scholarly journals Application of Kalman Filter and Breeding Ensemble Technique to Forecast the Tropical Cyclone Activity

2021 ◽  
Author(s):  
Cong Thanh ◽  
Dao Nguyen Quynh Hoa ◽  
Tran Tan Tien
Keyword(s):  
Kalman Filter ◽  
Tropical Cyclone ◽  
Wrf Model ◽  
Weather Forecast ◽  
Ensemble Prediction ◽  
Ensemble Prediction System ◽  
Ensemble Technique ◽  
Ensemble Transform Kalman Filter ◽  
Numerical Weather Forecast ◽  
Regional Atmospheric Modeling

Tropical cyclone (TC) is one of the major meteorology disasters, as they lead to deaths, destroy the infrastructure and the environment. Therefore, how to improve the predictability of TC’s activities, such as formation, track, and intensity, is very important and is considered an important task for current operational predicting TC centers in many countries. However, predicting TC’s activities has remained a big challenge for meteorologists due to our incomplete understanding of the multiscale interaction of TCs with the ambient environment and the limitation of numerical weather forecast tools. Hence, this chapter will exhibit some techniques to improve the ability to predict the formation and track of TCs using an ensemble prediction system. Particularly, the Local Ensemble Transform Kalman Filter (LETKF) scheme and its implementation in the WRF Model, as well as the Vortex tracking method that has been applied for the forecast of TCs formation, will be presented in subSection 1. Application of Breeding Ensemble to Tropical Cyclone Track Forecasts using the Regional Atmospheric Modeling System (RAMS) model will be introduced in subSection 2.

scholarly journals A Real-Time Convection-Allowing Ensemble Prediction System Initialized by Mesoscale Ensemble Kalman Filter Analyses

2015 ◽  
Vol 30 (5) ◽  
pp. 1158-1181 ◽  
Author(s):  
Craig S. Schwartz ◽  
Glen S. Romine ◽  
Morris L. Weisman ◽  
Ryan A. Sobash ◽  
Kathryn R. Fossell ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Real Time ◽  
Ensemble Kalman Filter ◽  
Wrf Model ◽  
Severe Weather ◽  
Ensemble Prediction ◽  
Limited Area ◽  
Ensemble Forecasts ◽  
Ensemble Prediction System ◽  
Weather Events

Abstract In May and June 2013, the National Center for Atmospheric Research produced real-time 48-h convection-allowing ensemble forecasts at 3-km horizontal grid spacing using the Weather Research and Forecasting (WRF) Model in support of the Mesoscale Predictability Experiment field program. The ensemble forecasts were initialized twice daily at 0000 and 1200 UTC from analysis members of a continuously cycling, limited-area, mesoscale (15 km) ensemble Kalman filter (EnKF) data assimilation system and evaluated with a focus on precipitation and severe weather guidance. Deterministic WRF Model forecasts initialized from GFS analyses were also examined. Subjectively, the ensemble forecasts often produced areas of intense convection over regions where severe weather was observed. Objective statistics confirmed these subjective impressions and indicated that the ensemble was skillful at predicting precipitation and severe weather events. Forecasts initialized at 1200 UTC were more skillful regarding precipitation and severe weather placement than forecasts initialized 12 h earlier at 0000 UTC, and the ensemble forecasts were typically more skillful than GFS-initialized forecasts. At times, 0000 UTC GFS-initialized forecasts had temporal distributions of domain-average rainfall closer to observations than EnKF-initialized forecasts. However, particularly when GFS analyses initialized WRF Model forecasts, 1200 UTC forecasts produced more rainfall during the first diurnal maximum than 0000 UTC forecasts. This behavior was mostly attributed to WRF Model initialization of clouds and moist physical processes. The success of these real-time ensemble forecasts demonstrates the feasibility of using limited-area continuously cycling EnKFs as a method to initialize convection-allowing ensemble forecasts, and future real-time high-resolution ensemble development leveraging EnKFs seems justified.

