Time series analysis of observed maximum and minimum air temperature at four urban cities of India during 1951-2015

A sixty - five year (1951-2015) long data for monthly minimum temperature (TMIN) and maximum temperature (TMAX), observed by the India Meteorological Department (IMD), is statistically analyzed at four urban stations namely Bhubaneswar, Delhi, Mumbai and Chennai of India. A bimodal nature in seasonality is noticed for TMAX and TMIN at all locations. Two peaks for TMAX and TMIN are observed in May and September. Exceptionally, Mumbai shows TMAX peaks during May and November and Delhi shows TMIN peaks during June and September. Higher standard deviations (SD) for TMAX is noted at Delhi with a maximum in March (1.78 °C), while for Chennai, the SD for TMIN is lesser compared to other cities. Two different periods 1951-1980 (P1, the first half of the study period) and 1981-2015 (P2, the second half of the study period) were identified from the time series of both TMAX and TMIN. A higher increasing trend is observed during P2 than P1 in all the cities except in TMIN at Mumbai. The highest increasing trend (0.040 °C/year) is observed for TMIN in Mumbai during P1 time, but the trend is almost constant (0.001 °C/year) during P2 time. The highest increasing trend for TMIN at Mumbai is mainly contributed by the increasing trend in post-monsoon and winter months in P1. Surprisingly, in both P1 and P2, the trends are less during monsoon months for all the cities. A consistent 5-year (3-year) band is observed throughout the wavelet power spectrum at the coastal cities Bhubaneswar, Mumbai (Chennai). However, the 5-year signal is not consistent at Delhi and it is observed only during the year 1975-1980. The global wavelet power spectrum showed that TMIN at Chennai has less power (0.6 °C2) corresponding to 3-year signal and Mumbai has highest power (12 °C2) corresponding to the 5-year signal in comparison to other cities.

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Investigation of Climate Change Anomaly by Using Nonparametric Test for Navsari District of South Gujarat

Indian Journal of Pure & Applied Biosciences ◽

10.18782/2582-2845.8632 ◽

2021 ◽

Vol 9 (3) ◽

pp. 266-275

Author(s):

Neeraj Kumar ◽

Keyword(s):

Climate Change ◽

Time Series ◽

Relative Humidity ◽

Nonparametric Test ◽

Maximum Temperature ◽

Rainfall Trend ◽

Regression Slope ◽

Sunshine Hours ◽

Increasing Trend ◽

Daily Sunshine

Navsari district of rainfall was shows highest increasing rainfall trend obtained September and negative January, July, October, November and December. The regression slope of the yearly time series is about 12.35 mm/36 years. Maximum temperature shows the highest increasing trend in month October, followed by December and August. The month highest decreasing trend was noticed that January, followed by February and July. The regression slope of the yearly time series is about 0.025°C/36 years. Minimum temperature highest values of the slope (0.109°C/36 year) with high value of regression Slope of determination (0.111°C), the annual Kendall’s tau statistic (0.492°C/36 year), the Kendall Score (310). All the month January to December shows increasing trend. The highest increasing trend found that November, followed by March and July, respectively. This finding shows that all the month shows increasing trend with the range between 0.308°C to 0.390°C. In case of RH-I the highest increasing trend shows September, followed by April and June. Similarly decreasing trend was found that January, followed by February and October, respectively. Relative humidity-II increasing trend was found only at the September month 0.084%, the increasing trend was detected in January to August and October to December, respectively. The strongest trend in the Bright sunshine hour’s decline of all month’s average daily sunshine hours was for the Navsari district. No significant trends were detected in all months and seasons for all weather elements. A similar trend was found in Sen’s slope and regression slope all the months for all the weather elements.

