Global Dynamical Downscaling of Reanalysis with Bias Correction
Kei YOSHIMURA1,2 and Masao KANAMITSU1
1Scripps Institution of Oceanography, University of California, San Diego, USA
2
Institute of Industrial Science, The University of Tokyo
Correspondence: k1yoshimura@ucsd.edu

INTRODUCTION
Reanalysis is now an indispensable dataset for climate studies. It provides analysis of a variety ofvariables, which are internally consistent within the framework of the numerical model used in the data
assimilation. However, its coarse spatial resolution has been problematic for various application studies. As
pointed out by von Storch et al. (2000), dynamical downscaling with the spectral nudging technique is considereda “poor person’s data assimilation technique”, dynamical downscaling is an alternative to regional dataassimilation. Therefore in this study, a global version of the dynamical downscaling is developed. The system
uses global spectral model and spectral nudging, and produces finer resolution global datasets from 200 km
resolution reanalysis. For this purpose, a modified version of the scale-selective bias correction (Kanamaru andKanamitsu, 2007), is developed. The major objective of this paper is to demonstrate that a “globalhigh-resolution” version of the NCEP global Reanalysis can be produced with relatively low computer cost.
METHODThe scale-selective bias correction (SSBC) scheme for a regional spectral model (RSM) developed by
Kanamaru and Kanamitsu (2007; KK07 hereafter) was modified for the global spectral model (GSM), and wasused as a base for this study. The GSM is based on the medium range forecast (MRF) model developed at NCEP
for making operational analysis and predictions (see Caplan et al, 1997), and was further improved at the Scripps
Institution of Oceanography (SIO). The RSM used by KK07 was also developed at NCEP and improved later atSIO (Kanamitsu et al., 2005). The physical parameterizations used by GSM and RSM are identical and the twomodels share many other components. The SSBC developed for RSM required modification due to differencesin the spectral basis functions used in GSM and RSM, as well as to the much wider area coverage that includes thetropics and extra-tropics.
Prior to the downscaling process, the driving reanalysis data were pre-processed; surface pressure wasrecalculated for higher resolution topography in the high-resolution global model with the hydrostatic relationship,and temperature, humidity, and wind fields were vertically interpolated to the new model sigma levels. This
process is basically the same as that of the RSM-SSBC’s correction for surface pressure.
In the RSM, the sine and cosine series for both x- and y-directions are used as basis functions, andnudging is applied directly to the two dimensional sine and cosine amplitudes. In GSM, the basis function is aspherical harmonics, and the SSBC equivalent of RSM is to apply the nudging to the amplitude of total
wavenumber. However, this implies that the nudging is uniformly applied in the zonal and meridional directions.
In reality, it is desirable to nudge differently for the zonal and meridional directions, since the atmospheric longwaves tend to have larger scale in east-west than in north-south. For this reason, SSBC for a specified zonal
scale is applied at each Gaussian latitude. The equations for nudging using a fully implicit time scheme arewritten as follows:m= Mf ( λ ,φ )= ∑ A( m ,φ ) e imλm=− MA f ( m ,φ )⎧⎪A( m ,φ ) = ⎨ 1⎪⎩α + 1 (A f ( m ,φ ) + α Aa ( m ,φ ) )2πRE cos φ)
L
2πRE cos φ(m ≤)L(m >(1)where f is a physical variable (full field), A is the Fourier coefficient, and the subscript f and a indicate forecast andanalysis (driving data), respectively. λ, φ, RE, m and M indicate longitude, latitude, radius of the earth,wavenumber, and the truncation wave number, respectively. α is a nudging coefficient, and L is a criticalnudging scale where waves longer than L will be nudged.
The original SSBC nudged zonal and meridional wind components at all sigma levels towards coarse
resolution reanalysis field by using a single weighting coefficient (α=0.9). KK07 applied it only to waves whose
physical wavelengths are 1000 km or longer. In GSM-SSBC, it was found that the nudging of temperature wasnecessary to improve simulation in the tropics. Furthermore, the removal of the correction and nudging tomoisture was needed to avoid excessive precipitation.
With this modified version of SSBC, a global downscaling by T248 (about 50 km) resolution model wasconducted (experiment named T248) for the year 2001. T62L28 6-hourly NCEP R2 was used for forcing.
Considering the spin-up of land surface parameters, the model was run from 1998, but the results for 2001 areshown below.
RESULTS(a) Global Temperature compared with CRU
Figure 1 shows the globally downscaled monthly mean temperature over land compared with the CRU(Climate Research Unit) dataset (version TS 2.1, Mitchell and Jones, 2005). From Figure 1d and 1e, it is foundthat the Arctic Islands, the extreme northern part of North America, and the eastern part of Siberia are slightly
warmer in both R2 and the downscaled analysis. There are slightly cooler biases in Central Africa, the Sahel,and the Amazon Basin. However, there is an obvious improvement associated with the global downscaling due
to more realistic surface topography, especially over mountain ranges. The clearest difference can be seen in theTibetan Plateau and in the Andes, but there are also improvements over the Pacific Coastal Ranges, the Alps, theEthiopian Plateau, the Mongolian Plateau, and many other locations. Although the other months’ results are notshown, the advantage of the downscaling is similar throughout the year.
(b) Global Precipitation compared with GPCPIn Figure 2, the downscaled analysis and original reanalysis R2 (T62 resolution) monthly precipitation arecompared with those of GPCP (Huffman et al., 2001), and T248 run without nudging (FCST) during July 2001.
Spatial contrasts associated with topography and coastlines become more apparent in the downscaled analysis, forexample, in the Himalaya and Sierra Madre Ranges, the Coast Ranges in British Columbia, and the western
coastline of India. The global average of the correlations is significantly higher for the downscaled analysis
throughout the year, indicating better representation of daily precipitation variations in the downscaled analysis.

