两腿间花蒂被吸得肿了,97精品超碰一区二区三区

基于最優(yōu)插值法的渤海CLDAS海面風客觀訂正

單位：1. 天津市海洋氣象重點實驗室, 天津 300074;
2. 天津海洋中心氣象臺, 天津 300074

分類號：P732.1

出版年·卷·期（頁碼）：2025·42·第四期（37-47）

摘要：

利用環(huán)渤海沿岸及海上125個站點的觀測數(shù)據(jù)，采用最優(yōu)插值法對中國氣象局陸面數(shù)據(jù)同化系統(tǒng)（CLDAS）10 m風實況分析數(shù)據(jù)進行融合訂正。結(jié)果表明：訂正后的CLDAS數(shù)據(jù)與觀測數(shù)據(jù)的相關(guān)系數(shù)由0.89增大到0.99，平均絕對誤差由1.02 m/s減小到0.27 m/s，均方根誤差由1.63 m/s減小到0.36 m/s。在渤海灣、萊州灣、遼東灣、渤海中部、渤海海峽不同海區(qū)中，萊州灣的訂正效果最好，平均絕對誤差和均方根誤差都減小了81.4%左右。不同風力等級的訂正效果顯示，3級以下風的平均絕對誤差由0.5～1.0 m/s減小到0.3 m/s以下，4～8級風的平均絕對誤差由1.4～4.7 m/s減小到1.0 m/s以下，9級及以上風的平均絕對誤差由5.9 m/s減小到1.1 m/s，且不同等級風的預報準確率也得到明顯提升。對2021年1月和12月兩次大風過程進行檢驗，發(fā)現(xiàn)訂正后的CLDAS數(shù)據(jù)的10 m風速明顯增大，變化趨勢和風速大值區(qū)與觀測數(shù)據(jù)更加接近。

Using the observation data from 125 stations along the coast and offshore in the Bohai Sea, the optimal interpolation method is used to correct the China Meteorological Administration Land Data Assimilation System（CLDAS） 10-meter wind data. The results show that through correction, the correlation coefficient between the observation data and CLDAS data increases from 0.89 to 0.99, the mean absolute error decreases from 1.02 m/s to 0.27 m/s, and the root mean square error decreases from 1.63 m/s to 0.36 m/s. Among the different areas of the Bohai Bay, Laizhou Bay, Liaodong Bay, central Bohai Sea, and Bohai Strait, the best correction effect is achieved in the Laizhou Bay, with the mean absolute error and root mean square error reduced both by about 81.4%. The correction effect of different wind levels shows that the mean absolute error of winds below level 3 decreases from 0.5～1.0 m/s to below 0.3 m/s, that between levels 4 and 8 decreases from 1.4～4.7 m/s to below 1.0 m/s, and that above level 9 decreases from 5.9 m/s to 1.1 m/s. The validation of two strong wind processes in 2021 shows sthat the corrected CLDAS 10-meter wind speed increases significantly, the trend and wind speed maximum area are more closely aligned with the observed data.

