Sentinel-5P OFFL AER AI: Offline UV Aerosol Index (COPERNICUS/S5P/OFFL/L3_AER_AI)

###Sentinel-5 Precursor

Sentinel-5 Precursor is a satellite launched on 13 October 2017
by the European Space Agency to monitor air pollution. The onboard sensor
is frequently referred to as Tropomi (TROPOspheric Monitoring Instrument).

All of the S5P datasets have two versions: Near Real-Time (NRTI) and Offline
(OFFL). The NRTI assets cover a smaller area than the OFFL assets, but
appear more quickly after acquisition. The OFFL assets contain data
from a single orbit (which, due to half the earth being dark, contains
data only for a single hemisphere).

The original Sentinel 5P Level 2 (L2) data is binned
by time, not by latitude/longitude. To make it possible to ingest the data
into Earth Engine, each Sentinel 5P L2 product is converted to L3, keeping
a single grid per orbit (that is, no aggregation across products
is performed).

Source products spanning the antimeridian are ingested as two Earth Engine
assets, with suffixes _1 and _2.

The conversion to L3 is done by the harpconvert
tool using the bin_spatial operation. The source data is filtered to
remove any values with QA values of less than 50.

###OFFL/L3_AER_AI

This dataset provides OFFL high-resolution imagery of the UV Aerosol Index
(UVAI), also called the Absorbing Aerosol Index (AAI).

The AAI is based on wavelength-dependent changes in Rayleigh scattering in
the UV spectral range for a pair of wavelengths. The difference between
observed and modelled reflectance results in the AAI. When the AAI is
positive, it indicates the presence of UV-absorbing aerosols like dust and
smoke.

The wavelengths used have very low ozone absorbtion, so unlike aerosol optical
thickness measurements, AAI can be calculated in the presence of clouds.
Daily global coverage is therefore possible.

For this L3 AER_AI product, the absorbing_aerosol_index is calculated with a
pair of measurements at the 354 nm and 388 nm wavelengths.

To make our OFFL L3 products, we find areas within the product’s bounding
box with data using a command like this:

harpconvert --format hdf5 --hdf5-compression 9
-a 'absorbing_aerosol_index_validity>50;derive(datetime_stop {time})'
S5P_OFFL_L2__AER_AI_20181030T213916_20181030T232046_05427_01_010200_20181105T210529.nc
grid_info.h5

We then merge all the data into one large mosaic (taking only one value for
pixels that may have different values for different times). From the
mosaic, we create a set of tiles containing orthorectified raster data.

Example harpconvert invocation for one tile:

harpconvert --format hdf5 --hdf5-compression 9
-a 'absorbing_aerosol_index_validity>50;derive(datetime_stop {time});
bin_spatial(2001, 50.000000, 0.01, 2001, -120.000000, 0.01);
keep(absorbing_aerosol_index,sensor_altitude,sensor_azimuth_angle,
     sensor_zenith_angle,solar_azimuth_angle,solar_zenith_angle)'
S5P_OFFL_L2__AER_AI_20181030T213916_20181030T232046_05427_01_010200_20181105T210529.nc
output.h5