Addressing the Challenge of Interpreting Microclimatic Weather Data Collected from Urban Sites

doi:10.4236/jpee.2013.15002

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Journal of Power and Energy Engineering, 2013, 1, 7-15

http://dx.doi.org/10.4236/jpee.2013.15002 Published Online October 2013 (http://www.scirp.org/journal/jpee)

Addressing the Challenge of I nterpreting Microclimat i c

Weather Data Collected from Urb an Sites

L. Bourikas1, T. Shen2, P. A. B. James1, D. H. C. Chow2, M. F. Jentsch3, J. Darkwa2, A. S. Bahaj1

1Sustainable Energy Research Group (SERG), University of Southampton, Southampton, UK; 2Centre for Sustainable Energy Tech-

nologies (CSET), University of Nottingham, Ningbo, China; 3Urban Energy Systems, Bauhaus-Universität, Weimar, G ermany.

Email: [email protected]

Received August 2013

ABSTRACT

This paper presents some installation and data analysis issues from an ongoing urban air temperature and humidity

measurement campaign in Hangzhou and Ningbo, China. The location of the measurement sites, th e positioning of the

sensors and the harsh conditions in an urban environment can result in missing values and observations that are unre-

presentative of the local urban microclimate. Missing data and erroneous values in micro-scale weather time series can

produce bias in the data analysis, false correlations and wrong conclusions when deriving the specific local weather

patterns. A methodology is presented for the identification of values that could be false and for determining whether

these are “noise”. Seven statistical methods were evaluated in their performance for replacing missing and erroneous

values in urban weather time series. The two methods that proposed replacement with the mean values from sensors in

locations with a Sky View Factor similar to that of the target sensor and the sensor s closest to the target’s location per-

formed well for all Day-Night and Cold-Warm days scenarios. However, during night time in warm weather the re-

placement with the mean values for air temperature of the nearest locations outperformed all other methods. The results

give some initial evidence of the distinctive urban microclimate development in time and space under different regional

weather forcings.

Keywords: Urban Microclimate Observations; Installation Challenges; Weather Data Time Series Analysis; Missing

Data

1. Introduction

In the current decade from 2010 to 2020 the urban popu-

lation of China is expected to exceed the rural one for the

first time in history [1]. In the Zhejiang Province, cities

such as Hangzhou and Ningbo are developing into finan-

cial centres attracting still more people from the countr y-

side. Over the next decade the borders of these urban

agglomerations are expected to expand further. The new

population will put pressures on the existing transporta-

tion, water supply, sewage and energy infrastructures.

The expansion of city borders and the changes in land

use as well as building density usually have a prominent,

and often ominous, effect on the air temperature devel-

opment within the urban canopy layer (the part of the

atmosphere from ground level up to average building

height) [ 2] .

An informed mitigation strategy at th e initial stages of

urban planning is, therefore, of paramount importance.

Common practice in making such informed urban design

decisions is to rely on simulation results. Simulation

models are typically dr iven by weather data for the place

of study. Commonly available weather data files have a

typical meteorological year format that is based on his-

torical hourly data which were usually collected at air-

ports. However, these historical datasets largely underes-

timate the effects of the urban microclimate and do not

represent of locations within the city where the local sp e-

cific microclimates develop [3].

Despite the “urbanisation” of regional weather predic-

tion models [4,5] and the development of micro-climatic

models that solve the urban energy balance [6, 7], there is

still a need for simple urban weather forecasting models

[8,9] that would produce urban and building simulation

ready weather data-series adapted to the local micro-

climate within an acceptable accuracy.

This paper reports on an on-going air temperature and

humidity measurement campaign in Hangzhou and

Ningbo, China and details key issues regarding the

weather data time series analysis as a baseline for pro-

ducing a methodology for the micro-climatic adaptation

of commonly available weather data files.

Addressing the Challenge of Interpreting Microclimatic Weather Data Collected from Urban Sites

1.1. Local Climate

The measurement network consists of 55 air temperature

and relative humidity (RH) sensors installed at a height

of 3 to 5 meters above ground level in the cities of

Hangzhou (30˚15'N 120˚10'E) and Ningbo (29˚52'N

121˚33'E) in Zhejiang Province, China (Figure 1).

Both Hangzhou and Ningbo have a humid subtropical

climate with a Cfa classification in the Köppen-Geiger

climate system [10]. The Cfa classification denotes a warm

temperate (C), fully humid (f) climate with hot summer

(a) [10].

