SCanning Imaging Absorption spectroMeter For Atmospheric CHartographY Data Research Articles

A comparison of OH nightglow volume emission rates as measured by SCIAMACHY and SABER

Abstract. Hydroxyl (OH) short-wave infrared emissions arising from OH(4-2, 5-2, 8-5, 9-6) as measured by channel 6 of the SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY (SCIAMACHY) are used to derive concentrations of OH(v=4, 5, 8, and 9) between 80 and 96 km. Retrieved concentrations are used to simulate OH(5-3, 4-2) integrated radiances at 1.6 µm and OH(9-7, 8-6) at 2.0 µm as measured by the Sounding of the Atmosphere using Broadband Emission Radiometry (SABER) instrument, which are not fully covered by the spectral range of SCIAMACHY measurements. On average, SABER “unfiltered” data are on the order of 40 % at 1.6 µm and 20 % at 2.0 µm larger than the simulations using SCIAMACHY data. “Unfiltered” SABER data are a product, which accounts for the shape, width, and transmission of the instrument's broadband filters, which do not cover the full ro-vibrational bands of the corresponding OH transitions. It is found that the discrepancy between SCIAMACHY and SABER data can be reduced by up to 50 %, if the filtering process is carried out manually using published SABER interference filter characteristics and the latest Einstein coefficients from the HITRAN database. Remaining differences are discussed with regard to model parameter uncertainties and radiometric calibration.

Consistent Nighttime Atomic Oxygen Concentrations From O2A‐band, O(1S) Green‐Line, and OH Airglow Measurements as Performed by SCIAMACHY

AbstractA new atomic oxygen data set was derived from nighttime SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY (SCIAMACHY) O2A‐band measurements. It is compared to atomic oxygen obtained from SCIAMACHY O(1S) green‐line and OH(9‐6)‐band measurements. The three data sets are considered independent of radiometry and, to some extent, methodology. A detailed comparison of atomic oxygen retrieved from these three nightglow measurements is reported for the first time. The agreement of absolute values within 15% supports the current understanding of the photochemistry of the three atomic oxygen proxies involved. As an alternative approach, the O2A‐band model recently proposed by Kalogerakis (2019) is used as well. It is striking that the SCIAMACHY data sets using different atomic oxygen proxies and mostly independent methodology agree much better with each other than with SABER data.

Atomic Oxygen Abundance Retrieved From SCIAMACHY Hydroxyl Nightglow Measurements

AbstractWe present a new atomic oxygen data set based on Scanning Imaging Absorption Spectrometer for Atmospheric CHartographY (SCIAMACHY) hydroxyl short‐wave infrared data. The OH spectra stem from the OH(9‐6) ro‐vibrational transition. These data and atomic oxygen obtained from SCIAMACHY O(1S) green line measurements, which is an independent data set in terms of methodology and radiometry, typically agree within 10–20%. This confirms the corresponding models of OH and O(1S), in particular, the relevance of the reaction between atomic oxygen and ozone in the photochemical model. The agreement between the SCIAMACHY data and the most recent Sounding of the Atmosphere using Broadband Emission Radiometry (SABER) atomic oxygen data set of Mlynczak et al. (2018, https://doi.org/10.1029/2018GL077377) is significantly better than for the previous SABER data set of Mlynczak et al. (2013, https://doi.org/10.1002/jgrd.50401). The agreement decreases if the SCIAMACHY forward model parameters are applied to SABER data. A similar agreement is achieved with the data of Panka et al. (2018, https://doi.org/10.1029/2018GL077677), which was retrieved from SABER data using a principally different retrieval approach.

