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Thomas Loridan, Ryan P. Crompton, and Eugene Dubossarsky

Abstract

Tropical cyclone (TC) risk assessment models and probabilistic forecasting systems rely on large ensembles to simulate the track trajectories, intensities, and spatial distributions of damaging winds from severe events. Given computational constraints associated with the generation of such ensembles, the representation of TC winds is typically based on very simple parametric formulations. Such models strongly underestimate the full range of TC wind field variability and thus do not allow for accurate representation of the risk profile. With this in mind, this study explores the potential of machine learning algorithms as an alternative to current parametric methods. First, a catalog of high-resolution TC wind simulations is assembled for the western North Pacific using the Weather Research and Forecasting (WRF) Model. The simulated wind fields are then decomposed via principal component analysis (PCA) and a quantile regression forest model is trained to predict the conditional distributions of the first three principal component (PC) weights. With this model, predictions can be made for any quantiles in the distributions of the PC weights thereby providing a way to account for uncertainty in the modeled wind fields. By repeatedly sampling the quantile values, probabilistic maps for the likelihood of attaining given wind speed thresholds can be easily generated. Similarly the inclusion of such a model as part of a TC risk assessment framework can greatly increase the range of wind field patterns sampled, providing a broader view of the threat posed by TC winds.

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Thomas Loridan and C. S. B. Grimmond

Abstract

A better understanding of links between the properties of the urban environment and the exchange to the atmosphere is central to a wide range of applications. The numerous measurements of surface energy balance data in urban areas enable intercomparison of observed fluxes from distinct environments. This study analyzes a large database in two new ways. First, instead of normalizing fluxes using net all-wave radiation only the incoming radiative fluxes are used, to remove the surface attributes from the denominator. Second, because data are now available year-round, indices are developed to characterize the fraction of the surface (built; vegetation) actively engaged in energy exchanges. These account for shading patterns within city streets and seasonal changes in vegetation phenology; their impact on the partitioning of the incoming radiation is analyzed. Data from 19 sites in North America, Europe, Africa, and Asia (including 6-yr-long observation campaigns) are used to derive generalized surface–flux relations. The midday-period outgoing radiative fraction decreases with an increasing total active surface index, the stored energy fraction increases with an active built index, and the latent heat fraction increases with an active vegetated index. Parameterizations of these energy exchange ratios as a function of the surface indices [i.e., the Flux Ratio–Active Index Surface Exchange (FRAISE) scheme] are developed. These are used to define four urban zones that characterize energy partitioning on the basis of their active surface indices. An independent evaluation of FRAISE, using three additional sites from the Basel Urban Boundary Layer Experiment (BUBBLE), yields accurate predictions of the midday flux partitioning at each location.

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Susanne Grossman-Clarke, Joseph A. Zehnder, Thomas Loridan, and C. Sue B. Grimmond

Abstract

The impact of 1973–2005 land use–land cover (LULC) changes on near-surface air temperatures during four recent summer extreme heat events (EHEs) are investigated for the arid Phoenix, Arizona, metropolitan area using the Weather Research and Forecasting Model (WRF) in conjunction with the Noah Urban Canopy Model. WRF simulations were carried out for each EHE using LULC for the years 1973, 1985, 1998, and 2005. Comparison of measured near-surface air temperatures and wind speeds for 18 surface stations in the region show a good agreement between observed and simulated data for all simulation periods. The results indicate consistent significant contributions of urban development and accompanying LULC changes to extreme temperatures for the four EHEs. Simulations suggest new urban developments caused an intensification and expansion of the area experiencing extreme temperatures but mainly influenced nighttime temperatures with an increase of up to 10 K. Nighttime temperatures in the existing urban core showed changes of up to ∼2 K with the ongoing LULC changes. Daytime temperatures were not significantly affected where urban development replaced desert land (increase by ∼1 K); however, maximum temperatures increased by ∼2–4 K when irrigated agricultural land was converted to suburban development. According to the model simulations, urban landscaping irrigation contributed to cooling by 0.5–1 K in maximum daytime as well as minimum nighttime 2-m air temperatures in most parts of the urban region. Furthermore, urban development led to a reduction of the already relatively weak nighttime winds and therefore a reduction in advection of cooler air into the city.

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Thomas Loridan, C. S. B. Grimmond, Brian D. Offerle, Duick T. Young, Thomas E. L. Smith, Leena Järvi, and Fredrik Lindberg

Abstract

Recent developments to the Local-scale Urban Meteorological Parameterization Scheme (LUMPS), a simple model able to simulate the urban energy balance, are presented. The major development is the coupling of LUMPS to the Net All-Wave Radiation Parameterization (NARP). Other enhancements include that the model now accounts for the changing availability of water at the surface, seasonal variations of active vegetation, and the anthropogenic heat flux, while maintaining the need for only commonly available meteorological observations and basic surface characteristics. The incoming component of the longwave radiation (L↓) in NARP is improved through a simple relation derived using cloud cover observations from a ceilometer collected in central London, England. The new L↓ formulation is evaluated with two independent multiyear datasets (Łódź, Poland, and Baltimore, Maryland) and compared with alternatives that include the original NARP and a simpler one using the National Climatic Data Center cloud observation database as input. The performance for the surface energy balance fluxes is assessed using a 2-yr dataset (Łódź). Results have an overall RMSE < 34 W m−2 for all surface energy balance fluxes over the 2-yr period when using L↓ as forcing, and RMSE < 43 W m−2 for all seasons in 2002 with all other options implemented to model L↓.

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