implementing urban climatology in the 'real world...

ASI3_Lecture 2_9/12/2011

1

ASI 3: CLIMATE CHANGE AND URBAN DESIGN School of Architecture, The Chinese University of Hong Kong, Hong Kong, 9-10 Dec 2011

Desert Architecture & Urban Planning1

photo

Implementing Urban Climatology in the 'Real World' - Theory and Practice

• Evyatar Erell• Associate Professor• Ben Gurion University of the Negev

Croucher Advanced Study Institute 2011-2012Urban Climatology for Tropical & Sub-tropical Regions



Acknowledgements

Prof. Terry Williamson

Adelaide University

Prof. David Pearlmutter

Ben Gurion University of the Negev


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Asking the right questions is the

key to setting appropriate

objectives.

Academic research does not

necessarily focus on the ‘right’

questions (from the perspective of

the planner).

However, although planning

questions are typically ‘wicked

problems’ 1, we still expect

academic research to be the key

to answering them…

Context

Image from Search Patterns by Peter Morville and Jeffery Callender



1. To create a planning framework for the urban area (or part of it) that will allow optimal exposure of individual buildings so that they may employ measures for improved thermal control, either to conserve energy or to improve comfort in the absence of AC (although these aims

may be mutually exclusive!).

In accordance with the local climate

In accordance with the building’s function

2. To design public open space that supports pedestrian activity in a comfortable environment (thermal & visual comfort, air quality)

In accordance with the local climate

In accordance with expected activities

Objectives


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Paradigm

Clear definition of goals

Unambiguous benefits

Integration in the design process

Complexity

Subsidiarity

Economics and sustainability

Adaptability

Variety

Manage expectations



Process

1. Analysis of local climate

2. Analysis of life styles and requirements of the project’s occupants, and identification of main environmental problems

3. Definition of planning objectives, with reference to

spatial scale: urban/neighborhood/building

elements dealt with: buildings/open space

temporal scale, on the annual cycle (summer-winter) or diurnal cycle (day-night)

4. Selection of appropriate design strategies


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Climatic planning strategies

Land use allocation (location)

Street orientation

Building density

Building typology (e.g. pavillion, row, courtyard)

Street cross-section

Vegetation

Colour (albedo?)

Special landscaping elements



Drawbacks:

The similarity between environmental conditions in the existing and planned settlements may not be sufficient.

The modern adaptation may differ critically in some aspects from the traditional solution.

The existing traditional solutions may not encompass the whole range of possible plans.

Generating solutions (i)

Study existing settlement patterns typical of specific environments to see which appears to be the most successful, and try to emulate the main features in new design.


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Analyze the processes occurring in urban regions to develop a model capable of predicting conditions in any given environment.

Generating solutions (ii)

Drawbacks:

Unless the model is complete and accurate, conclusions may be misleading.

Modeling the microclimate does not generate architectural form…



Scales of intervention

level policy project

who planning authority(ies) property developer or owner (buildings)

public utilities (infrastructure)

what establish objectives at an urban scale

integration in detailed project plans

define specific measures cost assessment

specify performance metrics implementation

check compliance maintenance

evaluate of progress feedback (to planning authorities)


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Planning authorities (or why cities are like camels)

“a camel is a horse designed by committee…”

Sir Alec Issigonis (or VOGUE Magazine)

modern architecture and urban planning are carried out by groups of professionals from diverse fields

information from multiple and sometimes conflicting sources must be reconciled

the problem is not to produce an idealized plan derived from climatic considerations



Application of computer modeling

Required:

A model to predict the air temperature at a given point in the urban canopy layer with sufficient accuracy to be a useful practical tool for use in simulation of building energy or thermal comfort.

The model should be simple to use

It should require only publicly available inputs: standard weather data and a simple morphological description of site

The CAT model deals with the intra-urban variations in canopy air temperature, at a spatial scale that is smaller than the resolution of meso-scale models.


7



T_base T_base=

Conceptual basis of CAT model

T_metT_urb

Turb = Tbase + T urbT_urb

Tbase = Tmet + T met

T_met

Erell & Williamson, 2006

A surface energy balance is calculated for the canyon volume at each of the two sites, and an empirical resistance is applied to estimate the effect of stability on energy exchange with the air above roof height.

Parameterizations are used to simplify the inputs.



