Storm surge prediction with cygnss winds

Warnock, A.; Ruf, Christopher S.; Morris, Mary

doi:10.1109/igarss.2017.8127624

Cited by 3 publications

(2 citation statements)

References 11 publications

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“…To further examine the data assimilation impact on the simulation of diurnal precipitation over the MC region, we conducted additional data assimilation with NASA research satellite CYGNSS data. CYGNSS was launched to measure ocean surface winds between approximately 38 • N and 38 • S latitudes ( [27,40,41] see sample CYGNSS data in Figure 2b). It provided a relatively frequent revisit time and was able to monitor the rapidly evolving wind over the MC region [36].…”

Section: Impact Of Cygnss Wind Datamentioning

confidence: 99%

The Use of Regional Data Assimilation to Improve Numerical Simulations of Diurnal Characteristics of Precipitation during an Active Madden–Julian Oscillation Event over the Maritime Continent

Cui

2023

Remote Sensing

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This study examines the impact of regional data assimilation on diurnal characteristics of precipitation and winds over the Maritime Continent (MC) using a set of cloud-permitting-scale (~3 km) numerical simulations with the mesoscale community Weather Research and Forecasting (WRF) model and the NCEP Gridpoint Statistical Interpolation (GSI)-based ensemble-3DVAR hybrid data assimilation system. Numerical experiments focus on January 2018, when a well-defined, active Madden–Julian Oscillation (MJO) propagated through the MC region. Available conventional and satellite data are assimilated. Results show that simulated precipitation with data assimilation generally agrees better with satellite-derived rainfall than the control simulation without data assimilation. Simulations with data assimilation also reproduce the diurnal cycle of precipitation better, especially for the timing of the precipitation peak. Data assimilation modulates the overstrong (overweak) diurnal forcing over the land (ocean) in the control simulation. The vertical phase shift of the thermodynamic environment, associated with the timing of vertical motion transition along with low-level water vapor supplies, results in maximum precipitation occurring later, especially over land. To further demonstrate the impact of data assimilation, an additional experiment assimilates NASA Cyclone Global Navigation Satellite System (CYGNSS)-derived ocean surface winds. The results indicate that the assimilation of CYGNSS data exhibits an evident impact on the diurnal variation of surface variables and a similar shift in the diurnal cycle of precipitation. Overall, this study highlights the importance of regional data assimilation in improving the representation of precipitation over the MC, paving the way for a better understanding of the interactions of local diurnal convective precipitation cycles with MJO.

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Section: Impact Of Cygnss Wind Datamentioning

confidence: 99%

The Use of Regional Data Assimilation to Improve Numerical Simulations of Diurnal Characteristics of Precipitation during an Active Madden–Julian Oscillation Event over the Maritime Continent

Cui

2023

Remote Sensing

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“…In addition, the satellite mission is also committed to GNSS radio occultation (GNSS-RO)related applications. It is worth mentioning that although the main objective of the above spaceborne GNSS-R tasks is to monitor ocean wind speed, these satellite tasks can also be used for sea surface height retrieval [8,9], sea ice detection [10,11], sea ice concentration (SIC) retrieval [12,13], sea ice thickness retrieval [14], tsunami detection [15], storm surge monitoring [16,17], wave height retrieval [18][19][20][21][22][23][24], rainfall detection [25,26], rainfall intensity retrieval [27,28], soil moisture retrieval [29] and biomass retrieval [30], etc.…”

Section: Introductionmentioning

confidence: 99%

GloWS-Net: A Deep Learning Framework for Retrieving Global Sea Surface Wind Speed Using Spaceborne GNSS-R Data

Zuo

et al. 2023

Remote Sensing

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Spaceborne Global Navigation Satellite System Reflectometry (GNSS-R) is a new remote sensing technology that uses GNSS signals reflected from the Earth’s surface to estimate geophysical parameters. Because of its unique advantages such as high temporal and spatial resolutions, low observation cost, wide coverage and all-weather operation, it has been widely used in land and ocean remote sensing fields. Ocean wind monitoring is the main objective of the recently launched Cyclone GNSS (CYGNSS). In previous studies, wind speed was usually retrieved using features extracted from delay-Doppler maps (DDMs) and empirical geophysical model functions (GMFs). However, it is a challenge to employ the GMF method if using multiple sea state parameters as model input. Therefore, in this article, we propose an improved deep learning network framework to retrieve global sea surface wind speed using spaceborne GNSS-R data, named GloWS-Net. GloWS-Net considers the fusion of auxiliary information including ocean swell significant wave height (SWH), sea surface rainfall and wave direction to build an end-to-end wind speed retrieval model. In order to verify the improvement of the proposed model, ERA5 and Cross-Calibrated Multi-Platform (CCMP) wind data were used as reference for extensive testing to evaluate the wind speed retrieval performance of the GloWS-Net model and previous models (i.e., GMF, fully connected network (FCN) and convolutional neural network (CNN)). The results show that, when using ERA5 winds as ground truth, the root mean square error (RMSE) of the proposed GloWS-Net model is 23.98% better than that of the MVE method. Although the GloWS-Net model and the FCN model have similar RMSE (1.92 m/s), the mean absolute percentage error (MAPE) of the former is improved by 16.56%; when using CCMP winds as ground truth, the RMSE of the proposed GloWS-Net model is 2.16 m/s, which is 20.27% better than the MVE method. Compared with the FCN model, the MAPE is improved by 17.75%. Meanwhile, the GloWS-Net outperforms the FCN, traditional CNN, modified CNN (MCNN) and CyGNSSnet models in global wind speed retrieval especially at high wind speeds.

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Improving GNSS-R Sea Surface Wind Speed Retrieval from FY-3E Satellite Using Multi-task Learning and Physical Information

Zhou,

Duan,

Ren

2023

Neural Information Processing

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Storm surge prediction with cygnss winds

Cited by 3 publications

References 11 publications

The Use of Regional Data Assimilation to Improve Numerical Simulations of Diurnal Characteristics of Precipitation during an Active Madden–Julian Oscillation Event over the Maritime Continent

The Use of Regional Data Assimilation to Improve Numerical Simulations of Diurnal Characteristics of Precipitation during an Active Madden–Julian Oscillation Event over the Maritime Continent

GloWS-Net: A Deep Learning Framework for Retrieving Global Sea Surface Wind Speed Using Spaceborne GNSS-R Data

Improving GNSS-R Sea Surface Wind Speed Retrieval from FY-3E Satellite Using Multi-task Learning and Physical Information

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