scholarly journals Challenging problems of quality assurance and quality control (QA/QC) of meteorological time series data

2021 ◽  
Author(s):  
B. Faybishenko ◽  
R. Versteeg ◽  
G. Pastorello ◽  
D. Dwivedi ◽  
C. Varadharajan ◽  
...  
Keyword(s):  
Time Series ◽  
Quality Control ◽  
Quality Assurance ◽  
Time Series Data ◽  
Meteorological Station ◽  
Barro Colorado Island ◽  
Phase Iii ◽  
Series Data ◽  
Statistical Framework ◽  
East River

AbstractRepresentativeness and quality of collected meteorological data impact accuracy and precision of climate, hydrological, and biogeochemical analyses and predictions. We developed a comprehensive Quality Assurance (QA) and Quality Control (QC) statistical framework, consisting of three major phases: Phase I—Preliminary data exploration, i.e., processing of raw datasets, with the challenging problems of time formatting and combining datasets of different lengths and different time intervals; Phase II—QA of the datasets, including detecting and flagging of duplicates, outliers, and extreme data; and Phase III—the development of time series of a desired frequency, imputation of missing values, visualization and a final statistical summary. The paper includes two use cases based on the time series data collected at the Billy Barr meteorological station (East River Watershed, Colorado), and the Barro Colorado Island (BCI, Panama) meteorological station. The developed statistical framework is suitable for both real-time and post-data-collection QA/QC analysis of meteorological datasets.

scholarly journals Challenging Problems of Quality Assurance and Quality Control (QA/QC) of Meteorological Time Series Data

2021 ◽  
Author(s):  
Boris Faybishenko ◽  
Roelof Versteeg ◽  
Gilberto Pastorello ◽  
Dipankar Dwivedi ◽  
Charuleka Varadharajan ◽  
...  
Keyword(s):  
Time Series ◽  
Quality Control ◽  
Quality Assurance ◽  
Missing Values ◽  
Time Series Data ◽  
Meteorological Data ◽  
Meteorological Station ◽  
Barro Colorado Island ◽  
Series Data ◽  
East River

Abstract Representativeness and quality of collected meteorological data impact accuracy and precision of climate, hydrological, and biogeochemical analyses and predictions. We developed a comprehensive Quality Assurance (QA) and Quality Control (QC) statistical framework, consisting of three major steps: Step 1—Preliminary data exploration, i.e., processing of raw datasets, with the challenging problems of time formatting and combining datasets of different lengths and different time intervals; Step 2—QA of the datasets, including detecting and flagging of duplicates, outliers, and extreme data; and Step 3—the development of time series of a desired frequency, imputation of missing values, visualization and a final statistical summary. The paper includes two use cases based on the time series data collected at the Billy Barr meteorological station (East River Watershed, Colorado), and the Barro Colorado Island (BCI, Panama) meteorological station. The developed statistical methods are suitable for both real-time and post-data-collection QA/QC analysis of meteorological datasets.

scholarly journals An Analysis of Potential Structural Change in the Demand for South Atlantic Fresh Peaches

HortScience ◽  
1992 ◽  
Vol 27 (10) ◽  
pp. 1129-1131 ◽  
Author(s):  
J.E. Epperson ◽  
M.C. Chien ◽  
W.O. Mizelle
Keyword(s):  
Time Series ◽  
Quality Control ◽  
Structural Change ◽  
Time Series Data ◽  
South Atlantic ◽  
Capita Consumption ◽  
Series Data ◽  
Cultivar Development ◽  
Per Capita ◽  
Per Capita Consumption

An analysis was conducted using time-series data to identify possible structural change in the farm-gate demand for South Atlantic fresh peaches [Prunus persica (L.) Batsch.]. Structural change was not found in the price-quantity relationship. However, a failing per capita consumption of South Atlantic fresh peaches was found to be associated with an increase in the per capita consumption of fresh fruits in general. Thus, measures such as promotion and advertising, uniform quality control, and cultivar development may increase the demand for South Atlantic fresh peaches.

scholarly journals Research on the Data-Driven Quality Control Method of Hydrological Time Series Data

Water ◽  
2018 ◽  
Vol 10 (12) ◽  
pp. 1712 ◽  
Author(s):  
Qun Zhao ◽  
Yuelong Zhu ◽  
Dingsheng Wan ◽  
Yufeng Yu ◽  
Xifeng Cheng
Keyword(s):  
Time Series ◽  
Quality Control ◽  
Confidence Intervals ◽  
Time Series Data ◽  
Control Method ◽  
Data Driven ◽  
Series Data ◽  
Hydrological Data ◽  
Quality Control Method ◽  
Time Space

Ensuring the quality of hydrological data has become a key issue in the field of hydrology. Based on the characteristics of hydrological data, this paper proposes a data-driven quality control method for hydrological data. For continuous hydrological time series data, two combined forecasting models and one statistical control model are constructed from horizontal, vertical, and statistical perspectives and the three models provide three confidence intervals. Set the suspicious level based on the number of confidence intervals for data violations, control the data, and provide suggested values for suspicious and missing data. For the discrete hydrological data with large time-space difference, the similar weight topological map between the neighboring stations is established centering on the hydrological station under the test and it is adjusted continuously with the seasonal changes. Lastly, a spatial interpolation model is established to detect the data. The experimental results show that the quality control method proposed in this paper can effectively detect and control the data, find suspicious and erroneous data, and provide suggested values.

