Advanced Along-Track Scanning Radiometer Research Articles

Evaluation of SCIAMACHY ESA/DLR Cloud Parameters Version 5.02 by Comparisons to Ground-Based and Other Satellite Data

This paper reports on the evaluation of long-term cloud products as retrieved from measurements of the SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY (SCIAMACHY) instrument with the DLR/ESA processor in its version 5.02 and the operational implementation of the Semi-Analytical CloUd Retrieval Algorithm SACURA. The comparison is performed against spaceborne and ground-based cloud data. The satellite records are the SCIAMACHY/SACURA in its scientific implementation and the Global retrieval of ATSR cloud parameters and evaluation (GRAPE) data set, in its version 3.2, generated for the nadir view of the Advanced Along-Track Scanning Radiometer (AATSR) instrument onboard ENVISAT. Ground-based data are derived from profiles of micro-pulse lidars, continuously operated at three Atmospheric Radiation Measurement (ARM) research facilities. They are, namely, North Slope Alaska, Southern Great Plains and Tropical Western Pacific-Nauru, located in three different latitude belts. It has been found that SCIAMACHY cloud top heights, inferred in the visible-near infrared, have a seasonal dependent overestimation in range 0.6 – 1.0 km when compared to the thermal infrared-derived AATSR cloud top heights. The comparison with the in-situ cloud retrievals reveals that SCIAMACHY cloud altitudes are more accurate for local cloud cover values > 0.6.

Toward “all weather,” long record, and real‐time land surface temperature retrievals from microwave satellite observations

AbstractThe land surface temperature can be estimated from satellite passive microwave observations, with limited contamination from the clouds as compared to the infrared satellite retrievals. With ∼60% cloud cover in average over the globe, there is a need for “all weather,” long record, and real‐time estimates of land surface temperature (Ts) from microwaves. A simple yet accurate methodology is developed to derive the land surface temperature from microwave conical scanner observations, with the help of precalculated land surface microwave emissivities. Different tests are conducted to optimize the algorithms. The method is applied to the Special Sensor Microwave/Imagers (SSM/I) observations over 2 years, regardless of the cloud cover. The results are compared to infrared estimates from International Satellite Cloud Climatology Project (ISCCP) and from Advanced Along Track Scanning Radiometer (AATSR), under clear‐sky conditions. Limited biases are observed (∼0.5 K for both comparisons) with a root‐mean‐square error (RMSE) of ∼5 K, to be compared to the RMSE of ∼3.5 K between ISCCP et AATSR. Cloud contamination in the AATSR estimates have been evidenced, and a simple filtering has been proposed. The microwave surface temperatures have also been carefully compared to in situ Ts time series from a collection of more than 20 stations over a large range of environments. Very good agreement is obtained for well‐controlled stations in vegetated environments (down to RMSE of ∼2.5 K for several stations), but the methodology encounter difficulties under cold conditions due to the large variability of snow and ice surface emissivities.

Investigating sea surface temperature diurnal variation over the Tropical Warm Pool using MTSAT-1R data

Abstract Diurnal variation (DV) of sea surface temperature (SST) plays an important role in air–sea interaction. We have validated four months (January to April 2010) of the version 3 Australian Bureau of Meteorology reprocessed Multifunction Transport SATellite-1R (v3 MTSAT-1R) SST data over the Tropical Warm Pool (TWP) region (90°E to 170°E, 25°S to 15°N) against both drifting buoy and Advanced Along-Track Scanning Radiometer (AATSR) SST data. Validation against collocated point measurements from drifting buoys, under conditions where the surface ocean is well-mixed, shows that overall the v3 MTSAT-1R SSTs perform well with an average bias of 0.00 °C and a 0.73 °C standard deviation (STD). The average daytime and night-time mean bias is − 0.06 °C and 0.08 °C, respectively. For all hours of the diurnal cycle, the mean biases are within ± 0.25 °C, indicating the consistency between day and night v3 MTSAT-1R data. However, on average, the v3 MTSAT-1R SSTs are overestimated at cold SSTs and underestimated at warm SSTs. Similar results are obtained from validation against the AATSR satellite SSTs but with smaller STD (0.48 °C) and smaller average daytime and night-time mean biases (− 0.04 °C and 0.06 °C, respectively). These results indicate that the v3 MTSAT-1R data set is suitable for SST DV investigations and validation of DV models. Using the validated v3 MSTAT-1R data, together with surface wind speed and solar shortwave insolation (SSI) outputs from the Australian Community Climate and Earth-System Simulator – Regional (ACCESS-R) numerical prediction model, we investigate SST DV events over the TWP region. Good correlation is found between DV events and low wind and high SSI conditions. The dominant role of wind speed in SST DV events over the SSI is also revealed.

