{"title":"基于地基多通道微波辐射计和毫米波云雷达改进大气温度和相对湿度剖面检索","authors":"Longwei Zhang, Yingying Ma, Lianfa Lei, Yujie Wang, Shikuan Jin, Wei Gong","doi":"10.3390/atmos15091064","DOIUrl":null,"url":null,"abstract":"Obtaining temperature and humidity profiles with high vertical resolution is essential for describing and predicting atmospheric motion, and, in particular, for understanding the evolution of medium- and small-scale weather processes, making short-range and near-term weather forecasting, and implementing weather modifications (artificial rainfall, artificial rain elimination, etc.). Ground-based microwave radiometers can acquire vertical tropospheric atmospheric data with high temporal and spatial resolution. However, the accuracy of temperature and relative humidity retrieval is still not as accurate as that of radiosonde data, especially in cloudy conditions. Therefore, improving the observation and retrieval accuracy is a major challenge in current research. The focus of this study was to further improve the accuracy of atmospheric temperature and humidity profile retrieval and investigate the specific effects of cloud information (cloud-base height and cloud thickness) on temperature and humidity profile retrieval. The observation data from the ground-based multichannel microwave radiometer (GMR) and the millimeter-wave cloud radar (MWCR) were incorporated into the retrieval process of the atmospheric temperature and relative humidity profiles. The retrieval was performed using the backpropagation neural network (BPNN). The retrieval results were quantified using the mean absolute error (MAE) and root mean square error (RMSE). The statistical results showed that the temperature profiles were less affected by the cloud information compared with the relative humidity profiles. Cloud thickness was the main factor affecting the retrieval of relative humidity profiles, and the retrieval with cloud information was the best retrieval method. Compared with the retrieval profiles without cloud information, the MAE and RMSE values of most of the altitude layers were reduced to different degrees after adding cloud information, and the relative humidity (RH) errors of some altitude layers were reduced by approximately 50%. The maximum reduction in the RMSE and MAE values for the retrieval of temperature profiles with cloud information was about 1.0 °C around 7.75 km, and the maximum reduction in RMSE and MAE values for the relative humidity profiles was about 10%, which was obtained around 2 km.","PeriodicalId":8580,"journal":{"name":"Atmosphere","volume":"1 1","pages":""},"PeriodicalIF":2.5000,"publicationDate":"2024-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Improving Atmospheric Temperature and Relative Humidity Profiles Retrieval Based on Ground-Based Multichannel Microwave Radiometer and Millimeter-Wave Cloud Radar\",\"authors\":\"Longwei Zhang, Yingying Ma, Lianfa Lei, Yujie Wang, Shikuan Jin, Wei Gong\",\"doi\":\"10.3390/atmos15091064\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Obtaining temperature and humidity profiles with high vertical resolution is essential for describing and predicting atmospheric motion, and, in particular, for understanding the evolution of medium- and small-scale weather processes, making short-range and near-term weather forecasting, and implementing weather modifications (artificial rainfall, artificial rain elimination, etc.). Ground-based microwave radiometers can acquire vertical tropospheric atmospheric data with high temporal and spatial resolution. However, the accuracy of temperature and relative humidity retrieval is still not as accurate as that of radiosonde data, especially in cloudy conditions. Therefore, improving the observation and retrieval accuracy is a major challenge in current research. The focus of this study was to further improve the accuracy of atmospheric temperature and humidity profile retrieval and investigate the specific effects of cloud information (cloud-base height and cloud thickness) on temperature and humidity profile retrieval. The observation data from the ground-based multichannel microwave radiometer (GMR) and the millimeter-wave cloud radar (MWCR) were incorporated into the retrieval process of the atmospheric temperature and relative humidity profiles. The retrieval was performed using the backpropagation neural network (BPNN). The retrieval results were quantified using the mean absolute error (MAE) and root mean square error (RMSE). The statistical results showed that the temperature profiles were less affected by the cloud information compared with the relative humidity profiles. Cloud thickness was the main factor affecting the retrieval of relative humidity profiles, and the retrieval with cloud information was the best retrieval method. Compared with the retrieval profiles without cloud information, the MAE and RMSE values of most of the altitude layers were reduced to different degrees after adding cloud information, and the relative humidity (RH) errors of some altitude layers were reduced by approximately 50%. The maximum reduction in the RMSE and MAE values for the retrieval of temperature profiles with cloud information was about 1.0 °C around 7.75 km, and the maximum reduction in RMSE and MAE values for the relative humidity profiles was about 10%, which was obtained around 2 km.