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Effect of Observation Network Design on MeteorologicalForecasts of Asian Dust Events
EUN-GYEONG YANG, HYUN MEE KIM, JINWOONG KIM, AND JUN KYUNG KAY
Atmospheric Predictability and Data Assimilation Laboratory, Department of Atmospheric
Sciences, Yonsei University, Seoul, South Korea
(Manuscript received 5 March 2014, in final form 25 August 2014)
ABSTRACT
To improve the prediction of Asian dust events on the Korean Peninsula, meteorological fields must be
accurately predicted because dust transport models require them as input. Accurate meteorological forecasts
could be obtained by integrating accurate initial conditions obtained from data assimilation processes in nu-
merical weather prediction. In data assimilation, selecting the appropriate observation location is important to
ensure that the initial conditions represent the surrounding meteorological flow. To investigate the effect of
observation network configuration on meteorological forecasts during Asian dust events on the Korean Pen-
insula, observing system simulation experiments using several simulated and real observation networks were
tested with the Weather Research and Forecasting modeling system for 11 Asian dust events affecting the
Korean Peninsula during a recent 6-yr period. First, the characteristics of randomly fixed and adaptively selected
observation networks were investigated with various observation densities. The adaptive observation strategy
could reduce forecast errorsmore efficiently than the fixed observation strategy. For both the fixed and adaptive
observation strategies, the mean forecast error reduction rates increased as the number of assimilated obser-
vations and the distance between observation sites increased up to 300km. Second, the effects of redistributing
the real observation sites and adding observation sites to the real observation network based on the adaptive
observation strategy were investigated. Adding adaptive observation sites to the real observation network in
statistically sensitive regions improved the forecast performancemore than redistributing real observation sites
did. The strategy of adding adaptive observation sites is used to suggest the optimal meteorological observation
network for meteorological forecasts of Asian dust transport events on the Korean Peninsula.
1. Introduction
Asian dust events are an important spring phenomenon
in East Asia. Recently, the importance of predictingAsian
dust events on the Korean Peninsula has been increased
because of the increasing number of events (Sugimoto
et al. 2003), the increasing occurrence frequency in fall and
winter in South Korea (Kim et al. 2013), and the longer
duration of individual events. To mitigate the social and
economic damage from Asian dust events, accurate fore-
casts of such events are essential.
Numericalmodels for predictingAsian dust events were
initially developed in the late 1990s. During the Asian
Pacific Regional Aerosol Characterization Experiment
(ACE-Asia; Huebert et al. 2003), the estimated vertical
dust fluxes and elevated dust concentration layers varied
across the different dust transport models (Uno et al. 2004;
Liu et al. 2003; Gong et al. 2003) as a result of the varying
surface wind speeds that were dependent on the model
resolution, uncertainties in the surface land use and soil
texture information, and the dust removal scheme. Liu
et al. (2003) simulated Asian dust storms with a high-
resolution regional dust model, and Uno et al. (2008)
presented detailed three-dimensional structures of Asian
dust outflow using a four-dimensional variational data
assimilation (4DVAR) scheme with a dust transport
model. However, as noted by Shao and Dong (2006), the
uncertainties of the model predictions remain large de-
spite the state-of-the-art dust models showing a certain
capability in predicting the onset and evolution of Asian
dust storms.
To accurately predict Asian dust, it is crucial to im-
prove the quality of the initial conditions of both the
Denotes Open Access content.
Corresponding author address: Hyun Mee Kim, Department of At-
mospheric Sciences, Yonsei University, 50 Yonsei-ro, Seodaemun-gu,
Seoul 120-749, South Korea.
E-mail: khm@yonsei.ac.kr
DECEMBER 2014 YANG ET AL . 4679
DOI: 10.1175/MWR-D-14-00080.1
� 2014 American Meteorological Society
meteorological fields and the dust emissions because
dust transport models require this information as input.
In terms of the effect of meteorological forecasts on
chemical transports, Sistla et al. (1996), Biswas and
Rao (2001), Zhang et al. (2007), and Liu et al. (2011)
showed that small uncertainties in meteorological in-
put to transport models result in large uncertainties
in ozone and CO2 predictions. In this sense, the un-
certainties in the meteorological forecasts could cause
large errors in Asian dust forecasts on the Korean
Peninsula (Kim et al. 2008; Kim and Kay 2010; Kim
et al. 2013).
The transport paths of Asian dust connecting the
dust source regions to the Korean Peninsula are
closely associated with extratropical pressure systems
over East Asia (e.g., Chung and Park 1997; Uno et al.
2001; Kim et al. 2013). During Asian dust events on
the Korean Peninsula, typically a high pressure sys-
tem follows behind a surface frontal cyclone over the
Korean Peninsula (e.g., Merrill and Kim 2004; Kim et al.
2008, 2013). Kim et al. (2008) showed that difficulties
in forecasting the path of the dust and the location and
speed of the high pressure system behind a surface
frontal cyclone over the Korean Peninsula caused an
incorrect forecast for the Asian dust event that oc-
curred during 7–9 April 2006. In addition, Jhun et al.
(1999) showed that the flow direction on the 850-hPa
layer plays a crucial role in transporting Asian dusts to
the Korean Peninsula. Murayama et al. (2001) and
Kim et al. (2010) confirmed that most aerosols asso-
ciated with Asian dust events observed in South Korea
are located below 5 km in height. Therefore, in the
present study, we focus on improving the meteoro-
logical forecasts of extratropical pressure systems and
associated lower to midtropospheric flow directions
over the Korean Peninsula during Asian dust events.
