A Multi-Resolution Model Evaluation of Pre-Flood Season Precipitation and Low-Level Flow Field Forecasts over the Guangdong-Hong Kong-Macao Region
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摘要: 利用国家级气象站和探空站的观测数据，对2014—2018年4—6月期间粤港澳地区9 km和3 km分辨率区域数值天气模式的降水及低层流场预报结果进行了综合评估。研究采用传统降水评分、邻域法，并利用多种统计指标评估了不同分辨率模式在前汛期的降水与低层流场预报技巧的差异及其时间、空间变化特征，分析了不同分辨率的适用性；进一步探索降水预报与低层流场预报技巧之间的相互联系，为未来中尺度模式在区域预报中的优化提供参考。(1) 9 km、3 km分辨率模式对南海夏季风爆发前降水、低层流场预报水平均相对稳定；两种分辨率模式预报降水、低层流场预报能力均具有一定日变化，两者对08:00—12:00时段的预报更准确，而对物理过程更复杂的午后和夜间预报技巧相对不足。(2) 9 km分辨率模式常高估降水量，而3 km模式的整体预报结果时间和空间偏差更小，具体表现为这一分辨率模式对季风爆发后及午后时段的降水预测更精准，且对珠江三角洲和广东省北部山区等复杂地形的降水预报误差更小；此外，3 km分辨率模式预报技巧更加稳定，尤其对小雨、中雨及强降水的预报精度更高。(3) 与降水预报评估结论相似，3 km分辨率模式对低层流场的预报同样更出色；尤其是近地面温度场和比湿的预报中，能够更好地捕捉午后近地面温度和湿度条件的变化；在复杂地形区域对近地面温湿度的预报更精准。Abstract: Based on observations from national meteorological and radiosonde stations, this study comprehensively evaluates the forecast performance of precipitation and low-level flow field over the Guangdong-Hong Kong-Macao region during the pre-flood season (April to June) from 2014 to 2018 using regional numerical weather prediction models with 9 km and 3 km horizontal resolutions. Traditional precipitation scoring methods and neighborhood technique were employed, along with various statistical indicators, to assess the differences in forecast skills between the models with different resolutions, focusing on both temporal and spatial variability. The study also analyzed the applicability of different resolutions and explored the relationship between the forecast skills for precipitation and low-level flow field, providing valuable insights for optimizing mesoscale models in future regional forecasts. The results show that: (1) Both the 9 km and 3 km resolution models exhibit relatively stable forecast performance for precipitation and low-level flow fields prior to the onset of the South China Sea summer monsoon. Both models show diurnal variations in forecasting capabilities, with improved accuracy during the 08:00 LST to 12:00 LST period, while their performance during the afternoon and night, which involve more complex physical processes, is comparatively inadequate. (2) The 9 km resolution model tends to overestimate precipitation amounts, whereas the 3 km model displays smaller temporal and spatial biases in overall forecasting, demonstrating more stable forecast skills, particularly with higher accuracy for light, moderate, and heavy rainfall events. The 3 km resolution model provides more precise predictions for precipitation following the monsoon onset and during the afternoon, with reduced forecast errors in complex terrains such as the Pearl River Delta and the northern mountainous regions of Guangdong. (3) Similarly, the 3 km resolution model excels in simulating low-level flow fields, particularly in near-surface temperature and specific humidity simulations, effectively capturing their variations during the afternoon. This model also demonstrates greater precision in simulating near-surface temperature and humidity over complex terrain areas.
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图 2 2014—2018年观测(a~c)、9 km分辨率(d~f)、3 km分辨率(g~i)的4月(a、d、g)、5月(b、e、h)、6月(c、f、i)的日降水(mm·d-1)空间分布
图 7 4月(a~b)、5月(c~d)、6月(e~d)观测与9 km分辨率模式预报(a、c、e)、3 km分辨率模式预报(b、d、f)对零星降水(0.1 mm·h-1)、对流降水(8 mm·h-1)的逐小时降水量POD评分
图 10 4月(a~b)、5月(c~d)、6月(e~f)的观测与9 km分辨率模式预报(a、c、e)、3 km分辨率模式预报(b、d、f)对零星降水(0.1 mm·h-1)、对流降水(8 mm·h-1)的逐小时降水量FSS评分
图 11 4月(a~c)、5月(d~f)6月(g~i)观测(a、d、g)、9 km WRF(b、e、h)、3 km WRF(c、f、i)模式预报的850 hPa风场与温度场(℃)
图 12 2014—2018年观测与9 km WRF预报(a、c、e)，3 km WRF预报(b、d、f)的10 m风矢量(箭头)及2 m温度(填色)的RMSE(单位：℃)在4月(a~b)、5月(c~d)、6月(e~f)的空间分布
图 14 2014—2018年4月(a~c)、5月(d~f)、6月(g~i)观测(a、d、g)、9 km WRF(b、e、h)、3 km WRF(c、f、i)模式预报的850 hPa风场(填色为比湿(g·kg-1))