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Prediction of maximum or minimum air temperature in a coastal location in West Bengal

MAUSAM ◽

10.54302/mausam.v64i4.750 ◽

2021 ◽

Vol 64 (4) ◽

pp. 671-680

Author(s):

SUKUMAR LALAROY ◽

SANJIB BANDYOPADHYAY ◽

SWETA DAS

Keyword(s):

Solar Radiation ◽

Air Temperature ◽

Minimum Temperature ◽

West Bengal ◽

Global Solar Radiation ◽

Maximum Temperature ◽

Accurate Estimation ◽

Radiation Data ◽

Sunshine Hour ◽

Minimum Air Temperature

bl 'kks/k i= dk mÌs'; Hkkjrh; rVh; LFkku vFkkZr~ if'peh caxky ds vyhiqj ¼dksydkrk½ esa izsf{kr HkweaMyh; lkSj fofdj.k dh enn ls gjxzhCl fofdj.k QkWewZyk ls rkjh[kokj la'kksf/kr KRS irk djuk gS ftlls fd vkxs ;fn U;wure rkieku ¼Tmin½ Kkr gks rks vf/kdre rkieku ¼Tmax½ dk iwokZuqeku nsus esa vFkok blds foijhr] mi;ksx fd;k tk ldsA HkweaMyh; lkSj fofdj.k ds chp lglaca/k dh x.kuk rkjh[kokj fd, x, /kwi ds ?kaVkokj vk¡dM+ksa ds vkSlr ds mi;ksx ftlesa vkaXLVªkse izsLdkWV QkewZyk ls izkIr fu;rkad as = 0-25 vkSj bs = 0-5 gS] ls dh xbZZ gSA blesa izsf{kr fd, x, HkweaMyh; lkSj fofdj.k vkadM+ksa dk v/;;u fd;k x;k gSA ;g fuf'pr :i ls dgk tkrk gS fd vkaxLVªkse izsldkWV QkewZyk HkweaMyh; lkSj fofdj.k dk lVhd vkdyu djrk gS vkSj ;g lgh ik;k tkrk gSA bl 'kks/k i= esa gjxzhCl fofdj.k QkewZyk ¼ftles KRS = 0-19 fy;k x;k gS½ ls rkjh[kokj izkIr fd, x, vf/kdre rkiekuksa rFkk U;wure rkiekuksa ds vkSlr ¼vkadM+s Hkkjr ekSle foKku foHkkx ds vyhiqj] dksydkrk ftyk & 24 ijxuk ds dk;kZy; ls izkIr½ dk mi;ksx djds HkweaMyh; lkSj fofdj.k ds chp lglaca/k dh x.kuk dh xbZ gS vkSj bldk v/;;u izsf{kr HkweaMyh; lkSj fofdj.k ds lkFk Hkh fd;k x;k gSA rkjh[kokj la'kksf/kr KRS dh x.kuk gjxzhCl fofdj.k QkewZyk ls dh xbZA blesa HkweaMyh; lkSj fofdj.k ds izsf{kr vkadM+ksa] rkjh[kokj vf/kdre rkiekuksa vkSj U;wure rkiekuksa ds vkSlr mi;ksx esa fy, x, gSaA bls fdlh LVs'ku ds vf/kdre rkiekuksa vkSj U;wure rkieku vkadMksa ds rkjh[kokj KRS ds mi;ksx ds }kjk vkl ikl ds {ks=ksa ds ok"iksRltZu ds fy, HkweaMyh; lkSj fofdj.k dk vkdyu djus ds fy, Hkh mi;ksx esa yk;k tk ldrk gSA The objective of this study is to find the date wise corrected KRS from the Hargreaves Radiation formula with the help of observed global solar radiation for the Indian coastal location namely Alipore (Kolkata) in West Bengal so that subsequently it can be used for predicting maximum temperature Tmax if minimum temperature Tmin is known or vice-versa. The correlation between the global solar radiation calculated by using date wise average sunshine hour data with constants as = 0.25 and bs = 0.5, from Angstrom Prescott formula with the observed global solar radiation data was studied. The assertion that the Angstrom Prescott formula gives nearly accurate estimation of global solar radiation has been found to be correct. Correlation between the global solar radiation calculated by using date wise average of Tmax and Tmin (sourced from IMD located at Alipore, Kolkata, District - South 24 parganas) from Hargreaves Radiation formula (taking KRS = 0.19 ) with the observed global solar radiation data was also studied. Date wise corrected KRS by Hargreaves Radiation formula was computed using the observed data of global solar radiation, date wise average of maximum temperature Tmax and minimum temperature Tmin. The date wise corrected KRS can be used for better prediction of Tmax and Tmin. Also it can be used for estimation of global solar radiation for reference evapo-transpiration of the neighbourhood areas by utilizing the date wise KRS with the Tmax and Tmin of the station.

