Laboratory for
Climatology and Remote Sensing

Abstract:

Die Arbeit stellt die Entwicklung eines Verfahrens zur Erkennung der Niederschlagsfläche in optischen Satellitendaten dar. Die Abgrenzung regnender Wolken beruht dabei auf dem Modellkonzept, dass diese über eine ausreichend große Kombination aus optischer Dicke und effektivem Tropfenradius verfügen müssen.

Abstract:

Climatic processes in northern Peru are evaluated on surface observation independent of modelling studies. The region is characterized by regular oscillations, but episodic El Niño-events introduce strong disturbances. Conceptual models based on observations, remote sensing data, and output of regional climate models are compared with data from a new station network. The results show regular oscillations of all climate variables on the annual and daily time scale. The daily cycle is probably associated with thermotidal forcings, causing gravity waves to emanate from the Andes Cordillera. Main factors are the interaction of large scale pressure systems like the Southeast Pacific High and the intertropical convergence zone (ITCZ). Also, there are regional factors: an extended sea-breeze system, the barrier-effect of the Andes, additional energy input by elevated radiation absorption at the mountain slopes, local wind systems, and the variations of the sea surface temperature. At the coast, a low-level jet works as a thermodynamic energy sink, suppressing deep convection and supporting the aridity. Those patterns are found in most of the station data and the processes of this climate can generally be confirmed. The overturning of this stable system with the onset of El Niño-conditions is possibly caused by disruptions of the regional circulation.

Abstract:

A scheme for the detection of fog and low stratus over land during daytime based on data of the MODIS (Moderate Resolution Imaging Spectroradiometer) instrument is presented. The method is based on an initial threshold test procedure in the MODIS solar bands 1–7 (0.62–2.155µm). Fog and low stratus detection generally relies on the definition of minimum and maximum fog and low stratus properties, which are converted to spectral thresholds by means of radiative transfer calculations (RTC). Extended sensitivity studies reveal that thresholds mainly depend on the solar zenith angle and, hence, illumination-dependent threshold functions are developed. Areas covered by snow, ice and mid-/high-level clouds as well as bright/hazy land surfaces are omitted from the initial classification result by means of a subsequent cloud-top height test based on MODIS IR band 31 (at 12 µm) and a NIR/VIS ratio test. The validation of the final fog and low stratus mask generally shows a satisfactory performance of the scheme. Validation problems occur due to the late overpass time of the TERRA platform and the time lag between SYNOP and satellite observations. Apparent misclassifications are mainly found at the edge of the fog layers, probably due to over- or underestimation of fog and low stratus cover in the transition zone from fog to haze.

Abstract:

The vertical distribution of liquid water content (LWC) in natural fog and low stratus is a crucial variable in many applications, e.g. the development of satellite based retrievals of ground fog. Unfortunately, there is very little data concerning fog LWC-profiles, mainly due to the lack of suitable operational instrumentation. A novel ground-based 94 GHz FMCW cloud radar could fill this gap if radar reflectivity Z could be converted to LWC by using appropriate Z–LWC relations. However, this relation strongly depends on drop size distribution (DSD) and is hardly known for natural fog types. In this sensitivity study, the influence of the DSD on the Z–LWC relation in different types and life cycle stages of natural fogs is analyzed using a radiative transfer code (RTC) and published fog drop size distributions. It could be shown that there is a direct but nonlinear relationship between LWC and radar reflectivity. The proportionality factor of the Z–LWC equation in particular reveals specific ranges for the different life cycle stages. If a proper classification of fog life cycle in the field is possible, the results could be used to properly convert Z to LWC.