Three-phase meteorological QA/QC workflow

Knowledge graph centered on Three-phase meteorological QA/QC workflow with 72 nodes and 163 connections. Top connected: groundwater storage dynamics, leaf water content, air temperature, carbon cycling, precipitation.

Method synopsis

A comprehensive statistical framework for quality assurance and quality control of meteorological time series data involving preliminary data exploration, quality assessment with outlier detection, and quality control with missing value imputation.

Synthesized from method descriptions across 2 papers using this protocol.

Procedure from a recent peer-reviewed implementation

Steps below were extracted from the most recent peer-reviewed implementation of this protocol in the corpus — Challenging problems of quality assurance and quality control (QA/QC) of meteorological time series data (2021), Stochastic environmental research and risk assessment. The protocol was originally introduced by Challenges in Building an End-to-End System for Acquisition, Management, and Integration of Diverse Data From Sensor Networks in Watersheds: Lessons From a Mountainous Community Observatory in East River, Colorado (2019), IEEE Access. Implementations in other papers (listed below) may differ.

1. Load and format time series data
   Convert timestamps from various formats (yyyy/mm/dd H:M:S, dd/yyyy, dd/mm/yyyy) to standardized as.POSIXct format. Check for missing timestamps, duplicates, and non-consecutive order. Combine datasets from different sensors with different time durations and frequencies into single vectorized files.
   Quantities: Two datasets: Billy Barr (7 meteorological variables, 2016-2017) and BCI (5 variables, 2018-2021)Duration: Initial data processing phaseConditions: Automated processing of raw meteorological station data
   Equipment: R programming language, as.POSIXct function, base R library functions
2. Detect and flag duplicate timestamps and values
   Use duplicated() function to identify duplicate date/time stamps and variable values. Apply distinct() function from dplyr package to eliminate duplicated rows. Flag all duplicated rows for quality assessment.
   Quantities: All duplicate entries across both datasetsDuration: Quality assurance phaseConditions: Systematic checking of all timestamp-variable combinations
   Equipment: duplicated() function, distinct() function, dplyr package

Typical Equipment

• R programming language
• imputeTS package
• highfrequency package
• dplyr package
• caTools package
• NSM3 package

Output Measurements

• cleaned time series data
• quality flags
• statistical summaries
• visualization plots

Papers Using This Protocol (2)

Species Studied With This Protocol

Places Where Used