Up until now montane cloud forest (MCF) in Taiwan has only been mapped for selected areas of vegetation plots. This paper presents the first comprehensive map of MCF distribution for the entire island. For its creation, a Random Forest model was trained with vegetation plots from the National Vegetation Database of Taiwan that were classified as “MCF” or “non-MCF”. This model predicted the distribution of MCF from a raster data set of parameters derived from a digital elevation model (DEM), Landsat channels and texture measures derived from them as well as ground fog frequency data derived from the Moderate Resolution Imaging Spectroradiometer. While the DEM parameters and Landsat data predicted much of the cloud forest’s location, local deviations in the altitudinal distribution of MCF linked to the monsoonal influence as well as the Massenerhebung effect (causing MCF in atypically low altitudes) were only captured once fog frequency data was included. Therefore, our study suggests that ground fog data are most useful for accurately mapping MCF.

The lack of long term and well distributed precipitation observations on the Tibetan Plateau (TiP) with its complex terrain raises the need for other sources of precipitation data for this area. Satellite-based precipitation retrievals can fill those data gaps. Before precipitation rates can be retrieved from satellite imagery, the precipitating area needs to be classified properly. Here, we present a feasibility study of a precipitation area delineation scheme for the TiP based on multispectral data with data fusion from the geostationary orbit (GEO, Insat-3D and Elektro-L2) and a machine learning approach (Random Forest, RF). The GEO data are used as predictors for the RF model, extensively validated by independent GPM (Global Precipitation Measurement Mission) IMERG (Integrated Multi-satellitE Retrievals for GPM) gauge calibrated microwave (MW) best-quality precipitation estimates. To improve the RF model performance, we tested different optimization schemes. Here, we find that (1) using more precipitating pixels and reducing the amount of non-precipitating pixels during training greatly improved the classification results. The accuracy of the precipitation area delineation also benefits from (2) changing the temporal resolution into smaller segments. We particularly compared our results to the Infrared (IR) only precipitation product from GPM IMERG and found a markedly improved performance of the new multispectral product (Heidke Skill Score (HSS) of 0.19 (IR only) compared to 0.57 (new multispectral product)). Other studies with a precipitation area delineation obtained a probability of detection (POD) of 0.61, whereas our POD is comparable, with 0.56 on average. The new multispectral product performs best (worse) for precipitation rates above the 90th percentile (below the 10th percentile). Our results point to a clear strategy to improve the IMERG product in the absence of MW radiances.

Abstract. In this paper a new technique for the detection of fog and low stratus in 1 km resolution from MSG SEVIRI data is presented. The method relies on the pan-sharpening of 3 km narrow-band channels using the 1 km high-resolution visible (HRV) channel. As solar and thermal channels had to be sharpened for the technique, a new approach based on an existing pan-sharpening method was developed using local regressions. A fog and low stratus detection scheme originally developed for 3 km SEVIRI data was used as the basis to derive 1 km resolution fog and low stratus masks from the sharpened channels. The sharpened channels and the fog and low stratus masks based on them were evaluated visually and by various statistical measures. The sharpened channels deviate only slightly from reference images regarding their pixel values as well as spatial features. The 1 km fog and low stratus masks are therefore deemed of high quality. They contain many details, especially where fog is restricted by complex terrain in its extent, that cannot be detected in the 3 km resolution.

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.

In this paper a new technique for the detection of fog and low stratus in 1 km resolution from MSG SEVIRI data is presented. The method relies on the pan-sharpening of 3 km narrow-band channels using the 1 km high-resolution visible (HRV) channel. As solar and thermal channels had to be sharpened for the technique, a new approach based on an existing pan-sharpening method was developed using local regressions. A fog and low stratus detection scheme originally developed for 3 km SEVIRI data was used as the basis to derive 1 km resolution fog and low stratus masks from the sharpened channels. The sharpened channels and the fog and low stratus masks based on them were evaluated visually and by various statistical measures. The sharpened channels deviate only slightly from reference images regarding their pixel values as well as spatial features. The 1 km fog and low stratus masks are therefore deemed of high quality. They contain many details, especially where fog is restricted by complex terrain in its extent, that cannot be detected in the 3 km resolution