SENSITIVITY ANALYSIS OF MACHINE LEARNING IN BRIGHTNESS TEMPERATURE PREDICTIONS OVER SNOW-COVERD REGIONS USING THE ADVANCED MICROWAVE SCANNING RADIOMETER

Snow is a critical component in the global energy and hydrologic cycle. Further, it is important to know the mass of snow because it serves as the dominant source of drinking water for more than one billion people worldwide. Since direct quantification of snow water equivalent (SWE) is complicated by spatial and temporal variability, space-borne passive microwave SWE retrieval products have been utilized over regional and continental-scales to better estimate SWE. Previous studies have explored the possibility of employing machine learning, namely an artificial neural network (ANN) or a support vector machine (SVM), to replace the traditional radiative transfer model (RTM) during brightness temperatures (Tb) assimilation. However, we still need to address the following question: What are the most significant parameters in the machine-learning model based on either ANN or SVM? The goal of this study is to compare and contrast sensitivity analysis of Tb with respect to each model input between the ANN- and SVM-based estimates. In general, the results suggest the SVM (relative to the ANN) may be more beneficial during Tb assimilation studies where enhanced SWE estimation is the main objective

Structure of the outer layers of cool standard stars

Context: Among late-type red giants, an interesting change occurs in the structure of the outer atmospheric layers as one moves to later spectral types in the Hertzsprung-Russell diagram: a chromosphere is always present, but the coronal emission diminishes and a cool massive wind steps in. Aims: Where most studies have focussed on short-wavelength observations, this article explores the influence of the chromosphere and the wind on long-wavelength photometric measurements. Methods: The observational spectral energy distributions are compared with the theoretical predictions of the MARCS atmosphere models for a sample of 9 K- and M-giants. The discrepancies found are explained using basic models for flux emission originating from a chromosphere or an ionized wind. Results: For 7 out of 9 sample stars, a clear flux excess is detected at (sub)millimeter and/or centimeter wavelengths. The precise start of the excess depends upon the star under consideration. The flux at wavelengths shorter than about 1 mm is most likely dominated by an optically thick chromosphere, where an optically thick ionized wind is the main flux contributor at longer wavelengths. Conclusions: Although the optical to mid-infrared spectrum of the studied K- and M-giants is well represented by a radiative equilibrium atmospheric model, the presence of a chromosphere and/or ionized stellar wind at higher altitudes dominates the spectrum in the (sub)millimeter and centimeter wavelength ranges. The presence of a flux excess also has implications on the role of these stars as fiducial spectrophotometric calibrators in the (sub)millimeter and centimeter wavelength range.Comment: 13 pages, 6 figures, 7 pages of online material, submitted to A&

Analyzing Machine Learning Predictions of Passive Microwave Brightness Temperature Spectral Difference Over Snow-Covered Terrain in High Mountain Asia

Snow is an important component of the terrestrial freshwater budget in high mountainAsia (HMA) and contributes to the runoff in Himalayan rivers through snowmelt. Despitethe importance of snow in HMA, considerable spatiotemporal uncertainty exists across the different estimates of snow water equivalent for this region. In order to better estimate snow water equivalent, radiative transfer models are often used in conjunction with microwave brightness temperature measurements. In this study, the efficacy of support vector machines (SVMs), a machine learning technique, to predict passive microwave brightness temperature spectral difference (1Tb) as a function of geophysical variables (snow water equivalent, snow depth, snow temperature, and snow density) is explored through a sensitivity analysis. The use of machine learning (as opposed to radiative transfer models) is a relatively new and novel approach for improving snow water equivalent estimates. The Noah-MP land surface model within the NASALand Information System framework is used to simulate the hydrologic cycle over HMA and model geophysical variables that are then used for SVM training. The SVMsserve as a nonlinear map between the geophysical space (modeled in Noah-MP) andthe observation space (1Tb as measured by the radiometer). Advanced MicrowaveScanning Radiometer-Earth Observing System measured passive microwave brightness temperatures over snow-covered locations in the HMA region are used as training data during the SVM training phase. Sensitivity of well-trained SVMs to each Noah-MP modeled state variable is assessed by computing normalized sensitivity coefficients. Sensitivity analysis results generally conform with the known first-order physics. Input states that increase volume scattering of microwave radiation, such as snow density and snow water equivalent, exhibit a plurality of positive normalized sensitivity coefficients. In general, snow temperature was the most sensitive input to the SVM predictions. The sensitivity of each state is location and time dependent. The signs of normalized sensitivity coefficients that indicate physical irrationality are ascribed to significant cross-correlation between Noah-MP simulated states and decreased SVM prediction capability at specific locations due to insufficient training data. SVM prediction pitfalls do exist that serve to highlight the limitations of this particular machine learning algorithm

