GPS水汽反演及降雨预报方法研究
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摘要
洪水被认为是世界范围内最频繁和最具毁灭性的自然灾害之一。要提高洪水预报的精度，就必须借助于短期降雨预报和先进的科技手段，需要从各个角度去探索准确预报的新方法。GPS水汽反演和降雨预报研究是当前气象遥感应用的一个前沿探索领域，本论文依托于中意科技合作项目“洪水风险规划、监测和实时预报的集成系统”，主要成果体现在以下方面：
(1) 提出了一个合理的GPS水汽监测网设计方案
根据GPS卫星信号传播原理和水汽垂直分布规律以及流域天气的地方性特征，确定了不同高度截角下一个GPS站所能测定大气水汽含量的有效范围，得出了滨江流域只需要3～4个GPS接收机就足以反映流域水汽变化的结论。在实地考察的基础上，借鉴上海地区GPS综合应用网和意大利Umbria流域GPS网络的建设经验，提出了滨江流域水汽监测网的设计方案。
(2) 模型订正与比较
在GPS水汽反演过程中，一般干延迟和大气加权平均温度的计算是采用“普适性”模型，目前应用较多的有Saastamoinen(sA)模型、Hopfield(H)模型和Bevis模型。然而在实际大气中，大气温湿廓线千变万化，在我国GPS水汽反演中如采用这些普适模型不一定能获得最优效果。本研究利用实际探空资料建立了大气干延迟和大气加权平均温度的局地订正模型，并对实际水汽计算模型进行了比较，得到了满意的结果。
(3) GPS水汽反演优化策略分析
利用GPS观测数据，使用多种数据处理方案，进行GPS水汽反演优化策略分析，初步解决了进行GPS水汽反演中引入IGS站的最佳个数、单站解算和联合解算的关系、网络布局、截止高度角、天顶延迟参数、时段长度、节点位置等选择问题。
(4) 建立了基于遗传算法的降雨预报神经网络模型
利用滨江流域的雨量站和周围探空站的观测资料，首次将遗传算法(GA)应用于流域面降雨量预报研究。采用最优子集回归方法进行预报因子的确定，其次从样本选择、网络结构、转移函数、训练算法选取等方面入手寻找一个优化的BP网络，然后将BP算法和遗传算法结合起来，建立了流域面降雨量预报的神经网络模型。比较结果表明GA-BP网络模型无论在拟合精度还是在预报精度上都高于其它统计方法。因此可以说，GA-BP网络是一种精度较高的降雨预报模型。
(5) 卫星云图参数化及在降雨预测中的应用
红外卫星云图参数化估计值，与局地降雨过程的发生、发展具有较为密切的关系。相关较密切的参数有平均亮温、亮温方差、等效云量、亮温面积指数(1级、5级、6级)。在不同分析视场下，云图参数与降雨的相关系数和预测模型方程的系数没有明显的差异。对预测方程检验的结果表明：利用红外资料估算未来6h的降雨量其总体样本的平均正确率为80％以上，但是在分级样本上差别很大，这对小区域短时降雨预报具有很好的参考价值。分月模型在估算结果中的改进并不明显，这可能与样本不足有关。
Flood is regarded as one of the most frequent and the most severe nature disaster over the world wide and short -term rainfall prediction and advanced technology should be applied in order to improve forecast precision and new methods for prediction should be explored in every field. Research on Retrieval of GPS Water Vapor and Method of Rainfall Forecast are keen field which draws all attention from wide world.The dissertation is accomplished on the basis of Sino-Italy Collaborative Project-'The integral system of flood risk programming, monitoring and real time forecasting" and the primary achievements and conclusion reached include:
(1) A reasonable design has been made for GPS water vapor monitoring network
According to the theory of GPS satellite signal propagation and vertical distribution of water vapor and local synoptic characteristic of Binjiang basin, the effective area where water vapor can be detected by GPS station has been determined under different cut-off angles. The conclusion has been reached that 3~4 GPS receivers are enough for water vapor monitoring in Binjiang basin. After having carried out the field survey and studied the constructive experience of GPS integrated application network in Shanghai and the Umbria basin GPS network in Italy, the reasonable design has been made for GPS water vapor monitoring network in Binjiang basin.
