ALBERT

Description: Quantification of model uncertainty in aerosol optical thickness retrieval from Ozone Monitoring Instrument (OMI) measurements Atmospheric Measurement Techniques Discussions, 6, 8509-8541, 2013 Author(s): A. Määttä, M. Laine, J. Tamminen, and J. P. Veefkind We study uncertainty quantification in remote sensing of aerosols in the atmosphere with top of the atmosphere reflectance measurements from the nadir-viewing Ozone Monitoring Instrument (OMI). Focus is on the uncertainty in aerosol model selection of pre-calculated aerosol models and on the statistical modelling of the model inadequacies. The aim is to apply statistical methodologies that improve the uncertainty estimates of the aerosol optical thickness (AOT) retrieval by propagating model selection and model error related uncertainties more realistically. We utilise Bayesian model selection and model averaging methods for the model selection problem and use Gaussian processes to model the smooth systematic discrepancies from the modelled to observed reflectance. The systematic model error is learned from an ensemble of operational retrievals. The operational OMI multi-wavelength aerosol retrieval algorithm OMAERO is used for cloud free, over land pixels of the OMI instrument with the additional Bayesian model selection and model discrepancy techniques. The method is demonstrated with four examples with different aerosol properties: weakly absorbing aerosols, forest fires over Greece and Russia, and Sahara dessert dust. The presented statistical methodology is general; it is not restricted to this particular satellite retrieval application.

Description: The Greenhouse Gas Climate Change Initiative (GHG-CCI): comparative validation of GHG-CCI SCIAMACHY/ENVISAT and TANSO-FTS/GOSAT CO 2 and CH 4 retrieval algorithm products with measurements from the TCCON network Atmospheric Measurement Techniques Discussions, 6, 8679-8741, 2013 Author(s): B. Dils, M. Buchwitz, M. Reuter, O. Schneising, H. Boesch, R. Parker, S. Guerlet, I. Aben, T. Blumenstock, J. P. Burrows, A. Butz, N. M. Deutscher, C. Frankenberg, F. Hase, O. P. Hasekamp, J. Heymann, M. De Mazière, J. Notholt, R. Sussmann, T. Warneke, D. Griffith, V. Sherlock, and D. Wunch Column-averaged dry-air mole fractions of carbon dioxide and methane have been retrieved from spectra acquired by the TANSO-FTS and SCIAMACHY instruments on board GOSAT and ENVISAT using a range of European retrieval algorithms. These retrievals have been compared with data from ground-based high-resolution Fourier Transform Spectrometers (FTS) from the Total Carbon Column Observing Network (TCCON). The participating algorithms are the Weighting Function Modified Differential Optical Absorption Spectroscopy (DOAS) algorithm (WFMD, University of Bremen), the Bremen Optimal Estimation DOAS algorithm (BESD, University of Bremen), the Iterative Maximum A Posteriori DOAS (IMAP, Jet Propulsion Laboratory (JPL) and Netherlands Institute for Space Research algorithm (SRON)), the proxy and full-physics versions of SRON's RemoTeC algorithm (SRPR and SRFP respectively) and the proxy and full-physics versions of the University of Leicester's adaptation of the OCO (Orbiting Carbon Observatory) algorithm (OCPR and OCFP respectively). The goal of this algorithm inter-comparison was to identify strengths and weaknesses of the various so-called Round Robin data sets generated with the various algorithms so as to determine which of the competing algorithms would proceed to the next round of the European Space Agency's (ESA) Greenhouse Gas Climate Change Initiative (GHG-CCI) project, which is the generation of the so-called Climate Research Data Package (CRDP), which is the first version of the Essential Climate Variable (ECV) "Greenhouse Gases" (GHG). For CO 2 , all algorithms reach the precision requirements for inverse modelling ( 〈 8 ppb), with only WFMD having a lower precision (4.7 ppm) than the other algorithm products (2.4–2.5 ppm). When looking at the seasonal relative accuracy (SRA, variability of the bias in space and time), none of the algorithms have reached the demanding 〈 0.5 ppm threshold. For CH 4 , the precision for both SCIAMACHY products (50.2 ppb for IMAP and 76.4 ppb for WFMD) fail to meet the 〈 34 ppb threshold, but note that this work focusses on the period after the 2005 SCIAMACHY detector degradation. The GOSAT X CH 4 precision ranges between 18.1 and 14.0 ppb. Looking at the SRA, all GOSAT algorithm products reach the 〈 10 ppm threshold (values ranging between 5.4 and 6.2 ppb). For SCIAMACHY, IMAP and WFMD have a SRA of 17.2 ppb and 10.5 ppb respectively.