These two cities combined had a total urban popula-

tion of 6.65 million people in 2011 with a mere 7.85 mil-

lion documented in the broader Municipality areas [12,

13].

1.2. Urban Context

Recent urbanization trends in combination with a climate

change induced warming effect led to a noticeable tem-

perature rise in developed Chinese cities [14]. According

to China’s national meteorological statistics for the pe-

riod 1981 to 2010, Hangzhou experienced on an average

27.2 days per year with average temperatures higher than

35˚C. Since 2003, this figure has increased to more than

35 days annually, making Hangzhou the third hottest city

in China [15,16].

From an urban planning perspective, Hangzhou’s ur-

ban grid spans across 3070 km2 [12] and contains two

large water bodies, namely the West Lake close to the

city centre and Xixi Wetland. Ningbo occupies an area of

2460 km2 [13] and is characterised by three main rivers,

the Fenghua, Yao and Yong Rivers, which meet in the

city centre. It is interesting to examine whether these

natural ecological resources could be utilised to alleviate

the current urban heat island effects and if so, to what

degree.

2. Measurement Equipment and Set-up

Specifications

At each measurement location (Figure 2) air temperature

Figure 1. The geographic location of Hangzhou and Ningbo

in China. World Image Source: [11].

and relative humidity iButto n sen sor s [17] with miniature

data loggers were installed on lamp posts (Figure 3) . The

data loggers record hourly values for air temperature at a

11-Bit (0.0625˚C) resolution and relative humidity (RH)

at a 12-Bit (0.04%) resolution [17]. The memory capaci-

ty of 8kb allows to store up to 110 days of hourly data.

At the end of each 110 day period the data are down-

loaded to a PC and the sensors repositioned at the same

location to continue recording.

All sensors were calibrated prior to installation and

their readings were inter-compared in an environmental

test chamber at the Centre for Sustainable Energy Tech-

nologies (CSET) at the University of Nottingham Ning-

bo.

The consultation report on meteorological observations

at urban sites [18] that complements the World Meteoro-

logical Organisation’s (WMO) Guide to Meteorological

Instruments and Methods of Observation [19] standard

provides extensive guidelines for the location selection

and on-site installation of weather stations. The network

installation and maintenance of the sensor network dis-

cussed here has proved so far that applying all the WMO

recommendations is quite a challenging task in practice.

In situ installations had to strike a balance between

being representative of the urban canopy layer (UCL)

and at the same time ensuring accessibility to the site and

easy maintenance.

The sensors are expected to be representative of the

temperature and RH of an area ranging from 100 to sev-

eral hundred meters in a direction upwind and around

each sensor [18]. The locations were carefully selected to

have homogeneous characteristics and the sensors were

installed at sites with a reasonable distance to the fringe

of different surface types.

With regard to radiation shielding, the shield design

and the iButton sensors were tested to assure that the

logged air temperature data are reliable and similar with-

in an accepted accuracy to the data collected with the

commonly used Stevenson screen design [23].

2.1. Experiences from th e Installation and First

Measurement P eriod

The first problem that was encountered was that initially

the local government did not consent to install objects on

lamp posts on the grounds that solar radiation shields

would impact on street aesthetics and pose a risk to pe-

destrians. An experimental setting was prepar ed to prove

the firmness and safety of the installation. In the end,

owing to the safety test report and the government’s

support to the research project, the Urban Administration

Bureau of Hangzhou and Ningbo approved the applica-

tion for the network’s installation.

Despite the permissions and public information notes

provided with the sensors, six solar radiation shields and

Addressing the Challenge of Interpreting Microclimatic Weather Data Collected from Urban Sites

Letter Index: C = commercial, R = residential, PG = public green, E = educational, I = indus-

trial, ER = Ecological Reserve, F = Farmland, SU-A = Special use-Airport.

Figure 2. Sensor network maps in Hangzhou (top) and Ningbo (bottom). The boxes give an eye-estimation of the albedo and

the bullet points describe the Sky View Factor (SVF) as calculated on site. The letters nearby the sensors indicate the Land

Use type as given in the Local Planning maps [20,21]. The dot circles show the distance from the city centre in kilometres.

Background Image Source: [22].

Figure 3. Sensors were installed on lampposts at a height of

3 to 5 meters due to security concerns and local authority

restrictions (left). The radiation shield (centre) and the

iButton sensor with the miniature data logger that are posi-

tioned inside the radiation shield (right). Central Image

Source: [23].

the sensors within have been removed since September

2012 (fou r in Ningbo and two in Hang z hou).

