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Author: Chester F. Ropelewski Publisher: Cambridge University Press ISBN: 1108573371 Category : Science Languages : en Pages : 391
Book Description
Sensational images and stories about variations in Earth's climate and their impacts on society are pervasive in the media. The scientific basis for these stories is often not understood by the general public, nor even by those with a scientific background in fields other than climate science. This book is a comprehensive resource that will enable the reader to understand and appreciate the significance of the flood of climate information. It is an excellent non-mathematical resource for learning the fundamentals of climate analysis, as well as a reference for non-climate experts that need to use climate information and data. The focus is on the basics of the climate system, how climate is observed and how the observations are transformed into datasets useful for monitoring the climate. Each chapter contains Discussion Questions. This is an invaluable textbook on climate analysis for advanced students, and a reference textbook for researchers and practitioners.
Author: Chester F. Ropelewski Publisher: Cambridge University Press ISBN: 1108573371 Category : Science Languages : en Pages : 391
Book Description
Sensational images and stories about variations in Earth's climate and their impacts on society are pervasive in the media. The scientific basis for these stories is often not understood by the general public, nor even by those with a scientific background in fields other than climate science. This book is a comprehensive resource that will enable the reader to understand and appreciate the significance of the flood of climate information. It is an excellent non-mathematical resource for learning the fundamentals of climate analysis, as well as a reference for non-climate experts that need to use climate information and data. The focus is on the basics of the climate system, how climate is observed and how the observations are transformed into datasets useful for monitoring the climate. Each chapter contains Discussion Questions. This is an invaluable textbook on climate analysis for advanced students, and a reference textbook for researchers and practitioners.
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.
Author: Vivian Siahaan Publisher: BALIGE PUBLISHING ISBN: Category : Computers Languages : en Pages : 281
Book Description
In this meteorological data analysis and prediction project using machine learning with Python, we begin by conducting data exploration to understand the dataset's structure and contents. We load the dataset and check for any missing values or anomalies that may require preprocessing. To gain insights into the data, we visualize the distribution of each feature, examining histograms, box plots, and scatter plots. This helps us identify potential outliers and understand the relationships between different variables. After data exploration, we preprocess the dataset, handling missing values through imputation techniques or removing rows with missing data, ensuring the data is ready for machine learning algorithms. Next, we define the problem we want to solve, which is predicting the weather summary based on various meteorological parameters. The weather summary serves as our target variable, while the other features act as input variables. We split the data into training and testing sets to train the machine learning models on one subset and evaluate their performance on unseen data. For the prediction task, we start with simple machine learning models like Logistic Regression or Decision Trees. We fit these models to the training data and assess their accuracy on the test set. To improve model performance, we explore more complex algorithms, such as Logistic Regression, K-Nearest Neighbors, Support Vector, Decision Trees, Random Forests, Gradient Boosting, Extreme Gradient Boosting, Light Gradient Boosting, and Multi-Layer Perceptron (MLP). We use grid search to tune the hyperparameters of these models and find the best combination that optimizes their performance. During model evaluation, we use metrics such as accuracy, precision, recall, and F1-score to measure how well the models predict the weather summary. To ensure robustness and reliability of the results, we apply k-fold cross-validation, where the dataset is divided into k subsets, and each model is trained and evaluated k times. Throughout the project, we pay attention to potential issues like overfitting or underfitting, striving to strike a balance between model complexity and generalization. Visualizations play a crucial role in understanding the model's behavior and identifying areas for improvement. We create various plots, including learning curves and confusion matrices, to interpret the model's performance. In the prediction phase, we apply the trained models to the test dataset to predict the weather summary for each sample. We compare the predicted values with the actual values to assess the model's performance on unseen data. The entire project is well-documented, ensuring transparency and reproducibility. We record the methodologies, findings, and results to facilitate future reference or sharing with stakeholders. We analyze the predictive capabilities of the models and summarize their strengths and limitations. We discuss potential areas of improvement and future directions to enhance the model's accuracy and robustness. The main objective of this project is to accurately predict weather summaries based on meteorological data, while also gaining valuable insights into the underlying patterns and trends in the data. By leveraging machine learning algorithms, preprocessing techniques, hyperparameter tuning, and thorough evaluation, we aim to build reliable models that can assist in weather forecasting and analysis.
Author: Hans von Storch Publisher: Cambridge University Press ISBN: 1139425099 Category : Science Languages : en Pages : 979
Book Description
Climatology is, to a large degree, the study of the statistics of our climate. The powerful tools of mathematical statistics therefore find wide application in climatological research. The purpose of this book is to help the climatologist understand the basic precepts of the statistician's art and to provide some of the background needed to apply statistical methodology correctly and usefully. The book is self contained: introductory material, standard advanced techniques, and the specialised techniques used specifically by climatologists are all contained within this one source. There are a wealth of real-world examples drawn from the climate literature to demonstrate the need, power and pitfalls of statistical analysis in climate research. Suitable for graduate courses on statistics for climatic, atmospheric and oceanic science, this book will also be valuable as a reference source for researchers in climatology, meteorology, atmospheric science, and oceanography.
Author: Chester F. Ropelewski
Author: Chester F. Ropelewski
Author: United States. Weather Bureau
Author: United States. Weather Bureau
Author: Abdelwaheb Hannachi
Author: United States. Environmental Data Service
Author: Vivian Siahaan
Author: 
Author: United States. Weather Bureau
Author: Hans von Storch
Author: Kelly T. Redmond