Advanced Along-Track Scanning Radiometer Research Articles

Atmospheric correction of ENVISAT/MERIS data over case II waters: the use of black pixel assumption in oxygen and water vapour absorption bands

The Medium Resolution Imaging Spectrometer (MERIS) sensor, with its good physical design, can provide excellent data for water colour monitoring. However, owing to the shortage of shortwave-infrared (SWIR) bands, the traditional near-infrared (NIR)–SWIR algorithm for atmospheric correction in inland turbid case II waters cannot be extended to the MERIS data directly, which limits its applications. In this study, we developed a modified NIR black pixel method for atmospheric correction of MERIS data in inland turbid case II waters. In the new method, two special NIR bands provided by MERIS data, an oxygen absorption band (O2 A-band, 761 nm) and a water vapour absorption band (vapour A-band, 900 nm), were introduced to keep the assumption of zero water-leaving reflectance valid according to the fact that both atmospheric transmittance and water-leaving reflectance are very small at these two bands. After addressing the aerosol wavelength dependence for the cases of single- and multiple-scattering conditions, we further validated the new method in two case lakes (Lake Dianchi in China and Lake Kasumigaura in Japan) by comparing the results with in situ measurements and other atmospheric correction algorithms, including Self-Contained Atmospheric Parameters Estimation for MERIS data (SCAPE-M) and the Basic ERS (European Remote Sensing Satellite) & ENVISAT (Environmental Satellite) (A)ATSR ((Advanced) Along-Track Scanning Radiometer) and MERIS (BEAM) processor. We found that the proposed method had acceptable accuracy in the bands within 560–754 nm (MERIS bands 5–10) (average absolute deviation (AAD) = 0.0081, average deviation (AD) = 0.0074), which are commonly used in the estimation models of chlorophyll-a (chl-a) concentrations. In addition, the performance of the new method was superior to that of the BEAM processor and only slightly worse than that of SCAPE-M in these bands. Considering its acceptable accuracy and simplicity both in principle and at implementation compared with the SCAPE-M method, the new method provides an option for atmospheric correction of MERIS data in inland turbid case II waters with applications aiming for chl-a estimation.

Comparison between different sources of atmospheric profiles for land surface temperature retrieval from single channel thermal infrared data

Different sources of atmospheric water vapor and temperature profiles were used with a radiative transfer model for retrieving land surface temperature (LST) from thermal infrared remote sensing data with the so-called single channel (SC) method. Retrieved LSTs were compared to concurrent ground measurements over homogeneous rice fields to assess the accuracy of the atmospheric profiles. These included radiosonde balloons launched at the test site near-concurrently to satellite overpasses, re-analysis profiles from the National Centers for Environmental Prediction (NCEP), and satellite sounder products from the Atmospheric Infrared Sounder (AIRS) and the Moderate Imaging Spectroradiometer (MODIS; MOD07 product). SC LSTs were computed for Enhanced Thematic Mapper+ (ETM+), Advanced Spaceborne Thermal Emission and Reflection radiometer (ASTER), MODIS, and Advanced Along-Track Scanning Radiometer (AATSR). Results show that radiosonde profiles provided the best agreement between ground-measured and satellite-derived LSTs, with root mean square difference (RMSD) better than 1.0 K and biases within ± 0.5 K for most of the cases. As an alternative to radiosonde profiles, NCEP and MOD07 data yielded reasonable results with RMSDs around 1.0 K, although LSTs derived from MOD07 profiles showed a slight overestimation (0.5 to 1.0 K) of the ground LSTs. AIRS profiles usually underestimated the ground LSTs by 1 to 2 K, probably due to the large temporal gap (2–3 h) with the other satellite measurements. We propose a test to assess the suitability of atmospheric profiles applicable to sensors with bands at 11 and 12 μm in the split-window. So this test plus the SC method may be called split-window SC algorithm, being significantly different from the simple SC method. It implies the calculation of the difference between the LST derived from both bands (T ig − T jg), which should be close to zero if the atmospheric profile is accurate and the surface emissivity is well known. A small range of T ig − T jg values around zero can be set for which the LST derived at 11 μm is accurate. Using only the profiles passing the test, LSTs derived from MODIS band 31 agreed with the ground data with a mean bias of − 0.1 K (ground minus satellite) and RMSD of 0.6 K, while AATSR band at 11 μm yielded a − 0.1 K bias and RMSD = 0.5 K.

