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Kulie, Mark S.

Publications and source records attributed to Kulie, Mark S..

A Comprehensive Northern Hemisphere Particle Microphysics Data Set From the Precipitation Imaging Package

Microphysical observations of precipitating particles are critical data sources for numerical weather prediction models and remote sensing retrieval algorithms. However, obtaining coherent data sets of particle microphysics is challenging as they are often unindexed, distributed across disparate institutions, and have not undergone a uniform quality control process. This work introduces a unified, comprehensive Northern Hemisphere particle microphysical data set from the National Aeronautics and Space Administration precipitation imaging package (PIP), accessible in a standardized data format and stored in a centralized, public repository. Data is collected from 10 measurement sites spanning 34° latitude (37°N–71°N) over 10 years (2014–2023), which comprise a set of 1,070,000 precipitating minutes. The provided data set includes measurements of a suite of microphysical attributes for both rain and snow, including distributions of particle size, vertical velocity, and effective density, along with higher-order products including an approximation of volume-weighted equivalent particle densities, liquid equivalent snowfall, and rainfall rate estimates. The data underwent a rigorous standardization and quality assurance process to filter out erroneous observations to produce a self-describing, scalable, and achievable data set. Case study analyses demonstrate the capabilities of the data set in identifying physical processes like precipitation phase-changes at high temporal resolution. Bulk precipitation characteristics from a multi-site intercomparison also highlight distinct microphysical properties unique to each location. This curated PIP data set is a robust database of high-quality particle microphysical observations for constraining future precipitation retrieval algorithms, and offers new insights toward better understanding regional and seasonal differences in bulk precipitation characteristics.

54 ENVIRONMENTAL SCIENCES↗

Multi–year analysis of rain–snow levels at Marquette, Michigan

This study uses observations from a ground-based instrument suite to investigate the rain-snow level in stratiform rainfall from January 2014 to April 2020 in the Upper Great Lakes Region. The height above the surface where ice melts to rain, the rain-snow level (RSL), influences microphysical assumptions in remote sensing precipitation retrievals and the ability of space-based radar to discriminate surface precipitation phase because of ground clutter. The instrument suite is installed at the Marquette, MI (MQT) National Weather Service station adjacent to Lake Superior. Rain events and the RSL are studied through a ground-based vertically profiling radar (Micro Rain Radar), a custom NASA-developed video disdrometer (Precipitation Imaging Package), and reanalyses products from ECMWF ERA5 and NASA MERRA-2. Further, distinct macro and micro physical characteristics are observed in precipitation events with shallow RSL ( < 1.8 km above ground level [AGL]) and intermediate RSL ( > 1.8 km AGL). Intermediate RSL correspond to rain events with relatively higher rain rates and a higher concentration of small drops in the drop size distributions (DSDs). Shallow RSL DSDs contain relatively higher concentrations of large drops with lower fall speeds suggesting that partially melted snowflakes may be reaching the surface. Reflectivity-rain-rate relationships are also impacted by microphysical differences associated with RSL regimes. Radar-detected RSL agree with reanalysis-derived melt levels- especially at wet-bulb isotherms of 0.5°C and 1°C. Seasonal differences such as shallow rain-snow levels in winter, fall, and spring have subsequent implications for satellite detectability.

54 ENVIRONMENTAL SCIENCES↗

Seasonal Characteristics of Non-Meteorological Radar Reflectivity Returns in Central Florida and Their Impact on TRMM Ground Validation

Radar data quality control is a major component to the Tropical Rainfall Measuring Mission (TRMM) Ground Validation (GV) effort. The quality control algorithm utilizes several adjustable height and reflectivity threshold parameters to remove non-precipitation echoes from ground-based radar data. Spurious radar reflectivity returns not removed during the quality control process may create biases in ground validation rainfall products used to evaluate rainfall measurements retrieved from aboard the TRMM satellite. To better evaluate the performance of the quality control algorithm, WSR-88D radar data from one primary GV site, Melbourne, Florida, are used to determine the seasonal characteristics of non-meteorological radar echoes in cast central Florida during the first year of the TRMM mission. It is demonstrated that unique spurious echo regimes develop throughout the year, leading to different levels of successful non-meteorological echo removal by the quality control algorithm. In addition to the type of non- precipitation echoes present, the success of the algorithm is also greatly dependent upon the amount of precipitation present. Less aggressive attempts to remove non-meteorological echoes must be employed when rain is observed so as not to remove these echoes as well. Therefore, precipitation statistics from ground validation monthly rainfall products will be utilized to further document the quality control algorithm performance. Moreover, these precipitation statistics will be used to demonstrate the relationship between dominant spurious echo regimes and rainfall amount. Finally, monthly statistics of contaminated ground validation radar data will be calculated and the effect on rainfall accumulation products will be discussed.

Robinson, Michael↗

Standard Reference Rainfall Products Used in the TRMM Ground Efforts

The Primary goal of the Tropical Rainfall Measuring Mission (TRMM) Ground Validation (GV) effort is to provide basic validation of satellite-derived precipitation measurements over monthly climatologies. To this end, the Joint Center for Earth Systems Technology (JCET) at the University of Maryland, Baltimore County produces monthly rainfall accumulation reference products for each of the four primary TRMM GV sites. These products, standard methodology for deriving monthly, gauge-adjusted rainfall products, are utilized for each primary site. The monthly rainfall GV reference products are developed in discrete, modular steps with distinct intermediate products. These developmental steps, which will be fully discussed, include: (1) extracting radar data over locations of rain gauges, (2) merging rain gauge and radar data in time and space with user-defined options, (3) quality control of radar and gauge merged data by tracking accumulations from each instrument, and (4) deriving Z_R relationship from quality-controlled merged data over monthly time scales. A summary of all gauge statistics and GV rainfall reference products available for TRMM science users will be presented. Basic reference product results and trends involving monthly accumulation, Z-R relationship, and gauge statistics for primary GV site will be discussed. Finally, the sample impact on monthly rainfall reference products, through varying the time interval between intermediate rainfall accumulations, will be analyzed and presented.

Marks, David A.↗

Operational Processing of Ground Validation Data for the Tropical Rainfall Measuring Mission

The Tropical Rainfall Measuring Mission (TRMM) satellite was successfully launched in November 1997. A primary goal of TRMM is to sample tropical rainfall using the first active spaceborne precipitation radar. To validate TRMM satellite observations, a comprehensive Ground Validation (GV) Program has been implemented for this mission. A key component of GV is the analysis and quality control of meteorological ground-based radar data from four primary sites: Melbourne, FL; Houston, TX; Darwin, Australia; and Kwajalein Atoll, RMI. As part of the TRMM GV effort, the Joint Center for Earth Systems Technology (JCET) at the University of Maryland, Baltimore County, has been tasked with developing and implementing an operational system to quality control (QC), archive, and provide data for subsequent rainfall product generation from the four primary GV sites. This paper provides an overview of the JCET operational environment. A description of the QC algorithm and performance, in addition to the data flow procedure between JCET and the TRNM science and Data Information System (TSDIS), are presented. The impact of quality-controlled data on higher level rainfall and reflectivity products will also be addressed, Finally, a brief description of JCET's expanded role into producing reference rainfall products will be discussed.

Kulie, Mark S.↗