Abstract. Within the framework of the UV-Indien network, nine ground stations have been equipped with ultraviolet broadband radiometers, five of them …Abstract. Within the framework of the UV-Indien network, nine ground stations have been equipped with ultraviolet broadband radiometers, five of them have also been equipped with an all-sky camera, and the main station in Saint-Denis de la Réunion is also equipped with a spectroradiometer. These stations are spatially distributed to cover a wide range of latitudes, longitudes, altitudes, and environmental conditions in five countries of the western Indian Ocean region (Comoros, France, Madagascar, Mauritius, and Seychelles), a part of the world where almost no measurements have been made so far. The distribution of the stations is based on the scientific interest of studying ultraviolet radiation not only in relation to atmospheric processes but also in order to provide data relevant to fields such as biology, health (prevention of skin cancer), and agriculture. The main scientific objectives of this network are to study the annual and inter-annual variability in the ultraviolet (UV) radiation in this area, to validate the output of numerical models and satellite estimates of ground-based UV measurements, and to monitor UV radiation in the context of climate change and projected ozone depletion in this region. A calibration procedure including three types of calibrations responding to the various constraints of sustaining the network has been put in place, and a data processing chain has been set up to control the quality and the format of the files sent to the various data centres. A method of clear-sky filtering of the data is also applied. Here, we present an intercomparison with other datasets, as well as several daily or monthly representations of the UV index (UVI) and cloud fraction data, to discuss the quality of the data and their range of values for the older stations (Antananarivo, Anse Quitor, Mahé, and Saint-Denis). Ground-based measurements of the UVI are used to validate satellite estimates – Ozone Monitoring Instrument (OMI), the TROPOspheric Monitoring Instrument (TROPOMI), and the Global Ozone Monitoring Experiment (GOME) – and model forecasts of UVI – Tropospheric Emission Monitoring Internet Service (TEMIS) and Copernicus Atmospheric Monitoring Service (CAMS). The median relative differences between satellite or model estimates and ground-based measurements of clear-sky UVI range between −34.5 % and 15.8 %. Under clear skies, the smallest UVI median difference between the satellite or model estimates and the measurements made by ground-based instruments is found to be 0.02 (TROPOMI), 0.04 (OMI), −0.1 (CAMS), and −0.4 (CAMS) at Saint-Denis, Antananarivo, Anse Quitor, and Mahé, respectively. The diurnal variability in UVI and cloud fraction, as well as the monthly variability in UVI, is evaluated to ensure the quality of the dataset. The data used in this study are available at https://doi.org/10.5281/zenodo.4811488 (Lamy and Portafaix, 2021a).more
The advanced scatterometer (ASCAT) is a fan beam scatterometer carried on board the Metop series of satellites. Its primary objective is to measure oc…The advanced scatterometer (ASCAT) is a fan beam scatterometer carried on board the Metop series of satellites. Its primary objective is to measure ocean backscatter for the retrieval of ocean wind vectors. Two ASCAT instruments (ASCAT-A and ASCAT-B) are operational and are independently calibrated using a number of ground-based transponders. The first seven years of data from ASCAT-A have recently been processed into a climate data record. This paper describes a number of methods for cross-validating the data from the two instruments and for assessing the quality and stability of the climate data record. The methods are based on backscatter from the Amazon rainforest, mean backscatter from the open ocean, comparison of measured and modeled ocean backscatter, and ocean cone metrics. These methods show that the climate data record, which covers the period January 2007 to March 2014, has a very high stability (with trends around 0.005 dB per year), good absolute and relative calibration (better than 0.1 dB), and a good across swath calibration (peak to peak variation of less than 0.1 dB). For operational data covering the period April 2015 to March 2016, the methods indicate that ASCAT-B backscatter is around 0.1-0.2 dB higher than ASCAT-A (depending on which beam is considered). This difference is due to a combination of factors: minor changes in calibration algorithms, a minor change in the behavior of the ASCAT-A internal calibration system, and the strategy used to update calibration files in the processing system.more
This work presents a validation of the downwelling surface shortwave radiation, or surface solar radiation (SSR), derived from the Satellite Applicati…This work presents a validation of the downwelling surface shortwave radiation, or surface solar radiation (SSR), derived from the Satellite Application Facility on Climate Monitoring (CM SAF) over Europe for a 23-year period of records on a monthly basis. This SSR product has been recently derived based on the visible channel of the Meteosat First Generation satellites, providing a dataset with a high spatial resolution (0.03° × 0.03°) covering the 1983–2005 period. The CM SAF SSR product is compared against a homogeneous dataset of surface observations from the Global Energy Balance Archive (GEBA) over Europe, which has been homogenized by means of the Standard Normal Homogeneity Test (SNHT). The results show a good agreement between both datasets (r2 = 0.86, p < 0.01), with a slight overestimation (bias of + 5.20 W m− 2) of the CM SAF records as compared to the surface observations on a monthly mean basis. Equally, there is a monthly mean absolute bias difference (MABD) of 8.19 W m− 2 that is below the accuracy threshold defined by the CM SAF. There is a clear maximum and minimum MABD during summer and winter, respectively, with an opposite cycle if the relative MABD values are considered. Moreover, the temporal stability of the CM SAF SSR is checked against the GEBA stations for the mean time series over Europe, as well as for each individual series. The results point to possible inhomogeneities in the CM SAF records around 1987 and 1994, possibly due to changes in the satellite instruments, although other factors such as the lack of aerosol retrievals in the CM SAF SSR are also discussed. Consequently, the study of the means and trends in the SSR derived from CM SAF is only recommended for the records after 1994.more
To achieve high-quality surface solar radiation (SSR) data for climate monitoring and analysis, the two satellite-derived monthly SSR datasets of CM S…To achieve high-quality surface solar radiation (SSR) data for climate monitoring and analysis, the two satellite-derived monthly SSR datasets of CM SAF CLARA-A2 and SARAH-E have been validated against a homogenized ground-based dataset covering 59 stations across China for 1993–2015 and 1999–2015, respectively. The satellite products overestimate surface solar irradiance by 10.0 W m−2 in CLARA-A2 and 7.5 W m−2 in SARAH-E on average. A strong urbanization effect has been noted behind the large positive bias in China. The bias decreased after 2004, possibly linked to a weakened attenuating effect of aerosols on radiation in China. Both satellite datasets can reproduce the monthly anomalies of SSR, indicated by a significant correlation around 0.8. Due to the neglection of temporal aerosol variability in the satellite algorithms, the discrepancy between the satellite-estimated and ground-observed SSR trends slightly increases in 1999–2015 as compared to 1993–2015. The seasonal performance of the satellite products shows a better accuracy during warm than cold seasons. With respect to the spatial performance, the effects from anthropogenic aerosols, dust aerosols and high elevation and snow-covered surfaces should be well considered in the satellite SSR retrievals to further improve the performance in the eastern, northwestern and southwestern parts of China, respectively.more
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