GRADHIST — A method for detection and analysis of oceanic fronts from remote sensing data

Abstract Oceanic shelf sea fronts have significant effects on local dynamics, ecology and climate. An assessment of the impact of climate change on frontal positions and frontal gradients requires reliable reference data and the possibility to monitor oceanic fronts. Therefore, the development of algorithms which automatically detect frontal positions from Earth Observation (EO) data is an important tool to analyse long EO time series, i.e. to process big data volumes. The development of GRADHIST was driven by the need to generate a climatology for North Sea fronts. GRADHIST is a new algorithm for the detection and mapping of oceanic fronts, which is based on a combination and refinement of the gradient algorithm of Canny (1986) and the histogram algorithm of Cayula and Cornillon (1992). GRADHIST preserves the main principles of both algorithms and can be applied to various ocean parameters as well as to different sensors with very little effort. GRADHIST was validated and tested using both synthetic and real data and applied to sea surface temperature and ocean colour parameters retrieved from satellite data; i.e. data from MODIS (Moderate Resolution Imaging Spectroradiometer), MERIS (MEdium Resolution Imaging Spectrometer), AVHRR (Advanced Very High Resolution Radiometer) and AATSR (Advanced Along-Track Scanning Radiometer). Selected results and statistical analysis of a new North Sea climatology for oceanic fronts are presented and discussed.

Independent uncertainty estimates for coefficient based sea surface temperature retrieval from the Along-Track Scanning Radiometer instruments

Abstract We establish a methodology for calculating uncertainties in sea surface temperature estimates from coefficient based satellite retrievals. The uncertainty estimates are derived independently of in-situ data. This enables validation of both the retrieved SSTs and their uncertainty estimate using in-situ data records. The total uncertainty budget is comprised of a number of components, arising from uncorrelated (e.g. noise), locally systematic (e.g. atmospheric), large scale systematic and sampling effects (for gridded products). The importance of distinguishing these components arises in propagating uncertainty across spatio-temporal scales. We apply the method to SST data retrieved from the Advanced Along Track Scanning Radiometer (AATSR) and validate the results for two different SST retrieval algorithms, both at a per pixel level and for gridded data. We find good agreement between our estimated uncertainties and validation data. This approach to calculating uncertainties in SST retrievals has a wider application to data from other instruments and retrieval of other geophysical variables.

Sampling uncertainty in gridded sea surface temperature products and Advanced Very High Resolution Radiometer (AVHRR) Global Area Coverage (GAC) data

Abstract Sea surface temperature (SST) data are often provided as gridded products, typically at resolutions of order 0.05° from satellite observations, to reduce data volume at the request of data users and facilitate comparison against other products or models. Sampling uncertainty is introduced in gridded products where the full surface area of the ocean within a grid cell cannot be fully observed because of cloud cover. In this paper we parameterise uncertainties in SST as a function of the percentage of clear-sky pixels available and the SST variability in that subsample. This parameterisation is developed from Advanced Along Track Scanning Radiometer (AATSR) data, but is applicable to all gridded L3U SST products at resolutions of 0.05–0.1°, irrespective of instrument and retrieval algorithm, provided that instrument noise propagated into the SST is accounted for. We also calculate the sampling uncertainty of ~ 0.04 K in global area coverage (GAC) Advanced Very High Resolution Radiometer (AVHRR) products, using related methods.

Estimating urban PM10 and PM2.5 concentrations, based on synergistic MERIS/AATSR aerosol observations, land cover and morphology data

