scholarly journals Theoretical Study and Numerical Experiment on the Influence of Trend Changes on Correlation Coefficient

Atmosphere ◽  
2021 ◽  
Vol 13 (1) ◽  
pp. 66
Author(s):  
Chaojiu Da ◽  
Lei Hu ◽  
Binglu Shen ◽  
Yuyin Yang ◽  
Shiquan Wan ◽  
...  
Keyword(s):  
Time Series ◽  
Global Warming ◽  
Numerical Experiment ◽  
Correlation Coefficient ◽  
Negative Correlation ◽  
Theoretical Study ◽  
Meteorological Data ◽  
Function Graph ◽  
Trend Coefficient ◽  
Linear Trends

When one of two time series undergoes an obvious change in trend, the correlation coefficient between the two will be distorted. In the context of global warming, most meteorological time series have obvious linear trends, so how do variations in these trends affect the correlation coefficient? In this paper, the correlation between time series with trend changes is studied theoretically and numerically. Adopting the trend coefficient, which reflects the nature and size of the trend change, we derive a formula r = f(k, l) for the correlation coefficient of time series X and Y with respective trend coefficients k and l. Analysis of the function graph shows that the changes in correlation coefficient with respect to the trend coefficients produce a twisted saddle surface, and the saddle point coordinates are given by the trend coefficients of time series X and Y with the opposite signs. The curve f(k, l) = f(0, 0) divides the coordinate planes into regions where f(k, l) > f(0, 0) and f(k, l) < f(0, 0). When the trend coefficients k and l are very small and the correlation coefficient is also very small, then k > 0 and l > 0 (or k < 0 and l < 0) amplifies a positive correlation, whereas k > 0 and l < 0 (or k < 0 and l > 0) amplifies a negative correlation, as found in previous research. Finally, experiments using meteorological data verify the reliability and effectiveness of the theory.

scholarly journals Business Days Time Series Weekly Trend and Seasonality

2021 ◽  
Vol 5 (1) ◽  
pp. 26
Author(s):  
Karlis Gutans
Keyword(s):  
Time Series ◽  
Global Warming ◽  
Exchange Rates ◽  
Temperature Data ◽  
Adjustment Method ◽  
Currency Exchange ◽  
Currency Exchange Rates ◽  
Time Series Decomposition ◽  
The World

The world changes at incredible speed. Global warming and enormous money printing are two examples, which do not affect every one of us equally. “Where and when to spend the vacation?”; “In what currency to store the money?” are just a few questions that might get asked more frequently. Knowledge gained from freely available temperature data and currency exchange rates can provide better advice. Classical time series decomposition discovers trend and seasonality patterns in data. I propose to visualize trend and seasonality data in one chart. Furthermore, I developed a calendar adjustment method to obtain weekly trend and seasonality data and display them in the chart.

scholarly journals Hydrological prediction in a non-stationary world

2007 ◽  
Vol 11 (1) ◽  
pp. 408-414 ◽  
Author(s):  
R. T. Clarke
Keyword(s):  
Time Series ◽  
Urban Development ◽  
Mean Value ◽  
South American ◽  
Drainage Basins ◽  
South Eastern ◽  
Rapid Changes ◽  
Hydrological Prediction ◽  
Random Fluctuations ◽  
Linear Trends

Abstract. The paper discusses evidence that common assumptions in the analysis of hydrological time series (homogeneous variability in random fluctuations about a constant mean value) may not be appropriate for some South American drainage basins. Relatively rapid changes have occurred, and are occurring, as a consequence of replacing mature forest by short crops and urban development. Some research claims to have detected non-linear trends in streamflow in rivers draining the south-eastern part of the sub-continent, together with decadal fluctuations and interannual peaks at ENSO timescales. The paper discusses the implications of such changes for hydrological practices now in widespread and largely unquestioned use.

scholarly journals Towards Reliable Probabilistic Time-Series Projections of Global Mean Surface Temperature

2021 ◽  
Author(s):  
Philip G. Sansom ◽  
Donald Cummins ◽  
Stefan Siegert ◽  
David B Stephenson
Keyword(s):  
Time Series ◽  
Global Warming ◽  
Surface Temperature ◽  
Climate Model ◽  
Future Projections ◽  
Perfect Model ◽  
Global Mean Surface Temperature ◽  
Time Series Approach ◽  
Energy Balance Models ◽  
Global Mean

