發(fā)布時(shí)間：2018-03-13 19:22

  本文選題：非線性自回歸神經(jīng)網(wǎng)絡(luò)　切入點(diǎn)：WRF大氣模式　出處：《清華大學(xué)》2015年碩士論文　論文類型：學(xué)位論文

【摘要】：深圳大鵬灣位于南海北部,西鄰珠江口,東接大鵬半島,沿岸建有港口、海水泳場、淺海養(yǎng)殖區(qū)、以及深水航道,人工作業(yè)繁忙。因此灣內(nèi)波浪的準(zhǔn)確預(yù)報(bào)對(duì)于大鵬灣內(nèi)作業(yè)安全、以及海洋災(zāi)害預(yù)警具有十分重要的意義。同時(shí)深圳市為社會(huì)經(jīng)濟(jì)發(fā)展以及公眾安全,在深圳周邊海域建立了全面的海洋環(huán)境浮標(biāo)監(jiān)測網(wǎng)絡(luò)。本文在該監(jiān)測網(wǎng)絡(luò)的基礎(chǔ)上開展了波浪在線預(yù)報(bào)模型的研究,該模型包括兩部分:基于數(shù)據(jù)驅(qū)動(dòng)模式的浮標(biāo)在線預(yù)報(bào)模型,以及基于海-氣耦合數(shù)值模式的波浪場區(qū)域預(yù)報(bào)模型。首先基于大鵬灣波浪浮標(biāo)在線監(jiān)測數(shù)據(jù),采用了數(shù)據(jù)驅(qū)動(dòng)模式實(shí)現(xiàn)大鵬灣單點(diǎn)波浪的在線實(shí)時(shí)預(yù)報(bào)。采用兩種非線性自回歸人工神經(jīng)網(wǎng)絡(luò)NAR、NARX對(duì)大鵬灣內(nèi)灣口、大梅沙與東涌浮標(biāo)站分別進(jìn)行了3h、6h以及12h的有效波高預(yù)報(bào)。結(jié)果顯示大梅沙與灣口站點(diǎn)3h與6h預(yù)報(bào)的平均誤差在0.1m以內(nèi);東涌3h、6h與12h預(yù)報(bào)誤差均在0.1m以內(nèi),預(yù)報(bào)相關(guān)系數(shù)在0.904以上�；诖髿庵谐叨饶Ｊ絎RF與淺水波浪數(shù)值模式SWAN的耦合模型WRF_SWAN建立了大鵬灣波浪場區(qū)域預(yù)報(bào)模式。并研究了WRF模式中不同參數(shù)如垂直層數(shù)、頂部氣壓、微物理過程對(duì)模擬結(jié)果的影響,以及采用地理信息管理GIS空間分析模型研究了土地覆被的變化對(duì)大鵬灣風(fēng)場的影響;此外,研究了SWAN模式中白冠破碎速率對(duì)波浪場的影響。模型參數(shù)率定后大鵬灣內(nèi)浮標(biāo)站點(diǎn)的有效波高計(jì)算結(jié)果平均誤差在0.29m以內(nèi)。為驗(yàn)證WRF_SWAN耦合模式的適應(yīng)性,將其應(yīng)用于2014年第15號(hào)臺(tái)風(fēng)“海鷗”襲擊大鵬灣期間的波浪場進(jìn)行后報(bào)驗(yàn)證;驗(yàn)證結(jié)果表明在臺(tái)風(fēng)“海鷗”期間大鵬灣的有效波高后報(bào)平均誤差在0.32m以內(nèi)。最后采用NOAA的全球預(yù)報(bào)系統(tǒng)GFS提供的氣象預(yù)報(bào)數(shù)據(jù)作為SWAN_WRF模式的預(yù)報(bào)初始與邊界條件對(duì)大鵬灣波浪進(jìn)行72h預(yù)報(bào)。灣口波浪預(yù)報(bào)絕對(duì)誤差為0.23m,精度滿足日常預(yù)報(bào)要求。計(jì)算結(jié)果說明本文所建的WRF_SWAN耦合模式適用于深圳大鵬灣的波浪預(yù)報(bào)。
[Abstract]:Dapeng Bay in Shenzhen is located in the northern part of the South China Sea, adjacent to the Pearl River Estuary to the west, and to the Dapeng Peninsula to the east. There are ports along the coast, sea water swimming fields, shallow water culture zones, and deep water channels. Therefore, accurate prediction of waves in the Bay is of great significance to operational safety in Mirs Bay and early warning of marine disasters. At the same time, Shenzhen City is responsible for social and economic development and public safety. A comprehensive marine environment buoy monitoring network has been established in the surrounding sea area of Shenzhen. Based on the monitoring network, the on-line wave prediction model has been studied in this paper. The model consists of two parts: the on-line prediction model of buoy based on data-driven model, and the prediction model of wave field based on coupled ocean-atmosphere numerical model. Firstly, the on-line monitoring data of wave buoy in Dapeng Bay are used. In this paper, a data-driven model is used to realize on-line real-time prediction of single point waves in Dapeng Bay. Two kinds of nonlinear autoregressive artificial neural networks (NARN NARX) are used to predict the mouth of Dapeng Bay. The effective wave height was predicted for 3 h and 12 h at Dameisha and Tung Chung buoy stations, respectively. The results show that the average error of 3 h and 6 h prediction for Dameisha and Bay mouth stations is less than 0.1 m, and the prediction errors for 3 h and 12 h in Tung Chung are within 0.1 m, respectively. The prediction correlation coefficient is more than 0.904. Based on the coupling model WRF_SWAN of mesoscale model WRF and shallow water wave numerical model SWAN, the regional prediction model of Mirs Bay wave field is established. The different parameters of WRF model, such as vertical layer number, top pressure, are studied. The effects of microphysical processes on simulation results and the effects of land cover changes on wind fields in Dapeng Bay are studied by using the GIS spatial analysis model of geographic information management. The effect of the breakup rate of the white crown on the wave field in the SWAN model is studied. The average error of the effective wave height calculated at the buoy station in Dapeng Bay is less than 0.29 m after the model parameter rate is fixed. In order to verify the adaptability of the WRF_SWAN coupling model, It was applied to the wave field during typhoon no. 15, 2014, when seagull attacked Mirs Bay. The results show that the average error of effective wave height in Dapeng Bay during typhoon "seagull" is less than 0.32 m. Finally, the meteorological forecast data provided by the global forecasting system (GFS) of NOAA are used as the initial prediction and boundary strip of SWAN_WRF model. The absolute error of wave prediction at the mouth of Dapeng Bay is 0.23m, which satisfies the requirement of daily forecast. The results show that the coupled WRF_SWAN model is suitable for the wave prediction of Dapeng Bay in Shenzhen.
【學(xué)位授予單位】：清華大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2015
【分類號(hào)】：P731.22

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