Retrieval of Precipitable Water from Observations in the Split Window over Varying Surface Temperatures

Kleespies, Thomas J.; McMillin, Larry M.

doi:10.1175/1520-0450(1990)029<0851:ropwfo>2.0.co;2

Cited by 89 publications

(49 citation statements)

References 8 publications

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“…2b, see also Schmetz et al, 2002). This is due to its higher sensitivity to water vapour absorption (Kleespies and McMillin, 1990) resulting in an emission spectrum at higher altitudes and lower temperatures. Integrated and weighted with the exact SRF, the brightness temperature in the I R 12.1 channel is colder than in the I R 10.8 channel.…”

Section: Positive T Wv−ir For the Detection Of Convective Cloudsmentioning

confidence: 99%

First results on a process-oriented rain area classification technique using Meteosat Second Generation SEVIRI nighttime data

2008

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Abstract.A new technique for process-oriented rain area classification using Meteosat Second Generation SEVIRI nighttime data is introduced. It is based on a combination of the Advective Convective Technique (ACT) which focuses on precipitation areas connected to convective processes and the Rain Area Delineation Scheme during Nighttime (RADS-N) a new technique for the improved detection of stratiform precipitation areas (e.g. in connection with mid-latitude frontal systems). The ACT which uses positive brightness temperature differences between the water vapour (WV) and the infrared (IR) channels ( T WV−IR ) for the detection of convective clouds and connected precipitating clouds has been transferred from Meteosat First Generation (MFG) Metesoat Visible and Infra-Red Imager radiometer (MVIRI) to Meteosat Second Generation (MSG) Spinning Enhanced Visible and InfraRed Imager (SEVIRI). RADS-N is based on the new conceptual model that precipitating cloud areas are characterised by a large cloud water path (cwp) and the presence of ice particles in the upper part of the cloud. The technique considers information about both parameters inherent in the channel differences

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Section: Positive T Wv−ir For the Detection Of Convective Cloudsmentioning

confidence: 99%

First results on a process-oriented rain area classification technique using Meteosat Second Generation SEVIRI nighttime data

2008

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“…Satellite data are useful to elucidate the mesoscale distribution of water vapor over the land, and some algorithms were developed using the split-window channel data (Chester et al, 1983;Kleespies and McMilline, 1990;Jedlovec, 1990;Guillory et al, 1992;Iwasaki,1994a). Since the methods of Chester et al (1993) and Guillory et al (1992) require information about the vertical profile of temperature, these are not suitable for the semibasin where the diurnal variation is pronounced as shown in Fig.…”

Section: Introductionmentioning

confidence: 99%

“…2. The algorithm of Iwasaki (1994a: referred to as 194) is an extension of Kleespies and McMilline (1990) and Jedlovec (1990), and can estimate precipitable water using only satellite data. Using the algorithm, satellite precipitable water was in good agreement with the sonde observation data launched at Tsukuba (see Fig.…”

Section: Introductionmentioning

confidence: 99%

Case Study on the Distribution of Precipitable Water Associated with Local Circulation Using the Split-Window Data of a NOAA Satellite

Iwasaki¹

1999

Journal of the Meteorological Society of Japan

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The algorithm of Iwasaki (1994 a) to estimate precipitable water over land was applied to 12 analysis units of a 30X30 array of pixels (about 33kmX33km at the nadir point for NOAA satellites) including mountainous regions, and retrievedvolume was compared with microwave radiometer data. The correlation between satellite and radiometer precipitable water was 0.81, slope and the y interception of the regression line are 1.08 and -1.68mm, respectively.Using the algorithm, the mesoscale distributions of precipitable water and its time change associated with thermally induced local circulations were visualized under the cloud-free conditions. Precipitable water on the coast increased by 5-20mm from morning to afternoon. At the same time, precipitable water increased by 0-20mm over the mountains and decreased by 0-15mm at the foot of the mountains.

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“…The averaged air temperature in the lower level, obtained by conventional radiosonde observations, was substituted for the mean-air temperature in the technique. Kleespies and McMillin (1990) proposed another technique to estimate precipitable water, also using split window data. The estimate is based on the ratio of the difference in the split window channel brightness temperature.…”

Section: Introductionmentioning

confidence: 99%

“…This paper presents a new algorithm to estimate precipitable water over land using the split window data from the NOAA satellite. The algorithm is an extension of the ideas proposed by Kleespies and McMillin (1990) and Jedlovec (1990). The author reviews the theory behind their techniques and points out two problems on the non-linear effect of air temperature and unresolved clouds in Section 3, and presents a new algorithm to overcome the two problems in Section 4.…”

Section: Introductionmentioning

confidence: 99%

Estimation of Precipitable Water over Land Using the Split-Window Data from the NOAA Satellite

Iwasaki

1994

Journal of the Meteorological Society of Japan

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A new algorithm to estimate precipitable water over land using the split-window data from the NOAA satellite is proposed. The estimation is based on the ratio of the split window channel brightness temperature difference and the ratio of the split window channel brightness temperature variance. The algorithm involves two main steps. The first is to extract high-confidence pixels which do not contain a large influence due to unresolved clouds, and to calculate the first-guess of precipitable water using the split-window variance ratio technique proposed by Jedlovec (1990). The second step corrects the first-guess of precipitable water using the difference between the mean-air temperature of the model atmosphere and the mean-air temperature calculated from the satellite data.By using this algorithm, the precipitable water over an area of about 33kmx33km can be estimated, while the influences due to unresolved clouds and unknown temperature profiles decrease. Compared with four radiosonde observations during August of 1992, the satellite-derived precipitable water data are in good agreement with the radiosonde data.

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Retrieval of Precipitable Water from Observations in the Split Window over Varying Surface Temperatures

Cited by 89 publications

References 8 publications

First results on a process-oriented rain area classification technique using Meteosat Second Generation SEVIRI nighttime data

First results on a process-oriented rain area classification technique using Meteosat Second Generation SEVIRI nighttime data

Case Study on the Distribution of Precipitable Water Associated with Local Circulation Using the Split-Window Data of a NOAA Satellite

Estimation of Precipitable Water over Land Using the Split-Window Data from the NOAA Satellite

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