Simulation of Satellite Passive-Microwave Observations in Rainy Atmospheres at the Meteorological Service of Canada

Burlaud, Corinne; Deblonde, G.; Mahfouf, Jean‐François

doi:10.1109/tgrs.2007.895335

Cited by 3 publications

(1 citation statement)

References 26 publications

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“…In comparison with lower frequency channels, Tb at 89 GHz channel (~3.36 mm wavelength) have higher spatial resolution (instantaneous field of view: 3.5 × 5.9 km 2 ), is sensitive to the presence of ice aloft associated with deep convection, and is commonly used to detect raining pixels and surface precipitation in the satellite retrieval over land [ Olson et al ., ]. Thus, high‐frequency microwave Tb can be utilized to examine precipitation processes and precipitation microphysics [ Wiedner et al ., 2004; Burlaud et al ., ; Matsui et al ., ; Li et al ., ; Gao et al ., ; Han et al ., ].…”

Section: Satellite Observations and Examples Of Simulated L1 Data Fromentioning

confidence: 97%

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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