scholarly journals Interpreting the time variability of world-wide GPS and GOME/SCIAMACHY integrated water vapour retrievals, using reanalyses as auxiliary tools

2018 ◽  
Author(s):  
Roeland Van Malderen ◽  
Eric Pottiaux ◽  
Gintautas Stankunavicius ◽  
Steffen Beirle ◽  
Thomas Wagner ◽  
...  
Keyword(s):  
Time Series ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Water Vapour ◽  
Southern Oscillation ◽  
Linear Trend ◽  
Time Variability ◽  
The North ◽  
Integrated Water Vapour

Abstract. This study investigates different aspects of the Integrated Water Vapour (IWV) variability at 118 globally distributed Global Positioning System (GPS) sites, using additionally UV/VIS satellite retrievals by GOME, SCIAMACHY and GOME-2 (denoted as GOMESCIA below), and ERA-Interim reanalysis output at these site locations. Apart from some spatial representativeness issues at especially coastal and island sites, those three datasets correlate rather well, the lowest correlation found between GPS and GOMESCIA (0.865 on average). In this paper, we first study the geographical distribution of the frequency distributions of the IWV time series, and subsequently analyse the seasonal IWV cycle and linear trend differences among the three different datasets. Finally, both the seasonal behaviour and the long-term variability are fitted together by means of a stepwise multiple linear regression of the station’s time series, with a selection of regionally dependent candidate explanatory variables. Overall, the variables that are most frequently used and explain the largest fractions of the IWV variability are the surface temperature and precipitation. Also the surface pressure and tropopause pressure (in particular for higher latitude sites) are important contributors to the IWV time variability. All these variables also seem to account for the sign of long-term trend in the IWV time series to a large extent, when considered as explanatory variable. Furthermore, the multiple linear regression linked the IWV variability at some particular regions to teleconnection patterns or climate/oceanic indices like the North Oscillation index for West USA, the El Niňo Southern Oscillation (ENSO) for East Asia, the East Atlantic (associated with the North Atlantic Oscillation, NAO) index for Europe.

scholarly journals Long term dynamics of OH<sup>∗</sup> temperatures over Middle Europe: Trends and solar correlations

2016 ◽  
Author(s):  
Christoph Kalicinsky ◽  
Peter Knieling ◽  
Ralf Koppmann ◽  
Dirk Offermann ◽  
Wolfgang Steinbrecht ◽  
...  
Keyword(s):  
Time Series ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Linear Trend ◽  
Temperature Time Series ◽  
Time Interval ◽  
Radio Flux ◽  
Solar Radio Flux ◽  
Temperature Time

Abstract. We present the analysis of annual average OH∗ temperatures in the mesopause region derived from measurements of the GRound based Infrared P-branch Spectrometer (GRIPS) at Wuppertal (51° N, 7° E) in the time interval 1988 to 2015. The current study uses a 7 year longer temperature time series compared to the latest analysis regarding the long term dynamics of OH* temperatures measured at Wuppertal. This additional time of observation leads to a change in characterisation of the observed long term dynamics. We perform a multiple linear regression using the solar radio flux F10.7cm (11-year cycle of solar activity) and time to describe the temperature evolution. The analysis leads to a linear trend of (−0.089&amp;pm;0.055) K year−1 and a sensitivity to the solar activity of (4.2&amp;pm;0.9) K (100 SFU)−1 (r2 of fit 0.6). However, one linear trend in combination with the 11-year solar cycle is not sufficient to explain all observed long term dynamics. Actually we find a clear trend break in the temperature time series in middle of 2006. Before this break point there is an explicit negative linear trend of (−0.22&amp;pm;0.08) K year−1 and after 2006 the linear trend turns positive with a value of (0.38&amp;pm;0.23) K year−1. This apparent trend break can also be described using a long periodic oscillation. One possibility is to use the 22-year solar cycle that describes the reversal of the solar magnetic field (Hale cycle). A multiple linear regression using the solar radio flux and the solar polar magnetic field as parameters leads to the regression coefficients Csolar = (5.0&amp;pm;0.7) K (100 SFU)−1 and Chale = (1.8&amp;pm;0.5) K (100 µT)−1 (r2 = 0.71). But the best way to describe the OH* temperature time series is to use the solar radio flux and a 24-year oscillation. A multiple linear regression using these parameters leads to a sensitivity to the solar activity of (4.3&amp;pm;0.7) K (100 SFU)−1 and an amplitude of the 24-year oscillation A = (1.95&amp;pm;0.43) K (r2 = 0.77). The most important finding here is that using these parameters for the multiple linear regression an additional linear trend is no longer needed. Moreover, with the knowledge of this 24-year oscillation the linear trends derived in this and in a former study of the Wuppertal data series can be reproduced by just fitting a line to the corresponding part (time interval) of the oscillation. This actually means that depending on the analysed time interval completely different linear trends with respect to magnitude and sign can be observed. This fact is of essential importance for any comparison between different observations and model simulations. After detrending the temperature time series regarding the 11-year solar cycle and the 24-year oscillation multi-annual oscillations (MAOs) remain. A harmonic analysis finds three pronounced oscillations with periods of (2.69&amp;pm;0.06) years, (3.15&amp;pm;0.07) years, and (4.54&amp;pm;0.17) years. The corresponding amplitudes are (1.03&amp;pm;0.33) K, (1.03&amp;pm;0.33) K, and (0.91&amp;pm;0.36) K, respectively.

