Possible Sources of Forecast Errors Generated by the Global/Regional Assimilation and Prediction System for Landfalling Tropical Cyclones.PartⅠ:Initial Uncertainties

This paper investigates the possible sources of errors associated with tropical cyclone （TC） tracks forecasted using the Global/Regional Assimilation and Prediction System （GRAPES）. The GRAPES forecasts were made for 16 landfaIling TCs in the western North Pacific basin during the 2008 and 2009 seasons, with a forecast length of 72 hours, and using the default initial conditions （＂initials＂, hereafter）, which are from the NCEP-FNL dataset, as well as ECMWF initials. The forecasts are compared with ECMWF forecasts. The results show that in most TCs, the GRAPES forecasts are improved when using the ECMWF initials compared with the default initials. Compared with the ECMWF initials, the default initials produce lower intensity TCs and a lower intensity subtropical high, but a higher intensity South Asia high and monsoon trough, as well as a higher temperature but lower specific humidity at the TC center. Replacement of the geopotential height and wind fields with the ECMWF initials in and around the TC center at the initial time was found to be the most efficient way to improve the forecasts. In addition, TCs that showed the greatest improvement in forecast accuracy usually had the largest initial uncertainties in TC intensity and were usually in the intensifying phase. The results demonstrate the importance of the initial intensity for TC track forecasts made using GRAPES, and indicate the model is better in describing the intensifying phase than the decaying phase of TCs. Finally, the limit of the improvement indicates that the model error associated with GRAPES forecasts may be the main cause of poor forecasts of landfalling TCs. Thus, further examinations of the model errors are required.

Possible Sources of Forecast Errors Generated by the Global/Regional Assimilation and Prediction System for Landfalling Tropical Cyclones. Part Ⅱ: Model Uncertainty

This paper investigates the possible sources of errors associated with tropical cyclone(TC) tracks forecasted using the Global/Regional Assimilation and Prediction System(GRAPES). In Part I, it is shown that the model error of GRAPES may be the main cause of poor forecasts of landfalling TCs. Thus, a further examination of the model error is the focus of Part II.Considering model error as a type of forcing, the model error can be represented by the combination of good forecasts and bad forecasts. Results show that there are systematic model errors. The model error of the geopotential height component has periodic features, with a period of 24 h and a global pattern of wavenumber 2 from west to east located between 60?S and 60?N. This periodic model error presents similar features as the atmospheric semidiurnal tide, which reflect signals from tropical diabatic heating, indicating that the parameter errors related to the tropical diabatic heating may be the source of the periodic model error. The above model errors are subtracted from the forecast equation and a series of new forecasts are made. The average forecasting capability using the rectified model is improved compared to simply improving the initial conditions of the original GRAPES model. This confirms the strong impact of the periodic model error on landfalling TC track forecasts. Besides, if the model error used to rectify the model is obtained from an examination of additional TCs, the forecasting capabilities of the corresponding rectified model will be improved.

Recommendations for improved tropical cyclone formation and position probabilistic Forecast products

Prediction of the potentially devastating impact of landfalling tropical cyclones(TCs)relies substantially on numerical prediction systems.Due to the limited predictability of TCs and the need to express forecast confidence and possible scenarios,it is vital to exploit the benefits of dynamic ensemble forecasts in operational TC forecasts and warnings.RSMCs,TCWCs,and other forecast centers value probabilistic guidance for TCs,but the International Workshop on Tropical Cyclones(IWTC-9)found that the“pull-through”of probabilistic information to operational warnings using those forecasts is slow.IWTC-9 recommendations led to the formation of the WMO/WWRP Tropical Cyclone-Probabilistic Forecast Products(TC-PFP)project,which is also endorsed as a WMO Seamless GDPFS Pilot Project.The main goal of TC-PFP is to coordinate across forecast centers to help identify best practice guidance for probabilistic TC forecasts.TC-PFP is being implemented in 3 phases:Phase 1(TC formation and position);Phase 2(TC intensity and structure);and Phase 3(TC related rainfall and storm surge).This article provides a summary of Phase 1 and reviews the current state of the science of probabilistic forecasting of TC formation and position.There is considerable variability in the nature and interpretation of forecast products based on ensemble information,making it challenging to transfer knowledge of best practices across forecast centers.Communication among forecast centers regarding the effectiveness of different approaches would be helpful for conveying best practices.Close collaboration with experts experienced in communicating complex probabilistic TC information and sharing of best practices between centers would help to ensure effective decisions can be made based on TC forecasts.Finally,forecast centers need timely access to ensemble information that has consistent,user-friendly ensemble information.Greater consistency across forecast centers in data accessibility,probabilistic forecast products,and warnings and their communication to users will produce more reliable information and support improved outcomes.

