Statistical downscaling of water vapour satellite measurements from profiles of tropical ice clouds

Abstract. Multi-scale interactions between the main players of the atmospheric water cycle are poorly understood, even in the present-day climate, and represent one of the main sources of uncertainty among future climate projections. Here, we present a method to downscale observations of relative humidity available from the Sondeur Atmosphérique du Profil d'Humidité Intertropical par Radiométrie (SAPHIR) passive microwave sounder at a nominal horizontal resolution of 10 km to the finer resolution of 90 m using scattering ratio profiles from the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) lidar. With the scattering ratio profiles as covariates, an iterative approach applied to a non-parametric regression model based on a quantile random forest is used. This allows us to effectively incorporate into the predicted relative humidity structure the high-resolution variability from cloud profiles. The finer-scale water vapour structure is hereby deduced from the indirect physical correlation between relative humidity and the lidar observations. Results are presented for tropical ice clouds over the ocean: based on the coefficient of determination (with respect to the observed relative humidity) and the continuous rank probability skill score (with respect to the climatology), we conclude that we are able to successfully predict, at the resolution of cloud measurements, the relative humidity along the whole troposphere, yet ensure the best possible coherence with the values observed by SAPHIR. By providing a method to generate pseudo-observations of relative humidity (at high spatial resolution) from simultaneous co-located cloud profiles, this work will help revisit some of the current key barriers in atmospheric science. A sample dataset of simultaneous co-located scattering ratio profiles of tropical ice clouds and observations of relative humidity downscaled at the resolution of cloud measurements is available at https://doi.org/10.14768/20181022001.1 (Carella et al., 2019).

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