Advanced Along-Track Scanning Radiometer Data Research Articles

Retrievals of key biophysical parameters at mesoscale from the Ts/VI scatterplot domain

Earth Observation (EO) is often combined with simulated process models to gain insight on key parameters for the physical mechanisms and interactions of the Earth's system. The objective of this study is two-fold: First, it explores the combined use of EO data from the Advanced Along-Track Scanning Radiometer (AATSR) with the SimSphere land biosphere model via the “triangle” to derive latent (LE) and sensible heat (H) fluxes and soil moisture content (SMC) for diverse European ecosystems. Secondly, it investigates the influence of atmospheric correction on the “triangle”-derived retrievals. Both non-atmospherically (1P) and atmospherically corrected (2P) AATSR data products are used. The data cover selected days from 2007 to 2011 at 12 sites of a European ground monitoring network. For all the predicted parameters, the 2P product leads to improved statistical validation measures. The present study for the first time: (1) assesses the triangle technique at 1km mesoscale resolution using AATSR data; (2) investigates the effects of atmospheric correction and surface heterogeneity on prediction accuracy. The findings provide important information regarding potential operational use of the technique.

Variational bias correction of satellite sea‐surface temperature data incorporating observations of the bias

We describe a variational bias‐correction system for satellite sea‐surface temperature (SST) data that includes the use of “observations‐of‐bias”. The bias‐correction scheme is designed to work in the historical period, when good quality low‐bias reference data were scarce, but can also take advantage of reference data when they are available. In testing with a simple Lorenz 63 model, our new scheme outperformed traditional variational bias correction. When compared with an offline bias‐correction method, the new scheme showed superior performance both when the bias was large and when reference observations were sparse. The bias‐correction scheme has also been tested using a three‐year assimilative run (2008–2010) of the Nucleus for European Modeling of the Ocean (NEMO) ocean general circulation model, with reference data from the Advanced Along Track Scanning Radiometer (AATSR) instrument withheld in 2009. In these tests, the new scheme was found to be more robust to missing reference observations than an offline scheme. Against AATSR data, the new bias‐correction method had lower biases and root‐mean‐square (RMS) errors than an offline scheme, but was degraded relative to a pure variational technique. However, in comparisons with drifting buoys, the new scheme outperformed both offline and pure variational methods.

Global Land Surface Temperature From the Along‐Track Scanning Radiometers

AbstractThe Leicester Along‐Track Scanning Radiometer (ATSR) and Sea and Land Surface Temperature Radiometer (SLSTR) Processor for LAnd Surface Temperature (LASPLAST) provides global land surface temperature (LST) products from thermal infrared radiance data. In this paper, the state‐of‐the‐art version of LASPLAST, as deployed in the GlobTemperature project, is described and applied to data from the Advanced Along‐Track Scanning Radiometer (AATSR). The LASPLAST retrieval formulation for LST is a nadir‐only, two‐channel, split‐window algorithm, based on biome classification, fractional vegetation, and across‐track water vapor dependences. It incorporates globally robust retrieval coefficients derived using highly sampled atmosphere profiles. LASPLAST benefits from appropriate spatial resolution auxiliary information and a new probabilistic‐based cloud flagging algorithm. For the first time for a satellite‐derived LST product, pixel‐level uncertainties characterized in terms of random, locally correlated, and systematic components are provided. The new GlobTemperature GT_ATS_2P Version 1.0 product has been validated for 1 year of AATSR data (2009) against in situ measurements acquired from “gold standard reference” stations: Gobabeb, Namibia, and Evora, Portugal; seven Surface Radiation Budget stations, and the Atmospheric Radiation Measurement station at Southern Great Plains. These data show average absolute biases for the GT_ATS_2P Version 1.0 product of 1.00 K in the daytime and 1.08 K in the nighttime. The improvements in data provenance including better accuracy, fully traceable retrieval coefficients, quantified uncertainty, and more detailed information in the new harmonized format of the GT_ATS_2P product will allow for more significant exploitation of the historical LST data record from the ATSRs and a valuable near‐real‐time service from the Sea and Land Surface Temperature Radiometers (SLSTRs).