A comparison of different optimization algorithms for retrieving aerosol optical depths from satellite data: an example of using a dual-angle algorithm

Optimization techniques are often used in remote sensing retrieval of surface or atmospheric parameters. Nevertheless, different algorithms may exhibit different performances for the same optimization problem. Comparison of some classic optimization approaches in this article aims to select the best method for retrieving aerosol opacity, or even for other parameters, from remotely sensed data. Eight frequently used optimization algorithms were evaluated using both simulated data and actual AATSR (advanced along track scanning radiometer) data. Several typical land cover types and aerosol opacity levels were also considered in the simulations to make the tests more representative. It was observed that the absolute error in retrieval would rise after a certain number of iterations due to the round-off error, and the algorithms showed different performances in the inversions without any a priori knowledge. When combined with reasonable a priori knowledge, the selection of various algorithms only slightly affected the retrieval accuracy. Given a summary of all the comparison tests, a special class named ‘trust-region methods’ (TR) was demonstrated to be the optimal choice in general cases. In contrast, some widely used optimization methods in aerosol research, for example, the Levenberg–Marquardt (LM) algorithm, seemed not to display a persuasive performance.

Optimized split-window coefficients for deriving surface temperatures from inland water bodies

Large inland water bodies constituting lakes, reservoirs and inland-seas are excellent proxy indicators for climate change. Using thermal infrared satellite data, a recent study found that a global set of inland water bodies showed significant warming in seasonal nighttime Lake Surface Water Temperatures (LSWTs) between 1985 and 2009. Split-window land surface temperature (LST) retrievals are typically tuned for a broad range of land surface emissivities and global atmospheric conditions, and are not optimized for inland water body surfaces, whereas split-window sea-surface temperatures (SSTs) are only tuned for a single emissivity (water), but over ocean atmospheres. Over inland water bodies, these two approaches can lead to region dependent errors in LSWTs, spurious trends, and inconsistencies between sensors in the long-term temperature record of inland water bodies. To address this issue, the primary goal of this paper was to develop a methodology for deriving a set of optimized split-window coefficients, individually tuned for the regional atmospheric conditions of 169 globally distributed, saline and freshwater inland water bodies from multiple satellite sensors including the Moderate Resolution Imaging Spectroradiometer (MODIS) on Terra and Aqua; Along Track Scanning Radiometer (ATSR) including ATSR-1, ATSR-2, AATSR; and Advanced Very High Resolution Radiometer (AVHRR-3). The new Inland Water-body Surface Temperature (IWbST) v1.0 algorithm was applied to Terra MODIS and Advanced Along Track Scanning Radiometer (AATSR) data and validated with in situ water temperature data from sites with widely contrasting atmospheric conditions: Lake Tahoe in California/Nevada, a high-elevation cool and dry site, and the Salton Sea in California, a low-elevation warm and humid site. Analysis showed improved accuracy in LSWTs in terms of bias and RMSE when compared to the standard MODIS LST and AATSR SST products. For example, the IWbST RMSE at Salton Sea was reduced by 0.4 K when compared to the operational MODIS product. For the AATSR data, the IWbST RMSE was reduced by 0.36 K at Tahoe and 0.29 K at Salton Sea when compared to results obtained using the operational AATSR split-window coefficients. The IWbST improvements are significant in relation to the current accuracy of water temperature retrievals from space (< 0.5 K), and will enable the derivation of long-term, accurate LSWTs consistently across multiple sensors for climate studies.

