ALBERT

Description: The objective of this study is to improve the characterization of satellite-derived atmospheric motion vectors (AMVs) and their errors to guide developments in the use of AMVs in numerical weather prediction. AMVs tend to exhibit considerable systematic and random errors that arise in the derivation or the interpretation of AMVs as single-level point observations of wind. One difficulty in the study of AMV errors is the scarcity of collocated observations of clouds and wind. This study uses instead a simulation framework: geostationary imagery for Meteosat-8 is generated from a high-resolution simulation with the Weather Research and Forecasting regional model, and AMVs are derived from sequences of these images. The forecast model provides the “truth” with a sophisticated description of the atmosphere. The study considers infrared and water vapor AMVs from cloudy scenes. This is the first part of a two-part paper, and it introduces the framework and provides a first evaluation in terms of the brightness temperatures of the simulated images and the derived AMVs. The simulated AMVs show a considerable global bias in the height assignment (60–75 hPa) that is not observed in real AMVs. After removal of this bias, however, the statistics comparing the simulated AMVs with the true model wind show characteristics that are similar to statistics comparing real AMVs with short-range forecasts (speed bias and root-mean-square vector difference typically agree to within 1 m s−1). This result suggests that the error in the simulated AMVs is comparable to or larger than that in real AMVs. There is evidence for significant spatial, temporal, and vertical error correlations, with the scales for the spatial error correlations being consistent with estimates for real data.

Description: Background Gene expression analysis is an important tool in contemporary research, with real-time PCR as the method of choice for quantifying transcription levels. Co-analysis of suitable reference genes is crucial for accurate expression normalisation. Reference gene expression may vary, e.g., among species or tissues; thus, candidate genes must be tested prior to use in expression studies. The domestic cat is an important study subject in both medical research and veterinary medicine. The aim of the present study was to develop TaqMan® real-time PCR assays for eight potential reference genes and to test their applicability for feline samples, including blood, lymphoid, endocrine, and gastrointestinal tissues from healthy cats, and neoplastic tissues from FeLV-infected cats. Results RNA extraction from tissues was optimised for minimal genomic DNA (gDNA) contamination without use of a DNase treatment. Real-time PCR assays were established and optimised for v-abl Abelson murine leukaemia viral oncogene homolog (ABL), β-actin (ACTB), β-2-microglobulin (B2M), β-glucuronidase (GUSB), hydroxymethyl-bilane synthase (HMBS), hypoxanthine phosphoribosyltransferase (HPRT), ribosomal protein S7 (RPS7), and tryptophan 5-monooxygenase activation protein, zeta polypeptide (YWHAZ). The presence of pseudogenes was confirmed for four of the eight investigated genes (ACTB, HPRT, RPS7, and YWHAZ). The assays were tested together with previously developed TaqMan® assays for feline glyceraldehyde-3-phosphate dehydrogenase (GAPDH) and the universal 18S rRNA gene. Significant differences were found among the expression levels of the ten candidate reference genes, with a ~106-fold expression difference between the most abundant (18S rRNA) and the least abundant genes (ABL, GUSB, and HMBS). The expression stability determined by the geNorm and NormFinder programs differed significantly. Using the ANOVA-based NormFinder program, RPS7 was the most stable gene in the tissues studied, followed by ACTB and ABL; B2M, HPRT, and the 18S rRNA genes were the least stable ones. Conclusion The reference gene expression stability varied considerably among the feline tissues investigated. No tested gene was optimal for normalisation in all tissues. For the majority of the tissues, two to three reference genes were necessary for accurate normalisation. The present study yields essential information on the correct choice of feline reference genes depending on the tissues analysed.