Quantitative Investigation of Radiometric Interactions between Snowfall, Snow Cover, and Cloud Liquid Water over Land

Abstract

Falling snow alters its own microwave signatures when it begins to accumulate on the ground, making retrieval of snowfall challenging. This paper investigates the effects of snow-cover depth and cloud liquid water content on microwave signatures of terrestrial snowfall using reanalysis data and multi-annual observations by the Global Precipitation Measurement (GPM) core satellite with particular emphasis on the 89 and 166 GHz channels. It is found that over shallow snow cover (snow water equivalent (SWE) ≤100kg m−2) and low values of cloud liquid water path (LWP 100–150 g m−2), the scattering of light snowfall (intensities ≤0.5mm h−1) is detectable only at frequency 166 GHz, while for higher snowfall rates, the signal can also be detected at 89 GHz. However, when SWE exceeds 200 kg m−2 and the LWP is greater than 100–150 g m−2, the emission from the increased liquid water content in snowing clouds becomes the only surrogate microwave signal of snowfall that is stronger at frequency 89 than 166 GHz. The results also reveal that over high latitudes above 60°N where the SWE is greater than 200 kg m−2 and LWP is lower than 100–150 g m−2, the snowfall microwave signal could not be detected with GPM without considering a priori data about SWE and LWP. Our findings provide quantitative insights for improving retrieval of snowfall in particular over snow-covered terrain.

A Machine Learning Snowfall Retrieval Algorithm for ATMS

“…In our study, we observed that the presence of liquid water has a strong impact on the detection of snowfall (increasing the rate of false alarms), while we did not notice any impact from snow-cover depth. This may be due to the categorization of snow cover at the time of the overpass that was performed by the PESCA algorithm and used as input in SLALOM-CT. Another explanation could be that our algorithm can better discriminate the signal produced by snowfall from those due to surface-related or atmospheric effects, compared with the analysis carried out in [97], where the analysis focused on GMI high-frequency window channels only (89 and 166 GHz). Moreover, our analysis showed that SLALOM-CT error statistics were almost independent of the surface category, which strongly affects the ground emissivity, as shown in Camplani et al [93].…”

“…In a recent study by Takbiri et al [97], it was shown that the liquid water content of clouds and the snow-cover depth impact the observed BT signals in high-frequency channels and can mask the relatively small signal due to snowfall. In particular, the emission due to liquid water tends to enhance the BTs, while a deeper snow cover tends to lower the surface emissivity, and these effects tend to mask the scattering signal produced by snowflakes.…”

“…These tests were performed in order to verify the presence of blind regions for SLALOM-CT (well-defined conditions which, if present, do not allow snowfall detection and retrieval). Takbiri et al [97] showed that snow-cover depth, concurrently with liquid water (probably supercooled), impacts the upwelling BT and can completely mask the signal related to snowfall. The main result of this analysis is that none of the considered statistical indexes showed any particular sensitivity to snow-cover depth (while the presence of liquid water strongly impacts the detection of snowfall, as seen in panels Figure 9c,d).…”

The State of Precipitation Measurements at Mid-to-High Latitudes

“…In particular, the snow depth, snow density and grain size of a snowpack on the surface complicates the retrieval of falling snow from passive microwave observations. Takbiri et al [159] related the precipitation scattering signal to the snow water equivalent of the snow cover and the liquid water path of the atmosphere. They highlighted a blind spot for a snow cover water equivalent above 200 kg m −2 and a liquid water path greater than 100-150 g m −2 where the microwave scattering signal was completely masked by the high emissivity of the surface and the liquid water content in snow clouds.…”