Skip to main content
Log in

The ARPAL atmospheric operational modeling chain and its applications: description and validation

  • Research
  • Published:
Bulletin of Atmospheric Science and Technology Aims and scope Submit manuscript

Abstract

The paper describes the model chain operational at the Meteo-Hydrological Center of the Liguria Region (ARPAL) based on the CNR-ISAC models BOLAM (BOlogna Limited Area Model) and MOLOCH (MOdello LOCale in Hybrid coordinates). Some of the chain applications and a statistical verification of its most recent implementation are also shown.

The first operational run of the BOLAM numerical weather prediction (NWP) model at ARPAL was launched in September 1999, in correspondence with the beginning of the Mesoscale Alpine Programme Special Observing Period field campaign. Since then, the collaboration between CNR-ISAC and ARPAL has allowed to maintain and update the operational chain, also thanks to a continuous upgrade of the computational resources available at ARPAL. Since 2005, the non-hydrostatic model MOLOCH has been added to the forecasting chain.

In the present operational setup, BOLAM is run over a European domain at the horizontal resolution of about 8 km, using ECMWF-IFS analysis and forecasts as initial and boundary conditions. MOLOCH model is nested in BOLAM, and it runs over a domain including the entire Italian territory, on a 1.5-km horizontal resolution grid. The forecast products, consisting in 72-h BOLAM and 48-h MOLOCH predictions, are available in 1.5 h after the reception of the ECMWF-IFS data. Four runs every day are performed.

Other modeling systems in cascade are driven by NWP models. For several years, wind fields obtained from the atmospheric modeling chain have been used to force the wave model WAVEWATCH III, implemented at different resolutions over the Mediterranean basin. Other applications in cascade consist in hydrological prediction for the basins of the Liguria Region, wildfire, and ocean circulation modeling. Moreover, the different initializations of the BOLAM and MOLOCH models provide an important contribution to the ARPAL operational Poor Man’s Ensemble prediction system.

A verification based on 3 years of data available from the ARPAL ground observing network shows a general capability of the high-resolution models in better forecasting heavy precipitation events, due to a better description of convective phenomena, while a less marked improvement with respect to large-scale models is shown for low precipitation thresholds. Results also show an improvement of model performance for all monitored variables, precipitation, 2-m temperature, and 10-m winds, when resolution increases and when model domains are enlarged.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

Data availability

Observational data from the regional monitoring network are publicly available and can be downloaded from this website: https://ambientepub.regione.liguria.it/SiraQualMeteo/script/PubAccessoDatiMeteo.asp. Model outputs are available from the authors upon reasonable request.

References

  • Apicella L, Puca S, Lagasio M, Meroni AN, Milelli M, Vela N, Garbero V, Ferraris L, Parodi A (2021) The predictive capacity of the high resolution weather research and forecasting model: a year-long verification over Italy. Bull Atmos Sci Technol 2:3

    Article  Google Scholar 

  • Arpagaus M, Rotach MW, Ambrosetti P, Ament F, Appenzeller C, Bauer HS, Behrendt A, Bouttier F, Buzzi A, Corazza M, Davolio S, Denhard M, Dorninger M, Fontannaz L, Frick J, Fundel F, German U, Gorgas T, Grossi G et al (2009) MAP D-PHASE: ‘demonstrating forecast capabilities for flood events in the Alpine region’. Veröffentlichungen der MeteoSchweiz 78:75

    Google Scholar 

  • Bougeault P, Binder P, Buzzi A, Dirks R, Kuettner J, Smith RB, Steinacker R, Volkert H (2001) The MAP special observing period. Bull Amer Meteor Soc 82:433–462

    Article  Google Scholar 

  • Bruno G, Pignone F, Silvestro F, Gabellani S, Schiavi F, Rebora N, Giordano P, Falzacappa M (2021) Performing hydrological monitoring at a national scale by exploiting rain-gauge and radar networks: the Italian case. Atmosphere 12:771

    Article  Google Scholar 

  • Buzzi A, Davolio S, Malguzzi P, Drofa O, Mastrangelo D (2014) Heavy rainfall episodes over Liguria of autumn 2011: numerical forecasting experiments. Nat Hazards Earth Syst Sci 14:1325–1340

    Article  Google Scholar 

  • Buzzi A, Tartaglione N, Malguzzi P (1998) Numerical simulations of the 1994 Piedmont flood: role of orography and moist processes. Mon Wea Rev 126:2369–2383

