集合同化方法在海浪同化中的試驗
發(fā)布時間：2018-11-04 16:25
【摘要】：目前，集合同化方法在海浪同化中還未得到全面深入的應用。本文基于第三代海浪數值預報模式WAVEWATCH III，設計了全球區(qū)域海浪同化系統，開展了集合最優(yōu)插值（Ensemble Optimal Interpolation，簡稱EnOI）三天海浪預報及長期的同化試驗，并與最優(yōu)插值（Optimal Interpolation，簡稱OI）同化結果進行了比較。發(fā)現，EnOI同化方法在改善海浪預報中起到了很好的作用�？紤]到EnOI中的歷史樣本會夸大背景誤差且引起虛假相關，為解決這一問題，本文又首次嘗試了通過在風場疊加隨機擾動的方法生成一組動態(tài)樣本，評估了歷史樣本與動態(tài)樣本的優(yōu)劣，為今后繼續(xù)開展集合卡爾曼濾波（Ensemble Kalman Filter，簡稱EnKF）同化的研究做好了準備。 主要開展了以下工作： （1）背景誤差信息在資料同化中是至關重要的，在開展同化前須對模式誤差有充分的認識。為此，首先對模式進行了10年的積分，通過NDBC浮標數據及Jason-1高度計資料對模式模擬結果的檢驗，發(fā)現模式對全球海浪模擬效果較好，并得到了有效的模式誤差，為后面同化工作中背景誤差協方差矩陣的構建及集合樣本的選取提供了依據。 （2）由于在全球范圍內同化觀測點資料較多，且模式網格設置較粗，為節(jié)省計算時間，對比分析了四種同化觀測點選取方案。發(fā)現觀測稀疏后并不會明顯削弱同化效果，采用五點平均的減薄方案濾去高頻擾動，能夠在不影響同化效果的前提下縮短同化計算時間。 （3）為了評估OI、EnOI同化在短期海浪預報中的改進效果，，開展了三天海浪預報同化試驗。發(fā)現，數據同化能夠很好地訂正初始場的偏差，并對初始化過程及三天預報過程有明顯改進。EnOI對模式的改進效果在時間序列上更為穩(wěn)定，對于36小時以內的預報，采用EnOI同化方案能夠獲得比OI更理想的同化效果。 （4）為了進一步考察EnOI在長時間序列上對海浪預報的同化效果，設計了為期一年的同化試驗。發(fā)現與OI相比，EnOI具有絕對的優(yōu)勢。采用EnOI同化方案后，全球海浪有效波高預報絕對誤差小于0.5m的概率達到83.79%，小于1m的概率高達96.03%，預報精度非�？捎^。 （5）由于EnOI是通過預先存儲好的歷史樣本來估計背景誤差，在積分過程中始終保持不變，這往往會夸大背景誤差并且引起較長時間尺度上大范圍的虛假相關。為解決這一問題，通過設計多組敏感性試驗繼續(xù)探討了EnKF初始樣本生成的最佳方法，并將其與EnOI歷史樣本做了比較。發(fā)現，相對于歷史樣本，擾動樣本能夠較好的呈現出模式誤差的結構和相關性。
[Abstract]:At present, the ensemble assimilation method has not been fully applied in ocean wave assimilation. In this paper, a global regional ocean wave assimilation system is designed based on the third generation wave numerical prediction model (WAVEWATCH III,). Three days of ocean wave prediction and long-term assimilation experiments are carried out with the ensemble optimal interpolation (Ensemble Optimal Interpolation, (EnOI), and the results are compared with the optimal interpolation (Optimal Interpolation,. OI) assimilation results were compared. It is found that EnOI assimilation method plays a good role in improving wave prediction. Considering that historical samples in EnOI exaggerate background errors and cause false correlation, in order to solve this problem, this paper first attempts to generate a set of dynamic samples by superimposing random disturbances in wind field. The advantages and disadvantages of historical and dynamic samples are evaluated, and the preparation for further research on EnKF assimilation based on ensemble Kalman filter (EnKF) is made. The main work is as follows: (1) background error information is very important in data assimilation. For this reason, the model is first integrated for 10 years, and the model simulation results are tested by NDBC buoy data and Jason-1 altimeter data. It is found that the model has a good effect on the global ocean wave simulation, and the effective model error is obtained. It provides a basis for the construction of background error covariance matrix and the selection of set samples in the later assimilation work. (2) in order to save calculation time, four assimilation observation point selection schemes are compared and analyzed because there are more data of assimilation observation points in the global scope and the model grid is coarse. It is found that the assimilation effect will not be significantly weakened after the observation is sparse, and the calculation time of assimilation can be shortened without affecting the assimilation effect by filtering the high-frequency disturbance by using the five-point average thinning scheme. (3) in order to evaluate the improved effect of OI,EnOI assimilation in short-term wave prediction, a three-day wave prediction assimilation experiment was carried out. It is found that the data assimilation can correct the deviation of the initial field well, and improve the initialization process and the 3-day prediction process obviously. The improved effect of EnOI on the model is more stable in time series, and is more stable for the prediction within 36 hours. The assimilation effect of EnOI assimilation scheme is better than that of OI. (4) in order to further investigate the assimilation effect of EnOI on wave prediction in long time series, a one-year assimilation experiment was designed. It is found that EnOI has an absolute advantage over OI. After adopting the EnOI assimilation scheme, the probability of global wave effective wave height prediction with absolute error less than 0.5 m is 83.79 and the probability of less than 1m is 96.03. The prediction accuracy is very considerable. (5) because the background error is estimated by pre-stored historical samples, the background error is always kept unchanged in the integration process, which often exaggerates the background error and leads to a large range of false correlation on a long time scale. In order to solve this problem, the optimal method of EnKF initial sample generation is discussed by designing multi-group sensitivity tests and compared with EnOI history sample. It is found that, compared with historical samples, disturbed samples can better present the structure and correlation of pattern errors.
