Derivation of New Daily Rainfall Values from TAO 1-Min Rain Gauge Data

IF 1.9 4区 地球科学 Q2 ENGINEERING, OCEAN Journal of Atmospheric and Oceanic Technology Pub Date : 2022-11-01 DOI:10.1175/jtech-d-22-0011.1
W. E. Cook, J. S. Greene
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Abstract

Daily rainfall accumulation estimates have been derived from 1-min volume data collected via self-syphon rain gauges deployed in the Tropical Atmosphere–Ocean (TAO) array of oceanographic buoys. The underlying high-resolution volume data were obtained directly from the Global Tropical Moored Buoy Array (GTMBA) Project Office of NOAA/Pacific Marine Environmental Laboratory. The derived accumulations have been incorporated into the Pacific Rainfall (PACRAIN) database as estimated daily values to augment existing sea level oceanic rainfall records gathered using traditional rain gauges. They have also been included in the PACRAIN historical, monthly gridded rainfall product. The methodology presented, which employs differencing of least squares–regressed sensor levels about 0000 UTC and rain gauge syphon events, is shown to offer improved error characteristics over the methodology used to compute previously published GTMBA rain rates. In particular, the PACRAIN method yields larger coefficients of determination and smaller standard errors than the duplicated GTMBA method when applied to synthetic rainfall data with noise magnitude and decorrelation times encompassing those observed in the real 1-min data. These results are shown to be consistent with mathematical expectations. Sources of instrument and catchment errors, as well as evaporation, are discussed in the context of their potential effects on accumulation estimates for periods of a day or longer. In this paper, we describe the derivation of daily rainfall amounts from raw rain gauge data obtained from buoy-mounted rain gauges. These new accumulation estimates expand the store of rainfall estimates from locations approximating the open-ocean conditions of the tropical Pacific Ocean. The derivation technique we describe exhibits better performance than the method used to generate previously published estimates using the same dataset.
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从TAO 1分钟雨量表数据推导新的日降雨量
根据部署在热带大气-海洋(TAO)海洋浮标阵列中的自虹吸管雨量计收集的1分钟体积数据,得出了每日降雨量累积估计值。基础高分辨率体积数据直接从NOAA/太平洋海洋环境实验室的全球热带系泊浮标阵列(GTMBA)项目办公室获得。所得累积量已作为估计日值纳入太平洋降雨数据库,以增加使用传统雨量计收集的现有海平面海洋降雨记录。它们也被纳入PACRAIN历史月度网格降雨产品中。所提出的方法采用了最小二乘法的差分——大约0000 UTC的回归传感器水平和雨量计虹吸事件,与之前公布的GTMBA降雨率计算方法相比,该方法具有改进的误差特性。特别是,当将PACRAIN方法应用于噪声幅度和去相关时间包括在真实1分钟数据中观察到的噪声幅度和解相关时间的合成降雨数据时,它比重复的GTMBA方法产生更大的确定系数和更小的标准误差。这些结果与数学预期一致。在对一天或更长时间的累积估计的潜在影响的背景下,讨论了仪器和集水区误差以及蒸发的来源。在本文中,我们描述了从浮标雨量计获得的原始雨量计数据中推导出的日降雨量。这些新的累积估计扩大了来自接近热带太平洋公海条件的位置的降雨量估计的存储范围。我们描述的推导技术比使用相同数据集生成先前发布的估计值的方法表现出更好的性能。
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来源期刊
CiteScore
4.50
自引率
9.10%
发文量
135
审稿时长
3 months
期刊介绍: The Journal of Atmospheric and Oceanic Technology (JTECH) publishes research describing instrumentation and methods used in atmospheric and oceanic research, including remote sensing instruments; measurements, validation, and data analysis techniques from satellites, aircraft, balloons, and surface-based platforms; in situ instruments, measurements, and methods for data acquisition, analysis, and interpretation and assimilation in numerical models; and information systems and algorithms.
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