Spatial and temporal variations of rainy days and mean daily rainfall intensity in northern Nigeria.

The spatial and temporal variations of rainy Gays arid daily rainfall intensity for northern Nigeria for using 54 years data are analysed, The extent and nature of non-random changes, such as trend and fluctuations are Investigated. In general, both, the rainy day frequency and mean daily rainfall intensity decreases northwards except for localized orographic effect in the north central Part of the region. There is statistical evidence or decreasing trend in the, number of rainy days over the period of study, but the trend analysis showed no significance or the mean daily rainfall intensity. This suggests that the recent decreasing rainfall trend In the region particularly In the Sahellan zone, In the result of decrease In the frequency of rainy days and not due to any significant change In the rainfall intensity.

Spatial and Temporal Variations of Extreme Precipitation in Central Asia during 1982–2020

As one of the largest arid and semi-arid regions in the world, central Asia (CA) is very sensitive to changes in regional climate. However, because of the poor continuity of daily observational precipitation records in CA, the spatial and temporal variations of extreme precipitation in recent decades remain unclear. Considering their good spatial and temporal continuity, gridded data, such as Climate Prediction Center (CPC) global precipitation, and reanalysis data, such as ERA-Interim (ERA), are helpful for exploring the spatial–temporal variations of extreme precipitation. This study evaluates how well CPC and ERA can represent observed precipitation extremes by comparing the differences in eight extreme precipitation indices and observation data at 84 meteorological stations. The results indicate that the CPC (except for 1979–1981) is more suitable for depicting changes in precipitation extremes. Based on the CPC data for the period 1982–2020, we found that seven indices of precipitation extremes, including consecutive wet days (CWD), max1-day precipitation amount (Rx1day), max5-day precipitation amount (Rx5day), number of heavy precipitation days (R10), very wet days (R95p), annual total precipitation in wet days (PRCPTOT), and simple precipitation intensity index (SDII) have increased by 0.2 d/10a, 0.9 mm/10a, 1.8 mm/10a, 0.3 d/10, 8.4 mm/10a, 14.3 mm/10a and 0.1 mm/d/10a, respectively, and the consecutive dry days (CDDs) have decreased by −3.10 d/10a. It is notable that CDDs decreased significantly in the north of Xinjiang (XJ) but increased in Kyrgyzstan (KG), Tajikistan (TI), and eastern Turkmenistan (TX). The other indices increased clearly in the west of XJ, north of Kazakhstan (KZ), and east of KG but decreased in the south of KG, TI, and parts of XJ. For most indices, the largest change occurred in spring, the main season of precipitation in CA. Therefore, the large-scale atmospheric circulation in April is analyzed to contrast between the most and least precipitation years for the region. A typical circulation pattern in April for those extremely wet years includes an abnormal low-pressure center at 850 hpa to the east of the Caspian Sea, which enhances the southerly winds from the Indian Ocean and hence the transportation of water vapor required for precipitation into CA. This abnormal circulation pattern occurred more frequently after 2001 than before, thus partly explaining the recent increasing trends of precipitation extremes in CA.

Spatial and Temporal Variations of Aerosol Optical Thickness over the China Seas from Himawari-8

Six years of hourly aerosol optical thickness (AOT) data retrieved from Himawari-8 were used to investigate the spatial and temporal variations, especially diurnal variations, of aerosols over the China Seas. First, the Himawari-8 AOT data were consistent with the AERONET measurements over most of the China Seas, except for some coastal regions. The spatial feature showed that AOT over high latitude seas was generally larger than over low latitude seas, and it is distributed in strips along the coastline and decreases gradually with increasing distance from the coastline. AOT undergoes diurnal variation as it decreases from 9:00 a.m. local time, reaching a minimum at noon, and then begins to increase in the afternoon. The percentage daily departure of AOT over the East China Seas generally ranged ±20%, increasing sharply in the afternoon; however, over the northern part of the South China Sea, daily departure reached a maximum of >40% at 4:00 p.m. The monthly variation in AOT showed a pronounced annual cycle. Seasonal variations of the spatial pattern showed that the largest AOT was usually observed in spring and varies in other seasons for different seas.

Spatial and temporal multivariate statistical analysis to assess drinking water quality in medical services

Drinking water quality supplied to medical services presents significant role regarding the health aspect of the society. Multivariate statistical techniques were applied for the interpretation of data obtained, i.e., cluster analysis (CA), principal component analysis (PCA), factor analysis (FA), and discriminant analysis (DA) to analyze and assess the spatial and temporal variations of drinking water quality in different medical services in Kuwait. This study was generated over a period of 11 years (2007–2017), including 19 parameters at fourteen different sites. Hierarchical CA obtained two groups regarding both spatial and temporal variations. For spatial variations, 14 sampling sites were grouped into Low Concentration (LC) and High Concentration (HC). For temporal variations, 12 months were grouped into Summer and Winter. DA provided better results by data reduction for the large data set with great discriminatory ability for both spatial and temporal variations, as only five parameters were used concerning the spatial variations to afford 68.4% of the cases being assigned correctly, and seven parameters were interpreted for the temporal variations affording 76.1% of correctly classified cases. The applied PCA/FA on the spatial variations resulted in five principle components (PCs) for the LC region, and the total variance is 74.84% and three PCs for the HC region explaining a total variance of 64.86%. For the temporal variations, summer yielded into five PCs with a total variance of 70.6%, whereas the winter resulted in three PCs describing 67.1% total variance. Thus, multivariate analysis provides better spatial and temporal variations assessment in contemplation of effective drinking water quality management and control.