The Impact of Targeted Dropwindsonde Observations on Tropical Cyclone Intensity Forecasts of Four Weak Systems during PREDICT

• Published in: Monthly weather review Vol. 142; no. 8; pp. 2860 - 2878
• Main Author: Torn, Ryan D
• Format: Journal Article
• Language: English
• Published: Washington American Meteorological Society 01.08.2014
• Subjects: Aircraft; Climatology; Cycles; Cyclones; Data assimilation; Ensemble forecasting; Error detection; Hurricanes; Kalman filters; Mathematical models; Meridional wind; Meteorology; Radiosondes; Sensitivity; Standard deviation; Studies; Tropical cyclone intensities; Tropical cyclones; Tropical environments; Tropics; Vortices; Weather; Weather forecasting; Wind; Wind observation; Wind shear
• ISSN: 0027-0644; 1520-0493
• DOI: 10.1175/MWR-D-13-00284.1

The value of assimilating targeted dropwindsonde observations meant to improve tropical cyclone intensity forecasts is evaluated using data collected during the Pre-Depression Investigation of Cloud-Systems in the Tropics (PREDICT) field project and a cycling ensemble Kalman filter. For each of the four initialization times studied, four different sets of Weather Research and Forecasting Model (WRF) ensemble forecasts are produced: one without any dropwindsonde data, one with all dropwindsonde data assimilated, one where a small subset of targeted dropwindsondes are identified using the ensemble-based sensitivity method, and a set of randomly selected dropwindsondes. For all four cases, the assimilation of dropwindsondes leads to an improved intensity forecast, with the targeted dropwindsonde experiment recovering at least 80% of the difference between the experiment where all dropwindsondes and no dropwindsondes are assimilated. By contrast, assimilating randomly selected dropwindsondes leads to a smaller impact in three of the four cases. In general, zonal and meridional wind observations at or below 700 hPa have the largest impact on the forecast due to the large sensitivity of the intensity forecast to the horizontal wind components at these levels and relatively large ensemble standard deviation relative to the assumed observation errors.