scholarly journals Tropospheric water vapour above Switzerland over the last 12 years

2009 ◽  
Vol 9 (16) ◽  
pp. 5975-5988 ◽  
Author(s):  
J. Morland ◽  
M. Collaud Coen ◽  
K. Hocke ◽  
P. Jeannet ◽  
C. Mätzler
Keyword(s):  
Time Series ◽  
Water Vapour ◽  
Grid Point ◽  
Microwave Radiometer ◽  
Regional Scale ◽  
Seasonal Trends ◽  
Climate Change Effects ◽  
Integrated Water Vapour ◽  
Mountain Weather

Abstract. Integrated Water vapour (IWV) has been measured since 1994 by the TROWARA microwave radiometer in Bern, Switzerland. Homogenization techniques were used to identify and correct step changes in IWV related to instrument problems. IWV from radiosonde, GPS and sun photometer (SPM) was used in the homogenisation process as well as partial IWV columns between valley and mountain weather stations. The average IWV of the homogenised TROWARA time series was 14.4 mm over the 1996–2007 period, with maximum and minimum monthly average values of 22.4 mm and 8 mm occurring in August and January, respectively. A weak diurnal cycle in TROWARA IWV was detected with an amplitude of 0.32 mm, a maximum at 21:00 UT and a minimum at 11:00 UT. For 1996–2007, TROWARA trends were compared with those calculated from the Payerne radiosonde and the closest ECMWF grid point to Bern. Using least squares analysis, the IWV time series of radiosondes at Payerne, ECMWF, and TROWARA showed consistent positive trends from 1996 to 2007. The radiosondes measured an IWV trend of 0.45±0.29%/y, the TROWARA radiometer observed a trend of 0.39±0.44%/y, and ECMWF operational analysis gave a trend of 0.25±0.34%/y. Since IWV has a strong and variable annual cycle, a seasonal trend analysis (Mann-Kendall analysis) was also performed. The seasonal trends are stronger by a factor 10 or so compared to the full year trends above. The positive IWV trends of the summer months are partly compensated by the negative trends of the winter months. The strong seasonal trends of IWV on regional scale underline the necessity of long-term monitoring of IWV for detection,understanding, and forecast of climate change effects in the Alpine region.

scholarly journals A precipitation-based regionalization for Western Iran and regional drought variability

2008 ◽  
Vol 12 (6) ◽  
pp. 1309-1321 ◽  
Author(s):  
T. Raziei ◽  
I. Bordi ◽  
L. S. Pereira
Keyword(s):  
Linear Trend ◽  
Climatic Variability ◽  
Annual Precipitation ◽  
Time Variability ◽  
Positive Trend ◽  
The South ◽  
Western Iran ◽  
The North ◽  
Precipitation Regimes

