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Haiyang-2 scatterometers (HY-2A/B/C/D) have limitations in high wind speed retrieval due to the complexity of the remote sensing mechanism and the influence of rainfall on the radar cross section under the conditions of tropical cyclones. In this study, we focus on the evaluation of Chinese scatterometer operational wind products from HY-2B/C/D over the period from July 2019 to December 2021. HY-2B/C/D scatterometer wind products are collocated with SMAP (Soil Moisture Active Passive) L-band radiometer remotely sensed measurements. The results show that the underestimation of high wind speed occurs in the HY-2B/C/D wind speed products. The machine learning algorithms are explored to improve this underestimation issue, including the back propagation neural network (BP-NN), K-nearest neighbor (KNN), support vector machine (SVM), decision tree (DT), random forest (RF), and Bayesian ridge (BR) regression algorithms. Comparisons show that the BP-NN algorithm shows the best performance with a small RMSE (root-mean-square error) of 3.40 m/s, and high correlation coefficient of 0.88, demonstrating an improvement of 20.4% in RMSE (root-mean-square error) compared with the HY-2B/C/D wind speed products. In addition, the revised high winds are in good agreement with the ground truth measurements from the SFMR (Stepped Frequency Microwave Radiometer), which are useful for tropical cyclone disaster prevention and mitigation and are of vital importance in the numerical simulation of storm surges. [ABSTRACT FROM AUTHOR]

In 2021, a novel NOAA-Saildrone project deployed five uncrewed surface vehicle Saildrones (SDs) to monitor regions of the Atlantic Ocean and Caribbean Sea frequented by tropical cyclones. One of the SDs, SD-1045, crossed Hurricane Sam (Category 4) on September 30, providing the first-ever surface-ocean videos of conditions in the core of a major hurricane and reporting near-surface winds as high as 40 m/s. Here, we present a comprehensive analysis and interpretation of the Saildrone ocean surface wind measurements in Hurricane Sam, using the following datasets for direct and indirect comparisons: an NDBC buoy in the path of the storm, radiometer tropical cyclone (TC) winds from SMAP and AMSR2, wind retrievals from the ASCAT scatterometers and SAR (RadarSat2), and HWRF model winds. The SD winds show excellent consistency with the satellite observations and a remarkable ability to detect the strength of the winds at the SD location. We use the HWRF model and satellite data to perform cross-comparisons of the SD with the buoy, which sampled different relative locations within the storm. Finally, we review the collective consistency among these measurements by describing the uncertainty of each wind dataset and discussing potential sources of systematic errors, such as the impact of extreme conditions on the SD measurements and uncertainties in the methodology. [ABSTRACT FROM AUTHOR]

The study intends to examine the impact of assimilation of ocean surface winds on the simulation of the tropical cyclones Ockhi, Mekunu, and Luban formed over the north Indian Oceans. We have conducted two assimilation experiments, cyclic control (CyclicCntl) and Hybrid, using the Weather Research and Forecast model. The CyclicCntl experiment and Hybrid experiment are the analysis generated without and with adding ocean surface winds, respectively. The ocean surface winds from Ocean Scatterometer, Advanced Scatterometer, and Windsat are assimilated using the ensemble three‐dimensional variational assimilation technique. The analysis generated by Hybrid and CyclicCntl is compared with the ocean surface winds observations from buoys, and it is found that the assimilation of ocean surface winds reduces the root mean square error between observation and analysis for zonal and meridional winds by 2–3 m s−1 (42%) compared to CyclicCntl simulations. The comparison with the International Best Track Archive for Climate Stewardship locations of cyclones derived from the analyses indicates that track error is maximum in genesis and landfall stage for both the Hybrid and CyclicCntl experiments, and the average track error is less than 80 km for the three cyclones. In a Hybrid experiment, during the intensifications from deep depression to the very severe cyclonic storm, the wind speed enhancement is dominant over the southeast and northwest sectors of the eyewall regions and subsequently in the rainband region. Thus, the assimilation of ocean surface winds from the three sensors improves the prediction of intensity and the evolution of tropical cyclones in general. Plain Language Summary: The study presents the impact of assimilation of ocean surface winds on the analysis of tropical cyclones (TCs) formed over the north Indian Ocean viz. Ockhi, Mekunu, and Luban. Ocean surface winds transfer heat, moisture, and momentum between ocean and atmosphere, and it is one of the essential parameters for TC evolution and movement. The ocean surface winds data are acquired from the three sensors viz. Advanced Scatterometer, Ocean Scatterometer, and Windsat. We have carried out two experiments with the Weather Research and Forecast (WRF) model. In the first experiment, cyclic control (CyclicCntl), generates analysis at each 6‐h interval without any data assimilation, while the Hybrid experiment generates a 6‐hourly analysis with data assimilation. The ocean surface winds retrieved from the above‐mentioned sensors are assimilated in the WRF model. The track and intensity generated using both the analyses are compared with International Best Track Archive for Climate Stewardship observations. The average track error is less than 80 km for all three cyclones. The intensity simulation is a challenging task in numerical prediction. The intensity simulation is better in the Hybrid experiment compared to CyclicCntl. During the intensifications from deep depression to the very severe cyclonic storm, the wind speed enhancement is dominant over the southeast and northwest sectors of the eyewall regions. Key Points: The assimilation of ocean surface winds reduces the root mean square error in zonal and meridional winds between analysis and observation by 2–3 m s−1 (42%)Assimilation of ocean surface winds improves the intensity simulation of the tropical cycloneDuring the intensification of the cyclone, the wind speed enhancement is dominant over the southeast and northwest sectors of the eyewall regions [ABSTRACT FROM AUTHOR]

