Improving active remote sensing retrievals of snowfall at microwave wavelengths: an emphasis on the global precipitation measurement mission’s dual-frequency precipitation radar

Chase, Randy J

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https://hdl.handle.net/2142/110456 Copy

Description

Title

Improving active remote sensing retrievals of snowfall at microwave wavelengths: an emphasis on the global precipitation measurement mission’s dual-frequency precipitation radar

Author(s)

Chase, Randy J

Issue Date

2021-04-16

Director of Research (if dissertation) or Advisor (if thesis)

Nesbitt, Stephen W.

Doctoral Committee Chair(s)

Nesbitt, Stephen W.

Committee Member(s)

• McFarquhar, Greg M.
• Lasher-Trapp, Sonia
• Di Girolamo , Larry

Department of Study

Atmospheric Sciences

Discipline

Atmospheric Sciences

Degree Granting Institution

University of Illinois at Urbana-Champaign

Degree Name

Ph.D.

Degree Level

Dissertation

Keyword(s)

• radar meteorology
• snowfall
• GPM-DPR
• neural networks

Abstract

Even though snowfall at the surface is often constrained to higher latitudes or altitudes, the contribution of solid-phase hydrometeors to the hydrologic cycle is not trivial and can be related to more than 50% of all surface rain events. Furthermore, the quantification of snow and ice in the atmospheric column is required to understand the Earth’s outgoing thermal radiative budget. Thus, the retrieval of snowfall from spaceborne radars that can sample remote regions of the world is invaluable for both atmospheric and climate sciences. One spaceborne radar capable of measuring snowfall is the Global Precipitation Measurement mission’s Dual-frequency Precipitation Radar (GPM-DPR). Initial evaluations of the retrieval of near-surface snowfall from GPM-DPR against the common global snowfall reference (i.e., CloudSat) showed large discrepancies between the two radar retrieval estimates. The large discrepancy between the CloudSat and GPM-DPR snowfall retrieval served as the main motivation for the work conducted here. Three tasks were formulated and conducted in this dissertation: (1) Evaluate the assumptions within the current GPM-DPR retrieval of snowfall; (2) Create an alternative retrieval for GPM-DPR; (3) Compare the new retrieval to the old retrieval methods. Task 1 is found in Chapter 2, Task 2 is in Chapter 3 and Task 3 is in Chapter 4. For Task 1, the investigation of ground-based measurements of both rain and snow and their particle size distributions allowed for the assessment of the main microphysical assumption of the GPM-DPR retrieval, which assumes that all hydrometeors obey the same empirical relationship between the precipitation rate (R) and the mass-weighted mean diameter (D_m). Rainfall observations showed that the default R-D_m relation for rainfall is plausible and shows general consistency with a Pearson ρ correlation coefficient of 0.63. However, snowfall observations showed that the R-D_m relation does not apply well for snowfall resulting in the underestimation of R. Furthermore, the low correlation between the log⁡〖(R〗) and D_m (ρ=0.23) suggests that an R-D_m retrieval is not optimal for snowfall retrievals and other methods should be explored. Motivated from the results of Task 1, an alternative retrieval for GPM-DPR was designed in Task 2 using a neural network, state-of-the-art particle scattering models and measured particle size distributions. The main result from Task 2 is that the neural network retrieval significantly improves (p<0.05) the mean squared error of the retrieval of ice water content (IWC) compared to old power-law methods and an estimate of the current GPM-DPR algorithm. This was shown in the evaluation of the retrieval on a subset of synthetic data that was not used in training the neural network as well as in three case studies from NASA field campaigns where independent observations of radar reflectivity and in-situ parameters were made. Finally, Task 3 evaluated the newly formulated retrieval from Task 2 against the operational CloudSat product (2C-SNOWPROFILE) and the current GPM-DPR algorithm. The evaluation is done using a premade coincident dataset of both CloudSat and GPM-DPR which allowed for the direct comparison of all retrieval methods. Comparing the three retrievals show that on average the neural network retrieval performs best, predicting R just below the melting layer to within 2%. A secondary result from Task 3 is that the 2C-SNOWPROFLE retrieval is likely underestimating R for moderate to intense snowfall events signified by a 35% reduction of R from -15°C to the melting layer.

Graduation Semester

2021-05

Type of Resource

Thesis

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http://hdl.handle.net/2142/110456

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Copyright 2021, Randy J. Chase

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