The C-band Advanced Scatterometer (ASCAT) has the advantages of good spatial-temporal coverage and low sensitivity to nonextreme rainfall. While the perceived wind speed underestimation issues of ASCAT sea surface wind (SSW) retrievals can be mitigated using appropriate high wind speed scalings, the low spatial resolution in ASCAT remains a challenge, which implicitly leads to the blurring effect in tropical cyclone (TC) inner-core regions. To overcome this issue, the 2-D variational (2DVAR) analysis method is modified from 12.5 to 1.8 km grid size, where the latter allows super-resolution (SR) spatial structure functions, empirically trained on synthetic aperture radar (SAR) data, to enhance TC structure retrievals of ASCAT. The method first employs triple collocation analysis to estimate observation and background errors under different TC categories. After that, the relevant spatial parameters during the data assimilation process are determined and linked to TC features. These analyses contribute to constructing SAR-learned structure functions, complementing ASCAT-observed TC characteristics, and then achieving TC vortex reconstruction and wind field SR. Validation studies demonstrate that the SR products possess the correct small-scale properties of TC inner-core structures, such as radius of maximum wind (RMW), TC asymmetry, and wind variability. Notably, the proposed SR approach can achieve a significant reduction in error standard deviations (SDs) of ($l,t$) wind components (by 37% and 33%, respectively) when compared to spatial interpolated results. The encouraging results suggest the feasibility of the method in enhancing the abundant but lower resolution scatterometer winds, potentially contributing to future advancements in TC advisories.

Accurate knowledge of Tropical Cyclone (TC) inner-core structures contributes to a better understanding of TC thermodynamics. The Advanced Scatterometer (ASCAT) can measure ocean surface winds at a good spatial-temporal coverage, but the TC inner structures are largely blurred by its 20-km footprint. In this study, the Two-Dimensional Variational (2DVAR) scheme is considered to enhance the TC inner-core structure, by "learning" background spatial error covariances from high-resolution Synthetic Aperture Radar (SAR) winds. We find that the length scales of the stream function are close to the radii of maximum wind speeds and length scales of the velocity potential are dependent on TC asymmetry scales. All these parameters can be provided by ASCAT data. Experimental results prove that the proposed method can enhance TC inner-core structures and thus achieve super-resolution. The promising results contribute to our long-term goal of developing a general method for providing TC inner-core structures from all scatterometer winds available for nowcasting, allowing temporal monitoring of TC winds.

Accurate retrieval of ocean surface wind speed (OSWS) has a vital impact on maritime transportation planning and extreme weather forecast. Current models leveraging deep-learning (DL) techniques have demonstrated considerable potential for satellite remote-sensing wind retrieval. However, these models tend to focus on synchronizing the retrieved wind speeds with the label, neglecting the inherent absolute error (AE) embedded within the label and thus resulting in retrieval errors. To mitigate the disruptive impact of AE on retrieval accuracy, we introduce a novel network called TCNet, which retrieves observations of cyclone global navigation satellite system (CYGNSS) as OSWS. The network constructs an AE module (AEM), guided by triple collocation (TC) method for improved accuracy in real-time wind retrieval by calculating the AE as loss value. These calculations guide the network training process, thereby enhancing retrieval accuracy. Meanwhile, the wind speed dataset imbalance and inherent averaging characteristics of networks frequently result in wind speed uncertaintines in extremes. Notably, this occurs as a gross underestimation of high-speed winds. Therefore, TCNet incorporates an adaptive penalty module (APM) to solve this problem. By assigning higher penalty factors to high-speed winds, the sensitivity of network to its retrieval is improved. Experimentally, the APM in TCNet exhibited a remarkable reduction of AE in high-speed wind retrieval and mitigates the understating of high-speed scenarios while maintaining an overall error that is not significantly increased. Importantly, TCNet demonstrated notable resistance to noise and portrayed excellent generalizability, providing fresh insights into weather forecasting, climate research, and other marine applications.

C-band scatterometer winds have been adjusted for extreme conditions and in this research extension to Ku-band scatterometers is investigated. With rain rates from the Global Precipitation Measurement mission collocated to the Ku-band scatterometer observations to identify and exclude rain contamination of winds, calibration of the Ku-band observations can be done. Using high-wind cases extracted from collocated C- and Ku-band observations, we develop a calibration model and extend the Ku-band winds to 35 m/s. Validation is obtained from the set not included in the model derivation, indicating a speed error less than 10% for wind speed larger than 30m/s. The modified speed is consistent with the Step Frequency Microwave Radiometer measurements, when collocated with another Ku-band scatterometer. A comparison for the Tropical Cyclone Manyi in 2018 shows the adjustedd wind fits better with the best-track information provided by the Chinese Meteorological Administration, while more details are revealed. Results can be improved after obtaining more collocations with the dual-frequency scatterometer "WindRad" onboard the FY-3E satellite. A method for wind direction enhancement in extreme conditions is also discussed.

Accurate knowledge of wind directions plays a critical role in atmospheric dynamics exploration, numerical weather prediction and Tropical Cyclone (TC) research. This study proposes a new method for wind direction retrieval from TC Synthetic Aperture Radar (SAR) images. Unlike conventional approaches, which estimate wind directions from singlepolarization imagery, the method utilizes dual-polarized (VV and VH) signals to obtain continuous wind directions across moderate and extreme wind speed regimes. The technique is developed based on the Histogram of Oriented Gradient descriptor and the Hann window function. In addition, the neighbouring information is introduced to alleviate sharp directional variations. As case studies, the wind directions in TCs Karl and Maria are derived and subsequently verified by simultaneous dropsonde and ASCAT (ambiguity-removed) measurements. The encouraging results suggest that the wind direction retrieval method based on dual-polarization SAR imagery can be useful in extracting wind direction and contribute to further exploitation of SAR images in TC studies.

How strong does the wind blow in a hurricane? This proves a question that is difficult to answer, but has far-reaching consequences for satellite meteorology, weather forecasting and hurricane advisories. In the EUMETSAT CHEFS project, KNMI, ICM and IFREMER worked with international colleagues to address this question to prepare for the EPS-SG SCA scatterometer, which introduces C-band cross-polarization measurements to improve the detection of hurricane-force winds. To calibrate the diverse available satellite, airplane and model winds, in-situ wind speed references are needed. Unfortunately, these prove rather inconsistent in the wind speed range of 15 to 25 m/s, casting doubt on the higher winds too. Should we trust dropsondes at high and extreme winds or perhaps put more confidence inthe moored buoy references? This dilemma will be presented to initiate a discussion with the international community gathered at IGARSS ‘21.