Statistics And Data Visualization In Climate With R And Python
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Statistics And Data Visualization In Climate With R And Python
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Author : Samuel S. Shen
language : en
Publisher:
Release Date : 2023
Statistics And Data Visualization In Climate With R And Python written by Samuel S. Shen and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023 with Climatology categories.
"A comprehensive overview of essential statistical concepts, useful statistical methods, data visualization, and computing tools for the climate and related sciences. This book is an invaluable reference for students and researchers in climatology and its connected fields who wish to learn data science, statistics, R and Python programming"--
Climate Adaptation Planning In California Using Google Earth Weadapt
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Author : V. K. Mehta
language : en
Publisher:
Release Date : 2009
Climate Adaptation Planning In California Using Google Earth Weadapt written by V. K. Mehta and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009 with Climatic changes categories.
Spring Meeting
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Author : American Geophysical Union. Meeting
language : en
Publisher:
Release Date : 2001
Spring Meeting written by American Geophysical Union. Meeting and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2001 with Geophysics categories.
Python For R Users
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Author : Ajay Ohri
language : en
Publisher: John Wiley & Sons
Release Date : 2017-11-03
Python For R Users written by Ajay Ohri and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-11-03 with Computers categories.
The definitive guide for statisticians and data scientists who understand the advantages of becoming proficient in both R and Python The first book of its kind, Python for R Users: A Data Science Approach makes it easy for R programmers to code in Python and Python users to program in R. Short on theory and long on actionable analytics, it provides readers with a detailed comparative introduction and overview of both languages and features concise tutorials with command-by-command translations—complete with sample code—of R to Python and Python to R. Following an introduction to both languages, the author cuts to the chase with step-by-step coverage of the full range of pertinent programming features and functions, including data input, data inspection/data quality, data analysis, and data visualization. Statistical modeling, machine learning, and data mining—including supervised and unsupervised data mining methods—are treated in detail, as are time series forecasting, text mining, and natural language processing. • Features a quick-learning format with concise tutorials and actionable analytics • Provides command-by-command translations of R to Python and vice versa • Incorporates Python and R code throughout to make it easier for readers to compare and contrast features in both languages • Offers numerous comparative examples and applications in both programming languages • Designed for use for practitioners and students that know one language and want to learn the other • Supplies slides useful for teaching and learning either software on a companion website Python for R Users: A Data Science Approach is a valuable working resource for computer scientists and data scientists that know R and would like to learn Python or are familiar with Python and want to learn R. It also functions as textbook for students of computer science and statistics. A. Ohri is the founder of Decisionstats.com and currently works as a senior data scientist. He has advised multiple startups in analytics off-shoring, analytics services, and analytics education, as well as using social media to enhance buzz for analytics products. Mr. Ohri's research interests include spreading open source analytics, analyzing social media manipulation with mechanism design, simpler interfaces for cloud computing, investigating climate change and knowledge flows. His other books include R for Business Analytics and R for Cloud Computing.
Statistical Analysis Of Climate Series
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Author : Helmut Pruscha
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-10-30
Statistical Analysis Of Climate Series written by Helmut Pruscha and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-10-30 with Mathematics categories.
The book presents the application of statistical methods to climatological data on temperature and precipitation. It provides specific techniques for treating series of yearly, monthly and daily records. The results’ potential relevance in the climate context is discussed. The methodical tools are taken from time series analysis, from periodogram and wavelet analysis, from correlation and principal component analysis, and from categorical data and event-time analysis. The applied models are - among others - the ARIMA and GARCH model, and inhomogeneous Poisson processes. Further, we deal with a number of special statistical topics, e.g. the problem of trend-, season- and autocorrelation-adjustment, and with simultaneous statistical inference. Programs in R and data sets on climate series, provided at the author’s homepage, enable readers (statisticians, meteorologists, other natural scientists) to perform their own exercises and discover their own applications.
Time Series Weather Forecasting And Prediction With Python
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Author : Vivian Siahaan
language : en
Publisher: BALIGE PUBLISHING
Release Date : 2023-07-12
Time Series Weather Forecasting And Prediction With Python written by Vivian Siahaan and has been published by BALIGE PUBLISHING this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-07-12 with Computers categories.
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. 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. 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. 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. 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. 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. 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. 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. 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. 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.
