Along-Track Scanning Radiometer Research Articles

Retrieval of aerosol optical depth over the Arctic cryosphere during spring and summer using satellite observations

Abstract. The climate in the Arctic has warmed much more quickly in the last 2 to 3 decades than at the mid-latitudes, i.e., during the Arctic amplification (AA) period. Radiative forcing in the Arctic is influenced both directly and indirectly by aerosols. However, their observation from ground or airborne instruments is challenging, and thus measurements are sparse. In this study, total aerosol optical depth (AOD) is determined from top-of-atmosphere reflectance measurements by the Advanced Along-Track Scanning Radiometer (AATSR) on board ENVISAT over snow and ice in the Arctic using a retrieval called AEROSNOW for the period 2003 to 2011. AEROSNOW incorporates an existing aerosol retrieval algorithm with a cloud-masking algorithm, alongside a novel quality-flagging methodology specifically designed for implementation in the high Arctic region (≥ 72∘ N). We use the dual-viewing capability of the AATSR instrument to accurately determine the contribution of aerosol to the reflection at the top of the atmosphere for observations over the bright surfaces of the cryosphere in the Arctic. The AOD is retrieved assuming that the surface reflectance observed by the satellite can be well parameterized by a bidirectional snow reflectance distribution function (BRDF). The spatial distribution of AOD shows that high values in spring (March, April, May) and lower values in summer (June, July, August) are observed. The AEROSNOW AOD values are consistent with those from collocated Aerosol Robotic Network (AERONET) measurements, with no systematic bias found as a function of time. The AEROSNOW AOD in the high Arctic was validated by comparison with ground-based measurements at the PEARL, OPAL, Hornsund, and Thule stations. The AEROSNOW AOD value is less than 0.15 on average, and the linear regression of AEROSNOW and AERONET total AOD yields a slope of 0.98, a Pearson correlation coefficient of R=0.86, and a root mean square error (RMSE) of =0.01 for the monthly scale in both spring and summer. The AEROSNOW observation of increased AOD values over the high Arctic cryosphere during spring confirms clearly that Arctic haze events were well captured by this dataset. In addition, the AEROSNOW AOD results provide a novel and unique total AOD data product for the springtime and summertime from 2003 to 2011. These AOD values, retrieved from spaceborne observation, provide a unique insight into the high Arctic cryospheric region at high spatial resolution and temporal coverage.

TRIMS LST: a daily 1 km all-weather land surface temperature dataset for China's landmass and surrounding areas (2000–2022)

Abstract. Land surface temperature (LST) is a key variable within Earth's climate system and a necessary input parameter required by numerous land–atmosphere models. It can be directly retrieved from satellite thermal infrared (TIR) observations, which contain many invalid pixels mainly caused by cloud contamination. To investigate the spatial and temporal variations in LST in China, long-term, high-quality, and spatiotemporally continuous LST datasets (i.e., all-weather LST) are urgently needed. Fusing satellite TIR LST and reanalysis datasets is a viable route to obtain long time-series all-weather LSTs. Among satellite TIR LSTs, the MODIS LST is the most commonly used, and a few corresponding all-weather LST products have been reported recently. However, the publicly reported all-weather LSTs were not available during the temporal gaps of MODIS between 2000 and 2002. In this study, we generated a daily (four observations per day) 1 km all-weather LST dataset for China's landmass and surrounding areas, the Thermal and Reanalysis Integrating Moderate-resolution Spatial-seamless (TRIMS) LST, which begins on the first day of the new millennium (1 January 2000). We used the enhanced reanalysis and thermal infrared remote sensing merging (E-RTM) method to generate the TRIMS LST dataset with the temporal gaps being filled, which had not been achieved by the original RTM method. Specifically, we developed two novel approaches, i.e., the random-forest-based spatiotemporal merging (RFSTM) approach and the time-sequential LST-based reconstruction (TSETR) approach, respectively, to produce Terra/MODIS-based and Aqua/MODIS-based TRIMS LSTs during the temporal gaps. We also conducted a thorough evaluation of the TRIMS LST. A comparison with the Global Land Data Assimilation System (GLDAS) and ERA5-Land LST demonstrates that the TRIMS LST has similar spatial patterns but a higher image quality, more spatial details, and no evident spatial discontinuities. The results outside the temporal gap show consistent comparisons of the TRIMS LST with the MODIS LST and the Advanced Along-Track Scanning Radiometer (AATSR) LST, with a mean bias deviation (MBD) of 0.09/0.37 K and a standard deviation of bias (SD) of 1.45/1.55 K. Validation based on the in situ LST at 19 ground sites indicates that the TRIMS LST has a mean bias error (MBE) ranging from −2.26 to 1.73 K and a root mean square error (RMSE) ranging from 0.80 to 3.68 K. There is no significant difference between the clear-sky and cloudy conditions. For the temporal gap, it is observed that RFSTM and TSETR perform similarly to the original RTM method. Additionally, the differences between Aqua and Terra remain stable throughout the temporal gap. The TRIMS LST has already been used by scientific communities in various applications such as soil moisture downscaling, evapotranspiration estimation, and urban heat island modeling. The TRIMS LST is freely and conveniently available at https://doi.org/10.11888/Meteoro.tpdc.271252 (Zhou et al., 2021).