Evaluation of SCIAMACHY Level-1 data versions using nadir ozone profile retrievals in the period 2003–2011

Abstract. Ozone profile retrieval from nadir-viewing satellite instruments operating in the ultraviolet–visible range requires accurate calibration of Level-1 (L1) radiance data. Here we study the effects of calibration on the derived Level-2 (L2) ozone profiles for three versions of SCanning Imaging Absorption spectroMeter for Atmospheric ChartograpHY (SCIAMACHY) L1 data: version 7 (v7), version 7 with m-factors (v7mfac) and version 8 (v8). We retrieve nadir ozone profiles from the SCIAMACHY instrument that flew on board Envisat using the Ozone ProfilE Retrieval Algorithm (OPERA) developed at KNMI with a focus on stratospheric ozone. We study and assess the quality of these profiles and compare retrieved L2 products from L1 SCIAMACHY data versions from the years 2003 to 2011 without further radiometric correction. From validation of the profiles against ozone sonde measurements, we find that the v8 performs better than v7 and v7mfac due to correction for the scan-angle dependency of the instrument's optical degradation. Validation for the years 2003 and 2009 with ozone sondes shows deviations of SCIAMACHY ozone profiles of 0.8–15 % in the stratosphere (corresponding to pressure range ∼ 100–10 hPa) and 2.5–100 % in the troposphere (corresponding to pressure range ∼ 1000–100 hPa), depending on the latitude and the L1 version used. Using L1 v8 for the years 2003–2011 leads to deviations of ∼ 1–11 % in stratospheric ozone and ∼ 1–45 % in tropospheric ozone. The SCIAMACHY L1 v8 data can still be improved upon in the 265–330 nm range used for ozone profile retrieval. The slit function can be improved with a spectral shift and squeeze, which leads to a few percent residue reduction compared to reference solar irradiance spectra. Furthermore, studies of the ratio of measured to simulated reflectance spectra show that a bias correction in the reflectance for wavelengths below 300 nm appears to be necessary.

Validation of Carbon Monoxide Total Column Retrievals from SCIAMACHY Observations with NDACC/TCCON Ground-Based Measurements

The objective was to validate the carbon monoxide (CO) total column product inferred from Scanning Imaging Absorption Spectrometer for Atmospheric Chartography (SCIAMACHY) full-mission (2003–2011) short-wave infrared (SWIR) nadir observations using the Beer InfraRed Retrieval Algorithm (BIRRA). Globally distributed Network for the Detection of Atmospheric Composition Change (NDACC) and Total Carbon Column Observing Network (TCCON) ground-based (g-b) measurements were used as a true reference. Weighted averages of SCIAMACHY CO observations within a circle around the g-b observing system were utilized to minimize effects due to spatial mismatch of space-based (s-b) and g-b observations, i.e., disagreements due to representation errors rather than instrument and/or algorithm deficiencies. In addition, temporal weighted averages were examined and then the unweighted (classical) approach was compared to the weighted (non-classical) method. The delivered distance-based filtered SCIAMACHY data were in better agreement with respect to CO averages as compared to square-shaped sampling areas throughout the year. Errors in individual SCIAMACHY retrievals have increased substantially since 2005. The global bias was determined to be in the order of − 10 parts per billion in volume (ppbv) depending on the reference network and validation strategy used. The largest negative bias was found to occur in the northern mid-latitudes in Europe and North America, and was partly caused by insufficient a priori estimates of CO and cloud shielding. Furthermore, no significant trend was identified in the global bias throughout the mission. The global analysis of the CO columns retrieved by the BIRRA shows results that are largely consistent with similar investigations in previous works.

Stratospheric CH<sub>4</sub> and CO<sub>2</sub> profiles derived from SCIAMACHY solar occultation measurements

