This data package is formatted as a hymetDP (Hydrological-Meteorological Data Pattern). For more information on hymetDP see https://github.com/EDIorg/ecocomDP. This Level 1 data package was derived from the Level 0 data package found here: https://pasta.lternet.edu/package/metadata/eml/knb-lter-jrn/210437020/34. The abstract below was extracted from the Level 0 data package and is included for context:
30-minute summary data at NPP G-IBPE met station. Average air temperature, relative humidity, wind speed and wind direction are measured and calculated based on 1-second scan rate of all sensors located at an automated meteorological station installed at Jornada LTER NPP G-IBPE site. Wind speed is measured at 75 cm, 150 cm, and 300 cm, wind direction at approximately 3m, and air temperature and relative humidity at approximate 2.5m. This climate station is operated by the Jornada LTER Program. This is an ONGOING dataset.
Collection of baseline climate data at Jornada LTER NPP sites, New Mexico, supports the environmental monitoring objectives of the Jornada LTER monitoring program. Measurements are aggregated at intervals of 5, 30, and 60 minutes and daily with precipitation additionally with 1 second aggregation during precipitation events. Data are downloaded hourly from each remote site via a 900 MHz spread spectrum wireless radio network. The met station variables measured include air temperature, precipitation, relative humidity, wind direction, wind speed. Solar and barometric pressure are measured at a subset of sites.
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.