Methodology: Model flow chart

reference site

canyon site

weather file

view factors

INPUTS SENSIBLE HEAT FROM CANYON SURFACES

SW (solar) radiation: dir, dif, reflected

LW radiation: sky, terrestrial

wind speed & convection

moisture availability (advection)

anthropogenic heat

heat storage

sensible heat flux

anthropogenic heat

TURBULENT MIXING

canyon geometry

thermal (buoyancy)

mixing coeff.

DBT PREDICTION

mechanical (wind)


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Validation

N

View of Adelaide Centre and measurement sitesurban canyon

Kent Town BoM

reference



CAT model validation - Adelaide

BoM urban

observed predicted

Absolute minimum 5.6 7.3 7.3

Mean daily minimum 9.5 10.5 10.2

Monthly mean 13.0 14.6 14.6

Mean daily maximum 17.1 17.3 17.8

Absolute maximum 22.0 22.0 22.3

CAT simulation results for May 2000


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y = 0.71x + 0.48

R2 = 0.70

-2

-1

0

1

2

3

4

5

6

7

-2 -1 0 1 2 3 4 5 6 7

measured UHI (K)

pre

dic

ted

UH

I (K

)CAT model validation (ii)

D = 0 if model results are no better than the trivial estimate (Tcanyon = Tref)

D = 1 if model results give a perfect fit with measured data

D < 0 if model results are worse than the trivial estimate

Williamson Degree of Confirmation (D) = 0.58

cool island

heat island

Williamson, 1995



CAT validation (iii): Gothenburg

Williamson Degree of Confirmation: 0.52Wind data in input file suspect


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Application example (i): Building energy consumption

(Erell Soebarto and Williamson, 2007)

The need for urbanized weather data in simulation



Predicted canyon heat island intensity (based on Australian BoM data for 1987):

Simulated diurnal and seasonal patterns

-4

-2

0

2

4

6

8

0 3 6 9 12 15 18 21 24

time of day

pre

dic

ted

tem

per

atu

re

dif

fere

nce

(K

)

Daytime cool island as well as marked nocturnal heat island

Canyon heat island is stronger in winter

-4

-2

0

2

4

6

8

1 2 3 4 5 6 7 8 9 10 11 12

month

pre

dic

ted

te

mp

era

ture

d

iffe

ren

ce

(K

)


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Building energy simulations

Performance Simulated with ENERWIN‐EC:

‐ Hourly thermal/energy simulation program

‐ Taking into account hourly weather, building geometry, envelope properties, internal loads and operations, HVAC operations

‐ Predicting hourly, monthly, and annual heating and cooling loads, energy end uses, energy costs, thermal comfort.



‐ Total floor area: 6,536 m2

‐ Envelope: insulated masonry walls (U=0.79); concrete roof (U=0.33), double glazed low‐e windows (U=1.8)

‐ Occupancy: 5 days/week

‐ Ventilation: 0.7 l/sec/m2

‐ Infiltration: 0.4 ach

‐ Internal gains: 18 W/m2 lighting and equip.

‐ HVAC system: Central cooling (COP 2.64); gas heating (COP 0.7)

‐ Thermostat settings, occupied (unoccupied): summer ‐ 24 (26); winter – 22 (20).

View of Case Study Building

Case study building


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peak

coolingkW

heatingkW

403345BoM data

391325CAT modified input

-12-20difference

annual

coolingGJ

heatingGJ

5721060BoM data

671842CAT modified input

+99-118difference

Small reduction in peak loads (cooling too!) is due to the moderating effect of urban thermal mass on air temperature.

Using ‘urbanized’ temperature in energy simulation

Annual energy budget reflects urban heat island, with increased cooling load and reduced heating requirement.



Application example (ii): Density

The effect of density on

a. building energy consumption for heating and cooling (Williamson, Soebarto and Erell, 2009)

b. Climate cooling potential(williamson & Erell, 2008)


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the urban street canyonThe basic unit

A semi infinite canyon, defined by its 2‐D section: width (W) and height of two sides (H)

Methodology

Generate climate data for different H/W ratios, from 0.25‐4. (1)

Choose a building.

Run simulation program to calculate energy performance with “standard” climate data.

Compare results for simulations carried out with ‘urbanized’ climate files.

