Quality Assurance and Quality Control (QA/QC) of Meteorological Time Series Data for Billy Barr, East River, Colorado USA

Faybishenko, Boris; Versteeg, Roelof; Pastorello, Gilberto; Dwivedi, Dipankar; Varadharajan, Charuleka; Agarwal, Deb

doi:10.15485/1823516

Title: Quality Assurance and Quality Control (QA/QC) of Meteorological Time Series Data for Billy Barr, East River, Colorado USA

Dataset
Other Related Research

Abstract

A comprehensive Quality Assurance (QA) and Quality Control (QC) statistical framework consists of three major phases: Phase 1—Preliminary raw data sets exploration, including time formatting and combining datasets of different lengths and different time intervals; Phase 2—QA of the datasets, including detecting and flagging of duplicates, outliers, and extreme values; and Phase 3—the development of time series of a desired frequency, imputation of missing values, visualization and a final statistical summary. The time series data collected at the Billy Barr meteorological station (East River Watershed, Colorado) were analyzed. The developed statistical framework is suitable for both real-time and post-data-collection QA/QC analysis of meteorological datasets.The files that are in this data package include one excel file, converted to CSV format (Billy_Barr_raw_qaqc.csv) that contains the raw meteorological data, i.e., input data used for the QA/QC analysis. The second CSV file (Billy_Barr_1hr.csv) is the QA/QC and flagged meteorological data, i.e., output data from the QA/QC analysis. The last file (QAQC_Billy_Barr_2021-03-22.R) is a script written in R that implements the QA/QC and flagging process. The purpose of the CSV data files included in this package is to provide input and output files implemented in the R script.

Authors:

; Versteeg, Roelof; Pastorello, Gilberto;

;

Lawrence Berkeley National Laboratory
Subsurface Insights

Publication Date:: 2021-01-01

Other Number(s):: paf_724_733

Research Org.:: Environmental System Science Data Infrastructure for a Virtual Ecosystem (ESS-DIVE) (United States)

Sponsoring Org.:: U.S. DOE > Office of Science > Biological and Environmental Research (BER)

Subject:: 54 ENVIRONMENTAL SCIENCES

Keywords:: QA/QC; R packages; East River Watershed; meteorological data; Air Temperature; Barometric Pressure; Meteorology – weather stations; Precipitation; Relative Humidity; Solar Radiation; Wind Direction; Wind Speed

Geolocation:: 39.034,-106.88|38.88,-106.88|38.88,-107.05|39.034,-107.05|39.034,-106.88

OSTI Identifier:: 1823516

DOI:: https://doi.org/10.15485/1823516

Project Location:

Citation Formats


                    Faybishenko, Boris, Versteeg, Roelof, Pastorello, Gilberto, Dwivedi, Dipankar, Varadharajan, Charuleka, and Agarwal, Deb. Quality Assurance and Quality Control (QA/QC) of Meteorological Time Series Data for Billy Barr, East River, Colorado USA.  United States: N. p., 2021. 
        Web.  doi:10.15485/1823516.

Copy to clipboard


                    Faybishenko, Boris, Versteeg, Roelof, Pastorello, Gilberto, Dwivedi, Dipankar, Varadharajan, Charuleka, & Agarwal, Deb. Quality Assurance and Quality Control (QA/QC) of Meteorological Time Series Data for Billy Barr, East River, Colorado USA.  United States.  doi:https://doi.org/10.15485/1823516

Copy to clipboard


                    Faybishenko, Boris, Versteeg, Roelof, Pastorello, Gilberto, Dwivedi, Dipankar, Varadharajan, Charuleka, and Agarwal, Deb. 2021.  
"Quality Assurance and Quality Control (QA/QC) of Meteorological Time Series Data for Billy Barr, East River, Colorado USA".  United States.  doi:https://doi.org/10.15485/1823516.  https://www.osti.gov/servlets/purl/1823516. Pub date:Fri Jan 01 00:00:00 EST 2021

