Harmonization of remote sensing land surface products : correction of clear-sky bias and characterization of directional effects

Tese de doutoramento, Ciências Geofísicas e da Geoinformação (Deteção Remota), Universidade de Lisboa, Faculdade de Ciências, 2018Land surface temperature (LST) is the mean radiative skin temperature of an area of land resulting from the mean energy balance at the surface. LST is an important climatological variable and a diagnostic parameter of land surface conditions, since it is the primary variable determining the upward thermal radiation and one of the main controllers of sensible and latent heat fluxes between the surface and the atmosphere. The reliable and long-term estimation of LST is therefore highly relevant for a wide range of applications, including, amongst others: (i) land surface model validation and monitoring; (ii) data assimilation; (iii) hydrological applications; and (iv) climate monitoring. Remote sensing constitutes the most effective method to observe LST over large areas and on a regular basis. Satellite LST products generally rely on measurements in the thermal infrared (IR) atmospheric window, i.e., within the 8-13 micrometer range. Beside the relatively weak atmospheric attenuation under clear sky conditions, this band includes the peak of the Earth’s spectral radiance, considering surface temperature of the order of 300K (leading to maximum emission at approximately 9.6 micrometer, according to Wien’s Displacement Law). The estimation of LST from remote sensing instruments operating in the IR is being routinely performed for nearly 3 decades. Nevertheless, there is still a long list of open issues, some of them to be addressed in this PhD thesis. First, the viewing position of the different remote sensing platforms may lead to variability of the retrieved surface temperature that depends on the surface heterogeneity of the pixel – dominant land cover, orography. This effect introduces significant discrepancies among LST estimations from different sensors, overlapping in space and time, that are not related to uncertainties in the methodologies or input data used. Furthermore, these directional effects deviate LST products from an ideally defined LST, which should correspond to the ensemble directional radiometric temperature of all surface elements within the FOV. In this thesis, a geometric model is presented that allows the upscaling of in situ measurements to the any viewing configuration. This model allowed generating a synthetic database of directional LST that was used consistently to evaluate different parametric models of directional LST. Ultimately, a methodology is proposed that allows the operational use of such parametric models to correct angular effects on the retrieved LST. Second, the use of infrared data limits the retrieval of LST to clear sky conditions, since clouds “close” the atmospheric window. This effect introduces a clear-sky bias in IR LST datasets that is difficult to quantify since it varies in space and time. In addition, the cloud clearing requirement severely limits the space-time sampling of IR measurements. Passive microwave (MW) measurements are much less affected by clouds than IR observations. LST estimates can in principle be derived from MW measurements, regardless of the cloud conditions. However, retrieving LST from MW and matching those estimations with IR-derived values is challenging and there have been only a few attempts so far. In this thesis, a methodology is presented to retrieve LST from passive MW observations. The MW LST dataset is examined comprehensively against in situ measurements and multiple IR LST products. Finally, the MW LST data is used to assess the spatial-temporal patterns of the clear-sky bias at global scale.Fundação para a Ciência e a Tecnologia, SFRH/BD/9646

An assessment of the quality of aerosol retrievals over the Red Sea and evaluation of the climatological cloud-free dust direct radiative effect in the region

Ground-based and satellite observations are used in conjunction with the Rapid Radiative Transfer Model (RRTM) to assess climatological aerosol loading and the associated cloud-free aerosol direct radiative effect (DRE) over the Red Sea. Aerosol optical depth (AOD) retrievals from the Moderate Resolution Imaging Spectroradiometer and Spinning Enhanced Visible and InfraRed Imager (SEVIRI) instruments are first evaluated via comparison with ship-based observations. Correlations are typically better than 0.9 with very small root-mean-square and bias differences. Calculations of the DRE along the ship cruises using RRTM also show good agreement with colocated estimates from the Geostationary Earth Radiation Budget instrument if the aerosol asymmetry parameter is adjusted to account for the presence of large particles. A monthly climatology of AOD over the Red Sea is then created from 5 years of SEVIRI retrievals. This shows enhanced aerosol loading and a distinct north to south gradient across the basin in the summer relative to the winter months. The climatology is used with RRTM to estimate the DRE at the top and bottom of the atmosphere and the atmospheric absorption due to dust aerosol. These climatological estimates indicate that although longwave effects can reach tens of W m−2, shortwave cooling typically dominates the net radiative effect over the Sea, being particularly pronounced in the summer, reaching 60 W m−2 at the surface. The spatial gradient in summertime AOD is reflected in the radiative effect at the surface and in associated differential heating by aerosol within the atmosphere above the Sea. This asymmetric effect is expected to exert a significant influence on the regional atmospheric and oceanic circulation

Spatio-temporal variability of warm rain events over southern West Africa from geostationary satellite observations for climate monitoring and model evaluation

