Conference Agenda

In August 2018, ESA’s Earth Explorer mission Aeolus has been successfully launched to space. Since then Aeolus has been demonstrating its capability to accurately measure atmospheric wind profiles from the ground to the lower stratosphere on a global scale deploying the first ever satellite borne wind lidar system ALADIN (Atmospheric Laser Doppler Instrument).

In order to identify and correct the systematic error sources, enhance the performance of ALADIN and the data quality of the wind products, several calibration and validation campaigns were implemented.

In the aspect of ALADIN calibration, the ALADIN laser frequency stability and its impact on wind measurement was assessed and the correction of wind bias for ALADIN using telescope temperatures was conducted. By monitoring the ALADIN laser frequency over more than 2 years, excellent frequency stability with pluse-to-pluse variations of about 10MHz (root mean square) is evident despite the permanent occurrence of short periods with significantly enhanced frequency noise (> 30 MHz). Analysis of the Aeolus wind error with respect to European Centre for Medium-Range Weather Forecasts (ECMWF) model winds shows that the temporally degraded frequency stability of the ALADIN laser transmitter has only a minor influence on the wind data quality on a global scale, which is primarily due to the small percentage of wind measurements for which the frequency fluctuations are considerably enhanced. Another systematic error source is related to small fluctuations of the temperatures across the 1.5 m diameter primary mirror of the telescope which cause varying wind biases along the orbit of up to 8 m s⁻¹. It was shown that the telescope temperature variations along the orbit are due to changes in the top-of-atmosphere reflected shortwave and outgoing longwave radiation of the Earth and the related response of the telescope's thermal control system. To correct for this effect ECMWF model-equivalent winds are used as a reference to describe the wind bias in a multiple linear regression model as a function of various temperature sensors located on the primary telescope mirror. In cases where the influence of the temperature variations is particularly strong it was shown that the bias correction can improve the orbital bias variation by up to 53 %.

Shortly after the launch of Aeolus, co-located airborne wind lidar observations, which employed a prototype of the satellite instrument – the ALADIN (Atmospheric LAser Doppler INstrument) Airborne Demonstrator (A2D), were performed in central Europe, meanwhile ground-based coherent Doppler wind lidars (CDLs) net was established over China, to verify the wind observations from Aeolus. In the first airborne validation campaign after the launch and still during the commissioning phase, four coordinated flights along the satellite swath were conducted in late autumn of 2018, yielding wind data in the troposphere with high coverage of the Rayleigh channel. Owing to the different measurement grids and LOS viewing directions of the satellite and the airborne instrument, intercomparison with the Aeolus wind product requires adequate averaging as well as conversion of the measured A2D LOS wind speeds to the satellite LOS (LOS*). The statistical comparison of the two instruments shows a positive bias (of 2.6 m s⁻¹) of the Aeolus Rayleigh winds (measured along its LOS*) with respect to the A2D Rayleigh winds as well as a standard deviation of 3.6 m s⁻¹. In China, by the simultaneous wind measurements with CDLs at 17 stations, the Rayleigh-clear and Mie-cloudy horizontal-line-of-sight (HLOS) wind velocities from Aeolus in the atmospheric boundary layer and the lower troposphere are compared with those from CDLs. Overall, 52 simultaneous Mie-cloudy comparison pairs and 387 Rayleigh-clear comparison pairs from this campaign are acquired. It is found that the standard deviation, the scaled MAD and the bias on ascending tracks are lower than those on descending tracks. From the comparison results of respective Baselines, marked misfits between the wind data from Aeolus Baselines 07 and 08 and wind data from CDLs in the atmospheric boundary layer and the lower troposphere are found. With the continuous calibration and validation and product processor updates, the performances of Aeolus wind measurements under Baselines 09 and 10 and Baseline 11 are improved significantly. Considering the influence of turbulence and convection in the atmospheric boundary layers and the lower troposphere, higher values for the vertical velocity are common in this region. Hence, as a special note, the vertical velocity could impact the HLOS wind velocity retrieval from Aeolus.

Aeolus has the capability to measurement wind profiles and aerosol optical properties profiles synchronously, which provide the possibility of the observation of aerosol transport and advection. Based on the observation of ALADIN, combined with the data of CALIOP (Cloud-Aerosol Lidar with Orthogonal Polarization), ECMWF (European Centre for Medium-Range Forecasts) and HYSPLIT (Hybrid Single-Particle Lagrangian Integrated Trajectory model), a long-term large-scale Saharan dust transport event which occurred between 14 and 27 June 2020 is tracked and the possibility of calculating the dust mass advection is explored. The quasi-synchronization observation results of 15, 16, 19, 24 and 27 June by ALADIN and CALIOP during the entire transport process show good agreement with the Dust Score Index data and the HYSPLIT trajectories, which indicates that the transport process of the same dust event is tracked by ALADIN and CALIOP, verifies that the dust transport spent around 2 weeks from the emission to the deposition and achieved the respective observations of this dust event's emission phase, development phase, transport phase, descent phase and deposition phase.

Global observations of column carbon dioxide concentrations and aerosol optical properties profiles are important for climate study and environment monitoring which is why China decided to implement the lidar mission ACDL (Aerosol and Carbon dioxide Detection Lidar) to measure CO2 and aerosol from space – has been launched to space successfully on 16 April 2022. The commissioning phase of ACDL is scheduled to be 6 months, during which the calibration and validation campaigns are implemented and the retrieval algorithms of column carbon dioxide concentration and aerosol optical properties profiles are improved. It is expected that with the calibrations and validations of ACDL and the updates of retrieval algorithms, the products of ACDL will be accurate and robust for science applications.

