13

Health-related Weather Forecasts in Korea:
Current Status and Perspectives
Jiyoung KIM1*, Heon-Ae KIM2, Byoung-Cheol CHOI1, Youngsin CHUN3,
Kyu Rang KIM1, Dae-Geun LEE1, Jae-Won OH4, Il Kyu YANG2 and Soo Hyun KIM2
1

Applied Meteorology Research Laboratory, National Institute of Meteorological Research,
Korea Meteorological Administration, Seoul 156-720, South Korea
e-mail: jykim@metri.re.kr
2
Headquarters for Industrial Meteorology and Social Welfare,
Korea Meteorological Administration, Seoul 156-720, South Korea
3
Typhoon and Asian Dust Research Laboratory, National Institute of Meteorological Research,
Korea Meteorological Administration, Seoul 156-720, South Korea
4
Departments of Pediatrics, College of Medicine, Hanyang University,
Kyunggi-Do 471-701, South Korea*corresponding author

Abstract
Global warming and consequent extreme weather events pose a growing threat to human health. In
particular, frequent outbreaks of hot, humid weather in summer have caused human casualties in various
places of the world. Airborne pollen can aggravate respiratory allergic diseases. The East Asian region is
known to have some of the heaviest emissions of anthropogenic pollutants in the world. Accordingly,
harmful air pollutants are ubiquitous and occur in dense concentrations in the region. They are produced
from various sources and processes in the atmosphere. All these pollutants pose a threat to patients withrespiratory diseases, including asthma. In addition to anthropogenic air pollutants, massive Asian dust
storms which have arisen in the 21st century are causing great inconvenience in people’s daily lives aswell as to industrial activities. It is necessary to supply the general public with diverse information on
the impacts of the weather and climate on health. It is expected that demands for the health-related
weather forecasts will dramatically increase in the future. This paper gives an overview of a series of research projects on health-related weather forecasts conducted in Korea with some future perspectives.
Key words: applied meteorology, biometeorology, heat-health watch warning system, heat waves,
KMA, medical meteorology, pollen, weather-related health

1. Introduction
In recent years, people have paid great attention to
the effects of weather on human health (Korea Meteorological Administration (KMA), 2004a, 2004b;
KMA, 2005). In fact, weather and climate have muchto do with health (Yun et al., 1989; World HealthOrganization (WHO), 2003). Many studies have been
conducted on the impacts of weather on health. Thesestudies have mentioned the effects of heat waves andheat-caused stress on mortality and morbidity (Kimet al., 2006a, 2007; Meteorological Research Institute(METRI), 2005), the effects of weather on contagious
diseases caused by pathogenic insects, and the effects
of extreme weather phenomena on food production
and the supply of fresh water. In fact, weather andclimate are among the typical natural phenomena that
Global Environmental Research
11: 13-22 (2007)
printed in Japan

have direct or indirect influences on the entire
ecosystem (WHO, 2003). However, it is very difficult
to determine how daily weather phenomena or the
climate of a few days may affect health. This is because the relationships between climate and health arenonlinear and physiological acclimation of humanbodies occurs through very complicated processes as
individual people have different metabolic profiles
and body compositions.
Nonetheless, we can see a statistical relationshipbetween climate and health and it allows us to makehealth-related weather forecasts. The basic concept
starts from the fact that people are well adapted toaverage or regular weather conditions. It is true that
we have very limited understanding of what human
responses may be when the weather conditions go
beyond a certain critical point. Beyond that critical©2007 AIRIES14

J. KIM et al.

point, a certain level of climate can be sustained forseveral hours, and for several days in extreme cases(Ministry of Environment (MOE), 2005; Kim et al.,2006b). In 2003, there were episodic heat wave eventsin Europe. In France, about 15,000 people died from
August 1 to 20, 2003, due to an abnormal heat wave.
This example shows the consequences of prolonged
harsh weather conditions. In particular, heat waves
may be accompanied by drought. A long spell ofheavy rains or snows, or severe air pollution for a sustained period of time can cause serious damage tohuman health. According to forecasts based on climate
models, such extreme weather phenomena are likely
to occur more frequently in the late 21st century. As
such, we need to construct an emergency preparedness
system as well as gain a deeper understanding of these
phenomena.
This study has made a general review of the research on health-related weather forecasts in Korea invarious fields and also includes future perspectives.