There were also some cases of private sites such as

factory grounds and residential compounds where per-

mission to install sensors was deni e d by the owne rs .

The difficulty to convince the authorities and private

owners to allow the use of the lamp posts in conjunction

with the need for sites with easy access which at the

same time, have to be free of obstructions make the cur-

rent selection of sites the best possible choice. However,

there are some cases where the sensors had to be posi-

tioned very close to airflow obstructions or in open park

locations that may affect the representativeness of their

readings for the local urban micro-climate conditio ns.

False measurements or missing data due to malfunc-

tion of the sensors will distort the signal of site specific

weather development, cause bias during the development

of urban weather adaptation models and produce invalid

correlations. Consequently, any false or missing values in

Addressing the Challenge of Interpreting Microclimatic Weather Data Collected from Urban Sites

the time series must be identified and effectively replaced.

2.2. Data Analysis and Interpretation in the

Urban Context

The weather data time series collected from the sensors’

network were, in a first step, examined to identify any

noise in the form of outliers in the datasets. It was found

that RH values were often above 100% regardless of the

sensor and its location. A simple algorithm was applied

to cap the RH values at 100% levels. The scatter and

frequency of the erroneous RH data indicates that the

most probable cause is a sensor’s drift due to pollution,

water spray and general degradation.

In a second stage, boxplots of the hourly air tempera-

ture distribution per week were created with IBM SPSS

Statistics Ver.19 [24] for each sensor’s location in

Hangzhou (see Figure 2). All outliers were traced back

to the data sample. Outliers that showed up in groups of

subsequent hours or were common to all sensors at a

specific time and day were not treated as potential errors.

The remaining outliers were compared against the av-

erage hourly air temperature for the same week and the

coldest and hottest days of this week respectively (Fig-

ure 4). The range of hourly changes on each measure-

ment site (Figure 5) was also assessed against the hourly

changes in weather data collected at Mantou Mountain,

National Principle weather Station (30˚13'N, 120˚26'E).

The interpretation of these tests’ results in view of site

specific attributes (i.e. Sky View Factor (where SVF is

an index (0 to 1) of the unobstructed sky area which can

be seen from a given point), albedo, building height to

street width aspect ratio, impermeable to permeable sur-

face ratio, surrounding materials’ properties) can lead to

conclusions for the weight of each parameter and their

role in the local urban micro-climate’s development in

time and space.

3. Case Study for the Replacement of

Missing/False Data in Urban Observations

Datasets

Seven cases (Table 1) have been investigated for replac-

ing false data readings and missing values in urban weath-

er measurements.

The first three cases involved the use of statistical pre-

diction models with the existing dataset and application

of these models to the forecast of missing data (Cases 1-3

in Table 1). These models estimate the level, seasonal

change and trend for the data that are put into the models

and predict the time series’ development [24].

The remaining four cases were based on the replace-

Figure 4. Comparison of hourly air temperature to the weekly trend and extreme days in the week.

Addressing the Challenge of Interpreting Microclimatic Weather Data Collected from Urban Sites

Figure 5. The box shows the range of air temperature change for the 50% of the values. The line inside the boxes is the me-

dian of the air temperature change distribution. The red stars denote the extreme outliers, changes in temperature at least

three times larger than the range of change in the 50% of the values. The whiskers extend 1.5 times the height of the box or if

there are not any values in that range then to the minimum and maximum values [24].

Table 1. Summary of the temperature estimation methods for missing data in the case study.

Cases Estimation Method Dataset from Sensors (see in Figure 6) Scenarios

Case 1 Forecast model 1 1 (00:00 to 05:00)

Case 2 Forecast model 1 to 6 2 (12:00 to 17:00)

Case 3 Forecast model 2,7,8

Case 4 Mean (2 hours before and after missing data) 1

Case 5A Linear Interpolation Average from sensors 1 to 6 Days

Case 5B Mean Average from sensors 1 to 6 13/01/2013

Case 6A Linear Interpolation Average from sensors 2,7,8 09/03/2013

Case 6B Mean Average from sensors 2,7,8

Case 7 Long-time Mean (10 years) Average from Mantou mountain, WMO station

ment of missing values with the mean value of the two

hours before and after the missing data (Case 4 in Table

1), replacement with the mean temperature from loca-

tions with similar to the target site’s SVF (marked with

blue points in Figure 6) (Case 5B in Table 1) and with

the mean temperature from the nearest sites to the target

site’s location (marked as 2, 7, 8 in Figure 6) (Case 6B

in Tab le 1). In Cases 5A and 6A the missing values were

replaced with the results from linear interpolation of the

average temperature in locations with similar to the tar-

get site’s SVF and linear interpolation of the average

temperature of the three nearest sites respectively. In

Case 7 it is proposed to fill gaps in the dataset with 10

year-long average temperature values from data collected

at Mantou Mountain, National Principle Station (marked

as 9 in Figure 6).

