Publisher's Note: Products purchased from Third Party sellers are not guaranteed by the publisher for quality, authenticity, or access to any online entitlements included with the product. One-stop weather database A valuable weather data resource for engineers and project managers, Michael Kjelgaard’s Engineering Weather Data is loaded with data you’ll find essential for designing buildings and HVAC systems in cities with different climates. You get table after table of important weather statistics, organized by city for easy look-up, including tables of weather data for cities throughout the US -- plus 355 cities in Canada and Mexico, and 100 cities throughout the rest of the world. Material is derived mostly from the National Weather Service (NWS), the National Renewable Energy Lab (NREL), and ASHRAE, and includes notes and methodologies for: *ASHRAE Design Conditions *Ventilation Heating and Cooling *Humidification Systems * Bin Data *Degree Day Data *Economizer System Savings *Air to Air Heat Recovery *Engineering Weather Data

This book reviews the principles of Doppler radar and emphasizes the quantitative measurement of meteorological parameters. It illustrates the relation of Doppler radar data and images to atmospherix phenomena such as tornados, microbursts, waves, turbulence, density currents, hurricanes, and lightning. Radar images and photographs of these weather phenomena are included. - Polarimetric measurements and data processing - An updated section on RASS - Wind profilers - Observations with the WSR-88D - An updated treatment of lightning - Turbulence in the planetary boundary layer - A short history of radar - Chapter problem sets

Advances in computer power and observing systems has led to the generation and accumulation of large scale weather & climate data begging for exploration and analysis. Pattern Identification and Data Mining in Weather and Climate presents, from different perspectives, most available, novel and conventional, approaches used to analyze multivariate time series in climate science to identify patterns of variability, teleconnections, and reduce dimensionality. The book discusses different methods to identify patterns of spatiotemporal fields. The book also presents machine learning with a particular focus on the main methods used in climate science. Applications to atmospheric and oceanographic data are also presented and discussed in most chapters. To help guide students and beginners in the field of weather & climate data analysis, basic Matlab skeleton codes are given is some chapters, complemented with a list of software links toward the end of the text. A number of technical appendices are also provided, making the text particularly suitable for didactic purposes. The topic of EOFs and associated pattern identification in space-time data sets has gone through an extraordinary fast development, both in terms of new insights and the breadth of applications. We welcome this text by Abdel Hannachi who not only has a deep insight in the field but has himself made several contributions to new developments in the last 15 years. - Huug van den Dool, Climate Prediction Center, NCEP, College Park, MD, U.S.A. Now that weather and climate science is producing ever larger and richer data sets, the topic of pattern extraction and interpretation has become an essential part. This book provides an up to date overview of the latest techniques and developments in this area. - Maarten Ambaum, Department of Meteorology, University of Reading, U.K. This nicely and expertly written book covers a lot of ground, ranging from classical linear pattern identification techniques to more modern machine learning, illustrated with examples from weather & climate science. It will be very valuable both as a tutorial for graduate and postgraduate students and as a reference text for researchers and practitioners in the field. - Frank Kwasniok, College of Engineering, University of Exeter, U.K.

A leading scientist argues that we must consider deploying climate engineering technology to slow the pace of global warming. Climate engineering—which could slow the pace of global warming by injecting reflective particles into the upper atmosphere—has emerged in recent years as an extremely controversial technology. And for good reason: it carries unknown risks and it may undermine commitments to conserving energy. Some critics also view it as an immoral human breach of the natural world. The latter objection, David Keith argues in A Scientist's Case for Climate Engineering, is groundless; we have been using technology to alter our environment for years. But he agrees that there are large issues at stake. A leading scientist long concerned about climate change, Keith offers no naïve proposal for an easy fix to what is perhaps the most challenging question of our time; climate engineering is no silver bullet. But he argues that after decades during which very little progress has been made in reducing carbon emissions we must put this technology on the table and consider it responsibly. That doesn't mean we will deploy it, and it doesn't mean that we can abandon efforts to reduce greenhouse gas emissions. But we must understand fully what research needs to be done and how the technology might be designed and used. This book provides a clear and accessible overview of what the costs and risks might be, and how climate engineering might fit into a larger program for managing climate change.

As climate has warmed over recent years, a new pattern of more frequent and more intense weather events has unfolded across the globe. Climate models simulate such changes in extreme events, and some of the reasons for the changes are well understood. Warming increases the likelihood of extremely hot days and nights, favors increased atmospheric moisture that may result in more frequent heavy rainfall and snowfall, and leads to evaporation that can exacerbate droughts. Even with evidence of these broad trends, scientists cautioned in the past that individual weather events couldn't be attributed to climate change. Now, with advances in understanding the climate science behind extreme events and the science of extreme event attribution, such blanket statements may not be accurate. The relatively young science of extreme event attribution seeks to tease out the influence of human-cause climate change from other factors, such as natural sources of variability like El Niño, as contributors to individual extreme events. Event attribution can answer questions about how much climate change influenced the probability or intensity of a specific type of weather event. As event attribution capabilities improve, they could help inform choices about assessing and managing risk, and in guiding climate adaptation strategies. This report examines the current state of science of extreme weather attribution, and identifies ways to move the science forward to improve attribution capabilities.