High-quality initial meteorological fields could be
obtained by constructing an optimal observation net-
work for atmospheric conditions during Asian dust
events and using the observations obtained from this
network to determine the initial meteorological con-
ditions. To construct the optimal observation network
for initial meteorological conditions and forecasts
for Asian dust events, the characteristics of the obser-
vation network should be studied. Morss et al. (2001)
and Liu and Rabier (2002) investigated the character-
istics of observation networks based on the density of
observation sites using a quasigeostrophic and one-
dimensional model, respectively. However, no studies
have considered the effect of the observation network
for meteorological forecast fields specifically during
Asian dust events using a realistic modeling sys-
tem. In addition, although the Korea Meteorological
Administration (KMA) collects meteorological and
dust [e.g., particulate matter 10 (PM10) concentration]
data from surface observation sites operated by the
KMA and the China Meteorological Administration
(CMA) over the Asian dust source regions, these sites
were determined subjectively, and the observational data
are insufficient to produce accurate Asian dust pre-
dictions (Kim et al. 2008, 2013). Therefore, to determine
an optimal observation network for meteorological
forecasts during Asian dust events, the characteristics
of observation networks were investigated for 11 Asian
dust events that affected the Korean Peninsula from
2005 to 2010 by performing observing system simula-
tion experiments (OSSEs) using theWeather Research
and Forecasting (WRF) Model. First, the character-
istics of randomly fixed and adaptive observation
networks were investigated for various observation
densities and distances between observation sites.
Adaptive observation strategies are used to design the
observation network because they employ more ob-
jective criteria to decide observational sites, which
improve meteorological transport forecasts associated
with Asian dust events, as mentioned in Kim et al.
(2013). The adaptive observation networks were de-
termined according to adjoint sensitivity analyses. The
characteristics of adjoint-based forecast sensitivity for
Asian dust events have been investigated by Kim et al.
(2008), Kim and Kay (2010), and Kim et al. (2013),
which found that adjoint-based forecast sensitivity is
a beneficial tool for determining observation sites and
improving the meteorological forecasts of Asian dust
events on the Korean Peninsula. Kim et al. (2013)
showed that the linearity assumption holds well for
meteorological forecasts of 46 Asian dust occurrences
from 2005 to 2010. Second, two experiments with the
modified real observation network (i.e., radiosonde
sites) were performed to determine an optimal obser-
vation network. Modifications to the real observation
network were performed either by redistributing ob-
servation sites within the real observation network or
by adding adaptive observation sites to the real ob-
servation network. Again, the adaptive observation
networks were determined using adjoint sensitivities.
Third, the optimal observation networks for Asian dust
events on the Korean Peninsula were suggested based
on the first and second experiments.
Section 2 presents the methodology and includes the
mathematical formulations, Asian dust cases, and ex-
perimental framework. Section 3 describes the results
of OSSEs with fixed and adaptive strategies, modified
real observation networks, and an optimal observation
network. Finally, section 4 presents the summary and
conclusions.
4680 MONTHLY WEATHER REV IEW VOLUME 142
2. Methodology
a. Adjoint-based forecast sensitivity
Adjoint sensitivities, which represent the gradient of
the response function with respect to the initial condition,
have been used to identify sensitive regions for various
meteorological features (Kim and Jung 2006; Kim et al.
2008; Jung and Kim 2009; Kim and Kay 2010; Kim et al.
2013). The forecast can be expressed as
xf 5N(x0) , (1)
where x0 represents the initial states and xf represents
the forecast states integrated by the nonlinear model
N. The variation of the forecast state dxf can be ex-
pressed as
Dxf ffi dxf 5›N
›x
���x5x
0
dx05Mdx0 , (2)
where dx0 and M(5›N/›x) represent the variation of
initial state and tangent linear model, respectively. The
response function R is defined as
R5 f (xf ) , (3)
where it can be any function of the forecast state that is
differentiable.
As mentioned in section 1, meteorological forecast
errors affecting Asian dust transport forecasts on the
Korean Peninsula are primarily associated with extra-
tropical weather systems and their forecasts (Kim et al.
2013) and flow directions in the lower to midtroposphere
(Jhun et al. 1999; Murayama et al. 2001; Kim et al. 2010)
over theKorean Peninsula. The location and speed of the
high pressure system behind a surface frontal cyclone
over the Korean Peninsula affect the flow direction in the
lower to midtroposphere over the peninsula, which af-
fects dust forecasts. Therefore, R in Eq. (3) is defined as
thewind forecast error from the surface tomidtroposphere
(approximately 500hPa) over the Korean Peninsula
(Fig. 1) measured as the kinetic energy at the final time:
R5
ðððs,x,y
1
2(u021 y021w02) dx dy ds , (4)
where u0, y0, and w0 are the forecast errors of zonal,
meridional, and vertical winds, respectively.
Following Errico (1997) and Kim and Jung (2006), the
sensitivity of R to the initial state can be obtained using
the adjoint model MT as
›R
›x05MT›R
›xf. (5)
b. Model
For the numerical experiments, the Advanced Re-
searchWRFversion 3.3 (Skamarock et al. 2008)was used.
The model was centered at 458N, 1158E, with a 60-km
horizontal grid spacing in 71 (zonal direction) by 55
(meridional direction) horizontal grid points (Fig. 1). The
domain had 41 vertical layers, and the top of the model
was at 50hPa. To calculate the adjoint-based forecast
sensitivity, the WRFPLUS system (Xiao et al. 2008;
Huang et al. 2009), which consists of the adjoint and
tangent linear version of the WRF, was used. For the
initial and lateral boundary conditions of the model, the
National Centers for Environmental Prediction (NCEP)
Final Analysis (FNL; 18 3 18 horizontal resolution) and
FIG. 1. The average surface pressure patterns (black line) and
500-hPa geopotential height (blue line) at (a) the initial time of the
model integrations (36 h ahead of the occurrence times of the
maximum PM10 concentration on the Korean Peninsula) and
(b) the occurrence times of the maximum PM10 concentration on
the Korean Peninsula. The area covered represents the model
domain and the box (red) over the Korean Peninsula denotes an
area where a response function is defined.
