| 微波辐射计温湿廓线反演方法改进试验 |
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| 引用本文： | 张雪芬,王志诚,茆佳佳,汪章维,张东明,陶法.微波辐射计温湿廓线反演方法改进试验[J].应用气象学报,2020,31(4):385-396. |
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| 作者姓名： | 张雪芬 王志诚 茆佳佳 汪章维 张东明 陶法 |
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| 作者单位： | 1.中国气象局气象探测中心, 北京 100081 |
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| 摘 要： | 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。
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| 关 键 词： | 地基微波辐射计 BP神经网络 温湿廓线 测云仪 |
| 收稿时间： | 2020-01-07 |
Experiments on Improving Temperature and Humidity Profile Retrieval for Ground-based Microwave Radiometer |
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| Affiliation: | 1.Meteorological Observation Center of CMA, Beijing 1000812.Zhejiang Atmospheric Detection Technology Support Center, Hangzhou 310018 |
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| Abstract: | Ground-based microwave radiometer (MWR) has a crucial role in scientific researches, weather modification service and climate change studies. MWR adopts passive remote sensing technology which has smaller volume, lower power consumption. Ground-based microwave radiometer detects atmospheric temperature and humidity by receiving atmospheric microwave radiation, which can conduct 24-hour unattended, high-resolution observation. It can detect short-time variation of atmospheric elements. Many studies show that different seasons, different weather conditions, quality control algorithms, and changes in environments have certain effects on retrieval results of MWR. In the case of cloudy condition, the uncertainty of cloud absorption coefficient leads to the increase of retrieval error and incorrect data of MWR especially. In order to improve temperature and relative humidity detection capabilities of MWR, the experiment builds BP neural network algorithm with 6 years(from 2011 to 2016) sounding data. The experiment builds two types of retrieval methods because there are some differences of microwave radiation transfer between clear and cloudy samples. The test uses measured brightness temperature data (requiring correction) and cloud data of millimeter-wavelength cloud radar as model inputs and then uses sounding data to evaluate model outputs (temperature and relative humidity profiles) from 2017 to 2018.Results show that correlation coefficients between outputs of 4 models (clear sky sample temperature model, cloudy sample temperature model, clear sky sample relative humidity model and the cloudy sample relative humidity model) and sounding data are 0.99, 0.99, 0.80 and 0.78. Taking sounding profiles as reference, root mean square errors (RMSE) of retrieval results of 4 models are 2.3℃, 2.3℃, 9%, 16%. Comparing with the MWR original profiles, RMSEs of 4 models are reduced by 0.4℃, 0.3℃, 11% and 9%, accuracies are improved by about 30%, 28%, 64% and 45%. In particular, the deviation of temperature model and humidity model within ±2℃ and ±20% account for 68%, 70% and 95%, 78%, which are 7%, 5% and 27%, 23% higher than MWR original profiles. The bias correction of brightness temperature and the training retrieval model of distinguishing weather samples are helpful to improve the retrieval accuracy of MWR temperature and humidity profiles. The network model combined with cloud radar information has obviously better effects on the retrieval result under cloudy samples.Through these experiments, the quality control of brightness temperature, combination of active and passive retrieval algorithms are well improved. The combination of active and passive retrieval effectively improves the performance of MWR, which will lay a foundation for the development of the comprehensive observation system of atmospheric profiles. |
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