These methods, instrumentation and/or protocols apply to all data in this dataset:Methods and protocols used in the collection of this data package |
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Description: |
Summary: A met station consisting of a 3-meter mast is installed on the east side of the site
between plots B and C. Air temperature, relative humidity, wind direction and wind speed sensors
are mounted on the mast. A vertical wind profile is measured at most sites with anemometers at
0.37m, 0.75m, 1.5m and 3m on the mast.
| Instrument(s): | Rain Gauge; Manufacturer: Qualimetrics. (Model: 6011-B Tipping Bucket Rain Gauge); Parameter:
Precipitation (Accuracy: 0.5% at <0.5 in/hr) |
| Description: |
Method - Sensor readings are taken at 1 second intervals by a Campbell Scientific Instruments (CSI)
CR1000 data logger and converted from analog voltage to calibrated digital data, if necessary, by
the CR1000 data logger program (e.g., C-Cali_CR1000_20150925.cr1). Minimum, average, and maximum
air temperature and relative humidity; average and maximum wind speed; average and standard
deviation of wind direction, and total precipitation are calculated by the CR1000 datalogger
program and stored in tables in the datalogger storage module at intervals of 5, 30, and 60 minutes
and daily. One minute precipitation totals are calculated during precipitation events. These data
are downloaded hourly from each remote site via a 900 MHz spread spectrum wireless radio network
using CSI LoggerNet software. The data are parsed, quality-checked, documented and archived in the
JRN-LTER Information Management office using data retrieval and parsing functions in the GCE Data
Toolbox for MATLAB (https://gce-lter.marsci.uga.edu/lter/research/tools/data_toolbox.htm).
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| Sampling Area and Study Extent |
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Sampling Description: |
Sensor readings are taken at 1 second intervals and aggregated into 5, 30, and 60 minute, and daily
data tables.
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Sampling Extent: | Geographic Region: |
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Description: | Precise geographic coordinates of the meteorological station are available upon request; please
contact the Data Manager. | | Bounding Coordinates: | Northern: | 32.605000 | Southern: | 32.561000 | Western: | -106.848000 | Eastern: | -106.764000 |
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These methods, instrumentation, and/or protocols apply to the data table csis-15_ppt_detailed_L2_rev_1.csv:Methods and protocols used in the collection of this data package |
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Description: |
Data processing using the GCE Data Toolbox for MATLAB
| Software: | Title: | GCE Data Toolbox for MATLAB | Author(s): | Organization: | Georgia Coastal Ecosystems LTER | Email Address: | |
| Abstract: |
The GCE Data Toolbox is a comprehensive library of functions for metadata-based analysis quality
control, transformation and management of ecological data sets. The toolbox is based on the GCE
Data Structure, a MATLAB specification for storing tabular data along with all information required
to interpret the data and generate formatted metadata (documentation). Metadata fields in the
structure are queried by toolbox functions for all operations, allowing functions to process and
format values appropriately based on the type of information they represent. This semantic
processing approach supports highly automated and intelligent data analysis and ensures data set
validity throughout all processing steps.
| Implementation Info: | Distribution: | | Url: | https://gce-svn.marsci.uga.edu/trac/GCE_Toolbox | Version Number: | Toolbox Version 3.9.9b (06-Mar-2019) |
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| Quality Control |
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Quality Control Step 1: | | Description: |
Quality control analysis was performed using the GCE Data Toolbox for MATLAB software. Column data
types are validated upon importing the table into MATLAB, and qualifier flags are automatically
generated for data values based on QA/QC criteria for each data column pre-defined in metadata
templates (e.g. value range checks, sanity checks, pattern checks). Automatically-assigned
qualifier flags are reviewed graphically and revised or augmented as deemed appropriate by GCE
information management staff, based on metadata information from the contributor, values of other
measured variables, or statistical tests. Qualifier flags are also assigned to values that are
imputed or derived, revised, or otherwise differ from values in the original data submission. A
column of coded qualifier flags is generated and included in the data table if flags are assigned
to any value in a data column.
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