Dynamical Downscaling of Global Reanalysis
Masao Kanamitsu1, Hideki Kanamaru2, Kei Yoshimura1,3, Yifeng Cui4, HenryJuang5 and Wataru Ohfuchi61

4.

Climate, Atmospheric Science and Physical Oceanography Division
Scripps Institution of Oceanography, University of California,
San Diego, CA, U.S.A.
2.
Food and Agriculture Organization
United Nations,Rome, Italy
3.
Institute of Industrial Science, University of Tokyo,
Tokyo, JapanSan Diego Super Computing Center, University of California, San Diego, San Diego,
CA, U.S.A.
5.
National Centers for Environmental Prediction
Camp Springs, MD, U.S.A.
6.
Earth Simulator Center,
Yokohama, JapanINTRODUCTION
Climate research, particularly application studies for water, agriculture, forestry,
fishery and energy management require fine scale multi-decadal information ofmeteorological, oceanographic and land states. Unfortunately, spatially and temporally
homogeneous multi-decadal observations of these variables in high horizontal
resolution are non-existent. Some long term surface records of temperature andprecipitation exist, but the number of observations is very limited and the measurements
are often contaminated by changes in instrumentation over time.
Someclimatologically important variables, such as soil moisture, surface evaporation, andradiation are not even measured on regular basis.
Reanalysis is one approach to obtaining long term homogeneous analysis ofneeded variables. Unfortunately, the horizontal resolution of global reanalysis is of theorder of 100 to 200 km, too coarse for many application studies. Recently, regional
reanalysis over North America was conducted (North American Regional Reanalysis,(NARR) Mesinger et al, 2006). The horizontal resolution of 32 km and the duration of25 years used in that study are still not completely satisfactory for application
requirements, but the product is definitely valuable.
In this paper, we present another attempt to produce even higher resolution
regional “reanalysis” over a longer period for the state of California using a dynamical
downscaling technique (California Reanalysis Downscaling at 10 km; CaRD10hereafter) with Scale Selective Bias Correction (SSBC, Kanamaru and Kanamitsu,2006). This method is based on the concept that small scale detail can be attained bylaterally forcing the high resolution regional model with large scale analysis. The
essential difference between the dynamical downscaling method and data assimilation,
which is used in NARR and in all the global reanalyses, is that the former does not
utilize station observations to correct model forecast error. In this context, thedynamical downscaling can be referred to as “regional data assimilation withoutobservation” (von Storch et al, 2000). Using the results from extensive validation of
the downscaled analysis with dense surface observations, we demonstrate that thedownscaling with higher resolution regional model outperforms lower resolution
regional data assimilation analysis. This is due to the fundamental difficulties in using
dense surface observation in the current state-of-the-art data assimilation system.
This work has been published as a two parts paper in Journal Climatology(Kanamitsu and Kanamaru, 2007; Kanamaru and Kanamitsu, 2007), and the readers areencouraged to read these original manuscripts for full detail.
CaRD10For the purpose of producing datasets for regional scale climate change
research and application, the NCEP/NCAR Reanalysis for the period 1948-2005 wasdynamically downscaled to hourly, 10 km resolution over California using the Regional
Spectral Model.
Extensive validation showed that, in general, the CaRD10 near-surface wind
and temperature fit better to regional scale station observations than the NCEP/NCAR
reanalysis used to force the regional model, supporting the premise that the regional
downscaling is a viable method to attain regional detail from large scale analysis. Thisadvantage of CaRD10 was found on all time scales, ranging from hourly to decadalscales, i.e. from diurnal variation to multi-decadal trend. In table 1, detailed
comparison of 10-meter wind analysis with airport station observations in California
between Reanalysis and CaRD10 is presented. It shows that the CaRD10 fits withnear surface observation much closer with the coarse resolution global analysis.

