The ESA MIPAS/Envisat level2-v8 dataset: 10 years of measurements retrieved with ORM v8.22

The observations acquired during the full mission of the Michelson Interferometer for Passive Atmospheric Sounding (MIPAS) instrument, aboard the European Space Agency Environmental Satellite (Envisat), have been analysed with version 8.22 of the Optimised Retrieval Model (ORM), originally developed as the scientific prototype of the ESA level-2 processor for MIPAS observations. The results of the analyses have been included into the MIPAS level-2 version 8 (level2-v8) database containing atmospheric fields of pressure, temperature, and volume mixing ratio (VMR) of MIPAS main targets H2O, O3, HNO3, CH4, N2O, and NO2, along with the minor gases CFC-11, ClONO2, N2O5, CFC-12, COF2, CCl4, CF4, HCFC-22, C2H2, CH3Cl, COCl2, C2H6, OCS, and HDO. The database covers all the measurements acquired by MIPAS in the nominal measurement mode of the full resolution (FR) part of the mission (from July 2002 to March 2004) and all the observation modes of the optimised resolution (OR) part (from January 2005 to April 2012). The number of species included in the MIPAS level2-v8 dataset makes it of particular importance for the studies of stratospheric chemistry. The database is considered by ESA the final release of the MIPAS level-2 products. The ORM algorithm is operated at the vertical grid coincident to the tangent altitudes of the observations or to a subset of them, spanning (in the nominal mode) the altitude range from 6 to 68 km in the FR phase and from 6 to 70 km in the OR period. In the latitude domain, FR profiles are spaced by about 4.7∘, while the OR profiles are spaced by about 3.7∘. For each retrieved species, the auxiliary data and the retrieval choices are described. Each product is characterised in terms of the retrieval error, spatial resolution, and “useful” vertical range in both phases of the MIPAS mission. These depend on the characteristics of the measurements (spectral and vertical resolution of the measurements), the retrieval choices (number of spectral points included in the analyses, number of altitudes included in the vertical retrieval grid), and the information content of the measurements for each trace species. For temperature, water vapour, ozone, and nitric acid, the number of degrees of freedom is significantly larger in the OR phase than in the FR one, mainly due to the finer vertical measurement grid. In the FR phase, some trace species are characterised by a smaller retrieval error with respect to the OR phase, mainly due to the larger number of spectral points used in the analyses, along with the reduced vertical resolution. The way of handling possible caveats (negative VMR, vertical grid representation) is discussed. The quality of the retrieved profiles is assessed through four criteria, two providing information on the successful convergence of the retrieval iterations, one on the capability of the retrieval to reproduce the measurements, and one on the presence of outliers. An easy way to identify and filter the problematic profiles with the information contained in the output files is provided. MIPAS level2-v8 data are available to the scientific community through the ESA portal (https://doi.org/10.5270/EN1-c8hgqx4).

Scan strategies for wind profiling with Doppler lidar – An LES-based evaluation

Lidar scan techniques for wind profiling rely on the assumption of a horizontally homogeneous wind field and stationarity for the duration of the scan. As this condition is mostly violated in reality, detailed knowledge of the resulting measurement error is required. The objective of this study is to quantify and compare the expected error associated with Doppler-lidar wind profiling for different scan strategies and meteorological conditions by performing virtual measurements implemented in a large-eddy simulation (LES) model. Various factors influencing the lidar retrieval error are analyzed through comparison of the wind measured by the virtual lidar with the ‘true’ value generated by the LES. These factors include averaging interval length, zenith angle configuration, scan technique and instrument orientation. For the first time, ensemble simulations are used to determine the statistically expected uncertainty of the lidar error. The analysis reveals a root-mean-square deviation (RMSD) of less than 1 m s−1 for 10 min averages of wind speed measurements in a moderately convective boundary layer, while RMSD exceeds 2 m s−1 in strongly convective conditions. Unlike instrument orientation and scanning scheme, the zenith angle configuration proved to have significant effect on the retrieval error. Horizontal wind speed error is reduced when a larger zenith angle configuration is used, but is increased for measurements of vertical wind. Results suggest that the scan strategy has a relevant effect on the lidar retrieval error and that instrument configuration should be chosen depending on the quantity of interest and the flow conditions in which the measurement is performed.

