| 05 Mar 2026
How Does Assimilating a Large Commercial GNSS RO Dataset Impact HAFS Hurricane Forecasts? An Evaluation in Support of the ROMEX Experiment
Abstract. While Global Navigation Satellite System (GNSS) radio occultation (RO) data assimilation improves tropical cyclone (TC) intensity forecasts, the scaling of these impacts with RO observation volume remains unclear. This observing system experiment (OSE) study evaluates the impact of assimilating the large commercial GNSS RO profile dataset from the Radio Occultation Modeling Experiment (ROMEX) on 84 Hurricane Analysis and Forecast System (HAFS) model forecasts of four 2022 Atlantic hurricanes. The ROMEX dataset contains about 20,000 daily global Spire and PlanetiQ profiles, which is roughly triple the volume of government-provided RO data that the National Centers for Environmental Prediction (NCEP) assimilated operationally in 2022. Compared to a Control experiment that uses only operational RO data, assimilating ROMEX data together with operational RO profiles in HAFS yields ~ 5–15 % relative skill improvement in minimum central sea-level pressure (PMIN) absolute intensity forecast errors in short-range forecasts, and it nearly eliminates a ~ 2–3 hPa PMIN over-intensification bias in medium-to-long range forecasts. Additionally, ROMEX commercial RO data assimilation reduces HAFS temperature and water vapor errors in the middle-to-upper troposphere. A sensitivity experiment shows that lower-tropospheric RO data assimilated below the 5-km impact height provide a substantial contribution to ROMEX forecast improvements relative to Control. These results demonstrate that quadrupling the volume of assimilated GNSS RO data yields a meaningful positive impact on regional model TC forecasts.
The manuscript "How Does Assimilating a Large Commercial GNSS RO Dataset
Impact HAFS Hurricane Forecasts? An Evaluation in Support of the ROMEX
Experiment" by William Miller et al. presents a thorough study on the impact
of large amounts of real GNSS-RO data on the prediction of four 2022 Atlantic
hurricanes. The manuscript is well readable and accessible to readers
unfamiliar with hurricane modeling. It is mostly very clear and almost ready
for publication.
The manuscript could still be slightly improved by addressing the minor
issues discussed below:
- Page 5-6, line 143ff: "...assimilated in the lower troposphere can
positively impact HAFS forecasts, given the tendency for these observations
to have larger forward operator errors and/or likelihood of rejection there,
compared to RO data from the middle or upper troposphere."
While the reader may understand the gist of this statement, it is slightly
inaccurate and ambiguous. In the lower troposphere, observations may have
larger errors due to the complex path that GNSS signals propagate and the
resulting processing to bending angles. On the other hand, forward modeling
may use an overly simple operator (e.g. 1d Abel integral instead of
ray-tracing), have a large representativity error, and model background
error is larger. I recommend writing "...to have larger forward modeling
errors..." or something similar to summarize this.
- Page 7, line 181: "a 15-foot peak storm surge". Can the authors improve
this so that a reader used to SI units does not have to calculate?
- Page 8, lines 199ff: The description of the model configuration lacks a
specification of the model top and number of model levels. What is the type
of nesting? 1-way, or 2-way with feedback to the coarser model?
(Presumably the former.)
It appears that the outer domain is not part of the ROMEX experiment in the
sense that the additional RO data are not assimilated there. The authors
might wish to clarify this already here. Only on page 15, lines 357-359
state that the outer domain stays the same for all experiments, and in the
conclusions on page 31, lines 706ff.
- Page 9, lines 236-237: "... background super-refractivity (SR) layer where
the vertical refractivity gradient is large."
Note that "large" could be specified more clearly as, e.g., above the
critical value, if this threshold is chosen.
- Page 4, line 97, and
page 9, line 245, 255: The official spelling of "MetOp" is now "Metop",
e.g., https://www.eumetsat.int/our-satellites/metop-series
- Page 12, lines 312ff: When discussing possibly extreme O-B outliers in the
lower troposphere, it is important to note that Spire's processing is known
to involve a screening of profiles before sending them out, unlike UCAR's
processing of COSMIC-2. Therefore, a fair comparison seems difficult.
However, the comparison of rejection rates above 25 hPa could be adjusted to
a top at about 1 hPa (~ 45 km), if possible.
- Page 15, Fig.5a: It is very difficult to understand the statistical
significance of the results from this plot. Would it be possible to present
the results in a different way, perhaps by splitting?
- Page 17, lines 400ff and Fig.8/9(b,d,f): In the mean differences shown here,
ERA5 should cancel out, right? Or is it mean absolute differences to ERA5?