The main scientific objectives concern the monitoring of the quality of the French and Chinese coastal waters using OLCI and COCTS/CZI space-borne sensors. The project is divided into different tasks: (1) Characterization of uncertainty of OLCI and COCTS/CZI ocean color products in coastal waters; (2) Development of novel regional EO datasets in coastal waters. The first task aims at evaluating the atmospheric correction and bio-optical algorithms of OLCI and COCTS/CZI in our two areas of interest using in-situ measurements collected by both teams and the second task aims at developing regional bio-optical algorithms for the Chinese/French coastal waters according to specific spectral configuration of COCTS and OLCI.

In the last year, even though COVID-19 hindered certain field work and reduced physical contact, in-situ atmospheric and oceanic optical data has been continuously collected in both Chinese and European coastal waters, e.g., the Bohai and East China Sea, the English Channel and Cabo Verde, thanks to field campaign and also permanent observing systems including several AERONET-OC sites. Level 2 products of OLCI/Sentinel 3 as well as those of COCTS/HY-1 were comprehensively validated by in-situ measurements. In-situ data are well quality controlled. Also, depending on local cross time, these L2 products in above-mentioned regions are compared with those provided by MODIS onboard AQUA annd TERRA, and VIIRS onboard SNPP and NOAA 20 satellites correspondingly. Temporal and spatial match-up follows protocols commonly accepted by the ocean color community.

In this report, detailed validation results will be presented, which give an overall quality assessment of operational ocean color products in the Chinese and European coastal waters. Uncertainty patterns will be analyzed and compared among different water mass. Suggestion regarding to improvement of these products are finally recommended.

Land Surface Temperature (LST) is one of the main quantities governing the energy exchange between surface and atmosphere. On the extensive Tibetan Plateau (TP), where in-situ observations are usually extremely sparse, accurate knowledge of the land surface energy balance is crucial for understanding and simulating regional processes of meteorology, hydrology and ecology. More specifically, all-weather LST products are required for accurately simulating soil heat transfer, which provides insights into changes in TP permafrost / seasonally frozen ground and regional climate change. However, LST products based on thermal infrared (TIR) remote sensing are limited to clear sky conditions. Within the Dragon 5 project ‘All-weather land surface temperature at high spatial resolution: validation and applications’, two recently developed all-weather satellite LST products are compared against in-situ measurements from LST validation stations and LST extracted from ERA5-Land data provided by the Copernicus Climate Change Service (C3S).

The two LST satellite products investigated here provide (nearly) gap-free all-weather LST and are based on two different retrieval approaches: 1) reanalysis and thermal infrared remote sensing merging (RTM) (Zhang et al., 2021), the idea of which is the temporal component decomposition method for merging TIR LST with passive microwave (PMW) LST (Zhang et al., 2019) and 2) merging of clear-sky MSG/SEVIRI LST with the surface temperature of a Soil-Vegetation-Atmosphere (SVAT) model (Martins et al., 2019). The in-situ LST for validating these two LST products are obtained from radiometric measurement obtained at the permanent LST validation sites ‘KIT-Forest’ (mixed forest; Germany), ‘Lake Constance’ (water surface; Germany - Switzerland), ‘Evora’ (cork oak tree forest; Portugal) and ‘Gobabeb’ (gravel plains; Namibia). For years 2019 to 2021 the research teams generated all-weather LST products over Europe and Africa and extracted spatial subsets centred on the four validation stations (51 x 51 pixel for 1 km satellite data; 11 x 11 pixel for 5 km satellite data). In order to ease LST product inter-comparisons and validations, all data, i.e. satellite LST and in-situ LST, are transformed into netCDF format before they are spatially and temporally matched.

The presentation provides a brief summary of the two all-weather LST retrieval algorithms, gives an overview of the progress made within the project so far and presents and discusses some examples of the two all-weather LST satellite data sets. Validation results obtained over the four validation stations will be presented and observed differences between the two all-weather LST data sets as well as their respective spatial variability over the validation sites will be discussed. Some examples of the first all-weather LST product, e.g. urban heat island analysis and surface evapotranspiration will also be presented.

References:
Martins, J. P. A., Trigo, I. F., Ghilain, N., Jimenez, C., Göttsche, F.-M., Ermida, S. L., Olesen, F.-S., Gellens-Meulenberghs, F., and Arboleda, A. (2019), An All-Weather Land Surface Temperature Product Based on MSG/SEVIRI Observations. Remote Sensing, vol. 11, no. 24, pp. 3044, doi: 10.3390/rs11243044

Zhang, X., Zhou, J., Gottsche, F.-M., Zhan, W., Liu, S., and Cao, R. (2019), A Method Based on Temporal Component Decomposition for Estimating 1-km All-Weather Land Surface Temperature by Merging Satellite Thermal Infrared and Passive Microwave Observations. IEEE Transactions on Geoscience and Remote Sensing, vol. 57, no. 7, pp. 4670 – 4691, doi: 10.1109/tgrs.2019.2892417

Zhang, X., Zhou, J., Liang, S., and Wang, D. (2021). A practical reanalysis data and thermal infrared remote sensing data merging (RTM) method for reconstruction of a 1-km all-weather land surface temperature. Remote Sensing of Environment, vol. 260, 112437, doi: 10.1016/j.rse.2021.112437