(2) Model correction and comparison
In the process of conversion from zenith day delay to GPS water vapor and precision evaluation. Saastamoinen(SA) model and Hopfield(H) for dry delay and Bevis model for atmospheric weighted temperature are adopted. However, the profile of temperature and humidity is variable in real atmosphere so that the optimum result for GPS water vapor may not be achieved by those popular models in our country. The paper have built the local correction models for dry delay and atmospheric weighted temperature and have made a comparison between two models for calculating actual water vapor on the basis of actual upper-air detecting data. AH results show that the conclusion is satisfied.
(3) The optimized strategy analysis on retrieval of GPS water vapor
The optimized strategy analysis on retrieval of GPS water vapor has been carried out under different data processing schemes by using GPS observation. The parameters selection problem has been resolved in GPS water vapor retrieval process such as the optimum number of adopted IGS stations, the relation between single station processing and united station processing, network layout, cut-off angle, zenith delay parameter, period, knot position and so on.
(4 ) Research on ANN model joined with GA for area rainfall forecast
The method is taken to join the genetic algorithm(GA) and BP algorithm together and supplementing mutually by optimizing the initial weights of ANN with GA, and some application has been made in the Binjiang basin for precipitation forecast. The ANN model by GA has been established which forecast variables are selected by optimized subclass regression technique and the optimized ANN model for basin area precipitation has been obtained. The experiment result of ANN model joined with GA for area rainfall forecast shows that this method can enhance the forecast precision of 6-hours
precipitation compared with other statistical methods, and its effectiveness and the reliability of the method has been proved.
(5) Parameterization of infrared satellite cloud imagery and its application in rainfall predication Obvious correlation exist between the probability of rain and parameterization estimate such as average brightness temperature(tb), brightness temperature variance(f), equivalent cloudage (CN),brightness temperature area index(Al-the first A5-the fifth grade, A6-the sixth grade). The statistical result shows that the average precision of rainfall intensity is over 80% which varies largely with rainfall intensity grades using infrared cloud imagery parameters and the size of analysis field has slight effect on it.The monthly model make less improvement on p
(1) 提出了一个合理的GPS水汽监测网设计方案
根据GPS卫星信号传播原理和水汽垂直分布规律以及流域天气的地方性特征，确定了不同高度截角下一个GPS站所能测定大气水汽含量的有效范围，得出了滨江流域只需要3～4个GPS接收机就足以反映流域水汽变化的结论。在实地考察的基础上，借鉴上海地区GPS综合应用网和意大利Umbria流域GPS网络的建设经验，提出了滨江流域水汽监测网的设计方案。
(2) 模型订正与比较
在GPS水汽反演过程中，一般干延迟和大气加权平均温度的计算是采用“普适性”模型，目前应用较多的有Saastamoinen(sA)模型、Hopfield(H)模型和Bevis模型。然而在实际大气中，大气温湿廓线千变万化，在我国GPS水汽反演中如采用这些普适模型不一定能获得最优效果。本研究利用实际探空资料建立了大气干延迟和大气加权平均温度的局地订正模型，并对实际水汽计算模型进行了比较，得到了满意的结果。
(3) GPS水汽反演优化策略分析
利用GPS观测数据，使用多种数据处理方案，进行GPS水汽反演优化策略分析，初步解决了进行GPS水汽反演中引入IGS站的最佳个数、单站解算和联合解算的关系、网络布局、截止高度角、天顶延迟参数、时段长度、节点位置等选择问题。
(4) 建立了基于遗传算法的降雨预报神经网络模型
利用滨江流域的雨量站和周围探空站的观测资料，首次将遗传算法(GA)应用于流域面降雨量预报研究。采用最优子集回归方法进行预报因子的确定，其次从样本选择、网络结构、转移函数、训练算法选取等方面入手寻找一个优化的BP网络，然后将BP算法和遗传算法结合起来，建立了流域面降雨量预报的神经网络模型。比较结果表明GA-BP网络模型无论在拟合精度还是在预报精度上都高于其它统计方法。因此可以说，GA-BP网络是一种精度较高的降雨预报模型。
(5) 卫星云图参数化及在降雨预测中的应用
红外卫星云图参数化估计值，与局地降雨过程的发生、发展具有较为密切的关系。相关较密切的参数有平均亮温、亮温方差、等效云量、亮温面积指数(1级、5级、6级)。在不同分析视场下，云图参数与降雨的相关系数和预测模型方程的系数没有明显的差异。对预测方程检验的结果表明：利用红外资料估算未来6h的降雨量其总体样本的平均正确率为80％以上，但是在分级样本上差别很大，这对小区域短时降雨预报具有很好的参考价值。分月模型在估算结果中的改进并不明显，这可能与样本不足有关。
