ALBERT

Description: An improved version is presented of the SCIAMACHY PMD Identification of Clouds and Ice/snow method (SPICI). SPICI uses the SCIAMACHY measurements in the wavelength range between 450 nm and 1.6 μm to make a distinction between clouds and ice/snow covered surfaces, specifically developed to identify cloud-free SCIAMACHY observations. For this purpose the SCIAMACHY Polarisation Measurement Devices (PMDs) are used because they provide higher spatial resolution compared to the main spectrometer measurements. The improvements (compared to Krijger et al., 2005) include a snow over vegetation detection and correction for SCIAMACHY PMD degradation.

Description: In the ultra-violet, visible and near infra-red wavelength range the presence of clouds can strongly affect the satellite-based passive remote sensing observation of constituents in the troposphere, because clouds effectively shield the lower part of the atmosphere. Therefore, cloud detection algorithms are of crucial importance in satellite remote sensing. However, the detection of clouds over snow/ice surfaces is particularly difficult in the visible wavelengths as both clouds an snow/ice are both white and highly reflective. The SCIAMACHY Polarisation Measurement Devices (PMD) Identification of Clouds and Ice/snow method (SPICI) uses the SCIAMACHY measurements in the wavelength range between 450 nm and 1.6 μm to make a distinction between clouds and ice/snow covered surfaces, specifically developed to identify cloud-free SCIAMACHY observations. For this purpose the on-board SCIAMACHY PMDs are used because they provide higher spatial resolution compared to the main spectrometer measurements. In this paper we expand on the original SPICI algorithm (Krijger et al., 2005a) to also adequately detect clouds over snow-covered forests which is inherently difficult because of the similar spectral characteristics. Furthermore the SCIAMACHY measurements suffer from degradation with time. This must be corrected for adequate performance of SPICI over the full SCIAMACHY time range. Such a correction is described here. Finally the performance of the new SPICI algorithm is compared with various other datasets, such as from FRESCO, MICROS and AATSR, focusing on the algorithm improvements.

Description: Since clouds play an essential role in the Earth's climate system, it is important to understand the cloud characteristics as well as their distribution on a global scale using satellite observations. The main scientific objective of SCIAMACHY (SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY) onboard the ENVISAT satellite is the retrieval of vertical columns of trace gases. On the one hand, SCIAMACHY has to be sensitive to low variations in trace gas concentrations which means the ground pixel size has to be large enough. On the other hand, such a large pixel size leads to the problem that SCIAMACHY spectra are often contaminated by clouds. SCIAMACHY spectral measurements are not well suitable to derive a reliable sub-pixel cloud fraction that can be used as input parameter for subsequent retrievals of cloud properties or vertical trace gas columns. Therefore, we use MERIS/ENVISAT spectral measurements with its high spatial resolution as sub-pixel information for the determination of MerIs Cloud fRation fOr Sciamachy (MICROS). Since MERIS covers an even broader swath width than SCIAMACHY, no problems in spatial and temporal collocation of measurements occur. This enables the derivation of a SCIAMACHY cloud fraction with an accuracy much higher as compared with other current cloud fractions that are based on SCIAMACHY's PMD (Polarization Measurement Device) data. We present our new developed MICROS algorithm, based on the threshold approach, as well as a qualitative validation of our results with MERIS satellite images for different locations, especially with respect to bright surfaces such as snow/ice and sands. In addition, the SCIAMACHY cloud fractions derived from MICROS are intercompared with other current SCIAMACHY cloud fractions based on different approaches demonstrating a considerable improvement regarding geometric cloud fraction determination using the MICROS algorithm.

