Sensitivity of GNSS Occultation Profiles to Horizontal Variability in the Troposphere: A Simulation Study
Sensitivity of GNSS Occultation Profiles to Horizontal Variability in the Troposphere: A Simulation Study
复制标题
GNSS 掩星剖面对对流层水平变化的敏感性:模拟研究
DOI:
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发表时间:
2004
期刊:
影响因子:
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通讯作者:
G. Kirchengast
中科院分区:
文献类型:
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作者:
U. Foelsche;G. Kirchengast
We investigated the sensitivity of atmospheric profiles retrieved from Global Navigation Satellite System (GNSS) radio occultation data to atmospheric horizontal variability errors. First, the errors in a quasi-realistic horizontally variable atmosphere relative to errors in a spherically symmetric atmosphere were quantified based on an ensemble of about 300 occultation events. This investigation was based on simulated data using a representative European Centre for Medium-Range Weather Forecasts (ECMWF) T511L60 analysis field with and without horizontal variability. Biases and standard deviations are, below 20 km, significantly smaller under a spherical symmetry assumption than corresponding errors in an atmosphere with horizontal variability. The differences are most pronounced below ~7 km height. Second, we assessed the relevance of either assuming the “true” profile vertically at a mean event location (the common practice) or along the actual 3D tangent point trajectory. Standard deviation and bias errors decrease significantly if the data are exploited along the tangent point trajectory. Third, the sensitivity of retrieval products to the angle-of-incidence of occultation rays relative to the boresight direction of the receiving antenna (aligned with the orbit plane of the Low Earth Orbit satellite) was analyzed based on the same ensemble of events for three different angle-of-incidence classes (0–10 deg, 20–30 deg, 40–50 deg; ensembles of about 100 events in each class). Below about 7 km, most errors were found to increase with increasing angle of incidence. Dry temperature biases between 7 km and 20 km exhibit no relevant increase with increasing angle of incidence, which is favorable regarding the climate monitoring utility of the data.