# Tropical Cyclone Winds from WindSat, AMSR2, and SMAP: Comparison with the HWRF Model
## 5 Discussion and Conclusions  
In this study, we have focused on assessing winds in tropical cyclones from the SMAP, WindSat, and AMSR2 radiometers by comparing them with the HWRF model. Because  
Figure 15: Biases (solid lines) and standard deviations (dashed lines) between the satellite and HWRF winds in the Atlantic Ocean for the following rain regimes: light rain (brown curves; 0–4 mm/h), moderate rain (green curves; 4–8 mm/h), and heavy rain (blue curves; >8 mm/h) for each of the three sensors: AMSR2 (**a**), WindSat (**b**), and SMAP (**c**). As with Figures 13 and 14, winds were binned vs. average satellite/HWRF winds. Winds were binned in a similar manner to those shown in Figures 13 and 14 to ensure sufficient population. The black dashed line is the zero bias line. SMAP is an L-band (1.4 GHz) radiometer, it has the unique ability to retrieve winds in tropical cyclones of up 70 m/s with minimal interference due to rain. Since WindSat and AMSR2 operate at slightly higher frequencies than SMAP, their signals are subject to attenuation by precipitation, which is prevalent in tropical cyclones. However, by using appropriate linear combinations between the WindSat and AMSR2 C- and X-band channels, a TC-wind algorithm was developed that allows these two sensors to measure winds in TCs, even in heavy precipitation. This TC wind algorithm has been trained with SMAP winds. This new algorithm has allowed for the creation of new \"TC-winds' products, which were specifically developed for TC conditions, and consist of winds in tropical cyclones as observed by AMSR2 and WindSat, as well as SMAP, all processed and distributed in near-real time with the exception of WindSat, which ceased operation in October 2020. For our study, these TC-winds were compared against the high-resolution HWRF 0-h analyses. We have shown that care must be taken when comparing the satellite data to a high-resolution model such as HWRF due to spatial scale differences between the two. Before any direct comparisons can be made, the HWRF winds need to be resampled to the spatial resolution of the satellite observations. This is achieved by calculating the spatial average of the HWRF winds weighted by the approximate antenna satellite gain pattern. For practical purposes, one can use a radially symmetric Gaussian weighting function, whose half-power width is the average of the antenna footprint diameters. We also noted that, while not as precise as the Gaussian weighting method, using a 25 km drop-in-the-bucket box average resampling can also provide an acceptable approximation of the antenna pattern of the main beam, for those looking for a slightly quicker way to repeat these types of analyses. It was found that, for most of the storms considered in this study, the HWRF winds resampled using the Gaussian weighting method were approximately equivalent to those seen by the satellites in regards to size, shape, storm features, and maximum intensity. After the spatial resampling, the HWRF winds surrounding a given satellite pass need to be interpolated to the time of the satellite observation. This time interpolation occasionally produces unphysical features (e.g., a storm with a double eye). In order to account for this, the storm centers of the HWRF winds surrounding a given satellite pass can be shifted so they are aligned with the storm center as seen by the satellite. Aligning the storm centers generally improves comparisons between the satellites and models (e.g., storm structure), particularly in cases where a more well-organized storm is moving rapidly between the two analysis times. However, the use of this shifting method tends to have a much smaller effect on overall statistics, especially when many storm scenes are considered. Ultimately, one needs to look at each individual storm case and judge if a simple time interpolation is sufficient, or if the more cumbersome shifting methodology needs to be applied.