    Article  Google Scholar 

  • Capecchi V, Pasi F, Gozzini B, Brandini C (2023) A convection-permitting and limited-area model hindcast driven by ERA5 data: precipitation performances in Italy. Clim Dyn 61:1411–1437

    Article  Google Scholar 

  • Capecchi V (2021) Reforecasting two heavy-precipitation events with three convection-permitting ensembles. Weather Forecast 36(3):769–790

    Article  Google Scholar 

  • Cassardo C, Loglisci N, Gandini D, Qian MW, Niu GY, Ramieri P, Pelosini R, Longhetto A (2002) The flood of November 1994 in Piedmont, Italy: a quantitative analysis and simulation. Hydrol Process 16:1275–1299

    Article  Google Scholar 

  • Cassola F, Ferrari F, Mazzino A (2015) Numerical simulations of Mediterranean heavy precipitation events with the WRF model: a verification exercise using different approaches. Atmos Res 164–165:3–18

    Google Scholar 

  • Corazza M, Buzzi A, Sacchetti D, Trovatore E, Ratto CF (2003) Simulating extreme precipitation with a mesoscale forecast model. Meteorol Atmos Phys 83(1):131–143

    Article  Google Scholar 

  • Corazza M, Sacchetti D, Antonelli M, Drofa O (2018) The ARPAL operational high resolution poor man’s ensemble, description and validation. Atmos Res 203:1–15

    Article  Google Scholar 

  • Courant R, Friedrichs K, Lewy H (1967) On the partial difference equations of mathematical physics. IBM J Res Develop 11:215–234

    Article  Google Scholar 

  • Davis A, Brown B, Bullock R (2006b) Object-based verification of precipitation forecast. Part II application to convective rain system. Mon Wea Rev 134:1785–1795

    Article  Google Scholar 

  • Davis A, Brown B, Bullock R (2006a) Object-based verification of precipitation forecasts. Part I: methodology and application to mesoscale rain areas. Mon Wea Rev 134:1772–1784

    Article  Google Scholar 

  • Davolio S, Malguzzi P, Drofa O, Mastrangelo D, Buzzi A (2020) The Piedmont flood of November 1994: a test-bed of forecasting capabilities of the CNR-ISAC meteorological model suite. Bull Atmos Sci Technol 1:263–282

    Article  Google Scholar 

  • Davolio S, Silvestro F, Malguzzi P (2015a) Effects of increasing horizontal resolution in a convection permitting model on flood forecasting: the 2011 dramatic events in Liguria (Italy). J Hydrometeorol 16:1843–1856

    Article  Google Scholar 

  • Davolio S, Ferretti R, Baldini L, Casaioli M, Cimini D, Ferrario ME, Gentile S, Loglisci N, Maiello I, Manzato A, Mariani S, Marsigli C, Marzano FS, Miglietta MM, Montani A, Panegrossi G, Pasi F, Pichelli E, Pucillo A, Zinzi A (2015b) The role of the Italian scientific community in the first HyMeX SOP: an outstanding multidisciplinary experience. Meteorol Z 24:261–267

    Article  Google Scholar 

  • Direttiva del Presidente del Consiglio dei Ministri (2004) Indirizzi operativi per la gestione organizzativa e funzionale del sistema di allertamento nazionale e regionale per il rischio idrogeologico ed idraulico ai fini di protezione civile. Gazzetta Ufficiale 59 (in Italian). https://www.protezionecivile.gov.it/it/normativa/direttiva-27-febbraio-2004--indirizzi-operativi-per-la-gestione-del-sistema-di-allertamento-nazionale-per-il-rischio-idrogeologico-e-idraulico/. Accessed 05 May 2023

  • Doms G, Baldauf M (2018) A description of the nonhydrostatic regional COSMO model, Part I: Dynamics and Numerics, v5.05. Offenbach, Germany, Deutscher Wetterdienst. 5.05 I. Tech Rep. https://www.dwd.de/EN/ourservices/cosmo_documentation/pdf_docu_v5_05/1_cosmo_dynamics_5_05_en.html. Accessed 10 May 2023

  • Ebert EE (2009) Neighborhood verification: a strategy for rewarding close forecasts. Weather Forecast 24:1498–1510

    Article  Google Scholar 

  • Faccini F, Luino F, Paliaga G, Sacchini A, Turconi L, de Jong C (2018) Role of rainfall intensity and urban sprawl in the 2014 flash flood in Genoa City, Bisagno catchment (Liguria, Italy). Appl Geog 98:224–241

