Development of China’s first space-borne aerosol-cloud high-spectral-resolution lidar: retrieval algorithm and airborne demonstration

Aerosols and clouds greatly affect the Earth’s radiation budget and global climate. Light detection and ranging (lidar) has been recognized as a promising active remote sensing technique for the vertical observations of aerosols and clouds. China launched its first space-borne aerosol-cloud high-spectral-resolution lidar (ACHSRL) on April 16, 2022, which is capable for high accuracy profiling of aerosols and clouds around the globe. This study presents a retrieval algorithm for aerosol and cloud optical properties from ACHSRL which were compared with the end-to-end Monte-Carlo simulations and validated with the data from an airborne flight with the ACHSRL prototype (A2P) instrument. Using imaging denoising, threshold discrimination, and iterative reconstruction methods, this algorithm was developed for calibration, feature detection, and extinction coefficient (EC) retrievals. The simulation results show that 95.4% of the backscatter coefficient (BSC) have an error less than 12% while 95.4% of EC have an error less than 24%. Cirrus and marine and urban aerosols were identified based on the airborne measurements over different surface types. Then, comparisons were made with U.S. Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) profiles, Moderate-resolution Imaging Spectroradiometer (MODIS), and the ground-based sun photometers. High correlations (R > 0.79) were found between BSC (EC) profiles of A2P and CALIOP over forest and town cover, while the correlation coefficients are 0.57 for BSC and 0.58 for EC over ocean cover; the aerosol optical depth retrievals have correlation coefficient of 0.71 with MODIS data and show spatial variations consistent with those from the sun photometers. The algorithm developed for ACHSRL in this study can be directly employed for future space-borne high-spectral-resolution lidar (HSRL) and its data products will also supplement CALIOP data coverage for global observations of aerosol and cloud properties

Aerosol—Cloud Interaction with Summer Precipitation over Major Cities in Eritrea

This paper presents the spatiotemporal variability of aerosols, clouds, and precipitation within the major cities in Eritrea and it investigates the relationship between aerosols, clouds, and precipitation concerning the presence of aerosols over the study region. In Eritrea, inadequate water supplies will have both direct and indirect adverse impacts on sustainable development in areas such as health, agriculture, energy, communication, and transport. Besides, there exists a gap in the knowledge on suitable and potential areas for cloud seeding. Further, the inadequate understanding of aerosol-cloud-precipitation (ACP) interactions limits the success of weather modification aimed at improving freshwater sources, storage, and recycling. Spatiotemporal variability of aerosols, clouds, and precipitation involve spatial and time series analysis based on trend and anomaly analysis. To find the relationship between aerosols and clouds, a correlation coefficient is used. The spatiotemporal analysis showed larger variations of aerosols within the last two decades, especially in Assab, indicating that aerosol optical depth (AOD) has increased over the surrounding Red Sea region. Rainfall was significantly low but AOD was significantly high during the 2011 monsoon season. Precipitation was high during 2007 over most parts of Eritrea. The correlation coefficient between AOD and rainfall was negative over Asmara and Nakfa. Cloud effective radius (CER) and cloud optical thickness (COT) exhibited a negative correlation with AOD over Nakfa within the June–July–August (JJA) season. The hybrid single-particle Lagrangian integrated trajectory (HYSPLIT) model that is used to find the path and origin of the air mass of the study region showed that the majority of aerosols made their way to the study region via the westerly and the southwesterly winds

Analysis of a Haze Event over Nanjing, China Based on Multi-Source Data

We analyzed a June 2018 Nanjing, China haze event using ground-based and spaceborne sensors, combined with sounding and HYSPLIT backward trajectory data, with the ground-based and spaceborne sensor data exhibiting good consistency. Water vapor content showed significant positive correlation with AOD (aerosol optical depth), and AOD measured in urban and industrial areas was much higher compared to other similar zones. The afternoon convection caused the aerosol uplift during the haze event. Higher aerosol concentration was detected below 2 km. Due to the summer afternoon convective movement, pollutants at high altitude were dominated by small particles, while the overall pollutant mix was dominated by mixed aerosols. During a stable period over June 11–18, a single, near-surface inversion layer, and occasional two inversion layers, stopped pollutant dispersal, with only very stable ocean air mass transport in the southeast direction available. The Air Quality Index drop which took place during June 28–30 was caused by two inversion layers, combined with the immigration of pollutants from inland air masses

