Evaluation of the High-Resolution Model Forecasts over the Taiwan Area during GIMEX

Transcription

1 836 WEATHER AND FORECASTING Evaluation of the High-Resolution Model Forecasts over the Taiwan Area during GIMEX JING-SHAN HONG Central Weather Bureau, Taipei, Taiwan (Manuscript received 2 July 2002, in final form 6 March 2003) ABSTRACT During the 2001 Green Island Mesoscale Experiment (GIMEX), the fifth-generation Pennsylvania State University NCAR Mesoscale Model (MM5) was run at a horizontal resolution of 5 km twice a day and forced by initial and boundary conditions from the operational models of the Central Weather Bureau of Taiwan. The purpose of the paper is to evaluate the performance of the surface forecasts of the high-resolution numerical model and to verify quantitative precipitation forecasts (QPFs) over Taiwan Island within the 2-month period. The major errors in the forecasts are surface warm and dry biases. The model also tends to predict a stronger surface wind speed and an inland wind component, which suggest that the model overpredicted the sea breeze, a result that is consistent with the surface warm bias. The underprediction of the precipitation and poor skill scores are possibly due to the inadequate description of the humidity in the initial condition, and a spinup problem due to the steep Central Mountain Range. 1. Introduction Because of the improved computational performance and the development of mesoscale numerical models, high-resolution ( 10 km), local-scale, real-time numerical weather prediction of mesoscale features can now provide considerable useful information in complex terrain, urban and coastal areas, and spatially inhomogeneous geographical regions. The high-resolution numerical forecasts also show considerable skill in predicting a number of local, orographically driven circulations, including gap winds, foehn-related phenomena, downslope winds, lee waves, and topographic organization of precipitation (Smith et al. 1997). Many of these features are simply not resolved by coarse-resolution models. Currently, real-time high-resolution numerical models are operational at a number of sites (Mass and Kuo 1998). Although improved skill in depicting the mesoscale features is suggested by the increased model resolution, more effort should be directed toward evaluating the performance of the high-resolution numerical model forecasts via the description of the forecast errors and the verification of quantitative precipitation forecasts (QPFs). There are many studies of model performance for individual cases (e.g., Roebber and Eise 2001; Gallus and Segal 2001) and over extended periods (e.g., Nutter Corresponding author address: Jing-Shan Hong, Meteorological Information Center, Central Weather Bureau, 64 Kung-Yuan Rd., Taipei, Taiwan. rfs14@cwb.gov.tw and Manobianco 1999; White et al. 1999; Case et al. 2002). However, only a few studies have documented the performance of high-resolution numerical models over a large number of forecasts. Since the dominant response from orographically driven circulations is in the near-surface layer, it is necessary to verify further the surface forecasts of high-resolution numerical models. Recently, Hanna and Yang (2001) evaluated the 4- day model performance for surface wind speeds and directions at the lowest model level at about 9 m AGL against observations using the fifth-generation Pennsylvania State University National Center for Atmospheric Research (PSU NCAR) Mesoscale Model (MM5; Grell et al. 1994) at 4-km resolution. Their results showed that the typical mean error (ME) and rootmean-square-error (RMSE) of the hourly average surface wind speed were 1.5 and m s 1, respectively, for a wide range of wind speeds. The ME and RMSE of the surface wind direction were about 2 and 66, respectively. They suggested that these uncertainties in wind speed and direction were primarily due to random turbulent processes that could not be simulated by models, as well as to subgrid variations in terrain and land use. However, the performance of the high-resolution forecasts may vary by region, and thus an evaluation of the performance of mesoscale forecasts of varying resolution in different areas should be informative. Case studies have suggested that mesoscale numerical models run at high resolution can predict realistic precipitation structures, especially for topographically organized precipitation systems (Colle and Mass 1996) American Meteorological Society

