Simulating Weather Events on a Real-world Map using Unity 3D

Hamza Noueihed, Heba Harb and Joe Tekli

Department of Electrical and Computer Engineering, School of Engineering,

Lebanese American University (LAU), 36 Byblos, Lebanon

Keywords: Simulator Modelling, Unity 3D, Virtual Sensors, Semantic Sensor Network, Weather Events.

Abstract: During the past decade, 3D simulation models have gained importance in the development of software

solutions that aim to mimic real-world events and phenomena with increasing levels of accuracy and detail.

In this paper, we introduce VOWES, a Virtual Outdoor Weather Event Simulator to replicate and measure

outdoor weather events in vivid 3D visualizations. We make use of the Unity 3D engine to build the simulator

environment and its virtual sensors, and integrate the Mapbox SDK and the WeatherStack API for realistic

real-world weather mapping. We have conducted a large battery of experiments involving 30 human testers,

considering various evaluation criteria. Results highlight VOWES’ quality and performance, and its ability to

simulate complex weather environments with large numbers of sensors and weather phenomena.

1 INTRODUCTION

With the rising interest in creating realistic and vivid

simulations, 3D models have been gaining increasing

importance in the development of software solutions

that aim to mimic real-world events and phenomena.

Simulation modelling allows creating and analysing

the behaviour of a digital prototype system

representing a physical real-world entity, aiming to

study and predict the latter’s behaviour and

performance in the real-world (Garcia-Dorado I. et al.

2017). Simulation software has become one of the

most commonly used techniques for virtual

demonstrations in different fields, especially 3D

models used to simulate real-world structures,

objects, and events, with increasing levels of accuracy

and detail, e.g., (Li X. et al. 2019, Zigon B. et al.

2018, Garcia-Dorado I. et al. 2017).

In this paper, we introduce VOWES, a Virtual

Outdoor Weather Event Simulator to represent

outdoor weather events and data in vivid 3D

visualizations. It is designed as a digital twin solution

to describe and replicate weather measurements,

events, sensors, and their properties from the real-

world, into a software simulation environment. We

make use of the Unity 3D engine to build and design

the simulator environment and its virtual sensors. We

develop special visualizations and behaviours to

https://orcid.org/0000-0003-3441-7974

present weather measurements, events, and sensors as

visible 3D structures with specifications controllable

by the user. We utilize the Mapbox SDK (MapBox

2021) to import high-resolution world maps showing

countries, cities, and buildings. In addition, we utilize

the WeatherStack API (WeatherStack 2021) to

capture real-time weather measurements and

conditions from the geographic area that is being

simulated and integrate them in the simulation

environment to allow for more realistic and accurate

simulations. Qualitative and performance evaluations

highlight the potential of the tool.

In the following, Section 2 reviews related

works. Section 3 describes the VOWES simulation

tool. Section 4 describes the experimental evaluation,

before concluding in Section 5 with future directions.

2 RELATED WORKS

With the rising interest in creating realistic and vivid

models, Unity 3D has been gaining increasing

importance as a powerful tool for the creation of 3D

visualizations, functions, and attributes, and their

integration with dedicated processing features and

metric measurements to achieve accurate outputs and

analyses. Unity is a cross-platform game engine

developed by Unity Technologies, which was

Noueihed, H., Harb, H. and Tekli, J.

Simulating Weather Events on a Real-world Map using Unity 3D.

DOI: 10.5220/0010989000003203

In Proceedings of the 11th International Conference on Smart Cities and Green ICT Systems (SMARTGREENS 2022), pages 86-93

ISBN: 978-989-758-572-2; ISSN: 2184-4968

announced and released in June 2005 at Apple Inc.'s

Worldwide Developers Conference as a Mac OS X-

exclusive game engine. Starting in 2018, the engine

has been extended to support more than 25 platforms

for creating two-dimensional (2D), three-dimensional

(3D), augmented reality, and virtual reality games.

Also, the Unity engine has been used for simulations

in various fields including architecture, engineering,

automotive, and construction, e.g., (Sanders B. et al.

2020, Sun L. et al. 2020, Wang R. et al. 2020). One

of its distinctive features is the power of its real-time

3D rendering, making it one of the world’s leading

real-time development platforms (Unity).

