Retrieval of sea surface barometric pressure under clear sky by passive microwave measurements

Author(s):  
Zijin Zhang ◽  
Xiaolong Dong
2005 ◽  
Author(s):  
G.D. Sandlin ◽  
L.A. Rose ◽  
G.L. Geernaert ◽  
J.P. Hollinger ◽  
F.A. Hansen

2020 ◽  
Vol 236 ◽  
pp. 111485 ◽  
Author(s):  
Emy Alerskans ◽  
Jacob L. Høyer ◽  
Chelle L. Gentemann ◽  
Leif Toudal Pedersen ◽  
Pia Nielsen-Englyst ◽  
...  

2022 ◽  
Vol 14 (2) ◽  
pp. 276
Author(s):  
Qiurui He ◽  
Zhenzhan Wang ◽  
Jiaoyang Li

Both the Microwave Humidity and Temperature Sounder (MWHTS) and the Microwave Temperature Sounder-II (MWTS-II) operate on the Fengyun-3 (FY-3) satellite platform, which provides an opportunity to retrieve the sea surface barometric pressure (SSP) with high accuracy by fusing the observations from the 60 GHz, 118.75 GHz, and 183.31 GHz channels. The theory of retrieving SSP using passive microwave observations is analyzed, and the sensitivity test experiments of MWHTS and MWTS-II to SSP as well as the test experiments of the contributions of MWHTS and MWTS-II to SSP retrieval are carried out. The theoretical channel combination is established based on the theoretical analysis, and the SSP retrieval experiment is carried out based on the Deep Neural Network (DNN) for the theoretical channel combination. The experimental results show that the retrieval accuracy of SSP using the theoretical channel combination is higher than that of MWHTS or MWTS-II. In addition, based on the test results of the contributions of MWHTS and MWTS-II to the retrieval SSP, the optimal theoretical channel combination can be built, and can further improve the retrieval accuracy of SSP from the theoretical channel combination.


2021 ◽  
Vol 12 (3) ◽  
pp. 46-47
Author(s):  
Nikita Saxena

Space-borne satellite radiometers measure Sea Surface Temperature (SST), which is pivotal to studies of air-sea interactions and ocean features. Under clear sky conditions, high resolution measurements are obtainable. But under cloudy conditions, data analysis is constrained to the available low resolution measurements. We assess the efficiency of Deep Learning (DL) architectures, particularly Convolutional Neural Networks (CNN) to downscale oceanographic data from low spatial resolution (SR) to high SR. With a focus on SST Fields of Bay of Bengal, this study proves that Very Deep Super Resolution CNN can successfully reconstruct SST observations from 15 km SR to 5km SR, and 5km SR to 1km SR. This outcome calls attention to the significance of DL models explicitly trained for the reconstruction of high SR SST fields by using low SR data. Inference on DL models can act as a substitute to the existing computationally expensive downscaling technique: Dynamical Downsampling. The complete code is available on this Github Repository.


Sign in / Sign up

Export Citation Format

Share Document