DECEMBER 2014 YANG ET AL . 4681
the InterimEuropean Centre forMedium-RangeWeather
Forecasts (ECMWF) Re-Analysis (ERA-Interim; 1.58 31.58 horizontal resolution) were used.
c. Cases
Of the 22 Asian dust events that affected the Korean
Peninsula from 2005 to 2010, 11 cases with relatively
large meteorological forecast errors were chosen for
the present study (Table 1) because our aim was to
reduce such errors. In Table 1, the origins of dusts were
determined using red–green–blue (RGB) imagery of
Moderate Resolution Imaging Spectroradiometer
(MODIS) data, Multifunctional Transport Satellite-1R
(MTSAT-1R) data, the surface and upper-air weather
chart, and PM10 concentrations, as in Kim et al. (2013).
The occurrence date indicates the day when PM10
concentrations observed on the Korean Peninsula
reached the maximum value. The transport time period
was determined by the time difference between the
maximumPM10 concentrations in the source regions and
that on the Korean Peninsula. Only the cases with
transport times longer than or equal to 36 h were chosen
to evaluate the impact of observation sites upwind of the
Korean Peninsula. The final and initial times for model
runs were determined according to the time when the
PM10 concentrations were at maxima on the Korean
Peninsula and 60h before the final time, respectively.
The average pressure patterns for the 11 Asian dust
cases are shown in Fig. 1. At 36 h ahead of the occur-
rence time of the maximum PM10 concentrations on the
Korean Peninsula, the average pressure pattern was
characterized by the upper trough extending south-
westward and strong surface pressure gradients west of
the surface cyclone over Mongolia and northeastern
China (Fig. 1a). The average synoptic pattern during
a dust event on the Korean Peninsula was characterized
by an upper trough extending southeastward and a well-
developed surface cyclone over the northern part of the
Korean Peninsula (Fig. 1b). Lee and Kim (1997) found
that the composite 500-hPa geopotential height of eight
Asian dust events had a trough in an area to the southeast
of Lake Baikal two days before Asian dust was observed
on the Korean Peninsula, and the trough developed and
moved toward Manchuria one day before Asian dust was
observed on the Korean Peninsula. In addition, Jhun et al.
(1999) confirmed that the durations of Asian dust events
on the Korean Peninsula were related to the location of
pressure troughs in the upper atmosphere and the ve-
locity of the pressure system. Therefore, the average
synoptic pattern of the 11 Asian dusts are similar to that
of the typicalAsian dusts affecting theKorean Peninsula
(e.g., Merrill and Kim 2004; Kim et al. 2006), which are
characterized by strong winds associated with a surface
cyclone over the arid area in Mongolia and northeastern
China at the initial time and a surface cyclone accom-
panying the eastward movement of the upper trough
over the Korean Peninsula at the occurrence time.
In terms of the linearity assumption on which the
adjoint sensitivity analysis is based, the ratios of line-
arly and nonlinearly evolved temperature perturbation
magnitudes in the verification area were calculated
for each case (not shown). The linearity holds generally
well for the 11 cases, which implies that the adjoint
sensitivity can be used to identify sensitive regions for
meteorological forecasts of the cases.
d. OSSEs
OSSEs were performed for the 11 Asian dust events.
The OSSEs require the reference atmospheric state
considered to be the ‘‘true state,’’ simulated observa-
tions, and a data assimilation system. The data assimi-
lation system used was the WRF three-dimensional
variational data assimilation (3DVAR) system (Barker
et al. 2004). The background error statistics for this
study were generated from 99-day statistics from 2
February to 31 May 2005 using gen_be utilities of
TABLE 1. Asian dust events that affected the Korean Peninsula from 2005 to 2010. The occurrence date andmaximum PM10 are based on
observations over South Korea.
Case No.
Occurrence
date Origin Run time
Transport
time (h)
Max PM10 (mgm23)
in South Korea
1 1 May 2005 Manchuria 1200 UTC 28 Apr–0000 UTC 1 May (60 h) 48 Baengnyeongdo, 500
2 19 Apr 2006 Inner Mongolia 0000 UTC 17 Apr–1200 UTC 19 Apr (60 h) 72 Gosan, 330
3 23 Apr 2006 Inner Mongolia 0000 UTC 21 Apr–1200 UTC 23Apr (60 h) 42 Baengnyeongdo, 550
4 1 May 2006 Gobi 1200 UTC 28 Apr–0000 UTC 1 May (60 h) 48 Gyegryelbi-do, 500
5 27 Mar 2007 Inner Mongolia 0000 UTC 25 Mar–1200 UTC 27 Mar (60 h) 42 Kwanaksan, 413
6 25 May 2007 Inner Mongolia 0000 UTC 23 May–1200 UTC 25 May (60 h) 48 Chupungnyeong, 493
7 2 Mar 2008 Inner Mongolia 0000 UTC 29 Feb–1200 UTC 2 Mar (60 h) 42 Daegu, 1428
8 25 Apr 2009 Inner Mongolia 1200 UTC 22 Apr–0000 UTC 25 Apr (60 h) 48 Gudeoksan, 307
9 27 Apr 2010 Inner Mongolia 1200 UTC 24 Apr–0000 UTC 27 Apr (60 h) 60 Ulsan, 164
10 8 May 2010 Inner Mongolia 1200 UTC 6 May–0000 UTC 9 May (60 h) 42 Jindo, 289
11 11 Nov 2010 Gobi 0000 UTC 9 Nov–1200 UTC 11 Nov (60 h) 36 Baengnyeongdo, 1664
4682 MONTHLY WEATHER REV IEW VOLUME 142
the WRF data assimilation system. The 60-h WRF
forecasts using ERA-Interim data as an input were
used as the true state (TRUE), and the 60-h WRF
forecasts using NCEP FNL data as an input were used
as the control state (CNTL) (Fig. 2). In OSSE, the
TRUE (i.e., nature run) needs to be within the vari-
ability of the real analyses. The mean states of the
TRUE are within the variability of the ERA-Interim
and the variability of the TRUE is quite consistent with
that of the ERA-Interim (Fig. 3). In addition, the po-
sition and intensity of the average surface pressure
patterns and 500-hPa geopotential height at the initial
time of the model integrations and the occurrence
times of the maximum PM10 concentration on the
Korean Peninsula are quite similar for the TRUE
(Fig. 1) and ERA-Interim analysis (not shown), which
implies that the TRUE in this study is accurate enough
to be regarded as the ‘‘truth.’’ The simulated observa-
tions were considered as the upper-air radiosonde ob-
servations and obtained by adding observational errors
to the TRUE. The simulated wind speed, wind di-
rection, and temperature at 850, 500, and 200 hPa were
extracted from the TRUE at the targeting time (0 h),
and the observational errors of the radiosonde (Irvine
et al. 2011) were added to these wind and temperature
variables. New analysis fields were then generated at
the targeting time by assimilating the simulated ob-
servations to the WRF 3DVAR. The selection criteria
dictating where to place the simulated observations are
explained in section 3. The EXP runs were obtained by
integrating the new analysis for 36 h. The 36-h in-
tegration time was chosen because the statistically
sensitive regions for the Asian dust events with model
integration times greater than 36 h show similar dis-
tributions in Kim et al. (2013). The difference between
the TRUE and CNTL at the verification time (i.e., final
time) was regarded as the forecast error without data
assimilation, whereas the difference between TRUE
and EXP at the verification time was regarded as the
forecast error after assimilating the simulated obser-
vations at certain observation sites.
