A follow-up study of the TOVS application for the Japanese Climatic Reanalysis: JRA-25

Masami Sakamoto, Kozo Okamoto, and Yoshito Yoshizaki

Japan Meteorological Agency, 1-3-4, Otemachi, Chiyoda-ku, Tokyo, Japan

Abstract Japan Meteorological Agency (JMA) and the Central Research Institute of Electric Power

Industry (CRIEPI) accomplished a 26-year climatic reanalysis: JRA-25. This reanalysis was

achieved on the basis of the numerical prediction and data assimilation techniques of JMA and

computational resources of CRIEPI. JMA had never experienced an operational assimilation of

equivalent Temperature of Black Body (TBB) of TOVS on board TIROS-N to NOAA-14. JRA-

25 developed an assimilation scheme for level-1d data for HIRS and MSU, and level-1c for SSU.

The result of the assimilation of TOVS shows some interesting features; stable and consistent in

the troposphere, but unstable in the stratosphere in comparison with other reanalyses.

As a follow-up study on the TOVS application in JRA-25, climatic reanalyses were compared

with each other and with the real TOVS observation. This study provided an opportunity to

explore how climatic reanalyses differ and what brought these differences.

Referring to the results of the follow-up study, the authors propose a strategy for a climatic

reanalysis assimilation of satellite sounding observations: TOVS / ATOVS and their predecessor

VTPR. One of keys to a successful application might be an exact assimilation with zero-bias from

the background, combined with General Circulation Model (GCM) improvements. If the GCM

can describe climatic trends and events more accurately, such strategy would be a mainstream for

next generation climatic reanalyses. But still it is very challenging as for a climatic reanalysis

concept at present, although it seems to become quite common to operational Numerical Weather

Predictions (NWPs) for medium-range forecasts. If some ‘trend-setting’ observation is still

necessary to supplement the climatological trends and events in a reanalysis, thermal micro-wave

sounders (MSU/AMSU-A) would be the best candidate.

Introduction Vertical sounding observations by TOVS and ATOVS on board NOAA satellites have been the

most important information sources for the climatic renalyses: like NCEP-NCAR (Kalnay et al.,

1996), ERA-15 (Gibson et al., 1997), and ERA-40 (Uppala et. al, 2005). In March 2003, JMA

and CRIEPI completed a 26-year climatic reanalysis: JRA-25, and this reanalysis also used these

satellite soundings as important observations to determine thermal and moisture profiles.

The GCM and assimilation techniques that have been adopted for climatic reanalyses were not

the same. In general, each reanalysis has been achieved on the basis of a GCM and an

assimilation system that the reanalysis executors developed for their operational NWP. TOVS

assimilation procedures also differed among reanalyses, and usually, but except for JRA-25, were

inheritances from the operational system for NWP.

ERA-15 used their Optimized Interpolation (OI) technique with T106 spectral GCM, and TOVS

observation was assimilated through the 1D-VAR physical retrieval method. Although the spatial

resolution of NCEP-NCAR reanalysis is T62, it is the pioneer of 3D-VAR based climatic

reanalyses, and they adopted NOAA/NESDIS thermal and moisture retrieval profiles from TOVS.

ERA-40 was the first climatic reanalysis that adopted a TOVS TBB assimilation with 3D-VAR

and TL159 semi-Lagrangian model. JRA-25 and ERA-40 used the same TOVS TBB dataset: the

ERA-40 TOVS level-1c dataset (Hernandez et. al, 2004), and the same radiative transfer model:

RTTOV version 6 (Saunders et. al., 1999).

Authors developed the assimilation procedure of TOVS for JRA-25, and experienced many

types of the issues: e.g. cloud detection, channel selection, bias setting, thinning, time-window,

error screening. The way to use TOVS in JRA-25 is described in the appendix of this issue. As a

result of such developments and experiments, JRA-25 adopted strict QC procedures, and

assimilated TOVS TBB sparsely. And JRA-25 also decided to use an automatic bias correction

procedure based on a 1D-VAR optimized value estimation.

To examine validity of the TOVS application and to design a strategy for satellite sounding

assimilation for a next generation climatic reanalysis, we started a follow-up study. The authors

compared climatic reanalyses with each other and with the real TOVS observation to find how

reanalyses differ and what brought the differences from a TOVS application point of view.

Comparisons of Reanalyses with each other and with TOVS Observation How to compare reanalyses with Real TOVS observation

JRA-25 and ERA-40 were compared with the ERA-40 TOVS level-1c dataset. For comparisons

in the same condition, pressure level 2.5-degree grid values for both reanalyses were used.

Pressure levels of the both are shown in Table 1. To estimate TBB for each channel, the forward

model of RTTOV version 6 (Saunders et. al., 1999) was used. As mentioned above, both JRA-25

and ERA-40 adopted RTTOV version 6 in their 3D-VAR assimilation process. Atmospheric

vertical profiles and surface variables that RTTOV requires were linearly interpolated into

observation spots. Each observation spot was categorized according to surface types and weather

conditions through similar procedures with the JRA-25 TOVS assimilation. Especially for the

accurate detection of low stratiform clouds prevailing over the ocean along western coasts of

continents (Klein and Hartmann 1993), the examination of the thermal lapse rate in the lower

troposphere were added. That is,

(1) T850 – Tsurf > 1.0 K,

(2) The spot is over the ocean.

The following two parameters were used to assess results of the comparison;

(1) Departure in TBB (DT): the quantity obtained by subtraction of a reanalysis derived TBB

(T*) from the corresponding real observation TBB (T), and its unit is Kelvin.

