Grundlagen der Darstellung

Simmer, Konrad

doi:10.1007/978-3-322-99388-5_1

Cited by 3 publications

(2 citation statements)

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“…Based on Simmer (1994), the non-scattering microwave radiative transfer is applied to calculate the brightness temperatures of each profile from 9555 radio-soundings and 58195 ERA5 profiles. This results in a data-set of 67750 profiles with corresponding calculated brightness temperatures which serve as base for the retrieval generation.…”

Section: Non-scattering Microwave Radiative Transfer Modelmentioning

confidence: 99%

Comment on egusphere-2024-919

2024

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Section: Non-scattering Microwave Radiative Transfer Modelmentioning

confidence: 99%

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2024

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“…The scattering properties which are functions of the hydrometeor species, wavelength and effective radii are read from the CRTM look-up tables (LUTs). The LUTs for thermal infrared wavelengths are constructed based on Mie scattering assuming spherical shape for liquid cloud hydrometeors and a modified gamma size distribution (Chen et al, 2008;Simmer, 1994). The ice phase hydrometeors are assumed to be non-spherical hexagonal column with a gamma size distribution (Yang et al, 2005).…”

Section: Overview Of Case Study Model and Da Configurationmentioning

confidence: 99%

Online Nonlinear Bias Correction in Ensemble Kalman Filter to Assimilate GOES‐R All‐Sky Radiances for the Analysis and Prediction of Rapidly Developing Supercells

Krishnamoorthy

Wang

Johnson

et al. 2022

J Adv Model Earth Syst

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The present study introduces the online non‐linear bias correction for the assimilation of all‐sky GOES‐16 Advanced Baseline Imager (ABI) channel 9 (6.9 μm) radiances in a rapidly cycled EnKF for convective scale data assimilation (DA). This study is the first to explore the use of the radar reflectivity as the anchoring observation for ABI all sky radiance assimilation. The online and offline nonlinear bias correction methods are compared and evaluated for a case of rapidly developing supercells over Oklahoma and Texas. The analysis and background of the online bias correction perform better than the offline approach during the suppression of spurious clouds and the establishment of non‐precipitating and precipitating regions when the supercell storms are observed to develop. The online approach not only improves the analysis and background over the radar anchored region but also the unanchored non‐precipitating regions compared to the offline approach. Both quantitative and subjective verification of the deterministic forecasts showed consistent superior performance from the online bias correction over the offline approach. Diagnostics reveal that the online bias correction retains useful information in the innovation, which in turn improves subsequent analysis, background and background ensemble spread for both the thermodynamic and dynamic fields. The effect is accumulated during the DA cycling that is responsible for the superior analysis and forecast of the supercells.

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2024

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Grundlagen der Darstellung

Cited by 3 publications

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Online Nonlinear Bias Correction in Ensemble Kalman Filter to Assimilate GOES‐R All‐Sky Radiances for the Analysis and Prediction of Rapidly Developing Supercells

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