This paper presents the spatiotemporal variability of warm rain events over southern West Africa (SWA) during the summer monsoon season for the first time, using Spinning Enhanced Visible Infrared Radiometer (SEVIRI) observations on the Meteosat geostationary satellites. The delineation of warm rain events is based on the principle that precipitating low-level clouds are associated with either sufficient water content or large cloud droplet size. Capitalising on the ability of spaceborne radar to resolve vertical cloud structures and detect the presence of precipitation, the delineation is trained by collocated SEVIRI and CloudSat observations. The resulting 12-years of observations from SEVIRI are used to examine the spatial, diurnal, seasonal and interannual variability of warm rain events over SWA. Warm rain events predominate during the monsoon in August, with little interannual variability, and persist over orography in the morning and the coasts after midday, likely enhanced by orographic lifting and land-sea breeze effects. Warm clouds have a much higher probability of precipitation along the coastlines of Liberia and Nigeria compared to the central SWA coastline and further inland. Finally, when evaluating an 8-day yet high-spatial resolution model simulation, we find that warm rain frequencies from the simulation agree well with SEVIRI near the coast but simulated warm cloud cover and thus warm rain frequencies are too low over the Gulf of Guinea. The probability of precipitation of warm clouds is also too low inland. The newly developed climatology creates opportunities to further investigate the diurnal cycle of warm rain, study aerosol-cloud-precipitation interactions, and assess the role of warm rain in the water cycle across Africa and beyond

Working with the enemy? Social work education and men who use intimate partner violence

This article examines service user involvement in social work education. It discusses the challenges and ethical considerations of involving populations who may previously have been excluded from user involvement initiatives, raising questions about the benefits and challenges of their involvement. The article then provides discussion of an approach to service user involvement in social work education with one of these populations, men who use violence in their intimate relationships, and concludes by considering the implications of their involvement for the social work academy

Evaluating the diurnal cycle in cloud top temperature from SEVIRI

The variability of convective cloud spans a wide range of temporal and spatial scales and is of fundamental importance for global weather and climate systems. Datasets from geostationary satellite instruments such as the Spinning Enhanced Visible and Infrared Imager (SEVIRI) provide high-time-resolution observations across a large area. In this study we use data from SEVIRI to quantify the diurnal cycle of cloud top temperature within the instrument's field of view and discuss these results in relation to retrieval biases. We evaluate SEVIRI cloud top temperatures from the new CLAAS-2 (CLoud property dAtAset using SEVIRI, Edition 2) dataset against Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) data. Results show a mean bias of +0.44 K with a standard deviation of 11.7 K, which is in agreement with previous validation studies. Analysis of the spatio-temporal distribution of these errors shows that absolute retrieval biases vary from less than 5 K over the southeast Atlantic Ocean up to 30 K over central Africa at night. Night- and daytime retrieval biases can also differ by up to 30 K in some areas, potentially contributing to biases in the estimated amplitude of the diurnal cycle. This illustrates the importance of considering spatial and diurnal variations in retrieval errors when using the CLAAS-2 dataset. Keeping these biases in mind, we quantify the seasonal, diurnal, and spatial variation of cloud top temperature across SEVIRI's field of view using the CLAAS-2 dataset. By comparing the mean diurnal cycle of cloud top temperature with the retrieval bias, we find that diurnal variations in the retrieval bias can be small but are often of the same order of magnitude as the amplitude of the observed diurnal cycle, indicating that in some regions the diurnal cycle apparent in the observations may be significantly impacted by diurnal variability in the accuracy of the retrieval. We show that the CLAAS-2 dataset can measure the diurnal cycle of cloud tops accurately in regions of stratiform cloud such as the southeast Atlantic Ocean and Europe, where cloud top temperature retrieval biases are small and exhibit limited spatial and temporal variability. Quantifying the diurnal cycle over the tropics and regions of desert is more difficult, as retrieval biases are larger and display significant diurnal variability. CLAAS-2 cloud top temperature data are found to be of limited skill in measuring the diurnal cycle accurately over desert regions. In tropical regions such as central Africa, the diurnal cycle can be described by the CLAAS-2 data to some extent, although retrieval biases appear to reduce the amplitude of the real diurnal cycle of cloud top temperatures. This is the first study to relate the diurnal variations in SEVIRI retrieval bias to observed diurnal cycles in cloud top temperature. Our results may be of interest to those in the observation and modelling communities when using cloud top properties data from SEVIRI, particularly for studies considering the diurnal cycle of convection

The impact of satellite derived land surface temperatures on numerical weather predication analyses and forecasts

Land surface temperature (LST) observations from a variety of satellite instruments operating in the infrared have been compared to estimates of surface temperature from the Met Office operational numerical weather prediction (NWP) model. The comparisons show that during the day the NWP model can under predict the surface temperature by up to 10 K in certain regions such as the Sahel and Southern Africa. By contrast at night the differences are generally smaller. Matchups have also been performed between satellite LSTs and observations from an in situ radiometer located in Southern England within a region of mixed land use. These matchups demonstrate good agreement at night and suggest that the satellite uncertainties in LST are less than 2 K. The Met Office surface analysis scheme has been adapted to utilize nighttime LST observations. Experiments using these analyses in an NWP model have shown a benefit to the resulting forecasts of near surface air temperature, particularly over Africa

The Science and Application of Satellite Based Fire Radiative Energy

The accurate measurement of ecosystem biomass is of great importance in scientific, resource management and energy sectors. In particular, biomass is a direct measurement of carbon storage within an ecosystem and of great importance for carbon cycle science and carbon emission mitigation. Remote Sensing is the most accurate tool for global biomass measurements because of the ability to measure large areas. Current biomass estimates are derived primarily from ground-based samples, as compiled and reported in inventories and ecosystem samples. By using remote sensing technologies, we are able to scale up the sample values and supply wall to wall mapping of biomass