Smart Computing Review, vol. 3, no. 6, December 2013
447
Smart Computing Review
Visualization of Upper Air
Data from Radiosonde
Stations using Google
Earth API
Jaeseok Yun, Min-Woo Ryu, and Sang-Shin Lee
Embedded Software Convergence Research Center, Korea Electronics Technology Institute / 68 Yatap-dong,
Bundang-gu, Seongnam 463-816, S. Korea / {jaeseok, minu, sslee}@keti.re.kr
* Corresponding Author: Jaeseok Yun
Received September 22, 2013; Revised November 23, 2013; Accepted November 30, 2013; Published
December 19, 2013
Abstract: Upper air data is meteorological data measured from weather balloons or aircraft over a
particular location. This data has been used to create graphical plots such as Skew-T log-P
diagrams for climate analysis and weather forecasting based on various numerical models. Upper
air data is generally measured with an upper air sounding system, a radiosonde, consisting of
multiple sensors for measuring meteorological conditions and a radio frequency transmitter for
transmitting measured signals to radiosonde stations. In this paper, we present the visualization of
upper air data collected from radiosonde stations using the Google Earth API. We first survey the
literature on upper air data archives and visualizations. In particular, we summarize how to access
and read the upper air dataset archived in the Integrated Global Radiosonde Archive provided by
the National Climatic Data Center of the National Oceanic and Atmospheric Administration. We
also demonstrate the web-based graphical tools provided by the Integrated Global Radiosonde
Archive: time-series plots, sounding plots, wind plots, and temperature plots. In order to implement
the visualization of upper air data collected from radiosonde stations, we investigate Google Earth
API objects and functions, which can be used to build a JavaScript web page with Google Earth
embedded. We propose and implement an algorithm which allows us to obtain an optimal view for
a given set of radiosonde stations and demonstrate the web pages we implemented using the Google
Earth API.
Keywords: Upper air data, radiosonde, radiosonde station, Integrated Global Radiosonde Archive,
visualization, Web-based interface, Google Earth API
This work was supported by the IT R&D program of MSIP/KEIT. [10044811, The development of Radiosonde and Automatic Upper Air
Sounding System for monitoring of a hazardous weather and improvement of weather forecasting]
DOI: 10.6029/smartcr.2013.06.006
448
Yun et al.: Visualization of Upper Air Data from Radiosonde Stations using Google Earth API
Introduction
U
pper air data is commonly used for climate studies, climate monitoring, weather analysis, and forecasting. There
exist a variety of methods of measuring upper air data, including by kite, aircraft, radiosonde, rawinsonde, pilot
balloon, and sounding rocket. Among them, radiosondes attached to weather balloons equipped with sensors and a radio
transmitter for measuring meteorological conditions has been widely deployed since the 1930s.
Many researchers have been working on the use of upper air data collected from radiosondes. For example, Elliott and
Gaffen present the use of upper air data from radiosondes in long-term climate studies [1]. Brönnimann presents the
procedure for re-evaluating the historical upper air data in 1939-44, which could correct the historical upper air data for the
effects of lag and radiation errors, and assess the accuracy and precision of the data [2]. Thorne et al. present HadAt, a new
analysis of the global upper air temperature record from 1958 to 2002 based on radiosonde data, thus creating a consistent
depiction of changes in upper air temperatures [3]. Stickler et al. present the Comprehensive Historical Upper-Air Network
(CHUAN), a consistent global historical upper-air dataset derived from heterogeneous data available from various sources
as well as from newly digitized data, including 3,987 station records worldwide [4].
Research on the visualization of upper air data has also been widely studied, using web-based interfaces in particular.
Papathomas et al. illustrate applications of computer graphics to the visualization of measured or modeled meteorological
data [5]. Treinish presents a case study in operational weather forecasting, which demonstrates the principles of taskspecific visualization design and implements 2D and 3D display and interaction tools, generating visualizations for the web
in meteorology [6][7]. Haase et al. present a range of solutions for meteorological visualization, including systems for the
production of TV weather forecasts, for the analysis of simulation output by experts, for weather information in the web,
and for meteorological visualization using Virtual Studio, augmented reality, and augmented video technology [8]. Murray
et al. present a freely available visualization and analysis framework, the Integrated Data Viewer for the Unidata Program
Center, which can produce a web-enabled application to display and work with satellite imagery, gridded data, surface,
upper air, and radar data within a unified interface [9].
