scholarly journals Typhoon Ensemble Prediction System Developed at the Japan Meteorological Agency

2009 ◽  
Vol 137 (8) ◽  
pp. 2592-2604 ◽  
Author(s):  
Munehiko Yamaguchi ◽  
Ryota Sakai ◽  
Masayuki Kyoda ◽  
Takuya Komori ◽  
Takashi Kadowaki
Keyword(s):  
Deterministic Model ◽  
Global Analysis ◽  
North Pacific Ocean ◽  
Japan Meteorological Agency ◽  
Ensemble Prediction ◽  
Prediction System ◽  
Global Spectral Model ◽  
Vector Method ◽  
Ensemble Prediction System ◽  
Ensemble Mean

Abstract The Japan Meteorological Agency (JMA) Typhoon Ensemble Prediction System (TEPS) and its performance are described. In February 2008, JMA started an operation of TEPS that was designed for providing skillful tropical cyclone (TC) track predictions in both deterministic and probabilistic ways. TEPS consists of 1 nonperturbed prediction and 10 perturbed predictions based on the lower-resolution version (TL319L60) of the JMA Global Spectral Model (GSM; TL959L60) and a global analysis for JMA/GSM. A singular vector method is employed to create initial perturbations. Focusing on TCs in the western North Pacific Ocean and the South China Sea (0°–60°N, 100°E–180°), TEPS runs 4 times a day, initiated at 0000, 0600, 1200, and 1800 UTC with a prediction range of 132 h. The verifications of TEPS during the quasi-operational period from May to December 2007 indicate that the ensemble mean track predictions statistically have better performance as compared with the control (nonperturbed) predictions: the error reduction in the 5-day predictions is 40 km on average. Moreover, it is found that the ensemble spread of tracks is an indicator of position error, indicating that TEPS will be useful in presenting confidence information on TC track predictions. For 2008 when TEPS was in operational use, however, it was also found that the ensemble mean was significantly worse than the deterministic model (JMA/GSM) out to 84 h.

scholarly journals The skill assessment of ENSO prediction issued by JMA ensemble prediction system and CFSv2

2021 ◽  
Vol 893 (1) ◽  
pp. 012047
Author(s):  
R Rahmat ◽  
A M Setiawan ◽  
Supari
Keyword(s):  
Southern Oscillation ◽  
Skill Score ◽  
Skill Assessment ◽  
Japan Meteorological Agency ◽  
Ensemble Prediction ◽  
In Situ Observation ◽  
Prediction System ◽  
Ensemble Prediction System ◽  
Enso Prediction ◽  
The Government

Abstract Indonesian climate is strongly affected by El Niño-Southern Oscillation (ENSO) as one of climate-driven factor. ENSO prediction during the upcoming months or year is crucial for the government in order to design the further strategic policy. Besides producing its own ENSO prediction, BMKG also regularly releases the status and ENSO prediction collected from other climate centers, such as Japan Meteorological Agency (JMA) and National Oceanic and Atmospheric Administration (NOAA). However, the skill of these products is not well known yet. The aim of this study is to conduct a simple assessment on the skill of JMA Ensemble Prediction System (EPS) and NOAA Climate Forecast System version 2 (CFSv2) ENSO prediction using World Meteorological Organization (WMO) Standard Verification System for Long Range Forecast (SVS-LRF) method. Both ENSO prediction results also compared each other using Student's t-test. The ENSO predictions data were obtained from the ENSO JMA and ENSO NCEP forecast archive files, while observed Nino 3.4 were calculated from Centennial in situ Observation-Based Estimates (COBE) Sea Surface Temperature Anomaly (SSTA). Both ENSO prediction issued by JMA and NCEP has a good skill on 1 to 3 months lead time, indicated by high correlation coefficient and positive value of Mean Square Skill Score (MSSS). However, the skill of both skills significantly reduced for May-August target month. Further careful interpretation is needed for ENSO prediction issued on this mentioned period.

