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51位
¥1,367円
評価: 0
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楽天Kobo電子書籍ストア
＜p＞Wouldn't it be useful to be able to accurately predict the weather simply by reading the clouds? Well, with this book, you can!＜/p＞ ＜p＞TV forecasts, online predictions and smartphone apps are all based on the same data ? a number-crunched overview of how air pressure and temperature affect the weather across a large geographical area.＜/p＞ ＜p＞But to get an idea of how the weather will develop for the precise spot where you're standing (or walking, sailing, golfing, fishing, etc) you don't need any equipment or a wifi connection ? you just need to look up.＜/p＞ ＜p＞This book will give you a broad understanding of why the clouds are symptoms of weather patterns, not causes. By reading these signs in the sky and referring to the explanatory colour photographs, you will discover exactly what those signs mean.＜/p＞ ＜p＞An at-a-glance guide to the clouds for anyone anywhere in the world, on land or at sea, this book will enable you to predict the weather by recognising cloud types, shapes, colour and behaviour. It will be an invaluable companion for anyone who enjoys outdoor activities.＜/p＞ ＜p＞'Well researched - practical information in an easy to assimilate form' - Professor Richard Collier, former President of the Royal Meteorological Society＜/p＞ ＜p＞'So good that my Yachtmaster candidates would do well to read it. I learned something from this book. I bet you do too' - Tom Cunliffe, author of ＜em＞The Complete Day Skipper＜/em＞ and ＜em＞The Complete Yachtmaster＜/em＞＜/p＞ ＜p＞'Absolutely brilliant; a must for anyone who does anything outside and for whom the weather might be important. Everyone, wherever they are in the world, will get something from this book' - Duncan Wells, author of＜em＞Stress-Free Sailing＜/em＞ and ＜em＞Stress-Free Motorboating＜/em＞＜/p＞画面が切り替わりますので、しばらくお待ち下さい。 ※ご購入は、楽天kobo商品ページからお願いします。※切り替わらない場合は、こちら をクリックして下さい。 ※このページからは注文できません。
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52位
¥7,172円
評価: 0
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ミヤケマーケット
fullion ストームグラス Weather forecast bottle Storm Glass天候によって中の結晶が変化します。 机や窓辺に置いておくだけで素敵なインテリアとしてお使い頂けます。 お客様のご意見にお答えし、日本語の説明書をご用意いたしました。こちらの商品は、写真に掲載された箱に入れ、丁寧に梱包した状態で国内から発送いたしますので、安心してお買い求めいただけます。サイズ：
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53位
¥5,748円
評価: 0
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MOAセレクト
・ニュースやラジオで予測しながら気象学者やウェザーキャスターを着用するのに最適です。 この面白いI'd Forecast That デザインを揺さぶって、天気予報と気象学の精神を示しましょう。・I'd Forecast That Weatherercaster Meteorology Funny Weathermanは、ユニークなスタイルセンスを持つ気象予報者や気候学者に最適なアイテムです。・2つの素材から作られている保護ケースは、傷やへこみから保護するポリカーボネート製シェルと耐久性としなやかな弾力性を併せ持ったTPU(熱可塑性ポリウレタン)素材を使用し、偶発的な落下による損傷を防ぎます。・簡単装着※在庫更新のタイミングにより、在庫切れの場合やむをえずキャンセルさせていただく可能性があります。ご了承のほどよろしくお願いいたします。関連する商品はこちらGalaxy S8+ I'd Forecast5,748円iPhone 12/12 Pro Wtf Wh5,748円iPhone 7 Plus/8 Plus I 5,748円Galaxy S8 I Am A Weathe5,748円iPhone XR I'd Smoke Tha5,748円iPhone XS Max I'd Rathe5,748円iPhone XR Smoker BBQ An5,414円Galaxy S10+ I'd Rather 5,748円iPhone XR アーチェリー 弓 アーチャ5,414円新着商品はこちら2024/4/19グルマンディーズ すみっコぐらし docomo8,341円2024/4/19ESR iPad Mini4 ケース クリア 3,980円2024/4/19Mediapad T5 10用 ケース LeT3,980円再販商品はこちら2024/4/19エレコム 傘・杖向け シリコンケース AirT3,980円2024/4/19オノカツ 十字穴付き トラス小ねじ ステンレス3,980円2024/4/19オノカツ ステンレス 低頭 六角穴付ボルト M3,980円2024/04/20 更新
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54位
¥2,639円
評価: 0
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楽天Kobo電子書籍ストア
＜p＞Monday, June 5, had long been planned for launching D-day, the start of the campaign to liberate Nazi-held Western Europe. Yet the fine weather leading up to the greatest invasion the world would ever see was deteriorating rapidly. Would it hold long enough for the bombers, the massed armada, and the soldiers to secure beachheads in Normandy? That was the question, and it was up to Ike’s chief meteorologist, James Martin Stagg, to give him the answer.＜/p＞ ＜p＞On the night of June 4, the weather hung on a knife’s edge. The three weather bureaus advising Staggーthe US Army Air Force, the Royal Navy, and the British Met Officeーeach provided differing forecasts. Worse, leading meteorologists in the USAAF and Met Office argued stormily. Stagg had only one chance to get it right. Were he wrong, thousands of men would perish, secrecy about when and where the Allies would land would be lost, victory in Europe would be delayed for a year, and the Communists might well take control of the continent.＜/p＞画面が切り替わりますので、しばらくお待ち下さい。 ※ご購入は、楽天kobo商品ページからお願いします。※切り替わらない場合は、こちら をクリックして下さい。 ※このページからは注文できません。
