A principal component based accurate fast vector radiative transfer model with very high resolution in the UV (200 nm) to NIR (2500 nm) has been developed. This model greatly reduces the number of necessary radiative transfer calculations, and no time-consume convolution process is needed to get the final radiance spectrum. The error in the obtained Stokes component is usually smaller than 0.1% and is much smaller than the uncertainty in the measured solar irradiance.

Knowledge of the spectral behavior of aerosol optical depth (AOD) over the solar spectrum is required for realistic estimates of aerosol radiative forcing. To describe this behavior over an extended period, individual AOD records provided by several co-located ground-based instruments with different design and operation characteristics need to be combined. There are three main challenges associated with such combining; these stem from differences between the instruments in terms of: (1) data quality, (2) continuity, and (3) the measurement wavelengths of each instrument . The first two challenges have been addressed successfully using our approach (Kassianov et al., 2021). Here we demonstrate how the third challenge can be addressed to generate combined AODs at different wavelengths. In particular, we consider generation of the combined AODs at five wavelengths (415, 500, 615, 673, 870 nm) for a 21-year period (1997-2018) using individual AOD records obtained from four instruments deployed at a US continental site. We also discuss wavelength-dependent uncertainties of the generated AODs and future applications of this extended approach for different sets of wavelengths and locations. Kassianov, E., Cromwell, E., Monroe, J. et al. Harmonized and high-quality datasets of aerosol optical depth at a US continental site, 1997–2018. Sci Data 8, 82 (2021). https://doi.org/10.1038/s41597-021-00866-2

In this paper, we will present a Single Field-of-view (FOV) Sounder Atmospheric Product (SiFSAP) and a Climate Fingerprinting Sounder Product (ClimFiSP). Both products are derived from hyperspectral Infrared remote sensors such as Atmospheric Infrared Sounder (AIRS) and Cross-track Infrared Sounder (CrIS). Compared to the current operational AIRS and CrIS level-2 algorithms, the SiFSAP algorithm has 3 advantages, which are listed in the technical review abstract. We have developed a ClimFiSP product, which is derived from spatiotemporally averaged level-1 hyperspectral radiances directly. Again, the ClimFiSP algorithm overcomes many issues associated with traditional level-1 to level-2 and then to level-3 approach. It can be used to derive climate change signals from multiple satellite sensors using consistent radiative kernels and a robust spectral fingerprinting method. We have applied this method to both AIRS and CrIS data and generated decade-long climate data records for atmospheric temperature, water vapor, cloud, trace gases, and surface skin temperature. Both SiFSAP and ClimFiSP are being transitioned to NASA data centers for routine generations of both level-2 and level-3 products.

Biomass burning aerosol play an important role in the Earth’s radiative budget, as it contains a significant amount of black carbon that absorbs solar radiation. The lidar measurements are conducted quasi-continuously over Warsaw at the Remote Sensing Laboratory (RSlab) of the Institute of Geophysics, Faculty of Physics, University of Warsaw since 2013. The measurements performed by the multiwavelength PollyXT lidar system allowed to identify and analyze the cases of biomass burning aerosols inflow over Warsaw from different directions and under different meteorological conditions. The main identified sources of aerosol from biomass combustion over Warsaw are North America and Eastern Europe (Ukraine), whereas the aerosol from North America was observed in the higher parts of the atmosphere, partly having features of stratospheric smoke. The presentation will report the optical properties based on several selected inflow cases, as well as the unique analysis of the fine-scale aerosol microphysical parameters for the selected case.

We present design and first performance results of an airborne differential absorption lidar laser transmitter that can measure CO2 and water isotopes at different wavelengths around 2 µm with the same setup. This laser will be integrated into an airborne lidar, intended to demonstrate future spaceborne instrument characteristics with high-energy (several tens of millijoules nanosecond-pulses) and high optical frequency-stability (less than a few hundreds of kilohertz long-term drift). The transmitter consists of a widely tunable OPO with successive OPA that are pumped by a Nd:YAG MOPA and generates the on- and offline wavelength of the addressed species with narrow bandwidth.

The main objective of this project was to evaluate the potential of a Near Infrared Region (NIR) sensitive video surveillance camera available in the market to detect chemical pollutants in air. Preliminary studies focused on binary liquid systems to ascertain the potential of the NIR-capable video sensor. The camera, operated on a multispectral mode using an external filter wheel, was able to acceptably quantify water concentration in a water/ethanol mixture. The chemometric models build for this data were capable of predicting the water concentration with an adequate precision (R-squared (R2) in a relation of predicted values vs reference values was 0,993). The air pollutants studied were nitrogen oxides (NOx), water as vapor (H2O), nitrogen (N2), acetylene (C2H2) and Ammonia (NH3). We analysed only 9 wavelengths which were selected based on the absorption profile of the identified air pollutants. A typical experimental procedure consisted of capturing band-filtered images of a gas or vapor sample inside a gas cell. Then, the pictures were pre-processed by an algorithm developed during this project, and finally submitted to Principal Component Analysis (PCA), Partial Least Squares Discriminant Analysis (PLS-DA) and linear discriminant analysis (LDA). Validation of the PCA models was tested with independent samples using classification analysis SIMCA. These samples were also used to test the PLS-DA and LDA models. We also had the opportunity to use a commercial short-wave infrared (SWIR) camera, which has higher sensitivity to the NIR region, than the video surveillance camera. This sensor was also successful in the detection and quantification of water in water/acetonitrile mixtures. The chemometric models build for this data were capable of predicting the water concentration with an adequate precision (R-squared (R2) in a relation of predicted values vs reference values was 0,995). Preliminary studies revealed some potential in gas sample discrimination with both video sensors. The project is currently on-going, and, in the future, we expect to develop chemometric models capable of discriminating a set of specific air pollutants.