Articles | Volume 25, issue 4
Nonlin. Processes Geophys., 25, 747–764, 2018
https://doi.org/10.5194/npg-25-747-2018

Special issue: Numerical modeling, predictability and data assimilation in...

Nonlin. Processes Geophys., 25, 747–764, 2018
https://doi.org/10.5194/npg-25-747-2018

Research article 07 Nov 2018

Research article | 07 Nov 2018

Data assimilation of radar reflectivity volumes in a LETKF scheme

Thomas Gastaldo et al.

Related authors

Review article: Observations for high-impact weather and their use in verification
Chiara Marsigli, Elizabeth Ebert, Raghavendra Ashrit, Barbara Casati, Jing Chen, Caio A. S. Coelho, Manfred Dorninger, Eric Gilleland, Thomas Haiden, Stephanie Landman, and Marion Mittermaier
Nat. Hazards Earth Syst. Sci., 21, 1297–1312, https://doi.org/10.5194/nhess-21-1297-2021,https://doi.org/10.5194/nhess-21-1297-2021, 2021
Short summary
Commercial microwave links as a tool for operational rainfall monitoring in Northern Italy
Giacomo Roversi, Pier Paolo Alberoni, Anna Fornasiero, and Federico Porcù
Atmos. Meas. Tech., 13, 5779–5797, https://doi.org/10.5194/amt-13-5779-2020,https://doi.org/10.5194/amt-13-5779-2020, 2020
Short summary
Sensitivity of forecast skill to the parameterisation of moist convection in a limited-area ensemble forecast system
Matteo Vasconi, Andrea Montani, and Tiziana Paccagnella
Nonlin. Processes Geophys. Discuss., https://doi.org/10.5194/npg-2018-21,https://doi.org/10.5194/npg-2018-21, 2018
Revised manuscript has not been submitted
Impact of multiple radar reflectivity data assimilation on the numerical simulation of a flash flood event during the HyMeX campaign
Ida Maiello, Sabrina Gentile, Rossella Ferretti, Luca Baldini, Nicoletta Roberto, Errico Picciotti, Pier Paolo Alberoni, and Frank Silvio Marzano
Hydrol. Earth Syst. Sci., 21, 5459–5476, https://doi.org/10.5194/hess-21-5459-2017,https://doi.org/10.5194/hess-21-5459-2017, 2017
Short summary
Sensitivity of sea-level forecasting to the horizontal resolution and sea surface forcing for different configurations of an oceanographic model of the Adriatic Sea
Lidia Bressan, Andrea Valentini, Tiziana Paccagnella, Andrea Montani, Chiara Marsigli, and Maria Stefania Tesini
Adv. Sci. Res., 14, 77–84, https://doi.org/10.5194/asr-14-77-2017,https://doi.org/10.5194/asr-14-77-2017, 2017
Short summary

Related subject area

Subject: Predictability, probabilistic forecasts, data assimilation, inverse problems | Topic: Climate, atmosphere, ocean, hydrology, cryosphere, biosphere
An early warning sign of critical transition in the Antarctic ice sheet – a data-driven tool for a spatiotemporal tipping point
Abd AlRahman AlMomani and Erik Bollt
Nonlin. Processes Geophys., 28, 153–166, https://doi.org/10.5194/npg-28-153-2021,https://doi.org/10.5194/npg-28-153-2021, 2021
Short summary
Training a convolutional neural network to conserve mass in data assimilation
Yvonne Ruckstuhl, Tijana Janjić, and Stephan Rasp
Nonlin. Processes Geophys., 28, 111–119, https://doi.org/10.5194/npg-28-111-2021,https://doi.org/10.5194/npg-28-111-2021, 2021
Short summary
Behavior of the iterative ensemble-based variational method in nonlinear problems
Shin'ya Nakano
Nonlin. Processes Geophys., 28, 93–109, https://doi.org/10.5194/npg-28-93-2021,https://doi.org/10.5194/npg-28-93-2021, 2021
Short summary
Fast hybrid tempered ensemble transform filter formulation for Bayesian elliptical problems via Sinkhorn approximation
Sangeetika Ruchi, Svetlana Dubinkina, and Jana de Wiljes
Nonlin. Processes Geophys., 28, 23–41, https://doi.org/10.5194/npg-28-23-2021,https://doi.org/10.5194/npg-28-23-2021, 2021
Short summary
A methodology to obtain model-error covariances due to the discretization scheme from the parametric Kalman filter perspective
Olivier Pannekoucke, Richard Ménard, Mohammad El Aabaribaoune, and Matthieu Plu
Nonlin. Processes Geophys., 28, 1–22, https://doi.org/10.5194/npg-28-1-2021,https://doi.org/10.5194/npg-28-1-2021, 2021
Short summary

Cited articles

Anderson, J. L.: Spatially and temporally varying adaptive covariance inflation for ensemble filters, Tellus A, 61, 72–83, https://doi.org/10.1111/j.1600-0870.2008.00361.x, 2009. a
Anderson, J. L. and Anderson, S. L.: A Monte Carlo Implementation of the Nonlinear Filtering Problem to Produce Ensemble Assimilations and Forecasts, Mon. Weather Rev., 127, 2741–2758, https://doi.org/10.1175/1520-0493(1999)127<2741:AMCIOT>2.0.CO;2, 1999. a
Baldauf, M., Seifert, A., Förstner, J., Majewski, D., Raschendorfer, M., and Reinhardt, T.: Operational Convective-Scale Numerical Weather Prediction with the COSMO Model: Description and Sensitivities, Mon. Weather Rev., 139, 3887–3905, https://doi.org/10.1175/MWR-D-10-05013.1, 2011. a
Bannister, R. N.: A review of operational methods of variational and ensemble-variational data assimilation, Q. J. Roy. Meteor. Soc., 143, 607–633, https://doi.org/10.1002/qj.2982, 2016. a
Berner, J., Fossell, K. R., Ha, S.-Y., Hacker, J. P., and Snyder, C.: Increasing the Skill of Probabilistic Forecasts: Understanding Performance Improvements from Model-Error Representations, Mon. Weather Rev., 143, 1295–1320, https://doi.org/10.1175/MWR-D-14-00091.1, 2015. a
Download
Short summary
Accuracy of numerical weather prediction forecasts is strongly related to the quality of initial conditions employed. To improve them, it seems advantageous to use radar reflectivity observations because of their high spatial and temporal resolution. This is tested in a high-resolution model whose domain covers Italy. Results show that the employment of reflectivity observations improves precipitation forecast accuracy, but the positive impact is lost after a few hours of forecast.