Multi-decadal trends and variability in burned area from the fifth version of the Global Fire Emissions Database (GFED5)

Abstract. Long-term records of burned area are needed to understand wildfire dynamics, assess fire impacts on ecosystems and air quality, and improve fire forecasts. Here, we fuse multiple streams of remote sensing data to create a 24 year (1997–2020) dataset of monthly burned area as a component of the fifth version of the Global Fire Emissions Database (GFED5). During 2001–2020, we use the Moderate Resolution Imaging Spectroradiometer (MODIS) MCD64A1 burned area product and adjust for the errors of commission and omission. Adjustment factors are estimated based on region, land cover, and tree cover fraction, using spatiotemporally aligned burned area from Landsat or Sentinel-2. Burned area in croplands, peatlands, and deforestation regions is estimated from MODIS active fire detections. Along-Track Scanning Radiometer (ATSR) and Visible and Infrared Scanner (VIRS) active fire data are used to extend the time series back to 1997. The global annual burned area during 2001–2020 is estimated to be 774 ± 63 Mha yr−1 or 5.9 ± 0.5 % of ice-free land. Burned area declined by 1.21 ± 0.66 % yr−1, a cumulative decrease of 24.2 ± 13.2 % over 20 years. The global reduction is primarily driven by a decrease in fires in savannas, grasslands, and croplands. Forest, peat, and deforestation fires did not exhibit significant long-term trends. The GFED5 global burned area is 93 % higher than MCD64A1, 61 % higher than GFED4s, and in closer agreement with products from higher-resolution satellite sensors. These data may reduce discrepancies between fire emission estimates from activity-based and atmospheric-based approaches, and improve our understanding of global fire impacts on the carbon cycle and climate system. The GFED5 global burned area product is freely accessible at https://doi.org/10.5281/zenodo.7668423 (Chen et al., 2023).

Bayesian Cloud Detection over Land for Climate Data Records

Cloud detection is a necessary step in the generation of land surface temperature (LST) climate data records (CDRs) and affects data quality and uncertainty. We present here a sensor-independent Bayesian cloud detection algorithm and show that it is suitable for use in the production of LST CDRs. We evaluate the performance of the cloud detection with reference to two manually masked datasets for the Advanced Along-Track Scanning Radiometer (AATSR) and find a 7.9% increase in the hit rate and 4.9% decrease in the false alarm rate when compared to the operational cloud mask. We then apply the algorithm to four instruments aboard polar-orbiting satellites, which together can produce a global, 25-year LST CDR: the second Along-Track Scanning Radiometer (ATSR-2), AATSR, the Moderate Resolution Spectroradiometer (MODIS Terra) and the Sea and Land Surface Temperature Radiometer (SLSTR-A). The Bayesian cloud detection hit rate is assessed with respect to in situ ceilometer measurements for periods of overlap between sensors. The consistency of the hit rate is assessed between sensors, with mean differences in the cloud hit rate of 4.5% for ATSR-2 vs. AATSR, 4.9% for AATSR vs. MODIS, and 2.5% for MODIS vs. SLSTR-A. This is important because consistent cloud detection performance is needed for the observational stability of a CDR. The application of a sensor-independent cloud detection scheme in the production of CDRs is thus shown to be a viable approach to achieving LST observational stability over time.

Improved cloud detection over sea ice and snow during Arctic summer using MERIS data

Abstract. The historic MERIS (Medium Resolution Imaging Spectrometer) sensor on board Envisat (Environmental Satellite, operation 2002–2012) provides valuable remote sensing data for the retrievals of summer sea ice in the Arctic. MERIS data together with the data of recently launched successor OLCI (Ocean and Land Colour Instrument) on board Sentinel 3A and 3B (2016 onwards) can be used to assess the long-term change of the Arctic summer sea ice. An important prerequisite to a high-quality remote sensing dataset is an accurate separation of cloudy and clear pixels to ensure lowest cloud contamination of the resulting product. The presence of 15 visible and near-infrared spectral channels of MERIS allows high-quality retrievals of sea ice albedo and melt pond fraction, but it makes cloud screening a challenge as snow, sea ice and clouds have similar optical features in the available spectral range of 412.5–900 nm. In this paper, we present a new cloud screening method MECOSI (MERIS Cloud Screening Over Sea Ice) for the retrievals of spectral albedo and melt pond fraction (MPF) from MERIS. The method utilizes all 15 MERIS channels, including the oxygen A absorption band. For the latter, a smile effect correction has been developed to ensure high-quality screening throughout the whole swath. A total of 3 years of reference cloud mask from AATSR (Advanced Along-Track Scanning Radiometer) (Istomina et al., 2010) have been used to train the Bayesian cloud screening for the available limited MERIS spectral range. Whiteness and brightness criteria as well as normalized difference thresholds have been used as well. The comparison of the developed cloud mask to the operational AATSR and MODIS (Moderate Resolution Imaging Spectroradiometer) cloud masks shows a considerable improvement in the detection of clouds over snow and sea ice, with about 10 % false clear detections during May–July and less than 5 % false clear detections in the rest of the melting season. This seasonal behavior is expected as the sea ice surface is generally brighter and more challenging for cloud detection in the beginning of the melting season. The effect of the improved cloud screening on the MPF–albedo datasets is demonstrated on both temporal and spatial scales. In the absence of cloud contamination, the time sequence of MPFs displays a greater range of values throughout the whole summer. The daily maps of the MPF now show spatially uniform values without cloud artifacts, which were clearly visible in the previous version of the dataset. The developed cloud screening routine can be applied to address cloud contamination in remote sensing data over sea ice. The resulting cloud mask for the MERIS operating time, as well as the improved MPF–albedo datasets for the Arctic region, is available at https://www.seaice.uni-bremen.de/start/ (Istomina et al., 2017).