2. Impacts of Weather, Climate, and Air
Quality on Human Health2.1 Heat waves and health
It is a known fact that severe hot weather in summer has very harmful effects on the human body.
Sultry weather, in particular, can be fatal to the elderly,
children and patients with chronic diseases (Kim et al.,2006a). This has been confirmed by the extremely
sultry weather in the northeastern region of the UnitedStates in 1995 and the heat waves that swept throughEurope in 2003. Korea also experienced a record heatwave in 1994, which was believed to have much to dowith the sustained drought in the East Asian region(Kim et al., 2006a, 2006b). Figure 1 shows a time

Fig. 1

series of the standardized daily mortality in Seoul inthe summer of 1994. It shows that the mortality in thesummer of 1994 was much higher than that of theyearly average of the other years. In particular, it
shows the highest daily mortality to have occurred on
July 25, one day after July 24, on which the highest
daily temperature was recorded. This shows atime-delayed effect between the temperature and thenumber of deaths of about one day. Kim et al. (2006a)
also found the threshold temperature in Seoul to beapproximately 31.2°C if the daily maximum temperature is plotted with daily death data.
Kim et al. (2007) carried out a cluster analysis ofmeteorological parameters in Seoul in order to classify
the synoptic air mass types in Seoul. They developed
an algorithm to estimate the excess deaths on days
with extreme temperatures. They applied a multiple
regression model. The independent variables for theestimation were the number of days with sustained
temperatures higher than the critical temperature,and the Apparent Temperature (a kind of heat index
derived from a function of temperature and humidity)of the day before the heat wave occurred. As shown in
Fig. 2, there was a high correlation coefficient of
about 0.67 between the observed and predicted values.
Kalkstein (1991) developed methods forecasting
the number of excess deaths through an air massclassification and determining the level of warnings tobase on the forecasts. It has been widely used in the
United States, China, Italy and Canada. Using asimilar method, Korea has built a system to forecasthigh temperatures and health risks, a system that canproperly reflect the meteorological characteristics ofKorea and adaptability of the Korean people toclimate.

Daily changes in the standard daily death rates in summer in Seoul (standard death rates
per million).

Red indicates data from the summer of 1994. Black indicates average daily death rates andstandard deviation from 1993 to 2004 (excluding 1994). This figure clearly shows a sharp rise inexcess deaths caused by sustained heat waves in late July 1994 (Seoul experienced the highest
daily temperature value on July 24). The blue solid line indicates the baseline death rate or daily
death rate on normal days.

Health-related Weather Forecasts in Korea: Current Status and Perspectives

Fig. 2 A comparison between observed and forecasted
excess deaths.

A coefficient of correlation of about 0.67 was obtained
according to a forecast equation of excess deaths using
the number of consecutive days when the temperature
passed the critical point in Seoul, and the Apparent
Temperature value (the temperature value of the day
before the heat waves hit Seoul).

2.2 Air pollution and health
Airborne particles and/or gaseous pollutants haveadverse effects on various parts of the human body,such as the respiratory and cardiovascular systems(Kang et al., 2006). In general, the level of air quality
is mainly dependent on the quantity and kinds of pollutants emitted in a region as well as by meteorological factors (e.g., atmospheric stability, the ventilationindex and synoptic weather conditions) (Lee & Park,
1997; Kim et al., 2006c, 2007). To predict the influences of air pollution on human health, first of all it
is necessary to forecast the chemical-specific pollution
levels for the next day, while classifying the pollutants
into gaseous and particulate matters. Each kind ofpollutant has different characteristics in terms of its
origin, creation, transport, transformation or removal(or sink in the atmosphere). Generally speaking,statistical models are used to employ statistical correlations between the concentration of each air pollutant (for example, sulfurous acid gas, nitrogen oxide,
carbon monoxide, ozone and airborne particulate matters) and meteorological factors.
In order to pursue a different approach, such as theuse of dynamic models (so-called chemical weather
forecast models or chemical transport models), it isnecessary to develop a numerical model, reflecting theemission inventory of each pollutant, its chemical
reaction processes, and its removal mechanisms with
the input of meteorological data obtained fromnumerical weather prediction models. The usage ofstatistical and dynamic models are evaluated differently according to the purposes of their users. These