Change location and re‐do experiment (Adelaide, Glasgow, Sde Boqer).

H

W

(1) Anthropogenic heat input was varied for each canyon width in an attempt to represent a realistic scenario of different development, traffic and pedestrian densities.

Density and building energy consumption



Simulated modification of air temperature (CAT)

LocationCanyon Aspect Ratio

4 2 1 0.5 0.25

Adelaide 5.9 5.5 5.2 3.7 3.1

Glasgow 4.1 3.8 3.3 2.9 2.4

Sde Boqer 4.6 4.3 3.9 3.6 2.5

Average Monthly Maximum Canyon Heat Island (K)

Average monthly maximum canyon cool island (K)

LocationCanyon Aspect Ratio

4 2 1 0.5 0.25

Adelaide 1.8 1.7 1.6 1.4 1.2

Glasgow 1.1 1.0 1.2 1.1 1.1

Sde Boqer 1.3 1.0 1.0 .8 .6


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ADELAIDE GLASGOW SDE BOQER

H/W heating cooling total heating cooling total heating cooling total

4 402 558 960 1167 164 1331 314 703 1017

2 411 550 961 1181 164 1345 320 693 1013

1 423 543 965 1208 161 1369 328 685 1013

0.5 437 539 976 1241 159 1400 339 676 1015

0.25 446 538 984 1265 158 1422 343 674 1017

Note: Calculation assumes NO mutual shading by similar adjacent buildings.

Annual energy budget (EnerWin)

* all values in GJ/year



ADELAIDE GLASGOW SDE BOQER

H/W heating cooling total heating cooling total heating cooling total

4 402 558 960 1167 164 1331 314 703 1017

2 411 550 961 1181 164 1345 320 693 1013

1 423 543 965 1208 161 1369 328 685 1013

0.5 437 539 976 1241 159 1400 339 676 1015

0.25 446 538 984 1265 158 1422 343 674 1017

Annual energy budget (EnerWin)

984623361141913112889654954700.25

953578375140511612899484594890.5

91750741013978913089313975341

89239849514574614119032886152

11112478641840241816117416710064

984623361141913112889654954700.25

953578375140511612899484594890.5

91750741013978913089313975341

89239849514574614119032886152

11112478641840241816117416710064

NO

mut

ual s

hadi

ngm

utua

l sha

ding


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-1750

-1500

-1250

-1000

-750

-500

-250

0

0 1 2 3 4

aspect ratio (H/W)

Ch

ang

e in

CC

P [

Kh

]

London Adelaide Sde Boqer

Effect of canyon aspect ratio on the absolute

change in the CCP of London, Adelaide and Sde

Boqer for the three-month summer period, based

on air temperature predicted by CAT with

anthropogenic heat input set to zero.

)(1

,,,,,1

hne

h

hhhnbhn

N

n

TTmN

CCPf

i

{ m=1h if Tb-Te ≥ Tcrit

m=0 if Tb-Te < Tcrit

)24

2cos(5.25.24,i

hb

hhT

h - Time of day

Tb - Building temperature

Te - External temperature

Tcrit - Fixed arbitrarily at 3K

* Artmann et al (2007)

Urban density and climate cooling potential *



-1750

-1500

-1250

-1000

-750

-500

-250

0

0 1 2 3 4

aspect ratio (H/W)

Ch

ang

e in

CC

P [

Kh

]


-1750

-1500

-1250

-1000

-750

-500

-250

0

0 1 2 3 4

aspect ratio (H/W)

Ch

ang

e in

CC

P [

Kh

]


Right: The addition of even a modest amount of anthropogenic

heat (representing a traffic flow rate corresponding to 20

vehicles per hour per meter of street width) exacerbates the

reduction in climate cooling potential in dense urban locations.

