scholarly journals Compositions of hydrogen and oxygen isotopes of precipitation in Xiamen, Southeast China Coast: Seasonal variations, synoptic processes, and typhoons impact

2021 ◽  
Author(s):  
Lin Yunpeng ◽  
Yunhai Li ◽  
Wang Liang ◽  
Yin Xijie ◽  
Zou Xiaochun ◽  
...  
Keyword(s):  
Time Series ◽  
Seasonal Variations ◽  
Precipitation Process ◽  
Global Changes ◽  
Fractionation Process ◽  
Southeast China ◽  
Moisture Sources ◽  
Typhoon Structure ◽  
Δd And Δ18o ◽  
China Coast

In this study, the δD and δ18O values of 162 precipitation samples (including 33 typhoon-related precipitation samples), collected in Xiamen, Southeast China coast, during June 2018 to August 2019, were investigated and analyzed. The results show that there are obvious seasonal variations in the δD and δ18O, which are mainly controlled by the East Asia Monsoon with significant influence of typhoon events in summer. The influence of moisture sources on δ18O values overrides the influence of precipitation fractionation process on δ18O values which leads to an inverse temperature effect in the study area. In comparison to the seasonal scale, the synoptic time-series variation of δD and δ18O is much more complicated. In general, there are three types of isotopic variations in the normal precipitation processes, which are obviously affected by re-evaporation processes and continuing equilibrium fractionation during condensation. The local meteorological parameters during normal precipitation, which mainly control the re-evaporation process, are the dominant factors for the variation patterns of δD and δ18O, whereas moisture sources control the overall isotope values of precipitation. The differences between the time-series of normal and typhoon-related precipitation are mainly controlled by the changes of physical processes and meteorologic parameters during the precipitation process. However, due to the unique atmospheric structure and dynamic processes of typhoons, the δD and δ18O of typhoon-related precipitation changes in stages gradually as the distance between the typhoon’s center and the study area changes. The uniformity of typhoon structure leads to a similar staged changes in different typhoon-related precipitation. The moisture source trajectory of typhoon-related precipitation shows a clear spiral structure (except for typhoon Yutu), and the moisture sources at different heights control the δD and δ18O values of typhoon-related precipitation. This study is important for quantifying the global changes of typhoon processes and paleotempestology studies.

scholarly journals The effects of daily meteorological perturbation on pregnancy outcome: follow-up of a cohort of young women undergoing IVF treatment

2019 ◽  
Vol 18 (1) ◽  
Author(s):  
Mingpeng Zhao ◽  
Haoyang Zhang ◽  
Tarah H. B. Waters ◽  
Jacqueline Pui Wah Chung ◽  
Tin Chiu Li ◽  
...  
Keyword(s):  
Time Series ◽  
Solar Radiation ◽  
Time Series Analysis ◽  
Subgroup Analysis ◽  
Seasonal Variations ◽  
Multiple Logistic Regression Analysis ◽  
Human Reproduction ◽  
Mixed Effect ◽  
Series Analysis ◽  
Patient Consent

