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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