Exploring Trends through “RainSphere”: Research data transformed into public knowledge
Exploring Trends through “RainSphere”: Research data transformed into public knowledge
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通过“RainSphere”探索趋势：研究数据转化为公共知识
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发表时间：
2017
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通讯作者：
D. Braithwaite
中科院分区：
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作者：
P. Nguyen;S. Sorooshian;A. Thorstensen;H. Tran;Phat Huynh;Thanh T. Pham;Hamed Ashouri;K. Hsu;A. Aghakouchak;D. Braithwaite
气候是一个既影响全球人口又受全球人口影响的感兴趣的领域，然而，从专门从事该领域的科学家到普通公众的关于我们气候系统的关键基础知识的转移严重不足。为了弥补这一差距，使人们能够了解历史气候和气候预测，开发了一个直观和用户友好的分析工具，称为水文气象和遥感中心雨球（设在http：//www.example.com;关于水文气象和遥感中心雨球的YouTube视频教程可在www.youtube.com？rainsphere.eng.uci.edu v=eI2-f88iGlY&feature= youtu.be）。CHRS RainSphere被设计为一个教育工具，使用户能够快速，轻松地进行分析的历史和未来的降水在空间尺度，范围从高度本地到全球，并在每日，每月或每年的时间尺度。通过自动生成的时间序列、空间图和基本趋势分析，用户可以快速浏览历史降水估计和针对其特定兴趣的未来预测。让数据以公众能够理解的方式为自己说话，不仅有助于传播对气候和气候变率的理解，而且还鼓励气候研究的独立调查和发现。CHRS RainSphere的核心是使用人工神经网络-气候数据记录（PERSIANN-CDR）的遥感信息降水估计（Ashouri等人，2015年）。这一基于卫星的降水产品提供了0.25°空间分辨率下南纬60°至北纬60°的每日降水估计数。PERSIANN-CDR是从母体PERSIANN算法（Hsu等人，1997年）衍生而来的，该算法利用人工神经网络，根据对地球静止轨道卫星红外信息和低近地轨道卫星无源微波信息的亮温反演，确定地表降雨率。已在多项研究中对PERSIANN产品进行了确认（即，Sorooshian et al. 2000; Miao et al. 2015，Ashouri et al. 2016）。PERSIANN-CDR提供了研究极端水文气象现象的能力。记录始于1983年1月1日，一直持续到现在。这30多年来对全球降水量的回顾性研究有助于解决一系列与历史降水量相关的问题，例如“今年6月的总降水量与6月的平均月总降水量相比如何？”或者“我国年降水量的趋势是什么？”这些和无数其他问题可以通过CHRS RainSphere使用PERSIANN-CDR来回答。CHRS RainSphere补充了PERSIANN-CDR过去的降水量估计，具有来自耦合模式相互比较项目第5阶段（CMIP 5）的全球降水量预测，该项目基于政府间气候变化专门委员会（IPCC）的三种碳排放情景（分别为低，稳定和高排放情景的RCP 2.6，RCP 4.5和RCP 8.5）。关于CMIP 5的更多细节可以在Taylor等人（2012）中找到。从加拿大气候数据和情景网站（http：//www.example.com）获得了来自29个CMIP 5模型的IPCC预测降水量的平均值。ccds-dscc.ec.gc.ca CMIP 5模型结果被内插到一个普通的1 × 1度网格中（查看更多附属机构：Nguyen水文气象和遥感中心（CHRS）和土木与环境工程系，加州大学欧文分校，欧文，加州，和农林大学，胡志明市，越南; SorooShiaN，ThorSTeNSeN，TraN，huyNh，Pham，aShouri，hSu，aghaKouchaK，aNd BraiThwaiTe-水文气象学和遥感中心（CHRS）和加州大学土木与环境工程系，欧文，欧文，加州
C limate is an area of interest that both influences and is influenced by the global population, and yet the transfer of critical basic knowledge about our climate system from scientists who specialize in the field to the general public is severely deficient. To bridge this gap and make understanding historical climate and climate projections accessible, an intuitive and user-friendly analysis tool called CHRS (Center for Hydrometeorology and Remote Sensing) RainSphere has been developed (hosted at http:// rainsphere.eng.uci.edu; a YouTube video tutorial on CHRS RainSphere is available at www.youtube.com /watch?v=eI2-f88iGlY&feature=youtu.be). CHRS RainSphere was designed as an educational tool that allows users to quickly and easily conduct analyses of historical and future precipitation at spatial scales that range from highly local to global and at daily, monthly, or annual time scales. With automatically generated time series, spatial plots, and basic trend analysis, users can swiftly explore historical precipitation estimates and future projections tailored to their specific interests. Allowing the data to speak for themselves in a way that the public can understand not only helps to spread the comprehension of climate and climate variability but also encourages independent inquisition and discovery of climate studies. At the heart of CHRS RainSphere is the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks–Climate Data Record (PERSIANN-CDR) (Ashouri et al. 2015). This satellite-based precipitation product provides daily precipitation estimates from 60°S to 60°N latitude at a 0.25° spatial resolution. PERSIANN-CDR is derived from the parent PERSIANN algorithm (Hsu et al. 1997), which utilizes an artificial neural network to assign a surface rain rate based on brightness temperature retrievals of infrared information from geostationary Earth-orbiting satellites and passive microwave information from low-Earthorbiting satellites. Validation of the PERSIANN product has been performed in several studies (i.e., Sorooshian et al. 2000; Miao et al. 2015, Ashouri et al. 2016). PERSIANN-CDR provides the ability to study extreme hydrometeorological phenomena. The record begins on 1 January 1983 and continues to the present date. This 30+-year retrospective look at global precipitation lends itself to a host of historical precipitation-related questions such as “How does this June’s total precipitation compare to the average monthly total precipitation for June?” or “What is the trend in annual precipitation for my country?” These and countless other questions can be answered using PERSIANN-CDR facilitated through CHRS RainSphere. Complementing the past precipitation estimates from PERSIANN-CDR, CHRS RainSphere features global precipitation projections from the Coupled Model Intercomparison Project, Phase 5 (CMIP5) based on three carbon emission scenarios (RCP2.6, RCP4.5, and RCP8.5 for low, stabilized, and high emissions scenarios, respectively) from the Intergovernmental Panel on Climate Change (IPCC). More details on CMIP5 can be found in Taylor et al. (2012). Ensemble mean IPCC projected precipitation data from 29 CMIP5 models were obtained from the Canadian Climate Data and Scenarios site (http:// ccds-dscc.ec.gc.ca). CMIP5 model results were interpolated to a common 1 × 1 degree grid (see more AFFILIATIONS: NguyeN—Center for Hydrometeorology and Remote Sensing (CHRS) and Department of Civil and Environmental Engineering, University of California, Irvine, Irvine, California, and Nong Lam University, Ho Chi Minh City, Vietnam; SorooShiaN, ThorSTeNSeN, TraN, huyNh, Pham, aShouri, hSu, aghaKouchaK, aNd BraiThwaiTe—Center for Hydrometeorology and Remote Sensing (CHRS) and Department of Civil and Environmental Engineering, University of California, Irvine, Irvine, California