2 OCTOBER 2003 HONG 837 However, although the high-resolution forecasts may result in more realistic looking structures, it is necessary to further verify the QPFs of the high-resolution numerical models over longer time periods. An improved understanding of the QPF means not only a better grasp of the model precipitation bias but it can also provide feedback for identifying weaknesses in the model precipitation processes. Colle et al. (1999) evaluated 36-h MM5 precipitation forecasts at 36- and 12-km resolution over the Pacific Northwest and compared them with the National Centers for Environmental Prediction (NCEP) Eta Model at 10-km resolution (Eta-10). Their MM5 simulation used the explicit moisture scheme of Hsie et al. (1984), the Kain Fritsch cumulus parameterization scheme, and the Zhang and Anthes (1982) planetary boundary scheme. They found the MM5 simulations needed h to spin up the model precipitation owing to the cold start process (no hydrometeors at the beginning of the simulation), and the most skillful 6-h forecast period was from 12 to 18 h. The 12-km MM5 generated too much model precipitation along the steep windward slopes and not enough precipitation in the lee of major barriers. Furthermore, Colle et al. (2000) verified the MM5 precipitation forecast during the cool seasons over the Pacific Northeast and showed a noticeable improvement in the skill scores when the horizontal resolution was increased from 36- to 12-km resolution; however, the skill was restricted to the heavy rainfall when the resolution was decreased from 12 to 4 km. On Taiwan, a subtropical island ( N) with complex orographic structures of a limited size (about 400 km long and 150 km wide), most of the orography has elevations extending above 3000 m within a distance of 50 km (Fig. 2). Thus, local circulation induced by orographic and land sea processes is dominant over Taiwan Island (Kuo and Chen 1990; Chen et al. 1999). The Green Island Mesoscale Experiment (GIMEX) over southeast Taiwan was conducted in the mei-yu season of May and June during The proposed scientific objectives of GIMEX were to better understand the mesoscale circulations and the mesoscale convective systems associated with the mei-yu front to the southeast of Taiwan (Chen 1993; Hsu and Sun 1994), to document the local circulations, including the split flow and lee vortices induced by Taiwan orography (Kuo and Chen 1990; Yu et al. 1999), and to improve our understanding of the structure of the boundary layer and the development of the land sea breeze and mountain valley wind. During the period, a real-time numerical weather prediction suite was run to provide high-resolution forecasts on Taiwan s scale. The MM5 was run at the horizontal resolution of 5 km twice a day and forced by the initial and boundary conditions of the operational models from the Central Weather Bureau (CWB) of Taiwan. The purpose of this paper is to evaluate the performance of the surface forecasts of the high-resolution model and to verify the QPFs over Taiwan for a 2-month FIG. 1. Computational domains for the four nested grids. period. Descriptions of the model configuration and the data used for verification are presented in sections 2 and 3, respectively. The evaluation of the surface variables and QPFs is given in section 4. Finally, the main conclusions are summarized. 2. Model description The nonhydrostatic version of the PSU NCAR MM5 version 3.4 was run twice daily (0000 and 1200 UTC) from 1 May through 30 June 2001 at CWB using a Fujitsu VPP5000 supercomputer with four processors. There were four nested domains, as shown in Fig. 1, with horizontal resolutions (dimension) of 135 (55 36), 45 (67 55), 15 (97 91), and 5 km ( ), respectively. There were 31 unevenly spaced levels in the vertical. A one-way interface was applied to all the nested domains so that the coarser domain supplied the boundary condition to the nested domain only and solutions from the fine domain did not feed back to its mother domain. The outermost domain covered most of the Asian and west Pacific area in order to better describe the evolution of the subtropical high over the Pacific Ocean and avoid the dilution from the lateral boundary problem due to the Tibetan Plateau. All the elevation datasets, including 5, 30, and 1, are created from the U.S. Geological Survey (USGS) global 30 dataset, and thus 5 and 30 averaged terrain data were analyzed to create the 135-, 45-, 15-, and 5- km model terrain grid, respectively. The highest terrain height over Taiwan for the 5-km resolution was about 2944 m. A 30 vegetation dataset from the USGS was used to initialize 25 surface land use categories. Based on Yang et al. s (2000) and Chien et al. s (2002) work, all the MM5 simulations used the explicit Goddard microphysics scheme (Lin et al. 1983; Tao and Simpson 1993) and the planetary boundary layer parameterization of NCEP s Medium-Range Forecast scheme (Hong and Pan 1996). The Kain Fritsch cumulus parameterization scheme (Kain and Fritsch 1990) was applied in the 135-, 45-, and 15-km domains, while