DT = global average (T – T*)

(2) Dependency (DP): a relative ratio of global standard deviation of T (SDT) after subtracting

the corresponding global standard deviation of T – T* to SDT itself. DP represents the

reanalysis accounts for how much portion of the spatial variation of the real observation, and

its unit is percent.

DP = 100 (%) x (SDT – SDD) / SDT,

where SDD is the global standard deviation of T – T*.

Comparison in Thermal Profiles in the Stratosphere Figure 1 shows global averaged temperature profiles in the upper troposphere and the

stratosphere for JRA-25 and ERA-40. As of pointing in the diagram, peaks of TOVS weighting

functions are discretely at around 100hPa (HIRS channel 3), 90hPa (MSU channel 4), 60hPa

(HIRS channel 2), 15hPa (SSU channel 1), 4hPa (SSU channel 2), and 1.5hPa (SSU channel 3).

By and large, the differences in temperature between JRA-25 and ERA-40 are smaller at the

altitudes where TOVS impacts were dominant, and larger in between them.

Figures 2 show time sequences of global mean temperature at 100 hPa and 30 hPa, of JRA-25,

of ERA-40, and of NCEP-NCAR. At 100hPa where TOVS contribution was dominant, the

differences among reanalyses are small and time sequences of three reanalyses are much alike. At

30hPa where the TOVS explicit impact was quite rare, the difference between JRA-25 and ERA-

40 is quite large; about 5 K on the average, and NCEP-NCAR shows the different trend in its

time sequence for spring in 1996.

Both a GCM forecast and satellite sounding system can control climatic tendencies in the

stratosphere. When a discrete satellite sounding observation has substantial bias from the

background, to reduce the observation cost, an analyzed field approaches to the observation at

around the heights where TOVS has peaks of its weighting functions. However, in between those

altitudes, analyzed field could be prone to remain not so far from the background, not to increase

the background cost. Actually, increments (differences of analyzed field from background) are

usually large and various in between the weighting function’s peaks because there is no

restriction from observation and an impact on TBB can be transmitted not only horizontally but

also vertically through spatial correlations of various types of analyzed variables.

When a GCM has chronical large bias in the same way (cooling or heating bias), that raises

larger discrepancies in between the peaks of weighting functions. Murai et. al. (2005) reported

that the GCM used in JRA-25 had considerable cooling bias in the lower and middle stratosphere,

and they improved long-wave absorption estimation in the GCM to overcome the problem in the

operation NWP system after the JRA-25 system was fixed. The lower temperature of JRA-25 in

the lower stratosphere in Figure 1 well corresponds to this cooling bias.

Comparison in Tropospheric Water Vapor Figures 3 show differences in specific humidity at 700hPa between JRA-25 and ERA-40.

Throughout the entire period of JRA-25, the specific humidity over continents, especially in the

tropics, is lesser than ERA-40’s (the upper figure). Because the both did not use HIRS channel 11

and the Special Sensor Microwave Imagers (SSM/I) over continents, the differences are found to

have risen from their GCM behaviors.

To separate the contribution of Total Column Water Vapor (TCWV) derived from SSM/I, the

differences for the earlier period: from 1979 to 1986 without SSM/I observation (the middle

figure), and for the later period: 1987 to 2001 with SSM/I (the lower figure) are shown.

Apparently difference over ocean, especially subtropical ocean, decreases in the later.

Figure 4 shows time sequences of global averaged specific humidity at 700hPa. JRA-25 became

a little drier in late years than in early years. On the other hand, after volcanic eruptions happened

in 1982 (El Chichon) and in 1991 (Pinatubo), ERA-40’s global mean specific humidity increased.

These tendencies directly reflected into global precipitation trends (Fig. 5), and made

considerable discontinuities.

These differences in specific humidity can derive in part from the difference in usage of HIRS

water vapor channels, especially at 700hPa of channel 11. Because both JRA-25 and ERA-40 did

not use HIRS channel 11 over lands, differences over ocean should be discussed here. To assure

these from a TOVS usage point of view, figures 4 show time sequences of DTs of this channel for

JRA-25 and ERA-40 and their difference, and figures 5 present these of DPs. These figures are

based on a statistics of clear ocean samples.

After volcanic eruptions JRA-25’s DT decreased, that means JRA-25 reduced this channel’s

positive bias after these events. As for ERA-40 DTs for NOAA-6 and NOAA-10, 11 un-changed,

and ERA-40 seems likely to have translated the cooler TBBs of this channel directly into more

water vapor at its target altitude. DP charts (Fig. 5) suggest ERA-40 has far larger dependencies

on this channel. JRA-25 adopted moderate usage of tropspheric channels and automatic bias

estimation, and therefore suffered little from those stratospheric aerosols influences.

One more note to describe here is with regard to the high DTs for ERA-40 in early years. As is

shown in Fig. 5 ERA-40’s DPs on this channel are quite large for TIROS-N and NOAA-6, 7, and

ERA-40’s specific humidity at 700hPa in Fig. 4 is not much than JRA-25. These might be a

situation, which is difficult to understand.

As far as the author monitored, ERA-40’s specific humidity along the NOAA-satellite tracks is

not less than JRA-25, i.e. based on the monitor of NOAA-6 impact at the beginning of 1980,

when NOAA-6 was in operation as the sole morning satellite. ERA-40 shows a 12hourly

vibration in specific humidity at 700hPa over ocean, which has peaks at 0600 and 1800 local

times. One possible situation might be that ERA-40 has larger specific humidity only along the

NOAA tracks, but elsewhere JRA-25 has larger one. As is shown in Fig. 3, JRA-25 seems to have

much water vapor over subtropical ocean extent in early years, probably except for around

NOAA tracks.