Reconciling satellite-derived atmospheric properties with fine-resolution land imagery: Insights for atmospheric correction

This paper investigates to what extent satellite measurements of atmospheric properties can be reconciled with fine-resolution land imagery, in order to improve the estimates of surface reflectance through physically based atmospheric correction. The analysis deals with mountainous area (Landsat scene of Peruvian Amazon/Andes, 72 E and 13 S), where the atmosphere is highly variable. Data from satellite sensors were used for characterization of the key atmospheric constituents: total water vapor (TWV), aerosol optical depth (AOD), and total ozone. Constituent time series revealed the season-dependent mean state of the atmosphere and its variability. Discrepancies between AOD from the Advanced Along-Track Scanning Radiometer (AATSR) and Moderate Resolution Imaging Spectroradiometer (MODIS) highlighted substantial uncertainty of atmospheric aerosol properties. The distribution of TWV and AOD over a Landsat scene was found to be exponentially related to ground elevation (mean R(sup 2) of 0.82 and 0.29, respectively). In consequence, the atmosphere-induced and seasonally varying bias of the top-of-atmosphere signal was also elevation dependent (e.g., mean Normalized Difference Vegetation Index bias at 500 m was 0.06 and at 4000 m was 0.01). We demonstrate that satellite measurements of key atmospheric constituents can be downscaled and gap filled with the proposed background + anomalies approach, to allow for a better compatibility with fine-resolution land surface imagery. Older images (i.e., predating the MODIS/ATSR era), without coincident atmospheric data, can be corrected using climatologies derived from time series of satellite retrievals. Averaging such climatologies over space compromises the quality of correction result to a much greater degree than averaging them over time. We conclude that the quality of both recent and older fine-resolution land surface imagery can be improved with satellite-based atmospheric data acquired to date.

The Operational Sea Surface Temperature and Sea Ice Analysis (OSTIA) system

This paper describes a new Sea surface temperature (SST) analysis that is produced with global coverage on a daily basis at the Met Office called the Operational SST and Sea Ice Analysis (OSTIA) system. OSTIA uses satellite SST data provided by international agencies via the Group for High Resolution SST (GHRSST) Regional/Global Task Sharing (R/GTS) framework. GHRSST products include data from microwave and infrared satellite instruments with accompanying uncertainty estimates. The system also uses in situ SST data available over the Global Telecommunications System (GTS) and a sea-ice concentration product from the EUMETSAT Ocean and Sea Ice Satellite Applications Facility (OSI-SAF). The SST analysis is a multi-scale optimal interpolation that is designed for applications in numerical weather prediction and ocean forecasting systems. The background error covariance matrix is specified using ocean model data and the analysis uses correlation length scales of 10km and 100km. The OSTIA system produces a foundation SST estimate (SSTfnd, which is the SST free of diurnal variability) at an output grid resolution of 1/20° (~6km) although the smallest analysis feature resolution is based on the correlation length scale of 10km. All satellite SST data are adjusted for bias errors based on a combination of ENVISAT Advanced Along Track Scanning Radiometer (AATSR) SST data and in situ SST measurements from drifting buoys. Data are filtered (based on surface wind speed data) to remove diurnal variability and AATSR data are adjusted to represent the SST at the same depth as drifting buoy measurements (0.2–1m) before bias adjustments are made. Global coverage outputs are provided each day in GHRSST L4 netCDF format. A variety of secondary products are also provided including weekly and monthly mean data sets. OSTIA products are continuously monitored and validation/verification activities demonstrate that SST products have zero mean bias and an accuracy of ~0.57K compared to in situ measurements. OSTIA is now used operationally as a boundary condition for all weather forecast models at the Met Office and at European Centre for Medium-range Weather Forecasting (ECMWF). OSTIA is produced by the Met Office as part of the European Union Global Monitoring for Environment and Security (GMES) MyOcean project.

A reprocessing for climate of sea surface temperature from the along-track scanning radiometers: Initial validation, accounting for skin and diurnal variability effects

