English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Modern analysis of earth’s systems requires significant volumes of increasingly fine resolution spatial and temporal information. For example, the MEaSUREs Calibrated Enhanced Passive Microwave Daily-Grid 2.0 Brightness Temperature data set, released by the National Snow and Ice Data Center (NSIDC), is no exception (Brodzik & Armstrong, 2018). The MEaSUREs dataset contains twice daily passive microwave imagery from 1978 through 2017, which can be particularly useful for spatial-temporal analysis of snow water equivalent (SWE). In addition to high temporal resolution, the MEaSUREs CETB dataset contains imagery that has been resampled from 25km down to 6.25km wide pixels, allowing for analysis to be done at a finer spatial scale than previously available versions of the data source. While this data is exceedingly useful, it can take considerable time to set up scripts for web access, geographic subsetting, and merging sequential images into useful time cubes. The MEaSUREs data set is also large: accessing a full time series of Northern Hemisphere imagery would require easily two terabytes of disc space, which most researchers do not have available on their local computer. SWEpy was developed in part to deal with these issues, while also providing basic data manipulation tools to facilitate SWE-based research. SWEpy is a Python library that allows for quick and easy web-access, geographic subsetting, and concatenation of MEaSUREs CETB files. The primary goal of SWEpy is to reduce the time needed for researchers to access and begin making use of MEaSUREs CETB data. A user with very little knowledge of Python can use SWEpy to access this data within minutes, and those more familiar with the scientific Python ecosystem can benefit from the wealth of tools available for subsequent analysis of CETB data.

| SWEpy: A Python Library for Quick and Easy Access and Manipulation of MEaSUREs CETB Files for SWE Analysis