參考文獻：

[1] 韓帥,師春香,姜志偉,等. CMA高分辨率陸面數(shù)據(jù)同化系統(tǒng)(HRCLDAS-V1.0)研發(fā)及進展[J]. 氣象科技進展, 2018, 8(1):102-108.HAN S, SHI C X, JIANG Z W, et al. Development and progress of high resolution CMA land surface data assimilation system[J]. Advances in Meteorological Science and Technology, 2018, 8(1):102-108.
[2] 師春香,潘旸,谷軍霞,等.多源氣象數(shù)據(jù)融合格點實況產(chǎn)品研制進展[J]. 氣象學報, 2019, 77(4):774-783.SHI C X, PAN Y, GU J X, et al. A review of multi-source meteorological data fusion products[J]. Acta Meteorologica Sinica,2019, 77(4):774-783.
[3] 孫帥,師春香,梁曉,等.不同陸面模式對我國地表溫度模擬的適用性評估[J]. 應用氣象學報, 2017, 28(6):737-749.SUN S, SHI C X, LIANG X, et al. Assessment of ground temperature simulation in China by different land surface models based on station observations[J]. Journal of Applied Meteorological Science, 2017, 28(6):737-749.
[4] 龍柯吉,師春香,韓帥,等.中國區(qū)域高分辨率溫度實況融合格點分析產(chǎn)品質(zhì)量評估[J]. 高原山地氣象研究, 2019, 39(3):67-74.LONG K J, SHI C X, HAN S, et al. Quality assessment of high resolution temperature merged grid analysis product in China[J]. Plateau and Mountain Meteorology Research, 2019, 39(3):67-74.
[5] 劉瑩,師春香,王海軍,等. CLDAS氣溫數(shù)據(jù)在中國區(qū)域的適用性評估[J]. 大氣科學學報, 2021, 44(4):540-548.LIU Y, SHI C X, WANG H J, et al. Applicability assessment of CLDAS temperature data in China[J]. Transactions of Atmospheric Sciences, 2021, 44(4):540-548.
[6] 齊鐸,劉松濤,張?zhí)烊A,等.基于格點的中國東北中北部2 m溫度數(shù)值預報檢驗及偏差訂正[J]. 干旱氣象, 2020, 38(1):81-88.QI D, LIU S T, ZHANG T H, et al. Grid temperature numerical forecast verification and correction over central and north region of northeast China[J]. Journal of Arid Meteorology, 2020, 38(1):81-88.
[7] 董春卿,郭媛媛,張磊,等.基于CLDAS的格點溫度預報偏差訂正[J]. 干旱氣象, 2021, 39(5):847-856.DONG C Q, GUO Y Y, ZHANG L, et al. Deviation correction method of grid temperature prediction based on CLDAS data[J]. Journal of Arid Meteorology, 2021, 39(5):847-856.
[8] 王丹,王建鵬,黨超琪,等. CLDAS實況產(chǎn)品在陜西氣溫網(wǎng)格預報檢驗與訂正中的應用[J]. 氣象, 2023, 49(8):946-957.WANG D, WANG J P, DANG C Q, et al. Application of CLDAS in test and correction of grid temperature forecast in Shaanxi province[J]. Meteorological Monthly, 2023, 49(8):946-957.
[9] 楊富燕,彭芳,于飛,等. CLDAS溫濕產(chǎn)品在貴州的適用性評估及訂正[J]. 高原氣象, 2023, 42(2):472-482.YANG F Y, PENG F, YU F, et al. Evaluation of applicability and correction for the CLDAS temperature and relative humidity products in Guizhou province[J]. Plateau Meteorology, 2023, 42(2):472-482.
[10] 董祝雷,趙艷麗,馮曉晶,等. CLDAS氣溫和降水產(chǎn)品在內(nèi)蒙古地區(qū)適用性分析[J]. 干旱氣象, 2023, 41(5):811-819.DONG Z L, ZHAO Y L, FENG X J, et al. Applicability assessment of CLDAS temperature and precipitation products in Inner Mongolia[J]. Journal of Arid Meteorology, 2023, 41(5):811-819.
[11] 穆建華,紀曉玲,賈樂,等.基于CLDAS的賀蘭山區(qū)5-9月降水時空分布特征及其與地形的關(guān)系分析[J]. 氣象科技, 2023, 51(6):824-834.MU J H, JI X L, JIA L, et al. Analysis of temporal and spatial distribution characteristics of precipitation from May to September in Helan Mountains and its relationship with topography based on CLDAS data[J]. Meteorological Science and Technology, 2023, 51(6):824-834.
[12] 朱科旭,管琴,白愛娟.青海省洪澇災害時空分布和致災雨量特征[J]. 沙漠與綠洲氣象, 2024, 18(1):81-88.ZHU K X, GUAN Q, BAI A J. Characteristics of spatial and temporal distribution and disaster-causing rainfall of flood disasters in Qinghai province[J]. Desert and Oasis Meteorology,2024, 18(1):81-88.
[13] 蔣雨荷,黃曉龍,李施穎,等.四川CLDAS 5 km小時能見度實況產(chǎn)品與能見度觀測站資料質(zhì)量對比評估[J]. 高原山地氣象研究, 2020, 40(3):73-78.JIANG Y H, HUANG X L, LI S Y, et al. Comparison and evaluation of the data quality between CLDAS 5 km hourly visibility products and visibility observation stations in Sichuan province[J]. Plateau and Mountain Meteorology Research, 2020,40(3):73-78.
[14] 龍柯吉,宋雯雯,黃曉龍.智能網(wǎng)格能見度實況融合格點分析產(chǎn)品在一次大霧天氣過程中的評估[J]. 氣象研究與應用, 2021, 42(4):50-55.LONG K J, SONG W W, HUANG X L. Evaluation of intelligent grid visibility live fusion grid analysis products in a dense fog weather[J]. Journal of Meteorological Research and Application,2021, 42(4):50-55.
