A cloud filtering method for microwave upper tropospheric humidity measurements
Résumé
The paper presents a cloud filtering method for upper tropospheric humidity (UTH) measurements at 183.31±1.00 GHz. The method uses two criteria: The difference between the brightness temperatures at 183.31±7.00 and 183.31±1.00 GHz, and a threshold for the brightness temperature at 183.31±1.00 GHz. The robustness of this cloud filter is demonstrated by a mid-latitudes winter case-study.
The paper then studies different biases on UTH climatologies. Clouds are associated with high humidity, therefore the dry bias introduced by cloud filtering is discussed and compared to the wet biases introduced by the clouds radiative effect if no filtering is done. This is done by means of a case study, and by means of a stochastic cloud database with representative statistics for midlatitude conditions.
The consistent result is that both cloud wet bias (0.8% RH) and cloud filtering dry bias (?2.4% RH) are modest for microwave data, where the numbers given are for the stochastic cloud dataset. This indicates that for microwave data cloud-filtered UTH and unfiltered UTH can be taken as error bounds for errors due to clouds. This is not possible for the more traditional infrared data, since the radiative effect of clouds is much stronger there.
The focus of the paper is on midlatitude data, since atmospheric data to test the filter for that case were readily available. The filter is expected to be applicable also to subtropical and tropical data, but should be further validated with case studies similar to the one presented here for those cases.
The paper then studies different biases on UTH climatologies. Clouds are associated with high humidity, therefore the dry bias introduced by cloud filtering is discussed and compared to the wet biases introduced by the clouds radiative effect if no filtering is done. This is done by means of a case study, and by means of a stochastic cloud database with representative statistics for midlatitude conditions.
The consistent result is that both cloud wet bias (0.8% RH) and cloud filtering dry bias (?2.4% RH) are modest for microwave data, where the numbers given are for the stochastic cloud dataset. This indicates that for microwave data cloud-filtered UTH and unfiltered UTH can be taken as error bounds for errors due to clouds. This is not possible for the more traditional infrared data, since the radiative effect of clouds is much stronger there.
The focus of the paper is on midlatitude data, since atmospheric data to test the filter for that case were readily available. The filter is expected to be applicable also to subtropical and tropical data, but should be further validated with case studies similar to the one presented here for those cases.
Domaines
Océan, Atmosphère
Origine : Accord explicite pour ce dépôt
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