Articles | Volume 25, issue 4
Nonlin. Processes Geophys., 25, 747–764, 2018

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

Nonlin. Processes Geophys., 25, 747–764, 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.

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Cited articles

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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,, 2015. a
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.