How to select low level warm water cloud fraction in MODIS MCD06COSP dataset

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harshbardhan
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How to select low level warm water cloud fraction in MODIS MCD06COSP dataset

by harshbardhan » Mon Nov 27, 2023 12:41 am America/New_York

Hi all,

I am wondering to filter low-level (cloud top pressure >= 680 hPa) and warm (cloud top temperature > 273.0 K) cloud fraction in MCD06COSP level-3 daily cloud properties (https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/products/MCD06COSP_D3_MODIS).

I can see two different flavours of cloud fraction ('Cloud Mask Fraction Low' and 'Cloud Retrieval Fraction Liquid') but not sure about selecting only low-level warm cloud fraction.
Since MCD06COSP dataset also provide 'cloud top pressure' as one of SDS, may I choose 'Cloud Retrieval Fraction Liquid' where 'cloud top pressure' >= 680 hPa. Any suggesting from experts!
Thanks

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Re: How to select low level warm water cloud fraction in MODIS MCD06COSP dataset

by LAADS_UserServices_M » Mon Nov 27, 2023 2:48 pm America/New_York

Below is a response from our SME:

Cloud Mask Fraction Low: it is based on cloud top pressure (CTP >= 680hpa) for daytime (SZA <= 85 deg). The daytime cutoff is less strict than cloud optical properties (SZA <= 81.3731 deg), so the data extends a bit farther poleward. It may include liquid, ice, or undetermined phase cloud.

Cloud Retrieval Fraction Liquid: is based on cloud phase for liquid cloud only and daytime (SZA <= 81.3731 deg). The daytime cutoff is slightly more restrictive than Cloud Mask Fraction.

Therefore, for low-level warm cloud fraction, we suggest using the overlapped pixels of “Cloud Mask Fraction Low” AND “Cloud Retrieval Fraction Liquid”.
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harshbardhan
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Re: How to select low level warm water cloud fraction in MODIS MCD06COSP dataset

by harshbardhan » Mon Nov 27, 2023 10:42 pm America/New_York

Okay, I will consider overlapping pixels of “Cloud Mask Fraction Low” AND “Cloud Retrieval Fraction Liquid” for low-level warm cloud selection.
Thanks for your kind suggestion.

I am using python (xarray and numpy)
mask = 1 * numpy.ones((180,360)) * numpy.isfinite(Cloud_Mask_Fraction_Low)
low_warm_cloud_fraction = Cloud_Retrieval_Fraction_Liquid.where(mask)

is that correct way?

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Re: How to select low level warm water cloud fraction in MODIS MCD06COSP dataset

by LAADS_UserServices_M » Tue Nov 28, 2023 9:57 am America/New_York

Yes, this is a correct way.
The logic is to find the pixels with valid cloud fraction values for both Cloud_Mask_Fraction_Low and Cloud_Retrieval_Fraction_Liquid, then for these pixels you can use either Cloud_Retrieval_Fraction_Liquid or Cloud_Mask_Fraction_Low.
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Re: How to select low level warm water cloud fraction in MODIS MCD06COSP dataset

by harshbardhan » Tue Nov 28, 2023 11:52 pm America/New_York

Thanks for your kind suggestion!

one more thing I want to ask, while computing cloud fraction in MODIS COSP dataset, do the computation consider the effect of overlap cloud layer? Some studies (e.g., DOI: 10.1126/sciadv.adh771) while using MODIS cloud fraction prefer random overlap assumption as:
CF_low = (CF_total - CF_high) / (1 - CF_high)
where CF_low, CF_high and CF_total are low cloud, high cloud and total cloud fraction.
Any comment on it!
Thanks

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Re: How to select low level warm water cloud fraction in MODIS MCD06COSP dataset

by LAADS_UserServices_M » Thu Nov 30, 2023 10:43 am America/New_York

In MODIS L3 COSP, the cloud fractions are aggregated at different pressure levels, there is no layer overlap:
Cloud_Mask_Fraction: (all pressure levels: total column)
Cloud_Mask_Fraction_Low: (CTP >= 680 hpa)
Cloud_Mask_Fraction_Mid: (440 hpa <= CTP < 680 hpa)
Cloud_Mask_Fraction_High: (CTP < 440 hpa)
Cloud_Mask_Fraction = Cloud_Mask_Fraction_Low + Cloud_Mask_Fraction_Mid + Cloud_Mask_Fraction_High

For L3 cloud fraction derived from cloud optical properties:
Cloud_Retrieval_Fraction_Total = Cloud_Fraction_Liquid + Cloud_Fraction_Ice + Uncetermined_Phase_Cloud
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Re: How to select low level warm water cloud fraction in MODIS MCD06COSP dataset

by harshbardhan » Thu Nov 30, 2023 10:52 pm America/New_York

Thanks for the clarification.

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