Flood is regarded as one of the most frequent and the most severe nature disaster over the world wide and short -term rainfall prediction and advanced technology should be applied in order to improve forecast precision and new methods for prediction should be explored in every field. Research on Retrieval of GPS Water Vapor and Method of Rainfall Forecast are keen field which draws all attention from wide world.The dissertation is accomplished on the basis of Sino-Italy Collaborative Project-'The integral system of flood risk programming, monitoring and real time forecasting" and the primary achievements and conclusion reached include:
(1) A reasonable design has been made for GPS water vapor monitoring network
According to the theory of GPS satellite signal propagation and vertical distribution of water vapor and local synoptic characteristic of Binjiang basin, the effective area where water vapor can be detected by GPS station has been determined under different cut-off angles. The conclusion has been reached that 3~4 GPS receivers are enough for water vapor monitoring in Binjiang basin. After having carried out the field survey and studied the constructive experience of GPS integrated application network in Shanghai and the Umbria basin GPS network in Italy, the reasonable design has been made for GPS water vapor monitoring network in Binjiang basin.
(2) Model correction and comparison
In the process of conversion from zenith day delay to GPS water vapor and precision evaluation. Saastamoinen(SA) model and Hopfield(H) for dry delay and Bevis model for atmospheric weighted temperature are adopted. However, the profile of temperature and humidity is variable in real atmosphere so that the optimum result for GPS water vapor may not be achieved by those popular models in our country. The paper have built the local correction models for dry delay and atmospheric weighted temperature and have made a comparison between two models for calculating actual water vapor on the basis of actual upper-air detecting data. AH results show that the conclusion is satisfied.
(3) The optimized strategy analysis on retrieval of GPS water vapor
The optimized strategy analysis on retrieval of GPS water vapor has been carried out under different data processing schemes by using GPS observation. The parameters selection problem has been resolved in GPS water vapor retrieval process such as the optimum number of adopted IGS stations, the relation between single station processing and united station processing, network layout, cut-off angle, zenith delay parameter, period, knot position and so on.
(4 ) Research on ANN model joined with GA for area rainfall forecast
The method is taken to join the genetic algorithm(GA) and BP algorithm together and supplementing mutually by optimizing the initial weights of ANN with GA, and some application has been made in the Binjiang basin for precipitation forecast. The ANN model by GA has been established which forecast variables are selected by optimized subclass regression technique and the optimized ANN model for basin area precipitation has been obtained. The experiment result of ANN model joined with GA for area rainfall forecast shows that this method can enhance the forecast precision of 6-hours
precipitation compared with other statistical methods, and its effectiveness and the reliability of the method has been proved.
(5) Parameterization of infrared satellite cloud imagery and its application in rainfall predication Obvious correlation exist between the probability of rain and parameterization estimate such as average brightness temperature(tb), brightness temperature variance(f), equivalent cloudage (CN),brightness temperature area index(Al-the first A5-the fifth grade, A6-the sixth grade). The statistical result shows that the average precision of rainfall intensity is over 80% which varies largely with rainfall intensity grades using infrared cloud imagery parameters and the size of analysis field has slight effect on it.The monthly model make less improvement on p
引文
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