DOI: 10.5281/zenodo.8226293
title: Monthly aggregated Water Vapor MODIS MCD19A2 (1 km): Yearly time-series (2012-2022)

description: This data is part of the Monthly aggregated Water Vapor MODIS MCD19A2 (1 km) dataset. Check the related identifiers section on the Zenodo side panel to access other parts of the dataset.
General Description
The monthly aggregated water vapor dataset is derived from MCD19A2 v061. The Water Vapor data measures the column above ground retrieved from MODIS near-IR bands at 0.94μm. The dataset time spans from 2000 to 2022 and provides data that covers the entire globe. The dataset can be used in many applications like water cycle modeling, vegetation mapping, and soil mapping. This dataset includes:

Monthly time-series:Derived from MCD19A2 v061, this data provides a monthly aggregated mean and standard deviation of daily water vapor time-series data from 2000 to 2022. Only positive non-cloudy pixels were considered valid observations to derive the mean and the standard deviation. The remaining no-data values were filled using the TMWM algorithm. This dataset also includes smoothed mean and standard deviation values using the Whittaker method. The quality assessment layers and the number of valid observations for each month can provide an indication of the reliability of the monthly mean and standard deviation values.
Yearly time-series:Derived from monthly time-series, this data provides a yearly time-series aggregated statistics of the monthly time-series data.
Long-term data (2000-2022):Derived from monthly time-series, this data provides long-term aggregated statistics for the whole series of monthly observations.
Data Details

Time period: 2012–2022
Type of data: Water vapor column above the ground (0.001cm)
How the data was collected or derived: Derived from MCD19A2 v061 using Google Earth Engine. Cloudy pixels were removed and only positive values of water vapor were considered to compute the statistics. The time-series gap-filling and time-series smoothing were computed using the Scikit-map Python package.
Statistical methods used: Four statistics were derived: standard deviation, percentiles 25, 50, and 75.
Limitations or exclusions in the data: The dataset does not include data for Antarctica.
Coordinate reference system: EPSG:4326
Bounding box (Xmin, Ymin, Xmax, Ymax): (-180.00000, -62.00081, 179.99994, 87.37000)
Spatial resolution: 1/120 d.d. = 0.008333333 (1km)
Image size: 43,200 x 17,924
File format: Cloud Optimized Geotiff (COG) format.
Support
If you discover a bug, artifact, or inconsistency, or if you have a question please use some of the following channels:

Technical issues and questions about the code: GitLab Issues
General questions and comments: LandGIS Forum
Name convention
To ensure consistency and ease of use across and within the projects, we follow the standard Open-Earth-Monitor file-naming convention. The convention works with 10 fields that describes important properties of the data. In this way users can search files, prepare data analysis etc, without needing to open files. The fields are:

generic variable name: wv = Water vapor
variable procedure combination: mcd19a2v061.seasconv = MCD19A2 v061 with gap-filling algorithm
Position in the probability distribution / variable type: m = mean | sd = standard deviation | n = number of observations | qa = quality assessment
Spatial support: 1km
Depth reference: s = surface
Time reference begin time: 20120101 = 2012-01-01
Time reference end time: 20221231 = 2022-12-31
Bounding box: go = global (without Antarctica)
EPSG code: epsg.4326 = EPSG:4326
Version code: v20230619 = 2023-06-19 (creation date)