Precipitation assessment of a superensemble forecast over South-East Asia

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1 Meteorol. Appl. 12, (2005) doi: /s Precipitation assessment of a superensemble forecast over South-East Asia Mastura Mahmud 1 &R.S.Ross 2 1 Geography Programme, School of Social, Development and Environment, Universiti Kebangsaan Malaysia, Bangi, 43600, Selangor, Malaysia mastura@pkrisc.cc.ukm.my 2 Department of Meteorology, Florida State University, Tallahassee, Florida, , USA A superensemble forecast that consists of six operational general circulation models is tested to simulate weather parameters over the equatorial South-East Asian region. The forecast technique, developed at Florida State University, is a unique ensemble that has proved to be one of the better prognosticators for weather forecasting. In this paper, the root mean square errors of the superensemble precipitation for a case of a severe tropical rainfall event during the winter monsoon of 2001 were smaller than the other global operational member or ensemble models used, indicating a skilled forecast. This trait is also reflected in the equitable threat score which indicates the association of the common rainfall threshold distribution between the forecast and observed best rainfall analysis from the Tropical Rainfall Measurement Mission (TRMM) and Special Sensor Microwave Imager (SSM/I) dataset, where although it is slightly underpredicted for light rain thresholds, its results are still superior to other individual member or even the ensemble mean forecasts. Higher correlation between the superensemble forecast and the observed is maintained for up to 3-day forecasts, which indicates its potential as a reliable and good forecast tool for predicting equatorial precipitation. 1. Introduction The inherent nature of the unstable and nonlinear dynamical atmosphere that limits the predictability of numerical models to forecast weather and climate has instigated numerous approaches in improving global and regional circulation models. The skill of a forecast is reduced due to the imperfect basis of the models caused by initial condition uncertainties and model ambiguities from inaccurate numerical schemes. Random errors that originate from unexplained processes can be overcome by incorporating physics such as parameterisation of stochastic or explicit type schemes (Allen et al. 2003). However, systematic errors that arise from the structure of the models, which is not able to fully represent the parameter examined, is a more challenging problem that is yet to be fully solved. Prediction of tropical precipitation is still considered to be one of the challenges in numerical forecasting. Lack of knowledge and under-representation of the cloud microphysics have been identified as the source of precipitation forecast inaccuracy (Shin et al. 2003). Techniques that enhance the rainfall prediction have included revised cumulus parameterisation schemes such as the modified Kuo (Krishnamurti et al. 1983), Relaxed Arakawa-Schubert (Moorthi & Suarez 1992), Simplified Arakawa-Schubert (Pan & Wu 1994), and the incorporation of realistic emphirical profiles in the cumulus scheme (Rajendran et al. 2004), to name a few. Initialisation processes where initial conditions from observations are invoked into the models have resulted in improved forecasts, but they can only generate high skills for Day 1 forecasts (Shin & Krishnamurti 1999). Thereafter, the forecast skills rapidly deteriorate. Downscaling techniques that are based on the assumption that large-scale fields can provide information on smaller scales (Arritt & Goering 1999) through the concept of limiting the area of the model have also been explored. Despite reducing the number of gridpoints, increasing spatial resolution and thus reducing computational cost, they also offer a disadvantage where lateral boundary errors are generated from the imperfect global circulation model output into the driving of the limited area model (Pan et al. 2000), as well as limiting the time range of the model forecast. Another technique to improve forecasts is the ensemble of member models that range from forecasts of ensemble mean, bias removed ensemble, and the superensemble (Krishnamurti et al. 1999, 2000a, 2000b, 2001, 2003a, 2003b). Further attempts also include utilising different combinations of ensemble configuration such as the multi-analysis, multi-convection and multi-model ensembles (Shin et al. 2003), along with an ensemble forecast assimilation (Nohara & Tanaka 2004) where ensemble members are 177

