A WRF-based rapid updating cycling forecast system of. BMB and its performance during the summer and Olympic. Games 2008

Transcription

1 A WRF-based rapid updating cycling forecast system of BMB and its performance during the summer and Olympic Games 2008 Min Chen 1, Shui-yong Fan 1, Jiqin Zhong 1, Xiang-yu Huang 2, Yong-Run Guo 2, Wei Wang 2, Yingchun Wang 1, Bill Kuo 2 1 Institute of Urban Meteorology, CMA, Beijing, China 2 National Center of Atmospheric Science, Boulder, CO 1. Introduction A real-time rapid updated cycling forecast system (BJ-RUC) based on WRF model and WRFVAR data assimilation system was built in Beijing Meteorological Bureau (BMB) with the primary goal to improve the short-range forecasts and support the weather service for the XXIX Olympic and Paralympic Summer Games held in Beijing, During the summer season and the Games, the operational NWP products of the system provided helpful guidance for forecasters, especially on the 0-6h short-range forecasts for convection, the temporally detailed and point-specific forecasts for all of the outdoor venues and the opening and closing ceremonies. A preliminary evaluation was performed and described in this paper for the operational forecasts of BJ-RUC system during the summer season from June to September of 2008 including the Olympic and Paralympics Games period. The motivation of this research is to evaluate the accuracy of forecasts from BJ-RUC system, especially focusing on the short-range forecasts and how the accuracy varies during the diurnal cycle, with forecast leading time, and as a function of the forecast cycle. 2. Description of BJ-RUC system The forecast and data assimilation components of BJ-RUC system are based on WRF model and WRFVAR. Figure 1 illustrates the 3 nested model domains with grid distances of 27, 9, 3km respectively. The model physics configuration is as follows: WSM6 microphysics parameterization; the K-F cumulus parameterization of MM5 version for 27-9 domains and no cumulus for the 3-km domain; YSU PBL parameterization; RRTM long wave radiation scheme and Dudhia short wave radiation scheme (Skamarock and Klemp et al., 2005). The cold-start run initiates every day at 12UTC with the global forecasts from the AVN or T213 of NMC/CMA as the background of data assimilation, while the other 7 cycles will run in the so-called hot-start styles with the assimilation backgrounds from the 3-hr forecast of the previous cycles. Data assimilation and 24-hour forecasts are performed in the 3 domains simultaneously. Figure 1 also shows the operational flow chart of BJ-RUC system. The data sources utilized by WRFVAR include the conventional and intensive surface and upper-air rawinsonde reports, aviation routine 1

2 weather report, ship, buoy via the Global Telecommunication System (GTS). Also used are high-frequency measurements from various special networks such as the AWS and GPS/IPW maintained by BMB. The background error statistics (BES) files for each nested domain were derived based on the NMC method independently. According to (Guo and Fan et al., 2007), the scaling factor of BE for 3-km domain was reduced to 0.5 (var_scaling=0.5) to increase weight of the analysis background, for the purpose of keeping dynamical balance of the 1-st guess while effectively absorbing observation information. this research are based on the 896 forecasts performed during the period 00UTC 1 June - 21UTC 31 September, 2008, including the Games periods. As nearly all of the forecast products are generated from the results of 3km domain, both bias and RMSE for the surface and upper-air forecast variables are calculated against the conventional surface and sounding observations in the 3-km domain area. In addition, Critical Success Index (CSI/TS) and bias scores for 3-hr and 6-hr accumulated precipitation are also calculated against precipitation observations from the 155 AWS stations in Beijing area. All of the station forecasts are interpolated from the model grid to the observation locations using bilinear interpolation from the four neighboring grid points. 1) Verification of forecast of precipitation Figure 1 The forecast domain of BJ RUC system and Operational flow chart of BJ RUC system. 3. Preliminary assessment of BJ-RUC system during the summer and Games periods The evaluated accuracy statistics in 2

3 Figure 2 CSI and bias scores of the 3 hr and 6 hr accumulated precipitation forecasts from all cycling runs of BJ RUC system CSI and bias scores of the 3-hr and 6-hr accumulated precipitation forecasts from all cycling runs of BJ-RUC system during the evaluated period are given in Figure2. Evidently, for most thresholds, the very initial 0-3hr and 0-6hr have the best forecast skill, especially for those larger thresholds beyond 5mm/3hr and 5mm/10hr. From these results, the spin-up problem of the model is not as significant as the cold-start runs. The forecast skill at the initial stage of integration will be improved greatly due to the rapid updating cycling assimilation style. But the large BIAS scores for 50mm/3hr and 50mm/6hr thresholds were due to the over-prediction for very few precipitation cases, such as the one occurring on 10 August, There is obvious diurnal cycle of precipitation in Beijing area, i.e. both rainfall amount and frequency present high values for late afternoon to early morning and reach minima around noon (Li and Yu et al., 2008). In 2008, the diurnal cycle of precipitation revealed by the accumulated 3-hr AWS rainfall was more significant. The largest precipitation occurred during 12-15UTC and 15-18UTC, while the smallest amount of precipitation occurred during 03-06UTC. For each 3-hr period such as 00-03utc, 03-06utc,,21-00utc, there are 8 comparable forecasts from cycles with different initial time. Therefore, the two issues concerned by the forecasters need to be raised: i. which cycle had the best forecast skill for each 3-hr period? Ii. For each cycle initiating from different time, how about its forecast performance for the diurnal cycle of precipitation in Beijing area? Figure 3 CSI and bias scores for each 3-hr interval period of cycles with different initial time. 3

