基于BP神经网络与遗传算法反演大气温湿廓线

为提高地基微波辐射计大气探测精度,融合BP神经网络与遗传算法,研究0~10 km大气温湿度廓线。首先,结合数据特征,基于数值模拟技术,建立一套TP/WVP-3000型号地基微波辐射计的一级数据质量控制和订正模型。然后,为减小训练样本代表性误差对模型反演精度的影响,利用遗传算法优化训练样本数据,建立一套精度更高的神经网络大气温湿度反演模型。最后,利用构建的反演模型,开展大气温湿度反演试验,结合探空资料和微波辐射计二级产品,评价反演模型精度。研究结果表明:(1)经过质量控制后的实测数据与模拟数据之间的相关性有显著提升;(2)经过质量控制与订正后建立的神经网络模型对比原微波辐射计二级产品的反演精度有一定提升,温度提升6.77%,湿度提升20.11%;(3)经过遗传算法优化后的训练样本所建立的神经网络反演模型对比原微波辐射计二级产品反演精度有进一步的提升,温度提升10.21%,湿度提升23.75%,反演结果与该地区同类型研究结果相比有着较大提升。      

地基微波辐射计遥感人工触发闪电的观测研究

为了深入研究雷电产生的微波热辐射特征,从2016年初夏开始利用地基微波辐射计在中国气象局雷电野外科学实验基地开展了连续4年的观测实验,根据雷电热效应的特征,制定了观测方案,并为地基微波辐射计设置了“引雷观测模式”。结果表明,地基微波辐射计具有对雷电热效应产生响应的能力。2017—2019年,辐射计一共成功捕获了30次人工触发闪电,观测效率逐年增长,平均为71.4 %;微波亮温脉冲幅度的最大值约125 K。结合其中28次触发事件的雷电流数据,分析了亮温脉冲幅度和雷电流积分量之间的相关性,并发现了最大亮温脉冲幅度与总电流作用积分之间可能存在指数关系。根据亮温观测数据估算了雷电热效应的持续时间,平均约0.5 s,其中25次触发事件的雷电流热效应持续时间与雷电流持续时间变化较为一致,相关系数约0.73。     

RETRIEVAL OF CLEAR SKY MOISTURE PROFILES IN THE PACIFIC USING MEASUREMENTS OF MICROWAVE HUMIDITY SOUNDER FROM FY-3A SATELLITE

An improved Smith iterative method, which generally applies to microwave bands, is described. The moisture profiles retrieved from the brightness temperature data of microwave humidity sounder onboard FY-3A satellite, pertaining to clear sky, are shown over western North Pacific by using a vector-discrete ordination radiative transfer model. The retrieved profiles are compared with those from MODIS products, and the results of single point show that they are in good agreement with the results for lower layers. The distribution of retrieved humidity at 500 hPa is basically consistent with the actual situation, and the values are lower than that of the MODIS products. Compared with MODIS products, RMS is within 5.76g/kg at every single level separately.     

MEASUREMENT OF WATER VAPOR AND CLOUD LIQUID WATER CONTENT BY A DUAL-CHANNEL GROUND-BASED MICROWAVE RADIOMETER

The design principle and structural features of a new type dual-channel ground-based microwave radiometerfor measurement of water vapor and liquid water in troposphere are discussed. A method independent ofradiosonde data is used for system absolute calibrations. A statistical method for retrieving the total watervapor and cloud liquid water content and a nonlinear iterative algorithm for retrieving the water vapor profilesin the troposphere are discussed. Comparison is conducted between the measurement of the integrated amountsof the water vapor and the tropospheric vapor profiles by the radiometer and the radiosonde data.     

微波辐射计温湿廓线反演方法改进试验

为提升地基微波辐射计在不同天气条件下, 特别是云天条件下温湿廓线的反演精度, 利用2011年1月—2016年12月中国气象局北京国家综合气象观测试验基地探空数据, 在微波辐射计反演温湿度廓线的过程中通过区分晴天和云天条件并引入全固态Ka波段测云仪云高及云厚信息, 对反演输入亮温进行质量控制和偏差订正, 建立BP神经网络模型, 采用2017年1月—2018年3月微波辐射计探测数据评估检验, 结果表明：在亮温订正前提下, 晴天温度模型、云天温度模型、晴天相对湿度模型和云天相对湿度模型反演结果与探空的相关系数分别为0.99, 0.99, 0.80和0.78, 均方根误差为2.3℃, 2.3℃, 9%和16%, 较微波辐射计自带产品（LV2产品）减小约0.4℃, 0.3℃, 11%和9%, 准确性提升约30%, 28%, 64%和45%;温度模型偏差在±2℃以内、湿度模型偏差在±20%以内的占比分别为68%, 70%和95%, 78%, 较LV2产品分别提高了7%, 5%和27%, 23%, 其中相对湿度改善明显。可见亮温订正、区分天气类型训练反演模型有利于改善地基微波辐射温湿廓线反演精度。     

基于雷达和微波辐射计的湖北省冷季“高架雷暴”特征分析

利用常规观测、加密自动气象站、三维闪电定位仪、天气雷达和地基微波辐射计资料等,对湖北冷季(2014年11月)发生的3次高架雷暴过程进行了分析。(1)3次过程发生在地面冷锋后部地面冷气团中,主要以短时强降水和频繁的雷电活动为主,是典型的冷季"高架雷暴",对流区位于地面冷锋后部500 km左右。(2)地面到925 hPa的冷垫,迫使暖湿气流爬升,在925 hPa逆温层附近触发对流,冷垫之上西南暖湿气流越强,对流越旺盛,雷达径向速度剖面可以明显看到1 km之下的冷垫。(3)冷季高架雷暴雷电活动剧烈,CG(地闪)占总闪比例60%以上,而+CG则占CG的40%左右,闪电频次和降水有很好的时空对应关系,CG出现在较强降水中心附近及周围,IC和CG突增对降水均有一定的时间提前量。CG更靠近强回波中心,且和≥30 dBZ的回波位置对应较好,IC则分布在雷暴单体外侧回波强度≥15 dBZ的区域。0Ⅱ等温线以上的(最大)回波强度达到43 dBZ以上或者18 dBZ回波顶高超过7.5 km是湖北冷季高架雷暴是否发生雷电的重要预警因子。(4)地基微波辐射计温度、湿度廓线和探空曲线基本吻合,可以看到明显的冷垫、...     

