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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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