Reprint 581

Use of Model Ensemble Products

for Weather Forecasting in Hong Kong

C.C. Lam, K.C. Yeung & H. Lam

WMO Training Workshop on Ensemble Prediction System,

Shanghai, China, 18-23 April 2005

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Use of Model Ensemble Products for Weather Forecasting in Hong Kong

Queenie C.C. Lam, K.C. Yeung and Hilda Lam Hong Kong Observatory

Email: cclam@hko.gov.hk; kcyeung@hko.gov.hk; hildalam@hko.gov.hk

Abstract

With the availability of model outputs from different meteorological centres, the Hong Kong Observatory (HKO) began to apply multi-model ensemble technique subjectively in its daily operation in late 1980's. To increase effectiveness of interpreting outputs from increasingly more models, an objective method of using multiple model outputs from ECMWF, JMA, NCEP and UKMO has been applied in operational forecasting of tropical cyclones (TCs) in recent years. The performance of model ensemble in TC track forecasting outperformed all individual models. A reduction of 10-15 % was found for 24-, 48- and 72-hour forecast errors when compared with the best individual model for TCs over the South China Sea and the western North Pacific in 2001-2003. An automatic scheme of detection and correction for persistent bias of ensemble forecast was developed for operational use. Further improvements of track errors by 10 % for 24-hour forecast and more than 20 % for 48- and 72-hour forecasts can be achieved in relevant cases. Multi-model ensemble method has also been applied to temperature forecasts at HKO. Ensemble Kalman-filtered temperature forecasts are routinely generated by using temperature forecasts from multiple global models and HKO's operational mesoscale model with different initial times. In deriving the ensemble forecast, higher weightings are assigned to later forecasts and members with better near real-time verification results. Local weather forecasts in text format are also generated automatically from the combination of JMA and ECMWF model. Verification results show that the performance of model ensemble automatic weather forecasts was comparable to that of the HKO's subjective forecasts.

The experience of using perturbation-based ensemble prediction system was first acquired from the application of ECMWF EPS meteograms in HKO. EPS rainfall prediction, trends and patterns on a day-to-day basis were evaluated for selected heavy rain events, as well as case studies of EPS wind speed forecasts in relation to the prediction of high winds in tropical cyclone situations in 2003. It appears that the extreme members in the ensemble, typically the ensemble MAX, offer the most valuable information for extreme rainfall and high-wind forecasts. Considering that probability assessment from multi-model ensemble is not desirable due to the small sampling and dependence between model runs, the HKO will explore the capability of probability forecasts generated from the recently acquired digital data of ECMWF EPS for heavy rain and high-wind situations in Hong Kong.

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1. Introduction The use of multi-model ensemble technique in daily forecasting operations at the Hong Kong Observatory (HKO) began in the late 1980's. Forecasters used model forecasts from the European Centre for Medium-Range Weather Forecasts (ECMWF) and the U.K. Met Office (UKMO), as well as the HKO Limited Area Model outputs as a reference, keeping in view of the relative strengths and weaknesses of these methods, then used professional judgment in combination with climatological and persistence techniques in producing a forecast. With increasing availability of model outputs from Japan Meteorological Agency (JMA) and the U.S. National Centers for Environmental Prediction (NCEP), the HKO employed objective methods to formalize the forecasters’ subjective process by generating multi-model ensemble forecasts. In recent years, the science of numerical model prediction has matured to the extent that they are now indispensable to forecasters.

Various studies (Atger, 1999; Ziehmann, 2000) have demonstrated the value of a multi-model ensemble forecast over predictions from a large ensemble generated from a single model. Studies using multi-model ensembles to generate tropical cyclone forecast (TC) track (Zhang and Krishnamurti, 1997; Elsberry and Carr, 2000; Aberson, 2001; Weber, 2003) and probabilistic rain forecasts (Hamill and Colucci, 1997, 1998; Evans et al., 2000; Ebert, 2001) performed better than those produced by an ensemble prediction system alone. The advantage of the so called “poor man's” ensemble, which is composed of outputs from different models and initial times, is attributed to the incorporation of the uncertainty sampled from the initial conditions via different sets of observational data, assimilation and initialization methods used by operational centres, and the uncertainty in model formulation via the variety of model physical parametrizations, numerics and resolutions. However, the disadvantage of its relatively small sample size compared with that of the dynamical EPSs with several tens of members (at the time of writing) makes the probability forecasts generated less stable.