Description: Since clouds play an essential role in the Earth's climate system, it is important to understand the cloud characteristics as well as their distribution on a global scale using satellite observations. The main scientific objective of SCIAMACHY (SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY) onboard the ENVISAT satellite is the retrieval of vertical columns of trace gases. On the one hand, SCIAMACHY has to be sensitive to low variations in trace gas concentrations which means the ground pixel size has to be large enough. On the other hand, such a large pixel size leads to the problem that SCIAMACHY spectra are often contaminated by clouds. SCIAMACHY spectral measurements are not well suitable to derive a reliable sub-pixel cloud fraction that can be used as input parameter for subsequent retrievals of cloud properties or vertical trace gas columns. Therefore, we use MERIS/ENVISAT spectral measurements with its high spatial resolution as sub-pixel information for the determination of MerIs Cloud fRation fOr Sciamachy (MICROS). Since MERIS covers an even broader swath width than SCIAMACHY, no problems in spatial and temporal collocation of measurements occur. This enables the derivation of a SCIAMACHY cloud fraction with an accuracy much higher as compared with other current cloud fractions that are based on SCIAMACHY's PMD (Polarization Measurement Device) data. We present our new developed MICROS algorithm, based on the threshold approach, as well as a qualitative validation of our results with MERIS satellite images for different locations, especially with respect to bright surfaces such as snow/ice and sands. In addition, the SCIAMACHY cloud fractions derived from MICROS are intercompared with other current SCIAMACHY cloud fractions based on different approaches demonstrating a considerable improvement regarding geometric cloud fraction determination using the MICROS algorithm.

Description: A suite of oxygenated volatile organic compounds (OVOCs – acetaldehyde, acetone, propanal, butanal and butanone) were measured concurrently in the surface water and atmosphere of the South China Sea and Sulu Sea in November 2011. A strong correlation was observed between all OVOC concentrations in the surface seawater along the entire cruise track, except for acetaldehyde, suggesting similar sources and sinks in the surface ocean. Additionally, several phytoplankton groups, such as haptophytes or pelagophytes, were also correlated to all OVOCs indicating that phytoplankton may be an important source for marine OVOCs in the South China and Sulu Seas. Humic and protein like fluorescent dissolved organic matter (FDOM) components seemed to be additional precursors for butanone and acetaldehyde. The atmospheric OVOC mixing ratios were relative high compared with literature values, suggesting the coastal region of North Borneo as a local hot spot for atmospheric OVOCs. The flux of atmospheric OVOCs was largely into the ocean for all 5 gases, with a few important exceptions near the coast of Borneo. The calculated amount of OVOCs entrained into the ocean seemed to be an important source of OVOCs to the surface ocean. When the fluxes were out of the ocean, marine OVOCs were found to be enough to control the local measured OVOC distribution in the atmosphere. Based on our model calculations, at least 0.4 ppb of marine derived acetone and butanone can reach the upper troposphere, where they may have an important influence on hydrogen oxide radical formation over the western Pacific Ocean.

Description: A suite of oxygenated volatile organic compounds (OVOCs – acetaldehyde, acetone, propanal, butanal and butanone) were measured concurrently in the surface water and atmosphere of the South China Sea and Sulu Sea in November 2011. A strong correlation was observed between all OVOC concentrations in the surface seawater along the entire cruise track, except for acetaldehyde, suggesting similar sources and sinks in the surface ocean. Additionally, several phytoplankton groups, such as haptophytes or pelagophytes, were also correlated to all OVOCs, indicating that phytoplankton may be an important source of marine OVOCs in the South China and Sulu seas. Humic- and protein-like fluorescent dissolved organic matter (FDOM) components seemed to be additional precursors for butanone and acetaldehyde. The measurement-inferred OVOC fluxes generally showed an uptake of atmospheric OVOCs by the ocean for all gases, except for butanal. A few important exceptions were found along the Borneo coast, where OVOC fluxes from the ocean to the atmosphere were inferred. The atmospheric OVOC mixing ratios over the northern coast of Borneo were relatively high compared with literature values, suggesting that this coastal region is a local hotspot for atmospheric OVOCs. The calculated amount of OVOCs entrained into the ocean seemed to be an important source of OVOCs to the surface ocean. When the fluxes were out of the ocean, marine OVOCs were found to be enough to control the locally measured OVOC distribution in the atmosphere. Based on our model calculations, at least 0.4 ppb of marine-derived acetone and butanone can reach the upper troposphere, where they may have an important influence on hydrogen oxide radical formation over the western Pacific Ocean.