An initial validation of the Along Track Scanning Radiometer (ATSR) Reprocessing for Climate (ARC) retrievals of sea surface temperature (SST) is presented. ATSR-2 and Advanced ATSR (AATSR) SST estimates are compared to drifting buoy and moored buoy observations over the period 1995 to 2008. The primary ATSR estimates are of skin SST, whereas buoys measure SST below the surface. Adjustment is therefore made for the skin effect, for diurnal stratification and for differences in buoy–satellite observation time. With such adjustments, satellite-in situ differences are consistent between day and night within ~0.01K. Satellite-in situ differences are correlated with differences in observation time, because of the diurnal warming and cooling of the ocean. The data are used to verify the average behaviour of physical and empirical models of the warming/cooling rates.Systematic differences between adjusted AATSR and in-situ SSTs against latitude, total column water vapour (TCWV), and wind speed are less than 0.1K, for all except the most extreme cases (TCWV <5kgm–2, TCWV >60kgm–2). For all types of retrieval except the nadir-only two-channel (N2), regional biases are less than 0.1K for 80% of the ocean. Global comparison against drifting buoys shows night time dual-view two-channel (D2) SSTs are warm by 0.06±0.23K and dual-view three-channel (D3) SSTs are warm by 0.06±0.21K (day-time D2: 0.07±0.23K). Nadir-only results are N2: 0.03±0.33K and N3: 0.03±0.19K showing the improved inter-algorithm consistency to ~0.02K. This represents a marked improvement from the existing operational retrieval algorithms for which inter-algorithm inconsistency is >0.5K. Comparison against tropical moored buoys, which are more accurate than drifting buoys, gives lower error estimates (N3: 0.02±0.13K, D2: 0.03±0.18K). Comparable results are obtained for ATSR-2, except that the ATSR-2 SSTs are around 0.1K warm compared to AATSR.

Long-term validation of AATSR SST data products using shipborne radiometry in the Bay of Biscay and English Channel

Radiometric measurements of the sea surface temperature (SST) made by the infrared SST autonomous radiometer (ISAR) deployed routinely between 2004 and 2009 from a passenger vessel traversing the English Channel and Bay of Biscay are used to validate satellite SST data products produced using the Advanced along-track scanning radiometer (AATSR) flown on Envisat. More than 1500 independent pairings between an ISAR record and an AATSR pixel, coincident within specified space–time matching windows, are analysed. These confirm good agreement between the in situ and the satellite derived SST estimates, based on the dual view AATSR algorithms, with a bias of less than 0.1K which is the accuracy limit of the ISAR. The standard deviation of the comparisons depends on the coincidence criteria: for a match-up window of 1km and 2h it is around 0.3K for the three channel (night only) algorithm and 0.4K for the 2 channel algorithm. Separate validation statistics are produced for the periods before and after 7 Dec 2005 when a change was made to the AATSR algorithms. It is shown that the error distribution was narrowed by introducing the new algorithm and further narrowed by using only the AATSR data that have the highest Confidence Value. This is the first systematic use of autonomous underway shipboard radiometry on a vessel of opportunity for validating satellite data. The methodology is carefully assessed and shown to provide an effective and reliable means of confirming the high quality and stability of the SST data products from AATSR.

Aerosol retrievals over China with the AATSR dual view algorithm

Satellites offer the opportunity to determine the spatial distribution of aerosol properties. In this study the AATSR (Advanced Along Track Scanning Radiometer) Dual View (ADV) algorithm is applied for observing aerosol optical depth (AOD) over Eastern China. A preliminary selection of representative aerosol models was made by a comparison of ADV retrieval for a range of models with AERONET data. The selected fine-, and coarse-mode aerosol models were subsequently used in the ADV retrievals over a wider area where no AERONET data was available. In addition to AOD at wavelengths of 0.555 and 0.659μm, a mixing ratio (the contribution of fine mode aerosol to the total aerosol extinction) was retrieved from the AATSR data for 2008. The results show a seasonal variation of the AODs and mixing ratios over the areas with strong anthropogenic activity; over rural areas the AODs and mixing ratios are low throughout the year. Over urban areas the lowest AODs were observed during the winter, and the highest during the summer. During the winter the fine mode aerosol mixing ratios were higher than during other seasons.

