arXiv subjects

Otto Hasekamp

Publications and source records attributed to Otto Hasekamp.

2 recordsLinked to original sources

arXiv2026

Collaborative Neural Networks significantly Improve Global Aerosol Retrievals From Multi-Angle Polarimeters

We present the RemoTAP-Hybrid algorithm for global aerosol products retrieved from Multi-Angle Polarimeters (MAP), and apply it to one-year aerosol retrievals from the SPEXone MAP instrument. For the first time, Multiple Collaborated Neural Networks (MCNN) are used to perform global aerosol MAP retrievals over land and ocean. The combined use of MCNN with a full physics inversion in RemoTAP-Hybrid, significantly improves accuracy and computational speed of SPEXone aerosol retrievals. We validate the retrieved Aerosol Optical Depth (AOD), Angstrom Exponent (AE), Single Scattering Albedo (SSA), Absorption AOD (AAOD), Fine mode AOD (FAOD), Coarse mode AOD (CAOD), and fine mode effective radius (reff_fine) with AERONET data. The validation suggests that aerosol retrievals can be further improved when extending the aerosol description to include a fine non-spherical mode. For AOD, the Root-Mean-Square Error (RMSE) is 0.051 (0.037) over land (ocean), while 78.3% (80.8%) of the retrievals are within the requirement formulated by the Global Climate Observing System (GCOS). The RMSE is 0.186 (0.207) for AE, 0.038 (0.044) for SSA, 0.023 (0.014) for AAOD, 0.047 (0.031) for FAOD, 0.043 (0.027) for CAOD, and 0.021 (0.026) for reff_fine over land (ocean). Compared to the current RemoTAP algorithm, RemoTAP-Hybrid shows improved agreement with AERONET for AOD, AE, and FAOD over land and ocean while the number of valid retrievals is increased by 4% (3%) over land (ocean) and the computational time is reduced by 43% (32%). Finally, we present the first global seasonal maps of aerosol optical and microphysical properties and chemical composition from SPEXone.

physics.ao-ph↗

arXiv2025

Towards a Climate OSSE Framework for Satellite Mission Design

The rich history of observing system simulation experiments (OSSEs) does not yet include a well-established framework for using climate models. The need for a climate OSSE is triggered by the need to quantify the value of a particular measurement for reducing the uncertainty in climate predictions, which differ from numerical weather predictions in that they depend on future atmospheric composition rather than the current state of the weather. However, both weather and climate modeling communities share a need for motivating major observing system investments. Here we outline a new framework for climate OSSEs that leverages the use of machine-learning to calibrate climate model physics against existing satellite data. We demonstrate its application using NASA's GISS-E3 model to objectively quantify the value of potential future improvements in spaceborne measurements of Earth's planetary boundary layer. A mature climate OSSE framework should be able to quantitatively compare the ability of proposed observing system architectures to answer a climate-related question, thus offering added value throughout the mission design process, which is subject to increasingly rapid advances in instrument and satellite technology. Technical considerations include selection of observational benchmarks and climate projection metrics, approaches to pinpoint the sources of model physics uncertainty that dominate uncertainty in projections, and the use of instrument simulators. Community and policy-making considerations include the potential to interface with an established culture of model intercomparison projects and a growing need to economically assess the value-driven efficiency of social spending on Earth observations.

physics.ao-ph↗