Unveiling aerosol–cloud interactions – Part 2: Minimising the effects of aerosol swelling and wet scavenging in ECHAM6-HAM2 for comparison to satellite data

Abstract. Aerosol–cloud interactions (ACIs) are uncertain and the estimates of the ACI effective radiative forcing (ERFaci) magnitude show a large variability. Within the Aerosol_cci project the susceptibility of cloud properties to changes in aerosol properties is derived from the high-resolution AATSR (Advanced Along-Track Scanning Radiometer) data set using the Cloud–Aerosol Pairing Algorithm (CAPA) (as described in our companion paper) and compared to susceptibilities from the global aerosol climate model ECHAM6-HAM2 and MODIS–CERES (Moderate Resolution Imaging Spectroradiometer – Clouds and the Earth's Radiant Energy System) data. For ECHAM6-HAM2 the dry aerosol is analysed to mimic the effect of CAPA. Furthermore the analysis is done for different environmental regimes. The aerosol–liquid water path relationship in ECHAM6-HAM2 is systematically stronger than in AATSR–CAPA data and cannot be explained by an overestimation of autoconversion when using diagnostic precipitation but rather by aerosol swelling in regions where humidity is high and clouds are present. When aerosol water is removed from the analysis in ECHAM6-HAM2 the strength of the susceptibilities of liquid water path, cloud droplet number concentration and cloud albedo as well as ERFaci agree much better with those of AATSR–CAPA or MODIS–CERES. When comparing satellite-derived to model-derived susceptibilities, this study finds it more appropriate to use dry aerosol in the computation of model susceptibilities. We further find that the statistical relationships inferred from different satellite sensors (AATSR–CAPA vs. MODIS–CERES) as well as from ECHAM6-HAM2 are not always of the same sign for the tested environmental conditions. In particular the susceptibility of the liquid water path is negative in non-raining scenes for MODIS–CERES but positive for AATSR–CAPA and ECHAM6-HAM2. Feedback processes like cloud-top entrainment that are missing or not well represented in the model are therefore not well constrained by satellite observations. In addition to aerosol swelling, wet scavenging and aerosol processing have an impact on liquid water path, cloud albedo and cloud droplet number susceptibilities. Aerosol processing leads to negative liquid water path susceptibilities to changes in aerosol index (AI) in ECHAM6-HAM2, likely due to aerosol-size changes by aerosol processing. Our results indicate that for statistical analysis of aerosol–cloud interactions the unwanted effects of aerosol swelling, wet scavenging and aerosol processing need to be minimised when computing susceptibilities of cloud variables to changes in aerosol.

Synergy of MODIS and AATSR for better retrieval of aerosol optical depth and land surface directional reflectance

This paper presents a new algorithm to simultaneously retrieve Aerosol Optical Depth (AOD) and land surface Bidirectional Reflectance Distribution Function (BRDF) from Advanced Along-Track Scanning Radiometer (AATSR) by adopting gradient optimization method. Different from traditional method the approach presented here can perform simultaneous retrieval from each individual AATSR swath rather than multiple days. A theoretical sensitivity study proves the proposed method is insensitive to the distortion of initial BRDF. The presented algorithm is tested on AATSR data around four different Aerosol Robotic Network (AERONET) sites representing various types of land surface. Compared with the four selected AERONET sites' AOD and BRDF-derived albedo from AERONET-based Surface Reflectance Validation Network (ASRVN) data in corresponding four AERONET sites, the presented algorithm proves considerable accuracy for various type of land surface with correlation of AOD ranging from 0.647 to 0.911 and correlation of BRDF-derived albedo ranging from 0.483 to 0.944. The intersensor comparison with Moderate Resolution Imaging Spectroradiometer (MODIS) 3km AOD dark target product reveals high coverage rate of the presented method especially in bright surface or nonvegetation area and the correlation between the two sensors reaches up to 0.967. The improved estimation of BRDF from AATSR retrieval in AERONET Beijing site is compared with MODIS MCD43B1 product. The relative differences in hemispherical albedo calculated from average BRDF shape function parameters between AATSR and MODIS product are 1.33%, 1.52%, 2.60% and 4.28% at 550nm, 670nm, 870nm and 1600nm respectively.

