Quantitative information on the spatial and temporal error structures in large-scale (regional or global) precipitation datasets is essential for hydrologic and climatic studies. A powerful tool to quantify error structures in large-scale datasets is triple collocation. In this paper, triple collocation is used to determine the spatial and temporal error characteristics of three precipitation datasets over Europe—that is, the precipitation-properties visible/near infrared (PP-VNIR) retrievals from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) instrument on board Meteosat Second Generation (MSG), weather radar observations from the European integrated weather radar system, and gridded rain gauge observations from the datasets of the Global Precipitation Climatology Centre (GPCC) and the European Climate Assessment and Dataset (ECA&D) project. For these datasets the spatial and temporal error characteristics are evaluated and their performance is discussed. Finally, weather radar and PP-VNIR retrievals are used to evaluate the diurnal cycles of precipitation occurrence and intensity during daylight hours for different European climate regions. The results suggest that the triple collocation method provides realistic error estimates. The spatial and temporal error structures agree with the findings of earlier studies and reveal the strengths and weaknesses of the datasets, such as inhomogeneity of weather radar practices across Europe, the effect of sampling density in the gridded rain gauge dataset, and the sensitivity to retrieval assumptions in the PP-VNIR dataset. This study can help us in developing satisfactory strategies for combining various precipitation datasets—for example, for improved monitoring of diurnal variations or for detecting temporal trends in precipitation.

The role of clouds remains the largest uncertainty in climate projections. They influence solar and thermal radiative transfer and the earth's water cycle. Therefore, there is an urgent need for accurate cloud observations to validate climate models and to monitor climate change. Passive satellite imagers measuring radiation at visible to thermal infrared (IR) wavelengths provide a wealth of information on cloud properties. Among others, the cloud top height (CTH) – a crucial parameter to estimate the thermal cloud radiative forcing – can be retrieved. In this paper we investigate the skill of ten current retrieval algorithms to estimate the CTH using observations from the Spinning Enhanced Visible and InfraRed Imager (SEVIRI) onboard Meteosat Second Generation (MSG). In the first part we compare ten SEVIRI cloud top pressure (CTP) data sets with each other. The SEVIRI algorithms catch the latitudinal variation of the CTP in a similar way. The agreement is better in the extratropics than in the tropics. In the tropics multi-layer clouds and thin cirrus layers complicate the CTP retrieval, whereas a good agreement among the algorithms is found for trade wind cumulus, marine stratocumulus and the optically thick cores of the deep convective system. In the second part of the paper the SEVIRI retrievals are compared to CTH observations from the Cloud–Aerosol LIdar with Orthogonal Polarization (CALIOP) and Cloud Profiling Radar (CPR) instruments. It is important to note that the different measurement techniques cause differences in the retrieved CTH data. SEVIRI measures a radiatively effective CTH, while the CTH of the active instruments is derived from the return time of the emitted radar or lidar signal. Therefore, some systematic differences are expected. On average the CTHs detected by the SEVIRI algorithms are 1.0 to 2.5 km lower than CALIOP observations, and the correlation coefficients between the SEVIRI and the CALIOP data sets range between 0.77 and 0.90. The average CTHs derived by the SEVIRI algorithms are closer to the CPR measurements than to CALIOP measurements. The biases between SEVIRI and CPR retrievals range from −0.8 km to 0.6 km. The correlation coefficients of CPR and SEVIRI observations vary between 0.82 and 0.89. To discuss the origin of the CTH deviation, we investigate three cloud categories: optically thin and thick single layer as well as multi-layer clouds. For optically thick clouds the correlation coefficients between the SEVIRI and the reference data sets are usually above 0.95. For optically thin single layer clouds the correlation coefficients are still above 0.92. For this cloud category the SEVIRI algorithms yield CTHs that are lower than CALIOP and similar to CPR observations. Most challenging are the multi-layer clouds, where the correlation coefficients are for most algorithms between 0.6 and 0.8. Finally, we evaluate the performance of the SEVIRI retrievals for boundary layer clouds. While the CTH retrieval for this cloud type is relatively accurate, there are still considerable differences between the algorithms. These are related to the uncertainties and limited vertical resolution of the assumed temperature profiles in combination with the presence of temperature inversions, which lead to ambiguities in the CTH retrieval. Alternative approaches for the CTH retrieval of low clouds are discussed.

Accurate calibration of satellite imagers is a prerequisite for using their measurements in climate applications. Here we present a method for the inter-calibration of geostationary and polar-orbiting imager solar channels based on regressions of collocated near-nadir reflectances. Specific attention is paid to correcting for differences in spectral response between instruments. The method is used to calibrate the solar channels of the Spinning Enhanced Visible and Infrared Imager (SEVIRI) on the geostationary Meteosat satellite with corresponding channels of the Moderate Resolution Imaging Spectroradiometer (MODIS) on the polar-orbiting Aqua satellite. The SEVIRI operational calibration is found to be stable during the years 2004 to 2009, but offset by −8, −6, and +3.5 % for channels 1 (0.6 μm), 2 (0.8 μm), and 3 (1.6 μm), respectively. These results are robust for a range of choices that can be made regarding data collocation and selection, as long as the viewing and illumination geometries of the two instruments are matched. Uncertainties in the inter-calibration method are estimated to be 1 % for channel 1 and 1.5 % for channels 2 and 3. A specific application of our method is the inter-calibration of polar imagers using SEVIRI as a transfer instrument. This offers an alternative to direct inter-calibration, which in general has to rely on high-latitude collocations. Using this method we have tied MODIS-Terra and Advanced Very High Resolution Radiometer (AVHRR) instruments on National Oceanic and Atmospheric Administration (NOAA) satellites 17 and 18 to MODIS-Aqua for the years 2007 to 2009. While reflectances of the two MODIS instruments differ less than 2 % for all channels considered, deviations of an existing AVHRR calibration from MODIS-Aqua reach −3.5 and +2.5 % for the 0.8 and 1.6 μm channels, respectively.

