Qualitative Precipitation Estimation from Satellite Data Based on Distributed Domain-Specific Architecture

IF 0.8 Q3 ENGINEERING, MULTIDISCIPLINARY Modelling and Simulation in Engineering Pub Date : 2021-02-16 DOI:10.1155/2021/8827900
S. Prongnuch, T. Wiangtong, S. Sitjongsataporn
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Abstract

This paper presents the qualitative precipitation estimation (QPE) based on data from the Himawari satellite and distributed-domain specific architecture. The QPE process consists of receiving and managing the raw data from the satellite every 10 minutes and calculating the rain-temperature relationship. The aim of this research is to decrease the QPE processing time by using distributed domain-specific architecture (DDSA), with 9 small computing boards are connected to a gigabit switch. Instead of using a high-performance PC, this distributed embedded system is also suitable for processing interval data receiving from the satellite every 10 minutes. The experimental results show that the proposed fast-satellite data processing algorithm is optimal for QPE processing on the DDSA platform, requiring 115.53 seconds processing time and low power consumption.
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基于分布式领域特定架构的卫星数据定性降水估计
本文提出了基于Himawari卫星数据和分布式域特定架构的定性降水估计(QPE)。QPE过程包括每10分钟接收和管理来自卫星的原始数据,并计算雨温关系。本研究的目的是通过将9个小型计算板连接到千兆交换机上,采用分布式特定领域架构(DDSA)来缩短QPE处理时间。该分布式嵌入式系统不使用高性能PC机,也适合处理每10分钟从卫星接收的间隔数据。实验结果表明,该快速卫星数据处理算法处理时间为115.53秒,功耗低,是DDSA平台上QPE处理的最优算法。
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来源期刊
Modelling and Simulation in Engineering
Modelling and Simulation in Engineering ENGINEERING, MULTIDISCIPLINARY-
CiteScore
2.70
自引率
3.10%
发文量
42
审稿时长
18 weeks
期刊介绍: Modelling and Simulation in Engineering aims at providing a forum for the discussion of formalisms, methodologies and simulation tools that are intended to support the new, broader interpretation of Engineering. Competitive pressures of Global Economy have had a profound effect on the manufacturing in Europe, Japan and the USA with much of the production being outsourced. In this context the traditional interpretation of engineering profession linked to the actual manufacturing needs to be broadened to include the integration of outsourced components and the consideration of logistic, economical and human factors in the design of engineering products and services.
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