[15] 郭陽,師春香,徐賓,等. CLDAS陸面融合實況數(shù)據(jù)對天津霧和霾判識的準確性分析[J]. 干旱氣象, 2023, 41(4):657-665.GUO Y, SHI C X, XU B, et al. Accuracy analysis of fog and haze identification based on CLDAS land surface fusion data in Tianjin[J]. Journal of Arid Meteorology, 2023, 41(4):657-665.
[16] 莊妍,陳正洪,鐘水新,等.基于CLDAS資料的恩施山區(qū)成霧潛勢指標特征及成因簡析[J]. 暴雨災害, 2023, 42(4):479-487.ZHUANG Y, CHEN Z H, ZHONG S X, et al. Characteristics and simple analysis of fogging potential in the mountain area of Enshi based on CLDAS data[J]. Torrential Rain and Disasters, 2023, 42(4):479-487.
[17] 李施穎,吳薇,黃曉龍.四川省CLDAS和ECMWF再分析數(shù)據(jù)風速產(chǎn)品評估[J]. 高原山地氣象研究, 2020, 40(4):70-76.LI S Y, WU W, HUANG X L. Wind speed evaluation of CLDAS and ERA5-Land in Sichuan[J]. Plateau and Mountain Meteorology Research, 2020, 40(4):70-76.
[18] 曾曉青,薛峰,姚莉,等.針對模式風場的格點預報訂正方案對比[J]. 應用氣象學報, 2019, 30(1):49-60.ZENG X Q, XUE F, YAO L, et al. Comparative study of different error correction methods on model output wind field[J]. Journal of Applied Meteorological Science, 2019, 30(1):49-60.
[19] 俞劍蔚,李聰,蔡凝昊,等.國家級格點實況分析產(chǎn)品在江蘇地區(qū)的適用性評估分析[J]. 氣象, 2019, 45(9):1288-1298.YU J W, LI C, CAI N H, et al. Applicability evaluation of the national gridded real-time observation datasets in Jiangsu province[J]. Meteorological Monthly, 2019, 45(9):1288-1298.
[20] 王丹,楊艷超,黨超琪,等. CLDAS 10 m風產(chǎn)品在陜西的質(zhì)量檢驗分析[J]. 陜西氣象, 2022(1):17-21.WANG D, YANG Y C, DANG C Q, et al. Quality inspection analysis of CLDAS 10 m wind products in Shaanxi[J]. Journal of Shaanxi Meteorology, 2022(1):17-21.
[21] 劉衛(wèi)華,金巍,王會品,等. 2020年CLDAS風場數(shù)據(jù)在遼寧省的適用性分析[J]. 氣象與環(huán)境學報, 2023, 39(6):61-68.LIU W H, JIN W, WANG H P, et al. Analysis of the applicability of CLDAS wind field data in Liaoning province in 2020[J]. Journal of Meteorology and Environment, 2023, 39(6):61-68.
[22] 王亞男,李英華,孫密娜.下墊面對渤海西部雷雨大風影響的數(shù)值模擬分析[J]. 海洋預報, 2019, 36(3):24-32.WANG Y N, LI Y H, SUN M N. Numerical simulation study of the effect of underlying sea surface on thunderstorm wind in the western Bohai Sea[J]. Marine Forecasts, 2019, 36(3):24-32.
[23] 劉志杰,劉彬賢,王銳,等.基于傳統(tǒng)和深度學習技術(shù)的黃渤海域大風預報方法研究[J]. 海洋預報, 2022, 39(6):34-43.LIU Z J, LIU B X, WANG R, et al. Research on the gale prediction methods in the Yellow Sea and Bohai Sea based on traditional and deep learning technologies[J]. Marine Forecasts,2022, 39(6):34-43.
[24] 孫曉磊,張唯,胡田田,等.弱冷空氣引起渤海灣大風的時空分布特征及環(huán)境條件分析[J]. 海洋預報, 2022, 39(6):44-54.SUN X L, ZHANG W, HU T T, et al. Spatial and temporal distribution characteristics and environmental conditions of gales caused by weak cold air over the Bohai Bay[J]. Marine Forecasts,2022, 39(6):44-54.
[25] 李建通,楊維生,郭林,等.提高最優(yōu)插值法測量區(qū)域降水量精度的探討[J]. 大氣科學, 2000, 24(2):263-270.LI J T, YANG W S, GUO L, et al. A study of improving precision of measuring regional precipitation in optimum interpolation method[J]. Chinese Journal of Atmospheric Sciences, 2000, 24(2):263-270.
[26] 韓焱紅,郜靜靜,苗蕾,等.基于最優(yōu)插值的雷達定量降水估測訂正及應用--以廣元市為例[J]. 高原山地氣象研究, 2019, 39(3):82-85.HAN Y H, GAO J J, MIAO L, et al. Correction for radar-derived quantitation precipitation estimation based on the optimum interpolation and its application--taking Guangyuan as an example[J]. Plateau and Mountain Meteorology Research, 2019,39(3):82-85.
[27] 潘旸,沈艷,宇婧婧,等.基于最優(yōu)插值方法分析的中國區(qū)域地面觀測與衛(wèi)星反演逐時降水融合試驗[J]. 氣象學報, 2012, 70(6):1381-1389.PAN Y, SHEN Y, YU J J, et al. Analysis of the combined gaugesatellite hourly precipitation over China based on the OI technique[J]. Acta Meteorologica Sinica, 2012, 70(6):1381-1389.
[28] 王福增,溫凱,何山,等.多源降水量數(shù)據(jù)融合分析及評價[J]. 蘭州大學學報(自然科學版), 2023, 59(5):647-654.WANG F Z, WEN K, HE S, et al. Analysis and evaluation of multi-source merging precipitation data[J]. Journal of Lanzhou University(Natural Sciences), 2023, 59(5):647-654.
[29] 蔡志穎,鄭艷,段晶晶,等.臺風“煙花”影響浙江期間GPM衛(wèi)星降水產(chǎn)品的評估及訂正[J]. 暴雨災害, 2023, 42(6):704-715.CAI Z Y, ZHENG Y, DUAN J J, et al. Evaluation and correction of GPM satellite precipitation products during typhoon“In-Fa”affecting Zhejiang[J]. Torrential Rain and Disasters, 2023, 42(6):704-715.

服務與反饋：

【文章下載】【發(fā)表評論】【查看評論】【加入收藏】