scholarly journals Dynamical downscaling of a multimodel ensemble prediction system: Application to tropical cyclones

2020 ◽  
Vol 21 (8) ◽  
Author(s):  
Manpreet Kaur ◽  
R. Phani Murali Krishna ◽  
Susmitha Joseph ◽  
Avijit Dey ◽  
Raju Mandal ◽  
...  
Keyword(s):  
Tropical Cyclones ◽  
System Application ◽  
Dynamical Downscaling ◽  
Ensemble Prediction ◽  
Prediction System ◽  
Multimodel Ensemble ◽  
Ensemble Prediction System

Extended Prediction of North Indian Ocean Tropical Cyclones

2012 ◽  
Vol 27 (3) ◽  
pp. 757-769 ◽  
Author(s):  
James I. Belanger ◽  
Peter J. Webster ◽  
Judith A. Curry ◽  
Mark T. Jelinek
Keyword(s):  
Tropical Cyclone ◽  
Indian Ocean ◽  
Tropical Cyclones ◽  
North Indian Ocean ◽  
Ensemble Prediction ◽  
Tropical Cyclone Genesis ◽  
Lead Times ◽  
Prediction System ◽  
Ensemble Prediction System ◽  
The North

Abstract This analysis examines the predictability of several key forecasting parameters using the ECMWF Variable Ensemble Prediction System (VarEPS) for tropical cyclones (TCs) in the North Indian Ocean (NIO) including tropical cyclone genesis, pregenesis and postgenesis track and intensity projections, and regional outlooks of tropical cyclone activity for the Arabian Sea and the Bay of Bengal. Based on the evaluation period from 2007 to 2010, the VarEPS TC genesis forecasts demonstrate low false-alarm rates and moderate to high probabilities of detection for lead times of 1–7 days. In addition, VarEPS pregenesis track forecasts on average perform better than VarEPS postgenesis forecasts through 120 h and feature a total track error growth of 41 n mi day−1. VarEPS provides superior postgenesis track forecasts for lead times greater than 12 h compared to other models, including the Met Office global model (UKMET), the Navy Operational Global Atmospheric Prediction System (NOGAPS), and the Global Forecasting System (GFS), and slightly lower track errors than the Joint Typhoon Warning Center. This paper concludes with a discussion of how VarEPS can provide much of this extended predictability within a probabilistic framework for the region.

scholarly journals A Probabilistic Multimodel Ensemble Approach to Seasonal Prediction

2009 ◽  
Vol 24 (3) ◽  
pp. 812-828 ◽  
Author(s):  
Young-Mi Min ◽  
Vladimir N. Kryjov ◽  
Chung-Kyu Park
Keyword(s):  
Real Time ◽  
Gaussian Approximation ◽  
Seasonal Prediction ◽  
Asia Pacific ◽  
Ensemble Prediction ◽  
Prediction System ◽  
Sampling Errors ◽  
Seasonal Forecasts ◽  
Multimodel Ensemble ◽  
Ensemble Prediction System

Abstract A probabilistic multimodel ensemble prediction system (PMME) has been developed to provide operational seasonal forecasts at the Asia–Pacific Economic Cooperation (APEC) Climate Center (APCC). This system is based on an uncalibrated multimodel ensemble, with model weights inversely proportional to the errors in forecast probability associated with the model sampling errors, and a parametric Gaussian fitting method for the estimate of tercile-based categorical probabilities. It is shown that the suggested method is the most appropriate for use in an operational global prediction system that combines a large number of models, with individual model ensembles essentially differing in size and model weights in the forecast and hindcast datasets being inconsistent. Justification for the use of a Gaussian approximation of the precipitation probability distribution function for global forecasts is also provided. PMME retrospective and real-time forecasts are assessed. For above normal and below normal categories, temperature forecasts outperform climatology for a large part of the globe. Precipitation forecasts are definitely more skillful than random guessing for the extratropics and climatological forecasts for the tropics. The skill of real-time forecasts lies within the range of the interannual variability of the historical forecasts.

scholarly journals Space weather forecasting with a Multimodel Ensemble Prediction System (MEPS)

Radio Science ◽  
2016 ◽  
Vol 51 (7) ◽  
pp. 1157-1165 ◽  
Author(s):  
R. W. Schunk ◽  
L. Scherliess ◽  
V. Eccles ◽  
L. C. Gardner ◽  
J. J. Sojka ◽  
...  
Keyword(s):  
Space Weather ◽  
Weather Forecasting ◽  
Ensemble Prediction ◽  
Prediction System ◽  
Multimodel Ensemble ◽  
Ensemble Prediction System ◽  
Space Weather Forecasting