Techniques for the Analysis and Understanding of Cosmic Evolution

abstract: The Cosmic Microwave Background (CMB) has provided precise information on the evolution of the Universe and the current cosmological paradigm. The CMB has not yet provided definitive information on the origin and strength of any primordial magnetic fields or how they affect the presence of magnetic fields observed throughout the cosmos. This work outlines an alternative method to investigating and identifying the presence of cosmic magnetic fields. This method searches for Faraday Rotation (FR) and specifically uses polarized CMB photons as back-light. I find that current generation CMB experiments may be not sensitive enough to detect FR but next generation experiments should be able to make highly significant detections. Identifying FR with the CMB will provide information on the component of magnetic fields along the line of sight of observation. The 21cm emission from the hyperfine splitting of neutral Hydrogen in the early universe is predicted to provide precise information about the formation and evolution of cosmic structure, complementing the wealth of knowledge gained from the CMB. 21cm cosmology is a relatively new field, and precise measurements of the Epoch of Reionization (EoR) have not yet been achieved. In this work I present 2σ upper limits on the power spectrum of 21cm fluctuations (Δ²(k)) probed at the cosmological wave number k from the Donald C. Backer Precision Array for Probing the Epoch of Reionization (PAPER) 64 element deployment. I find upper limits on Δ²(k) in the range 0.3 < k < 0.6 h/Mpc to be (650 mK)², (450 mK)², (390 mK)², (250 mK)², (280mK)², (250 mK)² at redshifts z = 10.87, 9.93, 8.91, 8.37, 8.13 and 7.48 respectively Building on the power spectrum analysis, I identify a major limiting factor in detecting the 21cm power spectrum. This work is concluded by outlining a metric to evaluate the predisposition of redshifted 21cm interferometers to foreground contamination in power spectrum estimation. This will help inform the construction of future arrays and enable high fidelity imaging and cross-correlation analysis with other high redshift cosmic probes like the CMB and other upcoming all sky surveys. I find future arrays with uniform (u,v) coverage and small spectral evolution of their response in the (u,v,f) cube can minimize foreground leakage while pursuing 21cm imaging.Dissertation/ThesisDoctoral Dissertation Physics 201

Microwave remote sensing and its application to soil moisture detection

The author has identified the following significant results. Experimental measurements were utilized to demonstrate a procedure for estimating soil moisture, using a passive microwave sensor. The investigation showed that 1.4 GHz and 10.6 GHz can be used to estimate the average soil moisture within two depths; however, it appeared that a frequency less than 10.6 GHz would be preferable for the surface measurement. Average soil moisture within two depths would provide information on the slope of the soil moisture gradient near the surface. Measurements showed that a uniform surface roughness similar to flat tilled fields reduced the sensitivity of the microwave emission to soil moisture changes. Assuming that the surface roughness was known, the approximate soil moisture estimation accuracy at 1.4 GHz calculated for a 25% average soil moisture and an 80% degree of confidence, was +3% and -6% for a smooth bare surface, +4% and -5% for a medium rough surface, and +5.5% and -6% for a rough surface

Millimetre wave brightness temperature predictions for Mauna Kea, Hawaii

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