 25-year re-analysis (JRA-25, Onogi et al., 2007) covering the 

period from 1979 to 2004. JRA-25 uses 3D-Var. The model has a spectral resolution of T106 

(equivalent to a horizontal grid size of around 120 km) and 40 vertical layers with the top level at 

0.4 hPa. Most of the observational data used in ERA-40 were used, however additional data such 

as wind profile retrievals surrounding tropical cyclones (TCRs) supplied by Dr. M. Fiorino and 

daily snow depth observations recorded in “Monthly Surface Meteorological Data in China”
41

published by CMA and digitized by MRI/JMA were also used. The reanalysis was conducted in 

two streams, one from 1979 to 1991, and one from 1991 to 2004. JRA-25 products (both sixhourly and monthly mean) are available from http://jra.kishou.go.jp/JRA-25/index_en.html.

A.5 JMA JRA-55

Since the workshop, JMA started their new atmospheric reanalysis, JRA-55 (Ebita et al., 2011), 

with plans to cover a 55-year period from 1958. The goal of JRA-55 is to provide a 

comprehensive atmospheric dataset suitable for studies of climate change or multi-decadal 

variability. The system uses an updated JMA operational NWP system, with a new radiation 

scheme to the forecast model and 4D-Var with Variational Bias Correction (VarBC) for satellite 

radiances. The model grid is TL319L60 (~60km) with a top layer at 0.1 hPa. A simple inflation 

factor (x 1.8) is applied to the background error covariances before 1972. The RAOBCORE v1.4 

correction of Haimberger (2007) has been applied to the radiosondes. The system uses timevarying greenhouse gas concentrations and prescribed ozone and aerosol properties. The entire 

JRA-55 production is expected to be completed in early 2013. Thereafter, JRA-55 will be 

continued as a new JCDAS on real time basis.

A.6 NOAA-CIRES 20CR

The “20th Century Reanalysis Project” version 2 (20CR) has recently been generated by Compo 

et al. (2011). Led by NOAA and CIRES, the 20CR project is an international collaborative effort 

to produce a comprehensive global atmospheric circulation dataset spanning the twentieth 

century, assimilating only surface observations of synoptic pressure and using the UK Met 

Office HadISST (Rayner et al., 2003) monthly sea surface temperature and sea ice distributions 

as boundary conditions. Radiative forcings of time-varying solar output, volcanic aerosols, and 

CO2 were also prescribed as in Saha et al. (2010). 

20CR provides the first estimates of global tropospheric variability spanning 1871 to the present 

at 6-hourly temporal and 2° spatial resolutions. It uses, together with an experimental 2008 

version of the global NWP model developed at NCEP to provide background “first guess” fields, 

a recently developed Ensemble Kalman Filter data assimilation method (Whitaker and Hamill, 

2002). This directly yields a global analysis every 6 hours as the most likely state of the 

atmosphere, and also yields the uncertainty of that analysis. Earlier work established the 

feasibility of producing such a reanalysis dataset from the 1850s to present using only surface 

observations (Whitaker et al., 2004; Anderson et al., 2005; Compo et al., 2006; Whitaker et al., 

2009). 

20CR was processed in 27 approximately 5-year streams (see Compo et al., 2011 Table III). 

These data are available six-hourly at 2° horizontal resolution on 24 pressure levels from NOAA 

ESRL (http://go.usa.gov/XTd) and NCAR (http://rda.ucar.edu/datasets/ds131.1) and will also be 

distributed via the NOAA National Model Archive and Distribution System (NOMADS; 

http://nomads.ncdc.noaa.gov). Selected fields from individual ensemble members are also 

available via the NERSC Science Gateway (http://portal.nersc.gov). Additionally, the complete 

spectral files for every ensemble member were archived, so any additional variable can be 

obtained for every member. Finally, the results of the assimilation of each observation in the 

ISPD, including the first guess and analysis uncertainty at the observation location, are also 