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55位
¥5,748円
評価: 0
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MOAセレクト
・ニュースやラジオで予測しながら気象学者やウェザーキャスターを着用するのに最適です。 この面白いI'd Forecast That デザインを揺さぶって、天気予報と気象学の精神を示しましょう。・I'd Forecast That Weatherercaster Meteorology Funny Weathermanは、ユニークなスタイルセンスを持つ気象予報者や気候学者に最適なアイテムです。・2つの素材から作られている保護ケースは、傷やへこみから保護するポリカーボネート製シェルと耐久性としなやかな弾力性を併せ持ったTPU(熱可塑性ポリウレタン)素材を使用し、偶発的な落下による損傷を防ぎます。・簡単装着※在庫更新のタイミングにより、在庫切れの場合やむをえずキャンセルさせていただく可能性があります。ご了承のほどよろしくお願いいたします。関連する商品はこちらiPhone XR I'd Forecast 5,748円iPhone 12/12 Pro Wtf Wh5,748円Galaxy S8 I Am A Weathe5,748円Galaxy S10+ I'd Rather 5,748円iPhone 7 Plus/8 Plus I 5,748円Galaxy S8 I'd Smoke Tha5,748円Galaxy S8 I'd Tap That 6,036円Galaxy S10 I'd Tap That6,036円Galaxy S9 I'd Print Tha6,050円新着商品はこちら2024/4/19グルマンディーズ すみっコぐらし docomo8,341円2024/4/19ESR iPad Mini4 ケース クリア 3,980円2024/4/19Mediapad T5 10用 ケース LeT3,980円再販商品はこちら2024/4/19エレコム 傘・杖向け シリコンケース AirT3,980円2024/4/19オノカツ 十字穴付き トラス小ねじ ステンレス3,980円2024/4/19オノカツ ステンレス 低頭 六角穴付ボルト M3,980円2024/04/20 更新
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56位
¥10,564円
評価: 0
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楽天Kobo電子書籍ストア
＜p＞This book offers a complete primer, covering the end-to-end process of forecast production, and bringing together a description of all the relevant aspects together in a single volume; with plenty of explanation of some of the more complex issues and examples of current, state-of-the-art practices.＜/p＞ ＜p＞＜em＞Operational Weather Forecasting＜/em＞ covers the whole process of forecast production, from understanding the nature of the forecasting problem, gathering the observational data with which to initialise and verify forecasts, designing and building a model (or models) to advance those initial conditions forwards in time and then interpreting the model output and putting it into a form which is relevant to customers of weather forecasts. Included is the generation of forecasts on the monthly-to-seasonal timescales, often excluded in text-books despite this type of forecasting having been undertaken for several years.＜/p＞ ＜p＞This is a rapidly developing field, with a lot of variations in practices between different forecasting centres. Thus the authors have tried to be as generic as possible when describing aspects of numerical model design and formulation. Despite the reliance on NWP, the human forecaster still has a big part to play in producing weather forecasts and this is described, along with the issue of forecast verification ? how forecast centres measure their own performance and improve upon it.＜/p＞ ＜p＞Advanced undergraduates and postgraduate students will use this book to understand how the theory comes together in the day-to-day applications of weather forecast production. In addition, professional weather forecasting practitioners, professional users of weather forecasts and trainers will all find this new member of the RMetS ＜em＞Advancing Weather and Climate＜/em＞ series a valuable tool.＜/p＞ ＜ul＞ ＜li＞Provides an end-to-end description of the weather forecasting process＜/li＞ ＜li＞Clearly structured and pitched at an accessible level, the book discusses the practical choices that operational forecasting centres have to make in terms of what numerical models they use and when they are run.＜/li＞ ＜li＞Takes a very practical approach, using real life case-studies to contextualize information＜/li＞ ＜li＞Discusses the latest advances in the area, including ensemble methods, monthly to seasonal range prediction and use of ‘nowcasting’ tools such as radar and satellite imagery＜/li＞ ＜li＞Full colour throughout＜/li＞ ＜li＞Written by a highly respected team of authors with experience in both academia and practice.＜/li＞ ＜li＞Part of the RMetS book series ‘Advancing Weather and Climate’＜/li＞ ＜/ul＞画面が切り替わりますので、しばらくお待ち下さい。 ※ご購入は、楽天kobo商品ページからお願いします。※切り替わらない場合は、こちら をクリックして下さい。 ※このページからは注文できません。
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57位
¥8,892円
評価: 0
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楽天Kobo電子書籍ストア
