Along-Track Scanning Radiometer Research Articles

New satellite record of sea surface temperature

Sea surface temperature is one of the key variables scientists track in studying climate changes; it is also important to meteorology and oceanography. Merchant et al. describe a new 20‐year record of sea surface temperature. The record was created using infrared imagery from the Along‐Track Scanning Radiometers (ATSR) as part of the ATSR Reprocessing for Climate (ARC) project.

A 20 year independent record of sea surface temperature for climate from Along‐Track Scanning Radiometers

A new record of sea surface temperature (SST) for climate applications is described. This record provides independent corroboration of global variations estimated from SST measurements made in situ. Infrared imagery from Along‐Track Scanning Radiometers (ATSRs) is used to create a 20 year time series of SST at 0.1° latitude‐longitude resolution, in the ATSR Reprocessing for Climate (ARC) project. A very high degree of independence of in situ measurements is achieved via physics‐based techniques. Skin SST and SST estimated for 20 cm depth are provided, with grid cell uncertainty estimates. Comparison with in situ data sets establishes that ARC SSTs generally have bias of order 0.1 K or smaller. The precision of the ARC SSTs is 0.14 K during 2003 to 2009, from three‐way error analysis. Over the period 1994 to 2010, ARC SSTs are stable, with better than 95% confidence, to within 0.005 K yr−1(demonstrated for tropical regions). The data set appears useful for cleanly quantifying interannual variability in SST and major SST anomalies. The ARC SST global anomaly time series is compared to the in situ‐based Hadley Centre SST data set version 3 (HadSST3). Within known uncertainties in bias adjustments applied to in situ measurements, the independent ARC record and HadSST3 present the same variations in global marine temperature since 1996. Since the in situ observing system evolved significantly in its mix of measurement platforms and techniques over this period, ARC SSTs provide an important corroboration that HadSST3 accurately represents recent variability and change in this essential climate variable.

Global warping coefficients for improving ATSR co-registration

Globally applicable warping coefficients for improving the co-registration between the forward and nadir views of the series of Along-Track Scanning Radiometer (ATSR) instruments are presented. The coefficients are derived from a polynomial-based warp, using a least squares fit on a set of automatically detected forward to nadir tie points. In-depth statistical analysis is presented here covering both an independent analysis of the warping coefficients using the Pyramidal-Gotcha algorithm and a global assessment using the Speeded Up Robust Features tie-pointing algorithm. This independent assessment shows that the co-registration between the nadir and forward views improves in all cases to pixel-level accuracy with geometric errors to, at worst, ±0.7 pixel. This global assessment analyses a worldwide distributed tie-point set to assess universal applicability. For Advanced ATSR and ATSR-1, the coefficients provide co-registration accuracies at the pixel level globally. The same is shown to be true for ATSR-2 prior to the gyroscope failure in January 2000.

Use of ATSR and SAR measurements for the monitoring and characterisation of night-time gas flaring from off-shore platforms: The North Sea test case

A method for the monitoring of night-time gas flaring of off-shore oil/gas extraction platforms using measurements of the Along Track Scanning Radiometer (ATSR) and the Synthetic Aperture Radar (SAR) is presented and discussed in detail. The positions of off-shore extraction sites are accurately estimated by using SAR data, while the flaring activity is estimated from night-time shortwave infrared (SWIR) radiance measured by ATSR. The North Sea area has been selected as test case and related flaring activity from 1991 to 2010 has been analysed at single site and at North Sea area scales. Results indicate a decline in the overall flaring activity during the time period considered in this work, although single sites can show positive flaring trends. The ATSR derived flaring time series has been compared to the crude oil production data provided by the US Energy Information Administration (EIA), showing very good agreement in terms of trend and seasonal behaviour. We present a simple inversion scheme aimed at the evaluation of the flame parameters (temperature and size) from night-time shortwave, middle and thermal infrared ATSR measurements, and results are discussed in detail. Finally, the possibility to estimate flaring efficiency from satellite measurements and from detailed technical information on flaring devices is envisaged. The proposed approach can be easily extended to other areas in which gas flaring from off-shore oil and gas extraction are an important economic and environmental factor.