The availability of an increasingly large amount of model outputs generated by deterministic forecast models and dynamical EPSs poses a challenge to an operational forecasting centre. In addition to improving the performance of a prediction system, an integrated and concise presentation of model guidance which can be directly and effectively applied in formulating forecast and warning strategies is equally important to forecasters on the bench. HKO’s attempt to effectively apply model ensemble output to tropical cyclone track prediction and operational forecasting is described in the following sections.

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2. Tropical cyclone track forecasting

Significant improvements in the model forecasting of tropical cyclone tracks were identified in the past decade (Lam, 2001). The HKO began experimenting with multi-model ensemble method based on the equally-weighted average of the forecast TC positions of ECMWF, JMA and UKMO global models in 1999 (Lee and Wong; 2002). The forecast positions of TC are determined from the ECMWF and JMA surface prognoses as the point of minimum mean seal-level pressure which is identified by overlapping parabolic interpolation (Manning and Haagenson, 1992). The resolution of the ECMWF and JMA prognostic data received via GTS are 2.5 degrees and 1.25 degrees respectively. The forecast TC positions of the UKMO global model are extracted from the TC guidance of UKMO received via the GTS. These positions are identified by the point of maximum relative vorticity at the 850-hPa level (Heming and Radford, 1998). The equally weighted multi-model ensemble method was put into operation in HKO in 2002 in view of its superiority of multi-model ensemble method over the forecasts of individual models and conventional methods such as climatological-persistence method. Based on 2002 data, future improvement in TC track forecast was achieved by including NCEP global model in the multi-model ensemble (Lee, 2003). NCEP model was added to the model ensemble starting from 2003 onwards. The forecast TC positions of the NCEP global model are extracted from the surface prognoses of mean-sea level pressure at 1-degree data resolution. To facilitate the formulation of forecasts and warning strategies in TC situations, a Tropical Cyclone Information Processing System (TIPS) was developed to generate and display the model ensemble TC track on-the-fly. On selection, forecast tracks from individual models or from different operational centres can be displayed in the TIPS. Once the operational TC track is determined, the TIPS, using statistical method based on historical data, can display valuable information such as the onset and cessation times of strong and gale force winds in Hong Kong as well as the time and distance of closest approach to Hong Kong to facilitate decision making. Satellite and radar imagery can be overlaid on the track display so that forecasters can easily project the extent and structure of rainbands associated with the TC along the forecast track. Figure 1 shows a sample layout of the TIPS in the case of Typhoon Aere in August 2004.

The model ensemble forecast track errors for TCs over the South China Sea and the western North Pacific (0-45 N, 100-180 E) from 2001 to 2003 were around 160 km, 230 km and 340 km for 24-, 48- and 72-hour forecasts respectively. A reduction of 10-15 % was found for 24-, 48- and 72-hour forecast errors compared with the best individual model (Table 1). When compared with the climatology-persistence (1/2(P+C)) method, the track errors have decreased to below 60% of the error using the 1/2 (P+C) method for 48-hour forecasts (Figure 2). The skill of model ensemble forecast translated into a noticeable improvement in

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HKO's official TC forecast.

To further improve on the model ensemble TC track forecast, an objective method to identify and correct persistent bias for model ensemble forecast has been developed in the HKO (Lam and Ma, 2004). Persistent bias is considered to have occurred at time T (the base time of the ensemble forecast) if :

(a) VT-24 points to the same quadrant (NE, SE, SW, NW) as VT-36 where VT is the 24-hour forecast error vector of ensemble forecast with base time T;

(b) the improvement produced by applying bias adjustment using VT-48 to the ensemble forecast with base time T-24 hour is better than 80 km (which is roughly half of the value of the mean 24-hour forecast error) or in case that the improvement cannot be ascertained due to lack of data, VT-24 has a magnitude of greater than 80 km; and

(c) the quadrant that successive error vector points to has changed less than 4 times, up to current time T.