Similar to Eq. (4), the forecast error at specific model
layer s over the Korean Peninsula was defined as
R(s)5
ððx,y
1
2(u021 y021w02) dx dy . (6)
FIG. 2. Schematic diagram of the observing system simulation experiments with various
observation strategies used in this study. True and control runs begin 24 h before the targeting
time. For the experimental runs using the fixed and adaptive strategies, the observations are
assimilated using 3DVAR at the targeting time.
FIG. 3. The average kinetic energy over the model domain for 11
dust cases. The blue solid line and shading represent the mean and
standard deviation of the ERA-Interim, respectively. The red solid
line and dotted bar represent the mean and standard deviation of
the true run, respectively.
DECEMBER 2014 YANG ET AL . 4683
The reduction rate of the forecast error at specific model
layer was then defined as
reduction rate(s)5RCNTL(s)2REXP(s)
RCNTL(s)3 100 (%),
(7)
where RCNTL and REXP indicate the forecast error of the
CNTL and EXP run at the verification time, respectively.
The vertically averaged reduction rates of the forecast
errors were obtained by averaging the reduction rates of
the forecast errors at a specific layer for the entire vertical
layer. Because the error reduction rateswere different for
each Asian dust event, the reduction rates of all of the
events were averaged to obtain the average reduction
rates of the forecast errors.
3. Results
a. OSSE with randomly fixed and adaptiveobservation networks
1) SELECTION OF OBSERVATION SITES
The fixed observation network was chosen randomly,
whereas the adaptive observation network was deter-
mined by adjoint sensitivities. To investigate the impact
of the number and distance of observation sites on the
forecasts, the number of the observation sites was varied
among 10, 20, 30, 40, 50, 60, 70, and 80 (Fig. 4), and the
distance between observation sites was varied among 120,
180, 240, and 300km (Fig. 5). The regions in the vicinity
(i.e., within 300km) of the boundaries were excluded
when selecting observation sites.
FIG. 4. Examples of observation distributions depending on the number of observation sites (top-left corner of each panel) for the
(a) fixed observation strategy and (b) adaptive observation strategy. The red shading represents the sensitive regions determined by the
adjoint sensitivities for the Asian dust event that occurred on 1 May 2005. The black dots indicate 10, 20, 30, 40, 50, 60, 70, and 80
observation sites. The shortest distance between observations is 120 km.
4684 MONTHLY WEATHER REV IEW VOLUME 142
The randomly fixed observation sites for each Asian
dust case were selected as follows. The initial 10 fixed
observations were chosen randomly (Fig. 4a) and were
the same for the 120-, 180-, 240-, and 300-km experi-
ments. An additional 10 observation sites were selected
randomly and added to the previous observation net-
work until a maximum of 80 observation sites were es-
tablished keeping the distance (i.e., 120, 180, 240, and
300 km) between the sites. The simulated observations
for each observation network were then produced and
assimilated. Each experiment was performed three
times with different sets of randomly fixed observation
networks to obtain general conclusions. Three different
sets of the initial 10 randomly fixed observations are
shown in Fig. 6.
To investigate the effect of the adaptive observation
network on the forecasts, the sensitive regions for each
Asian dust case were identified by adjoint sensitivities
and the adaptive observation sites were selected within
the sensitive regions as follows. The adjoint sensitivity
was calculated for each case, and the time interval be-
tween the verification and targeting time was 36 h.
One observation site was then determined in the most
sensitive region, and the second observation site was
selected in the second most sensitive region in consid-
eration of the distance from the first observation site.
The subsequent sites were selected in the sameway as the
previous sites (Figs. 4b and 5b), and then the simulated
observations were produced for the selected observation
sites and assimilated.
2) CHARACTERISTICS OF FIXED OBSERVATION
NETWORK
The OSSEs that used the various observation net-
works investigated in this study are listed in Table 2. The
vertically averaged reduction rates of the forecast errors
for FIX_EXP and ADP_EXP that were averaged for all
of the Asian dust cases are shown in Fig. 7. The error
reduction rates for FIX_EXP were increased as the
number of fixed observation sites increased, which im-
plies that assimilating more observations can cover
a much larger area in the model domain and resolve
more synoptic features to improve the initial condition,
as described in Morss et al. (2001). Liu and Rabier
(2002) also demonstrated that increasing the density of
observations remarkably improved the analysis accu-
racy in an experimental 1D framework. For the same
number of observations, different distances between
randomly fixed observation sites can result in a maxi-
mum of only 1.4%of the differences in the forecast error
reduction rates. For larger numbers (e.g., 60–80) of ob-
servation sites, the FIX_EXPs with observation sites
that were 240 and 300 km apart reduced the forecast
errors more than those with observation sites 120 and
180 km apart. In contrast, for the smaller numbers of
observation sites (e.g., 30–40), it is difficult to differen-
tiate the effects at a 240–300-km distance from those at
a 120–180-km distance, which implies that larger dis-
tances between the fixed observation sites are beneficial
for larger numbers of fixed observation sites.