Daily, Global, High-Resolution SST and Sea Ice Reanalysis for 1985–2007 Using the OSTIA System

Abstract A sea surface temperature (SST) and sea ice reanalysis has been produced at the Met Office based on the Operational SST and Sea Ice Analysis (OSTIA) system. The OSTIA reanalysis produces daily, high-resolution, global foundation SST and sea ice concentration fields from 1 January 1985 to 31 December 2007. The SST reanalysis uses reprocessed satellite and in situ observations that are assimilated using a multiscale optimal-interpolation-type scheme similar to that used in the near-real-time OSTIA system. Validation of the SST analysis using assimilated in situ observation-minus-background statistics shows that the accuracy of the analysis increases throughout the reanalysis period; the global root-mean-square difference is approximately 0.50 K by 2007. This approach to validation is supported in the recent period by results from comparisons with independent near-surface Argo data against which a global standard deviation error of 0.55 K was calculated. Assessment of the OSTIA reanalysis at high latitudes demonstrates that the SST and sea ice fields are more consistent with one another in the Southern Hemisphere than in the Northern Hemisphere. Comparison of the sea ice extents to those in a similar reanalysis shows OSTIA to have larger extents in the Northern Hemisphere, and the Southern Hemisphere extents are similar. The OSTIA reanalysis SSTs are shown to be regionally comparable with similar reanalyses, with the largest differences occurring at high latitudes in the summer hemisphere. Differences are observed around the ice edge and in regions with high SST gradients. The OSTIA reanalysis provides a valuable high-resolution addition to the satellite period SST data record that makes use of the (Advanced) Along-Track Scanning Radiometer [(A)ATSR] multimission data.

Sentinel-3 SLSTR active fire detection and FRP product: Pre-launch algorithm development and performance evaluation using MODIS and ASTER datasets

The Sentinel-3 Sea and Land Surface Temperature Radiometer (SLSTR) is a dual-view Earth observing instrument that builds on the heritage of the Along Track Scanning Radiometer (ATSR) series. SLSTR is planned for launch in 2013 on Sentinel-3, with two concurrently operating satellites planned for daily global coverage. Here we review the aspects of the SLSTR instrument design specifically targeted at biomass burning events, including operation of the SWIR channels at night and the inclusion of low-gain middle infrared and thermal IR channels that will minimise saturation over even high intensity fires. We detail the active fire detection and fire radiative power dataset to be provided from these SLSTR observations, which will be part of the SLSTR land product suite aimed at supporting both Global Monitoring for Environment and Security (GMES) operational services and scientific applications. We describe in detail the pre-launch active fire product algorithm, which uses data from the SLSTR near-nadir scan. The algorithm detects pixels containing actively burning fires, and uses the MIR radiance method to estimate their fire radiative power (FRP). We test the algorithm using a series of EOS MODIS scenes covering a range of fire-affected forest and savannah environments, comparing performance to that of the existing MODIS MOD14 ‘Fire and Thermal Anomaly’ products. Across 385 scenes covering Africa, South America and Australia, we find that the SLSTR algorithm applied to MODIS data detects in total 20% more fire pixels than does the MOD14 algorithm applied to the same data. Some scenes show very large differences, while others showed no differences, and some of the extra detections made by SLSTR maybe false alarms. For a better evaluation, we use the simultaneous high spatial resolution active fire detections made from ASTER to provide an independent accuracy assessment. Across 45 separate geographical regions covered simultaneously by ASTER and MODIS, we find that the SLSTR algorithm in fact detects 13% more correctly identified clusters of active fire pixels than the MOD14 algorithm, and that these contain 36% more active fire pixels. In particular, the SLSTR algorithm shows increased detection probabilities at small/low FRP fires, mainly due to the more liberal characteristics of its potential fire pixel detection stage. This performance enhancement comes, however, at the expense of a small (<2%) increase in commission error (i.e. false alarm rate) when compared to MOD14. The SLSTR algorithms ability to better detect low FRP fires maybe important, since these are usually the most common component of a region's fire regime.

Surface water temperature observations of large lakes by optimal estimation

Optimal estimation (OE) and probabilistic cloud screening were developed to provide lake surface water temperature (LSWT) estimates from the series of (advanced) along-track scanning radiometers (ATSRs). Variations in physical properties such as elevation, salinity, and atmospheric conditions are accounted for through the forward modelling of observed radiances. Therefore, the OE retrieval scheme developed is generic (i.e., applicable to all lakes). LSWTs were obtained for 258 of Earth's largest lakes from ATSR-2 and AATSR imagery from 1995 to 2009. Comparison to in situ observations from several lakes yields satellite in situ differences of −0.2 ± 0.7 K for daytime and −0.1 ± 0.5 K for nighttime observations (mean ± standard deviation). This compares with −0.05 ± 0.8 K for daytime and −0.1 ± 0.9 K for nighttime observations for previous methods based on operational sea surface temperature algorithms. The new approach also increases coverage (reducing misclassification of clear sky as cloud) and exhibits greater consistency between retrievals using different channel–view combinations. Empirical orthogonal function (EOF) techniques were applied to the LSWT retrievals (which contain gaps due to cloud cover) to reconstruct spatially and temporally complete time series of LSWT. The new LSWT observations and the EOF-based reconstructions offer benefits to numerical weather prediction, lake model validation, and improve our knowledge of the climatology of lakes globally. Both observations and reconstructions are publically available from http://hdl.handle.net/10283/88.

