Inconsistency of surface‐based (<scp>SYNOP</scp>) and satellite‐based (<scp>MODIS</scp>) cloud amount estimations due to the interpretation of cloud detection results

Kotarba, Andrzej Z.

doi:10.1002/joc.5011

Cited by 10 publications

(9 citation statements)

References 47 publications

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“…To compare surface measurement from SYNOP hemispheric view with the cloud identification at a spatial resolution 1×1 km 2 resolution satellite measurement, we calculated cloudiness as the percentage of cloudy scenes within a window of 20×20 km 2 around each SYNOP station. This is a similar distance to that used in previous studies to validate satellite based cloud identification SYNOP or similar surface measurements (Kotarba, 2017;Werkmeister et al, 2015;Minnis et al, 2003). The cloud detection data product was then compared to the three months (March, May and July) of SYNOP observations.…”

Section: Validationsupporting

confidence: 66%

A cloud identification algorithm over the Arctic for use with AATSR/SLSTR measurements

Jafariserajehlou¹,

Mei²,

Vountas³

et al. 2018

Preprint

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Abstract. The accurate identification of the presence of cloud in the ground scenes observed by remote sensing satellites is an end in itself. Our lack of knowledge of cloud at high latitudes increases the error and uncertainty in the evaluation and assessment of the changing impact of aerosol and cloud in a warming climate. A prerequisite for the accurate retrieval of Aerosol Optical Thickness, AOT, is the knowledge of the presence of cloud in a ground scene. In this study observations of the up welling radiance in the visible (VIS), near infrared (NIR), shortwave infrared (SWIR), and the thermal infrared (TIR) are used to determine the reflectance. We have developed a new cloud identification algorithm for application to the observations of Advanced Along-Track Scanning Radiometer (AATSR) on European Space Agency (ESA)-Envisat and Sea and Land Surface Temperature Radiometer (SLSTR) on-board the ESA Copernicus Sentinel-3A and -3B. The AATSR/SLSTR Cloud Identification Algorithm (ASCIA) developed addresses the requirements for the study AOT at high latitudes and utilizes time-series measurements. It is assumed that cloud free surfaces have unchanged or little changed patterns for a given sampling period, whereas cloudy or partly cloudy scenes show much higher variability in space and time. In this method, the Pearson Correlation Coefficient (PCC) parameter is used to measure the stability of the atmosphere-surface system observed by satellites. The cloud free surface is classified by analyzing the PCC values at the block scale 25 × 25 km2. Subsequently, the reflection of 3.7 μm is used for accurate cloud identification at the scene level either 1 × 1 km2 or 0.5 × 0.5 km2. The ASCIA data product has been validated by comparison with independent observations e,g. Surface synoptic observations (SYNOP), AErosol RObotic NETwork (AERONET) and the following satellite-products from i) ESA standard cloud product from AATSR L2 nadir cloud flag, ii) one method based on clear-snow spectral shape developed at IUP Bremen (Istomina et al., 2010), which we call, ISTO, iii) Moderate Resolution Imaging Spectroradiometer (MODIS). In comparison to ground based SYNOP measurements, we achieved a promising agreement better than 95 % and 83 % within ±2 and ±1 okta respectively. In general, ASCIA shows an improved performance in comparison to other algorithms applied to AATSR measurements for cloud identification at high latitudes.

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Section: Validationsupporting

confidence: 66%

A cloud identification algorithm over the Arctic for use with AATSR/SLSTR measurements

Jafariserajehlou¹,

Mei²,

Vountas³

et al. 2018

Preprint

View full text Add to dashboard Cite

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“…A 68% of all missing values present in MODIS LST products were interpolated in time through LWR, while the remaining gaps were filled by means of TPS in the spatial interpolation step. The fact that there were more missing values during day than at night might be explained by the MODIS cloud detection algorithm that uses thermal and reflective bands for day overpasses, but only thermal bands for cloud detection at night, i.e., clouds are more easily detected during the day than at night [43,44]. The difference in the percentages of missing values among water bodies and their borders might also be related to the MODIS cloud detection algorithm that uses different spectral thresholds for different types of covers [45].…”

