Preprints
https://doi.org/10.5194/amt-2021-348
https://doi.org/10.5194/amt-2021-348

  06 Dec 2021

06 Dec 2021

Review status: this preprint is currently under review for the journal AMT.

A kriging-based analysis of cloud Liquid Water Content using CloudSat data

Jean-Marie Lalande1, Guillaume Bourmaud1, Pierre Minvielle2, and Jean-François Giovannelli1 Jean-Marie Lalande et al.
  • 1IMS (Univ. Bordeaux, CNRS, Bordeaux INP), 33400 Talence, France
  • 2CESTA, DAM, CEA, 33114 Le Barp, France

Abstract. Spatiotemporal statistical learning has received increased attention in the past decade, due to spatially and temporally indexed data proliferation, especially collected from satellite remote sensing. In the mean time, observational studies of clouds are recognized as an important step to improve cloud representation in weather and climate models. Since 2006, the satellite CloudSat of NASA carries a 94 GHz cloud profiling radar and is able to retrieve, from radar reflectivity, microphysical parameter distribution such as water or ice content. The collected data is piled up with the successive satellite orbits of nearly two hours, leading to a large compressed database of 2 Tb (http://cloudsat.atmos.colostate.edu/).

These observations give the opportunity to extend the cloud microphysical properties beyond the actual measurement locations using an interpolation and prediction algorithm. In order to do so, we introduce a statistical estimator based on the spatiotemporal covariance and mean of the observations known as kriging. An adequate parametric model for the covariance and the mean is chosen from an exploratory data analysis. Beforehand, it is necessary to estimate the parameters of this spatiotemporal model; This is performed in a Bayesian setting. The approach is then applied to a subset of the CloudSat dataset.

Jean-Marie Lalande et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on amt-2021-348', Anonymous Referee #1, 07 Jan 2022
  • RC2: 'Comment on amt-2021-348', Anonymous Referee #2, 10 Jan 2022

Jean-Marie Lalande et al.

Jean-Marie Lalande et al.

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Short summary
In this paper, we describe the implementation of an interpolation/prediction estimator applied to cloud properties derived from CloudSat observations. The objective is also to estimate the uncertainty associated with the estimated quantity. The model developed in this study can be valuable for satellite applications (GPS, telecommunication) as well as for cloud product comparison. We believe this paper is written in a didactic way so as to be profitable to anyone interested by kriging estimator.