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Climatological Data

Climatological Data PDF Author:
Publisher:
ISBN:
Category : Iowa
Languages : en
Pages : 164

Book Description


Climatological Data

Climatological Data PDF Author:
Publisher:
ISBN:
Category : Iowa
Languages : en
Pages : 164

Book Description


Climatological Data

Climatological Data PDF Author: United States. Environmental Data Service
Publisher:
ISBN:
Category : Meteorology
Languages : en
Pages : 602

Book Description


Climatological Data, Minnesota

Climatological Data, Minnesota PDF Author: United States. Environmental Data Service
Publisher:
ISBN:
Category :
Languages : en
Pages : 680

Book Description


Climatological Data, Louisiana

Climatological Data, Louisiana PDF Author: United States. Environmental Data Service
Publisher:
ISBN:
Category :
Languages : en
Pages : 394

Book Description


Climatological Data, Nevada

Climatological Data, Nevada PDF Author: United States. Environmental Data Service
Publisher:
ISBN:
Category : Nevada
Languages : en
Pages : 502

Book Description


Climatological Data for the United States by Sections

Climatological Data for the United States by Sections PDF Author: United States. Weather Bureau
Publisher:
ISBN:
Category : Meteorology
Languages : en
Pages : 276

Book Description


Monthly Catalog of United States Government Publications

Monthly Catalog of United States Government Publications PDF Author:
Publisher:
ISBN:
Category : Government publications
Languages : en
Pages : 1040

Book Description


Patterns Identification and Data Mining in Weather and Climate

Patterns Identification and Data Mining in Weather and Climate PDF Author: Abdelwaheb Hannachi
Publisher: Springer Nature
ISBN: 3030670732
Category : Science
Languages : en
Pages : 600

Book Description
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.

Engineering Weather Data

Engineering Weather Data PDF Author: Michael J. Kjelgaard
Publisher: McGraw Hill Professional
ISBN:
Category : Nature
Languages : en
Pages : 822

Book Description
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

A Guide to Empirical Orthogonal Functions for Climate Data Analysis

A Guide to Empirical Orthogonal Functions for Climate Data Analysis PDF Author: Antonio Navarra
Publisher: Springer Science & Business Media
ISBN: 9048137020
Category : Science
Languages : en
Pages : 151

Book Description
Climatology and meteorology have basically been a descriptive science until it became possible to use numerical models, but it is crucial to the success of the strategy that the model must be a good representation of the real climate system of the Earth. Models are required to reproduce not only the mean properties of climate, but also its variability and the strong spatial relations between climate variability in geographically diverse regions. Quantitative techniques were developed to explore the climate variability and its relations between different geographical locations. Methods were borrowed from descriptive statistics, where they were developed to analyze variance of related observations-variable pairs, or to identify unknown relations between variables. A Guide to Empirical Orthogonal Functions for Climate Data Analysis uses a different approach, trying to introduce the reader to a practical application of the methods, including data sets from climate simulations and MATLAB codes for the algorithms. All pictures and examples used in the book may be reproduced by using the data sets and the routines available in the book . Though the main thrust of the book is for climatological examples, the treatment is sufficiently general that the discussion is also useful for students and practitioners in other fields. Supplementary datasets are available via http://extra.springer.com