Table 1. Vector RMSE of winds of two analyses and twelve land station
observations during 2000. Smaller RMSE is indicated in bold.
January
CaRD10August
NNR

CaRD10NNRBFL

2.33

2.85

1.59

2.03

BIH

3.45

4.12

4.00

4.41

CQT

2.29

3.25

0.96

1.45

FAT

2.94

3.63

1.72

1.96

LAX

2.81

3.58

1.62

2.28

LGB

2.63

4.07

2.35

2.81

RDD

4.185.35

1.52

1.87

SAC

3.52

4.41

2.34

2.55

SAN

2.94

2.87

2.68

2.93

SCK

3.73

4.44

2.39

3.23

SFO

4.12

4.25

4.53

4.62

SMX

4.01

4.02

1.74

1.28

All

3.24

3.90

2.29

2.62

CaRD10 was also compared with the NARR which is a data assimilation
regional analysis at 32 km resolution and 3-hourly output using the Eta model for theperiod 1979-present using the NCEP/DOE Reanalysis as lateral boundary conditions.
The large-scale component of atmospheric analysis is similar in CaRD10 and NARR
due to the use of SSBC. The CaRD10 daily winds fit better to station observations
than NARR over land everywhere and ocean where daily variability is large. The daily
near-surface temperature comparison shows a similar temporal correlation withobservations in CaRD10 and NARR. Several synoptic examples such as the Catalina
Eddy, Coastally Trapped Wind Reversal, and Santa Ana winds are better produced inCaRD10 than NARR. One example of Catalina Eddy is shown in Figure 1. Notethat the actual lateral boundary is located further (more than 500km) away from thedomain shown.
It is remarkable to see that the small scale eddy over coastal ocean isrealistically generated by downscaling. The NARR analysis has the eddy, but this isdue to the use of available coastal ocean buoy data. The difference in the shape of theeddy between CaRD10 and NARR is due to difference in model and resolution, sincebuoy observation is limited to the north of the center of the eddy. The satellite picture

Fig. 1. Comparison of the near surface streamline from CaRD10 (left), global
reanalysis (center) and NARR (right).
taken at the same time (Figure 2) show clear eddy formation and some suggestion ofthe superior per reproduction of CaRD10. Also note more detail flow features inCaRD10 than in NARR.
In Figure 3, the fit of the6-hour forecast guess with station observations of
10-meter wind over continental U.S. is plotted for
Global model and NARR. The straight horizontal lineis the estimated fit of CaRD10 based on Table 1. It isclearly shown that the model resolution is much more
important than the data assimilation for obtaining moreaccurate near surface wind analysis. Thesecomparisons suggest that the horizontal resolution of
the model has a large influence on theregional analysis, and the near-surface
observation is not properly assimilated in
the current state-of-the-art regional data
assimilation system.
TheCaRD10
near-surface
temperature and winds on monthly andhourly scales are similar to NARR withmore regional details available in CaRD10.
The spatial pattern of the two precipitation
analyses is similar but CaRD10 shows smaller

Fig 2. Satellite pictureof the Catalina Eddy.

Fig. 3. 1st guess fit of 10m
wind over U.S.

scale features despite a positive bias. The trends of 500-hPa height and precipitation are
similar in the two analyses but the near-surface temperature trend spatial patterns do notagree, suggesting the importance of regional topography, model physics, and land
surface schemes. A comparison of a major storm event shows that both analyses
suffer from budget residual. CaRD10’s large precipitation is related to wind direction,
spatial distribution of precipitable water, and a large moisture convergence.
CONCLUSIONS
Dynamical downscaling forced by a global analysis is a computationallyeconomical approach to regional scale long-term climate analysis and can provide ahigh quality climate analysis comparable to current state-of-the-art data assimilated
regional reanalysis. However, uncertainties in regional analyses can be large andcaution should be exercised when using them for climate applications. Dynamicallydownscaled analysis provides ways to study dynamics and thermodynamics of various
regional climate phenomena of different time scales because all produced variables are
dynamically, physically and hydrologically consistent, which is not possible with thestatistical downscaling.
REFERENCES
Mesinger, F., and Coauthors, 2006: North American Regional Reanalysis. Bull. Amer.
Met. Soc. 87, 343-360.
Kanamaru H. and M. Kanamitsu, 2006:

Scale Selective Bias Correction in a

Downscaling of Global Analysis using a Regional Model. Mon. Wea. Rev. 135,334-350.
von Storch, H., H. Langenberg, and F. Feser, 2000: A spectral nudging technique fordynamical downscaling purposes, Mon. Wea. Rev., 128, 3664-3673.
Kanamitsu, M. and H. Kanamaru, 2007: 57-Year California Reanalysis Downscaling
at 10km (CaRD10) Part I. System Detail and Validation with Observations. J.
Climate, 20, 5527-5552.
Kanamaru, H. and M. Kanamitsu, 2007: 57-Year California Reanalysis Downscaling
at 10km (CaRD10) Part II. Comparison with North American Regional
Reanalysis.

J. Climate, 20, 5553-5571.