Analysis of Long-term Variability through Temperature and Humidity Data in Urban Meteorological Observation Network

2021 ◽  
Author(s):  
Eun-Bi kang ◽  
Deok-Du Kang ◽  
Dong-In Lee
Keyword(s):  
Time Series ◽  
Hurst Exponent ◽  
Meteorological Data ◽  
Scientific Data ◽  
Observation Data ◽  
Grid Data ◽  
Weather System ◽  
Rescaled Range Analysis ◽  
Temperature And Humidity

<p>In the process of producing grid data using observation data, the density of the stations were found to have the greatest influence on spatial (Hwang and Ham, 2013). Currently, the resolution of Korea’s ground detection network is about 12 to 15km additional stations need to be set up to improve spatial accuracy. However, indiscriminate installation of observatories is an objective challenge because of the enormous cost and the various factors to consider. It is important to select major observation points on an objective basis based on the existing KMA (Korea Meteorological Administration)'s AWS(Automatic Weather System), ASOS(Automated Synoptic Observing System)  data to increase the representative and reliability of the observation data. However, the establishment of an observatory so far has been chosen for subjective observation purposes, which may make it difficult to derive scientific data. In this study there is identified the long-term variability of urban meteorological data using the Hurst exponent (H) obtained through Rescaled range analysis (R/S analysis). And additional observation points are proposed for each meteorological element through network analysis.</p><p>R/S analysis is an analysis that measures the variability of time series by standardizing observations over time to make them in a dimensionless ratio and analyze the changes according to the length of the data used. H between 0 and 1 provides a criterion for distinguishing the measure of correlation that a time series has. H = 0.5 means that the present event does not affect subsequently, however the other values are correlated, not independent, and continuum of influence (Hwang and Cha 2004). The meteorological factors data were obtained from SK planet, AWS, ASOS installed in Seoul. As a result, long-term relativity between temperature and humidity are shown to be at a minimum of 0.750 and a maximum of 0.941.</p><p>Key words :  R/S analysis, Hurst exponent, long-term relativity</p>

Rescue of groundwater level time series: how to identify and treat errors

2021 ◽  
Author(s):  
Jānis Bikše ◽  
Inga Retike ◽  
Andis Kalvāns ◽  
Aija Dēliņa ◽  
Alise Babre ◽  
...  
Keyword(s):  
Time Series ◽  
Groundwater Level ◽  
Monitoring Network ◽  
Observation Data ◽  
Automatic Data ◽  
Mean Values ◽  
Temporal Prediction ◽  
National Monitoring ◽  
Groundwater Drought

<p>Groundwater level time series are the basis for various groundwater-related studies. The most valuable are long term, gapless and evenly spatially distributed datasets. However, most historical datasets have been acquired during a long-term period by various operators and database maintainers, using different data collection methods (manual measurements or automatic data loggers) and usually contain gaps and errors, that can originate both from measurement process and data processing. The easiest way is to eliminate the time series with obvious errors from further analysis, but then most of the valuable dataset may be lost, decreasing spatial and time coverage. Some gaps can be easily replaced by traditional methods (e.g. by mean values), but filling longer observation gaps (missing months, years) is complicated and often leads to false results. Thus, an effort should be made to retain as much as possible actual observation data.</p><p>In this study we present (1) most typical data errors found in long-term groundwater level monitoring datasets, (2) provide techniques to visually identify such errors and finally, (3) propose best ways of how to treat such errors. The approach also includes confidence levels for identification and decision-making process. The aim of the study was to pre-treat groundwater level time series obtained from the national monitoring network in Latvia for further use in groundwater drought modelling studies.</p><p>This research is funded by the Latvian Council of Science, project “Spatial and temporal prediction of groundwater drought with mixed models for multilayer sedimentary basin under climate change”, project No. lzp-2019/1-0165.</p>

Scaling behavior of earthquakes’ inter-events time series

Open Physics ◽  
2009 ◽  
Vol 7 (3) ◽  
Author(s):  
Shahriar Shadkhoo ◽  
Fakhteh Ghanbarnejad ◽  
Gholam Jafari ◽  
Mohammad Tabar
Keyword(s):  
Time Series ◽  
Time Scales ◽  
Hurst Exponent ◽  
Detrended Fluctuation Analysis ◽  
Fluctuation Analysis ◽  
Data Set ◽  
Scaling Properties ◽  
Independent Events ◽  
Detrended Fluctuation

