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zxdawn / S5P-LNO2

Core code for the TROPOMI lightning NO2 retrievals
GNU General Public License v3.0
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Cluster of lightning isn't large enough #1

Closed zxdawn closed 2 years ago

zxdawn commented 2 years ago

Problem

The observed lightning flashes are clustered first and then classified into clean and polluted clusters. https://github.com/zxdawn/S5P-LNO2/blob/26009d2caab0429d3a434c9a79eaa83a0e16dcf9/main/s5p_lnox_main.py#L72-L85

However, that would split storms using the default 40 km https://github.com/zxdawn/S5P-LNO2/blob/26009d2caab0429d3a434c9a79eaa83a0e16dcf9/main/s5p_lnox_utils.py#L132-L136

This method works well for isolated storms like this: The LNO2 were produced and transported with wind: S5P_PAL__L2__NO2____20190810T212136_20190810T230306_09456_01_020301_1 S5P_PAL__L2__NO2____20190811T004435_20190811T022605_09458_01_020301_1

However, there're some storms that occurred closely but were still far from 40 km. The LNO2 produced by them could mix together: S5P_PAL__L2__NO2____20190610T051322_20190610T065452_08581_01_020301_1 S5P_PAL__L2__NO2____20190610T170352_20190610T184522_08588_01_020301_1

Solution

It's better to calculated the transported air containing LNO2 first, and then cluster and classify it.

zxdawn commented 2 years ago

OK. I come up with this easier idea:

We can set the mask to the minimum value (>0) of previous masks https://github.com/zxdawn/S5P-LNO2/blob/26009d2caab0429d3a434c9a79eaa83a0e16dcf9/main/s5p_lnox_functions.py#L244-L256

image

zxdawn commented 2 years ago

The updated image is shown in https://github.com/zxdawn/S5P-LNO2/issues/2#issue-1088830325