In this paper we present a method to visualize upper air data collected from radiosonde stations using the Google Earth
API. We investigate a freely-available upper air data archive and present a way to access and read it for obtaining the upper
air data we want to visualize. We also illustrate Google Earth API objects and functions related to obtain an optimal view
for a given set of radiosonde stations, and finally demonstrate the results of a JavaScript Google Earth webpage for
visualization.
Upper Air Data and Radiosonde
Upper air data is meteorological data usually measured above the lower troposphere, though no explicit lower limit is
specified. Figure 1 shows a way to measure upper air data based on weather balloons and radio frequency transmissions.
Figure 1. A schematic diagram for a radiosonde and radiosonde station
Smart Computing Review, vol. 3, no. 6, December 2013
449
Upper air data could be measured using a sounding system, a radiosonde, composed of multiple sensors and a radio
transmitter and attached to a weather balloon, which can measure various meteorological parameters such as pressure,
temperature, geopotential height, dew point depression, wind direction, and wind speed. The radiosonde could then
transmit the signals collected to a receiver at a radiosonde station at a radio frequency. Weather balloons equipped with
radiosondes can be automatically launched by the radiosonde launcher (e.g,. ARL-9000 Automated Weather Balloon
Radiosonde Launcher from Yankee Environmental Systems [10]), or manually.
Radiosondes are usually released two times a day (12 AM and 12 PM Greenwich time) and measure upper air
conditions over a particular location. The radiosonde-based method has been largely employed for local-scale dispersion
modeling, though there are several other collection methods for collecting upper air data such as aircraft and pilot balloons.
The collected upper air data can be used to create graphical plots of data (e.g., Skew-T log-P diagrams) for weather analysis
and forecasting based on various numerical models.
Upper Air Data Archive and Visualization
■ Upper Air Data Archive
Upper air data collected from radiosondes launched around the world are archived and shared with all countries. One
common online archive for upper air data is provided by the Integrated Global Radiosonde Archive (IGRA) of the National
Climatic Data Center (NCDC), which consists of radiosonde and pilot balloon observations at over 1,500 globally
distributed stations [11]. Durre et al. present an overview of the IGRA upper air data archive [12]. IGRA is the largest
upper air data archive compiled from 11 source datasets including several Global Telecommunication Systems (GTS) and
country-specific archived datasets: NCDC historical GTS, NCDC real-time GTS, National Center for Atmospheric
Research (NCAR) GTS, National Centers for Environmental Prediction (NCEP) GTS, Russian GTS, U.S. Air Force,
Australian GTS, and country-specific datasets (U.S., Australian, Argentine, South Korea). Table 1 summarizes the data
sources for the IGRA upper air data archive, which is quoted from the IGRA web page [13]. The IGRA‘s upper air data has
been available since 1938, but many stations have been extended from 1970 to the present. It should be noted that most of
data source offer historical datasets of upper air data, and NCDC Real-Time GTS is the only data source updating upper air
data at present.
Table 1. Data sources for the IGRA upper air data archive
Data Source
†
Core
Other
Large
Scale
Countryspecific
†
NCDC Historical GTS
NCAR/NCEP GTS
NCEP GTS
NCDC Real-Time GTS
Russian GTS
U.S. Air Force
Australian GTS
U.S.
Australian
Argentine
South Korean
Period of Record
Area of Coverage
# of Stations
% of IGRA Soundings
1963-1970
1970-1972
1973-1999
2000-present
1998-2001
1946-1973
1990-1993
1946-2001
1938-1989
1958-1991
1984-1992
Global
Global
Global
Global
Global
Global
Southern Hemisphere
U.S. & U.S. military
Australia & territories
Argentina
South Korea
820
848
1517
1093
923
292
170
150
17
8
4
7.94
3.01
64.06
7.13
1.59
4.49
0.15
9.81
1.63
0.18
0.01
GTS: Global Telecommunication Systems, a global network for the transmission of meteorological data [14]14].
