PROCEEDINGS of the
23rd International Congress on Acoustics
9 to 13 September 2019 in Aachen, Germany

Sound and Weather – A complex Relationship
Jan LORDIECK1; Bryce Timothy LAWRENCE2
1

TU Dortmund, Germany

2

TU Dortmund, Germany

ABSTRACTThis paper presents a short study on the interrelationship of weather, landscape position and the metricallyquantified sound environment in a public park in Gelsenkirchen, Germany, indicating that cloud coverage
may have a significant influence on NDSI, AEI, and ADI values. The results inform the conclusion that dense
low-hanging cloud coverage may influence the sound environment by dampening soundwaves and masking
background anthrophonies, in a type of ‘background noise absorber’ effect, constituting a higher fidelity
sound environment where avifauna and human noises are suppressed. Although the effect is purely physical,
it may constitute a psychoacoustic effect in the human perception of soundscapes. The paper presents theseresults and invites further thinking around the idea of the ‘background noise absorber’ effect and the potential
for quantification of other psychoacoustic effects of weather on soundscape perception.
Keywords: Sound, Weather

1. INTRODUCTION
Soundscape Ecology is an emerging field of research that tries to describe the complex relationshipbetween an area’s spatial structure, biotic structure and the resulting composition of sounds. One partof this composition are geophonic sounds that originate from the geophysical environment. Thesesounds can be temporarily static, like the constant rushing of a river, or dynamic like the sounds
generated by fluctuating winds or rainstorms. Biological organisms are the source of biophonic sounds,
for example the birds’ chorus at dawn, but also human voices. However, sounds of human-madeobjects are called anthrophonic sounds. These sounds can be both static and dynamic. The constantbackground noise of a motorway or an air-conditioning unit can be static in an area whereas the soundof a rescue helicopter changes based on distance and movement. Hence, the soundscape of an area isdefined as “the collection of biological, geophysical and anthropogenic sounds that emanate from alandscape and which vary over space and time reflecting important ecosystem processe s and humanactivities”(1). (1,2)
Ecosystem processes as well as human activities are in the long -term determined by the generalgeophysical environment and human systems, which take shape in the natural and built environment(2). However, in the short term the sound of an area is determined by dynamic factors like the current
weather and active human activities. Nevertheless, the sound environment of an area in the short term
influence a person’s perception of the area at a specific moment. It can be assumed that a public park
has a much higher recreational potential when the actual sound environment is mainly influenced bybiophonic sounds than a sound environment influenced by gardeners working with machines ( 3). The
evaluation of different sound environments through influencing weather conditions in the same urban
park is less obvious. It is unclear if, for example, a warmer day results in a more pleasant soundenvironment for humans than a colder day. The knowledge of such influences could be beneficial forthe timing of annoying garden care, estimating the daily number of visitors in the morning for
personnel planning or planning of recreation trips.

1
2

jan.lordieck@tu-dortmund.debryce.lawrence@tu-dortmund.de6681

2. Purpose and theoretical background
This study aims to explore the influence of the weather conditions on the sound environment in an
urban park. Therefore, a field study with sound and temperature measurements as well as weather
observations in a public park is conducted. Sound metrics, which are originally designed for soundenvironment quantification from the field of soundscape ecology, are applied in research on the soundenvironment in this study. Sound metric results are statistically analyzed against weather data and birdobservations to answer the following research question:
What is the influence of weather conditions on the sound environment in a public park?
Weather conditions can influence the sound environment in different ways. Klein et al. states thatthe sound environment is determined by weather first through the influence of temperature, relative
humidity and atmospheric pressure on sound frequency propagation in the air, and secondly throughthe sounds caused by weather events for example rain and third the influence of environmental
conditions on sound produced by animals (4). Regarding to the first assumption, it holds that in general
high-frequency ranges (which include biophonic sounds) are more strongly muffled during weather
periods with more dense air (colder; higher humidity), than low frequency geophonic andanthrophonic sounds (5). Wind has an influence on the muffling of sound depending on speed anddirection (5).
Sounds caused by weather events, for example rain falling on the ground or wind rustling throughleaves, are low frequency geophonic sounds (2). Thus, on the one hand weather events influence thesound environment due to physical propagation of sound in the atmosphere and on the other hand
weather may influence the behavior of animals and thus the sound environment (6,7,8). The author
interprets the literature to mean that the physical influence of cold, humid, or rainy weather dampens
both the propagation of high frequency biophonic sounds in the sound environment and the soundproducing activity of animals, resulting in a sound environment with a higher proportion of low
frequency sounds that travel more easily through dense air. Hence, cold and harsh weather conditions
could lead to a sound environment that is different to conditions resulting from warmer weather in thesame location and season.