Abstract Background Human reproduction follows a seasonal pattern with respect to spontaneous conception, a phenomenon wherein the effect of meteorological fluctuations might not be unique. However, the effect of seasonal variations on patients who underwent in vitro fertilization (IVF) treatment is unclear. We aimed to evaluate the effects of meteorological variation on the pregnancy rate in a cohort undergoing IVF treatment by performing multivariable analyses. Methods We conducted a cohort study in a sub-tropical region with prominent seasonal variations (2005–2016). Women aged < 35 years who were treated with a long ovarian stimulation protocol and underwent fresh embryo transfer (ER) were included. Data on gonadotropin administration (CYCL), oocyte retrieval (OR), ER, and pregnancy outcomes were prospectively recorded. For each patient, the daily average of meteorological data (temperature, humidity, sunlight duration, solar radiation) was recorded from the date of CYCL to ER. Multiple logistic regression analysis adjusted for age, fertilization method, year of the cycle, gonadotropin dose, and transferred embryo grade was performed to determine the relationship between the meteorological parameters and clinical pregnancy. Patients with one successful cycle and one failed cycle were subtracted for a case-control subgroup analysis through mixed effect logistics regressions. Time-series analysis of data in the epidemic level was conducted using the distributed lag linear and non-linear models (DLNMs). Results There were 1029 fresh cycles in 860 women (mean age 31.9 ± 2.0 years). Higher mean temperature from CYCL to OR (adjusted odds ratio [aOR] 1.04; 95% confidence interval [CI] 1.01–1.07, P = 0.01) increased the odds of pregnancy, while OR to ER did not show any statistical significance. Compared to that in winter, the odds of becoming pregnant were higher during higher temperature seasons, summer and autumn (aOR 1.47, 95%CI 0.97–2.23, P = 0.07 (marginally significant) and aOR 1.73, 95%CI 1.12–2.68, P = 0.02, respectively). Humidity, sunlight duration, and solar radiation had no effect on the outcome. The subgroup analysis confirmed this finding. The time-series analysis revealed a positive association between temperature and relative risk for pregnancy. Conclusions In IVF treatment, the ambient temperature variation alters the pregnancy rates; this aspect must be considered when obtaining patient consent for assisted conception.

scholarly journals Long-term monitoring programme of the hydrological variability in the Mediterranean Sea: a first overview of the HYDROCHANGES network

Ocean Science ◽  
2013 ◽  
Vol 9 (2) ◽  
pp. 301-324 ◽  
Author(s):  
K. Schroeder ◽  
C. Millot ◽  
L. Bengara ◽  
S. Ben Ismail ◽  
M. Bensi ◽  
...  
Keyword(s):  
Time Series ◽  
Mediterranean Sea ◽  
Sampling Interval ◽  
Global Changes ◽  
Hydrological Variability ◽  
Long Term Monitoring ◽  
The Mediterranean ◽  
Set Up ◽  
Term Monitoring

Abstract. The long-term monitoring of basic hydrological parameters (temperature and salinity), collected as time series with adequate temporal resolution (i.e. with a sampling interval allowing the resolution of all important timescales) in key places of the Mediterranean Sea (straits and channels, zones of dense water formation, deep parts of the basins), constitute a priority in the context of global changes. This led CIESM (The Mediterranean Science Commission) to support, since 2002, the HYDROCHANGES programme (http//www.ciesm.org/marine/programs/hydrochanges.htm), a network of autonomous conductivity, temperature, and depth (CTD) sensors, deployed on mainly short and easily manageable subsurface moorings, within the core of a certain water mass. The HYDROCHANGES strategy is twofold and develops on different scales. To get information about long-term changes of hydrological characteristics, long time series are needed. But before these series are long enough they allow the detection of links between them at shorter timescales that may provide extremely valuable information about the functioning of the Mediterranean Sea. The aim of this paper is to present the history of the programme and the current set-up of the network (monitored sites, involved groups) as well as to provide for the first time an overview of all the time series collected under the HYDROCHANGES umbrella, discussing the results obtained thanks to the programme.

scholarly journals Assessing potential storm tide inundation hazard under climate change: a case study of Southeast China coast

2019 ◽  
Author(s):  
Bingchuan Nie ◽  
Qingyong Wuxi ◽  
Jiachun Li ◽  
Feng Xu
Keyword(s):  
Climate Change ◽  
Tropical Cyclone ◽  
Trend Analysis ◽  
Sea Level ◽  
Coupling Effect ◽  
Storm Tide ◽  
Southeast China ◽  
China Coast ◽  
Water Elevation

