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By combining AVHRR data from the NOAA satellites with information from a database of in situ measurements, large-scale maps can be generated of the microphysical parameters most immediately significant for the modelling of global circulation and climate. From the satellite data, the clouds can be classified into cumuliform, stratiform and cirrus classes and then into further sub-classes by cloud top temperature. At the same time a database of in situ measurements made by research aircraft is classified into the same sub-classes and a statistical analysis is used to derive relationships between the sub-classes and the cloud microphysical properties. These two analyses are then linked to give estimates of the microphysical properties of the satellite observed clouds. Examples are given of the application of this technique to derive maps of the probability of occurrence of precipitating clouds and of precipitating water content derived from a case study within the International Cirrus Experiment (ICE) held in 1989 over the North Sea.

This contribution to the EUCREX mission 206 series aims at evaluating the performances of three methods considered for the retrieval of cloud microphysical properties (optical depth, droplet effective radius and liquid water path (LWP)) from Advanced Very High Resolution Radiometer (AVHRR) satellite images. These procedures, namely a set of empirical parameterizations, a physically based inversion code, and a neuronal approach, are tested with the mission 206 case study. The influence of horizontal averaging on the retrieved cloud properties is also investigated to assess the limitations of the comparison between satellite observations and aircraft measurements performed at a smaller scale.

The classification of pixels making up a satellite image is seen here, not only in order to cluster or discriminate these pixels, but exactly as an inversion procedure. The implemented adjustable combination of neural networks (ACNN) technique is built as a combination of self-organized neural networks in order to be particularly robust. After a sensitivity study, this method was considered for the extraction of cloud optical thickness and droplet effective radius from AVHRR imagery. The validation of this algorithm was conducted in well-documented cases issued from the EUCREX campaigns by comparisons against the CRTVL inversion package developed by Nakajima and Nakajima (1995) and also in situ measurements.

The influence of two initialization schemes implemented for cirrus cloud simulation with a mesoscale model (RAMS) when run at two distinct horizontal resolutions (40 and 13.5 km) is investigated. The first method combines both first guess and analyzed data issued from the European Centre for Medium-Range Weather Forecasts (ECMWF). Indeed, the former provides a good estimation of the amplitude of relative humidity, but the field is phase shifted while the latter gives a correct phase but a bad amplitude (because of the difficulty of measuring relative humidity at pressure levels lower than 400 mb). The second scheme uses information at three scale levels: ECMWF three-dimensional fields, an atmospheric vertical profile, and a cloud cover mask derived from a National Oceanic and Atmospheric Administration Advanced Very High Resolution Radiometer satellite image. An interpolation algorithm that conserves the power spectrum slopes is used. Finally, several comparisons between the model outputs and observational data collected during the European Cloud and Radiation Experiment campaign are made. At a horizontal scale of 13.5 km, the authors’ new assimilation procedure allows a much better characterization of the whole three-dimensional cloud field (i.e., both horizontally and vertically) in terms of optical thickness and ice water content distributions, whereas the simulations are not improved at a larger horizontal scale (40 km).

[1] This paper reports on the first experiences with Advanced Technology Microwave Sounder (ATMS) data at the European Centre for Medium-Range Weather Forecasts (ECMWF), both in terms of the contribution to the calibration/validation exercise and in terms of initial assimilation trials. Comparisons in antenna temperature space against short-term forecasts are used to establish the fidelity of the data. Monitoring of ATMS data against short-term forecasts shows that the data are generally of good quality, with a noise performance that is well within specification and after appropriate averaging, comparable to or better than that of the Advanced Microwave Sounding Unit-A (AMSU-A). Biases vary smoothly with scan positions, even before an appropriate antenna pattern correction has been established, and ATMS looks better than AMSU-A in this regard. Outer scan positions can be assimilated without restrictions due to biases, and together with the wider swath, this leads to a better coverage from one ATMS compared to one AMSU-A. There are indications of larger interchannel and spatial error correlations in ATMS data than for AMSU-A, possibly linked to a weak striping effect. The analysis and forecast impact in initial assimilation trials over two seasons are significantly positive in the short range over the Southern Hemisphere and in the long range over the Northern Hemisphere, with an otherwise overall neutral impact. Experiments in the context of a depleted observing system suggest that ATMS gives a comparable forecast impact to that from a single AMSU-A/Microwave Humidity Sounder combination.

International audience; In this conclusion paper, remote sensing retrievals of cloud optical thickness performed during the EUCREX mission 206 are analyzed. The comparison with estimates derived from in situ measurements demonstrates that the adiabatic model of cloud microphysics is more realistic than the vertically uniform plane parallel model (VUPPM) for parameterization of optical thickness. The analysis of the frequency distributions of optical thickness in the cloud layer then shows that the adiabatic model provides a good prediction when the cloud layer is thick and homogeneous, while it overestimates significantly the optical thickness when the layer is thin and broken. Finally, it is shown that the effective optical thickness over the whole sampled cloud is smaller than the adiabatic prediction based on the mean geometrical thickness of the cloud layer. The high sensitivity of the optical thickness on cloud geometrical thickness suggests that the effect of aerosol and droplet concentration on precipitation efficiency, and therefore on cloud extent and lifetime, is likely to be more significant than the Twomey effect.