3 838 WEATHER AND FORECASTING no cumulus scheme was used in the 5-km domain. The cloud-radiation scheme and the five-layer soil model (Dudhia 1996) were used for the radiation and surface processes, respectively. For the purpose of verification against surface data, similarity theory was used to extend the lowest sigma level data at (about 36 m AGL) to the 2-m temperature, 2-m relative humidity, and 10-m wind levels. The initial atmospheric conditions and the sea surface temperature were obtained from the operational data assimilation system at CWB, where the analysis package is the optimal interpolation scheme (OI) (Barker 1992) at 45-km resolution and then interpolated to all the MM5 domains. Boundary conditions were forced from the forecasts of the operational global model at CWB with an update interval of 6h. The 48-h forecast for all the MM5 domains was performed during the 2-month period. 3. Data collection and evaluation method The model forecasts were run twice daily (0000 and 1200 UTC) from 0000 UTC 1 May through 1200 UTC 30 June During the verification period, two typhoons affected the Taiwan area. During these typhoon periods, the prediction of mesoscale features and the precipitation distribution around Taiwan is closely related to an accurate track forecast (Wu and Kuo 1999). In this study, there were no additional typhoon bogusing procedures used to create the initial conditions; thus, an enormous forecast error might be produced from the inadequate typhoon forecasts and affect a large area, including both the surface forecasts and the QPFs around Taiwan. For this reason, only 109 forecasts out of the 122 were verified during GIMEX. The verification procedure excluded the typhoon periods from 0000 UTC 10 May to roughly 1200 UTC 13 May and from 1200 UTC 21 June to 1200 UTC 23 June. The Central Weather Bureau operates 25 conventional surface stations around the Taiwan area. The forecasts of the 5-km-resolution domain were evaluated for 18 of the 25 stations, as shown in Fig. 2. Most of the stations had an observational interval of 1 h during the daytime ( LST) and 3 h during the nighttime ( LST). Because the surface forecast errors were possible due to terrain differences between the model grid and stations, especially over the abrupt orography, the forecasts and grid parameters at the stations were represented by the grid point around the station where the model terrain height was closest to the station height. Comparisons of surface parameters between the station and the screened model grid over stations are listed in Table 1. Notice that the PY and LT stations are located on separate islands that are too small to be described by the numerical mesh, such that the numerical model defines these locations as ocean. Surface observations from the conventional stations were used to verify the model forecasts of the 2-m temperature, 2-m relative humidity, and 10-m wind fields every 3 h. The accuracy of the model forecasts is quantified using the ME, RMSE, and the root-mean-square vector error (RMSVE), which are defined as follows: N 1 i i N i 1 N 1 2 i i N i 1 N 1 O F 2 O F 2 i i i i N i 1 ME (O F ), RMSE (O F ), and RMSVE (u u ) ( ), where O and F are the observation and the forecast, N is the total number of the forecasts, and (u, ) are the east and north wind component. Notice that the positive ME means the model has a low bias. In 1982, the CWB initiated a weather hazard prevention program and constructed an Automatic Rainfall and Meteorological Telemetry System (ARMTS). This ARMTS network, completed in August 1997, includes 220 rainfall stations that only measure precipitation and 94 rainfall meteorological stations that measure precipitation, wind, pressure, and temperature. The locations of the ARMTS stations are depicted in Fig. 2. The model precipitation of the 5-km-resolution domain at ARMTS stations was represented by using the spatial average of the surrounding four grids around each station and compared to the observed precipitation of the ARMTS network. A number of scores were computed from the elements of a rain no-rain contingency table (Mesinger and Black 1992; Wilks 1995; Mesinger 1996), including bias score (Bias), equitable threat score (ET), probability of detection (POD), false alarm ratio (FAR), and Kuipers skill score (KSS), which are defined as A B Bias, A C A E ET, where A B C E (A B)(A C) E, A B C D A POD, A C B FAR, and A B AD BC KSS. (A C)(B D) The 2 2 contingency table includes four categories that are defined relative to some specific rain threshold: both observed and forecast (A), forecast but not observed (B), observed but not forecast (C), and neither observed nor forecast (D). The Bias score is a way to

