Uncertainties in Numerical Weather Prediction

Produk Detail:
  • Author : Haraldur Olafsson
  • Publisher : Elsevier
  • Pages : 364 pages
  • ISBN : 0128157100
  • Rating : /5 from reviews
CLICK HERE TO GET THIS BOOK >>>Uncertainties in Numerical Weather Prediction

Download or Read online Uncertainties in Numerical Weather Prediction full in PDF, ePub and kindle. this book written by Haraldur Olafsson and published by Elsevier which was released on 08 December 2020 with total page 364 pages. We cannot guarantee that Uncertainties in Numerical Weather Prediction book is available in the library, click Get Book button and read full online book in your kindle, tablet, IPAD, PC or mobile whenever and wherever You Like. Uncertainties in Numerical Weather Prediction is a comprehensive work on the most current understandings of uncertainties and predictability in numerical simulations of the atmosphere. It provides general knowledge on all aspects of uncertainties in the weather prediction models in a single, easy to use reference. The book illustrates particular uncertainties in observations and data assimilation, as well as the errors associated with numerical integration methods. Stochastic methods in parameterization of subgrid processes are also assessed, as are uncertainties associated with surface-atmosphere exchange, orographic flows and processes in the atmospheric boundary layer. Through a better understanding of the uncertainties to watch for, readers will be able to produce more precise and accurate forecasts. This is an essential work for anyone who wants to improve the accuracy of weather and climate forecasting and interested parties developing tools to enhance the quality of such forecasts. Provides a comprehensive overview of the state of numerical weather prediction at spatial scales, from hundreds of meters, to thousands of kilometers Focuses on short-term 1-15 day atmospheric predictions, with some coverage appropriate for longer-term forecasts Includes references to climate prediction models to allow applications of these techniques for climate simulations

Uncertainties in Numerical Weather Prediction

Uncertainties in Numerical Weather Prediction
  • Author : Haraldur Olafsson,Jian-Wen Bao
  • Publisher : Elsevier
  • Release : 08 December 2020
GET THIS BOOK Uncertainties in Numerical Weather Prediction

Uncertainties in Numerical Weather Prediction is a comprehensive work on the most current understandings of uncertainties and predictability in numerical simulations of the atmosphere. It provides general knowledge on all aspects of uncertainties in the weather prediction models in a single, easy to use reference. The book illustrates particular uncertainties in observations and data assimilation, as well as the errors associated with numerical integration methods. Stochastic methods in parameterization of subgrid processes are also assessed, as are uncertainties associated with

Modeling Uncertainty of Numerical Weather Predictions Using Learning Methods

Modeling Uncertainty of Numerical Weather Predictions Using Learning Methods
  • Author : Ashkan Zarnani,University of Alberta. Department of Electrical and Computer Engineering
  • Publisher : Unknown
  • Release : 24 June 2021
GET THIS BOOK Modeling Uncertainty of Numerical Weather Predictions Using Learning Methods

Weather forecasting is one of the most vital tasks in many applications ranging from severe weather hazard systems to energy production. Numerical weather prediction (NWP) systems are commonly used state-of-the-art atmospheric models that provide point forecasts as deterministic predictions arranged on a three-dimensional grid. However, there is always some level of error and uncertainty in the forecasts due to inaccuracies of initial conditions, the chaotic nature of weather, etc. Such uncertainty information is crucial in decision making and optimization processes

Computational Science ICCS 2019

Computational Science     ICCS 2019
  • Author : João M. F. Rodrigues,Pedro J. S. Cardoso,Jânio Monteiro,Roberto Lam,Valeria V. Krzhizhanovskaya,Michael H. Lees,Jack J. Dongarra,Peter M.A. Sloot
  • Publisher : Springer
  • Release : 07 June 2019
GET THIS BOOK Computational Science ICCS 2019

The five-volume set LNCS 11536, 11537, 11538, 11539 and 11540 constitutes the proceedings of the 19th International Conference on Computational Science, ICCS 2019, held in Faro, Portugal, in June 2019. The total of 65 full papers and 168 workshop papers presented in this book set were carefully reviewed and selected from 573 submissions (228 submissions to the main track and 345 submissions to the workshops). The papers were organized in topical sections named: Part I: ICCS Main Track Part II: ICCS Main Track; Track of Advances in High-Performance Computational Earth Sciences: Applications

A Case Study of the Persistence of Weather Forecast Model Errors

A Case Study of the Persistence of Weather Forecast Model Errors
  • Author : Barbara Sauter
  • Publisher : Unknown
  • Release : 24 June 2021
GET THIS BOOK A Case Study of the Persistence of Weather Forecast Model Errors

Decision makers could frequently benefit from information about the amount of uncertainty associated with a specific weather forecast. Automated numerical weather prediction models provide deterministic weather forecast values with no estimate of the likely error. This case study examines the day-to-day persistence of forecast errors of basic surface weather parameters for four sites in northern Utah. Although exceptionally low or high forecast errors on one day are more likely to be associated with a similar quality forecast the following day,

Mathematical Problems in Meteorological Modelling

Mathematical Problems in Meteorological Modelling
  • Author : András Bátkai,Petra Csomós,István Faragó,András Horányi,Gabriella Szépszó
  • Publisher : Springer
  • Release : 08 November 2016
GET THIS BOOK Mathematical Problems in Meteorological Modelling

This book deals with mathematical problems arising in the context of meteorological modelling. It gathers and presents some of the most interesting and important issues from the interaction of mathematics and meteorology. It is unique in that it features contributions on topics like data assimilation, ensemble prediction, numerical methods, and transport modelling, from both mathematical and meteorological perspectives. The derivation and solution of all kinds of numerical prediction models require the application of results from various mathematical fields. The present

Masters of Uncertainty

Masters of Uncertainty
  • Author : Phaedra Daipha
  • Publisher : University of Chicago Press
  • Release : 17 November 2015
GET THIS BOOK Masters of Uncertainty

Though we commonly make them the butt of our jokes, weather forecasters are in fact exceptionally good at managing uncertainty. They consistently do a better job calibrating their performance than stockbrokers, physicians, or other decision-making experts precisely because they receive feedback on their decisions in near real time. Following forecasters in their quest for truth and accuracy, therefore, holds the key to the analytically elusive process of decision making as it actually happens. In Masters of Uncertainty, Phaedra Daipha develops

Improving Medium range Streamflow Forecasting Across U S Middle Atlantic Region

Improving Medium range Streamflow Forecasting Across U S  Middle Atlantic Region
  • Author : Ridwan Siddique
  • Publisher : Unknown
  • Release : 24 June 2021
GET THIS BOOK Improving Medium range Streamflow Forecasting Across U S Middle Atlantic Region

Short- to medium-range (forecast lead times from 0 to 14 days) streamflow forecasts are subject to uncertainties from various sources. A major source of uncertainty is due to the weather or meteorological forcing. In turn, the uncertainties from the meteorological forcing are propagated into the streamflow forecasts when using the meteorological forecasts (i.e., the outputs from a Numerical Weather Prediction (NWP) model) as forcing to hydrological models. Additionally, the hydrological models themselves are another important source of uncertainty, where uncertainty arises