available courtesy of NCAR (http://rda.ucar.edu/datasets/ds132.0).
42

!OO+/81U(VW(<4>>&%=(,-(D+&/&*=#+#($,(I&$+

Reanalysis Organization Time Span Resolution Method Radiances VarBC

GEOS-1 DAO 3/1985-11/1995 2"2.5"14L OI No No

ERA-15 ECWMF 1979-1993 T106L31 OI No No

NCEP-NCAR R1 NCEP 1948-present T62L28 3D-Var No No

NCEP-DOE R2 NCEP 1979-present T62L28 3D-Var No No

NARR NCEP 1979-present 32km 3D-Var Yes Yes

CFSR NCEP 1979-present T382L64 3D-Var Yes Yes

CFSR-Lite * NCEP 1948-present T126L64 Hybrid 

EnKF

Yes Yes

ERA-40 ECMWF 9/1957-8/2002 T159L60 3D-Var Yes No

JRA-25 JMA 1979-2004 T106L40 3D-Var Yes No

ERA-Interim ECMWF 1979-present T255L60 4D-Var Yes Yes

MERRA GMAO 1979-present ! "2/3"72L 3D-Var Yes Yes

JRA-55 * JMA 1958-present TL319L60 4D-Var Yes Yes

20CR NOAA-CIRES 1871-2010 T62L28 EnKF No No

*not yet at real-time
43

!OO+/81U(.W(!'%,/=>#

20CR (NOAA-CIRES) Twentieth Century Reanalysis

3D-Var Three-dimensional Variational assimilation

4D-Var Four-dimensional Variational assimilation

ACRE Atmospheric Circulation Reconstructions over the Earth

AGCM Atmospheric General Circulation Model

AIRS Advanced Infra-Red Sounder

AMSU Advanced Microwave Sounding Unit

CAMS Climate Anomaly Monitoring System

CCSP (U.S.) Climate Change Science Program

CDAS Climate Data Assimilation System

CERES Clouds and the Earth’s Radiant Energy System

CFS Climate Forecast System

CFSR Climate Forecast System Reanalysis

CHAMP Challenging Minisatellite Payload

CIRES Cooperative Institute for Research in Environmental Sciences

CMA Chinese Meteorological Administration

CMAP CPC Merged Analysis of Precipitation

COSMIC Constellation Observing System for Meteorology Ionosphere and Climate

CPC (NOAA) Climate Prediction Center

CRIEPI Central Research Institute of Electric Power Industry

DAO Data Assimilation Office

DOE Department of Energy

ECMWF European Center for Medium-range Weather Forecasting

EnKF Ensemble Kalman Filter Assimilation

EIG Eastward Inertio-gravity wave

EMC Environmental Modeling Center

ENSO El Niño-Southern Oscillation

EOS (NASA’s) Earth Observing System

ER Equatorial Rossby wave

ERS European Remote Sensing Satellite

ESA European Space Agency

ESRL (NOAA) Earth System Research Laboratory

EUMETSAT European Organisation for the Exploitation of Meteorological Satellites

GCOS Global Climate Observing System

GEOS Goddard Earth Observing System

GES DISC Goddard Earth Sciences Data and Information Services Center

GFS Global Forecast System

GHCN Global Historical Climate Network

GMAO Global Modeling and Assimilation Office

GMS Geostationary Meteorological Satellite

GOME Global Ozone Monitoring Experiment

GPCP Global Precipitation Climatology Project

GSI Gridpoint Statistical Interpolation

GSICS Global Space-based Inter-Calibration System

IAU Incremental Analysis Update

ISPD International Surface Pressure Databank

JRA Japanese Re-Analysis

JMA Japan Meteorological Agency
44

KMA Korea Meteorological Administration

MERRA Modern Era Retrospective-analysis for Research and Applications

MJO Madden-Julian Oscillation

MODIS Moderate Resolution Imaging Spectroradiometer

MRG Mixed Rossby-gravity wave 

MSU Microwave Sounding Unit (part of TOVS)

NARR North American Regional Reanalysis

NASA National Aeronautics and Space Administration

NCAR National Centers for Atmospheric Research

NCAS National Centre for Atmospheric Science

NCDC National Climatic Data Center

NCEP National Centers for Environmental Prediction

NCEP R1 NCEP-NCAR Reanalysis 1

NCEP R2 NCEP-DOE Reanalysis 2

NCO NCEP Central Operations

NESDIS National Environmental Satellite, Data, and Information Service

NOAA National Oceanic and Atmospheric Administration

NOMADS NOAA National Model Archive and Distribution System

NWP Numerical Weather Prediction

OMI Ozone Monitoring Instrument

QBO Quasi-Biennial Oscillation

QC Quality Control

R1 NCEP-NCAR Reanalysis 1

R2 NCEP-NCAR Reanalysis 2

RAOBCORE Radiosonde Observation Correction using Reanalyses

RMS Root Mean Square

SBUV Solar Backscatter Ultraviolet Spectral Radiometer

SNO Simultaneous Nadir Overpass

SSM/I Special Sensor Microwave Imager

SSU Stratospheric Sounding Unit

TCR Tropical Cyclone

TRMM Tropical Rainfall Measuring Mission

VarBC Variational Bias Correction

WCRP World Climate Research Program

WOAP WCRP Observations and Analyses Panel 
45

!OO+/81U(IW(!'G/,F*+83+>+/$#

The GMAO gratefully acknowledges support from NASA’s Modeling, Analysis, and Prediction program 

for the development and production of MERRA. Support from the NASA Center for Climate Simulation 

(NCCS) at Goddard Space Flight Center, in providing computational resources and a production 

environment for timely delivery of MERRA, was essential to the project. Support from the Goddard Earth 

Sciences Data and Information Services Center (GES DISC) for having MERRA distributed online is also 

greatly appreciated.

