Toward data assimilation of ship-induced aerosol–cloud interactions
Geoengineering News
- Lekha Patel
- Affiliation: Statistical Sciences, Sandia National Laboratories, Albuquerque, New Mexico, USA
- Lyndsay Shand
- Affiliation: 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.2022.21
Abstract
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
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