A new automated daytime cumulonimbus/towering cumulus (Cb/TCu) cloud detection method for the months of May–September is presented that combines information on cloud physical properties retrieved from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) on board Meteosat Second Generation (MSG) satellites and weather radar reflectivity factors. First, a pixel-based convective cloud mask (CCM) is constructed on the basis of cloud physical properties [cloud-top temperature, cloud optical thickness (COT), effective radius, and cloud phase] derived from SEVIRI. Second, a logistic regression model is applied to determine the probability of Cb/TCu clouds for the collection of pixels that pass the CCM. In this model, MSG-SEVIRI cloud physical properties and weather radar reflectivity factors are used as potential predictor sources. The predictand is derived from aviation routine weather reports (METAR) made by human observers at Amsterdam Airport Schiphol for 2004–07. Results show that the CCM filters out >70% of the “no” events (no Cb/TCu cloud) and that >93% of the “yes” events (Cb/TCu cloud) are retained. Most skillful predictors are derived from radar reflectivity factors and the COT of high resolution. The derived probabilities from the combined MSG and radar method clearly show skill over sample climatology. Probability thresholds are used to convert derived probabilities into derived group memberships (i.e., yes/no Cb/TCu clouds). When comparing verification scores between the combined MSG and radar method and either the radar-only method or the MSG-only method, the combined MSG and radar method shows slightly better performance. When comparing the combined MSG and radar method with the current Royal Netherlands Meteorological Institute (KNMI) radar-based Cb/TCu cloud detection method, the two methods show comparable probability of detection, but the former shows a false-alarm ratio that is about 8% lower. Moreover, a big advantage of the newly developed method is that it provides probabilities, in contrast to the current KNMI method.

Climate services are becoming the backbone to translate climate knowledge, data & information into climate-informed decision-making at all levels, from public administrations to business operators. It is essential to assess the technical and scientific quality of the provided climate data and information products, including their value to users, to establish the relation of trust between providers of climate data and information and various downstream users. The climate data and information products (i.e., from satellite, in-situ and reanalysis) shall be fully traceable, adequately documented and uncertainty quantified and can provide sufficient guidance for users to address their specific needs and feedbacks. This paper discusses details on how to apply the quality assurance framework to deliver timely assessments of the quality and usability of Essential Climate Variable (ECV) products. It identifies an overarching structure for the quality assessment of single product ECVs (i.e., consists of only one single variable), multi-product ECVs (i.e., more than one single parameter), thematic products (i.e., water, energy and carbon cycles), as well as the usability assessment. To support a traceable climate service, other than rigorously evaluating the technical and scientific quality of ECV products, which represent the upstream of climate services, how the uncertainty propagates into the resulting benefit (utility) for the users of the climate service needs to be detailed.

Marine stratocumulus (Sc) clouds play an essential role in the earth radiation budget. Here, we compare liquid water path (LWP), cloud optical thickness (τ), and cloud droplet effective radius (r_e) retrievals from 2 years of collocated Spinning Enhanced Visible and Infrared Imager (SEVIRI), Moderate Resolution Imaging Spectroradiometer (MODIS), and Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) observations; estimate the effect of biomass burning smoke on passive imager retrievals; and evaluate the diurnal cycle of South Atlantic marine Sc clouds. The effect of absorbing aerosols from biomass burning on the retrievals was investigated using the aerosol index (AI) obtained from the Ozone Monitoring Instrument (OMI). SEVIRI and MODIS LWPs were found to decrease with increasing AI relative to TMI LWP, consistent with well-known negative visible/near-infrared (VIS/NIR) retrieval biases in τ and r_e. In the aerosol-affected months of July–August–September, SEVIRI LWP – based on the 1.6 µm r_e – was biased low by 14 g m⁻² ( ∼ 16 %) compared to TMI in overcast scenes, while MODIS LWP showed a smaller low bias of 4 g m⁻² ( ∼ 5 %) for the 1.6 µm channel and a high bias of 8 g m⁻² ( ∼ 10 %) for the 3.7 µm channel compared to TMI. Neglecting aerosol-affected pixels reduced the mean SEVIRI–TMI LWP bias considerably. For 2 years of data, SEVIRI LWP had a correlation with TMI and MODIS LWP of about 0.86 and 0.94, respectively, and biases of only 4–8 g m⁻² (5 %–10 %) for overcast cases. The SEVIRI LWP diurnal cycle was in good overall agreement with TMI except in the aerosol-affected months. Both TMI and SEVIRI LWP decreased from morning to late afternoon, after which a slow increase was observed. Terra and Aqua MODIS mean LWPs also suggested a similar diurnal variation. The relative amplitude of the 2-year-mean and seasonal-mean LWP diurnal cycle varied between 35 % and 40 % from morning to late afternoon for overcast cases. The diurnal variation in SEVIRI LWP was mainly due to changes in τ, while r_e showed only little diurnal variability.