Sarah Riley
Open source developer
Montana State University
Biography
Sarah Riley (formerly Spencer Riley) is an open source developer currently pursuing a doctoral degree from Montana State University studying solar physics. She has experience developing various Python and R algorithms for data analytics, and is currently working on analyzing high-resolution spectroscopic data from the Daniel K. Inouye Solar Telescope.
Education
-
school
Ph.D in PhysicsFall 2022 -- Current
Montana State University
-
school
B.Sc in PhysicsFocus in Astrophysics & Atmospheric Physics
Minor in MathematicsFall 2017 -- May 2022New Mexico Institute of Mining and Technology
Development Skills
Python R Bash HTML Docker Raspberrry Pi IDL Javascript
Work History
Projects
Papers
Atmospheric precipitable water vapor and its correlation with clear-sky infrared temperature observations
PublishedConferences
Probing elementary scales of solar flare energetics with the Daniel K. Inouye Solar Telescope
Abstract
With the emergence of new high-resolution, high-cadence spectroscopic observations from the Daniel K. Inouye Solar Telescope, we have the opportunity to study the dynamics of the chromosphere on unprecedented elementary scales, and explore physical mechanisms transporting energy across the solar atmosphere on these scales. To achieve these objectives, we have developed a data analysis technique to derive dynamical signatures with their evolution, and use them to constrain numerical models. We characterize more than a million spectral profiles observed in flaring active regions by DKIST into groups of similar spectral features via the Catalog Clustering Scheme to investigate how the features vary during different stages of individual heating events. The representative profiles, which define each of the spectral groups, at varying stages of the heating can be compared with synthetic spectra from atmospheric radiative hydrodynamic models driven by a prescribed heating mechanism. By matching observed and modeled profiles, we can identify the heating mechanisms during the evolution of an observed flare. Our analysis of the 2022 December 27 flare have been successful in characterizing the spectra and contextualizing the spectral features with respect to the evolution of the individual heating events. In the next steps of our analysis we will study the 2023 May 03 low-cadence observations and potentially a dataset in 2025 with the highest time cadence possible by DKIST's Visible Spectropolarimeter.
Identification of chromospheric dynamical signatures in solar flares with DKIST
Abstract
With the emergence of new high-resolution, high-cadence spectral data from the Daniel K. Inouye Solar Telescope, we can probe solar flare energetics and dynamics in the chromosphere on unprecedented small scales. We utilize available Ca II 854.2 nm spectral data to diagnose the dynamics of the flaring chromosphere. In addition, we co-aligned AIA 1600$ {A}$ with DKIST observations to provide a history of localized heating in the vicinity of the spectral measurements. The aim is to characterize tens of thousands of spectra from a 27 December 2022 C4 solar flare into groups of similar spectral features via a hierarchical k-means algorithm and investigate how the spectral features vary during different stages of the localized heating events defined by AIA 1600$ {A}$ lightcurves. We find that within a sixty-minute interval centered on the spectral observations, there are strong populations of double-peaked spectral groups in the early gradual phase of the localized heating. Whil e red-enhanced asymmetric spectral groups have strong populations in the later gradual phase and the pre-heating phase.
The Precipitable-water Model Analysis Tool: An open-source suite for estimating precipitable water with low-cost instrumentation
Abstract
The Precipitable-Water Model Analysis Tool (PMAT) is an open-source software suite designed to study the correlation between atmospheric brightness temperature and precipitable water (PWAT) data. PMAT addresses the need for an easily integrated analysis workflow to quantitatively characterize the relationship between regional PWAT and localized atmospheric brightness temperature observations for areas that lack high to moderate-resolution data collection infrastructure. The workflow contains three primary modules: deployment, pre-processing, and analysis. The deployment mechanism allows users to implement the software suite on local and cloud-based systems, which facilitates access to the generated data products and visualizations. The pre-processing stage involves aggregating atmospheric brightness temperature data collected in the field with data from radiosondes and local ground stations. The final element consists of an iterative algorithm that generates an average regression model from the collected data. This model allows for the estimation of PWAT through measured atmospheric brightness temperature. PMAT has already been configured and deployed for use in the South-Central New Mexico climate zone using a series of handheld infrared thermometers as the source of atmospheric brightness temperature data. We plan to expand the scope of the research with community science endeavors in South Dakota; while also expanding the suite’s analysis capabilities with machine learning and additional statistical products for predictive modeling.
