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![Rain Field Prediction in Land-Falling Tropical Cyclones: From a Spatio-Temporal Perspective Using Ground-Based Doppler Weather Observations and "Big-Spatial-Data" Technologies için kapak resmi Rain Field Prediction in Land-Falling Tropical Cyclones: From a Spatio-Temporal Perspective Using Ground-Based Doppler Weather Observations and "Big-Spatial-Data" Technologies için kapak resmi](/client/assets/d79c3e4af2b6d196/ctx/images/no_image.png)
Rain Field Prediction in Land-Falling Tropical Cyclones: From a Spatio-Temporal Perspective Using Ground-Based Doppler Weather Observations and "Big-Spatial-Data" Technologies
Başlık:
Rain Field Prediction in Land-Falling Tropical Cyclones: From a Spatio-Temporal Perspective Using Ground-Based Doppler Weather Observations and "Big-Spatial-Data" Technologies
Yazar:
Tang, Jingyin, author.
ISBN:
9780438122222
Yazar Ek Girişi:
Fiziksel Tanımlama:
1 electronic resource (115 pages)
Genel Not:
Source: Dissertation Abstracts International, Volume: 79-11(E), Section: A.
Advisors: Corene J. Matyas.
Özet:
Doppler weather radars are ideal instruments to observe precipitation in tropical cyclones when they move over land. Methodological and technical challenges arise in the use of data from radars in research due to the high spatio-temporal resolution, large volume and large transmission rate of radar data. Efficient yet accurate methods are needed to mosaic data from dozens of radars to better understand precipitation processes in tropical cyclones. Accurate observational data and reliable prediction models are both essential to improve forecasting quality, while technical innovations are critical to provide those data in time. Thus, this dissertation focuses on improving short-term predictability of tropical cyclone precipitation with Doppler weather radar observations, from both methodological and technical perspectives. It also aims to develop algorithms that prioritize high efficiency, convenience and feasibility for both meteorological and geographic communities. The analysis of historical weather events should utilize radar data from both sides of a moving temporal window and process them in a flexible data architecture which is not available in most standalone software tools or real-time systems. The first study presents a map-reduce-based playback framework using Apache Spark's computational engine to interpolate large volumes of radar Level-II data onto 3D grids. Designed as being friendly to use on a high-performance computing cluster, these methods may also be executed on a low-end configured machine. A protocol is designed to enable interoperability with Geographic Information System and spatial analysis functions in this framework. Open-source software is utilized to enhance radar usability in the non-specialist community. Case studies during tropical cyclone landfall shows this framework's capability of efficiently creating a large scale high-resolution 3D radar mosaic with integration of GIS functions for spatial analysis. In the second study, the ability to use a GIS for geospatial analysis with radar data is extended to provide a comprehensive, scalable geospatial analytical library with strong compatibility of the market-dominating ArcGIS software stack on a personal workstation, high-performance computing cluster, or modern Cloud Computing platforms. This cross-platform geospatial library "arc4nix" permits the application of a wide range of geospatial methods that exist in ArcGIS and enables the geographic community to perform sophisticated analysis of weather radar data with minimal technical difficulties. It also supports parallel computational tasks using multiple CPU cores and computers for large-scale analyses. In the third study, improved forecasting model based on the semi-Lagrangian advection scheme, variational analysis technique and Geographic Information System is presented. The combination of these methods increases reliable rainfall prediction period in tropical cyclones to about 7 hours. The model presented in this dissertation is proven to improve the tropical cyclone rainfall predictability and rainfall data processing ability in better spatio-temporal resolution and accuracy.
Notlar:
School code: 0070
Tüzel Kişi Ek Girişi:
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Yer Numarası | Demirbaş Numarası | Shelf Location | Lokasyon / Statüsü / İade Tarihi |
---|---|---|---|
XX(696651.1) | 696651-1001 | Proquest E-Tez Koleksiyonu | Arıyor... |
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