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

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.

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

Falling snow alters its own microwave signatures when it begins to accumulate on the ground making retrieval of precipitation 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 measurements by the Global Precipitation Measurement (GPM) core satellite with particular emphasis on the 89 and 166 GHz channels. It is found that over snow cover shallower than 10 cm and low values of cloud liquid water path (LWP ≤125gm−2), the scattering of light snowfall (<0.5mmh−1) is detectable only at frequency 166 GHz while for higher intensities the signal can be also detected at 89 GHz. However, when snow depth exceeds ∼20 cm and the LWP is greater than ∼125gm−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 GHz than 166 GHz. The results also reveal that over high latitudes above 60∘ N where the snow cover is thicker than 20 cm and LWP is lower than 125 gm−2 the microwave snowfall signal could not be detected with GPM. Our results provide quantitative insights for improving retrieval of snowfall in particular over snow-covered terrain.

Impact of Snow Properties on Ka- and Ku-band Winter and Melt Season Microwave Signatures of Arctic Sea Ice

<p>We present the baseline and detailed assessment of Ka- and Ku-band microwave signatures of winter (Legs 1 and 2) and melt season (Leg 4) snow-covered sea ice, acquired during the 2019-2020 MOSAiC International Arctic Drift Expedition. The microwave signatures were acquired using a surface-based, fully-polarimetric, Ku- and Ka-band radar (KuKa radar), acquired coincident with <em>in situ</em> meteorological and snow/sea ice geophysical property measurements. The KuKa radar mimicked the center frequencies of presently operational Ku- and Ka-band satellite radar altimeter and scatterometer missions.</p><p>Preliminary observations, supported by microwave backscatter modeling indicates dominant Ka-band snow surface scattering and its strong sensitivity due to snow surface roughness and its changes, induced by snow accumulation, wind-driven redistribution/erosion. For Ku-band, winter backscatter signatures originate from the snow/sea ice interface. We also showcase the winter backscatter sensitivity through its impact during the November 2019 warm storm.  During advanced melt, the Ka- and Ku-band signatures demonstrates sensitivity to snow surface melt/refreeze diurnal cycling, caused by fluctuations in liquid water content. During the melt cycle, scattering loss and absorption dominated both frequencies, while refrozen snow surface scattering dominated the refreeze cycle (observed during morning and evening scans). </p><p>Observations from the KuKa radar will in turn provide critical understanding of snow/sea ice geophysical processes over the annual cycle, that will improve the accuracy of satellite-based retrievals of snow/sea ice critical state variables such as snow depth, sea ice thickness,  freeze-up and melt-onset timings etc, from operational and forthcoming missions such as AltiKa, CryoSat-2, Sentinel-3, ScatSat-1, CRISTAL etc. </p>

Impact of Microphysical Parameterizations on Simulated Hurricanes—Using Multi-Parameter Satellite Data to Determine the Particle Size Distributions that Produce Most Realistic Storms

Understanding and forecasting hurricanes remains a challenge for the operational and research communities. To accurately predict the Tropical Cyclone (TC) evolution requires properly reflecting the storm’s inner core dynamics by using: (i) high-resolution models; (ii) realistic physical parameterizations. The microphysical processes and their representation in cloud-permitting models are of crucial importance. In particular, the assumed Particle Size Distribution (PSD) functions affect nearly all formulated microphysical processes and are among the most fundamental assumptions in the bulk microphysics schemes. This paper analyzes the impact of the PSD assumptions on simulated hurricanes and their synthetic radiometric signatures. It determines the most realistic, among the available set of assumptions, based on comparison to multi-parameter satellite observations. Here we simulated 2005′s category-5 Hurricane Rita using the cloud-permitting community Weather Research and Forecasting model (WRF) with two different microphysical schemes and with seven different modifications of the parametrized hydrometeor properties within one of the two schemes. We then used instrument simulators to produce satellite-like observations. The study consisted in evaluating the structure of the different simulated storms by comparing, for each storm, the calculated microwave signatures with actual satellite observations made by (a) the passive microwave radiometer that was carried by the Tropical Rainfall Measuring Mission (TRMM) satellite—the TRMM microwave imager TMI, (b) TRMM’s precipitation radar (PR) and (c) the ocean-wind-vector scatterometer carried by the QuikSCAT satellite. The analysis reveals that the different choices of microphysical parameters do produce significantly different microwave signatures, allowing an objective determination of a “best” parameter combination whose resulting signatures are collectively most consistent with the wind and precipitation observations obtained from the satellites. In particular, we find that assuming PSDs with larger number of smaller hydrometeors produces storms that compare best to observations.

Measurements of Soil Surface Roughness During the Fourth Microwave Water and Energy Balance Experiment: April 18 through June 13, 2005

Passive microwave signatures have been used to retrieve geophysical parameters, such as soil temperature [Njoku and Li, 1999], moisture [Jackson et al., 1995], and surface roughness [Wegmüller and Mätzler, 1999]. One of the challenges in the parameter retrieval is the effect of soil surface roughness on the microwave emission. We conducted soil surface roughness measurements as part of our fourth Microwave Water and Energy Balance Experiment (MicroWEX-4) to understand the effects of surface roughness on microwave signatures at 6.7 GHz (λ = 4.48 cm). The dataset will also be used to develop and validate surface roughness models. In this report, we summarize briefly the theoretical background of surface roughness characteristics and discuss methodology and results of the roughness experiments. This document is Circular 1483, one of a series of the Department of Agricultural and Biological Engineering, UF/IFAS Extension. Original publication date November 2005. CIR1483/AE363: Measurements of Soil Surface Roughness During the Fourth Microwave Water and Energy Balance Experiment: April 18–June 13, 2005 (ufl.edu)