Data for "Detection of large-scale cloud microphysical changes within a major shipping corridor after implementation of the IMO 2020 fuel sulfur regulations"

<p>Processed data used for the manuscript &quot;Detection of large-scale cloud microphysical changes within a major shipping corridor after implementation of the IMO 2020 fuel sulfur regulations&quot;.</p> <p>Includes input data for kriging algorithm as &quot;SSF1deg_shipkrige_Terra.nc&quot; and output data files as &quot;Data_Terra_[VAR]_[YEAR]_C_M[MONTH].nc&quot; for [VAR] Acld (overcast albedo) or cer (cloud droplet effective radius), [YEAR] the starting year of a three-year period starting with 2002 and ending at 2020 or &quot;clim&quot; for the 2002-2019 climatology, and [MONTH] 1to12 (annual mean) or 9to11 (austral spring).</p> <p>For the output data, &quot;Obs&quot; is the original data, &quot;Est&quot;&nbsp;is the mean counterfactual field obtained via kriging, &quot;lowEst&quot; and &quot;highEst&quot; are the 2.5th and 97.5th percentiles of the kriged fields for each grid box, &quot;krSims&quot; stores the results of the 5,000 simulated kriged fields, &quot;Semivariance&quot; is the binned empirical variogram values, &quot;pVal&quot; is the raw field significance (not adjusted for multiple testing), &quot;nOut&quot; is the number of individually significant grid boxes, &quot;tran&quot; is the transform applied (none for cer, logit for Acld), &quot;iniPhi&quot; and &quot;iniSigma2&quot; are the initial values for the fitted variogram, &quot;Phi&quot; and &quot;Sigma2&quot; are the fitted values using weighted least squares, and &quot;parSel&quot; is the list of selected regressors for the mean function that minimize the Bayesian information criterion.</p>