The goodness-of-fit of the estimated values to the ob-

served data was examined under two scenarios; the pre-

diction and replacement of 6 subsequent hours during

night-time (00:00 - 05:00) (Scenario 1) and daytime

(12:00 - 17:00) (Scenario 2). The target sensor selected

for this case study (marked as 1 in Figure 6) is located in

a commercial area 8 km North West of Hangzhou’s

commercial city centre.

Addressing the Challenge of Interpreting Microclimatic Weather Data Collected from Urban Sites

Figure 6. Locations of the sensors used for the case study. The target location for the case study is marked as 1. The blue

points denote locations with similar to the target site’s SVF. The National Principle Station, Mantou Mountain’s location is

marked as 9. Image Source: [22].

There were two days selected in the scenarios; one

cold cloudy day in January (13/01/2013) and one warm

sunny day in March (09/03/2013). On the 13th of January

the sky conditions at Mantou Mountain’s National Prin-

ciple S tatio n were reported as mostly cloudy with light rain

showers while the winds were blow ing from a North West-

North direction with speeds from 1 to 3 m/s. The 9th of

March was the fifth day in a row with a clear sky, the

wind at the Mantou Mountain was blowing from a South

West-South direction with speeds from 1 to 4 m/s [25].

Statistical Forecast Modelling

Three prediction models were created with historical ob-

servations from the target sensor (marked as 1 in Figure

6, Case 1), the average temperature from locations with

the same summer time SVF (blue bullets in Figure 6,

Case 2 in Tab le 1) and the average temperature from the

three nearest sites to the target sensor’s location (marked

as 2, 7 & 8 in Figure 6, Case 3 in Table 1).

Hourly weather observations cannot be considered as

stationary (statistical properties such as the mean and

variance would be time independent [26]) with respect to

the time scale of weather development within the urban

canopy layer. For this reason autoregressive integrated

moving average (ARIMA) models were excluded from

the SPSS built-in forecasting procedure. Only seasonal

exponential smoothing models have been considered and

the most appropriate one was selected with the Expert

Modeler component of IBM SPSS Ve r. 1 9 [ 24].

The data observations currently available are for the

period from 01/01/2013 to 10/03/2013. Control tests were

carried out with 7, 5, 3 and 2 days’ samples for January,

February and March. Ta bles 2 and 3 show the Root Mean

Square Error (RMSE) and the Mean Absolute Error

(MAE) for a single day ’s temperat ure forecast each month.

The results in this preliminary analysis show that for

the dates and months studied in the case study the models

should be built with 7 days’ of data in January and with 5

days’ of data in Mar ch (Tab les 2 and 3). It also needs to

be pointed out that the weather conditions in the last few

days before the missing values have a larger effect to the

model’s output than the monthly weather trend. A stable,

homogeneous weather conditions’ pattern and a strong di-

urnal signal in the prediction dataset (data put into the mo-

dels) seem to increase the accuracy of the first few fore-

casted hours and give a 24-hour periodicity trend to the

results.

It should be noted that the models created for the pur-

poses of this study are simple and that they are only eva-

luated for their capability to fill missing values in an ur-

ban weather dataset. A weather forecasting model would

need to include more variables such as rainfall, wind

speed and solar radiation and closely map their effects on

the site specific weather development.

4. Results

The goodness-of-fit of all methods was evaluated with

the fit of t he predicte d va l ue s to the 6 h ou rs observat ions

Addressing the Challenge of Interpreting Microclimatic Weather Data Collected from Urban Sites

Table 2. Root Mean Square Error results indicating the goodness-of-fit of the predicted values to observed data.

RMSE Model - 2 days data Model - 3 days data Model - 5 day s data Model - 7 days data

January 2.361 2.014 1.654 1.442

February 1.179 0.619 1.006 0.870

March 1.950 1.464 1.374 1.643

Table 3. Mean Absolute Error serving as an additional indication of the model results’ fit to data.

