A time series analysis on the seasonality of air temperature anomalies

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 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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Outdoor Air Temperature and Mortality in the Netherlands: A Time-Series Analysis

American Journal of Epidemiology ◽

10.1093/oxfordjournals.aje.a116680 ◽

1993 ◽

Vol 137 (3) ◽

pp. 331-341 ◽

Cited By ~ 250

Author(s):

Anton E. Kunst ◽

Casper W. N. Looman ◽

Johan P. Mackenbach

Keyword(s):

Time Series ◽

The Netherlands ◽

Time Series Analysis ◽

Air Temperature ◽

Outdoor Air ◽

Series Analysis

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A time-series analysis of mortality and air temperature in Greater Beirut

The Science of The Total Environment ◽

10.1016/j.scitotenv.2004.02.027 ◽

2004 ◽

Vol 330 (1-3) ◽

pp. 71-80 ◽

Cited By ~ 47

Author(s):

A ELZEIN ◽

M TEWTELSALEM ◽

G NEHME

Keyword(s):

Time Series ◽

Time Series Analysis ◽

Air Temperature ◽

Series Analysis

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Validation of Rainfall Reanalysis Data to Explore Changes in Oldeman Agricultural Climate Patterns Due to Variability of Surface Temperature Anomalies with Time Series Analysis Techniques (Case Study of Dumai City for 30 Years Period)

Proceedings of International Conference on Multidiciplinary Research ◽

10.32672/pic-mr.v4i1.3773 ◽

2022 ◽

Vol 4 (1) ◽

pp. 221-228

Author(s):

Bagus Primohadi Syahputra ◽

Dedi Sucahyono

Keyword(s):

Time Series ◽

Time Series Analysis ◽

Surface Temperature ◽

Reanalysis Data ◽

Analysis Techniques ◽

Temperature Anomalies ◽

Series Analysis ◽

Climate Patterns

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Exploring Short-Term Climate Change Effects on Rangelands and Broad-Leaved Forests by Free Satellite Data in Aosta Valley (Northwest Italy)

Climate ◽

10.3390/cli9030047 ◽

2021 ◽

Vol 9 (3) ◽

pp. 47

Author(s):

Tommaso Orusa ◽

Enrico Borgogno Mondino

Keyword(s):

Climate Change ◽

Remote Sensing ◽

Time Series ◽

Snow Cover ◽

Air Temperature ◽

Google Earth ◽

Water Requirement ◽

Temperature Anomalies ◽

Climate Change Effects ◽

Air Temperature Anomalies

Satellite remote sensing is a power tool for the long-term monitoring of vegetation. This work, with reference to a regional case study, investigates remote sensing potentialities for describing the annual phenology of rangelands and broad-leaved forests at the landscape level with the aim of detecting eventual effects of climate change in the Alpine region of the Aosta Valley (Northwest (NW) Italy). A first analysis was aimed at estimating phenological metrics (PMs) from satellite images time series and testing the presence of trends along time. A further investigation concerned evapotranspiration from vegetation (ET) and its variation along the years. Additionally, in both the cases the following meteorological patterns were considered: air temperature anomalies, precipitation trends and the timing of yearly seasonal snow melt. The analysis was based on the time series (TS) of different MODIS collections datasets together with Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) collection obtained through Google Earth Engine. Ground weather stations data from the Centro Funzionale VdA ranging from 2000 to 2019 were used. In particular, the MOD13Q1 v.6, MOD16A2 and MOD10A1 v.6 collections were used to derive PMs, ET and snow cover maps. The SRTM (shuttle radar topography mission) DTM (digital terrain model) was also used to describe local topography while the Coordination of Information on the Environment (CORINE) land cover map was adopted to investigate land use classes. Averagely in the area, rangelands and broad-leaved forests showed that the length of season is getting longer, with a general advance of the SOS (start of the season) and a delay in the EOS (end of the season). With reference to ET, significant increasing trends were generally observed. The water requirement from vegetation appeared to have averagely risen about 0.05 Kg·m−2 (about 0.5%) per year in the period 2000–2019, for a total increase of about 1 Kg·m−2 in 20 years (corresponding to a percentage difference in water requirement from vegetation of about 8%). This aspect can be particularly relevant in the bottom of the central valley, where the precipitations have shown a statistically significant decreasing trend in the period 2000–2019 (conversely, no significant variation was found in the whole territory). Additionally, the snowpack timing persistence showed a general reduction trend. PMs and ET and air temperature anomalies, as well as snow cover melting, proved to have significantly changed their values in the last 20 years, with a continuous progressive trend. The results encourage the adoption of remote sensing to monitor climate change effects on alpine vegetation, with particular focus on the relationship between phenology and other abiotic factors permitting an effective technological transfer.

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