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.

Credibility and statistical characteristics of CAMSRA and MERRA-2 AOD reanalysis products over the Sichuan Basin during 2003–2018

Owing to its large population, unique topography, and rapid industrialization and urbanization, the Sichuan Basin (SCB) is one of the most polluted regions in China, as reflected by the large aerosol optical depth (AOD). In this study, the credibility of two long-term atmospheric composition reanalysis datasets—the Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2) and Copernicus Atmosphere Monitoring Service Reanalysis (CAMSRA)—are evaluated using Moderate Resolution Imaging Spectroradiometer (MODIS) AOD products over the SCB during 2003–2018. The time series of the three datasets at seasonal, monthly and annual scales exhibit some interesting similarities and differences, but with all of them demonstrating a prominent slowing down of the severe air pollution status over the SCB since 2012 (−0.25/7yrs for MODIS and CAMSRA and −0.12/7yrs for MERRA-2). CAMSRA shows good consistency with MODIS at all time scales (correlation coefficients varying from 0.89 to 0.97 at four seasons) despite its systematic overestimation (～0.2) throughout the 16-year study period. MERRA-2 has a negative deviation during 2003–2006 and then maintains an increasing positive deviation in the following years (0.05–0.2). This positive bias increases as MODIS AOD declines prominently from 2014. The good temporal consistency of CAMSRA is partly attributable to the bias correction of the assimilated remotely sensed AOD from MODIS and AATSR (Advanced Along-Track Scanning Radiometer). The differences between the reanalysis and MODIS are obvious during October–December, which may be related to the cloud contamination effects as well as the uncertainties of emissions in each model system. The uniform decreasing trend of the three datasets since 2012 supports the effects of the tough emissions reduction policy, which is also evident in AOD changes of anthropogenic species. It is concluded that atmospheric composition reanalysis data with good spatial and temporal coverage can be considered in future air quality analyses over the SCB. However, the bias and regional dependence should be handled carefully.

Cloud<i>_</i>cci ATSR-2 and AATSR data set version 3: a 17-year climatology of global cloud and radiation properties

Abstract. We present version 3 (V3) of the Cloud_cci Along-Track Scanning Radiometer (ATSR) and Advanced ATSR (AATSR) data set. The data set was created for the European Space Agency (ESA) Cloud_cci (Climate Change Initiative) programme. The cloud properties were retrieved from the second ATSR (ATSR-2) on board the second European Remote Sensing Satellite (ERS-2) spanning 1995–2003 and the AATSR on board Envisat, which spanned 2002–2012. The data are comprised of a comprehensive set of cloud properties: cloud top height, temperature, pressure, spectral albedo, cloud effective emissivity, effective radius, and optical thickness, alongside derived liquid and ice water path. Each retrieval is provided with its associated uncertainty. The cloud property retrievals are accompanied by high-resolution top- and bottom-of-atmosphere shortwave and longwave fluxes that have been derived from the retrieved cloud properties using a radiative transfer model. The fluxes were generated for all-sky and clear-sky conditions. V3 differs from the previous version 2 (V2) through development of the retrieval algorithm and attention to the consistency between the ATSR-2 and AATSR instruments. The cloud properties show improved accuracy in validation and better consistency between the two instruments, as demonstrated by a comparison of cloud mask and cloud height with co-located CALIPSO data. The cloud masking has improved significantly, particularly in its ability to detect clear pixels. The Kuiper Skill score has increased from 0.49 to 0.66. The cloud top height accuracy is relatively unchanged. The AATSR liquid water path was compared with the Multisensor Advanced Climatology of Liquid Water Path (MAC-LWP) in regions of stratocumulus cloud and shown to have very good agreement and improved consistency between ATSR-2 and AATSR instruments. The correlation with MAC-LWP increased from 0.4 to over 0.8 for these cloud regions. The flux products are compared with NASA Clouds and the Earth's Radiant Energy System (CERES) data, showing good agreement within the uncertainty. The new data set is well suited to a wide range of climate applications, such as comparison with climate models, investigation of trends in cloud properties, understanding aerosol–cloud interactions, and providing contextual information for co-located ATSR-2/AATSR surface temperature and aerosol products. The following new digital identifier has been issued for the Cloud_cci ATSR-2/AATSRv3 data set: https://doi.org/10.5676/DWD/ESA_Cloud_cci/ATSR2-AATSR/V003 (Poulsen et al., 2019).

