Identifying precipitating clouds in Greece using multispectral infrared Meteosat Second Generation satellite data

Feidas, H.; Giannakos, Apostolos

doi:10.1007/s00704-010-0316-5

Cited by 38 publications

(53 citation statements)

References 41 publications

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“…Brightness temperature ΤIR10.8 is an indication of the vertical extent of the cloud because, in general, brightness temperature of the system depends on the cloud-top height [2,16,17,18,19,20].…”

Section: A Spectral Parametersmentioning

confidence: 99%

Image Segmentation Method for Identifying Convective and Stratiform Rain using MSG SEVIRI Data

Sehad¹,

Lazri²,

Ameur³

et al. 2014

IJIGSP

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Abstract-This paper provides a new method for the classification of rainfall areas in convective and stratiform rain using MSG/SEVIRI (Spinning Enhanced Visible and Infrared) data. The proposed approach is based on spectral and temporal properties of clouds. The spectral parameters used are: brightness temperature (BT) and brightness temperature differences (BTDs), and the temporal parameter (RCT 10.8 ) is the rate of change of (BT) in the 10.8µm channel over two consecutive images. The developed rain area classification technique (RACT-DN) is based on two multilayer perceptron neural networks (MLP-D for daytime and MLP-N for nighttime) which relies on the correlation of satellite data with convective and stratiform rain. The two algorithms (MLP-D and MLP-N) are trained using as reference data from ground meteorological radar over northern Algeria. The results show that RACT-DN classifier gives accurate discrimination between convective and stratiform areas during daytime and nighttime.

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Section: A Spectral Parametersmentioning

confidence: 99%

Image Segmentation Method for Identifying Convective and Stratiform Rain using MSG SEVIRI Data

Sehad¹,

Lazri²,

Ameur³

et al. 2014

IJIGSP

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“…Techniques for rain area delineation relying on different spectral parameters have already been developed (e.g., Thies et al 2008b, c;Feidas and Giannakos 2010). Since the objective of the present study is to explicitly evaluate the potential of classifying rainy clouds as convective and stratiform based on spectral properties of cloud tops, only precipitating gauge data are considered.…”

Section: Data Preprocessingmentioning

confidence: 99%

“…In order to develop convective/stratiform rain type classification techniques, this study has adopted the procedure proposed by Feidas and Giannakos (2010) for the discrimination between precipitating and nonprecipitating clouds over Greece. In our case, two different methods were used to develop schemes for discriminating between convective and stratiform rainy clouds.…”

Section: Techniques' Developmentmentioning

confidence: 99%

“…It lies on the assumption that the probability of convective rain depends on the spectral parameters' values, thus we could use thresholds in the spectral domains to distinguish between "convective" or "stratiform" rain areas. This technique has been used in many studies to classify clouds into two (e.g., rain and no-rain) or more types (Inoue 1987a(Inoue , b, 1997Inoue and Aonashi 2000;Lutz et al 2003;Feidas and Giannakos 2010).…”

Section: A Thresholding Schemementioning

confidence: 99%

“…The second method relies on an empirical algorithm for classifying rain area which is based on the estimation of the probability of convective rainfall (PCR) on a pixel basis using multispectral matrices of all the four spectral parameters (Bellon et al 1980;Cheng et al 1993;Kurino 1997;Nauss and Kokhanovsky 2006;Thies et al 2008b, c;Feidas and Giannakos 2010). The PCR parameter is calculated as a function of the four variables T 10.8 , T 10.8 -T 12.1 , T 8.7 -T 10.8 , and T 6.2 -T 10.8 and represents the percentage of rainy pixels that have been identified as convective raining by the gauge data.…”

Section: B Probability Of Convective Rain Techniquementioning

confidence: 99%

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Classifying convective and stratiform rain using multispectral infrared Meteosat Second Generation satellite data

