Abstract
The Advanced Scatterometer (ASCAT) on the Meteorological Operational (MetOp) series of satellites is designed to provide data for…Abstract
The Advanced Scatterometer (ASCAT) on the Meteorological Operational (MetOp) series of satellites is designed to provide data for the retrieval of ocean wind fields. Three transponders were used to give an absolute calibration and the worst-case calibration error is estimated to be 0.15–0.25 dB.
In this paper the calibrated data are validated by comparing the backscatter from a range of naturally distributed targets against models developed from European Remote Sensing Satellite (ERS) scatterometer data.
For the Amazon rainforest it is found that the isotropic backscatter decreases from −6.2 to −6.8 dB over the incidence angle range. The ERS value is around −6.5 dB. All ASCAT beams are within 0.1 dB of each other. Rainforest backscatter over a 3-yr period is found to be very stable with annual changes of approximately 0.02 dB.
ASCAT ocean backscatter is compared against values from the C-band geophysical model function (CMOD-5) using ECMWF wind fields. A difference of approximately 0.2 dB below 55° incidence is found. Differences of over 1 dB above 55° are likely due to inaccuracies in CMOD-5, which has not been fully validated at large incidence angles. All beams are within 0.1 dB of each other.
Backscatter from regions of stable Antarctic sea ice is found to be consistent with model backscatter except at large incidence angles where the model has not been validated. The noise in the ice backscatter indicates that the normalized standard deviation of the backscatter values Kp is around 4.5%, which is consistent with the expected value.
These results agree well with the expected calibration accuracy and give confidence that the calibration has been successful and that ASCAT products are of high quality.more
Abstract
The accuracy and precision are determined of cloud liquid water path (LWP) retrievals from the Spinning Enhanced Visible and Infr…Abstract
The accuracy and precision are determined of cloud liquid water path (LWP) retrievals from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) on board Meteosat-8 using 1 yr of LWP retrievals from microwave radiometer (MWR) measurements of two CloudNET stations in northern Europe. The MWR retrievals of LWP have a precision that is superior to current satellite remote sensing techniques, which justifies their use as validation data. The Cloud Physical Properties (CPP) algorithm of the Satellite Application Facility on Climate Monitoring (CM-SAF) is used to retrieve LWP from SEVIRI reflectances at 0.6 and 1.6 μm. The results show large differences in the accuracy and precision of LWP retrievals from SEVIRI between summer and winter. During summer, the instantaneous LWP retrievals from SEVIRI agree well with those from the MWRs. The accuracy is better than 5 g m−2 and the precision is better than 30 g m−2, which is similar to the precision of LWP retrievals from MWR. The added value of the 15-min sampling frequency of Meteosat-8 becomes evident in the validation of the daily median and diurnal variations in LWP retrievals from SEVIRI. The daily median LWP values from SEVIRI and MWR are highly correlated (correlation > 0.95) and have a precision better than 15 g m−2. In addition, SEVIRI and MWR reveal similar diurnal variations in retrieved LWP values. The peak LWP values occur around noon. During winter, SEVIRI generally overestimates the instantaneous LWP values from MWR, the accuracy drops to about 10 g m2, and the precision to about 30 g m−2. The most likely reason for these lower accuracies is the shortcoming of CPP, and similar one-dimensional retrieval algorithms, to model inhomogeneous clouds. It is suggested that neglecting cloud inhomogeneities leads to a significant overestimation of LWP retrievals from SEVIRI over northern Europe during winter.more
Abstract. Validation of cloud properties retrieved from passive spaceborne imagers is essential for cloud and climate applications but complicated due…Abstract. Validation of cloud properties retrieved from passive spaceborne imagers is essential for cloud and climate applications but complicated due to the large differences in scale and observation geometry between the satellite footprint and the independent ground based or airborne observations. Here we illustrate and demonstrate an alternative approach: starting from the output of the COSMO-EU weather model of the German Weather Service realistic three-dimensional cloud structures at a spatial scale of 2.33 km are produced by statistical downscaling and microphysical properties are associated to them. The resulting data sets are used as input to the one-dimensional radiative transfer model libRadtran to simulate radiance observations for all eleven low resolution channels of MET-8/SEVIRI. At this point, both cloud properties and satellite radiances are known such that cloud property retrieval results can be tested and tuned against the objective input "truth". As an example, we validate a cloud property retrieval of the Institute of Atmospheric Physics of DLR and that of EUMETSAT's Climate Monitoring Science Application Facility CMSAF. Cloud detection and cloud phase assignment perform well. By both retrievals 88% of the pixels are correctly classified as clear or cloudy. The DLR algorithm assigns the correct thermodynamic phase to 95% of the cloudy pixels and the CMSAF retrieval to 84%. Cloud top temperature is slightly overestimated by the DLR code (+3.1 K mean difference with a standard deviation of 10.6 K) and to a very low extent by the CMSAF code (−0.12 K with a standard deviation of 7.6 K). Both retrievals account reasonably well for the distribution of optical thickness for both water and ice clouds, with a tendency to underestimation. Cloud effective radii are most difficult to evaluate but the APICS algorithm shows that realistic histograms of occurrences can be derived (CMSAF was not evaluated in this context). Cloud water path, which is a combination of the last two quantities, is slightly underestimated by APICS, while CMSAF shows a larger scattering.more