3. Research Design and Methodology
To investigate the influence of weather on the sound environment, a case study method using handmeasurement devices in an urban park was developed. Due to the short duration of the study, a single
semester, and the necessity to keep the workload manageable, a single case within one season was
analyzed. Given the small scale of the study, the author considers the study as an explorative casestudy.
3.1 Sample Design and Field Work
The Nordsternpark in Gelsenkirchen, Germany, a public park in the western part of the city andpart of the IBA Emscherpark, was selected as the case study because the study aims to observe impacts
of weather on recreation areas and because the location is in close proximity to the author’s homewhich makes it easily accessible and keeps the necessary amount of time manageable. To ensure thestudy collected observations during different weather conditions and to eliminate sound environment
variations that may occur due to land cover, six sound measurements points within Nordsternpark on
mixed grassland/shrub land cover were identified using a fishnet with a cell size of 200m x 200m in
ArcGIS. Measurements were taken only on Fridays and Mondays at the same time and in the sameorder from 16 November to 3 December, 2018. In total thirty measurements were taken in theNordsternpark and a two-sided Kruskal-Wallis Test was enabled to be used to determine if the soundmetrics in the group of recordings were statistically different from each other and if they correlatedwith weather observations. The sample design is displayed in Figure 1.

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Figure 1: Sample Design
Sound was recorded for three minutes at each point to balance the amount of time per measurement
and the quality of the measurements (see Joo et al. as an example using three-minute recordings (9)).
Recordings were made with the XL2 Audio and Acoustic Analyzer from NTi AUDIO. The temperature
was measured with an already available customary thermometer, but humidity and air pressure are leftout because of a missing suitable measurement device. The weather was nominally ranked within
three categories of cloudiness, windiness and raininess on a scale from zero to two. Whereas zeromeans sunny, no wind until breeze and no rain; one means some clouds, medium strength wind andlight rain; two means sky completely covered by clouds, strong breeze and medium strength to heavy
rain. The limits of the study are that the results only apply for the eastern part of the Nordsternparkand cannot be generalized on the park as whole or on other public parks, and that the results are only
valid for the winter season. Even with limitations, the explorative case study enables initial insights
to the topic and may serve as a basis for further research.
3.2 Soundscape Metrics
After field data collection, sound files were analysed with RStudio and the soundscape ecology
package (10). Table 1 displays the calculated soundscape metrics and the set parameters. The
soundscape ecology package metrics were selected because they are widely applied and coverdifferent characteristics of a sound environment. The resulting data set is then merged with thecollected weather data to prepare the dataset for further statistical analysis in RStudio.
3.3 Statistics
The first step of the statistical analysis is a data description to introduce the variables and identify
possible errors in the dataset before data analysis. The cleaned dataset is used to investigate therelationship between weather and soundscape metrics. The Kolmogorov-Smirnov test was used toverify that the sound and weather datasets were at least partially non-normally distributed. Therefore,
the main inferential statistical methods applied are the Kruskal-Wallis-Test and the calculation of6683

Table 1 – Soundscape Metrics
Soundscape

Description

Value Range

Set parameters

Acoustic

Assesses the complexity of the

0 to unlimited

Minimum frequency = 1000Hz

Complexity

sound regarding to dB and Hz by

Maximum frequency = 11500Hz

Index (ACI)

computing the average absolute

Calculation distance = 5s

Metricamplitude difference between points
in time next to each other in eachselected frequency bin.