If persistent bias has been established at time T, VT-24 will be used as a correction vector

and added to the 24-, 48-, and 72-hour ensemble forecasts to form the bias adjusted forecasts. Verification of bias-adjusted forecasts using TCs in the above-mentioned dataset show that 10 % reduction in error can be achieved for the 24-hour position forecasts and more than 20 % reduction for the 48- and 72-hour position forecasts (Table 2 and Figure 3) when compared with non-adjusted forecasts. An operational web-based graphical tool to calculate persistent bias adjustment was developed at the HKO to generate TC forecast tracks with automatic persistent bias correction (Figure 4). 3. Time-lagged model forecasts

Multi-model ensemble method has also been applied to medium-range temperature forecasts at the HKO. ECMWF, JMA and NCEP global model forecasts as well as mesoscale model forecasts from both the 20-km and 60-km versions of HKO's Operational Regional Spectral Model (ORSM) with different initial times are used in the time-lagged multi-model ensemble system. To remove systematic error of model forecast of surface temperature and to "compensate" partly for the limitations of model forecast with the prescribed orography and boundary layer parametrizations, the Kalman-filtering (KF) technique (Persson, 1991) is applied to hourly surface temperature forecasts for each model run. Hourly forecasts are interpolated from 6-hourly forecasts for global models and from 3-hourly forecasts for 60-km ORSM. Hourly outputs from 20-km ORSM are used directly. The hourly KF ensemble forecast is calculated by assigning higher weightings to those KF members with better verification results for the past 7 days. An integrated plot of time series of KF forecasts for individual members, KF ensemble mean, direct model output (DMO) spread and ensemble mean is displayed (Figure 5). To facilitate real-time verification, actual

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observations are also plotted at hourly intervals on the time series.

The time-lagged ensemble approach is also applied to frequently updated ORSM for short-range forecasting. The ORSM is run in two modes of operation: a 3-hourly (6-hourly) analysis-forecast cycle for 20-km (60-km) resolution model, and another with cold-start every 72 hours. Both mesoscale models use JMA global model outputs as boundary data. Forecast data with the same valid time from all contributing model runs are grouped into a single distribution and displayed in the form of an EPS meteogram (Figure 6). In addition to the quartile distribution, forecast from the latest model run, as well as the ensemble average (simple arithmetic mean), are also plotted on the meteogram. Plots of meteograms for temperature, wind, relative humidity and rainfall forecasts are updated at every 3 hours for forecasters' reference. An interactive mode of choosing display options is also available to allow real-time comparison with actual observations and more flexible display of combinations of forecast elements, validation range and forecasts from different grid points in the vicinity of Hong Kong. 4. Automatic worded forecast

Local weather forecasts in text format are automatically generated by an objective model interpretation scheme using outputs from the combined JMA and ECMWF global models for 7 days ahead. Forecast elements include surface wind, state of sky, precipitation, range of temperature and relative humidity. Low visibility due to haze, mist or fog will also be mentioned in the forecast, where appropriate. In addition, alert in the form of a warning symbol will also be shown in the output when the predicted weather conditions meet the criteria for local weather warnings, e.g. very hot weather, cold weather or thunderstorm warning. The outputs of worded forecasts and weather cartoons with warning symbols are designed to simulate the weather bulletins prepared by forecasters to facilitate effective assimilation of model information (Figure 7).