FIG. 5. Examples of observation distributions depending on the shortest distance (numbers at the top-left corner of each panel) between
observation sites for the (a) fixed observation strategy and (b) adaptive observation strategy (red shading is as in Fig. 4). The black dots
indicate 20 observation sites.
DECEMBER 2014 YANG ET AL . 4685
Figure 8 shows the standard deviation of error re-
duction rates for three different FIX_EXPs using dif-
ferent sets of numbers and distances of randomly fixed
observation sites. As the number of observation sites
increases, the standard deviations generally tend to de-
crease. For a small number of observations, it is difficult
to resolve the synoptic atmospheric phenomena in the
entire domain. Therefore, the error reduction rates vary
depending on the specific sets of observation networks
with the large standard deviation. In contrast, a large
number of observations could resolve the atmospheric
phenomena in the entire domain. Therefore, the error
reduction rates do not vary widely based on the specific
sets of observation networks, although the observation
locations are completely different for each observation
set. As a result, the standard deviation of the error re-
duction rates is small. The FIX_EXP with a distance of
120 km generally showed the largest standard deviation
(Fig. 8), which implies that the result of the FIX_EXP
with 120-km distance varied greatly depending on the
specific configurations of the observation locations. This
result may have been related to the larger error re-
duction rates of the 120-km FIX_EXP than those of the
180-km FIX_EXP shown in Fig. 7.
Therefore, a larger number of observation sites can
provide more stable forecast error reduction rates. For
larger numbers of observation sites, increased distances
up to 300 km between the randomly fixed observation
sites led to a greater reduction in the forecast error.
3) CHARACTERISTICS OF THE ADAPTIVE
OBSERVATION NETWORK
Similar to the FIX_EXP, the average error reduction
rates increased in the ADP_EXP as the number of ob-
servation sites increased (Fig. 7). However, the forecast
error reduction rates of the ADP_EXP oscillated after
reaching an almost 10% error reduction, which implies
that the 10% error reduction rate may be a saturation
rate in the given framework. Bengtsson and Gustavsson
(1972) showed that increasing the number of satellite
observations beyond a specific value is almost in-
significant in an objective analysis and that it is difficult
to diminish the RMS error below a certain value. Morss
et al. (2001) also showed that adding more observations
in the quasigeostrophic (QG) model to the existing
dense observations only resulted in a small additional
benefit because the analysis errors are small in a dense
observation network.
When the distances between observation sites are
greater, the forecast errors are reduced more because
observations with greater distances can cover most of
the sensitive regions. For 10–30 observation sites, the
ADP_EXPs with 240- and 300-km distances reduced
the forecast errors to a greater extent than those with
120- and 180-km distances. For 40–80 observations, the
ADP_EXPs with a 180-km distance reduced the error as
much as those with 240- and 300-km distances, whereas
the ADP_EXPwith a 120-km distance did not reduce the
error as much as the other experiments. This finding
implies that a 180-km distance EXP is enough to cover
the entire sensitive region for the larger number of ob-
servation sites but not for the smaller number. In contrast,
FIG. 6. Three different sets of 10 randomly fixed observation
locations.
4686 MONTHLY WEATHER REV IEW VOLUME 142
the 120-km distance showed the lowest reduction rate
among all strategies up to 60 observation sites and
yielded similar results to the other distances for 70–80
observation sites, which indicates that the ADP_EXP
with the 120-km distance cannot cover the domain us-
ing the smaller number of observation sites. Similar to
the FIX_EXP, increased distances between the adap-
tively selected observation sites reduced the forecast
errors to a greater extent for the larger numbers of
observation sites.
For both FIX_EXP and ADP_EXP, the average er-
ror reduction rates generally increase as the numbers of
observation sites increase and the distance between
the observation sites becomes greater up to 300 km.
Overall, the ADP_EXP shows a greater error reduction
rate than the FIX_EXP, excluding the ADP_EXP with
a 120-km distance. The smaller error reduction rates of
theADP_EXP compared to the FIX_EXP for the 120-km
distance were caused by observation sites that were too
closely clustered in the sensitive regions (Fig. 5b). The
discrepancies between the ADP_EXPs with various
distances are generally larger than those between the
FIX_EXPs (Fig. 7) because the observation locations
of the ADP_EXPs vary greatly compared to those of
the FIX_EXPs depending on the distances between the
observation sites (cf. Figs. 5a and 5b). However, ex-
cluding the 120-km distance, the discrepancies between
the ADP_EXPs decrease considerably and become
similar to those between the FIX_EXPs as the
number of observation sites increases, which implies
that the ADP_EXPs with the distance from 180 to
300 km result in similar error reduction rates for the
larger number of observation sites. The FIX_EXP can
reduce the forecast error bymore than 8% formore than
70 observation sites, whereas the ADP_EXP can reduce
the forecast error to the same extent for more than 40
observation sites, excluding the 120-km experiments.
Therefore, compared to the randomly fixed observation
network, the adaptive observation network is more ef-
ficient at reducing the forecast error with lower numbers
of observation sites if the observation sites are ade-
quately separated.
The OSSEs (i.e., FIX_EXPs and ADP_EXPs) with
the cycling data assimilation (26 and 0 h) and with
simulated observations extracted from six layers (850,
700, 500, 300, and 200 hPa) were also tested to evaluate
the effect of the cycling and doubling the vertical layers
on the OSSE results. The cycling data assimilation and
doubling the vertical layers to extract the simulated
observations did not affect major features of the results
(not shown).
b. OSSE with a modified real observation network
1) REDISTRIBUTION AND ADDITION STRATEGY
Because the 77 real upper-air observation sites were
already established in East Asia, experiments with the
real observation network in the model domain (Fig. 9)
were performed to suggest the observation network that
is optimal to decrease the meteorological forecast error.
