City-Scale Weather Monitoringwith Campus Networks for Disaster Management:
Case Study in Hyderabad
Hideya Ochiai
Masato Yamanouchi
Y K Reddy
Hiroshi Esaki
The University of Tokyo
Keio University
Indian Meteorological Department The University of Tokyo
jo2lxq@hongo.wide.ad.jp masato-y@kmd.keio.ac.jp
yeredla@yahoo.com
hiroshi@wide.ad.jp
Abstract—A weather station with communication capability
allows remote weather monitoring, which observations help us
to estimate flooding and to make an alarm for evacuation frompotential weather-related disasters. Rainfall often comes with avery local weather phenomenon, and to capture them, we haveto deploy many weather stations geographically densely. This
paper provides our study on a city-scale weather monitoring
system for disaster management applications. We have identified
a compact observation scheme for city-scale weather monitoringwith campus networks, and developed our weather stations.
In this work, we have deployed our weather stations at 18
locations over the city of Hyderabad India, and evaluated theperformance of our city-scale weather monitoring system. With
our preliminary analysis on the observations of 15th June 2014,
we confirmed that our system had detected very heavy, local andshort-term rainfall, sudden temperature changes, sudden windchanges, and rapid and very local air pressure changes. These
results indicate that we can use this type of city-scale weather
monitoring system for capturing the local weather phenomenon
and for disaster management applications.

Fig. 1. Weather observation should be ideally made on (a) WMO scheme,
but for disaster management purposes, we have chosen (b) compact method
for city-scale weather monitoring.

Keywords—Weather Monitoring System, Disaster Management,
Time-Series Data Collection

communication features, implemented and tested on the cityof Hyderabad, India.
I.

I NTRODUCTION

Protecting a city from weather-related disasters has entereda new age with the spread of Internet as the communication
infrastructure. Almost all the universities, colleges and schools
have Internet in their campuses these days. Weather stations
with Internet connectivity allow remote weather monitoring,which observations help us to estimate flooding and to makean alarm for evacuation from potential disasters.
In many countries, weather stations are already deployed
all over the country. However, those weather stations areusually geographically not dense enough to observe mesogamma scale (i.e., 2km - 20km scale, or we call city-scalein this paper) weather phenomenon. They sometimes fail todetect very heavy rainfall occurred next to a weather station.
In order to observe such rainfall, it is necessary to explore aweather monitoring system that we can deploy geographicallydensely over the city.
This paper provides our study on a practical city-scale
weather monitoring system that uses campus networks forcollecting data. In this work, we have based on Live E! weather
monitoring platform [2], improved the deployment styles and

The ideal weather monitoring system should be based on
the observation standard of world meteorological organization(WMO). However, in this work, we explore a system of simpler
but geographically denser weather monitoring that can be used
for weather-related disaster management applications: i.e., todetect local heavy rainfall and to make an alarm.
The weather stations in our work are based on Vaisala
WXT520 (see Figure 1). This is a compact weather sensor
that observes temperature, humidity, air pressure, rainfall, windspeed and direction. In this work, we have deployed it about 2
meters high from the ground with about every 10 km: totallyat 18 locations over Hyderabad. We recognize that, according
to WMO, to precisely observe the weather, we have to deploy(1) a wind sensor at 10 meters high, (2) temperature, humidity
and air pressure sensors at 1.5 meters high, and (3) rain gauge
at 0.3 meters high. However, the cost of installing such aweather station is so high, and we cannot generally deploy
them geographically so dense.
The weather stations we have deployed transmit observations to an Internet server with an HTTP-based protocol. To
connect such Internet weather stations, we have used campus

Fig. 2.
System architecture for city-scale weather monitoring. Weather
stations deployed in a city periodically submit their observations to the data
storage server on the Internet. Applications such as data distribution, trend
analysis and alarm notification use the data gathered at the server.

