Climate Monitoring Satellite Application Facility Research Articles

Global Cloudiness and Cloud Top Information from AVHRR in the 42-Year CLARA-A3 Climate Data Record Covering the Period 1979–2020

This paper investigates the quality of global cloud fraction and cloud-top height products provided by the third edition of the CM SAF cLoud, Albedo and surface RAdiation dataset from the AVHRR data (CLARA-A3) climate data record (CDR) produced by the EUMETSAT Climate Monitoring Satellite Application Facility (CM SAF). Compared with with CALIPSO–CALIOP cloud lidar data and six other cloud CDRs, including the predecessor CLARA-A2, CLARA-A3 has improved cloud detection, especially over ocean surfaces, and improved geographical variation and cloud detection efficiency. In addition, CLARA-A3 exhibits remarkable improvements in the accuracy of its global cloud-top height measurements. For example, in tropical regions, previous underestimations for high-level clouds are reduced by more than 2 km. By taking advantage of more realistic descriptions of global cloudiness, this study attempted to estimate trends in the observable fraction of low-level clouds, acknowledging their importance in producing a net climate cooling effect. The results were generally inconclusive in the tropics, mainly due to the interference of El Nino modes during the period under study. However, the analysis found small negative trends over oceanic surfaces outside the core tropical region. Further studies are needed to verify the significance of these results.

Optimization of weather forecasting for cloud cover over the European domain using the meteorological component of the Ensemble for Stochastic Integration of Atmospheric Simulations version 1.0

Abstract. We present the largest sensitivity study to date for cloud cover using the Weather Forecasting and Research model (WRF V3.7.1) on the European domain. The experiments utilize the meteorological part of a large-ensemble framework, ESIAS-met (Ensemble for Stochastic Integration of Atmospheric Simulations). This work demonstrates the capability and performance of ESIAS for large-ensemble simulations and sensitivity analysis. The study takes an iterative approach by first comparing over 1000 combinations of microphysics, cumulus parameterization, planetary boundary layer (PBL) physics, surface layer physics, radiation scheme, and land surface models on six test cases. We then perform more detailed studies on the long-term and 32-member ensemble forecasting performance of select combinations. The results are compared to CM SAF (Climate Monitoring Satellite Application Facility) satellite images from EUMETSAT (European Organisation for the Exploitation of Meteorological Satellites). The results indicate a high sensitivity of clouds to the chosen physics configuration. The combination of Goddard, WRF single moments 6 (WSM6), or CAM5.1 microphysics with MYNN3 (Mellor–Yamada Nakanishi Niino level 3) or ACM2 (Asymmetrical Convective Model version 2) PBL performed best for simulating cloud cover in Europe. For ensemble-based probabilistic simulations, the combinations of WSM6 and SBU–YLin (Stony Brook University Y. Lin) microphysics with MYNN2 and MYNN3 performed best.

Comparison of Surface Solar Irradiance from Ground Observations and Satellite Data (1990–2016) over a Complex Orography Region (Piedmont—Northwest Italy)

Climate Monitoring Satellite Application Facility (CM SAF) surface solar irradiance (SSI) products were compared with ground-based observations over the Piedmont region (north-western Italy) for the period 1990–2016. These products were SARAH-2.1 (Surface Solar Radiation DataSet—Heliosat version 2.1) and CLARA-A2 (Cloud, Albedo and Surface Radiation dataset version A2). The aim was to contribute to the discussion on the representativeness of satellite SSI data including a focus on high-elevation areas. The comparison between SSI averages shows that for low OCI (orographic complexity index) stations, satellite series have higher values than corresponding ground-based observations, whereas for high OCI stations, SSI values for satellite records are mainly lower than for ground stations. The comparison between SSI anomalies highlights that satellite records have an excellent performance in capturing SSI day-to-day variability of ground-based low OCI stations. In contrast, for high OCI stations, the agreement is much lower, due to the higher uncertainty in both satellite and ground-based records. Finally, if the temporal trends are considered, average low-elevation ground-based SSI observations show a positive trend, whereas satellite records do not highlight significant trends. Focusing on high-elevation stations, the observed trends for ground-based and satellite records are more similar with the only exception of summer. This divergence seems to be due to the relevant role of atmospheric aerosols on SSI trends.