(a)

(b)

(d)

(c)

(e)

Figure 1: Global monthly mean air temperature at 2 meter surface over land in July 2001. (a) CRU observation,(b) Reanalysis 2, and (c) T248 simulation. Differences from CRU (a) of R2 and T248 are shown in (d) and (e),respectively. The differences are rendered in CRU’s resolution (0.5 degree).

(a)(b)(c)(d)

Figure 2: Global distribution of monthly precipitation in July 2001. (a) GPCP, (b) R2, (c) T248 nudged simulation
results, and (d) T248 forecast simulation results are shown.

(c) Validation over Japan with AMeDAS
Next, we compared our results with more than 1000 Automated Meteorological Data Acquisition System(AMeDAS), in-situ meso-scale surface observatories covering all of Japan, for wind speed, humidity, temperature,and precipitation. The average AMeDAS station location interval is about 20 km, and most of the observations
are hourly. The averages of correlation coefficient of the four surface variables between the downscaled analysis
and AMeDAS in January and July 2001 showed clearly a large improvement in January precipitation, and asomewhat smaller improvement in the temperature fields for both months. By averaging the coefficients overthe region for all months, the wind speed, temperature, and precipitation of the downscaled analysis became closer
to the AMeDAS observations than those of Reanalysis. Only the humidity fields stayed similar or became worsethan R2, because humidity was not nudged and corrected in T248. Because the analysis of surface moistureplays an important role at the regional scales, improving the moisture field would make the whole simulation
better.
In Figure 3, the temporal variations of the variables (a) wind speed, (b) temperature, (c) humidity, and (d)precipitation for the first 10 days in July 2001 at a single grid point at 140.0E and 36.0N (near Tsukuba, Japan) areshown. We see that the improvement of the correlation coefficient was from better reproduction of diurnalvariations of wind speed and temperature. The diurnal cycle of absolute humidity is weak, but other fluctuations
were better reproduced in the T248 downscaling. In precipitation, the downscaled analysis captured a rain eventon 6 July, 2001, which was very sharp and short according to the observation, whereas R2 did not have any
precipitation in that period.
(d) Synoptic/Sub-synoptic Scale Weather Patterns
Some typical intense synoptic- and subsynoptic-scale atmospheric phenomena are selected and thedownscaled analysis is compared with the corresponding coarse resolution Reanalysis; a Santa Ana wind event inSouthern California, a Mistral in West Europe, and the katabatic wind in Antarctica. Figure 4 shows dailysnapshots of temperature anomaly (deviation from monthly mean), wind, and surface elevation of those events forR2 and the downscaled analysis, and captured more vivid temperature contrasts and more detailed wind patterns
associated with the complex topography, whereas winds in R2 were more uniform.
(a)