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 Verification of Ensemble-Based Uncertainty Circles around Tropical Cyclone Track Forecasts

2011 ◽  
Vol 26 (5) ◽  
pp. 664-676 ◽  
Author(s):  
Thierry Dupont ◽  
Matthieu Plu ◽  
Philippe Caroff ◽  
Ghislain Faure
Keyword(s):  
Tropical Cyclone ◽  
Large Error ◽  
Ensemble Prediction ◽  
Probabilistic Forecasting ◽  
Cyclone Track ◽  
Ensemble Prediction System ◽  
Weather Forecasts ◽  
Probabilistic Forecasts ◽  
Medium Range ◽  
Uncertainty Information

Abstract Several tropical cyclone forecasting centers issue uncertainty information with regard to their official track forecasts, generally using the climatological distribution of position error. However, such methods are not able to convey information that depends on the situation. The purpose of the present study is to assess the skill of the Ensemble Prediction System (EPS) from the European Centre for Medium-Range Weather Forecasts (ECMWF) at measuring the uncertainty of up to 3-day track forecasts issued by the Regional Specialized Meteorological Centre (RSMC) La Réunion in the southwestern Indian Ocean. The dispersion of cyclone positions in the EPS is extracted and translated at the RSMC forecast position. The verification relies on existing methods for probabilistic forecasts that are presently adapted to a cyclone-position metric. First, the probability distribution of forecast positions is compared to the climatological distribution using Brier scores. The probabilistic forecasts have better scores than the climatology, particularly after applying a simple calibration scheme. Second, uncertainty circles are built by fixing the probability at 75%. Their skill at detecting small and large error values is assessed. The circles have some skill for large errors up to the 3-day forecast (and maybe after); but the detection of small radii is skillful only up to 2-day forecasts. The applied methodology may be used to assess and to compare the skill of different probabilistic forecasting systems of cyclone position.

scholarly journals Initial perturbations based on Ensemble Transfrom Kalman Filter with rescaling method for ensemble forecast

2021 ◽  
Author(s):  
Jingzhuo Wang ◽  
Jing Chen ◽  
Hanbin Zhang ◽  
Hua Tian ◽  
Yining Shi
Keyword(s):  
Growth Rate ◽  
Kalman Filter ◽  
Weather Forecasting ◽  
Initial Perturbation ◽  
Ensemble Forecast ◽  
Ensemble Prediction ◽  
Forecast Errors ◽  
Model Uncertainties ◽  
Ensemble Prediction System ◽  
Probabilistic Forecasts

AbstractEnsemble forecast is a method to faithfully describe initial and model uncertainties in a weather forecasting system. Initial uncertainties are much more important than model uncertainties in the short-range numerical prediction. Currently, initial uncertainties are described by Ensemble Transform Kalman Filter (ETKF) initial perturbation method in Global and Regional Assimilation and Prediction Enhanced System-Regional Ensemble Prediction System (GRAPES-REPS). However, an initial perturbation distribution similar to the analysis error cannot be yielded in the ETKF method of the GRAPES-REPS. To improve the method, we introduce a regional rescaling factor into the ETKF method (we call it ETKF_R). We also compare the results between the ETKF and ETKF_R methods and further demonstrate how rescaling can affect the initial perturbation characteristics as well as the ensemble forecast skills. The characteristics of the initial ensemble perturbation improve after applying the ETKF_R method. For example, the initial perturbation structures become more reasonable, the perturbations are better able to explain the forecast errors at short lead times, and the lower kinetic energy spectrum as well as perturbation energy at the initial forecast times can lead to a higher growth rate of themselves. Additionally, the ensemble forecast verification results suggest that the ETKF_R method has a better spread-skill relationship, a faster ensemble spread growth rate and a more reasonable rank histogram distribution than ETKF. Furthermore, the rescaling has only a minor impact on the assessment of the sharpness of probabilistic forecasts. The above results all suggest that ETKF_R can be effectively applied to the operational GRAPES-REPS.

scholarly journals Verification of 24-h Quantitative Precipitation Forecasts over the Pacific Northwest from a High-Resolution Ensemble Kalman Filter System

