Estimating Antarctic surface melt rates using passive microwave data calibrated with weather station observations
Valeria Di Biase (Universiteit Utrecht)
Peter Kuipers Munneke (Universiteit Utrecht)
Bert Wouters (TU Delft - Physical and Space Geodesy)
Michiel R. van den Broeke (Universiteit Utrecht)
Maurice van Tiggelen (Universiteit Utrecht)
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Abstract
We present a dataset of Antarctic annual surface melt rates (6.25 km resolution, 2011–2021) from 19 GHz Special Sensor Microwave Imager/Sounder (SSMIS). First, melt occurrence is detected via thresholds for brightness temperature, diurnal variation, and winter anomaly, calibrated with Automatic Weather Station (AWS) data. Second, AWS-driven surface energy balance modeling yields an empirical relation between annual melt days and water-equivalent melt volume. SSMIS-derived melt volumes correlate well with AWS-based melt estimates (R2=0.83). Compared to QuikSCAT and RACMO2.4p1 outputs, SSMIS captures a similar spatial melt pattern but estimates a total melt volume approximately 15 % lower than RACMO2.4, on the decadal average.
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