15

models leave much to be desired. The limits of model
performance are mainly attributable to the adverse
characteristics of the pollutants, their chemical reaction mechanisms, their complex transformation
processes, and uncertainties in the meteorological datautilized. It is also important to archive three-dimensional observation data to verify the accuracy ofmodel outputs. Once the density level of air pollution
can be accurately forecasted, it will be possible toforecast adverse impacts of air pollution on humanhealth through the use of statistically predefined pollutant-health relationships (Kim et al., 2006c; Hong &Cho, 2001).
Research on air pollution and health forecasts in
Korea has focused on the causes and processes ofpollution (Hong et al., 2003). At the same time, someresearch has been conducted on air pollution, diseases
and deaths (Hong et al., 1999; Kwon & Cho, 1999;
Hong & Cho, 2001; Kim et al., 2003; Kim et al., 2004,
2006; Kang et al., 2006). The outcome of the previous
research shows that air pollutants have various effects
on human body according to individual physical andchemical properties (Kwon et al., 2001; Lee & Kim,
2001; Lee et al., 2000a, 2000b; Lee & Schwartz, 1999;
Lee et al., 1999). Lee and Park (1997) conducted
research on ways to forecast potential air pollution byclassifying synoptic meteorological conditions, opening the way for making relevant weather forecasts
based on pollution conditions. Something should bedone to enable weather forecasts to be made using theconditions and prospects for air pollution and air
quality. It may be difficult to determine how certain
harmful pollutants may be related to certain diseases
as well as how we can apply the revealed results tohealth-related weather forecasts. However, in a few
years, these difficulties can be overcome and it should
be possible to make forecasts about the overall effects
of atmospheric environment on health.
2.3 Health impacts of airborne pollenWith many people spending time outdoors, a growing number of people are contracting allergic diseases
caused by pollen. The advanced countries in Europe
and North America have long realized the danger andhave warned people who are sensitive to allergies ofthe seriousness of allergic diseases (Vázquez et al.,
2003; Smith & Emberlin, 2006). In 1819, John Bostock
disclosed that pollen can cause diseases in humans.
Since the late 1960s, the United States and European
nations have continued epidemiological studies ofvarious pollen types. In the 1970s, scientists in Japan,
the Philippines and Taiwan released results of studies
on pollen. In the 1980s, research was conducted on
the relationship between pollen and allergic diseases(Oh et al., 1998). Korea, however, remains in the earlystages in terms of its research on pollen forecasts.
There are nine pollen observation sites, which areoperated by the Korean Academy of Pediatric Allergy
and Respiratory Disease and the KMA throughout

16

J. KIM et al.

Korea. Pollen grains are sampled by Burkard Seven
Day Recording Volumetric Spore Traps. Then the pollen grains are counted using a microscope and theobtained data are stored in a pollen database. The pollen data used in this study were provided by the
Korean Academy of Pediatric Allergy and Respiratory
Disease. Figures 3 and 4 show the pollen collector andlaboratory analysis, respectively.
The pollen calendar in Fig. 5 shows the pollen species and their monthly distribution. Generally, pollen
is observed all year round, except for the wintermonths (November to February). The distribution differs for each species of pollen. The pollens of trees
and shrubs are observed from March to May; those of
grass from May to September; and those of weedsfrom August to October. In the case of trees andshrubs, pine trees account for about 70% of all tree
pollen. However, pine pollens are weak allergens. The
remaining 30% of tree pollens, including those ofalder trees, white birches and oaks, are moderate tostrong allergens despite the smaller numbers. Also
despite their small numbers, those of grasses andweeds are also highly likely to cause allergies. In particular, the pollens of Artemisia vulgaris and Humulus
japonicus are highly likely to cause allergies. Daily
fluctuations in the amount of pollen have to do with avariety of meteorological factors, such as temperature,
rainfall and the duration of sunshine. According tomultiple regression equations for the meteorologicalfactors and the concentration of pollen, different meteorological elements affect the concentration of pollens
each year. Temperature and rainfall are especiallydecisive in determining pollen concentrations.
Ten weather elements that are thought to affect theconcentration of pollen are used to develop equations
for the pollen forecasts. Forecast equations for eachvariety of pollen and for each month have been developed based on a multivariate regression analysis(Tables 1 & 2). The forecast equations for April tree
pollens showed accuracies of 72.7% for pine pollen

Fig. 3 A pollen collector installed at the KMA’s
Shindaebang-dong field in Seoul.

Fig. 4 Researcher handling pollen
filters to conduct a microscopic analysis in a laboratory.

Pollen Calendar (Seoul, 1997-2005)
Juniper
Salix
Maple

CorylusCorylus

Trees

Populus
Ulmus
Quercus
BetulaPine
Alnus Miller
Plane tree
MugwortWeeds

HumulusChen
ambrosioides
Artemisia
Ambrosia
Grass

Jan.

|

Feb.
1-5

|

Mar.
6-10

|

Apr.

11-20

|

May

21-40

|

Jun.

41-80

|

Jul.

