Synergy of stereo cloud top height and ORAC optimal estimation cloud retrieval: evaluation and application to AATSR

In this paper we evaluate the retrievals of cloud top height when stereo derived heights are combined with the radiometric cloud top heights retrieved from the ORAC (Optimal Retrieval of Aerosol and Cloud) algorithm. This is performed in a mathematically rigorous way using the ORAC optimal estimation retrieval framework, which includes the facility to use independent a priori information. Key to the use of a priori information is a characterisation of their associated uncertainty. This paper demonstrates the improvements that are possible using this approach and also considers their impact on the microphysical cloud parameters retrieved. The AATSR instrument has two views and three thermal channels so is well placed to demonstrate the synergy of the two techniques. The stereo retrieval is able to improve the accuracy of the retrieved cloud top height when compared to collocated Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO), particularly in the presence of boundary layer inversions and high clouds. The impact on the microphysical properties of the cloud such as optical depth and effective radius was evaluated and found to be very small with the biggest differences occurring over bright land surfaces and for high clouds. Overall the cost of the retrievals increased indicating a poorer radiative fit of radiances to the cloud model, which currently uses a single layer cloud model. Best results and improved fit to the radiances may be obtained in the future if a multi-layer model is used

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

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

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 man- ually 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 opera- tional 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

Post-processing to remove residual clouds from aerosol optical depth retrieved using the Advanced Along Track Scanning Radiometer

Cloud misclassification is a serious problem in the retrieval of aerosol optical depth (AOD), which might considerably bias the AOD results. On the one hand, residual cloud contamination leads to AOD overestimation, whereas the removal of high-AOD pixels (due to their misclassification as clouds) leads to underestimation. To remove cloudcontaminated areas in AOD retrieved from reflectances measured with the (Advanced) Along Track Scanning Radiometers (ATSR-2 and AATSR), using the ATSR dual-view algorithm (ADV) over land or the ATSR single-view algorithm (ASV) over ocean, a cloud post-processing (CPP) scheme has been developed at the Finnish Meteorological Institute (FMI) as described in Kolmonen et al. (2016). The application of this scheme results in the removal of cloudcontaminated areas, providing spatially smoother AOD maps and favourable comparison with AOD obtained from the ground-based reference measurements from the AERONET sun photometer network. However, closer inspection shows that the CPP also removes areas with elevated AOD not due to cloud contamination, as shown in this paper. We present an improved CPP scheme which better discriminates between cloud-free and cloud-contaminated areas. The CPP thresholds have been further evaluated and adjusted according to the findings. The thresholds for the detection of high-AOD regions (> 60% of the retrieved pixels should be high-AOD (> 0.6) pixels), and cloud contamination criteria for lowAOD regions have been accepted as the default for AOD global post-processing in the improved CPP. Retaining elevated AOD while effectively removing cloud-contaminated pixels affects the resulting global and regional mean AOD values as well as coverage. Effects of the CPP scheme on both spatial and temporal variation for the period 2002-2012 are discussed. With the improved CPP, the AOD coverage increases by 10-15% with respect to the existing scheme. The validation versus AERONET shows an improvement of the correlation coefficient from 0.84 to 0.86 for the global data set for the period 2002-2012. The global aggregated AOD over land for the period 2003-2011 is 0.163 with the improved CPP compared to 0.144 with the existing scheme. The aggregated AOD over ocean and globally (land and ocean together) is 0.164 with the improved CPP scheme (compared to 0.152 and 0.150 with the existing scheme, for ocean and globally respectively). Effects of the improved CPP scheme on the 10-year time series are illustrated and seasonal and temporal changes are discussed. The improved CPP method introduced here is applicable to other aerosol retrieval algorithms. However, the thresholds for detecting the high-AOD regions, which were developed for AATSR, might have to be adjusted to the actual features of the instruments.Peer reviewe

Introduction to GlobSnow Snow Extent products with considerations for accuracy assessment

Highlights • GlobSnow Snow Extent provides 17-years data record for Fractional snow cover (FSC). • Snow extent products cover the Northern Hemisphere in 0.01 deg. resolution. • FSC retrieval uses SCAmod method enabling fractional snow mapping also in forests. • Landsat TM/ETM+-based reference is not always representative for validation of FSC

Application of Stereo-Photogrammetric Methods to the Advanced Along Track Scanning Radiometer for the Atmospheric Sciences