Harmonization of Space-Borne Infra-Red Sensors Measuring Sea Surface Temperature

Sea surface temperature (SST) is observed by a constellation of sensors, and SST retrievals are commonly combined into gridded SST analyses and climate data records (CDRs). Differential biases between SSTs from different sensors cause errors in such products, including feature artefacts. We introduce a new method for reducing differential biases across the SST constellation, by reconciling the brightness temperature (BT) calibration and SST retrieval parameters between sensors. We use the Advanced Along-Track Scanning Radiometer (AATSR) and the Sea and Land Surface Temperature Radiometer (SLSTR) as reference sensors, and the Advanced Very High Resolution Radiometer (AVHRR) of the MetOp-A mission to bridge the gap between these references. Observations across a range of AVHRR zenith angles are matched with dual-view three-channel skin SST retrievals from the AATSR and SLSTR. These skin SSTs act as the harmonization reference for AVHRR retrievals by optimal estimation (OE). Parameters for the harmonized AVHRR OE are iteratively determined, including BT bias corrections and observation error covariance matrices as functions of water-vapor path. The OE SSTs obtained from AVHRR are shown to be closely consistent with the reference sensor SSTs. Independent validation against drifting buoy SSTs shows that the AVHRR OE retrieval is stable across the reference-sensor gap. We discuss that this method is suitable to improve consistency across the whole constellation of SST sensors. The approach will help stabilize and reduce errors in future SST CDRs, as well as having application to other domains of remote sensing.

On the retrieval of aerosol optical depth over cryosphere using passive remote sensing

The lack of aerosol information over the cryosphere introduces large uncertainties to our understanding of phenomenon, known as the Arctic Amplification (AA) and its feedback mechanisms. The aerosol optical depth (AOD) describes the optical characteristics of aerosol loading. This paper describes a novel algorithm, which retrieves AOD above snow-covered regions from the measurements of the up-welling radiation at the top of atmosphere, observed by the Advanced Along-Track Scanning Radiometer (AATSR) and the Sea and Land Surface Temperature Radiometer (SLSTR) instruments. The algorithm optimizes the generic eXtensible Bremen Aerosol/cloud and surfacE parameters Retrieval (XBAER) approach for longer wavelengths over the cryosphere. The algorithm utilizes the characteristics of solar bidirectional distribution properties of snow and aerosol at wavelength 3.7 μm to derive above-snow-AOD. Since the impact of fine-mode aerosol on 3.7 μm is ignorable, the retrieved AOD in this manuscript represents mainly coarse-mode dominated part. A novel method to extract the solar reflection part at 3.7 μm is presented and used in the surface parameterization. Two aerosol types (sea salt-dominated and dust-dominated) are used and the best-fit type is derived by an iterative procedure, using a Look-Up-Table (LUT) approach. Sensitivity studies of the impact on the retrieved AOD using XBAER algorithm, which investigate the impacts of aerosol type, snow surface emissivity and potential cloud contamination under typical AATSR observation conditions, are presented. The sensitivity studies show that the surface parametrization and aerosol typing are suitable for the retrieval of above-snow-AOD over the Arctic snow-covered region. AOD observations retrieved in this study from AATSR (2002–2012) observation collocated with those from the Aerosol Robotic Network (AERONET) sites over Greenland show good agreement. 72.1% of the match-ups fall into the expected error envelope of (±0.15AOD ± 0.025). The AATSR derived above-snow-AOD at 0.55 μm research product has also been compared with Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) aerosol product, the Mineral Aerosols Profiling from Infrared Radiances (MAPIR) derived Infrared Atmospheric Sounding Interferometer (IASI) AOD research product, and the Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) AOD simulations over Greenland on April 2011. The comparison reveals that all datasets show similar patterns for the AOD above Greenland. The AOD is smaller in central Greenland and larger over the coastline regions. The XBAER derived above-snow-AOD has improved coverage, as compared to that of the existing AATSR aerosol product. The transition between above-snow-AOD and AOD derived over surrounding ocean surfaces does not indicate any systematic errors. Two aerosol transport events have been well-captured by the XBAER derived above-snow-AOD research product. The new algorithm is also applied to the SLSTR onboard Sentinel-3 demonstrating new SLSTR above-snow-AOD data products, and its value for research in the changing AOD during the period of Arctic Amplification.