Normalized

Anthrophonicdisturbanceof-1 to 1

Difference

soundscape. Computes the ratio of

Maximum Anthrophonic Frequency = 2000 Hz

Soundscapeanthrophonic to biophonic acoustic

Minimum Biophonic Frequency = 2000 Hz

Index (NDSI)

components.

Maximum Biophonic Frequency = 11500 Hz

Acoustic

Assesses the diversity of Hz byDiversity

selecting the relative amplitude on adB threshold = -50dB

Index (ADI)

determined number of bins between

Frequency steps = 500 Hz

0 to 1Minimum Anthrophonic Frequency = 1000 Hz

Maximum frequency = 11500Hz

two frequency levels and applying
the Shannon entropy Index.

Acoustic

Assesses the evenness of Hz by

Evenness

conducting the same steps as thedB threshold = -50dB

Index (AEI)ADI

Frequency steps = 500 Hz

but

applying

coefficient

for

the

0 to 1

Gini

Maximum frequency = 11500Hz

distribution

inequality.

Bioacoustic

Assessestherelativeavian

Index (BI)abundance by computing the mean

0 to unlimited

Minimum frequency = 2000Hz
Maximum frequency = 11500HzdB spectrum and calculating the area
under the curve between

twofrequency levels.

Table adapted from Sueur (11)spearman’s correlation coefficients, which both do not require normal distributed variables. (12)The Kruskal-Wallis test helps to decide if two or more independent population distributions are
identical without assuming a normal distribution. The null hypothesis is thereby that the respective
populations are identical which can be rejected if the resulting p-value is below 0.05. In this study the
Kruskal-Wallis test is conducted for the soundscape metrics, temperature, cloudiness, windiness andraininess. Whereas the Kruskal-Wallis test only compares the distributions, a correlation analysis
enables discovering a direction of the possible interrelationship between two variables. Hence, the6684

Spearman’s correlation coefficients between the soundscape metrics, temperature and observed
weather conditions are calculated, interpreted and appropriate plots are displayed. (12)4. Results
The descriptive statistics of the different variables show that the overall aim of the meas uring
process, collecting sound data at different weather condition, is accomplished. Table 2 shows that thetemperature has a range of roughly eight degrees, moreover measurements at different cloudiness,windiness and raininess levels are taken. Thereby the maximum value of raininess is one, thus theresults are not biased by heavy rain. Furthermore, the soundscape metrics enable different
interpretations. The ACI shows values in a relatively small range which implies a stable complexityof sounds in general. The mean and median NDSI values are below 0, thus the sound environment
contains biophonic sound which are generally disturbed through anthrophonic sounds that could result
from the nearby main streets and the motorway. The distribution of ADI and AEI mean values showthat the sound environment in general is even, with a medium sized proportion of biophonic sounds,
due to a medium sized mean BI.

Group

Table 2 - Descriptive Statistics
Mean
Median Min
Max
Standard

Variable

Distribution

metrics

Soundscape

Weather

deviation
Temperature

8.673

8.95

4.8

12.7

2.46

bimodalCloudiness

/

2

0

2

/

/

Windiness

/

1

0

2

/

/

Raininess

/

0

0

1

/

/

ACI

2476

2464

2399

2666

59.67

Normal distributed

NDSI-0.551-0.590-0.731-0.0380.16

Normal distributed

ADI

0.33

0.02

0

1.62

0.55

Right-skewed

AEI

0.94

0.96

0.82

0.96

0.04

Left-skewed

BI

10.70

10.62

6.46

14.92

2.07

Normal distributedn = 30The second step of the analysis is the application of the Kruskal-Wallis-Test to investigate changes
in the distributions of soundscape metrics under different weather conditions. Table 3 shows the pvalues resulting from the Kruskal-Wallis Test of the soundscape metrics grouped by an indicator for
the weather conditions. For only two cases, the null hypothesis can be rejected which is the ADI and
AEI grouped by the cloudiness. Hence, it can be stated, that different cloudiness levels influence thediversity or evenness of the sound environment in Nordsternpark.

