Giulia Panegrossi

Research Activity

My research interests include remote sensing of clouds and precipitation; passive microwave precipitation retrieval algorithms also based on machine learning; radiative transfer through precipitating clouds; microphysics characterisation of precipitating clouds using models and observations; development of microphysics schemes; cloud electrification; nowcasting techniques; combined EO and modeling techniques for the analysis of heavy precipitation systems. My current scientific activity is mainly related to the development of passive microwave (PMW) precipitation retrieval algorithms for the cross-track and conically scanning microwave radiometers in the EPS-SG mission, and PMW snowfall retrieval with focus on high-latitudes, global precipitation climatology, extreme events in the Mediterranean area, water cycle, hydrological applications. This activity is carried out within different international and national projects. 

Main current official roles

  • Co-Coordinator of the ISAC Research MacroArea Climate And Meteorology, modelling and Earth Observations (CAMEO) (since October 2018)
  • EUMETSAT H SAF Science Manager (since October 2019) 
  • EPS-SG MWI/ICI mission Science Advisory Group - Member (since May 2023)
  • Chair of the IPWG Focus Group on Snowfall (since Sept. 2022)
  • Comitato tecnico scientifico CETEMPS, Università dell'Aquila, Italy (Member since Sep. 2023)
  • Precipitation Measurement Mission (PMM) Science Team Member (since 2014)

Recent Research Projects

  1. EUMETSAT Satellite Application Facility on Support to Operational Hydrology and Water Management (HSAF) (CDOP-4 2022-2027): Science Manager, Co-PI, Development and delivery of operational precipitation products exploiting all current and future microwave radiometers on board LEO satellites.
  2. ESA RainCast study (ESA ITT AO/1-9324/18/NL/IA) (Jan. 2019-Jun. 2023): “A multi-platform and multi-sensor study to address the requirement from the research and operational communities for global precipitation measurements”, Co-PI and responsible of the CNR-ISAC unit. Assessment of snowfall detection and estimate capabilities of spaceborne MW active and passive sensors. 
  3. Studi scientifici per la missione Wind Velociy Radar Nephoscope (WIVERN) (ASI Call for ideas 2022) (2023-2025) (Co-I)
  4. Cost Action CA19109 European network for Mediterranean cyclones in weather and climate (MEDCYCLONES) https://e-services.cost.eu/action/CA19109 (2020-2024) (Member)
  5. AEROMET AEROspatial data assimilation for METeorological weather prediction (Lazio Innova, RSI 2020) (2021-2023) 
  6. Copernicus C3S_312b_Lot1 (2018-2021) "Essential Climate Variable (ECV) products derived from observations Lot 1: precipitation, surface radiation budget, water vapour, cloud properties, and Earth radiation budget":  Co-PI, Leads the Precipitation ECV Climate Data Record generation (global daily and monthly mean precipitation estimate) based on passive microwave long-term Level 1 fundamental climate data record. 
  7. GAMES (EUMETSAT ITT 19/218140)  Geolocation Assessment/validation Methods for EPS-SG ICI and MWI (GAMES) (2020-2021).
  8. H SAF and GPM scientific Collaboration proposal (2014-present) “H-SAF and GPM: precipitation algorithm development and validation activity”, approved by the NASA PMM Research Program and endorsed by EUMETSAT. PI, and responsible for the scientific collaboration between H-SAF and the NASA/JAXA GPM related to precipitation retrieval algorithm development and member of the Land surface Working Group and Passive Microwave retrieval Algorithm working group.

