Retrieval of Atmospheric Temperature Profile from Historical Data and Ground-Based Observations by Using a Machine Learning Algorithm
The atmospheric temperature profile is an important parameter to describe the state of the atmosphere, and it is crucial to climate change research, weather forecasting, and atmospheric parameter retrieval. A machine learning algorithm that incorporates historical observations and ground-based measu...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-05-01
|
| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/15/11/2717 |
| _version_ | 1797596823137812480 |
|---|---|
| author | Hongkun Wang Dong Liu Yingwei Xia Wanyi Xie Yiren Wang |
| author_facet | Hongkun Wang Dong Liu Yingwei Xia Wanyi Xie Yiren Wang |
| author_sort | Hongkun Wang |
| collection | DOAJ |
| description | The atmospheric temperature profile is an important parameter to describe the state of the atmosphere, and it is crucial to climate change research, weather forecasting, and atmospheric parameter retrieval. A machine learning algorithm that incorporates historical observations and ground-based measurements was developed in this study to retrieve the atmospheric temperature profile. Specifically, the deep learning network considered historical observations for the same period and temporally correlated temperature profiles. It combined multi-layer perceptron (MLP) and the convolutional neural network (CNN). MLP derived the features from the ground factors, and CNN captured the essential features associated with the temperature profiles at the current time from latent historical data. Then, the features of the two parts were concatenated to obtain the final network. The construction and parameters of the model were optimized to determine the best model configuration and retrieval performance. The results of the model were evaluated against those of other methods on the same dataset. The model showed a good retrieval precision, which was equivalent to a small retrieval bias, root-mean-square error, and mean absolute error at all altitudes. The analysis of the application of this model to the retrieval of atmospheric temperature profiles indicates that the method is feasible. |
| first_indexed | 2024-03-11T02:58:27Z |
| format | Article |
| id | doaj.art-4095b54a4c9240a8b61befa8114db4c5 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-11T02:58:27Z |
| publishDate | 2023-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-4095b54a4c9240a8b61befa8114db4c52023-11-18T08:27:44ZengMDPI AGRemote Sensing2072-42922023-05-011511271710.3390/rs15112717Retrieval of Atmospheric Temperature Profile from Historical Data and Ground-Based Observations by Using a Machine Learning AlgorithmHongkun Wang0Dong Liu1Yingwei Xia2Wanyi Xie3Yiren Wang4Institutes of Physical Science and Information Technology, Anhui University, Hefei 230601, ChinaKey Laboratory of Atmospheric Optics, Anhui Institute of Optics and Fine Mechanics, Chinese Academy of Sciences, Hefei 230088, ChinaOpto-Electronics Applied Technology Research Center, Anhui Institute of Optics and Fine Mechanics, Chinese Academy of Sciences, Hefei 230031, ChinaKey Laboratory of Atmospheric Optics, Anhui Institute of Optics and Fine Mechanics, Chinese Academy of Sciences, Hefei 230088, ChinaKey Laboratory of Atmospheric Optics, Anhui Institute of Optics and Fine Mechanics, Chinese Academy of Sciences, Hefei 230088, ChinaThe atmospheric temperature profile is an important parameter to describe the state of the atmosphere, and it is crucial to climate change research, weather forecasting, and atmospheric parameter retrieval. A machine learning algorithm that incorporates historical observations and ground-based measurements was developed in this study to retrieve the atmospheric temperature profile. Specifically, the deep learning network considered historical observations for the same period and temporally correlated temperature profiles. It combined multi-layer perceptron (MLP) and the convolutional neural network (CNN). MLP derived the features from the ground factors, and CNN captured the essential features associated with the temperature profiles at the current time from latent historical data. Then, the features of the two parts were concatenated to obtain the final network. The construction and parameters of the model were optimized to determine the best model configuration and retrieval performance. The results of the model were evaluated against those of other methods on the same dataset. The model showed a good retrieval precision, which was equivalent to a small retrieval bias, root-mean-square error, and mean absolute error at all altitudes. The analysis of the application of this model to the retrieval of atmospheric temperature profiles indicates that the method is feasible.https://www.mdpi.com/2072-4292/15/11/2717atmospheric temperature profileMLPCNNretrievalground-based observationhistorical observations |
| spellingShingle | Hongkun Wang Dong Liu Yingwei Xia Wanyi Xie Yiren Wang Retrieval of Atmospheric Temperature Profile from Historical Data and Ground-Based Observations by Using a Machine Learning Algorithm Remote Sensing atmospheric temperature profile MLP CNN retrieval ground-based observation historical observations |
| title | Retrieval of Atmospheric Temperature Profile from Historical Data and Ground-Based Observations by Using a Machine Learning Algorithm |
| title_full | Retrieval of Atmospheric Temperature Profile from Historical Data and Ground-Based Observations by Using a Machine Learning Algorithm |
| title_fullStr | Retrieval of Atmospheric Temperature Profile from Historical Data and Ground-Based Observations by Using a Machine Learning Algorithm |
| title_full_unstemmed | Retrieval of Atmospheric Temperature Profile from Historical Data and Ground-Based Observations by Using a Machine Learning Algorithm |
| title_short | Retrieval of Atmospheric Temperature Profile from Historical Data and Ground-Based Observations by Using a Machine Learning Algorithm |
| title_sort | retrieval of atmospheric temperature profile from historical data and ground based observations by using a machine learning algorithm |
| topic | atmospheric temperature profile MLP CNN retrieval ground-based observation historical observations |
| url | https://www.mdpi.com/2072-4292/15/11/2717 |
| work_keys_str_mv | AT hongkunwang retrievalofatmospherictemperatureprofilefromhistoricaldataandgroundbasedobservationsbyusingamachinelearningalgorithm AT dongliu retrievalofatmospherictemperatureprofilefromhistoricaldataandgroundbasedobservationsbyusingamachinelearningalgorithm AT yingweixia retrievalofatmospherictemperatureprofilefromhistoricaldataandgroundbasedobservationsbyusingamachinelearningalgorithm AT wanyixie retrievalofatmospherictemperatureprofilefromhistoricaldataandgroundbasedobservationsbyusingamachinelearningalgorithm AT yirenwang retrievalofatmospherictemperatureprofilefromhistoricaldataandgroundbasedobservationsbyusingamachinelearningalgorithm |