A New Satellite-Based Retrieval of Low-Cloud Liquid-Water Path Using Machine Learning and Meteosat SEVIRI Data

Clouds are one of the major uncertainties of the climate system. The study of cloud processes requires information on cloud physical properties, in particular liquid water path (LWP). This parameter is commonly retrieved from satellite data using look-up table approaches. However, existing LWP retrievals come with uncertainties related to assumptions inherent in physical retrievals. Here, we present a new retrieval technique for cloud LWP based on a statistical machine learning model. The approach utilizes spectral information from geostationary satellite channels of Meteosat Spinning-Enhanced Visible and Infrared Imager (SEVIRI), as well as satellite viewing geometry. As ground truth, data from CloudNet stations were used to train the model. We found that LWP predicted by the machine-learning model agrees substantially better with CloudNet observations than a current physics-based product, the Climate Monitoring Satellite Application Facility (CM SAF) CLoud property dAtAset using SEVIRI, edition 2 (CLAAS-2), highlighting the potential of such approaches for future retrieval developments.

The Climate Monitoring SAF Outgoing Longwave Radiation from AVHRR

Data from the Advanced Very High Resolution Radiometer (AVHRR) have been used to create several long-duration data records of geophysical variables describing the atmosphere and land and water surfaces. In the Climate Monitoring Satellite Application Facility (CM SAF) project, AVHRR data are used to derive the Cloud, Albedo, and Radiation (CLARA) climate data records of radiation components (i.a., surface albedo) and cloud properties (i.a., cloud cover). This work describes the methodology implemented for the additional estimation of the Outgoing Longwave Radiation (OLR), an important Earth radiation budget component, that is consistent with the other CLARA variables. A first step is the estimation of the instantaneous OLR from the AVHRR observations. This is done by regressions on a large database of collocated observations between AVHRR Channel 4 (10.8 µm) and 5 (12 µm) and the OLR from the Clouds and Earth’s Radiant Energy System (CERES) instruments. We investigate the applicability of this method to the first generation of AVHRR instrument (AVHRR/1) for which no Channel 5 observation is available. A second step concerns the estimation of daily and monthly OLR from the instantaneous AVHRR overpasses. This step is especially important given the changes in the local time of the observations due to the orbital drift of the NOAA satellites. We investigate the use of OLR in the ERA5 reanalysis to estimate the diurnal variation. The developed approach proves to be valuable to model the diurnal change in OLR due to day/night time warming/cooling over clear land. Finally, the resulting monthly mean AVHRR OLR product is intercompared with the CERES monthly mean product. For a typical configuration with one morning and one afternoon AVHRR observation, the Root Mean Square (RMS) difference with CERES monthly mean OLR is about 2 Wm−2 at 1° × 1° resolution. We quantify the degradation of the OLR product when only one AVHRR instrument is available (as is the case for some periods in the 1980s) and also the improvement when more instruments are available (e.g., using METOP-A, NOAA-15, NOAA-18, and NOAA-19 in 2012). The degradation of the OLR product from AVHRR/1 instruments is also quantified, which is done by “masking” the Channel 5 observations.

Probabilistic Cloud Masking for the Generation of CM SAF Cloud Climate Data Records from AVHRR and SEVIRI Sensors

Cloud screening in satellite imagery is essential for enabling retrievals of atmospheric and surface properties. For climate data record (CDR) generation, cloud screening must be balanced, so both false cloud-free and false cloudy retrievals are minimized. Many methods used in recent CDRs show signs of clear-conservative cloud screening leading to overestimated cloudiness. This study presents a new cloud screening approach for Advanced Very-High-Resolution Radiometer (AVHRR) and Spinning Enhanced Visible and Infrared Imager (SEVIRI) imagery based on the Bayesian discrimination theory. The method is trained on high-quality cloud observations from the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) lidar onboard the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) satellite. The method delivers results designed for optimally balanced cloud screening expressed as cloud probabilities together with information on for which clouds (minimum cloud optical thickness) the probabilities are valid. Cloud screening characteristics over 28 different Earth surface categories were estimated. Using independent CALIOP observations (including all observed clouds) in 2010 for validation, the total global hit rates for AVHRR data and the SEVIRI full disk were 82% and 85%, respectively. High-latitude oceans had the best performance, with a hit rate of approximately 93%. The results were compared to the CM SAF cLoud, Albedo, and surface RAdiation dataset from AVHRR data–second edition (CLARA-A2) CDR and showed general improvements over most global regions. Notably, the Kuipers’ Skill Score improved, verifying a more balanced cloud screening. The new method will be used to prepare the new CLARA-A3 and CLAAS-3 (CLoud property dAtAset using SEVIRI, Edition 3) CDRs in the EUMETSAT Climate Monitoring Satellite Application Facility (CM SAF) project.