A Unified Scheme of Stochastic Physics and Bias Correction in an Ensemble Model to Reduce Both Random and Systematic Errors

2019 ◽  
Vol 34 (6) ◽  
pp. 1675-1691 ◽  
Author(s):  
Yu Xia ◽  
Jing Chen ◽  
Jun Du ◽  
Xiefei Zhi ◽  
Jingzhuo Wang ◽  
...  
Keyword(s):  
Bias Correction ◽  
Weather Prediction ◽  
Systematic Errors ◽  
Ensemble Prediction ◽  
Ensemble Model ◽  
Prediction System ◽  
Ensemble Prediction System ◽  
Probabilistic Forecasts ◽  
Ensemble Mean ◽  
Stochastic Physics

Abstract This study experimented with a unified scheme of stochastic physics and bias correction within a regional ensemble model [Global and Regional Assimilation and Prediction System–Regional Ensemble Prediction System (GRAPES-REPS)]. It is intended to improve ensemble prediction skill by reducing both random and systematic errors at the same time. Three experiments were performed on top of GRAPES-REPS. The first experiment adds only the stochastic physics. The second experiment adds only the bias correction scheme. The third experiment adds both the stochastic physics and bias correction. The experimental period is one month from 1 to 31 July 2015 over the China domain. Using 850-hPa temperature as an example, the study reveals the following: 1) the stochastic physics can effectively increase the ensemble spread, while the bias correction cannot. Therefore, ensemble averaging of the stochastic physics runs can reduce more random error than the bias correction runs. 2) Bias correction can significantly reduce systematic error, while the stochastic physics cannot. As a result, the bias correction greatly improved the quality of ensemble mean forecasts but the stochastic physics did not. 3) The unified scheme can greatly reduce both random and systematic errors at the same time and performed the best of the three experiments. These results were further confirmed by verification of the ensemble mean, spread, and probabilistic forecasts of many other atmospheric fields for both upper air and the surface, including precipitation. Based on this study, we recommend that operational numerical weather prediction centers adopt this unified scheme approach in ensemble models to achieve the best forecasts.

scholarly journals Sensitivity of Global Ensemble Forecasts to the Initial Ensemble Mean and Perturbations: Comparison of EnKF, Singular Vector, and 4D-Var Approaches

2010 ◽  
Vol 138 (10) ◽  
pp. 3886-3904 ◽  
Author(s):  
Mark Buehner ◽  
Ahmed Mahidjiba
Keyword(s):  
Ensemble Prediction ◽  
Boreal Summer ◽  
Prediction System ◽  
Ensemble Forecasts ◽  
Ensemble Prediction System ◽  
Var Analysis ◽  
Initial Ensemble ◽  
Ensemble Mean ◽  
The Tropics ◽  
The Impact

Abstract This study examines the sensitivity of global ensemble forecasts to the use of different approaches for specifying both the initial ensemble mean and perturbations. The current operational ensemble prediction system of the Meteorological Service of Canada uses the ensemble Kalman filter (EnKF) to define both the ensemble mean and perturbations. To evaluate the impact of different approaches for obtaining the initial ensemble perturbations, the operational EnKF approach is compared with using either no initial perturbations or perturbations obtained using singular vectors (SVs). The SVs are computed using the (dry) total-energy norm with a 48-h optimization time interval. Random linear combinations of 60 SVs are computed for each of three regions. Next, the impact of replacing the initial ensemble mean, currently the EnKF ensemble mean analysis, with the higher-resolution operational four-dimensional variational data assimilation (4D-Var) analysis is evaluated. For this comparison, perturbations are provided by the EnKF. All experiments are performed over two-month periods during both the boreal summer and winter using a system very similar to the global ensemble prediction system that became operational on 10 July 2007. Relative to the operational configuration that relies on the EnKF, the use of SVs to compute initial perturbations produces small, but statistically significant differences in probabilistic forecast scores in favor of the EnKF both in the tropics and, for a limited set of forecast lead times, in the summer hemisphere extratropics, whereas the results are very similar in the winter hemisphere extratropics. Both approaches lead to significantly better ensemble forecasts than with no initial perturbations, though results are quite similar in the tropics when using SVs and no perturbations. The use of an initial-time norm that does not include information on analysis uncertainty and the lack of linearized moist processes in the calculation of the SVs are two factors that limit the quality of the resulting SV-based ensemble forecasts. Relative to the operational configuration, use of the 4D-Var analysis to specify the initial ensemble mean results in improved probabilistic forecast scores during the boreal summer period in the southern extratropics and tropics, but a near-neutral impact otherwise.

