Evolution of reanalysis at ECMWF
Sakari Uppala1, Dick Dee1, Shinya Kobayashi1,2 and Adrian Simmons1
1

European Centre for Medium-Range Weather Forecasts, United Kingdom
2
Japan Meteorological Agency, Japan
Correspondence: Sakari.Uppala@ecmwf.int

INTRODUCTION
Reanalysis has come to be seen as an iterative process, where developments in modelling, data-analysis
techniques and computing power are used together with new data rescue efforts and experience from otherreanalyses to produce a succession of reanalyses of increasing quality. Currently ECMWF is producing its 3rd
generation reanalysis, ERA-Interim, which benefits from the analysis and model developments since ERA-40, in
particular in the assimilation of satellite radiances. Through three decades of NWP development and feedback
from the scientific community reanalyses have achieved partly the qualities required for climate change studies.
The components of hydrological cycle, surface fluxes and surface analyses together with the boundary layer
quantities have improved also over the sensitive and data sparse polar areas. Comparisons with stratospheric
datasets show that we can now have a good confidence on reanalyses up to 10hPa level through the years ofsatellite observations. The stratospheric circulation and low frequency variability also agree well with independent
observational time series. Future reanalyses will further benefit from the ongoing work on the use of cloudy
radiances. Equally, during the data sparse historical periods the modern analysis schemes such as 4D-Var havedemonstrated their capabilities, when properly tuned, to transfer information from data-dense to data-sparse areas.

FGGE “reanalyses” at ECMWF and GFDL
After the International Geophysical Year (IGY) 1958, the observing system started to develop towards an
integrated global system, including polar areas. In support of this, several measurement campaigns andexperiments, including satellite and conventional observations, were carried out, one of them GATE (the GARP
Atlantic Tropical Experiment) in 1974. It was the first major experiment of the Global Atmospheric ResearchProgramme, whose goal was to understand the predictability of the atmosphere and extend the time range of daily
weather forecasts to over two weeks. After a careful assessment of the results from GATE the requirements forthe global observing system were specified and the First GARP Global Experiment was implemented fromDecember 1978 to November 1979, with two Special Observation Periods in January/ February and May/ June.
The first “reanalyses” of the FGGE year, the Main Level IIIb analyses, were carried out at ECMWF, Bengtsson etal. (1982), and simultaneously at GFDL, Ploshay et al. (1983). At that time, global data assimilation (e.g Lorenc,1981), was improving rapidly, and with it forecast quality. The initial aim then was to learn how to use the new
global observing systems and special observations deployed. Observing system experiments clearly demonstrated
the value of each component, e.g. Uppala et al. (1985). Atmospheric Motion Vectors were proven important for
the analysis over tropical regions, whereas retrieved satellite temperature profiles were of paramount importancefor the extra-tropical analyses over ocean areas. Aircraft data were an invaluable addition to the global observing
system. The drifting buoy data in the southern ocean provided good quality pressure observations with highfrequency over data sparse areas. The FGGE reanalysis was later repeated, the Final Level IIIb analyses, with animproved analysis system and improved observation collection and quality at ECMWF , Uppala (1987), and at
GFDL, Ploshay et al. (1991).

FGGE reanalyses, through worldwide distribution by the World Data Centers A and B are in many ways theprecursors of todays reanalyses, the same logistics apply. Careful preparation, collection and quality control of theinput observations together with the validation of the analysis products by numereous scientists have lead toimprovements in the assimilation of data and therefore to improved operational and reanalysis products and theirapplications.

ERA-15 reanalysis of 1979-1993
The FGGE, and later operational analyses, were for several years inspiration in atmospheric research. Soon it wasrealized that this is not enough, and reanalysis of longer periods was suggested. In parallel with NCEP’sreanalysis activity, Kalnay et al. (1996), ECMWF embarked on its first long reanalysis ERA-15 in collaboration
with PCMDI, JMA, WCRP, COLA, NCAR and NCEP. The project had several validation partners and wasperformed during 1994-1996, (Gibson et al. 1997).
The Optimum Interpolation analysis and the assimilating model at ECMWF had undergone several refinements
and the model resolution of T106L31 was adopted. New input observations were acquired in addition to theECMWF’s operational archives: NESDIS Cloud Cleared Radiances, FGGE, COADS, ALPEX, JMA, PAOBS andTOGA COARE datasets. New updated boundary forcing fields, monthly GISST until October 1981 and NCEP OI
weekly sea surface analyses from then on. The sea ice was determined from SMMR and SSM/I data (Nomura,1995). ERA-15 assimilated temperature and humidity profiles, which were generated by a 1D-Var physicalretrieval of pre-processed HIRS and MSU radiances.
The hydrological cycle was studied in detail and weaknesses were identified. ERA-15 was performing better than
NCEP and NASA reanalyses in the extra tropics, but all reanalyses showed excessive precipitation in the tropics.
None of the reanalyses had a closed hydrological budget. Due to ERA-15 model spinup the 12 to 24 hour forecast
gave the most consistent picture especially in winter over land. Also there was an incorrect southward shift in theITCZ over Africa in 1987 most likely due to the assimilation and bias correction of satellite data. Despite these
deficiencies ERA-15 offered the best available dataset to study daily variability. The signal from El Chichon andPinatubo eruptions in the lower stratospheric temperature anomaly was well captured from the satellite radiances,while the near surface temperature trends were not considered useful.