Abstract. Stratospheric profiles of methane (CH4) and carbon dioxide (CO2) have been derived from solar occultation measurements of the SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY (SCIAMACHY). The retrieval is performed using a method called onion peeling DOAS (ONPD), which combines an onion peeling approach with a weighting function DOAS (differential optical absorption spectroscopy) fit in the spectral region between 1559 and 1671 nm. By use of updated pointing information and optimisation of the data selection as well as of the retrieval approach, the altitude range for reasonable CH4 could be broadened from 20 to 40 km to about 17 to 45 km. Furthermore, the quality of the derived CO2 has been assessed such that now the first stratospheric profiles (17–45 km) of CO2 from SCIAMACHY are available. Comparisons with independent data sets yield an estimated accuracy of the new SCIAMACHY stratospheric profiles of about 5–10 % for CH4 and 2–3 % for CO2. The accuracy of the products is currently mainly restricted by the appearance of unexpected vertical oscillations in the derived profiles which need further investigation. Using the improved ONPD retrieval, CH4 and CO2 stratospheric data sets covering the whole SCIAMACHY time series (August 2002–April 2012) and the latitudinal range between about 50 and 70° N have been derived. Based on these time series, CH4 and CO2 trends have been estimated. CH4 trends above about 20 km are not significantly different from zero and the trend at 17 km is about 3 ppbv year−1. The derived CO2 trends show a general decrease with altitude with values of about 1.9 ppmv year−1 at 21 km and about 1.3 ppmv year−1 at 39 km. These results are in reasonable agreement with total column trends for these gases. This shows that the new SCIAMACHY data sets can provide valuable information about the stratosphere.

UTLS water vapour from SCIAMACHY limb measurementsV3.01 (2002-2012).

The SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY (SCIAMACHY) aboard the Envisat satellite provided measurements from August 2002 until April 2012. SCIAMACHY measured the scattered or direct sunlight using different observation geometries. The limb viewing geometry allows the retrieval of water vapour at about 10-25 km height from the near-infrared spectral range (1353-1410 nm). These data cover the upper troposphere and lower stratosphere (UTLS), a region in the atmosphere which is of special interest for a variety of dynamical and chemical processes as well as for the radiative forcing. Here, the latest data version of water vapour (V3.01) from SCIAMACHY limb measurements is presented and validated by comparisons with data sets from other satellite and in situ measurements. Considering retrieval tests and the results of these comparisons, the V3.01 data are reliable from about 11 to 23 km and the best results are found in the middle of the profiles between about 14 and 20 km. Above 20 km in the extra tropics V3.01 is drier than all other data sets. Additionally, for altitudes above about 19 km, the vertical resolution of the retrieved profile is not sufficient to resolve signals with a short vertical structure like the tape recorder. Below 14 km, SCIAMACHY water vapour V3.01 is wetter than most collocated data sets, but the high variability of water vapour in the troposphere complicates the comparison. For 14-20 km height, the expected errors from the retrieval and simulations and the mean differences to collocated data sets are usually smaller than 10 % when the resolution of the SCIAMACHY data is taken into account. In general, the temporal changes agree well with collocated data sets except for the Northern Hemisphere extratropical stratosphere, where larger differences are observed. This indicates a possible drift in V3.01 most probably caused by the incomplete treatment of volcanic aerosols in the retrieval. In all other regions a good temporal stability is shown. In the tropical stratosphere an increase in water vapour is found between 2002 and 2012, which is in agreement with other satellite data sets for overlapping time periods.

Nighttime atomic oxygen in the mesopause region retrieved from SCIAMACHY O(1S) green line measurements and its response to solar cycle variation

AbstractThis paper presents new data sets relating to the abundance of atomic oxygen in the upper mesosphere and lower thermosphere, which were derived from the nighttime green line emission measurements of the SCIAMACHY (Scanning Imaging Absorption Spectrometer for Atmospheric CHartographY) instrument on the European Environmental Satellite (Envisat). These are compared to recently published data sets from the same SCIAMACHY green line measurements through the application of a different photochemical model and to data collected by the Sounding of the Atmosphere using Broadband Emission Radiometry instrument. We find that the retrieved atomic oxygen concentration depends on the choice of the underlying photochemical model. These dependencies explain a large proportion of the differences between recently published data sets. The impact of the 11 year solar cycle on volume emission rates and atomic oxygen abundances was analyzed for various data sets, with the finding that the solar cycle effect varies with the atomic oxygen data set used. The solar cycle impact on the SCIAMACHY data increases with altitude. Above 96 km, it is significantly larger than predicted by Hamburg Model of the Neutral and Ionized Atmosphere. Investigations indicate that these variations are primarily driven by total density compression/expansion variations during the solar cycle, rather than different photolysis rates.

