scholarly journals Comparison of nitric oxide measurements in the mesosphere and lower thermosphere from ACE-FTS, MIPAS, SCIAMACHY, and SMR

2014 ◽  
Vol 7 (12) ◽  
pp. 12735-12794 ◽  
Author(s):  
S. Bender ◽  
M. Sinnhuber ◽  
T. von Clarmann ◽  
G. Stiller ◽  
B. Funke ◽  
...  
Keyword(s):  
Nitric Oxide ◽  
Time Series ◽  
Regression Analysis ◽  
Linear Regression Analysis ◽  
Lower Thermosphere ◽  
Data Sets ◽  
Annual Variations ◽  
Annual Variability ◽  
Geographical Regions ◽  
Mesosphere And Lower Thermosphere

Abstract. We compare the nitric oxide measurements in the mesosphere and lower thermosphere (60 to 150 km) from four instruments: ACE-FTS, MIPAS, SCIAMACHY, and SMR. We use the daily zonal mean data in that altitude range for the years 2004–2010 (ACE-FTS), 2005–2012 (MIPAS), 2008–2012 (SCIAMACHY), and 2003–2012 (SMR). We first compare the data qualitatively with respect to the morphology, focussing on the major features, and then compare the time series directly and quantitatively. In three geographical regions, we compare the vertical density profiles on coincident measurement days. Since none of the instruments delivers continuous daily measurements in this altitude region, we carried out a multi-linear regression analysis. This regression analysis considers annual and semi-annual variability in form of harmonic terms and inter-annual variability by responding linearly to the solar Lyman-α radiation index and the geomagnetic Kp index. This analysis helps to find similarities and differences in the individual data sets with respect to the inter-annual variations caused by geomagnetic and solar variability. We find that the data sets are consistent and that they only disagree on minor aspects. SMR and ACE-FTS deliver the longest time series in the mesosphere and they both agree remarkably well. The shorter time series from MIPAS and SCIAMACHY also agree with them where they overlap. The data agree within ten to twenty percent when the number densities are large, but they can differ by 50 to 100% in some cases.

scholarly journals Comparison of nitric oxide measurements in the mesosphere and lower thermosphere from ACE-FTS, MIPAS, SCIAMACHY, and SMR

2015 ◽  
Vol 8 (10) ◽  
pp. 4171-4195 ◽  
Author(s):  
S. Bender ◽  
M. Sinnhuber ◽  
T. von Clarmann ◽  
G. Stiller ◽  
B. Funke ◽  
...  
Keyword(s):  
Nitric Oxide ◽  
Time Series ◽  
Regression Analysis ◽  
Linear Regression Analysis ◽  
Lower Thermosphere ◽  
Michelson Interferometer ◽  
Data Sets ◽  
Annual Variability ◽  
Geographical Regions ◽  
Mesosphere And Lower Thermosphere

Abstract. We compare the nitric oxide measurements in the mesosphere and lower thermosphere (60 to 150 km) from four instruments: the Atmospheric Chemistry Experiment–Fourier Transform Spectrometer (ACE-FTS), the Michelson Interferometer for Passive Atmospheric Sounding (MIPAS), the SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY (SCIAMACHY), and the Sub-Millimetre Radiometer (SMR). We use the daily zonal mean data in that altitude range for the years 2004–2010 (ACE-FTS), 2005–2012 (MIPAS), 2008–2012 (SCIAMACHY), and 2003–2012 (SMR). We first compare the data qualitatively with respect to the morphology, focussing on the major features, and then compare the time series directly and quantitatively. In three geographical regions, we compare the vertical density profiles on coincident measurement days. Since none of the instruments delivers continuous daily measurements in this altitude region, we carried out a multi-linear regression analysis. This regression analysis considers annual and semi-annual variability in the form of harmonic terms and inter-annual variability by responding linearly to the solar Lyman-α radiation index and the geomagnetic Kp index. This analysis helps to find similarities and differences in the individual data sets with respect to the inter-annual variations caused by geomagnetic and solar variability. We find that the data sets are consistent and that they only disagree on minor aspects. SMR and ACE-FTS deliver the longest time series in the mesosphere, and they agree with each other remarkably well. The shorter time series from MIPAS and SCIAMACHY also agree with them where they overlap. The data agree within 30 % when the number densities are large, but they can differ by 50 to 100 % in some cases.