The objective interpretation scheme contains a set of criteria for determining each type of

weather elements. Inputs include JMA and ECMWF DMO of 6-hour accumulated rainfall, wind, vertical velocity, temperature and dew point temperature, derived parameters of divergence and vorticity on the surface and upper levels for four grid points nearest to Hong Kong. Calibration is applied to the DMO forecast of rainfall intensity categories using past historical local rainstorms. Forecasters' experience and rules of thumb are incorporated into the objective scheme for categorizing weather types. The maximum and minimum surface temperature forecasts from DMO are post-processed by using KF and linear regression (LR) methods. In deriving the model ensemble, equal weightings are used except for temperature and the state of sky. For temperatures, the mean errors for DMO, KF, LR maximum and minimum temperatures and those derived from time-lagged multi-model ensemble forecasts

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are calculated for the past 7 days with higher weightings assigned to more recent forecasts. The set of temperature forecast with minimum weighted mean error is chosen for the model ensemble automatic weather forecast. In view of much coarser temporal resolution of the upper level data of the JMA global model available beyond 72-hour forecasts, the weightings for the state of sky of the JMA and ECMWF outputs are taken as 1 and 3 respectively.

Verification statistics for September 2003 to December 2004 show that the average scores (in the range of 0-100) for Day 1 to Day 7 were 87.8, 87.5 and 89.1 for the JMA model, ECMWF model and model ensemble respectively. The model ensemble automatic weather forecast outperformed those forecasts from individual members. It consistently outperformed forecasts derived from persistence method (by 14 marks on average). The average score for HKO's subjective forecast during the period was 86.7. The performance of the model ensemble automatic weather forecast was comparable to that of the HKO's subjective forecast and it was even more superior than the latter in the medium-range forecast. A plot of skill scores is shown in Figure 8. It seems that further improvement in official forecast skill can be achieved with optimal utilization of model ensemble forecast. 5. Dynamic EPS forecast

By courtesy of the ECMWF, EPS meteograms for two model grid points in the vicinity of Hong Kong were made available to the HKO in image format in 2003 for evaluation purpose. A preliminary study on the performance and application of ECMWF EPS forecasts in the prediction of heavy rain and high winds in Hong Kong was conducted (Yeung et al., 2004). The period of study was from January to October 2003. The 6-hourly rainfall forecasts were verified against the analysed rainfall over Hong Kong using standard verification measures and skill scores as applied to the five EPS parameters of an ensemble distribution (i.e. the ensemble minimum, the 25 % quartile, the median, the 75 % quartile and the maximum). EPS rainfall prediction, trends and patterns on a day-to-day basis were evaluated for ten selected heavy rain events, as well as case studies of EPS wind speed forecasts in relation to the prediction of high winds in tropical cyclone situation. Details of the evaluation results are given in the study of Yeung et al. (2004).

Some preliminary findings are given as follows :

(a) Brier scores suggested that EPS rainfall forecasts are in general more skilful than the higher-resolution deterministic forecast for small amounts of precipitation up to the 10 mm/6 hr threshold. There is no significant difference in skill levels between the 00-UTC and 12-UTC sets of EPS meteograms (Figure 9a).

(b) Throughout the forecast range up to 10 days, Brier scores of EPS rainfall forecasts are

consistently lower than those of deterministic forecasts (Figure 9b).

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(c) For rain forecast, EPS performs well in forecasting "no rain" but tends to under-predict the

rainfall amount in significant rainfall events. The ensemble MAX is the only capable of producing rainfall over 10 mm/6 hr. The upper bound of the forecast errors is sharp and linearly increasing with forecast rainfall while negative forecast errors are distributed rather randomly and do not seem to have a clear lower bound (Figure 10).

(d) The intrinsic limitations in model capability to forecast heavy rain on the finer temporal

and spatial scales are also reflected in the diurnal fluctuations of the skill scores and poorer EPS performances in monsoon trough scenarios which typically produce the heaviest rain.

(e) For rainfall trend forecasts on a day-to-day basis, the D+2 forecast of the highest probable

rainfall in the course of an anticipated rain event appears to be the most reliable.

As a general observation, extreme members in the ensemble, typically the ensemble MAX, offer the most valuable information for extreme rainfall and high wind forecasts. Calibrations and other post-processing are also required for both deterministic and probabilistic use of EPS rainfall forecasts.