networks of universities, colleges and research organizations.
In Live E! weather monitoring[2], we have deployedweather stations in varieties of manner just considering the useof campus networks. We sometimes deployed on top of a building and sometimes near buildings or trees, which degraded
the performance of the weather station. The deployment was
mainly made in Japan, where the power-supply and Internet
connectivity is very reliable. Thus, we did not consider thetolerance to the loss of them, which happens frequently now
in India. In this work, to maximize the performance, we havedefined our uniform observation scheme first, and then redesigned weather station systems to fit to these requirements.
We recognize that 3G and GPRS communication is alsoavailable these days all over the world. However, in thiswork, we try not to rely on such communication services,which we have to pay the communication fee every month.
This deployment and weather monitoring is made on a five
year research project called DISANET. If we have taken such
communication scheme, we will have a risk of stopping thesystem right after the project.
This paper is organized as follows. Section 2 describes thedesign of our city-scale weather monitoring system. Section
3 provides the deployment details. In section 4, we describe
the result of our preliminary analysis. Section 5 provides thediscussion and related work. We conclude this paper in section
6.
II.

C ITY-S CALE W EATHER M ONITORING

This section describes the system architecture for city-scale
weather monitoring. We also describe the identified weather
observation scheme for this project, and the design of weather
station and communication scheme.
A. System Architecture
The architecture that we have adopted for this work (i.e.,city-scale weather monitoring) itself is quite common. As
figure 2, we setup a data collection server on the Internet with
global and static IP address. We install weather stations on theFig. 3. The design of a weather station (Wi-Fi + Self-Power model). Thissystem is autonomous and can be easily deployed into an open space. We
don’t have to deploy long power and Ethernet cables (e.g., 100 meters) fromthe power point and network switch of the site organization.

sites with, in many cases, private IP addresses under network
address translators (NAT). Each weather station submits its
observations to the server. The server provides data to multiple
applications: e.g., alert notification for disaster management,
trend analysis for weather-related research, and distribution
to agricultural organizations and water-management organizations.
The weather stations should be deployed geographicallyuniformly as much as possible in order to observe wide-area
weather phenomenon with sufficient density.
B. Weather Observation in this ProjectThough Vaisala WXT520, a compact weather sensor
we chose for city-scale weather monitoring, is much more
lightweight than WMO-based weather stations, there are somerequirements, especially in selecting the location, to maximize
the performance of its observation.
1) A weather station should be deployed into an open
space, because buildings, mountains and trees become fatal
obstacles for weather observation:•

It cannot correctly observe temperature and humidity
if obstacles block the sunlight.

•

It cannot correctly observe rainfall if obstacles are
nearby, because they sometimes block the rain comingfrom that direction.

•

It cannot correctly observe wind if obstacles are
nearby, because they change the wind speed anddirection around the obstacles.

2) Weather monitoring should be made in the same style
as much as possible, because the observation value changes,
for example, depending on the height from the ground:•

The temperature at the terrace of a building at thedaytime is usually lower than the temperature of 2.0
meters high from the ground.

•

The wind speed at the terrace of building is usuallylarger than the wind speed near the ground.

•

If we have deployed them without considering theseeffects, we cannot easily compare the observations
among different sites because they have different
features.

In this work, we try to deploy weather stations in open
spaces as much as possible. We have also chosen 2 meters as
a standard height of the sensor from the ground.
As for observation interval, to detect sudden weatherchanges expected to happen in city-scale weather phenomenon,
we consider one minute as our observation interval; i.e., aweather station submits observations every minute.
C. Weather Station System Design
Open spaces usually do not have network and power supply
around 50 or 1000 meters range. To provide network and power
to a weather station deployed in an open space, we propose touse (1) long range Wi-Fi to provide network connectivity, and(2) self-power system to reduce civil works.
Figure 3 shows the design of a weather station we haveidentified in this work. This weather station has a self-powersystem with solar panel and battery, is connected over longrange Wi-Fi to the campus network of the site organization.
The sensor is attached on top of a pole concreted by cement on
the ground. The standard height is 2 meters. The data logger
and Wi-Fi module is also attached to the pole.
Typically, a weather sensor itself (e.g., Vaisala WXT520)
provides observations over serial communication lines. So, in
this design, we use an embedded computer to read observations
from the sensor on its serial cable and to send it to the data
storage server over TCP/IP/Ethernet. The Ethernet interface isconnected to a Wi-Fi client bridge, which associates to a Wi-Fi
access point. The Wi-Fi access point should be deployed so as
to provide radio connectivity to the weather observation site.
In many cases, the terrace of a building works. With the pair of
Wi-Fi access point and client bridge, this system connects theembedded computer of the data logger to the campus network.
To provide power to the embedded computer and the otherequipment, this system has charge controller and DC-DC
converter(s). The charge controller manages the solar powerand battery charging, and supplies the power to the DC-DC
converter(s). The DC-DC converters appropriately change thevoltage and provide electricity for the embedded computer,
weather sensor and Wi-Fi client bridge.
There are some options for the design of a weather station.
For example, if the power is available near the weather station,
we can use it with uninterruptible power supply (UPS). If the
network and power is available at the same location and it
is not difficult to deploy cable, we can use PoE injector andsplitter to provide network and power over a single Ethernet
cable. If we use weather resistant cable with proper civil works,
those approaches may also work.
D. Communication for Weather Data Collection
In this work, we have used facility information accessprotocol (FIAP)[5] to collect weather observations from thestations. FIAP defines time-series data model and data storage

