A Method of Aggregating Heterogeneous Subgrid Land-Cover Input Data for Multiscale Urban Parameterization

AbstractA method of representing grid-scale heterogeneous development density for urban climate models from probability density functions of subgrid-resolution observed data is proposed. Derived values are evaluated in relation to normalized Shannon entropy to provide guidance in assessing model input data. Urban fraction for dominant-class and mosaic urban contributions is estimated by combining analysis of 30-m-resolution National Land Cover Database 2006 data products for continuous impervious surface area and categorical land cover. The aim of the method is to reduce model error through improvement of urban parameterization and representation of observations employed as input data. The multiscale variation of parameter values is demonstrated for several methods of utilizing input. This approach provides multiscale and spatial guidance for determining where parameterization schemes may be misrepresenting heterogeneity of input data, along with motivation for employing mosaic techniques that are based u...

[1]  C. Willmott.  ON THE VALIDATION OF MODELS , 1981 .

[2]  G. D. Jenerette,et al.  Multiscale Modeling and Evaluation of Urban Surface Energy Balance in the Phoenix Metropolitan Area , 2015 .

[3]  E. S. Krayenhoff,et al.  Initial results from Phase 2 of the international urban energy balance model comparison , 2011 .

[4]  A. Clappier,et al.  An Urban Surface Exchange Parameterisation for Mesoscale Models , 2002 .

[5]  L. Bounoua,et al.  Exploring the influence of impervious surface density and shape on urban heat islands in the northeast United States using MODIS and Landsat , 2012 .

[6]  R. Treadon,et al.  A Tutorial on Lateral Boundary Conditions as a Basic and Potentially Serious Limitation to Regional Numerical Weather Prediction , 1997 .

[7]  Bert Guindon,et al.  Multispectral analysis for manmade surface extraction from RapidEye and SPOT5 , 2012 .

[8]  Limin Yang,et al.  Development of a 2001 National land-cover database for the United States , 2004 .

[9]  Robert E. Davis,et al.  Statistics for the evaluation and comparison of models , 1985 .

[10]  Christopher A. Barnes,et al.  Completion of the 2006 National Land Cover Database for the conterminous United States. , 2011 .

[11]  Kevin W. Manning,et al.  The community Noah land surface model with multiparameterization options (Noah-MP): 1. Model description and evaluation with local-scale measurements , 2011 .

[12]  E. Vivoni,et al.  Seasonal dynamics of a suburban energy balance in Phoenix, Arizona , 2014 .

[13]  Xiaoxiao Li,et al.  Object-based land-cover classification for metropolitan Phoenix, Arizona, using aerial photography , 2014, Int. J. Appl. Earth Obs. Geoinformation.

[14]  Nazmul Hossain,et al.  Change of impervious surface area between 2001 and 2006 in the conterminous United States , 2011 .

[15]  J. Dudhia,et al.  Coupling an Advanced Land Surface–Hydrology Model with the Penn State–NCAR MM5 Modeling System. Part I: Model Implementation and Sensitivity , 2001 .

[16]  Jorge E. Gonzalez,et al.  A Land–Atmospheric Interaction Study in the Coastal Tropical City of San Juan, Puerto Rico , 2010 .

[17]  Todd D. Ringler,et al.  A Multiscale Nonhydrostatic Atmospheric Model Using Centroidal Voronoi Tesselations and C-Grid Staggering , 2012 .

[18]  Jason Ching,et al.  A perspective on urban canopy layer modeling for weather, climate and air quality applications , 2013 .

[19]  Nathaniel A. Brunsell,et al.  Evaluating the impact of urban morphology configurations on the accuracy of urban canopy model temperature simulations with MODIS , 2014 .

[20]  William C. Skamarock,et al.  A time-split nonhydrostatic atmospheric model for weather research and forecasting applications , 2008, J. Comput. Phys..

[21]  Alberto Martilli,et al.  A new Building Energy Model coupled with an Urban Canopy Parameterization for urban climate simulations—part II. Validation with one dimension off-line simulations , 2009 .

[22]  M. J. Best,et al.  Trade‐offs and responsiveness of the single‐layer urban canopy parametrization in WRF: An offline evaluation using the MOSCEM optimization algorithm and field observations , 2010 .

[23]  J. Luvall,et al.  Climate Impacts of Land-Cover and Land-Use Changes in Tropical Islands under Conditions of Global Climate Change , 2013 .

[24]  C. Grimmond,et al.  Multi‐site evaluation of an urban land‐surface model: intra‐urban heterogeneity, seasonality and parameter complexity requirements , 2012 .

[25]  Hiroyuki Kusaka,et al.  Thermal Effects of Urban Canyon Structure on the Nocturnal Heat Island: Numerical Experiment Using a Mesoscale Model Coupled with an Urban Canopy Model , 2004 .

[26]  Maria Tombrou,et al.  The International Urban Energy Balance Models Comparison Project: First Results from Phase 1 , 2010 .

[27]  Kevin W. Manning,et al.  The Integrated WRF/Urban Modeling System: Development, Evaluation, and Applications to Urban Environmental Problems , 2010 .

[28]  F. Kimura,et al.  Coupling a Single-Layer Urban Canopy Model with a Simple Atmospheric Model: Impact on Urban Heat Island Simulation for an Idealized Case , 2004 .

[29]  Steven J. Burian,et al.  National Urban Database and Access Portal Tool , 2009 .

[30]  Fei Chen,et al.  Development and evaluation of a mosaic approach in the WRF‐Noah framework , 2013 .