Technical note: Intercomparison of three AATSR Level 2 (L2) AOD products over China

Abstract. One of four main focus areas of the PEEX initiative is to establish and sustain long-term, continuous, and comprehensive ground-based, airborne, and seaborne observation infrastructure together with satellite data. The Advanced Along-Track Scanning Radiometer (AATSR) aboard ENVISAT is used to observe the Earth in dual view. The AATSR data can be used to retrieve aerosol optical depth (AOD) over both land and ocean, which is an important parameter in the characterization of aerosol properties. In recent years, aerosol retrieval algorithms have been developed both over land and ocean, taking advantage of the features of dual view, which can help eliminate the contribution of Earth's surface to top-of-atmosphere (TOA) reflectance. The Aerosol_cci project, as a part of the Climate Change Initiative (CCI), provides users with three AOD retrieval algorithms for AATSR data, including the Swansea algorithm (SU), the ATSR-2ATSR dual-view aerosol retrieval algorithm (ADV), and the Oxford-RAL Retrieval of Aerosol and Cloud algorithm (ORAC). The validation team of the Aerosol-CCI project has validated AOD (both Level 2 and Level 3 products) and AE (Ångström Exponent) (Level 2 product only) against the AERONET data in a round-robin evaluation using the validation tool of the AeroCOM (Aerosol Comparison between Observations and Models) project. For the purpose of evaluating different performances of these three algorithms in calculating AODs over mainland China, we introduce ground-based data from CARSNET (China Aerosol Remote Sensing Network), which was designed for aerosol observations in China. Because China is vast in territory and has great differences in terms of land surfaces, the combination of the AERONET and CARSNET data can validate the L2 AOD products more comprehensively. The validation results show different performances of these products in 2007, 2008, and 2010. The SU algorithm performs very well over sites with different surface conditions in mainland China from March to October, but it slightly underestimates AOD over barren or sparsely vegetated surfaces in western China, with mean bias error (MBE) ranging from 0.05 to 0.10. The ADV product has the same precision with a low root mean square error (RMSE) smaller than 0.2 over most sites and the same error distribution as the SU product. The main limits of the ADV algorithm are underestimation and applicability; underestimation is particularly obvious over the sites of Datong, Lanzhou, and Urumchi, where the dominant land cover is grassland, with an MBE larger than 0.2, and the main aerosol sources are coal combustion and dust. The ORAC algorithm has the ability to retrieve AOD at different ranges, including high AOD (larger than 1.0); however, the stability deceases significantly with increasing AOD, especially when AOD > 1.0. In addition, the ORAC product is consistent with the CARSNET product in winter (December, January, and February), whereas other validation results lack matches during winter.

Seismic infrared anomalies detection in the case of the Wenchuan earthquake using bi-angular advanced along-track scanning radiometer data

<p>The paper has developed Robust Satellite data analysis Technique (RST) to detect seismic anomalies in the case of the Wenchuan earthquake occurred on May 12, 2008, using the bi-angular Advanced Along-Track Scanning Radiometer (AATSR) gridded brightness temperature (BT) data based on spatial/temporal continuity and confutation analysis. The proposed method has been applied to analyze the Wenchuan earthquake with longitude from 95°E to 111°E and latitude from 23°N to 39°N, and a full data-set of 7 years data from 2003 to 2009 during the months of April and May has been analyzed. Combining with the rupture structure data of the Wenchuan earthquake, the analyzed results indicate that: the main structure activity (the Yingxiu-Beichuan fault) characteristics of the Wenchuan earthquake can be identified and extracted using the bi-angular AATSR nadir and forward brightness temperature data, and the seismic infrared radiation anomalies detected by the AATSR nadir brightness temperature is more close to the Yingxiu-Beichuan fault.</p>

Satellite detection of IR precursors using bi-angular advanced along-track scanning radiometer data: a case study of Yushu earthquake

The paper has developed and proposed a synthesis analysis method based on the robust satellite data analysis technique (RST) to detect seismic anomalies within the bi-angular advanced along-track scanning radiometer (AATSR) gridded brightness temperature (BT) data based on spatial/temporal continuity analysis. The proposed methods have been applied to analyze the Yushu (Qinghai, China) earthquake occurred on 14th April 2010, and a full AATSR data-set of 8 years data from March 2003 to May 2010 with longitude from 91°E to 101°E and latitude from 28°N to 38°N has been analyzed. Combining with the tectonic explanation of spatial and temporal continuity of the abnormal phenomena, the analyzed results indicate that the infrared radiation anomalies detected by the AATSR BT data with nadir view appear and enhance gradually along with the development and occurring of the earthquake, especially along the Ganzi-Yushu fault, Nu River fault and Jiali-Chayu fault; more infrared anomalies along the earthquake fault zone (Lancangjiang fault and Ning Karma Monastery-Deqin fault) are detected using the proposed synthesis analysis method, which can also characterize the activity of seismic faults more precisely.