A linear method for the retrieval of sun-induced chlorophyll fluorescence from GOME-2 and SCIAMACHY data

Abstract. Global retrievals of near-infrared sun-induced chlorophyll fluorescence (SIF) have been achieved in the last few years by means of a number of space-borne atmospheric spectrometers. Here, we present a new retrieval method for medium spectral resolution instruments such as the Global Ozone Monitoring Experiment-2 (GOME-2) and the SCanning Imaging Absorption SpectroMeter for Atmospheric CHartographY (SCIAMACHY). Building upon the previous work by Guanter et al. (2013) and Joiner et al. (2013), our approach provides a solution for the selection of the number of free parameters. In particular, a backward elimination algorithm is applied to optimize the number of coefficients to fit, which reduces also the retrieval noise and selects the number of state vector elements automatically. A sensitivity analysis with simulated spectra has been utilized to evaluate the performance of our retrieval approach. The method has also been applied to estimate SIF at 740 nm from real spectra from GOME-2 and for the first time, from SCIAMACHY. We find a good correspondence of the absolute SIF values and the spatial patterns from the two sensors, which suggests the robustness of the proposed retrieval method. In addition, we compare our results to existing SIF data sets, examine uncertainties and use our GOME-2 retrievals to show empirically the relatively low sensitivity of the SIF retrieval to cloud contamination.

Retrieving the availability of light in the ocean utilising spectral signatures of vibrational Raman scattering in hyper-spectral satellite measurements

Abstract. The availability of light in the ocean is an important parameter for the determination of phytoplankton photosynthesis processes and primary production from satellite data. It is also a useful parameter for other applications, e.g. the determination of heat fluxes. In this study, a method was developed utilising the vibrational Raman scattering (VRS) effect of water molecules to determine the number of photons available in the ocean water, which is expressed by the depth integrated scalar irradiance E0. Radiative transfer simulations with the SCIATRAN fully coupled ocean–atmosphere radiative transfer model (RTM) show clearly the relationship of E0 with the strength of the VRS signal measured at the top of the atmosphere (TOA). Taking advantage of VRS structures in hyper-spectral satellite measurements, a retrieval technique to derive E0 in the wavelength region from 390 to 444.5 nm was developed. This approach uses the weighting function differential optical absorption spectroscopy (WF-DOAS) technique, applied to TOA radiances, measured by the Scanning Imaging Absorption Spectrometer for Atmospheric Chartography (SCIAMACHY). Based on the approach of Vountas et al. (2007), where the DOAS method was used to fit modelled spectra of VRS, the method was improved by using the weighting function of VRS (VRS-WF) in the DOAS fit. This was combined with a look-up table (LUT) technique, where the E0 value was obtained for each VRS satellite fit directly. The VRS-WF and the LUT were derived from calculations with the SCIATRAN RTM (Rozanov et al., 2014). RTM simulations for different chlorophyll a concentrations and illumination conditions clearly show that low fit factors of VRS retrieval results correspond to low amounts of light in the water column and vice versa. Exemplarily, 1 month of SCIAMACHY data were processed and a global map of the depth integrated scalar irradiance E0 was retrieved. Spectral structures of VRS were clearly identified in the radiance measurements of SCIAMACHY. The fitting approach led to consistent results and the WF-DOAS algorithm results of VRS correlated clearly with the chlorophyll concentration in case-I water. Comparisons of the diffuse attenuation coefficient, extracted by VRS fit results, with the established GlobColour Kd(490) product show consistent results.