Operational sea surface temperature bias adjustment using AATSR data

One objective of the sea surface temperature (SST) component of the Global Monitoring for the Environment and Security (GMES) MERSEA project was to define the combined use of all SST products in an optimal interpolation scheme. Most SST products are now available in a common format but show regional differences between datasets that must be removed before use. We have defined a simple method using the SST derived from the Advanced Along Track Scanning Radiometer (AATSR) onboard the European Space Agency (ESA) ENVISAT platform as a common reference for all SST products. This method is based on an analysis of the differences between a given dataset and the AATSR SST over the 10 preceding days and over a 5° regular grid. These space–time characteristics have been defined empirically in a preliminary experiment. The efficiency of the method has been evaluated on a daily basis for a two year period (2007–2008) based on two SST products from the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT): the Spinning Enhanced Visible and Infra-Red Imager (SEVIRI) derived SST over the Atlantic and the Advanced Very High Resolution Radiometer (AVHRR) SST derived at fine scale over the North-East Atlantic. They have been compared to the AATSR and buoy SSTs and to the Naval Oceanographic Office (NAVOCEANO) AVHRR global SST product. The SEVIRI SST bias, which was reduced from −0.40K to −0.02K for the 0–20N latitude band by this approach provides a good example of the performance of the method. Reductions in the bias are also demonstrated for the North Atlantic, where cloudiness related problems were expected.

Use of MODIS-derived surface reflectance data in the ORAC-AATSR aerosol retrieval algorithm: Impact of differences between sensor spectral response functions

The aerosol component of the Oxford-Rutherford Appleton Laboratory (RAL) Aerosol and Clouds (ORAC) retrieval scheme for the Advanced Along-Track Scanning Radiometer (AATSR) uses data derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) to constrain the brightness of the surface. However, the spectral response functions of the channels used (centred near 550nm, 660nm, 870nm, and 1.6μm) do not exactly match between the two sensors. It is shown that failure to account for differences between the instruments' spectral response functions leads to errors of typically 0.001–0.01 in spectral surface albedo, and distinct biases, dependent on wavelength and surface type. A technique based on singular value decomposition (SVD) is used to reduce these random errors by an average of 35% at 670nm and over 60% at the other wavelengths used. The technique reduces the biases so that they are negligible. In principle, the method can be extended to any combination of sensors. The SVD-based scheme is applied to AATSR data from the month of July 2008 and found to increase the number of successful aerosol retrievals, the speed of retrieval convergence, and improve the level of consistency between the measurements and the retrieved state. Additionally, retrieved aerosol optical depth at 550nm shows an improvement in correspondence when compared to Aerosol Robotic Network (AERONET) data.

The effect of extratropical cyclones on satellite-retrieved aerosol properties over ocean

Extratropical cyclones may have a signicant effect on column aerosol properties over ocean. European Centre for Medium Range Weather Forecasts (ECMWF) derived storm-centric composites of MODerate resolution Imaging Spectroradiometer (MODIS) and Advanced Along-Track Scanning Radiometer (AATSR) aerosol optical depth and aerosol size parameters are produced for the North Atlantic and the South Atlantic oceans. It is found that retrieved aerosol optical depth and aerosol size both increase near the center of the composite extratropical cyclones. Using composites of ECMWF ERA-Interim reanalysis data, it is demonstrated that wind speed is a considerably more likely explanatory variable than relative humidity for the aerosol observations. A comparison of composites for both MODIS and AATSR, which uses a wind speed dependent sea-surface brightness model in the aerosol retrieval, suggests that although surface brightness eects may contribute towards some of the observations, wind speed dependent emission of sea salt also appears to make a signicant contribution to the observed aerosol properties.

Long-term accuracy assessment of land surface temperatures derived from the Advanced Along-Track Scanning Radiometer

