scholarly journals Is a scaling factor required to obtain closure between measured and modelled atmospheric O<sub>4</sub> absorptions? – A case study for two days during the MADCAT campaign

2018 ◽  
Author(s):  
Thomas Wagner ◽  
Steffen Beirle ◽  
Nuria Benavent ◽  
Tim Bösch ◽  
Kai Lok Chan ◽  
...  
Keyword(s):  
Radiative Transfer ◽  
Scaling Factor

Abstract. In this study the consistency between MAX-DOAS measurements and radiative transfer simulations of the atmospheric O4 absorption is investigated on two mainly clear days during the MAD-CAT campaign in Mainz, Germany, in Summer 2013. In recent years several studies indicated that measurements and radiative transfer simulations of the atmospheric O4 absorption can only be brought into agreement if a so-called scaling factor (

The 27–28 October 1986 FIRF IFO Cirrus Case Study: Comparison of Radiative Transfer Theory with Observations by Satellite and Aircraft

1990 ◽  
Vol 118 (11) ◽  
pp. 2356-2376 ◽  
Author(s):  
Bruce A. Wielicki ◽  
J.T. Suttles ◽  
Andrew J. Heymsfield ◽  
Ronald M. Welch ◽  
James D. Spinhirne ◽  
...  
Keyword(s):  
Radiative Transfer ◽  
Radiative Transfer Theory ◽  
Transfer Theory

scholarly journals A Radiative Transfer Case Study for 3-d cloud effects in the UV

2001 ◽  
Vol 28 (9) ◽  
pp. 1683-1686 ◽  
Author(s):  
Ralf Meerkötter ◽  
Markus Degünther
Keyword(s):  
Radiative Transfer ◽  
Transfer Case

Case-study of a principal-component-based radiative transfer forward model and retrieval algorithm using EAQUATE data

2007 ◽  
Vol 133 (S3) ◽  
pp. 243-256 ◽  
Author(s):  
Xu Liu ◽  
Daniel K. Zhou ◽  
Allen Larar ◽  
William L. Smith ◽  
Stephen A. Mango
Keyword(s):  
Radiative Transfer ◽  
Principal Component ◽  
Forward Model ◽  
Retrieval Algorithm

scholarly journals Evaluation of radiative transfer schemes for mesoscale model data assimilation: a case study

2006 ◽  
Vol 7 ◽  
pp. 193-198 ◽  
Author(s):  
A. Memmo ◽  
C Faccani ◽  
R. Ferretti ◽  
S. Di Michele ◽  
F. S. Marzano
Keyword(s):  
Radiative Transfer ◽  
Transfer Equation ◽  
Radiative Transfer Equation ◽  
Mesoscale Model ◽  
Three Dimensional ◽  
Weather Forecast ◽  
Model Data ◽  
Brightness Temperatures ◽  
Microwave Imager

Abstract. The assimilation of Special Sensor Microwave Imager (SSM/I) data into the Mesoscale Model 5 (MM5) allows for improving the weather forecast. However the results suggested an update the Radiative Transfer Equation (RTE) within the three-dimensional variational (3DVAR) algorithm which is tailored for non rainy conditions only. To this purpose, a new RTE algorithm is tested, in order to account for radiometric response in rainy regions. The new brightness temperatures (TB) are estimated by using hydrometeor profiles from the MM5 mesoscale model, running with two different microphysical parameterizations. The goodness of the results is assessed by comparing the new TB with those of the original RTE algorithm in the 3DVAR code and the SSM/I observed data. The results confirm a better reliability of the new RTE compared to the old one.

The impact of common assumptions on canopy radiative transfer simulations: A case study in Citrus orchards

2009 ◽  
Vol 110 (1-2) ◽  
pp. 1-21 ◽  
Author(s):  
J. Stuckens ◽  
B. Somers ◽  
S. Delalieux ◽  
W.W. Verstraeten ◽  
P. Coppin
Keyword(s):  
Radiative Transfer ◽  
Citrus Orchards ◽  
The Impact

scholarly journals Satellite Observation for Evaluating Cloud Properties of the Microphysical Schemes in Weather Research and Forecasting Simulation: A Case Study of the Mei-Yu Front Precipitation System

2020 ◽  
Vol 12 (18) ◽  
pp. 3060
Author(s):  
Kao-Shen Chung ◽  
Hsien-Jung Chiu ◽  
Chian-Yi Liu ◽  
Meng-Yue Lin
Keyword(s):  
Radiative Transfer ◽  
Wrf Model ◽  
Skill Score ◽  
Radiative Transfer Model ◽  
Weather Research And Forecasting ◽  
Heavy Rainfall Event ◽  
Transfer Model ◽  
High Cloud ◽  
Weather Research

Radiative transfer model can be used to convert the geophysical variables (e.g., atmospheric thermodynamic state) to the radiation field. In this study, the Community Radiative Transfer Model (CRTM) is used to connect regional Weather Research and Forecasting (WRF) model outputs and satellite observations. A heavy rainfall event caused by the Mei-Yu front on the June 1, 2017, in the vicinity of Taiwan, was chosen as a case study. The simulated cloud performance of WRF with four microphysics schemes (i.e., Goddard (GCE), WRF single-moment 6 class (WSM), WRF double-moment 6 class (WDM), and Morrison (MOR) schemes) was investigated objectively using multichannel observed satellite radiances from a Japanese geostationary satellite Himawari-8. The results over the East Asia domain (9 km) illustrate that all four microphysics schemes overestimate cloudy pixels, in particular, the high cloud of simulation with MOR when comparing with satellite data. Sensitivity tests reveal that the excess condensation of ice at ≥14 km with MOR might be associated with the overestimated high cloud cover. However, GCE displayed an improved performance on water vapor channel in clear skies. When focusing on Taiwan using a higher (3 km) model resolution, each scheme displayed a decent performance on cloudy pixels. In the grid-by-grid skill score analysis, the distribution of high clouds was the most accurate among the three cloud types. The results also suggested that all schemes required a longer simulation time to describe the low cloud horizontal extend.

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