In this context, several Unity 3D-based

simulation solutions have been developed in the

literature. In (Wazir H. and Annaz F. 2015), the

authors design a Unity-3D simulator to help navigate

unmanned aerial vehicles (UAVs). The latter is

coupled with sensors and physical hardware allowing

to collect data from the UAV’s surrounding

environment and feeding it into the virtual simulation

for processing and analysis. The authors emphasize

the importance of Unity 3D in presenting a realistic

and precise model while tracing the performance of

UAVs in the real-world. In (Buyuksalih I. et al.

2017), the authors develop a Unity 3D virtual

environment to study the properties and potential

prospects of using solar energy on buildings in a

highly populated urban area. They mimic building

structured using dedicated 3D visualizations, and

mimic solar energy measurements based on values

and calculations accumulated from a real world urban

area in the city of Istanbul. The authors specifically

address the challenge of attaining high accuracy in

predicting solar energy outcomes with the influence

of the buildings’ shadow casting. The authors extend

their simulated environment to represent and study

the underground utility systems in the city, where the

whole city map is translated into a dedicated

underground 3D model. In (Jain V. and Mahdavi A.

2016), the authors design and integrate virtual sensors

to measure light conditions in both indoor and

outdoor environments. They focus on monitoring

daylight conditions and design artificial light sources

to map different sunlight conditions in the real world.

They accumulate measurements from real sensors

and map the data to the virtual sensors to create

realistic conditions in the virtual environment. The

authors utilize dedicated CAD software to create both

indoor and outdoor environments with high degrees

of precision and accuracy. Unity 3D is used to

animate the CAD environment and handle light

condition variations and sensor simulations. In

(Wazir H. and Annaz F. 2015), the authors show how

environmental events, such as fire, can be

demonstrated in a 3D manner. They attempt to imitate

real-life scenarios and study the level of stress that

different people might face in simulating different

kinds of fire events. The authors highlight the

capabilities of Unity 3D in visualizing and animating

complex objects and events such as fire, flame,

smoke, and their propagation.

3 VOWES SIMULATION TOOL

We design and develop our VOWES simulation tool

using the Unity 3D game engine to build the

environment and its virtual sensors, and integrate

them with real-world 3D maps and a weather API for

realistic weather mapping. We develop special

visualizations and behaviours to present weather

measurements, events, and sensors as visible 3D

structures with specifications controllable by the user.

The following subsections describe the main

components of our simulator tool.

3.1 Virtual 3D World

To achieve a realistic 3D simulation of outdoor

environmental events and measurements, we use

Unity’s flexibility in integrating third-party APIs to

acquire dynamic 3D maps and real-world weather

measurements. More specifically we utilize the

Mapbox SDK (MapBox 2021) to import high-

resolution world maps showing countries, cities, and

buildings, and we integrate the WeatherStack API

(WeatherStack 2021) to capture real-time weather

measurements and conditions from the geographic

area that is being simulated.

Mapbox offers APIs, SDKs, and live-updating

map data, allowing to build better mapping,

navigation, and search experiences across different

platforms (MapBox 2021). We utilize the Mapbox

SDK to import the 3D maps of real-life cities and

allow the user to explore and visualize those cities

from within the Unity 3D environment, with high

levels of detail, where particular locations or

buildings can be easily leveraged for procedurally

generating user-specific experiences or styling. Users

are prompted to select their city of choice upon

launching a new simulation project. Consequently,

the data layers are imported and built into the Unity

3D environment, including buildings data, points of

interest (POIs), roads, and real-time traffic data,

where the data can be fully customized within Unity

3D’s development environment (e.g., changing the

layout of certain buildings, adding a building,

Simulating Weather Events on a Real-world Map using Unity 3D

a. 3D visualization of the city of New York, with sample

weather measurements and some of their

arameters

b. Sample visualization of the city of New York, shown

durin

a snow storm in late Januar

2021

Figure 1: VOWES simulation tool snapshots of the city of New York.

removing or changing the properties of a road, etc.,

cf. Figure 1.a). In addition, we utilize the

WeatherStack API (WeatherStack 2021) to acquire

real-time weather data for the selected city being

simulated by the user 2 , while storing a 14-day

historical record of the weather information. The

historical record is useful to allow weather

forecasting through the simulator. Following the

user’s selection of the city of interest, and upon

launching the simulation project, the tool

automatically acquires and processes the real-data

weather information and presents the corresponding

visualizations and behaviours on-screen (Figure 1.b).