MAE Model - 2 days data Model - 3 days data Model - 5 days data Model - 7 days data

January 2.10 1.73 1.35 1.08

February 0.98 0.52 0.89 0.80

March 1.70 1.16 1.11 1.26

that were assumed as missing in the scenarios (Table 1).

Figure 7 shows that the replacement of missing urban

temperature measurements with the long-term average

temperature from the nearby National Principle Station

will lead to large deviations from the reality. In the case

of a hot day such as the one studied in March, the error

can be as large as 16˚C (F igure 8). The 10˚C to 16˚C air

temperature difference between the historical dataset

from the Mantou Mountain and the location in the city

strengthens the view that historical datasets collected at

airports and rural locations do not represent of locations

within the city where a distinctive micro-climate develops.

In the Cases 5B and 6B, the missing air temperature

values at location 1 were replaced with direct substitution

with the mean temperature from sensors across the city

that have similar SVF and from sensors within a small

distance (<4 km) to the site of interest.

It is important to point ou t that on a day with clear sky

(Mar_Day marked as green bar) both methods performed

well at the daily temperature peak point. The large daily

variation of air temperature during warm days and the

associated high values at noon could not be satisfactorily

predicted by the statistical models (Cases 1 to 3 in Table

1) and linear interpolation methods (Cases 5A and 6A in

Table 1). Generally, these methods appear to be applica-

ble only during days with small diurnal temperature dif-

ferences.

Figure 8 shows that all the estimation methods had a

similar performance during the mostly cloudy day that

was studied in January. Even Case 4 that replaces the

missing values with the mean data of the 2 hours before

and after the gap in the dataset returns values close to the

measured data. However, in Case 4 all 6 missing values

are replaced with the same mean value. Therefore, this

method should only be used when a single value is missing

and the change in the values before and after is not large.

5. Conclusions

The air temperature data collected from an urban meas-

urement network in Hangzhou, China were examined to

Figure 7. Goodness-of-fit of the estimated January (Jan)

and March (Mar) temperature data for each method in

relation to the observed temperature.

Figure 8. Mean absolute difference between the tempera-

ture predictions and the observed air temperatures for each

case.

identify missing or erroneous values. At the beginning of

the analysis the measurements from each location that

C ase 1

C ase 2

C ase 3

C ase 4

C ase 5A

C ase 5B

C ase 6A

C ase 6B

C ase 7

R oot Mean Square Error

J an_Night

Mar_Night

Jan_Day

Mar_Day

C ase 1

C ase 2

C ase 3

C ase 4

C ase 5A

C ase 5B

C ase 6A

C ase 6B

C ase 7

Mean Absolute Erro r ( |O bserved - Estimated | )

Jan_Night

Mar_Night

Jan_Day

Mar_Day

Addressing the Challenge of Interpreting Microclimatic Weather Data Collected from Urban Sites

did not fit the weekly temperature trend were isolated.

The weekly distribution of hourly air temperature and the

range of hourly changes revealed any values that could

be false. These were further compared against the weekly

average temperatures and the extreme days in the week.

Seven methods for replacing missing values were eva-

luated in their performance in the context of urban mea-

surement datasets. It was observed that the replacement

of missing and false values with the mean from nearby

sites and with the mean from sites with characteristics

similar to those of the target location returns equally

good results. There were not significant differences in the

performance of these two methods during day-night time

and on cold-warm days. However, the night-time tem-

perature estimates during warm weather when the urban

temperature difference to the rural surroundings is ex-

pected to be larger show that the mean of the nearby sites

fits the measured data the best. This might be due to the

night cooling potential of remote sites, away from the

city centre that were included in the dataset of Case 5

when they had a SVF similar to one of the site of interest.

6. Acknowledgements

L.B. would like to thank th e “Liveable Cities Proj ect” for

funding a visit to Hangzhou and Ningbo in China for

researching on the urban micro-climate and to collabo-

rate with the Centre for Sustainable Energy Technologies

at the University of Nottingha m Ningbo (EPSRC funded :

EP/J017698/1).

The installation work of the sensors’ network in

Hangzhou and Ningbo is supported by the Ningbo Natu-

ral Science Foundation (No. 2012A610173) and the Ningbo

Housing and Urban-Rural Development Committee (No.

201206).

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