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Hourly rainfall measurements of 1919 national-level meteorological stations from 1981 through 2012 are used to document,for the first time,the climatology of extreme rainfall in hourly through 24-h accumulation periods in China. Rainfall amounts for 3-,6-,12- and 24-h periods at each station are constructed through running accumulation from hourly rainfall data that have been screened by proper quality control procedures. For each station and for each accumulation period,the historical maximum is found,and the corresponding 50-year return values are estimated using generalized extreme value theory. Based on the percentiles of the two types of extreme rainfall values among all the stations,standard thresholds separating Grade I,Grade II and Grade III extreme rainfall are established,which roughly correspond to the 70th and 90th percentiles for each of the accumulation periods. The spatial characteristics of the two types of extreme rainfall are then examined for different accumulation periods. The spatial distributions of extreme rainfall in hourly through 6-h periods are more similar than those of 12- and 24-h periods. Grade III rainfall is mostly found over South China,the western Sichuan Basin,along the southern and eastern coastlines,and in the large river basins and plains. There are similar numbers of stations with Grade III extreme hourly rainfall north and south of 30°N,but the percentage increases to about 70% south of 30°N as the accumulation period increases to 24 hours,reflecting richer moisture and more prolonged rain events in southern China. Potential applications of the extreme rainfall climatology and classification standards are suggested at the end.

Background： Hemoglobin A 1 c （HbA1 c） measurement is of great value for the diagnosis and monitoring of diabetes. Many manufacturers have developed various experiments to determine the HbAlc concentration. However, the longitudinal use of these tests requires strict quality management. This study aimed to analyze the quality of HbAlc measurement systems in China using six sigma techniques to help improve their performances. Methods： A total of 135 laboratories were involved in this investigation in 2015. Bias values and coefficients of variation were collected from an HbA 1 c trueness verification external quality assessment program and an internal quality control program organized by the National Center of Clinical Laboratories in China. The sigma （σ） values and the quality goal index （QGI） were used to evaluate the performances of different groups, which were divided according to principles and instruments. Results： The majority of participants （88, 65.2%） were scored as ＂improvement needed （σ 〈 3）＂, suggesting that the laboratories needed to improve their measurement performance. Only 8.2% （11/135） of the laboratories were scored as ＂world class （σ≥ 6）＂. Among all the 88 laboratories whose σ values were below 3, 52 （59.1%） and 23 （26.1%） laboratories needed to improve measurement precision （QGI 〈8.0） and trueness （QGI 〉 1.2）, respectively; the remaining laboratories （13, 14.8%） needed to improve both measurement precision and trueness. In addition, 16.1% （5/31） and 15.0% （3/20） of the laboratories in ＂TO SOH＂ and ＂ARKRAY＂ groups, respectively, were scored as ＂world class＂, whereas none of the laboratories in ＂BIO-RAD＂ group were scored as ＂world class＂. Conclusions： This study indicated that, although participating laboratories were laboratories with better performance in China, the performances were still unsatisfactory. Actions should be taken to improve HbAlc measurement performance before we can include HbAlc assays in diabetes diagnosis in China.

A heavy rainfall case related to Mesoscale Convective Systems （MCSs） over the Korean Peninsula was selected to investigate the impact of radar data assimilation on a heavy rainfall forecast. The Weather Research and Forecasting （WRF） three-dimensional variational （3DVAR） data assimilation system with tuning of the length scale of the background error covariance and observation error parameters was used to assimilate radar radial velocity and reffectivity data. The radar data used in the assimilation experiments were preprocessed using quality-control procedures and interpolated/thinned into Cartesian coordinates by the SPRINT/CEDRIC packages. Sensitivity experiments were carried out in order to determine the optimal values of the assimilation window length and the update frequency used for the rapid update cycle and incremental analysis update experiments. The assimilation of radar data has a positive influence on the heavy rainfall forecast. Quantitative features of the heavy rainfall case, such as the maximum rainfall amount and Root Mean Squared Differences （RMSDs） of zonal/meridional wind components, were improved by tuning of the length scale and observation error parameters. Qualitative features of the case, such as the maximum rainfall position and time series of hourly rainfall, were enhanced by an incremental analysis update technique. The positive effects of the radar data assimilation and the tuning of the length scale and observation error parameters were clearly shown by the 3DVAR increment.