2 Mastura Mahmud & R. S. Ross accumulated into a single forecast by a filter. All these techniques have produced better forecasting results that generate smaller forecast errors than an individual model. The superiority of the superensemble technique forecasts against other current operational and ensemble models have been demonstrated by the low values of the root mean square errors, systematic errors (Krishnamurti et al. 1999; Krishnamurti et al. 2001), higher equitable threat scores (Ross & Krishnamurti 2005) and skill scores (Stefanova & Krishnamurti 2002). These studies have shown that the superensemble forecasts present better climate and weather predictions than any multi-model, multi-analysis forecasts. 2. Objective The aim of this research is to investigate the performance of the superensemble technique on forecasting the weather over the equatorial region of South-East Asia. Of particular interest is the skill of the prediction of precipitation, as the daily pressure distribution changes are small and do not contribute to the accuracy of the daily forecast except during the existence of low pressure tropical disturbances particularly during the north-east monsoon when heavy rainfall would cause flooding to countries such as Malaysia, Indonesia and southern Thailand due to the above events. The outstanding skill of the superensemble (over 30% higher than other individual models) in forecasting tropical precipitation (Krishnamurti 2004) motivated this study. The multi-model superensemble forecast is investigated over a selected region of the maritime continent monsoon in South-East Asia for the year A comparison of forecast outputs from the Florida State University (FSU) Superensemble is performed against other global operational models such as the Bureau of Meteorology Research Centre (BMRC), National Center for Environmental Prediction (NCEP), Japan Meteorological Agency ( JMA), RecherchéenPrévision Numérique (RPN), Florida State University (FSU), Naval Research Laboratory (NRL) and the ensemble average of these models. Precipitation verification is obtained from the European Centre for Medium Range Weather Forecasts (ECMWF)-FSU output data incorporated with the Tropical Rainfall Measurement Mission (TRMM) and Special Sensor Microwave Imager (SSM/I) dataset in the FSU global spectral model. 3. The superensemble technique The superensemble is a technique developed by Krishnamurti et al. (1999) to improve prediction of weather and climate by utilising low-order modes. The superensemble forecast is constructed from multimodel climate and weather forecasts (Stefanova & 178 Krishnamurti 2002), where a set of regression coefficients was obtained from the forecasts and observed data. Weightings were acquired through the least squares minimisation of the difference between the model and analysis (Krishnamurti et al. 1999). The construction of the superensemble consists of two parts. The first part is named the training phase where the least square regression statistics of the forecast is based on the outputs from observed and analysis data of the member models (Krishnamurti et al. 2003a). The evaluating period during this forecast phase is executed using the highest quality dataset, thereby greatly improving the superensemble compared to training when poorer datasets are utilised (Krishnamurti et al. 2000a, 2000b). The training phase provides linear regression coefficients based on the past errors of the models (Krishnamurti et al. 2003a). During the training phase, the forecasts from the multiple member models and the observed fields are utilised to produce statistical values that constitute the required statistics of the superensemble model. The statistics of the models are determined from the multiple linear regression of forecasts against an output from the Florida State University (FSU) global spectral model that incorporates the observed (analysis) best estimate rainfall, which is obtained from the TRMM and SSM/I dataset and the rain-rate algorithms developed by NASA Goddard Space Flight Center (Krishnamurti et al. 2001). The superensemble forecast is denoted by the formula below: S = O + N a i (F i F i ) i=1 where S = superensemble forecast, O = time mean of the observed state during the training period, a i = regression coefficient by a minimization process for model i, i = model index, N = number of models. F i = mean of prediction by model i, F i = forecast by model i (Ross & Krishnamurti 2005). The second part of the technique involves the predictions of the member models that were combined according to their weighting. Thus selective datasets improve the distribution of weights among the member models and for all the variables for the training phase (Krishnamurti et al. 2003a). High weightings are assigned to models with high skills. The distribution of the weightings is then determined for each of the member models (Krishnamurti et al. 2003a, 2003b). Weightings of fractional positive or negative values are defined over a region according to the member models past performance (Krishnamurti et al. 2003a). The weightings are computed at each grid by a function, G, which is then passed to the forecast stage. The mean

