Time Series Analysis of Daily Solar Radiation and Air Temperature Measurements for Use in Computing Potential Evapotranspiration

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.

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Time-series analysis of tides and similar motions of the sea surface

Journal of Applied Probability ◽

10.2307/3212302 ◽

1967 ◽

Vol 4 (1) ◽

pp. 103-112 ◽

Cited By ~ 3

Author(s):

D. E. Cartwright

Keyword(s):

Time Series ◽

Solar Radiation ◽

Time Series Analysis ◽

Sea Level ◽

Sea Surface ◽

Survey Article ◽

Series Analysis ◽

Non Linear ◽

Exciting Forces ◽

Linear Effects

This survey article, most of whose results are described in greater detail in Munk and Cartwright (1966), which will hereafter be abbreviated to MC, describes methods which aim to separate the response of the sea level at a given place due to various exciting forces such as gravity, solar radiation, non-linear effects, and weather. In so doing, it provides predictors for sea level which are formally simpler and somewhat more accurate than those given by the classical methods.

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Time-series analysis of GPS monitoring data from a long-span bridge considering the global deformation due to air temperature changes

Journal of Civil Structural Health Monitoring ◽

10.1007/s13349-015-0124-9 ◽

2015 ◽

Vol 5 (4) ◽

pp. 415-425 ◽

Cited By ~ 8

Author(s):

Hien Van Le ◽

Mayuko Nishio

Keyword(s):

Time Series ◽

Time Series Analysis ◽

Air Temperature ◽

Monitoring Data ◽

Temperature Changes ◽

Series Analysis ◽

Long Span ◽

Long Span Bridge ◽

Gps Monitoring ◽

Global Deformation

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Mapping air temperature using time series analysis of LST: the SINTESI approach

Nonlinear Processes in Geophysics ◽

10.5194/npg-20-513-2013 ◽

2013 ◽

Vol 20 (4) ◽

pp. 513-527 ◽

Cited By ~ 15

Author(s):

S. M. Alfieri ◽

F. De Lorenzi ◽

M. Menenti

Keyword(s):

Time Series ◽

Fourier Series ◽

Linear Regression ◽

Time Series Analysis ◽

Interannual Variability ◽

Air Temperature ◽

Land Surface ◽

Reference Location ◽

Series Analysis ◽

Annual Variability

Abstract. This paper presents a new procedure to map time series of air temperature (Ta) at fine spatial resolution using time series analysis of satellite-derived land surface temperature (LST) observations. The method assumes that air temperature is known at a single (reference) location such as in gridded climate data with grid size of the order of 35 km × 35 km. The LST spatial and temporal pattern within a grid cell has been modelled by the pixel-wise ratios r (x,y,t) of the LST at any location to the LST at a reference location. A preliminary analysis of these patterns over a decade has demonstrated that their intra-annual variability is not negligible, with significant seasonality, even if it is stable throughout the years. The intra-annual variability has been modeled using Fourier series. We have evaluated the intra-annual variability by theoretically calculating the yearly evolution of LST (t) for a range of cases as a function of terrain, land cover and hydrological conditions. These calculations are used to interpret the observed LST (x,y,t) and r (x,y,t). The inter-annual variability has been evaluated by modeling each year of observations using Fourier series and evaluating the interannual variability of Fourier coefficients. Because of the negligible interannual variability of r (x,y,t), LST (x,y,t) can be reconstructed in periods of time different from the ones when LST observations are available. Time series of Ta are generated using the ratio r (x,y,t) and a linear regression between LST and Ta. Such linear regression is applied in two ways: (a) to estimate LST at any time from observations or forecasts of Ta at the reference location; (b) to estimate Ta from LST at any location. The results presented in this paper are based on the analysis of daily MODIS LST observations over the period 2001–2010. The Ta at the reference location was gridded data at a node of a 35 km × 35 km grid. Only one node was close to our study area and was used for the work presented here. The regression of Ta on LST was determined using concurrent observations of Ta at the four available weather stations in the Valle Telesina (Italy), our study area. The accuracy of our estimates is consistent with literature and with the combined accuracy of LST and Ta. We obtained comparable error statistics when applying our method to LST data during periods different but adjacent to the periods used to model of r (x,y,t). The method has also been evaluated against Ta observations for earlier periods of time (1984–1988), although available data are rather sparse in space and time. Slightly larger deviation were obtained. In all cases five days of averages from estimated and observed Ta were compared, giving a better accuracy.

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Time series analysis of hourly global horizontal solar radiation

Solar Energy ◽

10.1016/0038-092x(88)90016-3 ◽

1988 ◽

Vol 41 (5) ◽

pp. 423-429 ◽

Cited By ~ 31

Author(s):

J.M. Gordon ◽

T.A. Reddy

Keyword(s):

Time Series ◽

Solar Radiation ◽

Time Series Analysis ◽

Series Analysis

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Time series analysis of ambient air-temperature during the period 1970–2016 over Sydney, Australia

The Science of The Total Environment ◽

10.1016/j.scitotenv.2018.08.144 ◽

2019 ◽

Vol 648 ◽

pp. 1627-1638 ◽

Cited By ~ 19

Author(s):

I. Livada ◽

A. Synnefa ◽

S. Haddad ◽

R. Paolini ◽

S. Garshasbi ◽

...

Keyword(s):

Time Series ◽

Time Series Analysis ◽

Air Temperature ◽

Ambient Air ◽

Ambient Air Temperature ◽

Series Analysis ◽

Sydney Australia

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Weather Forecasting in Coastal Districts of Odisha and Andhra Pradesh by Using Time Series Analysis

International Journal of Emerging Research in Management and Technology ◽

10.23956/ijermt.v6i8.122 ◽

2018 ◽

Vol 6 (8) ◽

pp. 85

Author(s):

Naresh Patnaik ◽

F Baliarsingh

Keyword(s):

Time Series ◽

Wind Speed ◽

Relative Humidity ◽

Solar Radiation ◽

Time Series Analysis ◽

Andhra Pradesh ◽

Maximum Temperature ◽

Climatic Parameters ◽

Series Analysis ◽

Annual Maximum Temperature

Climate change in world is always one of the most important topics in Water Resources. Now the issue is so predominant that it is gradually restricting out social life, peace and harmony. Climate change is a change in the statistical distribution of weather pattern of an area, when such changes occur for a long period of time. Weather is the state of atmosphere at a particular place and time. Climate is the long term statistical expression of short term weather. This study presents a comprehensive assessment of the future climate pattern/weather prediction by taking different climatic parameters such as temperature, precipitation, solar radiation, wind speed and relative humidity by using time series analysis. The study area of research work covers the coastal districts of Odisha and some parts of Andhra Pradesh. The climatic parameters are collected over last 20 years (1993-2013) from the selected 10 stations and the prediction is made using Time Series Analysis (ARIMA Model). The annual maximum temperature, solar radiation of all districts indicates a statistically significant increase in trend, whereas in the case of wind speed and relative humidity indicates significant deceasing trend. The annual rain fall shows an increasing trend of 2.69 mm/year in all station except Srikakulam, Khordha, Jagatsinghpur and Balasore which shows a decreasing trend of 1.94, 1.29, 0.56 and 1.18 mm/year respectively. As a whole the annual maximum temperature and solar radiation shows an increase trend of 0.16 ⁰C and 0.073 MJ/m² per year respectively. Further the wind speed and relative humidity of all stations indicates a decreasing trend of 0.056 m/s and 0.003(Units in fraction) per year respectively.

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