https://www.cambridge.org/core/journals/environmental-data-science/article/toward-data-assimilation-of-shipinduced-aerosolcloud-interactions/FC4BFA2D09DC808B2DB4587F8263F3B6
23 December 2022 Authors Lekha PatelAffiliation: Statistical Sciences, Sandia National Laboratories, Albuquerque, New Mexico, USA Lyndsay ShandAffiliation: Statistical Sciences, Sandia National Laboratories, Albuquerque, New Mexico, USA Citation: Patel, L., & Shand, L. (2022). Toward data assimilation of ship-induced aerosol–cloud interactions. *Environmental Data Science,* *1*, E31. doi:10.1017/eds.2022.21Abstract Satellite imagery can detect temporary cloud trails or ship tracks formed from aerosols emitted from large ships traversing our oceans, a phenomenon that global climate models cannot directly reproduce. Ship tracks are observable examples of marine cloud brightening, a potential solar climate intervention that shows promise in helping combat climate change. In this paper, we demonstrate a simulation-based approach in learning the behavior of ship tracks based upon a novel stochastic emulation mechanism. Our method uses wind fields to determine the movement of aerosol–cloud tracks and uses a stochastic partial differential equation (SPDE) to model their persistence behavior. This SPDE incorporates both a drift and diffusion term which describes the movement of aerosol particles via wind and their diffusivity through the atmosphere, respectively. We first present our proposed approach with examples using simulated wind fields and ship paths. We then successfully demonstrate our tool by applying the approximate Bayesian computation method-sequential Monte Carlo for data assimilation. ------------------------------ Keywords Aerosol–cloud interactions <https://www.cambridge.org/core/search?filters[keywords]=Aerosol%E2%80%93cloud%20interactions>atmospheric modeling <https://www.cambridge.org/core/search?filters[keywords]=atmospheric%20modeling>data assimilation <https://www.cambridge.org/core/search?filters[keywords]=data%20assimilation>stochastic simulation-based learning <https://www.cambridge.org/core/search?filters[keywords]=stochastic%20simulation-based%20learning> Figure 1. Visible ship tracks (left) on April 12, 2019, compared with no visible tracks (right) on April 7, 2019, with 3 hr of known ship locations (shown in red). Images ( km × km) taken at 12:00 GMT with ABI spectral band C06 off the coast of California. Figure 3. *Top*: Simulation snapshots are taken 4 hr apart, with , hr, hr, , hr, hr, , and . Ships (red, blue, purple, and yellow) indicated by colored dotted trajectories have initial conditions respectively, with heads (orange). Wind direction is shown via yellow arrows and tracks indicated by white trajectories. *Bottom*: Approximate posterior densities for (left) and (right) are shown with estimated values (red), true values (black), and 95% credible intervals (blue). Here, , , component-wise, with ; and computed at the 80% quantiles of accepted parameter distances at the previous iteration (Filippi et., 2013) *Source: Cambridge University Press* -- You received this message because you are subscribed to the Google Groups "geoengineering" group. To unsubscribe from this group and stop receiving emails from it, send an email to [email protected]. To view this discussion on the web visit https://groups.google.com/d/msgid/geoengineering/CAHJsh9_VD1%2Bq0AJSOq%2BVU%2BH%3DOFqtjSN7gk2GXHrPC316tU7icw%40mail.gmail.com.