High Repetition Rate Mid-Infrared Differential Absorption Lidar for Atmospheric Pollution Detection

Developments in mid-infrared Differential Absorption Lidar (DIAL), for gas remote sensing, have received a significant amount of research in recent years. In this paper, a high repetition rate tunable mid-infrared DIAL, mounted on a mobile platform, has been built for long range remote detection of gas plumes. The lidar uses a solid-state tunable optical parametric oscillator laser, which can emit laser pulse with repetition rate of 500 Hz and between the band from 2.5 μm to 4 μm. A monitoring channel has been used to record the laser energy in real-time and correct signals. Convolution correction technology has also been incorporated to choose the laser wavelengths. Taking NO2 and SO2 as examples, lidar system calibration experiment and open field observation experiment have been carried out. The observation results show that the minimum detection sensitivity of NO2 and SO2 can reach 0.07 mg/m3, and 0.31 mg/m3, respectively. The effective temporal resolution can reach second level for the high repetition rate of the laser, which demonstrates that the system can be used for the real-time remote sensing of atmospheric pollution gas

Occurrence and Discrepancy of Surface and Column Mole Fractions of CO2 and CH4 at a Desert Site in Dunhuang, Western China

Carbon dioxide (CO2) and methane (CH4) are the two major radiative forcing factors of greenhouse gases. In this study, surface and column mole fractions of CO2 and CH4 were first measured at a desert site in Dunhuang, west China. The average column mole fractions of CO2 (XCO2) and CH4 (XCH4) were 413.00 ± 1.09 ppm and 1876 ± 6 ppb, respectively, which were 0.90 ppm and 72 ppb lower than their surface values. Diurnal XCO2 showed a sinusoidal mode, while XCH4 appeared as a unimodal distribution. Ground observed XCO2 and XCH4 were compared with international satellites, such as GOSAT, GOSAT-2, OCO-2, OCO-3, and Sentinel-5P. The differences between satellites and EM27/SUN observations were 0.26% for XCO2 and −0.38% for XCH4, suggesting a good consistency between different satellites and ground observations in desert regions in China. Hourly XCO2 was close to surface CO2 mole fractions, but XCH4 appeared to have a large gap with CH4, probably because of the additional chemical removals of CH4 in the upper atmosphere. It is necessary to carry out a long-term observation of column mole fractions of greenhouse gases in the future to obtain their temporal distributions as well as the differences between satellites and ground observations

Applications of Cell-Ratio Constant False-Alarm Rate Method in Coherent Doppler Wind Lidar

A cell-ratio constant false-alarm rate (CR-CFAR) method for detecting the Doppler frequency shift is proposed to improve the accuracy of velocity measured by coherent Doppler wind lidar (CWL) in low signal-to-noise ratio (SNR) environments. The method analyzes the spectrum to solve issues of weak signal submergence in noise encountered in the widely used periodogram method. This characteristic is that the signal region slope is larger than the noise region slope in the frequency spectrum. We combined the ratio and CFAR to propose the CR-CFAR method. The peak area is discriminated from the spectrum using this method. By removing background noise, the peak signal is obtained along with the Doppler shift. To verify the CR-CFAR method, a campaign experiment using both CWL and a commercial Doppler lidar was performed in Hami, China (42°32′ N, 94°03′ E) during 1–7 June 2016. The results showed that the proposed method significantly improved the reliability of CWL data under low SNR conditions. The height—at which both horizontal wind speed correlativity and horizontal wind direction correlativity exceeded 0.99—increased by 65 m. The relative deviation of the horizontal wind speed at 120 m decreased from 40.37% to 11.04%. We used the CR-CFAR method to analyze continuous data. A greater number of wind field characteristics were obtained during observation compared to those obtained using the common wind field inversion method

Particulate Characteristics during a Haze Episode Based on Two Ceilometers with Different Wavelengths

