A 14‐year European Cloud Climatology from NOAA/AVHRR data in comparison to surface observations

Meerkötter, R.; König, Christine; Bissolli, Peter; Gesell, Gerhard; Mannstein, Hermann

doi:10.1029/2004gl020098

Cited by 47 publications

(41 citation statements)

References 11 publications

(14 reference statements)

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“…Using the Stephens et al (1984) extension of the original cloud property parameterization scheme allows simultaneous retrieval of cloud effective radius and optical depth from a two-channel approach like the often-used method of Nakajima and King (1990). This is a major update compared to the original APOLLO scheme, where cloud effective radius was assumed to be constant at 10 µm (e.g., Klüser and Holzer-Popp, 2010).…”

Section: Discussionmentioning

confidence: 99%

“…The red curves connect calculation results with constant effective radius. The black curves indicate constant optical depth, which is mostly related to the reflectance of the non-absorbing channel (Stephens et al, 1984;Nakajima and King, 1990). Orthogonality in the red and black curves would correspond to the possibility of retrieving optical depth and effective radius independently, as is more or less the case for very high optical depths.…”

Section: Optical Depth and Effective Radiusmentioning

confidence: 99%

“…Moreover also for cloud properties a probabilistic treatment has been implemented in APOLLO_NG. While in Stephens (1978) absorption was used only for angular correction of the red channel reflectance, now the contrast between one non-absorbing and one absorbing channel ("absorbing" here means that cloud droplets absorb at the respective wavelengths) is used for inferring optical depth and effective radius (Stephens, 1984;Nakajima and King, 1990). Reflectance of liquid water (left) and ice (right) clouds at the absorbing versus the non-absorbing channel for the absorbing channel being centered at 1.6 µm (top) and 3.7 µm (bottom).…”

Section: Optical Depth and Effective Radiusmentioning

confidence: 99%

“…The simultaneous retrieval of cloud optical depth and effective radius (Nakajima and King, 1990) is performed consecutively for each observation for potential liquid water and ice phase clouds. The approach is to calculate optical depths with the equations of Coakley and Chylek (1975) and Stephens et al (1984) from a set of 10 different reflectance values R 1,sim in the red (non-absorbing) channel ranging from 0.05 to 0.95.…”

Section: Optical Depth and Effective Radiusmentioning

confidence: 99%

“…The evolution of varying versions for different purposes and sensors made it desirable to harmonize the sensor dependent versions again. Necessary requirements like the introduction of a cloud droplet effective radius retrieval along with the optical depth estimation (Nakajima and King, 1990), the use of modern representations of ice cloud optical properties (Baum et al, 2014) and the requirement for more flexible cloud detection (Merchant et al, 2005;Holzer-Popp et al, 2013) consequently motivated the development of a new cloud detection and cloud retrieval scheme based on the physical principles of APOLLO.…”

Section: Introductionmentioning

confidence: 99%

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

Klüser¹,

Killius²,

Gesell³

2015

Atmos. Meas. Tech.

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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.

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Section: Discussionmentioning

confidence: 99%

Section: Optical Depth and Effective Radiusmentioning

confidence: 99%

Section: Optical Depth and Effective Radiusmentioning

confidence: 99%

Section: Optical Depth and Effective Radiusmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

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

Klüser¹,

Killius²,

Gesell³

2015

Atmos. Meas. Tech.

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Impact of Moderate Resolution Imaging Spectroradiometer (MODIS) cloud mask interpretation on cloud amount estimation

Kotarba

2015

JGR Atmospheres

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Cloud masks serve as a basis for estimates of cloud amount, which is an essential parameter for studying the Earth's radiation budget. The most commonly used cloud mask is a simple thematic classification, which includes qualitative information on the presence of clouds in the satellite's instantaneous field of view (IFOV). Cloud mask classes have to be “translated” into a quantitative measure, in order to be used for cloud amount calculations. The assignment of cloud fractions to cloud mask classes is a subjective process and increases uncertainty in cloud amount estimates. We evaluated this degree of uncertainty using the Moderate Resolution Imaging Spectroradiometer (MODIS) cloud mask product. Together with the operational MODIS cloud mask interpretation, we investigated two extreme alternatives: “rigorous” (only “confident cloudy” IFOVs were 100% cloudy) and “tolerant” (only “confident clear” IFOVs were 0% cloudy). Results showed that the range of uncertainty was 14.3% in Europe and controlled by the frequency of small convective clouds. Comparison with surface‐based observations suggests that the rigorous interpretation of the cloud mask is more accurate than that used operationally for MODIS level 3 product generation. The rigorous approach resulted in the smallest bias (−0.7%), the smallest root‐mean‐square error (4.6%), the small standard deviation (6%), and the strongest correlation (0.935). These results suggest that for climatological applications the rigorous scenario should be considered as a more accurate “best guess” over land.

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Spatial Representativeness of Surface‐Measured Variations of Downward Solar Radiation

et al. 2017

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When using time series of ground‐based surface solar radiation (SSR) measurements in combination with gridded data, the spatial and temporal representativeness of the point observations must be considered. We use SSR data from surface observations and high‐resolution (0.05°) satellite‐derived data to infer the spatiotemporal representativeness of observations for monthly and longer time scales in Europe. The correlation analysis shows that the squared correlation coefficients (R2) between SSR times series decrease linearly with increasing distance between the surface observations. For deseasonalized monthly mean time series, R2 ranges from 0.85 for distances up to 25 km between the stations to 0.25 at distances of 500 km. A decorrelation length (i.e., the e‐folding distance of R2) on the order of 400 km (with spread of 100–600 km) was found. R2 from correlations between point observations and colocated grid box area means determined from satellite data were found to be 0.80 for a 1° grid. To quantify the error which arises when using a point observation as a surrogate for the area mean SSR of larger surroundings, we calculated a spatial sampling error (SSE) for a 1° grid of 8 (3) W/m2 for monthly (annual) time series. The SSE based on a 1° grid, therefore, is of the same magnitude as the measurement uncertainty. The analysis generally reveals that monthly mean (or longer temporally aggregated) point observations of SSR capture the larger‐scale variability well. This finding shows that comparing time series of SSR measurements with gridded data is feasible for those time scales.

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A 14‐year European Cloud Climatology from NOAA/AVHRR data in comparison to surface observations

Cited by 47 publications

References 11 publications

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

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

Impact of Moderate Resolution Imaging Spectroradiometer (MODIS) cloud mask interpretation on cloud amount estimation

Spatial Representativeness of Surface‐Measured Variations of Downward Solar Radiation

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