Additionally, you can find in Figures 5, DP for JRA-25 increased after SSM/I assimilation

started. Probably that is because SSM/I can account for some portion of residual variation of

HIRS channel-11 in JRA-25.

Comparison in Lower Stratospheric Temperature Figure 8 shows global averaged anomaly time series of lower stratospheric temperature of JRA-

25 and ERA-40, which are estimated from thickness between 50 and 100 hPa. The most

noticeable features in the both time series are higher temperature periods after volcanic eruptions.

Other than these, ERA-40 has a considerable discontinuity at the beginning of 1989 and it

corresponds to the starting point of ERA-40’s calculation stream 1. JRA-25 has a lower

temperature period between May 1993 and July 1996.

Figure 9 shows time sequences of global averaged total column ozone of JRA-25 and ERA-40.

JRA-25’s ozone dataset was processed with the JMA Chemical Transport Model (CTM) using

NASA’s TOMS products and ERA-40’s meteorological profiles. This process used TOMS

observation only of Nimbus-7 and of Earth Probe, not of Metor-3. Therefore, for the period from

May 1993 to July 1996, ozone depth decreased as a result of the CTM bias tendencies. And a

little thicker ozone of JRA-25 since 1989 seems to have reflected ERA-40’s higher temperature.

Generally, GCM thermal biases in the stratosphere is closely connected with the heating ratio by

the short wave absorption of the ozone layer. ERA-40’s ozone dataset, which was used in short

wave absorption estimation in their GCM, was prepared beforehand (Dethof and Hólm 2004).

However, they re-estimate ozone concentration in the actual execution of ERA-40, ozone

concentration or total ozone depth that they provide as a part of ERA-40 products (shown in

Figure 9) is different from the original Dethof and Hólm’s dataset.

To monitor this from a TOVS application aspect, Figures 10 shows DT time series for MSU

channel 4, and Figures 11 provides its DP time series of JRA-25, of ERA-40, and their

differences. As is shown in Figures 11, MSU channel 4 has large DPs for the both reanalyses, and

that means this channel had a dominant impact on thermal profiles at its target altitude of 90hPa.

ERA-40 has large negative DT after starting its stream 1, which means they are most likely to

have assigned large negative bias to this channel. That was possible to result in higher

temperature in the lower stratosphere. A large positive DT of NOAA-12 for JRA-25 seems to be

noticeable, but ERA-40’s difference in DT between NOAA-11 and 12 is much alike with that of

JRA-25. In the difference time series chart of both DTs, ERA-40’s lower DT (higher atmospheric

temperature) in and after 1989 is the most conspicuous. The application of MSU channel-4 in

JRA-25 seems rather consistent, except for the thinner ozone influence.

Discussions Problems in JRA-25 System and its TOVS Application

There were three major problems in the JRA-25 system with regard to the TOVS application;

these were the non time-coincident usage of observation, an initialization process with a statistical

diurnal mode correction, and a chronical thermal bias in the GCM.

Since Gille et. al. (1979) started satellite sounding impact studies, coincidence of background

forecast and observation has been one of the most important issues in a satellite observation

application. ERA-40 had some procedure to interpolate the background forecast into an

observation time and to compare them at nearly the same time in 3D-VAR system (Uppala et. al.

2005). But JRA-25 did not have such an arrangement, and observation was compared with a

background on one of main synoptic (analysis) hours: at 0000, 0600, 1200, or 1800 UTC. It is no

wonder if the non-coincident usage of observation, especially of massive satellite sounding with

the global coverage, would be negative contribution. A coincident use of observation in 3D-VAR

had not been listed in the development schedule in JMA, because 4D-VAR was about to

introduce to the operational global NWP when JRA-25’s calculation started. The JMA 4D-VAR

assimilation technique came into operation in February 2005.

A non-leaner normal-mode initialization procedure was adopted in JRA-25 for a convenience in

calculation. After this initialization process, a set of diurnal variation of atmospheric tides was

introduced to correct initial fields of the forecast. This set of diurnal variation was deduced only

from a GCM run for the Northern-Hemisphere summer season. Therefore, statistical diurnal

mode did not have any seasonal variations and the same set of diurnal mode was applied

regardless annual or inter-annual climate change. This initialization arrangement and statistical

diurnal modes were inheritances from the operational JMA NWP system at the time, and the

upper and middle stratosphere, where atmospheric tides are not negligible, suffered enormously.

The thermal bias in GCM derive radiation estimation was mentioned above. As JRA-25 used the

automatic bias estimation for TOVS assimilation using GCM background forecast as the first

guess in 1D-VAR, the bias in the GCM was able to affect the bias estimation.

The stratospheric thermal tendencies suffered from these problems the most seriously. Fictitious

impacts like shown in Figure 12 was found in an assimilation experiment prior to the JRA-25

calculation. To attenuate these problems, JRA-25 had to adopt various arrangement described in

the appendix. Nevertheless, at the beginning of 1995 when SSU observation was absent, JRA-

25’s upper stratospheric temperature show a noticeable discontinuity as is shown in Figure 13.

Although JRA-25 show rather consistent behavior in the troposphere and attained a few

advantages described above with the moderate usage of TOVS and its automatic bias estimation,

a lot of issues still remain to be improved.

