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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
Accurate medium-range global weather forecasting with 3D neural networks
Weather forecasting is important for science and society. At present, the most accurate forecast system is the numerical weather prediction (NWP) method, which represents atmospheric states as discretized grids and numerically solves partial differential equations that describe the transition between those states
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. However, this procedure is computationally expensive. Recently, artificial-intelligence-based methods
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have shown potential in accelerating weather forecasting by orders of magnitude, but the forecast accuracy is still significantly lower than that of NWP methods. Here we introduce an artificial-intelligence-based method for accurate, medium-range global weather forecasting. We show that three-dimensional deep networks equipped with Earth-specific priors are effective at dealing with complex patterns in weather data, and that a hierarchical temporal aggregation strategy reduces accumulation errors in medium-range forecasting. Trained on 39 years of global data, our program, Pangu-Weather, obtains stronger deterministic forecast results on reanalysis data in all tested variables when compared with the world’s best NWP system, the operational integrated forecasting system of the European Centre for Medium-Range Weather Forecasts (ECMWF)
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. Our method also works well with extreme weather forecasts and ensemble forecasts. When initialized with reanalysis data, the accuracy of tracking tropical cyclones is also higher than that of ECMWF-HRES.
Three-dimensional deep neural networks can be trained to forecast global weather patterns, including extreme weather, with accuracy greater than or equal to that of the best numerical weather prediction models.
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
What Is the Predictability Limit of Midlatitude Weather?
Understanding the predictability limit of day-to-day weather phenomena such as midlatitude winter storms and summer monsoonal rainstorms is crucial to numerical weather prediction (NWP). This predictability limit is studied using unprecedented high-resolution global models with ensemble experiments of the European Centre for Medium-Range Weather Forecasts (ECMWF; 9-km operational model) and identical-twin experiments of the U.S. Next-Generation Global Prediction System (NGGPS; 3 km). Results suggest that the predictability limit for midlatitude weather may indeed exist and is intrinsic to the underlying dynamical system and instabilities even if the forecast model and the initial conditions are nearly perfect. Currently, a skillful forecast lead time of midlatitude instantaneous weather is around 10 days, which serves as the practical predictability limit. Reducing the current-day initial-condition uncertainty by an order of magnitude extends the deterministic forecast lead times of day-to-day weather by up to 5 days, with much less scope for improving prediction of small-scale phenomena like thunderstorms. Achieving this additional predictability limit can have enormous socioeconomic benefits but requires coordinated efforts by the entire community to design better numerical weather models, to improve observations, and to make better use of observations with advanced data assimilation and computing techniques.
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
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
WeatherBench 2: A Benchmark for the Next Generation of Data‐Driven Global Weather Models
WeatherBench 2 is an update to the global, medium‐range (1–14 days) weather forecasting benchmark proposed by (Rasp et al., 2020, https://doi.org/10.1029/2020ms002203), designed with the aim to accelerate progress in data‐driven weather modeling. WeatherBench 2 consists of an open‐source evaluation framework, publicly available training, ground truth and baseline data as well as a continuously updated website with the latest metrics and state‐of‐the‐art models: https://sites.research.google/weatherbench. This paper describes the design principles of the evaluation framework and presents results for current state‐of‐the‐art physical and data‐driven weather models. The metrics are based on established practices for evaluating weather forecasts at leading operational weather centers. We define a set of headline scores to provide an overview of model performance. In addition, we also discuss caveats in the current evaluation setup and challenges for the future of data‐driven weather forecasting. Plain Language Summary Traditionally, weather forecasts are made by models that attempt to replicate the physical processes of the atmosphere. This has been very successful over the last few decades as better computers, better observations and model upgrades have lead to steadily improving weather forecasts. However, with rapid advances in artificial intelligence (AI), the question can be asked whether one can simply learn a weather model from past observations or reanalyzes. In the last couple of years, we have seen tremendous progress with state‐of‐the‐art AI models rivaling the best “traditional” weather models in skill. WeatherBench 2 is a benchmark data set designed to evaluate and compare the quality of AI and traditional models. By setting a standard for evaluation, alongside providing open‐source data and code, this project aims to accelerate this research direction and lead to better weather prediction. Key Points WeatherBench 2 is a framework for evaluating and comparing data‐driven and traditional numerical weather forecasting models It provides an evaluation framework, publicly available data sets and a website to assess the state‐of‐the‐art weather models The evaluation protocol has been designed following best practices established in the operational weather forecasting community
Journal Article
The weather machine : a journey inside the forecast
\"From the acclaimed author of Tubes, a lively and surprising tour of the infrastructure behind the weather forecast, the people who built it, and what it reveals about our climate and our planet. The weather is the foundation of our daily lives. It's a staple of small talk, the app on our smartphones, and often the first thing we check each morning. Yet behind these quotidian interactions is one of the most expansive machines human beings have ever constructed -- a triumph of science, technology and global cooperation. But what is this 'weather machine' and who created it? In The Weather Machine, Andrew Blum takes readers on a fascinating journey through an everyday miracle. In a quest to understand how the forecast works, he visits old weather stations and watches new satellites blast off. He follows the dogged efforts of scientists to create a supercomputer model of the atmosphere and traces the surprising history of the algorithms that power their work. He discovers that we have quietly entered a golden age of meteorology -- our tools allow us to predict weather more accurately than ever, and yet we haven't learned to trust them, nor can we guarantee the fragile international alliances that allow our modern weather machine to exist. Written with the sharp wit and infectious curiosity Andrew Blum is known for, The Weather Machine pulls back the curtain on a universal part of our everyday lives, illuminating our relationships with technology, the planet, and the global community\"--Jacket.
Book
Probabilistic Predictions from Deterministic Atmospheric River Forecasts with Deep Learning
Deep-learning (DL) postprocessing methods are examined to obtain reliable and accurate probabilistic forecasts from single-member numerical weather predictions of integrated vapor transport (IVT). Using a 34-yr reforecast, based on the Center for Western Weather and Water Extremes West-WRF mesoscale model of North American West Coast IVT, the dynamically/statistically derived 0–120-h probabilistic forecasts for IVT under atmospheric river (AR) conditions are tested. These predictions are compared with the Global Ensemble Forecast System (GEFS) dynamic model and the GEFS calibrated with a neural network. In addition, the DL methods are tested against an established, but more rigid, statistical–dynamical ensemble method (the analog ensemble). The findings show, using continuous ranked probability skill score and Brier skill score as verification metrics, that the DL methods compete with or outperform the calibrated GEFS system at lead times from 0 to 48 h and again from 72 to 120 h for AR vapor transport events. In addition, the DL methods generate reliable and skillful probabilistic forecasts. The implications of varying the length of the training dataset are examined, and the results show that the DL methods learn relatively quickly and ∼10 years of hindcast data are required to compete with the GEFS ensemble.
Journal Article