This study evaluates alternative spatio-temporal approaches for quantitative estimation of daily mean Particulate Matter ( PM ) concentrations. Both fine ( PM 2.5) and coarse ( PM 10) concentrations were estimated over the area of London (UK) for the 2002–2012 time period, using Aerosol Optical Thickness ( AOT ) derived from MERIS (Medium Resolution Imaging Spectrometer)/AATSR (Advanced Along-Track Scanning Radiometer) synergistic observations at 1 km × 1 km resolution. Relative humidity, temperature and the K-Index obtained from MODIS (Moderate Resolution Imaging Spectroradiometer) sensor were used as additional predictors. High-resolution (100 m × 100 m) local urban land cover and morphology datasets were incorporated in the analysis in order to capture the effects of local scale emissions and sequestration. Spatial (2-D) and spatio-temporal (3-D) kriging were applied to in situ urban PM measurements to investigate their association with satellite-derived AOT while accounting for differences in spatial support. Linear mixed-effects models with day-specific and site-specific random intercepts and slopes were estimated to associate satellite-derived products with kriged PM concentration and their predictive performance was evaluated. • Urban PM concentration estimation using AOT derived from MERIS/AATSR synergy at 1 km • Local urban land use/cover and morphology data were among the additional predictors. • Spatial/space–time kriging was used to homogenize data of different spatial supports. • Mixed-effect models with day- and site-specific random effects were evaluated. • Satellite-derived predictors captured spatial variability of PM 10 and PM 2.5.

The ADV/ASV AATSR aerosol retrieval algorithm: current status and presentation of a full-mission AOD dataset

ABSTRACTAn advanced along-track scanning radiometer (AATSR) global multi-year aerosol retrieval algorithm is described. Over land, the AATSR dual-view (ADV) algorithm utilizes the measured top of the atmosphere (TOA) reflectance in both the nadir and forward views to decouple the contributions of the atmosphere and the surface to retrieve aerosol properties. Over ocean, the AATSR single-view (ASV) algorithm minimizes the discrepancy between the measured and modelled TOA reflectances in one of the views to retrieve the aerosol information using an ocean reflectance model. Necessary steps to process global, multi-year aerosol information are presented. These include cloud screening, the averaging of measured reflectance, and post-processing. Limitations of the algorithms are also discussed. The main product of the aerosol retrieval is the aerosol optical depth (AOD) at visible/near-infrared wavelengths. The retrieved AOD is validated using data from the surface-based AERONET and maritime aerosol network (MAN) sun photometer networks as references. The validation shows good agreement with the reference (r = 0.85, RMSE = 0.09 over land; r = 0.83, RMSE = 0.09 at coasts and r = 0.96, RMSE = 0.06 over open ocean). The results of the aerosol retrievals are presented for the full AATSR mission years 2002–2012 with seasonally averaged time series for selected regions.

Characterizing the Aerosol and Surface Reflectance Over Australia Using AATSR

We present a new algorithm that simultaneously retrieves aerosol properties and land surface bidirectional reflectance distribution function (BRDF) over Australian from Advanced Along-Track Scanning Radiometer images. Three key components are addressed: 1) an analytical radiative transfer formulation, based on Green's function, linking top-of-atmosphere (TOA) reflectance to the surface BRDF; 2) a novel approach to modeling BRDF using an extended compositing period; and 3) a set of representative aerosol models based on a published typology of Australian aerosols. Due to the generally low aerosol loadings and widespread bright surfaces over Australia, BRDF modeling is crucial. By using a 9-month compositing period, 90% of the Australian continent can be modeled with an error in the forward-to-nadir reflectance ratio of 2.5% or less. Comparison with suitably processed Moderate Resolution Imaging Spectroradiometer BRDF/albedo products demonstrates satisfactory agreement. For the studying period from 2002 to 2008, validation of aerosol optical depth (AOD) against eight sun photometers across Australia encompassing widely different atmospheric and surface regimes shows high accuracy, with a mean absolute error in AOD at 550 nm of 0.03 and a bias of 0.007. About 60% of the matchup points are within an absolute error of 0.03, 80% are within 0.05, and 96% are within 0.1. The algorithm selects for each cell an optimal aerosol type from a set of four predefined candidate models. Continental aerosol maps derived from the new method indicate broadscale agreement with known seasonal aerosol sources, while providing new insights into the spatial and temporal distributions of aerosol over the Australian continent.

Exploiting the sensitivity of two satellite cloud height retrievals to cloud vertical distribution

