Laboratory for
Climatology and Remote Sensing

Abstract:

Fog has a substantial influence on various ecosystems and it impacts economy, traffic systems and human life in many ways. In order to be able to deal with the large number of influence factors, a spatially explicit high-resoluted data set of fog frequency distribution is needed. In this study, a hybrid approach for fog retrieval based on Meteosat Second Generation (MSG) data and ground truth data is presented. The method is based on a random forest (RF) machine learning model that is trained with cloud base altitude (CBA) observations from Meteorological Aviation Routine Weather Reports (METAR) as well as synoptic weather observations (SYNOP). Fog is assumed where the model predicts CBA values below a dynamically derived threshold above the terrain elevation. Cross validation results show good accordance with observation data with a mean absolute error of 298 m in CBA values and an average Heidke Skill Score of 0.58 for fog occurrence. Using this technique, a 10 year fog baseline climatology with a temporal resolution of 15 min was derived for Europe for the period from 2006 to 2015. Spatial and temporal variations in fog frequency are analyzed. Highest average fog occurrences are observed in mountainous regions with maxima in spring and summer. Plains and lowlands show less overall fog occurrence but strong positive anomalies in autumn and winter.

Abstract:

As part of an interdisciplinary research programme, the spatial and temporal variability of precipitation in southern Ecuador has been investigated since January 2002. The study site is located at the northern margin of the Podocarpus National Park in the vicinity of Loja, about 500 km south of Quito, at altitudes ranging from 1800 to 3200 m.a.s.l. Due to its low density, the conventional rainfall station network fails to register the highly variable spatial distribution of rain, whereas contributions by fog are not accounted at all. Hence, for the first time in a tropical montane forest setting, a weather radar was used, covering a radius of 60 km and reaching from the Amazon Basin to the coastal plains of the region. Furthermore, a network of sampling stations supplies data about the altitudinal gradient of fog and rainwater inputs. The precipitation distribution in the study area proves to be far more variable than previously thought and is strongly coupled to the orographic characteristics and the special topographical setting of the landscape. Maxima in precipitation occur especially in the eastern parts of the radar range on slopes exposed to advected moisture from the Amazon Basin, whereas the highest crests of the Andes receive less precipitation. The study area has two cloud condensation levels, occurring at 1500–2000 and 2500–3500 m.a.s.l., respectively. At 1800–2000 m.a.s.l., fog is estimated to contribute an additional input of 5% of conventionally measured rainfall, increasing to about 35% at the highest measurement station (3200 m.a.s.l.). In contrast to some other tropical mountains, there seems to be no maximum zone of water input, although the gradient remains positive up to the highest altitudes. The unusual precipitation distribution is thought to reflect the contrasting climatological influences operating in the study area.

Abstract:

The mountain cloud forest of Taiwan can be delimited from other forest types using a map of the ground fog frequency. In order to create such a frequency map from remotely sensed data, an algorithm able to detect ground fog is necessary. Common techniques for ground fog detection based on weather satellite data cannot be applied to fog occurrences in Taiwan as they rely on several assumptions regarding cloud properties. Therefore a new statistical method for the detection of ground fog in mountainous terrain from MODIS Collection 051 data is presented. Due to the sharpening of input data using MODIS bands 1 and 2, the method provides fog masks in a resolution of 250 m per pixel. The new technique is based on negative correlations between optical thickness and terrain height that can be observed if a cloud that is relatively plane-parallel is truncated by the terrain. A validation of the new technique using camera data has shown that the quality of fog detection is comparable to that of another modern fog detection scheme developed and validated for the temperate zones. The method is particularly applicable to optically thinner water clouds. Beyond a cloud optical thickness of ? 40, classification errors significantly increase.

Abstract:

Fog is a crucial driver of epiphyte richness in tropical montane cloud forests but its spatial occurrence and role in tropical lowland areas is poorly understood. Recent studies in French Guiana have reported high epiphyte richness in previously undescribed “tropical lowland cloud forest” (LCF) due to radiation fog. Here, we analyze the spatial extent of fog/low stratus (FLS) in lowland forests of French Guiana using the frequency distribution by means of night-time MODIS (Moderate Resolution Imaging Spectroradiometer) satellite data for the years 2007–2010. The analysis is based on a newly developed dynamic threshold-test method relying on brightness temperature differences between thermal and mid-infrared bands. Individual thresholds for the discrimination between fog/low stratus and cloud-free pixels were retrieved by radiative transfer calculations and validated using discriminant analysis. The thresholds dynamically depend on total precipitable water (TPW) and the terrain-induced maximum possible sub-pixel fog coverage. The results of the new retrieval were validated using in-situ data and compared to results from existing fog detection algorithms, showing an improvement of the new detection scheme regarding the capability to detect sub-pixel fog coverage under varying TPW. FLS frequency maps derived from the novel fog classification scheme indicate a widespread distribution of night-time fog in river valleys, marking a multitude of potential areas for LCF throughout French Guiana. LCF is probably not only a local phenomenon but also may be widely distributed in river valleys in the lowland tropics, with significant consequences for biodiversity mapping in tropical lowland areas.