A Climate Downscaling Deep Learning Model considering the Multiscale Spatial Correlations and Chaos of Meteorological Events
المؤلفون المشاركون
Yuan, Shijin
Mu, Bin
Qin, Bo
Qin, Xiaoyun
المصدر
Mathematical Problems in Engineering
العدد
المجلد 2020، العدد 2020 (31 ديسمبر/كانون الأول 2020)، ص ص. 1-17، 17ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2020-11-16
دولة النشر
مصر
عدد الصفحات
17
التخصصات الرئيسية
الملخص EN
Climate downscaling is a way to provide finer resolution data at local scales, which has been widely used in meteorological research.
The two main approaches for climate downscaling are dynamical and statistical.
The traditional dynamical downscaling methods are quite time- and resource-consuming based on general circulation models (GCMs).
Recently, more and more researchers construct a statistical deep learning model for climate downscaling motivated by the single-image superresolution (SISR) process in computer vision (CV).
This is an approach that uses historical climate observations to learn a low-resolution to high-resolution mapping and produces great enhancements in terms of efficiency and effectiveness.
Therefore, it has provided an appreciable new insight and successful downscaling solution to multiple climate phenomena.
However, most existing models only make a simple analogy between climate downscaling and SISR and ignore the underlying dynamical mechanisms, which leads to the overaveraged downscaling results lacking crucial physical details.
In this paper, we incorporate the a priori meteorological knowledge into a deep learning formalization for climate downscaling.
More specifically, we consider the multiscale spatial correlations and the chaos in multiple climate events.
Depending on two characteristics, we build up a two-stage deep learning model containing a stepwise reconstruction process and ensemble inference, which is named climate downscaling network (CDN).
It can extract more local/remote spatial dependencies and provide more comprehensive captures of extreme conditions.
We evaluate our model based on two datasets: climate science dataset (CSD) and benchmark image dataset (BID).
The results demonstrate that our model shows the effectiveness and superiority in downscaling daily precipitation data from 2.5 degrees to 0.5 degrees over Asia and Europe.
In addition, our model exhibits better performance than the other traditional approaches and state-of-the-art deep learning models.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Mu, Bin& Qin, Bo& Yuan, Shijin& Qin, Xiaoyun. 2020. A Climate Downscaling Deep Learning Model considering the Multiscale Spatial Correlations and Chaos of Meteorological Events. Mathematical Problems in Engineering،Vol. 2020, no. 2020, pp.1-17.
https://search.emarefa.net/detail/BIM-1200753
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Mu, Bin…[et al.]. A Climate Downscaling Deep Learning Model considering the Multiscale Spatial Correlations and Chaos of Meteorological Events. Mathematical Problems in Engineering No. 2020 (2020), pp.1-17.
https://search.emarefa.net/detail/BIM-1200753
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Mu, Bin& Qin, Bo& Yuan, Shijin& Qin, Xiaoyun. A Climate Downscaling Deep Learning Model considering the Multiscale Spatial Correlations and Chaos of Meteorological Events. Mathematical Problems in Engineering. 2020. Vol. 2020, no. 2020, pp.1-17.
https://search.emarefa.net/detail/BIM-1200753
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1200753
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
تقوم هذه الخدمة بالتحقق من التشابه أو الانتحال في الأبحاث والمقالات العلمية والأطروحات الجامعية والكتب والأبحاث باللغة العربية، وتحديد درجة التشابه أو أصالة الأعمال البحثية وحماية ملكيتها الفكرية. تعرف اكثر