{"title":"比较基于南极洲上空冷杉致密化模型和微波数据的冷杉温度曲线检索","authors":"Xiaofeng Wang, Lu An, P. Langen, Rongxing Li","doi":"10.5194/isprs-archives-xlviii-1-2024-691-2024","DOIUrl":null,"url":null,"abstract":"Abstract. The firn temperature is a crucial parameter for understanding firn densification processes of the Antarctic Ice Sheet (AIS). Simulations with firn densification models (FDM) can be conceptualized as a function that relies on forcing data, comprising temperature and surface mass balance, together with tuning parameters determined based on measured depth-density profiles from different locations. The simulated firn temperature is obtained in the firn densification models by solving the one-dimensional heat conduction equation. Microwave satellite data on brightness temperature at different frequencies can also provide remote sensing monitoring of firn temperature variations across the AIS (i.e., the L-band up to 1500 meters). The firn temperature can be estimated by the microwave emission model and the regression method, but these two methods need more observations of temperature profiles for correction and validation. Therefore, we compiled a dataset with temperature profiles and temperature observations with depth around 10 meters. In this work, two methods were used to simulate/retrieve firn temperature across the Antarctic ice sheet. One method estimated the temperature profiles by solving the one-dimensional heat conduction equation driven by reanalyses and regional climate models, which are used in the simulation of FDMs. The other one established a relationship between the multi-frequency brightness temperature data from microwave remote sensing satellites and the firn temperature.\n","PeriodicalId":505918,"journal":{"name":"The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences","volume":" 7","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Comparing firn temperature profile retrieval based on the firn densification model and microwave data over the Antarctica\",\"authors\":\"Xiaofeng Wang, Lu An, P. 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The firn temperature can be estimated by the microwave emission model and the regression method, but these two methods need more observations of temperature profiles for correction and validation. Therefore, we compiled a dataset with temperature profiles and temperature observations with depth around 10 meters. In this work, two methods were used to simulate/retrieve firn temperature across the Antarctic ice sheet. 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引用次数: 0
摘要
摘要冷杉温度是了解南极冰盖(AIS)冷杉致密化过程的关键参数。杉岩致密化模型(FDM)的模拟可以理解为一种函数,它依赖于包括温度和地表质量平衡在内的强迫数据,以及根据不同地点测量的深度-密度剖面确定的调整参数。在冷杉致密化模型中,模拟冷杉温度是通过求解一维热传导方程得到的。不同频率亮度温度的微波卫星数据也可对整个澳大利亚国际空间站(即 L 波段至 1500 米)的杉林温度变化进行遥感监测。杉林温度可通过微波发射模型和回归法估算,但这两种方法需要更多的温度剖面观测数据进行修正和验证。因此,我们编制了一个数据集,其中包含温度曲线和深度在 10 米左右的温度观测数据。在这项工作中,使用了两种方法来模拟/检索南极冰盖上的枞树温度。一种方法是通过求解由再分析和区域气候模型驱动的一维热传导方程来估算温度曲线,这些模型被用于模拟FDM。另一种方法则在微波遥感卫星提供的多频亮度温度数据与杉岩温度之间建立了一种关系。
Comparing firn temperature profile retrieval based on the firn densification model and microwave data over the Antarctica
Abstract. The firn temperature is a crucial parameter for understanding firn densification processes of the Antarctic Ice Sheet (AIS). Simulations with firn densification models (FDM) can be conceptualized as a function that relies on forcing data, comprising temperature and surface mass balance, together with tuning parameters determined based on measured depth-density profiles from different locations. The simulated firn temperature is obtained in the firn densification models by solving the one-dimensional heat conduction equation. Microwave satellite data on brightness temperature at different frequencies can also provide remote sensing monitoring of firn temperature variations across the AIS (i.e., the L-band up to 1500 meters). The firn temperature can be estimated by the microwave emission model and the regression method, but these two methods need more observations of temperature profiles for correction and validation. Therefore, we compiled a dataset with temperature profiles and temperature observations with depth around 10 meters. In this work, two methods were used to simulate/retrieve firn temperature across the Antarctic ice sheet. One method estimated the temperature profiles by solving the one-dimensional heat conduction equation driven by reanalyses and regional climate models, which are used in the simulation of FDMs. The other one established a relationship between the multi-frequency brightness temperature data from microwave remote sensing satellites and the firn temperature.