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Stochastic Simulation of Non-stationary Meteorological Time-series - Daily Precipitation Indicators, Maximum and Minimum Air Temperature Simulation using Latent and Transformed Gaussian Processes

Proceedings of the 7th International Conference on Simulation and Modeling Methodologies, Technologies and Applications ◽

10.5220/0006358801730179 ◽

2017 ◽

Cited By ~ 1

Author(s):

Nina Kargapolova

Keyword(s):

Time Series ◽

Stochastic Simulation ◽

Air Temperature ◽

Gaussian Processes ◽

Daily Precipitation ◽

Minimum Air Temperature

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Prediction of temperatures in cattle dung for estimating development times of coprophilous organisms

Bulletin of Entomological Research ◽

10.1017/s0007485300013006 ◽

1988 ◽

Vol 78 (2) ◽

pp. 235-240 ◽

Cited By ~ 4

Author(s):

J. N. Matthiessen ◽

M. J. Palmer

Keyword(s):

Air Temperature ◽

Meteorological Data ◽

Maximum Temperature ◽

Daily Maximum Temperature ◽

Threshold Temperature ◽

Cattle Dung ◽

Daily Maximum ◽

Daily Minimum ◽

Maximum Temperatures ◽

Minimum Air Temperature

AbstractIn studies in Western Australia, temperatures in air and one- and two-litre pads of cattle dung set out weekly and ranging from one to 20 days old were measured hourly for 438 days over all seasons, producing 1437 day x dung-pad observations. Daily maximum temperatures (and hence thermal accumulation) in cattle dung pads could not be accurately predicted using meteorological data alone. An accurate predictor of daily maximum dung temperature, using multiple regression analysis, required measurement of the following factors: maximum air temperature, hours of sunshine, rainfall, a seasonal factor (the day number derived from a linear interpolation of day number from day 0 at the winter solstice to day 182 at the preceding and following summer solstices) and a dung-pad age-specific intercept term, giving an equation that explained a 91·4% of the variation in maximum dung temperature. Daily maximum temperature in two-litre dung pads was 0·6°C cooler than in one-litre pads. Daily minimum dung temperature equalled minimum air temperature, and daily minimum dung temperatures occurred at 05.00 h and maximum temperatures at 14.00 h for one-litre and 14.30 h for two-litre pads. Thus, thermal summation in a dung pad above any threshold temperature can be computed using a skewed sine curve fitted to daily minimum air temperature and the calculated maximum dung temperature.

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Spatio -Temporal Long-Term Variability and Trend Analysis of Precipitation and Temperature Series in the Ghataprabha (K3) Sub basin of Krishna Basin

International Journal of Innovative Technology and Exploring Engineering - Special Issue ◽

10.35940/ijitee.k1814.0981119 ◽

2019 ◽

Vol 8 (11) ◽

pp. 2547-2552

Keyword(s):

India Meteorological Department ◽

Well Being ◽

Annual Rainfall ◽

Maximum Temperature ◽

Study Region ◽

Changing Climate ◽

Increasing Trend ◽

Precipitation And Temperature ◽

Krishna Basin

Changing Climate is one of the most significant ecological issue, with the implications for agricultural production, water resource, energy and some other aspects of human well-being. Analysis of changing climate is important to assess climate-induced changes through the analysis of variability of climatic parameters such as temperature, precipitation, runoff and groundwater to suggest feasible adaptation strategies. This paper aims the long-term variability of rainfall and temperature using gridded daily data obtained from India Meteorological Department with 0.250 resolution from 1901-2016 for precipitation and 10 resolution from 1969-2005 for temperature (re-gridded to IMD 0.250 gridded location) in Ghataprabha sub basin (K3) of Krishna basin. The analysis of variability and trend in precipitation and temperature carried out by using coefficient of variation (CV), rainfall and temperature anomaly and also Mann-Kendall (MK) test was used to detect the time series trend. Statistical analysis of variability and trend in annual, Indian Summer Monsoon (ISMR) rainfall and temperature observed that i) there is an intra and inter annual variability of precipitation in the sub basin ii) test results revealed that the annual and ISMR trend appears to be increased by 0.12 & 0.14, iii) the Mann-Kendal trend test also analysed for annual minimum, mean and maximum temperature over the K3 sub basin (1969-2005) shows increasing trend by 0.06, 0.21 and 0.40. This analysis revealed that, there is an increasing trend in annual rainfall and temperature observed over the study region.

