scholarly journals Tropical Cyclone Wind Speeds from WindSat, AMSR and SMAP: Algorithm Development and Testing

2021 ◽  
Vol 13 (9) ◽  
pp. 1641
Author(s):  
Thomas Meissner ◽  
Lucrezia Ricciardulli ◽  
Andrew Manaster
Keyword(s):  
Tropical Cyclone ◽  
Surface Wind ◽  
Ocean Surface ◽  
Algorithm Development ◽  
Wind Speeds ◽  
Ocean Surface Wind ◽  
Satellite Microwave ◽  
Microwave Radiometers

The measurement of ocean surface wind speeds in precipitation from satellite microwave radiometers is a challenging task. Rain attenuates the signal that is emitted from the ocean surface.

scholarly journals Evaluation of the Ocean Surface Wind Speed Change following the Super Typhoon from Space-Borne GNSS-Reflectometry

2020 ◽  
Vol 12 (12) ◽  
pp. 2034 ◽  
Author(s):  
Hongsu Liu ◽  
Shuanggen Jin ◽  
Qingyun Yan
Keyword(s):  
Wind Speed ◽  
Correlation Coefficient ◽  
Surface Wind ◽  
Ocean Surface ◽  
Surface Wind Speed ◽  
Wind Speeds ◽  
Mean Error ◽  
Super Typhoon ◽  
The Mean ◽  
Ocean Surface Wind

Ocean surface wind speed is an essential parameter for typhoon monitoring and forecasting. However, traditional satellite and buoy observations are difficult to monitor the typhoon due to high cost and low temporal-spatial resolution. With the development of spaceborne GNSS-R technology, the cyclone global navigation satellite system (CYGNSS) with eight satellites in low-earth orbit provides an opportunity to measure the ocean surface wind speed of typhoons. Though observations are made at the extremely efficient spatial and temporal resolution, its accuracy and reliability are unclear in an actual super typhoon case. In this study, the wind speed variations over the life cycle of the 2018 Typhoon Mangkhut from CYGNSS observations were evaluated and compared with European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis-5 (ERA-5). The results show that the overall root-mean-square error (RMSE) of CYGNSS versus ECMWF was 4.12 m/s, the mean error was 1.36 m/s, and the correlation coefficient was 0.96. For wind speeds lower and greater than 15 m/s, the RMSE of CYGNSS versus ECMWF were 1.02 and 4.36 m/s, the mean errors were 0.05 and 1.61 m/s, the correlation coefficients were 0.91 and 0.90, and the average relative errors were 9.8% and 11.6%, respectively. When the typhoon reached a strong typhoon or super typhoon, the RMSE of CYGNSS with respect to ERA-5 from ECMWF was 5.07 m/s; the mean error was 3.57 m/s; the correlation coefficient was 0.52 and the average relative error was 11.0%. The CYGNSS estimation had higher precision for wind speeds below 15 m/s, but degraded when the wind speed was above 15 m/s.

scholarly journals The use of reflected GPS signals to retrieve ocean surface wind speeds in tropical cyclones

Radio Science ◽  
2013 ◽  
Vol 48 (4) ◽  
pp. 371-387 ◽  
Author(s):  
Stephen J. Katzberg ◽  
Jason Dunion ◽  
George G. Ganoe
Keyword(s):  
Tropical Cyclones ◽  
Surface Wind ◽  
Ocean Surface ◽  
Wind Speeds ◽  
Ocean Surface Wind

Influence of Assimilating CYGNSS Ocean Surface Wind Data on Tropical Cyclone Analyses and Predictions

2020 ◽  
Author(s):  
Bachir Annane ◽  
Mark Leidner ◽  
Ross Hoffman ◽  
Feixiong Huang ◽  
James Garrisson
Keyword(s):  
Tropical Cyclones ◽  
Surface Wind ◽  
Ocean Surface ◽  
Convective Precipitation ◽  
Weather Research And Forecasting ◽  
Wind Speeds ◽  
Ocean Surface Winds ◽  
Ocean Surface Wind ◽  
The Impact ◽  
Hwrf Model

<div> <div><em>For the analysis and forecasting of tropical cyclones, the main benefits of data from the CYGNSS constellation of satellites are the increased revisit frequency compared with polar-orbiting satellites and the ability to provide ocean surface wind observations through convective precipitation. Consequently, CYGNSS delivers an improved capability to observe the structure and evolution of ocean surface winds in and around tropical cyclones. This study quantifies the impact of assimilating CYGNSS delay-Doppler maps, CYGNSS retrieved wind speeds and derived CYGNSS wind vectors on 6-hourly analyses and 5-day forecasts of developing tropical cyclones, using the 2019 version of NOAA's operational Hurricane Weather Research and Forecasting (HWRF) model.</em></div> </div>

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

2019 ◽  
Vol 46 (5) ◽  
pp. 2984-2992 ◽  
Author(s):  
Zhiqiang Cui ◽  
Zhaoxia Pu ◽  
Vijay Tallapragada ◽  
Robert Atlas ◽  
Christopher S. Ruf
Keyword(s):  
Numerical Simulations ◽  
Surface Wind ◽  
Ocean Surface ◽  
Impact Study ◽  
Wind Speeds ◽  
Ocean Surface Wind

A Systematic Comparison of QuikSCAT and SAR Ocean Surface Wind Speeds

2004 ◽  
Vol 42 (2) ◽  
pp. 283-291 ◽  
Author(s):  
F.M. Monaldo ◽  
D.R. Thompson ◽  
W.G. Pichel ◽  
P. Clemente-Colon
Keyword(s):  
Surface Wind ◽  
Ocean Surface ◽  
Systematic Comparison ◽  
Wind Speeds ◽  
Ocean Surface Wind

scholarly journals Higher Ocean Surface Wind Speeds During Marine Cold Air Outbreaks

2017 ◽  
Author(s):  
Erik W. Kolstad
Keyword(s):  
Large Scale ◽  
Weather Forecasting ◽  
Weather Prediction ◽  
Surface Wind ◽  
Ocean Surface ◽  
Strong Wind ◽  
Cold Air ◽  
Wind Speeds ◽  
Ocean Surface Wind ◽  
Cold Air Outbreaks

Marine cold air outbreaks (MCAOs) are large-scale phenomena in which cold air masses are advected over open ocean. It is well-known that these events are linked to the formation of polar lows and other mesoscale phenomena associated with high wind speeds, and that they therefore in some cases represent a hazard to maritime activities. However, it is still unknown whether MCAOs are generally conducive to higher wind speeds than normal. Here this is investigated by comparing the behaviour of ocean surface wind speeds during MCAOs in three atmospheric reanalysis products with different horizontal grid spacings, along with case studies using a convection-permitting numerical weather prediction model. The study regions are the Labrador Sea and the Greenland–Iceland–Norwegian (GIN) Seas, where MCAOs have been shown to be important for air–sea interaction and deep water formation. The main findings are: 1) Wind speeds during the most extreme MCAO events are stronger than normal and higher than wind speeds during less severe events; 2) The peak times of MCAO usually occur when baroclinic waves pass over the regions; and 3) Reanalyses with grid spacings of more than 50 km appear to underestimate winds driven by the large ocean–atmosphere energy fluxes during MCAOs. It is also shown that while the strong wind episodes during MCAOs generally last for just a few days, MCAOs can persist for up to 50 days. These findings demonstrate that it would be worthwhile to forecast MCAOs, and that it might be possible to do this beyond the standard weather forecasting range of up to 10 days.

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