Soundscape metricTemperature

Table 3 - Kruskal-Wallis-Test
CloudinessWindiness

Raininessgrouped by
ACI

0.41990.48110.20870.1345

NDSI

0.69840.08975

0.08170.5060ADI

0.52440.0027**0.56770.2122

AEI

0.51400.0025**0.50430.2120BAI

0.52230.67090.30910.3184Significance is indicated if p-value is below 0.05(*), below 0.01 (**) and below 0.001(***)

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The results of the correlation analysis shown in Table 4 and Figure 2 give evidence about thedirection of the relationship between the ADI, AEI and cloudiness. With a significant correlation
coefficient of r = -0.61 and r = 0.61 respectively, it can be stated that the cloudier the sky is the moreeven or less diverse is the sound environment in the park. Moreover, a less strong but also significant
correlation between the cloudiness and the NDSI is found (Figure 3). Thus, with an increasing levelof cloudiness, the NDSI increases towards more biophonic sounds.
Table 4: Correlation Coefficients
Temperature
CloudinessWindiness

Raininess

ACI

0.15

-0.12

-0.31

0.28

NDSI

0.15

0.36*

0.00

0.12

ADI

-0.20

-0.61***

-0.19

-0.23

AEI

0.20

0.61***

0.21

0.23

BAI

0.20

0.01

-0.22

-0.19

Significance is indicated if p-value is below 0.05(*), below 0.01 (**) and below 0.001(***)
Figure 2: ADI and AEI at different cloudiness levels

Figure 3: NDSI at different cloudiness levels

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5. Discussion
The results may be interpreted together. If bird human activity in the park is less intense during
cloudy weather conditions, the sound environment contains fewer biophonic sounds in the foreground,leaving anthrophonic road noise in the background. Hence, the sound composition is less diverse andmore even. However, the positive correlation of NDSI and cloudiness indicates a less anthrophonicdisturbed sound environment at higher cloudiness levels. Considering the fewer biophonic sounds at
higher cloudiness levels, the remaining possibility of an increasing NDSI value is a ‘dampening’ ofanthrophonic sounds. Thus, the ratio of biophonic and anthrophonic sounds is tilted slightly more
towards biophonic sounds during cloudy conditions even though anthrophonic sounds still dominatedthe park. A possible explanation is that a dense low-hanging cloud coverage may influence the soundenvironment by dampening anthrophonic soundwaves and thus masking background anthrophonies.
The greater cloud coverage may produce a type of ‘background noise absorber’ effect constituting ahigher fidelity soundscape due to its physical characteristics.
The boxplots shown in Figure 2 and 3 illustrate the findings that the distribution of ADI and AEI
values change strongly with different cloudiness levels. In sunny weather conditions in November,the sound environment in the Nordsternpark is slightly diverse and not fully even, but when thecloudiness reaches level one or two, the sound environment is almost totally even. The NDSI is higher
with higher levels of cloudiness, however, the distribution at cloudiness level 1 is similar to level 2.
Thus, the taken assumption that an even sound environment with only a few biophonic sounds can beless disturbed through stronger dampened anthrophonic sounds holds, when comparing the
distribution of the soundscape metrics’ values at different cloudiness levels. One possible reason for
not finding significant results between cloudiness and other metrics is the small sample size of 30
observations which reduces the potential to find significant correlations or p-values with the KruskalWallis Test. Nevertheless, the results do not force rejecting the hypothesis of relations between thetemperature, windiness, raininess and the sound environment; rather they give an outlook for possible
future studies on the relation of weather and the sound environment. Thus, the general hypothesis thatharsh weather conditions influence the sound environment in the short run still applies and must not
be rejected. However further exploration is required to fully understand the results of this study.