81-160

|

Aug.

|

Sep.

|

Oct.

|

Nov.

|

161-320 321-640 641-1200 1201-2400

pollen counts (10-day moving average)

Fig. 5 A pollen calendar of Seoul.

The calendar was created based on the pollen count data measured over the past nine years (1997 - 2005).

Dec.

Health-related Weather Forecasts in Korea: Current Status and Perspectives

17

Table 1 Regression models for pine and other trees.

Variables analyzed in the models were: daily mean temperature (MeanT), daily precipitation (PRE), dailymean wind speed (WIND), daily mean relative humidity (HUM), daily maximum temperature (MaxT), daily
minimum temperature (MinT), diurnal temperature range (DR), continued hours of precipitation (RT),accumulated sunshine hours (AS) and accumulated mean temperature (AccumT).

Pollen species

Regression models

R2

P

Tree (pine)

1.609848 – 0.328230×WIND + 0.001628×AS

0.474

0.02

Tree (others)

0.494386 + 0.002296×AccumT – 0.009812×PRE – 0.012852×AS + 0.047051×MeanT

0.693

0.00

Weeds

3.105090 – 0.000521×AccumT – 0.011980×AS + 0.036886×DR + 0.025268×PRE

0.450

0.09

Table 2 Regression models for pine and other trees, as well as weeds.

A clustering method is applied in the model development. The same variables are used in the analysis as in
Table 1.

PollenspeciesTrees(pine)

cluster

Regression models

R2

P

1
2

1.925318 – 0.008858×AS + 0.069458×RT+ 0.022064×HUM – 0.450859×WIND– 0.894559 + 0.314224×MeanT – 0.002260×AccumT – 0.083394×DR
1.982743 – 0.103875×RT+0.077967×PRE + 0.002173×AccumT – 0.077289×
MeanT – 0.078673×WIND
2.938907 – 0.033750×AS
3.734477 – 0.039974×RT – 0.032431×AS + 0.074371×WIND – 0.000864×
AccumT + 0.022925×PRE – 0.037523×MaxT
1.544558 + 0.005103×HUM – 0.209519×PRE – 0.010390×AS + 0.0056975×RT– 0.031770 + 0.102436×MaxT – 0.000941×AccumT
4.668491 – 0.001345×AccumT+ 0.215592×MeanT – 0.094537×MaxT– 8.12693 + 0.00217×AccumT+ 0.07439×MeanT
4.619065 – 0.213817×MeanT+ 0.076789×MaxT

0.61
0.69

0.01
0.00

0.99

0.03

0.38

0.03

0.78

0.13

0.45
0.50
0.38
0.99
0.38

0.04
0.00
0.01
0.10
0.00

3
4

Trees
(others)

Weeds

1
2
3
1
2
3

and 77.3% for the other tree pollens. The forecast
equation for October weed pollens achieved an accuracy of 74.2%. The equations for May tree and shrub,
excluding pine tree, pollens showed accuracies of
42.9% and 57.1%, respectively. The equation for September weed pollens showed an accuracy of 46.7%. In
order to enhance the accuracy of the pollen forecast
equations, it will be necessary to secure consistency ofobserved data. It will also be necessary to evaluate thecurrent index for pollen allergies for suitability to the
Korean people based on experimental data.
The KMA has a plan to develop an accurate pollen
index forecast model by improving the existing forecast equations. It is also going to build a nationwide
pollen observation network in close cooperation withrelated agencies for the purpose of securing quality
data on pollen. Ultimately, it will supply the people
with comprehensive Internet services on pollen observation and forecasts.

3. Health-related Weather Services of the
KMA
With the arrival of an era of knowledge and information providing impetus, people are demanding avariety of weather services. Weather information is
widely used in preventing natural disasters, reducing
work-related accidents, increasing national competitiveness and enhancing public health. Since thefive-day workweek system was introduced, people
have enjoyed increased leisure time and have recognized the increased importance of weather informationin their daily lives. To meet such a variety of demands
for weather information, the KMA is providing people
with Web services containing a variety of weather
information in such a way that can benefit the industrial sectors and the general public alike, using climate
statistics and weather forecast models.
In order to effectively manage and supply suchapplied meteorological information, the KMA has
built a hub system for industrial climate informationthat can handle all processes, including gathering
basic meteorological data, producing applied meteorological information, and providing Web services. At
the same time, it has also built an information system
so that Internet users can have easy access to the hub
system’s homepage (http://industry.kma.go.kr) to get avariety of applied meteorological information that isbeing provided through several different information
channels.
Since the hub system was established in 2003, a
total of 19 classes of information have been provided
for the benefit of people’s daily lives and for thepromotion of their health. The kinds of informationthat are provided for the general public all year roundinclude effective humidity, calculated based on therelative humidity of a few days before, with weights
used to reflect the time lapse; evaporation loss, or the

