Planetary boundary layer height variability over Athens, Greece, based on the synergy of Raman lidar and radiosonde data: application of the Kalman filter and other techniques (2011-2016)

The temporal evolution of the Planetary Boundary Layer height over Athens, Greece for a 5-year period (2011-2016) is presented. Using the EOLE Raman lidar system, the range-corrected lidar signals were selected around 12:00 UTC and 00:00 UTC for a total of 332 cases (165 days and 167 nights). The Kalman filter and other techniques were used to determine PBL height. The mean PBL height was found to be around 1617±324 m (12:00 UTC) and 892±130 m (00:00 UTC).Peer ReviewedPostprint (published version

Retrieval of Cloud Properties for the Copernicus Atmospheric Missions Sentinel-4 (S4) and TROPOMI / Sentinel-5 Precursor (S5P) using deep neural networks

Due to their fast computational performance and accuracy, neural networks are nowadays commonly used in the context of remote sensing. The issue of performance is especially important in the context of big data and near-real-time (NRT) operational processing. Classical retrieval algorithms typically use a radiative transfer model (RTM) as a forward model to solve the inverse problem of inferring the quantities of interest from the measured spectra. However, these RTMs are often computationally very expensive and therefore replacing them by a NN is desirable to increase performance. But the application of NNs is not straightforward and there are at least two main approaches: 1. NNs used as forward model, where a NN accurately approximates the radiative transfer model and can thus replace it in the inversion algorithm 2. NNs for solving the inverse problem, where a NN is trained to infer the atmospheric parameters from the measurement directly The first approach is more straightforward to apply. However, the inversion algorithm still faces many challenges, as the spectral fitting problem is generally ill-posed. Therefore, local minima are possible and the results often depend on the selection of the a-priori values for the retrieval parameters. For the second case, some of these issues can be avoided: no a-priori values are necessary, and as the training of the NN is performed globally, i.e. for many training samples at once, this approach is potentially less affected by local minima. However, due to the black-box nature of a NN, no indication about the quality of the results is available. In order to address this issue, novel methods like Bayesian neural networks (BNNs) or invertible neural networks (INNs) have been presented in recent years. This allows the characterization of the retrieved values by an estimate of uncertainty describing a range of values that are probable to produce the observed measurement. We apply and evaluate both approaches for the retrieval of cloud properties and consider their potential as operational algorithms for current (Sentinel-5P) and future (Sentinel-4) Copernicus atmospheric composition missions

Distinguishing between cloud and aerosol layers in the TROPOMI/Sentinel-5P measurements

TROPOMI on board of Sentinel-5 Precursor (S5P) provides continuous daily distribution of several cloud properties, which are required as input for trace-gas retrievals. The operational TROPOMI cloud retrieval is a two-step algorithm. At first, the OCRA (Optical Cloud Recognition Algorithm) computes a radiometric cloud fraction using a broad-band UV/VIS color space approach and later the ROCINN (Retrieval of Cloud Information using Neural Networks) retrieves the cloud height, cloud optical thickness and cloud albedo from NIR measurements in and around the oxygen A-band (~760nm). Within the ROCINN algorithm two different models are possible; the Clouds-as-Reflecting-Boundaries (CRB), where the cloud is a simple Lambertian reflector and the Clouds-as-Layers (CAL), where the cloud is a homogeneous layer of scattering liquid-water spherical particles. There is evidence that some TROPOMI cloud retrievals are contaminated by aerosols. This is particularly true in the following cases: (a) when there is co-existence of clouds and aerosols in the same TROPOMI footprint and (b) when there is a pure aerosol layer, appearing in the TROPOMI cloud product. The latter is usually the case of OCRA deriving an elevated radiometric cloud fraction corresponding to the given aerosol conditions. Then, ROCINN is triggered and returns two additional cloud parameters. Often, the false alarms of elevated OCRA cloud fraction can be identified when ROCINN retrieves a cloud height at the surface level. However, there are cases in which ROCINN cloud outputs do not refer to the surface properties of the scene, but to aerosol layers present in the same TROPOMI footprint. Especially for dust aerosols, which are usually large particles and comparable to the cloud droplet size, we expect more frequently those mixed retrievals. In particular, dust layers with large concentrations (i.e., high aerosol optical depth (AOD)) are better candidates for erroneously retrieved clouds in the TROPOMI L2 product. The TROPOMI aerosol algorithm (TropOMAER) makes use of the L1b reflectances in the UV to derive aerosol information in cloud-free and above-cloud aerosol scenes. With the use of ground-based active and passive remote sensing instruments, we are able to characterize well the vertically resolved cloud and aerosol layers in the lower troposphere. In this work, synergistic ground-based measurements from a PollyXT multiwavelength-Raman-polarization lidar and an AERONET sun-photometer are used to discriminate dust aerosols from clouds in TROPOMI measurements. We have selected ground-based observation sites over which the atmospheric column frequently contains large contributions of desert dust particles

