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Sensitivity Analysis and Impact of the Kappa?Correction of Residual Ionospheric Biases on Radio Occultation Climatologies

ABSTRACT: Abstract A new model was recently introduced to correct for higher?order ionospheric residual biases in radio occultation (RO) data. The model depends on the ?1 and ?2 dual?frequency bending angle difference squared, and a factor ?, which varies with time, season, solar activity, and height, needing only the F10.7 solar radio flux index as additional background information. To date, this kappa?correction was analyzed in simulation studies. In this study, we test it on real observed Metop?A RO data. The goal is to improve the accuracy of monthly mean RO climate records, potentially raising the accuracy of RO data toward higher stratospheric altitudes. We performed a thorough analysis of the kappa?correction, evaluating its ionospheric sensitivity during the solar cycle for monthly RO climatologies and comparing the kappa?corrected RO stratospheric climatologies to three other data sets from reanalysis and passive infrared sounding. We find a clear dependence of the kappa?correction on solar activity, geographic location, and altitude; hence, it reduces systematic errors that vary with the solar cycle. From low to high solar activity conditions, the correction can increase from values of about 0.2 K to more than 2.0 K at altitudes between 40 to 45 km. The correction shifts RO climatologies toward warmer temperatures. With respect to other data sets, however, we found it difficult to draw firm conclusions, because the biases in the other data sets appear to be at similar magnitude as the size of the kappa?correction. Further validation with more accurate data will be useful. Key Points The kappa?correction model is tested for the impact of its correction of higher?order ionospheric biases in radio occultation data The magnitude of the kappa?correction varies over the solar cycle from 0.2 K to more than 2.0 K at about 40 km The correction shifts the stratospheric data toward warmer temperatures; firm validation based on other data sets was difficult

SUBMITTER: Danzer J

PROVIDER: S-EPMC7507777 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature

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Project description:There is currently a gap in satellite observations of the moisture structure during heavy precipitation conditions, since infrared and microwave sounders cannot sense water-vapour structure near the surface in the presence of intense precipitation. Conversely, Global Navigation Satellite System (GNSS) radio occultations (RO) can profile the moisture structure with high precision and vertical resolution, but cannot indicate the presence of precipitation directly. Polarimetric RO (PRO) measurements have been proposed as a method to characterize heavy rain in GNSS RO, by measuring the polarimetric differential phase delay induced by large size hydrometeors. Previous studies have shown that the PRO polarimetric phase shift is sensitive to the path-integrated rain rate under intense precipitation scenarios, but there is no current method to invert PRO measurements into quantitative estimates of the path-averaged rain rate. In this manuscript, a probabilistic inversion approach to the GNSS PRO observables is proposed, where the GPM precipitation products are used for the construction of an a priori look-up table (LUT) database. The performance of the LUTs is assessed for use in the inversion of satellite-based GNSS PRO observations, based on synthetically generated PRO data of actual events, which correspond to co-locations between GNSS RO profiles and the TRMM observations. The synthetic data include end-to-end propagation effects of the polarimetric observables and a simple separation algorithm to isolate the hydrometeor component of the observation. The assessment results in agreement better than ±1 mm/hr between the reference LUT and the actual rain statistics of the synthetic data, proving the suitability of the GPM-based probabilistic inversion tool. These findings indicate that the GNSS PRO products are capable of extending the current GNSS RO ones by associating indications of rain-rate probabilities at different altitudes, at ?250 m vertical resolution and under intense precipitation scenarios with the standard vertical thermodynamic profiles.

Project description:BackgroundLess than two percent of the human genome is protein coding, yet that small fraction harbours the majority of known disease causing mutations. Despite rapidly falling whole genome sequencing (WGS) costs, much research and increasingly the clinical use of sequence data is likely to remain focused on the protein coding exome. We set out to quantify and understand how WGS compares with the targeted capture and sequencing of the exome (exome-seq), for the specific purpose of identifying single nucleotide polymorphisms (SNPs) in exome targeted regions.ResultsWe have compared polymorphism detection sensitivity and systematic biases using a set of tissue samples that have been subject to both deep exome and whole genome sequencing. The scoring of detection sensitivity was based on sequence down sampling and reference to a set of gold-standard SNP calls for each sample. Despite evidence of incremental improvements in exome capture technology over time, whole genome sequencing has greater uniformity of sequence read coverage and reduced biases in the detection of non-reference alleles than exome-seq. Exome-seq achieves 95% SNP detection sensitivity at a mean on-target depth of 40 reads, whereas WGS only requires a mean of 14 reads. Known disease causing mutations are not biased towards easy or hard to sequence areas of the genome for either exome-seq or WGS.ConclusionsFrom an economic perspective, WGS is at parity with exome-seq for variant detection in the targeted coding regions. WGS offers benefits in uniformity of read coverage and more balanced allele ratio calls, both of which can in most cases be offset by deeper exome-seq, with the caveat that some exome-seq targets will never achieve sufficient mapped read depth for variant detection due to technical difficulties or probe failures. As WGS is intrinsically richer data that can provide insight into polymorphisms outside coding regions and reveal genomic rearrangements, it is likely to progressively replace exome-seq for many applications.

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Sensitivity Analysis and Impact of the Kappa?Correction of Residual Ionospheric Biases on Radio Occultation Climatologies

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