Explicitly determined sea ice emissivity and emission temperature over the Arctic for surface‐sensitive microwave channels

Abstract

Data assimilation of satellite microwave measurements is one of the important keys to improving weather forecasting over the Arctic region. However, the use of surface‐sensitive microwave‐sounding channel measurements for data assimilation or retrieval has been limited, especially during winter, due to the poorly constrained sea ice emissivity. In this study, aiming at more use of those channel measurements in the data assimilation, we propose an explicit method for specifying the surface radiative boundary conditions (namely emissivity and emitting layer temperature of snow and ice). These were explicitly determined with a radiative transfer model for snow and ice and with snow/ice physical parameters (i.e. snow/ice depths and vertical distributions of temperature, density, salinity, and grain size) simulated from the thermodynamically driven snow/ice growth model. We conducted 1D‐Var experiments in order to examine whether this approach can help to use the surface‐sensitive microwave temperature channel measurements over the Arctic sea ice region for data assimilation. Results show that (1) the surface‐sensitive microwave channels can be used in the 1D‐Var retrieval, and (2) the specification of the radiative boundary condition at the surface using the snow/sea ice emission model can significantly improve the atmospheric temperature retrieval, especially in the lower troposphere (500 hPa to surface). The successful retrieval suggests that useful information can be extracted from surface‐sensitive microwave‐sounding channel radiances over sea ice surfaces through the explicit determination of snow/ice emissivity and emitting layer temperature.

Simultaneous Inference of Sea Ice State and Surface Emissivity Model Using Machine Learning and Data Assimilation

“…A particular issue for physical models of sea ice is the impossibility of providing snow pit and ice core measurements globally. To deal with this is necessary to make empirical adjustments to fit the observations at each location -for example, modifying the snow and sea ice optical properties (Kang et al, 2023) or similarly the surface emissivity (Tonboe et al, 2013). Alternatively, very detailed assumptions of snow and sea ice microphysical structure need to be made a-priori, though so far this approach has been validated only for a limited set of conditions (e.g., Arctic winter, Rückert et al, 2023).…”

“…For physical retrieval techniques including data assimilation in weather forecasting, the interaction of the sea ice and its snow cover with electromagnetic radiation needs to be represented with an observation model. Typically this is done in terms of surface emissivity (e.g., Tonboe et al, 2013) or by using a multi-layer model for radiative transfer in the sea ice and snow (e.g., Kang et al, 2023;Rückert et al, 2023;Scarlat et al, 2020). However, physical modeling of snow and ice radiative transfer at microwave frequencies is difficult, in particular due to the importance of centimeter to micron scale details of the snow and ice, including air inclusions in ice or grain shapes and their layout in snow, which strongly affect the optical properties of the snow and ice.…”

Assessing the sea ice microwave emissivity up to submillimeter waves from airborne and satellite observations

“…The Tool to Estimate Land Surface Emissivity from Microwave to Submillimeter Waves (TELSEM 2 ; Wang et al, 2017b) climatology for sea ice and land surfaces additionally extrapolates the emissivity up to 700 GHz to provide first-guess emissivities for upcoming satellite missions such as ICI. To simultaneously retrieve atmospheric and sea ice and snow properties, radiative transfer models of sea ice and atmosphere are combined (Rückert et al, 2023;Kang et al, 2023). Kang et al (2023) additionally simulated sea ice growth to increase the temporal consistency of the retrieved sea ice and snow properties.…”

“…To simultaneously retrieve atmospheric and sea ice and snow properties, radiative transfer models of sea ice and atmosphere are combined (Rückert et al, 2023;Kang et al, 2023). Kang et al (2023) additionally simulated sea ice growth to increase the temporal consistency of the retrieved sea ice and snow properties. However, the sea ice radiative transfer models might only be valid below 100 GHz.…”

Modeling Snow and Ice Microwave Emissions in the Arctic for a Multi‐Parameter Retrieval of Surface and Atmospheric Variables From Microwave Radiometer Satellite Data

“…and lapse rate feedback) but is hampered by the sparseness of in situ measurements and the challenges using satellite microwave observations for atmospheric sounding over sea ice (Crewell et al, 2021;Kang et al, 2023). The surface contributions to the signal dominate the microwave brightness temperatures measured at the top of the atmosphere compared to the atmospheric emissions, with the surface signal being transmitted through the atmosphere even up to 480 GHz at the poles because of the dry atmosphere (Wang et al, 2017).…”