Lee wave detection over the Mediterranean Sea using the Advanced Infra-Red WAter Vapour Estimator (AIRWAVE) total column water vapour (TCWV) dataset

Abstract. Atmospheric gravity waves generated downstream by orography in a stratified airflow are known as lee waves. In the present study, such mesoscale patterns have been detected, over water and in clear-sky conditions, using the Advanced Infra-Red WAter Vapour Estimator (AIRWAVE) total column water vapour (TCWV) dataset, which contains about 20 years of day and night products, obtained from the thermal infrared measurements of the Along Track Scanning Radiometer (ATSR) instrument series. The high accuracy of such data, along with the native 1 km×1 km spatial resolution, allows the investigation of small-scale features such as lee waves. In this work, we focused on the Mediterranean Sea, the largest semi-enclosed basin on the Earth. The peculiarities of this area, which is characterised by complex orography and rough coastlines, lead to the development of these structures over both land and sea. We developed an automatic tool for the rapid detection of areas with high probability of lee wave occurrence, exploiting the TCWV variability in spatial regions with a 0.15∘×0.15∘ area. Through this analysis, several occurrences of structures connected with lee waves have been observed. The waves are detected in spring, autumn and summer seasons, with TCWV values usually falling in the range of 15 to 35 kg m−2. In this article, we describe some cases over the central (Italy) and the Eastern Mediterranean Basin (Greece, Turkey and Cyprus). We compared a case of perturbed AIRWAVE TCWV fields due to lee waves occurring over the Tyrrhenian Sea on 18 July 1997 with the sea surface winds from the synthetic aperture radar (SAR), which sounded the same geographical area, finding a good agreement. Another case has been investigated in detail: on 2 August 2002 the Aegean Sea region was almost simultaneously sounded by both the second sensor of the ATSR series (ATSR-2) and the Advanced ATSR (AATSR) instruments. The AIRWAVE TCWV fields derived from the two sensors were successfully compared with the vertically integrated water vapour content simulated with the Weather Research and Forecasting (WRF) numerical model for the same time period, confirming our findings. Wave parameters such as amplitude, wavelength and phase are described through the use of the Morlet continuous wavelet transformation (CWT). The performed analysis derived typical wavelengths from 6 to 8 km and amplitudes of up to 20 kg m−2.

VALIDATION OF AEROSOL PRODUCTS FROM ESA/AATSR OVER CHINA AND AOD FUSION BASED ON UNCERTAINTIES

Abstract. Aerosols play an important role in climate changes and environmental changes as well as on human health. ADV v3.11, ORAC v4.10, and SU v4.32 was three new versions of Advanced Along-Track Scanning Radiometer (AATSR) aerosol datasets which are published on Climate Change Initiative (CCI). In order to evaluate the accuracy of three AATSR aerosol optical depth (AOD) datasets, and to improve the spatial coverage and accuracy of AATSR AOD dataset, this study completed two works: the first part is validated the accuracy of three AOD datasets; the second part is fused three AATSR AOD datasets based on uncertainty of each dataset. After comparing with AERONET and CARSNET ground-based data over China, results show that the root-mean-square error (RMSE) for ADV v3.11, ORAC v4.10, SU v4.32 and fused AOD are 0.22, 0.17, 0.18, 0.17 respectively.

Intercomparison in spatial distributions and temporal trends derived from multi-source satellite aerosol products

Abstract. Satellite-derived aerosol products provide long-term and large-scale observations for analysing aerosol distributions and variations, climate-scale aerosol simulations, and aerosol–climate interactions. Therefore, a better understanding of the consistencies and differences among multiple aerosol products is important. The objective of this study is to compare 11 global monthly aerosol optical depth (AOD) products, which are the European Space Agency Climate Change Initiative (ESA-CCI) Advanced Along-Track Scanning Radiometer (AATSR), Advanced Very High Resolution Radiometer (AVHRR), Multi-angle Imaging SpectroRadiometer (MISR), Moderate Resolution Imaging Spectroradiometer (MODIS), Sea-viewing Wide Field-of-view Sensor (SeaWiFS), Visible Infrared Imaging Radiometer (VIIRS), and POLarization and Directionality of the Earth's Reflectance (POLDER) products. AErosol RObotic NEtwork (AERONET) Version 3 Level 2.0 monthly measurements at 308 sites around the world are selected for comparison. Our results illustrate that the spatial distributions and temporal variations of most aerosol products are highly consistent globally but exhibit certain differences on regional and site scales. In general, the AATSR Dual View (ADV) and SeaWiFS products show the lowest spatial coverage with numerous missing values, while the MODIS products can cover most areas (average of 87 %) of the world. The best performance is observed in September–October–November (SON) and the worst is in June–July–August (JJA). All the products perform unsatisfactorily over northern Africa and Middle East, southern and eastern Asia, and their coastal areas due to the influence from surface brightness and human activities. In general, the MODIS products show the best agreement with the AERONET-based AOD values on different spatial scales among all the products. Furthermore, all aerosol products can capture the correct aerosol trends at most cases, especially in areas where aerosols change significantly. The MODIS products perform best in capturing the global temporal variations in aerosols. These results provide a reference for users to select appropriate aerosol products for their particular studies.

