Download
slide1 n.
Skip this Video
Loading SlideShow in 5 Seconds..
Juan Ruiz 1,2 , Celeste Saulo 1,2 , Soledad Cardazzo 1 , Eugenia Kalnay 3 PowerPoint Presentation
Download Presentation
Juan Ruiz 1,2 , Celeste Saulo 1,2 , Soledad Cardazzo 1 , Eugenia Kalnay 3

Juan Ruiz 1,2 , Celeste Saulo 1,2 , Soledad Cardazzo 1 , Eugenia Kalnay 3

126 Vues Download Presentation
Télécharger la présentation

Juan Ruiz 1,2 , Celeste Saulo 1,2 , Soledad Cardazzo 1 , Eugenia Kalnay 3

- - - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript

  1. 3 1 2 Probabilistic Cuantitative Precipitation Forecast Calibration and Verification over South America Juan Ruiz1,2, Celeste Saulo1,2, Soledad Cardazzo1, Eugenia Kalnay3 1Departamento de Cs. de la Atmósfera y los Océanos (FCEyN-UBA), 2 Centro de Investigaciones del Mar y la Atmósfera (CONICET-UBA), 3 University of Maryland GOAL The aim of this work is to quantify the improvement in forecasts due to the implementation of a short range regional ensemble system over South America. The focus is in the probabilistic quantitative precipitation forecast (PQPF) where different methodologies are tested in order to improve forecast reliability. Although these methodologies have been applied to short range forecasts, they can be easily applied to longer range forecasts and to other variables as well. ENSEMBLE GENERATION RESULTS: BREEDING (summer 2002-2003 SALLJEX) • Several techniques have been tested for ensemble generation: • Ensembles based on initial and boundary condition perturbation: In this case we selected methods to generate perturbations with information of the “errors of the day” in order to represent the uncertainty in the initial and boundary conditions. We used SLAF (Scaled Lagged Averaged Forecast) (Dalcher et. al. 1988) and Breeding of the Growing Modes (Toth and Kalnay 1993). Both of them were implemented in the WRF regional model. • Ensemble based on combination of different models: The MASTER Laboratory Super Model Ensemble (SMES) has been used. (Silva Dias et. al. 2006). (http://www.master.iag.usp.br/graf_phplot/index.php) The SMES combines the operational forecasts available over South America. Regional and global models are included in this ensemble as well as forecasts started from different data assimilation systems. • In all cases the 24 and 48 ensemble forecasts were verified and calibrated. The SALLEX and ANA (Agencia Nacional del Agua, Brasil) rain gauge network data is used for the verification and calibration of the breeding ensemble (11 members and 40 km horizontal resolution). Forecast verification / calibration is performed independently over 3 different regions 24 hour forecasts (BSS decomposition) : Uncalibrated (dark red) and its confidence limits (bootstrap). Grey (control forecast) Violet (ensemble mean) Green (HC98) Blue (HC98 with bias correction) Yellow (Modified HC98). The calibration applied to the ensemble mean and the control forecast shows the best results. PQPF CALIBRATION • ¿Why we need to calibrate?: Because probabilities estimated directly from the ensemble are not reliable. This is because the different members of the ensemble have biases which should be corrected before the computation of the probabilities. • ¿How do we calibrate the probabilities?: There are several methodologies but most of them use a dataset composed of forecasts and its verification to quantify model biases and to construct an statistical model to remove those errors from the forecasted probabilities. • Tested methodologies: • Calibration based in the rank histogram: Hamill y Colucci (1998) –HC98- • Calibration using the a conditional probability approach: Gallus and Seagal 2007. 48 hour forecasts (region 1) In this case the ensemble mean is significantly better than the control forecast. Forecast value ( 2.5 mm threshold) Forecast value is computed using a very simple decision making model. The score shows the relative increment in the forecast value over the climatology. The value is plotted as a function of the cost / loss ratio (i.e. the ratio between the cost of protection and the loss produced by the unexpected occurrence of a particular phenomena) RESULTS: SLAF AND SMES (October – December 2006) Southern Region (South of 20º S), Northern Region (north of 20º S). Red: Uncalibrated Blue: Rank histogram calibration. Circles: Conditional probability calibration for the ensemble mean. Triangles: Conditional probability calibration for the control forecast. SLAF(left) and SMES(rigth) Forecast calibration increases forecast value over all regions and mainly in cases where the cost / loss relation is high. Brier Skill Score Operational implementation The calibrated and uncalibrated PQPF obtained from the SMES ensemble and from the WRF-CIMA forecasts are being computed every day. These experimental products are available at CIMA web page: http://wrf.cima.fcen.uba.ar • The results show that: • SLAF seems to be better over the southern region. • A single forecast can achieve results which are similar to the ones obtained with the ensemble for very short forecast lead times. (i.e. less than 24 hours). • Calibration significantly improve forecast reliability.