The accuracy of land surface temperatures (LSTs) derived from the Advanced Along-Track Scanning Radiometer (AATSR) was assessed in a test site in Valencia, Spain from 2002 to 2008. AATSR LSTs were directly compared with concurrent ground measurements over homogeneous, full-vegetated rice fields in the conventional temperature-based (T-based) method. We also applied the new radiance-based (R-based) method over bare soil and water surfaces, where ground LST measurements were not available. In the R-based method, ground LSTs are simulated from AATSR brightness temperatures in the 11μm band and radiative transfer simulations using surface emissivity data and atmospheric water vapor and temperature profiles. The accuracy of the R-based ground LSTs depends on how well the profiles used in simulations represent the actual atmosphere at the time of AATSR observations. This can be checked with the difference δ(T11-T12) between the actual AATSR and the profile-based simulated difference in the 11 and 12μm brightness temperatures (T11 and T12, respectively). We found that for −0.6K<δ(T11-T12)<0.6K, the R-based LSTs were accurate within ±1.0K and can be used for LST validation. For the data analyzed here, the AATSR operational algorithm overestimated the ground LST by 2 to 5K, showing that the auxiliary data utilized within the retrieval scheme (biome classification and fractional vegetation cover maps at 0.5°×0.5° resolution) should be improved and provided at the same spatial resolution as the AATSR data (1km2). When the AATSR algorithm was optimized with biome and fractional vegetation cover selected according to the nature of each surface, LST errors showed negligible average biases and rmse=±0.5K for full vegetation and water, and ±1.1K for bare soil. Furthermore, we checked an alternative algorithm explicitly dependent on emissivity, which provided accurate LSTs for all the surfaces studied, with small biases, rmse from ±0.4 to ±0.6K and most LST errors within ±1.0K. The algorithm requires monthly emissivity maps at 1km2, which can be derived from classification and fractional vegetation cover estimated from optical AATSR data. The results of this paper show the high LST accuracy achievable with AATSR data in ideal conditions. While it is necessary to establish and maintain highly homogeneous T-based validation sites, the R-based method provides an alternative for the semi-operational, long-term evaluation of LST products at global scale, since it is applicable over surfaces with varied LST and atmospheric regimes where ground LST measurements are not feasible.

ATSR infrared radiometric calibration and in-orbit performance

Three Along Track Scanning Radiometers (ATSRs) form a series of space-borne instruments specifically optimised to provide accurate remotely sensed measurements of Sea Surface Temperature (SST), which is a key geophysical parameter required to inform the debate on climate change and global warming. These sensors' well-calibrated, high quality data have wide applicability and are being used in a much wider range of earth observation studies and applications, in addition to the planned SST mission. Each successive instrument has been an incremental improvement over its predecessor. Since early 1991 the ATSR sensors have provided global observations from the European Space Agency's Earth Observation satellites; namely ATSR-1 on ERS-1 (European Remote-sensing Satellite), then ATSR-2 on ERS-2 and then AATSR (Advanced ATSR) on ENVISAT. The missions have been operated with good overlaps between successive sensors; AATSR is currently the operational instrument. The fundamental requirement for each ATSR instrument is a design that is capable of delivering absolutely calibrated infrared data; therefore this paper concentrates on how the sensors provide the calibrated radiometric observations required for the SST retrieval algorithms to work. It does not discuss the validation of the algorithms to produce SST derived from these basic observations. Described are the rigorous pre-launch measurements over a range of simulated flight environments which verify that this aim has been achieved, a calibration which exercises the same brightness temperature algorithm that is then used when calibrating in-flight measurements. Crucially, the ATSRs measure calibration source radiances in-flight without interrupting Earth-viewing which permits continuous gain and offset monitoring and calibration. For clarity where it is thought necessary for a data user's better understanding of the calibration for flight data, some detail is provided concerning the differences between the three different ATSRs.

Using AATSR data to assess the quality of in situ sea-surface temperature observations for climate studies

In situ data are widely used to provide a ground truth for the calibration and validation of satellite sea-surface temperature (SST) retrievals. They are also used to monitor long-term changes in the climate. For these applications, and others, it is necessary to understand the uncertainties in the data. Near-coincident SST observations from the Advanced Along-Track Scanning Radiometer (AATSR) and in situ platforms were used to understand the characteristics of errors in the measurements. The mean random error on the AATSR retrievals was found to be 0.14K. The in situ errors were modelled as a constant offset plus a random error. For ships, the standard deviation of the constant offset was estimated to be 0.71K and the mean random error was 0.74K. For drifting buoys, the standard deviation of the constant offset was estimated to be 0.29K and the mean random error was 0.26K. These results suggest that there is a need to revisit current assessments of the adequacy of in situ observing systems. The trend in global-average SST between 1991 and 2007 calculated from in situ data was compared to its counterpart calculated from the ATSR instruments. The in situ record warms more slowly than the ATSR record, probably due to a decrease in the fraction of relatively warm-biassed ship observations contributing to the global-average SST over the period and a corresponding increase in the fraction of relatively unbiassed drifting buoy observations.

