the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
The impact of GNSS Zenith Total Delay data assimilation on the short-term precipitable water vapor and precipitation forecast over Italy using the WRF model
Rosa Claudia Torcasio
Alessandra Mascitelli
Eugenio Realini
Stefano Barindelli
Giulio Tagliaferro
Silvia Puca
Stefano Dietrich
Abstract. The impact of assimilating GNSS-ZTD (Global Navigation Satellite Systems - Zenith Total Delay) on the precipitable water vapor and precipitation forecast over Italy is studied for the month of October 2019, characterized by several moderate to intense precipitation events. The WRF (Weather Research and Forecasting) model 4.1.3 is used with its 3DVar data assimilation system to assimilate ZTD observations from 388 GNSS receivers distributed over the country. The dataset was built collecting data from all the major national and regional GNSS permanent networks, achieving dense coverage over the whole area.
Results show that WRF underestimates the atmospheric water vapor content for the period, and GNSS-ZTD data assimilation improves this underestimation.
The water vapor forecast is verified for the forecast hours 1–6 h after the last data assimilation time, while the precipitation forecast is verified in the phases 0–3 h and 3–6 h after the last data assimilation time. More than 3000 rain gauges spread over Italy were used to verify the precipitation forecast.
The application of GNSS-ZTD data assimilation to a case study showed an improvement of the precipitation forecast in different ways, the main drawback being the prediction of false alarms.
Considering the study over the whole period, GNSS-ZTD data assimilation had a positive impact on the precipitable water vapor and rainfall forecast, with an improvement of the performance up to 6 hours, and with statistically significant results for moderate to intense rainfall thresholds (25–30 mm/3 h).
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Rosa Claudia Torcasio et al.
Status: final response (author comments only)
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RC1: 'Comment on nhess-2023-18', Anonymous Referee #1, 22 Mar 2023
- AC1: 'Reply on RC1', Stefano Federico, 06 Apr 2023
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RC2: 'Comment on nhess-2023-18', Anonymous Referee #2, 28 May 2023
The paper studies the impact of GNSS data assimilation over Italy with a focus on a 6-hour forecast based on precipitable water vapor and precipitation. It is evident from the study that GNSS DA improves the underestimation of water vapor by WRF.
I mostly agree with the points mentioned by Referee #1, however, I think that reframing the aim of the paper and adding some additional analysis that could bring some novelty to the paper. From the reply comments of the author to the reviewer, I see the author has already started to prepare results in the direction of data thinning experiments which is good.
My major concern to add is that the data assimilation experiments need some more elaboration:
- regarding the observations assimilated in the BCKG experiment.
- regarding the GNSS data used for assimilation and if bias correction was performed before the assimilation of GNSS data.
My minor concern to add is about the tense usage in the text throughout the journal should be uniform. Also, the paragraph construction should be more refined regarding the main point to express in a context.
Citation: https://doi.org/10.5194/nhess-2023-18-RC2 -
AC2: 'Reply on RC2', Stefano Federico, 05 Jun 2023
The comment was uploaded in the form of a supplement: https://nhess.copernicus.org/preprints/nhess-2023-18/nhess-2023-18-AC2-supplement.pdf
Rosa Claudia Torcasio et al.
Rosa Claudia Torcasio et al.
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