4 OCTOBER 2003 HONG 839 FIG. 2. Topography (m) and distribution of the conventional surface stations used to evaluate the MM5 surface forecasts (squares). The rainfall meteorological stations (large dots) and the rainfall stations (small dots) are shown. The contours for the terrain height are 1000 and 2000 m, respectively. determine if the model precipitation, averaged over many cases, is overforecast (Bias 1) or underforecast (Bias 1) for the given threshold and cannot assure the accuracy of the forecasts. The equitable threat score is similar to the threat score, except it corrects for the expected number of hits by chance. The POL and FAR measure the portion of rain events successfully forecast by the model and the model s tendency to forecast rain where none was observed, respectively. Through algebraic manipulation, the KSS score can be expressed in terms of the sum of the accuracy for events (POL) and nonevents minus one as A D KSS 1. A C B D The minus 1 is to normalize the score to the values range from 1 to1.thekss gives equal emphasis to the ability of the forecast model to correctly predict events and nonevents. A perfect prediction has values of B and C equal to 0 and yields BIAS POD ET KSS 1 and FAR Verification of surface forecasts The statistics of the surface forecast errors of the 5- km resolution domain during May and June 2001 are displayed in Tables 2 and 3. Because of the spinup problem, due in part to the complexity of the orography over Taiwan, the model went through a pronounced adjustment process during the first few hours, which is most significant in the surface layer. Thus, in order to avoid a large error in the precipitation bias score due to the adjustment processes, only the periods between 6 and 48 h of the forecasts were evaluated against station observations (Tables 2 and 3). An important concern is the significance of the results. The Student s t test was used to measure the statistical significance of the bias. As shown in Table 2, the temperature bias ( 0.3) at PY

5 840 WEATHER AND FORECASTING TABLE 1. Comparisons between the station height and the terrain height of the model grids over the station. The last column is the model land use category used for the station. The sequence of the stations listed in the table is roughly distributed from northwestern to southwestern Taiwan (bold) and then from southeastern to northeastern Taiwan (bold italics). Stations PY, LY, LT, and PH (roman bold) are on islands. Station ID Station height (m) Model terrain (m) Land use category PY KL TP HC TC CI TN KS HN TW TT CK HL SO IL LY LT PH Water bodies Irrigated cropland and pasture Urban and built-up land Irrigated cropland and pasture Shrubland Irrigated cropland and pasture Shrubland Shrubland Cropland/woodland mosaic Irrigated cropland and pasture Dryland cropland and pasture Mixed forest Grassland Dryland cropland and pasture Grassland Mixed forest Water bodies Cropland/woodland mosaic is statistically significant at the 99% confidence level because the absolute value of the bias is greater than Tables 2 and 3 show that all the statistics are significant at the 99% confidence level except for the wind direction at some stations. The reason for the significance of the results is the large number of data points. The average observed surface temperature was 27.8 C with a higher average temperature ( 28 C) over the western plain of Taiwan and a lower one over the eastern coastal region. The predicted surface temperature had an average ME of 0.3 C and an RMSE of C (details in the table caption). The RMSE was between 1.6 and roughly C; in general, the larger the ME, the larger the RMSE Further detailed analysis found that the systematic temperature mean errors were related in part to the differences between the model terrain and station height. For example, CK and TW are located on a foothill with a large terrain gradient, while LY is located on the mountaintop of the small island. The limited resolution of the numerical model terrain results in poor representation of the surface characteristics near these three surface observation stations (see Table 1). As a result, a cold bias occurred for CK and TW whereas a large warm bias occurred for LY, where the station TABLE 2. The statistics of the forecast errors for surface temperature and relative humidity evaluated at the conventional stations. Here, Obs, ME, and RMSE are the mean observations, mean error, and root mean square error, respectively. Test is the value of the bias that is statistically significant at the 99% level. The bottom row is the average for stations distributed across Taiwan Island (PY, PH, LT, and LY excluded). Station PY KL TP HC TC CI TN KS HN TW TT CK HL SO IL LY LT PH Avg Temperature ( C) Obs ME RMSE Test Relative humidity (%) Obs ME RMSE Test