    Article  Google Scholar 

  • Faccini F, Luino F, Sacchini A, Turconi L (2015) The 4th October 2010 flash flood event in Genoa Sestri Ponente (Liguria, Italy). Dis Adv 8:1–15

    Google Scholar 

  • Ferrari F, Cassola F, Tuju PE, Mazzino A (2021) RANS and LES face to face for forecasting extreme precipitation events in the Liguria region (northwestern Italy). Atmos Res 259:105654

    Article  Google Scholar 

  • Ferrero E, Balsamo G (2020) The 1994 Piedmont flood revisited. ECMWF Newsletter 162 (Winter 2019/20):8-9. https://www.ecmwf.int/sites/default/files/elibrary/012020/19356-newsletter-no-162-winter-201920_1.pdf. Accessed 10 May 2023

  • Ferretti R, Pichelli E, Gentile S, Maiello I, Cimini D, Davolio S, Miglietta MM, Panegrossi G, Baldini L, Pasi F, Marzano FS, Zinzi A, Mariani S, Casaioli M, Bartolini G, Loglisci N, Montani A, Marsigli C, Manzato A et al (2014) Overview of the first HyMeX special observation period over Italy: observations and model results. Hydrol Earth Syst Sci 18:1953–1977

    Article  Google Scholar 

  • Giannoni F, Roth G, Rudari R (2000) A semi-distributed rainfall-runoff model based on a geomorphologic approach. Phys Chem Earth, Part B: Hydrol, Oceans Atmos 25:665–671

    Article  Google Scholar 

  • Gilleland E, Ahijevych D, Brown BG, Casati B, Ebert EE (2009) Intercomparison of spatial forecast verification methods. Weather Forecast 24:1416–1430

    Article  Google Scholar 

  • Giovannini L, Davolio S, Zaramella M, Zardi D, Borga (2021) Multi-model convection-resolving simulations of the October 2018 Vaia storm over Northeastern Italy. Atmos Res 253:105455

    Article  Google Scholar 

  • Hogan RJ, Bozzo A (2018) A flexible and efficient radiation scheme for the ECMWF model. J Adv Modeling Earth Syst 10:1990–2008

    Article  Google Scholar 

  • Lira Loarca A, Caceres-Euse A, De Leo F, Besio G (2022) Wave modeling with unstructured mesh for hindcast, forecast and wave hazard applications in the Mediterranean Sea. Appl Ocean Res 122:103118

    Article  Google Scholar 

  • Malguzzi P, Grossi G, Buzzi A, Ranzi R, Buizza R (2006) The 1966 ‘century’ flood in Italy: a meteorological and hydrological revisitation. J Geophys Res 111:D24106

    Article  Google Scholar 

  • Manzato A, Cicogna A, Pucillo A (2016) 6-hour maximum rain in Friuli Venezia Giulia: Climatology and ECMWF-based forecasts. Atmos Res 169B:465–484

    Article  Google Scholar 

  • Mariani S, Casaioli M, Coraci E, Malguzzi P (2015) New high-resolution BOLAM-MOLOCH suite for the SIMM forecasting system: assessment over two HyMeX intense observation periods. Nat Hazards Earth Sys Sci 15:1–24

    Article  Google Scholar 

  • Mentaschi L, Besio G, Cassola F, Mazzino A (2015) Performance evaluation of Wavewatch III in the Mediterranean Sea. Ocean Model 90:82–94

    Article  Google Scholar 

  • Regione Liguria (2000) Legge regionale 17 febbraio 2000, n. 9. s.l.:Bollettino Ufficiale n. 4 (in Italian). https://lrv.regione.liguria.it/liguriass_prod/articolo?urndoc=urn:nir:regione.liguria:legge:2000-02-17;9. Accessed 05 May 2023

  • Regione Liguria (2006) Legge regionale 4 agosto 2006, n. 20. s.l.:Bollettino Ufficiale n. 12 (in Italian). https://lrv.regione.liguria.it/liguriass_prod/articolo?urndoc=urn:nir:regione.liguria:legge:2006-08-04;20. Accessed 05 May 2023

  • Regione Liguria (2016) Legge regionale 18 novembre 2016, n. 28. s.l.:Bollettino Ufficiale n. 21 (in Italian). https://lrv.regione.liguria.it/liguriass_prod/articolo?urndoc=urn:nir:regione.liguria:legge:2016-11-18;28. Accessed 05 May 2023

