Assessment of the Stability of Passive Microwave Brightness Temperatures for NASA Team Sea Ice Concentration Retrievals

Abstract

<jats:p>Gridded passive microwave brightness temperatures (TB) from special sensor microwave imager and sounder (SSMIS) instruments on three different satellite platforms are compared in different years to investigate the consistency between the sensors over time. The orbits of the three platforms have drifted over their years of operation, resulting in changing relative observing times that could cause biases in TB estimates and near-real-time sea ice concentrations derived from the NASA Team algorithm that are produced at the National Snow and Ice Data Center. Comparisons of TB histograms and concentrations show that there are small mean differences between sensors, but variability within an individual sensor is much greater. There are some indications of small changes due to orbital drift, but these are not consistent across different frequencies. Further, the overall effect of the drift, while not definitive, is small compared to the intra- and interannual variability in individual sensors. These results suggest that, for near-real-time use, the differences in the sensors are not critical. However, for long-term time series, even the small biases should be corrected for. The strong day-to-day, seasonal, and interannual variability in TB distributions indicate that time-varying algorithm coefficients in the NASA team algorithm would lead to improved, more consistent sea ice concentration estimates.</jats:p>

Resolution enhanced sea ice concentration: a new algorithm applied to AMSR2 microwave radiometry data

“…As a result, by applying the reSICCI3LF algorithm to AMSR2 data, we can obtain the benefits of both the SICCI3LF and N90LIN algorithms to produce a higher-resolution SIC with low measurement uncertainty. The ability to better represent transitional ice zones, such as the ice edge, is crucial for producing accurate synoptic forecasts (Meier and Stewart, 2020) with higher resolution SIC shown to improve the accuracy of ice edge forecasting (Posey and others, 2015). The assimilation of this SIC into the Barents-2.5 km ocean/ice forecasting model and its impacts are presented in Durán Moro and others (2022).…”

“…Passive microwave (PMW) sensors have enabled frequent and large-scale measurement of polar sea ice concentration and its derived quantities (sea ice extent and area) since the late 1970s, thanks to their polar orbits and wide swaths. PMW sensors are particularly suited to polar applications due to the microwave emissions they observe being independent of solar illumination and having generally low sensitivity to atmospheric influences in the atmospheric window frequencies, except for in the presence of intense precipitation and windroughened open ocean (Meier and Stewart, 2020), with greater sensitivity to atmospheric water vapour occurring in the 89 GHz channel (Ivanova and others, 2015). This has meant that PMW sensors have produced a near continuous climate data record that has enabled the identification of changes to the ice melt timing and duration (Stroeve and others, 2014;Serreze and others, 2016) and reductions in Arctic summer sea ice extents (Stroeve and Notz, 2018).…”

The Determination of the Snow Optical Grain Diameter and Snowmelt Area on the Greenland Ice Sheet Using Spaceborne Optical Observations

“…To map surface melt over the Greenland ice sheet between 2017 and 2019, we used the MEaSUREs Greenland daily surface melt flag [12,83]. It used, for that period, data from the SSM/I F17 and F19 sensors on board the Defense Meteorological Satellite Program (DMSP) satellites [84] and a snowpack microwave emission model and dynamic threshold adjusted for each year to detect melting/nonmelting surface conditions. It is available at a 25 km resolution on an EASEv2 grid and only fully glaciated pixels are considered.…”