Abstract

Abstract. The Michelson Interferometer for Passive Atmospheric Sounding (MIPAS) onboard ENVISAT has the potential to be particularly useful for studying high, thin clouds, which have been difficult to observe in the past. This paper details the development, implementation and testing of an optimal-estimation-type retrieval for three macrophysical cloud parameters (cloud top height, cloud top temperature and cloud extinction coefficient) from infrared spectra measured by MIPAS. A preliminary estimation of a parameterisation of the optical and geometrical filling of the measurement field-of-view by cloud is employed as the first step of the retrieval process to improve the choice of a priori for the macrophysical parameters themselves. Preliminary application to single-scattering simulations indicates that the retrieval error stemming from uncertainties introduced by noise and by a priori variances in the retrieval process itself is small – although it should be noted that these retrieval errors do not include the significant errors stemming from the assumption of homogeneity and the non-scattering nature of the forward model. Such errors are preliminarily and qualitatively assessed here, and are likely to be the dominant error sources. The retrieval converges for 99% of input cases, although sometimes fails to converge for vetically-thin (<1 km) clouds. The retrieval algorithm is applied to MIPAS data; the results of which are qualitatively compared with CALIPSO cloud top heights and PARASOL cloud opacities. From comparison with CALIPSO cloud products, it must be noted that the cloud detection method used in this algorithm appears to potentially misdetect stratospheric aerosol layers as cloud. This algorithm has been adopted by the European Space Agency's "MIPclouds" project.

Highlights

  • Much of atmospheric infrared remote sensing is based upon analysis of data to estimate constituent concentrations – where the presence of cloud particles in the measurements is treated as a source of error – it is possible to isolate measurements of cloud in order to determine the properties of clouds themselves

  • This study details an algorithm for modelling cloud top height, cloud top temperature and extinction coefficient using a simple non-scattering model in a manner suitable for an operational processor

  • The retrieval algorithm has been applied to singlescattering simulations for which the assumption of homogeneity is satisfied – and in this case, it appears that cloud top height and cloud top temperature can generally be successfully retrieved to within 250 m and 3 K, and half an order of magnitude of the extinction coefficient, it is important to bear in mind that these values do not represent anything near the real error range when the algorithm is applied to real measurements for which the assumptions of homogeneity almost certainly will not be met

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Summary

Introduction

Much of atmospheric infrared remote sensing is based upon analysis of data to estimate constituent concentrations – where the presence of cloud particles in the measurements is treated as a source of error – it is possible to isolate measurements of cloud in order to determine the properties of clouds themselves. Clouds (especially high cloud such as cirrus) represent one of the largest uncertainties in climate studies (Intergovernmental Panel on Climate Change, 2008) – and in order to have reliable estimates of radiative forcing and climatic impact, accurate distributions of cloud frequencies and properties must be available. Satellite instruments provide an opportunity to study the properties of clouds on a global scale

Overview of MIPAS-ENVISAT
Overview of clouds from satellites
Generalities of cloud measurement
Specifics of high cloud measurement by MIPASlike instruments
Cloud information from MIPAS
Algorithm description
Microwindows
Continuum radiance
Cloud effective fraction
State vector
Measurement vector
A priori information
Cloud forward model
Pencil-beams
FOV convolution
Definition of cloud forward model
Statistical combination
Spike tests
Error inflation
Operational considerations
Retrieval error and real error
Limitations of the cloud forward model: extinction range of sensitivity
Limitations of cloud forward model: scattering
Validation using KOPRA simulations
Limitations of cloud forward model: homogeneity
Using the CFM
Using KOPRA simulations
Water vapour continuum
Pointing error
Application of algorithm
30 August 2009
Comparison of CEF and CI detection mechanisms
Findings
Conclusions
Full Text
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