Impacts of Assimilating Additional Reconnaissance Data on Operational GFS Tropical Cyclone Forecasts

Sippel, Jason A.; Wu, Xingren; Ditchek, Sarah D.; Tallapragada, Vijay; Kleist, Daryl T.

doi:10.1175/WAF-D-22-0058.1

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Impacts of Assimilating Additional Reconnaissance Data on Operational GFS Tropical Cyclone Forecasts

2022
By Sippel, Jason A. ; Wu, Xingren ; Ditchek, Sarah D. ; ...
Source: Weather and Forecasting, 37(9), 1615-1639

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Journal Title:

Weather and Forecasting
Personal Author:

Sippel, Jason A. ; Wu, Xingren ; Ditchek, Sarah D. ; Tallapragada, Vijay ; Kleist, Daryl T.

Sippel, Jason A. ; Wu, Xingren ; Ditchek, Sarah D. ; Tallapragada, Vijay ; Kleist, Daryl T. Less -
NOAA Program & Office:

OAR (Oceanic and Atmospheric Research) ; AOML (Atlantic Oceanographic and Meteorological Laboratory) ; CIMAS (Cooperative Institute for Marine and Atmospheric Studies) ; NWS (National Weather Service) ; NCEP (National Centers for Environmental Prediction)

OAR (Oceanic and Atmospheric Research) ; AOML (Atlantic Oceanographic and Meteorological Laboratory) ; CIMAS (Cooperative Institute for Marine and Atmospheric Studies) ; NWS (National Weather Service) ; NCEP (National Centers for Environmental Prediction) Less -
Description:

This study reviews the recent addition of dropwindsonde wind data near the tropical cyclone (TC) center as well as the first-time addition of high-density, flight-level reconnaissance observations (HDOBs) into the National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS). The main finding is that the additional data have profound positive impacts on subsequent TC track forecasts. For TCs in the North Atlantic (NATL) basin, statistically significant improvements in track extend through 4–5 days during reconnaissance periods. Further assessment suggests that greater improvements might also be expected at days 6–7. This study also explores the importance of comprehensively assessing data impact. For example, model or data assimilation changes can affect the so-called “early” and “late” versions of the forecast very differently. It is also important to explore different ways to describe the error statistics. In several instances the impacts of the additional data strongly differ depending on whether one examines the mean or median errors. The results demonstrate the tremendous potential for further improving TC forecasts. The data added here were already operationally transmitted and assimilated by other systems at NCEP, and many further improvements likely await with improved use of these and other reconnaissance observations. This demonstrates the need of not only investing in data assimilation improvements, but also enhancements to observational systems in order to reach next-generation hurricane forecasting goals.
Keywords:

[+]

Cyclone Forecasting Dropwindsondes Hurricanes Numerical Weather Forecasting Typhoons
Source:

Weather and Forecasting, 37(9), 1615-1639
DOI:

https://doi.org/10.1175/WAF-D-22-0058.1
Format:

PDF
Document Type:

Journal Article
Funding:

Grant no. NA19OAR0220188
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Submitted
Main Document Checksum:

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urn:sha256:22e1ded2e75f483006d9289667e7794f1aab7eb509bcf878f950ba6185fdf858
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https://repository.library.noaa.gov/view/noaa/49022/noaa_49022_DS1.pdf
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Impacts of Assimilating Additional Reconnaissance Data on Operational GFS Tropical Cyclone Forecasts

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