3.2 Virtual Weather Measurements

and Events

We develop a dedicated weather simulation module

using Unity 3D’s Particle System graphics (Unity

2020) to create dynamic weather objects, visualizing

and simulating the behaviours of weather

measurements (e.g., wind, humidity, temperature)

and weather events (e.g., storm, tornado, fire). We

utilize Unity’s particle system to render small images,

called particles, and control their collective behaviour

to produce visual effects where every particle within

the system presents an individual graphical element

in the effect. Every particle system is modelled as a

3D sphere object with mutable boundaries, serving as

a container for a blob of particles associated with the

target weather measurement or event. The object’s

properties can be defined and fine-tuned by the user

through controllable parameters (e.g., coverage,

value, dissipation) as seen in Figure 1.

WeatherStack API is utilized by more than 75k companies

worldwide, providing multi-year history and live data

(WeatherStack, 2021).

3.3 Virtual Sensors and Multi-sensors

We define a virtual sensor as a spherical Unity 3D

game object with mutable boundaries, having user-

controllable properties including location

(coordinates of the sphere’s centre point),

measurement range (radius of the sphere), sampling

rate (frequency of capture), and sampling accuracy

(precision of capture, cf. Figure 2). Every weather

measurement is associated with an identifying tag,

which is assigned to the corresponding virtual sensor

objects once its measurable feature is chosen by the

user. The user can easily toggle between the sensors’

measurable features using their identifying tags. The

tags help identify all virtual sensor game objects

without the need for any additional manual code

writing or Unity scripting. A virtual multi-sensor is

modelled as a set of multiple overlapping 3D sphere

objects where each sphere object represents an

individual virtual sensor. This allows a multi-sensor

to capture multiple weather measurements from its

constituent virtual sensors and allows flexibility and

modularity in designing different kinds of virtual

sensors. The sensed values are based on the user-

chosen properties for the corresponding weather

measurement or the event object. Knowingly, the

sensor starts first finding the contact points with the

weather game object, estimating the corresponding

weather value at each point, accumulating the average

of all points, and showing the output values to the user

through the database console. This process is done

continuously until no weather item is detected within

the sensing range. As soon as the collision ends, the

function on collision exist indicates that contact has

been broken between the sensor and the weather

SMARTGREENS 2022 - 11th International Conference on Smart Cities and Green ICT Systems

game objects, signaling the end of the weather

measurements sensing process.

Figure 2: Virtual sensor configuration panel.

a. Conceptual ER describing an extract of the database

. Sam

le data

roduced b

the VOWES simulator tool

Figure 3: Extract of the VOWES database schema and

sample data.

3.4 Environment Data Storage

The data generated through the VOWES simulation

environment, including virtual weather

measurements and events, as well as virtual sensor

properties and readings, are organized and stored in a

http://sigappfr.acm.org/Projects/VOWES/

relational database structure. Figure 3 shows the

database conceptual schema and corresponding

sample data snapshots from the simulator tool. The

data from every simulation project is saved in the

database, with its timestamp under the user’s account,

and can be utilized by the user to save, exit, reload,

refresh and query the simulation project. The data is

also essential to allow the development of data

monitoring, mining, and extrapolation functionalities,

including project versioning, temporal querying,

measurement forecasting, and event prediction. For

instance, while VOWES does not currently perform

forecasting and prediction, yet it will allow

visualizing predicted events once their data becomes

available. In other words, VOWES will allow the user

to easily fast-forward (or fast-backward) in time to

visualize the weather environment and its events in

the future (or in the past), according to the available

temporal data in its database. The predicted events

and their measurements will plug into VOWES and

benefit from its visualization functionalities. The

latter are outside the scope of this work and will be

addressed in a future study.

4 EMPIRICAL EVALUATION

We have conducted qualitative and performance

evaluations to assess the VOWES’ tool, considering

three evaluation criteria: i) simulation accuracy, ii)

user friendliness, and iii) time performance. The

prototype system is available online

4.1 Simulation Accuracy

An essential feature in our simulator is the

functionality of the virtual sensor (and virtual multi-

sensor) component(s). As described in Section 4, a

virtual sensor is designed to mimic the behaviour of a

real sensor in the virtual simulation environment, by

capturing weather measurements (e.g., temperature,

humidity, wind) based on the occurring weather event.

