DOI: 10.3369/tethys.2018.15.15

pp.: 3 - 17


This study uses the Weather Research and Forecasting model (WRF) and the three-dimensional variational data assimilation system (WRF 3DVAR), in cold and warm starts, with the aim of finding out an appropriate nowcasting method that would have improved the forecast of precipitation maxima in the mesoscale convective system that occurred in Catalonia (NE Spain) on March 21, 2012 at 20 UTC. We assimilated radar data using different configurations, qualitatively verifying the increase of rainwater produced by the assimilation of reflectivity.

Key words: WRF, 3DVAR, radar, precipitation
, and

DOI: 10.3369/tethys.2015.12.02

pp.: 13 - 31


A wide range of approaches for radiative transfer computations leads to several parameterizations. Differences in these approximations bring about distinct results for the radiative fluxes, even under the same atmospheric conditions. Since the transfer of solar and terrestrial radiation represents the primordial physical process that shapes the atmospheric circulation, these deviations must have an impact on the numerical weather prediction (NWP) model performance.


, , and

DOI: 10.3369/tethys.2010.7.07

pp.: 75 - 86


The meteorological model WRF-ARW (Weather Research and Forecasting - Advanced Research WRF) is a new generation model that has a worldwide growing community of users. In the framework of a project that studies the feasibility of implementing it operationally at the Meteorological Service of Catalonia, a verification of the forecasts produced by the model in several cases of precipitation observed over Catalonia has been carried out.


Creative Commons License

This work is licensed under a Creative Commons Attribution 3.0 Unported License

Indexed in Scopus, Thomson-Reuters Emerging Sources Citation Index (ESCI), Scientific Commons, Latindex, Google Scholar, DOAJ, ICYT (CSIC)

Partially funded through grants CGL2007-29820-E/CLI, CGL2008-02804-E/, CGL2009-07417-E and CGL2011-14046-E of the Spanish Ministry of Science and Innovation

Syndicate content