ECMWF reanalysis is the product of many years of outstanding scientific research and development by a 

large number of individuals at ECMWF and elsewhere. Human as well as technical resources for 

computing, data services, and user support at ECMWF are greatly appreciated. Preparation and 

production of ERA-Interim has been supported by staff secondments from the NCAS, the JMA, and the 

KMA.

NOAA/NCEP acknowledges the NOAA Climate Program Office (reanalysis grant) for funding part of 

CFSR. Contributions from the staff of EMC, CPC, NCEP Central Operations (NCO), and NESDIS are 

also gratefully acknowledged.

JRA-25 was operated as a joint research project between JMA and CRIEPI and was supported by many 

staff and research scientists. CRIEPI’s Fujitsu VP5000 was the primary supercomputer used for JRA-25 

production.

The JRA-55 project has been supported by a large number of the JMA staff members in many aspects 

including planning of the project, preparation of observational data, testing the data assimilation system, 

production and verification. Their support is greatly appreciated.

20CR gratefully acknowledges support provided by the US Department of Energy, Office of Science 

Innovative and Novel Computational Impact on Theory and Experiment (DOE INCITE) program, and 

Office of Biological and Environmental Research (BER), and by the NOAA Climate Goal. The project 

used resources of the National Energy Research Scientific Computing Center and of the National Center 

for Computational Sciences at Oak Ridge National Laboratory.

International collaborations with data providers from many institutions have been invaluable and essential 

to all the reanalyses and are gratefully acknowledged.
46

Previous Volumes in This Series

Volume 1

September 1994

Documentation of the Goddard Earth Observing System (GEOS) general 

circulation model - Version 1 

L.L. Takacs, A. Molod, and T. Wang

Volume 2

October 1994

Direct solution of the implicit formulation of fourth order horizontal 

diffusion for gridpoint models on the sphere 

Y. Li, S. Moorthi, and J.R. Bates

Volume 3

December 1994

An efficient thermal infrared radiation parameterization for use in general 

circulation models 

M.-D. Chou and M.J. Suarez 

Volume 4

January 1995

Documentation of the Goddard Earth Observing System (GEOS) Data 

Assimilation System - Version 1 

James Pfaendtner, Stephen Bloom, David Lamich, Michael Seablom, 

Meta Sienkiewicz, James Stobie, and Arlindo da Silva

Volume 5

April 1995

Documentation of the Aries-GEOS dynamical core: Version 2 

Max J. Suarez and Lawrence L. Takacs

Volume 6

April 1995

A Multiyear Assimilation with the GEOS-1 System: Overview and Results 

Siegfried Schubert, Chung-Kyu Park, Chung-Yu Wu, Wayne Higgins, 

Yelena Kondratyeva, Andrea Molod, Lawrence Takacs, Michael 

Seablom, and Richard Rood

Volume 7

September 1995

Proceedings of the Workshop on the GEOS-1 Five-Year Assimilation

Siegfried D. Schubert and Richard B. Rood

Volume 8

March 1996

Documentation of the Tangent Linear Model and Its Adjoint of the 

Adiabatic Version of the NASA GEOS-1 C-Grid GCM: Version 5.2 

Weiyu Yang and I. Michael Navon

Volume 9

March 1996

Energy and Water Balance Calculations in the Mosaic LSM 

Randal D. Koster and Max J. Suarez

Volume 10

April 1996

Dynamical Aspects of Climate Simulations Using the GEOS General 

Circulation Model 

Lawrence L. Takacs and Max J. Suarez

Volume 11

May 1997

Documentation of the Tangent Linear and its Adjoint Models of the 

Relaxed Arakawa-Schubert Moisture Parameterization Package of the 

NASA GEOS-1 GCM (Version 5.2) 

Weiyu Yang, I. Michael Navon, and Ricardo Todling

Volume 12

August 1997

Comparison of Satellite Global Rainfall Algorithms 

Alfred T.C. Chang and Long S. Chiu

Volume 13

December 1997

Interannual Variability and Potential Predictability in Reanalysis Products 

Wie Ming and Siegfried D. Schubert
47

Volume 14

August 1998

A Comparison of GEOS Assimilated Data with FIFE Observations 

Michael G. Bosilovich and Siegfried D. Schubert

Volume 15

June 1999

A Solar Radiation Parameterization for Atmospheric Studies 

Ming-Dah Chou and Max J. Suarez

Volume 16

November 1999

Filtering Techniques on a Stretched Grid General Circulation Model 

Lawrence Takacs, William Sawyer, Max J. Suarez, and Michael S. 