【學位授予單位】：國家海洋環(huán)境預報中心
【學位級別】：碩士
【學位授予年份】：2014
【分類號】：P731.33
本文編號：2310434
[Abstract]:At present, the ensemble assimilation method has not been fully applied in ocean wave assimilation. In this paper, a global regional ocean wave assimilation system is designed based on the third generation wave numerical prediction model (WAVEWATCH III,). Three days of ocean wave prediction and long-term assimilation experiments are carried out with the ensemble optimal interpolation (Ensemble Optimal Interpolation, (EnOI), and the results are compared with the optimal interpolation (Optimal Interpolation,. OI) assimilation results were compared. It is found that EnOI assimilation method plays a good role in improving wave prediction. Considering that historical samples in EnOI exaggerate background errors and cause false correlation, in order to solve this problem, this paper first attempts to generate a set of dynamic samples by superimposing random disturbances in wind field. The advantages and disadvantages of historical and dynamic samples are evaluated, and the preparation for further research on EnKF assimilation based on ensemble Kalman filter (EnKF) is made. The main work is as follows: (1) background error information is very important in data assimilation. For this reason, the model is first integrated for 10 years, and the model simulation results are tested by NDBC buoy data and Jason-1 altimeter data. It is found that the model has a good effect on the global ocean wave simulation, and the effective model error is obtained. It provides a basis for the construction of background error covariance matrix and the selection of set samples in the later assimilation work. (2) in order to save calculation time, four assimilation observation point selection schemes are compared and analyzed because there are more data of assimilation observation points in the global scope and the model grid is coarse. It is found that the assimilation effect will not be significantly weakened after the observation is sparse, and the calculation time of assimilation can be shortened without affecting the assimilation effect by filtering the high-frequency disturbance by using the five-point average thinning scheme. (3) in order to evaluate the improved effect of OI,EnOI assimilation in short-term wave prediction, a three-day wave prediction assimilation experiment was carried out. It is found that the data assimilation can correct the deviation of the initial field well, and improve the initialization process and the 3-day prediction process obviously. The improved effect of EnOI on the model is more stable in time series, and is more stable for the prediction within 36 hours. The assimilation effect of EnOI assimilation scheme is better than that of OI. (4) in order to further investigate the assimilation effect of EnOI on wave prediction in long time series, a one-year assimilation experiment was designed. It is found that EnOI has an absolute advantage over OI. After adopting the EnOI assimilation scheme, the probability of global wave effective wave height prediction with absolute error less than 0.5 m is 83.79 and the probability of less than 1m is 96.03. The prediction accuracy is very considerable. (5) because the background error is estimated by pre-stored historical samples, the background error is always kept unchanged in the integration process, which often exaggerates the background error and leads to a large range of false correlation on a long time scale. In order to solve this problem, the optimal method of EnKF initial sample generation is discussed by designing multi-group sensitivity tests and compared with EnOI history sample. It is found that, compared with historical samples, disturbed samples can better present the structure and correlation of pattern errors.
【學位授予單位】：國家海洋環(huán)境預報中心
【學位級別】：碩士
【學位授予年份】：2014
【分類號】：P731.33
【參考文獻】
中國期刊全文數據庫 前10條
1 王關鎖;喬方利;楊永增;;基于MPI的LAGFD-WAM海浪數值模式并行算法研究[J];海洋科學進展;2007年04期
2 尹寶樹,王濤,范順庭;YW-SWP海浪數值預報模式及其應用[J];海洋與湖沼;1994年03期
3 文圣常;GENERALIZED WIND WAVE SPECTRA AND THEIR APPLICATIONS[J];Science in China,Ser.A;1960年03期
4 侯一筠,樓順里,謝強,楊聯貴;淺水非線性波的演化方程[J];科學通報;1998年03期
5 齊鵬;范秀梅;;高度計波高數據同化對印度洋海域海浪模式預報影響研究[J];海洋預報;2013年04期
6 任啟峰;張杰;尹訓強;楊永增;;Envisat ASAR海浪譜資料的最優(yōu)插值同化試驗[J];熱帶海洋學報;2010年05期
7 袁業(yè)立,華鋒,潘增弟,孫樂濤;LAGFD-WAM海浪數值模式——Ⅱ.區(qū)域性特征線嵌入格式及其應用[J];海洋學報(中文版);1992年06期
8 侯一筠;旋轉流體中的非線性慣性波[J];海洋學報(中文版);1995年01期
9 ;Theoretical wind wave frequency spectra in shallow water[J];Acta Oceanologica Sinica;1988年03期
10 孫明華;;一個集合海浪預報系統及其初步試驗[J];應用氣象學報;2011年06期
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