Seasonal and diurnal rain effects on Ku-band satellite link designs in rainy tropical regions

2000 ◽  
Vol 36 (9) ◽  
pp. 841 ◽  
Author(s):  
Q.W. Pan ◽  
J.E. Allnutt ◽  
F. Haidara
2008 ◽  
Vol 2 (2) ◽  
pp. 147-151 ◽  
Author(s):  
J.S. Mandeep ◽  
S.I.S. Hassan ◽  
K. Tanaka
Keyword(s):  

Author(s):  
Hassan Dao ◽  
Islam Md. Rafiqul ◽  
Megat Farez Azril Zuhairi ◽  
Megat Norulazmi Megat Mohamed Noor ◽  
Sayed Aziz Sayed Hussin

2013 ◽  
Vol 31 (1) ◽  
pp. 61
Author(s):  
V. Ramachandran ◽  
Ashneel Prasad

Dependence of radio wave attenuation by cloud water content has been investigated by analyzing simultaneous records of the strength of Ku-band satellite downlink and 'insolation'. Preliminary analysis suggests that with increasing cloud coverage in the satellite downlink path, the cloud induced attenuation also increases. The cloud attenuation showed a logarithmic dependence on reduction in insolation. In Fiji, a tropical island country, the maximum attenuation of Ku-band signals by cloud was ~ 11%.


2021 ◽  
Author(s):  
Xingou Xu ◽  
Ad Stoffelen

Abstract. Wind retrieval parameters, i.e., quality indicators and the 2DVAR analysis speeds, are explored with the aim to improve wind speed retrieval during rain for tropical regions. We apply the well-researched support vector machine (SVM) method in machine learning (ML) to solve this complex problem in a data-orientated regression. To guarantee the effectiveness of SVM, the inputs are extensively analysed to evaluate their appropriateness for this problem, before the results are produced. Subsequently, triple collocation shows that the similarity of the resolved Ku-band (OSCAT-2) wind speed in rain is better than the 2DVAR speed, with respect to the collocated C-band (ASCAT) speed, which is much less affected by rain. The comparisons between distributions and differences between data of rain-contaminated winds, corrected winds and good quality C-band winds, illustrate that the rain-distorted wind distributions become more nominal with SVM, hence eliminating rain-induced biases and error variance. Further confirmation is obtained from a case with synchronous Himawari-8 observation indicating rain (clouds) in the scene. Furthermore, the determination of simultaneous rain rate is attempted to retrieve both wind and rain. Although, additional observations or higher resolution may be required to better assess the accuracy of the wind and rain retrievals, the Machine Learning (ML) results demonstrate benefits of such methodology in geophysical retrieval and nowcasting applications.


2017 ◽  
Vol 66 (2) ◽  
pp. 897-911 ◽  
Author(s):  
Ali M. Al-Saegh ◽  
Aduwati Sali ◽  
Jit Singh Mandeep ◽  
Fernando Perez Fontan

2000 ◽  
Vol 36 (10) ◽  
pp. 894 ◽  
Author(s):  
K.I. Timothy ◽  
J.T. Ong ◽  
E.B.L. Choo

Sign in / Sign up

Export Citation Format

Share Document