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(yīng)用。本文基于第三代海浪數(shù)值預(yù)報模式WAVEWATCH III，設(shè)計了全球區(qū)域海浪同化系統(tǒng)，開展了集合最優(yōu)插值（Ensemble Optimal Interpolation，簡稱EnOI）三天海浪預(yù)報及長期的同化試驗，并與最優(yōu)插值（Optimal Interpolation，簡稱OI）同化結(jié)果進(jìn)行了比較。發(fā)現(xiàn)，EnOI同化方法在改善海浪預(yù)報中起到了很好的作用。考慮到EnOI中的歷史樣本會夸大背景誤差且引起虛假相關(guān)，為解決這一問題，本文又首次嘗試了通過在風(fēng)場疊加隨機(jī)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(zhǔn)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(guān)重要的，在開展同化前須對模式誤差有充分的認(rèn)識。為此，首先對模式進(jìn)行了10年的積分，通過NDBC浮標(biāo)數(shù)據(jù)及Jason-1高度計資料對模式模擬結(jié)果的檢驗，發(fā)現(xiàn)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(xié)方差矩陣的構(gòu)建及集合樣本的選取提供了依據(jù)。 （2）由于在全球范圍內(nèi)同化觀測點資料較多，且模式網(wǎng)格設(shè)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(xiàn)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(yù)報中的改進(jìn)效果，，開展了三天海浪預(yù)報同化試驗。發(fā)現(xiàn)，數(shù)據(jù)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(yù)報過程有明顯改進(jìn)。EnOI對模式的改進(jìn)效果在時間序列上更為穩(wěn)定，對于36小時以內(nèi)的預(yù)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(jìn)一步考察EnOI在長時間序列上對海浪預(yù)報的同化效果，設(shè)計了為期一年的同化試驗。發(fā)現(xiàn)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(yù)報絕對誤差小于0.5m的概率達(dá)到83.79%，小于1m的概率高達(dá)96.03%，預(yù)報精度非�？捎^。 （5）由于EnOI是通過預(yù)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(guān)。為解決這一問題，通過設(shè)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(xiàn)，相對于歷史樣本，擾動樣本能夠較好的呈現(xiàn)出模式誤差的結(jié)構(gòu)和相關(guān)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(xué)位授予單位】：國家海洋環(huán)境預(yù)報中心
【學(xué)位級別】：碩士
【學(xué)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(xué)位授予單位】：國家海洋環(huán)境預(yù)報中心
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：P731.33
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