Abstract Quantifying the risk of global warming exceeding critical targets such as 2.0 ◦ C requires reliable projections of uncertainty as well as best estimates of Global Mean Surface Temperature (GMST). However, uncertainty bands on GMST projections are often calculated heuristically and have several potential shortcomings. In particular, the uncertainty bands shown in IPCC plume projections of GMST are based on the distribution of GMST anomalies from climate model runs and so are strongly determined by model characteristics with little influence from observations of the real-world. Physically motivated time-series approaches are proposed based on fitting energy balance models (EBMs) to climate model outputs and observations in order to constrain future projections. It is shown that EBMs fitted to one forcing scenario will not produce reliable projections when different forcing scenarios are applied. The errors in the EBM projections can be interpreted as arising due to a discrepancy in the effective forcing felt by the model. A simple time-series approach to correcting the projections is proposed based on learning the evolution of the forcing discrepancy so that it can be projected into the future. This approach gives reliable projections of GMST when tested in a perfect model setting. When applied to observations this leads to projected warming of 2.2 ◦ C (1.7 ◦ C to 2.9 ◦ C) in 2100 compared to pre-industrial conditions, 0.4 ◦ C lower than a comparable IPCC anomaly estimate. The probability of staying below the critical 2.0 ◦ C warming target in 2100 more than doubles to 0.28 compared to only 0.11 from a comparably IPCC estimate.

scholarly journals Monitoring Changes to Arctic Vegetation and Glaciers at Ny-Ålesund, Svalbard, Based on Time Series Remote Sensing

2021 ◽  
Vol 13 (19) ◽  
pp. 3845
Author(s):  
Guangbo Ren ◽  
Jianbu Wang ◽  
Yunfei Lu ◽  
Peiqiang Wu ◽  
Xiaoqing Lu ◽  
...  
Keyword(s):  
Climate Change ◽  
Remote Sensing ◽  
Time Series ◽  
High Latitude ◽  
Global Climate ◽  
Meteorological Data ◽  
Remote Sensing Data ◽  
Vegetation Coverage ◽  
Coastal Regions ◽  
Average Annual Temperature

Climate change has profoundly affected global ecological security. The most vulnerable region on Earth is the high-latitude Arctic. Identifying the changes in vegetation coverage and glaciers in high-latitude Arctic coastal regions is important for understanding the process and impact of global climate change. Ny-Ålesund, the northern-most human settlement, is typical of these coastal regions and was used as a study site. Vegetation and glacier changes over the past 35 years were studied using time series remote sensing data from Landsat 5/7/8 acquired in 1985, 1989, 2000, 2011, 2015 and 2019. Site survey data in 2019, a digital elevation model from 2009 and meteorological data observed from 1985 to 2019 were also used. The vegetation in the Ny-Ålesund coastal zone showed a trend of declining and then increasing, with a breaking point in 2000. However, the area of vegetation with coverage greater than 30% increased over the whole study period, and the wetland moss area also increased, which may be caused by the accelerated melting of glaciers. Human activities were responsible for the decline in vegetation cover around Ny-Ålesund owing to the construction of the town and airport. Even in areas with vegetation coverage of only 13%, there were at least five species of high-latitude plants. The melting rate of five major glaciers in the study area accelerated, and approximately 82% of the reduction in glacier area occurred after 2000. The elevation of the lowest boundary of the five glaciers increased by 50–70 m. The increase in precipitation and the average annual temperature after 2000 explains the changes in both vegetation coverage and glaciers in the study period.

scholarly journals FORECASTING URBAN EXPANSION BASED ON NIGHT LIGHTS

2016 ◽  
Vol XLI-B8 ◽  
pp. 1049-1054 ◽  
Author(s):  
D. Stathakis
Keyword(s):  
Time Series ◽  
Linear Regression ◽  
Correlation Coefficient ◽  
Urban Expansion ◽  
Image Correlation ◽  
Spatially Explicit ◽  
Expansion Model ◽  
Night Lights