A large ensemble decadal prediction system with MPI-ESM

2021 ◽  
Author(s):  
Sebastian Brune ◽  
Vimal Koul ◽  
David Marcolino Nielsen ◽  
Laura Hövel ◽  
Holger Pohlmann ◽  
...  
Keyword(s):  
North Atlantic ◽  
Prediction Skill ◽  
Ensemble Prediction ◽  
Prediction System ◽  
Decadal Prediction ◽  
Ensemble Size ◽  
Large Ensemble ◽  
Ensemble Prediction System ◽  
Atlantic Sector ◽  
Ensemble Mean

<p>Current state-of-the-art decadal ensemble prediction systems are run with an ensemble size of 10 to 40 members, their retrospective forecasts of the past are used to assess the system's prediction skill. Here, we present an attempt for a large ensemble decadal prediction system for the time period 1960-today, with an ensemble size of 80 members, based on the low resolution version of the Max Planck Institute Earth system model (MPI-ESM-LR). The ensemble is forced with CMIP6 conditions and initialized every year in November through a weakly coupled assimilation using atmospheric reanalyses via nudging and observed oceanic temperature and salinity profiles via a 16-member ensemble Kalman filter. To generate ensemble members beyond 16, we use additional physical perturbations at stratospheric height. The analysis of our large ensemble prediction system presented here aims for answering two questions: (1) How does the ensemble mean deterministic prediction skill for global and North Atlantic key climate indices change with ensemble size? (2) How well may the 80-member ensemble serve as a basis for a robust statistical analysis of probabilities of extremes in the North Atlantic sector? Preliminary results for global and regional air surface temperature show that in terms of ensemble mean ACC and full ensemble CPRSS with reference data, the 80-member ensemble leads to similar prediction skill as the 16-member ensemble. This indicates that the additional ensemble members may lead to a better sampling of the distribution of model trajectories, paving the way for a more robust statistical probabilistic analysis.</p>

scholarly journals Regional Differences in the Prediction of Extratropical Cyclones by the ECMWF Ensemble Prediction System

2009 ◽  
Vol 137 (3) ◽  
pp. 893-911 ◽  
Author(s):  
Lizzie S. R. Froude
Keyword(s):  
Regional Differences ◽  
Propagation Speed ◽  
Ensemble Prediction ◽  
Extratropical Cyclones ◽  
Spatial Distributions ◽  
Prediction System ◽  
Ensemble Spread ◽  
Ensemble Prediction System ◽  
Mean Error ◽  
Ensemble Mean

Abstract A regional study of the prediction of extratropical cyclones by the European Centre for Medium-Range Weather Forecasts (ECMWF) Ensemble Prediction System (EPS) has been performed. An objective feature-tracking method has been used to identify and track the cyclones along the forecast trajectories. Forecast error statistics have then been produced for the position, intensity, and propagation speed of the storms. In previous work, data limitations meant it was only possible to present the diagnostics for the entire Northern Hemisphere (NH) or Southern Hemisphere. A larger data sample has allowed the diagnostics to be computed separately for smaller regions around the globe and has made it possible to explore the regional differences in the prediction of storms by the EPS. Results show that in the NH there is a larger ensemble mean error in the position of storms over the Atlantic Ocean. Further analysis revealed that this is mainly due to errors in the prediction of storm propagation speed rather than in direction. Forecast storms propagate too slowly in all regions, but the bias is about 2 times as large in the NH Atlantic region. The results show that storm intensity is generally overpredicted over the ocean and underpredicted over the land and that the absolute error in intensity is larger over the ocean than over the land. In the NH, large errors occur in the prediction of the intensity of storms that originate as tropical cyclones but then move into the extratropics. The ensemble is underdispersive for the intensity of cyclones (i.e., the spread is smaller than the mean error) in all regions. The spatial patterns of the ensemble mean error and ensemble spread are very different for the intensity of cyclones. Spatial distributions of the ensemble mean error suggest that large errors occur during the growth phase of storm development, but this is not indicated by the spatial distributions of the ensemble spread. In the NH there are further differences. First, the large errors in the prediction of the intensity of cyclones that originate in the tropics are not indicated by the spread. Second, the ensemble mean error is larger over the Pacific Ocean than over the Atlantic, whereas the opposite is true for the spread. The use of a storm-tracking approach, to both weather forecasters and developers of forecast systems, is also discussed.