Fig. 4. Transmission of weather observations into time-series data storage.
We presented weather observations onto FIAP’s time-series data model. The
embedded computer transmits the observation snapshot to the data storage
every minute.

for such time-series data, which we can also apply to weather
monitoring applications.
As figure 4, we consider that a weather station has multiple
elemental sensors. Those sensors individually and periodicallygenerate observations as time-series data. In order to manage
the observations, data logger assigns a text-based unique
identifier for time-series data. Here, in weather monitoring
applications, each identifier may contain the name of the site
and the type of the sensor. For example, we can design theidentifier for temperature sensor of site A as ”A/Temperature”
in the text.
Data logger submits the observations by FIAP’s WRITE
procedure. If the network is working, it submits data every
minute after getting the observations from the sensors. However, the network sometimes becomes unavailable because of
power failure or Wi-Fi link disruption. To get the tolerance fortemporal communication failure, the failure should be detected
and observations should be re-submitted after the recovery of
the network.
The data logger should have an adjusted clock to putcorrect timestamps to the observations. We can use network
time protocol (NTP) to synchronize its clock to a network time
server.
III.

D EPLOYMENT IN H YDERABAD , I NDIA

As figure 5 shows, we deployed weather stations at 18
locations in Hyderabad, India. We made the deployment so as

Fig. 5.

Weather station deployment map in Hyderabad, India. We have deployed at 18 locations. They cover most of the area of Hyderabad.

TABLE I.

W EATHER S TATION D EPLOYMENT D ETAILS

Location Name
IMD Hyderabad, Begumpet
Loyola Academy, Alwal
GITAM University, Rudraram
Shamshabad Airport
IIT Hyderabad, Yeddumailaram
IIT Hyderabad, Kandi
NGRI, Uppal
IIIT Hyderabad, Gachibowli
NRCM, Chengicherla
GRIET, Nijampet
CBIT, Gandipet
CMRIT, Kandlakoya
CGWB, Tattiannaram
Geethanjali Eng.Clg., Cheeryal
MVSR, Nadargul
ANGRAU, Rajendranagar
TITS, Indresham
Vardhaman Eng.Clg., Shamshabad

Model
Wi-Fi + Site PowerPower over Ethernet
Power over Ethernet
Wi-Fi + Site Power
Wi-Fi + Site PowerGPRS + Self Power
Wi-Fi + Site Power
Wi-Fi + Site Power
Wi-Fi + Self-Power
Wi-Fi + Site Power
Wi-Fi + Self-Power
Wi-Fi + Site Power
Wi-Fi + Self-Power
Wi-Fi + Site-Power
Wi-Fi + Self-Power
Wi-Fi + Self-Power
Wi-Fi + Self-Power
Wi-Fi + Self-PowerDate
Sep 2010
Nov 2012
Jun 2013
Sep 2013
Sep 2013
Sep 2013
Oct 2013
Nov 2013
Nov 2013
May 2014
May 2014
May 2014
May 2014
Jun 2014
Jun 2014
Jun 2014
Jun 2014
Jun 2014

feasible for all the sites. All the deployment has been finishedon the 10th of June 2014.
For the deployment based on Wi-Fi + Self Power model,
we have used a pair of EnGenius ENH500 for long range
Wi-Fi communication. As this Wi-Fi unit requires DC24V
700mA, we used 180W solar panel (18V, 10A) and 12V 100Ah
Pb battery. And, we used a 24V-output DC-DC converter forthis Wi-Fi module, and a 5V-output DC-DC converter for theembedded computer and Vaisala WXT520.
We have also used GPRS solution for IIT Hyderabad,
Kandi, where no Internet was provided in the campus at
the time of deployment. GPRS model was also helpful for
installation, but we now pay the communication fee (about
the equivalent of 5 USD) every month.
IV.