Variability of OH(3–1) emission altitude from 2003 to 2011: Long-term stability and universality of the emission rate–altitude relationship

Vertical volume emission rate profiles of the OH(3–1) Meinel emission near the mesopause are retrieved from nighttime limb-emission observations with the SCIAMACHY (Scanning Imaging Absorption spectroMeter for Atmospheric CHartographY) instrument on the Envisat satellite. Mean emission altitudes are determined by weighting altitude with the vertical OH(3–1) volume emission rate profile. Analysis of the SCIAMACHY data set from January 2003 to December 2011 shows a clear semi-annual variation of the mean emission altitudes at low latitudes – where SCIAMACHY performs measurements throughout the year – with an amplitude of 0.5–1.0km. Confirming earlier studies, we find a near constant, or universal, scaling of mean OH emission altitude and vertically integrated emission rate, which can be employed by ground-based observers to infer indirect information on OH emission altitude and its variability, if measurements of OH emission rates are performed. For this purpose we provide climatological fit parameters for the altitude/emission rate relationship. No obvious long-term trends or 11-year solar cycle signatures are present in the OH emission altitude time series, partly contradicting earlier studies. The long-term stability in OH emission altitude at the local time of the SCIAMACHY nighttime observations (10 p.m.) strengthens the use of ground-based OH rotational temperature measurements to study middle atmospheric climate change.

Mapping of North American methane emissions with high spatial resolution by inversion of SCIAMACHY satellite data

We estimate methane emissions from North America with high spatial resolution by inversion of Scanning Imaging Absorption Spectrometer for Atmospheric Chartography (SCIAMACHY) satellite observations using the Goddard Earth Observing System Chemistry (GEOS-Chem) chemical transport model and its adjoint. The inversion focuses on summer 2004 when data from the Intercontinental Chemical Transport Experiment-North America (INTEX-A) aircraft campaign over the eastern U.S. are available to validate the SCIAMACHY retrievals and evaluate the inversion. From the INTEX-A data we identify and correct a water vapor-dependent bias in the SCIAMACHY data. We conduct an initial inversion of emissions on the horizontal grid of GEOS-Chem (1/2° × 2/3°) to identify correction tendencies relative to the Emission Database for Global Atmospheric Research (EDGAR) v4.2 emission inventory used as a priori. We then cluster these grid cells with a hierarchical algorithm to extract the maximum information from the SCIAMACHY observations. A 1000 cluster ensemble can be adequately constrained, providing ~100 km resolution across North America. Analysis of results indicates that the Hudson Bay Lowland wetlands source is 2.1 Tg a−1, lower than the a priori but consistent with other recent estimates. Anthropogenic U.S. emissions are 30.1 ± 1.3 Tg a−1, compared to 25.8 Tg a−1 and 28.3 Tg a−1 in the EDGAR v4.2 and Environmental Protection Agency (EPA) inventories, respectively. We find that U.S. livestock emissions are 40% greater than in these two inventories. No such discrepancy is apparent for overall U.S. oil and gas emissions, although this may reflect some compensation between overestimate of emissions from storage/distribution and underestimate from production. We find that U.S. livestock emissions are 70% greater than the oil and gas emissions, in contrast to the EDGAR v4.2 and EPA inventories where these two sources are of comparable magnitude.

Terrestrial carbon sink observed from space: variation of growth rates and seasonal cycle amplitudes in response to interannual surface temperature variability

Abstract. The terrestrial biosphere is currently acting as a net carbon sink on the global scale, exhibiting significant interannual variability in strength. To reliably predict the future strength of the land sink and its role in atmospheric CO2 growth, the underlying biogeochemical processes and their response to a changing climate need to be well understood. In particular, better knowledge of the impact of key climate variables such as temperature or precipitation on the biospheric carbon reservoir is essential. It is demonstrated using nearly a decade of SCIAMACHY (SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY) nadir measurements that years with higher temperatures during the growing season can be robustly associated with larger growth rates in atmospheric CO2 and smaller seasonal cycle amplitudes for northern mid-latitudes. We find linear relationships between warming and CO2 growth as well as seasonal cycle amplitude at the 98% significance level. This suggests that the terrestrial carbon sink is less efficient at higher temperatures during the analysed time period. Unless the biosphere has the ability to adapt its carbon storage under warming conditions in the longer term, such a temperature response entails the risk of potential future sink saturation via a positive carbon-climate feedback. Quantitatively, the covariation between the annual CO2 growth rates derived from SCIAMACHY data and warm season surface temperature anomaly amounts to 1.25 ± 0.32 ppm yr−1 K−1 for the Northern Hemisphere, where the bulk of the terrestrial carbon sink is located. In comparison, this relationship is less pronounced in the Southern Hemisphere. The covariation of the seasonal cycle amplitudes retrieved from satellite measurements and temperature anomaly is −1.30 ± 0.31 ppm K−1 for the north temperate zone. These estimates are consistent with those from the CarbonTracker data assimilated CO2 data product, indicating that the temperature dependence of the model surface fluxes is realistic.