Note on the quality of the (A)ATSR land surface temperature record from 1991 to 2009

Land-surface temperature (LST) is a key parameter in the physical study of atmosphere–land interactions as well as for global warming and climate change monitoring on a longer timescale. Remote sensing provides an excellent way to measure LST at a global scale with appropriate spatial and temporal resolution. To retrieve LSTs, measured radiances at the sensor have to be corrected for surface emissivity, atmospheric effects and contaminating clouds. This study is based on the LST data provided by the Along-Track Scanning Radiometers (ATSR-1 and ATSR-2) and the Advanced ATSR (AATSR) on board the three European Space Agency (ESA) satellites, European Remote-Sensing Satellite 1 (ERS-1), ERS-2 and Enviromental Satellite (ENVISAT). The analysis covers data from August 1991 up to December 2009 and contains detailed investigations on global as well as on regional scales with a temporal resolution of 1 month, outlining problems and restrictions within the time series mainly due to cloud contamination and effects of calibration drifts on the cloud detection tests. It is demonstrated that the trends observed, for cooling as well as for warming, are likely to be related to the trends in cloud contamination rather than the trends in the actual LST.

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.

Forest fire risk assessment in parts of Northeast India using geospatial tools

Forest fire is a major cause of changes in forest structure and function. Among various floristic regions, the northeast region of India suffers maximum from the fires due to age-old practice of shifting cultivation and spread of fires from jhum fields. For proper mitigation and management, an early warning of forest fires through risk modeling is required. The study results demonstrate the potential use of remote sensing and Geographic Information System (GIS) in identifying forest fire prone areas in Manipur, southeastern part of Northeast India. Land use land cover (LULC), vegetation type, Digital elevation model (DEM), slope, aspect and proximity to roads and settlements, factors that influence the behavior of fire, were used to model the forest fire risk zones. Each class of the layers was given weight according to their fire inducing capability and their sensitivity to fire. Weighted sum modeling and ISODATA clustering was used to classify the fire zones. To validate the results, Along Track Scanning Radiometer (ATSR), the historical fire hotspots data was used to check the occurrence points and modeled forest fire locations. The forest risk zone map has 55.63% of agreement with ATSR dataset.

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.

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.

An infrared desert dust index for the Along-Track Scanning Radiometers

A new aerosol index for the Along-Track Scanning Radiometers (ATSRs) is presented that provides a means to detect desert dust contamination in infrared SST retrievals. The ATSR Saharan dust index (ASDI) utilises only the thermal infrared channels and may therefore be applied consistently to the entire ATSR data record (1991 to present), for both day time and night time observations. The derivation of the ASDI is based on a principal component (PC) analysis (PCA) of two unique pairs of channel brightness temperature differences (BTDs). In 2-D space (i.e. BTD vs BTD), it is found that the loci of data unaffected by aerosol are confined to a single axis of variability. In contrast, the loci of aerosol-contaminated data fall off-axis, shifting in a direction that is approximately orthogonal to the clear-sky axis. The ASDI is therefore defined to be the second PC, where the first PC accounts for the clear-sky variability. The primary ASDI utilises the ATSR nadir and forward-view observations at 11 and 12μm (ASDI2). A secondary, three-channel nadir-only ASDI (ASDI3) is also defined for situations where data from the forward view are not available. Empirical and theoretical analyses suggest that ASDI is well correlated with aerosol optical depth (AOD: correlation r is typically >0.7) and provides an effective tool for detecting desert mineral dust. Overall, ASDI2 is found to be more effective than ASDI3, with the latter being sensitive only to very high dust loading. In addition, use of ASDI3 is confined to night time observations as it relies on data from the 3.7μm channel, which is sensitive to reflected solar radiation. This highlights the benefits of having data from both a nadir- and a forward-view for this particular approach to aerosol detection.