A New Method for Generating Initial Condition Perturbations in a Regional Ensemble Prediction System: Blending

2014 ◽  
Vol 142 (5) ◽  
pp. 2043-2059 ◽  
Author(s):  
Yong Wang ◽  
Martin Bellus ◽  
Jean-Francois Geleyn ◽  
Xulin Ma ◽  
Weihong Tian ◽  
...  
Keyword(s):  
Large Scale ◽  
Dynamical Downscaling ◽  
Ensemble Prediction ◽  
Small Scale ◽  
Lateral Boundary Condition ◽  
Limited Area ◽  
Initial Condition ◽  
Prediction System ◽  
Ensemble Prediction System ◽  
Blending Method

Abstract A blending method for generating initial condition (IC) perturbations in a regional ensemble prediction system is proposed. The blending is to combine the large-scale IC perturbations from a global ensemble prediction system (EPS) with the small-scale IC perturbations from a regional EPS by using a digital filter and the spectral analysis technique. The IC perturbations generated by blending can well represent both large-scale and small-scale uncertainties in the analysis, and are more consistent with the lateral boundary condition (LBC) perturbations provided by global EPS. The blending method is implemented in the regional ensemble system Aire Limitée Adaptation Dynamique Développement International-Limited Area Ensemble Forecasting (ALADIN-LAEF), in which the large-scale IC perturbations are provided by the European Centre for Medium-Range Weather Forecasts (ECMWF-EPS), and the small-scale IC perturbations are generated by breeding in ALADIN-LAEF. Blending is compared with dynamical downscaling and breeding over a 2-month period in summer 2007. The comparison clearly shows impact on the growth of forecast spread if the regional IC perturbations are not consistent with the perturbations coming through LBC provided by the global EPS. Blending can cure the problem largely, and it performs better than dynamical downscaling and breeding.

scholarly journals Study on the Construction of Initial Condition Perturbations for the Regional Ensemble Prediction System of North China

Atmosphere ◽  
2019 ◽  
Vol 10 (2) ◽  
pp. 87
Author(s):  
Hanbin Zhang ◽  
Min Chen ◽  
Shuiyong Fan
Keyword(s):  
North China ◽  
Dynamical Downscaling ◽  
Ensemble Forecast ◽  
Precipitation Forecast ◽  
Ensemble Prediction ◽  
Initial Condition ◽  
Prediction System ◽  
Ensemble Prediction System ◽  
Ensemble Performance ◽  
Range Forecasts

The regional ensemble prediction system (REPS) of North China is currently under development at the Institute of Urban Meteorology, China Meteorological Administration, with initial condition perturbations provided by global ensemble dynamical downscaling. To improve the performance of the REPS, a comparison of two initial condition perturbation methods is conducted in this paper: (i) Breeding, which was specifically designed for the REPS, and (ii) Dynamical downscaling. Consecutive tests were implemented to evaluate the performances of both methods in the operational REPS environment. The perturbation characteristics were analyzed, and ensemble forecast verifications were conducted. Furthermore, a heavy precipitation case was investigated. The main conclusions are as follows: the Breeding perturbations were more powerful at small scales, while the downscaling perturbations were more powerful at large scales; the difference between the two perturbation types gradually decreased with the forecast lead time. The downscaling perturbation growth was more remarkable than that of the Breeding perturbations at short forecast lead times, while the perturbation magnitudes of both schemes were similar for long-range forecasts. However, the Breeding perturbations contained more abundant small-scale components than downscaling for the short-range forecasts. The ensemble forecast verification indicated a slightly better downscaling ensemble performance than that of the Breeding ensemble. A precipitation case study indicated that the Breeding ensemble performance was better than that of downscaling, particularly in terms of location and strength of the precipitation forecast.

Study of perturbing method in regional BGM ensemble prediction system

2012 ◽  
Vol 4 (1) ◽  
pp. 65
Author(s):  
Xiao Yu-Hua ◽  
He Guang-Bi ◽  
Chen Jing ◽  
Deng Guo
Keyword(s):  
Ensemble Prediction ◽  
Prediction System ◽  
Ensemble Prediction System
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