\",\"PeriodicalId\":8580,\"journal\":{\"name\":\"Atmosphere\",\"volume\":\"1 1\",\"pages\":\"\"},\"PeriodicalIF\":2.5000,\"publicationDate\":\"2024-09-03\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Atmosphere\",\"FirstCategoryId\":\"89\",\"ListUrlMain\":\"https://doi.org/10.3390/atmos15091064\",\"RegionNum\":4,\"RegionCategory\":\"地球科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"ENVIRONMENTAL SCIENCES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Atmosphere","FirstCategoryId":"89","ListUrlMain":"https://doi.org/10.3390/atmos15091064","RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ENVIRONMENTAL SCIENCES","Score":null,"Total":0}
引用次数: 0
摘要
获得高垂直分辨率的温度和湿度剖面对于描述和预测大气运动,特别是了解中、小尺度天气过程的演变、进行短期和近期天气预报以及实施天气改变(人工降雨、人工消雨等)至关重要。地基微波辐射计可以获取高时间和空间分辨率的垂直对流层大气数据。但是,温度和相对湿度的检索精度仍不及无线电探空仪数据,尤其是在多云条件下。因此,提高观测和检索精度是当前研究的一大挑战。本研究的重点是进一步提高大气温湿度剖面检索的精度,并研究云信息(云基高度和云厚度)对温湿度剖面检索的具体影响。在大气温度和相对湿度剖面的检索过程中纳入了地面多通道微波辐射计和毫米波云雷达的观测数据。检索使用了反向传播神经网络(BPNN)。检索结果使用平均绝对误差(MAE)和均方根误差(RMSE)进行量化。统计结果表明,与相对湿度曲线相比,温度曲线受云层信息的影响较小。云层厚度是影响相对湿度剖面检索的主要因素,有云层信息的检索是最佳的检索方法。与无云层信息的检索剖面相比,加入云层信息后大部分高度层的 MAE 值和 RMSE 值都有不同程度的降低,部分高度层的相对湿度(RH)误差降低了约 50%。在 7.75 千米附近,有云层信息的温度剖面的 RMSE 和 MAE 值最大减少了约 1.0 °C,相对湿度剖面的 RMSE 和 MAE 值最大减少了约 10%,这是在 2 千米附近获得的。
Improving Atmospheric Temperature and Relative Humidity Profiles Retrieval Based on Ground-Based Multichannel Microwave Radiometer and Millimeter-Wave Cloud Radar
Obtaining temperature and humidity profiles with high vertical resolution is essential for describing and predicting atmospheric motion, and, in particular, for understanding the evolution of medium- and small-scale weather processes, making short-range and near-term weather forecasting, and implementing weather modifications (artificial rainfall, artificial rain elimination, etc.). Ground-based microwave radiometers can acquire vertical tropospheric atmospheric data with high temporal and spatial resolution. However, the accuracy of temperature and relative humidity retrieval is still not as accurate as that of radiosonde data, especially in cloudy conditions. Therefore, improving the observation and retrieval accuracy is a major challenge in current research. The focus of this study was to further improve the accuracy of atmospheric temperature and humidity profile retrieval and investigate the specific effects of cloud information (cloud-base height and cloud thickness) on temperature and humidity profile retrieval. The observation data from the ground-based multichannel microwave radiometer (GMR) and the millimeter-wave cloud radar (MWCR) were incorporated into the retrieval process of the atmospheric temperature and relative humidity profiles. The retrieval was performed using the backpropagation neural network (BPNN). The retrieval results were quantified using the mean absolute error (MAE) and root mean square error (RMSE). The statistical results showed that the temperature profiles were less affected by the cloud information compared with the relative humidity profiles. Cloud thickness was the main factor affecting the retrieval of relative humidity profiles, and the retrieval with cloud information was the best retrieval method. Compared with the retrieval profiles without cloud information, the MAE and RMSE values of most of the altitude layers were reduced to different degrees after adding cloud information, and the relative humidity (RH) errors of some altitude layers were reduced by approximately 50%. The maximum reduction in the RMSE and MAE values for the retrieval of temperature profiles with cloud information was about 1.0 °C around 7.75 km, and the maximum reduction in RMSE and MAE values for the relative humidity profiles was about 10%, which was obtained around 2 km.
期刊介绍:
Atmosphere (ISSN 2073-4433) is an international and cross-disciplinary scholarly journal of scientific studies related to the atmosphere. It publishes reviews, regular research papers, communications and short notes, and there is no restriction on the length of the papers. Our aim is to encourage scientists to publish their experimental and theoretical research in as much detail as possible. Full experimental and/or methodical details must be provided for research articles.