Probabilistic approach to cloud and snow detection on Advanced Very High Resolution Radiometer (AVHRR) imagery

Abstract

Abstract. Derivation of probability estimates complementary to geophysical data sets has gained special attention over the last years. Information about a confidence level of provided physical quantities is required to construct an error budget of higher-level products and to correctly interpret final results of a particular analysis. Regarding the generation of products based on satellite data a common input consists of a cloud mask which allows discrimination between surface and cloud signals. Further the surface information is divided between snow and snow-free components. At any step of this discrimination process a misclassification in a cloud/snow mask propagates to higher-level products and may alter their usability. Within this scope a novel probabilistic cloud mask (PCM) algorithm suited for the 1 km × 1 km Advanced Very High Resolution Radiometer (AVHRR) data is proposed which provides three types of probability estimates between: cloudy/clear-sky, cloudy/snow and clearsky/snow conditions. As opposed to the majority of available techniques which are usually based on the decision-tree approach in the PCM algorithm all spectral, angular and ancillary information is used in a single step to retrieve probability estimates from the precomputed look-up tables (LUTs). Moreover, the issue of derivation of a single threshold value for a spectral test was overcome by the concept of multidimensional information space which is divided into small bins by an extensive set of intervals. The discrimination between snow and ice clouds and detection of broken, thin clouds was enhanced by means of the invariant coordinate system (ICS) transformation. The study area covers a wide range of environmental conditions spanning from Iceland through central Europe to northern parts of Africa which exhibit diverse difficulties for cloud/snow masking algorithms. The retrieved PCM cloud classification was compared to the Polar Platform System (PPS) version 2012 and Moderate Resolution Imaging Spectroradiometer (MODIS) collection 6 cloud masks, SYNOP (surface synoptic observations) weather reports, Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) vertical feature mask version 3 and to MODIS collection 5 snow mask. The outcomes of conducted analyses proved fine detection skills of the PCM method with results comparable to or better than the reference PPS algorithm.

Daytime Low Stratiform Cloud Detection on AVHRR Imagery

“…Over North Africa, the LSC product reports higher probability over the coastal region, where low clouds form during the night and remain in the morning. Conversely, in the PPS product, they are not apparent as opposed to other deserted areas which are misclassified as clouds [48]. All of the aforementioned issues adversely influence the PPS LSC discrimination accuracy which was assessed during the validation analysis ( Figure 5).…”

“…This information is further employed during the algorithm training phase to construct two look-up vectors (LUVs) containing the precomputed probability estimates (separately suited for the 1.6/3.7 µm channel configurations). The classification process considers only pixels marked as overcast by the PCM cloud mask [48] and is limited to localisation of input data within the LUV composed of a bitwise combination of six features:…”

“…Due to the data availability issues, the analysed time frame was split between the year 2008 for NOAA17,18 satellites and the year 2011 for the NOAA16 platform. On the basis of pre-processed AVHRR data, the cloud mask [48] was derived in order to delineate spectral signature of clouds from the surface background. For the sake of comparison with other classification methods, the LSC cover was extracted from the PPS cloud-type product.…”

Impact of AVHRR Channel 3b Noise on Climate Data Records: Filtering Method Applied to the CM SAF CLARA-A2 Data Record

“…To understand this, we need to recall some of the most essential principles for the cloud screening process. As elaborated in detail in [7,8], the 3.7 micron channel offers a very important complementary capability for cloud screening compared to the more traditional visible and infrared channels. For visible channels, clouds are generally brighter than the surface and the same is true for infrared channels provided that the signal is inverted so that the coldest targest are shown as bright targets and vice versa.…”

Cross-Domain Ground-Based Cloud Classification Based on Transfer of Local Features and Discriminative Metric Learning

“…Ebert [3] proposed a pattern recognition algorithm to classify eighteen surface and cloud types in high-latitude advanced very high resolution radiometer (AVHRR) imagery based on several spectral and textural features. Recently, a probabilistic approach to cloud and snow detection on AVHRR imagery proposed by Musial et al [4]. Lamei et al [5] investigated a texture-based method which is based on 2-D Gabor functions for satellite image representation.…”