Author: Dánnell Quesada ChacónPublisher:
ISBN:
Category :
Languages : en
Pages : 0
Book Description
Deep Learning Based High-resolution Statistical Downscaling to Support Climate Impact Modelling
Author: Dánnell Quesada ChacónPublisher:
ISBN:
Category :
Languages : en
Pages : 0
Book Description
Downscaling Techniques for High-Resolution Climate Projections
Author: Rao KotamarthiPublisher: Cambridge University Press
ISBN: 1108587062
Category : Science
Languages : en
Pages : 213
Book Description
Downscaling is a widely used technique for translating information from large-scale climate models to the spatial and temporal scales needed to assess local and regional climate impacts, vulnerability, risk and resilience. This book is a comprehensive guide to the downscaling techniques used for climate data. A general introduction of the science of climate modeling is followed by a discussion of techniques, models and methodologies used for producing downscaled projections, and the advantages, disadvantages and uncertainties of each. The book provides detailed information on dynamic and statistical downscaling techniques in non-technical language, as well as recommendations for selecting suitable downscaled datasets for different applications. The use of downscaled climate data in national and international assessments is also discussed using global examples. This is a practical guide for graduate students and researchers working on climate impacts and adaptation, as well as for policy makers and practitioners interested in climate risk and resilience.
Downscaling Techniques for High-Resolution Climate Projections
Author: Rao KotamarthiPublisher: Cambridge University Press
ISBN: 110847375X
Category : Nature
Languages : en
Pages : 213
Book Description
A practical guide to understanding, using and producing downscaled climate data, for researchers, graduate students, policy makers and practitioners.
Empirical-statistical Downscaling
Author: Rasmus E. BenestadPublisher: World Scientific
ISBN: 9812819126
Category : Science
Languages : en
Pages : 228
Book Description
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.
Statistical Downscaling and Bias Correction for Climate Research
Author: Douglas MaraunPublisher: Cambridge University Press
ISBN: 1107066050
Category : Mathematics
Languages : en
Pages : 365
Book Description
A comprehensive and practical guide, providing technical background and user context for researchers, graduate students, practitioners and decision makers. This book presents the main approaches and describes their underlying assumptions, skill and limitations. Guidelines for the application of downscaling and the use of downscaled information in practice complete the volume.
Deep Recurrent Learned Dynamic Downscaling
Author: Jean-Yves Djamen-KepaouPublisher:
ISBN:
Category :
Languages : en
Pages : 0
Book Description
"Global climate models represent major climate system components of the planet in order to generate long term, sparse, accurate realizations of future climatic events across the entire globe. Downscaling is the method by which these low resolution realizations are converted into high resolution simulations of climate events which can then be used by stakeholders and policy makers. Regional climate models dynamically downscale simulated climate by conditioning global climate models on location-specific physical processes. Although these models are robust and reliable, they are computationally expensive when compared to statistical approaches for modeling a general relationship between global climate behaviour and local climate behavior. Therefore, there is need for downscaling methods that leverage the computational efficiency of statistical models while maintaining the performance of regional climate models.In this thesis, we build upon previously proposed deep learning methods for dynamical downscaling through estimation of a regional climate model. Our proposed model is a generative adversarial network that leverages the effects of temporal dependencies within spatio-temporal climate events"--
Precipitation: Advances in Measurement, Estimation and Prediction
Author: Silas C. MichaelidesPublisher: Springer Science & Business Media
ISBN: 3540776559
Category : Science
Languages : en
Pages : 552
Book Description
This volume is the outcome of contributions from 51 scientists who were invited to expose their latest findings on precipitation research and in particular, on the measurement, estimation and prediction of precipitation. The reader is presented with a blend of theoretical, mathematical and technical treatise of precipitation science but also with authentic applications, ranging from local field experiments and country-scale campaigns to multinational space endeavors.
Proceedings of COMPSTAT'2010
Author: Yves LechevallierPublisher: Springer Science & Business Media
ISBN: 3790826049
Category : Computers
Languages : en
Pages : 627
Book Description
Proceedings of the 19th international symposium on computational statistics, held in Paris august 22-27, 2010.Together with 3 keynote talks, there were 14 invited sessions and more than 100 peer-reviewed contributed communications.
Spatiotemporal Super-Resolution with Generative Machine Learning for Creating Renewable Energy Resource Data Under Climate Change Scenarios
Author: Publisher:
ISBN:
Category :
Languages : en
Pages : 0
Book Description
As we plan for a future with higher penetrations of renewables and increasing electrification, it becomes more important to understand how the electricity grid will operate under a variety of weather events. We must also consider that the weather our future grid will experience will be different and possibly more extreme than the historical weather that we have extensive data for. We can use data from global climate models (GCMs) to help understand how our climate may change over the next several decades, but there is often a significant gap between the low-resolution GCM data and the high-resolution weather data required to study power systems under specific weather events. Therefore, our objective in this work is to develop tools that can bridge this gap by using low-resolution GCM data to create realistic high-resolution weather datasets that can be used to study renewable energy generation and electricity demand. To accomplish this objective, we have developed a set of generative machine learning models that can rapidly downscale GCM daily average output data at an approximate grid resolution of 100km to hourly data at an approximate 4 km grid resolution. The models can be used to create high resolution data from nearly any GCM included in the Coupled Model Intercomparison Project (CMIP) Phase 5 or 6. Our methods include all datasets regularly used to study the integration of wind and solar power plants as well as changes in electricity demand due to heating and cooling loads. These models and datasets enable power systems modelers to study climate change-influenced weather events and their impact on the grid. We have downscaled and validated wind, solar, temperature, and humidity data with very promising results. The generative machine learning methods are computationally efficient and produce data that has similar statistical characteristics to current state-of-the-art historical datasets. We have trained initial generative models and produced an initial dataset collectively referred to as Sup3rCC: Super-Resolved Renewable Energy Resource Data with Climate Change Impacts. The data covers a (mostly) historical period from 2015-2025 and a future period from 2050-2059. We have also taken hypothetical high-electrification load data and scaled the heating and cooling loads with respect to the 2050-2059 high-resolution Sup3rCC meteorology. The results show how future levels of renewable energy generation and electrified load may be impacted by climate change, setting the stage for capacity expansion models to consider a dynamic climate through model years.
Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support
Author: Danail StoyanovPublisher: Springer
ISBN: 3030008894
Category : Computers
Languages : en
Pages : 401
Book Description
This book constitutes the refereed joint proceedings of the 4th International Workshop on Deep Learning in Medical Image Analysis, DLMIA 2018, and the 8th International Workshop on Multimodal Learning for Clinical Decision Support, ML-CDS 2018, held in conjunction with the 21st International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2018, in Granada, Spain, in September 2018. The 39 full papers presented at DLMIA 2018 and the 4 full papers presented at ML-CDS 2018 were carefully reviewed and selected from 85 submissions to DLMIA and 6 submissions to ML-CDS. The DLMIA papers focus on the design and use of deep learning methods in medical imaging. The ML-CDS papers discuss new techniques of multimodal mining/retrieval and their use in clinical decision support.