Remote sensing of aerosols over snow using infrared AATSR observations

Abstract. Infrared (IR) retrievals of aerosol optical thickness (AOT) are challenging because of the low reflectance of aerosol layer at longer wavelengths. In this paper we present a closer analysis of this problem, performed with radiative transfer (RT) simulations for coarse and accumulation mode of four main aerosol components. It shows the strong angular dependence of aerosol IR reflectance at low solar elevations resulting from the significant asymmetry of aerosol phase function at these wavelengths. This results in detectable values of aerosol IR reflectance at certain non-nadir observation angles providing the advantage of multiangle remote sensing instruments for a retrieval of AOT at longer wavelengths. Such retrievals can be of importance e.g. in case of a very strong effect of the surface on the top of atmosphere (TOA) reflectance in the visible spectral range. In the current work, a new method to retrieve AOT of the coarse and accumulation mode particles over snow has been developed using the measurements of Advanced Along Track Scanning Radiometer (AATSR) on board the ENVISAT satellite. The algorithm uses AATSR channel at 3.7 μm and utilizes its dual-viewing observation technique, implying the forward view with an observation zenith angle of around 55 degrees and the nadir view. It includes cloud/snow discrimination, extraction of the atmospheric reflectance out of measured brightness temperature (BT) at 3.7 μm, and interpolation of look-up tables (LUTs) for a given aerosol reflectance. The algorithm uses LUTs, separately simulated with RT forward calculations. The resulting AOT at 500 nm is estimated from the value at 3.7 μm using a fixed Angström parameter. The presented method has been validated against ground-based Aerosol Robotic Network (AERONET) data for 4 high Arctic stations and shows good agreement. A case study has been performed at W-Greenland on 5 July 2008. The day before was characterized by a noticeable dust event. The retrieved AOT maps of the region show a clear increase of AOT in the Kangerlussuaq area. The area of increased AOT was detected on 5 July on the ice sheet east of Kangelussuaq, opposite to the observed north easterly wind at ground level. This position can be explained by a small scale atmospheric circulation transporting the mobilized mineral dust upslope, after its intrusion into the upper branch of the circulation. The performed study of atmospheric reflectance at 3.7 μm also shows possibilities for the detection and retrievals of cloud properties over snow surfaces.

Examining the Effects of Dust Aerosols on Satellite Sea Surface Temperatures in the Mediterranean Sea Using the Medspiration Matchup Database

Abstract Dust aerosol plumes from the Sahara cover the Mediterranean Sea regularly during the summer months (June–August) and occasionally during other seasons. Dust can absorb infrared longwave radiation, thus causing a drop in sea surface temperature (SST) retrievals from satellite. To quantify the magnitude of this absorption and to understand the sources of the biases that might be introduced when trying to validate SST algorithms with in situ bulk temperatures, the effects of the dust absorption are studied using the Medspiration Match-up Database. This database provides in situ and satellite SSTs derived from the Advanced Very High Resolution Radiometer (AVHRR) and the Advanced Along-Track Scanning Radiometer (AATSR), and the difference between skin and bulk measurements is calculated in order to obtain errors or residuals, which are classified by ranges and compared to an aerosol optical thickness product derived from the sensors. The behavior of the residuals is studied and there is clear correspondence between higher residual values and higher aerosol concentrations, though in some cases this relation is not so evident, especially during autumn months. Residuals for this period are then related to other atmospheric effects and layer boundary physical processes by using ancillary data (e.g., soundings).

Global retrieval of ATSR cloud parameters and evaluation (GRAPE): dataset assessment

