Sea Surface Rainfall Detection and Intensity Retrieval Based on GNSS-Reflectometry Data from the CYGNSS Mission

2021 ◽  
pp. 1-1
Author(s):  
Jinwei Bu ◽  
Kegen Yu
Keyword(s):  
Sea Surface ◽  
Surface Rainfall ◽  
Gnss Reflectometry ◽  
Rainfall Detection

scholarly journals Sea surface topography retrieved from GNSS reflectometry phase data of the GEOHALO flight mission

2014 ◽  
Vol 41 (3) ◽  
pp. 954-960 ◽  
Author(s):  
A. M. Semmling ◽  
J. Beckheinrich ◽  
J. Wickert ◽  
G. Beyerle ◽  
S. Schön ◽  
...  
Keyword(s):  
Surface Topography ◽  
Sea Surface ◽  
Sea Surface Topography ◽  
Gnss Reflectometry ◽  
Phase Data

An Evaluation of the Proposed Mechanism of the Adaptive Infrared Iris Hypothesis Using TRMM VIRS and PR Measurements

2005 ◽  
Vol 18 (20) ◽  
pp. 4185-4194 ◽  
Author(s):  
Anita D. Rapp ◽  
Christian Kummerow ◽  
Wesley Berg ◽  
Brian Griffith
Keyword(s):  
Surface Temperature ◽  
Deep Convection ◽  
Cloud Amount ◽  
Tropical Rainfall Measuring Mission ◽  
Convective Cloud ◽  
Sea Surface ◽  
Central Pacific ◽  
Convective Clouds ◽  
Brightness Temperatures ◽  
Surface Rainfall

Abstract Significant controversy surrounds the adaptive infrared iris hypothesis put forth by Lindzen et al., whereby tropical anvil cirrus detrainment is hypothesized to decrease with increasing sea surface temperature (SST). This dependence would act as an iris, allowing more infrared radiation to escape into space and inhibiting changes in the surface temperature. This hypothesis assumes that increased precipitation efficiency in regions of higher sea surface temperatures will reduce cirrus detrainment. Tropical Rainfall Measuring Mission (TRMM) satellite measurements are used here to investigate the adaptive infrared iris hypothesis. Pixel-level Visible and Infrared Scanner (VIRS) 10.8-μm brightness temperature data and precipitation radar (PR) rain-rate data from TRMM are collocated and matched to determine individual convective cloud boundaries. Each cloudy pixel is then matched to the underlying SST. This study examines single- and multicore convective clouds separately to directly determine if a relationship exists between the size of convective clouds, their precipitation, and the underlying SSTs. In doing so, this study addresses some of the criticisms of the Lindzen et al. study by eliminating their more controversial method of relating bulk changes of cloud amount and SST across a large domain in the Tropics. The current analysis does not show any significant SST dependence of the ratio of cloud area to surface rainfall for deep convection in the tropical western and central Pacific. Results do, however, suggest that SST plays an important role in the ratio of cloud area and surface rainfall for warm rain processes. For clouds with brightness temperatures between 270 and 280 K, a net decrease in cloud area normalized by rainfall of 5% per degree SST was found.

scholarly journals TSUNAMI LEAD WAVE RECONSTRUCTION BASED ON NOISY SEA SURFACE HEIGHT MEASUREMENTS

2016 ◽  
Vol XLI-B1 ◽  
pp. 525-528 ◽  
Author(s):  
Kegen Yu
Keyword(s):  
Sea Surface Height ◽  
Sea Surface ◽  
Reconstruction Method ◽  
Mean Square ◽  
Wavelet Theory ◽  
Decomposition Stage ◽  
True Value ◽  
Gnss Reflectometry ◽  
Simulation Results ◽  
High Pass