Recent publications

Mediterranean cyclones and severe weather events

  1. Panegrossi, G.; D’Adderio, L.P.; Dafis, S.; Rysman, J.-F.; Casella, D.; Dietrich, S.; Sanò, P. Warm Core and Deep Convection in Medicanes: A Passive Microwave-Based Investigation. Remote Sens. , 15, 2838. https://doi.org/10.3390/rs15112838, 2023
  2. D’Adderio, L. D. Casella, S. Dietrich,. P. Sanò, G. Panegrossi, GPM-CO observations of Medicane Ianos: comparative analysis of precipitation structure between development and mature phase, Atmos. Res., https://doi.org/10.1016/j.atmosres.2022.106174, 2022
  3. D’Adderio, L. D. Casella, S. Dietrich,. P. Sanò, G. Panegrossi, GPM-CO observations of Medicane Ianos: comparative analysis of precipitation structure between development and mature phase, Atmos. Res., https://doi.org/10.1016/j.atmosres.2022.106174, 2022
  4. Comellas Prat, A.; Federico, S.; Torcasio, R.C.; D’Adderio, L.P.; Dietrich, S.; Panegrossi, G. Evaluation of the Sensitivity of Medicane Ianos to Model Microphysics and Initial Conditions Using Satellite Measurements. Remote Sens. 13, 4984. https://doi.org/10.3390/rs13244984, 2021
  5. Hourngir, D., G. Panegrossi, D. Casella, P. Sanò, L.P. D’Adderio, C. Liu, A 4-year climatological analysis based on GPM observations of deep convective events in the Mediterranean region, Remote Sens. 13(9), 1685; https://doi.org/10.3390/rs130916852021
  6. Marra, A. C., S. Federico, M. Montopoli, E. Avolio, L. Baldini, D. Casella, L. P. D’Adderio, S. Dietrich, P. Sanò, R. C. Torcasio, and G. Panegrossi, The Precipitation Structure of the Mediterranean Tropical-Like Cyclone Numa: Analysis of GPM Observations and NumericalWeather Prediction Model Simulations, Remote Sens. 2019, 11, 1690; doi:10.3390/rs11141690, 2019.
  7. Marra A. C., F. Porcu', L. Baldini, M. Petracca, D. Casella, S. Dietrich, A. Mugnai, P. Sanò, G. Vulpiani, G. Panegrossi, Observational analysis of an exceptionally intense hailstorm over the Mediterranean area: Role of the GPM Core Observatory, Atmos. Res., 182, 72-90, doi: 10.1016/j.atmosres.2017.03.019, 2017
  8. Panegrossi G., D. Casella, S. Dietrich, A. C. Marra, M. Petracca, P. Sanò, A. Mugnai, L. Baldini, N. Roberto, E. Adirosi, R. Cremonini, R. Bechini, G. Vulpiani, and F. Porcù: Use of the GPM constellation for monitoring heavy precipitation events over the Mediterranean region, IEEE J. of Sel. Topics in Appl. Earth Obs. and Rem. Sens. (J-STARS), Volume 9, Issue 6, Pages: 2733 - 2753, doi: 10.1109/JSTARS.2016.2520660, 2016.