This thesis studies photogrammetric techniques applied to the ATSR instruments for the extraction of atmospheric parameters with the objective of generating new scientific datasets. The atmospheric parameters under observation are cloud top height, smoke plume injection height, and tropospheric wind components. All have important applications in various tasks, including the initialisation and validation of climate models. To generate accurate stereo measurements from the ATSR imagery the forward and nadir views need to be accurately co-registered. Currently this is not the case, with differences of up to 2 pixels in both axes recorded. In this thesis an automated image tie-pointing and image warping algorithm that improves ATSR co-registration to ≤1 pixel is presented. This thesis also identifies the census stereo matching algorithm for application to the ATSR instruments. When compared against a collocated DEM, census outperforms the previous stereo matching algorithm applied to the ATSR instrument, known as M4, significantly: RMSE ~700m vs. ~1200m; bias ~60m vs ~600m; R2 ~0.9 vs ~0.7. Furthermore, this thesis reviews the M6 algorithm developed for application within the ESA ALANIS Smoke Plume project. Using census a climatological cloud fraction by altitude dataset over Greenland is generated and demonstrated to agree well with current observational datasets from MISR, MODIS and AATSR. The 11μm channel stereo output provides insights into high cloud characteristics over Greenland and appears to be, in comparison with CALIOP, practically unbiased. The ALANIS Smoke plume project is introduced and the inter-comparison of the M6 algorithm against MISR and CALIOP is presented. M6 demonstrates some ability for determining smoke plumes injection heights above 1km in elevation. However, the smoke plume masking approach currently employed is demonstrated to be lacking in quality. Finally, this thesis presents the determination of cloud tracked tropospheric winds from the ATSR2-AATSR tandem operation using the Farneback optical flow algorithm. This algorithm offers accuracy on the order of 0.5 ms-1 at full image resolution, which is unprecedented in comparison to similarly derived datasets

European Capacity for Monitoring and Assimilating Space-based Climate Change Observations - Status and Prospects

This report, which is based on the findings of a workshop at Ispra in March 2009, provides the scientific background to a forthcoming Commission response to the Space and Competitiveness councils requests that the commission assess the needs for full access to standardised climate change data, the means to provide these data and together with ESA, EUMETSAT and the scientific community define how GMES services can contribute effectively to providing these data. The report therefore focuses primarily, but not exclusively, on space-based Climate data sources. Standardised climate data are needed for climate monitoring, prediction and research, while climate information informs the policy cycle at four key points - Policy definition; Management and scenario building; Reporting requirements; Alarm functions. The workshop identified the 44 Essential Climate Variables defined by GCOS as the minimum set of standardised climate data that the commission should be considering and a gap analysis for the provision of these observations was undertaken. In addition European capacity is analysed according to maturity, differentiating between sustained operational capacity (Envelope Missions/EUMETSAT), non-operationally funded repetitive capacity and additional infrastructure needs in order to fill the gaps are identified. Finally the report discusses co-ordination and governance issues and how to overcome them. The key findings and recommendations are contained in an executive summary.JRC.DDG.H.2-Climate chang

Cloud clearing techniques over land for land surface temperature retrieval from the Advanced Along Track Scanning Radiometer

We present five new cloud detection algorithms over land based on dynamic threshold or Bayesian techniques, applicable to the Advanced Along Track Scanning Radiometer (AATSR) instrument and compare these with the standard threshold based SADIST cloud detection scheme. We use a manually classified dataset as a reference to assess algorithm performance and quantify the impact of each cloud detection scheme on land surface temperature (LST) retrieval. The use of probabilistic Bayesian cloud detection methods improves algorithm true skill scores by 8-9 % over SADIST (maximum score of 77.93 % compared to 69.27 %). We present an assessment of the impact of imperfect cloud masking, in relation to the reference cloud mask, on the retrieved AATSR LST imposing a 2 K tolerance over a 3x3 pixel domain. We find an increase of 5-7 % in the observations falling within this tolerance when using Bayesian methods (maximum of 92.02 % compared to 85.69 %). We also demonstrate that the use of dynamic thresholds in the tests employed by SADIST can significantly improve performance, applicable to cloud-test data to provided by the Sea and Land Surface Temperature Radiometer (SLSTR) due to be launched on the Sentinel 3 mission (estimated 2014)

Retrieval of aerosol optical thickness over snow and ice surfaces in the Arctic using Advanced Along Track Scanning Radiometer

Aerosols in the Arctic cause radiative forcing and a variety of climatic feedbacks, which affect climate of both local and global scales. In order to assess the state of the Arctic climate, information on the aerosol type and amount is needed. Harsh conditions and remoteness of the Arctic region result in very few ground based measurements of aerosol optical thickness. Remote sensing has the potential to provide the necessary temporal and spatial coverage. A non-trivial task of aerosol retrieval over a very bright surface is being solved within the thesis; the developed retrieval consists of cloud screening over snow and two types of aerosol retrieval over snow - in the visible and infrared spectral regions. A number of validation and case studies has been performed to assess the quality of the retrieval. The developed algorithm applies to the data of Advanced Along Track Scanning Radiometer and produces maps of aerosol optical thickness over snow and ice