The 30-year TAMSAT African rainfall climatology and time-series (TARCAT) dataset

African societies are dependent on rainfall for agricultural and other water-dependent activities, yet rainfall is extremely variable in both space and time and reoccurring water shocks, such as drought, can have considerable social and economic impacts. To help improve our knowledge of the rainfall climate, we have constructed a 30-year (1983–2012), temporally consistent rainfall dataset for Africa known as TARCAT (TAMSAT African Rainfall Climatology And Time-series) using archived Meteosat thermal infra-red (TIR) imagery, calibrated against rain gauge records collated from numerous African agencies. TARCAT has been produced at 10-day (dekad) scale at a spatial resolution of 0.0375°. An intercomparison of TARCAT from 1983 to 2010 with six long-term precipitation datasets indicates that TARCAT replicates the spatial and seasonal rainfall patterns and interannual variability well, with correlation coefficients of 0.85 and 0.70 with the Climate Research Unit (CRU) and Global Precipitation Climatology Centre (GPCC) gridded-gauge analyses respectively in the interannual variability of the Africa-wide mean monthly rainfall. The design of the algorithm for drought monitoring leads to TARCAT underestimating the Africa-wide mean annual rainfall on average by −0.37 mm day−1 (21%) compared to other datasets. As the TARCAT rainfall estimates are historically calibrated across large climatically homogeneous regions, the data can provide users with robust estimates of climate related risk, even in regions where gauge records are inconsistent in time

High-resolution satellite-based cloud detection for the analysis of land surface effects on boundary layer clouds

The observation of boundary layer clouds with high-resolution satellite data can provide comprehensive insights into spatiotemporal patterns of land-surface-driven modification of cloud occurrence, such as the diurnal variation of the occurrence of fog holes and cloud enhancements attributed to the impact of the urban heat island. High-resolution satellite-based cloud-masking approaches are often based on locally optimised thresholds that can be affected by the local surface reflectance, and they therefore introduce spatial biases in the detected cloud cover. In this study, geostationary satellite observations are used to develop and validate two high-resolution cloud-masking approaches for the region of Paris to show and improve applicability for analyses of urban effects on clouds. Firstly, the Local Empirical Cloud Detection Approach (LECDA) uses an optimised threshold to separate the distribution of visible reflectances into cloudy and clear sky for each individual pixel accounting for its locally specific brightness. Secondly, the Regional Empirical Cloud Detection Approach (RECDA) uses visible reflectance thresholds that are independent of surface reflection at the observed location. Validation against in-situ cloud fractions reveals that both approaches perform similarly, with a probability of detection (POD) of 0.77 and 0.69 for LECDA and RECDA, respectively. Results show that with the application of RECDA a decrease of cloud cover during typical fog or low-stratus conditions over the urban area of Paris for the month of November is likely a result of urban effects on cloud dissipation. While LECDA is representative for the widespread usage of locally optimised approaches, comparison against RECDA reveals that the cloud masks obtained from LECDA result in regional biases of ±5 % that are most likely caused by the differences in surface reflectance in and around the urban areas of Paris. This makes the regional approach, RECDA, a more appropriate choice for the high-resolution satellite-based analysis of cloud cover modifications over different surface types and the interpretation of locally induced cloud processes. Further, this approach is potentially transferable to other regions and temporal scales for analysing long-term natural and anthropogenic impacts of land cover changes on clouds

Climate Data Records from Meteosat First Generation Part II: Retrieval of the In-Flight Visible Spectral Response