Forecasting urban expansion models are a very powerful tool in the hands of urban planners in order to anticipate and mitigate future urbanization pressures. In this paper, a linear regression forecasting urban expansion model is implemented based on the annual composite night lights time series available from National Oceanic and Atmospheric Administration (NOAA). The product known as 'stable lights' is used in particular, after it has been corrected with a standard intercalibration process to reduce artificial year-to-year fluctuations as much as possible. Forecasting is done for ten years after the end of the time series. Because the method is spatially explicit the predicted expansion trends are relatively accurately mapped. Two metrics are used to validate the process. The first one is the year-to-year Sum of Lights (SoL) variation. The second is the year-to-year image correlation coefficient. Overall it is evident that the method is able to provide an insight on future urbanization pressures in order to be taken into account in planning. The trends are quantified in a clear spatial manner.

scholarly journals ANALISIS KORELASI CURAH HUJAN DAN SUHU PERMUKAAN LAUT WILAYAH INDONESIA, SERTA IMPLIKASINYA UNTUK PRAKIRAAN CURAH HUJAN (STUDI KASUS KABUPATEN CILACAP) (CORRELATION ANALYSIS OF RAINFALL AND INDONESIA SEA SURFACE TEMPERATURE, AND ITS ...

Agromet ◽  
2007 ◽  
Vol 21 (2) ◽  
pp. 46 ◽  
Author(s):  
W. Estiningtyas ◽  
F. Ramadhani ◽  
E. Aldrian
Keyword(s):  
Time Series ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Negative Correlation ◽  
Prediction Models ◽  
Time Series Data ◽  
Sea Surface ◽  
Rainfall Pattern ◽  
Series Data ◽  
Rainfall Prediction

<p>Significant decrease in rainfall caused extreme climate has significant impact on agriculture sector, especialy food crops production. It is one of reason and push developing of rainfall prediction models as anticipate from extreme climate events. Rainfall prediction models develop base on time series data, and then it has been included anomaly aspect, like rainfall prediction model with Kalman filtering method. One of global parameter that has been used as climate anomaly indicator is sea surface temperature. Some of research indicate, there are relationship between sea surface temperature and rainfall. Relationship between Indonesian rainfall and global sea surface temperature has been known, but its relationship with Indonesian’s sea surface temperature not know yet, especialy for rainfall in smaller area like district. So, therefore the research about relationship between rainfall in distric area and Indonesian’s sea surface temperature and it application for rainfall prediction is needed. Based on Indonesian’s sea surface temperature time series data Januari 1982 until Mei 2006 show there are zona of Indonesian’s sea surface temperature (with temperature more than 27,6 0C) dominan in Januari-Mei and moved with specific pattern. Highest value of spasial correlation beetwen Cilacap’s rainfall and Indonesian’s sea surface temperature is 0,30 until 0,50 with different zona of Indonesian’s sea surface temperature. Highest positive correlation happened in March and July. Negative correlation is -0,30 until -0,70 with highest negative correlation in May and June. Model validation resulted correlation coeffcient 85,73%, fits model 20,74%, r2 73,49%, RMSE 20,5% and standart deviation 37,96. Rainfall prediction Januari-Desember 2007 period indicated rainfall pattern is near same with average rainfall pattern, rainfall less than 100/month. The result of this research indicate Indonesian’s sea surface temperature can be used as indicator rainfall condition in distric area, that means rainfall in district area can be predicted based on Indonesian’s sea surface temperature in zona with highest correlation in every month.</p><p>------------------------------------------------------------------</p><p>Penurunan curah hujan yang cukup signifikan akibat iklim ekstrim telah membawa dampak yang cukup signifikan pula pada sektor pertanian, terutama produksi tanaman pangan. Hal ini menjadi salah satu alasan yang mendorong semakin berkembangnya model-model prakiraan hujan sebagai upaya antipasi terhadap kejadian iklim ekstrim. Model prakiraan hujan yang pada awalnya hanya berbasis pada data time series, kini telah berkembang dengan memperhitungkan aspek anomali iklim, seperti model prakiraan hujan dengan metode filter Kalman. Salah satu indikator global yang dapat digunakan sebagai indikator anomali iklim adalah suhu permukaan laut. Dari berbagai hasil penelitian diketahui bahwa suhu permukaan laut ini memiliki keterkaitan dengan kejadian curah hujan. Hubungan curah hujan Indonesia dengan suhu permukaan laut global sudah banyak diketahui, tetapi keterkaitannya dengan suhu permukaan laut wilayah Indonesia belum banyak mendapat perhatian, terutama untuk curah hujan pada cakupan yang lebih sempit seperti kabupaten. Oleh karena itu perlu dilakukan penelitian yang mengkaji hubungan kedua parameter tersebut serta mengaplikasikannya untuk prakiraan curah hujan pada wilayah Kabupaten. Hasil penelitian berdasarkan data suhu permukaan laut wilayah Indonesia rata-rata Januari 1982 hingga Mei 2006 menunjukkan zona dengan suhu lebih dari 27,6 0C yang dominan pada bulan Januari-Mei dan bergerak dengan pola yang cukup jelas. Korelasi spasial antara curah hujan kabupaten Cilacap dengan SPL wilayah Indonesia rata-rata bulan Januari-Desember menunjukkan korelasi positip tertinggi antara 0,30 hingga 0,50 dengan zona SPL yang beragam. Korelasi tertinggi terjadi pada bulan Maret dan Juli. Sedangkan korelasi negatip berkisar antara -0,30 hingga -0,70 dengan korelasi negatip tertinggi pada bulan Mei dan Juni. Validasi model prakiraan hujan menghasilkan nilai koefisien korelasi 85,73%, fits model 20,74%, r2 sebesar 73,49%, RMSE 20,5% dan standar deviasi 37,96. Hasil prakiraan hujan bulanan periode Januari-Desember 2007 mengindikasikan pola curah hujan yang tidak jauh berbeda dengan rata-rata selama 19 tahun (1988-2006) dengan jeluk hujan kurang dari 100 mm/bulan. Hasil penelitian mengindikasikan bahwa SPL wilayah Indonesia dapat digunakan sebagai indikator untuk menunjukkan kondisi curah hujan di suatu wilayah (kabupaten), artinya curah hujan dapat diprediksi berdasarkan perubahan SPL pada zona-zona dengan korelasi yang tertinggi pada setiap bulannya.</p>

