A Preliminary Impact Study of CYGNSS Ocean Surface Wind Speeds on Numerical Simulations of Hurricanes

Cui, Zhiqiang; Pu, Zhaoxia; Tallapragada, Vijay; Atlas, Robert; Ruf, Christopher S.

doi:10.1029/2019gl082236

Cited by 32 publications

(21 citation statements)

References 24 publications

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“…In the past few decades, TC prediction has observed moderate improvements (Alley et al, ; DeMaria et al, ; Rappaport et al, ). These improvements have mainly resulted from better assimilation of high‐resolution remote sensing observations (e.g., Cui et al, ; Honda et al, ; McNally et al, ; Zhang et al, ; Zhang et al, ; Zhang & Weng, ), increased model resolution (e.g., Bender et al, ; Davis et al, ; Gopalakrishnan et al, ; Hazelton et al, ), and improved model physics (e.g., Gallus & Bresch, ; Gopalakrishnan et al, ; Bao et al, ; Chen, Zhang, et al, ).…”

Section: Introductionmentioning

confidence: 99%

Development of a Convection‐Permitting Air‐Sea‐Coupled Ensemble Data Assimilation System for Tropical Cyclone Prediction

Chen

Zhang

2019

J Adv Model Earth Syst

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A regional-scale fully coupled data assimilation (DA) system based on the ensemble Kalman filter is developed for a high-resolution coupled atmosphere-ocean model. Through the flow-dependent covariance both within and across the oceanic and atmospheric domains, the fully coupled DA system is capable of updating both atmospheric and oceanic state variables simultaneously by assimilating either atmospheric and/or oceanic observations. The potential impacts of oceanic observations, including sea-surface temperature, sea-surface height anomaly, and sea-surface current, in addition to the observation of the minimum surface pressure at the storm center (HPI), on tropical cyclone analysis and prediction are examined through observing system simulation experiments of Hurricane Florence (2018). Results show that assimilation of oceanic observations not only resulted in better analysis and forecast of the oceanic variables but also considerably reduced analysis and forecast errors in the atmospheric fields, including the intensity and structure of Florence. Compared to weakly coupled DA in which the analysis update is performed separately for the atmospheric and oceanic domains, fully coupled DA reduces the forecast errors of tropical cyclone track and intensity. Results show promise in potential further improvement in tropical cyclone prediction through assimilation of both atmospheric and oceanic observations using the ensemble-based fully coupled DA system.Plain Language Summary Air-sea interactions are critical to tropical cyclone (TC) development. However, oceanic state variables are still poorly initialized and are inconsistent with atmospheric initial fields in most operational coupled TC forecast models. In this study, the ensemble Kalman filter is used to develop a regional-scale air-sea fully coupled data assimilation (DA) system for coupled atmosphere-ocean prediction. The potential impacts of oceanic observations, including sea surface temperature, sea surface height anomaly, and sea surface current on TC analysis and prediction, are examined through observing system simulation experiments of Hurricane Florence (2018). Results show that, using the flow-dependent covariance within and across the oceanic and atmospheric domains, the fully coupled DA system is capable of updating both atmospheric and oceanic state variables simultaneously by assimilating either atmospheric and/or oceanic observations. The track, intensity, and sea surface height forecasts of Florence are improved by assimilating oceanic observations. When compared to weakly coupled DA, in which the analysis update is performed separately for the atmospheric and oceanic domains, fully coupled DA reduces the forecast errors of TC track and intensity. It shows great promise in potential further improvements in TC prediction through using the ensemble-based fully coupled DA system.

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Section: Introductionmentioning

confidence: 99%

Development of a Convection‐Permitting Air‐Sea‐Coupled Ensemble Data Assimilation System for Tropical Cyclone Prediction

Chen

Zhang

2019

J Adv Model Earth Syst

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“…Their results suggested that the simulated CYGNSS data would positively impact the forecasts of TCs in both track and intensity [35][36][37][38][39]. The assimilation of real CYGNSS observation was recently investigated by Cui et al [14]; the authors indicated the potential of CYGNSS data in improving the track, intensity, and structure of TCs. Unfortunately, due to data quality issues, Cui et al [14] were only able to assimilate CYGNSS v2.0 fully developed seas (FDS) data, which greatly underestimates the wind field within deep convection regions related to TCs.…”

Section: Introductionmentioning

confidence: 99%

“…Each CYGNSS observatory is capable of sampling 4 different specular reflection tracks at a frequency of 1 Hz, and ocean surface wind speed is retrieved along the specular tracks at a resolution of approximately 25 km. It is expected that the CYGNSS will be able to accurately monitor the rapidly changing wind of precipitating regions over the tropics (e.g., [13]), and to improve forecasting operations (e.g., [14]).…”