Urban density and anthropogenic heat


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Comments on urban density

Radiant exchange often has a greater impact on building energy

consumption than air temperature

The effect of density on building performance depends on

whether the building is air conditioned (fixed temperature) or

free-running

Although dense cities promote warmth (i.e. UHI) – energy costs

for cooling typical office buildings may actually be smaller in

certain dense urban configurations



Application example (iii): Colour

The effect of albedo modification on outdoor thermal comfort


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Correlation between calculated thermal stress and subjective thermal sensation

Givoni, 1963

ITS – Index of Thermal Stress (warm climates)



Rn - net radiation

C - convection

M - metabolic heat

W – mechanical work

Givoni, 1963

Calculation of ITS (i)

fEITS

1

)( WMCRE n )]12.0(6.0exp[1

max

E

E

f

)42(3.0max aPvpVE

p – clothing coefficient

V – wind speed

Pva – vapour pressure of air

ITS - the sweat rate required for thermal equilibrium, expressed as equivalent latent heatE - required cooling rate by sweatingf - sweat efficiency


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Pearlmutter, Shaviv and Berliner (2007)

Calculation of ITS (ii): Radiant exchange

Net Radiation (W m-2 of body area):

Short-wave (incident fluxes):Kdir = direct radiationKdif = diffuse radiationhKh= horizontal reflected radiationvKv= vertical reflected radiation

1-s= skin absorptivity

Long-wave (absorbed fluxes):Ld = downward sky radiationLh = horizontally emitted radiationLv = vertically emitted radiation

εTs4 = radiation emitted by body

Rn = (Kdir+ Kdif + hKh+ vKv)(1-s)+ Ld+Lh+ Lv - εTs

4

Radiant energy exchanges between cylindrical body and urban environment



Calculation of ITS (iii): Convective exchange

Convection (W m-2 of body area):

Temperature difference (oC):

T=Ts-Tz

Ts=body skin temperature (35oC)

Tz = air temperature (oC)

Heat transfer coefficient (W m-2 K):

hC= 8.3uz0.6

uz = wind speed (m/s)

Cz = hC ∆T

Convective energy exchanges between cylindrical body and urban environment

Pearlmutter, Shaviv and Berliner (2007)


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Pedestrian heat stress

-200

0

200

400

600

800

1000

6 8 10 12 14 16 18

time of day

he

at

str

es

s (

W)

0.20

0.45

0.70

-200

0

200

400

600

800

1000

6 8 10 12 14 16 18

time of day

hea

t st

ress

(W

)

0.20

0.45

0.70

N-S canyon with H/W=2 Open space (represented by a N-S ‘canyon’ with H/W=0.1)

Calculations of heat stress carried out for an open space and a street canyon

in Adelaide using site data generated by CAT, with all surfaces having uniform

albedos of 0.2, 0.45, 0.7 (weather data for the day of Nov. 25, 1987).



Pedestrian heat stress

Calculations of heat stress carried out for N-S and E-W street canyons in

Adelaide using site data generated by CAT, with all surfaces having uniform

albedos of 0.2, 0.45 or 0.7 (weather data for the day of Nov. 25, 1987).

N-S canyon with H/W=2 E-W canyon with H/W=2

-200

0

200

400

600

800

1000

6 8 10 12 14 16 18

time of day

hea

t st

ress

(W)

0.200.450.70

-200

0

200

400

600

800

1000

6 8 10 12 14 16 18

time of day

he

at s

tre

ss

(W

)

0.200.450.70


20



Canyon air temperature prediction with CAT

15

20

25

30

35

40

24 4 8 12 16 20 24

time of day

DB

T (

oC

)

ref0.200.450.70

-3

-2

-1

0

1

2

3

4

6 8 10 12 14 16 18

time of day

T

( o

C)

0.20

0.45

0.70

N-S street canyon in Adelaide with H/W=2; all surfaces have uniform

albedos of 0.2, 0.45, or 0.7 (weather data for the day of Nov. 25, 1987).

Temperature difference relative to weather station

Air temperature at the reference weather station (measured) and at street canyon (predicted).



External heat load on a pedestrian

N-S canyon in Adelaide with H/W=2, at noon, for surfaces with different albedos (0.2, 0.45, 0.7)

Notes:

External fluxes were calculated per unit area of the pedestrian body (represented by a cylinder).

The graph does not show dissipation of heat in the form of LW radiation given off by the person or latent heat loss by sweat. -100

100

300

500

700

900

1100

0.20 0.45 0.70

surface albedo

ex

tern

al f

lux

es

(W

/m2 )

SW reflected

SW sun

LW terrestrial

LW sky

convection


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External heat load on a pedestrian

N-S canyon

-100

100

300

500

700

900

600 800 1000 1200 1400 1600 1800

time of day

flux

(W/m

2 )

SW (reflected)

SW (sun)

LW (terrestrial)

LW (sky)

convection

Street canyon in Adelaide with H/W=2 withuniform surface albedos of 0.45.