18

J. KIM et al.

quantity of moisture in the air; an ultraviolet (UV)
index that forecasts the relatively indexed strength ofincoming ultraviolet radiation reaching the Earth’s
surface; and an air pollution index that forecasts thelikelihood of air pollution based on the weather conditions and existing pollutants in the air. During spring,
the KMA supplies the general public with a dust storm
index based on the standard density index of PM10.
Additionally, the KMA provides people with a wildfire danger index reflecting meteorological and topographical factors, in cooperation with the Korea Forest
Service during the period of autumn to spring.
During summer, the KMA posts a food poisoning
index that has been developed through an analysis of
the growth rate of microbes, according to changes intemperature; a heat wave index that reflects thesultriness people feel; a discomfort index that refers to
the degree of unpleasant feelings weather conditions
cause people; and a decomposition index that
expresses the possibility of decomposition of foods.
During winter, the KMA notifies the general public of
the possibility of frostbite based on wind-chill effects,
as well as the possibility of freezing and bursting ofpipes as a result of the onset of cold waves.
Information regarding the proper time for househeating is also provided. More detailed information on
the various indices mentioned above is described in
KMA (2004a).
AsthmaCVA

Since 2004, the KMA has taken an interest in theeffects of weather on health and has collected data on
the daily number of hospital in-patients over the pastten years, focusing on diseases that are likely to beaffected by weather conditions. Based on these data,
we provide health forecasts based on regression
models developed through a correlation analysis ofmeteorological factors and the daily numbers of hospital in-patients (KMA, 2004b, 2005). The diseases,
selected through a correlation analysis, include asthma,
cerebral apoplexy, skin diseases and pulmonary diseases. Dividing the entire country into six to nine categorical areas, the KMA operates a numerical forecasting system and releases daily forecasts. The KMA isproviding Web services on weather-related healthforecasts based on a three-level notice system: “normal,” “warning” and “danger.” They are issued twice
a day based on the estimated numbers of people hospitalized for three consecutive days (i.e., that day, thefollowing day, and the day after that). A more detailed
description of the three categories is given in KMA(2004b). Figure 6 shows health and weather information provided on the industrial climate information
hub homepage.
Besides such information, the KMA is providing
industrial climate information on the same homepage
so that the consumers can easily use weekly weather
forecasts in industrial sectors like agriculture, fisheries,

Frostbite Skin diseases

Pulmonary diseases

Bronchial asthma with periodically occurring difficulty in breathing. Occurrence is affected by .
personal health state as well as meteorological factors (temperature, pressure and wind)
12/20

12/21

12/22

Issued at 0300 LST 20 DEC.

AsthmaWeather

T(°C)

WS

Seoul

cloudy3.0

2.5

Chungju

mist

1.9

0.5

Gangwonovercast

6.5

1.0

Daejeon

clear

2.7

0.5

Jeonjuovercast

4.7

0.5

Daegu

clear

0.8

0.5

Gwangjuscattered

1.7

1.0

Busan

scattered

9.6

1.0

Jeju

scattered

10.8

2.0

RegionNormalWarning

DangerMeteorological state

Index

Fig. 6 Health-related weather forecasts provided by the KMA’s homepage (http://www.
kma.go.kr).
Significant health risk factors are forecasted in three levels (i.e., normal, alert and danger) for thesubsequent three days.

Health-related Weather Forecasts in Korea: Current Status and Perspectives

livestock industries, construction, leisure industries,
logistics, transportation, and energy industries. Theseeight industrial sectors are classified into two to nine
sub-sectors each, which receive three or five –stagesof information. Experts in the individual industrial
sectors developed this kind of information based on
research conducted in 2003. The KMA’s short-term,
weekly, regional, and numerical forecast models areused in the processes. There will likely be moredemand for meteorological information in people’s
daily lives and in the industrial sectors. Accordingly,
the KMA plans to improve its practical applied
weather service through more adequate validation, as
well as to develop new kinds of applied meteorological indices, to meet such diverse demand.