Evaluation of Five Satellite Top-of-Atmosphere Albedo Products over Land

Five satellite top-of-atmosphere (TOA) albedo products over land were evaluated in this study including global products from the Advanced Very High Resolution Radiometer (AVHRR) (TAL-AVHRR), Moderate Resolution Imaging Spectroradiometer (MODIS) (TAL-MODIS), and Clouds and the Earth’s Radiant Energy System (CERES); one regional product from the Climate Monitoring Satellite Application Facility (CM SAF); and one harmonized product termed Diagnosing Earth’s Energy Pathways in the Climate system (DEEP-C). Results showed that overall, there is good consistency among these five products, particularly after the year 2000. The differences among these products in the high-latitude regions were relatively larger. The percentage differences among TAL-AVHRR, TAL-MODIS, and CERES were generally less than 20%, while the differences between TAL-AVHRR and DEEP-C before 2000 were much larger. Except for the obvious decrease in the differences after 2000, the differences did not show significant changes over time, but varied among different regions. The differences between TAL-AVHRR and the other products were relatively large in the high-latitude regions of North America, Asia, and the Maritime Continent, while the differences between DEEP-C and CM SAF in Europe and Africa were smaller. Interannual variability was consistent between products after 2000, before which the differences among the three products were much larger.

Long-term record of top-of-atmosphere albedo over land generated from AVHRR data

Top-of-atmosphere (TOA) albedo is a fundamental component of Earth's energy budget. To date, long-term global land TOA albedo products with spatial resolution higher than 20-km are not available. In this study, we propose a novel algorithm to retrieve TOA albedo from multispectral imager observations acquired by Advanced Very High Resolution Radiometer (AVHRR), which provides the longest continuous record of global satellite observations since 1981. Direct estimation models were established first to derive instantaneous TOA broadband albedo under various atmospheric and surface conditions, including cloudy-sky, clear-sky (snow-free) and snow-cover conditions. To perform long-term series analysis, the instantaneous TOA albedo were then converted to daily/monthly mean values based on the diurnal curves from multi-year Clouds and the Earth's Radiant Energy System (CERES) 3-hourly flux dataset. Calibration differences between sequential AVHRR sensors were further mitigated by invariant targets normalization. The retrieved TOA albedo at 0.05° × 0.05° was validated against two TOA albedo datasets, CM SAF (Climate Monitoring Satellite Application Facility) flux data and CERES flux data, at spatial resolutions of 0.05° × 0.05°, 20 km × 20 km and 1° × 1°. The instantaneous TOA albedo had an overall Root-Mean-Square-Error (RMSE) of 0.047 when compared with 20-km CERES fluxes, whereas the 1° by 1° monthly mean TOA albedo showed closer agreements with both CM SAF and CERES, with RMSE ranging from 0.029 to 0.040 and from 0.022 to 0.031, respectively. Moreover, our product was found to be highly consistent with both CERES and CM SAF at long-term trend detection. The extensive validation indicated the robustness of our algorithm and subsequently, comparable data quality with existing datasets. With global coverage and long time series (1981–2017), our product is expected to provide valuable information for climate change studies.