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.

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.

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.

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.

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.

Actual daily evapotranspiration estimated from MERIS and AATSR data over the Chinese Loess Plateau

Abstract. The Chinese Loess Plateau is located in the north of China and has a significant impact on the climate and ecosystem evolvement over the East Asian continent. Estimates of evapotranspiration (ET) at a regional scale are in crucial need for climate studies, weather forecasts, hydrological surveys, ecological monitoring and water resource management. In this research, the ET of the Chinese Loess Plateau was estimated by using an energy balance approach and data collected during the LOess Plateau land-atmosphere interaction pilot EXperiments 2005 (LOPEX05). With the combined data of the Medium Resolution Imaging Spectrometer (MERIS), the Advanced Along-Track Scanning Radiometer (AATSR) and some other variables such as air temperature, crop height and wind speed, the instantaneous net radiation, sensible heat flux and soil heat flux were calculated; the instantaneous latent heat flux was derived as the residual term of energy balance, and then converted to daily ET value by sunshine duration. The calculated daily ET from the model showed a good match with the measurements of the eddy covariance systems deployed in LOPEX05. The minimum relative error of this approach is 9.0%, the cause of the bias was also explored and discussed.

Evaluation of AATSR and TMI Satellite SST Data

Abstract The purpose of this paper is to investigate two satellite instruments for SST: the infrared (IR) Advanced Along Track Scanning Radiometer (AATSR) and the microwave (MW) Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI). Because of its dual view, AATSR has a potential for lower biases than other IR products such as the Advanced Very High Resolution Radiometer (AVHRR), while the tropical TMI record was available for a longer period of time than the global MW instrument, the Advanced Microwave Scanning Radiometer (AMSR). The results show that the AATSR IR retrievals are good quality with biases lower than or as low as other satellite retrievals between 50°S and 50°N. Furthermore, the dual-view algorithm reduces the influence of aerosol contamination. However, the AATSR coverage is roughly half that of AVHRR. North of 50°N there appear to be biases and high variability in summer daytime retrievals, with smaller but consistent biases observed below 50°S. TMI data can significantly improve coverage offshore in regions where IR retrievals are reduced by cloud cover. However, TMI data have small-scale biases from land contamination that should be removed by modifying the land–sea mask to remove more coastal regions.

Land Surface Temperature From the Advanced Along-Track Scanning Radiometer: Validation Over Inland Waters and Vegetated Surfaces

The land surface temperature (LST) product of the Advanced Along-Track Scanning Radiometer (AATSR) was validated with ground measurements at the following two thermally homogeneous sites: Lake Tahoe, CA/NV, USA, and a large rice field close to Valencia, Spain. The AATSR LST product is based on the split-window technique using the 11- and 12- mum channels. The algorithm coefficients are provided for 13 different land-cover classes plus one lake class (index i). Coefficients are weighted by the vegetation-cover fraction (f). In the operational implementation of the algorithm, i and f are assigned from a global classification and monthly fractional vegetation-cover maps with spatial resolutions of 0.5deg times 0.5deg. Since the validation sites are smaller than this, they are misclassified in the LST product and treated incorrectly despite the fact that the higher resolution AATSR data easily resolve the sites. Due to this problem, the coefficients for the correct cover types were manually applied to the AATSR standard brightness temperature at sensor product to obtain the LST for the sites assuming they had been correctly classified. The comparison between the ground-measured and the AATSR-derived LSTs showed an excellent agreement for both sites, with nearly zero average biases and standard deviations les 0.5degC. In order to produce accurate and precise estimates of LST, it is necessary that the land-cover classification is revised and provided at the same resolution as the AATSR data, i.e., 1 km rather than the 0.5deg resolution auxiliary data currently used in the LST product.