6 OCTOBER 2003 HONG 841 TABLE 3. Same as in Table 2 but for wind direction and wind speed. Station PY KL TP HC TC CI TN KS HN TW TT CK HL SO IL LY LT PH Avg Wind direction ( ) Obs ME RMSE Test Wind speed (m s 1 ) Obs ME RMSE RMSVE Test heights of CK and TW are lower than the model terrain and LY is higher. An objective correction could be applied to correct the bias in order to provide more useful information to forecasters. The objective correction could be statistically based, for example, the regression method or something similar to model output statistics (MOS; Vislocky and Fritsch 1995), or physically based, for example, corrected following the lapse rate to account for the terrain difference between the model and the station. The later is straightforward and easy to implement, but only suitable for temperature, and not for other variables. For example, if we correct the averaged surface temperature forecasts by using the lapse rate of 6.5 C km 1, the mean error could be reduced to about 0.9, 0.7, and 0 C for LY, CK, and TW, respectively. The diurnal variation was absent at the LT and PY (not shown), due to an incorrect land sea representation in the model (represented as sea points, as revealed in Table 1). The forecast surface temperature then was closely related to the sea surface temperature. The misuse of the land sea index also contributed to the verification for the wind direction, particularly in low flow regimes (e.g., wind speed less than 10 m s 1, not shown). Thus the better description of the model terrain and the land sea index is expected to improve the forecast performance. The relative humidity displayed in Table 2 shows that the average observation was about 78.3% during the period of the study. The model forecasts tended to possess a dry bias of 10% and as RMSE of about 10% 20%. The dry bias was not only at surface levels but over the whole atmosphere. From the radiosonde observations at Penchiao station (near TP), both the initial analysis and forecasts had the mean dry bias about 10% 15% in the troposphere (not shown here). The surface also had a dry bias of 14.5% at the initial time [as shown in Fig. 4 (middle)]. Thus, the dry bias found throughout the whole atmosphere was mostly due to an incorrect moisture analysis. However, it is difficult to improve the moisture analysis using the OI analysis scheme because of the deficiency of the conventional observations over the ocean area. A variational analysis scheme is suggested because it can assimilate more remote observations, such as precipitable water (Kuo et al. 1996) or GPS/MET data (Zou et al. 1995). Table 3 shows that the model also had a tendency to predict stronger winds than observed by about 1.3 m/ s. The average RMSE s for the wind direction and wind speed were 67.7 and m s 1, respectively. An average RMSVE for the wind speed was about 3.6 m s 1. Larger RMSE s ( 3) or RMSVE s ( 4) are found for PY, KL, SO, LT, LY, TW, HN, and KS, which were distributed over the northern and southern ends of Taiwan. Because of the elongated topography, as shown in Fig. 2, the most pronounced orographically induced local circulations would occur over the north and south ends of Taiwan. The features for such an orographically induced local circulation were sensitive to the upstream condition, which relied heavily on the accurate description of the synoptic system. If the synoptic forecasts have slight temporal or spatial phase errors, then the high-resolution forecasts might produce a large variability in the surface wind forecast errors. Figure 3 shows the observed and forecast winds averaged over the evaluation period. The figure shows that the average wind field during May and June was dominated by the prevailing southerly wind component and modified by the sea breeze over Taiwan Island. The model forecast wind speed was not only stronger than the observations but also had a directional bias of a more onshore wind component. The results implied that