Feidas

Giannakos

2011

Theor Appl Climatol

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This paper investigates the potential for developing schemes that classify convective and stratiform precipitation areas using the high infrared spectral resolution of the Meteosat Second Generation-Spinning Enhanced Visible and Infrared Imager (MSG-SEVIRI). Two different classification schemes were proposed that use the brightness temperature (BT) Τ 10.8 along with the brightness temperature differences (BTDs) Τ 10.8 -Τ 12.1 , Τ 8.7 -Τ 10.8 , and Τ 6.2 -Τ 10.8 as spectral parameters, which provide information about cloud parameters. The first is a common multispectral thresholding scheme used to partition the space of the spectral cloud parameters and the second is an algorithm based on the probability of convective rain (PCR) for each pixel of the satellite data. Both schemes were calibrated using as a reference convective\stratiform rain classification fields derived from 87 stations in Greece for six rainy days with high convective activity. As a result, one single infrared technique (TB 10 ) and two multidimensional techniques (BTD all and PCR) were constructed and evaluated against an independent sample of rain gauge data for four daily convective precipitation events. It was found that the introduction of BTDs as additional information to a technique works in improving the discrimination of convective from stratiform rainy pixels compared to the single infrared technique BT 10 . During the training phase, BTD all performed slightly better than BT 10 while PCR technique outperformed both threshold techniques. All techniques clearly overestimate the convective rain occurrences detected by the rain gauge network. When evaluating against the independent dataset, both threshold techniques exhibited the same performance with that of the dependent dataset whereas the PCR technique showed a notable skill degradation. As a result, BTD all performed best followed at a short distance by PCR and BT 10 . These findings showed that it is possible to apply a convective/ stratiform rain classification algorithm based on the enhanced infrared spectral resolution of MSG-SEVIRI, for nowcasting or climate purposes, despite the highly variable nature of convective precipitation.

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Near Real-Time Estimation of High Spatiotemporal Resolution Rainfall from Cloud Top Properties of the MSG satellite and Commercial Microwave Link Rainfall Intensities

Kumah

Maathuis

et al. 2022

Preprint

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High spatiotemporal resolution rainfall is needed in predicting flash floods, local climate impact studies and agriculture management. Rainfall estimation techniques like satellites and the commercial microwave links (MWL) rainfall estimation have independently made significant advancements in high spatiotemporal resolution rainfall estimation. However, their combination for rainfall estimation has received little attention, while it could benefit many applications in ungauged areas. This study investigated the usability of the random forest (RF) algorithm trained with MWL rainfall and Meteosat Second Generation (MSG) based cloud top properties for estimating high spatiotemporal resolution rainfall in the sparsely gauged Kenyan Rift Valley. Our approach retrieved cloud top properties for use as predictor variables from rain areas estimated from the MSG data and estimated path average rainfall intensities from the MWL to serve as the target variable. We trained and validated the RF algorithm using parameters derived through optimal parameter tuning. The RF rainfall intensity estimates were compared with gauge, MWL, Global Precipitation Measurement (GPM) Integrated Multi-satellitE Retrievals for GPM (IMERG) and European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) Multisensor Precipitation Estimate (MPE) to evaluate its rainfall intensities from point and spatial perspectives. The results can be described as good, considering they were achieved in near real-time, pointing towards a promising rainfall estimation alternative based on the RF algorithm applied to MWL and MSG data. The applicative benefits of this technique could be huge, considering that many ungauged areas have a growing MWL network and MSG and, in the future, Meteosat Third Generation (MTG) coverage.

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Identifying precipitating clouds in Greece using multispectral infrared Meteosat Second Generation satellite data

Cited by 38 publications

References 41 publications

Image Segmentation Method for Identifying Convective and Stratiform Rain using MSG SEVIRI Data

Image Segmentation Method for Identifying Convective and Stratiform Rain using MSG SEVIRI Data

Classifying convective and stratiform rain using multispectral infrared Meteosat Second Generation satellite data

Near Real-Time Estimation of High Spatiotemporal Resolution Rainfall from Cloud Top Properties of the MSG satellite and Commercial Microwave Link Rainfall Intensities

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