CLARA-A2: the second edition of the CM SAF cloud and radiation data record from 34 years of global AVHRR data

Abstract. The second edition of the satellite-derived climate data record CLARA (The CM SAF Cloud, Albedo And Surface Radiation dataset from AVHRR data – second edition denoted as CLARA-A2) is described. The data record covers the 34-year period from 1982 until 2015 and consists of cloud, surface albedo and surface radiation budget products derived from the AVHRR (Advanced Very High Resolution Radiometer) sensor carried by polar-orbiting, operational meteorological satellites. The data record is produced by the EUMETSAT Climate Monitoring Satellite Application Facility (CM SAF) project as part of the operational ground segment. Its upgraded content and methodology improvements since edition 1 are described in detail, as are some major validation results. Some of the main improvements to the data record come from a major effort in cleaning and homogenizing the basic AVHRR level-1 radiance record and a systematic use of CALIPSO-CALIOP cloud information for development and validation purposes. Examples of applications studying decadal changes in Arctic summer surface albedo and cloud conditions are provided.

Trends and variability of cloud fraction cover in the Arctic, 1982–2009

Climatology, trends and variability of cloud fraction cover (CFC) data over the Arctic (north of 70°N), were analysed over the 1982–2009 period. Data, available from the Climate Monitoring Satellite Application Facility (CM SAF), are derived from satellite measurements by AVHRR. Climatological means confirm permanent high CFC values over the Atlantic sector during all the year and during summer over the eastern Arctic Ocean. Lower values are found in the rest of the analysed area especially over Greenland and the Canadian Archipelago, nearly continuously during all the months. These results are confirmed by CFC trends and variability. Statistically significant trends were found during all the months over the Greenland Sea, particularly during the winter season (negative, less than −5 % dec −1) and over the Beaufort Sea in spring (positive, more than +5 % dec −1). CFC variability, investigated by the Empirical Orthogonal Functions, shows a substantial “non-variability” in the Northern Atlantic Ocean. Statistically significant correlations between CFC principal components elements and both the Pacific Decadal Oscillation index and Pacific North America patterns are found.

CMSAF products Cloud Fraction Coverage and Cloud Type used for solar global irradiance estimation

Two products provided by the climate monitoring satellite application facility (CMSAF) are the instantaneous Cloud Fractional Coverage (iCFC) and the instantaneous Cloud Type (iCTY) products. Previous studies based on the iCFC product show that the simple solar radiation models belonging to the cloudiness index class n CFC = 0.1–1.0 have rRMSE values ranging between 68 and 71 %. The products iCFC and iCTY are used here to develop simple models providing hourly estimates for solar global irradiance. Measurements performed at five weather stations of Romania (South-Eastern Europe) are used. Two three-class characterizations of the state-of-the-sky, based on the iCTY product, are defined. In case of the first new sky state classification, which is roughly related with cloud altitude, the solar radiation models proposed here perform worst for the iCTY class 4–15, with rRMSE values ranging between 46 and 57 %. The spreading error of the simple models is lower than that of the MAGIC model for the iCTY classes 1–4 and 15–19, but larger for iCTY classes 4–15. In case of the second new sky state classification, which takes into account in a weighted manner the chance for the sun to be covered by different types of clouds, the solar radiation models proposed here perform worst for the cloudiness index class n CTY = 0.7–0.1, with rRMSE values ranging between 51 and 66 %. Therefore, the two new sky state classifications based on the iCTY product are useful in increasing the accuracy of solar radiation models.

Simple solar radiation modelling for different cloud types and climatologies

The instantaneous Cloud Fraction Coverage (iCFC) and Cloud Type (iCTY) products of the Climate Monitoring Satellite Application Facility are used to develop simple relationships between solar global irradiance and cloud amount and types. Radiometric measurements from five Romanian weather stations are used. Solar radiation relationships are proposed for clear sky, overcast sky and cloudy sky. A procedure to average the iCTY data is proposed, and eight classes of averaged iCTY values are considered. Two procedures are used to define the overcast sky and two cloudy sky solar radiation models are considered. Overcast skies consisting of stratiform clouds (CTY classes 8 to 14) are the most challenging when solar radiation modelling is considered. The overcast sky models have lower accuracy at high irradiance values. The best cloudy sky model has relative root mean square error values ranging between 17.6 % (for CTY classes 1 to 4) and 67.6 % (for CTY classes 12 to 14). For most CTY classes, the model performs worse at intermediate irradiance values.

