Abstract
AbstractMass–dimensional relationships have been published for decades to characterize the microphysical properties of ice cloud particles. Classical retrieval methods employ a simplifying assumption that restricts the form of the mass–dimensional relationship to a power law, an assumption that was proved inaccurate in recent studies. In this paper, a nonstandard approach that leverages optimal use of in situ measurements to remove the power-law constraint is presented. A model formulated as a linear system of equations relating ice particle mass to particle size distribution (PSD) and ice water content (IWC) is established, and the mass retrieval process consists of solving the inverse problem with numerical optimization algorithms. First, the method is applied to a synthetic crystal dataset in order to validate the selected algorithms and to tune the regularization strategy. Subsequently, the method is applied to in situ measurements collected during the High Altitude Ice Crystal–High Ice Water Content field campaigns. Preliminary results confirm the method is efficient at retrieving size-dependent masses from real data despite a significant amount of noise: the IWC values calculated from the retrieved masses are in good agreement with reference IWC measurements (errors on the order of 10%–15%). The possibility to retrieve ice particle size–dependent masses combined with the flexibility left for sorting datasets as a function of parameters such as cloud temperature, cloud type, or convective index makes this approach well suited for studying ice cloud microphysical properties.
Highlights
Introduction aMotivationThe representation of ice particles, especially the parameterization of their microphysical properties, is an important part in atmospheric models and spaceborne sensors retrieval algorithms
Given the conditions of Darwin flight 16 (D16), the coefficients of the matrix Se are derived from ice water content (IWC) relative uncertainty values uIWC, calculated using Eq (12): The analysis considers only data points measured during level flight periods in areas where IWC values are uIWC(%) 5 0:0204 IWC20:2344 uIWC(%) 5 0:023 579 IWC20:025 532 for for
These results suggest that 1) ice particles observed in the D16, Darwin flight 19 (D19), and Cayenne flight 26 (C26) mesoscale convective systems (MCSs) at comparable temperature levels may have similar mass properties and 2) in the same cloud, particles sampled at different temperature levels may have different mass properties
Summary
This spread in the particles’ mass within a bin is expected to be even more pronounced in real data: OAP images reveal that several particle habits are mixed within the ice cloud particle populations sampled in airborne field campaigns These considerations give an insight into the level of noise included in PSD data, which is detrimental to the accuracy of the mass retrieval process. It limits the source of noise in input data to the binning process, since the characteristic dimension of a sphere is insensitive to 2D projection. Given the size range (10 2 940 mm) and the number of bins (93 spaced 10-mm-wide bins) of this synthetic test case, there are 93 unknown reference masses to be solved
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