APOLLO_NG – a probabilistic interpretation of the APOLLO legacy for AVHRR heritage channels

Abstract

Abstract. The cloud processing scheme APOLLO (AVHRR Processing scheme Over cLouds, Land and Ocean) has been in use for cloud detection and cloud property retrieval since the late 1980s. The physics of the APOLLO scheme still build the backbone of a range of cloud detection algorithms for AVHRR (Advanced Very High Resolution Radiometer) heritage instruments. The APOLLO_NG (APOLLO_NextGeneration) cloud processing scheme is a probabilistic interpretation of the original APOLLO method. It builds upon the physical principles that have served well in the original APOLLO scheme. Nevertheless, a couple of additional variables have been introduced in APOLLO_NG. Cloud detection is no longer performed as a binary yes/no decision based on these physical principles. It is rather expressed as cloud probability for each satellite pixel. Consequently, the outcome of the algorithm can be tuned from being sure to reliably identify clear pixels to conditions of reliably identifying definitely cloudy pixels, depending on the purpose. The probabilistic approach allows retrieving not only the cloud properties (optical depth, effective radius, cloud top temperature and cloud water path) but also their uncertainties. APOLLO_NG is designed as a standalone cloud retrieval method robust enough for operational near-realtime use and for application to large amounts of historical satellite data. The radiative transfer solution is approximated by the same two-stream approach which also had been used for the original APOLLO. This allows the algorithm to be applied to a wide range of sensors without the necessity of sensorspecific tuning. Moreover it allows for online calculation of the radiative transfer (i.e., within the retrieval algorithm) giving rise to a detailed probabilistic treatment of cloud variables. This study presents the algorithm for cloud detection and cloud property retrieval together with the physical principles from the APOLLO legacy it is based on. Furthermore a couple of example results from NOAA-18 are presented.

Automated Improvement of Geolocation Accuracy in AVHRR Data Using a Two-Step Chip Matching Approach—A Part of the TIMELINE Preprocessor

“…At DLR, cloud cover parameters are extracted from AVHRR data relying on the AVHRR Processing scheme Over cLouds, Land and Ocean (APOLLO), an algorithm that was designed in the late 1980s and continuously improved and extended since [2,[17][18][19]. Recently, a new version of APOLLO (APOLLO_NG) was developed, now including a probabilistic interpretation of cloud contamination for each pixel within a scene [3]. APOLLO provides a huge set of cloud parameters, including optical depth and effective radius, cloud top phase, temperature, and cloud water path.…”

A Fully Automatic Instantaneous Fire Hotspot Detection Processor Based on AVHRR Imagery—A TIMELINE Thematic Processor

“…Furthermore, as described in Section 2, within the TIMELINE project, enhanced algorithms for cloud and water detection have been developed [93,94]. These enhanced cloud and water masks will replace the currently used simplified algorithms for cloud and water detection described in Section 3.1.…”

Response to Anonymous Referee 2

“…The SWIR and TIR, such as 1.6 µm and beyond, where liquid water and ice absorb provide useful information. There is a large reduction in the reflectance between clear snow/ice and clouds between 0.87 and 1.6 µm (Kokhanovsky, 2006). Our routine takes advantage of this contrast through the PCC calculation.…”