Advancing the uncertainty characterisation of cloud masking in passive satellite imagery: Probabilistic formulations for NOAA AVHRR data

Two alternative methods for probabilistic cloud masking of images from the Advanced Very High Resolution Radiometer (AVHRR) sensor have been examined. Both methods are based on Bayesian theory and were trained using data from the Cloud–Aerosol Lidar with Orthogonal Polarization (CALIOP) lidar onboard the Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) satellite. Results were evaluated by comparing to independent CALIPSO–CALIOP observations and to a one-year ground-based cloud dataset composed from five different remote sensing systems over the observation site in Cabauw in the Netherlands. In addition, results were compared to two different cloud masks; one derived from the geostationary Spinning Enhanced Visible and Infrared Imager (SEVIRI) sensor and one from the Climate Monitoring Satellite Application Facility Clouds (CMSAF), Albedo and Radiation dataset from AVHRR data (CLARA-A1). It was demonstrated that the probabilistic methods compare well with the referenced satellite datasets and for daytime conditions they provide even better performance than the reference methods. Among the two probabilistic approaches, it was found that the formulation based on a Naïve Bayesian formulation (denoted PPS-Prob Naïve) performed clearly superior to the formulation based on a linear summation of conditional cloud probabilities (denoted PPS-Prob SPARC) for daytime conditions. For the study based on the observations over the Cabauw site, the overall daytime Kuipers Skill Score for PPS-Prob Naïve was 0.84, for PPS-Prob SPARC 0.79, for CLARA-A1 0.74 and for SEVIRI 0.66. Corresponding results for night-time conditions were less favourable for the probabilistic formulations (Kuipers Skill Score 0.74 for PPS_Prob Naïve, 0.68 for PPS-Prob SPARC, 0.80 for CLARA-A1 and 0.79 for SEVIRI) but still relatively close to the reference dataset. The Cabauw distribution of cloudiness occurrences in different octa categories was reproduced very closely by all methods, including the probabilistic formulations. Results based on Cabauw observations were also largely in good agreement with results deduced from comparisons with the CALIPSO–CALIOP cloud mask.The PPS-Prob Naïve approach will be implemented in an upcoming version of the Polar Platform System (PPS) cloud software issued by the EUMETSAT Nowcasting Satellite Application Facility (NWC SAF). It will also be used in the second release of the CMSAF CLARA cloud climate data record based on historic AVHRR GAC data (to be denoted CLARA-A2).

Simple models to compute solar global irradiance from the CMSAF product Cloud Fractional Coverage

Simple models are proposed to compute solar global irradiance by using the hourly Cloud Fractional Coverage (CFC) data provided by the Climate Monitoring Satellite Application Facility (CMSAF). The models are tested against measurements performed in five Romanian weather stations. The cloudy sky models based on CFC (n, for short) are compared with cloudy sky models based on ground-based estimates of point cloudiness (C, for short). Two models were proposed here for clear sky and overcast sky defined as n = 0 and n = 1, respectively. Two types of cloudy sky regression models were built on the basis of these clear sky and overcast sky models. Eight cloudy sky models based on n have been tested in a particular location. The bias error is good or good enough for all cloudiness classes. The spreading error is good for n = 0 ÷ 0.3; good enough for n = 0.3 ÷ 0.7 and poor for n > 0.7. For low zenith angle (Z = 0° ÷ 30°) the bias error of the eight models is generally good enough or poor. Generally, best fit models based on C perform better than best fit models based on n. One model (D1) has been selected for further testing. The sub-model D1TOT has been obtained by fitting the model D1 to all available measured data. The accuracy of sub-model D1TOT is good and good enough for all stations at low and intermediate zenith angles (Z < 70°). The performance of a model based on n is significantly better than that of a model based on C, for all zenith angle classes. D1 sub-models were developed by using data from particular stations. Generally, all sub-models have good or good enough performance when used in stations other than the origin one, for cloudiness classes n < 0.7. In case of skies with n = 0.3 ÷ 0.7, the performance of the sub-models based on n is obviously worse than that of sub-models based on C. For low zenith angles (Z = 0° ÷ 70°), the performance of D1 sub-models is good or good enough, when applied in the origin station or other stations, and it is comparable with that of models based on C.

