No abstract

The climatological characteristics of drought in South Korea were investigated using daily precipitation data for 1777-2008. The effective drought index was used to quantify the drought intensity. As a result, five characteristics were discovered. First, South Korea can be divided into four drought subregions (the central, southern, and east coastal regions and Jeju Island) using hierarchical cluster analysis. Second, a map for longterm drought conditions in the four subregions is created that allows identification of the spatiotemporal distribution of droughts for the 231 yr at a glance. Third, droughts in South Korea have time scales that depend on the onset season. Spring (March-May) droughts tend to be short (#200 days) because the summer (June-September) rainy season follows. Summer droughts tend to be long (.200 days) because the dry season (October-February) follows. In the dry season, droughts tend to be sustained or become severe rather than being initiated or relieved. Fourth, 5-, 14-, 34-, and 115-yr drought cycles were identified by spectral analysis. The 5-yr cycle was dominant in all of the regions, the 14-yr cycle was observed over the southern and east coastal regions, and the 34-yr cycle was observed over the central region. Fifth, the most extreme drought occurred in 1897-1903 (return period: 233 yr) and was associated with the 115-yr drought cycle. After this drought, severe droughts (return period of .10 yr) occurred in they were caused by the consecutive shortage of summer rainfall for two or more years.

Atmospheric Motion Vectors (AMVs) calculated by six different institutions (Brazil Center for Weather Prediction and Climate Studies/CPTEC/INPE, European Organization for the Exploitation of Meteorological Satellites/EUMETSAT, Japan Meteorological Agency/JMA, Korea Meteorological Administration/KMA, Unites States National Oceanic and Atmospheric Administration/NOAA, and the Satellite Application Facility on Support to Nowcasting and Very short range forecasting/NWCSAF) with JMA’s Himawari-8 satellite data and other common input data are here compared. The comparison is based on two different AMV input datasets, calculated with two different image triplets for 21 July 2016, and the use of a prescribed and a specific configuration. The main results of the study are summarized as follows: (1) the differences in the AMV datasets depend very much on the ‘AMV height assignment’ used and much less on the use of a prescribed or specific configuration; (2) the use of the ‘Common Quality Indicator (CQI)’ has a quantified skill in filtering collocated AMVs for an improved statistical agreement between centers; (3) Among the six AMV operational algorithms verified by this AMV Intercomparison, JMA AMV algorithm has the best overall performance considering all validation metrics, mainly due to its new height assignment method: ‘Optimal estimation method considering the observed infrared radiances, the vertical profile of the Numerical Weather Prediction wind, and the estimated brightness temperature using a radiative transfer model’.

Eight Beagle dogs were anesthetized and were imaged using a single channel helical CT scanner. The contrast medium used in this study was iohexol (300 mg I/ml) and doses were 0.5 ml/kg for a cine scan, 3 ml/kg for an enhanced scan. The flow rate for contrast material administration was 2 ml/sec for all scans. This study was divided into three steps, with unenhanced, cine and enhanced scans. The enhanced scan was subdivided into the arterial phase and the venous phase. All of the enhanced scans were reconstructed in 1 mm intervals and the scans were interpreted by the use of reformatted images, a cross sectional histogram, maximum intensity projection and shaded surface display. For the cine scans, optimal times were a 9-sec delay time post IV injection in the arterial phase, and an 18-sec delay time post IV injection in the venous phase. A nine-sec delay time was acceptable for the imaging of the canine hepatic arteries by CT angiography. After completion of arterial phase scanning, venous structures of the liver were well visualized as seen on the venous phase.