Development for Next Climatic Reanalysis Climatic reanalyses to date have relied on both GCM forecasting and data assimilation

techniques based on the operational NWP development. An assimilation process with 3D-VAR or

OI technique has provided chiefly spatial features in an analyzed field or an initial condition for

GCM forecasting. A GCM forecasting has been accounting for temporal variability providing the

background forecasts to a data assimilation process. Those functions for each process would be

also the same for newly introduced techniques like a 4D-VAR and an Ensemble Kalman Filter,

because they rely on GCM’s forward and adjoint models in their temporal estimations processes.

On the other hand, climatic reanalyses have used climatic trends in observation systems as

leading guides for climatic trends and events. Among them, the satellite sounding observation

system has provided comprehensive atmospheric tendencies. The whole observation systems in

climatic analyses have controlled temporal features through the following two ways;

1. Providing precise spatial features to GCM initial conditions, they can lead climatic trends

through accurate GCM forecasting. For such a usage, the averaged observation bias from the

background should be zero.

2. Observation systems can control climatic trends directly using trend information in their time

series. For such a purpose, biases from the background is non-zero, and it directly induce

climatic trends and events.

The Later one seems rather risky, because climatic researchers usually discuss about ‘anomaly’:

the small portion of meteorological variations. Disagreement among observation systems and

discontinuities in observations are always controversial among climatic reanalysis users. Adding

to it, non-zero averaged biases, even they are small, contradict against the assumption in an

assimilation model. Therefore, operational NWP systems, other than operational climatic analysis

systems like NCEP’s CDAS and JMA’s JCDAS, are usually trying to avoid using such biases.

In 2006, JMA introduced an adaptive bias correction arrangement into the operational global

NWP for its 4D-VAR TBB assimilation of ATOVS, SSM/I, TMI on TRMM, and AMSR-E on

Aqua. Since such an arrangement can keep the averaged observation bias from the background

nearly zero, it would provide the most effective observation impact on the analyzed field.

However, in comparison with an assimilation cycle with fixed bias correction coefficients, the

adaptive bias correction brings slightly different seasonal variation features, especially in the mid

and upper stratosphere.

What if a reanalysis introduces such adaptive bias correction for all the satellite-observing

systems? In case the GCM has enough ability to describe correct climatic trends and events, such

arrangement will work positively. Generally, such arrangement for GCM requires many types of

historical datasets like SST, Sea-ice, snow coverage, atmospheric constituents, and aerosols. JRA-

25 introduced originally developed SST, sea-ice, ozone concentration, and snow coverage to

control climatic features in the GCM, but still the result was far from satisfaction. Probably in a

next try, it is going to remain difficult for a GCM to fit every observation and every climate

situation for several decades. Considering these backgrounds, next climatic reanalysis executors

have to select observing system that can provide reliable guidance for climatic trends and events.

Figure 14 shows averaged DTs and DPs for the both reanalyses, for all the satellite, and for the

period from 1979 to 1999. The most remarkable features in this chart are JRA-25’s lower DPs for

HIRS water vapor channels and for SSU.

ERA-40’s larger dependencies on HIRS water vapor channels seem to have resulted in the

inconsistent tropospheric water vapor and precipitation behaviors mentioned above. HIRS TBB

trends can easily suffer from thick aerosols and coverage of thin cirrus, and the influences by the

related events like volcanic eruptions are inevitable. On the other hand, HIRS have good spatial

resolution. If averaged observation biases were zero from the background forecast, it can provide

better climatic and meteorological features. HIRS with the adoptive bias correction arrangement

can contribute to reanalysis trends through realistic spatial features in the initial condition.

Difference in DP of SSU chiefly came from JRA-25’s disadvantages: the non time-coincidence

in 3D-VAR and the false usage of the initialization procedure. However, we found that the

observation biases of SSU, not only for JRA-25 but also for ERA-40, are quite different among

the spacecraft, as is shown in Figure 15. The coefficients of a radiative transfer models for this

instrument is also not so accurate. As there is no clue to know climatic trends in the upper and

middle stratosphere, it might be the good idea to use SSU sparsely with an adaptive bias

correction measures to represent large-scale stratospheric events. In the upper and mid

stratosphere, there are already another climatic trend-setting imformation of atmospheric

constituents tendencies.

As for MSU observation, the both reanalyses shows quite large DPs for each channel in

comparison with HIRS channels at the similar target altitudes. As MSU observation has the least

difficulties derived from detection of stratospheric aerosols and thin cirrus, its TBB trends and

events must be quite accurate. Adding to that, climatic trends in MSU observation have been

enthusiastically investigated to know deep atmospheric layers behavior for decades (Christy et. al,

2000, and Mears et. al, 2003). Therefore, TOVS researchers have provided comprehensive vital

information on MSU trends. MSU and of its successor: AMSU-A should be the best candidates to

use as ‘climatic trendsetters’ without adaptive bias correction schemes.

When a next reanalysis expand its target period back to 1970s, the conductors have to encounter

an application of VTPR mounted on NOAA-2 to 5. Because VTPR had neither visible channels

nor near-IR channels, it must be difficult to conduct cloud rejection. Therefore, steady efforts and

fundamental investigation like by Shi and Bates (2006) will be required. Like HIRS, VTPR is

also a sensitive instrument to thin cirrus and thick aerosols. Therefore, VTPR should be used with

an adaptive bias correction arrangement aiming at better initial fields for GCM forecasting.

Conclusions JRA-25 adopted the assimilation of sparser TOVS TBB and the automatic bias estimation for it,

and it achieved the following results;

1. Behaviors in tropospheric water vapor over ocean are rather consistent, and therefore global averaged precipitation seems stable throughout the TOVS application period.

