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Dust storms pose significant threats to human health and property. Accurate forecasting is crucial for taking precautionary measures. Dust models have suffered from uncertainties from emission and transport factors. Data assimilation can help refine biased models by incorporating available observations, leading to improved analyses and forecasts. The Ensemble Kalman Filter (EnKF) is a widely-used assimilation algorithm that effectively tunes models, particularly in terms of intensity adjustment. However, when the position of the simulation does not align consistently with the observations which is referred to as position error, the EnKF algorithm struggles. This is because its background covariance normally represents intensity uncertainty, while the positional errors in the long distance transport are difficult to be quantified and were usually neglected. In this paper, we propose a novel Neighboring Time Ensemble Kalman Filter (NTEnKF). In addition to the original ensembles quantifying dust loading variation, this methodology introduces extra ensembles from neighboring time for describing the potential spread of dust position. The enlarged ensemble captures both intensity and positional errors, allowing observations to be thoroughly resolved into the assimilation calculations. We tested this method on three major dust storm events that occurred in spring 2021. The results show that position error significantly degraded dust forecasting in terms of RMSE and NMB, and hindered the EnKF from assimilating valid observations. In contrast, the NTEnKF yielded substantial improvements in both dust analysis fields and forecasts compared to the EnKF. [ABSTRACT FROM AUTHOR]

Cloud masking is a key initial step in the retrieval of geophysical properties from satellite data. Despite decades of research, problems still exist of over- or underdetection of clouds. High aerosol loadings, in particular from dust storms or fires, are often classified as clouds, and vice versa. In this paper, we present a cloud mask created using machine learning for the Advanced Himawari Imager (AHI) aboard Himawari-8. In order to train the algorithm, a parallax-corrected collocated data set was created from AHI and Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) lidar data. Artificial neural networks (ANNs) were trained on the collocated data to identify clouds in AHI scenes. The resulting neural network (NN) cloud masks are validated and compared to cloud masks produced by the Japanese Meteorological Association (JMA) and the Bureau of Meteorology (BoM) for a number of different solar and viewing geometries, surface types and air masses. Here, five case studies covering a range of challenging scenarios for cloud masks are also presented to demonstrate the performance of the masking algorithm. The NN mask shows a lower false positive rate (FPR) for an equivalent true positive rate (TPR) across all categories, with FPRs of 0.160 and 0.259 for the NN and JMA masks, respectively, and 0.363 and 0.506 for the NN and BoM masks, respectively, at equivalent TPR values. This indicates the NN mask accurately identifies 1.13 and 1.29 times as many non-cloud pixels for the equivalent hit rate when compared to the JMA and BoM masks, respectively. The NN mask was shown to be particularly effective in distinguishing thick aerosol plumes from cloud, most likely due to the inclusion of the 0.47 and 0.51 µ m bands. The NN cloud mask shows an improvement over current operational cloud masks in most scenarios, and it is suggested that improvements to current operational cloud masks could be made by including the 0.47 and 0.51 µ m bands. The collocated data are made available to facilitate future research. [ABSTRACT FROM AUTHOR]

Cloud masking is a key initial step in the retrieval of geophysical properties from satellite data. Despite decades of research problems still exists of over or under detection of cloud. High aerosol loadings, in particular from dust storms or fires, are often classified as cloud and vice versa. In this paper we present a cloud mask created using machine learning for the Advanced Himawari Imager (AHI) on board Himawari-8. In order to train the algorithm a parallax-corrected collocated data set was created from AHI and Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) lidar data. Artificial neural networks (ANNs) were trained on the collocated data to identify clouds in AHI scenes. The resulting NN cloud masks are validated and compared to cloud masks produced by the Japanese Meteorological Association (JMA) and the Bureau of Meteorology (BoM) for a number of different solar and viewing geometries, surface types and airmasses. 5 case studies covering a range of challenging scenarios for cloud masks are also presented to demonstrate the performance of the masking algorithm. The NN mask shows a lower false positive rate (FPR) for an equivalent true positive rate (TPR) across all categories, with FPRs of 0.106 and 0.198 for the NN and JMA masks respectively and 0.314 and 0.464 for the NN and BoM masks respectively at equivalent TPR values. This indicates the NN mask accurately identifies 1.11 and 1.28 times as many non-cloud pixels, for the equivalent hit rate when compared to the JMA and BoM masks respectively. The NN mask was shown to be particularly effective in distinguishing thick aerosol plumes from cloud, most likely due to the inclusion of the 0.47 μm and 0.51 μm bands. The NN cloud mask shows an improvement over current operational cloud masks in most scenario and it is suggested that improvements to current operational cloud masks could be made by including the 0.47 μm and 0.51 μm bands. The collocated data are made available to facilitate future research. [ABSTRACT FROM AUTHOR]

When calibrating simulations of dust clouds, both the intensity and the position are important. Intensity errors arise mainly from uncertain emission and sedimentation strengths, while position errors are attributed either to imperfect emission timing or to uncertainties in the transport. Though many studies have been conducted on the calibration or correction of dust simulations, most of these focus on intensity solely and leave the position errors mainly unchanged. In this paper, a grid-distorted data assimilation, which consists of an image-morphing method and an ensemble-based variational assimilation, is designed for realigning a simulated dust plume to correct the position error. This newly developed grid-distorted data assimilation has been applied to a dust storm event in May 2017 over East Asia. Results have been compared for three configurations: a traditional assimilation configuration that focuses solely on intensity correction, a grid-distorted data assimilation that focuses on position correction only and the hybrid assimilation that combines these two. For the evaluated case, the position misfit in the simulations is shown to be dominant in the results. The traditional emission inversion only slightly improves the dust simulation, while the grid-distorted data assimilation effectively improves the dust simulation and forecasting. The hybrid assimilation that corrects both position and intensity of the dust load provides the best initial condition for forecasting of dust concentrations. [ABSTRACT FROM AUTHOR]