In the real observation network, the distances between
radiosonde observation sites are generally from 200 to
400 km. The OSSEs in Table 2 were applied to the 11
Asian dust cases, and the mean reduction rates of the
forecast errors were obtained by averaging the error
reduction rates for each case.
The RDS_ADP_EXP was implemented as follows.
First, the distances from each observation site to the
other observation sites were calculated and ordered by
distance. The two observation sites with the shortest
distance each other were selected among all of the real
observation sites. The observation site with the shorter
distance to the next closest observation site was selected
from the two observation sites and removed. Second, the
observation site in the most sensitive grid point was se-
lected. If the distances between the selected observation
TABLE 2. The observing system simulation experiments using various observation networks.
Type of observation
network Expt name
Characteristics
of expt
Fixed and adaptive
observation network
FIX_EXP OSSE using the randomly selected fixed observation sites
ADP_EXP OSSE using the observation sites determined by the
adaptive strategy
Modified real observation
network by redistribution
RDS_FIX_EXP OSSE using the real observation sites partly redistributed by the
randomly fixed strategy
RDS_ADP_EXP OSSE using the real observation sites partly redistributed by the
adaptive strategy
Modified real observation
network by addition
ADD_FIX_EXP OSSE using the real observation sites, added new observation sites
by the randomly fixed strategy
ADD_ADP_EXP OSSE using the real observation sites, added new observation sites
by the adaptive strategy
DECEMBER 2014 YANG ET AL . 4687
site and the existing observation sites (i.e., real or
adaptive observation sites) were more than 180 km,1
then the selected observation site replaced the real ob-
servation site that was removed; if not, then the obser-
vation sites in the second most sensitive grid point were
selected and replaced the real observation site that was
removed. Third, observations at the new observation
network were assimilated. The RDS_ADP_EXP was
conducted by varying the number of redistributed ob-
servation sites from 1 to 17 (approximately 22% of the
total 77 sites) by increments of 1. When the number of
redistributed sites increased, the distances between
the real observation sites were recalculated and the
entire process was repeated. Figure 9a is an example of
a new observation network containing 17 redistributed
observation sites. RDS_FIX_EXP was the same as
RDS_ADP_EXP except that the removed real obser-
vation sites were replaced by the randomly chosen
fixed observation sites.
For ADD_ADP_EXP, the adaptive observation sites
determined by adjoint sensitivities were added to the
real observation network. Experiments were conducted
by varying the number of newly added observation sites
from 1 to 13 by increments of 1 in consideration of the
180-km distance among the added adaptive observation
sites and existing observation sites. The selection of the
added observation sites in the sensitive regions was
the same as in RDS_ADP_EXP. Figure 9b is an ex-
ample of a newly organized real observation network
in which 13 observation sites were added adaptively.
ADD_FIX_EXP was the same as ADD_ADP_EXP
except that the newly added observation sites were
selected randomly.
2) CHARACTERISTICS OF THE REDISTRIBUTED
OBSERVATION NETWORK
Figure 10a shows the mean error reduction rates of
RDS_ADP_EXP and RDS_FIX_EXP. The forecast
error was reduced by 8.9%, on average, when the ob-
servations were assimilated on the real observation sites
without redistribution (black bar in Fig. 10a). Except for
the experiment with one redistributed observation site,
RDS_ADP_EXP showed greater mean error reduction
rates thanRDS_FIX_EXP (Fig. 10a), which implies that
adaptive redistribution is a better strategy for improving
forecast performance than random redistribution for the
observation sites 180 km apart when more than one
observation site are redistributed. In addition, com-
pared to the error reduction rate of the real observation
network (black bar), adaptive redistribution of less than
five observation sites of the real observation network
degrades the forecast performance. Therefore, the ac-
curacy of the forecasts in redistributed real observation
networks was improved when more than a certain
number of observation sites were adaptively redis-
tributed, which implies that the number of redistributed
FIG. 7. Means of vertically averaged error reductions depending on the number of observation
sites for the fixed observation strategy (dashed) and adaptive observation strategy (solid).
1 The distances between the observation sites were set at
180 km because the greater distances between observation sites
cannot be guaranteed given the homogeneously distributed real
observation sites.
4688 MONTHLY WEATHER REV IEW VOLUME 142
observations should be large enough to cover entire
sensitive regions. The linear regression result between
the number of adaptively redistributed observation sites
and forecast error reduction rates is shown in Fig. 10a.
An increasing trend of mean error reduction rates with
more adaptively redistributed observation sites is shown
in Fig. 10a.
3) CHARACTERISTICS OF THE ADDED
OBSERVATION NETWORK
Figure 10b shows the mean forecast error reduction
rate for the newly organized real observation network,
which consists of real observation sites and added
adaptive observation sites. Adding adaptive observa-
tion sites to the real observation network always im-
proves the forecast and reduces the forecast error by
more than 8.9%. Generally, the error reduction rate
of the adaptive addition strategy is always greater
than 10% for more than four observation sites added.
ADD_FIX_EXP can reduce much smaller forecast
errors than ADD_ADP_EXP, even if ADD_FIX_
EXP improves forecast performance (Fig. 10b), which
indicates that the forecast improvement is caused
not just by adding more observation sites and covering
larger regions but by adding observation sites and
covering sensitive regions. Thus, it can be concluded
that assimilating observations in sensitive regions de-
termined by large adjoint sensitivities have a notable
impact on forecast performance and that adding
adaptive observation sites can effectively improve
forecasts.