Abstract. This work presents a study on the sensitivity of two satellite cloud height retrievals to cloud vertical distribution. The difference in sensitivity is exploited by relating the difference in the retrieved cloud heights to cloud vertical extent. The two cloud height retrievals, performed within the Freie Universität Berlin AATSR MERIS Cloud (FAME-C) algorithm, are based on independent measurements and different retrieval techniques. First, cloud-top temperature (CTT) is retrieved from Advanced Along Track Scanning Radiometer (AATSR) measurements in the thermal infrared. Second, cloud-top pressure (CTP) is retrieved from Medium Resolution Imaging Spectrometer (MERIS) measurements in the oxygen-A absorption band and a nearby window channel. Both CTT and CTP are converted to cloud-top height (CTH) using atmospheric profiles from a numerical weather prediction model. First, a sensitivity study using radiative transfer simulations in the near-infrared and thermal infrared was performed to demonstrate, in a quantitative manner, the larger impact of the assumed cloud vertical extinction profile, described in terms of shape and vertical extent, on MERIS than on AATSR top-of-atmosphere measurements. Consequently, cloud vertical extinction profiles will have a larger influence on the MERIS than on the AATSR cloud height retrievals for most cloud types. Second, the difference in retrieved CTH (ΔCTH) from AATSR and MERIS are related to cloud vertical extent (CVE), as observed by ground-based lidar and radar at three ARM sites. To increase the impact of the cloud vertical extinction profile on the MERIS-CTP retrievals, single-layer and geometrically thin clouds are assumed in the forward model. Similarly to previous findings, the MERIS-CTP retrievals appear to be close to pressure levels in the middle of the cloud. Assuming a linear relationship, the ΔCTH multiplied by 2.5 gives an estimate on the CVE for single-layer clouds. The relationship is stronger for single-layer clouds than for multi-layer clouds. Due to large variations of cloud vertical extinction profiles occurring in nature, a quantitative estimate of the cloud vertical extent is accompanied with large uncertainties. Yet, estimates of the CVE provide an additional parameter, next to CTH, that can be obtained from passive imager measurements and can be used to further describe cloud vertical distribution, thus contributing to the characterization of a cloudy scene. To further demonstrate the plausibility of the approach, an estimate of the CVE was applied to a case study. In light of the follow-up mission Sentinel-3 with AATSR and MERIS like instruments, Sea and Land Surface Temperature Radiometer (SLSTR) and (Ocean and Land Colour Instrument) OLCI, respectively, for which the FAME-C algorithm can be easily adapted, a more accurate estimate of the CVE can be expected. OLCI will have three channels in the oxygen-A absorption band, possibly providing enhanced information on cloud vertical distributions.

Indirect estimation of absorption properties for fine aerosol particles using AATSR observations: a case study of wildfires in Russia in 2010

Abstract. The Advanced Along-Track Scanning Radiometer (AATSR) on board the ENVISAT satellite is used to study aerosol properties. The retrieval of aerosol properties from satellite data is based on the optimized fit of simulated and measured reflectances at the top of the atmosphere (TOA). The simulations are made using a radiative transfer model with a variety of representative aerosol properties. The retrieval process utilizes a combination of four aerosol components, each of which is defined by their (lognormal) size distribution and a complex refractive index: a weakly and a strongly absorbing fine-mode component, coarse mode sea salt aerosol and coarse mode desert dust aerosol). These components are externally mixed to provide the aerosol model which in turn is used to calculate the aerosol optical depth (AOD). In the AATSR aerosol retrieval algorithm, the mixing of these components is decided by minimizing the error function given by the sum of the differences between measured and calculated path radiances at 3–4 wavelengths, where the path radiances are varied by varying the aerosol component mixing ratios. The continuous variation of the fine-mode components allows for the continuous variation of the fine-mode aerosol absorption. Assuming that the correct aerosol model (i.e. the correct mixing fractions of the four components) is selected during the retrieval process, also other aerosol properties could be computed such as the single scattering albedo (SSA). Implications of this assumption regarding the ratio of the weakly/strongly absorbing fine-mode fraction are investigated in this paper by evaluating the validity of the SSA thus obtained. The SSA is indirectly estimated for aerosol plumes with moderate-to-high AOD resulting from wildfires in Russia in the summer of 2010. Together with the AOD, the SSA provides the aerosol absorbing optical depth (AAOD). The results are compared with AERONET data, i.e. AOD level 2.0 and SSA and AAOD inversion products. The RMSE (root mean square error) is 0.03 for SSA and 0.02 for AAOD lower than 0.05. The SSA is further evaluated by comparison with the SSA retrieved from the Ozone Monitoring Instrument (OMI). The SSA retrieved from both instruments show similar features, with generally lower AATSR-estimated SSA values over areas affected by wildfires.