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Time Series Trend and Variability Analysis of Temperature and Rainfall in West Shewa Zone of Oromia, Ethiopia

10.21203/rs.3.rs-410118/v1 ◽

2021 ◽

Author(s):

Daniel Assefa ◽

Mesfin Mengistu

Keyword(s):

Time Series ◽

Minimum Temperature ◽

Kendall Test ◽

Annual Rainfall ◽

Maximum Temperature ◽

Variability Analysis ◽

Increasing Trend ◽

Systematic Planning ◽

Temperature Records ◽

C 30

Abstract BackgroundThe paper focus on time series trend and variability analysis of observed rainfall and temperature records from 16 stations during 1985-2015. ResultsBoth the summer and annual rainfall have an increasing trend but not statistically significant. Regards to variability, low to very high levels of variability were recorded according to the seasons and annual rainfall, whereas, moderate to extremely high levels of variability were observed. The result of the Mann Kendall test portrays that the mean minimum temperature was raised by 0.05 oC, while the maximum temperature was increased rose by 0.03 oC/30 years. The monthly maximum temperature also shows an increasing trend with the lowest record during August (22.05 oC) and the highest in the March (26.49 oC) except in the month of November and December. Similarly, an increasing trend was observed with a mean monthly minimum temperature with the lowest mean of 8.42Co in December and the highest mean of 11.12 oC recorded in April. Besides, a low level of variability was seen both in the case of minimum and maximum temperature were observed in all months. ConclusionsTherefore, since the observed trends of both temperature and total rainfall show abnormal shifts, there is an urgent need for policymakers to design systematic planning and management activities to rain-fed agriculture.

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Principal Component Analysis to Explore Climate variability of Pakistan

Pakistan Journal of Engineering Technology & Science ◽

10.22555/pjets.v4i1.526 ◽

2016 ◽

Vol 4 (1) ◽

Author(s):

Syed Afrozuddin Ahmed ◽

Junaid Saghir Siddiqi

Keyword(s):

Time Series ◽

Total Variation ◽

Minimum Temperature ◽

Principal Component ◽

Component Analysis ◽

Maximum Temperature ◽

P Value ◽

Annual Data ◽

Increasing Trend ◽

Time Series Plot

Various studies have reported that global warming causes unstable climate and many serious impact to physical environment and public health. The climatic or environmental structure data was processed by coding, editing, tabulating, recoding, restructuring in terms of retabulating was carried out.Applying different statistical methods, techniques and procedures for the evaluation.To study the global warming effects on overall environmental conditions of Pakistan. Annual data of maximum and minimum temperature of four provincial capitals have been taken from 1947 to 2012. The data isconsideredas representative environmental components, use for further analysis. Time series plot shows difference of behaviors in maximum and minimum temperatures of Karachi and Lahore while bend of Quetta indicates increasing trend and Peshawar shows flat and smooth. The fit of trend line, maximum temperature of Karachi, has significant regression coefficient b = 0.0504 with p-value 0.000 and R2equal to 70.2%. The minimum temperature has decreasing trend but it is insignificant. The data of Lahore shows decreasing and increasing trends for maximum and minimum temperatures respectively shows the differences reducing with the passage of time and expected to have cooler weather than the past. Quetta and Peshawar temperatures fit of trend lines and graphs, revealed that both cities getting warmer with the passage of time.Principal component analysis is performed for the purpose of finding if there is/are any general environmental factor/structure which could be considered as Pakistani climate The PC1 is constructed by six manifest variables and represent the environmental factor called as “Index of Pakistan weather”. Explain 42.74% of the total variation. The time series plot of this index seems to have increasing trend. The PC2 represents the temperature of Karachi, Quetta and Lahore. PC3 is the contrast between of minimum and maximum temperature. PC4 represents complex contrast between maximum and minimum temperature explain 9.0% of total variation of temperature. PC5 represent contrast between Karachi and Peshawar weather and its contribution to the total or overall variation of Pakistani weather is only 3.5%.

