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  • 1. Bennartz, Ralf
    et al.
    Hoschen, Heidrun
    Picard, Bruno
    Schroder, Marc
    Stengel, Martin
    Sus, Oliver
    Bojkov, Bojan
    Casadio, Stefano
    Diedrich, Hannes
    Eliasson, Salomon
    SMHI, Research Department, Atmospheric remote sensing.
    Fell, Frank
    Fischer, Jurgen
    Hollmann, Rainer
    Preusker, Rene
    Willén, Ulrika
    SMHI, Research Department, Climate research - Rossby Centre.
    An intercalibrated dataset of total column water vapour and wet tropospheric correction based on MWR on board ERS-1, ERS-2, and Envisat2017In: Atmospheric Measurement Techniques, ISSN 1867-1381, E-ISSN 1867-8548, Vol. 10, no 4, p. 1387-1402Article in journal (Refereed)
  • 2.
    Eliasson, Salomon
    et al.
    SMHI, Research Department, Atmospheric remote sensing.
    Karlsson, Karl-Göran
    SMHI, Research Department, Atmospheric remote sensing.
    van Meijgaard, Erik
    Meirink, Jan Fokke
    Stengel, Martin
    Willén, Ulrika
    SMHI, Research Department, Climate research - Rossby Centre.
    The Cloud_cci simulator v1.0 for the Cloud_cci climate data record and its application to a global and a regional climate model2019In: Geoscientific Model Development, ISSN 1991-959X, E-ISSN 1991-9603, Vol. 12, no 2, p. 829-847Article in journal (Refereed)
  • 3.
    Eliasson, Salomon
    et al.
    SMHI, Research Department, Atmospheric remote sensing.
    Tetzlaff, Anke
    SMHI, Research Department, Atmospheric remote sensing.
    Karlsson, Karl-Göran
    SMHI, Research Department, Atmospheric remote sensing.
    Prototyping an improved PPS cloud detection for the Arctic polar night2007Report (Other academic)
    Abstract [en]

    A new Polar Platform Systems (PPS) Cloud Mask (CM) test sequence is required for improving cloud detection during Arctic winter conditions. This study introduces a test sequence, called Ice Night Sea (INS), that to a greater extent successfully detects clouds over ice surfaces and which is less sensitive to cloud free misclassification.The test sequence uses a combination of Numerical Weather Prediction (NWP) fields and Advanced Very High Resolution Radiometer (AVHRR) satellite data. Only the infrared (IR) AVHRR channels can be exploited during night conditions. Training target data from winter 2001-2002, collected over a large area north of the Atmospheric Radiation Measurement (ARM) site at Barrow, Alaska, were used to assess the general atmospheric state of the Arctic and to perform a qualitative validation of CM test sequences. Results clearly show that the atmospheric conditions during Arctic winter severely hamper cloud detection efforts. Very cold surface temperatures and immense surface temperature inversions lead to a diminished separability between surfaces and clouds. One particular problem is that the IR brightness temperatures for the shortest wavelength (3.7μm - henceforth T37) are strongly affected by noise. The use of an IR noise filter was shown to improve results significantly. In addition, the problem of misclassifying cracks in the pack ice as Cirrus clouds was basically solved by using a dedicated filter using the local variance of T37.Using an inverse version of a typical daytime Cirrus test (based on just two IR channels and normally applied successfully outside the Arctic region), it is shown that we can detect a substantial part of the warmsemi-transparent clouds commonly found in the Arctic. Running the test sequences on training target data revealed an improvement in correct cloud free target classification of around 30% but only a marginal improvement for cloudy training targets. However, visual inspection of results obtained for about 50 scenes covering a large part of the Arctic region in January 2007 clearly indicated improvements also for the cloudy portion of the scenes. The INS CM test sequence awaits a more rigorous and quantitative validation, e.g. based on comparisons with CLOUDSAT/CALIPSO satellite data sets.

  • 4. Holl, G.
    et al.
    Eliasson, Salomon
    SMHI, Research Department, Atmospheric remote sensing.
    Mendrok, J.
    Buehler, S. A.
    SPARE-ICE: Synergistic ice water path from passive operational sensors2014In: Journal of Geophysical Research - Atmospheres, ISSN 2169-897X, E-ISSN 2169-8996, Vol. 119, no 3, p. 1504-1523Article in journal (Refereed)
    Abstract [en]

    This article presents SPARE-ICE, the Synergistic Passive Atmospheric Retrieval Experiment-ICE. SPARE-ICE is the first Ice Water Path (IWP) product combining infrared and microwave radiances. By using only passive operational sensors, the SPARE-ICE retrieval can be used to process data from at least the NOAA 15 to 19 and MetOp satellites, obtaining time series from 1998 onward. The retrieval is developed using collocations between passive operational sensors (solar, terrestrial infrared, microwave), the CloudSat radar, and the CALIPSO lidar. The collocations form a retrieval database matching measurements from passive sensors against the existing active combined radar-lidar product 2C-ICE. With this retrieval database, we train a pair of artificial neural networks to detect clouds and retrieve IWP. When considering solar, terrestrial infrared, and microwave-based measurements, we show that any combination of two techniques performs better than either single-technique retrieval. We choose not to include solar reflectances in SPARE-ICE, because the improvement is small, and so that SPARE-ICE can be retrieved both daytime and nighttime. The median fractional error between SPARE-ICE and 2C-ICE is around a factor 2, a figure similar to the random error between 2C-ICE ice water content (IWC) and in situ measurements. A comparison of SPARE-ICE with Moderate Resolution Imaging Spectroradiometer (MODIS), Pathfinder Atmospheric Extended (PATMOS-X), and Microwave Surface and Precipitation Products System (MSPPS) indicates that SPARE-ICE appears to perform well even in difficult conditions. SPARE-ICE is available for public use.