How can the in-flight spectral response functions of a series of decades-old broad band radiometers in Space be retrieved post-flight? This question is the key to developing Climate Data Records from the Meteosat Visible and Infrared Imager on board the Meteosat First Generation (MFG) of geostationary satellites, which acquired Earth radiance images in the Visible (VIS) broad band from 1977 to 2017. This article presents a new metrologically sound method for retrieving the VIS spectral response from matchups of pseudo-invariant calibration site (PICS) pixels with datasets of simulated top-of-atmosphere spectral radiance used as reference. Calibration sites include bright desert, open ocean and deep convective cloud targets. The absolute instrument spectral response function is decomposed into generalised Bernstein basis polynomials and a degradation function that is based on plain physical considerations and able to represent typical chromatic ageing characteristics. Retrieval uncertainties are specified in terms of an error covariance matrix, which is projected from model parameter space into the spectral response function domain and range. The retrieval method considers target type-specific biases due to errors in, e.g., the selection of PICS target pixels and the spectral radiance simulation explicitly. It has been tested with artificial and well-comprehended observational data from the Spinning Enhanced Visible and Infrared Imager on-board Meteosat Second Generation and has retrieved meaningful results for all MFG satellites apart from Meteosat-1, which was not available for analysis

High-resolution satellite-based cloud detection for the analysis of land surface effects on boundary layer clouds

Abstract. Continental boundary layer clouds play an essential role in the climate system and are driven by processes linked to the land surface. The observation of boundary layer clouds with high-resolution satellite data can provide comprehensive insights into spatiotemporal patterns of land surface-driven modification of cloud occurrence, such as the diurnal variation of the occurrence of fog holes and cloud enhancements attributed to the impact of the urban heat island. High-resolution satellite-based cloud masking approaches are often based on locally-optimized thresholds that are compared against satellite-observed visible and/or infrared radiances to separate cloudy from clear-sky observations that can be affected by the local surface reflectance. Therefore, spatial differences in surface albedo, as found in and around urban areas or forests, can introduce spatial biases in the detected cloud cover that may impede the analysis of spatial pattern changes due to land surface influences. In this study, two approaches for cloud masking using the High Resolution Visible channel of the Spinning Enhanced Visible and Infrared Imager aboard Meteosat Second Generation are developed and validated for the region of Paris to show and improve applicability for analyses of urban effects on clouds. Firstly, the Local Empirical Cloud Detection Approach (LECDA) uses an optimized threshold to separate the distribution of visible reflectances into cloudy and clear sky for each individual pixel accounting for its locally specific brightness. Secondly, the Regional Empirical Cloud Detection Approach (RECDA) uses visible reflectance thresholds that are independent of surface reflection at the observed location. Validation against in-situ cloud fractions reveals that both approaches perform similarly with a Heidke Skill Score of 0.69 and 0.71, respectively. While the LECDA is representative for the widespread usage of locally-optimized approaches, comparison against RECDA reveals that the cloud masks obtained from LECDA can result in regional biases of +−5 % that are caused by the differences in surface reflectance. This makes the regional approach RECDA a more appropriate choice for the high-resolution satellite-based analysis of cloud cover changes over different surface types and the interpretation of locally induced cloud processes. </jats:p

Assessment of the EUMETSAT Multi Decadal Land Surface Albedo Data Record from Meteosat Observations

Surface albedo, defined as the ratio of the surface-reflected irradiance to the incident irradiance, is one of the parameters driving the Earth energy budget and it is for this reason an essential variable in climate studies. Instruments on geostationary satellites provide suitable observations allowing long-term monitoring of surface albedo from space. In 2012, EUMETSAT published Release 1 of the Meteosat Surface Albedo (MSA) data record. The main limitation effecting the quality of this release was non-removed clouds by the incorporated cloud screening procedure that caused too high albedo values, in particular for regions with permanent cloud coverage. For the generation of Release 2, the MSA algorithm has been replaced with the Geostationary Surface Albedo (GSA) one, able to process imagery from any geostationary imager. The GSA algorithm exploits a new, improved, cloud mask allowing better cloud screening, and thus fixing the major limitation of Release 1. Furthermore, the data record has an extended temporal and spatial coverage compared to the previous release. Both Black-Sky Albedo (BSA) and White-Sky Albedo (WSA) are estimated, together with their associated uncertainties. A direct comparison between Release 1 and Release 2 clearly shows that the quality of the retrieval improved significantly with the new cloud mask. For Release 2 the decadal trend is less than 1% over stable desert sites. The validation against Moderate Resolution Imaging Spectroradiometer (MODIS) and the Southern African Regional Science Initiative (SAFARI) surface albedo shows a good agreement for bright desert sites and a slightly worse agreement for urban and rain forest locations. In conclusion, compared with MSA Release 1, GSA Release 2 provides the users with a significantly more longer time range, reliable and robust surface albedo data record