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Effets de la scarification du site sur le micro-environnement

Canadian Journal of Forest Research ◽

10.1139/x80-078 ◽

1980 ◽

Vol 10 (4) ◽

pp. 476-482 ◽

Cited By ~ 3

Author(s):

André P. Plamondon ◽

Denis C. Ouellet ◽

Gaston Déry

Keyword(s):

Soil Water ◽

Air Temperature ◽

Soil Surface ◽

The Other ◽

Maximum Temperature ◽

Soil Water Tension ◽

Air Temperatures ◽

Soil Temperatures ◽

Water Tension ◽

Minimum Air Temperature

Soil and air temperatures, and soil water tension were measured at two sites from June 1972 to August 1973 in order to determine the effect of scarification. This study is part of a project concerning yellow birch regeneration. The minimum air temperature at 30 cm height and at the soil surface were, respectively, 0.5 and 1.0 °C higher at the scarified site; on the other hand, the maximum temperature at 30 cm was lower. The soil temperatures during the summer were 2 to 4 °C higher at the scarified site according to the level considered. Soil water tension was much lower in the scarified station between 0 and 15 cm depth, but the effect decreased during the second summer of the study.

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Artificial Detection of Lower-Frequency Periodicity in Climatic Studies by Wavelet Analysis Demonstrated on Synthetic Time Series

Journal of Applied Meteorology and Climatology ◽

10.1175/jamc-d-18-0331.1 ◽

2019 ◽

Vol 58 (9) ◽

pp. 2077-2086 ◽

Cited By ~ 1

Author(s):

Assaf Hochman ◽

Hadas Saaroni ◽

Felix Abramovich ◽

Pinhas Alpert

Keyword(s):

Time Series ◽

Wavelet Analysis ◽

Power Spectrum ◽

Low Frequency ◽

Real Data ◽

Careful Analysis ◽

Wavelet Power Spectrum ◽

Natural Oscillations ◽

Local Singularity ◽

Climatic Time Series

AbstractThe continuous wavelet transform (CWT) is a frequently used tool to study periodicity in climate and other time series. Periodicity plays a significant role in climate reconstruction and prediction. In numerous studies, the use of CWT revealed dominant periodicity (DP) in climatic time series. Several studies suggested that these “natural oscillations” would even reverse global warming. It is shown here that the results of wavelet analysis for detecting DPs can be misinterpreted in the presence of local singularities that are manifested in lower frequencies. This may lead to false DP detection. CWT analysis of synthetic and real-data climatic time series, with local singularities, indicates a low-frequency DP even if there is no true periodicity in the time series. Therefore, it is argued that this is an inherent general property of CWT. Hence, applying CWT to climatic time series should be reevaluated, and more careful analysis of the entire wavelet power spectrum is required, with a focus on high frequencies as well. A conelike shape in the wavelet power spectrum most likely indicates the presence of a local singularity in the time series rather than a DP, even if the local singularity has an observational or a physical basis. It is shown that analyzing the derivatives of the time series may be helpful in interpreting the wavelet power spectrum. Nevertheless, these tests are only a partial remedy that does not completely neutralize the effects caused by the presence of local singularities.

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Forecasting mean monthly maximum and minimum air temperature of Jalandhar district of Punjab, India using seasonal ARIMA model

Journal of Agrometeorology ◽

10.54386/jam.v24i1.777 ◽

2022 ◽

Vol 24 (1) ◽

Author(s):

BALJEET KAUR ◽

NAVNEET KAUR ◽

K. K. GILL ◽

JAGJEEVAN SINGH ◽

S. C. BHAN ◽

...

Keyword(s):

Time Series ◽

Air Temperature ◽

Goodness Of Fit ◽

Time Series Data ◽

Arima Model ◽

Kendall Test ◽

Series Data ◽

Data Series ◽

Augmented Dickey Fuller ◽

Minimum Air Temperature

The long-term air temperature data series from 1971-2019 was analyzed and used for forecasting mean monthly air temperature at the district level. The Augmented Dickey-Fuller test, Kwiatkowski-Phillips-Schmidt-Shin test, and Mann-Kendall test were employed to test the stationarity and trend of the time series. The mean monthly maximum air temperature did not show any significant variation while an increasing trend of 0.04°C per annum was observed in mean monthly minimum air temperature, which was detrended. Box-Jenkins autoregressive integrated moving–averages were used to forecast the forthcoming 5 years (2020-2024) air temperature in the district Jalandhar of Punjab. The goodness of fit was tested against residuals, the autocorrelation function, and the histogram. The fitted model was able to capture dynamics of the time series data and produce a sensible forecast.

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