Remote Sensing of clouds and precipitation 

  1. Camplani, A., Casella, D., Sanò, P., and Panegrossi, G.: The High lAtitude sNowfall Detection and Estimation aLgorithm for ATMS (HANDEL-ATMS): a new algorithm for the snowfall retrieval at high latitudes, Atmos. Meas. Tech. Discuss. [preprint], https://doi.org/10.5194/amt-2023-94, in review, 2023.
  2. Lombardi, A., Tomassetti, B., Colaiuda, V., Di Antonio, L., Tuccella, P., Montopoli, M., Ravazzani, G., Marzano, F. S., Lidori, R., and Panegrossi, G.: On the combined use of rain gauges and GPM IMERG satellite rainfall products: testing cellular automata-based interpolation methodology on the Tanaro river basin in Italy, Hydrol. Earth Syst. Sci. Discuss. [preprint], https://doi.org/10.5194/hess-2023-214, in review, 2023.
  3. Vahedizade S., A. Ebtehaj, S. Tamang, Y. You, G. Panegrossi, S. Ringerud, F. J. Turk, On the Effects of Cloud Water Content on Passive Microwave Snowfall Retrievals, Rem. Sens. of Envi., 280,113187, https://doi.org/10.1016/j.rse.2022.113187, 2022
  4. Rahimi R., A. Ebtehaj, G. Panegrossi, L. Milani, S. E. Ringerud and F. J. Turk, "Vulnerability of Passive Microwave Snowfall Retrievals to Physical Properties of Snowpack: A Perspective From Dense Media Radiative Transfer Theory," in IEEE Transactions on Geoscience and Remote Sensing, vol. 60, pp. 1-13, 2022, Art no. 5304713, doi: 10.1109/TGRS.2022.3184530, 2022
  5. Sanò P., D. Casella, A. Camplani, L. P. D’Adderio, G. Panegrossi, A machine learning snowfall retrieval algorithm for ATMS, Remote Sens. 202214(6),1467; https://doi.org/10.3390/rs14061467
  6. D. Casella, G. Panegrossi, P. Sanò, Bengt Rydberg, Vinia Mattioli, Christophe Accadia, Mario Papa, Frank S. Marzano, Mario Montopoli, "Can We Use Atmospheric Targets for Geolocating Spaceborne Millimeter-Wave Ice Cloud Imager (ICI) Acquisitions?," in IEEE Transactions on Geoscience and Remote Sensing, vol. 60, pp. 1-22, Art no. 5302622, doi: 10.1109/TGRS.2022.3145638, 2022
  7. Turk, J. F., S. E Ringerud, A. Camplani, D. Casella, R. J Chase, A. Ebtehaj, J. Gong, M. Kulie, G. Liu, L. Milani, G. Panegrossi, R. Padulles, J.-F. Rysman, P. Sanò, S. Vahedizade, N. Wood, Applications of a CloudSat-TRMM and CloudSat-GPM Satellite Coincidence Dataset, 13, 2264. https://doi.org/10.3390/rs13122264, 2021
  8. Camplani, A., D. Casella, P. Sanò, G. Panegrossi, The Passive microwave Empirical frozen Surface Classification Algorithm (PESCA): application to GMI and ATMS, J. of Hydrometeorol., https://doi.org/10.1175/JHM-D-20-0260.1, 2021
  9. Bagaglini L., P. Sanò, D. Casella, E. Cattani, G. Panegrossi, The Passive microwave Neural network Precipitation Retrieval algorithm for Climate applications (PNPR-CLIM): design and application, Remote Sens.13(9), 1701; https://doi.org/10.3390/rs130917012021
  10. Turk J., Ringerud, S., You Y., Camplani A., Casella D., Panegrossi G., Sanò P., Ebtehaj A., Guilloteau C., Utsumi N., Prigent C., Peters-Lidard C., Adapting Passive Microwave-Based Precipitation Algorithms to Variable Microwave Land Surface Emissivity to Improve Precipitation Estimation from the GPM Constellation, J. of Hydrometeorol., https://doi.org/10.1175/JHM-D-20-0296.1, 2021
  11. Mroz K., M. Montopoli, G. Panegrossi, L. Baldini, P. Kirtsetter, Quality assessment of spaceborne active and passive microwave snowfall products over the continental United States, J. of Hydrometeorology, https://doi.org/10.1175/JHM-D-20-0222.1, 2021
  12. Battaglia A., G. Panegrossi, What Can We Learn from the CloudSat Radiometric Mode Observations of Snowfall over the Ice-Free Ocean?, Remote Sens.,12, 3285; doi:10.3390/rs12203285, 2020.
  13. Milani L.,M. S. Kulie, D. Casella P. Kirstetter, G. Panegrossi, V. Petkovic, S. E. Ringerud1, J-F Rysman, P. Sanò, N.-Y .Wang, Y. You, G. Skofronick-Jackson, Extreme Lake-Effect Snow from a GPM Microwave Imager Perspective: Observational Analysis and Precipitation Retrieval Evaluation, J. of Atmos. and Ocean. Tech., https://doi.org/10.1175/JTECH-D-20-0064.1, 2020.