4. Asian Dust (Hwangsa) Forecasting in
Korea
4.1 The KMA’s warning system for Asian Dust
Since the 1980s, the KMA has been forecasting
Asian Dust events when they are expected within thenext few days. This forecasting is based on the
Hwangsa Trajectory Model calculated on the isentropic surfaces from the global circulation model;
GMS (Geostationary Meteorological Satellite) andNOAA (National Oceanic and Atmospheric Administration) satellite imageries produced using differencesin brightness temperature, PM10, including thosefrom the Ministry of Environment; Lidar data fromthe GAW (Global Atmosphere Watch) observatory:
and synoptic reports from the 42 observatories in

19

Korea.
Severe Asian Dust events occurred in many partsof the Korean peninsula between the 21st and the 23rdof March 2002. The daily mean concentration ofPM10 observed at the GAW observatory was recorded
at levels up to 1,407 µg/m3. However, the TSP (Total
Suspended Particulate) was almost 3,000 µg/m3 at the
top of Mt. Gwanak, located in Seoul. Most of theelementary schools and domestic airports were closed
and factories for semi-conductors and other precision
products reduced their working hours. The KMA had
issued warnings for Asian Dust events April 10th,
2002, in order to inform the public and disasterprevention authorities of the potential risk from Asian
Dust events. The criteria for the special information
notes and warnings for Asian Dust events are shownin Table 3.
Since 2007, the KMA operates the new Asian Dust
Warming System. According to the new system,
advisories and warnings are issued when the hourly
average dust (PM10) concentrations are expected to
exceed 400 and 800 µg/m3 for over two hours,
respectively.
4.2 Asian Dust observation network
4.2.1 Dust Monitoring by the KMA
The KMA observes and monitors Asian dust
events in the following ways:
1) Visual observation for occurrences of Asian Dust
events in the meteorological observatory
2) PM10 at 19 sites (including Anmyon-do andBaekryeong-do located in the western part of theTable 3 ‘Hwangsa’ warning reports in Korea since 2002.

Watch

Advisory

Warning

When the hourly averaged dust
concentration (PM10) is expected to exceed 300 ㎍ m-3 forover two hours.

When the hourly averaged dust
concentration (PM10) is expected to exceed 500 ㎍ m-3 forover two hours.

When the hourly averaged dust
concentration (PM10) is expected to exceed 1,000 ㎍ m-3
for over two hours,

20

J. KIM et al.

Korean peninsula, and Mt. Gwanak in Seoul)3) Lidar measurement at four sites including the
GAW observatory (Anmyon-do) to determine thevertical distribution4) International meteorological information throughthe Global Telecommunication System at 3-hourintervals5) Satellite images obtained from NOAA, TERRA
and SeaWiFs (Sea-viewing Wide-Field of view
Sensor) to determine the horizontal distribution
and intensity
4.2.2 Air Pollution Monitoring by the Ministry ofEnvironment
The MOE maintains 174 automatic air pollutionmonitoring stations to measure atmospheric pollutants
including dust (PM10) in the air. The system collects
data at five minutes interval and electronicallytransmits the data to the MOE and KMA in real time
to support the operation of the warning system.
4.2.3 New Cooperation between the KMA and
CMA
The KMA is cooperating with the China Meteorological Administration (CMA) on Asian Dust forecasting. They have established and have been operating an
Asian Dust joint monitoring network with five monitoring stations since 2005 spring. Three of thesestations are located on the eastern coast of China andthe rest within the major Asian Dust path. The network has been operating since March 2005, sharing
information and exchanging experts. Five more sites
are being added in early 2007.

4.3 Forecast models of Asian Dust by the KMA
4.3.1 Hwangsa Trajectory Model (HTM)
In 2000, the National Institute of MeteorologicalResearch developed a trajectory model for routine
forecasting of the air stream, including dust, 72 hours
in the future at an interval of 6 hours. Synoptic conditions in the source region were analyzed based on thesynoptic code from 1993 to 1995. A total of 47 trajectories are initiated in the source region on the isentropic surface of 295K, 300K and 305K. The KMA
has utilized the HTM in order to forecast the air
stream including Hwangsa (Asian Dust) since 2000.
4.3.2 Asian Dust Aerosol Model (ADAM)
A dust concentration model for research (named
ADAM) was developed by Prof. Soon-Ung Park at
Seoul National University (In & Park, 2002; Chun,
1997; Chung, 1996). Furthermore, the National Institute of Meteorological Research/KMA has preparedthis model for routine operation in order to forecast
dust concentrations 48 hours in the future at an interval of 3 hours. Dust emissions in the source region are
simulated based on the friction velocity, and verified
by surface observation, satellite images and Lidar
measurements in Korea (for the Dust Measurement
Network, see Fig. 7).

5. Conclusion and Perspectives
This paper has reviewed the current status ofhealth-related weather forecasts in Korea and presented some future perspectives. In Korea, health-Ulleungdo

Fig. 7 The ‘Hwangsa’ monitoring network of the KMA.

