Further Improvement of SFMR Surface Wind Speeds in Heavy Precipitation

The Stepped Frequency Microwave Radiometer (SFMR) is useful for retrieving surface wind speeds and rain rates within tropical cyclones, but during the past several Atlantic hurricane seasons, it has become more apparent that the wind speed in the lower wind regimes (i.e. tropical depression and weak tropical storm) are biased high in the presence of moderate to high rain rates. With more emphasis of monitoring these weaker systems with the NOAA WP-3D and Air Force Reserve C-130J flights in recent years, being able to provide better surface wind speeds in these situations is important for operational intensity estimates. Recently, a bias correction model was developed using a database of GPS dropwindsonde wind speeds and this correction was implemented in-house at the National Hurricane Center. There are still several issues that need addressing in relation to providing a better algorithm for estimating surface wind speeds in moderate to heavy precipitation.

In this work, we go several steps further by looking into the current algorithm performance, addressing issues with surface emissivity and with absorption in the rain column. Firstly, an updated bias correction model is provided to include the 2012 Atlantic hurricane season data (the previous version only included data up to 2011). This correction did not change much from the previous version and still produces the largest corrections in weak winds and heavy rain conditions. Examination of the current operational algorithm accuracy is then addressed by verifying the surface emissivity portion of the algorithm in rain-free conditions and by verifying the rain-absorption model through eliminating the effect of rain on the brightness temperature. Surface emissivity values were recalculated using only data that had less than 2 mm/hr SFMR rain rate and a new wind-emissivity model was produced for rain-free conditions. To evaluate the absorption due to rain, vertical profiles of NOAA P-3 Tail Doppler radar (TA) data are used in conjunction with Precipitation Imaging Probe (PIP) rain rates to produce probability density functions and to produce a new rain-absorption model for the algorithm. The new rain algorithm expands the range of attainable rain rates to be more consistent with other estimates from airborne instrumentation. Additionally, comparisons of the algorithm using the combined model updates are performed against the operational algorithm and for winds speeds with the bias correction applied. Results indicate that the updated algorithm provides an improvement in the surface wind speed estimation in moderate to heavy precipitation while continuing to produce similar wind speeds to the operational version at higher wind regimes.