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Sebastian S. Harkema

Publications and source records attributed to Sebastian S. Harkema.

8 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS2023

Electrification Within Wintertime Stratiform Regions Sampled During the 2020/2022 NASA IMPACTS Field Campaign

Two nor'easter events—sampled during the NASA Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) field campaign—were examined to characterize the microphysics in relation to the underlying electrification processes within wintertime stratiform regions. A theoretical model was developed to determine whether accretion or diffusion growth regimes were preferential during periods of greatest electrification. Model simulation with electrification parameterization was used to provide supplemental context to the physical processes of in-cloud microphysics and electrification. The strongest electric fields (i.e., ∼80 V m −1 at 20 km) during the 2020 NASA IMPACTS deployment was associated with large non-rimed ice crystals colliding with each other. During the 29–30 January 2022 science flight, the NASA P-3 microphysical probe data demonstrated that non-inductive charging was possible off the coastline of Cape Cod, Massachusetts. Later in the science flight, when the NASA P-3 and ER-2 were coordinating with each other, measured electric fields consistently were less than 8 V m −1 and electrification was subdued owing to reduced concentrations of graupel and large ice hydrometeors. Altogether, the in-situ observations provide evidence for the non-riming collisional charging mechanism and demonstrates that graupel and supercooled liquid water may not be necessary for weak electrification within wintertime stratiform regions. Model output from simulation of both events suggested that the main synoptic snowbands were associated with elevated hydrometeor snow charge density and electric fields.

snowfall↗
NASA NTRSDate not supplied

Evaluating Meteorological Dust Events and Machine-Learning Based Dust Identification in Geostationary Satellite Imagery

NASA scientists in the Short-term Prediction Research and Transition Center (SPoRT) developed a physically-based machine learning approach to identify dust in satellite imagery with a focus on night-time dust detection (Berndt et al. 201; DustTracker-AI). NASA/NOAA Geostationary Environmental Operational Satellite-16 (GOES-16) imagery was used for training and model inputs. The training, testing and validation data set consists of 28 events in the Southwest United States, capturing dust and null events in the region from 2018-2020.With 83 distinct images and millions of pixels a random forest model was trained and validated, correctly labeling 85% of dust pixels.For the first time, the model was run in near-real time production during the spring of 2022 and dust probability visualizations were made available to NOAA National Weather Service (NWS) forecasters to assess its utility for dust forecasting. Results indicated the model helped increase the confidence in the presence of dust and enabled dust tracking for a longer period of time into the night-time hours. Forecaster assessment and running the model in near real-time allowed for the team to determine the types of events missed, captured, and false alarms. To gain additional context on model performance,the SPoRT team sought to gather more detailed information on the training database(e.g., meteorological characteristics and drivers). The goal of this project was to identify the meteorological drivers for the dust events and create a database which synthesized information from observations, forecaster discussions, and analyses pertaining to the dust events to understand the types of events currently used to train the model. A more detailed meteorological synopsis was created for each dust event in the training, testing, and validation datasets. Following the completion of the database and documentation, the classification details revealed that 88% of the dust events were synoptically driven while mesoscale events were less prevalent in model datasets. Meteorological conditions found such as mixing layer depth and wind velocity had mean values of 645mb and 21kt respectively.With conditions of deep mixed layers and moderate to strong surface winds a mesoscale thunderstorm outflow event was considered and subsequently added to the model training data set to test the impact of additional mesoscale training data. The model was retrained and then qualitatively tested on a sample thunderstorm outflow case that the original model was unable to identify. Preliminary results showed potential that the addition of more mesoscale events included in the training data could help to better identify indistinct and localized dust events.

Connor Welch↗