| 19 Aug 2026
Impact of GNSS zenith total delay and tropospheric gradient Assimilation on severe weather: a case study of Super Typhoon Koinu
Abstract. In this study, we investigate the impact of assimilating Global Navigation Satellite System (GNSS) Zenith Total Delays (ZTDs) and tropospheric gradients (TGs) on the simulation of Super Typhoon Koinu (2023) using the Weather Research and Forecasting (WRF) model. Four data assimilation experiments are conducted: a control experiment assimilating conventional observations only, experiments separately assimilating ZTDs and tropospheric gradients on top of conventional observations, and a combined experiment assimilating both ZTDs and tropospheric gradients on top of the conventional observations. A two-month assimilation experiment using observations from approximately 250 GNSS stations demonstrates the overall benefit of GNSS data, with reductions in root-mean-square error (RMSE) of up to 60 % in ZTD when both ZTDs and tropospheric gradients are assimilated. Assimilation of tropospheric gradients alone also reduces the ZTD RMSE, demonstrating that gradients provide independent and complementary information beyond the vertically integrated moisture constraint of ZTDs. The impact of Typhoon Koinu is then examined as it approaches Taiwan between 3 and 6 October 2023. Continuous assimilation of GNSS observations improves the representation of the atmospheric moisture field surrounding the cyclone, leading to changes in the spatial distribution of integrated water vapor and the azimuthal moisture asymmetry around the storm. These moisture adjustments produce small but systematic improvements in the environmental deep-layer steering flow and, consequently, modest reductions in typhoon track error after landfall over Taiwan. The combined assimilation of ZTDs and tropospheric gradients also provides the most accurate simulation of cyclone intensity. The results demonstrate that tropospheric gradients complement ZTDs by improving the representation of horizontal moisture variability, thereby enhancing the simulation of the tropical cyclone environment. This study highlights the potential of assimilating multiple GNSS tropospheric products to improve tropical cyclone prediction in numerical weather prediction systems.
Specific comments
Lin 20_ correct spaces “azimuthal moisture”
Lin31_ typo “crustal”
Lin 33_ What is “GNSS meteorology”
Lin 43: spaces “heavy rainfall prediction”
Lin 62_ explain a bit what is an observation operator
Fig1_ the left figure could have more clear the station points it they were in another colour, for example red
Lin141_ Why to place this “Future work will investigate the impact of GNSS observations using convection -permitting simulations.” here? : You are doing a description of the model you are using now. This sentence would be placed elsewhere
Lin 145_ you say the simulation starts on 1 sep 2023, until when? How long forecast? And when the typhoon occurred exactly? Please explain this a bit more
Lin 168_ but what was the sigmao value used in this study?
FIG3 _ I find the legend too long. The explanations should be given in the txt , not there.
Lin 228_ “mo ving “ typo
Lin 228 and 230_ Specify more the dates, and the phases of the cyclone
Lin 261_ Could a real radar reflectivity image be provided so to compared with the simulated ones? And this for the different experiments?
Anyhow, which of the runs is this figure 4a from?
Lin267- A more clear explanation of how the statistics were calculated may be needed. Because if what you do is compare O-B of ZTD of CTRL that (had not ZTD in the analysis) with the others that have it, it is logical that the RMS reduces due to you are approaching the analysis to this obs now. So a more detailed explanation should be needed here.
Lin_309_ Do this image and the simulated reflectivity one are plot over the same area so to make easy the comparison of the structures? It is hard to see what it is said in the text. So if not better to remove or change this comparison.
Lin 365_ Space in “spatial IWV”
Fig 4,5,6,7,8_again I find the legend too long. The part of the explanations should be given in the text , not there.
Lin 514; space at “environmental steering”
Lin 530_ space “complem entary”
General comments.
I have found many times the idea of “ZTD observations strongly constrain the column-integrated moisture field, whereas tropospheric gradients provide additional
information on its horizontal organization” in many of the sections, so some redundancy of this idea can be found along the text.
This work where 4 experiments have been run and compared is very useful to study the possible complementary information that the gradients give to the ZTD vertical profiles of humidity, and in this case in a severe case study that occurred in the tropics.
It would be interesting to see its impact in an operational environment from a model that apart of conventional observations, assimilates other satellite observations, because in that situation where ZTD by itself is beneficial too, as it has been proven and written in many papers, the addition of gradients and its positive impact could be demonstrated.