2017 ◽  
Vol 32 (3) ◽  
pp. 1185-1208 ◽  
Author(s):  
Phillipa Cookson-Hills ◽  
Daniel J. Kirshbaum ◽  
Madalina Surcel ◽  
Jonathan G. Doyle ◽  
Luc Fillion ◽  
...  
Keyword(s):  
Kalman Filter ◽  
High Resolution ◽  
Data Assimilation ◽  
Pacific Northwest ◽  
Ensemble Kalman Filter ◽  
Ensemble Prediction ◽  
Forecast Errors ◽  
Ensemble Prediction System ◽  
The Pacific ◽  
Quantitative Precipitation Forecasts

Abstract Environment and Climate Change Canada (ECCC) has recently developed an experimental high-resolution EnKF (HREnKF) regional ensemble prediction system, which it tested over the Pacific Northwest of North America for the first half of February 2011. The HREnKF has 2.5-km horizontal grid spacing and assimilates surface and upper-air observations every hour. To determine the benefits of the HREnKF over less expensive alternatives, its 24-h quantitative precipitation forecasts are compared with those from a lower-resolution (15 km) regional ensemble Kalman filter (REnKF) system and to ensembles directly downscaled from the REnKF using the same grid as the HREnKF but with no additional data assimilation (DS). The forecasts are verified against rain gauge observations and gridded precipitation analyses, the latter of which are characterized by uncertainties of comparable magnitude to the model forecast errors. Nonetheless, both deterministic and probabilistic verification indicates robust improvements in forecast skill owing to the finer grids of the HREnKF and DS. The HREnKF exhibits a further improvement in performance over the DS in the first few forecast hours, suggesting a modest positive impact of data assimilation. However, this improvement is not statistically significant and may be attributable to other factors.

scholarly journals Improvements in tropical cyclone forecasting through ensemble prediction system at NCMRWF in India

2020 ◽  
Vol 9 (2) ◽  
pp. 106-116
Author(s):  
Anumeha Dube ◽  
Raghavendra Ashrit ◽  
Sushant Kumar ◽  
Ashu Mamgain
Keyword(s):  
Tropical Cyclone ◽  
Ensemble Prediction ◽  
Prediction System ◽  
Ensemble Prediction System

scholarly journals Fire weather index: the skill provided by ECMWF ensemble prediction system

2020 ◽  
Author(s):  
Francesca Di Giuseppe ◽  
Claudia Vitolo ◽  
Blazej Krzeminski ◽  
Jesús San-Miguel
Keyword(s):  
Weather Forecast ◽  
Research Centre ◽  
Ensemble Prediction ◽  
Fire Weather ◽  
Prediction System ◽  
Weather Index ◽  
Fire Weather Index ◽  
Ensemble Prediction System ◽  
Medium Range Weather Forecast ◽  
Medium Range

Abstract. In the framework of the EU Copernicus program, the European Centre for Medium-range Weather Forecast (ECMWF) on behalf of the Joint Research Centre (JRC) is forecasting daily fire weather indices using its medium range ensemble prediction system. The use of weather forecast in place of local observations can extend early warnings up to 1–2 weeks allowing for greater proactive coordination of resource-sharing and mobilization within and across countries. Using one year of pre-operational service in 2017 and the fire weather index (FWI) here we assess the capability of the system globally and analyze in detail three major events in Chile, Portugal and California. The analysis shows that the skill provided by the ensemble forecast system extends to more than 10 days when compared to the use of mean climate making a case of extending the forecast range to the sub-seasonal to seasonal time scale. However accurate FWI prediction does not translate into accuracy in the forecast of fire activity globally. Indeed when all 2017 detected fires are considered, including agricultural and human induced burning, high FWI values only occurs in 50 % of the cases and only in Boreal regions. Nevertheless for very important events mostly driven by weather condition, FWI forecast provides advance warning that could be instrumental in setting up management strategies.

scholarly journals Model Error Representation in an Operational Ensemble Kalman Filter