(weather data for the day of Nov. 25, 1987)

E-W canyon

-100

100

300

500

700

900

600 800 1000 1200 1400 1600 1800

time of day

flu

x (W

/m2 )



Scenario testing

What if urban scale albedo modification reduced air temperature by 2K (as some models suggest)?

-200

0

200

400

600

800

1000

6 8 10 12 14 16 18

time of day

hea

t st

ress

(W

)

-2K

base N-S canyon, H/W=2

Base case:

- reference DBT not modified

- albedo of canyon surfaces = 0.45

‘-2K’ case:

- reference DBT reduced by 2K

- albedo of canyon surfaces = 0.7


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Shading and air movement

Raffles Hotel, Singapore 7th Ave. Mall, Beer Sheva

Effective microclimate modification in small, well-defined urban spaces:

Clarke Quay, Singapore



Layered building facades

Arcades provide shade (and protection from rain): Israel (left), Portugal (above).


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Modeling complex street cross-sections

basic street canyon street with overhangs

street with pergolas

street with sidewalk trees

boulevard with center treesrecessed colonnades

Simulate each with respect to - air flow; temperature; thermal comfort; air quality; visual comfort; accoustics… for different aspect ratios, wind speed and direction etc.



Application example (iv): Vegetation

The effect of vegetation on outdoor thermal comfort

(Shashua-Bar, Erell and Pearlmutter, 2010)


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MeshExposedTrees

Bare

Grass

Gro

un

d co

ver

Overhead cover

Trees-Bare Exposed-Bare Mesh-Bare

Mesh-GrassExposed-GrassTrees-Grass

Configurations monitored



Normalized* air temperature

* Courtyard air temperature at a given hour was adjusted proportionally for the ratio between simultaneously measured ambient temperature, and average ambient temperature for that hour over the entire study period

Clear / GrassClear / Bare

Trees / GrassTrees / Bare

Shade / GrassShade / Bare


25



Exposed-Grass

Exposed-Bare

Thermal comfort



Trees-Bare

Mesh-Bare

Mesh-Grass

Trees-Grass

Thermal comfort


26



Comments on vegetation

Vegetation may make a substantial contribution to thermal comfort

even when its effect on air temperature is small.

This is done by reducing the exposure of a pedestrian to radiation –

both solar and long wave

Reduction of surface temperature is often the primary contribution of

vegetation to thermal comfort

To the extent that reduction in air temperature is due to evapo-

transpiration, there is an equivalent increase in the latent heat of the

air – so in humid climates the net effect on building energy

consumption may be negligible.



In buildings as well as for pedestrians outdoors, dissipating excess heat is a problem: Available environmental sinks are ineffective in warm humid climates.

High air temperatures are often perceived as the problem – yet, unlike cold climates, convective exchange contributes only a small part of the environmental load on a person or on a building, which is dominated by radiant loads.

Therefore, reduction of environmental loads is an essential pre-requisite for good climatic design, which means control of radiant loads –primarily by shading (of people and surfaces), but also through selective use of high albedo materials and vegetation.

Final thoughts on UMC in warm humid climates


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A cascade of models at appropriate scales

global climate change

regional resolution

urban effects

street canyons

buildings

thermal comfort

X

Spatial scales

Time scales

1-way vs. 2-way coupling?

Processes (physical, chemical etc.)



Final thoughts on computer modeling and simulation

There is much to be learned from models of different kinds at different scales (in time and space) – but care must be taken to avoid drawing the wrong conclusions from inappropriate use of any particular model.

Formulating the question appropriately is the first step

Building energy simulation must take into account the effects of the surrounding urban environment.

The problem is complex, but the analysis needs to address all relevant factors: Focusing on one, e.g. air temperature, risks erroneous conclusions.

How do atmospheric models link to models employed by other disciplines (e.g. transport)?


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Evyatar ErellBen Gurion University of the NegevDesert Architecture and Urban Planning,Jacob Blaustein Institutes for Desert Research,Sde Boqer Campus, 84990ISRAELTel: +972 8 6596878 Email: [email protected] Website: www.bgu.ac.il/CDAUP/evyatar-erell.html

End, Thank You



References and additional reading

Artmann N., Manz H. and Heiselberg P. (2007). "Climatic potential for passive cooling of buildings by night-time ventilation in Europe", Applied Energy 84:187-201.

