Abstract
This thesis presents the retrieval, evaluation, and application of cloud physical property datasets (cloud phase, cloud particle effective radius, and precipitation occurrence and intensity) obtained from Spinning Enhanced Visible and Infrared Imager (SEVIRI) reflectance measurements using the Cloud Physical Properties (CPP) retrieval algorithm. In Chapter 3 it is shown that
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the CPP cloud-phase retrieval algorithm has sufficient accuracy (< 5%) and precision (< 10%) for climate monitoring purposes through comparisons with ground-based radar and lidar cloud-phase observations. In addition, the increase in ice cloud occurrence frequency throughout the day resulting from convection can be followed well.
In Chapter 4, the effect of different horizontal sampling resolutions on the cloud particle effective radius (re) and cloud-phase retrievals in case of broken and inhomogeneous overcast clouds is quantified using both simulations and retrievals. At low cloud fractions, the retrieved low-resolution re is overestimated by up to 5 μm compared to at high resolution, due to the contribution of the underlying surface to the observed reflectances. In about 4% of the cases this overestimation leads to cloud-phase misclassifications, which is reduced to 2% when applying an additional cloud-top temperature check in the cloud-phase retrieval algorithm.
The accuracy of CPP precipitation retrievals is evaluated with TRMM-PR and CMORPH observations in Chapter 5. Rain occurrence frequency from CPP-PP agrees well with TRMM-PR-observed values (corr=0.86), while rain rates agree to a lesser extent (corr=0.50). Investigation of the precipitation intensity frequency distributions from CPP reveal good agreement with TRMM-PR and rain gauge observations, although at moderate rain rates CPP overestimates relative to the rain gauges. Further, it is demonstrated that CPP is suitable to monitor both the seasonal and diurnal cycle of rainfall during daytime. CPP detects a larger dynamical range of this diurnal cycle than CMORPH, possibly due to the higher temporal and spatial resolution.
Chapter 6 presents a study on the relation between soil moisture observed by AMSR-E and the precipitation occurrence frequency and intensity from CPP over West Africa.
During the afternoon, precipitation occurrence frequency over dry soils becomes significantly higher than over wet soils, whereas for precipitation intensity no significant difference is discerned. The study demonstrates that the combination of satellite-based soil moisture and precipitation observations can be helpful in improving the understanding of the land surface–precipitation interaction over tropical areas.
The thesis concludes with a number of recommendations on future algorithm improvements and potential research applications. For both cloud phase and precipitation properties, extension of the algorithm to include nighttime observations would be desirable to enable detailed studies on the full diurnal cycle. Further, the SEVIRI High-Resolution Visible (HRV) channel could be incorporated to correct retrieved cloud physical properties for broken and inhomogeneous cloud cases.
Finally, the accurate cloud phase and precipitation datasets combined with the high SEVIRI spatial and temporal sampling resolution enables possibilities for detailed research on climate monitoring, nowcasting applications, evaluation of cloud schemes in climate models, studies on land surface-precipitation interactions (with a special focus on the diurnal cycle), and assimilation of the datasets in weather and climate models
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