Abstract. The spatial distribution of the seasonal and annual precipitation was analyzed in western Iran using data from 140 stations covering the period 1965–2000. Applying the Precipitation Concentration Index (PCI), the intra-annual precipitation variability was also studied. Furthermore, nine precipitation-derived parameters were used to regionalize climate in western Iran using principal component analysis and clustering techniques. Results suggest that five spatially homogenous sub-regions can be identified characterized by different precipitation regimes. The spatial pattern of seasonal precipitation seems to be highly controlled by the wide latitudinal extent of the region and by the pronounced orographic relieves, and the time of occurrence of the maximum precipitation varies from spring in the north to winter in the south. The time variability of dry and wet periods in the identified sub-regions was analyzed using the Precipitation Index (PI) and the existence of any long-term trend was tested. Results show that the northern and southern regions of western Iran are characterized by different climatic variability. Furthermore, a negative long-term linear trend in the north and a weak positive trend in the south of the study area have been detected though they are not statistically significant.

scholarly journals A precipitation-based regionalization for Western Iran and regional drought variability

2008 ◽  
Vol 5 (4) ◽  
pp. 2133-2167 ◽  
Author(s):  
T. Raziei ◽  
I. Bordi ◽  
L. S. Pereira
Keyword(s):  
Linear Trend ◽  
Climatic Variability ◽  
Annual Precipitation ◽  
Time Variability ◽  
Positive Trend ◽  
The South ◽  
Western Iran ◽  
The North ◽  
Precipitation Regimes

Abstract. The spatial distribution of the seasonal and annual precipitation was analyzed in western Iran using data from 140 stations covering the period 1965–2000. Applying the Precipitation Concentration Index (PCI), the intra-annual precipitation variability was also studied. Furthermore, nine precipitation-derived parameters were used to regionalize climate in western Iran using principal component analysis and clustering techniques. Results suggest that five spatially homogenous sub-regions can be identified characterized by different precipitation regimes. The spatial pattern of seasonal precipitation seems to be highly controlled by the wide latitudinal extent of the region and by the pronounced orographic relieves, and the time of occurrence of the maximum precipitation varies from spring in the north to winter in the south. The time variability of dry and wet periods in the identified sub-regions was analyzed using the Precipitation Index (PI) and the existence of any long-term trend was tested. Results show that the northern and southern regions of western Iran are characterized by different climatic variability. Furthermore, a negative long-term linear trend in the north and a weak positive trend in the south of the study area have been detected though they are not statistically significant.

Análise da relação entre O3, NO e NO2 usando técnicas de regressão linear múltipla.

GEOgraphia ◽  
2018 ◽  
Vol 20 (43) ◽  
pp. 124
Author(s):  
Amaury De Souza ◽  
Priscilla V Ikefuti ◽  
Ana Paula Garcia ◽  
Debora A.S Santos ◽  
Soetania Oliveira
Keyword(s):  
Time Series ◽  
Linear Regression ◽  
Nitrogen Dioxide ◽  
Multiple Linear Regression ◽  
Quality Parameters ◽  
Environmental Pollutant ◽  
Environmental Research ◽  
Linear Regression Models ◽  
Mato Grosso ◽  
The Impact

Análise e previsão de parâmetros de qualidade do ar são tópicos importantes da pesquisa atmosférica e ambiental atual, devido ao impacto causado pela poluição do ar na saúde humana. Este estudo examina a transformação do dióxido de nitrogênio (NO2) em ozônio (O3) no ambiente urbano, usando o diagrama de séries temporais. Foram utilizados dados de concentração de poluentes ambientais e variáveis meteorológicas para prever a concentração de O3 na atmosfera. Foi testado o emprego de modelos de regressão linear múltipla como ferramenta para a predição da concentração de O3. Os resultados indicam que o valor da temperatura e a presença de NO2 influenciam na concentração de O3 em Campo Grande, capital do Estado do Mato Grosso do Sul. Palavras-chave: Ozônio. Dióxido de nitrogênio. Séries cronológicas. Regressões. ANALYSIS OF THE RELATIONSHIP BETWEEN O3, NO AND NO2 USING MULTIPLE LINEAR REGRESSION TECHNIQUES.Abstract: Analysis and prediction of air quality parameters are important topics of current atmospheric and environmental research due to the impact caused by air pollution on human health. This study examines the transformation of nitrogen dioxide (NO2) into ozone (O3) in the urban environment, using the time series diagram. Environmental pollutant concentration and meteorological variables were used to predict the O3 concentration in the atmosphere. The use of multiple linear regression models was tested as a tool to predict O3 concentration. The results indicate that the temperature value and the presence of NO2 influence the O3 concentration in Campo Grande, capital of the State of Mato Grosso do Sul.Keywords: Ozone. Nitrogen dioxide. Time series. Regressions. ANÁLISIS DE LA RELACIÓN ENTRE O3, NO Y NO2 UTILIZANDO MÚLTIPLES TÉCNICAS DE REGRESIÓN LINEAL.Resumen: Análisis y previsión de los parámetros de calidad del aire son temas importantes de la actual investigación de la atmósfera y el medio ambiente, debido al impacto de la contaminación atmosférica sobre la salud humana. Este estudio examina la transformación del dióxido de nitrógeno (NO2) en ozono (O3) en el entorno urbano, utilizando el diagrama de series de tiempo. Las concentraciones de los contaminantes ambientales de datos y variables climáticas fueron utilizadas para predecir la concentración de O3 en la atmósfera. El uso de múltiples modelos de regresión lineal como herramienta para predecir la concentración de O3 se puso a prueba. Los resultados indican que el valor de la temperatura y la presencia de NO2 influyen en la concentración de O3 en Campo Grande, capital del Estado de Mato Grosso do Sul.Palabras clave: Ozono. Dióxido de nitrógeno. Series de tiempo. Regresiones.