Aerosol Optical Depth over the Arctic Snow-Covered Regions Derived from Dual-Viewing Satellite Observations

Aerosol properties over the Arctic snow-covered regions are sparsely provided by temporal and spatially limited in situ measurements or active Lidar observations. This introduces large uncertainties for the understanding of aerosol effects on Arctic climate change. In this paper, aerosol optical depth (AOD) is derived using the advanced along-track scanning radiometer (AATSR) instrument. The basic idea is to utilize the dual-viewing observation capability of AATSR to reduce the impacts of AOD uncertainties introduced by the absolute wavelength-dependent error on surface reflectance estimation. AOD is derived assuming that the satellite observed surface reflectance ratio can be well characterized by a snow bidirectional reflectance distribution function (BRDF) model with a certain correction direct from satellite top of the atmosphere (TOA) observation. The aerosol types include an Arctic haze aerosol obtained from campaign measurement and Arctic background aerosol (maritime aerosol) types. The proper aerosol type is selected during the iteration step based on the minimization residual. The algorithm has been used over Spitsbergen for the spring period (April–May) and the AOD spatial distribution indicates that the retrieval AOD can capture the Arctic haze event. The comparison with AERONET observations shows promising results, with a correlation coefficient R = 0.70. The time series analysis shows no systematical biases between AATSR retrieved AOD and AERONET observed ones.

Cool skin signals observed from Advanced Along-Track Scanning Radiometer (AATSR) and in situ SST measurements

Nighttime cool skin sea surface temperature (SST) signals, defined in this study as the differences between the SSTskin from the Advanced Along-Track Scanning Radiometer (AATSR) onboard Envisat satellite and in situ SSTs from drifting buoys and moorings, ΔT = SSTskin − SSTinsitu, are investigated on a global scale from July 2002 to April 2012. Global mean ΔT, averaged over the full study period, is −0.13 K, with most values falling between −0.1 and −0.2 K. The dominant role of wind speed on the ΔT is shown, with weaker winds usually corresponding to a cooler skin. The effect of air-sea temperature difference is also significant: warm skin (ΔT > 0 K) can be observed under large positive air-sea temperature differences. Other geophysical variables, such as the total column water vapor, in situ SST, and net heat flux, also affect ΔT, but to a lesser degree. Significant increase of ΔT size with SSTinsitu is observed when SSTinsitu is >28 °C. Tropical waters, such as the tropical Indian Ocean and the tropical warm pool (western Pacific and eastern Indian Ocean), are more frequently covered with a cool skin, largely due to the calm winds, very warm waters (especially for SSTinsitu >28 °C), and other environmental conditions supporting the development of large cool skin events. The ΔT seasonal pattern in the southern hemisphere is more regular, compared to the northern hemisphere. In both hemispheres, larger cool skin signals are seen during the local summer, mainly due to weaker winds. According to several previous cool skin models, higher winds tend to result in thinner cool skin layer depths, and hence in smaller ΔT amplitudes, regardless of stronger evaporation and heat loss. Given that wind is closely coupled with waves and turbulent mixing with wave breaking, the dependencies of ΔT on a few wave parameters are also investigated. A strong (moderate) dependency of ΔT on wave height (wave steepness) is identified, while the dependency of ΔT on wave breaking probability is less discernible.

Angular variations of brightness surface temperatures derived from dual-view measurements of the Advanced Along-Track Scanning Radiometer using a new single band atmospheric correction method

Surface temperatures derived from remote sensing data over heterogeneous, non-isothermal land surfaces depend on the viewing and solar angles mainly due to variations in sunlit and shaded fractions of the different elements in the field of view. The near-simultaneous dual-view capability of the Advanced Along-Track Scanning Radiometer (AATSR) can be used to estimate differences in brightness surface temperatures (BSTs) between the nadir (satellite zenith angle of 0°–21.7°) and forward views (53°–55.6°) in the 11 and 12 μm bands. BST is defined as the black-body temperature corresponding to the radiance at surface level (that is, corrected for atmospheric absorption and emission). We developed an automated, pixel-by-pixel atmospheric correction method to obtain the BST for the 11 and 12 μm bands at nadir and forward views. It uses atmospheric profiles from the NCEP reanalysis product provided at 1° × 1° spatial resolution every 6 h, the MODTRAN 5 radiative transfer model, and a digital elevation model with spatial resolution of 1 km. The method provides the atmospheric transmittance and upwelling radiance at the AATSR resolution (1 km) for the two bands and views, taking into account the geographical coordinates, altitude and zenith observation angle of each pixel, and the AATSR overpass time. The method was applied to eight daytime and nighttime AATSR scenes over the Iberian Peninsula on the four seasons, for which the nadir-forward BST differences were obtained. For sea surfaces, the angular BST difference was uniform and independent on the time and season with an average about +0.7 K. This is expected since the sea surface is homogeneous and flat, the only angular effect being due to the known angular variation of emissivity. Nadir-forward BST differences were usually positive for daytime data over land surfaces, since AATSR observes from the North so there is a larger fraction of sunlit elements in the nadir view in the northern hemisphere. We found a clear seasonal dependence as expected, the largest (smallest) angular BST differences being obtained in summer (winter). The spatial variability of the daytime angular BST differences over land was also highest (lowest) in summer (winter). For the nighttime data, the angular variation was much smaller and uniform for all seasons. In order to analyze the daytime angular BST differences over land, we used concurrent Leaf Area Index (LAI) data obtained from the MODIS product MOD15A2. For a given date, the largest differences were found for surfaces with LAI values ranging from 0.5 to 2, due to the large change in the proportions of sunlit and shaded elements observed by the two views. For such cases in summer, differences up to 8 K were found, with average values of 4 K and high variability. For LAI values larger than 3, the angular differences decreased to about 2 K and were more uniform. We also found a positive correlation between the angular BST difference and the nadir BST.

