Probabilistic Cloud Masking for the Generation of CM SAF Cloud Climate Data Records from AVHRR and SEVIRI Sensors

Karlsson, Karl‐Göran; Johansson, Erik; Håkansson, Nina; Sedlar, Joseph; Eliasson, Salomon

doi:10.3390/rs12040713

Cited by 11 publications

(8 citation statements)

References 30 publications

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“…The combination of measured radiances and a climatology allows for the estimation of a probability that a pixel is cloudy, given the measured radiance. For the purpose of this study we only use the binary cloud mask, which classifies a pixel as cloudy if the cloud probability exceeds a threshold of 50% and clear‐sky otherwise (Karlsson et al, 2020). The MSG CM SAF cloud mask covers a circular area with a maximum extent from 81.3° S–81.3° N and from 81.25° W–81.25° E on a pixel basis.…”

Section: Model Descriptions and Measurement Datamentioning

confidence: 99%

Impact of Saharan dust outbreaks on short‐range weather forecast errors in Europe

Hermes,

Quinting,

Grams

et al. 2024

Quart J Royal Meteoro Soc

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Mineral dust, the most abundant atmospheric aerosol by mass, interacts with radiation directly and alters cloud properties indirectly. Many operational numerical weather prediction models account for aerosol direct effects by using climatological mean concentrations and neglect indirect effects. This simplification may lead to shortcomings in model forecasts during outbreaks of Saharan dust towards Europe, when climatological mean dust concentrations deviate strongly from actual concentrations. This study investigates errors in model analyses and short‐range forecasts during such events. We investigate a pronounced dust event in March 2021 using the pre‐operational ICOsahedral Nonhydrostatic weather and climate model with Aerosols and Reactive Trace gases (ICON‐ART) with prognostic calculation of dust and the operational European Centre for Medium‐Range Weather Forecasts (ECMWF) Integrated Forecasting System (IFS) model, which deploys a dust climatology. We compare model analysis and forecast with measurements from satellite and in situ instruments. We find that inclusion of prognostic aerosol and direct radiative effects from dust improves forecasts of surface radiation during clear‐sky conditions. However, dust‐induced cirrus clouds are strongly underestimated, highlighting the importance of representing indirect effects adequately. These findings are corroborated by systematic quantification of forecast errors against satellite measurements. For this we construct an event catalogue with 49 dust days over Central Europe between January 2018 and March 2022. We classify model cells by simulated and observed cloudiness and simulated dustiness in the total atmospheric column. We find significant overestimations of brightness temperature for cases with dust compared with cases without dust. For surface shortwave radiation, we find median overestimations of 6.2% during cloudy conditions with dust optical depth greater than 0.1, however these are not significant compared with cloudy conditions without dust. Our findings show that the pre‐operational ICON‐ART and the operational IFS model still do not reproduce cloudiness adequately during events with Saharan dust over Central Europe. Missing implementations of prognostic dust, particularly of indirect effects on cloud formation, lead to significant underestimations of cloudiness and potentially overestimations of surface radiation.

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Section: Model Descriptions and Measurement Datamentioning

confidence: 99%

Impact of Saharan dust outbreaks on short‐range weather forecast errors in Europe

Hermes,

Quinting,

Grams

et al. 2024

Quart J Royal Meteoro Soc

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“…The products used in this article are based on version PPSv2018-patch5, which will also be implemented in the CLARA-A3 products. The following PPS products are used for the TOA flux retrieval: PPS probabilistic cloud mask [26,27], PPS classic cloud mask with snow presence flag [19,28], and PPS-CPP cloud phase, optical thickness, and quality flag [29].…”

Section: External Input Datamentioning

confidence: 99%

Retrieval of Daily Mean Top-of-Atmosphere Reflected Solar Flux Using the Advanced Very High Resolution Radiometer (AVHRR) Instruments