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>

Optical modeling of volcanic ash particles using ellipsoids

AbstractThe single‐scattering properties of volcanic ash particles are modeled here by using ellipsoidal shapes. Ellipsoids are expected to improve the accuracy of the retrieval of aerosol properties using remote sensing techniques, which are currently often based on oversimplified assumptions of spherical ash particles. Measurements of the single‐scattering optical properties of ash particles from several volcanoes across the globe, including previously unpublished measurements from the Eyjafjallajökull and Puyehue volcanoes, are used to assess the performance of the ellipsoidal particle models. These comparisons between the measurements and the ellipsoidal particle model include consideration of the whole scattering matrix, as well as sensitivity studies on the point of view of the Advanced Along Track Scanning Radiometer (AATSR) instrument. AATSR, which flew on the ENVISAT satellite, offers two viewing directions but no information on polarization, so usually only the phase function is relevant for interpreting its measurements. As expected, ensembles of ellipsoids are able to reproduce the observed scattering matrix more faithfully than spheres. Performance of ellipsoid ensembles depends on the distribution of particle shapes, which we tried to optimize. No single specific shape distribution could be found that would perform superiorly in all situations, but all of the best‐fit ellipsoidal distributions, as well as the additionally tested equiprobable distribution, improved greatly over the performance of spheres. We conclude that an equiprobable shape distribution of ellipsoidal model particles is a relatively good, yet enticingly simple, approach for modeling volcanic ash single‐scattering optical properties.

Natural variability of aerosol inherent optical properties: consequences for atmospheric correction over water

A good knowledge of the inherent optical properties (IOPs) of aerosols is a strong requirement for accurately performing atmospheric correction over the ocean. For several decades, IOPs have been computed using standard aerosol models (SAMs) that characterize the micro-physical properties of aerosols. These SAMs were used in the last generation of the Medium Resolution Imaging Spectrometer (MERIS) auxiliary data files (ADFs) to feed the atmospheric correction algorithm. Alternatively, Aerosol Robotic Network (AERONET) measurements can also provide IOPs. We built a database with the aerosol IOPs encountered over four AERONET stations in the North Sea plus one at the Acqua Alta Oceanographic Tower (AAOT, Venice, Italy). Several thousands of data sequences containing the aerosol IOPs were processed with filtering techniques and statistical methods to produce 16 classes of IOPs. An analysis of the dispersion of the IOPs within each class was conducted to evaluate the induced errors in the MERIS level-2 (L2) products in European coastal waters. We also investigated the reduction in the errors that can be achieved if there is access to auxiliary meteorological data (i.e. the relative humidity) or by using the bidirectionality in the satellite measurements, such as for advanced along-track scanning radiometer (AATSR) data.

Bayesian cloud detection for MERIS, AATSR, and their combination

Abstract. A broad range of different of Bayesian cloud detection schemes is applied to measurements from the Medium Resolution Imaging Spectrometer (MERIS), the Advanced Along-Track Scanning Radiometer (AATSR), and their combination. The cloud detection schemes were designed to be numerically efficient and suited for the processing of large numbers of data. Results from the classical and naive approach to Bayesian cloud masking are discussed for MERIS and AATSR as well as for their combination. A sensitivity study on the resolution of multidimensional histograms, which were post-processed by Gaussian smoothing, shows how theoretically insufficient numbers of truth data can be used to set up accurate classical Bayesian cloud masks. Sets of exploited features from single and derived channels are numerically optimized and results for naive and classical Bayesian cloud masks are presented. The application of the Bayesian approach is discussed in terms of reproducing existing algorithms, enhancing existing algorithms, increasing the robustness of existing algorithms, and on setting up new classification schemes based on manually classified scenes.

Upwelling Parameters From Bias-Corrected Composite Satellite SST Maps in the Gulf of Finland (Baltic Sea)

This letter proposes a method for using the operational ship of opportunity temperature data at a fixed depth for bias correction of satellite sea surface temperature (SST) images. The bias-corrected SST imagery from MODerate Resolution Imaging Spectroradiometer (MODIS) and Advanced Along-Track Scanning Radiometer (AATSR) sensors were used to calculate mean upwelling characteristics in the Gulf of Finland (GoF, Baltic Sea). First, we determined that the operational flow through temperature data at a 4-m depth can be used for validation and bias correction of satellite SST images in cases of wind speed over 5 m · s <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-1</sup> . The composite sea temperature maps were calculated from bias-corrected images collected during upwelling events in the GoF, in 2000-2009. Mean upwelling characteristics were estimated from composite maps for both the northern and southern coasts of the gulf.

