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Charles C. Watson Jr. and Mark E. Johnson

The results of hurricane loss models are used regularly for multibillion dollar decisions in the insurance and financial services industries. These models are proprietary, and this “black box” nature hinders analysis. The proprietary models produce a wide range of results, often producing loss costs that differ by a ratio of three to one or more. In a study for the state of North Carolina, 324 combinations of loss models were analyzed, based on a combination of nine wind models, four surface friction models, and nine damage models drawn from the published literature in insurance, engineering, and meteorology. These combinations were tested against reported losses from Hurricanes Hugo and Andrew as reported by a major insurance company, as well as storm total losses for additional storms. Annual loss costs were then computed using these 324 combinations of models for both North Carolina and Florida, and compared with publicly available proprietary model results in Florida. The wide range of resulting loss costs for open, scientifically defensible models that perform well against observed losses mirrors the wide range of loss costs computed by the proprietary models currently in use. This outcome may be discouraging for governmental and corporate decision makers relying on this data for policy and investment guidance (due to the high variability across model results), but it also provides guidance for the efforts of future investigations to improve loss models. Although hurricane loss models are true multidisciplinary efforts, involving meteorology, engineering, statistics, and actuarial sciences, the field of meteorology offers the most promising opportunities for improvement of the state of the art.

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Mark C. Green, Judith C. Chow, John G. Watson, Kevin Dick, and Daniel Inouye

Abstract

Many populated valleys in the western United States experience increased concentrations of particulate matter with diameter of less than 2.5 μm (PM2.5) during winter stagnation conditions. Further study into the chemical components composing wintertime PM2.5 and how the composition and level of wintertime PM2.5 are related to meteorological conditions can lead to a better understanding of the causes of high PM2.5 and aid in development and application of emission controls. The results can also aid in short-term air-pollution forecasting and implementation of periodic emission controls such as burning bans. This study examines relationships between PM2.5 concentrations and wintertime atmospheric stability (defined by heat deficit) during snow-covered and snow-free conditions from 2000 to 2013 for five western U.S. urbanizations: Salt Lake City, Utah; Reno, Nevada; Boise, Idaho; Missoula, Montana; and Spokane, Washington. Radiosonde data were used where available to calculate daily heat deficit, which was compared with PM2.5 concentration for days with snow cover and days with no snow cover. Chemically speciated PM2.5 data were compared for snow-cover and snow-free days to see whether the chemical abundances varied by day category. Wintertime PM2.5 levels were highly correlated with heat deficit for all cities except Spokane, where the airport sounding does not represent the urban valley. For a given static stability, snow-cover days experienced higher PM2.5 levels than did snow-free days, mainly because of enhanced ammonium nitrate concentrations. Normalizing average PM2.5 to the heat deficit reduced year-to-year PM2.5 variability, resulting in stronger downward trends, mostly because of reduced carbonaceous aerosol concentrations. The study was limited to western U.S. cities, but similar results are expected for other urban areas in mountainous terrain with cold, snowy winters.

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Cynthia Rosenzweig, William D. Solecki, Lily Parshall, Barry Lynn, Jennifer Cox, Richard Goldberg, Sara Hodges, Stuart Gaffin, Ronald B. Slosberg, Peter Savio, Frank Dunstan, and Mark Watson

This study of New York City, New York's, heat island and its potential mitigation was structured around research questions developed by project stakeholders working with a multidisciplinary team of researchers. Meteorological, remotely-sensed, and spatial data on the urban environment were brought together to understand multiple dimensions of New York City's heat island and the feasibility of mitigation strategies, including urban forestry, green roofs, and high-albedo surfaces. Heat island mitigation was simulated with the fifth-generation Pennsylvania State University-NCAR Mesoscale Model (MM5). Results compare the possible effectiveness of mitigation strategies at reducing urban air temperature in six New York City neighborhoods and for New York City as a whole. Throughout the city, the most effective temperature-reduction strategy is to maximize the amount of vegetation, with a combination of tree planting and green roofs. This lowered simulated citywide surface urban air temperature by 0.4°C on average, and 0.7°C at 1500 Eastern Standard Time (EST), when the greatest temperature reductions tend to occur. Decreases of up to 1.1°C at 1500 EST occurred in some neighborhoods in Manhattan and Brooklyn, where there is more available area for implementing vegetation planting. New York City agencies are using project results to guide ongoing urban greening initiatives, particularly tree-planting programs.

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Keith A. Browning, Alan M. Blyth, Peter A. Clark, Ulrich Corsmeier, Cyril J. Morcrette, Judith L. Agnew, Sue P. Ballard, Dave Bamber, Christian Barthlott, Lindsay J. Bennett, Karl M. Beswick, Mark Bitter, Karen E. Bozier, Barbara J. Brooks, Chris G. Collier, Fay Davies, Bernhard Deny, Mark A. Dixon, Thomas Feuerle, Richard M. Forbes, Catherine Gaffard, Malcolm D. Gray, Rolf Hankers, Tim J. Hewison, Norbert Kalthoff, Samiro Khodayar, Martin Kohler, Christoph Kottmeier, Stephan Kraut, Michael Kunz, Darcy N. Ladd, Humphrey W. Lean, Jürgen Lenfant, Zhihong Li, John Marsham, James McGregor, Stephan D. Mobbs, John Nicol, Emily Norton, Douglas J. Parker, Felicity Perry, Markus Ramatschi, Hugo M. A. Ricketts, Nigel M. Roberts, Andrew Russell, Helmut Schulz, Elizabeth C. Slack, Geraint Vaughan, Joe Waight, David P. Wareing, Robert J. Watson, Ann R. Webb, and Andreas Wieser

The Convective Storm Initiation Project (CSIP) is an international project to understand precisely where, when, and how convective clouds form and develop into showers in the mainly maritime environment of southern England. A major aim of CSIP is to compare the results of the very high resolution Met Office weather forecasting model with detailed observations of the early stages of convective clouds and to use the newly gained understanding to improve the predictions of the model.

A large array of ground-based instruments plus two instrumented aircraft, from the U.K. National Centre for Atmospheric Science (NCAS) and the German Institute for Meteorology and Climate Research (IMK), Karlsruhe, were deployed in southern England, over an area centered on the meteorological radars at Chilbolton, during the summers of 2004 and 2005. In addition to a variety of ground-based remote-sensing instruments, numerous rawinsondes were released at one- to two-hourly intervals from six closely spaced sites. The Met Office weather radar network and Meteosat satellite imagery were used to provide context for the observations made by the instruments deployed during CSIP.

This article presents an overview of the CSIP field campaign and examples from CSIP of the types of convective initiation phenomena that are typical in the United Kingdom. It shows the way in which certain kinds of observational data are able to reveal these phenomena and gives an explanation of how the analyses of data from the field campaign will be used in the development of an improved very high resolution NWP model for operational use.

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