Spatial and seasonal variations of aerosols over China from two decades of multi-satellite observations – Part 2: AOD time series for 1995–2017 combined from ATSR ADV and MODIS C6.1 and AOD tendency estimations

Abstract. Understanding long-term variations in aerosol loading is essential for evaluating the health and climate effects of airborne particulates as well as the effectiveness of pollution control policies. The expected satellite lifetime is about 10 to 15 years. Therefore, to study the variations of atmospheric constituents over longer periods information from different satellites must be utilized. Here we introduce a method to construct a combined annual and seasonal long time series of AOD at 550 nm using the Along-Track Scanning Radiometers (ATSR: ATSR-2 and AATSR combined) and the MODerate resolution Imaging Spectroradiometer on Terra (MODIS/Terra), which together cover the 1995–2017 period. The long-term (1995–2017) combined AOD time series are presented for all of mainland China, for southeastern (SE) China and for 10 selected regions in China. Linear regression was applied to the combined AOD time series constructed for individual L3 (1∘ × 1∘) pixels to estimate the AOD tendencies for two periods: 1995–2006 (P1) and 2011–2017 (P2), with respect to the changes in the emission reduction policies in China. During P1, the annually averaged AOD increased by 0.006 (or 2 % of the AOD averaged over the corresponding period) per year across all of mainland China, reflecting increasing emissions due to rapid economic development. In SE China, the annual AOD positive tendency in 1995–2006 was 0.014 (3 %) per year, reaching maxima (0.020, or 4 %, per year) in Shanghai and the Pearl River Delta regions. After 2011, during P2, AOD tendencies reversed across most of China with the annually averaged AOD decreasing by −0.015 (−6 %) per year in response to the effective reduction of the anthropogenic emissions of primary aerosols, SO2 and NOx. The strongest AOD decreases were observed in the Chengdu (−0.045, or −8 %, per year) and Zhengzhou (−0.046, or −9 %, per year) areas, while over the North China Plain and coastal areas the AOD decrease was lower than −0.03 (approximately −6 %) per year. In the less populated areas the AOD decrease was small. The AOD tendency varied by both season and region. The increase in the annually averaged AOD during P1 was mainly due to an increase in summer and autumn in SE China (0.020, or 4 %, and 0.016, or 4 %, per year, respectively), while during winter and spring the AOD actually decreased over most of China. The AOD negative tendencies during the 2011–2017 period were larger in summer than in other seasons over the whole of China (ca. −0.021, or −7 %, per year) and over SE China (ca. −0.048, or −9 %, per year). The long-term AOD variations presented here show a gradual decrease in the AOD after 2011 with an average reduction of 30 %–50 % between 2011 and 2017. The effect is more visible in the highly populated and industrialized regions in SE China, as expected.

Spatial and seasonal variations of aerosols over China from two decades of multi-satellite observations – Part 1: ATSR (1995–2011) and MODIS C6.1 (2000–2017)

