Browsing by Author "Diedenhoven, Bastiaan van"
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Item The CHROMA cloud top pressure retrieval algorithm for the Plankton, Aerosol, Cloud, ocean Ecosytem (PACE) satellite mission(EGU, 2023-02-23) Sayer, Andrew; Lelli, Luca; Cairns, Brian; Diedenhoven, Bastiaan van; Ibrahim, Amir; Knobelspiesse, Kirk D.; Korkin, Sergey; Werdell, P. JeremyThis paper provides the theoretical basis and simulated retrievals for the Cloud Height Retrieval from O2 Molecular Absorption (CHROMA) algorithm. Simulations are performed for the Ocean Color Instrument (OCI), which is the primary payload on the forthcoming NASA Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) mission, and the Ocean Land Colour Instrument (OLCI) currently flying on the Sentinel 3 satellites. CHROMA is a Bayesian approach which simultaneously retrieves cloud optical thickness (COT), cloud top pressure/height (CTP/CTH), and (with a significant prior constraint) surface albedo. Simulated retrievals suggest that the sensor and algorithm should be able to meet the PACE mission goal for CTP error, which is ±60 mb for 65 % of opaque (COT≥ 3) single-layer clouds on global average. CHROMA will provide pixel-level uncertainty estimates, which are demonstrated to have skill at telling low-error situations from high-error ones. CTP uncertainty estimates are well-calibrated in magnitude, although COT uncertainty is overestimated relative to observed errors. OLCI performance is found to be slightly better than OCI overall, demonstrating that it is a suitable proxy for the latter in advance of PACE’s launch. CTP error is only weakly sensitive to correct cloud phase identification or assumed ice crystal habit/roughness. As with other similar algorithms, for simulated retrievals of multi-layer systems consisting of optically thin cirrus clouds above liquid clouds, retrieved height tends to be underestimated because the satellite signal is dominated by the optically-thicker lower layer. Total (liquid plus ice) COT also becomes underestimated in these situations. However, retrieved CTP becomes closer to that of the upper ice layer for ice COT≈3 or higher.Item Retrieval of liquid water cloud properties from POLDER-3 measurements using a neural network ensemble approach(Copernicus Publications on behalf of the European Geosciences Union, 2019-03-18) Noia, Antonio Di; Hasekamp, Otto P.; Diedenhoven, Bastiaan van; Zhang, ZhiboThis paper describes a neural network algorithm for the estimation of liquid water cloud optical properties from the Polarization and Directionality of Earth’s Reflectances-3 (POLDER-3) instrument aboard the Polarization & Anisotropy of Reflectances for Atmospheric Sciences coupled with Observations from a Lidar (PARASOL) satellite. The algorithm has been trained on synthetic multi-angle, multi-wavelength measurements of reflectance and polarization and has been applied to the processing of 1 year of POLDER-3 data. Comparisons of the retrieved cloud properties with Moderate Resolution Imaging Spectroradiometer (MODIS) products show that the neural network algorithm has a low bias of around 2 in cloud optical thickness (COT) and between 1 and 2 µm in the cloud effective radius. Comparisons with existing POLDER-3 datasets suggest that the proposed scheme may have enhanced capabilities for cloud effective radius retrieval, at least over land. An additional feature of the presented algorithm is that it provides COT and effective radius retrievals at the native POLDER-3 Level 1B pixel level.Item Vertical profiles of droplet size distributions derived from cloud-side observations by the research scanning polarimeter: Tests on simulated data(Elsevier, 2020-02-25) Alexandrov, Mikhail D.; Miller, Daniel J.; Rajapakshe, Chamara; Fridlind, Ann; Diedenhoven, Bastiaan van; Cairns, Brian; Ackerman, Andrew S.; Zhang, ZhiboThe Research Scanning Polarimeter (RSP) is an airborne along-track scanner measuring the polarized and total reflectances with high angular resolution. It allows for accurate characterization of liquid water cloud droplet sizes using the rainbow structure in the polarized reflectance. RSP's observations also provide constraints on the cumulus cloud's 2D cross section, yielding estimates of its geometric shape. In this study for the first time we evaluate the possibility to retrieve vertical profiles of microphysical characteristics along the cloud side by combining these micro- and macrophysical retrieval methods. First we constrain cloud's geometric shape, then for each point on the bright side of its surface we collect data from different scans to obtain the multi-angle polarized reflectance at that point. The rainbow structures of the reflectances from multiple points yield the corresponding droplet size distributions (DSDs), which are then combined into vertical profiles. We present the results of testing the proposed profiling algorithm on simulated data obtained using large eddy simulations and 3D radiative transfer computations. The virtual RSP measurements were used for retrieval of DSD profiles, which then were compared to the actual data from the LES-model output. A cumulus congestus cloud was selected for these tests in preparation for analysis of real measurements made during the Cloud, Aerosol and Monsoon Processes Philippines Experiment (CAMP²Ex). We demonstrate that the use of the non-parametric Rainbow Fourier Transform (RFT) allows for adequate retrieval of the complex altitude-dependent bimodal structure of cloud DSDs.