3 Precipitation assessment of superensemble forecast over SE Asia square error of the forecast is minimised and coefficients a i derived from estimating the minimum of the function: G = t-train t=1 (S t O t ) 2 where O = observed state, t = time, and t-train = length of the training period. The training period of 120 days for the models was found to predict a much better weather forecast than the individual operational models and the ensemble mean of the models (Krishnamurti et al. 2001; Ross & Krishnamurti 2005). An ensemble mean that only assigns a weight of 1.0 to all the models (inclusive of the poor and good performances of the models) does not correct the bias of the models based on their previous performance (Krishnamurti et al. 2001). It is the removal of collective bias of the superensemble that enhances the forecast capability of this technique (Krishnamurti et al. 2003a, 2003b). Several operational weather models are included in this study. The participating multi-models in this assessment consist of the NCEP Aviation Model, BMRC, JMA, NRL, RPN, and FSU. Krishnamurti et al. (2000a, 2003a) found that a minimum of six member models produces a high skill in the superensemble forecast. 4. Area of research The area chosen for the study lies within the South- East Asian region, from 90 E to 120 E and from 5 S to 20 N. The countries of this region include Malaysia, Indonesia, Singapore, Indonesia and Brunei; and Thailand, Myanmar, Cambodia, Laos and Vietnam (these latter comprising the subregion of Indochina). The area of study falls within the maritime continent where latent heating released is considered as one of the important energy sources for the global circulation. Two main climate types preside over the South- East Asian region. Malaysia and Indonesia generally experience an equatorial climate that is characterised by warm and humid weather throughout the year. The region is influenced by the prevailing north-easterly winds from November to March during the northeast monsoon. The climate over mainland Indochina is tropical, with a dry season occurring during the northeast monsoon. The north-east monsoon that occurs in equatorial Southeast Asia is often the season when most rain falls, and at times can cause widespread flooding. A report on Global Extreme Flood Events by the Dartmouth Flood Observatory (WWW1) lists the widespread flooding that affected southern Thailand, north-eastern parts of the Malaysian peninsula and northern Sumatra (Indonesia) in The flooding that occurred from 21 to 30 November claimed 170 lives in Indonesia, 73 in Thailand and 50 in Malaysia. A large number of people were evacuated: 20,000 people in Indonesia, 40,682 in Thailand and 8,000 in Malaysia. The estimated cost of the damage for Indonesia and Thailand was US$ 94.5 million. A total of 749,756 hectares of farmland was destroyed in Sumatra and Songkhla in Thailand (WWW1). Heavy rainfall also occurred in late December 2001 when tropical cyclone Vamei hit the southern tip of the Malaysian peninsula and northern Sumatra, damaging much agricultural land as well as disrupting the lives of the local population. The flooding in Kuantan, in the central east coast area of the Malaysian peninsula, was the result of three main rainfall events on 14, 21 and 26 December and was widely regarded as the worst flooding for 30 years (WWW2). One of the elements of forecasting that is of interest in equatorial areas is rainfall. Precipitation over the equator from convective clouds is usually short-lived and intense, or may originate from a cloud system associated with a cyclonic vortex. To date, achieving a high accuracy of daily precipitation forecasting over the tropics is still a challenge. This is due to the character of precipitation over the tropics which is actively convective and variable temporally. Its accurate predictability may be helped by the information from the TRMM satellite that provides invaluable data on rainfall distribution over the tropical region. Simple statistical analyses were performed on the data to find the skill scores of the forecasts and the observed data. The best estimate of the observed rainfall data over latitudes 55 Nto55 S is obtained from the FSU global spectral model that utilises the physical initialisation of the TRMM and SSM/I dataset (Turk et al. 2001) and the 2A12 and GPROF algorithms (Kummerow et al. 2000). 5. Results 5.1. Case study: daily analysis A heavy rainfall event, which caused widespread flooding over the central east coast of the Malaysian peninsula, occurred on 21 December Figure 1 Figure 1. Daily rainfall total from 13 to 29 December 2001 at Kuantan, Malaysian peninsula. 179