  14. Chen F., W. T. Crow, L. Ciabatta, P. Filippucci, G. Panegrossi, A. C. Marra, S. Puca, and C. Massari, Enhanced large-scale validation of satellite-based land rainfall products, F. J. of Hydrometeor., https://doi.org/10.1175/JHM-D-20-0056.1, 2020
  15. Rysman J.-F., G. Panegrossi, P. Sanò, A. C. Marra, S. Dietrich, L. Milani, M. S. Kulie, D. Casella, A. Camplani, C. Claud, L. Edel, Retrieving surface snowfall with GPM Microwave Imager: A new module for SLALOM algorithm, Geophys. Res. Let., doi :10.1029/2019GL084576, 2019 
  16. D’Adderio L.P., F.
Porcù, G. Panegrossi, A.C. Marra, P. Sanò, S.
Dietrich, Comparison of the GPM DPR Single- and Double-Frequency Products Over the Mediterranean Area, IEEE Trans. Geosci. Remote Sens, doi: 10.1109/TGRS.2019.2928871, 2019
  17. Rysman, J.-F., G. Panegrossi, A. C. Marra, S. Dietrich, L. Milani, M. Kulie, SLALOM: An all-surface snow water path retrieval algorithm for the GPM Microwave Imgaer, Remote Sens. 10(8), 1278; https://doi.org/10.3390/rs10081278, 2018.
  18. Sanò P., G. Panegrossi, D. Casella, A. C. Marra, L. P. D’Adderio, J.-F. Rysman, S. Dietrich, The Passive Microwave Neural Network Precipitation Retrieval (PNPR) algorithm for the Conical Scanning GMI Radiometer, Remote Sens. 10, 1122; doi:10.3390/rs10071122, 2018 
  19. Milani, L., M. Kulie, D. Casella, S. Dietrich, T. L'Ecuyer, G. Panegrossi, F. Porcu', P. Sano', N. Wood, CloudSat Snowfall Estimates over Antarctica and the Southern Ocean: An Assessment of Independent Retrieval Methodologies and Multi-Year Snowfall Analysis, Atmos. Res., 213, 121-135, DOI: 10.1016/j.atmosres.2018.05.015, 2018
  20. Capozzi V., M. Montopoli, V. Mazzarella, A. C. Marra, N. Roberto, G. Panegrossi, S. Dietrich and G. Budillon, Multi-Variable Classification Approach for the Detection of Lightning Activity Using a Low-Cost and Portable X Band Radar, Remote Sens.10(11), 1797; https://doi.org/10.3390/rs10111797, 2018
  21. Amaral, L. Martins Costa, S. Barbieri, D. Vila, S. Puca, G. Vulpiani, G. Panegrossi, T. Biscaro, P. Sanò, M. Petracca, A. C. Marra, M. Gosset, S. Dietrich, Assessment of Ground-Reference Data and Validation of the H-SAF Precipitation Products in Brazil, Remote Sens.10(11), 1743; doi:10.3390/rs10111743, 2018.
  22. Derin, Y.,  E. Anagnostou, M. Anagnostou, J. Kalogiros, D. Casella, A. C. Marra, G. Panegrossi P. Sanò, , Passive Microwave Rainfall Error Analysis Using High- Resolution X-Band Dual-Polarization Radar Observations in Complex Terrain, IEEE Transactions on Geoscience and Remote Sensing pp(99):1-22, DOI 10.1109/TGRS.2017.2763622, 2018
  23. Panegrossi G., J-F. Rysman, D. Casella, A. C. Marra, P. Sanò, and M. S. Kulie, CloudSat-based assessment of GPM Microwave Imager snowfall observation capabilities, Rem. Sensing, 9(12), 1263; doi:10.3390/rs9121263, 2017.
  24. Casella, D., Panegrossi G., Dietrich S., Marra A.C., Sanò P., M. S. Kulie, B. T. Johnson,  Evaluation of the GPM-DPR snowfall detection capability: comparison with CloudSat, Atmos. Res., 197, 64-75, doi :10.1016/j.atmosres.2017.06.018, 2017
  25. Casella D., L. M. Amaral, S. Dietrich, A. C. Marra, P. Sanò, and G. Panegrossi, The Cloud Dynamics and Radiation Database algorithm for AMSR2: exploitation of the GPM observational dataset for operational applications, IEEE J. of Sel. Topics in Appl. Earth Obs. and Rem. Sens. (J-STARS), 10(8), DOI : 10.1109/JSTARS.2017.2713485, 2017
  26. Ciabatta L., Marra A. C., Panegrossi G., Casella D., Sanò P., Dietrich S., Massari C., Brocca L., Daily precipitation estimation through different microwave sensors: Verification study over Italy, J. of Hydrology, 545, 436-450, doi: 10.1016/j.jhydrol.2016.12.057, 2017.
  27. Sanò, P., Panegrossi, G., Casella, D., Marra, A. C., Di Paola, F., and Dietrich, S.: The new Passive microwave Neural network Precipitation Retrieval (PNPR) algorithm for the cross-track scanning ATMS radiometer: description and verification study over Europe and Africa using GPM and TRMM spaceborne radars, Atmos. Meas. Tech., 9, 5441-5460, doi:10.5194/amt-9-5441-2016, 2016.

