No AccessOther papers2 Mar 2017Non-Traditional Approaches to Weather Observations in Developing CountriesAuthors/Editors: John T. SnowJohn T. Snowhttps://doi.org/10.1596/26122SectionsAboutPDF (8.2 MB) ToolsAdd to favoritesDownload CitationsTrack Citations ShareFacebookTwitterLinked In Abstract: In many developing countries, given their poor economic circumstances, weather observing networks are usually installed using funds from international development and aid agencies to enhance the capability of local national hydro-meteorological services (NHMS) and to accomplish humanitarian objectives. This paper discusses non-traditional approaches for establishing sustainable weather observing networks in developing countries, beginning with a brief overview of the importance of data from such networks to a NHMS. Some of the challenges inherent in establishing and maintaining weather and climate observing networks in developing countries are described. This is followed by a discussion of how these challenges may be addressed through the development of weather observing networks based on facilities and capabilities of the local cellular telephone network, such as the open lattice tower pictured in the background of the cover photograph. The paper reviews and summarizes currently available scientific, technical, and commercial literature regarding use of cell phone towers as observing sites. It provides a few illustrative examples of non-traditional technologies well-suited to making cell-tower based observations. The paper concludes with recommendations concerning how one may work with NHMSs in developing countries to improve the sustainability of their observing networks. These recommendations are focused on partnerships, in the sense of true business relationships, involving the NHMSs with, for example, local telephone companies, other in-country utilities, and commercial and private sector instrument manufacturers and data services. For more publications on IFC Sustainability please visit www.ifc.org/sustainabilitypublications. Previous bookNext book FiguresreferencesRecommendeddetails View Published: January 2013 Copyright & Permissions Related TopicsAgricultureEnvironmentInformation and Communication TechnologiesScience and Technology DevelopmentUrban Development KeywordsWEATHERMEASUREMENT PDF DownloadLoading ...

ities, with their many large buildings of varying heights, heavy traffic, and paved streets and parking areas, can create their own distinct local weather. For example, cities experience what is known as the urban heat island effect, where the storage of heat by buildings and paved areas can result in night-time surface temperatures up to 10° C above those of surrounding rural areas (Figure 1). The paved surfaces, traffic, and high buildings in cities can also lead to urban flooding events, changes in local precipitation patterns, elevated concentrations of gaseous pollutants and aerosols , and the channeling of wind between buildings. The high density of people and their dependence on infrastructure makes urban areas especially vulnerable to the impacts of weather events like severe thunderstorms, heat and cold waves, and winter storms with heavy ice and snow, which can disrupt traffic and the supply of electricity. Over the past decades, the field of urban meteorology has grown from simple observations and forecasts of the general weather for metropolitan areas to scientific and technological advances that allow predictions of a wide set of environmental parameters such as temperature, aerosol concentration, and precipitation with relatively precise timing and location. As these capabilities have improved, the uses for urban weather information and its value to emergency managers, city planners, national security officials, and other users have increased. Despite these advances, many users' needs in terms of precision in timing or location, timely access, or specific variables are currently not sufficiently Although all weather is driven by large scale weather patterns, the characteristics of urban settings—such as buildings of varying heights and large areas of paved streets and parking lots—can generate a unique urban weather environment. Given that three out of five people worldwide are expected to live in an urban environment by 2030, accurately forecasting urban weather is becoming increasingly important to protect these densely-populated areas from the impacts of adverse weather events. Currently, the diverse needs of users of meteorological data in the urban setting, such as emergency managers and urban planners, are not being well met by the scientific community, mainly because of limited communication between the two communities. A clear mechanism to help the urban meteorological community better identify user groups, reach out to them, and maintain an ongoing dialogue would lead to better urban weather forecasting and planning in the future. Figure 1. The urban heat-island effect. Thermal imaging …

The Nation’s weather services and the wind energy industry share several common goals including enhancing the Nation’s economy and the quality of life for its citizens. One of the key tools weather forecasters use in preparing forecasts and severe weather warnings is the Nation’s network of weather surveillance radars. The federal government installed the network in 1990 – 1997; soon after, the wind energy industry began to deploy a new generation of large wind turbines around the country. Over the last decade, operators of weather surveillance radars have become increasingly aware of the interaction between individual weather radars and nearby (<100 km) wind farms (Vogt et al 2007). Experience has shown that when wind farms are located “close” to weather radars, the towers and rotating turbine blades can negatively impact radar data quality and degrade the performance of critical weather detection algorithms that use these data. In this paper we will focus on wind turbine interaction with the Weather Surveillance Radar1988 Doppler (WSR-88D) radar. These radars are the product of the Next Generation Weather Radar (NEXRAD) Program, a joint effort of the Departments of Commerce (DOC), Defense (DOD), and Transportation (DOT) – the NEXRAD tri-agencies. We will refer to the WSR-88D as the “NEXRAD”, which is a common name for these radars. This paper has several purposes: • Inform the wind energy industry of the mission and location of the NEXRAD radars; • Provide the wind energy industry basic information on how NEXRAD radars work and how wind farms can impact the NEXRAD radars; and • Inform the wind energy industry of the NEXRAD Program’s desire to work in a collaborative and non-interfering manner.