Following the infection dynamics of the tropical trematode Oligogonotylus mayae in its intermediate and definitive hosts for 13 years

2020 ◽  
Vol 94 ◽  
Author(s):  
A.L. May-Tec ◽  
N.A. Herrera-Castillo ◽  
V.M. Vidal-Martínez ◽  
M.L. Aguirre-Macedo
Keyword(s):  
Time Series ◽  
Cross Correlation ◽  
Southern Oscillation ◽  
Fish Host ◽  
Snail Host ◽  
Monthly Data ◽  
Infection Dynamics ◽  
Infection Parameters ◽  
Over Time

Abstract We present a time series of 13 years (2003–2016) of continuous monthly data on the prevalence and mean abundance of the trematode Oligogonotylus mayae for all the hosts involved in its life cycle. We aimed to determine whether annual (or longer than annual) environmental fluctuations affect these infection parameters of O. mayae in its intermediate snail host Pyrgophorus coronatus, and its second and definitive fish host Mayaheros urophthalmus from the Celestun tropical coastal lagoon, Yucatan, Mexico. Fourier time series analysis was used to identify infection peaks over time, and cross-correlation among environmental forcings and infection parameters. Our results suggest that the transmission of O. mayae in all its hosts was influenced by the annual patterns of temperature, salinity and rainfall. However, there was a biannual accumulation of metacercarial stages of O. mayae in M. urophthalmus, apparently associated with the temporal range of the El Niño-Southern Oscillation (five years) and the recovery of the trematode population after a devasting hurricane. Taking O. mayae as an example of what could be happening to other trematodes, it is becoming clear that environmental forcings acting at long-term temporal scales affect the population dynamics of these parasites.

scholarly journals Flexible approach for quantifying average long-term changes and seasonal cycles of tropospheric trace species

2019 ◽  
Author(s):  
David D. Parrish ◽  
Richard G. Derwent ◽  
Simon O'Doherty ◽  
Peter G. Simmonds
Keyword(s):  
Time Series ◽  
Seasonal Cycle ◽  
Linear Trend ◽  
Power Series Expansion ◽  
Mathematical Representation ◽  
Seasonal Cycles ◽  
Data Sets ◽  
Significant Information ◽  
Long Term Changes

Abstract. We present an approach to derive a systematic mathematical representation of the statistically significant features of the average long-term changes and seasonal cycle of concentrations of trace tropospheric species. The results for two illustrative data sets (time series of baseline concentrations of ozone and N2O at Mace Head, Ireland) indicate that a limited set of seven or eight parameter values provides this mathematical representation for both example species. This method utilizes a power series expansion to extract more information regarding the long-term changes than can be provided by oft-employed linear trend analyses. In contrast, the quantification of average seasonal cycles utilizes a Fourier series analysis that provides less detailed seasonal cycles than are sometimes represented as twelve monthly means; including that many parameters in the seasonal cycle representation is not usually statistically justified, and thereby adds unnecessary noise to the representation and prevents a clear analysis of the statistical uncertainty of the results. The approach presented here is intended to maximize the statistically significant information extracted from analyses of time series of concentrations of tropospheric species regarding their mean long-term changes and seasonal cycles, including non-linear aspects of the long-term trends. Additional implications, advantages and limitations of this approach are discussed.