It is important to compare the redistribution strategy
with the addition strategy to determine which one is
more effective at modifying the real upper-air observa-
tion network. As shown in Figs. 10a and 10b, adding
adaptive observation sites improves the forecast per-
formance compared to the redistribution of observation
sites. For the 180-km distance, irregular numbers (e.g., 9,
14, 15, and 17) of the 77 real observation sites must be
redistributed to obtain a forecast error reduction rate
greater than 10% (Fig. 10a), whereas adding more than
four adaptive observation sites can reduce the same
amount of the forecast error (Fig. 10b). Because the
total number of observation sites in the addition strategy
is greater than that in the redistribution strategy, the
forecast error reduction rate of the addition strategy
could be greater than that of the redistribution strategy.
To enable a fair comparison, the error reduction rate per
observation site was obtained by dividing the error re-
duction rate by the total number of observation sites.
The error reduction rate per observation site was almost
same as that in Fig. 10 (not shown).
The impact of adding a small number of observation
sites was similar to that of redistributing a large number
of observation sites. Therefore, the addition strategy is
a more efficient method of reducing forecast error than
the redistribution strategy. Furthermore, adding adaptive
observation sites improves the forecast performance to
a greater extent than adding random observation sites.
Thus, the forecast improvement is not caused by in-
creased observations but by observations assimilated in
sensitive regions.
FIG. 8. Standard deviations of error reduction rates from three different sets of randomly fixed
observations for several numbers of observation sites and distances between them.
DECEMBER 2014 YANG ET AL . 4689
c. OSSE with an optimal meteorological observationnetwork
1) STATISTICALLY SENSITIVE REGIONS
Until now, each sensitive region for each Asian dust
event had been used to implement the experiments, and
the results were averaged statistically. In reality, how-
ever, it is unfeasible to detect each sensitive region and
assimilate the adaptively selected observations for each
case. Thus, it is crucial to determine the statistically
sensitive regions for the 11 Asian dust events and verify
whether the observation sites in the sensitive regions
have positive effects on the meteorological forecast
performance of Asian dust events. To obtain statistically
sensitive regions, sensitive grids that include the most
sensitive upper 5% of all grids in the domain were se-
lected for each case using adjoint sensitivities. These
grids were considered to have one frequency, and the
frequencies were then integrated for all 11 cases.
Figure 11 shows the statistically sensitive regions
with an average 500-hPa geopotential height at the
targeting time for the 11 Asian dust cases. Two sensi-
tive regions were identified: the maximum sensitivity
was located in the Gobi, and the next maximum sen-
sitivities were located in northeastern China (Man-
churia), the Liaodung Peninsula, and North Korea.
These sensitive regions coincide with the northern dust
source regions that are identified as statistically sensi-
tive regions for 46 Asian dust events affecting the Ko-
rean Peninsula from 2005 to 2010 in Kim et al. (2013).
The mean pressure trough in the upper air was located
over sensitive regions in the Gobi. The sensitive re-
gions were located under the upper trough in the strong
pressure gradient regions behind the frontal cyclone (cf.
Fig. 1a). The locations of the upper trough and surface
cyclone were associated with the statistically sensitive
regions before and during the Asian dust event on the
Korean Peninsula (Kim et al. 2013).
2) CHARACTERISTICS OF THE OPTIMAL
METEOROLOGICAL OBSERVATION NETWORK
It is important to verify whether these statistically
sensitive regions have a significant impact on the mete-
orological forecast performance of individual Asian dust
events. Based on the procedures described in section 3b,
the adaptive addition strategy was selected to organize
the new observation network. Similar to the previous
experiments, additional observation sites were selected in
statistically sensitive regions and added to the real ob-
servation network in consideration of the 180-km dis-
tance between the new observation sites and the existing
observation sites. The number of additional observation
sites was then increased from 1 to 13. Figure 11 shows the
newly organized optimal observation network with 13
added observation sites superimposed on statistically
sensitive regions and 500-hPa geopotential height. The
mean error reduction rates for the newly organized ob-
servation network are shown in Fig. 12a. Regardless of
the number of observation sites added, the average per-
formance of the forecast was improved. The mean error
reduction rate was 10%, with a maximum of 10.6% when
eight observation sites were added. When more than
eight observation sites were added, the error reduction
rates fluctuated around 10%, which was the saturation
point in this framework.
Because the statistically sensitive regions were in-
directly associated with each case, cross-validation-type
experiments were also performed to obtain a more
general result (Fig. 12b). The 11 cases were divided into
two groups: one group for prescribing the statistically
FIG. 9. New observation network with (a) 17 redistributed ob-
servation sites (circle with a dot) and (b) 13 added observation sites
(circle with a dot) added to the 77 real radiosonde observation sites
(black dot). The red shading is as in Fig. 4.
4690 MONTHLY WEATHER REV IEW VOLUME 142
sensitive regions and the other for the impact experi-
ments. For more than nine observation sites added, the
average of several different configurations of the cross-
validation experiments show more error reduction than
the original experiment shown in Fig. 12a, which may
be due to the averaging effect of the several cross-
validation experiments. However, the characteristics of
themean error reduction rates were generally similar for
the original experiment (Fig. 12a) and the average of the
cross-validation experiments (Fig. 12b), which reaffirms
that the statistically sensitive regions and adaptive ad-
dition strategy have a significant impact on the meteo-
rological forecast of Asian dust events.
Forecasting was improved when statistically sensitive
regions were considered for each case, although the sta-
tistically sensitive regions were not directly associated
FIG. 10. Mean error reduction rates for the new observation networks established by (a) re-
distributing real observation sites adaptively (RDS_ADP_EXP) or randomly (RDS_FIX_EXP)
and (b) adding observation sites adaptively (ADD_ADP_EXP) or randomly (ADD_FIX_EXP)
to the real radiosonde observation network. The black bar represents the original real radiosonde
observation network, and color bars represent the newly organized observation networks with
a distance of 180km. On the x axis, the number indicates the number of observation sites that
were (a) redistributed in and (b) added to the real observation sites. The solid lines (black) denote
the trends of mean error reduction rates obtained by simple linear regression analysis for the
(a) adaptive redistribution and (b) adaptive addition strategies.