Top‐down isoprene emissions over tropical South America inferred from SCIAMACHY and OMI formaldehyde columns

We use formaldehyde (HCHO) vertical column measurements from the Scanning Imaging Absorption spectrometer for Atmospheric Chartography (SCIAMACHY) and Ozone Monitoring Instrument (OMI), and a nested‐grid version of the GEOS‐Chem chemistry transport model, to infer an ensemble of top‐down isoprene emission estimates from tropical South America during 2006, using different model configurations and assumptions in the HCHO air‐mass factor (AMF) calculation. Scenes affected by biomass burning are removed on a daily basis using fire count observations, and we use the local model sensitivity to identify locations where the impact of spatial smearing is small, though this comprises spatial coverage over the region. We find that the use of the HCHO column data more tightly constrains the ensemble isoprene emission range from 27–61 Tg C to 31–38 Tg C for SCIAMACHY, and 45–104 Tg C to 28–38 Tg C for OMI. Median uncertainties of the top‐down emissions are about 60–260% for SCIAMACHY, and 10–90% for OMI. We find that the inferred emissions are most sensitive to uncertainties in cloud fraction and cloud top pressure (differences of ±10%), the a priori isoprene emissions (±20%), and the HCHO vertical column retrieval (±30%). Construction of continuous top‐down emission maps generally improves GEOS‐Chem's simulation of HCHO columns over the region, with respect to both the SCIAMACHY and OMI data. However, if local time top‐down emissions are scaled to monthly mean values, the annual emission inferred from SCIAMACHY are nearly twice those from OMI. This difference cannot be explained by the different sampling of the sensors or uncertainties in the AMF calculation.

Improvement to the PhytoDOAS method for identification of coccolithophores using hyper-spectral satellite data

Abstract. The goal of this study was to improve PhytoDOAS, which is a new retrieval method for quantitative identification of major phytoplankton functional types (PFTs) using hyper-spectral satellite data. PhytoDOAS is an extension of the Differential Optical Absorption Spectroscopy (DOAS, a method for detection of atmospheric trace gases), developed for remote identification of oceanic phytoplankton groups. Thus far, PhytoDOAS has been successfully exploited to identify cyanobacteria and diatoms over the global ocean from SCIAMACHY (SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY) hyper-spectral data. This study aimed to improve PhytoDOAS for remote identification of coccolithophores, another functional group of phytoplankton. The main challenge for retrieving more PFTs by PhytoDOAS is to overcome the correlation effects between different PFT absorption spectra. Different PFTs are composed of different types and amounts of pigments, but also have pigments in common, e.g. chl a, causing correlation effects in the usual performance of the PhytoDOAS retrieval. Two ideas have been implemented to improve PhytoDOAS for the PFT retrieval of more phytoplankton groups. Firstly, using the fourth-derivative spectroscopy, the peak positions of the main pigment components in each absorption spectrum have been derived. After comparing the corresponding results of major PFTs, the optimized fit-window for the PhytoDOAS retrieval of each PFT was determined. Secondly, based on the results from derivative spectroscopy, a simultaneous fit of PhytoDOAS has been proposed and tested for a selected set of PFTs (coccolithophores, diatoms and dinoflagellates) within an optimized fit-window, proven by spectral orthogonality tests. The method was then applied to the processing of SCIAMACHY data over the year 2005. Comparisons of the PhytoDOAS coccolithophore retrievals in 2005 with other coccolithophore-related data showed similar patterns in their seasonal distributions, especially in the North Atlantic and the Arctic Sea. The seasonal patterns of the PhytoDOAS coccolithophores indicated very good agreement with the coccolithophore modeled data from the NASA Ocean Biochemical Model (NOBM), as well as with the global distributions of particulate inorganic carbon (PIC), provided by MODIS (MODerate resolution Imaging Spectroradiometer)-Aqua level-3 products. Moreover, regarding the fact that coccolithophores belong to the group of haptophytes, the PhytoDOAS seasonal coccolithophores showed good agreement with the global distribution of haptophytes, derived from synoptic pigment relationships applied to SeaWiFS chl a. As a case study, the simultaneous mode of PhytoDOAS has been applied to SCIAMACHY data for detecting a coccolithophore bloom which was consistent with the MODIS RGB image and the MODIS PIC map of the bloom, indicating the functionality of the method also in short-term retrievals.