(c)

(b)

(d)

Figure 3: Temporal variations of Reanalysis 2 (thin solid line with open square), T248 (black thick line withclosed circle), and AMeDAS observation (gray thick line) are compared for (a) surface wind speed, (b) surface
temperature, (c) surface humidity, and (d) precipitation.

(a)

(b)

(c)

(d)

(e)

(f)

Figure 4 (topright): Daily averaged temperature anomalies (shades), winds (arrows), and topography (graycontour; 300 m interval and 0 m line are omitted). Left and right columns show Reanalysis 2 with linear
interpolation to the same resolution with T248 and T248-nudged run, respectively. (a, b) Santa Ana wind inSouthern California on 3 January 2001, (c, d) Mistral wind in West Europe on 22 December, 2001, and (e, f)katabatic winds in Antarctic Peninsula on 29 July 2001are shown. Number of arrows is horizontally cropped to
1/4 in (f).
SUMMARY AND CONCLUSIONSIn this study, an attempt has been made to develop a global version of dynamical downscaling using theglobal spectral model (GSM) with a spectral nudging technique. A modified version of the scale-selective bias
correction (SSBC) (Kanamaru and Kanamitsu, 2007) was applied. The global downscaling is free of the lateralboundary noise which contaminates regional downscaling, and is a way to produce computationally efficient
high-resolution global reanalysis datasets from coarse resolution data assimilation analysis.

SSBC was modified for GSM in three different ways. First, the large-scale temperature of the scale
greater than 2000 km was nudged at every Gaussian Latitude in addition to the zonal and meridional componentsof wind. This was necessary to reduce the large-scale temperature bias in the stratosphere in equatorial tropics.
Second, the nudging of perturbation field was applied instead of the nudging of perturbation tendency. Large
scale biases that can occur in the perturbation tendency nudging may not occur in the nudging of the perturbation
itself. Third, humidity was not nudged or corrected. With this nudging scheme, large-scale features of thereanalysis were well maintained in the downscaling. The departures of geopotential height of downscaled
analysis from reanalysis were in a range of 5 to 10 meters at all of the pressure levels.
Using a T248L28 (about 50 km resolution) global model, downscaling was performed for the entire yearof 2001, using T62L28 NCEP/NCAR R2 as a large-scale forcing. Surface variables and precipitation werecompared with R2 and available high-resolution observations. The global temperature fields compared with
CRU temperature showed that the downscaled analysis better matched with observation due to the better
topography. Monthly averaged precipitation, its seasonality and daily variation were compared with those ofCRU and GPCP. The daily variability in the downscaled precipitation was better reproduced in the downscaled
analysis than in the R2 throughout the year. Over Japan, the comparison with more than 1000 AMeDAS in-situ
observations showed that the downscaled analysis fits better to observation than R2 for surface temperature, windspeed, and precipitation. The fit of humidity was not significantly improved. The improvement of diurnal
variation of surface temperature was significant. In addition, three typical synoptic/sub-synoptic scale weatherfeatures were selected for comparison, namely the Santa Ana in Southern California, the Mistral in Southern
France, and katabatic winds in Antarctica. The global downscale clearly showed realistic regional-scale featureswith respect to temperature and wind.
One of the purposes of this study is to determine whether this global downscaling can serve as areplacement of the global high-resolution reanalysis without performing an expensive high-resolution global data
assimilation. From the present result, this seems to be the case at least for surface meteorological variables andprecipitation. However, in order to confirm this, it is also necessary to investigate the fit of the downscaling toobservations in the free atmosphere and to compare the results with the high-resolution data assimilation analysis.
Since this would require a full objective analysis system capable of using high-density surface observation, it isbeyond our capability at this time.
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