＜p＞＜em＞Weather Analysis and Forecasting: Applying Satellite Water Vapor Imagery and Potential Vorticity Analysis, Second Edition,＜/em＞ is a step-by-step essential training manual for forecasters in meteorological services worldwide, and a valuable text for graduate students in atmospheric physics and satellite meteorology. In this practical guide, P. Santurette, C.G. Georgiev, and K. Maynard show how to interpret water vapor patterns in terms of dynamical processes in the atmosphere and their relation to diagnostics available from numerical weather prediction models. In particular, they concentrate on the close relationship between satellite imagery and the potential vorticity fields in the upper troposphere and lower stratosphere. These applications are illustrated with color images based on real meteorological situations over mid-latitudes, subtropical and tropical areas.＜/p＞ ＜ul＞ ＜li＞Presents interpretation of the water vapor channels 6.2 and 7.3μm as well as advances based on satellite data to improve understanding of atmospheric thermodynamics＜/li＞ ＜li＞Improves by new schemes the understanding of upper-level dynamics, midlatitudes cyclogenesis and fronts over various geographical areas＜/li＞ ＜li＞Provides analysis of deep convective phenomena to better understand the development of strong thunderstorms and to improve forecasting of severe convective events＜/li＞ ＜li＞Includes efficient operational forecasting methods for interpretation of data from NWP models＜/li＞ ＜li＞Offers information on satellite water vapor images and potential vorticity fields to analyse and forecast convective phenomena and thunderstorms＜/li＞ ＜/ul＞画面が切り替わりますので、しばらくお待ち下さい。 ※ご購入は、楽天kobo商品ページからお願いします。※切り替わらない場合は、こちら をクリックして下さい。 ※このページからは注文できません。
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58位
¥11,865円
評価: 0
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楽天Kobo電子書籍ストア
＜p＞＜em＞The Gap Between Weather and Climate Forecasting: Sub-seasonal to Seasonal Prediction＜/em＞ is an ideal reference for researchers and practitioners across the range of disciplines involved in the science, modeling, forecasting and application of this new frontier in sub-seasonal to seasonal (S2S) prediction. It provides an accessible, yet rigorous, introduction to the scientific principles and sources of predictability through the unique challenges of numerical simulation and forecasting with state-of-science modeling codes and supercomputers. Additional coverage includes the prospects for developing applications to trigger early action decisions to lessen weather catastrophes, minimize costly damage, and optimize operator decisions.＜/p＞ ＜p＞The book consists of a set of contributed chapters solicited from experts and leaders in the fields of S2S predictability science, numerical modeling, operational forecasting, and developing application sectors. The introduction and conclusion, written by the co-editors, provides historical perspective, unique synthesis and prospects, and emerging opportunities in this exciting, complex and interdisciplinary field.＜/p＞ ＜ul＞ ＜li＞Contains contributed chapters from leaders and experts in sub-seasonal to seasonal science, forecasting and applications＜/li＞ ＜li＞Provides a one-stop shop for graduate students, academic and applied researchers, and practitioners in an emerging and interdisciplinary field＜/li＞ ＜li＞Offers a synthesis of the state of S2S science through the use of concrete examples, enabling potential users of S2S forecasts to quickly grasp the potential for application in their own decision-making＜/li＞ ＜li＞Includes a broad set of topics, illustrated with graphic examples, that highlight interdisciplinary linkages＜/li＞ ＜/ul＞画面が切り替わりますので、しばらくお待ち下さい。 ※ご購入は、楽天kobo商品ページからお願いします。※切り替わらない場合は、こちら をクリックして下さい。 ※このページからは注文できません。
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59位
¥4,059円
評価: 0
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楽天Kobo電子書籍ストア
＜p＞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.＜/p＞ ＜p＞In ＜em＞Masters of Uncertainty＜/em＞, Phaedra Daipha develops a new conceptual framework for the process of decision making, after spending years immersed in the life of a northeastern office of the National Weather Service. Arguing that predicting the weather will always be more craft than science, Daipha shows how forecasters have made a virtue of the unpredictability of the weather. Impressive data infrastructures and powerful computer models are still only a substitute for the real thing outside, and so forecasters also enlist improvisational collage techniques and an omnivorous appetite for information to create a locally meaningful forecast on their computer screens. Intent on capturing decision making in action, Daipha takes the reader through engrossing firsthand accounts of several forecasting episodes (hits and misses) and offers a rare fly-on-the-wall insight into the process and challenges of producing meteorological predictions come rain or come shine. Combining rich detail with lucid argument, ＜em＞Masters of Uncertainty＜/em＞ advances a theory of decision making that foregrounds the pragmatic and situated nature of expert cognition and casts into new light how we make decisions in the digital age.＜/p＞画面が切り替わりますので、しばらくお待ち下さい。 ※ご購入は、楽天kobo商品ページからお願いします。※切り替わらない場合は、こちら をクリックして下さい。 ※このページからは注文できません。