IGRA‘s upper air data can be accessed via HTTP or FTP [15][16]. The IGRA archive includes a couple of directories,
but we focus on data_por and data_y2d. data_por involves a period of record data files, including the base version,
through 2008 plus daily updates, while data_y2d has involved daily updates since January 1, 2009. These data files are
presented in one file per radiosonde station. We can see the list of all stations in IGRA in igra-stations.txt, including
the country codes for 193 countries, station number, station name, latitude, longitude, elevation, first year of record, and
last year of record for 1,538 stations. South Korea has the country code of KS and 13 radiosonde stations, as summarized in
Table 2. Note that only four stations including OSAN AB, POHANG, KWANGJU AB, and CHEJU provide daily updates
of upper air data at present. Station numbers could be used as the ID of a station, and latitude, longitude, and elevation
values will be used as parameters sent to the Google Earth APIs for visualization in the following section below.
Yun et al.: Visualization of Upper Air Data from Radiosonde Stations using Google Earth API
450
Although data_por and data_y2d directories include upper air dataset files with different extensions, e.g.,
47138.dat.gz and 47138.y2d.gz respectively for the POHANG station (station ID: 47138), both dataset files use the same
header and data record format. Table 3 and Table 4 illustrate the header and data record format of the IGRA upper air
dataset files, respectively. Figure 2 and Figure 3 show examples of the header and data record format of the IGRA upper air
dataset files, respectively. Accordingly, by reading an IGRA dataset file with upper air data collected from a radiosonde
launched from a particular radiosonde station (e.g., XXXXX.dat.gz or XXXXX.y2d.gz), we can understand upper air
conditions including temperature, dewpoint depression, wind direction, and wind speed at a particular geopotential height.
Table 1. List of IGRA radiosonde stations in South Korea
Country
Code
KS
KS
KS
KS
KS
KS
KS
KS
KS
KS
KS
KS
KS
Station
Number
47103
47107
47110
47118
47122
47132
47134
47138
47142
47153
47158
47161
47185
Station Name
PAENGNYONG-DO ISLAND
KANGNUNG AFB
SEOUL/KIMPO
HOENGSONG AFB
OSAN AB
TAEJON AFB
YECHON
POHANG
TAEGU
PUSAN WEST
KWANGJU AB
SACHON
CHEJU
Latitude
(-90~90)
37.97
37.75
37.55
37.43
37.10
36.33
36.62
36.03
35.90
35.18
35.12
35.08
33.28
Longitude
(-180~180)
124.67
128.95
126.80
127.95
127.03
127.38
128.35
129.38
128.65
128.93
126.82
128.08
126.17
Elevation
177
6
20
101
52
64
120
6
35
4
13
8
73
Frist Year of Last Year of
Record
Record
1973
2000
1973
2000
1973
1990
1973
2000
1957
2012
1973
1987
1980
2000
1966
2012
1973
2000
1973
2000
1973
2012
1975
2000
1954
2012
Table 2. The header record format for IGRA dataset files
Variable Name
Header Record Indicator
Station Number
Year
Month
Day
Observation Hour
Release Time
Number of levels
Columns
1-1
2-6
7-10
11-12
13-14
15-16
17-20
21-24
Description
# character
WMO station number
00-23 UTC
0000-2359 UTC, 9999 = missing
number of subsequent data records
Figure 1. An example of a header record format for IGRA dataset files
■ Upper Air Data Visualization
Visualization-based analysis based on various charts and maps have been employed in attempts to analyze and forecast
weather using various numerical models. The IGRA upper air data archive also provides useful graphical tools in a webbased interface. As shown in Figure 3, the IGRA web-based interface provides useful maps and charts for the visualization
of upper air data: time-series plots for fixed-pressure level, sounding plots, wind and temperature plots for a user-specified
time, pressure level, and area. Among them, wind and temperature plots are currently not working. Figure 4-(a) is the plot
for fixed pressure level (1000 mb) based on the upper air data sets gathered from the radiosonde station (station ID: 47185)
in CHEJU, S. Korea from January to December 2008. We lost a lot of data from July through September and in December,
due to lack of archive data for the CHEJU radiosonde station. Figure 4-(b) is the sounding plot based on the upper air data
Smart Computing Review, vol. 3, no. 6, December 2013
451
set gathered from the CHEJU radiosonde station on November 11, 2008. Because the web interface for creating wind and
temperature plots for a user-specified time, pressure level, and area is not working currently, we show the sample plots
linked from the IGRA‘s maps and charts web page.
