[1] The climatological diurnal cycle of precipitation in the tropics is analyzed using data from rain gauges on ocean buoys and satellite measurements by the Tropical Rainfall Measuring Mission (TRMM) satellite. The ocean buoy data are from the NOAA/Pacific Marine Environmental Laboratory Tropical Atmosphere-Ocean/Triangle Trans-Ocean buoy Network in the tropical Pacific Ocean. TRMM data are from the precipitation radar (PR) and the TRMM microwave imager (TMI). Climatological hourly mean precipitation rates are analyzed in terms of the diurnal and semidiurnal harmonics. Both data sets confirm an early morning peak in precipitation over ocean regions. The amplitude of the diurnal harmonic over the oceans is typically less than 25% of the mean precipitation rate. Over tropical land masses the rainfall peaks in the afternoon and evening hours. The relative amplitude of the diurnal harmonic over land is larger than over the ocean, often exceeding 50% of the mean rain rate. Previously noted differences between the TMI and PR rainfall retrievals persist in the diurnal cycle. On average the TMI measures more rainfall than the PR and has a larger diurnal variation. Phase differences between the two instruments do not show a consistent bias.

Background: For the past few years, scientific controversy has surrounded the large number of errors in forensic and literature mitochondrial DNA (mtDNA) data. However, recent research has shown that using mtDNA phylogeny and referring to known mtDNA haplotypes can be useful for checking the quality of sequence data.

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In this study, the effect of zero measurements on the spatial correlation function of rainfall is analyzed for the quantification of a rainfall field. The use of a bivariate mixed distribution function made it possible to analyze and compare the spatial correlation functions for these three different data sets: only the positive measurements at both gauge locations, positive measurements at either one or both gauge locations, and all measurements including zero at both locations. As an example, the spatial correlation functions are derived for the Geum River Basin, Korea and evaluated for the wet and dry seasons, respectively. Results show that the effect of zero measurements on spatial correlation structures is significant during the wet season, when the interstation correlations were estimated significantly lower than those during the dry season. It was also found that only the case considering positive measurements are valid for the quantification of rainfall field. Even during the wet season, the inter-station correlation coefficients derived by considering the zero measurements show their high variability along with many abnormally looking high estimates, which made the quantification of the spatial correlation function become very ambiguous.

We examine the characteristic climatic changes recorded in the long-term precipitation data for Seoul, Korea. Since precipitation data for Seoul has been accumulated for over 230 years, it is very useful in studying climatic changes in the monsoon region.

Abstract:Even though rain rate is notorious for its spatial and temporal intermittency, its effect on the second-order statistics of rain rate, especially the inter-station correlation coefficients, has not been intensively evaluated before. This study has derived and compared the inter-station correlation coefficient of rain rate for three cases of data: (1) only the positive measurements at both locations; (2) the positive measurements at either one or both locations; (3) all the measurements including zero measurement at both locations. For these three cases, the inter-station correlation coefficients are analytically derived by applying the mixed bivariate log-normal distribution. As an application example, the model parameters are estimated using the rain rate data collected at the Geum River basin, Korea, and the resulting inter-station correlation coefficients are evaluated and compared with those estimated by applying the Gaussian distribution. We could find that highly biased inter-station correlation coefficients are unavoidable when simply estimating them under the assumption of Gaussian distribution, or even when using the log-transformed rain rate data.

To define the stability of methicillin-resistant Staphylococcus aureus (MRSA) in vivo, 22 isolates collected at one New York institution in 1989 and 1990 were studied. All 22 belonged to one of two distinct methicillin-resistant phenotypes (class 3 or 2), which were precisely identified as belonging to two distinct genotypes. Genotypic classification was based on restriction analysis of chromosomal DNA with EcoRI and HindIII and Southern analysis of ClaI digests using two DNA probes. One was specific for the mec gene; the other was specific for transposon Tn554. The findings suggest that the MRSA isolates studied were representative of two genetically distinct MRSA "clones," each with a unique strain-specific methicillin-resistant phenotype that is stable under the conditions of invasive disease, carriage, and spread from patient to patient.

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