7 842 WEATHER AND FORECASTING FIG. 4. The statistics for (top) surface temperature ( C), (middle) surface relative humidity (%), and (bottom) surface wind speed (m s 1 ), for all observations stations excluding PY, PH, LT, and LY during the evaluation period. Results are for the forecasts initialized at 1200 UTC and plotted as a function of forecast time (0 48 h) and the valid local time. The heavy line is the observations and the dashed line is the forecasts. The vertical line shows the RMSE of the forecast errors. The model forecasts were verified at the 1-h observational interval during the daytime and the 3-h observational interval at night. FIG. 3. The averaged surface wind for the observations (bold bar) and the forecast during the evaluation period. The full bar is 5 m s 1. the model tended to predict stronger sea-breeze circulations, which was consistent with the warm bias as mentioned. Figure 4 shows the fields averaged over the stations on Taiwan Island and displayed as a function of the forecast time for the 1200 UTC forecasts. In order to enhance the diurnal variations, the fields in Fig. 4 were averaged at a 1-h observation interval during the daytime and a 3-h observation interval at night. The results from the 0000 UTC forecasts were similar (not shown). The fields for the surface temperature, relative humidity, and wind speed shown in Fig. 4 exhibit significant diurnal variations: ME and RMSE during nighttime were larger than those during daytime. Figure 4 (top) also shows that a warm bias occurred during nighttime, whereas a slight cold bias could be found during the afternoon because the sea breeze was well developed (not shown). Thus, the stronger predicted sea breeze tended to offset the radiational heating effect and then resulted in a cold or small warm bias for the stations distributed along the coastal region. Table 2 shows that most of the cold bias or slight warm bias (e.g., KL, HL, HC, PH, TT, and HN) occurred for the stations distributed across the coastal region. In contrast, stations located across the inland or the plain area (TP, IL, TC, and TN) had a larger warm bias. The skill of the rainfall forecasts based on the contingency table was computed at 12-h intervals between forecast hours 0 and roughly 48. Figure 5 shows the ET scores as a function of the rain threshold. The skill drops rapidly for thresholds between 0.1 and 15 mm 12 h 1. The figure also indicates that the most skillful 12-h forecast period was h with ET scores dropping from approximately 0.23 to 0.09 as the rain threshold is FIG. 5. Equitable threat scores as a function of rain threshold at a 12-h interval (0 12-, , , and h forecasts).

8 OCTOBER 2003 HONG 843 FIG. 6. Same as in Fig. 5 but for KSS. FIG. 8. Same as in Fig. 5 but for FAR. increased, whereas the worst ET was for the 0 12-h forecast period, decreasing from 0.22 to The ET scores for the h forecast period ranged from 0.16 to 0.05 and was comparable to the h forecast period. Chien et al. (2002) evaluated the MM5 precipitation results for 15-km resolution during the 1998 mei-yu season. Their results showed the ET score is between 0.3 and 0.1 for thresholds from 0.3 to 50 mm 12 h 1. They also found that the h rainfall forecast gave the highest score. However, since the verification score may vary by the verification and model resolution (Gallus 2002), it is more meaningful to compare the scores for similar resolutions. The curve of the KSS scores as shown in Fig. 6 is very similar to ET with values range from 0.35 to 0.1. The POD (Fig. 7) decreased from about 55% to about 10%. The measures of POD for and h forecasts are better than the other forecast intervals for thresholds below 15 mm 12 h 1. Since the KSS score is a normalized form of the summation of the accuracy for events and nonevents, the relatively low KSS and POD implied the low accuracy for nonevents. Consistent with the low accuracy for events and nonevents, the FAR (Fig. 8) increased with the threshold, from about 40% to 80%. The Bias scores (Fig. 9) were less than one for all of the rain thresholds and forecast periods. The underprediction of precipitation was consistent with the dry bias, as depicted in Table 2. The scores decreased from the 0.1- to 1-mm thresholds and were almost constant for the other rain thresholds. The figure also shows that the Bias scores at all thresholds decreased as the forecast time increased. Figure 10a shows the total observed precipitation during the verification period. The figure indicates that the most precipitation was distributed over the Central Mountain Range (CMR), and the southwest and northeast of Taiwan. Figure 10b shows accumulated precipitation for the 0 12-h forecasts. The figure indicates that the model predicted too much precipitation over the eastern CMR, which was the windward side for the prevailing southeast wind (Fig. 3). Usually, the numerical model needs to go through an adjustment process during the first few forecast hours. In this study, the initial condition of the 5-km resolution domain is interpolated from the rather coarse resolution of 45 km and from P to coordinates. Thus, the initial condition over steep mountain areas is highly imbalanced and the spinup problem could be pronounced. The spinup process could induce organized vertical motions that are correlated with the topography. The humidified atmo- FIG. 7. Same as in Fig. 5 but for POD. FIG. 9. Same as in Fig. 5 but for Bias scores.