ERA-40 reanalysis of 1957-2002
The new variational analysis technique (Courtier et al., 1996) became available soon after completion ofERA-15. A new project, ERA-40 reanalysis of the period 1957-2002 Uppala et al (2005), applied the First
Guess at Appropriate Time version of the variational 3D-Var analysis method with a T159L60 model and madecomprehensive use of historical conventional and satellite data, most of them provided by NCAR.
The principal change from ERA-15 in ERA-40’s use of satellite data was its direct variational assimilation ofraw radiances, in which basic model variables were adjusted to improve jointly the fits of simulated ‘model
equivalent’ radiances to measured radiances, and the fits of the model to other types of observation.
Radiances from the VTPR, HIRS, MSU, SSU and AMSU-A instruments were assimilated in this way,following the approach implemented operationally for assimilation of data from AMSU-A, MSU and a small
number of HIRS channels. AMSU-A provides microwave temperature-sounding data with greater verticalresolution than MSU, but has been flown only since 1998. This was the first time the VTPR and SSU
radiances had been used in multivariate data assimilation. The use of the additional HIRS data also
compensated for the absence of microwave humidity data prior to the launch of SSM/I in 1987. In ERA-15
new satellite bias adjustments were calculated on a monthly basis. In ERA-40, the bias tuning for eachchannel was much more stable. In most cases a single bias adjustment was applied for the lifetime of asatellite. From August 1987 onwards ERA-40, unlike ERA-15, also assimilated 1D-Var retrievals of TCWV
and surface wind speed over sea from SSM/I radiances and surface winds over sea from the scatterometerson the ERS-1 and ERS-2 satellites from 1993 onwards. Altimeter data from the ERS satellites were used inERA-40’s ocean wave analysis and analysis of ozone was based on SBUV and TOMS ozone retrievals. In
preparation for ERA-40 EUMETSAT initiated a project to reprocess satellite image data from Meteosat-2
and Meteosat-3 in order to generate meteorological products using their latest algorithms.
The quality of both analyses and observations can be inferred from the comprehensive statistics ofobservation-minus-background and observation-minus-analysis differences, Figure 1. The background fit in theNorthern Hemisphere is closer, more confined and more stable than in the Southern Hemisphere. A general
improvement in the fit occurs over the period of ERA-40. The improvement in the background fit at the end of
1978 is a consequence of the major improvement of the overall observing system mentioned before. It isparticularly marked in the southern hemisphere, where a distinct improvement can be seen also at the beginning of1973 when satellite sounder data (VTPR) were first used. The data fits improve gradually from 1979 onwards,
more so for the southern than for the northern hemisphere, and become similar for the two hemispheres by the endof the period. These and other results presented in Uppala et al. (2005) indicate a general improvement over timeof the post-FGGE observing system, especially for the southern hemisphere.

Figure 1 Daily values of the root-mean-square background (red) and analysis (blue) fits to 12UTC SYNOP and SHIP
measurements of surface pressure (hPa) over the extratropical northern and southern hemispheres.

ERA-Interim reanalysis 1989 onwards
After completion of ERA-40 effort was devoted to development of a new reanalysis system derived from thelatest version of the operational ECMWF system. In 2006 a new reanalysis was started from January 1989, toproduce an interim reanalysis (ERA-Interim) for the data-rich 1990s and 2000s, to be continued as an ECMWF
Climate Data Assimilation System (ECDAS) until superseded by a new extended reanalysis. The main advancesof the ERA-Interim data assimilation over the ERA-40 system are: 12 hour 4D-Var, T255 horizontal resolution,better formulation of background error constraint, new humidity analysis, improved model physics, quality controlof data drawing on experience from ERA-40 and JRA-25, variational bias correction of satellite radiance data,improvements in radiosonde temperature and surface pressure bias handling, more extensive use of radiances,improved fast radiative transfer model and assimilation of rain affected SSM/I radiances through 1D-Var.
ERA-Interim uses mostly the observations prepared for ERA-40 supplemented by data for later years from
ECMWF’s operational archive. Boundary forcing fields are taken from ERA-40 until 2001, and from ECMWF
operations for later dates. However a few new datasets have been acquired: a uniform ERS altimeter wave-heightdataset from ESA, reprocessed winds and clear-sky radiances from EUMETSAT, reprocessed ozone profiles fromGOME data from the Rutherford Appleton Laboratory from 1995 onwards and CHAMP GPS radio occultation
measurements, reprocessed by UCAR. Subsequent occultation data from the constellation of CHAMP, GRACEand COSMIC receivers will be used from ECMWF’s operational archive.