Abstract. A methodology for assessing the storm tide inundation under TCI (tropical cyclone intensification) and SLR (sea level rise) is proposed, which integrates the trend analysis, numerical analysis and GIS-based analysis. In the trend analysis, the potential TCI and SLR can be estimated based on the long-term historical data of TC (tropical cyclone) and MSL (mean sea level) considering the non-stationary and spatially non-uniform effect; the numerical simulation is relied on the ADCIRC+SWAN model, which is capable of taking into account the tide-surge-wave coupling effect to improve the precision of water elevation prediction; the water elevation is then analyzed on the GIS platform, the potential inundation regions can be identified. Based on this methodology, a case study for the Southeast China coast, one of the storm surge prone areas in China, is presented. The results show that the high water elevation tends to occur in the bays and around the estuaries, the maximal water elevations caused by the typhoon wind of 100-year recurrence period can reach as high as 6.06 m, 5.82 m and 5.67 m around Aojiang, Feiyunjiang and Oujiang river estuaries, respectively. Non-stationary TCI and SLR due to climate change can further deteriorate the situation and enhance the risk of inundation there, i.e. the potential inundation area would expand by 108 % to about 798 km2 compared with the situation without considering TCI and SLR. In addition, the remotely sensed maps and inundation durations of the hardest hit regions are provided, which will aid the prevention and mitigation of storm tide inundation hazard and future coastal management there.

Comparison of Tropospheric Zenith Wet Delay from VLBI and GNSS Estimations

2021 ◽  
Author(s):  
Vicky Jia Liu ◽  
Maaria Nordman ◽  
Nataliya Zubko
Keyword(s):  
Time Series ◽  
Seasonal Variations ◽  
Elevation Angle ◽  
Correlation Coefficients ◽  
Satellite System ◽  
Tropospheric Delay ◽  
Polar Regions ◽  
Long Baseline ◽  
Zenith Wet Delay ◽  
Space Geodetic Techniques

&lt;p&gt;Tropospheric delay is one of the major error sources for space geodetic techniques such as Very Long Baseline Interferometry (VLBI) and Global Navigation Satellite System (GNSS). In this study, we compared the agreement of tropospheric zenith wet delay (ZWD) seasonal variations derived from VLBI and GNSS observations at 8 stations that are located at all around the globe. We have analysed time series of 8 years, starting in 2012 until end of 2019. Results show that VLBI_ZWD present clear seasonal variations which depend on the location of each station, in the tropics the variability is more pronounced than in mid-latitudes or polar regions. Furthermore, the VLBI_ZWD also shows a reasonably good agreement with seasonal fit model. When comparing zenith wet delays derived from co-located GNSS and VLBI stations at &amp;#160;cut-off elevation angle, they agree quite well, which is proved by the high correlation coefficients, varying from 0.6 up to 0.95. The biases between the techniques are in mm level and standard errors of the whole time series are in few centimetres.&lt;/p&gt;

scholarly journals Seasonal Cycles of Phytoplankton Expressed by Sine Equations Using the Daily Climatology from Satellite-Retrieved Chlorophyll-a Concentration (1997–2019) Over Global Ocean

2020 ◽  
Vol 12 (16) ◽  
pp. 2662 ◽  
Author(s):  
Zexi Mao ◽  
Zhihua Mao ◽  
Cédric Jamet ◽  
Marc Linderman ◽  
Yuntao Wang ◽  
...  
Keyword(s):  
Time Series ◽  
Chlorophyll A ◽  
Seasonal Variations ◽  
Global Ocean ◽  
Boreal Winter ◽  
Seasonal Cycles ◽  
Chlorophyll A Concentration ◽  
Chl A ◽  
Global Oceans ◽  
Sine Equation

