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Sarah E Ringerud

Publications and source records attributed to Sarah E Ringerud.

5 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS2022

166 GHZ Ice Scattering Signal in Snowfall Events Over Ocean

Snowfall retrieval algorithms for spaceborne passive microwave (PMW) sensors have been developed and refined in recent years, but many complicating issues still affect their accuracy and reliability. Previous work showed that the Global Precipitation Measurement (GPM) mission Goddard PROFiling (GPROF) algorithm snowfall retrieval performance strongly depends on the snowfall type. In particular, PMW-based detection of shallow cumuliform snowfall (SCS), which accounts for 36%-70% of global snowfall frequency, can be very challenging. The snowfall scattering signal can be contaminated by the background surface or supercooled cloud liquid water emission. A scattering index (SI) approach that exploits the GPM Microwave Imager (GMI) dual-polarization 166 GHz channels is developed to analyze its behavior in presence of SCS over ocean. Case studies show that, compared to the SI at 89 GHz, it can isolate the SCS snowfall scattering signal in extremely dry conditions. Some issues are still observed in presence of supercooled liquid water.

Lisa Milani↗
NASA NTRS2022

Vulnerability of Passive Microwave Snowfall Retrievals to Physical Properties of Snowpack: A Perspective from Dense Media Radiative Transfer Theory

The uncertainty of passive microwave retrievals of snowfall is notoriously high where the high-frequency surface emissivity is significantly reduced and varies markedly in response to changes of snowpack physical properties. Using the dense media radiative transfer theory, this article studies the potential effects of terrestrial snow-cover depth, density, and6grain size on high-frequency channels 89 and 166 GHz of the radiometer onboard the Global Precipitation Measurement (GPM) core satellite, which are commonly used to capture the snowfall scattering signals. Integrating the inference across all feasible grain sizes, ranges of snowpack density and depth are identified over which the snowfall scattering signatures can be time varying and potentially obscured. Using 10 years of reanalysis data, the seasonal vulnerability of snowfall retrievals to changes of snowpack emissivity in the Northern Hemisphere is mapped in a probabilistic sense and connections are made with uncertainties of the GPM passive microwave snowfall retrievals. It is found that among different snow classes, relatively light Arctic tundra snow in fall, with a density below 260kg m−3, and shallow prairie snow during the winter, with a depth of less than 40cm, can reduce the surface emissivity and obscure the snowfall passive microwave signatures. It is demonstrated that, during the winter, the highly vulnerable areas are over Kazakhstan, and Mongolia with taiga and prairie snow. In the fall, these areas are largely over tundra and taiga snow in north of Russia and the Arctic Archipelagos as well as prairies in Canada and the Great Plains in the United States.

Reyhaneh Rahimi↗
NASA NTRS2021

Applications of a CloudSat-TRMM and CloudSat-GPM Satellite Coincidence Dataset

The Global Precipitation Measurement (GPM) Dual-Frequency Precipitation Radar (DPR) (Ku- and Ka-band, or 14 and 35 GHz) provides the capability to resolve the precipitation structure under moderate to heavy precipitation conditions. In this manuscript, the use of near-coincident observations between GPM and the CloudSat Profiling Radar (CPR) (W-band, or 94 GHz) are demonstrated to extend the capability of representing light rain and cold-season precipitation from DPR and the GPM passive microwave constellation sensors. These unique triple-frequency data have opened up applications related to cold-season precipitation, ice microphysics, and light rainfall and surface emissivity effects.

GPM↗
NASA NTRS2021

Extreme Lake-Effect Snow from a GPM Microwave Imager Perspective: Observational Analysis and Precipitation Retrieval Evaluation

This study focuses on the ability of the Global Precipitation Measurement (GPM) passive microwave sensors to detect and provide quantitative precipitation estimates (QPE) for extreme lake-effect snowfall events over the United States lower Great Lakes region. GPM Microwave Imager (GMI) high frequency channels can clearly detect intense shallow convective snowfall events. However, GMI Goddard PROfiling (GPROF) QPE retrievals produce inconsistent results when compared against the Multi-Radar/Multi-Sensor (MRMS) ground-based radar reference dataset. While GPROF retrievals adequately capture intense snowfall rates and spatial patterns of one event, GPROF systematically underestimates intense snowfall rates in another event. Furthermore, GPROF produces abundant light snowfall rates that do not conform with MRMS observations. Ad-hoc precipitation rate thresholds are suggested to partially mitigate GPROF’s overproduction of light snowfall rates. The sensitivity and retrieval efficiency of GPROF to key parameters (2-meter temperature, total precipitable water, and background surface type) used to constrain the GPROF a-priori retrieval database are investigated. Results demonstrate that typical lake-effect snow environmental and surface conditions, especially coastal surfaces, are underpopulated in the database and adversely affect GPROF retrievals. For the two presented case studies, using snow cover a-priori database in the locations of originally deemed as coastline improves retrieval. This study suggests that it is particularly important to have more accurate GPROF surface classifications and better representativeness of the a-priori databases to improve intense lake-effect snow detection and retrieval performance.

Lisa Milani↗