C. PENGARUH UTANG LUAR NEGERI DAN EKSPOR TERHADAP PERTUMBUHAN EKONOMI (Studi pada Produk Domestik Bruto Indonesia Periode 2015-2019)

2021 ◽  
Vol 4 (1) ◽  
pp. 22
Author(s):  
Farah Syahri Maulidiyah
Keyword(s):  
Economic Growth ◽  
Time Series ◽  
Regression Analysis ◽  
Linear Regression Analysis ◽  
Foreign Debt ◽  
External Debt ◽  
Multiple Linear Regression Analysis ◽  
Analysis Method ◽  
Indonesian Government ◽  
Independent Variable

ABSTRACT The purpose of this research is to analyze the influence of exports and foreign debt which can affect Indonesia's GDP (Gross Domesty Product). The variables of this research are the foreign debt value of the Indonesian government and the value of Indonesian exports as the independent variable, and the value of Indonesia's GDP as the dependent variable. The data used are supporting data for the 2015-2019 period from the time series (time series) of Bank Indonesia and BPS. The data analysis method used multiple linear regression analysis. The results of this study are the value of the Indonesian government's foreign debt and the value of Indonesia's exports have a significant effect. Meanwhile, the results of the partial test (t-test) show that the value of foreign debt and exports of the Indonesian government greatly affects the value of Indonesia's GDP. Keywords : External Debt, Export, Economic Growth (Menggunakan template jurnal sinta 2 JESP (Jurnal Ekonomi dan Studi Pembangunan) eISSSN : 2502-7115 l pISSN : 2502-7115 Universitas Negeri Malang).

scholarly journals Pengaruh Suku Bunga, Loan to Deposit Ratio (LDR) dan Pertumbuhan Kredit terhadap Inflasi di Indonesia

Jurnal Ecogen ◽  
2019 ◽  
Vol 1 (3) ◽  
pp. 557
Author(s):  
Putri Yeni ◽  
Syamsul Amar ◽  
Alpon Satrianto
Keyword(s):  
Time Series ◽  
Regression Analysis ◽  
Interest Rate ◽  
Interest Rates ◽  
Linear Regression Analysis ◽  
Secondary Data ◽  
Multiple Linear Regression Analysis ◽  
Credit Growth ◽  
Descriptive Research ◽  
Positive Effect

This study aims to analyze the influence of interest rates, Loan to Deposit Ratio (LDR) and credit growth to inflation in Indonesia. This type of research is descriptive research and uses secondary data in the form of time-series from 2007 to 2016 using the method of multiple linear regression analysis. The results of this study indicate that interest rates have a significant and positive effect on inflation in Indonesia. The Loan to Deposit Ratio (LDR) has a significant and positive effect on inflation in Indonesia. Credit growth has a significant and positive effect on inflation in Indonesia. Based on the results of this study it can be concluded that there is a significant influence between interest rates, Loan to Deposit Ratio (LDR) and credit growth to inflation in Indonesia. Keyword: Inflation, Interest Rate, Loan to Deposit Ratio (LDR), Credit Growth

scholarly journals Mean winds, temperatures and the 16- and 5-day planetary waves in the mesosphere and lower thermosphere over Bear Lake Observatory (42° N, 111° W)

2012 ◽  
Vol 12 (3) ◽  
pp. 1571-1585 ◽  
Author(s):  
K. A. Day ◽  
M. J. Taylor ◽  
N. J. Mitchell
Keyword(s):  
Zonal Wind ◽  
Lower Thermosphere ◽  
Planetary Waves ◽  
Zonal Winds ◽  
Annual Variability ◽  
Bear Lake ◽  
Meridional Winds ◽  
The Mean ◽  
Mesosphere And Lower Thermosphere ◽  
Wind Shears

Abstract. Atmospheric temperatures and winds in the mesosphere and lower thermosphere have been measured simultaneously using the Aura satellite and a meteor radar at Bear Lake Observatory (42° N, 111° W), respectively. The data presented in this study is from the interval March 2008 to July 2011. The mean winds observed in the summer-time over Bear Lake Observatory show the meridional winds to be equatorward at meteor heights during April−August and to reach monthly-mean velocities of −12 m s−1. The mean winds are closely related to temperatures in this region of the atmosphere and in the summer the coldest mesospheric temperatures occur about the same time as the strongest equatorward meridional winds. The zonal winds are eastward through most of the year and in the summer strong eastward zonal wind shears of up to ~4.5 m s−1 km−1 are present. However, westward winds are observed at the upper heights in winter and sometimes during the equinoxes. Considerable inter-annual variability is observed in the mean winds and temperatures. Comparisons of the observed winds with URAP and HWM-07 reveal some large differences. Our radar zonal wind observations are generally more eastward than predicted by the URAP model zonal winds. Considering the radar meridional winds, in comparison to HWM-07 our observations reveal equatorward flow at all meteor heights in the summer whereas HWM-07 suggests that only weakly equatorward, or even poleward flows occur at the lower heights. However, the zonal winds observed by the radar and modelled by HWM-07 are generally similar in structure and strength. Signatures of the 16- and 5-day planetary waves are clearly evident in both the radar-wind data and Aura-temperature data. Short-lived wave events can reach large amplitudes of up to ~15 m s−1 and 8 K and 20 m s−1 and 10 K for the 16- and 5-day waves, respectively. A clear seasonal and short-term variability are observed in the 16- and 5-day planetary wave amplitudes. The 16-day wave reaches largest amplitude in winter and is also present in summer, but with smaller amplitudes. The 5-day wave reaches largest amplitude in winter and in late summer. An inter-annual variability in the amplitude of the planetary waves is evident in the four years of observations. Some 41 episodes of large-amplitude wave occurrence are identified. Temperature and wind amplitudes for these episodes, AT and AW, that passed the Student T-test were found to be related by, AT = 0.34 AW and AT = 0.62 AW for the 16- and 5-day wave, respectively.