AbstractIn this paper, we investigate the statistical and scaling properties of the California earthquakes’ inter-events over a period of the recent 40 years. To detect long-term correlations behavior, we apply detrended fluctuation analysis (DFA), which can systematically detect and overcome nonstationarities in the data set at all time scales. We calculate for various earthquakes with magnitudes larger than a given M. The results indicate that the Hurst exponent decreases with increasing M; characterized by a Hurst exponent, which is given by, H = 0:34 + 1:53/M, indicating that for events with very large magnitudes M, the Hurst exponent decreases to 0:50, which is for independent events.

scholarly journals Long-term trends of ambient nitrate (NO<sub>3</sub><sup>−</sup>) concentrations across China based on ensemble machine-learning models

2020 ◽  
Author(s):  
Rui Li ◽  
Lulu Cui ◽  
Yilong Zhao ◽  
Wenhui Zhou ◽  
Hongbo Fu
Keyword(s):  
Tibetan Plateau ◽  
Sichuan Basin ◽  
Meteorological Data ◽  
Ground Level ◽  
Absolute Error ◽  
River Delta ◽  
Gradient Boosting ◽  
Observation Data ◽  
Extreme Gradient Boosting

Abstract. High loadings of nitrate (NO3−) in the aerosol over China significantly exacerbates the air quality and poses a great threaten on ecosystem safety through dry/wet deposition. Unfortunately, limited ground-level observation data makes it challenging to fully reflect the spatial pattern of NO3− level across China. Up to date, the long-term monthly NO3− datasets at a high resolution were still missing, which restricted the assessment of human health and ecosystem safety. Therefore, a unique monthly NO3− dataset at 0.25° resolution over China during 2005–2015 was developed by assimilating surface observation, satellite product, meteorological data, land use types and other covariates using an ensemble model combining random forest (RF), gradient boosting decision tree (GBDT), and extreme gradient boosting (XGBoost). The new developed product featured excellent cross-validation R2 value (0.78) and relatively lower root-mean-square error (RMSE: 1.19 μg/m3) and mean absolute error (MAE: 0.81 μg/m3). Besides, the dataset also exhibited relatively robust performance at the spatial and temporal scale. Moreover, the dataset displayed good agreement with (R2 = 0.85, RMSE = 0.74 μg/m3, and MAE = 0.55 μg/m3) some unlearning data collected from previous studies. The spatiotemporal variations of the developed product were also shown. The estimated NO3− concentration showed the highest value in North China Plain (NCP) (3.55 ± 1.25 μg/m3), followed by Yangtze River Delta (YRD (2.56 ± 1.12  g/m3)), Pearl River Delta (PRD (1.68 ± 0.81 μg/m3)), Sichuan Basin (1.53 ± 0.63 μg/m3), and the lowest one in Tibetan Plateau (0.42 ± 0.25 μg/m3). The higher ambient NO3− concentrations in NCP, YRD, and PRD were closely linked to the dense anthropogenic emissions. Apart from the intensive human activities, poor terrain condition might be a key factor for the serious NO3− pollution in Sichuan Basin. The lowest ambient NO3− concentration in Tibetan Plateau was contributed by the scarce anthropogenic emission and favorable meteorological factors (e.g., high wind speed). In addition, the ambient NO3− concentration showed marked increasing tendency of 0.10 μg/m3/year during 2005–2014 (p 

Correlation structure of end-expiratory lung volume in anesthetized rats with intact upper airway

2000 ◽  
Vol 278 (6) ◽  
pp. R1446-R1452 ◽  
Author(s):  
Xiaobin Zhang ◽  
Eugene N. Bruce
Keyword(s):  
Time Series ◽  
Lung Volume ◽  
Hurst Exponent ◽  
Upper Airway ◽  
Correlation Structure ◽  
Original Time Series ◽  
Dispersional Analysis ◽  
Anesthetized Rats ◽  
Original Time

The correlation structure of breath-to-breath fluctuations of end-expiratory lung volume (EEV) was studied in anesthetized rats with intact airways subjected to positive and negative transrespiratory pressure (i.e., PTRP and NTRP, correspondingly). The Hurst exponent, H, was estimated from EEV fluctuations using modified dispersional analysis. We found that H for EEV was 0.5362 ± 0.0763 and 0.6403 ± 0.0561 with PTRP and NTRP, respectively (mean ± SD). Both H were significantly different from those obtained after random shuffling of the original time series. Also, H with NTRP was significantly greater than that with PTRP ( P = 0.029). We conclude that in rats breathing through the upper airway, a positive long-term correlation is present in EEV that is different between PTRP and NTRP.

scholarly journals A low-cost development of automatic weather station based on Arduino for monitoring precipitable water vapor