Akkermans

Clerbaux

2021

Remote Sensing

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The records of the Advanced Very High Resolution Radiometer (AVHRR) instrument observations can resolve the current lack of a long global climate data record of Reflected Solar Flux (RSF), by transforming these measurements into broadband flux at the top-of-atmosphere. This paper presents a methodology for obtaining daily mean RSF (Wm−2) from AVHRR. First, the narrowband reflectances are converted to broadband reflectance using empirical regressions with the Clouds and the Earth’s Radiant Energy System (CERES) observations. Second, the anisotropy is corrected by applying Angular Distribution Models (ADMs), which convert directional reflectance into a hemispherical albedo. Third, the instantaneous albedos are temporally interpolated by a flexible diurnal cycle model, capable of ingesting any number of observations at any time of day, making it suitable for any orbital configuration of NOAA and MetOp satellites. Finally, the twilight conditions prevailing near sunrise and sunset are simulated with an empirical model. The entire day is then integrated into a single daily mean RSF. This paper furthermore demonstrates the methodology by validating a full year (2008) of RSF daily means with the CERES SYN1deg data record, both on daily and subdaily scale. Several configurations are tested, each excluding particular satellites from the constellation in order to mimic orbital changes (e.g., orbital drift), and to assess their relative importance to the daily mean RSF. The best performance is obtained by the combination of at least one mid-morning (NOAA-17 or MetOp-A) and one early afternoon (NOAA-18) orbit. In this case, the RMS difference with CERES is about 7 Wm−2. Removing NOAA-18 degrades the performance to an RMS difference of 12 Wm−2, thereby providing an estimate of the impact of NOAA-19’s orbital drift between 2016 and 2020. Very early or late observations (NOAA-15, NOAA-16) provide little added value, and both mid-morning orbits turn out to be almost interchangeable given their close temporal proximity.

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“…The algorithms based on Bayesian inference involve the estimation of the joint probability density function (PDF) of clear/cloudy‐sky spectral features which is often simplified by assuming the independence of spectral features. The cloud‐masking algorithms (Merchant et al ., 2005; Karlsson et al ., 2020) based on the Bayesian approach require ancillary information, such as numerical weather prediction data or some reference cloud observations.…”

Section: Introductionmentioning

confidence: 99%

“…An essential feature of the proposed algorithm is that it is a stand‐alone algorithm based solely on satellite imager counts in various channels and does not depend on any data from external sources, such as surface types or any meteorological observations. The proposed algorithm is developed based on a true Bayesian formulation, unlike other probabilistic cloud‐masking algorithms (Heidinger et al ., 2012; Hollstein et al ., 2015; Karlsson et al ., 2020) that are based on the naive Bayesian approach. The use of a truly Bayesian formulation makes the proposed algorithm computationally expensive.…”

Section: Introductionmentioning

confidence: 99%

Probabilistic cloud mask from visible and infrared sensors on‐board geostationary platform

Ojha

Singh

Dube

2023

Quart J Royal Meteoro Soc

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We present a novel technique for developing a probabilistic cloud mask using sensors on a geostationary platform. The clear‐sky probability of a pixel is estimated using the past 45 days' observations by making use of the Gaussian mixture model, spectral clustering, and Bayes' theorem. The proposed cloud‐masking algorithm is applied on INSAT‐3D imager observations for the whole years 2017 and 2018. The INSAT‐3D imager cloud mask generated using the proposed method is compared against the Moderate‐resolution Imaging Spectroradiometer (MODIS) MYD35 cloud‐mask product. The skill of the algorithm, against the MODIS cloud‐mask product, is evaluated for day and night as well as for land and ocean separately. Overall, the Kuipers skill score (KSS) is found to be 0.82. The best performance of the algorithm is obtained during daytime over the land with KSS=0.84$$ \mathrm{KSS}=0.84 $$.

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Probabilistic Cloud Masking for the Generation of CM SAF Cloud Climate Data Records from AVHRR and SEVIRI Sensors

Cited by 11 publications

References 30 publications

Impact of Saharan dust outbreaks on short‐range weather forecast errors in Europe

Impact of Saharan dust outbreaks on short‐range weather forecast errors in Europe

Retrieval of Daily Mean Top-of-Atmosphere Reflected Solar Flux Using the Advanced Very High Resolution Radiometer (AVHRR) Instruments

Probabilistic cloud mask from visible and infrared sensors on‐board geostationary platform

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