UV index values and trends in Santiago, Chile (33.5°S) based on ground and satellite data

We report on the surface UV index (UVI) variations in Santiago (Chile) a city with high air pollution and complex surrounding topography. Ground-based UV measurements were continuously carried out between January 1995 and December 2011, by using a multi-channel filter radiometer (PUV-510). Ground-based measurements and satellite-derived data retrieved from the Total Ozone Mapping Spectrometer (TOMS), the Ozone Monitoring Instrument (OMI), and the Scanning Imaging Absorption spectroMeter for Atmospheric CHartographY (SCIAMACHY), were compared. We found that satellite-derived UVI products largely overestimate surface UVI. Our ground-based UVI measurements were significantly lower than TOMS-derived UVI data: (46.1±6.3)% (in the period 1997–2003), and OMI-derived UVI data: (47.0±6.3)% (in the period 2005–2007). Clear-sky SCIAMACHY-derived UVI were found to be also nearly systematically greater than ground-based UVI measurements in the period 2002–2011. An exceptionally long period of clear skies between December 2010 and January 2011 was used to test further satellite-derived UVI data; in the whole period, OMI and SCIAMACHY data were 53.1% and 38.3% greater than our ground-based measurements, respectively. These differences are presumably due to aerosol load associated with the local pollution and the complex topography surrounding Santiago. In addition, linear regression allowed us to estimate trends that we use for forecasting. Methodological details are provided below.

Water vapour profiles from SCIAMACHY solar occultation measurements derived with an onion peeling approach

Abstract. A new retrieval method has been developed to derive water vapour number density profiles from solar occultation measurements of the SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY (SCIAMACHY). This method is intentionally kept simple and based on a combination of an onion peeling approach with a modified DOAS (Differential Optical Absorption Spectroscopy) fit in the wavelength region around 940 nm. Reasonable resulting water vapour profiles are currently obtained in the altitude range 15–45 km. Comparisons of the SCIAMACHY profiles with water vapour data provided by the Atmospheric Chemistry Explorer Fourier Transform Spectrometer (ACE-FTS) show an average agreement within about 5% between 20 and 45 km. SCIAMACHY water vapour data tend to be systematically higher than ACE-FTS. These results are in principal confirmed by comparisons with water vapour profiles derived from model data of the European Centre for Medium Range Weather Forecasts (ECMWF), although ECMWF concentrations are systematicly lower than both corresponding SCIAMACHY and ACE-FTS data at all altitudes.