A reprocessing for climate of sea surface temperature from the along-track scanning radiometers: A new retrieval scheme

We present a new coefficient-based retrieval scheme for estimation of sea surface temperature (SST) from the Along Track Scanning Radiometer (ATSR) instruments. The new coefficients are banded by total column water vapour (TCWV), obtained from numerical weather prediction analyses. TCWV banding reduces simulated regional retrieval biases to <0.1K compared to biases ~0.2K for global coefficients. Further, detailed treatment of the instrumental viewing geometry reduces simulated view-angle related biases from ~0.1K down to <0.005K for dual-view retrievals using channels at 11 and 12μm. A novel analysis of trade-offs related to the assumed noise level when defining coefficients is undertaken, and we conclude that adding a small nominal level of noise (0.01K) is optimal for our purposes.When applied to ATSR observations, some inter-algorithm biases appear as TCWV-related differences in SSTs estimated from different channel combinations. The final step in coefficient determination is to adjust the offset coefficient in each TCWV band to match results from a reference algorithm. This reference uses the dual-view observations of 3.7 and 11μm. The adjustment is independent of in situ measurements, preserving independence of the retrievals. The choice of reference is partly motivated by uncertainty in the calibration of the 12μm of Advanced ATSR.Lastly, we model the sensitivities of the new retrievals to changes to TCWV and changes in true SST, confirming that dual-view SSTs are most appropriate for climatological applications.

Gas flaring monitoring from space using the ATSR instrument series

Gas flaring flames are characterised by high temperatures and ATSR instruments are equipped with the appropriate spectral bands to detect them. In order to monitor gas flaring on global scale a new active flame detection scheme from satellite night-time Short Wavelength Infra Red measurements (SWIR, 1.6μm) has been developed and tested using the Along Track Scanning Radiometer (ATSR) family measurements. The new algorithm, called ALGO3, is based on the verified assumption that, at SWIR wavelengths, the background contribution to the night-time total radiation measured by ATSR is negligible, while that emitted by active flames is fully detectable. ALGO3 products are suitable for detecting gas flares, due to their peculiar high temperature/small area flames. Flaring sites have been discriminated according to time persistency criteria, i.e. location for which hot spots are found at frequencies higher than 4 times a year are assumed to be industrial settlements. Continuity and consistency between the ATSR missions has been verified, and results relative to 1991–2009 time window are reported. Validation of flaring site discrimination has been performed by visual inspection of high resolution Earth surface images. The comparison of ALGO3 retrievals with light count data from the Defense Meteorological Satellite Program (DMSP) Operational Linescan System (OLS) show very good agreement.

Towards a bias correction of the AVHRR Pathfinder SST data from 1985 to 1998 using ATSR

There are many sea surface temperature (SST) datasets derived from various satellite instruments with different characteristics in terms of orbit, coverage, retrieval, calibration, accuracy and measurement type. This paper presents the results of a project to combine SSTs from two instruments: the Along-Track Scanning Radiometer (ATSR) SSTs and the Advanced Very High Resolution Radiometer (AVHRR) Pathfinder SSTs. The dual-view nature of the (A)ATSR series of radiometers enables high-quality sea surface temperatures to be retrieved. These SSTs are ideal for using as a reference in order to bias-adjust the AVHRR series of instruments. In addition a method to correct for aerosol effects in the AVHRR SSTs is employed using aerosol optical depth from the Total Ozone Mapping Spectrometer. The method enables positive characteristics of both datasets to be combined to provide a dataset of potentially high-quality, long time-series, large data coverage satellite sea surface temperatures for climate monitoring purposes.An AVHRR SST dataset at ¼ degree spatial resolution with associated systematic biases and uncertainties is produced for climate research covering the period 1985 to 1998. The method is used to produce improved AVHRR SSTs for the period 1991 to the present (when the ATSRs are available) and additionally the method can be used to estimate biases on AVHRR SSTs prior to the first ATSR launch (1985 to 1991).The bias-correction has the effect of reducing an observed cool bias in the AVHRR SSTs, relative to the Hadley Centre Sea-Ice and Sea Surface Temperature dataset (HadISST), especially in cases of high volcanic and Saharan dust aerosol. The time-series indicates that the bias correction scheme is effective during the 1990s at decreasing the cool bias in the AVHRR SSTs. The scheme is especially effective in times of high atmospheric volcanic aerosol levels such as the period following the Mount Pinatubo eruption in 1991.

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.