To test the accuracy of the weather measurements

made by virtual sensors, we refer to the real-time

weather values given by the integrated weather API,

which are set as the initial values for any weather

measurement or event as seen in Figure 4.a and b. The

weather values provided by the weather API are

regularly updated in the simulation tool, to highlight

the real weather conditions in the chosen geo-location

being simulated. We also test the performance of the

Simulating Weather Events on a Real-world Map using Unity 3D

a. Sample wind measurement b. Sample temperature measurement c. Parameters’ panel

d. Wind speed readings with no dissipation (cf. a)

e. Tem

erature readin

s with 30% dissi

ation

(

cf. b

)

Figure 4: Display of weather measurement properties (a b, c) and sensor readings (d, e).

virtual sensors by checking their readings in

comparison with the selected weather measurements

and their associated properties (e.g., value,

dissipation, location, coverage). For example, if we

select a wind measurement and set dissipation to 0%

(cf. Figure 4.a), we expect the sensor to capture the

same specified wind speed value returned by the API

as long as it occurs within its coverage area,

regardless of its collision location (cf. Figure 4.d). Yet

if we set the temperature dissipation parameter to

50%, and we incrementally move the virtual sensor

away from the weather measurement’s location, we

expect the sensor to capture temperature values at a

decreasing rate of 50% considering the sensor’s

collision location w.r.t. the temperature measurement

location (cf. Figure 4.e). We follow the above

approach by modifying all the weather measurement

properties and checking the virtual sensors’

measurements accordingly. For every property, we

consider 10 variations of equal spans (e.g.,

temperature varies between -30, -20, …, 60 Celsius,

dissipation varies between 0, 10, 20, …, 100%). The

results produced for all property variations and tests

concur with the virtual sensors’ expected

measurements, denoting their simulation accuracy.

4.2 User-friendliness

The VOWES tool is designed to allow non-expert

users who have no previous knowledge about the

Graphical User Interface

simulation tool to be able to easily utilize it and

benefit from its functionalities. Hence, we evaluate

the tool’s user-friendliness by performing two kinds

of evaluations: i) GUI

testing, and ii) usability

testing. The former aims at checking the GUI’s input

fields and components, while the latter aims at

checking the ease/difficulty of usage of the software

tool by non-expert users.

GUI Testing: In this experiment, we check the

display of input fields and buttons on the screen

considering the aspects of size, alignment, and

content. We also check the menu and parameter

panels of the application by testing their buttons and

mouse hovering functionality, and their impact on the

main display. This is applied on all user-interfaces in

the whole simulator, starting from testing the

capability of generating more than one project

simultaneously (through the main page), to the ability

to select a country/city and viewing it in a 3D

environment, as well as scrolling and zooming in and

out of the map with high resolution and details. We

also evaluate and test the ability to add weather

measurements and events in the same simulation

project, and we test the functionality of the designed

buttons by pressing each button more than 50 times

consecutively. In addition, we make sure that all the

weather measurements are movable around the map,

by relocating every one of them more than once.

SMARTGREENS 2022 - 11th International Conference on Smart Cities and Green ICT Systems

Table 1: Simulation tool usability evaluation criteria.

Criterion Description Evaluation question

Stability

It is the ability of the software tool to function over a long period

of time without crashin

Given the criterion’s description, how satisfied are you with the

stabilit

of the simulation tool?

Look and Feel

It refers to the first impression a user has after using the software

tool.

Given the criterion’s description, how satisfied are you with the look

and feel of the simulation tool?

Ease of Use

It describes how easy and straightforward it is to use and

mani

ulate the software tool.

Given the criterion’s description, how satisfied are you with the ease

of use of simulation tool?

Functionality

It refers to the capacity of the software tool to provide useful

functions and features servin

its main ob

ective.

Given the criterion’s description, how satisfied are you with the

functionalit

of the simulation tool?

Responsiveness

It refers to the time it takes the software tool to execute a certain

action or behaviour.

Given the criterion’s description, how satisfied are you with the

res

onsiveness and overall s

eed of this a

lication?

Format

It refers to the materials and options provided (e.g., buttons,

instructions

)

and their or

anization within the software tool.