2. Lower stratospheric temperature shows good agreement with the ozone concentration trend, which was used for short-wave absorption estimation in GCM.

However, it has the following disadvantage derived from 1) the non time-coincedent 3D-VAR, 2) the false initialization procedure usage, and 3) chronical thermal bias derived from long-wave absorption estimation errors in theGCM;

3. Thermal tendencies in the upper and middle stratosphere seems quite unstable and inaccurate.

4. Missing of TOVS observation induced inconsistent thermal features in the stratosphere.

According to the result of this follow-up study, the following strategies seem to be promising;

I. GCM improvement aiming at climatic trends and events will be desirable.

II. MSU (AMSU-A) is the best candidate for the ‘climatic trend-setter’, HIRS and SSU can

contribute to the climatic trends through better initial conditions for a GCM forecasting. An

application of VTPR will require through investigation for cloud detection, and it should be

used in the similar way with HIRS.

Acknowledgements

Authors would sincerely appreciate the TOVS data provision by ECMWF and original data

contributions by NESDIS / NOAA and UK-MET office, and NCAR. We would also thank T.

Kurino and Y. Tahara of MSC / JMA, H. Owada of NPD / JMA, M. Kazumori of NCEP / NOAA

for their advices, and thank to all JRA-25 co-workers for their support.

References Christy, J. R., R. W. Spencer, and W. D. Braswell 2000: MSU Tropospheric Temperatures:

Dataset Construction and Radiosonde Comparisons. J. Atmospheric and Oceanic Technology, 17, 1153 - 1170.

Dethof, A. and Hólm, E. 2004: Ozone assimilation in the ERA-40 reanalysis project. Quart. J. R.

Meteorol. Soc., 130. Gibson, J.K., P. Kållberg, S. Uppala, A.Hernandez, A. Nomura and E. Serrano 1997: ERA

Description. ECMWF ERA-15 Project Report Series, 1, 71. Gille, J. C., M.Halem, and R. Atlas 1979: Time-Continuous Assimilation of Remote-Sounding

Data and Its Effect on Weather Forecasting. Mon. Wea. Rev., 107, 140-171. Hernandez, A., G. Kelly and S. Uppala, 2004: The TOVS/ATOVS observing system in ERA-40,

ERA-40 Project Report Series, 16. Ishii, M., A. Shouji, S. Sugimoto and T. Matsumoto 2005: Objective Analyses of Sea-Surface

Temperature and Marine Meteorological Variables for the 20th Century Using ICOADS and the KOBE Collection. Int. J. of Climatology, 25, 865-879.

Kalnay, E., and Coauthors 1996: The NCEP/NCAR 40-year reanalysis project. Bull. Amer. Meteor. Soc., 77, 437-471.

Klein, Stephen A. and Dennis L. H Hartmann 1993: The Seasonal Cycle of Low Stratiform

Clouds. J. Climate 6, 1587 – 1606. McMillin, L.M. and C.Dean, 1982: Evaluation of a New Operational for Producing Clear

Radiances, J. of Applied Meteorology 21, 1005-1014. Mears, Carl A., Matthias C. Schabel, and Frank J. Wentz 2003: A Reanalysis of the MSU

Channel 2 Tropospheric Temperature Record. J. Climate 16, 3650-3664. Murai, S., S. Yabu, H. Kitagawa 2005: Development of a new radiation scheme for the global

atmospheric NWP model. Proceeding of 21st Conference on Weather Analysis and Forecasting/17th Conference on Numerical Weather Prediction, Aug 1-5, 2005, P1.66.

Sakamoto, M., S. Kobayashi, K. Kato, T. Matsumoto, H. Koide, K. Onogi, T. Ose, and H.

Hatsushika. 2005: Ongoing Japanese Long-term Reanalysis Project (JRA-25); Assimilation of NOAA Polar-orbiter Satellite Sounder Data. Proceeding of the 85th American Meteorological Society annual meeting, Ninth Symposium on Integrated Observing and Assimilation Systems for Atmosphere, Oceans, and Land Surface, Jan 8-14, 2005, P1.10.

Saunders, R., M. Matricardi, and P. Brunel, 1999: An improved fast radiative transfer model for

assimilation of satellite radiance observations. Q.J.R. Meteorol. Soc. 125, 1407-1425. Shi, L. and J. J. Bates, 2006: Vertical Temperature Profile Radiometer Brightness Temperature

Dataset and its Statistics. Proceedings of 14th Conference on Satellite Meteorology and Oceanography, 86th AMS Annual Meeting, Atlanta, GA, 30 January - 2 February 2006, American Meteorological Society, Boston, MA, P2.1.

Uppala, S.M., and Coauthors 2005: The ERA-40 re-analysis, Quart. J. R. Meteorol. Soc., 131,

2961-3012. Wylie, D. P. and W P. Menzel, 1999: Eight Years of High Cloud Statistics Using HIRS, J.

Climate, 12, 170-184.

Appendix: The way to use TOVS in JRA-25 The Author describes here the way to use TOVS in JRA-25 briefly. Note that JRA-25 adopted

the global spectral model with a special resolution of T106 and 40 vertical layers and 3DVAR

assimilation technique, which were inheritances from the JMA operational NWP at the time.

Cloud detection The level-1d data, which are combined data of HIRS and MSU observations, were used for

convenience of the Quality Control (QC) of the tropospheric observing channels. The cloud

distinction procedures were designed chiefly for HIRS because an MSU Instantaneous Field of

View (IFOV) is, even at around nadir, about 36 times as wide as HIRS’s and it is difficult to

distinct small cloud portion in an IFOV. Therefore the cloud distinction for tropospheric channels

for MSU was too strict and not necessarily suitable.