Determination of land surface reflectance using the AATSR dual-view capability

Abstract. In this study, a method is presented to retrieve the surface reflectance using the radiances measured at the top of the atmosphere for the two views provided by the Advanced Along-Track Scanning Radiometer (AATSR). In the first step, the aerosol optical depth (AOD) is obtained using the AATSR dual-view algorithm (ADV) by eliminating the effect of the surface on the measured radiances. Hence the AOD is independent of surface properties and can thus be used in the second step to provide the aerosol part of the atmospheric correction which is needed for the surface reflectance retrieval. The method is applied to provide monthly maps of both AOD and surface reflectance at two wavelengths (555 and 659 nm) for the whole year of 2007. The results are validated versus surface reflectance provided by the AERONET-based Surface Reflectance Validation Network (ASRVN). Correlation coefficients are 0.8 and 0.9 for 555 and 659 nm, respectively. The standard deviation is 0.001 for both wavelengths and the absolute error is less than 0.02. Pixel-by-pixel comparison with MODIS (Moderate Resolution Imaging Spectrometer) monthly averaged surface reflectances show a good correlation (0.91 and 0.89 for 555 and 659 nm, respectively) with somewhat higher values (up to 0.05) obtained by ADV over bright surfaces. The difference between the ADV- and MODIS-retrieved surface reflectances is smaller than ±0.025 for 68.3% of the collocated pixels at 555 nm and 79.9% of the collocated pixels at 659 nm. An application of the results over Australia illustrates the variation in the surface reflectances for different land cover types. The validation and comparison results suggest that the algorithm can be successfully used for both the AATSR and ATSR-2 (which has characteristics similar to AATSR) missions, which together cover a 17-year period of measurements (1995–2012), as well as a prototype for the Sea and Land Surface Temperature Radiometer (SLSTR) planned to be launched in the fall of 2015 onboard the Sentinel-3 satellite.

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.

On Uncertainties of the Priestley-Taylor/LST-Fc Feature Space Method to Estimate Evapotranspiration: Case Study in an Arid/Semiarid Region in Northwest China

Accurate evapotranspiration (ET) estimation is very crucial for water resource management, particularly for the arid and semi-arid region. The remote sensing-based Priestley-Taylor method (RS-PT method) can estimate ET at regional scale, using the feature space of remotely sensed land surface temperature (LST) and vegetation index (VI). This study evaluates the RS-PT feature space method over an arid and semi-arid region in northwest China using satellite data from the moderate-resolution space-borne sensor Advanced Along-Track Scanning Radiometer (AATSR), the observations from the high-resolution airborne sensor Wide-angle Infrared Dual-mode line/area Array Scanner (WiDAS) and ground measurements of heat fluxes collected in summer 2008. The results show that the mean difference for latent heat flux (LE) estimates resulting from different domain sizes is 69.5 W/m2. When using high-resolution images from airborne measurements, the dry boundary is strongly affected by the pixels of impervious surfaces, which lead to a mean difference of 15.36 W/m2 for LE estimates. In addition, the physically based Surface Energy Balance Index (SEBI) model is used to analyze the accuracy of dry/wet boundaries in the RS-PT method. Compared with the SEBI-estimated relative evaporative fraction (Λr), the RS-PT method underestimated Λr by ~0.11. For the RS-PT method, the uncertainty in the determination of the dry/wet boundaries has a significant impact on the accuracy of the ET estimate, not only depending on the size of the area to build the feature space, but also on the land covers.

Impact of satellite-based lake surface observations on the initial state of HIRLAM. Part II: Analysis of lake surface temperature and ice cover

This paper presents results from a study on the impact of remote-sensing Lake Surface Water Temperature (LSWT) observations in the analysis of lake surface state of a numerical weather prediction (NWP) model. Data assimilation experiments were performed with the High Resolution Limited Area Model (HIRLAM), a three-dimensional operational NWP model. Selected thermal remote-sensing LSWT observations provided by the Moderate Resolution Imaging Spectroradiometer (MODIS) and Advanced Along-Track Scanning Radiometer (AATSR) sensors onboard the Terra/Aqua and ENVISAT satellites, respectively, were included into the assimilation. The domain of our experiments, which focussed on two winters (2010–2011 and 2011–2012), covered northern Europe. Validation of the resulting objective analyses against independent observations demonstrated that the description of the lake surface state can be improved by the introduction of space-borne LSWT observations, compared to the result of pure prognostic parameterisations or assimilation of the available limited number of in-situ lake temperature observations. Further development of the data assimilation methods and solving of several practical issues are necessary in order to fully benefit from the space-borne observations of lake surface state for the improvement of the operational weather forecast. This paper is the second part of a series of two papers aimed at improving the objective analysis of lake temperature and ice conditions in HIRLAM.