Abstract. The Along-Track Scanning Radiometers (ATSRs) provide a long time-series of measurements suitable for the retrieval of cloud properties. This work evaluates the freely-available Global Retrieval of ATSR Cloud Parameters and Evaluation (GRAPE) dataset (version 3) created from the ATSR-2 (1995–2003) and Advanced ATSR (AATSR; 2002 onwards) records. Users are recommended to consider only retrievals flagged as high-quality, where there is a good consistency between the measurements and the retrieved state (corresponding to about 60% of converged retrievals over sea, and more than 80% over land). Cloud properties are found to be generally free of any significant spurious trends relating to satellite zenith angle. Estimates of the random error on retrieved cloud properties are suggested to be generally appropriate for optically-thick clouds, and up to a factor of two too small for optically-thin cases. The correspondence between ATSR-2 and AATSR cloud properties is high, but a relative calibration difference between the sensors of order 5–10% at 660 nm and 870 nm limits the potential of the current version of the dataset for trend analysis. As ATSR-2 is thought to have the better absolute calibration, the discussion focusses on this portion of the record. Cloud-top heights from GRAPE compare well to ground-based data at four sites, particularly for shallow clouds. Clouds forming in boundary-layer inversions are typically around 1 km too high in GRAPE due to poorly-resolved inversions in the modelled temperature profiles used. Global cloud fields are compared to satellite products derived from the Moderate Resolution Imaging Spectroradiometer (MODIS), Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) measurements, and a climatology of liquid water content derived from satellite microwave radiometers. In all cases the main reasons for differences are linked to differing sensitivity to, and treatment of, multi-layer cloud systems. The correlation coefficient between GRAPE and the two MODIS products considered is generally high (greater than 0.7 for most cloud properties), except for liquid and ice cloud effective radius, which also show biases between the datasets. For liquid clouds, part of the difference is linked to choice of wavelengths used in the retrieval. Total cloud cover is slightly lower in GRAPE (0.64) than the CALIOP dataset (0.66). GRAPE underestimates liquid cloud water path relative to microwave radiometers by up to 100 g m−2 near the Equator and overestimates by around 50 g m−2 in the storm tracks. Finally, potential future improvements to the algorithm are outlined.

Drought prediction using the Along Track Scanning Radiometer (ATSR2) on board ERS2 satellite

Frequent monitoring and estimation of the areas under drought danger is important for agriculture, hydrology, weather, natural hazards and crop yield prediction. The evaporation fraction, calculated as a function of the evapotranspiration and net radiation over heterogeneous land surface, is used as a drought indicator in this study, where areas with low evaporation fraction are under drought danger. A two-source energy balance model has been applied to calculate the fluxes arising from the heterogeneous target, vegetation and soil. The fluxes have been retrieved from the Along Track Scanning Radiometer (ATSR2) on board the European Research Satellite (ERS2). The main advantage of the algorithm is the separation of the canopy and soil fluxes. The fluxes have been calculated as a function of the aerodynamic temperatures of the components of the surface (vegetation and soil), retrieved from the ATSR2 satellite data. The relationship between canopy biophysical properties and evaporation are examined, using the ATSR2 observations. All variables are retrieved from the ATSR2 satellite data corrected for the atmospheric effects. The algorithm for drought prediction has a long-term application for processing data from the ATSR2/ERS2, the Advanced Along Track Scanning Radiometer (AATSR) on board ENVISAT, ASTER/Terra/NASA and the future high resolution ESA/Land Surface mission (SPECTRA).

Some implications of sampling choices on comparisons between satellite and model aerosol optical depth fields

Abstract. The comparison of satellite and model aerosol optical depth (AOD) fields provides useful information on the strengths and weaknesses of both. However, the sampling of satellite and models is very different and some subjective decisions about data selection and aggregation must be made in order to perform such comparisons. This work examines some implications of these decisions, using GlobAerosol AOD retrievals at 550 nm from Advanced Along-Track Scanning Radiometer (AATSR) measurements, and aerosol fields from the GEOS-Chem chemistry transport model. It is recommended to sample the model only where the satellite flies over on a particular day; neglecting this can cause regional differences in model AOD of up to 0.1 on monthly and annual timescales. The comparison is observed to depend strongly upon thresholds for sparsity of satellite retrievals in the model grid cells. Requiring at least 25% coverage of the model grid cell by satellite data decreases the observed difference between the two by approximately half over land. The impact over ocean is smaller. In both model and satellite datasets, there is an anticorrelation between the proportion p of a model grid cell covered by satellite retrievals and the AOD. This is attributed to small p typically occuring due to high cloud cover and lower AODs being found in large clear-sky regions. Daily median AATSR AODs were found to be closer to GEOS-Chem AODs than daily means (with the root mean squared difference being approximately 0.05 smaller). This is due to the decreased sensitivity of medians to outliers such as cloud-contaminated retrievals, or aerosol point sources not included in the model.