to cover most of the city area.
Table I shows the model and the deployment date of eachweather station. At an early stage, we started with (1) Wi-Fi+ Site-Power or (2) Power over Ethernet models. However,these schemes required heavy civil works – we had to ditch
for more than 50 meters with 30 cm depth and bury cablecarefully under the ground sometimes across a road. So, inthe latter part of the deployment, we have adopted (3) Wi-Fi +Self-Power model (as described in Section II.C). This modelallowed the reduction of civil works and made deployment

D ETECTION OF C ITY-S CALE W EATHERP HENOMENON

Weather conditions are not uniform over a city. Rainfall
usually happens locally, temperature sometimes changes quite
rapidly and wind direction also changes suddenly. The local
phenomenon may even change air pressure. As a preliminary
evaluation of our city-scale weather monitoring system, we
have analyzed the pattern of rainfall, temperature, wind direction and air pressure.
We have made this analysis on the observations of theevening of 15th June 2014. We have detected very heavy andFig. 6. Heavy rainfall detected by our city-scale weather monitoring system on 15th of June 2014. It was suddenly detected at (a) Loyola Academy, and thenit moved to (b) IMD Begumpet. Finally, it moved to (c) NGRI and CGWB. Other sites did not detect rainfall. This result indicates that our weather monitoring
system have successfully detected very local short-term heavy rainfall.

short-term rainfall in a part of the city, which is one of thelocal weather phenomenons that we target with this system.
A. Local Short-Term Heavy Rain
Figure 6 shows how the rainfall was observed on 15th June2014 by our weather monitoring system.
On the day, Loyola Academy begun to detect rainfall at
18:35 and finished at 18:45. The duration was only 10 minutes.
The maximum intensity of the rain was 70.1[mm/h] at 18:38.
Next, IMD Begumpet detected rainfall from 18:52, recording
the intensity of 63.9[mm/h] at 18:55. The rain has continued
for 11 minutes at IMD. Then, it moved to NGRI and CGWB.
NGRI started to detect at 19:03, and CGWB at 19:04. The
maximum intensity was 73.7[mm/h] at 19:06 at NGRI, and36.7[mm/h] at 19:10 at CGWB. The rain finished at 19:13
and 19:20 respectively. Other sites did not have rain.
Here, the distance between Loyola Academy and IMD isabout 7 km, IMD and NGRI is 8 km, NGRI and CGWB is 6km.
This result indicates that our city-scale weather monitoring
system has certainly detected such very local, short-term andheavy rainfall. It has recorded how the rainfall begun, moved
and ended over the city.
B. Sudden Temperature Drop
Figure 7 shows how the temperature of Loyola Academy,
IMD, NGRI, CGWB and CBIT have changed from 18:00 to
20:00. CBIT is located at east side of Hyderabad (about 20
km east from IMD). CBIT did not have rainfall on the day.
As the graph shows, the temperature of Loyola Academy
started to decrease first, and then the temperature of IMD,NGRI, and CGWB decreased in this order. This corresponds
to the order of the rainfall.
Especially, the temperature of CGWB has dropped rapidly– from 32.6 to 26.9 degrees Celsius in 15 minutes (19:00 –19:15). On the other hand, the temperature of CBIT has justgradually decreased as usual.
This result indicates that our weather monitoring system
has detected sudden temperature drop happened locally.

Fig. 7. Temperature patterns observed with our city-scale weather monitoring
system on 15th June 2014. Temperature suddenly dropped at the sites of rain.
Especially, the temperature of CGWB dropped about 2.8 degrees Celsius from
19:02 to 19:06, while the temperature of CBIT did not decrease so fast.