4 Mastura Mahmud & R. S. Ross Table 1. The root mean square errors and correlation of precipitation for the eight member models. Member models Forecast RPN JMA BMR NCE FSU NRL Superensemble Ensemble (a) Root Mean Square Error (RMSE) Day Day Day Day (b) Correlation Day Day Day Day shows the daily rainfall total at Kuantan, on the central east coast of the Malaysian peninsula, which was hit by heavy rain from 14 December An intercomparison of the performance precipitation forecast of a single day for all the models was carried out. A shortrange forecast of four days was performed to simulate the event. The root mean square error (RMSE) of precipitation for the superensemble forecast on 21 December 2001 was consistently small, at a value of 1.5 mm/day on Day 1 forecast, and its small magnitude of less than 2 was maintained until Day 4 forecast. The errors by the majority of the member models were higher than the forecast for the superensemble forecast by more than 80% compared to the superensemble forecast (Table 1). Most of the other individual member models exhibited higher RMSE of more than 10 mm/day for the Day 1 forecast. The RMSE for the ensemble forecast appeared to be high, which considered the poor and good individual models. Errors as large as these can differentiate between the levels of confidence a forecast makes. The magnitudes of the RMSE for the best and the rest of the member models are found to be slightly higher than either the seasonal or the monthly values. Comparatively, the RMSE of the one-day case-study event for a four-day forecast was larger than the magnitude of the RMSE for the monthly event by as much as 70% for the worst-performing member model. We found that the magnitudes of the RMSE for the ensemble and individual models over the South-East Asian region were larger than those found for the entire global area from 55 Nto55 S (Ross & Krishnamurti 2005). Our magnitudes of RMSE of less than 2 mm/day for all the four-day forecasts were considerably smaller than the magnitudes of the RMSE of approximately 5 mm/day for the first-day forecast over the globe. The small values of RMSE promise to provide good forecasts over our region of study, and will be beneficial to the local forecasters in predicting heavy rainfall. We believe that the location of South-East Asia near the equatorial western Pacific region is subjected to the near persistent existence of the ITCZ that meanders approximately within 5 Nto5 S throughout the year to be the cause of this, accounted for by the TRMM and SSM/I data that utilised the five different rain-rate algorithms. All the other individual member and ensemble models exhibited higher magnitudes of more than 10 mm/day for all the four-day forecasts. This is in contrast to the lower RMSE values of less than 10 mm/day for a three-day forecast analysis for the global area during the same year (Ross & Krishnamurti 2005). The precipitation correlation indicates the success of the forecast model in predicting the location and amount of rainfall in the form of an areal mean (Ross & Krishnamurti 2005). Table 1 illustrates the correlation at 0.76 of the superensemble forecast on Day 1 forecast that is slightly higher than for the rest of the models. Other member models such as the NRL, JMA and NCEP exhibit high correlations within the range of values from 0.75 to 0.6, but these correlations rapidly deteriorate over subsequent forecast days. The ensemble mean did not perform as well its correlation was even lower than some of the individual member models, due to the influence of the poorer models. In our analysis, the worst model revealed a negative correlation. The observed rainfall estimate from the TRMM-2A12 and the SSM/I GPROF algorithms compared to the forecasts from the individual member models on the third-day forecast is shown in Figure 2. The visual distribution of precipitation for the Day 1 forecast, with a cut off of less than 3 mm/day, indicates that the location of the areas of precipitation were generally correct for the superensemble, NCEP, JMA and NRL. The slightly negative correlation for the worst model for the Day 1 forecast shows that the model was unable to forecast the correct location of precipitation area over the Malaysian peninsula, Sumatra and parts of western Borneo. It is apparent that the areal precipitation for the superensemble and JMA forecasts are larger than the observed precipitation area, and hence exhibit a higher correlation of the area of precipitation. The relationship between the pattern correlation of precipitation and the RMSE for all the models is examined. The scatter plot shown in Figure 3 indicates 180

