Integrating Remote-Sensing and Assimilation Data to Improve Air Temperature on Hot Weather in East China
Land-surface characteristics (LSCs) and land-soil moisture conditions can modulate energy partition at the land surface, impact near-surface atmosphere conditions, and further affect land–atmosphere interactions. This study investigates the effect of land-surface-characteristic parameters (LSCPs) in...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-08-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/13/17/3409 |
| _version_ | 1797520931318398976 |
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| author | Suosuo Li Yuanpu Liu Yongjie Pan Zhe Li Shihua Lyu |
| author_facet | Suosuo Li Yuanpu Liu Yongjie Pan Zhe Li Shihua Lyu |
| author_sort | Suosuo Li |
| collection | DOAJ |
| description | Land-surface characteristics (LSCs) and land-soil moisture conditions can modulate energy partition at the land surface, impact near-surface atmosphere conditions, and further affect land–atmosphere interactions. This study investigates the effect of land-surface-characteristic parameters (LSCPs) including albedo, leaf-area index (LAI), and soil moisture (SM) on hot weather by in East China using the numerical model. Simulations using the Weather Research and Forecasting (WRF) Model were conducted for a hot weather event with a high spatial resolution of 1 km in domain 3 by using ERA-Interim forcing fields on 20 July 2017 until 16:00 UTC on 25 July 2017. The satellite-based albedo and LAI, and assimilation-based soil-moisture data of high temporal–spatial resolution, which are more accurate to match fine weather forecasts and high-resolution simulations, were used to update the default LSCPs. A control simulation with the default LSCPs (WRF_CTL), a main sensitivity simulation with the updated LSCP albedo, LAI and SM (WRF_CHAR), and a series of other sensitivity simulations with one or two updated LSCPs were performed. Results show that WRF_CTL could reproduce the spatial distribution of hot weather, but overestimated air temperature (Ta) and maximal air temperature (Tamax) with a warming bias of 1.05 and 1.32 °C, respectively. However, the WRF_CHAR simulation reduced the warming bias, and improved the simulated Ta and Tamax with reducing relative biases of 33.08% and 29.24%, respectively. Compared to the WRF_CTL, WRF_CHAR presented a negative sensible heat-flux difference, positive latent heat flux, and net radiation difference of the area average. LSCPs modulated the partition of available land-surface energy and then changed the air temperature. On the basis of statistical-correlation analysis, the soil moisture of the top 10 cm is the main factor to improve warming bias on hot weather in East China. |
| first_indexed | 2024-03-10T08:05:29Z |
| format | Article |
| id | doaj.art-f9c9bebaf59c45f0b1aaf5242ed1bd02 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T08:05:29Z |
| publishDate | 2021-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-f9c9bebaf59c45f0b1aaf5242ed1bd022023-11-22T11:08:28ZengMDPI AGRemote Sensing2072-42922021-08-011317340910.3390/rs13173409Integrating Remote-Sensing and Assimilation Data to Improve Air Temperature on Hot Weather in East ChinaSuosuo Li0Yuanpu Liu1Yongjie Pan2Zhe Li3Shihua Lyu4Key Laboratory of Land Surface Process and Climate Change in Cold and Arid Regions, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, ChinaKey Laboratory of Arid Climatic Change and Reduction Disaster of Gansu Province, Institute of Arid Meteorology, China Meteorological Administration, Lanzhou 730020, ChinaKey Laboratory of Land Surface Process and Climate Change in Cold and Arid Regions, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, ChinaKey Laboratory of Land Surface Process and Climate Change in Cold and Arid Regions, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, ChinaPlateau Atmosphere and Environment Key Laboratory of Sichuan Province, School of Atmospheric Sciences, Chengdu University of Information Technology, Chengdu 610225, ChinaLand-surface characteristics (LSCs) and land-soil moisture conditions can modulate energy partition at the land surface, impact near-surface atmosphere conditions, and further affect land–atmosphere interactions. This study investigates the effect of land-surface-characteristic parameters (LSCPs) including albedo, leaf-area index (LAI), and soil moisture (SM) on hot weather by in East China using the numerical model. Simulations using the Weather Research and Forecasting (WRF) Model were conducted for a hot weather event with a high spatial resolution of 1 km in domain 3 by using ERA-Interim forcing fields on 20 July 2017 until 16:00 UTC on 25 July 2017. The satellite-based albedo and LAI, and assimilation-based soil-moisture data of high temporal–spatial resolution, which are more accurate to match fine weather forecasts and high-resolution simulations, were used to update the default LSCPs. A control simulation with the default LSCPs (WRF_CTL), a main sensitivity simulation with the updated LSCP albedo, LAI and SM (WRF_CHAR), and a series of other sensitivity simulations with one or two updated LSCPs were performed. Results show that WRF_CTL could reproduce the spatial distribution of hot weather, but overestimated air temperature (Ta) and maximal air temperature (Tamax) with a warming bias of 1.05 and 1.32 °C, respectively. However, the WRF_CHAR simulation reduced the warming bias, and improved the simulated Ta and Tamax with reducing relative biases of 33.08% and 29.24%, respectively. Compared to the WRF_CTL, WRF_CHAR presented a negative sensible heat-flux difference, positive latent heat flux, and net radiation difference of the area average. LSCPs modulated the partition of available land-surface energy and then changed the air temperature. On the basis of statistical-correlation analysis, the soil moisture of the top 10 cm is the main factor to improve warming bias on hot weather in East China.https://www.mdpi.com/2072-4292/13/17/3409land-surface characteristicshot weatherair temperatureWRF modelEast China |
| spellingShingle | Suosuo Li Yuanpu Liu Yongjie Pan Zhe Li Shihua Lyu Integrating Remote-Sensing and Assimilation Data to Improve Air Temperature on Hot Weather in East China Remote Sensing land-surface characteristics hot weather air temperature WRF model East China |
| title | Integrating Remote-Sensing and Assimilation Data to Improve Air Temperature on Hot Weather in East China |
| title_full | Integrating Remote-Sensing and Assimilation Data to Improve Air Temperature on Hot Weather in East China |
| title_fullStr | Integrating Remote-Sensing and Assimilation Data to Improve Air Temperature on Hot Weather in East China |
| title_full_unstemmed | Integrating Remote-Sensing and Assimilation Data to Improve Air Temperature on Hot Weather in East China |
| title_short | Integrating Remote-Sensing and Assimilation Data to Improve Air Temperature on Hot Weather in East China |
| title_sort | integrating remote sensing and assimilation data to improve air temperature on hot weather in east china |
| topic | land-surface characteristics hot weather air temperature WRF model East China |
| url | https://www.mdpi.com/2072-4292/13/17/3409 |
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