ALBERT

Description: We demonstrate that daily pollutant transport patterns in East Asia are visible from satellite images when inspected with corresponding synoptic weather analyses. Transport pathways of air pollutants in East Asia are investigated using satellite observations, surface weather charts, and chemical-transport model simulations. It is found that during cool season (fall to spring), pollutant transports in East Asia are largely determined by synoptic weather patterns associated with high pressure system over southern China, which is extended from the Siberia High, and low pressure system over Manchuria, which is initiated by Altai-Sayan cyclogenesis. Based on the relative location and strength of these weather systems, three types of synoptic weather patterns that may contribute to pollutants transport in East Asia, especially in China and Korea, are identified: i.e., (1) a strengthening of the Siberian High and its southeastward propagation; (2) a high-pressure system over southern China followed by a frontal passage associated with a northern low-pressure system; and (3) a stagnant high-pressure system over southern China. For all three patterns, the high-pressure system in southern China is essential for the development of regional air pollution, while frontal activities associated with low-pressure system provide a forcing mechanism to transport those pollutants eastward or southeastward. Observed and simulated surface PM distributions show good agreement in both aerosol optical depth and NO2 column density further implying that anthropogenic emissions also contribute to regional events of high surface PM concentrations. It is argued that the quasi-periodic migration of synoptic weather systems in East Asia works as an efficient pump of pollutants; i.e., regional air pollutions developed under high-pressure systems are transported downstream by low-pressure systems.

Description: A HYSPLIT inverse system that is based on variational data assimilation and a Lagrangian dispersion transfer coefficient matrix (TCM) is evaluated using the Cross Appalachian Tracer Experiment (CAPTEX) data collected from six controlled releases. For simplicity, the initial tests are applied to release 2 for which the HYSPLIT has the best performance. Before introducing model uncertainty terms, the tests using concentration differences in the cost function results in severe underestimation while those using logarithm concentrations differences results in overestimation of the release rate. Adding model uncertainty terms improves results for both choices of the metric variables in the cost function. A cost function normalization scheme is later introduced to avoid spurious minimal source term solutions when using logarithm concentration differences. The scheme is effective in eliminating the spurious solutions and it also helps to improve the release estimates for both choices of the metric variables. The tests also show that calculating logarithm concentration differences generally yield better results than calculating concentration differences and the estimates are more robust for a reasonable range of model uncertainty parameters. This is further confirmed with nine ensemble HYSPLIT runs in which meteorological fields were generated with varying planetary boundary layer (PBL) schemes. In addition, it is found that the emission estimate using a combined TCM by taking the average or median values of the nine TCMs is similar to the median of the nine estimates using each of the TCMs individually. The inverse system is then applied to the other CAPTEX releases with a fixed set of observational and model uncertainty parameters and the largest relative error among the six releases is 53.3%. At last, the system is tested for its capability to find a single source location as well as its source strength. In these tests, the location and strength that yield the best match between the predicted and the observed concentrations are considered as the inverse modeling results. The estimated release rates are mostly not as good as the cases in which the exact release locations are assumed known, but they are all within a factor of 3 for all the six releases. However, the estimated location may have large errors.

Description: A Hybrid Single-Particle Lagrangian Integrated Trajectory version 4 (HYSPLIT-4) inverse system that is based on variational data assimilation and a Lagrangian dispersion transfer coefficient matrix (TCM) is evaluated using the Cross-Appalachian Tracer Experiment (CAPTEX) data collected from six controlled releases. For simplicity, the initial tests are applied to release 2, for which the HYSPLIT has the best performance. Before introducing model uncertainty terms that will change with source estimates, the tests using concentration differences in the cost function result in severe underestimation, while those using logarithm concentration differences result in overestimation of the release rate. Adding model uncertainty terms improves results for both choices of the metric variables in the cost function. A cost function normalization scheme is later introduced to avoid spurious minimal source term solutions when using logarithm concentration differences. The scheme is effective in eliminating the spurious solutions and it also helps to improve the release estimates for both choices of the metric variables. The tests also show that calculating logarithm concentration differences generally yields better results than calculating concentration differences, and the estimates are more robust for a reasonable range of model uncertainty parameters. This is further confirmed with nine ensemble HYSPLIT runs in which meteorological fields were generated with varying planetary boundary layer (PBL) schemes. In addition, it is found that the emission estimate using a combined TCM by taking the average or median values of the nine TCMs is similar to the median of the nine estimates using each of the TCMs individually. The inverse system is then applied to the other CAPTEX releases with a fixed set of observational and model uncertainty parameters, and the largest relative error among the six releases is 53.3 %. At last, the system is tested for its capability to find a single source location as well as its source strength. In these tests, the location and strength that yield the best match between the predicted and the observed concentrations are considered as the inverse modeling results. The estimated release rates are mostly not as good as the cases in which the exact release locations are assumed known, but they are all within a factor of 3 for all six releases. However, the estimated location may have large errors.