DECEMBER 2014 YANG ET AL . 4691
with each case. The mean error reduction rate when
considering sensitive regions for each case (Fig. 10b) was
10.4%, with a maximum of 11.2%, whereas for the sta-
tistically sensitive regions (Fig. 12a), the mean error re-
duction was 10%, with a maximum of 10.6%. Better
forecast performance shown in Fig. 10b is expected be-
cause each sensitive region was considered for each case.
This finding indicated that adding observation sites in
specific sensitive regions of each case was more efficient
than adding observation sites in statistically sensitive re-
gions because the statistically sensitive regions may not
be relevant to specific sensitive regions associated with
each case. Overall, the mean error reduction rate of the
addition strategy in the statistically sensitive regions was
greater than that of the random addition strategy and less
than that of the adaptive addition strategies for individual
cases (cf. Figs. 10b and 12a). For both ADD_ADP_EXP
and the addition experiment in the statistically sensitive
regions, the mean error reduction appeared to be satu-
rated when the number of observation sites approached
8 or 9 (Figs. 10b and 12a) even though this feature may
be relaxed when performing the cross-validation ex-
periments using more cases. Nevertheless, adding ob-
servation sites in statistically sensitive regions improved
meteorological forecast performance. Therefore, statis-
tically sensitive regions can provide useful guidance for
establishing new observation sites or for using available
observations (e.g., satellite observations) to more accu-
rately detect meteorological states.
4. Summary and conclusions
Because Asian dust phenomena occur upwind of the
Korean Peninsula and move eastward and meteorolog-
ical forecasting is crucial for the accurate prediction of
Asian dust transport events to the peninsula, the ob-
servation network upwind of the Korean Peninsula
should be designed appropriately to represent the flow
fields. The observations obtained from the observation
network can be assimilated to produce more reliable
initial meteorological conditions.
To investigate the effect of observation network de-
sign on the meteorological forecasts during Asian dust
events on the Korean Peninsula, 11 dust events affecting
South Korea in a recent 6-yr period were selected and
a series of observation system simulation experiments
were conducted using theWRF modeling system, which
includes the WRF adjoint model and 3DVAR. The
impact of the distribution of observation sites on the
forecast performance was investigated by assimilating
the simulated observations from randomly fixed and
adaptively selected observation sites. The adaptive ob-
servation strategy was able to reduce the forecast error
more efficiently than the fixed observation strategy. For
the adaptive observation strategy, as the distance be-
tween observation sites became greater up to 300 km,
the reduction of forecast errors increased, on average, in
the current framework. As the number of observation
sites increased, the reduction of the forecast error in-
creased for both randomly fixed and adaptive strategies.
However, for the adaptive observation network, as the
reduction rate of forecast error reached the saturation
point (10%), the forecast error was no longer reduced
but instead fluctuated even though more observation
sites were used.
To incorporate an adaptive observation network into
the real observation network, the effects of redistributing
the real observation network and adding adaptive ob-
servation sites were investigated. The real upper-air ob-
servation network consists of 77 radiosonde sites in the
model domain, which include the Korean Peninsula,
China, Mongolia, and Russia. Adding adaptive observa-
tion sites to the real observation network produced
a greater improvement for the forecast performance than
did redistributing the existing observation sites.
Because it is not feasible to use each sensitive region
for each case to design an optimal observation network,
statistically sensitive regions for 11 Asian dust events
were used. The sensitive regions appeared near the up-
per trough and the accompanying surface cyclone, which
are related to Asian dust events. When additional ob-
servations were added to statistically sensitive regions
over the real observation network, the forecast accuracy
FIG. 11. Statistically sensitive regions with respect to the initial
meteorological conditions. Shaded regions indicate the frequencies
of each sensitive grid of 11 Asian dust events. The mean of 500-hPa
geopotential height (solid blue line) for 11 Asian dust events, 77
real radiosonde observation sites (black dot), and 13 optimal ob-
servation sites (circle with a dot).
4692 MONTHLY WEATHER REV IEW VOLUME 142
was improved, although the performance resulting from
adding adaptive observation sites in the statistically sen-
sitive regions was slightly less than that in sensitive re-
gions of individual cases. Therefore, adding observations
in statistically sensitive regions should have a beneficial
impact on the meteorological forecast performance of
Asian dust events. Furthermore, statistically sensitive
regions could provide useful guidance for the effective
placement observation sites to detect meteorological
conditions accurately.
Asmentioned in the introduction, to accurately predict
Asian dust, it is crucial to improve the quality of the initial
conditions of both the meteorological fields and the dust
emissions because dust transport models require this in-
formation as input. Therefore, future work would include
using a response function that incorporates the errors in
dust emission and further using the adjoint sensitivity
distributions for both meteorological and dust emission
processes of Asian dust events, which would provide
more comprehensive conclusions regarding the optimal
FIG. 12. Mean error reduction rate for the new observation network established by adding
optimal observation sites to the real radiosonde observation network. The black bar represents
the original real radiosonde observation network, and the gray bars represent the newly or-
ganized observation network obtained by adding more observations to the real radiosonde
observation network. The x axis indicates the number of optimal observation sites added to the
77 real radiosonde observation sites. Results of (a) the original experiment and (b) the average
of the several cross-validation experiments.
DECEMBER 2014 YANG ET AL . 4693
observation network of Asian dust forecasts. In addition,
using other adaptive strategies that can calculate the
sensitive regions by considering the effect of redistribut-
ing and adding observation sites would further help with
understanding the effect of the observation network on
the meteorological forecasts of Asian dust events.
Acknowledgments. The authors thank the three
anonymous reviewers for their valuable comments. This
research was supported by the Korea Meteorological
Administration Research and Development Program
under Grant CATER 2012-2030 and the Basic Science
Research Program through the National Research
Foundation of Korea (NRF) funded by the Ministry of
Education, Science and Technology (2010-0028062).
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