Distributed neural systems enable flexible attention updating during category learning

In order to accurately categorize novel items, humans learn to selectively attend to stimulus dimensions that are most relevant to the task. Models of category learning describe the interconnected cognitive processes that contribute to selective attention as observations of stimuli and category feedback are progressively acquired. The Adaptive Attention Representation Model (AARM), for example, provides an account whereby categorization decisions are based on the perceptual similarity of a new stimulus to stored exemplars, and dimension-wise attention is updated on every trial in the direction of a feedback-based error gradient. As such, attention modulation as described by AARM requires interactions among orienting, visual perception, memory retrieval, error monitoring, and goal maintenance in order to facilitate learning across trials. The current study explored the neural bases of attention mechanisms using quantitative predictions from AARM to analyze behavioral and fMRI data collected while participants learned novel categories. GLM analyses revealed patterns of BOLD activation in the parietal cortex (orienting), visual cortex (perception), medial temporal lobe (memory retrieval), basal ganglia (error monitoring), and prefrontal cortex (goal maintenance) that covaried with the magnitude of model-predicted attentional tuning. Results are consistent with AARM’s specification of attention modulation as a dynamic property of distributed cognitive systems.

Impact of 3D Cloud Structures on the Atmospheric Trace Gas Products from UV-VIS Sounders – Part I: Synthetic dataset for validation of trace gas retrieval algorithms

Retrievals of trace gas concentrations from satellite observations are mostly performed for clear regions or regions with low cloud coverage. However, even fully clear pixels can be affected by clouds in the vicinity, either by shadowing or by scattering of radiation from clouds in the clear region. Quantifying the error of retrieved trace gas concentrations due to cloud scattering is a difficult task. One possibility is to generate synthetic data by three-dimensional (3D) radiative transfer simulations using realistic 3D atmospheric input data, including 3D cloud structures. Retrieval algorithms may be applied on the synthetic data and comparison to the known input trace gas concentrations yields the retrieval error due to cloud scattering. In this paper we present a comprehensive synthetic dataset which has been generated using the Monte Carlo radiative transfer model MYSTIC. The dataset includes simulated spectra in two spectral ranges (400–500 nm and the O2A-band from 755–775 nm). Moreover it includes layer air mass factors (layer-AMF) calculated at 460 nm. All simulations are performed for a fixed background atmosphere for various sun positions, viewing directions and surface albedos. Two cloud setups are considered: The first includes simple box-clouds with various geometrical and optical thicknesses. This can be used to systematically investigate the sensitivity of the retrieval error on solar zenith angle, surface albedo and cloud parameters. Corresponding 1D simulations are also provided. The second includes realistic three-dimensional clouds from an ICON large eddy simulation (LES) for a region covering Germany and parts of surrounding countries. The scene includes cloud types typical for central Europe such as shallow cumulus, convective cloud cells, cirrus, and stratocumulus. This large dataset can be used to quantify the trace gas concentration retrieval error statistically. Along with the dataset the impact of horizontal photon transport on reflectance spectra and layer-AMFs is analyzed for the box-cloud scenarios. Moreover, the impact of 3D cloud scattering on the NO2 vertical column density (VCD) retrieval is presented for a specific LES case. We find that the retrieval error is largest in cloud shadow regions, where the NO2 VCD is underestimated by more than 20 %. The dataset is available for the scientific community to assess the behavior of trace gas retrieval algorithms and cloud correction schemes in cloud conditions with 3D structure.

The ESA MIPAS/ENVISAT Level2-v8 dataset: 10 years of measurements retrieved with ORM v8.22