5 Precipitation assessment of superensemble forecast over SE Asia Figure 2. The comparison of the observed TRMM and SSM/I rainfall to the 1-day forecast valid on 21 December 2001 from the Superensemble, FSU, NCEP, NRL, RPN, BMR and JMA models. It appears that the superensemble, RPN, NCE forecasts are able to capture the intense rainfall over the central to southern Malaysian peninsula. The minimum threshold of precipitation is set at 5 mm/day accumulations for 24-hour rainfall. The unit on the bar scale is mm/day. the relationship between the RMSE and correlation for precipitation for up to four days forecast valid on 21 December It illustrates that the longer the period of forecast, the higher the RMSE and the lower the correlation. The models revealed linear trends. Most of the member models exhibit the same magnitude of a high degree regression between the points, with the exception for the superensemble, which is better. Its very low RMSE but a high degree of correlation is a vast improvement compared to the other member models. Two trend lines with their respective regression equations are chosen to find the relationship between these two parameters; one is the superensemble and the other is the NCEP. The next better model ranked from the superensemble is the ensemble mean model due to its relatively high linearity, high correlation and low RMSE. The distribution of precipitation predicted by the models and the observed precipitation can be seen in the equitable threat score on 21 December

6 Mastura Mahmud & R. S. Ross y = x R 2 = RMSE (mm/day) Superensemble FSU RPN BMRC NRL JMA NCEP Ensemble Linear (Superensemble) Linear (NCEP) y = x R 2 = Correlation Figure 3. The scatter plot of the RMSE and correlation for a short-range precipitation of forecast four days valid on 21 December 2001 over the South-East Asian region. (Table 2). The equitable adjustment to the threat score accounts for the probability of the areas of overlap between the forecast and observed precipitation. The superensemble forecast showed the highest overlap between the forecast and observation at an equitable threat score magnitude of more than 58% success, particularly for the lower precipitation thresholds of 0.2, 2.0, 5.0 and 10.0 mm/day. Most of the other member models were unable to generate high precipitation thresholds of more than 35 mm/day except for the superensemble, NCEP and RPN, and even so, their equitable threat score values were fairly small, at best only with 20% correctly located. The performances of the other operational models were less successful in predicting the probability of the areas of higher precipitation (Figure 2). The visual interpretation for the one-day forecast generally showed that some of the models produced a realistic distribution of rainfall although there were some models that reflected the disparity in the location of the heavier intensities. Forecasts from models such as the superensemble, NRL and NCEP were able to capture the intense rainfall magnitude of 50 mm/day over central and southern parts of the Malaysian peninsula. For this event, the Table 2. The equitable threat score for different thresholds on 21 December 2001 for Day 1 and Day 3 forecasts over South-East Asia. Threshold Member models (mm/day) RPN JMA BMR NCE FSU NRL Superensemble Ensemble Day 1 forecast valid on 21 December Day 3 forecast valid on 21 December

7 Precipitation assessment of superensemble forecast over SE Asia Figure 4. The three-day forecast of the superensemble and other models valid on 21 December 2001 and the observed TRMM and SSM/I image. The ensemble forecast is not included. The unit on the bar scale is mm/day. superensemble underforecasted the large area of heavier precipitation over the southern South China Sea and Borneo. The third-day forecast performance was also investigated in this case study. The equitable threat score for the superensemble forecast was still high at 0.87 for the lower threshold precipitation of 0.2 mm/day. The superensemble equitable threat score was still higher than the rest of the other models at the 2.0, 5.0 and 10.0 mm/day thresholds, although the probability of the correct placement has now reduced to only approximately 50%, a reduction of about 8% compared with the first-day forecast. The ability of the RPN and NCEP models to correctly place the higher threshold of precipitation had deteriorated by the third day of forecasts, to the extent that the equitable threat score values were negative. Generally, the magnitudes of the equitable threat score in correctly locating the different thresholds of rainfall have reduced by the third day of forecast. Visual representation of the forecast precipitations and the observed TRMM and SSM/I data for the third day of the forecast, valid on 21 December, 2001 is shown in Figure