Table 3. The data record format for IGRA dataset files
Variable Name
Major Level Type
Columns
1-1
Description
1 = standard pressure level, 2 = significant
thermodynamic level, 3 = additional wind level
1 = surface, 2 = tropopause, 0 = other
units of Pa (mb * 100)
A, B, or blank†
units of meters
A, B, or blank
Minor Level Type
2-2
Pressure
3-8
Pressure Flag
9-9
Geopotential Height
10-14
Geopotential Height Flag
15-15
Temperature
16-20
units of degrees C * 10
Temperature Flag
21-21
A, B, or blank
Dewpoint Depression
22-26
units of degrees C * 10
Wind Direction
27-31
units of degrees (0-360, inclusive)
Wind Speed
32-36
units of (m/s)*10
†
The flag indicates whether the corresponding value was checked by procedures based on climatological
means and standard deviations. ‗blank‘ means no climatological check, ‗A‘ means that the value falls
within "tier-1" climatological limits, and ‗B‘ means that the value passes checks based on both the tier-1
climatology and a "tier-2" climatology [17].
Figure 2. An example of a data record format for IGRA dataset files
Figure 3. Examples of the web-based visualization of upper air data. These charts and maps can be generated with the
archived data and web-based interfaces provided by Integrated Global Radiosonde Archive (IGRA): (a) time-series plots
for fixed-pressure level, (b) sounding plots, (c) 500 mb wind plot for North America, (d) 850 mb temperature plot for
Europe
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Yun et al.: Visualization of Upper Air Data from Radiosonde Stations using Google Earth API
Visualization of Upper Air Data using Google Earth API
■ Google Earth API
Google offers the Google Earth plug-in for web browsers and its JavaScript-based Google Earth API, with which we can
embed the Google Earth interface and its 3D rendering capabilities into any webpage [18]. For example, Sun et al. present a
3D-assisted driving system based on GPS, mesh-wireless networks, and Google Earth to provide 3D information for truck
drivers and real-time monitored truck information for the remote dispatcher, reducing the number of surface mining
accidents due to low visibility conditions and truck blind spots [19]. Du et al. demonstrate the framework of a Geographic
Information System (GIS) based on Keyhole Markup Language (KML: a Descriptive Markup Language adopted by Google
Earth) having the characteristics of describing and expressing the geographical information. They performed a case study of
GIS development in a rural renewable energy planning and evaluation system [20]. Isikdag and Zlatanova present a way of
drawing and visualizing simple geometric representations of buildings directly in the Google Earth environment, showing
the practical feasibility of using the tool as a common language between urban planning professionals and citizens [21].
Accordingly, using the Google Earth API, we can imagine web and 3D map-based visualization tools for drawing markers,
lines, and stations, weather images over terrain, and even render 3D upper air data models.
■ Visualization using Google Earth API
Drawing images over terrain or rendering 3D upper air data models using the Google Earth API is a sophisticated project,
thus in this paper we only consider marking radiosonde stations and obtain an optimal viewpoint to display them in a web
page. Marking radiosonde stations can be implemented with the basic placemark function as follows:
// Create the placemark.
var placemark = ge.createPlacemark('');
// Set the placemark's location.
var point = ge.createPoint('');
point.setLatitude(latitude);
point.setLongitude(longitude);
point.setAltitude(altitude);
point.setAltitudeMode(ge.ALTITUDE_ABSOLUTE);
placemark.setName(name);
placemark.setGeometry(point);
// Add the placemark to Earth.
ge.getFeatures().appendChild(placemark);
The Latitude variable is given as a value between -90 and 90, i.e., a latitude of -90 degree represents the South Pole,
and the latitude of 90 degree represents the North Pole. Similarly, the longitude variable is given as a value between 180 and 180, i.e., the longitude of 0 degree means Greenwich in London, and the longitude of 180 degrees means the
international dateline. The Altitude variable is set by entering a value in meters. There exist five options for altitude:
clamped to ground, clamped to sea floor, relative to ground, relative to sea floor, and absolute. We also tag a name below a
placemark using the setName function.