Nearly 100 000 vortex detections produced by the Mesocyclone Detection Algorithm (MDA) are analyzed to gain insight into the effectiveness of the detection algorithm in identifying various types of tornado-producing events. Radar and algorithm limitations prevent raw vortex detections from being very useful without further discrimination. Filtering techniques are developed to remove spurious vortex detections and discriminate between vortices that are and are not related to mesocyclones.To investigate whether various vortex detections ( and their attributes) are associated with severe weather phenomena, they are compared with available tornado reports to determine if detections with certain types of attributes can be associated with tornadic events. Tornado reports are used since the ground truth tornado set is more reliable than other databases of severe weather phenomena. Basic skill scores and more advanced principal component methods are used to quantify the correlation between vortex detection attributes and tornadoes.The results of this analysis reveal that only a very small percentage (<5%) of vortex detections, using the most basic definition, are associated with the occurrence of a tornado. Percentages increase to approximately 10% as the criteria for defining a vortex detection as a mesocyclone detection become more strict; however, many tornadic events are only associated with weaker detections and are "missed'' when the detection threshold is increased. Several velocity-derived detection attributes are shown to have weak to moderate predictive ability when determining whether a detection is ( or is not) tornadic.

In Spring 2002, the NEXRAD stakeholder agencies requested that the NEXRAD Technical Advisory Committee (TAC) develop a “strategic directions” document for the long-term evolution of the total NEXRAD system. This document would address both the WSR-88D system and the national radar network, and describe possible enhancements for the 2007-2020 time frame. The intent is to guide the evolution of the radar through the final 15 to 20 years of its design life cycle, taking advantage of new technologies as they become available. A select group of 15 weather radar experts provided short discussions of the emphases they believe the NEXRAD Program should have for the period 2007-2020. In providing their comments, these experts were asked to consider both enhancements and upgrades to the WSR88D system, and broader strategies for the national network as a whole. The responses were analyzed by members of the TAC and synthesized into a series of points. TAC members also incorporated their own perspectives in preparing this paper. This is a continually evolving document that provides possible “strategic directions” for the radar and the national network. The presentation and paper will present the most significant points from the “strategic directions” document and suggest areas where additional research and new technology development is needed to realize the potential inherent in the NEXRAD system.

The introduction of the WSR-88D Doppler radar into nationwide use has greatly enhanced our ability to study storm-scale vortices such as mesocyclones and tornado vortex signatures. During the past decade several algorithms have been developed by the NOAA National Severe Storms Laboratory (NSSL) to diagnose these vortices and determine their characteristics using WSR-88D data. One such algorithm is the Mesocyclone Detection Algorithm (MDA) (Stumpf et al. 1998). Using the MDA, many mesocyclone attributes have become available for study. One important avenue of research is determining the correlation of mesocyclone attributes with the occurrence (or non-occurrence) of severe weather phenomena such as tornadoes, strong winds, and hail; however, work in determining this correlation has been rather limited. Past works attempting to determine such this correlation include: Desrochers and Donaldson 1992 and Marzban et al. 1999. Most of these works have looked at rather limited sample sizes due to the manual mesocyclone-tornado correlation technique used and the necessity of using high-resolution (level II) radar data from tape archives. Other works (including Mitchell et al. 2000) attempting to find a correlation using large data sets only look at the presence or lack thereof of a mesocyclone detection in association with a tornado track. The initial focus of the work reported here is on resolving the correlation between tornado reports (or lack thereof) and mesocyclone detections with their associated attributes using several statistical procedures. This correlation is being determined using a climatological perspective rather than a small-scale, case-by-case perspective used previously. The eventual goal is to demonstrate the practicality and usefulness of a mesocyclone climatology based on the MDA.

WeatherVolume 53, Issue 3 p. 66-72 Back to basics: The tornado, Nature's most violent wind: Part 2 — Formation and current research John T. Snow, Corresponding Author John T. Snow College of Geosciences, University of Oklahoma, USACollege of Geosciences, The University of Oklahoma, Sarkeys Energy Center, Suite 710, 100 East Boyd Street, Norman, Oklahoma 73019–0628, USA.Search for more papers by this authorAmy Lee Wyatt, Amy Lee Wyatt College of Geosciences, University of Oklahoma, USASearch for more papers by this author John T. Snow, Corresponding Author John T. Snow College of Geosciences, University of Oklahoma, USACollege of Geosciences, The University of Oklahoma, Sarkeys Energy Center, Suite 710, 100 East Boyd Street, Norman, Oklahoma 73019–0628, USA.Search for more papers by this authorAmy Lee Wyatt, Amy Lee Wyatt College of Geosciences, University of Oklahoma, USASearch for more papers by this author First published: 30 April 2012 https://doi.org/10.1002/j.1477-8696.1998.tb03962.xCitations: 9AboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat Citing Literature Volume53, Issue3March 1998Pages 66-72 RelatedInformation