Retrieval of Venus' cloud parameters from VIRTIS nightside spectra in the latitude band 25°-55°N

2017 ◽  
Vol 144 ◽  
pp. 16-31 ◽  
Author(s):  
Davide Magurno ◽  
Tiziano Maestri ◽  
Davide Grassi ◽  
Giuseppe Piccioni ◽  
Giuseppe Sindoni
2021 ◽  
Vol 13 (1) ◽  
pp. 152
Author(s):  
Haklim Choi ◽  
Xiong Liu ◽  
Gonzalo Gonzalez Abad ◽  
Jongjin Seo ◽  
Kwang-Mog Lee ◽  
...  

Clouds act as a major reflector that changes the amount of sunlight reflected to space. Change in radiance intensity due to the presence of clouds interrupts the retrieval of trace gas or aerosol properties from satellite data. In this paper, we developed a fast and robust algorithm, named the fast cloud retrieval algorithm, using a triplet of wavelengths (469, 477, and 485 nm) of the O2–O2 absorption band around 477 nm (CLDTO4) to derive the cloud information such as cloud top pressure (CTP) and cloud fraction (CF) for the Geostationary Environment Monitoring Spectrometer (GEMS). The novel algorithm is based on the fact that the difference in the optical path through which light passes with regard to the altitude of clouds causes a change in radiance due to the absorption of O2–O2 at the three selected wavelengths. To reduce the time required for algorithm calculations, the look-up table (LUT) method was applied. The LUT was pre-constructed for various conditions of geometry using Vectorized Linearized Discrete Ordinate Radiative Transfer (VLIDORT) to consider the polarization of the scattered light. The GEMS was launched in February 2020, but the observed data of GEMS have not yet been widely released. To evaluate the performance of the algorithm, the retrieved CTP and CF using observational data from the Global Ozone Monitoring Experiment-2 (GOME-2), which cover the spectral range of GEMS, were compared with the results of the Fast Retrieval Scheme for Clouds from the Oxygen A band (FRESCO) algorithm, which is based on the O2 A-band. There was good agreement between the results, despite small discrepancies for low clouds.


2001 ◽  
Vol 32 ◽  
pp. 975-976
Author(s):  
S. HENNING ◽  
E. WEIN GARTNER ◽  
S. SCHMIDT ◽  
M. WENDISCH ◽  
U. BALTENSPERGER

1998 ◽  
Vol 16 (3) ◽  
pp. 331-341 ◽  
Author(s):  
J. Massons ◽  
D. Domingo ◽  
J. Lorente

Abstract. A cloud-detection method was used to retrieve cloudy pixels from Meteosat images. High spatial resolution (one pixel), monthly averaged cloud-cover distribution was obtained for a 1-year period. The seasonal cycle of cloud amount was analyzed. Cloud parameters obtained include the total cloud amount and the percentage of occurrence of clouds at three altitudes. Hourly variations of cloud cover are also analyzed. Cloud properties determined are coherent with those obtained in previous studies.Key words. Cloud cover · Meteosat


1997 ◽  
Author(s):  
Jean-Marc Theriault ◽  
Luc R. Bissonnette ◽  
Gilles Roy
Keyword(s):  

1985 ◽  
Vol 88 (2) ◽  
pp. 539-542 ◽  
Author(s):  
S. E. Zabolotskii ◽  
V. P. Kalinushkin ◽  
T. M. Murina ◽  
M. G. Ploppa ◽  
K. Tempelhoff

1988 ◽  
Author(s):  
Edwin W. Eloranta ◽  
Christian J. Grund

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