Accuracy of CM-SAF solar irradiance incident on horizontal surface

The Climate Monitoring Satellite Application Facility (CM-SAF) provides estimates of global solar irradiance incident on horizontal surface at Earth surface. Measurements performed in 2010 at five Romanian meteorological stations are used to test the accuracy of the CM-SAF irradiance data. The dataset contains null solar global irradiance values, which cannot be explained by very large values of the zenith angle neither by overcast sky conditions. Sub-databases have been created. The database Z85 consists of irradiance data, without filtering and processing. The database Z85SIS+ remove all null irradiance values. For a given database, the root mean square error (RMSE) with respect to the ground-based measurements is rather similar for all stations, i.e. around 35 % for Z85 and 24 % for Z85SIS+. On average, the database Z85SIS+ has smaller mean bias error (MBE) than the database Z85, independent of the degree of cloudiness. For the database Z85, MBE (RMSE) ranges, depending on station, between −9.4 and −1.2 % (35.3 and 39.1 %). For database Z85SIS+, the MBE (RMSE) ranges, depending on station, between −4.0 and 0.1 % (23.0 and 29.1 %). On overcast sky, we found for some stations MBE = −0.1 % and RMSE = 46.4 % when the database Z85SIS+ has been considered. The accuracy of the database Z85 is lower; we found MBE = −7.0 % and RMSE = 58.8 % as extreme cases.

CLARA-A1: a cloud, albedo, and radiation dataset from 28 yr of global AVHRR data

Abstract. A new satellite-derived climate dataset – denoted CLARA-A1 ("The CM SAF cLoud, Albedo and RAdiation dataset from AVHRR data") – is described. The dataset covers the 28 yr period from 1982 until 2009 and consists of cloud, surface albedo, and radiation budget products derived from the AVHRR (Advanced Very High Resolution Radiometer) sensor carried by polar-orbiting operational meteorological satellites. Its content, anticipated accuracies, limitations, and potential applications are described. The dataset is produced by the EUMETSAT Climate Monitoring Satellite Application Facility (CM SAF) project. The dataset has its strengths in the long duration, its foundation upon a homogenized AVHRR radiance data record, and in some unique features, e.g. the availability of 28 yr of summer surface albedo and cloudiness parameters over the polar regions. Quality characteristics are also well investigated and particularly useful results can be found over the tropics, mid to high latitudes and over nearly all oceanic areas. Being the first CM SAF dataset of its kind, an intensive evaluation of the quality of the datasets was performed and major findings with regard to merits and shortcomings of the datasets are reported. However, the CM SAF's long-term commitment to perform two additional reprocessing events within the time frame 2013–2018 will allow proper handling of limitations as well as upgrading the dataset with new features (e.g. uncertainty estimates) and extension of the temporal coverage.

The CMSAF hourly solar irradiance database (product CM54): Accuracy and bias corrections with illustrations for Romania (south-eastern Europe)

Product CM54 of CMSAF (Climate Monitoring Satellite Application Facility) consists of Surface Incoming Shortwave Radiation (SIS). This product is obtained by using the MAGICSOL algorithm, which needs only the broadband visible channel as Meteosat satellites input. Hourly, daily and monthly averaged data sets are available, covering on a regular 0.03×0.03° grid the Meteosat scene up to a scanning angle of 70°. The CM54 product has been tested by using global hourly averaged solar irradiance data measured in 2010 in five Romanian meteorological stations. The available satellite database is structured into three sub-databases. Two databases (Z85 and Z75) consist of recordings associated with solar zenith angle Z<85° and Z<75°, respectively. A third database (Z85SIS+) was obtained by removing from the database Z85 the null irradiance values. The databases Z85 and Z75 underestimate the measured values and their RMSE is relatively similar, around 35%. The database Z85SIS+ has MBE and RMSE values around 0.1% and 25%, respectively. Independent of the database, MBE increases while RMSE decreases by increasing the fractional cloudiness class. The database Z85SIS+ has an MBE between −1% and 1%, independent of the cloudiness class. The RMSE of Z85SIS+ database is about 9%, 20% and 37% for clear skies, vaguely and partly cloudy skies and overcast skies, respectively. The CM54 product overestimates the ground-based measurements at small zenith angles and underestimates at very large zenith angles. Regression relationships have been prepared to remove the bias errors from the database Z85SIS+. These relationships are function of location, total cloud cover amount and class of zenith angle.