6. Discussions and concluding remarks

The Hong Kong Observatory has been applying multi-model ensemble technique in forecasting tropical cyclone tracks and location-specific weather elements. Studies have been carried out in the HKO to demonstrate the advantages of using ensemble output over the classical deterministic method. The relatively low cost solution of multi-model ensemble forecasting has been proved very useful for adding values to model forecasts. Typically, ensemble outputs are more capable of achieving the smallest mean error and standard deviation of error distribution compared with individual members. To achieve this result, the performance of the ensemble members should be comparable to each other and no single member consistently outperformed the others. Weights from each member in deriving the ensemble can be set according to the past model performance. As higher resolution mesoscale models are more capable of forecasting weaker and smaller-size features than global models, the addition of mesoscale models in the ensemble may benefit to the overall performance of model ensemble.

Time-lagged ensemble forecasts have also been applied in the Observatory to estimate the spread and distribution of forecast elements based on combinations of mesoscale models or global models. It provides a composite display of frequently updated mesoscale model outputs for the same valid time in the form of meteograms for weather elements like

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temperture, wind, relative humidity and rainfall. In the multi-model ensemble temperature forecast, the extreme value will decrease after averaging due to the phase differences of individual members. The effect is most significant in the cold surge event where timing of arrival of cold air may be different among ensemble members. To obtain the extreme values of quantitative forecast for a weather element, phase differences of individual members may need to be removed before compiling the ensemble average. To further increase the effectiveness of assimilation of model information by forecasters, local worded forecasts are generated automatically from the combination of JMA and ECMWF models. The automatic weather forecasts based on model ensemble consistently outperformed forecasts derived from individual models and by the persistence method for all forecast ranges from September 2003 to December 2004. The performance was comparable to that of the HKO's subjective forecast and it was even more superior in the medium-range forecast.

The probabilistic assessment from multi-model ensemble is not desirable due to the small sampling and dependence between model runs, the HKO starts to explore the use of dynamic EPS outputs with ECMWF EPS meteograms. Through the validation results of a preliminary study from January to October 2003, usefulness of EPS forecasts in terms of accuracy, bias and relative value for a small region like Hong Kong is better understood. Judging from the verification results alone, the rainfall information contained in an EPS forecast probably should not be applied directly to operational forecasting due to problems of bias and large RMS errors. Calibration and other post-processing will be required for both deterministic and probabilistic use. The EPS wind speed forecasts based on extreme members, meanwhile, are more promising for high wind predictions in Hong Kong. The EPS far exceeds it deterministic counterparts in terms of performance in validation results. Efforts to explore the use of dynamic EPS outputs particularly in forecasting heavy rain and tropical cyclones will be continued.

The study of Richardson (2001) revealed that an EPS constructed by a combination of

ECMWF and UKMO models and analyses consistently outperformed the ECWMF EPS in probabilistic forecasts. An optimal way of combining outputs from dynamic models (multiple models and single-model EPS) and statistical models, which account for the error distribution of model members for bias removal, to generate a hybrid ensemble may produce more skillful deterministic and probabilistic forecasts. Interpretation and visualization techniques for ensemble outputs as well as translation to economic value will be further explored to maximize the cost effectiveness of model guidance. With ever-increasing computer power, rescaling and downscaling techniques of the EPS using mesoscale models may provide better probabilistic forecasts for severe weather to a small region.