6. Conclusion and Outlook
The study applied the calculation and statistical interpretation of soun dscape metrics incombination with weather conditions on the actual sound environment in a public park and finds somesignificant results. Hence, the hypothesis of the negative interrelationship between weather conditions
and the composition of sounds in a public park is not rejected. It is found that an increasing level ofcloudiness results in a totally even sound environment in the eastern part of the Nordsternpark,
Gelsenkirchen which either reduces animal activity or masks animal noises and results in a soundenvironment characterized by road noise from the nearby traffic corridors. However, backgroundanthrophonies are apparently muffled by a possible `background noise absorber´ effect of dense cloud
coverage. Although, these results may not be directly applicable on other case studies or able to fully
substantiate the `noise absorber effect´ of denser cloud coverage due to the limited research design,
the results give a first insight into the topic and can serve as basis for further research. Hence, tofurther prove the hypothesis a broader study must be conducted. Thereby, the sample size should be
increased and the indicators for the weather conditions should be reported using more precise
equipment such as a weather station. Moreover, the humidity and the air pressure as two major
meteorological indicators should also be measured and analysed. Therefore, automated measuring
devices are especially suitable. Another aspect worth investigating could be the possible differences
in influences on the sound environment because elevation influenced temperature change due toconvection or sun influenced temperature change due to solar irradiance. The understand ing of thefactors influencing the sound environment in the short run and the soundscape in the long run isessential for designing pleasant urban parks.

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REFERENCES
1. Pijanowski, Bryan C.; Villanueva-Rivera, Luis J.; Dumyahn, Sarah L.; Farina, Almo; Krause,
Bernie L.; Napoletano, Brian M.; Gage, Stuart H.; Pieretti, Nadia 2011: Soundscape Ecology: The
Science of Sound in the Landscape. In: BioScience, Vol. 61, No.: 203 –216
2. Pijanowski, Bryan C.; Farina, Almo; Gage, Stuart H.; Dumyahn, Sarah L.; Krause, Berni e L.
2011a: What is soundscape ecology?: An introduction and overview of an emerging new science.
In: Landscape Ecology, Vol. 26, No. 9: 1213–1232
3. Li, Jing; Burroughs, Kaitlin; Halim, Mirza Farzana; Penbrooke, Teresa L.; Seekamp, Erin; Smith,Jordan W. 2018: Assessing soundscape preferences and the impact of specific sounds on outdoor
recreation activities using qualitative data analysis and immersive virtual environment technology.
In: Journal of Outdoor Recreation and Tourism, Vol. 24, No.: 66–73
4. Klein, Ewan; Chapple, Simon; Fainberg, Joachim; Magill, Cat; Parker, Martin; Raab, Charles;
Silvertown, Jonathan 2018: Capturing the sound of an urban greenspace. In: The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Science s, Vol. 42, No.
11: 19–26
5. Henn, Hermann 2008: Ingenieurakustik. Wiesbaden: Springer Fachmedien
6. Bruni, Adrianna; Mennill, Daniel J.; Foote, Jennifer R. 2014: Dawn chorus start time variation ina temperate bird community: Relationships with seasonality, wea ther, and ambient light. In:
Journal of Ornithology, Vol. 155, No. 4: 877–890
7. Lengagne, Thierry; Slater, Peter J. B. 2002: The effects of rain on acoustic communication: Tawnyowls have good reason for calling less in wet weather. In: Proceedings. Biologic al sciences, Vol.
269, No. 1505: 2121–2125
8. Snell-Rood, Emilie C. 2012: The effect of climate on acoustic signals: Does atmospheric soundabsorption matter for bird song and bat echolocation? In: The Journal of the Acoustical Societyof America, Vol. 131, No. 2: 1650–1658
9. Joo, Wooyeong; Gage, Stuart H.; Kasten, Eric P. 2011: Analysis and interpretation of variability
in soundscapes along an urban rural gradient. In: Landscape and Urban Planning, No. 103: 259 –276
10. Villanueva-Rivera, Luis J.; Pijanowski, Bryan C. 2018: R-Package ‘soundecology’.
11. Sueur, Jérôme 2018: Sound Analysis and Synthesis with R. Use R! Cham: Springer International
Publishing
12. 12. Bühl, Achim 2012. SPSS 20: Einführung in die modern Datenanalyse. Pearson, Munich, 13thedition.

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