8 Mastura Mahmud & R. S. Ross Table 3. The bias scores for the first three-day forecasts at the different precipitation thresholds valid on 21 December Threshold Models (mm/day) RPN JMA BMR NCE FSU NRL Superensemble Ensemble Bias for Day 1 forecast Bias for Day 2 forecast Bias for Day 3 forecast The bias score is the ratio of the number of grids of the forecast precipitation to the observed precipitation. In the representation of Figure 4, the bias skill is taken as the deviation from the magnitude of 1, where a bias of more than 1 indicates over-forecasting of the areas of precipitation with different thresholds categorised to it. A negative value indicates under-forecasting of the area of precipitation, while a positive value indicates an over-forecast. Ideally, a value of zero is sought, which indicates complete accuracy in locating the rainfall area. Figure 4 shows that the most of the models were successful in detecting the areal precipitation of the lowest threshold of 0.2 mm/day, but tended to over-forecast those within the low threshold range of 2.0 and 5.0 mm/ day. The skill score of the superensemble technique was weak in handling higher precipitation threshold forecasts of more than 10 mm/day (Table 3). It was able to correctly place the higher intensity of 75 mm/day, but with a reduced number of grids at 40% accuracy to the observed rainfall threshold. However, for this case study of a heavy rainfall event, the RPN, BMRC and NCEP models overpredicted a higher number of grid points of the heavier precipitation thresholds of more than 35 mm/day. The RPN and NCEP forecasts were able to encapsulate the exact location of intense precipitation at the appropriate grids from the visual distribution shown in Figure 4. On the other hand, the BMRC displayed a high number of grid points for the heavier precipitation 184 threshold of 35 mm/day, i.e. an over-forecast, although the correct placement of precipitation threshold was only indicated by an equitable threat score overlap of 19. As for the JMA model that exhibited a high correlation, the bias score showed its ability in only capturing the lower threshold precipitation of 10 mm/day or less. The evaluation for the three-day forecasts valid on 21 December 2001 demonstrates that most of the models are unable to capture the correct location of the heavy rainfall. The correlation analysis shows that the forecast for Day 3, valid on 21 December for all the participating models, deteriorated slightly compared to the firstday forecast. The superensemble forecast correlation is still slightly better than the rest of the member models at a value of 0.67, though the area of heavy precipitation is not correctly located. Models such as BMRC, JMA and NRL exhibit a low correlation of above 0.3 compared with the observed TRMM and SSM/I data. The RPN model tends to over-forecast the higher threshold rainfall of more than 30 mm/day. Most of the models presented a high skill in forecasting the very low level rainfall threshold of 0.2 mm/day, but tended to over-forecast the light rainfall threshold of 2.0 to 5 mm/day by more than 50%. Models such as the NCEP and JMA also tended to over-forecast the higher precipitation threshold of 25 to 75 mm/day.

9 Precipitation assessment of superensemble forecast over SE Asia The statistical analyses of the correlation, equitable threat score and bias illustrated the slightly higher competence of the superensemble forecast compared with the other models. The combination of these statistical analyses showed that the pattern correlation presented the general synoptic-scale areal location of precipitation between the forecast and the observed data. The equitable threat score accounts for the probability of the areas of overlap of intense or low rainfall rates and the bias score indicates the comparative number of grids of the precipitating area. Not all the high correlations indicate the correct placement of the intense rainfall, as shown by either the bias or the equitable threat score. The above case study illustrates the higher capability of the superensemble forecast in predicting the position of the precipitating areas and the amount of rainfall over the rest of the individual operational models. Further examples of the success of the superensemble compared with other member models include the forecasts for different rainfall systems such as a tropical flood event in Mozambique, the typhoons in western Philippines (Krishnamurti et al. 2003a) and the Pacific (Vijaya Kumar et al. 2003), heavy rainfall in Vietnam and numerous examples of hurricane forecasts in the USA (Krishnamurti et al., 2003a; Williford et al., 2003). The superensemble presented the highest skill compared with other individual models (Krishnamurti et al. 2003a). Typhoon-related precipitation forecasts that utilised three ensemble configurations of multi-analysis, multi-convection and multi-model ensembles of the FSU model based on 30 cases also demonstrated the superiority of the superensemble