A fast SEVIRI simulator for quantifying retrieval uncertainties in the CM SAF cloud physical property algorithm

Abstract. The uncertainties in the cloud physical properties derived from satellite observations make it difficult to interpret model evaluation studies. In this paper, the uncertainties in the cloud water path (CWP) retrievals derived with the cloud physical properties retrieval algorithm (CPP) of the climate monitoring satellite application facility (CM SAF) are investigated. To this end, a numerical simulator of MSG-SEVIRI observations has been developed that calculates the reflectances at 0.64 and 1.63 μm for a wide range of cloud parameter values, satellite viewing geometries and surface albedos using a plane-parallel radiative transfer model. The reflectances thus obtained are used as input to CPP, and the retrieved values of CWP are compared to the original input of the simulator. Cloud parameters considered in this paper refer to e.g. sub-pixel broken clouds and the simultaneous occurrence of ice and liquid water clouds within one pixel. These configurations are not represented in the CPP algorithm and as such the associated retrieval uncertainties are potentially substantial. It is shown that the CWP retrievals are very sensitive to the assumptions made in the CPP code. The CWP retrieval errors are generally small for unbroken single-layer clouds with COT &gt; 10, with retrieval errors of ~3% for liquid water clouds to ~10% for ice clouds. In a multi-layer cloud, when both liquid water and ice clouds are present in a pixel, the CWP retrieval errors increase dramatically; depending on the cloud, this can lead to uncertainties of 40–80%. CWP retrievals also become more uncertain when the cloud does not cover the entire pixel, leading to errors of ~50% for cloud fractions of 0.75 and even larger errors for smaller cloud fractions. Thus, the satellite retrieval of cloud physical properties of broken clouds as well as multi-layer clouds is complicated by inherent difficulties, and the proper interpretation of such retrievals requires extra care.

A new solar radiation database for estimating PV performance in Europe and Africa

The Photovoltaic Geographical Information System (PVGIS) is a web application for the estimation of the performance of photovoltaic (PV) systems in Europe and Africa, which has become widely used by the PV community in Europe. We here present the results of adapting the solar radiation data calculated from satellite data in the Climate Monitoring Satellite Application Facility (CM-SAF) to PVGIS. The CM-SAF solar radiation database is characterized by very low overall bias and shows good accuracy at validation sites. The application to PVGIS brings important improvements relative to the existing solar radiation databases within PVGIS.

A New Algorithm for the Satellite-Based Retrieval of Solar Surface Irradiance in Spectral Bands

Accurate solar surface irradiance data is a prerequisite for an efficient planning and operation of solar energy systems. Further, it is essential for climate monitoring and analysis. Recently, the demand on information about spectrally resolved solar surface irradiance has grown. As surface measurements are rare, satellite derived information with high accuracy might fill this gap. This paper describes a new approach for the retrieval of spectrally resolved solar surface irradiance from satellite data. The method combines a eigenvector-hybrid look-up table approach for the clear sky case with satellite derived cloud transmission (Heliosat method). The eigenvector LUT approach is already used to retrieve the broadband solar surface irradiance of data sets provided by the Climate Monitoring Satellite Application Facility (CM-SAF). This paper describes the extension of this approach to wavelength bands and the combination with spectrally resolved cloud transmission values derived with radiative transfer corrections of the broadband cloud transmission. Thus, the new approach is based on radiative transfer modeling and enables the use of extended information about the atmospheric state, among others, to resolve the effect of water vapor and ozone absorption bands. The method is validated with spectrally resolved measurements from two sites in Europe and by comparison with radiative transfer calculations. The validation results demonstrate the ability of the method to retrieve accurate spectrally resolved irradiance from satellites. The accuracy is in the range of the uncertainty of surface measurements, with exception of the UV and NIR ( ≥ 1200 nm) part of the spectrum, where higher deviations occur.