To obtain a viewpoint to optimally display radiosonde stations in a web-based plugin window, we first need to
understand the camera and view [22]. Google Earth defines a ‗view‘ as the image we can see in the plugin window, and
‗camera‘ as the viewer‘s location in space. There are two different ways to define a view: Camera or LootAt KML
elements. With the Camera view, we can specify the location and orientation of the viewer in space by setting latitude,
longitude, and altitude values for the viewer‘s placement and heading, tilt, and roll angle values for the viewer‘s orientation.
Similarly, with a LookAt, we can specify the point on the Earth that is being viewed by setting latitude and longitude
values, the distance of the viewpoint from the point of interest by setting an altitude value, and the angle of the view by
setting range, tilt, and heading values. For our visualization application, we choose the LookAt element because
radiosonde stations are located on the Earth, and thus the longitude and latitude values of the given radiosonde stations are
the parameters necessary to optimize the view. Controlling the LootAt element can be implemented with the LootAt
object as follows:
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453
// Create a new LookAt.
var lookAt = ge.createLookAt('');
// Set the position and orientation values.
lookAt.setLatitude(latitude);
lookAt.setLongitude(longtitude);
lookAt.setAltitude(altitude)
lookAt.setRange(range);
lookAt.setTilt(tilt);
lookAt.setHeading(heading);
// Update the view in Google Earth.
ge.getView().setAbstractView(lookAt);
Latitude, longitude, and altitude values are given to specify the point being viewed, and range, tilt, and heading values
are given to specify the orientation of the view, as shown in Figure 5.
Figure 4. Methods for setting parameters for a LootAt object
■ Implementation of Visualization of Upper Air Data
In order to build a JavaScript page that graphically visualizes upper air data using the Google Earth API, we assumed that a
set of radiosonde stations would first be provided. Next, with the latitude and longitude values of the given radiosonde
stations, we need to estimate and set the parameters of the LootAt object in order to produce an optimal view in a 3D web
page using Google Earth. We assume that a list of radiosonde stations, S; the corresponding latitude, LT; and longitude, LG;
are given as follows:
S = {s1, s2, …, sN}
LT = {lt1, lt2, …, ltN}, N = the number of radiosonde stations.
LG = {lg1, lg2, …, lgN}
Figure 6 illustrates a way of estimating an optimal view for a given set of radiosonde stations, S. As shown in Figure 6,
we first need to calculate distance measures (e.g., distance matrix) between radiosonde stations, in both longitude and
latitude. It should be noted that distance measures in longitude should be calculated in a clockwise direction, but also in a
counterclockwise direction. Subsequently, we can find the maximum distance, distlg and distlt, between radiosonde stations
in longitude and latitude, respectively. We define threshold values, THlg and THlt, for longitude and latitude, respectively.
The threshold values can be used to prevent the selection of radiosonde stations that are far away from each other. As an
example, two radiosonde stations located in the North Pole and South Pole could not be practically displayed in a Google
Earth web page, because the position of the viewer in space would have to be too far from the Earth to see them both. For
our implementation, we set both threshold, THlg and THlt, to 90, though they could be set to different values. After deciding
that all distance measures are smaller than the threshold value—namely, all stations are close enough to obtain an optimal
view, we can extract radiosonde stations having the maximum distance, distlg and distlt. Accordingly, using the latitude and
longitude of those radiosonde stations, we finally extract an optimal viewpoint, lto and lgo, and an optimal range value,
rangeo. For appropriately setting rangeo, we used a scale factor proportional to distlg and distlt. Figure 7 illustrates a flow
chart diagram of the algorithm for estimating an optimal viewpoint and range value for a given set of radiosonde stations.