To investigate the particulate characteristics of a haze episode, data from two ceilometers with wavelengths of 532 nm and 910 nm, respectively, were studied intensively. By combining the data from the ceilometers with data from a sounding balloon, an automatic meteorological station, and a Grimm 180 PM instrument, analyses of the haze process of a short haze event were performed. The results showed that the relatively calm weather conditions were favorable to the occurrence of the haze and that higher relative humidity had a great influence on visibility. The extinction profiles from the ceilometers reflected the existence of an inverted structure of the temperature profiles and demonstrated the extinction differences at two different wavelengths. Because extinction has a positive correlation with relative humidity, the effect of hygroscopic growth was analyzed at the two different wavelengths. As hygroscopic growth of the particles proceeded, the longer wavelength became more sensitive to the large particles, and vice versa. The hygroscopic growth factor and the Angstrom exponent showed a negative correlation, and the correlation coefficients at 532 nm and 910 nm were 0.54 and 0.86, respectively. The accumulation mode particles were more stable through time than the coarse mode particles, and the variation of the coarse mode particles coincided well with the variation of the Angstrom exponent from the two ceilometers

Application of M5 Model Tree in Passive Remote Sensing of Thin Ice Cloud Microphysical Properties in Terahertz Region

This article presents a new method for retrieving the Ice Water Path (IWP), the median volume equivalent sphere diameter (Dme) of thin ice clouds (IWP < 100 g/m2, Dme < 80 μm) in the Terahertz band. The upwelling brightness temperature depressions caused by the ice clouds at 325.15, 448.0, 664.0 and 874.0 GHz channels are simulated by the Atmospheric Radiative Transfer Simulator (ARTS). The simulated forward radiative transfer models are taken as historical data for the M5 model tree algorithm to construct a set of piecewise functions which represent the relation of simulated brightness temperature depressions and IWP. The inversion results are optimized by an empirical relation of the IWP and the Dme for thin ice clouds which is summarized by previous studies. We inverse IWP and Dme with the simulated brightness temperature and analyze the inversion performance of selected channels. The 874.4 ± 6.0 GHz channel provides the most accurate results, because of the strong brightness temperature response to the change of IWP in the forward radiative transfer model. In order to improve the thin ice clouds IWP and Dme retrieval accuracy at the middle-high frequency channels in Terahertz band, a dual-channel inversion method was proposed that combines the 448.0± 3.0 GHz and 664.0 ± 4.2 GHz channel. The error analysis shows that the results of the 874.4 ± 6.0 GHz channel and the dual-channel inversion are reliable, and the IWP inversion results meet the error requirement range proposed by previous studies

Multi-Year Comparison of CO2 Concentration from NOAA Carbon Tracker Reanalysis Model with Data from GOSAT and OCO-2 over Asia

Accurate knowledge of the carbon budget on global and regional scales is critically important to design mitigation strategies aimed at stabilizing the atmospheric carbon dioxide (CO2) emissions. For a better understanding of CO2 variation trends over Asia, in this study, the column-averaged CO2 dry air mole fraction (XCO2) derived from the National Oceanic and Atmospheric Administration (NOAA) CarbonTracker (CT) was compared with that of Greenhouse Gases Observing Satellite (GOSAT) from September 2009 to August 2019 and with Orbiting Carbon Observatory 2 (OCO-2) from September 2014 until August 2019. Moreover, monthly averaged time-series and seasonal climatology comparisons were also performed separately over the five regions of Asia; i.e., Central Asia, East Asia, South Asia, Southeast Asia, and Western Asia. The results show that XCO2 from GOSAT is higher than the XCO2 simulated by CT by an amount of 0.61 ppm, whereas, OCO-2 XCO2 is lower than CT by 0.31 ppm on average, over Asia. The mean spatial correlations of 0.93 and 0.89 and average Root Mean Square Deviations (RMSDs) of 2.61 and 2.16 ppm were found between the CT and GOSAT, and CT and OCO-2, respectively, implying the existence of a good agreement between the CT and the other two satellites datasets. The spatial distribution of the datasets shows that the larger uncertainties exist over the southwest part of China. Over Asia, NOAA CT shows a good agreement with GOSAT and OCO-2 in terms of spatial distribution, monthly averaged time series, and seasonal climatology with small biases. These results suggest that CO2 can be used from either of the datasets to understand its role in the carbon budget, climate change, and air quality at regional to global scales