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"Weather forecasting"
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The secret world of weather : how to read signs in every cloud, breeze, hill, street, plant, animal, and dewdrop
\"The weather changes as we walk around a tree or turn down a street. There is a secret world of weather one that we all live in, but very few see. Each day we pass dozens of small weather signs that reveal what the weather is doing all around us and what is about to happen. The clues are easy to spot when you know how, but remain invisible to most people. In The Secret World of Weather you'll discover the simple rules that explain the weather signs. And you'll learn rare skills that enhance every minute you spend outdoors, whether you are in a town, on a beach or in a wilder spot. As the author of the international bestsellers The walker's guide and How to read water, Tristan Gooley knows how to de-code the phenomena and signs to look for. As he says, 'I want you to get to know these signs as I have, as characters. By studying their habits and behaviours, the signs come to life and the meaning reveals itself. From this flows an ability to read what is happening and what is about to happen'. This is the ultimate guide to exploring an undiscovered world, one that hides in front of our eyes\"--Publisher's description.
Book
The quiet revolution of numerical weather prediction
Advances in numerical weather prediction represent a quiet revolution because they have resulted from a steady accumulation of scientific knowledge and technological advances over many years that, with only a few exceptions, have not been associated with the aura of fundamental physics breakthroughs. Nonetheless, the impact of numerical weather prediction is among the greatest of any area of physical science. As a computational problem, global weather prediction is comparable to the simulation of the human brain and of the evolution of the early Universe, and it is performed every day at major operational centres across the world.
Journal Article
Minding the weather : how expert forecasters think
\"This book presents an integration of recent research that reveals the knowledge and reasoning of expert weather forecasters, explaining how they understand and predict the weather. The book also discusses different styles and approaches to forecasting. It summarizes what weather forecasters themselves have said about their reasoning, the literature of what makes for sound training of forecasters, studies of forecast accuracy, attempts to generate computer systems to forecast weather, and the technologies used in forecasting (including displays and visualizations) in such systems as radar and satellite. It presents a case study in which forecasters at a facility were probed for their reasoning and a proficiency scale was developed. In addition, the book summarizes and integrates the newest cutting-edge research in applied cognitive science, human factors engineering, cognitive systems engineering, and human-computer interaction that has investigated aspects of the psychology of weather forecasting, including knowledge and mental models, perceptual and display interpretation skills, and reasoning heuristics and strategies\"--Provided by publisher.
Book
Temporal convolutional neural (TCN) network for an effective weather forecasting using time-series data from the local weather station
Non-predictive or inaccurate weather forecasting can severely impact the community of users such as farmers. Numerical weather prediction models run in major weather forecasting centers with several supercomputers to solve simultaneous complex nonlinear mathematical equations. Such models provide the medium-range weather forecasts, i.e., every 6 h up to 18 h with grid length of 10–20 km. However, farmers often depend on more detailed short-to medium-range forecasts with higher-resolution regional forecasting models. Therefore, this research aims to address this by developing and evaluating a lightweight and novel weather forecasting system, which consists of one or more local weather stations and state-of-the-art machine learning techniques for weather forecasting using time-series data from these weather stations. To this end, the system explores the state-of-the-art temporal convolutional network (TCN) and long short-term memory (LSTM) networks. Our experimental results show that the proposed model using TCN produces better forecasting compared to the LSTM and other classic machine learning approaches. The proposed model can be used as an efficient localized weather forecasting tool for the community of users, and it could be run on a stand-alone personal computer.
Journal Article
What is a forecast?
What does it mean to forecast the weather? How do weather forecasters do their jobs? Readers will learn the ins and outs of weather forecasting in this book\"--Provided by publisher.
Book
ERA-Interim/Land: a global land surface reanalysis data set
ERA-Interim/Land is a global land surface reanalysis data set covering the period 1979-2010. It describes the evolution of soil moisture, soil temperature and snowpack. ERA-Interim/Land is the result of a single 32-year simulation with the latest ECMWF (European Centre for Medium-Range Weather Forecasts) land surface model driven by meteorological forcing from the ERA-Interim atmospheric reanalysis and precipitation adjustments based on monthly GPCP v2.1 (Global Precipitation Climatology Project). The horizontal resolution is about 80 km and the time frequency is 3-hourly. ERA-Interim/Land includes a number of parameterization improvements in the land surface scheme with respect to the original ERA-Interim data set, which makes it more suitable for climate studies involving land water resources. The quality of ERA-Interim/Land is assessed by comparing with ground-based and remote sensing observations. In particular, estimates of soil moisture, snow depth, surface albedo, turbulent latent and sensible fluxes, and river discharges are verified against a large number of site measurements. ERA-Interim/Land provides a global integrated and coherent estimate of soil moisture and snow water equivalent, which can also be used for the initialization of numerical weather prediction and climate models.
Journal Article
The Met Office Unified Model Global Atmosphere 6.0/6.1 and JULES Global Land 6.0/6.1 configurations
We describe Global Atmosphere 6.0 and Global Land 6.0 (GA6.0/GL6.0): the latest science configurations of the Met Office Unified Model and JULES (Joint UK Land Environment Simulator) land surface model developed for use across all timescales. Global Atmosphere 6.0 includes the ENDGame (Even Newer Dynamics for General atmospheric modelling of the environment) dynamical core, which significantly increases mid-latitude variability improving a known model bias. Alongside developments of the model's physical parametrisations, ENDGame also increases variability in the tropics, which leads to an improved representation of tropical cyclones and other tropical phenomena. Further developments of the atmospheric and land surface parametrisations improve other aspects of model performance, including the forecasting of surface weather phenomena. We also describe GA6.1/GL6.1, which includes a small number of long-standing differences from our main trunk configurations that we continue to require for operational global weather prediction. Since July 2014, GA6.1/GL6.1 has been used by the Met Office for operational global numerical weather prediction, whilst GA6.0/GL6.0 was implemented in its remaining global prediction systems over the following year.
Journal Article
Probabilistic Weather Prediction with an Analog Ensemble
Abstract
This study explores an analog-based method to generate an ensemble [analog ensemble (AnEn)] in which the probability distribution of the future state of the atmosphere is estimated with a set of past observations that correspond to the best analogs of a deterministic numerical weather prediction (NWP). An analog for a given location and forecast lead time is defined as a past prediction, from the same model, that has similar values for selected features of the current model forecast. The AnEn is evaluated for 0–48-h probabilistic predictions of 10-m wind speed and 2-m temperature over the contiguous United States and against observations provided by 550 surface stations, over the 23 April–31 July 2011 period. The AnEn is generated from the Environment Canada (EC) deterministic Global Environmental Multiscale (GEM) model and a 12–15-month-long training period of forecasts and observations. The skill and value of AnEn predictions are compared with forecasts from a state-of-the-science NWP ensemble system, the 21-member Regional Ensemble Prediction System (REPS). The AnEn exhibits high statistical consistency and reliability and the ability to capture the flow-dependent behavior of errors, and it has equal or superior skill and value compared to forecasts generated via logistic regression (LR) applied to both the deterministic GEM (as in AnEn) and REPS [ensemble model output statistics (EMOS)]. The real-time computational cost of AnEn and LR is lower than EMOS.
Journal Article