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60位
¥1,322円
評価: 0
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楽天Kobo電子書籍ストア
＜p＞In this project, we embarked on a journey of exploring time-series weather data and performing forecasting and prediction using Python. The objective was to gain insights into the dataset, visualize feature distributions, analyze year-wise and month-wise patterns, apply ARIMA regression to forecast temperature, and utilize machine learning models to predict weather conditions. Let's delve into each step of the process.＜/p＞ ＜p＞To begin, we started by exploring the dataset, which contained historical weather data. We examined the structure and content of the dataset to understand its variables, such as temperature, humidity, wind speed, and weather conditions. Understanding the dataset is crucial for effective analysis and modeling.＜/p＞ ＜p＞Next, we visualized the distributions of different features. By creating histograms, box plots, and density plots, we gained insights into the range, central tendency, and variability of the variables. These visualizations allowed us to identify any outliers, skewed distributions, or patterns within the data.＜/p＞ ＜p＞Moving on, we explored the dataset's temporal aspects by analyzing year-wise and month-wise distributions. This involved aggregating the data based on years and months and visualizing the trends over time. By examining these patterns, we could observe any long-term or seasonal variations in the weather variables.＜/p＞ ＜p＞After gaining a comprehensive understanding of the dataset, we proceeded to apply ARIMA regression for temperature forecasting. ARIMA (Autoregressive Integrated Moving Average) is a powerful technique for time-series analysis. By fitting an ARIMA model to the temperature data, we were able to make predictions and assess the model's accuracy in capturing the underlying patterns.＜/p＞ ＜p＞In addition to temperature forecasting, we aimed to predict weather conditions using machine learning models. We employed various classification algorithms such as Logistic Regression, Decision Trees, Random Forests, Support Vector Machines (SVM), K-Nearest Neighbors (KNN), Adaboost, Gradient Boosting, Extreme Gradient Boosting (XGBoost), Light Gradient Boosting (LGBM), and Multi-Layer Perceptron (MLP). These models were trained on the historical weather data, with weather conditions as the target variable.＜/p＞ ＜p＞To evaluate the performance of the machine learning models, we utilized several metrics: accuracy, precision, recall, and F1 score. Accuracy measures the overall correctness of the predictions, while precision quantifies the proportion of true positive predictions out of all positive predictions. Recall, also known as sensitivity, measures the ability to identify true positives, and F1 score combines precision and recall into a single metric.＜/p＞ ＜p＞Throughout the process, we emphasized the importance of data preprocessing, including handling missing values, scaling features, and splitting the dataset into training and testing sets. Preprocessing ensures the data is in a suitable format for analysis and modeling, and it helps prevent biases or inconsistencies in the results.＜/p＞ ＜p＞By following this step-by-step approach, we were able to gain insights into the dataset, visualize feature distributions, analyze temporal patterns, forecast temperature using ARIMA regression, and predict weather conditions using machine learning models. The evaluation metrics provided a comprehensive assessment of the models' performance in capturing the weather conditions accurately.＜/p＞ ＜p＞In conclusion, this project demonstrated the power of Python in time-series weather forecasting and prediction. Through data exploration, visualization, regression analysis, and machine learning modeling, we obtained valuable insights and accurate predictions regarding temperature and weather conditions. This knowledge can be applied in various domains such as agriculture, transportation, and urban planning, enabling better decision-making based on weather forecasts.＜/p＞画面が切り替わりますので、しばらくお待ち下さい。 ※ご購入は、楽天kobo商品ページからお願いします。※切り替わらない場合は、こちら をクリックして下さい。 ※このページからは注文できません。
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