4 To answer the above questions, figure 3 shows the CSI and bias scores of 3-hr accumulated precipitation forecasts initiating from different time for each 3-hr valid period for 2 selected thresholds (1mm/3hr, 5mm/3hr), showing the difference of precipitation forecast skill as a function of time during the diurnal cycle with different leading time. For 0.1mm/3hr, no big fluctuation of CSI varies along with valid time can be identified for the average CSI scores (figures not shown). But for the thresholds beyond 1mm/3hr, all cycles, whatever their initial time are, have identical diurnal tendencies for precipitation forecasts, i.e. better skills in the afternoon and nighttime (09-00utc of the next day) than those in the morning and this situation is more significant for larger thresholds. The best forecast skill was at 15-18utc, corresponding to 23-02BJT, while the worst skill was at noon and early afternoon (03-06utc, valid at 11-14BJT). The very initial 0-3hr or 3-6hr forecasts of the cycling runs initiating from 09,12,15,18,21utc were of the better precipitation forecast skills, which would be ascribed to the fact that the spin-up problem has been better resolved via the rapid updating cycling assimilation style than the cold-start runs, leading to significant improved precipitation forecast performance. Herein, the cycling runs initiating from 09, 12, 15, 18, 21utc and their 0-6 hour short-range precipitation forecasts would be more consultable for forecasters. 2) Verification of Surface forecasts From Figure 4, very significant systematic error for 2-m temperature and specific humidity can be identified. For 2-m temperature, there is 1 warm bias during daytime and cold bias during nighttime with the maximum -4.5 at 21 UTC, corresponding to 05 Beijing Time (BJT), local early morning. And for 2-m specific humidity, the dry bias during all day also has the maximum at 21UTC, the local early morning. The magnitudes of the u, v bias were around ±0.3m/s and 0.1~-0.5m/s. 10-m u component had positive bias during the time from afternoon to the early morning, while negative bias from 0-6utc, valid at 8-14BJT. 10-m v component had negative bias, whatever the forecast lengths. When comparing with RMSE, it was found that model s systematic error could only account for very small part of the model forecasts error, and the large RMSE were due to the non-systematic error, such as random error, etc. Some possible explanations of the errors relating to surface forecasts have been discussed by Liu and Warner et al. (2008) and more investigations are needed to improve the forecast skill. At each 3-hourly identical valid time, the analysis of the cycle initiating at the current time do have the smallest RMSE, i.e. best quality when comparing with the forecasts from other 7 cycles, especially at 12UTC, the cold-start initial time. In addition, significant distances between analysis and 3-hr forecast of previous cycles can be identified. For cold-start runs initiating from 12UTC, the distance is the largest, that is, the model forecast errors accumulated during previous runs would have a chance to be reduced to a new low level; for hot-start runs, these distances can be regarded as analysis increments, which modify the 4

5 backgrounds (3-hr forecast of previous cycle) and push them toward observations. Thus it can be seen, the variational data assimilation is performed effectively. Also for the surface forecast valid at the time with 3-hour interval, nearly all of the best forecast quality comes from the very most adjacent cycle, while other cycles, whose forecast range among 6-24 hours would have similar forecast scores. So it can be found that the initial conditions with latest observations assimilated would have positive impacts on forecast quality at least during the first 6-hr integrations, which is also consistent with the better precipitation forecast for the very first 0-6 hour for BJ-RUC system. From this point of view, the poor quality of the short-range numerical forecast would have been greatly improved. Figure 4 RMS and bias scores for surface variables (2-m temperature, 2-m specific humidity, 10-m u, 10-m v). 4. Summary In this paper, the performance of operational short-range forecast of BJ-RUC system during the summer season from June to September of 2008 including the Olympic and Paralympics 5

6 Games period is preliminarily evaluated during the diurnal cycle, with forecast leading time, and as a function of the forecast cycle. We find from the results that the great improvement of forecast skill at the first several hours of integration can be ascribed to the two factors, the first is the rapid updating cycling assimilation style, and the other is the positive impacts brought by assimilation of latest observations lasting at least for 6-hour integration. BJ-RUC system has some systematic errors of precipitation forecast that the worst skill is found at noon when considering the forecast performance for the diurnal cycle of precipitation. Further investigation is still under way. Acknowledgements The research is supported by Ministry of Science and Technology of PRC under Contracts 2008BAC37B00 and special meteorological technology project by CMA (GYHY ).. References 1. Guo, Y. R. and S. Y. Fan, et al. (2007). "Application of WRFVar (3DVar) to a High Resolution (3-km) Model over Beijing Area.". 2. Li, J. and R. C. Yu, et al. (2008). "Diurnal variations of summer precipitation in Beijing." Chinese Science Bulletin 53 (12): Liu, Y. and T. T. Warner, et al. (2008). "The Operational Mesogamma-Scale Analysis and Forecast System of the US Army Test and Evaluation Command. Part II: Interrange Comparison of the Accuracy of Model Analyses and Forecasts." Journal of Applied Meteorology and Climatology 47 (4): Skamarock, W. C. and J. B. Klemp, et al. (2005). "A description of the Advanced Research WRF Version 2." NCAR Tech Notes-468+ STR. 6