Ground Passive Microwave Remote Sensing of Atmospheric Profiles Using WRF Simulations and Machine Learning Techniques

IF 2.8 3区 地球科学 Q3 METEOROLOGY & ATMOSPHERIC SCIENCES
Lulu Zhang, Meijing Liu, Wenying He, Xiangao Xia, Haonan Yu, Shuangxu Li, Jing Li
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Abstract

Microwave radiometer (MWR) demonstrates exceptional efficacy in monitoring the atmospheric temperature and humidity profiles. A typical inversion algorithm for MWR involves the use of radiosonde measurements as the training dataset. However, this is challenging due to limitations in the temporal and spatial resolution of available sounding data, which often results in a lack of coincident data with MWR deployment locations. Our study proposes an alternative approach to overcome these limitations by harnessing the Weather Research and Forecasting (WRF) model’s renowned simulation capabilities, which offer high temporal and spatial resolution. By using WRF simulations that collocate with the MWR deployment location as a substitute for radiosonde measurements or reanalysis data, our study effectively mitigates the limitations associated with mismatching of MWR measurements and the sites, which enables reliable MWR retrieval in diverse geographical settings. Different machine learning (ML) algorithms including extreme gradient boosting (XGBoost), random forest (RF), light gradient boosting machine (LightGBM), extra trees (ET), and backpropagation neural network (BPNN) are tested by using WRF simulations, among which BPNN appears as the most superior, achieving an accuracy with a root-mean-square error (RMSE) of 2.05 K for temperature, 0.67 g m−3 for water vapor density (WVD), and 13.98% for relative humidity (RH). Comparisons of temperature, RH, and WVD retrievals between our algorithm and the sounding-trained (RAD) algorithm indicate that our algorithm remarkably outperforms the latter. This study verifies the feasibility of utilizing WRF simulations for developing MWR inversion algorithms, thus opening up new possibilities for MWR deployment and airborne observations in global locations.

利用 WRF 模拟和机器学习技术对大气剖面进行地面被动微波遥感
微波辐射计(MWR)在监测大气温度和湿度剖面方面具有卓越的功效。微波辐射计的典型反演算法包括使用无线电探空仪测量数据作为训练数据集。然而,由于现有探空数据在时间和空间分辨率上的局限性,这往往会导致缺乏与 MWR 部署位置重合的数据,因而具有挑战性。我们的研究提出了另一种方法来克服这些限制,即利用天气研究与预报(WRF)模型著名的模拟能力,这种能力具有很高的时间和空间分辨率。通过使用与 MWR 部署地点相匹配的 WRF 模拟来替代无线电探空仪测量或再分析数据,我们的研究有效地缓解了与 MWR 测量和地点不匹配相关的限制,从而能够在不同的地理环境中进行可靠的 MWR 检索。利用 WRF 模拟测试了不同的机器学习(ML)算法,包括极梯度提升(XGBoost)、随机森林(RF)、光梯度提升机(LightGBM)、额外树(ET)和反向传播神经网络(BPNN)。温度的均方根误差(RMSE)为 2.05 K,水蒸气密度(WVD)为 0.67 g m-3,相对湿度(RH)为 13.98%。我们的算法与探空训练(RAD)算法的温度、相对湿度和水汽密度检索结果比较表明,我们的算法明显优于后者。这项研究验证了利用 WRF 模拟开发 MWR 反演算法的可行性,从而为全球各地的 MWR 部署和机载观测提供了新的可能性。
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来源期刊
Journal of Meteorological Research
Journal of Meteorological Research METEOROLOGY & ATMOSPHERIC SCIENCES-
CiteScore
6.20
自引率
6.20%
发文量
54
期刊介绍: Journal of Meteorological Research (previously known as Acta Meteorologica Sinica) publishes the latest achievements and developments in the field of atmospheric sciences. Coverage is broad, including topics such as pure and applied meteorology; climatology and climate change; marine meteorology; atmospheric physics and chemistry; cloud physics and weather modification; numerical weather prediction; data assimilation; atmospheric sounding and remote sensing; atmospheric environment and air pollution; radar and satellite meteorology; agricultural and forest meteorology and more.
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