scholarly journals Dynamics of morbidity of mental disorders of neurotic origin in the Ternopil region in the context of global warmin

2021 ◽  
Vol 11 (6) ◽  
pp. 186-191
Author(s):  
S. N. Vadzyuk ◽  
T. V. Kharkovska
Keyword(s):  
Growth Rate ◽  
Global Warming ◽  
Mental Disorders ◽  
Correlation Coefficient ◽  
Heat Waves ◽  
Scientific Data ◽  
Educational Process ◽  
Archival Data ◽  
Primary Analysis ◽  
Average Annual Temperature

Relevance: Over the last century, the modern climate is characterized by an average annual increase in global air temperature. According to existing scientific data, heat waves are often accompanied by mental, behavioral disorders that are combined with mood swings and increased anxiety. That is why the analysis of the incidence of mental disorders of the neurotic spectrum in the Ternopil region in the context of global warming is relevant.Materials and methods: To analyze the incidence of mental disorders of the neurotic spectrum, the archival data of the Ternopil Regional Clinical Psychoneurological Hospital for 10 years, from 2010 to 2019 were analyzed. The average annual air temperatures were determined according to the archival data of hydrometeorological observations by the meteorological station of the city of Ternopil during 2010-2019. The growth rate of these indicators was estimated by the time series method. Pearson's correlation coefficient was used to measure the degree of linear dependence between two variables, namely the rate of hospitalization growth and the average annual temperature in the region. Tables and graphs are built using Microsoft Excel and Microsoft Word.Results: The primary analysis of the incidence of mental disorders of the neurotic spectrum in Ternopil region showed that the percentage growth rate for the year ranged from -19.55% to + 20.62% and it was difficult to determine the direction of this phenomenon. In this regard, we carried out the alignment of the series by increasing the period and determining the arithmetic mean of the data for three years, respectively, we obtained data showing that for 10 years the prevalence of mental disorders of the neurotic spectrum in Ternopil region increased, the growth rate ranged from + 1.66% to + 6.32%.Conclusions: Analyzing the obtained results, we found that in the conditions of global warming in Ternopil region there is a dynamics of growth in the incidence of mental disorders of neurotic origin. Also, by calculating the correlation coefficient, a direct correlation between the increase in the average annual temperature and the increase in morbidity rates was proved. The data obtained should be taken into account in the development of protocols for the prevention and treatment of mental illness associated with the effects of climate change, and will also be used in the educational process at the Departments of Physiology, Pathological Physiology and Psychiatry.

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