Basically, according to McMillin and Dean (1982), window channel observations (channel 8:

11μm IR, channel 18: 3.7μm, and 19: 4.0μm) were used to determine existence of clouds.

CONDITIONS FOR CLEAR SPOTS (Daytime)

・ | RT(18) - RT(8) | < or = 10.00 K.

CONDITIONS FOR CLEAR SPOTS (Night)

・ RT(18) – RT(8) < or = 2.00 K,

・ RT(8) – RT(18) < or = 4.00 K,

・ RT(19) – RT(18) < or = 2.00 K,

・ and RT(18) – RT(19) < or = 4.00 K.

Here RT(8), RT(18), and RT(19) are brightness temperatures of HIRS channel 8, 18, and 19

respectively.

To reject contaminated spots by thin cirrus or/and thick aerosols, a comparison of RT(8) with

surface skin temperature was adopted. Supposing that global clear portion rate of sky would be

ranging from 15 to 25% (Wylie and Menzel, 1999), the threshold to distinct cirrus / aerosol

contamination was automatically modulated in the assimilation system so that the clear sky rate

would keep within the range.

One more procedure was added to assure homogeneity of weather condition in MSU IFOV. At

the conversion stage from level-1c into level-1d, each MSU observation was combined with

single spot of HIRS observation by the way that the distance between both centers becomes less

than 1.25deg in scan angle (the nearest neighbor method). The average and standard deviation of

RT(8) were calculated in each MSU IFOV extent, and the following tests were examined,

・ | RT(8) – AVG8 | < or = STD8,

where AVG8 and STD8 are the average and the standard deviation of HIRS channel 8 TBBs in

each MSU IFOV respectively. In case there are less than eight HIRS observations (not enough

samples) in a MSU IFOV, that level-1d spot was classified as cloudy one. And when more than

one spots of HIRS observation existed in the range within 1.25deg from a MSU IFOV center, the

spot that had a smaller absolute difference between RT(8) and AVG8 (,and if the TBBs for the

spots were the same, the one closest to the MSU center) was picked up.

Channel selection According to manual data quality checks and assimilation experiments for TOVS, the channel

selection for JRA-25 was determined. (Table 2.)

Basically, use of tropospheric observing channels was limited to clear spots over ocean to avoid

the influences from variation in surface emission and cloud contaminations. Surface type

distinctions were examined separately for HIRS and MSU; a land / sea occupation ratio in a range

within 0.75deg in latitude and longitude from the center of IFOV was examined for HIRS, and a

2.25deg range for MSU. If the ratio of land portion was greater than 5%, the spot was regarded as

on shores or over land.

Since manual screening showed that near infrared channels, except for HIRS channel 15, 18 and

19, seemed very noisy, they were not used. HIRS channel 1 and 17, which have broader

weighting functions, were apparently negative in assimilation experiments. As a result, JRA-25

channel selection was consequently quite similar to ERA-40’s (Hernandez et. al., 2004).

Bias Correction

Usually observations from different sources, even from different instrument for the same

observation spot, can insist on the different profiles. And a background field also can persist in

another different profile. If any arrangement is not made, the observations could have

considerable amount of biases from the background forecasts and that could contradict the

assumption of the assimilation model. To harmonize difference among observations and the

background field, an automatic bias setting procedure for TOVS was introduced.

The 1 D-VAR technique can provide an optimized profile for each spot of observation. The

differences from optimized solutions were accumulated as biases to be corrected for each channel.

The biases were sampled for each TBB classes and scan positions respectively, because a

preliminary statistical study showed the biases of the observation depend on scan positions and

TBB values.

The bias correction amounts were automatically updated as the following,

Cn+1 i = Cn i X 15 / 16 + D n i X 1/16,

where Cn i was the bias to be corrected for i-th channel in the n-th assimilation cycle. And D n i

was an averaged values of the difference between 1D-VAR optimized values and observations.

When effective sample number of each class of TBB and of each scan position is less than 5.0: at

the n-th assimilation cycle:

n-1

∑ { N m i X (15 n-m-1 /16n-m) } ＜ 5.0, m=0

the usage of the i-th channel is suspended. N m i is a sample number of i-th channel observation

for a class of TBB and for a scan position at the m-th assimilation.

Finally, to avoid meaningless over-fitting to the background and further distortion from

observation absolute values, the following arrangements were made,

・ When the standard deviation of the difference between observation and background

exceeds twice of the observation error, the application of the channel is suspended.

・ When the bias to be corrected (C) for a channel is larger than twice of the observation

error (E), C is deducted to the following modified value (C’);

C’ = 2 X E + 0.5 X (C – 2 X E).

Thinning and Time Window

Density of observation to be assimilated has a considerable impact on assimilation. Besides,

uniformity of observation density was desirable for JRA-25 assimilation system, because an

increment derived from a dense observations area can aggravate the behaviors over sparse areas.

As a NOAA satellite covers the whole globe for 12 hours, a 6-hour time window makes wide

no-observation extents. In assimilation experiments prior to JRA-25, the observation that had

noticeable increment in the middle and upper stratosphere, where larger forecast error and wider

spatial correlation among analyzed variables were present, were inclined to lead to unfavorable

distortion in increment over no-observation extents (Fig. 14). To prevent uneven distribution of

observation, the time window for the stratospheric channels was extended up to 12 hours when

only single satellite was in operation or when more than one satellite observations showed

substantial differences.