图 15 观测(OBS)与9 km WRF预报(a、c、e)，3 km WRF预报(b、d、f)02、08、14、20时次925 hPa低层水汽通量与24 h平均水汽通量(填色，单位：kg·m-1·s-1)在4月(a~b)、5月(c~d)、6月(e~f)的RMSE值空间分布
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[1] 郑彬, 梁建茵, 林爱兰, 等. 华南前汛期的锋面降水和夏季风降水——Ⅰ.划分日期的确定[J]. 大气科学, 2006, 30(6): 1 207-1 216. [2] 张柳红, 伍红雨, 向昆仑, 等. 1961—2021年粤港澳大湾区暴雨气候变化特征[J]. 中山大学学报: 自然科学版(中英文), 2023, 62(4): 32-44. [3] 朱鹏程, 王东海, 曾智琳, 等. 粤港澳大湾区汛期降水的多模式集成预报方法的评估检验[J]. 热带气象学报, 2024, 40(2): 258-271. [4] 罗聪, 曾沁, 高亭亭, 等. 精细化逐时滚动温度预报方法及检验[J]. 热带气象学报, 2012, 28(4): 552-556. [5] 宝兴华, 杨舒楠. WRF-EnKF系统对中国南方一次暴雨过程确定性预报的试验[J]. 气象, 2015, 41(5): 566-576. [6] 王睿, 伍志方, 林青, 等. 改进的精细分辨率雷达探测强对流效果评估[J]. 热带气象学报, 2023, 39(3): 323-336. [7] LI Y, LU G, WU Z, et al. Evaluation of optimized WRF precipitation forecast over a complex topography region during flood season[J]. Atmos, 2016, 7(11): 145. [8] 周天军, 钱永甫. 模式水平分辨率影响积云对流参数化效果的数值试验[J]. 高原气象, 1996, 15(2): 204-211. [9] KALA J, ANDRYS J, LYONS T J, et al. Sensitivity of WRF to driving data and physics options on a seasonal time-scale for the southwest of Western Australia[J]. 2015, Climate Dyn, 44(3): 633-659. [10] 林晓霞, 冯业荣, 陈子通, 等. 华南区域高分辨率数值模式前汛期预报初步评估[J]. 热带气象学报, 2021, 37(4): 656-668. [11] 马玉芬, 肖莲媛, 李火青, 等. WRF模式中不同土地利用数据对新疆一次高温天气预报的影响[J]. 沙漠与绿洲气象, 2019, 13(1): 52-62. [12] WU J, LU J, CHEN X. Performance of the WRF model in simulating convective rainfall events in the humid subtropical monsoon climate region—Poyang Lake basin[J]. Theor Appl Climatol 2023, 153(1): 889-911. [13] 袁金南, 郑彬. 广东热带气旋降水年代际变化特征的分析[J]. 热带气象学报, 2010, 26(4): 385-393. [14] SCHUMACHER V, FERNÁNDEZ A, JUSTINO F, et al. WRF high resolution dynamical downscaling of precipitation for the central andes of chile and argentina[J]. Front Earth Sci, 2020, 8: 328. [15] ZHANG M, MENG Z. Warm-sector heavy rainfall in Southern China and its WRF simulation evaluation: A low-level-jet perspective[J]. Mon Wea Rev, 2019, 147(12): 4 461-4 480. [16] EVANS J P, MCCABE M F. Regional climate simulation over Australia's Murray-Darling basin: A multitemporal assessment[J]. J Geophys Res, 2010, 115(D14). [17] ANDRYS J, LYONS T J, KALA J. Multidecadal evaluation of WRF downscaling capabilities over Western Australia in simulating rainfall and temperature extremes[J]. J Appli Meteorol Climatol, 2015, 54(2): 370-394. [18] SOARES P M M, CARDOSO R M, MIRANDA P M A, et al. WRF high resolution dynamical downscaling of ERA-Interim for Portugal[J]. Clim Dyn, 2012, 39(9-10): 2 497-2 522. [19] 俞碧玉, 朱科锋. 多种空间检验方法在不同分辨率模式降水预报评估中的应用[J]. 气象科学, 2022, 42(3): 341-355. [20] 陈百炼, 杨富燕, 陈子通, 等. SSIM在高分辨率降水数值预报产品空间检验中的应用[J]. 热带气象学报, 2024, 40(4): 537-546. [21] ARROYO R C, RODRIGO J S, GANKARSKI P. Modelling of atmospheric boundary-layer flow in complex terrain with different forest parameterizations[J]. J Phys: Conf Ser, 2014, 524: 012119. [22] DESER C, ALEXANDER M A, XIE S P, et al. Sea surface temperature variability: Patterns and mechanisms[J]. Annu Rev Mar Sci, 2010, 2: 115-143. [23] DAI A G. Global precipitation and thunderstorm frequencies. Part Ⅱ: diurnal variations[J]. J Climate, 2001, 14(6): 1 112-1 128. [24] DU Y, CHEN G. Climatology of low-level jets and their impact on rainfall over Southern China during the early-summer rainy season[J]. J Climate, 2019, 32(24): 8 813-8 833. [25] 叶朗明, 苗峻峰. 华南一次典型回流暖区暴雨过程的中尺度分析[J]. 暴雨灾害, 2014, 33(4): 342-350. [26] CHEN, G X, LAN R Y, ZENG W X, et al. "Diurnal variations of rainfall in surface and satellite observations at the monsoon coast (South China)"[J]. J Climate, 2018, 31(5): 1 703-1 724. [27] SINGH J, SINGH N, OJHA N, et al. Effects of spatial resolution on WRF v3.8.1 simulated meteorology over the central Himalaya[J]. Geosci Model Dev, 2021, 14(3): 1 427-1 443. -
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