Dust Detection and Optical Depth Retrieval Using MSG‑SEVIRI Data

Satellite Monitoring of the Dust Storm over Northern China on 15 March 2021

Northern China was hit by a severe dust storm on 15 March 2021, covering a large area and bring devastating impact to a degree that was unprecedented in more than a decade. In the study, we carried out a day-and-night continuous monitoring to the path of the moving dust, using multi-spectral data from the Chinese FY-4A satellite combined with the Japanese Himawary-8 from visible to near-infrared, mid-infrared and far-infrared bands. We monitored the whole process of the dust weather from the occurrence, development, transportation and extinction. The HYSPLIT(Hybrid Single Particle Lagrangian Integrated Trajectory) backward tracking results showed the following two main sources of dust affecting Beijing during the north China dust storm: one is from western Mongolia; the other is from arid and semi-arid regions of northwest of China. Along with the dust storm, the upper air mass, mainly from Siberia, brought a significant decrease in temperature. The transport path of the dust shown by the HYSPLIT backward tracking is consistent with that revealed by the satellite monitoring. The dust weather, which originated in western Mongolia, developed into the “3.15 dust storm” in north China, lasting more than 40 h, with a transport distance of 3900 km, and caused severe decline in air quality in northern China, the Korean peninsula and other regions. It is the most severe dust weather in the past 20 years in east Asia.

Quantitative Aerosol Optical Depth Detection during Dust Outbreaks from Meteosat Imagery Using an Artificial Neural Network Model

This study presents the development of an artificial neural network (ANN) model to quantitatively estimate the atmospheric aerosol load (in terms of aerosol optical depth, AOD), with an emphasis on dust, over the Mediterranean basin using images from Meteosat satellites as initial information. More specifically, a back-propagation ANN model scheme was developed to estimate visible (at 550 nm) aerosol optical depth (AOD550 nm) values at equal temporal (15 min) and spatial (4 km) resolutions with Meteosat imagery. Accuracy of the ANN model was thoroughly tested by comparing model estimations with ground-based AOD550 nm measurements from 14 AERONET (Aerosol Robotic NETwork) stations over the Mediterranean for 34 selected days in which significant dust loads were recorded over the Mediterranean basin. Using a testbed of 3076 pairs of modeled and measured AOD550 nm values, a Pearson correlation coefficient (rP) equal to 0.91 and a mean absolute error (MAE) of 0.031 were found, proving the satisfactory accuracy of the developed model for estimating AOD550 nm values.

Improvement in Surface Solar Irradiance Estimation Using HRV/MSG Data

The Advanced Model for the Estimation of Surface Solar Irradiance (AMESIS) was developed at the Institute of Methodologies for Environmental Analysis of the National Research Council of Italy (IMAA-CNR) to derive surface solar irradiance from SEVIRI radiometer on board the MSG geostationary satellite. The operational version of AMESIS has been running continuously at IMAA-CNR over all of Italy since 2017 in support to the monitoring of photovoltaic plants. The AMESIS operative model provides two different estimations of the surface solar irradiance: one is obtained considering only the low-resolution channels (SSI_VIS), while the other also takes into account the high-resolution HRV channel (SSI_HRV). This paper shows the difference between these two products against simultaneous ground-based observations from a network of 63 pyranometers for different sky conditions (clear, overcast and partially cloudy). Comparable statistical scores have been obtained for both AMESIS products in clear and cloud situation. In terms of bias and correlation coefficient over partially cloudy sky, better performances are found for SSI_HRV (0.34 W/m2 and 0.995, respectively) than SSI_VIS (−33.69 W/m2 and 0.862) at the expense of the greater run-time necessary to process HRV data channel.