Satellite Data Simulator Unit

Masunaga, Hirohiko; Matsui, Toshihisa; Tao, Wei‐Kuo; Hou, Arthur Y.; Kummerow, Christian D.; Nakajima, Teruyuki; Bauer, Péter; Olson, William S.; Sekiguchi, M; Nakajima, Takashi Y.

doi:10.1175/2010bams2809.1

Cited by 86 publications

(33 citation statements)

References 3 publications

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“…The G-SDSU is an end-to-end multi-satellite simulator unit, which has been developed at NASA GSFC in collaboration with several universities and other institutions (Masunaga et al, 2010, Matsui et al, 2013. The purpose of G-SDSU is to convert the NU-WRF geophysical parameter output into the satellite-observable signals.…”

Section: G-sdsumentioning

confidence: 99%

Integrated modeling of aerosol, cloud, precipitation and land processes at satellite-resolved scales

Peters‐Lidard

Kemp

Matsui

et al. 2015

Environmental Modelling & Software

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102

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Section: G-sdsumentioning

confidence: 99%

Integrated modeling of aerosol, cloud, precipitation and land processes at satellite-resolved scales

Peters‐Lidard

Kemp

Matsui

et al. 2015

Environmental Modelling & Software

Self Cite

102

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“…The infrared 11-mm TBB from the TRMM 1B01 product and the 13.8-GHz reflectivity and orbital precipitation from TRMM 2A25 are used for the TRMM Triple-Sensor Three-Step Evaluation Framework (T3EF) method (Masunaga et al 2008;Matsui et al 2009). T3EF involves three steps: 1) obtaining the joint histograms of TBB and PTH, 2) the CFADs of the PR reflectivities, and 3) the cumulative probability distribution of the TRMM Microwave Imager (TMI) 85-GHz brightness temperatures (Masunaga and Kummerow 2006;Matsui et al 2009). The PTH is identified as the highest altitude of the layer above 17 dBZ of PR reflectivity.…”

Section: B Observational Data and Satellite Forward Operatorsmentioning

confidence: 99%

“…We use the Satellite Data Simulator Unit (SDSU; Masunaga et al 2010), version 2.1.4, to compare the radiances of the observation data with those of the NICAM data. In SDSU, the extinction and scattering properties of hydrometeors are calculated on the basis of the Mie theory.…”

Section: B Observational Data and Satellite Forward Operatorsmentioning

confidence: 99%

“…However, ground radar observations and aircraft data have spatial and sampling limits. One is to directly compare the output data of CSRMs and retrieve physical parameters from satellite data, and the other is to compare radiances in satellite data with simulated radiances from CSRM output data using satellite simulators (Masunaga et al 2010;Bodas-Salcedo et al 2011;Hashino et al 2013). Two approaches are used to evaluate CSRMs using satellite data for the spatial and vertical distributions of cloud systems.…”

Section: Introductionmentioning

confidence: 99%

“…We investigate the horizontal distribution of cloud sizes, the joint probability distributions of cloud-top temperature, precipitation-top height (PTH), and contoured frequency by altitude diagrams (CFADs) of radar reflectivities for each category using a TRMM precipitation radar (PR) and infrared scanner following the approach proposed by Masunaga and Kummerow (2006) and Matsui et al (2009). We investigate the horizontal distribution of cloud sizes, the joint probability distributions of cloud-top temperature, precipitation-top height (PTH), and contoured frequency by altitude diagrams (CFADs) of radar reflectivities for each category using a TRMM precipitation radar (PR) and infrared scanner following the approach proposed by Masunaga and Kummerow (2006) and Matsui et al (2009).…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Evaluation of Precipitating Hydrometeor Parameterizations in a Single-Moment Bulk Microphysics Scheme for Deep Convective Systems over the Tropical Central Pacific

Roh

Satoh

2014

Journal of the Atmospheric Sciences

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Cloud microphysics of deep convective systems over the tropical central Pacific simulated by a cloud systemresolving model using satellite simulators are evaluated in terms of the joint histogram of cloud-top temperature and precipitation echo-top heights. A control experiment shows an underestimation of stratiform precipitation and a higher frequency of precipitating deep clouds with top heights higher than 12 km when compared with data from the Tropical Rainfall Measuring Mission. The comparison shows good agreement for horizontal distribution and statistical cloud size distributions of deep convective systems. Biases in the joint histogram are improved by changing cloud microphysics parameters of a single-moment bulk microphysics scheme. The effects of size distribution of precipitating hydrometeors are examined. Modification of the particle size distributions of rain, snow, and graupel size distributions based on observed relationships improves cloud precipitation statistics. This study implies that a single-moment bulk cloud microphysics scheme can be improved by employing comparison of satellite observations and diagnostic relationships.

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Introducing multisensor satellite radiance-based evaluation for regional Earth System modeling

Matsui

Santanello

Shi

et al. 2014

J. Geophys. Res. Atmos.

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Earth System modeling has become more complex, and its evaluation using satellite data has also become more difficult due to model and data diversity. Therefore, the fundamental methodology of using satellite direct measurements with instrumental simulators should be addressed especially for modeling community members lacking a solid background of radiative transfer and scattering theory. This manuscript introduces principles of multisatellite, multisensor radiance-based evaluation methods for a fully coupled regional Earth System model: NASA-Unified Weather Research and Forecasting (NU-WRF) model. We use a NU-WRF case study simulation over West Africa as an example of evaluating aerosol-cloud-precipitation-land processes with various satellite observations. NU-WRF-simulated geophysical parameters are converted to the satellite-observable raw radiance and backscatter under nearly consistent physics assumptions via the multisensor satellite simulator, the Goddard Satellite Data Simulator Unit. We present varied examples of simple yet robust methods that characterize forecast errors and model physics biases through the spatial and statistical interpretation of various satellite raw signals: infrared brightness temperature (Tb) for surface skin temperature and cloud top temperature, microwave Tb for precipitation ice and surface flooding, and radar and lidar backscatter for aerosol-cloud profiling simultaneously. Because raw satellite signals integrate many sources of geophysical information, we demonstrate user-defined thresholds and a simple statistical process to facilitate evaluations, including the infrared-microwave-based cloud types and lidar/radar-based profile classifications.

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Satellite Data Simulator Unit

Cited by 86 publications

References 3 publications

Integrated modeling of aerosol, cloud, precipitation and land processes at satellite-resolved scales

Integrated modeling of aerosol, cloud, precipitation and land processes at satellite-resolved scales

Evaluation of Precipitating Hydrometeor Parameterizations in a Single-Moment Bulk Microphysics Scheme for Deep Convective Systems over the Tropical Central Pacific

Introducing multisensor satellite radiance-based evaluation for regional Earth System modeling

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