C. Sudden Change of Wind Direction
Figure 8 shows how the wind directions of Loyola
Academy, IMD, NGRI, CGWB and CBIT have changed from18:00 to 20:00.
As the graph shows, before 18:45, the wind directions at all
the sites were from west. However, wind of Loyola Academy
and IMD changed after that. At Loyola Academy, the winddirection has rotated and finally it came from south east after
19:15. At IMD, it has changed to come from north. The windof NGRI started to change at 19:04, and it did not settle before
20:00. The wind of CGWB suddenly changed to come from
north at 19:15 but finally it came from south east. The windof CBIT was almost settled: though it has small differences,
it came from west all the time.
This result indicates that our weather monitoring system
could take the geographical snapshots of wind directions,which represent very local and rapidly changing weather
phenomenon.
D. Local Air Pressure Changes
Air pressure is usually considered to be almost uniform
over the city. However, we have also detected sudden air

estimating the local situations in disaster management.

Fig. 8. Wind directions observed with our city-scale weather monitoring
system on 15th June 2014. Before 18:45, the wind came from west at all thesites. After 18:45, wind direction started to change from Loyola Academy,
IMD, NGRI and CGWB in this order. Finally, they settled to different
directions. The system has observed local wind flows generated by such cityscale weather phenomenon.

With the advent of sensor networking technologies, many
researchers have so far developed weather and environment
monitoring systems. However, those works did not address
the observation method for city-scale weather monitoring that
we targeted in this paper. The work of IrisNet[3] focused on
the integration of heterogeneous sensors provided by multiple
operational domains. Semantic sensor web[6], [1] developed
a framework which allows to describe varieties of observation
features and to integrate heterogeneous sensors on a webcentric information infrastructure. The development and deployment studies of Sensor Asia[4] discussed the issues ofeasy installation of field servers (a kind of weather station) anddesigned the sensor system for such requirements. Our previous work[2] also focused on the development of information
infrastructure for weather stations, however, without making astandard observation scheme. In this paper, assuming disastermanagement applications, we have identified an observation
method for city-scale weather monitoring as the requirement
for the system. Then, we developed, implemented and testedon the real field.
VI.

Fig. 9. Air pressure observed with our city-scale weather monitoring systemon 15th June 2014. The absolute values of air pressure are different by location
because of the altitude from the sea level is not the same. Sudden peak
appeared in IMD and NGRI’s observations at the time when they had heavy
rainfall.

pressure changes. Figure 9 shows the trend of air pressure
at Loyola Academy, IMD, NGRI, CGWB and CBIT. The
absolute values are different because the altitude of the sensor
from the sea level is not the same. The air pressure of all thesites gradually increased from 18:00 to 20:00, this is a global
trend always observed in Hyderabad. However, the system
has detected sudden increase of air pressure at IMD (about
0.9[hPa] at 18:54) and NGRI (about 0.5[hPa] at 19:04). Thisis about one or two minutes before the maximum rain intensity
recorded. After the rainfall, the pressure has decreased morethan before the rainfall.
V.

D ISCUSSION AND R ELATED W ORK

The data observed with our city-scale weather monitoring
system is not compatible to the data of WMO-standard weather
stations, especially regarding to wind and rainfall. However,
if we consider using this system for disaster management
applications, it may have enough performance to estimate
flooding and to make an alarm for some areas of the city.
In our preliminary evaluation, we have confirmed that it can
detect very local weather phenomenon, which is necessary for

C ONCLUSION

We have studied a city-scale weather monitoring system fordisaster management applications. For weather-related disastermanagement, it is necessary to capture very local weather phenomenon, especially rainfall to estimate flooding and to makean alarm. To maximize the performance of the monitoring
system, we have identified the requirements for weather observation within the limits of compact scheme, and developed
weather stations considering the communication features. We
have deployed weather stations at 18 locations over the cityof Hyderabad, India. According to our preliminary analysis on
the observations of 15th June 2014, our system had detectedvery heavy, local and short-term rainfall, sudden temperaturechanges, sudden wind changes, and rapid and very local airpressure changes. These results indicate that we can use thistype of city-scale weather monitoring system for capturing
the local weather phenomenon and for disaster management
applications.
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