NWP and lightning 

  1. Torcasio, Rosa Claudia, Stefano Federico *, Albert Comellas Prat, Giulia Panegrossi, Leo Pio D'Adderio, Stefano Dietrich, Impact of lightning data assimilation on the short-term precipitation forecast over Central Mediterranean Sea, Remote Sens. 13(4), 682; https://doi.org/10.3390/rs130406822021
  2. Federico, S., M. Petracca, G. Panegrossi, C. Transerici, and S. Dietrich, Impact of lightning data assimilation on the precipitation forecast at different forecast ranges, Adv. in Sci. and Res., 14, 187-194, https://doi.org/10.5194/asr-14-187-2017, 2017.
  3. Federico, S., Petracca, M., Panegrossi, G., and Dietrich, S.: Improvement of RAMS precipitation forecast at the short-range through lightning data assimilation, Nat. Hazards Earth Syst. Sci., 17, 61-76, doi:10.5194/nhess-17-61-2017, 2017

Book Chapters

  1. Panegrossi, G., D. Casella, P. Sanò, A. Camplani, A. Battaglia, Recent Advances and Challenges in Snowfall detection and Estimation, pp. 333-376, Chapter 12 in In: Precipitation Science (ISBN: 978-0-12-822973-6), Ed. Silas Michaelides, Eds. Elsevier, Nov. 2021
  2. D’Adderio, Leo Pio, F. Porcù, G. Panegrossi, A. Tokai, G. Vulpiiani, S. Dietrich, Rainfall Microphysical characterization over the Mediterranean area during the GPM era, pp. 503-560, Chapter 17 In: Precipitation Science (ISBN: 978-0-12-822973-6), Ed. Silas Michaelides, Eds. Elsevier, Nov. 2021
  3. Massari, C., Camici, S., Ciabatta, L., Penna, D., Marra, A. C., & Panegrossi, G. (2020). Floods in the Mediterranean area: The role of soil moisture and precipitation. In Water Resources in the Mediterranean Region (pp. 191-218). Elsevier
  4. Panegrossi, G. et al., Heavy precipitation systems in the Mediterranean area: The role of GPM. In: Satellite Precipitation Measurement. V. Levizzani, C. Kidd, D. B. Kirschbaum, C. D. Kummerow, K. Nakamura, F. J. Turk, Eds. Advances in Global Change Research69, Springer Nature, Cham, 819-841, doi:10.1007/978-3-030-35798-6_1, 2020