Assimilating X- and S-Band Radar Data for a Heavy Precipitation Event in Italy
Abstract
:1. Introduction
2. Materials and Methods
2.1. Synoptic Conditions
2.2. Observed Precipitation Measurements
2.3. Radar Data
2.4. Satellite and Lighting Data
2.5. Modeling Setup
3. Quantitative Precipitation Forecast Verification
4. Results
5. Discussion
6. Conclusions
- Assimilating reflectivity data from X- and S-band radars and radial velocity data from S-band radar significantly improves the descriptions of atmospheric humidity content and low-level winds, resulting in better QPFs.
- The application of a simplified ocean model, although modifying the low-level jet associated with the event, scarcely impacts the short-range forecast (length shorter than 12 h) of precipitation.
- The novel QPF verification method introduced in this paper, based on roto-translation RMSE-minimisation, confirmed and reinforced the results achieved with standard verification scores, thereby providing more information about the position error of the WRF simulations.
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Parameter | Value |
---|---|
Rows × Columns | 440 × 400 |
Grid spacing | 3 km |
Vertical levels | 50 |
Time step | 12 s |
Cumulus convection | explicit (no parameterisation) |
Micro-physics option | Thompson scheme [68] |
Boundary-layer option | Yonsei University [69] |
Land-surface option | Unified Noah model [70] |
Radiation option | Rapid radiative transfer model [71] |
Turbulence option | Yonsei University+2D Smagorisnski [69] |
Forecast Code | Data Assimilated |
---|---|
C | none (control run) |
S | Convectional data from weather stations: |
pressure, 2-m temperature | |
2-m relative humidity | |
10-m wind speed and direction | |
as in S plus reflectivity data from X- and S-band radars, | |
and radial velocity data from the S-band radar | |
as in | |
(it differs from the experiment because | |
it implements a simplified marine model) |
Event Observed | |||
---|---|---|---|
yes | no | ||
Event Forecast | yes | A | B |
no | C | D |
Forecast Code | |||
---|---|---|---|
C | 65.8 | −50.3 | < |
S | 65.9 | −50.5 | < |
46.3 | −2.5 | 0.95 | |
40.4 | −2.9 | 0.94 |
Forecast Code | |||||
---|---|---|---|---|---|
C | 29.7 | −0.36 | +0.87 | 100 | |
S | 29.0 | −0.30 | +0.75 | 86 | |
29.9 | −0.24 | −0.18 | 27 | ||
30.1 | +0.48 | +0.41 | 60 |
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Capecchi, V.; Antonini, A.; Benedetti, R.; Fibbi, L.; Melani, S.; Rovai, L.; Ricchi, A.; Cerrai, D. Assimilating X- and S-Band Radar Data for a Heavy Precipitation Event in Italy. Water 2021, 13, 1727. https://doi.org/10.3390/w13131727
Capecchi V, Antonini A, Benedetti R, Fibbi L, Melani S, Rovai L, Ricchi A, Cerrai D. Assimilating X- and S-Band Radar Data for a Heavy Precipitation Event in Italy. Water. 2021; 13(13):1727. https://doi.org/10.3390/w13131727
Chicago/Turabian StyleCapecchi, Valerio, Andrea Antonini, Riccardo Benedetti, Luca Fibbi, Samantha Melani, Luca Rovai, Antonio Ricchi, and Diego Cerrai. 2021. "Assimilating X- and S-Band Radar Data for a Heavy Precipitation Event in Italy" Water 13, no. 13: 1727. https://doi.org/10.3390/w13131727