Monitoring Asian Dust Storms from NOAA-20 CrIS Double CO₂ Band Observations

Sand and dust storms (SDSs) are common environmental hazards in spring in Asian continent and have significant impacts on human health, weather, and climate. While many technologies have been developed to monitor SDSs, this study investigates the spectral characteristics of SDSs in satellite hyperspectral infrared observations and propose a new methodology to monitor the storms. An SDS emission and scattering index (SESI) is based on the differential responses of infrared CO2 shortwave and longwave IR bands to the scattering and emission of sand and dust particles. For a severe dust storm process during 14–17 March 2021, the SESI calculated by the Cross-track Infrared Sounder (CrIS) observations shows very negative values in the dusty region and is consistent with the spatial distribution of dust identified from the true-color RGB imagery and the dust RGB imagery of the Visible Infrared Imaging Radiometer Suite (VIIRS) on the NOAA-20 Satellite. The use of the SESI index in the near-surface layer allows for monitoring of the dust storm process and enables an effective classification between surface variations and dust weather events

Monitoring Asian Dust Storms from NOAA-20 CrIS Double CO2 Band Observations

Sand and dust storms (SDSs) are common environmental hazards in spring in Asian continent and have significant impacts on human health, weather, and climate. While many technologies have been developed to monitor SDSs, this study investigates the spectral characteristics of SDSs in satellite hyperspectral infrared observations and propose a new methodology to monitor the storms. An SDS emission and scattering index (SESI) is based on the differential responses of infrared CO2 shortwave and longwave IR bands to the scattering and emission of sand and dust particles. For a severe dust storm process during 14–17 March 2021, the SESI calculated by the Cross-track Infrared Sounder (CrIS) observations shows very negative values in the dusty region and is consistent with the spatial distribution of dust identified from the true-color RGB imagery and the dust RGB imagery of the Visible Infrared Imaging Radiometer Suite (VIIRS) on the NOAA-20 Satellite. The use of the SESI index in the near-surface layer allows for monitoring of the dust storm process and enables an effective classification between surface variations and dust weather events

Optimization of Hourly PM_2.5 Inversion Model Integrating Upper-Air Meteorological Elements

PM2.5 is directly related to the air quality and poses a threat to human health, thus high-precision monitoring of PM2.5 is necessary. The dispersion and accumulation of PM2.5 are affected by meteorological elements near the ground and in the upper air. Nevertheless, the current PM2.5 inversion models based on the deep neural network only consider ground elements. To further optimize the model and improve the inversion accuracy, a PM2.5 hourly inversion model integrating upper-air meteorological data was proposed, and a parametric rectified linear unit was used as the activation function of the model. The results showed that among the input elements, PM2.5 had the highest correlation with AOD, with a correlation coefficient of 0.33. The proposed model achieved the highest accuracy on the test set, with RMSE, MAE, and $\text{R}^{2}$ of $14.39 \mu \text{g}/\text{m}^{3}$ , $9.67 \mu \text{g}/\text{m}^{3}$ , and 0.83, respectively. Compared with the deep neural network models for surface meteorological data and surface+850hPa meteorological data, the RMSE of the proposed model on the test set was reduced by 23.13% and 17.05%, respectively. Meanwhile, the RMSE of the proposed model on the test set was reduced by 56.15%, 39.99%, 14.60% and 5.76%, respectively, compared with adaptive boosting, gradient boosting regression, random forest, and the integrated model of these three models. During the heating season in Shanxi Province, the high-value areas of PM2.5 were mainly distributed in the basin area, the PM2.5 concentration reached the highest in November and peaked at 11 a.m. during the day

Hyperspectral Infrared Atmospheric Sounder (HIRAS) Atmospheric Sounding System

Accurate atmospheric temperature and moisture profiles are essential for weather forecasts and research. Satellite-based hyperspectral infrared observations are meaningful in detecting atmospheric profiles, especially over oceans where conventional observations can seldom be used. In this study, a HIRAS (Hyperspectral Infrared Atmospheric Sounder) Atmospheric Sounding System (HASS) was introduced, which retrieves atmospheric temperature and moisture profiles using a one-dimension variational scheme based on HIRAS observations. A total of 274 channels were optimally selected from the entire HIRAS spectrum through information entropy analyses, and a group of retrieval experiments were independently performed for different HIRAS fields of views (FOVs). Compared with the ECMWF reanalysis data version-5 (ERA5), the RMSEs of temperature (relative humidity) for low-, mid-, and high-troposphere layers were 1.5 K (22.3%), 1.0 K (33.2%), and 1.3 K (38.5%), respectively, which were similar in magnitude to those derived from other hyperspectral infrared sounders. Meanwhile, the retrieved temperature RMSEs with respect to the satellite radio occultation (RO) products increased to 1.7 K, 1.8 K, and 1.9 K for the low-, mid-, and high-troposphere layers, respectively, which could be attributed to the accurate RO temperature products in the upper atmospheres. It was also found that the RMSE varied with the FOVs and latitude, which may be caused by the current angle-dependent bias correction and unique background profiles