  • Roebber PJ (2009) Visualizing multiple measures of forecast quality. Weather Forecast 24:601–608

    Article  Google Scholar 

  • Rossa A, Nurmi P, Ebert E (2008) Overview of methods for the verification of quantitative precipitation forecasts. In: Michaelides S (ed) Precipitation: advances in measurements, estimation and prediction. Springer, Berlin, Heidelberg, pp 417–450

    Google Scholar 

  • Rotach MW, Ambrosetti P, Ament F, Appenzeller C, Arpagaus M, Bauer HS, Behrendt A, Bouttier F, Buzzi A, Corazza M, Davolio S, Denhard M, Dorninger M, Fontannaz L, Frick J, Fundel F, Germann U, Gorgas T, Hegg C et al (2009) MAP D-PHASE: real-time demonstration of weather forecast quality in the Alpine region. Bull Am Meteor Soc 90:1321–1336

    Article  Google Scholar 

  • Sacchetti D, Corazza M (2009) dphase_arpalmol: MOLOCH operational model forecasts run by ARPAL-CFMI for the MAP D-PHASE project. World Data Center for Climate (WDCC) at DKRZ. http://eudat7-ingest.dkrz.de/dataset/6d6c7729-525d-578a-8bb6-567068818e8b, accessed 10 May 2023.

  • Senatore A, Davolio S, Furnari L, Mendicino G (2020) Reconstructing flood events in Mediterranean coastal areas using different reanalyses and high-resolution meteorological models. J Hydrometeorol 21:1865–1887

    Article  Google Scholar 

  • Silvestro F, Gabellani S, Giannoni F, Parodi A, Rebora N, Rudari R, Siccardi F (2012) A hydrological analysis of the 4 November 2011 event in Genoa. Nat Hazards Earth Syst Sci 12:2743–2752

    Article  Google Scholar 

  • Skamarock WC, Klemp JB, Dudhia J, Gill DO, Barker DM, Duda MG, Huang X-Y, Wang W, Powers JG (2008) A description of the advanced research WRF version 3. NCAR Technical note NCAR/TN-475+STR, pp 113. https://doi.org/10.5065/D68S4MVH

  • Steppeler J, Doms G, Schaettler U, Bitzer HW, Gassmann A, Damrath U, Gregoric G (2003) Meso-gamma scale forecasts using the nonhydrostatic model LM. Meteorol Atmos Phys 82:75–96

    Article  Google Scholar 

  • Tiesi A, Miglietta MM, Conte D, Drofa O, Davolio S, Malguzzi P, Buzzi A (2016) Heavy rain forecasting by model initialization with LAPS: a case study. IEEE J Sel Top Appl Earth Obs Remote Sens 9(6):2619–2627

    Article  Google Scholar 

  • Trini Castelli S, Bisignano A, Donateo A, Landi TC, Martano P, Malguzzi P (2020) Evaluation of the turbulence parametrization in the MOLOCH meteorological model. Q J Roy Meteor Soc 146:124–140

    Article  Google Scholar 

  • Visconti G, Marzano FS (2008) An independent overview of the national weather service in Italy. Bull Am Meteor Soc 89:1279–1284

    Article  Google Scholar 

  • Wilks D (2006) Statistical methods in the atmospheric sciences. Academic Press, London

    Google Scholar 

Download references

Acknowledgements

The authors would like to acknowledge the contribution of Andrea Buzzi and Piero Malguzzi who since the 90s have developed the ISAC NWP models and promoted the collaboration with ARPAL.

Author information

Authors and Affiliations

Authors

Contributions

All authors conceived the study, discussed and analyzed the results. O.D. and S.D. developed and provided the models. D.S, M.C., and F.C. implemented the operational chain. M.C., F.C., and S.D. wrote the manuscript. D.S., M.C., M.T., and L.P. prepared the figures. All authors reviewed the manuscript.

Corresponding author

Correspondence to F. Cassola.

Ethics declarations

Competing interests

The authors declare no competing interests.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Sacchetti, D., Cassola, F., Corazza, M. et al. The ARPAL atmospheric operational modeling chain and its applications: description and validation. Bull. of Atmos. Sci.& Technol. 5, 1 (2024). https://doi.org/10.1007/s42865-024-00064-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s42865-024-00064-z

Keywords

Navigation