Vertical Profiles of Aerosol Optical and Microphysical Properties During a Rare Case of Long-range Transport of Mixed Biomass Burning-polluted Dust Aerosols from the Russian Federation-kazakhstan to Athens, Greece

Multi-wavelength aerosol Raman lidar measurements with elastic depolarization at 532 nm were combined with sun photometry during the HYGRA-CD campaign over Athens, Greece, on May-June 2014. We retrieved the aerosol optical [3 aerosol backscatter profiles (baer) at 355-532-1064 nm, 2 aerosol extinction (aaer) profiles at 355-532 nm and the aerosol linear depolarization ratio (δ) at 532 nm] and microphysical properties [effective radius (reff), complex refractive index (m), single scattering albedo (ω)]. We present a case study of a long distance transport (~3.500-4.000 km) of biomass burning particles mixed with dust from the Russian Federation-Kazakhstan regions arriving over Athens on 21-23 May 2014 (1.7-3.5 km height). On 23 May, between 2-2.75 km we measured mean lidar ratios (LR) of 35 sr (355 nm) and 42 sr (532 nm), while the mean Ångström exponent (AE) aerosol backscatter-related values (355nm/532nm and 532nm/1064nm) were 2.05 and 1.22, respectively; the mean value of δ at 532 nm was measured to be 9%. For that day the retrieved mean aerosol microphysical properties at 2-2.75 km height were: reff=0.26 μm (fine mode), reff=2.15 μm (coarse mode), m=1.36+0.00024i, ω=0.999 (355 nm, fine mode), ω=0.992(355 nm, coarse mode), ω=0.997 (532 nm, fine mode), and ω=0.980 (532 nm, coarse mode)

Comparison of Cloud Parameters from GOME-2 and Assessment of Cloud Impact on Tropospheric NO2 and HCHO Retrievals

In recent decades, there has been an increasing interest in making use of satellite measurements for identifying trends in atmospheric composition and climate. Instruments like GOME-2 and TROPOMI are dedicated to air-quality and global trace gas monitoring. For the accurate retrieval of columnar information of the trace gases, cloud correction is necessary. This work is meant to examine the quality of the GOME-2 operational cloud product from AC SAF and to propose enhancements of the current dataset to improve the retrieval of the NO2 and HCHO tropospheric gases

A tropospheric NO2 research product from TROPOMI for air quality applications in Europe

This study focuses on a tropospheric NO₂ research product from TROPOMI measurements over Europe based on an improved retrieval algorithm. We present an overview of the DLR NO₂ algorithm and validation with ground-based measurements. In addition, the use of TROPOMI tropospheric NO₂ columns for air quality purposes in Europe will be discussed. The DLR NO₂ retrieval algorithm for TROPOMI consists of mainly three steps: (1) the spectral fitting of the slant column based on the differential optical absorption spectroscopy (DOAS) method, (2) the separation of stratospheric and tropospheric contributions, and (3) the conversion of the slant column to a vertical column using an air mass factor (AMF) calculation. To calculate the NO₂ slant columns, a 405-465 nm fitting window is applied in the DOAS fit for consistency with other NO₂ retrievals from OMI and TROPOMI. Absorption cross-sections of interfering species and a linear intensity offset correction are applied. The stratospheric NO₂ columns are estimated using a directionally dependent STRatospheric Estimation Algorithm from Mainz (DSTREAM) method to correct for the dependency of the stratospheric NO₂ on the viewing geometry. For AMF computation, the climatological OMI surface albedo database is replaced by the geometry-dependent effective Lambertian equivalent reflectivity (GE_LER) and directionally dependent (DLER) data obtained from TROPOMI measurements with higher spatial resolution. As surface albedo is an important parameter for accurate retrieval of trace gas columns, the effect of surface albedo in TROPOMI NO₂ retrieval was investigated by comparing results applying different surface albedo datasets. Mesoscale-resolution a priori NO₂ profiles obtained from the regional chemistry transport model POLYPHEMUS/DLR and LOTOS-EUROS are used. The cloud correction in this TROPOMI NO₂ retrieval is improved using the Clouds-As-Layers (CAL) model from the ROCINN cloud algorithm which is more representative of the real situation than the Clouds-As-Reflecting-Boundaries (CRB) model. Validation of the TROPOMI tropospheric NO₂ columns is performed by comparisons with ground-based MAX-DOAS measurements at nine European stations with urban/suburban conditions. The improved DLR tropospheric NO₂ product shows a similar seasonal variation and good agreement with MAX-DOAS measurements. In particular, the retrievals applying a priori NO₂ profiles from the regional model with a high spatial resolution and recent emission inventory improve an underestimation in TROPOMI tropospheric NO₂ columns in polluted urban areas. Finally, we present the use of the TROPOMI tropospheric NO₂ research product in the regional POLYPHEMUS and LOTOS-EUROS chemistry transport models to analyse the effect of traffic emission on air quality in Germany with the framework of the S-VELD project