2018
DOI: 10.3390/rs10030436
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Optimal Interpolation of Precipitable Water Using Low Earth Orbit and Numerical Weather Prediction Data

Abstract: The National Meteorological Satellite Center/Korean Meteorological Administration (NMSC/KMA) receives data directly from low Earth orbit (LEO) satellites 19;B;, and generates Level 2 products (e.g., temperature and humidity profile) in near real time. Total precipitable water (TPW) and layer precipitable water (LPW) are also generated using the retrieved humidity profiles. Today, forecasters need meteorologically-significant data fields composited from all available data sources, not multiple maps of observati… Show more

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Cited by 10 publications
(3 citation statements)
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“…The analysis produced by SI is a weighted average of the in situ data (typically within a search radius) and the background field with the weights being calculated so that the error variance of the resultant analysis is a minimum, with respect to both the in situ data and the background field. SI relies on the assumption that corrections to the background field depend linearly on the background-observation residuals, that the background and observation errors are unbiased and uncorrelated, and that rainfall errors are nonstationary and anisotropic (Heo et al 2018). The ramifications of these assumptions are discussed in section 4a.…”
Section: B Si Algorithmmentioning
confidence: 99%
“…The analysis produced by SI is a weighted average of the in situ data (typically within a search radius) and the background field with the weights being calculated so that the error variance of the resultant analysis is a minimum, with respect to both the in situ data and the background field. SI relies on the assumption that corrections to the background field depend linearly on the background-observation residuals, that the background and observation errors are unbiased and uncorrelated, and that rainfall errors are nonstationary and anisotropic (Heo et al 2018). The ramifications of these assumptions are discussed in section 4a.…”
Section: B Si Algorithmmentioning
confidence: 99%
“…The Editorial Office of Remote Sensing wish to report an error in the aforementioned published paper [1]. The titles of Figures 5 and 6 as well as Table 3 were incorrect, which were originally: We apologize for any inconvenience caused to the readers by this change.…”
mentioning
confidence: 99%
“…Data assimilation is an advanced technique whereby real-time observations are assimilated with predictions from models established with historical data in order to achieve an improved estimate of the evolving states of a system [20,21]. With the continuous advances of pervasive sensing and monitoring of the environment [22,23], it can be foreseen that data assimilation will become even more popular going forward [24]. Nevertheless, existing methodologies on data assimilation in the DT of water treatment facilities are deemed insufficient.…”
Section: Chapter Onementioning
confidence: 99%