9 844 WEATHER AND FORECASTING FIG. 10. The accumulated precipitation during the evaluation period: (a) the observations, (b) 0 12-h forecasts, (c) h forecasts, (d) h forecasts, and (e) h forecasts. The contour line interval is 200 mm. sphere, typical in the mei-yu season, is able to provide sufficient moisture and results in heavy rainfall during the spinup process. Inferring from the above, Fig. 10b suggests that the forecasts had a spinup problem and produced heavy rainfall during the first few hours. This spinup problem is hypothesized to cause the poor scores for the 0 12-h forecast. Figure 10c gives the accumulated precipitation for the h forecasts and shows that the model predicted similar levels of precipitation, in total amount and in distribution, as the

10 OCTOBER 2003 HONG 845 FIG. 10. (Continued) observations. This result was consistent with that of the QPF h forecast, which was the most skillful. The and h forecasts (Figs. 10d and 10e) were less than the observations but consistent with the verification result that the Bias score decreased as the forecast time increased. Thus, a better understanding of the initial humidity field, and the spinup problem due to the steep CMR, are important issues for improving the QPFs of the high-resolution forecasts. 5. Summary The performance of the 5-km resolution MM5 forecast was evaluated during May and June Overall, the model had an average surface warm bias of 0.3 C and an RMSE of C. Larger biases of the surface temperature occurred where the differences between the model terrain and station height were large. The inland wind direction bias implied that the model overforecast the sea breeze, and was consistent with the warm bias. The reduced radiational heating effect due to the stronger predicted sea breeze might lead to the cold bias of the stations located around the coastal region. The forecast relative humidity was 10% drier than observations. The model also tended to predict stronger winds than observed by about 1.3 m s 1. The averaged RMSE for the wind direction and wind speed was 67.7 and ms 1, respectively. The average RMSVE for the wind speed was about 3.6 m s 1. Larger RMSE values for the surface wind speed were found at stations distributed across both the north and south ends of Taiwan due to the elongated topography. The large variability in the forecast errors of the wind speed occurring across the south and north ends of Taiwan might be attributed to the model bias; however, inaccurate description of the synoptic pattern in the initial condition or the forecasts could be an important factor in such an error distribution. The forecast errors exhibit significant diurnal variations; the ME and RMSE during the nighttime were larger than those during the daytime. The warm bias occurred during nighttime, whereas the small cold bias could be found during the afternoon. The Bias scores were less than one for all the rain thresholds and decreased as the forecast time increased. The underprediction of the precipitation was consistent with the dry bias for the surface relative humidity. The h forecast was the most skillful 12-h forecast period, with ET scores from approximately 0.23 to 0.09, whereas the worst was the 0 12-h forecast period when it decreased from 0.22 to The low KSS scores were consistent with the low POD and high FAR which showed that the skill at rainfall forecast was limited for both rain and no-rain events. This paper sheds light on the quantitative evaluation of the forecasts of a high-resolution numerical model over Taiwan. The forecast problems around the Taiwanese area are difficult and are made more challenging by the complex/steep topography and the deficiency of the observations over open ocean. The underprediction and poor skill scores of the precipitation forecasts are documented and attributed to the inadequate moisture description in the initial conditions and the spinup problem over the abrupt orography. Thus, efforts to reduce the spinup problem over steep mountain areas and to better describe the moisture field around data-sparse oceans are needed, and could be a way to further improve the ability of quantitative precipitation forecasts over Taiwan. Acknowledgments. The author extends his sincere gratitude to the Meteorological Information Center at the Central Weather Bureau in Taiwan for offering its utmost support. The computations were performed on a Fujitsu VPP5000 computer with four processors. Additional thanks to Prof. Ben Jong-Dao Jou for the encouragement on this research and to Dr. Ming-Dean Cheng for the helpful discussion. The author also wishes to thank the anonymous reviewers for their valuable comments on the manuscript. This research was supported by the National Science Council of Taiwan under Grants NSC M and NSC M AP4. REFERENCES Barker, E. H., 1992: Design of the navy s multivariate optimal interpolation analysis system. Wea. Forecasting, 7, Case, J. L., J. Manobianco, A. V. Dianic, M. M. Wheeler, D. E. Harms,

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