The global coverage of Chlorophyll-a concentration (Chl-a) has been continuously available from ocean color satellite sensors since September 1997 and the Chl-a data (1997–2019) were used to produce a climatological dataset by averaging Chl-a values at same locations and same day of year. The constructed climatology can remarkably reduce the variability of satellite data and clearly exhibit the seasonal cycles, demonstrating that the growth and decay of phytoplankton recurs with similarly seasonal cycles year after year. As the shapes of time series of the climatology exhibit strong periodical change, we wonder whether the seasonality of Chl-a can be expressed by a mathematic equation. Our results show that sinusoid functions are suitable to describe cyclical variations of data in time series and patterns of the daily climatology can be matched by sine equations with parameters of mean, amplitude, phase, and frequency. Three types of sine equations were used to match the climatological Chl-a with Mean Relative Differences (MRD) of 7.1%, 4.5%, and 3.3%, respectively. The sine equation with four sinusoids can modulate the shapes of the fitted values to match various patterns of climatology with small MRD values (less than 5%) in about 90% of global oceans. The fitted values can reflect an overall pattern of seasonal cycles of Chl-a which can be taken as a time series of biomass baseline for describing the state of seasonal variations of phytoplankton. The amplitude images, the spatial patterns of seasonal variations of phytoplankton, can be used to identify the transition zone chlorophyll fronts. The timing of phytoplankton blooms is identified by the biggest peak of the fitted values and used to classify oceans as different bloom seasons, indicating that blooms occur in all four seasons with regional features. In global oceans within latitude domains (48°N–48°S), blooms occupy approximately half of the ocean (50.6%) during boreal winter (December–February) in the northern hemisphere and more than half (58.0%) during austral winter (June–August) in the southern hemisphere. Therefore, the sine equation can be used to match the daily Chl-a climatology and the fitted values can reflect the seasonal cycles of phytoplankton, which can be used to investigate the underlying phenological characteristics.

Using Time Series to Incorporate Seasonal Variations in Pavement Design

1996 ◽  
Vol 1539 (1) ◽  
pp. 33-43 ◽  
Author(s):  
Hesham A. Ali ◽  
Neville A. Parker
Keyword(s):  
Time Series ◽  
Structural Properties ◽  
Seasonal Variations ◽  
Monitoring Program ◽  
Statistical Technique ◽  
Pavement Design ◽  
Seasonal Monitoring ◽  
Program Data ◽  
Series Technique

Analysis of the seasonal monitoring program data of the long term pavement performance program indicated that some pavement structural properties often follow predictable seasonal patterns. Time series is a statistical technique that may be used to develop periodic functions to predict the values of such properties as a function of time. The application of time series technique in characterizing the seasonal variations of pavement structural properties as simulated functions is presented. In addition, the incorporation of such variations in both empirical and mechanistic-empirical methods of flexible pavement design is demonstrated. To this end, a computer program, seasonal variation in pavement design, was written to carry out the required calculations and to facilitate the comparison between empirical and mechanistic-empirical design methods.

Seasonal variation in liver function tests: a time-series analysis of outpatient data

2009 ◽  
Vol 46 (5) ◽  
pp. 377-384 ◽  
Author(s):  
Kazunori Miyake ◽  
Noriko Miyake ◽  
Shigemi Kondo ◽  
Yoko Tabe ◽  
Akimichi Ohsaka ◽  
...  
Keyword(s):  
Time Series ◽  
Seasonal Variation ◽  
Liver Function ◽  
Seasonal Variations ◽  
Time Series Data ◽  
Liver Function Tests ◽  
Series Data ◽  
Test Results ◽  
Men And Women ◽  
Function Tests

Background Long-term physiological variations, such as seasonal variations, affect the screening efficiency at medical checkups. This study examined the seasonal variation in liver function tests using recently described data-mining methods. Methods The ‘latent reference values’ of aspartate aminotransferase (AST), alanine aminotransferase (ALT), alkaline phosphatase (ALP), gamma-glutamyltransferase ( γGT), cholinesterase (ChE) and total bilirubin (T-Bil) were extracted from a seven-year database of outpatients (aged 20–79 yr; comprising approximately 1,270,000 test results). After calculating the monthly means for each variable, the time-series data were separated into trend and seasonal components using a local regression model (Loess method). Then, a cosine function model (cosinor method) was applied to the seasonal component to determine the periodicity and fluctuation range. A two-year outpatient database (215,000 results) from another hospital was also analysed to confirm the reproducibility of these methods. Results The serum levels of test results tended to increase in the winter. The increase in AST and ALT was about 6% in men and women, and was greater than that in ChE, ALP (in men and women) and γGT (in men). In contrast, T-Bil increased by 3.6% (men) and 5.0% (women) in the summer. The total protein and albumin concentrations did not change significantly. AST and ALT showed similar seasonal variation in both institutions in the comparative analysis. Conclusions The liver function tests were observed to show seasonal variations. These seasonal variations should therefore be taken into consideration when establishing either reference intervals or cut-off values, which are especially important regarding aminotransferases.

