Comparison of a Split-window and a Multi-spectral Cloud Classification for MODIS Observations

Lutz, Hans-Joachim; Inoue, Toshiro; Schmetz, Johannes

doi:10.2151/jmsj.81.623

Cited by 46 publications

(35 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The APICS cloud masking algorithm has inherited its structure from the EUMETSAT scenes detection algorithm (Lutz, 1999(Lutz, , 2002Lutz et al, 2003). It is based on six groups of threshold tests applied to several SEVIRI channels.…”

Section: Cloud Detectionmentioning

confidence: 99%

Validation of cloud property retrievals with simulated satellite radiances: a case study for SEVIRI

Bugliaro

Zinner²,

Keil³

et al. 2011

Atmos. Chem. Phys.

View full text Add to dashboard Cite

Abstract. Validation of cloud properties retrieved from passive spaceborne imagers is essential for cloud and climate applications but complicated due to the large differences in scale and observation geometry between the satellite footprint and the independent ground based or airborne observations. Here we illustrate and demonstrate an alternative approach: starting from the output of the COSMO-EU weather model of the German Weather Service realistic three-dimensional cloud structures at a spatial scale of 2.33 km are produced by statistical downscaling and microphysical properties are associated to them. The resulting data sets are used as input to the one-dimensional radiative transfer model libRadtran to simulate radiance observations for all eleven low resolution channels of MET-8/SEVIRI. At this point, both cloud properties and satellite radiances are known such that cloud property retrieval results can be tested and tuned against the objective input "truth". As an example, we validate a cloud property retrieval of the Institute of Atmospheric Physics of DLR and that of EUMETSAT's Climate Monitoring Science Application Facility CMSAF. Cloud detection and cloud phase assignment perform well. By both retrievals 88% of the pixels are correctly classified as clear or cloudy. The DLR algorithm assigns the correct thermodynamic phase to 95% of the cloudy pixels and the CMSAF retrieval to 84%. Cloud top temperature is slightly overestimated by the DLR code (+3

show abstract

Section: Cloud Detectionmentioning

confidence: 99%

Validation of cloud property retrievals with simulated satellite radiances: a case study for SEVIRI

Bugliaro

Zinner²,

Keil³

et al. 2011

Atmos. Chem. Phys.

View full text Add to dashboard Cite

show abstract

“…The advantage of the technique is to classify optically thin cirrus type cloud without using visible data. The classifications produced reliable results in representing highlevel clouds in comparison with conventional schemes (Lutz et al 2003).…”

Section: Introductionmentioning

confidence: 93%

Cloud Properties over the Bay of Bengal Derived from NOAA-9 Split Window Data and the TRMM PR Product

Inoue

Ueda

Inoue³

2006

SOLA

Self Cite

View full text Add to dashboard Cite

We studied the features of convective activity over the Bay of Bengal (BoB) in terms of cloud type and rainfall. In this paper, six cloud types were classified over the tropics using split window (11 and 12 µm) data measured by the Advanced Very High Resolution Radiometer (AVHRR) on board NOAA-9. The spatiotemporal structure of Dense Cirrus (D-Ci) and Cumulonimbus (Cb) type clouds is surveyed in this study. As for the summer monsoon season, D-Ci-type clouds higher than 10% can be seen over the entire BoB. The spatiotemporal structure of the D-Ci-type cloudiness coincides with those of outgoing longwave radiation (OLR) value of less than 230 W/m 2 over the BoB. The Cb-type cloudiness, which is less than D-Ci-type, concentrates over the northeastern part of the BoB in the boreal summer. The enhanced precipitation, derived from Tropical Rainfall Measurements Mission (TRMM) Precipitation Rader (PR), coincides with the D-Ci-type clouds area higher than 20% and the low OLR value less than 190 W/m 2 as well as large amount of Cb-type cloudiness in the boreal summer.

show abstract

“…The DT WV7.3-IR12.0 is effective in distinguishing between high-level and low-level/mid-level clouds (Lutz et al 2003). The 7.3-lm channel is dominated by atmospheric water vapor absorption.…”

Section: Parameters For Identification Of Raining Pixelsmentioning

confidence: 99%

Rainfall detection over northern Algeria by combining MSG and TRMM data

Ouallouche

Ameur

2014

Appl Water Sci

View full text Add to dashboard Cite

In this paper, a new method to delineate rain areas in northern Algeria is presented. The proposed approach is based on the blending of the geostationary meteosat second generation (MSG), infrared channel with the low-earth orbiting passive tropical rainfall measuring mission (TRMM). To model the system designed, we use an artificial neural network (ANN). We seek to define a relationships between three parameters calculated from TRMM microwave imager (TMI) associated with four parameters from infrared sensors of MSG satellite and two classes (rain, no-rain) from precipitation radar (PR) TRMM data. The seven spectral parameters issued from MSG and TMI are used as input data. Rain/no-rain classes from PR are used as the output data of this ANN. Two steps are necessary: training and validation. Results in the developed scheme are compared with the results of a reference method which is the scattering index (SI) method. The result shows that the developed model works very well and overcomes the shortcomings of the SI method.

show abstract

Comparison of a Split-window and a Multi-spectral Cloud Classification for MODIS Observations

Cited by 46 publications

References 18 publications

Validation of cloud property retrievals with simulated satellite radiances: a case study for SEVIRI

Validation of cloud property retrievals with simulated satellite radiances: a case study for SEVIRI

Cloud Properties over the Bay of Bengal Derived from NOAA-9 Split Window Data and the TRMM PR Product

Rainfall detection over northern Algeria by combining MSG and TRMM data

Contact Info

Product

Resources

About