Description: The effect of phase partitioning of semivolatile compounds on the measured CCN activity of aerosol particles Atmospheric Measurement Techniques Discussions, 6, 8413-8433, 2013 Author(s): S. Romakkaniemi, A. Jaatinen, A. Laaksonen, A. Nenes, and T. Raatikainen The effect of inorganic semivolatile aerosol compounds on the CCN activity of aerosol particles was studied by using a computational model for a DMT-CCN counter, a cloud parcel model for condensation kinetics and experiments to quantify the modelled results. Concentrations of water vapour and semivolatiles as well as aerosol trajectories in the CCN column were calculated by a computational fluid dynamics model. These trajectories and vapour concentrations were then used as an input for the cloud parcel model to simulate mass transfer kinetics of water and semivolatiles between aerosol particles and the gas phase. Two different questions were studied: (1) how big fraction of semivolatiles is evaporated from particles before activation in the CCN counter? (2) How much the CCN activity can be increased due to condensation of semivolatiles prior to the maximum water supersaturation in the case of high semivolatile concentration in the gas phase? The results show that, to increase the CCN activity of aerosol particles, a very high gas phase concentration (as compared to typical ambient conditions) is needed. We used nitric acid as a test compound. A concentration of several ppb or higher is needed for measurable effect. In the case of particle evaporation, we used ammonium nitrate as a test compound and found that it partially evaporates before maximum supersaturation is reached in the CCN counter, thus causing an underestimation of CCN activity. The effect of evaporation is clearly visible in all supersaturations, leading to an underestimation of the critical dry diameter by 10 to 15 nanometres in the case of ammonium nitrate particles in different supersaturations. This result was also confirmed by measurements in supersaturations between 0.1 and 0.7%.

Description: Spectral Aerosol Extinction Monitoring System (SÆMS): setup, observational products, and comparisons Atmospheric Measurement Techniques Discussions, 6, 8647-8677, 2013 Author(s): A. Skupin, A. Ansmann, R. Engelmann, and H. Baars A Spectral Aerosol Extinction Monitoring System (SÆMS) is presented that allows us to continuously measure the spectral extinction coefficient of atmospheric aerosol particles along an about 2.7 km long optical path at 30–50 m height above ground at Leipzig (51.3° N, 12.4° E), Germany. The fully automated instrument measures the ambient aerosol extinction coefficients from 300–1000 nm. The main goal of SÆMS observations are long-term studies of the relationship between particle extinction and relative humidity from below 40 % to almost 100 %. The setup is presented and observations (a case study and statistical results for 2009) are discussed in terms of time series of 550 nm particle optical depth, Ångström exponent, and particle size distribution retrieved from the spectrally resolved extinction. The SÆMS measurements are compared with simultaneously performed EARLINET lidar, AERONET photometer, and in situ aerosol observations of particle size distribution and related extinction coefficients at the roof of our institute. Consistency between the different measurements is found which corroborates the quality of the SÆMS observations.