The Statistical Analysis Of Global Climate Change Studies
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Author : James William Hardin
language : en
Publisher:
Release Date : 1992
The Statistical Analysis Of Global Climate Change Studies written by James William Hardin and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1992 with Climatic changes categories.
Statistical Mining And Data Visualization In Atmospheric Sciences
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Author : Timothy J. Brown
language : en
Publisher: Springer Science & Business Media
Release Date : 2013-03-14
Statistical Mining And Data Visualization In Atmospheric Sciences written by Timothy J. Brown and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-03-14 with Computers categories.
Statistical Mining and Data Visualization in Atmospheric Sciences brings together in one place important contributions and up-to-date research results in this fast moving area. Statistical Mining and Data Visualization in Atmospheric Sciences serves as an excellent reference, providing insight into some of the most challenging research issues in the field.
R For Cloud Computing
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Author : A Ohri
language : en
Publisher: Springer
Release Date : 2014-11-14
R For Cloud Computing written by A Ohri and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-11-14 with Computers categories.
R for Cloud Computing looks at some of the tasks performed by business analysts on the desktop (PC era) and helps the user navigate the wealth of information in R and its 4000 packages as well as transition the same analytics using the cloud. With this information the reader can select both cloud vendors and the sometimes confusing cloud ecosystem as well as the R packages that can help process the analytical tasks with minimum effort, cost and maximum usefulness and customization. The use of Graphical User Interfaces (GUI) and Step by Step screenshot tutorials is emphasized in this book to lessen the famous learning curve in learning R and some of the needless confusion created in cloud computing that hinders its widespread adoption. This will help you kick-start analytics on the cloud including chapters on both cloud computing, R, common tasks performed in analytics including the current focus and scrutiny of Big Data Analytics, setting up and navigating cloud providers. Readers are exposed to a breadth of cloud computing choices and analytics topics without being buried in needless depth. The included references and links allow the reader to pursue business analytics on the cloud easily. It is aimed at practical analytics and is easy to transition from existing analytical set up to the cloud on an open source system based primarily on R. This book is aimed at industry practitioners with basic programming skills and students who want to enter analytics as a profession. Note the scope of the book is neither statistical theory nor graduate level research for statistics, but rather it is for business analytics practitioners. It will also help researchers and academics but at a practical rather than conceptual level. The R statistical software is the fastest growing analytics platform in the world, and is established in both academia and corporations for robustness, reliability and accuracy. The cloud computing paradigm is firmly established as the next generation of computing from microprocessors to desktop PCs to cloud.
Climate Data Computing
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Author :
language : en
Publisher:
Release Date : 2018
Climate Data Computing written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018 with Electronic books categories.
This dissertation solves two important problems in the modern analysis of big climate data. The first is the efficient visualization and fast delivery of big climate data, and the second is a computationally extensive principal component analysis (PCA) using spherical harmonics on the Earth’s surface. The second problem creates a way to supply the data for the technology developed in the first. These two problems are computationally difficult, such as the representation of higher order spherical harmonics Y400, which is critical for upscaling weather data to almost infinitely fine spatial resolution. For the first problem, the 4D Visual Delivery (4DVD) software technology has been created as a web application to provide appealing visualizations of climate data and to easily deliver big climate data to end users quickly and efficiently. The need for such an application is first discussed and is followed up with the initial design requirements. From here, a novel interconnected system is designed that seamlessly integrates a database, web server, and front-end computing. WebGL and JavaScript are used to generate feature rich maps and time series of global climate data. The delivered data can be sorted and their statistical properties can be easily computed. All this is done with minimal processing from the server because the computations are moved instead to the end user’s web browser, taking a heavy burden off of the back-end. The second topic is the calculation of an annual mean global climate of surface air temperature anomalies through the use of optimal interpolation of existing weather stations by using the eigenvalues and vectors calculated via a spherical harmonics representation of the global climate field. An optimal interpolation is made for the weather station data by using a multivariate regression built on these eigenvalues and vectors which is calculated via an SVD algorithm in the spectral space. The background, mathematics, and code logic are explored for three of the main difficulties: transform of the high order spherical harmonic functions, the calculation of eigenvalues and vectors in complex space in Java, and the calculation of the optimal weights when involving large complex matrices. In the research, code optimizations and numerical verifications of the statistical theory are made and a novel cloud computing approach was explored to find optimal weights over Apache Spark, which is a distributed computing library. Cloud computing was carried out to generate many results of this research, and the cloud computing procedure is documented in this dissertation for other researchers.