Fox-Rabinowitz

Volume 17

July 2000

Atlas of Seasonal Means Simulated by the NSIPP-1 Atmospheric GCM 

Julio T. Bacmeister, Philip J. Pegion, Siegfried D. Schubert, and Max J. 

Suarez

Volume 18

December 2000

An Assessment of the Predictability of Northern Winter Seasonal Means 

with the NSIPP1 AGCM 

Philip J. Pegion, Siegfried D. Schubert, and Max J. Suarez

Volume 19

July 2001

A Thermal Infrared Radiation Parameterization for Atmospheric Studies 

Ming-Dah Chou, Max J. Suarez, Xin-Zhong, and Michael M.-H. Yan

Volume 20

August 2001

The Climate of the FVCCM-3 Model 

Yehui Chang, Siegfried D. Schubert, Shian-Jiann Lin, Sharon Nebuda, 

and Bo-Wen Shen

Volume 21

September 2001

Design and Implementation of a Parallel Multivariate Ensemble Kalman 

Filter for the Poseidon Ocean General Circulation Model 

Christian L. Keppenne and Michele M. Rienecker

Volume 22

August 2002

Coupled Ocean-Atmosphere Radiative Model for Global Ocean 

Biogeochemical Models 

Watson W. Gregg

Volume 23

November 2002

Prospects for Improved Forecasts of Weather and Short-term Climate 

Variability on Subseasonal (2-Week to 2-Month) Time Scales 

Siegfried D. Schubert, Randall Dole, Huang van den Dool, Max J. 

Suarez, and Duane Waliser

Volume 24

July 2003

Temperature Data Assimilation with Salinity Corrections: Validation for 

the NSIPP Ocean Data Assimilation System in the Tropical Pacific Ocean, 

1993–1998 

Alberto Troccoli, Michele M. Rienecker, Christian L. Keppenne, and 

Gregory C. Johnson

Volume 25

December 2003

Modeling, Simulation, and Forecasting of Subseasonal Variability 

Duane Waliser, Siegfried D. Schubert, Arun Kumar, Klaus 

Weickmann, and Randall Dole

Volume 26

April 2005

Documentation and Validation of the Goddard Earth Observing System 

(GEOS) Data Assimilation System – Version 4

Senior Authors: S. Bloom, A. da Silva and D. Dee

Contributing Authors: M. Bosilovich, J-D. Chern, S. Pawson, S. 

Schubert, M. Sienkiewicz, I. Stajner, W-W. Tan, and M-L. Wu
48

Volume 27

December 2008

The GEOS-5 Data Assimilation System - Documentation of Versions 5.0.1, 

5.1.0, and 5.2.0.

M.M. Rienecker, M.J. Suarez, R. Todling, J. Bacmeister, L. Takacs,

H.-C. Liu, W. Gu, M. Sienkiewicz, R.D. Koster, R. Gelaro, I. Stajner,

and J.E. Nielsen

Volume 28

April 2012

The GEOS-5 Atmospheric General Circulation Model: Mean Climate and 

Development from MERRA to Fortuna

Andrea Molod, Lawrence Takacs, Max Suarez, Julio Bacmeister, InSun Song, and Andrew Eichmann

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Standard Form 298 (Rev. 8-98) Prescribed by ANSI Std. Z39-18 

30-06-2012 Technical Memorandum

Technical Report Series on Global Modeling and Data Assimilation,

Volume 29; Atmospheric Reanalyses—Recent Progress and Prospects for

the Future. A Report from a Technical Workshop, April 2010

Max J. Suarez, Editor

Michele M. Rienecker, Dick Dee, Jack Woollen, Gilbert P. Compo,

Kazutoshi Onogi, Ron Gelaro, Michael G. Bosilovich, Arlindo da Silva,

Steven Pawson, Siegfried Schubert, Max Suarez, Dale Barker, Hirotaka

Kamahori, Robert Kistler, and Suranjana Saha

NASA, Goddard Space Flight Center

Greenbelt, MD 20771

National Aeronautics and Space Administration

Washington, DC 20546-0001

NASA GSFC

TM–2012-104606/Vol29

Unclassified-Unlimited, Subject Category: 47

Report available from the NASA Center for Aerospace Information, 7115 Standard Drive, Hanover, MD 21076. (443)757-5802

In April 2010, developers representing each of the major reanalysis centers met at Goddard Space Flight Center to discuss technical

issues – system advances and lessons learned – associated with recent and ongoing atmospheric reanalyses and plans for the future.

The meeting included overviews of each center’s development efforts, a discussion of the issues in observations, models and data

assimilation, and, finally, identification of priorities for future directions and potential areas of collaboration. This report summarizes

the deliberations and recommendations from the meeting as well as some advances since the workshop.

Meteorology, Climatology, Modeling

 Unclassified Unclassified Unclassified

Unclassified

56

Max Suarez

(301) 614-5292