where forecasts were closer to observed values than any combination of ensemble configurations and ensemble means (Shin et al. 2003). Annual precipitation analysis for the global area (Ross & Krishnamurti 2005) and the South-East Asian region (Mahmud 2004) in 2001 also showed the high skill of the superensemble. Seasonal and monthly statistics for South-East Asia presented better skills than daily statistics (not shown). Forecasts based on the seasonal and multi-seasonal precipitation simulations over the northern hemisphere, southern hemisphere, the globe, Europe, North America and the South Asian monsoon region have all displayed this trait (Krishnamurti et al. 2000a). similar findings for selected regions such as Africa and tropical America. Individual heavy rainfall events, such as the case study investigated here, was the result of a cold surge event and was well simulated by the superensemble forecast over the other individual operational forecast models, although it had a tendency to over-forecast the regions of lower threshold rainfall (less than 0.2 mm/day). Extremely heavy rainfall thresholds (more than 75 mm/ day) were not well simulated by any of the models. This is a feature that has to be improved as heavy rainfall of more than 200 mm/day is a common feature over the Malaysian peninsula, Sumatra and southern Thailand during the early part of the north-east monsoon period. Results from the ensemble mean are ranked as the next best forecast since it incorporates the skill from the best and the worst operational individual models. The RMSE for the precipitation of the superensemble forecast is approximately 80% lower than that of the ensemble or individual operational models over this region. We have verified this with the results from the global superensemble forecast and found that over the selected area of equatorial South-East Asia, the RMSEs are as small as 2 mm/day. This case study focused on an event that recorded a daily rainfall total of 350 mm, and we found that none of the models could handle this intense threshold. The example examined here is not of a large-scale precipitation event but of a synoptic-scale event. The best available rainfall estimates of the combined TRMM-SSM/I algorithm were used as the benchmark training set that greatly improved the forecast of the superensemble, which emerged as a better estimate of the forecast of rainfall in terms of its correct location but still required improvement in forecasting the higher intensities of the rainfall. Other models tended to over-forecast either the intensity or the geographical location of the heavy rainfall. We recommend that further work be undertaken to improve this, possibly by employing rain gauge data as well as using different rain-rate algorithms of the rain obtained from the TRMM and SSM/I dataset. Further improvement could be achieved by using member models of higher resolution with improved physics and initialisation of precipitation (Krishnamurti et al. 2003a). Concluding remarks The forecast skill of a superensemble for a precipitation event over the South-East Asian region during the north-east monsoon, the season when the heaviest rains fall over many equatorial countries, appears to be promising, and is currently one of the better predictors of rainfall compared to many operational forecasts, particularly the skill for the 1 to 3-day forecast. This was also verified by Krishnamurti et al. (2000a) who showed Acknowledgements The lead author is indebted to Professor T. N. Krishnamurti, the developer of the superensemble technique, for his encouragement and support. The authors also acknowledge Brian Mackey for his efforts in making the precipitation data available. Our appreciation is extended to Nihat Cubukcu, V. Sanjay and Darlene Oosterhof for their assistance on the computing technicalities, graphics and administration, 185

10 Mastura Mahmud & R. S. Ross respectively. The modelling groups and centres such as the RPN, NRL, BMRC, NCEP, JMA, FSU, TRMM and SSM/I are acknowledged for their available datasets without which this work would not be possible. The lead author is indebted to Professor Krishnamurti and the Fullbright Programme for their support. This research is funded by the FSU Research Foundation Center of Excellence Award Grant , FSU Research Foundation Grant , NSF ATM , NASA NAG5-9662, NOAA NA06GPO512 and NOAA NA16GP1365. References Allen, M., Kettleborough, J. & Stainforth, D. (2003) Model errors in weather and climate. ECMWF Seminar on Predictability of Weather and Climate, European Centre Medium Range Weather Forecasting, Reading, United Kingdom, 9 13 September 2002, Arritt, R. W. & Goering, D. E. (1999) Inferences of effects of global climate change on mesoscale climate over the central US. 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