高原地区基于微波辐射计反演大气廓线的神经网络算法研究

以2007~2018年西宁二十里铺气象站探空资料为模拟样本,利用MonoRTM模式模拟中心频率21.985~58.759GHz的35通道亮温,应用BP神经网络对模拟数据进行反复训练,构建最优反演模型,并以2019年探空资料为测试样本,对比分析了不同季节和不同天气条件下BP神经网络与微波辐射计的反演效果。结果表明：晴空条件下,BP神经网络与微波辐射计在温度反演上效果最佳,水汽密度次之,相对湿度最差,其中冬春季BP神经网络反演效果优于微波辐射计,夏秋季反之;有云条件下,BP神经网络温度反演效果在冬、春和夏季均优于微波辐射计,其水汽密度反演效果在四季均较微波辐射计有明显提升,其相对湿度反演效果在冬、春和夏季均较微波辐射计更佳。晴空和有云条件下,BP神经网络在不同季节反演温度、水汽密度和相对湿度的平均绝对误差和标准偏差均小于微波辐射计,尤其是相对湿度的反演精度提升最为明显。晴空条件下,BP神经网络反演温度廓线在春、夏和秋季效果最佳,反演水汽密度廓线在中低层精度较高,反演相对湿度廓线的精度较差,但基本和探空资料趋势一致;有云条件下,BP神经网络反演温度廓线与晴空时基本一致,较微波辐射计精度更高,反演水汽密度和相对湿度廓线在8km以上效果较好。     

EFFECTS OF A CLOUD FILTERING METHOD FOR FENGYUN-3C MICROWAVE HUMIDITY AND TEMPERATURE SOUNDER MEASUREMENTS OVER OCEAN ON RETRIEVALS OF TEMPERATURE AND HUMIDITY

For Microwave Humidity and Temperature sounder (MWHTS) measurements over the ocean, a cloud filtering method is presented to filter out cloud- and precipitation-affected observations by analyzing the sensitivity of the simulated brightness temperatures of MWHTS to cloud liquid water, and using the root mean square error (RMSE) between observation and simulation in clear sky as a reference standard. The atmospheric temperature and humidity profiles are retrieved using MWHTS measurements with and without filtering by multiple linear regression (MLR), artificial neural networks (ANN) and one- dimensional variational (1DVAR) retrieval methods, respectively, and the effects of the filtering method on the retrieval accuracies are analyzed. The numerical results show that the filtering method can improve the retrieval accuracies of the MLR and the 1DVAR retrieval methods, but have little influence on that of the ANN. In addition, the dependencies of the retrieval methods upon the testing samples of brightness temperature are studied, and the results show that the 1DVAR retrieval method has great stability due to that the testing samples have great impact on the retrieval accuracies of the MLR and the ANN, but have little impact on that of the 1DVAR.     

A COMPARISON OF THE RETRIEVAL OF ATMOSPHERIC TEMPERATURE PROFILES USING OBSERVATIONS OF THE 60 GHZ AND 118.75 GHZ ABSORPTION LINES

The Microwave Temperature Sounder-Ⅱ(MWTS-Ⅱ) and Microwave Humidity and Temperature Sounder(MWHTS) onboard the Fengyun-3 C(FY-3 C) satellite can be used to detect atmospheric temperature profiles. The MWTS-II has 13 temperature sounding channels around the 60 GHz oxygen absorption band and the MWHTS has 8 temperature sounding channels around the 118.75 GHz oxygen absorption line. The data quality of the observed brightness temperatures can be evaluated using atmospheric temperature retrievals from the MWTS-Ⅱ and MWHTS observations. Here, the bias characteristics and corrections of the observed brightness temperatures are described. The information contents of observations are calculated, and the retrieved atmospheric temperature profiles are compared using a neural network(NN) retrieval algorithm and a one-dimensional variational inversion(1 D-var) retrieval algorithm. The retrieval results from the NN algorithm show that the accuracy of the MWTS-Ⅱ retrieval is higher than that of the MWHTS retrieval, which is consistent with the results of the radiometric information analysis. The retrieval results from the 1 D-var algorithm show that the accuracy of MWTS-Ⅱ retrieval is similar to that of the MWHTS retrieval at the levels from 850-1,000 h Pa, is lower than that of the MWHTS retrieval at the levels from 650-850 h Pa and 125-300 h Pa, and is higher than that of MWHTS at the other levels. A comparison of the retrieved atmospheric temperature using these satellite observations provides a reference value for assessing the accuracy of atmospheric temperature detection at the 60 GHz oxygen band and 118.75 GHz oxygen line. In addition, based on the comparison of the retrieval results, an optimized combination method is proposed using a branch and bound algorithm for the NN retrieval algorithm, which combines the observations from both the MWTS-Ⅱand MWHTS instruments to retrieve the atmospheric temperature profiles. The results show that the optimal combination can further improve the accuracy of MWTS-Ⅱ retrieval and enhance the detection accuracy of atmospheric temperatures near the surface.