Presenter

Alessandro Ipe - ETRO-VUB / Royal Meteorological Institute

Abstract

The climate system of the Earth is fundamentally determined by the Earth radiation budget (ERB) and its regional distribution. The Earth receives energy from the Sun and radiates energy back to space. In a stable climate system, both incoming and outgoing energy flows are balanced. An external perturbation, e.g. an increase of greenhouse gases or a variation of aerosols concentrations, results in a radiative forcing which is driving a change of the climate system. The final effect of this radiative forcing is influenced by various feedbacks mechanisms.

As we mentioned, the ERB at the top of the atmosphere (TOA) consists of the incoming solar energy and outgoing radiation. While the first component of this budget is known with high accuracy, the exact amount of energy leaving the atmosphere into space is still subject to speculation. This has driven the launch of the Geostationary Earth Radiation Budget (GERB) experiment whose goal is to provide TOA reflected solar and emitted thermal fluxes. This project aims to resolve the diurnal cycle of the outgoing fluxes by providing measurements at high temporal sampling, and combining these to the high spatial resolution of the Meteosat Second Generation (MSG) satellite data. However, these quantities can not be directly measured from narrow field–of–view (FOV) broadband radiometers. Therefore, they need to be estimated from models linking directional radiance measurements to such hemispheric fluxes. This angular modeling plays a central part in the GERB processing. Due to the fixed scene geometry implied by the geostationary orbit, these models must priorly be built from low Earth orbit (LEO) radiometers. These instruments allow to measure the radiance field of several specific scenes in several geometries and thus to derive their associated fluxes. These specific scenes represent broad classes of surface types and cloudy conditions with radiance fields that have a similar angular behaviour for all the scene class members. Thus, to apply such models to GERB measurements, each footprint must be characterized in terms of the features used to stratify these models, i.e. cloud properties (cloud mask, cloud optical depth and cloud thermodynamic phase) and surface geotypes.

In this work, we propose to address this issue. First, we briefly introduce the concepts of physics behind specific cloud parameters needed in such scene identification. Then, we provide an extensive review of the literature about the techniques developed and commonly used to estimate these parameters from satellite–borne sensors. We present the complete design of the method currently implemented in the GERB Edition 1 processing starting from the development and testing of the prototype algorithm on the previous generation satellite data (Meteosat–7) up to its adaptation and validation to the current GERB and Spinning Enhanced Visible and InfraRed Imager (SEVIRI) instruments. We pursue with a proposal for a nighttime cloud detection algorithm which will provide valuable information once included in the end– user products for the next Edition processing. Finally, we conclude this work by providing a critical review of the Edition 1 scene identification and suggesting various improvements of its current weaknesses which will benefit future GERB product editions.