scholarly journals ANALISIS FAKTOR-FAKTOR YANG MEMPENGARUHI IMPOR BERAS DI INDONESIA

2019 ◽  
Vol 11 (2) ◽  
pp. 183-201
Author(s):  
Yona Namira ◽  
Iskandar Andi Nuhung ◽  
Mudatsir Najamuddin
Keyword(s):  
Time Series ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Time Series Data ◽  
Series Data ◽  
Test Results ◽  
The Us ◽  
Rice Prices ◽  
Us Dollar ◽  
Spss Software

This study aims to 1) identify factors that affect the import of rice in Indonesia 2) analyze the influence of these factors on imports of rice in Indonesia. The data used in this research are time series data from 1994 to 2013 from the Central Statistics Agency (BPS), the Ministry of Agriculture, Ministry of Commerce, National Logistics Agency (Bulog), and Bank Indonesia. Multiple linear regression through SPSS software version 21 was employed to analyze the data. The test results together indicated the variables of productions, consumptions, stocks of rice, domestic rice prices, international rice prices and the rupiah against the US dollar affect the imports of rice in Indonesia.

scholarly journals Pengaruh Industri Sedang dan Besar Terhadap Tingkat Kemiskinan di Kabupaten Gresik Tahun 2002-2016

2021 ◽  
Vol 4 (1) ◽  
pp. 25-31
Author(s):  
Rohmatul Janah ◽  
Ida Nuraini
Keyword(s):  
Time Series ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Time Series Data ◽  
Industrial Sector ◽  
Poverty Rate ◽  
Domestic Product ◽  
Series Data ◽  
Insignificant Effect ◽  
Poverty Levels

This research is aimed at studying the influence of medium and large industries on poverty levels in Gresik on 2002-2016. The variables used in this study is medium and large industries, a labour of medium and large industries, gross regional domestic product (GRDP) of industrial sector and poverty rate. The method used in this study used multiple linear regression and used time-series data. The results of this study simultaneously are the variables of the amount of medium and large industries, the labour medium and large industries, and the gross regional domestic product (GRDP) of the industrial sector to poverty rate is significant. While medium and large industries to poverty rate have negative and insignificant effect with a coefficient value of -0,208905. The labour of medium and large industries to poverty rate has a positive and significant effect with a coefficient value of 0,130822,  the gross regional domestic product (GRDP) of industrial to poverty rate has a negative and significant effect with a coefficient value of -0,169431.

scholarly journals PENGARUH INVESTASI DAN TENAGA KERJA TERHADAP PRODUK DOMESTIK REGIONAL BRUTO (PDRB) DAN PENDAPATAN ASLI DAERAH (PAD) KOTA BALIKPAPAN

2015 ◽  
Vol 5 (2) ◽  
pp. 159
Author(s):  
Johana Rosmalia ◽  
Rusdiah Iskandar ◽  
Fitriadi Fitriadi
Keyword(s):  
Time Series ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Pathway Analysis ◽  
Secondary Data ◽  
Strong Relationship ◽  
Domestic Product ◽  
Regression Formula

This study used secondary data in the form of time series which are analyzed using Pathway analysis with multiple linear regression formula. The purpose of this study was to determine the effect of investment and labor to gross regional domestic product (GRDP) and regional revenues in Balikpapan city.  The results of the study was shown that Y = 0.202 - 0.098 X1 + 0.244 X2 + 0.825 X3. The results showed that investment, labor and gross regional domestic product (GRDP) have jointly effect on the regional revenues in Balikpapan city. Partially; the investment has no significant effect on gross regional domestic product (GRDP), labor has significant effect on gross regional domestic product (GRDP), gross regional domestic product (GRDP) has significant effect to regional revenues in Balikpapan city, the investment has no significant effect on regional revenues in Balikpapan city, and labor has no significant effect on regional revenues in Balikpapan city. The contribution of investment, labor and gross regional domestic product (GRDP) variable was about 93.5 % and it means that those have very strong relationship; meanwhile, the rest, about 6.5 %, has been influenced by other factors.

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