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Yun et al.: Visualization of Upper Air Data from Radiosonde Stations using Google Earth API
Figure 5. Estimation of an optimal view for a given set of radiosonde stations
Figure 6. An algorithm for achieving an optimal viewpoint and range value for a given set of radiosonde stations
We implemented the algorithm presented in Figure 7 in a JavaScript web page using the Google Earth API. In order to
show the performance of the proposed algorithm, we first choose five radiosonde stations in South Korea (OSAN AB,
POHANG, GWANGJU AB, CHEJU, and KANGNUNG AFB) and executed the JavaScript web page in a web browser,
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considering five cases that (a) two (OSAN AB and POHANG), (b) three (OSAN AB, POHANG, and GWANGJU AB), (c)
four (OSAN AB, POHANG, GWANGJU AB, and CHEJU), and (d) five (OSAN AB, POHANG, GWANGJU AB, CHEJU,
and KANGNUNG AFB) radiosonde stations are selected. Figure 8 presents the results of the four cases. From the figure we
can see that the viewpoint and range for each case can be automatically controlled according to the latitude and longitude
values of the radiosonde stations selected. Accordingly, we can conclude that the algorithm we proposed performs well in
all cases, and thus dynamically obtaining an optimal view when a particular set of radiosonde stations is selected. Of course,
by reading the IGRA upper air data archive provided for each radiosonde station, we can graphically visualize that upper
air data including temperature, wind speed, direction, and dewpoint depression on the Google Earth web page using marks,
strings, lines, images, a 3D rendering model, and animations.
Figure 7. Implementation of the visualization of radiosonde stations on Google Earth. (a) Two stations (OSAN AB and
POHANG), (b) three stations (OSAN AB, POHANG, and GWANGJU AB), (c) four stations (OSAN AB, POHANG,
GWANGJU AB, and CHEJU), (d) five stations (OSAN AB, POHANG, GWANGJU AB, CHEJU, and KANGNUNG AFB)
Conclusion
We have presented the visualization of upper air data collected from radiosonde stations using Google Earth API. A
radiosonde is a sounding system that can measure various meteorological conditions in the upper air. Upper air data
collected from radiosonde stations worldwide has been archived and shared with all countries. One common upper air data
archive is provided by the IGRA of the NCDC, and we can access the upper air data collected from over 1,500 globally
distributed radiosonde stations. We have summarized Google Earth API objects and functions in order to develop a
JavaScript web page with Google Earth embedded, which enables a visualization of upper air data collected from
radiosonde stations. We also proposed and implemented an algorithm which allows us to obtain an optimal view for a given
set of radiosonde stations, and showed the web pages we implemented using the Google Earth API.
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Smart Computing Review, vol. 3, no. 6, December 2013
457
Jaeseok Yun is a senior researcher in the Convergence Emerging Industries R&D Division at
Korea Electronics Technology Institute (KETI), Seongnam, South Korea. He earned his MS and
PhD in Mechatronics from Gwangju Institute of Science and Technology (GIST), Gwangju,
South Korea in 1999 and 2006, respectively. He has joined Embedded Software Convergence
Research Center at KETI in September 2009. He worked as a postdoctoral research scientist in
Ubiquitous Computing Research Group in the School of Interactive Computing at Georgia
Institute of Technology, GA, USA, from September 2006 to August 2009. His research interests
include ubiquitous computing, wearable computing, Internet of Things (IoT), human-computer
interaction, and context-aware computing.
Min-Woo Ryu is a senior researcher in the Convergence Emerging Industries R&D Division at
Korea Electronics Technology Institute (KETI), Seongnam, South Korea. He received his MS
and PhD in Computer Science from KwangWoon University, Seoul, South Korea in 2009 and
2012, respectively. His research interests include vehicle ad hoc network, information centric
network, Internet of Things (IoT), ontology, and intelligence software.
Sang-Shin Lee is a managerial researcher in the Convergence Emerging Industries R&D
Division at Korea Electronics Technology Institute (KETI), Seongnam, South Korea. He received
his B.S. degree in Mathematics from Hankuk University of Foreign Studies (HUFS) in 1997, his
M.S and Ph.D degrees, both in Computer Engineering from Hankuk University of Foreign
Studies, South Korea in 2000 and 2012, respectively. His research interests include ubiquitous
computing, Internet of Things (IoT), wireless sensor networks, and network protocols.
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