Observations were thinned in a density with one observation per two model grid cells inside the

normal 6-hour window. When the time window extended, observations outside of 6-hour window

were thinned in half density of the inside.

Error Screening

Error detection was implemented in three ways; 1) manual screening, 2) systematic error

detection, and 3) comparison in the assimilation cycle.

1) Manual Screening

To plan counter measures for observation errors, manual screening was intensively examined.

Koji Kato of the Meteorological Satellite Center (MSC) of JMA classified the errors into several

types, based on the result of his visual monitoring (Sakamoto et. al, 2005). According to his

classifications some types of errors were systematically detected, but others required further

manual screening.

2) Systematic Error Detection

Systematically two types of errors were detected: geometric errors and TBB errors. As for

geometric errors, the geographical consistency of sub-satellite points (nadir and near nadir

footprints geometry) and the scan patterns of each instrument were examined. For TBB errors,

Shinya Kobayashi of Climate Prediction Division / JMA examined earth observed data and the

calibration data (warm target and space view records). A lot of calibration errors and noisy

observation periods, especially for HIRS, were discovered and made into an errors list.

3) Comparison in the Assimilation Cycle

Errors not detected in above two procedures were detected by comparison with the optimized

solution of 1D-VAR. If the difference between observation and optimized value is larger than

four times of the observation error for the channel, the data were rejected. For instance, level-1c

SSU has only three channels and larger observation errors. 1D-VAR optimized values easily

approach to the observation within smaller number of iterations and pass through this check

easily. Therefore, for level-1d cloudy spots that also have only three available channels, this

comparison does not seem to have worked effectively either.

Preliminary QC test showed that differences between background and observation at the limb

spots are very noisy, so SSU observation at the scan position 1 and 8 and MSU (level-1d) at 1, 2,

10, and 11 were rejected.

Table 1. Pressure Levels of 2.5deg Grid Data for Both Reanalyses. JRA-25

(23 levels) ERA-40

(23 levels) 0.4 hPa 1.0 hPa 1.0 hPa 2.0 hPa 2.0 hPa 3.0 hPa 3.0 hPa 5.0 hPa 5.0 hPa 7.0 hPa 7.0 hPa 10 hPa 10 hPa 20 hPa 20 hPa 30 hPa 30 hPa 50 hPa 50 hPa 70 hPa 70 hPa 100 hPa 100 hPa 150 hPa 150 hPa 200 hPa 200 hPa 250 hPa 250 hPa 300 hPa 300 hPa 400 hPa 400 hPa 500 hPa 500 hPa 600 hPa 600 hPa 700 hPa 700 hPa

775 hPa 850 hPa 850 hPa 925 hPa 925 hPa 1000 hPa 1000 hPa

Table 2. Channel selection of TOVS for JRA-25.

Instrument Channel Number

Central Wave Number / Frequency Condition to use

2 679 cm-1 No restriction 3 691 cm-1 No restriction 4 704 cm-1 Clear ocean 5 716 cm-1 Clear ocean 6 732 cm-1 Clear ocean 7 748 cm-1 Clear ocean

8 898 cm-1 Not assimilated, just for cloud detection

10 1217 cm-1 Clear ocean 11 1364 cm-1 Clear ocean 12 1484 cm-1 Clear ocean 15 2240 cm-1 Clear ocean

18 2512 cm-1 Not assimilated, just for cloud detection

HIRS

19 2671 cm-1 Not assimilated, just for cloud detection

2 53.73 GHz Clear ocean 3 54.96 GHz Clear ocean MSU 4 57.95 GHz No restriction 1 668 cm-1 No restriction 2 668 cm-1 No restriction SSU 3 668 cm-1 No restriction

Fig. 1 Global averaged Temperature Profiles of JRA-25 and of ERA-40.

The solid line shows a JRA-25 temperature profile averaged for the period from June 1995 to April 1997, the broken line shows the same for ERA-40. Arrows point the peaks of the TOVS weighting functions for the nadir position.

HIRS channel 4

MSU channel 4 HIRS channel 3

HIRS channel 2

SSU channel 1

SSU channel 2

SSU channel 3

K

hPa

Fig. 2 Global averaged Temperature (K) at 100hPa (upper) and at 30hPa (lower). In each diagram, JRA-25 time sequence was shown as the black thick line, ERA-40 as the thin green line, and NCEP-NCAR as medium blue line respectively.

Temperature at 100hPa

Temperature at 30hPa

JRA-25

ERA-40

NCEP-NCAR

JRA-25

ERA-40

NCEP-NCAR

Fig. 3 Differences in specific humidity (g / kg) at 700hPa

between JRA-25 and ERA-40. Figure a) shows the deference (JRA-25 – ERA40) for the period from 1979 to 2004, b) from 1979 to 1986 when SSM/I observation was not available, and c) from 1987 to 2004 when both used SSM/I retrievals.

a) 1979 – 2004 (whole period of JRA-25)

b) 1979 – 1986

c) 1987 – 2004

Fig.4 Global averaged specific humidity time sequences at 700hPa (g / kg).

Black solid line shows the time sequence for JRA-25, green dashed line for ERA-40. Red arrows indicate volcanic eruption events of El Chichon in Mar. 82 and of Pinatubo in Jun. 91 respectively. The blue arrow in each figure shows the time when SSM/I started in Jul. 1987.

Fig.5 Global averaged precipitation rate time sequences (mm / day).

Black solid line shows the time sequence for JRA-25, green dashed line for ERA-40. Red arrows indicate volcanic eruption events of El Chichon in Mar. 82 and of Pinatubo in Jun. 91 respectively.