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References Aberson, S.D., 2001: The ensemble of tropical cyclone track forecasting models in the North Atlantic basin (1976-2000). Bull. Amer. Meteor. Soc., 82, 1895-1904. Atger, F., 1999: The skill of ensemble prediction systems. Mon. Wea. Rev., 127, 1941-1953. Ebert, E.E., 2001: Ability of a poor man's ensemble to predict the probability and distribution of precipitation. Mon. Wea. Rev., 129, 2461-2480. Elsberry, R.L., and L.E. Carr III, 2000: Consensus of dynamical tropical cyclone track forecasts - Errors versus spread. Mon. Wea. Rev., 128, 4131-4138. Evans, R.E., M.S. J. Harrison, R.J. Graham, and K.R. Mylne, 2000: Joint medium-range ensembles from the Met. Office and ECMWF systems. Mon. Wea. Rev., 128, 3104-3127. Hamill, T.M. and S.J. Coluccci, 1997: Verification of Eta-RSM short-range ensemble forecasts. Mon.Wea. Rev., 125, 1312-1327. Hamill, T.M. and S.J. Coluccci, 1998: Evaluation of Eta-RSM ensemble probabilistic predictions forecasts. Mon.Wea. Rev., 126, 711-724. Heming, J.T. and A.M. Radford, 1998: The performance of the United Kingdom Meteorological Office global model in predicting the tracks of Atlantic tropical cyclones in 1995. Mon. Wea. Rev., 126, 1323-1331. JMA, 2002 : Outline of the operational numerical weather prediction at the Japan Meteorological Agency. Appendix to WMO Numerical Weather Prediction Progress Report, March 2002. Lam, C.C., 2001: Performance of the ECMWF model in forecasting the tracks of tropical cyclones in the South China Sea and parts of the western North Pacific. Meteorol. Appl., 8, 339-344. Lam, H. and H.M. Ma, 2004: An automatic scheme to correct for persistent bias in multiple model ensemble tropical cyclone track forecasts. HKO internal manuscript.

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Lee, T.C. and M.S. Wong, 2002: The use of multiple-model ensemble techniques for tropical cyclone track forecast at the Hong Kong Observatory. Presented at the WMO Commission for Basic Systems Technical Conference on Data Processing and Forecasting Systems, Cairns, Australia, 2-3 December 2002. Lee, T.C., 2003: Performance of global models in tropical cyclone track forecasting in 2002. HKO internal document. Manning, K.W. and P.L. Haagenson, 1992: Data ingest and objective analysis for the PSU/NCAR modeling system: Programs DATAGRID and RAWINS. NCAR Technical Note, NCAR/TN-376+IA, 209 pp. Molteni, F., R. Buizza, T. N. Palmer, and T. Petroliagis, 1996 : The ECMWF Ensemble Prediction System: Methodology and vailidation. Quart. J. Roy. Meteor. Soc., 122, 73-119. Persson, A.O., 1991: Kalmanfiltering - A new approach to adaptive statistical interpretation of numerical meteorological forecasts. WMO Technical Document No. 421, XX-27–XX-32. Richardson, D.S., 2001: Ensembles using multiple models and analyses. Q.J.R. Meteorol. Soc., 127, 1847-1864. Stensrud, D. J., H.E. Brooks, J. Du, M.S. Tracton, and E.Rogers, 1999: Using ensembles for short-range forecasting. Mon. Wea. Rev., 127, 433-446. Weber, H.C., 2003: Hurricane track prediction using a statistical ensemble of numerical models. Mon.Wea. Rev., 131, 749-770. Yeung, Linus H.Y., Edwin S.T. Lai, Queenie C.C. Lam, Philip K.Y. Chan, and P. Cheung, 2004: Performance and application of ECMWF EPS forecasts in the prediction of heavy rain and high winds in Hong Kong. ECMWF Technical Memorandum No. 446, ECMWF, Reading, U.K. Zhang, Z., and T.N. Krishnamurti, 1997: Ensemble forecasting of hurricane tracks. Bull. Amer. Meteor. Soc., 78, 2785-2795. Ziehmann, C., 2000: Comparison of a single-model EPS with a mulit-model ensemble consisting of a few operational models. Tellus, 52A, 280-299.

