The Response of 36- and 89-GHz Microwave Channels to Convective Snow Clouds over Ocean: Observation and Modeling

Katsumata, Masaki; Uyeda, Hiroshi; Iwanami, Koyuru; Liu, Guosheng

doi:10.1175/1520-0450(2000)039<2322:troagm>2.0.co;2

Cited by 30 publications

(24 citation statements)

References 21 publications

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“…Aircraft observations over ocean by downward-looking microwave radiometers confirmed the brightness temperature depression caused by ice scattering. For example, Katsumata et al [2000] reported that brightness temperature at 89 GHz drops about 15 K over convective snowfall cells compared to their neighboring clear-sky region, and Noh et al [2006] found that brightness temperatures at 150 GHz are about 50 K lower than neighboring clear-sky regions for some snowing clouds. The minimum detectable signature by currently available and planned future sensors has been studied by radiative transfer model simulations (G. SkofronickJackson et al, personal communication, 2012) and by comparisons between satellite radiometer and surface radar observations (S. Munchak and G. Skofronick-Jackson, personal communication, 2012).…”

Section: Introductionmentioning

confidence: 99%

Detecting snowfall over land by satellite high‐frequency microwave observations: The lack of scattering signature and a statistical approach

Liu

Seo

2013

JGR Atmospheres

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[1] It has been long believed that the dominant microwave signature of snowfall over land is the brightness temperature decrease caused by ice scattering. However, our analysis of multiyear satellite data revealed that on most of occasions, brightness temperatures are rather higher under snowfall than nonsnowfall conditions, likely due to the emission by cloud liquid water. This brightness temperature increase masks the scattering signature and complicates the snowfall detection problem. In this study, we propose a statistical method for snowfall detection, which is developed by using CloudSat radar to train high-frequency passive microwave observations. To capture the major variations of the brightness temperatures and reduce the dimensionality of independent variables, the detection algorithm is designed to use the information contained in the first three principal components resulted from Empirical Orthogonal Function (EOF) analysis, which capturẽ 99% of the total variances of brightness temperatures. Given a multichannel microwave observation, the algorithm first transforms the brightness temperature vector into EOF space and then retrieves a probability of snowfall by using the CloudSat radar-trained lookup table. Validation has been carried out by case studies and averaged horizontal snowfall fraction maps. The result indicated that the algorithm has clear skills in identifying snowfall areas even over mountainous regions.Citation: Liu, G., and E.-K. Seo (2013), Detecting snowfall over land by satellite high-frequency microwave observations: The lack of scattering signature and a statistical approach,

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Section: Introductionmentioning

confidence: 99%

Detecting snowfall over land by satellite high‐frequency microwave observations: The lack of scattering signature and a statistical approach

Liu

Seo

2013

JGR Atmospheres

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“…This is even truer in case of young ice because of its horizontal inhomogeneity. Katsumata et al [43] observed snow-producing clouds over the Sea of Japan using the Airborne Microwave Radiometer (AMR), a prototype simulator of the Advanced Microwave Scanning Radiometer (AMSR). The observations, although limited below 89 GHz by the instrument design, showed that the retrieved liquid and snow water amounts compared reasonably well with coincident radar observations.…”

Section: Passive Microwave Methodsmentioning

confidence: 99%

Detection and Measurement of Snowfall from Space

2011

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Snowfall detection and measurement represent highly difficult problems in modern hydrometeorology. Ground measurements are complicated due to detection technology limitations, snow drift and accumulation issues, and error definition. The snowfall detection from space is in turn affected by all detection limitations that characterize the measurement of rainfall with the addition of several complications, such as the indirect character of remote sensing precipitation estimation, the presence of frozen or snow-covered terrain, and the unknown vertical distribution of hydrometeors in the cloud column. Several methods for the retrieval of snowfall intensity from satellite have been proposed in recent times using passive and active sensors. No satisfactory answer to the general problem of quantitative snowfall intensity determination has been found to date, but several studies contribute to delineate a working framework for the future operational retrieval algorithms.

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“…At higher microwave frequencies, snowfall characterization from space is a challenging task, but possible through the analysis of the scattering signal from frozen hydrometeors (e.g. Katsumata et al, 2000;Bennartz and Bauer, 2003;Skofronick-Jackson and Johnson, 2011). The second, and main reason, is the complex nature and high variability of the microphysical properties (size, composition, density and shape), and thus radiative properties, of the frozen particles (Johnson et al, 2012).…”

Section: Introductionmentioning

confidence: 99%

Meso-scale modelling and radiative transfer simulations of a snowfall event over France at microwaves for passive and active modes and evaluation with satellite observations

et al. 2015

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Abstract. Microwave passive and active radiative transfer simulations are performed with the Atmospheric Radiative Transfer Simulator (ARTS) for a mid-latitude snowfall event, using outputs from the Meso-NH mesoscale cloud model. The results are compared to the corresponding microwave observations available from MHS and CloudSat. The spatial structures of the simulated and observed brightness temperatures show an overall agreement since the large-scale dynamical structure of the cloud system is reasonably well captured by Meso-NH. However, with the initial assumptions on the single-scattering properties of snow, there is an obvious underestimation of the strong scattering observed in regions with large frozen hydrometeor quantities. A sensitivity analysis of both active and passive simulations to the microphysical parametrizations is conducted. Simultaneous analysis of passive and active calculations provides strong constraints on the assumptions made to simulate the observations. Good agreements are obtained with both MHS and CloudSat observations when the single-scattering properties are calculated using the "soft sphere" parametrization from Liu (2004), along with the Meso-NH outputs. This is an important step toward building a robust data set of simulated measurements to train a statistically based retrieval scheme.

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The Response of 36- and 89-GHz Microwave Channels to Convective Snow Clouds over Ocean: Observation and Modeling

Cited by 30 publications

References 21 publications

Detecting snowfall over land by satellite high‐frequency microwave observations: The lack of scattering signature and a statistical approach

Detecting snowfall over land by satellite high‐frequency microwave observations: The lack of scattering signature and a statistical approach

Detection and Measurement of Snowfall from Space

Meso-scale modelling and radiative transfer simulations of a snowfall event over France at microwaves for passive and active modes and evaluation with satellite observations

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