The observations acquired during the full mission of the Michelson Interferometer for Passive Atmospheric Sounding (MIPAS) instrument, on board the European Space Agency ENVISAT satellite, have been analysed with version 8.22 of the Optimised Retrieval Model (ORM), originally developed as the scientific prototype of the ESA level 2 processor for MIPAS observations. The results of the analyses have been included into the MIPAS level 2 version 8 (level2-v8) database containing atmospheric fields of pressure, temperature and volume mixing ratio of MIPAS main targets H2O, O3, HNO3, CH4, N2O, and NO2, along with the minor gases CFC-11, ClONO2, N2O5, CFC-12, COF2, CCl4, CF4, HCFC-22, C2H2, CH3Cl, COCl2, C2H6, OCS, HDO. The database covers all the measurements acquired by MIPAS in the nominal measurement mode of the Full Resolution (FR) part of the mission (from July 2002 to March 2004) and all the observation modes of the Optimised Resolution (OR) part (from January 2005 to April 2012). The number of species included in the MIPAS level2-v8 data-set makes it of particular importance for the studies of stratospheric chemistry. The database is considered by ESA the final release of the MIPAS level 2 products. The ORM algorithm is operated at the vertical grid coincident to the tangent altitudes of the observations or to a subset of them, spanning (in the nominal mode) the altitude range from 6 to 68 km in the FR phase and from 6 to 70 km in the OR period. In the latitude domain, FR profiles are spaced by about 4.7 degrees while the OR profiles are spaced by about 3.7 degrees. For each retrieved species the auxiliary data and the retrieval choices are described. Each product is characterised in terms of the retrieval error, spatial resolution, and 'useful' vertical range in both phases of the MIPAS mission. These depend on the characteristics of the measurements (spectral and vertical resolution of the measurements), on the retrieval choices (number of spectral points included in the analyses, number of altitudes included in the vertical retrieval grid), and on the information content of the measurements for each trace species. For temperature, water vapour, ozone and nitric acid the number of degrees of freedom is significantly larger in the OR phase than in the FR one, mainly due to the finer vertical measurement grid. In the FR phase some trace species are characterised by a smaller retrieval error with respect to the OR phase, mainly due to the larger number of spectral points used in the analyses, along with the reduced vertical resolution. The way of handling possible caveats (negative VMR, vertical grid representation) is discussed. The quality of the retrieved profiles is assessed through four criteria, two providing information on the successful convergence of the retrieval iterations, one on the capability of the retrieval to reproduce the measurements, and one on the presence of outliers. An easy way to identify and filter the problematic profiles with the information contained in the output files is provided. MIPAS level2-v8 data are available to the scientific community through the ESA portal https://earth.esa.int/eogateway/.

Impact of Aerosol Vertical Distribution on Aerosol Optical Depth Retrieval from Passive Satellite Sensors

When retrieving Aerosol Optical Depth (AOD) from passive satellite sensors, the vertical distribution of aerosols usually needs to be assumed, potentially causing uncertainties in the retrievals. In this study, we use the Moderate Resolution Spectroradiometer (MODIS) and Visible Infrared Imaging Radiometer Suite (VIIRS) sensors as examples to investigate the impact of aerosol vertical distribution on AOD retrievals. A series of sensitivity experiments was conducted using radiative transfer models with different aerosol profiles and surface conditions. Assuming a 0.2 AOD, we found that the AOD retrieval error is the most sensitive to the vertical distribution of absorbing aerosols; a −1 km error in aerosol scale height can lead to a ~30% AOD retrieval error. Moreover, for this aerosol type, ignoring the existence of the boundary layer can further result in a ~10% AOD retrieval error. The differences in the vertical distribution of scattering and absorbing aerosols within the same column may also cause −15% (scattering aerosols above absorbing aerosols) to 15% (scattering aerosols below absorbing aerosols) errors. Surface reflectance also plays an important role in affecting the AOD retrieval error, with higher errors over brighter surfaces in general. The physical mechanism associated with the AOD retrieval errors is also discussed. Finally, by replacing the default exponential profile with the observed aerosol vertical profile by a micro-pulse lidar at the Beijing-PKU site in the VIIRS retrieval algorithm, the retrieved AOD shows a much better agreement with surface observations, with the correlation coefficient increased from 0.63 to 0.83 and bias decreased from 0.15 to 0.03. Our study highlights the importance of aerosol vertical profile assumption in satellite AOD retrievals, and indicates that considering more realistic profiles can help reduce the uncertainties.