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Table 1 Mean errors and standard deviations (in km) of (a) 24-hour (b) 48-hour and (c) 72-hour forecast of TC tracks over 0-45 N, 100-180 E. (a)

Year Sample size

ECMWF UKMO NCEP JMA Ensemble

2001 249 232±182 135±84 - 152±93 132±92 2002 243 182±126 125±79 - 134±90 114±78 2003 213 190±133 146±105 139±90 133±89 116±83

Overall 705 210±150 135±89 139±90 140±91 121±85 (b)

Year Sample size

ECMWF UKMO NCEP JMA Ensemble

2001 196 345±301 257±182 - 263±179 225±175 2002 195 265±176 215±136 - 214±153 188±138 2003 170 302±221 256±196 241±166 232±154 199±146

Overall 561 304±239 243±172 241±166 236±163 204±154 (c)

Year Sample size

ECMWF UKMO NCEP JMA Ensemble

2001 128 438±354 382±206 - 377±273 318±211 2002 153 370±261 348±238 - 314±196 290±208 2003 115 436±304 359±216 365±286 330±227 283±187

Overall 396 415±305 363±221 365±286 340±232 297±203 Table 2 Comparison of track errors with and without persistent bias correction for TCs over

the South China Sea and the western North Pacific in 2001-2003.

Forecast hour

Total number of TC ensemble forecasts (mean track error±sd in km)

Number of samples meeting the criteria of exhibiting persistent bias (% against total)

Mean track error for the sample cases with no bias correction applied (±sd in km)

Mean track error for sample cases with bias correction applied (±sd in km)

Improvem-ent with 95% of confidence interval (km)

24 505 (128±98) 53 (10%) 157±101 143±79 14±39

48 463 (216±170) 45 (10%) 229±141 179±109 50±42

72 374 (335±258) 40 (11%) 344±241 272±228 72±44

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Fig.1 Display of forecast tracks of model ensemble and individual members on the Tropical Cyclone Information Prediction System (TIPS) in the case of Typhoon Aere (0417).

Fig.2 Performance of HKO's official TC track forecast in terms of error ratio with respect to climatology-persistence method from 1975 to 2003 over the area of 10-30 N, 105-125 E.

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Fig.3 Mean track position errors for TCs over the South China Sea and the western North Pacific with and without bias adjustment in 2001-2003.

Fig.4 An operational tool used in HKO to detect persistent bias and calculate correction vector. The operational observed TC track in black line with cross marks indicating observed position of TC at the hour shown. J, E, U, N indicate 24-hr forecast positions from JMA, ECMWF, UKMO, and NCEP models respectively. Ensemble 24-hr forecast position in pink dot. Dotted line joins the ensemble forecast position with the position of the TC. Dark arrow (VT) is 24-hour ensemble forecast error vectors with base time "T".

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Fig.5 Time-lagged model ensemble temperature forecast with and without Kalman-filtering for a specific location at HKO Headquarters. Hourly observations in grey dots are also plotted in real-time for verification purpose.

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(a)

(b)

Fig.6 (a) Time-lagged model ensemble wind forecast generated by using HKO's mesocsale models updated every 3 hours with different cycle run strategies. Horizontal axis is marked with date/hour in UTC. (b) Same as (a) but with forecast temperature and rainfall.

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Fig.7 Local worded forecast automatically generated from the combination of JMA and ECMWF model (automatic weather forecast) for 7 days ahead.

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Fig.8 Performance of automatic weather forecast for Day 1 to Day 7 relative to no-skill persistence method for September 2003 to December 2004. CFO denotes subjective weather forecast issued by the Central Forecasting Office of the HKO.

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(a) (b)

Fig.9 (a) Comparison of Brier Scores for ECMWF EPS (dark lines) and TL511 deterministic forecast (blue lines) forecasts. The 00-UTC and 12-UTC ensembles are indicated by solid and dashed lines respectively. Brier Skill Scores for EPS are also shown as red lines. (b) Brier Scores against forecast range. This plot compares the Brier Scores of ECMWF EPS (broken lines) and TL511 deterministic forecast (solid lines). (a) (b)

Fig.10 (a) Mean forecast errors of ECMWF ensemble parameters versus observed rainfall and (b) the parameters themselves. Insets show the corresponding plots in linear scale. Only cases with non-zero observed rainfall are included.