Results for “POLARIZED-LIGHT”

NASA NTRS1991

Ammonia-water mixtures at high pressures - Melting curves of ammonia dihydrate and ammonia monohydrate and a revised high-pressure phase diagram for the water-rich region

The phase relations of some mixtures of ammonia and water are investigated to create a phase diagram in pressure-temperature-composition space relevant to the geophysical study of bodies in the outer solar system. The mixtures of NH3(x)H2O(1-x), where x is greater than 0.30 but less than 0.51, are examined at pressures and temperatures ranging from 0-6.5 GPa and 125-400 K, respectively. The ruby luminescence technique monitors the pressure and a diamond-anvil cell compresses the samples, and the phases are identified by means of normal- and polarized-light optical microscopy. The melting curve for NH3H2O(2) is described by the equation T = 176 + 60P - 8.5P squared for the ranges of 0.06-1.4 GPa and 179-243 K. The equation for NH3H2O is T = 194 + 37P - P squared, which represents a minor correction of a previous description by Johnson et al. (1985). Observed phase transitions are consistent with the high-pressure stability limit of NH3H2O(2), and the transition boundary is found to be linear.

Boone, S.↗

NASA NTRS2020

Low-level liquid cloud properties during ORACLES retrieved using airborne polarimetric measurements and a neural network algorithm

In this study we developed a neural network (NN) that can be used to retrieve cloud microphysical properties from multiangular and multispectral polarimetric remote sensing observations. This effort builds upon our previous work, which explored the sensitivity of neural network input, architecture, and other design requirements for this type of remote sensing problem. In particular this work introduces a framework for appropriately weighting total and polarized reflectances, which have vastly different magnitudes and measurement uncertainties. The NN is trained using an artificial training set and applied to research scanning polarimeter (RSP) data obtained during the ORACLES field campaign (ObseRvations of Aerosols above CLouds and their intEractionS). The polarimetric RSP observations are unique in that they observe the same cloud from a very large number of angles within a variety of spectral bands, resulting in a large dataset that can be explored rapidly with a NN approach. The usefulness of applying a NN to a dataset such as this one stems from the possibility of rapidly obtaining a retrieval that could be subsequently applied as a first guess for slower but more rigorous physical-based retrieval algorithms. This approach could be particularly advantageous for more complicated atmospheric retrievals – such as when an aerosol layer lies above clouds like in ORACLES. For RSP observations obtained during ORACLES 2016, comparisons between the NN and standard parametric polarimetric (PP) cloud retrieval give reasonable results for droplet effective radius (r(e): R=0.756, RMSE=1.74 µm) and cloud optical thickness (τ: R=0.950, RMSE=1.82). This level of statistical agreement is shown to be similar to comparisons between the two most well-established cloud retrievals, namely, the polarimetric and the bispectral total reflectance cloud retrievals. The NN retrievals from the ORACLES 2017 dataset result in retrievals of r(e) (R=0.54, RMSE=4.77 µm) and τ (R=0.785, RMSE=5.61) that behave much more poorly. In particular we found that our NN retrieval approach does not perform well for thin (τ<3), inhomogeneous, or broken clouds. We also found that correction for above-cloud atmospheric absorption improved the NN retrievals moderately – but retrievals without this correction still behaved similarly to existing cloud retrievals with a slight systematic offset.

SPECTRAL-RESOLUTION LIDAR↗