Section: Introductionmentioning

confidence: 99%

“…The assimilation of real CYGNSS observation was recently investigated by Cui et al [14]; the authors indicated the potential of CYGNSS data in improving the track, intensity, and structure of TCs. Unfortunately, due to data quality issues, Cui et al [14] were only able to assimilate CYGNSS v2.0 fully developed seas (FDS) data, which greatly underestimates the wind field within deep convection regions related to TCs. A recent study (Ruf et al [40]) indicated that, with a number of improvements to the data calibration, v2.1 is of much better quality than the previous version; this is especially true for the young seas/limited fetch (YSLF) product.…”

Section: Introductionmentioning

confidence: 99%

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A Study on Assimilation of CYGNSS Wind Speed Data for Tropical Convection during 2018 January MJO

Mecikalski

Lang

2020

Remote Sensing

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The National Aeronautics and Space Administration (NASA) Cyclone Global Navigation Satellite System (CYGNSS) mission was launched in December 2016. CYGNSS provides ocean surface wind speed retrieval along specular reflection tracks at an interval resolution of approximately 25 km. With a median revisit time of 2.8 h covering a ±35° latitude, CYGNSS can provide more frequent and accurate measurements of surface wind over the tropical oceans under heavy precipitation, especially within tropical cyclone cores and deep convection regions, than traditional scatterometers. In this study, CYGNSS v2.1 Level 2 wind speed data were assimilated into the Weather Research and Forecasting (WRF) model using the WRF Data Assimilation (WRFDA) system with hybrid 3- and 4-dimensional variational ensemble technology. Case studies were conducted to examine the impact of the CYGNSS data on forecasts of tropical cyclone (TC) Irving and a westerly wind burst (WWB) during the Madden–Julian oscillation (MJO) event over the Indian Ocean in early January 2018. The results indicate a positive impact of the CYGNSS data on the wind field. However, the impact from the CYGNSS data decreases rapidly within 4 h after data assimilation. Also, the influence of CYGNSS data only on precipitation forecast is found to be limited. The assimilation of CYGNSS data was further explored with an additional experiment in which CYGNSS data was combined with Global Precipitation Mission (GPM) Integrated Multi-satellitE Retrievals for GPM (IMERG) hourly precipitation and Advanced Scatterometer (ASCAT) wind vector and were assimilated into the WRF model. A significant positive impact was found on the tropical cyclone intensity and track forecasts. The short-term forecast of wind and precipitation fields were also improved for both TC Irving and the WWB event when the combined satellite data was assimilated.

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“…All of them agree on the potentiality of CYGNSS wind speed assimilation in improving both kinematic and thermodynamic features of TC forecasts. While this article is being written, only Cui et al [10] show a study based on real CYGNSS wind speed assimilation in forecast experiments. These experiments relate to hurricanes Harvey and Irma, which made landfall on U.S. coasts during summer 2017.…”

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confidence: 99%

Rationale of GNSS Reflected Delay–Doppler Map (DDM) Distortions Induced by Specular Point Inaccuracies

Grieco

Stoffelen

Portabella

2020

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Global navigation satellite system reflectometry (GNSS-R)-derived winds from the cyclone GNSS (CYGNSS) satellite constellation are expected to significantly improve weather forecasts in the tropical region. Delay-Doppler maps (DDMs) acquired by the TechDemosat-1 (TDS-1) GNSS-R satellite mission suffer from distortions that are highly correlated to on-board specular point estimation inaccuracies. Such distortions may affect wind retrievals, especially when multilook approaches aiming at exploiting the ambiguity-free area of the DDM are applied. This article demonstrates: that CYGNSS DDMs are also affected by such distortions; the rationale of DDM shape asymmetries induced by specular point location inaccuracies; and a simple strategy for reducing such induced distortions. Two different datasets have been used, consisting of both regular and raw intermediate frequency CYGNSS measurements. The results show that, similar to TDS-1, the CYGNSS DDM distortions are correlated to specular point location inaccuracies. Furthermore, such inaccuracies are significantly reduced if more accurate specular point related parameters are used to recompress the raw GNSS-R echo, highlighting some sampling issues that are common to both TDS-1 and CYGNSS missions. These results suggest that multilook wind retrieval approaches aiming at exploiting also the peripheral parts of the DDM may be seriously compromised by such distortions. The latter may be substantially reduced by oversampling the outcoming DDM and by a posteriori choosing the proper DDM subsample. For future upcoming GNSS-R missions, it is strongly recommended to store the raw data for eventual reprocessing in case of miscalibration or processing issues such as those shown in this article.Index Terms-Delay-Doppler map (DDM) distortions, global navigation satellite system (GNSS), reflectometry.

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A Preliminary Impact Study of CYGNSS Ocean Surface Wind Speeds on Numerical Simulations of Hurricanes

Cited by 32 publications

References 24 publications

Development of a Convection‐Permitting Air‐Sea‐Coupled Ensemble Data Assimilation System for Tropical Cyclone Prediction

Development of a Convection‐Permitting Air‐Sea‐Coupled Ensemble Data Assimilation System for Tropical Cyclone Prediction

A Study on Assimilation of CYGNSS Wind Speed Data for Tropical Convection during 2018 January MJO

Rationale of GNSS Reflected Delay–Doppler Map (DDM) Distortions Induced by Specular Point Inaccuracies

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