scholarly journals THE COMPARISON OF TWO PRECIPITATION PREDICTION METHODS

2020 ◽  
Vol XLII-3/W10 ◽  
pp. 1025-1032
Author(s):  
X. Q. Mo ◽  
G. W. Lan ◽  
Y. L. Du ◽  
Z. X. Chen
Keyword(s):  
Time Series ◽  
Regression Analysis ◽  
Linear Regression ◽  
Time Series Analysis ◽  
Daily Precipitation ◽  
Linear Regression Analysis ◽  
Time Series Model ◽  
Residual Error ◽  
Actual Situation ◽  
Monthly Average

Abstract. Precipitation forecasts play the role in flood control and drought relief. At present, the time series analysis and the linear regression analysis are two of most commonly used methods. The time series analysis is relatively simple as it only requires historical precipitation data. The model of the linear regression analysis can ensure high accuracy for causality analysis and short, medium and long-term prediction. Guilin is the region of the heavy rain center in Guangxi, which frequently suffers serious losses from rainstorms. Selecting a better model to predict precipitation has the important reference significance for improving the accuracy of precipitation weather forecast. In this research, the two methods are used to predict precipitation in Guilin. According to data of the monthly maximum precipitation, monthly average daily precipitation and monthly total precipitation from 2014 to 2016, this paper establishes the time series model and linear regression analysis model to predict precipitation in 2017 and compare the forecast results. The results show that the monthly average daily precipitation model is best with the accuracy of the time series model, and the residual error of predicted precipitation is 3.08 mm, but the change trend of predicted precipitation is not accord with the actual situation. The residual error is only 0.45 mm through using inter-annual linear regression equation to predict the precipitation, but the predicted summer precipitation is quite different from the actual one. The linear equation established by different seasons is used to predict the precipitation with residual error of 3.25 mm, and it is coincident for the predicted precipitation trend with the actual situation. Furthermore, the predictions fitting errors of spring, summer, autumn and winter are all less than 20%, which are within the scope of the specification prediction error.

scholarly journals Pengaruh Pertumbuhan Ekonomi, Tingkat Inflasi, dan Pertumbuhan Penduduk terhadap Tingkat Pengangguran Terbuka di Indonesia

2019 ◽  
Vol 18 (1) ◽  
pp. 52
Author(s):  
Irma Yuni Astuti ◽  
Nanik Istiyani ◽  
Lilis Yuliati
Keyword(s):  
Economic Growth ◽  
Time Series ◽  
Regression Analysis ◽  
Linear Regression ◽  
Population Growth ◽  
Multiple Linear Regression ◽  
Time Series Data ◽  
Linear Regression Analysis ◽  
Multiple Linear Regression Analysis ◽  
Series Data

This study aims to determine the effect of economic growth, inflation and population growth in open unemployment rate in Indonesia. The type of data used in this study is secondary data in the form of time series data and variable data used are annual data in the period 1986-2017 with the object of research in the country o Indonesia. The data sources used in this study were obtained from the Central Statistics Agency (BPS) Indonesia and World Bank. The analytical method used in this study is multiple linear regression analysis with the Ordinary Least Square (OLS) technique. The estimation of time series data with multiple linear regression analysis shows that the economics growth variable has a positive and not significant effect on the level of open unemployment, the inflation variable has a positive and not significant effect on the level of open unemployment, and the population growth variable has a negative and significant effect on the level of open unemployment in Indonesia. Keywords: Open Unemployment, Economic Growth, Inflation, Population Growth

scholarly journals Trend Analysis of Red Chili Price-Formation Models

2020 ◽  
Vol 40 (1) ◽  
pp. 57
Author(s):  
Siti Mir'atul Khasanah ◽  
Mochammad Maksum ◽  
Endy Suwondo
Keyword(s):  
Time Series ◽  
Regression Analysis ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Production Cost ◽  
Linear Regression Analysis ◽  
Multiple Linear Regression Analysis ◽  
Price Formation ◽  
Changing Trends ◽  
Continuous Demand

Red chili’s characteristic flavor has been a popular element in Indonesian cuisine. A large and continuous demand for red chili is inconsistent with production volumes, causing frequent and extreme price fluctuations throughout the year. This study explores the changing trends in red chili prices to identify the influencing factors. The study was conducted in the Sleman district of Yogyakarta, Indonesia. Time-series datasets of monthly production rates and prices of chili for 3 years were subject to multiple linear regression analysis. The study found a rising trend in prices in the Sleman Regency from January 2014 to December 2016. The factors significantly influencing the red chili prices was the price of cayenne pepper. The production cost of chili, the price of tomatoes, and the price of chili for the previous 2 months had only partial and nonsignificant effects. The timing of great Muslim celebrations, such as Eid Al-Fitr and Eid Al-Adha had no significant effect on the price of red chili. However, Christmas and New Year events were associated with higher prices.

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