Abstract
The results of a deterministic calibration for the nonhydrostatic convection-permitting LAM-EPS AEMET-γSREPS are shown. LAM-EPS AEMET-γSREPS is a multiboundary condition, multimodel ensemble forecast system developed for Spain. Machine learning tools are used to calibrate the members of the ensemble. Machine learning (hereafter ML) has been considerably successful in many problems, and recent research suggests that meteorology and climatology are not an exception. These machine learning tools range from classical statistical methods to contemporary successful and powerful methods such as kernels and neural networks. The calibration has been done for airports located in many regions of Spain, representing different climatic conditions. The variables to be calibrated are the 2-meter temperature, the 10-meter wind speed, and the precipitation in 24 hours. Classical statistical methods perform very well with the temperature and the wind speed; the precipitation is a subtler case: it seems there is not a general rule, and for each point, a decision has to be taken of what method (if any) improves the direct output of the model, but even recognizing this, a slight improvement can be shown with ML methods for the precipitation.
Highlights
It is constituted by 20 members and runs at 2.5 kilometres resolution, and it is convection permitting. It uses two branches of the European model Harmonie (ALARO and AROME), the WRF-ARW from NOAA-NCAR, and the NMMB from NOAA-NCEP. e boundary conditions come from 5 Global NWP models: ECMWF/ IFS, NOAA-NCEP/GFS, Canadian CMC/GEM, Japanese JMA/GSM, and Meteo-France/ARPEGE
Multimodel is the approach to take into account NWP model’s errors and uncertainties mainly in the mesoscale, and the initial and boundary conditions uncertainties are dealt through multiboundaries approach which are more related to synoptic uncertainties. e multiboundaries and multimodel design of AEMET-cSREPS is the same as its predecessor AEMETSREPS [5] because of the same reason: better performance in terms of more consistent EPS, with better skill than using other EPS approaches as multiphysics, stochastic parameterizations, multiparameters, and boundary conditions from a global EPS [6]
It was decided to use a deterministic approach with different machine learning (ML) methods; that is, it was decided to calibrate each of the 20 members as if they were a deterministic model, and 5 airports that represented different climatic conditions of Spain were chosen; these airports were Madrid-Adolfo Suarez-Barajas, Barcelona-El Prat, Vigo-Peinador, Palma de Mallorca-Son San Juan, and Malaga-Costa del Sol
Summary
Is is an ambitious and original shortrange ensemble which mixes different boundary conditions and NWP models (so, it is a multiboundary, multimodel ensemble). It is constituted by 20 members and runs at 2.5 kilometres resolution, and it is convection permitting. It was decided to use a deterministic approach with different machine learning (ML) methods; that is, it was decided to calibrate each of the 20 members as if they were a deterministic model, and 5 airports that represented different climatic conditions of Spain were chosen; these airports were Madrid-Adolfo Suarez-Barajas, Barcelona-El Prat, Vigo-Peinador, Palma de Mallorca-Son San Juan, and Malaga-Costa del Sol. Madrid has an airport in the middle of the Iberian Peninsula, with a continental and dry climate; two airports were close to the coast (Barcelona and Palma de Mallorca): one of them (Palma de Mallorca) is in an island with Mediterranean climate.
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