In the figure, sites marked with stars (★) and filled circles (●)
indicate LIDAR and PM10 sites, respectively.

Health-related Weather Forecasts in Korea: Current Status and Perspectives

related weather forecasts have been developed as apart of research on human biometeorology. Research
is currently underway into the impacts of heat waves
on health, considering synoptic air mass types for
estimating daily excess mortalities. In addition, aforecast model of daily excess deaths based on
multiple regression equations is being successfully
developed. Dust storm warning systems of the KMA
are currently operational. The dust forecast model will
be continuously improved. The data from the dust
monitoring network installed in China and Korea will
be used to verify and improve dust model performance. The information, including a ventilation index
for air quality, is being provided on the KMA homepage. On its industrial climate information homepage,
the KMA is also providing the general public withhealth-related weather forecasts on asthma, cerebral
apoplexy, frostbite, skin diseases and pulmonary diseases based on meteorological data. The KMA is currently developing statistical models in the hope of discovering and forecasting the impacts of pollen on
human respiratory diseases during spring to autumn.
The KMA is going to provide operational pollen forecasts in 2008.
The current health-related weather forecasts in
Korea aim to meet positively the public demand for
quality of life. However, it will be necessary to continue intensive R&D efforts to enhance accuracy andcredibility of forecast information and achieve aclearer understanding of the diverse mechanisms
among weather, climate and health. Efforts are being
made to galvanize research by establishing interdisciplinary research communities and groups that comprise meteorologists, climatologists and experts inpreventive medicine, atmospheric environment andhealth statistics. The results of these research programs should contribute greatly to enhancing the quality of health-related weather forecast services that may
be applied to policy-making processes at the relevant
government offices.

Acknowledgements
This study was supported by the KMA’s R&D program under grant metri-2007-B-7. J. Kim was supported by the KRF’s research program under grantKRF-2006-C00005. Y. Chun expresses her gratitude
for the support from the KMA R&D program under
grant CATER 2006-3202.
References
Chun, Y. (1997) Long range transport of Yellow Sand withspecial emphasis on the dust rise conditions in the source regions. PhD. Thesis, Seoul National University, Seoul, Korea.
Chung, K. Y. (1996) A simulation of long-range transport of
Yellow Sand observed in Korea. PhD. Thesis, Seoul National
University, Seoul, Korea.

21

Hong, Y. C., J. H. Leem, E. H. Ha and D. C. Christiani (1999)
PM10 exposure, gaseous pollutants, and daily mortality in
Inchon, South Korea. Environmental Health Perspectives,
107: 873-878.
Hong, Y. C. and S. H. Cho (2001) Health effects of ambientparticulate pollutants. Korean Journal of Preventive Medicine, 34: 103-108. (in Korean)Hong, Y. C., J. H. Rha, J. T. Lee, E. H. Ha, H. J. Kwon and H.
Kim (2003) Ischemic stroke associated with decrease in temperature. Epidemiology, 14: 473-478.
In, H.-J. and S.-U. Park (2002) A simulation of long-range
transport of Yellow Sand observed in April 1998 in Korea.
Atmospheric Environment, 36: 4173-4187.
Kalkstein, L. S. (1991) A new approach to evaluate the impact
of climate on human mortality. Environmental Health Perspectives, 96: 145-150.
Kang, C.-M., S. K. Park, Y. Sunwoo, B.-W. Kang and H. S. Lee(2006) Respiratory health effects of fine particles in Seoul.
Journal of the Korean Society for Atmospheric Environment,
22: 554-563. (in Korean)Kim, H., Y. S. Kim and Y. C. Hong (2003) The lag-effect pattern in the relationship of particulate air pollution to daily
mortality in Seoul, Korea. International Journal of Biometeorology, 48: 25-30.
Kim, J., D.-G. Lee, I.-S. Park, B.-C. Choi and J. S. Kim(2006a) Influences of heat waves on daily mortality in South
Korea. Atmosphere, 16: 269-278. (in Korean)Kim, J., I.-S. Park, D.-G. Lee and S.-Y. Bang (2006b) Heatrelated mortality in major cities of South Korea during
1991-2004. Proceedings of the 6th Conference of the European Meteorological Society and the 6th European Conference on Applied Climatology, Ljublanja, Slovenia, 4-8 September 2006.
Kim, J., S. C. Yoon, A. Jefferson and S. W. Kim (2006c) Aerosol hygroscopic properties during Asian dust, pollution, andbiomass burning episodes at Gosan, Korea in April 2001.
Atmospheric Environment, 40: 1550-1560.
Kim, J., D.-G. Lee, B.-C. Choi and I.-S. Park (2007) Synoptic
air mass classification using cluster analysis and relation todaily mortality in Seoul, South Korea. Atmosphere, 17: 45-53.
(in Korean)Kim, S.-Y., J. T. Lee, Y. C. Hong, K. J. Ahn and H. Kim (2004)
Determining the threshold effect of ozone on daily mortality:
an analysis of ozone and mortality in Seoul, Korea, 19951999. Environmental Research, 94: 113-119.
Kim, S.-Y., H. Kim and J. Kim (2006) Effects of air pollution
on asthma in Seoul: Comparisons across subject characteristics. Korean Journal of Preventive Medicine, 39: 309-316.
(in Korean)
Korea Meteorological Administration (2004a) Development of
Weekly Industrial Meteorology Forecast System. KMA
Report. (in Korean)
Korea Meteorological Administration (2004b) Research andDevelopment Program of the Industrial Meteorology Information on the Weather-Health Relationship(I). KMA Report.
(in Korean)
Korea Meteorological Administration (2005) Research andDevelopment Program of the Industrial Meteorology Information on the Weather-Health Relationship(II). KMA Report.
(in Korean)Kwon, H.-J. and S.-H. Cho (1999) Air pollution and daily
mortality in Seoul. Korean Journal of Preventive Medicine,
32: 191-199. (in Korean)Kwon, H.-J., S. H. Cho, F. Nyberg and G. Pershangen (2001)
Effects of ambient air pollution on daily mortality in a cohortof patients with congestive heart failure. Epidemiology, 21:413-419.
Lee, B. R. and S. U. Park (1997) Method for forecasting air
pollution potential using synoptic meteorological conditions.