Description: Validation of SCIAMACHY O 2 A band cloud heights using Cloudnet radar/lidar measurements Atmospheric Measurement Techniques Discussions, 6, 8603-8645, 2013 Author(s): P. Wang and P. Stammes For the first time two SCIAMACHY O 2 A band cloud height products are validated using ground-based radar/lidar measurements between January 2003 and December 2011. The products are the ESA Level 2 (L2) version 5.02 cloud top height and the FRESCO (Fast Retrieval Scheme for Clouds from the Oxygen A band) version 6 cloud height. The radar/lidar profiles are obtained at the Cloudnet sites of Cabauw and Lindenberg, and are averaged for one hour centered at the SCIAMACHY overpass time to achieve an optimal temporal and spatial match. In total we have about 220 cases of single layer clouds and 200 cases of multi-layer clouds. The FRESCO cloud height and ESA L2 cloud top height are compared with the Cloudnet cloud top height and Cloudnet cloud middle height. We find that the ESA L2 cloud top height has a better agreement with the Cloudnet cloud top height than the Cloudnet cloud middle height. The ESA L2 cloud top height is on average 0.44 km higher than the Cloudnet cloud top height, with a standard deviation of 3.07 km. The FRESCO cloud height is closer to the Cloudnet cloud middle height than the Cloudnet cloud top height. The mean difference between the FRESCO cloud height and the Cloudnet cloud middle height is −0.14 km with a standard deviation of 1.88 km. The SCIAMACHY cloud height products are further compared to the Cloudnet cloud top height and the Cloudnet cloud middle height in 1 km bins. For single layer clouds, the difference between the ESA L2 cloud top height and the Cloudnet cloud top height is less than 1 km for each cloud bin at 3–7 km, which is 24 % percent of the data. The difference between the FRESCO cloud height and the Cloudnet cloud middle height is less than 1 km for each cloud bin at 0–6 km, which is 85 % percent of the data. The results are similar for multi-layer clouds, but the percentage of cases having a bias within 1 km is smaller than for single layer clouds. Since globally about 60 % of all clouds are low clouds and 42 % are single-layer low clouds, we expect that globally for a large percentage of cases the FRESCO cloud height would be close to the cloud middle height.

Description: Characteristics of cloud liquid water path from SEVIRI on the Meteosat Second Generation 2 satellite for several cloud types Atmospheric Measurement Techniques Discussions, 6, 8743-8782, 2013 Author(s): A. Kniffka, M. Stengel, M. Lockhoff, R. Bennartz, and R. Hollmann In this study the temporal and spatial characteristics of liquid water path (LWP) of low, middle level and high clouds are analysed using space-based observations of the Spinning Enhanced Visible and Infrared Imager (SEVIRI) onboard the Meteosat Second Generation 2 (MSG2) satellite. Both geophysical quantities are part of the dataset CLAAS (CLoud property dAtAset using SEVIRI) and are generated by EUMETSAT's Satellite Application Facility on Climate Monitoring (CM SAF). In this article we focus on the statistical properties of LWP retrieved at daylight associated with the individual cloud type. Our results reveal that each cloud type possesses a characteristic LWP distribution. These frequency distributions are constant with time in the entire SEVIRI field of view, but vary for smaller regions like Central Europe. The average LWP is higher over land than over sea, in case of low clouds 15–27% for 2009 and the variance of the frequency distributions is enhanced. Also, the average diurnal cycle of LWP is related to cloud type where most pronounced diurnal variations were detected for middle level clouds. With SEVIRI it is possible to distinguish between intrinsic LWP variability and variations driven by cloud amount. The relative amplitude of the intrinsic diurnal cycle can exceed the cloud amount driven amplitude.

Description: Remote sensing of atmospheric trace gas columns: an efficient approach for regularization and calculation of total column averaging kernels Atmospheric Measurement Techniques Discussions, 6, 4999-5031, 2013 Author(s): T. Borsdorff, O. P. Hasekamp, A. Wassmann, and J. Landgraf A concept is proposed to retrieve the vertical column densities of atmospheric trace gases from remote sensing measurements. It combines the numerical simplicity of a least-squares profile scaling retrieval with the numerically robust calculation of the total column averaging kernel using an analytic expression. The approach enables calculation of the total column averaging kernel on arbitrary vertical grids. Formally, the proposed method is equivalent to Tikhonov regularization of the first kind with an infinite regularization strength. Due to its efficiency it is particularly suited for implementation in operational data processing with high demands on processing time. To demonstrate the method, we apply it to CO column retrieval from simulated measurements in the 2.3 μm spectral region and to O 3 column retrieval from the UV, which represents ideal measurements of a series of space-borne spectrometers like SCIAMACHY, TROPOMI, GOME, and GOME-2. For both spectral ranges, we consider clear-sky and cloudy scenes where clouds are modelled as an elevated Lambertian surface. Here, the smoothing error for the clear-sky and cloudy atmosphere is significant and reaches several percent, depending on the reference profile which is used for scaling. This underlines the importance of the column averaging kernel for a proper interpretation of retrieved column densities. Furthermore, we show that the total column smoothing error is affected by a discretization error when total column averaging kernels are not represented on a fine enough vertical grid. For both retrievals this effect becomes negligible by using a vertical grid with 20–40 equally thick layers between 0 and 50 km.