2009 ◽  
Vol 137 (7) ◽  
pp. 2126-2143 ◽  
Author(s):  
P. L. Houtekamer ◽  
Herschel L. Mitchell ◽  
Xingxiu Deng
Keyword(s):  
Kalman Filter ◽  
Ensemble Kalman Filter ◽  
Model Error ◽  
Ensemble Prediction ◽  
Isotropic Model ◽  
Error Sources ◽  
Ensemble Prediction System ◽  
Stochastic Perturbations ◽  
Ensemble Mean ◽  
The Impact

Since 12 January 2005, an ensemble Kalman filter (EnKF) has been used operationally at the Meteorological Service of Canada to provide the initial conditions for the medium-range forecasts of the ensemble prediction system. One issue in EnKF development is how to best account for model error. It is shown that in a perfect-model environment, without any model error or model error simulation, the EnKF spread remains representative of the ensemble mean error with respect to a truth integration. Consequently, the EnKF can be used to quantify the impact of the various error sources in a data-assimilation cycle on the quality of the ensemble mean. Using real rather than simulated observations, but still not simulating model error in any manner, the rms ensemble spread is found to be too small by approximately a factor of 2. It is then attempted to account for model error by using various combinations of the following four different approaches: (i) additive isotropic model error perturbations; (ii) different versions of the model for different ensemble members; (iii) stochastic perturbations to physical tendencies; and (iv) stochastic kinetic energy backscatter. The addition of isotropic model error perturbations is found to have the biggest impact. The identification of model error sources could lead to a more realistic, likely anisotropic, parameterization. Using different versions of the model has a small but clearly positive impact and consequently both (i) and (ii) are used in the operational EnKF. The use of approaches (iii) and (iv) did not lead to further improvements.

scholarly journals Novel Metrics for Evaluation of Ensemble Forecasts of Tropical Cyclone Structure

2009 ◽  
Vol 137 (9) ◽  
pp. 2830-2850 ◽  
Author(s):  
Daniel Veren ◽  
Jenni L. Evans ◽  
Sarah Jones ◽  
Francesca Chiaromonte
Keyword(s):  
Tropical Cyclone ◽  
Forecast Accuracy ◽  
Ensemble Member ◽  
Life Cycles ◽  
Ensemble Prediction ◽  
Structure Evolution ◽  
Track Forecast ◽  
Forecast Errors ◽  
Reference Structure ◽  
Ensemble Prediction System

Abstract Predicting extratropical transition (ET) of a tropical cyclone poses a significant challenge to numerical forecast models because the storm evolution depends on both the timing of the phasing between the tropical cyclone and midlatitude weather systems and the structures of each system. Ensemble prediction systems offer the potential for assessing confidence in numerical guidance during ET cases. Thus, forecasts of storm structure changes during ET from the European Centre for Medium-Range Weather Forecasts (ECMWF) ensemble prediction system (EPS) are explored using two novel validation approaches. The evolution of the (initially tropical) storm structure is characterized in the framework of the cyclone phase space (CPS) and the validation metrics are based on separation between the EPS forecasts and verifying analyses in the CPS. The first validation approach utilizes two metrics and most closely resembles traditional forecast validation techniques. The second approach involves clustering the ensemble member initializations and operational analyses during the life cycles of each tropical cyclone to provide a reference structure evolution against which to evaluate the EPS forecasts. Application of these metrics is demonstrated for two case studies of ET in the western North Pacific: Typhoons Tokage (2004) and Maemi (2003). Both validation approaches identify a decline in EPS structure forecast accuracy for all valid times coinciding with ET onset and beyond, as well as during a weakening tropical stage prior to ET for Tokage. While track forecast errors contribute to structure errors in the EPS forecasts, they are not an overwhelming factor. The two validation approaches highlight the inability of ensemble member forecasts to appropriately weaken the warm core prior to and during ET, and the effects this has on forecasts of ET timing. The analyses adopted in this study provide a basis for future assessments of ensemble forecast skill of cyclone structure during ET.

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