22

J. KIM et al.

Journal of the Korean Meteorological Society, 33: 641-656.
(in Korean)Lee, J. T. and J. Schwartz (1999) Reanalysis of the effects of
air pollution and daily mortality in Seoul, Korea: a casecrossover design. Environmental Health Perspectives, 107:633-636.
Lee, J. T., D. Shin and Y. Chung (1999) Air pollution and daily
mortality in Seoul and Ulsan, Korea. Environmental Health
Perspectives, 107: 149-154.
Lee, J. T., H. Kim, Y. C. Hong, H. J. Kwon, J. Schwartz and D.
C. Christiani (2000a) Air pollution and daily mortality inseven major cities of Korea, 1991-1997. Environmental
Research, 84: 247-254.
Lee, J. T., H. Kim and J. Schwartz (2000b) Bidirectional
case-crossover studies of air pollution: bias from skewed andincomplete waves. Environmental Health Perspectives, 108:1107-1111.
Lee, J. T. and H. Kim (2001) Epidemiologic methods and study
designs for investigating adverse health effects of ambient air
pollution. Korean Journal of Preventive Medicine, 34:
119-126. (in Korean)
Meteorological Research Institute (2005) The Development of
Biometeorology Technique. The Final Report of the Basic
Research Project of METRI(I). (in Korean)

Ministry of Environment (2005) Health Effect of Climate
Change and Adaptation Strategies, Ministry of Environment
Report. (in Korean)
Oh, J.-W., H.-B. Lee, H.-R. Lee, B.-Y. Pyun, Y.-M. Ahn, K.-E.
Kim, S.-Y. Lee and S.-I. Lee (1998) Aerobiological study ofpollen and mold in Seoul, Korea. Allergology International,
47:263-270.
Smith, M. and J. Emberlin (2006) A 30-day-ahead forecast
model for grass. International Journal of Biometeorology,
50:233-242.
Vázquez, L. M., C. Galán and E. Domínguez-Vilches (2003)
Influence of meteorological parameters on olea pollen concentrations in Cordoba (South-Western Spain). International
Journal of Biometeorology, 48:83-90.
WHO (2003) A.J. McMichael, D.H. Campbell-Lendrum, C.F.
Corvalan, K.L. Ebi, A. Githeko, J.D. Scheraga and A.
Wordward, eds., Climate change and human health: risks
and responses. World Health Organization, Swiss.
Yun, D. J., K. S. Park, H. K. Hwang, C. O. Ahn, D. K. Yun, Y.
J. Kwon, D. C. Park, Y. H. Yun and C. M. Nam (1989) Occurrence of pediatric diseases in relation to the environment,seasons and atmospheric phenomena (weather). Korean
Journal of Preventive Medicine, 22: 283-289. (in Korean)(Received January 21 2007, Accepted April 10 2007)