Erell, E. and Williamson, T. (2006). Simulating air temperature in an urban street canyon in all weather conditions using measured data at a reference meteorological station, International Journal of Climatology, 26, 1671-1694.

Erell E., Soebarto V. and Williamson T. (2007). “Accounting for urban microclimate in computer simulation of building energy performance”. In Wittkopf S. and Tan B. K. (Eds.) Sun, Wind and Architecture, Proceedings of PLEA 2007, 24th International Conference on Passive and Low Energy Architecture, Singapore, November 22-24, 2007, pp. 593-600.

Erell E. (2008). “The application of urban climate research in the design of cities”, Advances in Building Energy Research, 2:95-121.

Erell E., Pearlmutter D. and Williamson T. (2010). Urban climate: Designing Spaces Between Buildings. Earthscan/James & James Science Publishers, London, 266p.

Givoni B (1963). Estimation of the effect of climate on man — development of a new thermal index. PhD thesis, Technion-Israel Institute of Technology.

Mills G., Cleugh H., Emmanuel R., Endlicher W., Erell E., McGranahan G., Ng E., Nickson A., Rosenthal J. and Steemer K. (2010). "Climate Information for Improved Planning and Management of Mega Cities (needs perspective)". Procedia Environmental Sciences, 1:228-246.


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References and additional reading

Pearlmutter D., Shaviv E. and Berliner P. (2007). "Integrated modeling of pedestrian energy exchange and thermal comfort in urban street canyons", Building & Environment, 42:2396-2409.

Ritchey, Tom (2005). "Wicked Problems: Structuring Social Messes with Morphological Analysis“. Swedish Morphological Society, www.swemorph.com.

Shashua-Bar L., Pearlmutter D. and Erell E. (2011). "The influence of trees and grass on outdoor thermal comfort in a hot-arid environment". International Journal of Climatology, 31(10): 1498–1506.

Williamson, T. J. (1995)."A confirmation technique for thermal performance simulation models", Building Simulation '95, Madison, Wisconsin, U.S.A.

Williamson T. and Erell E. (2008). “The Implications for Building Ventilation of the Spatial and Temporal Variability of Air Temperature in the Urban Canopy Layer”. International Journal of Ventilation, 7(1):23-35.

Williamson T.J., Erell E. and Soebarto V. (2009). "Assessing the error from failure to account for urban microclimate in computer simulation of building energy performance". In Building Simulation 2009, Proceedings of the 11th International IBPSA Conference, Glasgow, Scotland, July 27-30, 2009, pp. 497-504.



‘wicked problems’

In social studies, a ‘wicked problem’ is a problem that is difficult or impossible to solve because of incomplete, contradictory, and changing requirements that are often difficult to recognize.

1. There is no definitive formulation of a wicked problem (defining wicked problems is itself a wicked problem).

2. Wicked problems have no stopping rule.3. Solutions to wicked problems are not true-or-false, but better or worse.4. There is no immediate and no ultimate test of a solution to a wicked problem.5. Every solution to a wicked problem is a "one-shot operation"; because there is no opportunity to

learn by trial and error, every attempt counts significantly.6. Wicked problems do not have an enumerable (or an exhaustively describable) set of potential

solutions, nor is there a well-described set of permissible operations that may be incorporated into the plan.

7. Every wicked problem is essentially unique.8. Every wicked problem can be considered to be a symptom of another problem.9. The existence of a discrepancy representing a wicked problem can be explained in numerous ways.

The choice of explanation determines the nature of the problem's resolution.10. The planner has no right to be wrong (planners are liable for the consequences of the actions they

generate).Ritchey, 2005


30



Williamson degree of confirmation

),(),( meUvmUCs

U(m,v) is the Theil inequality factor between the measured value and the trivial guess

U(e,m) is the Theil inequality factor between the estimated value and the measured one

),(

),(),(

vmU

meUvmUD

The Williamson degree of confirmation (D) is the confirmation factor Cs normalized by the likely magnitude of the error, represented by the Theil inequality factor between the measured values and the trivial guess.

Williamson, 1995

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