Copy to clipboard


                    
@article{osti_1823516,

  title        = {Quality Assurance and Quality Control (QA/QC) of Meteorological Time Series Data for Billy Barr, East River, Colorado USA},

  author       = {Faybishenko, Boris and Versteeg, Roelof and Pastorello, Gilberto and Dwivedi, Dipankar and Varadharajan, Charuleka and Agarwal, Deb},

  abstractNote = {A comprehensive Quality Assurance (QA) and Quality Control (QC) statistical framework consists of three major phases: Phase 1—Preliminary raw data sets exploration, including time formatting and combining datasets of different lengths and different time intervals; Phase 2—QA of the datasets, including detecting and flagging of duplicates, outliers, and extreme values; and Phase 3—the development of time series of a desired frequency, imputation of missing values, visualization and a final statistical summary. The time series data collected at the Billy Barr meteorological station (East River Watershed, Colorado) were analyzed. The developed statistical framework is suitable for both real-time and post-data-collection QA/QC analysis of meteorological datasets.The files that are in this data package include one excel file, converted to CSV format (Billy_Barr_raw_qaqc.csv) that contains the raw meteorological data, i.e., input data used for the QA/QC analysis. The second CSV file (Billy_Barr_1hr.csv) is the QA/QC and flagged meteorological data, i.e., output data from the QA/QC analysis. The last file (QAQC_Billy_Barr_2021-03-22.R) is a script written in R that implements the QA/QC and flagging process. The purpose of the CSV data files included in this package is to provide input and output files implemented in the R script.},

  doi          = {10.15485/1823516},

  journal      = {},

  number       = ,

  volume       = ,

  place        = {United States},

  year         = {2021},

  month        = {1}

}

Copy to clipboard

Dataset:

View Dataset

DOI: https://doi.org/10.15485/1823516

Save / Share:

Export Metadata

Save to My Library

Similar records in DOE Data Explorer and OSTI.GOV collections:

QA/QC-ed Groundwater Level Time Series in PLM-1 and PLM-6 Monitoring Wells, East River, Colorado (2016-2022)

Dataset Faybishenko, Boris ; Versteeg, Roelof ; Williams, Kenneth ; ...

This data set contains QA/QC-ed (Quality Assurance and Quality Control) water level data for the PLM1 and PLM6 wells. PLM1 and PLM6 are location identifiers used by the Watershed Function SFA project for two groundwater monitoring wells along an elevation gradient located along the lower montane life zone of a hillslope near the Pumphouse location at the East River Watershed, Colorado, USA. These wells are used to monitor subsurface water and carbon inventories and fluxes, and to determine the seasonally dependent flow of groundwater under the PLM hillslope. The downslope flow of groundwater in combination with data on groundwater chemistrymore »« less
QA/QC of the East River, Colorado, discharge and geochemical time series datasets (Almont, BCC, and Pump House) to be used for modeling of hydrogeochemical balance

Dataset Faybishenko, Boris ; Fox, Patricia

The following datasets were QA/QC-ed (Quality Assurance/Quality Control): 1. Brush Creek Confluence (BCC) discharge data (from Helen Malenda, USGS, Colorado School of Mines), which were calculated using the pressure transducer data and rating curves. The original 15 min time series data were presented as mean daily discharge. 2. Almont discharge data from United States Geological Survey (USGS). The original data were in 15 min time intervals, and were averaged to mean daily discharge time series. 3. Pump House discharge data as mean daily discharge (downloaded from the SFA portal). 4. BCC and Pump House chemistry data from SFA data portalmore »« less
Meteorological data from the Billy Bar site at the East River, Colorado

Dataset Faybishenko, Boris ; Gochis, Dave ; Versteeg, Roelof ; ...

This dataset includes Quality Assessed and Quality Controlled (QA/QC) meteorological data from the Billy Bar field site in the East River Watershed, Colorado in order to inform watershed hydrobiogeochemical processes. The data includes 1-hour aligned time series of Solar Radiation, Wind Speed, Wind Direction, Air Temperature, Relative Humidity, Barometric Pressure, and Precipitation. For each parameter, the QA flags are given: 1 is for extreme (potential abnormal values), and 0 is for values in the expected range. The Rmarkdown document is a stand-alone file/notebook that preserves the text, code, and code results, as well as formatting contained in the original Rmore »« less
Challenging problems of quality assurance and quality control (QA/QC) of meteorological time series data

Journal Article Faybishenko, B. ; Versteeg, R. ; Pastorello, G. ; ... - Stochastic environmental research and risk assessment

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 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. Themore »« less
Challenging problems of quality assurance and quality control (QA/QC) of meteorological time series data

Journal Article Faybishenko, B. ; Versteeg, R. ; Pastorello, G. ; ... - Stochastic environmental research and risk assessment

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 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.more »« less

Similar Records