This book presents the fundamentals of data assimilation and reviews the application of satellite remote sensing in hydrological data assimilation. Although hydrological models are valuable tools to monitor and understand global and regional water cycles, they are subject to various sources of errors. Satellite remote sensing data provides a great opportunity to improve the performance of models through data assimilation.

Statistical Methods in the Atmospheric Sciences, Third Edition, explains the latest statistical methods used to describe, analyze, test, and forecast atmospheric data. This revised and expanded text is intended to help students understand and communicate what their data sets have to say, or to make sense of the scientific literature in meteorology, climatology, and related disciplines. In this new edition, what was a single chapter on multivariate statistics has been expanded to a full six chapters on this important topic. Other chapters have also been revised and cover exploratory data analysis, probability distributions, hypothesis testing, statistical weather forecasting, forecast verification, and time series analysis. There is now an expanded treatment of resampling tests and key analysis techniques, an updated discussion on ensemble forecasting, and a detailed chapter on forecast verification. In addition, the book includes new sections on maximum likelihood and on statistical simulation and contains current references to original research. Students will benefit from pedagogical features including worked examples, end-of-chapter exercises with separate solutions, and numerous illustrations and equations. This book will be of interest to researchers and students in the atmospheric sciences, including meteorology, climatology, and other geophysical disciplines. - Accessible presentation and explanation of techniques for atmospheric data summarization, analysis, testing and forecasting - Many worked examples - End-of-chapter exercises, with answers provided

South America is a unique place where a number of past climate archives are ava- able from tropical to high latitude regions. It thus offers a unique opportunity to explore past climate variability along a latitudinal transect from the Equator to Polar regions and to study climate teleconnections. Most climate records from tropical and subtropical South America for the past 20,000 years have been interpreted as local responses to shift in the mean position and intensity of the InterTropical Conv- gence Zone due to tropical and extratropical forcings or to changes in the South American Summer Monsoon. Further South, the role of the Southern Hemisphere westerly winds on global climate has been highly investigated with both paleodata and coupled climate models. However the regional response over South America during the last 20,000 years is much more variable from place to place than pre- ously thought. The factors that govern the spatial patterns of variability on millennial scale resolution are still to be understood. The question of past natural rates and ranges of climate conditions over South America is therefore of special relevance in this context since today millions of people live under climates where any changes in monsoon rainfall can lead to catastrophic consequences.

Empirical-statistical downscaling (ESD) is a method for estimating how local climatic variables are affected by large-scale climatic conditions. ESD has been applied to local climate/weather studies for years, but there are few ? if any ? textbooks on the subject. It is also anticipated that ESD will become more important and commonplace in the future, as anthropogenic global warming proceeds. Thus, a textbook on ESD will be important for next-generation climate scientists.

More accurate forecasts of climate conditions over time periods of weeks to a few years could help people plan agricultural activities, mitigate drought, and manage energy resources, amongst other activities; however, current forecast systems have limited ability on these time- scales. Models for such climate forecasts must take into account complex interactions among the ocean, atmosphere, and land surface. Such processes can be difficult to represent realistically. To improve the quality of forecasts, this book makes recommendations about the development of the tools used in forecasting and about specific research goals for improving understanding of sources of predictability. To improve the accessibility of these forecasts to decision-makers and researchers, this book also suggests best practices to improve how forecasts are made and disseminated.

In this book the Sea Surface Temperature (SST) patterns of decadal-to-multidecadal variability observed and simulated by 17 general circulation models (GCMs) are analyzed. Furthermore, their impact on precipitation in West Africa and South America and the atmospheric mechanisms involved are assessed. Through this analysis, the effect of external forcings on these impacts and the relative contribution of decadal-to-multidecadal variability patterns of SST to precipitation are presented in depth. Finally, a humid period in the West African region of the Sahel during the 19th century, previously little documented, is analyzed using an atmospheric GCM. The monsoons of West Africa and South America have shown changes in the timescales of a few decades. Previous work suggests a relationship with patterns of decadal-to-multidecadal variability of SST, such as global warming and the Atlantic and Pacific variability. However, the dynamics underlying this relationship and its simulation by current GCMs had not been addressed in a consistent manner. This is the main motivation of this book. The results of this book not only represent a great step forward in our understanding of the changes in the precipitation regimes of the studied regions, but they can also be of great help for the improvement of decadal prediction systems and the associated social consequences.