Figure 2 Time series of monthly mean total precipitation rate averaged over Tropical Oceans from the ERA-40,
ERA-Interim and JRA-25 reanalyses, from SSM/I retrievals and from GPCP. Units: mm day-1.

Several of the problems experienced in ERA-40 have been eliminated or significantly reduced in ERA-Interim, mostnotably a too-strong tropical oceanic precipitation that was marked from the early 1990s onwards, Figure 2.
Precipitation, which is higher in both ERA-Interim and ERA-40 than in the Global Precipitation Climatology Project(GPCP) over the tropical oceans, is closer to GPCP in ERA-Interim. ERA-40 and ERA-Interim are nevertheless incloser agreement with each other than either is to the GPCP estimate. Total column water vapour from ERA-Interim issignificantly lower than from ERA-40 (not shown), and closer to the SSM/I product by Remote Sensing Systems. The
excess of precipitation over evaporation seen in ERA-40 is much reduced in ERA-Interim. Precipitation remains
higher than evaporation, however, consistent with the indications that rainfall over the tropical oceans is still
somewhat too high, notwithstanding uncertainties in the accuracy of the observation-based estimates. ERA-40 also
suffers from a too-strong Brewer-Dobson circulation in the stratosphere. The studies of “age of air” in the preparatory
assimilations show that ERA-Interim provides a much improved dataset for driving models of stratospheric chemicaltransport and stratosphere/ troposphere exchange.
Ten-day forecasts were run 12-hourly from both the ERA-40 and ERA-Interim analyses. We can see a substantial
improvement in forecast skill of ERA-40 over ECMWF operations for 1989-1990. ERA-Interim in turn improves
substantially on ERA-40, especially in the southern hemisphere. The use of 4D-Var in ERA-Interim is likely to bea key factor behind the larger improvement in the southern hemisphere, where a more sophisticated assimilation
technique is able to use the satellite data more effectively. Even during 1999-2001 when operations already used
4D-Var and a higher resolution T511 assimilating model ERA-Interim forecasts perform better than operations
and more so again in the Southern Hemisphere. Other improvements to the model and the analysis together withthe variational radiance bias correction contribute to this. By comparing the performance between 1989-1990 and
1999-2001 on both hemispheres we can conclude that ERA-Interim forecasts have a more uniform quality in timeand space than forecasts from the ERA-40, implying a more homogeneous analysis and product quality inERA-Interim.

Figure 3 Mean anomaly correlation of 500hPa geopotential; ERA-Interim (red), ERA-40 (blue) and original
operations (black) for the extratropical northern (upper panels) and southern (lower panels) hemispheres for two
periods 1989-1990 (left) and 1999-2001 (right).

Relative performances of ECMWF reanalyses
By comparing the data assimilations of the earlier reanalyses of June 1979 using global (radiosonde-background) windstatistics we can see steady stepwise improvements in the background quality from Optimum Interpolation in FGGEand ERA-15 reanalyses to 3D-Var in ERA-40 and further to 4D-Var in ERA-Interim (June 1979 was rerun using theERA-Interim system). The radiosonde network is concentrated over land areas in the Northern Hemisphere with agood multi-type conventional data coverage and therefore their statistics show smaller, even though significant,improvements than the statistics against aircraft winds, which are mostly located over ocean areas with plenty ofsatellite data and relatively few conventional data. We can conclude, based also on the forecast performance discussedearlier, that variational analysis schemes, 4D-Var more so than 3D-Var, together with the variational bias correction
have improved the assimilation of satellite data and thereby improved the quality of analyses over ocean areas. The
performance of T799L91 resolution operational assimilation in June 2007 gives a further indication of the reanalysis
quality potential, given the latest model version, the high resolution and the necessary computer resources.

Figure 4 Data assimilation performance in reanalysis of 1979 and in operations 2007 measured by the global wind
RMS (radiosonde–background) on the left and (aircraft– background) on the right.

SUMMARY
The development of data assimilation techniques and worldwide scientific validation work has resulted in a sequenceof reanalyses with improving quality. Given the resources the data assimilation capabilities already now would allow
production of higher quality reanalysis products. The next extended reanalysis at ECMWF, 1938 onwards, could startin 2011 pending on funding. Together with higher resolution and improvements in the analysis methods thedata-assimilation can be configured for reanalysis purposes. Also possible coupling of atmospheric, ocean and icemodels needs to be considered. Collaborative work on the improved observational input and boundary forcing data,
the ongoing homogenization work on radiances and conventional data, as well as the reprocessing of data will further
enhance the role of reanalyses in applications and in climate change assessments.
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