Comparing firn temperature profile retrieval based on the firn densification model and microwave data over the Antarctica

• Published in: International archives of the photogrammetry, remote sensing and spatial information sciences. Vol. XLVIII-1-2024; pp. 691 - 696
• Main Authors: Wang, Xiaofeng, An, Lu, Langen, Peter L., Li, Rongxing
• Format: Journal Article Conference Proceeding
• Language: English
• Published: Gottingen Copernicus GmbH 11.05.2024; Copernicus Publications
• Subjects: Antarctic ice sheet; Antarctic temperatures; Brightness temperature; Climate models; Conduction heating; Conductive heat transfer; Densification; Density profiles; Firn; Firn density; Glaciation; Heat conduction; Ice sheets; Mass balance; Measuring instruments; Microwave emission; Parameters; Regional climate models; Regional climates; Regression models; Remote monitoring; Remote sensing; Satellite data; Satellites; Surface radiation temperature; Temperature data; Temperature profile; Temperature profiles; Temperature variations
• ISSN: 2194-9034; 1682-1750; 2194-9034
• DOI: 10.5194/isprs-archives-XLVIII-1-2024-691-2024

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.