Toward high precision XCO2 retrievals from TanSat observations

<p>TanSat is the 1<sup>st</sup> Chinese carbon dioxide measurement satellite, launched in 2016. Preliminary TanSat XCO<sub>2</sub> retrievals have been introduced in previous studies based on the 1.6 m weak CO<sub>2</sub> band. In this study, the University of Leicester Full Physics (UoL-FP) algorithm is implemented for TanSat nadir mode XCO<sub>2</sub> retrievals. We develop a spectrum correction method to reduce the retrieval errors by an online fitting of an 8<sup>th</sup> order Fourier series. The model and a priori is developed by analyzing the solar calibration measurement. This correction provides a significant improvement to the O<sub>2</sub> A band retrieval. Accordingly, we extend the previous TanSat single CO<sub>2</sub> weak band retrieval to a combined O<sub>2</sub> A and CO<sub>2</sub> weak band retrieval. A Genetic Algorithm (GA) has been applied to determine the threshold values of post-screening filters. In total, 18.3% of the retrieved data is identified as high quality compared. The same quality control parameters have been used in the bias correction due to the stronger correlation with the XCO<sub>2</sub> retrieval error. A footprint independent multiple linear regression is applied to determine the sounding XCO<sub>2</sub> retrieval error and bias correction. Twenty sites of the Total Column Carbon Observing Network (TCCON) have been selected to validate our new approach of the TanSat XCO<sub>2</sub> retrieval. We show that our new approach produces a significant improvement of the XCO<sub>2</sub> retrieval accuracy and precision when compared with TCCON with an average bias and RMSE of -0.08 and 1.47 ppm respectively. The methods used in this study, such as continuum correction, can help to improve the XCO<sub>2</sub> retrieval from TanSat and subsequently the Level-2 data production, and hence will be applied in the TanSat operational XCO<sub>2</sub> processing.</p>

Characterisation of the artificial neural network CiPS for cirrus cloud remote sensing with MSG/SEVIRI

Cirrus clouds remain one of the key uncertainties in atmospheric research. To better understand the properties and physical processes of cirrus clouds, accurate large-scale observations from satellites are required. Artificial neural networks (ANNs) have proved to be a useful tool for cirrus cloud remote sensing. Since physics is not modelled explicitly in ANNs, a thorough characterisation of the networks is necessary. In this paper the CiPS (Cirrus Properties from SEVIRI) algorithm is characterised using the space-borne lidar CALIOP. CiPS is composed of a set of ANNs for the cirrus cloud detection, opacity identification and the corresponding cloud top height, ice optical thickness and ice water path retrieval from the imager SEVIRI aboard the geostationary Meteosat Second Generation satellites. First, the retrieval accuracy is characterised with respect to different land surface types. The retrieval works best over water and vegetated surfaces, whereas a surface covered by permanent snow and ice or barren reduces the cirrus detection ability and increases the retrieval errors for the ice optical thickness and ice water path if the cirrus cloud is thin (optical thickness less than approx. 0.3). Second, the retrieval accuracy is characterised with respect to the vertical arrangement of liquid, ice clouds and aerosol layers as derived from CALIOP lidar data. The CiPS retrievals show little interference from liquid water clouds and aerosol layers below an observed cirrus cloud. A liquid water cloud vertically close or adjacent to the cirrus clearly increases the average retrieval errors for the optical thickness and ice water path, respectively, only for thin cirrus clouds with an optical thickness below 0.3 or ice water path below 5.0 g m−2. For the cloud top height retrieval, only aerosol layers affect the retrieval error, with an increased positive bias when the cirrus is at low altitudes. Third, the CiPS retrieval error is characterised with respect to the properties of the investigated cirrus cloud (ice optical thickness and cloud top height). On average CiPS can retrieve the cirrus cloud top height with a relative error around 8 % and no bias and the ice optical thickness with a relative error around 50 % and bias around ±10 % for the most common combinations of cloud top height and ice optical thickness. Similarities with physically based retrieval methods are evident, which implies that even though the retrieval methods differ in the implementation of physics in the model, the retrievals behave similarly due to physical constraints. Finally, we also show that the ANN retrievals have a low sensitivity to radiometric noise in the SEVIRI observations. For optical thickness and ice water path the relative uncertainty due to noise is less than 10 % down to sub-visual cirrus. For the cloud top height retrieval the uncertainty due to noise is around 100 m for all cloud top heights.