First steps towards the assimilation of IASI ozone data into the MOCAGE-PALM system

Abstract. With the use of data assimilation, we study the quality of the Infrared Atmospheric Sounding Interferometer (IASI) total ozone column measurements. The IASI data are provided by the inversion of IASI radiances performed at the Laboratoire ATmosphères, Milieux, Observations Spatiales (LATMOS). This data set is initially compared on a five-month period to a three-dimensional time varying ozone field that we take as a reference. This reference field results from the combined assimilation of ozone profiles from the Microwave Limb Sounder (MLS) instrument and of total ozone columns from the SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY (SCIAMACHY) instrument. It has low systematic and random errors when compared to ozonesondes and Ozone Monitoring Instrument (OMI) data. The comparison shows that on average, the LATMOS-IASI data tends to overestimate the total ozone columns by 2% to 8%. The random observation error of the LATMOS-IASI data is estimated to about 7%, except over polar regions and deserts where it is higher. The daytime data have generally lower biases but higher random error than the nighttime data. Using this information, the LATMOS-IASI data are then assimilated, combined with the MLS data. This first LATMOS-IASI data assimilation experiment shows that the resulting analysis is quite similar to the one obtained from the combined MLS and SCIAMACHY data assimilation. The differences are mainly due to the lack of SCIAMACHY measurements during polar night, and to the higher LATMOS-IASI random errors especially over the southern polar region.

Quantitative observation of cyanobacteria and diatoms from space using PhytoDOAS on SCIAMACHY data

Abstract. In this study the technique of Differential Optical Absorption Spectroscopy (DOAS) has been adapted for the retrieval of the absorption and biomass of two major phytoplankton groups (PhytoDOAS) from data of the Scanning Imaging Absorption Spectrometer for Atmospheric Chartography (SCIAMACHY) satellite sensor. SCIAMACHY measures back scattered solar radiation in the UV-Vis-NIR spectral regions with a high spectral resolution (0.2 to 1.5 nm). In order to identify phytoplankton absorption characteristics in the SCIAMACHY data in the range of 430 to 500 nm, phytoplankton absorption spectra measured in-situ during two different RV "Polarstern" expeditions were used. The two spectra have been measured in different ocean regions where different phytoplankton groups (cyanobacteria and diatoms) dominated the phytoplankton composition. Results clearly show distinct absorption characteristics of the two phytoplankton groups in the SCIAMACHY spectra. Using these results in addition to calculations of the light penetration depth derived from DOAS retrievals of the inelastic scattering (developed by Vountas et al., 2007), globally distributed pigment concentrations for these characteristic phytoplankton groups for two monthly periods (February–March 2004 and October–November 2005) were determined. This satellite information on cyanobacteria and diatoms distribution clearly matches the concentrations based on high pressure liquid chromatography (HPLC) pigment analysis of collocated water samples and concentrations derived from a global model analysis with the NASA Ocean Biogeochemical Model (Gregg et al., 2003; Gregg and Casey 2007). The quantitative assessment of the distribution of key phytoplankton groups from space enables various biogeochemical regions to be distinguished and will be of great importance for the global modeling of marine ecosystems and biogeochemical cycles which enables the impact of climate change in the oceanic biosphere to be estimated.

SO<sub>2</sub> Retrieval from SCIAMACHY using the Weighting Function DOAS (WFDOAS) technique: comparison with Standard DOAS retrieval

Abstract. Atmospheric SO2 can be measured by remote sensing of scattered sunlight from space, using its unique absorption features in the ultraviolet region. However, the sensitivity of the SO2 measurement depends critically on spectral interference, surface albedo and varies with wavelength as Rayleigh scattering increases at shorter wavelengths. The Weighting Function Differential Optical Absorption Spectroscopy (WFDOAS) method was used to pinpoint these problems and improve the retrieval. The Ring spectra included in the WFDOAS fit were determined as a function of total ozone column density, solar zenith angle, surface albedo, and effective scene altitude. The WFDOAS SO2 retrieval from SCIAMACHY (Scanning Imaging Absorption Spectrometer for Atmospheric Chartography) data onboard the ENVISAT satellite are presented here and compared to those of the Standard DOAS (SDOAS) method for cases of background conditions and volcanic eruption. The study demonstrates the problems in the SO2 retrieval with SDOAS, such as the positive offsets over remote (clean) regions and the negative offsets at high solar zenith angles and high ozone, could be attributed to imperfect correction for the varying ozone dependence of the Ring effect and could be solved by the WFDOAS approach.