scholarly journals Spatial and seasonal variations of aerosols over China from two decades of multi-satellite observations – Part 1: ATSR (1995–2011) and MODIS C6.1 (2000–2017)

2018 ◽  
Vol 18 (15) ◽  
pp. 11389-11407 ◽  
Author(s):  
Larisa Sogacheva ◽  
Gerrit de Leeuw ◽  
Edith Rodriguez ◽  
Pekka Kolmonen ◽  
Aristeidis K. Georgoulias ◽  
...  
Keyword(s):  
Time Series ◽  
Seasonal Variations ◽  
Eastern Coast ◽  
Deep Blue ◽  
Modis Aod ◽  
Yearly Average ◽  
Offset Correction ◽  
Moderate Resolution ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
The Difference

Abstract. Aerosol optical depth (AOD) patterns and interannual and seasonal variations over China are discussed based on the AOD retrieved from the Along-Track Scanning Radiometer (ATSR-2, 1995–2002), the Advanced ATSR (AATSR, 2002–2012) (together ATSR) and the MODerate resolution Imaging Spectroradiometer (MODIS) aboard the Terra satellite (2000–2017). The AOD products used were the ATSR Dual View (ADV) v2.31 AOD and the MODIS/Terra Collection 6.1 (C6.1) merged dark target (DT) and deep blue (DB) AOD product. Together these datasets provide an AOD time series for 23 years, from 1995 to 2017. The difference between the AOD values retrieved from ATSR-2 and AATSR is small, as shown by pixel-by-pixel and monthly aggregate comparisons as well as validation results. This allows for the combination of the ATSR-2 and AATSR AOD time series into one dataset without offset correction. ADV and MODIS AOD validation results show similar high correlations with the Aerosol Robotic Network (AERONET) AOD (0.88 and 0.92, respectively), while the corresponding bias is positive for MODIS (0.06) and negative for ADV (−0.07). Validation of the AOD products in similar conditions, when ATSR and MODIS/Terra overpasses are within 90 min of each other and when both ADV and MODIS retrieve AOD around AERONET locations, show that ADV performs better than MODIS in autumn, while MODIS performs slightly better in spring and summer. In winter, both ADV and MODIS underestimate the AERONET AOD. Similar AOD patterns are observed by ADV and MODIS in annual and seasonal aggregates as well as in time series. ADV–MODIS difference maps show that MODIS AOD is generally higher than that from ADV. Both ADV and MODIS show similar seasonal AOD behavior. The AOD maxima shift from spring in the south to summer along the eastern coast further north. The agreement between sensors regarding year-to-year AOD changes is quite good. During the period from 1995 to 2006 AOD increased in the southeast (SE) of China. Between 2006 and 2011 AOD did not change much, showing minor minima in 2008–2009. From 2011 onward AOD decreased in the SE of China. Similar patterns exist in year-to-year ADV and MODIS annual AOD tendencies in the overlapping period. However, regional differences between the ATSR and MODIS AODs are quite large. The consistency between ATSR and MODIS with regards to the AOD tendencies in the overlapping period is rather strong in summer, autumn and overall for the yearly average; however, in winter and spring, when there is a difference in coverage between the two instruments, the agreement between ATSR and MODIS is lower. AOD tendencies in China during the 1995–2017 period will be discussed in more detail in Part 2 (a following paper: Sogacheva et al., 2018), where a method to combine AOD time series from ADV and MODIS is introduced, and combined AOD time series are analyzed.

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