  • 5.
    Sheldon, Johnston, Marston
    et al.
    SMHI, Research Department, Atmospheric remote sensing.
    Eliasson, Salomon
    SMHI, Research Department, Atmospheric remote sensing.
    Eriksson, P.
    Forbes, R. M.
    Gettelman, A.
    Raisanen, P.
    Zelinka, M. D.
    Diagnosing the average spatio-temporal impact of convective systems - Part 2: A model intercomparison using satellite data2014In: Atmospheric Chemistry And Physics, ISSN 1680-7316, E-ISSN 1680-7324, Vol. 14, no 16, p. 8701-8721Article in journal (Refereed)
    Abstract [en]

    The representation of the effect of tropical deep convective (DC) systems on upper-tropospheric moist processes and outgoing longwave radiation is evaluated in the EC-Earth3, ECHAM6, and CAM5 (Community Atmosphere Model) climate models using satellite-retrieved data. A composite technique is applied to thousands of deep convective systems that are identified using local rain rate maxima in order to focus on the temporal evolution of the deep convective processes in the model and satellite-retrieved data. The models tend to over-predict the occurrence of rain rates that are less than approximate to 3 mm h(-1) compared to Tropical Rainfall Measurement Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA). While the diurnal distribution of oceanic rain rate maxima in the models is similar to the satellite-retrieved data, the land-based maxima are out of phase. Despite having a larger climatological mean uppertropospheric relative humidity, models closely capture the satellite-derived moistening of the upper troposphere following the peak rain rate in the deep convective systems. Simulated cloud fractions near the tropopause are larger than in the satellite data, but the ice water contents are smaller compared with the satellite-retrieved ice data. The models capture the evolution of ocean-based deep convective systems fairly well, but the land-based systems show significant discrepancies. Over land, the diurnal cycle of rain is too intense, with deep convective systems occurring at the same position on subsequent days, while the satellite-retrieved data vary more in timing and geographical location. Finally, simulated outgoing longwave radiation anomalies associated with deep convection are in reasonable agreement with the satellite data, as well as with each other. Given the fact that there are strong disagreements with, for example, cloud ice water content, and cloud fraction, between the models, this study supports the hypothesis that such agreement with satellite-retrieved data is achieved in the three models due to different representations of deep convection processes and compensating errors.

  • 6.
    Sheldon, Johnston, Marston
    et al.
    SMHI, Research Department, Atmospheric remote sensing.
    Eriksson, P.
    Eliasson, Salomon
    SMHI, Research Department, Atmospheric remote sensing.
    Jones, Colin
    SMHI, Research Department, Climate research - Rossby Centre.
    Forbes, R. M.
    Murtagh, D. P.
    The representation of tropical upper tropospheric water in EC Earth V22012In: Climate Dynamics, ISSN 0930-7575, E-ISSN 1432-0894, Vol. 39, no 11, p. 2713-2731Article in journal (Refereed)
    Abstract [en]

    Tropical upper tropospheric humidity, clouds, and ice water content, as well as outgoing longwave radiation (OLR), are evaluated in the climate model EC Earth with the aid of satellite retrievals. The Atmospheric Infrared Sounder and Microwave Limb Sounder together provide good coverage of relative humidity. EC Earth's relative humidity is in fair agreement with these observations. CloudSat and CALIPSO data are combined to provide cloud fractions estimates throughout the altitude region considered (500-100 hPa). EC Earth is found to overestimate the degree of cloud cover above 200 hPa and underestimate it below. Precipitating and non-precipitating EC Earth ice definitions are combined to form a complete ice water content. EC Earth's ice water content is below the uncertainty range of CloudSat above 250 hPa, but can be twice as high as CloudSat's estimate in the melting layer. CERES data show that the model underestimates the impact of clouds on OLR, on average with about 9 W m(-2). Regionally, EC Earth's outgoing longwave radiation can be similar to 20 W m(-2) higher than the observation. A comparison to ERA-Interim provides further perspectives on the model's performance. Limitations of the satellite observations are emphasised and their uncertainties are, throughout, considered in the analysis. Evaluating multiple model variables in parallel is a more ambitious approach than is customary.

  • 7. Stengel, Martin
    et al.
    Schlundt, Cornelia
    Stapelberg, Stefan
    Sus, Oliver
    Eliasson, Salomon
    SMHI, Research Department, Atmospheric remote sensing.
    Willén, Ulrika
    SMHI, Research Department, Climate research - Rossby Centre.
    Meirink, Jan Fokke
    Comparing ERA-Interim clouds with satellite observations using a simplified satellite simulator2018In: Atmospheric Chemistry And Physics, ISSN 1680-7316, E-ISSN 1680-7324, Vol. 18, no 23, p. 17601-17614Article in journal (Refereed)
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