The NASA CYGNSS satellite constellation measures ocean surface winds using the existing network of the Global Navigation Satellite System (GNSS) and was designed for measurements in tropical cyclones (TCs). Here, we focus on using a consistent methodology to validate multiple CYGNSS wind data records currently available to the public, some focusing on low to moderate wind speeds, others for high winds, a storm-centric product for TC analyses, and a wind dataset from NOAA that applies a track-wise bias correction. Our goal is to document their differences and provide guidance to users. The assessment of CYGNSS winds (2017–2020) is performed here at global scales and for all wind regimes, with particular focus on TCs, using measurements from radiometers that are specifically developed for high winds: SMAP, WindSat, and AMSR2 TC-winds. The CYGNSS high-wind products display significant biases in TCs and very large uncertainties. Similar biases and large uncertainties were found with the storm-centric wind product. On the other hand, the NOAA winds show promising skill in TCs, approaching a level suitable for tropical meteorology studies. At the global level, the NOAA winds are overall unbiased at wind regimes from 0–30 m/s and were selected for a test assimilation into a global wind analysis, CCMP, also presented here.

The Indian Space Research Organisation launched the Oceansat-2 scatterometer (OSCAT) for atmospheric and oceanographic applications. In this paper, a case study has been performed to assess the impact of OSCAT-retrieved wind vectors on the simulation of tropical cyclone Phet over the Arabian Sea. Three-dimensional variational data assimilation of the Weather Research and Forecasting model is used for this purpose. In addition to OSCAT winds, wind speed and precipitable water derived from the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) are also used for assimilation to evaluate the impact of scatterometer and radiometer data on tropical cyclone prediction. Results show that an \sim60-km track error is observed in control and TMI experiments when compared with Joint Typhoon Warning Center observed cyclone center at 1800 UTC 01 June 2010. An approximately 40-km track error is determined in the initial center position of OSCAT experiments. The mean track error forecast is less in OSCAT experiments (\sim80 km) in comparison with TMI experiments (\sim 110 km). Only OSCAT data experiments are able to predict the track of the cyclone toward the Oman coast. Assimilation of scatterometer wind direction improves the track forecast; but it degrades the forecast of the intensity, maximum magnitude, and evolution of the cyclone. None of these experiments are able to capture the observed minimum sea level pressure (964 hPa at 1200 UTC 02 June 2010) accurately. TMI experiments are slightly better than OSCAT experiments in capturing the intensity of cyclone Phet, whereas wind direction from OSCAT improves the track forecast of the cyclone. [ABSTRACT FROM AUTHOR]

The NASA CYGNSS satellite constellation measures ocean surface winds using the existing network of the Global Navigation Satellite System (GNSS) and was designed for measurements in tropical cyclones (TCs). Here, we focus on using a consistent methodology to validate multiple CYGNSS wind data records currently available to the public, some focusing on low to moderate wind speeds, others for high winds, a storm-centric product for TC analyses, and a wind dataset from NOAA that applies a track-wise bias correction. Our goal is to document their differences and provide guidance to users. The assessment of CYGNSS winds (2017–2020) is performed here at global scales and for all wind regimes, with particular focus on TCs, using measurements from radiometers that are specifically developed for high winds: SMAP, WindSat, and AMSR2 TC-winds. The CYGNSS high-wind products display significant biases in TCs and very large uncertainties. Similar biases and large uncertainties were found with the storm-centric wind product. On the other hand, the NOAA winds show promising skill in TCs, approaching a level suitable for tropical meteorology studies. At the global level, the NOAA winds are overall unbiased at wind regimes from 0–30 m/s and were selected for a test assimilation into a global wind analysis, CCMP, also presented here. [ABSTRACT FROM AUTHOR]