Given the criterion’s description, how satisfied are you with the

format of this simulation tool?

Navigation

It refers to the interactions that allow users to navigate across,

into, and back-out of the software's format (e.g., back to the

main

e, o

enin

/closin

side menus, zoom in/out

)

Given the criterion’s description, how satisfied are you with the

navigation of this simulation tool?

Icon Intuitiveness

It reflects how easy it is to guess a button’s resulting action or

ehaviour before a user

resses it.

Given the criterion’s description, how satisfied are you with the

intuitiveness of the icons of this simulation tool?

User Interface

It is the means through which a user controls a software

lication and interacts with it.

Given the criterion’s description, how satisfied are you with the

interface of this simulation tool?

We apply the same testing on the virtual sensors,

where we perform 10 consecutive addition, renaming,

deletion, and movement operations on every sensor in

the simulation exercise. Similarly, we test up to 10

separate projects by launching every project using a

different city map, populating it with weather

measurements, weather events, and virtual sensors,

saving it, closing it, re-opening it, and verifying that

the sensors, measurements, events, and their values

and locations are correctly loaded and initialized

respectively. Furthermore, we test the parameter

panels associated with every visual component by

checking the functionality of its buttons and range

sliders (describing coverage, value, and dissipation,

cf. Figure 4.c) and observing their impact on the

visual component. Results of all GUI tests were

successful and allowed fine-tuning and improving the

visual aspects and behaviour in the simulation tool.

a. Educational level

. Field of stud

c. Ex

erience with Unit

Figure 5: Non-expert testers’ education levels, majors, and

experience with Unity 3D

Usability Testing: We also created an online

survey

to evaluate the usability and user-friendliness

of our simulation tool considering nine evaluation

criteria: i) stability, ii) look and feel, iii) ease of use,

iv) functionality, v) responsiveness, vi) format, vii)

navigation, viii) icon intuitiveness, and ix) user

Available at: https://forms.gle/F6odKynC9pcvmCzq6

interface (cf. Table 1). A total of 30 non-expert testers

(undergraduate and graduate students, cf. Figure 5)

were invited to contribute to the experiment, where

they independently rated every evaluation criterion

on an integer scale from 0 to 4 (i.e., from highly

dissatisfied to highly satisfied). Tests were conducted

on a network version of the tool made available

through the university’s computer labs, where every

computer lab consists of an HP ProLiant ML350

Generation 5 (G5) Dual-Core Intel XeonTM 5000

processor with 2.66 GHz processing speed and 16 GB

of RAM. A total of 170 responses were collected,

with every criterion receiving 30 rating scores.

Results in Figure 6 show the average rating scores and

their standard deviations aggregated for every

criterion. Most testers are satisfied with the tool’s

usability, producing an overall average rating of 3 out

of 4 considering all criteria combined. Three criteria

received average scores below 3: look and feel (2.80),

ease of use (2.80), and responsiveness (2.80). Tester

discussions revealed that the latter are generally due

to the perceived loading time delays of certain Unity

3D components, visual effects, or animations, which

probably require increased processing power. This is

a common issue with most 3D rendering

environments due to their high processing and

memory requirements and can be improved with the

usage of GPUs and other enhancements. Few testers

recommended including additional features like: i)

considering the impact of outside weather conditions

on indoor environments (e.g., indoor heating/cooling

systems), and ii) including pollution-related

measurements (e.g., carbon dioxide concentration).

We plan to consider the latter in a future study.

Undergraduatestudents

Graduatestudents

Doctoralstudents

ComputerScience/Engineering

Engineering(other)

Architecture

Sciences

Other

NeverheardofUnity3D

Know,butneverusedUnity3D

KnowandusedUnity3D

Simulating Weather Events on a Real-world Map using Unity 3D

Figure 6: Average tester ratings for every usability

criterion.