Description: Mixing layer height retrievals by multichannel microwave radiometer observations Atmospheric Measurement Techniques Discussions, 6, 4971-4998, 2013 Author(s): D. Cimini, F. De Angelis, J.-C. Dupont, S. Pal, and M. Haeffelin The mixing layer height (MLH) is a key parameter for boundary layer studies, including meteorology, air quality, and climate. MLH estimates are inferred from in situ radiosonde measurements or remote sensing observations from instruments like lidar, wind profiling radar, or sodar. Methods used to estimate MLH from radiosonde profiles are also used with atmospheric temperature and humidity profiles retrieved by microwave radiometers (MWR). This paper proposes an alternative approach to estimate MLH from MWR data, based on direct observations (brightness temperatures, Tb) instead of retrieved profiles. To our knowledge, MLH estimates directly from Tb observations has never been attempted before. The method consists of a multivariate linear regression trained with an a priori set of collocated MWR Tb observations (multi-frequency and multi-angle) and MLH estimates from a state-of-the-art lidar system. Results show that the method is able to follow both the diurnal cycle and the day-to-day variability as suggested by the lidar measurements, and also it can detect low MLH values that are below the full overlap limit (~ 200 m) of the lidar system used. Statistics of the comparison between MWR- and reference lidar-based MLH retrievals show mean difference within 10 m, RMS within 340 m, and correlation coefficient higher than 0.77. Monthly mean analysis for day-time MLH from MWR, lidar, and radiosonde shows consistent seasonal variability, peaking at ~ 1200–1400 m in June and decreasing down to ~ 600 m in October. Conversely, night-time monthly mean MLH from all methods are within 300–500 m without any significant seasonal variability. The proposed method provides results that are more consistent with radiosonde estimates than MLH estimates from MWR retrieved profiles. MLH monthly mean values agree well within 1 std with bulk Richardson number method applied at radiosonde profiles at 11:00 and 23:00 UTC. The method described herewith operates continuously and it is expected to work with analogous performances for the entire diurnal cycle, except during considerable precipitation, demonstrating new potential for atmospheric observation by ground-based microwave radiometry.

Description: Stratospheric aerosol particle size information in Odin-OSIRIS limb scatter spectra Atmospheric Measurement Techniques Discussions, 6, 5065-5099, 2013 Author(s): L. A. Rieger, A. E. Bourassa, and D. A. Degenstein The Optical Spectrograph and InfraRed Imaging System (OSIRIS) on-board the Odin satellite has now taken over a decade of limb scatter measurements that have been used to retrieve the Version 5 stratospheric aerosol extinction product. This product is retrieved using a representative particle size distribution to calculate scattering cross sections and scattering phase functions for the forward model calculations. In this work the information content of OSIRIS measurements with respect to stratospheric aerosol is systematically examined for the purpose of retrieving particle size information along with the extinction coefficient. The benefit of using measurements at different wavelengths and scattering angles in the retrieval is studied and it is found that incorporation of the 1530 nm radiance measurement is key for a robust retrieval of particle size information. It is also found that using OSIRIS measurements at different solar geometries simultaneously provides little additional benefit. Based on these results, an improved aerosol retrieval algorithm is developed that couples the retrieval of aerosol extinction and mode radius of a log-normal particle size distribution. Comparison of these results with coincident measurements from SAGE III show agreement in retrieved extinction to within approximately 10% over the bulk of the aerosol layer, which is comparable to Version 5. The retrieved particle size, when converted to Ångström coefficient, shows good qualitative agreement with SAGE II measurements made at somewhat shorter wavelengths.

Description: SAGE version 7.0 algorithm: application to SAGE II Atmospheric Measurement Techniques Discussions, 6, 5101-5171, 2013 Author(s): R. P. Damadeo, J. M. Zawodny, L. W. Thomason, and N. Iyer This paper details the SAGE version 7.0 algorithm and how it is applied to SAGE II. Changes made between the previous (v6.2) and current (v7.0) versions are described and their impacts on the data products explained for both coincident event comparisons and time-series analysis. Users of the data will notice a general improvement in all of the SAGE II data products, which are now in better agreement with more modern data sets (e.g. SAGE III) and more robust for use with trend studies.