2021 ◽  
Vol 24 (2) ◽  
pp. 744
Author(s):  
Wayan Suparta ◽  
Aris Warsita ◽  
Ircham Ircham
Keyword(s):  
Water Vapor ◽  
Meteorological Data ◽  
Low Cost ◽  
Precipitable Water ◽  
Precipitable Water Vapor ◽  
Weather Station ◽  
Automatic Weather Station ◽  
Weather System ◽  
Spatial And Temporal Scales

Water vapor is the engine of the weather system. Continuous monitoring of its variability on spatial and temporal scales is essential to help improve weather forecasts. This research aims to develop an automatic weather station at low cost using an Arduino microcontroller to monitor precipitable water vapor (PWV) on a micro-scale. The surface meteorological data measured from the BME280 sensor is used to determine the PWV. Our low-cost systems also consisted of a DS3231 real-time clock (RTC) module, a 16×2 liquid crystal display (LCD) module with an I<sup>2</sup>C, and a micro-secure digital (micro-SD) card. The core of the system employed the Arduino Uno surface mount device (SMD) R3 board. The measurement results for long-term monitoring at the tested sites (ITNY and GUWO) found that the daily mean error of temperature and humidity values were 1.30% and 3.16%, respectively. While the error of air pressure and PWV were 0.092% and 2.61%, respectively. The PWV value is higher when the sun is very active or during a thunderstorm. The developed weather system is also capable of measuring altitude on pressure measurements and automatically stores daily data. With a total cost below 50 dollars, all major and support systems developed are fully functional and stable for long-term measurements.

SUNSPOTS DATA ANALYSIS USING TIME SERIES

Fractals ◽  
2008 ◽  
Vol 16 (03) ◽  
pp. 259-265 ◽  
Author(s):  
YUSUF H. SHAIKH ◽  
A. R. KHAN ◽  
M. I. IQBAL ◽  
S. H. BEHERE ◽  
S. P. BAGARE
Keyword(s):  
Time Series ◽  
Hurst Exponent ◽  
Sunspot Cycle ◽  
Fractal Dimensions ◽  
Data Sets ◽  
Range Analysis ◽  
Data Set ◽  
Rescaled Range ◽  
Rescaled Range Analysis ◽  
Sunspot Data

The record of the sunspot number visible on the sun is regularly collected over the centuries by various observatories for studying the different factors influencing the sunspot cycle and solar activity. Sunspots appear in cycles, and last several years. These cycles follow a certain pattern which is well known. We analyzed monthly and yearly averages of sunspot data observed from year 1818 to 2002 using rescaled range analysis. The Hurst exponent calculated for monthly data sets are 0.8899, 0.8800 and 0.8597 and for yearly data set is 0.7187. Fractal dimensions1 calculated are 1.1100, 1.1200, 1.1403 and 1.2813. From the study of Hurst exponent and fractal dimension, we conclude that time series of sunspots show persistent behavior. The fundamental tool of signal processing is the fast Fourier transform technique (FFT). The sunspot data is also analyzed using FFT. The power spectrum of monthly and yearly averages of sunspot shows distinct peaks at 11 years confirming the well known 11-year cycle. The monthly sunspot data is also analyzed using FFT to filter the noise in the data.

scholarly journals ERROR ASSESSMENT IN MODELING WITH FRACTAL BROWNIAN MOTIONS

Fractals ◽  
2013 ◽  
Vol 21 (03n04) ◽  
pp. 1350018 ◽  
Author(s):  
BINGQIANG QIAO ◽  
SIMING LIU
Keyword(s):  
Time Series ◽  
Fractal Dimension ◽  
Hurst Exponent ◽  
Long Term Memory ◽  
Error Assessment ◽  
Data Sampling ◽  
Brownian Motions ◽  
Term Memory ◽  
Rescaled Range

To model a given time series F(t) with fractal Brownian motions (fBms), it is necessary to have appropriate error assessment for related quantities. Usually the fractal dimension D is derived from the Hurst exponent H via the relation D = 2-H, and the Hurst exponent can be evaluated by analyzing the dependence of the rescaled range 〈|F(t + τ) - F(t)|〉 on the time span τ. For fBms, the error of the rescaled range not only depends on data sampling but also varies with H due to the presence of long term memory. This error for a given time series then can not be assessed without knowing the fractal dimension. We carry out extensive numerical simulations to explore the error of rescaled range of fBms and find that for 0 < H < 0.5, |F(t + τ) - F(t)| can be treated as independent for time spans without overlap; for 0.5 < H < 1, the long term memory makes |F(t + τ) - F(t)| correlated and an approximate method is given to evaluate the error of 〈|F(t + τ) - F(t)|〉. The error and fractal dimension can then be determined self-consistently in the modeling of a time series with fBms.

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