4.3 Time Performance

The following paragraphs highlight and discuss the

time results obtained during the tool’s: i) setup phase

and ii) simulation phase. Experiments were

conducted on an HP ProLiant ML350 Generation 5

(G5) Dual-Core Intel XeonTM 5000 processor with

2.66 GHz processing speed and 16 GB of RAM.

Setup Phase: The simulation tool allows the user

to visualize sensors, weather measurements (e.g.,

wind, humidity, temperature), and weather events

(e.g., storm, fire, tornado) as objects with editable and

controllable parameters. As such, we evaluate the

tool’s setup phase by measuring the time to create and

load large numbers of game objects, ranging over: 20,

40, 60, 80, and 100 different objects where half of

them represent sensors and the other half represent

weather events and measurements. We start by

adding 10 sensors and 10 weather phenomena with

random values for their attributes. Then, we measure

the time consumed to save and then load these game

objects from the database, along with their respective

features. Also, we measure the time to search,

refresh, and export the game objects’ data from the

database, to keep track of all the sensors and weather

phenomena placed or edited in a project environment.

Results in Figure 7 show that most setup operations

run in almost instantaneous time, where search,

refresh and export operations share almost identical

performance levels with execution time increasing by

approximately 179𝜇s for every added game object.

Simulation Phase: This phase demonstrates the

sensors’ behaviour in action, where sensors are

Figure 7

Execution time of setup phase operations.

Figure 8: Execution time of simulation phase operations.

detecting the weather measurements within their

coverage areas, based on the features specified by the

user. Each sensor works following its internal

sampling rate, collecting data from the environment

and storing them in the database. As a continuation of

the setup phase evaluation, we create 50 sensors with

a sampling rate of 0.1s (i.e., every 0.1s, all sensors

carry out their reading calculations simultaneously

and store the results in the database). We evaluate the

time performance of sensor reading queries

considering large numbers of data tuples ranging

over: 10k, 20k, 30k, 40k, and 50k. We evaluate

export, search, refresh, delete, and undo queries, by

executing every query 10 times and computing the

average execution time. Results in Figure 8 reflect

efficient simulation time, where the maximum

consumed time was detected at 945ms to export 50k

tuples (i.e., almost 3.7MB) into an external CSV file.

This highlights the tool’s time performance in

running large simulation projects, and its ability to

simulate complex weather environments with large

numbers of sensors and weather phenomena.

5 CONCLUSION

This paper introduces VOWES, a Virtual Outdoor

Weather Event Simulator to replicate and measure

outdoor weather events and data in 3D. We make use

of the Unity 3D engine to build the simulator

3,30

3,10

3,00 3,00 3,00 3,00 3,00

2,80 2,80 2,80

0,0

0,5

1,0

1,5

2,0

2,5

3,0

3,5

4,0

Averagetesterratings

0,79

1,06

0,98 0,98

1,02

1,13

1,06

1,13 1,08 1,19

0,0

0,5

1,0

1,5

Stdev

145

307

109

537

208

749

292

977

325

200

400

600

800

1000

1200

Save Load Search Refresh Exportto

CSV

Time(ms)

Setupoperations

20 40 60 80 100Game‐objects

175

113

369

301

203

572

513

323

791

758

398

945

901

470

200

400

600

800

1000

ExportTo

CSV

Search Refresh Deletea

Tuple

Undolast

action

Time(ms)

Simulationoperations

10 20 30 40 50thousandtuples

SMARTGREENS 2022 - 11th International Conference on Smart Cities and Green ICT Systems

environment and introduce special visualizations and

behaviours to present weather measurements, events,

and sensors. We integrate the Mapbox SDK to import

high-resolution world maps, and the WeatherStack

API to capture real-time weather measurements and

conditions, allowing for more realistic and accurate

simulations. Empirical evaluations are promising and

highlight the system’s quality and potential.

We are currently extending VOWES to integrate

a knowledge base structure, providing a formally

description of the simulator’s components (Noueihed

H. et al. 2022). We are also investigating the impact

of data collection (Moataz S. et al., 2020, Ebrahimi

D. et al. 2019, Ebrahimi D. et al. 2018), and data

duplication and de-duplication techniques (Shazad F.

et al. 2022, Mansour E. et al. 2020) on the quality and

time performance of the tool. We also plan to

investigate different machine learning models

(Fuentes S. et al. 2020, Oses N. et al. 2020) and

evolutionary developmental techniques (Salloum G.

and Tekli J. 2021, Abboud R. and Tekli J. 2019), to

perform weather measurement forecasting and event

prediction (Hewage P. et al. 2021, Moreno R. et al.

2020). Forecasting and prediction will be added as

plug-and-play layers, allowing for model

transparency and extensibility.

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