Presentation on theme: "D.S.Hammond, L.Chapman & J.E.Thornes"— Presentation transcript:
1 D.S.Hammond, L.Chapman & J.E.Thornes Improving estimates of roughness length (Z0) in a road weather prediction model using airborne LIDAR dataD.S.Hammond, L.Chapman & J.E.ThornesSchool of Geography, Earth and Environmental Sciences, University of Birmingham, Edgbaston, Birmingham, B15 2TT, UK2010 Standing International Road Weather Commission Biennial Conference, Quebec City, Canada, February 5th -7th 2010.
2 Geographical & Infrastructure Parameters used to drive the road weather prediction model Geographical ParametersInfrastructureSky View Factor (ψs)Road TypeAltitudeRoughness Length (Z0)SlopeTraffic DensityAspectEmissivityLatitudeAlbedoLongitudeSome parameters can be easily obtained e.g. Altitude and slope from DEM, sky view from fisheye photography or by proxy techniquesOther parameters are less well defined in the model – we’ve been looking at ways of better parameterising some of these key variables in the modelToday I’m going to focus on one of these less well defined variables, looking at work we’ve recently undertaken to improve parameterisation of roughness length in the route based forecast model
3 Roughness length (Z0)Air flow in boundary layer largely controlled by frictional drag imposed on flow by the underlying surfaceSource: Oke (1992)So first of all, what do we mean by roughness length?Well, air flow in the boundary layer is largely controlled by frictional drag imposed on flow by the underlying rigid surfaceThis frictional drag retards the air flow close to the ground, causing a sharp decrease in wind speed near the surfaceZ0 is a measure used for quantifying the aerodynamic roughness of a surface that causes this frictional dragZ0 - measure of the aerodynamic roughness of a surface“Height at which the neutral wind profile extrapolates to a zero wind speed.” (Oke, 1992)
4 How is Z0 currently parameterised in the road weather prediction model Simple look-up table of Z0 values assimilated from scientific literatureOrdinal dataset of Z0 – major oversimplificationRuralSemi-ruralSuburbanUrbanCity CentreMotorway0.500.751.002.00A-road0.25B-roadC-roadParameterised with respect to ordinal land use and road type classifications at each forecast point in the model using a simple look-up table of Z0 values assimilated from scientific literatureWhat we end up with is an ordinal classification of Z0 which is a major oversimplificationFails to account for variations in the surface elements within land use classes, and takes no account of wind direction and the surface elements within the upwind fetchModified from Chapman (2002)
5 Example of Z0 classification If we visualise this in a GIS – we can see from this example that all the forecast points located on the motorway or a C classified road passing through a semi-rural land use area are assigned a Z0 value of 0.5m.Whilst all the points located on a C classified road that passes through a rural land use area are auto assigned a Z0 value of 0.25mWanted a method of parameterising Z0 which gets us away from this simple ordinal classificationLooking for a method which can provide realistic estimates of surface roughness at each forecast point along our route, taking into account the surface elements within the upwind fetch – this would be a vast improvement on the current parameterisation
7 Calculate Z0eff using areal area average of local Z0 values MethodologyProcess LIDAR dataDSM – DTM = ZHApply f0 = 0.1 rule of thumbZ0 = 0.1 x (ZH)Calculate Z0eff using areal area average of local Z0 valuesZ0eff = <Z0>
8 Methodology ArcMap Focal Mean neighbourhood function Z0247.5°292.5°Upwind Fetch (m)5 distances of upwind fetch = 5 Z0eff datasetsPrevailing westerly windThis was done in a GIS by running a focal mean neighbourhood function on the roughness modified LIDAR data at each forecast pointThe neighbourhood function was modified to account for 5 different distances of upwind fetch, from 100m out to 500m, for a prevailing westerly wind directionZ0eff roughness values were calculated for each forecast point based on the average of the local Z0 values within the defined upwind areas
9 Distribution of Z0eff values If we take a look at the percentage distribution of Z0eff values around the route, we see that the roughness values are positively skewed towards the lower end of the roughness scale, as we might expect given the predominantly rural to suburban nature of the routeMaximum Z0eff values occur with an upwind fetch of just 100m, and are mainly located in the urbanised city centre as you’ll see in the next slide, where values up to 3.1 metres are foundAs the distance of upwind fetch increases, the range of roughness values around the route decreases, most likely due to the dampening of average surface element heights by an increasing proportion of lower-rise surface elements within the defined neighbourhood area over larger fetches
12 OWEN Land Use (Owen et al, 2006) Statistical AnalysisAre there significant differences in Z0eff values between land use categories?2 land use datasets used in the comparisonKruskal-Wallis rank-order statistical analysisENTICE Land UseOWEN Land Use (Owen et al, 2006)1. Rural1. Villages/farms6. Urban2. Semi-Rural2. Suburban7. Light urban/open water3. Suburban3. Light suburban8. Woodland/open land4. Urban4. Dense suburban5. City Centre5. Urban/transportRoughness values were compared using 2 different land use datasetsExisting ENTICE proxy land use classes used in the model, derived via a spatial density analysis of vector road data to locate dense areas of the road network, based on the assumption that more heavily urbanised areas have a denser road network than suburban and rural areasMore comprehensive land-cover dataset of the West Midlands produced by Sue Owen at Lancaster university, derived from dimensionality reduction of 25 spatial land-cover attributes using principal components analysis
13 Kruskal-Wallis Analysis Results of Kruskal-Wallis analyses were highly significant (p < 0.001) over all 5 distances of upwind fetch for both land use datasetsSignificant differences do exist in the Z0eff values between at least two land use classes in each dataset, but it doesn’t reveal where these differences existWilcoxon rank-sum TestsAnalysis performed on the Z0eff values within each independent land use classResults of the Kruskal-Wallis analyses were highly significant over all 5 distances of upwind fetch for both the ENTICE and OWEN land use datasetsThis tells us that significant differences do exist in the Z0eff values between at least two of the land use classes in each dataset, but it doesn’t reveal where these differences occurBut by examining the mean rank scores assigned to each land use category, we can get a good feel for where these differences are likely to occurFor example, with the ENTICE land use dataset the lowest roughness values a found in the rural category which has a much lower mean rank score than the city centre categoryOverall the vast majority of the land use comparisons are statistically significant for both land use datasetsNew method of roughness parameterisation does distinguish well between different land use categories around the route
14 Multiple Regression on Thermal Mapping data 20 nights Thermal Mapping data (dependent variable)ENTICE GPD parameters (independent variables)Sky ViewRoad TypeAltitudeSlopeAspectZ0effThermalMappingDataAs an initial indicator as to the potential influence of the new Z0eff values on road surface temperature prediction, regression analysis was performed on 20 nights thermal mapping data for the study route, using parameters from the ENTICE geographical parameter database as the independent variables in the statistical model, one of which was surface roughness1st run - Existing Z0 classification2nd run - New Z0eff dataset
15 Statistical Model Performance As we can see from the plot of R2 values, statistical prediction of RST improved on all but 1 of the 20 nights which gives us confidence that the new roughness parameterisation will improve predictions of surface temperature in the numerical model
16 Potential Future Improvements Distance of upwind fetch calculated for each individual forecast point as a function of obstacle heightSame technique could be used to assimilate a look-up table of Z0eff values for various directions of upwind fetchLimitationsTechnique assumes constant direction of upwind flow, with each portion of the upstream surface considered to be an equal contributor to the aerodynamic character at a given forecast pointTechnique fails to account for moving surface elements, such as vehicle traffic
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