Section: Discussionmentioning

confidence: 99%

A New Fully Gap-Free Time Series of Land Surface Temperature from MODIS LST Data

2017

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Temperature time series with high spatial and temporal resolutions are important for several applications. The new MODIS Land Surface Temperature (LST) collection 6 provides numerous improvements compared to collection 5. However, being remotely sensed data in the thermal range, LST shows gaps in cloud-covered areas. We present a novel method to fully reconstruct MODIS daily LST products for central Europe at 1 km resolution and globally, at 3 arc-min. We combined temporal and spatial interpolation, using emissivity and elevation as covariates for the spatial interpolation. The reconstructed MODIS LST for central Europe was calibrated to air temperature data through linear models that yielded R 2 values around 0.8 and RMSE of 0.5 K. This new method proves to scale well for both local and global reconstruction. We show examples for the identification of extreme events to demonstrate the ability of these new LST products to capture and represent spatial and temporal details. A time series of global monthly average, minimum and maximum LST data and long-term averages is freely available for download.

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“…The AATSR data are selected from several years starting from 2006, during strong Arctic haze episode, which originated predominantly from agricultural fires burning in eastern Europe. The event has been reported previously (Law and Stohl, 2007). A second episode in 2008 is also considered, for which valida- tion data are available from SYNOP stations.…”

Section: Methodsmentioning

confidence: 99%

Response to Anonymous Referee 1

Jafariserajehlou¹

2019

Preprint

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The accurate identification of the presence of cloud in the ground scenes observed by remote-sensing satellites is an end in itself. The lack of knowledge of cloud at high latitudes increases the error and uncertainty in the evaluation and assessment of the changing impact of aerosol and cloud in a warming climate. A prerequisite for the accurate retrieval of aerosol optical thickness (AOT) is the knowledge of the presence of cloud in a ground scene. In our study, observations of the upwelling radiance in the visible (VIS), near infrared (NIR), shortwave infrared (SWIR) and the thermal infrared (TIR), coupled with solar extraterrestrial irradiance, are used to determine the reflectance. We have developed a new cloud identification algorithm for application to the reflectance observations of the Advanced Along-Track Scanning Radiometer (AATSR) on European Space Agency (ESA)-Envisat and Sea and Land Surface Temperature Radiometer (SLSTR) on board the ESA Copernicus Sentinel-3A and-3B. The resultant AATSR-SLSTR cloud identification algorithm (ASCIA) addresses the requirements for the study AOT at high latitudes and utilizes time-series measurements. It is assumed that cloudfree surfaces have unchanged or little changed patterns for a given sampling period, whereas cloudy or partly cloudy scenes show much higher variability in space and time. In this method, the Pearson correlation coefficient (PCC) parameter is used to measure the "stability" of the atmospheresurface system observed by satellites. The cloud-free surface is classified by analysing the PCC values on the block scale 25 × 25 km 2. Subsequently, the reflection at 3.7 µm is used for accurate cloud identification at scene level: with areas of either 1 × 1 or 0.5 × 0.5 km 2. The ASCIA data product has been validated by comparison with independent observations, e.g. surface synoptic observations (SYNOP), the data from AErosol RObotic NETwork (AERONET) and the following satellite products: (i) the ESA standard cloud product from AATSR L2 nadir cloud flag; (ii) the product from a method based on a clear-snow spectral shape developed at IUP Bremen (Istomina et al., 2010), which we call ISTO; and (iii) the Moderate Resolution Imaging Spectroradiometer (MODIS) products. In comparison to ground-based SYNOP measurements, we achieved a promising agreement better than 95 % and 83 % within ±2 and ±1 okta respectively. In general, ASCIA shows an improved performance in comparison to other algorithms applied to AATSR measurements for the identification of clouds in a ground scene observed at high latitudes.

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Inconsistency of surface‐based (SYNOP) and satellite‐based (MODIS) cloud amount estimations due to the interpretation of cloud detection results

Cited by 10 publications

References 47 publications

A cloud identification algorithm over the Arctic for use with AATSR/SLSTR measurements

A cloud identification algorithm over the Arctic for use with AATSR/SLSTR measurements

A New Fully Gap-Free Time Series of Land Surface Temperature from MODIS LST Data

Response to Anonymous Referee 1

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