Fig.6 DT Time Sequence of HIRS ch. 11 for JRA-25 and ERA-40 (K). Red arrows indicate volcanic eruption events of El Chichon in Mar. 82 and of Pinatubo in Jun. 91 respectively. The blue arrow in each figure shows the time when SSM/I started in Jul. 1987.

a) DT for JRA-25 HIRS ch. 11 TIROS-N

NOAA-6

NOAA-7 NOAA-8

NOAA-9 NOAA-10

NOAA-11 NOAA-12

NOAA-14 K

b) DT for ERA-40 HIRS ch. 11 TIROS-N

NOAA-6

NOAA-7 NOAA-8

NOAA-9 NOAA-10

NOAA-11

NOAA-12 NOAA-14 K

c) Difference of a from b TIROS-N

NOAA-6

NOAA-7 NOAA-8

NOAA-9

NOAA-10 NOAA-11

NOAA-12

NOAA-14 K

Fig.7 DP Time Sequence of HIRS ch. 11 for JRA-25 and ERA-40 (%).

Red arrows indicate volcanic eruption events of El Chichon in Mar. 82 and of Pinatubo in Jun. 91 respectively. The blue arrow in each figure shows the time when SSM/I started in Jul. 1987.

a) DP for JRA-25 HIRS ch. 11

b) DP for ERA-40 HIRS ch. 11

c) Difference of a from b

NOAA-14

NOAA-14

NOAA-12

NOAA-14

NOAA-12

NOAA-12

NOAA-11

NOAA-11

NOAA-11

NOAA-10

NOAA-10

NOAA-10

NOAA-9

NOAA-9

NOAA-9

NOAA-8

NOAA-8

NOAA-8

NOAA-7

NOAA-7

NOAA-7

NOAA-6

NOAA-6

NOAA-6

TIROS-N

TIROS-N

TIROS-N

%

%

%

Fig.8. Global averaged Temperature in the lower Stratosphere (K) estimated from thickness between 50 and 100hPa. Red arrows indicate volcanic eruption events of El Chichon in Mar. 82 and of Pinatubo in Jun. 91 respectively. The green arrow shows the starting point of ERA-40’s stream 1.

Fig.9. Global averaged Total Column Depth of Ozone (DU).

Red arrows indicate volcanic eruption events of El Chichon in Mar. 82 and of Pinatubo in Jun. 91 respectively. The blue arrows shows TOMS observation application periods in JRA-25; of Nimbus-7 up to May. 1993, and Earth Probe since Jul. 1996.

Fig.10. DT Time Sequence of MSU ch. 4 for JRA-25 and ERA-40 (K).

The green arrow indicates starting point of ERA-40 stream 1, and the blue one shows the absent period of TOMS ozone observation in JRA-25.

a) DT for JRA-25 MSU ch. 4

b) DT for ERA-40 MSU ch. 4

c) Difference of a from b

TIROS-N

TIROS-N

TIROS-N

NOAA-6

NOAA-6

NOAA-6

NOAA-7

NOAA-7

NOAA-7

NOAA-8

NOAA-8

NOAA-8 NOAA-9

NOAA-9

NOAA-9

NOAA-10

NOAA-10

NOAA-10 NOAA-11

NOAA-11

NOAA-11

NOAA-12

NOAA-12

NOAA-12

NOAA-14

NOAA-14

NOAA-14

K

K

K

Fig.11. DP Time Sequence of MSU ch. 4 for JRA-25 and ERA-40 (%).

The green arrow indicates starting point of ERA-40 stream 1, and the blue one shows the absent period of TOMS observation input in JRA-25.

TIROS-N

TIROS-N

TIROS-N

NOAA-6

NOAA-6

NOAA-6

NOAA-7

NOAA-7

NOAA-7

NOAA-8

NOAA-8

NOAA-8

NOAA-9

NOAA-9

NOAA-9

NOAA-10

NOAA-10

NOAA-10

NOAA-11

NOAA-11

NOAA-11

NOAA-12

NOAA-12

NOAA-12

NOAA-14

NOAA-14

NOAA-14

%

%

%

a) DP for JRA-25 MSU ch. 4

b) DP for ERA-40 MSU ch. 4

c) Difference of a from b

Fig.12 Departures (K) for SSU channel 2 and Temperature Increment (K) at 5hPa. This figure shows a result of an assimilation experiment, not of JRA-25 actual assimilation. This experiment used the same observation of NOAA-11 SSU from 21UTC Dec. 31 1990 to 03UTC Jan. 1 1991 and the same 3D-VAR system with JRA-25, but time-window and data thinning were not modified. Apparent negative fake impacts are around south of Australia, and around Canada, positive one is on the Southern American Continent. Here departure mean difference of real observation and background, and increment is the difference analyzed value and background.

Fig.13 Temperature time sequence at 10hPa.

Black thick line: JRA-25, green thin line: ERA-40, blue doted line: NCEP-NCAR.

Fig.14. DT (K) and DP (%) for JRA-25 and ERA-40 (1979 – 1999).

Averaged values of DT (K) and DP (%) shown in this figure are based on all the satellite observation in the period, after taking into account the error observation reject periods of JRA-25 and ERA-40.

NOAA-11 ended NOAA-14 SSU started

K

(a) DT of SSU channel 2 against JRA-25 (K) for each satellite and for each year

(b) DT of SSU channel 2 against ERA-40 (K) for each satellite and for each year

Fig.15 DT of SSU channel 2 against JRA-25 (a) and ERA-40 (b).