The satellite-derived HOAPS (Hamburg Ocean Atmosphere Parameters and Fluxes from Satellite Data) precipitation estimates have been validated against i…The satellite-derived HOAPS (Hamburg Ocean Atmosphere Parameters and Fluxes from Satellite Data) precipitation estimates have been validated against in-situ precipitation measurements from optical disdrometers, available from OceanRAIN (Ocean Rainfall And Ice-phase precipitation measurement Network) over the open-ocean by applying a statistical analysis for binary estimates. In addition to using directly collocated pairs of data, collocated data were merged within a certain temporal and spatial threshold into single events, according to the observation times. Although binary statistics do not show perfect agreement, simulations of areal estimates from the observations themselves indicate a reasonable performance of HOAPS to detect rain. However, there are deficits at low and mid-latitudes. Weaknesses also occur when analyzing the mean precipitation rates; HOAPS underperforms in the area of the intertropical convergence zone, where OceanRAIN observations show the highest mean precipitation rates. Histograms indicate that this is due to an underestimation of the frequency of moderate to high precipitation rates by HOAPS, which cannot be explained by areal averaging.more
Accurate determination of the amount of incoming solar radiation at Earth’s surface is important for both climate studies and solar power applications…Accurate determination of the amount of incoming solar radiation at Earth’s surface is important for both climate studies and solar power applications. Satellite-based datasets of solar radiation offer wide spatial and temporal coverage, but careful validation of their quality is a necessary prerequisite for reliable utilization. Here we study the retrieval quality of one polar-orbiting satellite-based dataset (CLARA-A1) and one geostationary satellite-based dataset (SARAH), using in situ observations of solar radiation from the Finnish and Swedish meteorological measurement networks as reference. Our focus is on determining dataset quality over high latitudes as well as evaluating daily mean retrievals, both of which are aspects that have drawn little focus in previous studies. We find that both datasets are generally capable of retrieving the levels and seasonal cycles of solar radiation in Finland and Sweden well, with some limitations. SARAH exhibits a slight negative bias and increased retrieval uncertainty near the coverage edge, but in turn offers better precision (less scatter) in the daily mean retrievals owing to the high sampling rate of geostationary imaging.more
The Satellite Application Facility on Climate Monitoring (CM-SAF) Spectral Resolved Irradiance (SRI) and National Renewable Energy Laboratory National…The Satellite Application Facility on Climate Monitoring (CM-SAF) Spectral Resolved Irradiance (SRI) and National Renewable Energy Laboratory National Solar Radiation Database Spectral on Demand (NSRDB-S) satellite-based spectral irradiance products are tested here against benchmark data and models at seven ground stations: one in Spain for CM-SAF SRI and six in North America for NSRDB-S. Benchmarks include WISER spectroradiometers, spectra modeled from SolarSIM-G measurements, the First Solar model of spectral mismatch factor (SMM), and the SMARTS radiative code with two alternate input sources: AErosol RObotic NETwork (AERONET) and the Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) reanalysis. The satellite products are tested in terms of their ability to estimate photovoltaic (PV) spectral effects for six PV module technologies. Spectra are also compared directly. CM-SAF SRI generally outperforms First Solar and "no spectral effects " benchmarks, except for cadmium telluride modules. For NSRDB-S, predictions of long-term spectral derate factors show less skill than for instantaneous SMMs. Spectra comparisons reveal systematic differences between NSRDB-S and benchmark spectra, likely due to the NSRDB-S treatment of aerosols. Meanwhile, the mean SMARTS spectra with AERONET and MERRA-2 inputs are in good agreement, showing promise for the use of MERRA-2 as input to clear-sky models.more
The Satellite Application Facility on Climate Monitoring (CM-SAF) Spectral Resolved Irradiance (SRI) and National Renewable Energy Laboratory (NREL) N…The Satellite Application Facility on Climate Monitoring (CM-SAF) Spectral Resolved Irradiance (SRI) and National Renewable Energy Laboratory (NREL) National Solar Radiation Database Spectral on Demand (NSRDB-S) satellite-based spectral irradiance products are tested here against benchmark data and models at seven ground stations: one in Spain for CM-SAF SRI and six in North America for NSRDB-S. Benchmarks include WISER spectroradiometers, spectra modeled from SolarSIM-G measurements and the SMARTS radiative code with two alternate input sources: AErosol RObotic NETwork (AERONET) and the ModernEra Retrospective analysis for Research and Applications, Version 2 (MERRA-2) reanalysis. The satellite products are tested in terms of their ability to estimate photovoltaic (PV) spectral effects for six PV module technologies. The spectra are also compared directly under clear-sky conditions. Both CM-SAF SRI and NSRDB-S outperformed the simple benchmark of neglecting spectral effects in terms of predicting instantaneous spectral mismatch factors, but only CM-SAF SRI did better at predicting the long-term spectral derate factors. The clear-sky results revealed systematic differences between NSRDB-S and benchmark spectra, likely due to the NSRDB-S treatment of aerosols. Meanwhile, the mean SMARTS spectra with AERONET and MERRA-2 inputs were in good agreement, showing promise for the use of MERRA-2 as input to clear-sky models.more