Abstract. Aerosol optical depth (AOD) patterns and interannual and seasonal variations over China are discussed based on the AOD retrieved from the Along-Track Scanning Radiometer (ATSR-2, 1995–2002), the Advanced ATSR (AATSR, 2002–2012) (together ATSR) and the MODerate resolution Imaging Spectroradiometer (MODIS) aboard the Terra satellite (2000–2017). The AOD products used were the ATSR Dual View (ADV) v2.31 AOD and the MODIS/Terra Collection 6.1 (C6.1) merged dark target (DT) and deep blue (DB) AOD product. Together these datasets provide an AOD time series for 23 years, from 1995 to 2017. The difference between the AOD values retrieved from ATSR-2 and AATSR is small, as shown by pixel-by-pixel and monthly aggregate comparisons as well as validation results. This allows for the combination of the ATSR-2 and AATSR AOD time series into one dataset without offset correction. ADV and MODIS AOD validation results show similar high correlations with the Aerosol Robotic Network (AERONET) AOD (0.88 and 0.92, respectively), while the corresponding bias is positive for MODIS (0.06) and negative for ADV (−0.07). Validation of the AOD products in similar conditions, when ATSR and MODIS/Terra overpasses are within 90 min of each other and when both ADV and MODIS retrieve AOD around AERONET locations, show that ADV performs better than MODIS in autumn, while MODIS performs slightly better in spring and summer. In winter, both ADV and MODIS underestimate the AERONET AOD. Similar AOD patterns are observed by ADV and MODIS in annual and seasonal aggregates as well as in time series. ADV–MODIS difference maps show that MODIS AOD is generally higher than that from ADV. Both ADV and MODIS show similar seasonal AOD behavior. The AOD maxima shift from spring in the south to summer along the eastern coast further north. The agreement between sensors regarding year-to-year AOD changes is quite good. During the period from 1995 to 2006 AOD increased in the southeast (SE) of China. Between 2006 and 2011 AOD did not change much, showing minor minima in 2008–2009. From 2011 onward AOD decreased in the SE of China. Similar patterns exist in year-to-year ADV and MODIS annual AOD tendencies in the overlapping period. However, regional differences between the ATSR and MODIS AODs are quite large. The consistency between ATSR and MODIS with regards to the AOD tendencies in the overlapping period is rather strong in summer, autumn and overall for the yearly average; however, in winter and spring, when there is a difference in coverage between the two instruments, the agreement between ATSR and MODIS is lower. AOD tendencies in China during the 1995–2017 period will be discussed in more detail in Part 2 (a following paper: Sogacheva et al., 2018), where a method to combine AOD time series from ADV and MODIS is introduced, and combined AOD time series are analyzed.

ITCZ trend analysis via Geodesic P-spline smoothing of the AIRWAVE TCWV and cloud frequency datasets

The Inter Tropical Convergence Zone (ITCZ) is the region of the Earth's atmosphere where the trade winds converge. This region is characterized by rising air, strong convection, clouds and heavy precipitation and it is tightly related to changes in climate patterns on a global scale. For these reasons assessing the ITCZ migrations is of extreme importance for climate monitoring. This can be achieved through the use of satellite data of different kind. In the last decades several quantities have been used as proxies for this purpose, e.g. infrared radiance measured at the top of atmosphere (TOA), precipitation datasets or vertical and horizontal wind components. In this work the ITCZ position is determined and its time evolution is analysed using the Total Column Water Vapour (TCWV) data, retrieved using the Advanced Infra-Red Water Vapour Estimator algorithm (AIRWAVE). AIRWAVE was developed for the retrieval of the TCWV from the Along Track Scanning Radiometer (ATSR) instrument series, operational from 1991 to 2012. It allows the TCWV retrieval from infra-red channels at 11 and 12 μm exploiting the ATSR nadir and forward viewing geometries, for day/night and cloud-free sea surface scenarios. The information on cloud coverage from ATSRs is used as correlative information in order to expand the ITCZ analysis to land scenes. The TCWV and cloud frequency datasets are analysed with a Geodesic P-spline efficient spatial smoothing method specifically developed to extract information from large datasets. The posterior distribution of the model is considered for identification of the ITCZ both over sea and land with associated uncertainty quantification. The resulting AIRWAVE/cloud frequency monthly fields are analysed to detect trends in the ITCZ latitudinal displacement over the 20 years of the ATSR family lifetime. Results indicate that no significant trends can be detected in the 1991–2012 time period.

A Generalized Evaluation Scheme for Comparing Temperature Products from Satellite Observations, Numerical Weather Model, and Ground Measurements Over the Tibetan Plateau

Ground surface temperature (GST) measurements are scarce in Tibetan plateau (TP), whereas the satellite observations and numerical weather model outputs are good alternatives to fill the spatial gaps among ground stations. However, the evaluation of different temperature products is challenging due to the distinct temporal and spatial dimensions in their acquisition methods. This paper intended to develop an evaluation framework for comparing the performances of various temperature data, including the Advanced Along-Track Scanning Radiometer (AATSR) satellite land surface temperature (LST) data, the high Asia refined (HAR) analysis numerical outputs, and the GST. In the proposed framework, we introduce a diurnal temperature cycle model and an aggregated weighted method to solve the temporal and spatial mismatch problem between different data sets. The results over TP show that the evaluation framework solves the temporal and spatial matching among different data sets. AATSR LST and HAR outputs are consistent regardless of the heterogeneous and weather conditions at Linzhi site indicating that the fully homogeneous land surface conditions are not the only way for the satellite/simulation validations. Our results suggest that the proposed framework of time normalization and spatial aggregation method is appropriate for evaluating satellite thermal infrared retrieved data sets and numerical simulations even when the proper ground measurements are insufficient. Since it performs well in the high elevation and complex land surface-conditioned TP region, it will be easy to be adopted in the other regions with a variety of data sets.