This paper presents a Tsunami lead wave reconstruction method using noisy sea surface height (SSH) measurements such as observed by a satellite-carried GNSS reflectometry (GNSS-R) sensor. It is proposed to utilize wavelet theory to mitigate the strong noise in the GNSS-R based SSH measurements. Through extracting the noise components by high-pass filters at decomposition stage and shrinking the noise by thresholding prior to reconstruction, the noise is greatly reduced. Real Tsunami data based simulation results demonstrate that in presence of SSH measurement error of standard deviation 50 cm the accuracy in terms of root mean square error (RMSE) of the lead wave height (true value 145.5 cm) and wavelength (true value 592.0 km) estimation is 21.5 cm and 56.2 km, respectively. The results also show that the proposed wavelet based method considerably outperforms the Kalman filter based method on average. The results demonstrate that the proposed wave reconstruction approach has the potential for Tsunami detection and parameter estimation to assist in achieving reliable Tsunami warning.

scholarly journals Sea Surface Height Estimation with Multi-GNSS and Wavelet De-noising

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Fade Chen ◽  
Lilong Liu ◽  
Fei Guo
Keyword(s):  
Correlation Coefficient ◽  
Tide Gauge ◽  
Cost Effective ◽  
Sea Surface Height ◽  
Sea Surface ◽  
Observation Data ◽  
Estimation Errors ◽  
Remote Sensing Technique ◽  
Long Term Stability ◽  
Gnss Reflectometry

Abstract This paper presents a new sea surface height (SSH) estimation using GNSS reflectometry (GNSS-R). It is a cost-effective remote sensing technique and owns long-term stability besides high temporal and spatial resolution. Initial in-situ SSH estimates are first produced by using the SNR data of BDS (L1, L5, L7), GPS (L1, L2, L5), and GLONASS (L1, L2), of MAYG station, which is located in Mayotte, France near the Indian Ocean. The results of observation data over a period of seven days showed that the root mean square error (RMSE) of SSH estimation is about 32 cm and the correlation coefficient is about 0.83. The tidal waveform is reconstructed based on the initial SSH estimates by utilizing the wavelet de-noising technique. By comparing the tide gauge measurements with the reconstructed tidal waveform at SSH estimation instants, the SSH estimation errors can be obtained. The results demonstrate that the correlation coefficient and RMSE of the wavelet de-noising based SSH estimation is 0.95 and 19 cm, respectively. Compared with the initial estimation results, the correlation coefficient is improved by about 14.5%, while the RMSE is reduced by 40.6%.

scholarly journals TSUNAMI LEAD WAVE RECONSTRUCTION BASED ON NOISY SEA SURFACE HEIGHT MEASUREMENTS

2016 ◽  
Vol XLI-B1 ◽  
pp. 525-528
Author(s):  
Kegen Yu
Keyword(s):  
Sea Surface Height ◽  
Sea Surface ◽  
Reconstruction Method ◽  
Mean Square ◽  
Wavelet Theory ◽  
Decomposition Stage ◽  
True Value ◽  
Gnss Reflectometry ◽  
Simulation Results ◽  
High Pass

This paper presents a Tsunami lead wave reconstruction method using noisy sea surface height (SSH) measurements such as observed by a satellite-carried GNSS reflectometry (GNSS-R) sensor. It is proposed to utilize wavelet theory to mitigate the strong noise in the GNSS-R based SSH measurements. Through extracting the noise components by high-pass filters at decomposition stage and shrinking the noise by thresholding prior to reconstruction, the noise is greatly reduced. Real Tsunami data based simulation results demonstrate that in presence of SSH measurement error of standard deviation 50 cm the accuracy in terms of root mean square error (RMSE) of the lead wave height (true value 145.5 cm) and wavelength (true value 592.0 km) estimation is 21.5 cm and 56.2 km, respectively. The results also show that the proposed wavelet based method considerably outperforms the Kalman filter based method on average. The results demonstrate that the proposed wave reconstruction approach has the potential for Tsunami detection and parameter estimation to assist in achieving reliable Tsunami warning.

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