Graphical Exploratory Data Analysis for Categorical Longitudinal and Time Series Data

2013 ◽  
Author(s):  
Stephen J. Tueller ◽  
Richard A. Van Dorn ◽  
Georgiy Bobashev ◽  
Barry Eggleston
Keyword(s):  
Time Series ◽  
Data Analysis ◽  
Exploratory Data Analysis ◽  
Time Series Data ◽  
Series Data ◽  
Exploratory Data

Faktor-Faktor Yang Mempengaruhi Nilai Tukar Dollar Amerika Serikat Terhadap Rupiah Tahun 2000–2013

2017 ◽  
Vol 1 (2) ◽  
pp. 177-191
Author(s):  
Rizki Rahma Kusumadewi ◽  
Wahyu Widayat
Keyword(s):  
Time Series ◽  
Exchange Rate ◽  
Money Supply ◽  
Time Series Data ◽  
The United States ◽  
Economic Conditions ◽  
Series Data ◽  
Arch Model ◽  
United States Dollar ◽  
The Exchange Rate

Exchange rate is one tool to measure a country’s economic conditions. The growth of a stable currency value indicates that the country has a relatively good economic conditions or stable. This study has the purpose to analyze the factors that affect the exchange rate of the Indonesian Rupiah against the United States Dollar in the period of 2000-2013. The data used in this study is a secondary data which are time series data, made up of exports, imports, inflation, the BI rate, Gross Domestic Product (GDP), and the money supply (M1) in the quarter base, from first quarter on 2000 to fourth quarter on 2013. Regression model time series data used the ARCH-GARCH with ARCH model selection indicates that the variables that significantly influence the exchange rate are exports, inflation, the central bank rate and the money supply (M1). Whereas import and GDP did not give any influence.

An Anomaly Detection Method with Exemplar Subsequence for Time Series Data

2016 ◽  
Vol 136 (3) ◽  
pp. 363-372
Author(s):  
Takaaki Nakamura ◽  
Makoto Imamura ◽  
Masashi Tatedoko ◽  
Norio Hirai
Keyword(s):  
Time Series ◽  
Anomaly Detection ◽  
Time Series Data ◽  
Detection Method ◽  
Series Data

The Comparison of Land Subsidence between East and West Side of Bangkok, Thailand

2020 ◽  
Vol 17 (3) ◽  
pp. 1
Author(s):  
Angkana Pumpuang ◽  
Anuphao Aobpaet
Keyword(s):  
Time Series ◽  
Land Subsidence ◽  
Time Series Data ◽  
Series Data ◽  
Industrial Estates ◽  
Vertical Displacements ◽  
Land Deformation ◽  
Extensive Farming ◽  
Chao Phraya River ◽  
Set Up

The land deformation in line of sight (LOS) direction can be measured using time series InSAR. InSAR can successfully measure land subsidence based on LOS in many big cities, including the eastern and western regions of Bangkok which is separated by Chao Phraya River. There are differences in prosperity between both sides due to human activities, land use, and land cover. This study focuses on the land subsidence difference between the western and eastern regions of Bangkok and the most possible cause affecting the land subsidence rates. The Radarsat-2 single look complex (SLC) was used to set up the time series data for long term monitoring. To generate interferograms, StaMPS for Time Series InSAR processing was applied by using the PSI algorithm in DORIS software. It was found that the subsidence was more to the eastern regions of Bangkok where the vertical displacements were +0.461 millimetres and -0.919 millimetres on the western and the eastern side respectively. The districts of Nong Chok, Lat Krabang, and Khlong Samwa have the most extensive farming area in eastern Bangkok. Besides, there were also three major industrial estates located in eastern Bangkok like Lat Krabang, Anya Thani and Bang Chan Industrial Estate. By the assumption of water demand, there were forty-eight wells and three wells found in the eastern and western part respectively. The number of groundwater wells shows that eastern Bangkok has the demand for water over the west, and the pumping of groundwater is a significant factor that causes land subsidence in the area.Keywords: Subsidence, InSAR, Radarsat-2, Bangkok

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