Atmospheric Precipitable Water and its Correlation with Clear Sky Infrared Temperature Observations
Abstract
Precipitable water vapor (PWAT) is the vertically integrated amount of water vapor in the atmosphere, and it is a valuable predictor for weather forecasting. Currently, the use of sophisticated instrumentation can limit the number of PWAT measurement sites, which affects the accuracy of forecast models in regards to storm formation, strength, and the potential for precipitation. We have analyzed relationships between PWAT and zenith clear sky temperature measurements for the dry climate zone found in the North American Desert Southwest, specifically over Socorro, New Mexico (34$^{\circ}$N, 107$^{\circ}$W). Daily measurements of the ground and zenith sky temperatures have been made at Socorro for two complete annual cycles using low-cost infrared thermal sensors. Radiosonde measurements of PWAT from National Weather Service stations located in nearby Albuquerque, and Santa Teresa, New Mexico, are input into our dataset and analysed via a newly developed computational tool. Our results show that an exponential relationship between PWAT and zenith clear sky temperature holds for the Desert Southwest, but with parameters that are different from those obtained previously over the more moist climate zone of the Gulf Coast of Texas. Model simulations can accurately reproduce the observed relationship between PWAT and temperature, and the results suggest that half of the signal in temperature is directly related to changes in opacity due to changes in PWAT, while the other half is due to changes in air temperature that usually accompany changes in PWAT.
Atmospheric Precipitable Water and its Correlation with Clear Sky Infrared Temperature Readings
Abstract
Precipitable water is primarily measured using radiosondes, ground-based global positioning systems (GPS), sun photometers, and microwave radiometry (MWRI). This limits the number of precipitable water measurement sites, which affects forecast accuracy in regards to storm formation, strength, and the potential for precipitation. Socorro, NM is among the sites that do not have the capability to measure precipitable water. Our research builds upon a previous study which determined an exponential relationship between infrared clear sky temperature measurements and precipitable water over the Gulf Coast of Texas. We are analyzing this relationship for the climate zone found in Socorro, NM. Daily ground and clear sky temperature measurements are being taken with low-cost infrared thermometers. Radiosonde precipitable water measurements from Albuquerque and Santa Teresa NWS monitoring sites are input into our dataset and analysed via our newly developed computational tool; which shows that there is a correlation ($R^2 = 0.707$) between clear sky temperature and precipitable water. Our research demonstrates the capability to measure and analyze precipitable water with low cost instrumentation in higher altitude arid climate zones similar to those found in the desert Southwest. We are building a platform to expand our tools and methods so that they can be used to determine and analyse similar correlations over a greater variety of climate zones.
Atmospheric Precipitable Water and its Correlation with Clear Sky Infrared Temperature Readings: Data Analysis
Abstract
Precipitable water can be defined as the total amount of water vapor that exists in a vertical column of air, traditionally measured via radiosondes. Global Positioning System (GPS) networks and microwave-infrared radiometers can also be used to measure precipitable water by analyzing signal delay; these methods are used by NOAA. Precipitable water measurements can be used to forecast extreme weather events and the potential for precipitation. Based on a previous that analyzed the relationship between precipitable water and zenith sky temperature using infrared thermometers, we have developed a rigorous computational utility to study this same correlation for the Socorro, NM climate system. This research highlights the impact of using low-cost instrumentation to accurately forecast precipitation in regions where the data is not available. After thirty-five weeks, the results of our analysis show an exponential correlation ($R^2 = 0.707$) between precipitable water and clear sky temperature, similar to the trends found in previous studies. However, as we continue to collect data, the intrinsic properties of the correlation are continuously evolving. With the availability of our computational tool, we aim to widen our data to include a diverse set of climate systems to further study the relationship between clear sky temperature and precipitable water while also continuing to collect data for the Socorro, NM area.