High-resolution satellite-based cloud detection for the analysis of land surface effects on boundary layer clouds

&amp;lt;p&amp;gt;In this study, geostationary satellite observations are used to develop and validate two high-resolution cloud-masking approaches for the region of Paris to show and improve applicability for analyses of urban effects on clouds.&amp;amp;#160;&amp;lt;/p&amp;gt; &amp;lt;p&amp;gt;Firstly, the Local Empirical Cloud Detection Approach (LECDA) uses an optimised threshold to separate the distribution of visible reflectances into cloudy and clear sky for each individual pixel accounting for its locally specific brightness. Secondly, the Regional Empirical Cloud Detection Approach (RECDA) uses visible reflectance thresholds that are independent of surface reflection at the observed location.&amp;lt;/p&amp;gt; &amp;lt;p&amp;gt;Results show that&amp;lt;/p&amp;gt; &amp;lt;ul&amp;gt; &amp;lt;li aria-level=&amp;quot;1&amp;quot;&amp;gt;A decrease of cloud cover during typical fog or low-stratus conditions over the urban area of Paris for the month of November is likely a result of urban effects on cloud dissipation.&amp;lt;/li&amp;gt; &amp;lt;li aria-level=&amp;quot;1&amp;quot;&amp;gt;The regional approach, RECDA, is a more appropriate choice for the high-resolution satellite-based analysis of cloud cover modifications over different surface types than LECDA with regional biases of &amp;amp;#177;5 %.&amp;lt;/li&amp;gt; &amp;lt;/ul&amp;gt; &amp;lt;p&amp;gt;This approach can provide comprehensive insights into spatiotemporal patterns of land-surface-driven modification of cloud occurrence and locally induced cloud processes, such as the diurnal variation of the occurrence of fog holes and cloud enhancements attributed to the impact of the urban heat island. Further, it is potentially transferable to other regions and temporal scales for analysing long-term natural and anthropogenic impacts of land cover changes on clouds.&amp;lt;/p&amp;gt;</jats:p

High-resolution satellite-based cloud detection for the analysis of land surface effects on boundary layer clouds

&amp;lt;p&amp;gt;This study aims at improving an empirical cloud masking approach for the high-resolution analysis of land surface effects on boundary layer clouds.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;The observation of boundary layer clouds with high-resolution satellite data can provide comprehensive insights into spatiotemporal patterns of land surface-driven modification of cloud occurrence, such as the diurnal variation of the occurrence of fog holes and cloud enhancements attributed to the impact of the urban heat island. High-resolution satellite-based cloud masking approaches are often based on locally-optimized thresholds that are compared against satellite-observed reflectances to separate cloudy from clear-sky observations that can be affected by the local surface reflectance. Therefore, spatial differences in surface albedo, as found in and around urban areas or forests, can introduce spatial biases in the detected cloud cover that may impede the analysis of spatial pattern changes due to land surface influences. In this study, two approaches for cloud masking using the High Resolution Visible channel of the Spinning Enhanced Visible and Infrared Imager aboard Meteosat Second Generation are developed and validated for the region of Paris to show and improve applicability for analyses of urban effects on clouds. Firstly, a local approach that uses an optimized threshold to separate the distribution of visible reflectances into cloudy and clear sky for each individual pixel accounting for its locally specific brightness. Secondly, a regional approach that uses visible reflectance thresholds that are independent of surface reflection at the observed location. While the first approach is representative for the widespread usage of locally-optimized approaches, derived cloud masks result in regional biases that are caused by the differences in surface reflectance. This makes the regional approach a more appropriate choice for the high-resolution satellite-based analysis of cloud cover changes over different surface types and the interpretation of locally induced cloud processes.&amp;lt;/p&amp;gt;</jats:p