SELECTIVE ENSEMBLE MEAN TECHNIQUE FOR TROPICAL CYCLONE TRACK FORECASTS USING MULTI-MODEL ENSEMBLES

A selective ensemble mean technique for tropical cyclone(TC)track forecasts,which excludes from the ensemble those models that have large position errors at short lead times,was applied to a set of TC track forecasts produced by 11 operational global deterministic models.The position errors of the resulting selective ensemble mean TC track forecasts were verifi ed for 91 TCs in the western North Pacifi c from 2010 to 2013 that reached an intensity classifi cation of'tropical storm'or stronger.The TC position errors of the selective ensemble mean were smaller than those of a simple 11-member ensemble mean by 14.4%,7.4%and 4.7%at forecast times of24,48 and 72 hours,respectively.However,the errors were larger than those of the best single-model-based deterministic forecasts,which were ECMWF forecasts.The correlation between TC position errors at short and long lead times was weak,which partially explains why the selective ensemble mean technique in this study had lesser skill than ECMWF forecasts.For operational forecasting,simple ensemble mean forecasts by ECMWF and NCEP generally provide the best forecast performance for verifi cation samples from 2010 to 2013.

NORTH WESTERN PACIFIC TROPICAL CYCLONE ENSEMBLE FORECAST PROJECT

The WMO/TCP and WWRP launched the North Western Pacific Tropical Cyclone Ensemble Forecast Project(NWP-TCEFP) in 2009 to explore the utility of ensemble forecasts, including multi-model ensemble forecasts of TCs, and to promote such products for operational TC forecasting. Operational global mediumrange ensembles, which have been exchanged in real-time in a CXML format under the initiative of the WMO GIFS-TIGGE Working Group, have been used to create ensemble products of TC tracks that were used by the Typhoon Committee Members and forecasters participating in the SWFDP in Southeast Asia through a password-protected website developed and maintained by the JMA. In some cases many or all of the ensembles have simultaneously predicted small or large ensemble spreads in TC tracks. The implication is that multi-model ensemble products provide forecasters with additional information on forecast certainty or uncertainty and thus increase the level of confidence in the forecasts. Another important outcome of the project was the responses to surveys conducted by the WMO/TCP and WWRP and also by the WMO GIFS-TIGGE Working Group. The responses confirmed that ESCAP/WMO Typhoon Committee Members have routinely accessed the website and have recognized the usefulness of the ensemble products available on the website for operational TC forecasting.

ADVANCES IN UNDERSTANDING DIFFICULT CASES OF TROPICAL CYCLONE TRACK FORECASTS

Although tropical cyclone track forecast errors have substantially decreased in recent decades,there are still cases each season with large uncertainties in the forecasts and/or very large track errors.As such cases are challenging for forecasters,it is important to understand the mechanisms behind the low predictability.For this purpose the research community has developed a number of tools.These tools include ensemble and adjoint sensitivity models,ensemble perturbation experiments and nudging experiments.In this report we discuss definitions of difficult cases for tropical cyclone track forecasts,diagnostic techniques to understand sources of errors,lessons learnt in recent years and recommendations for future work.

Large tropical cyclone track forecast errors of global numerical weather prediction models in western North Pacific basin

Although tropical cyclone(TC)track forecast errors(TFEs)of operational warning centres have substantially decreased in recent decades,there are still many cases with large TFEs.The International Grand Global Ensemble(TIGGE)data are used to study the possible reasons for the large TFE cases and to compare the performance of different numerical weather prediction(NWP)models.Forty-four TCs in the western North Pacific during the period 2007-2014 with TFEs(+24 to+120 h)larger than the 75 th percentile of the annual error distribution(with a total of 93 cases)are identified.Four categories of situations are found to be associated with large TFEs.These include the interaction of the outer structure of the TC with tropical weather systems,the intensity of the TC,the extension of the subtropical high(SH)and the interaction with the westerly trough.The crucial factor of each category attributed to the large TFE is discussed.Among the TIGGE model predictions,the models of the European Centre for Medium-Range Weather Forecasts and the UK Met Office generally have a smaller TFE.The performance of different models in different situations is discussed.

TROPICAL CYCLONE PREDICTION ON SUBSEASONAL TIME-SCALES

Here we discuss recent progress in understanding tropical cyclone(TC)subseasonal variability and its prediction.There has been a concerted effort to understand the sources of predictability at subseasonal time-scales,and this effort has continued to make progress in recent years.Besides the Madden-Julian Oscillation(MJO),other modes of variability affect TCs at these time-scales,in particular various equatorial waves.Additionally,TC activity is also modulated by extratropical processes via Rossby wave breaking.There has also been progress in the ability of models to simulate the MJO and its modulation of TC activity.Community efforts have created multi-model ensemble datasets,which have made it possible to evaluate the forecast skill of the MJO and TCs on subseasonal time-scales in multiple forecasting systems.While there is positive skill in some cases,there is strong dependence on the ensemble system considered,the basin examined,and whether the storms have extratropical influences or not.Furthermore,the definition of skill differs among studies.Forecasting centers are currently issuing subseasonal TC forecasts using various techniques(statistical,statistical-dynamical and dynamical).There is also a strong interest in the private sector for forecasts with 3-4 weeks lead time.