Ensemble mean forecast skill and applications with the T213 ensemble prediction system

2016 ◽  
Vol 33 (11) ◽  
pp. 1297-1305
Author(s):  
Sijia Li ◽  
Yuan Wang ◽  
Huiling Yuan ◽  
Jinjie Song ◽  
Xin Xu
Keyword(s):  
Forecast Skill ◽  
Ensemble Prediction ◽  
Prediction System ◽  
Ensemble Prediction System ◽  
Ensemble Mean

scholarly journals Why Should Ensemble Spread Match the RMSE of the Ensemble Mean?

2014 ◽  
Vol 15 (4) ◽  
pp. 1708-1713 ◽  
Author(s):  
V. Fortin ◽  
M. Abaza ◽  
F. Anctil ◽  
R. Turcotte
Keyword(s):  
Good Practice ◽  
Ensemble Prediction ◽  
Square Root ◽  
Prediction System ◽  
Mean Square ◽  
Ensemble Spread ◽  
Ensemble Prediction System ◽  
Correct Equation ◽  
Ensemble Mean ◽  
The Impact

Abstract When evaluating the reliability of an ensemble prediction system, it is common to compare the root-mean-square error of the ensemble mean to the average ensemble spread. While this is indeed good practice, two different and inconsistent methodologies have been used over the last few years in the meteorology and hydrology literature to compute the average ensemble spread. In some cases, the square root of average ensemble variance is used, and in other cases, the average of ensemble standard deviation is computed instead. The second option is incorrect. To avoid the perpetuation of practices that are not supported by probability theory, the correct equation for computing the average ensemble spread is obtained and the impact of using the wrong equation is illustrated.

scholarly journals Global analysis of seasonal streamflow predictability using an ensemble prediction system and observations from 6192 small catchments worldwide

2013 ◽  
Vol 49 (5) ◽  
pp. 2729-2746 ◽  
Author(s):  
Albert I. J. M. van Dijk ◽  
Jorge L. Peña-Arancibia ◽  
Eric F. Wood ◽  
Justin Sheffield ◽  
Hylke E. Beck
Keyword(s):  
Global Analysis ◽  
Ensemble Prediction ◽  
Prediction System ◽  
Ensemble Prediction System ◽  
Seasonal Streamflow ◽  
Small Catchments

scholarly journals Impact of Resolution Degradation of the Initial Condition on Typhoon Track Forecasts

2010 ◽  
Vol 25 (5) ◽  
pp. 1568-1573 ◽  
Author(s):  
Takemasa Miyoshi ◽  
Takuya Komori ◽  
Hitoshi Yonehara ◽  
Ryota Sakai ◽  
Munenhiko Yamaguchi
Keyword(s):  
High Resolution ◽  
Data Assimilation ◽  
Initial Conditions ◽  
Global Analysis ◽  
Weather Prediction ◽  
Japan Meteorological Agency ◽  
Ensemble Prediction ◽  
Initial Condition ◽  
Ensemble Prediction System ◽  
Typhoon Track

Abstract The operational numerical weather prediction (NWP) systems at the Japan Meteorological Agency (JMA) indicated that the typhoon track forecasts made by the control member of the ensemble prediction system (EPS) tended to be worse than those made by the high-resolution global NWP. The control forecast of the EPS with horizontal triangular truncation at 319 wavenumbers and 60 vertical levels (T319/L60 resolution) was initialized by eliminating the higher-wavenumber components of the global analysis at T959/L60 resolution. When the data assimilation cycle was performed at the lower T319/L60 resolution, the forecast gave typhoon track forecasts closer to the high-resolution global NWP. Therefore, it stands to reason that the resolution transform of the initial condition must be responsible for the degradation of the typhoon track forecasts at least to considerable extent. To improve the low-resolution forecast, two approaches are tested in this study: 1) applying a smoother spectral truncation for the resolution transform and 2) performing noncycled lower-resolution data assimilation during preprocessing. Results from the single case study of Typhoon Nuri (2008) indicate almost no impact from the former approach, but a significant positive impact when using the latter approach. The results of this study illuminate the importance of considering a model’s resolving capability during data assimilation. Namely, if the initial conditions contain features caused by unresolved scales, degraded forecasts may result.

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