Vision for Producing Fresh Water Using Space Power

An escalating climate crisis is stressing the Earth\u27s environment. One significantly affected area is the global water infrastructure that includes hydropower, flood defense, drainage, and irrigation systems. The effect of adverse climate change on freshwater systems aggravates population growth and weakens economic conditions. In the western U.S., for example, reduced water supplies plus increased demand are likely to provoke more interstate and urban-rural competition for over-allocated water resources. Seawater desalination has existed for decades as a proven technology for supplying water in coastal areas; however, desalination processes are energy intensive and this has reduced their widespread use. It is noted that California offshore oil and gas platforms already use seawater desalination to produce fresh water for platform personnel and equipment. It is proposed that as California coastal oil and gas platforms come to the end of their productive lives, they be re-commissioned for use as large-scale fresh water production facilities. Solar arrays, mounted on the platforms, are able to provide some of the power needed for seawater desalination during the daytime. However, for efficient fresh water production, a facility must be operated 24 hours a day. The use of solar power transmitted from orbiting satellites (Solar Power Satellites - SPS) to substantially augment the solar array power generated from natural sunlight is a feasible concept. We discuss the architecture of using a SPS in geosynchronous orbit (GEO) to enable 24 hours a day operations for fresh water production through seawater desalination. Production of industrial quantities of fresh water on re-commissioned oil and gas platforms, using energy transmitted from solar power satellites, is a breakthrough concept for addressing the pressing climate, water, and economic issues of the 21st Century using space assets

A strategic architecture for growing a space economy utilizing foundational space weather

We face unprecedented resource stresses in the 21st Century such as global climate disruptions, freshwater scarcity, expanding energy demands, and the threat of global pandemics. Historically, societies have relieved resource stress by increasing trade, innovating technologically, expanding territorially, regulating, redistributing, making alliances, creating new economic models, training new skills, as well as conducting war. Do we continue depleting our already strained resources leading to more regulation, redistribution, alliances, new economics, and war or do we grow our resources using innovation, expansion, new economics, and new skills? We present the argument for evolving space development using asteroid mining as the primary activity for frontier expansion aided by Low Earth Orbit (LEO), Moon, and Mars waystations. Forecast space weather is a necessary technology baseline for developing this pathway. All activity off Earth will require a fundamental knowledge of how the energetics of space will affect technological progress. We discuss the critical elements this space economy expansion, including technical feasibility and infrastructure development, economic and geopolitical viability complete with the US National Space Weather Program dialogue, ethical and legal considerations, and risk management. This discussion helps us understand how a space economy is feasible with the aggregation of many existing and new technologies into more advanced systems engineering projects.Comment: The Anemomilos Plan is the short titl

Progress Towards Real-Time Radiation Measurements on Aircraft

The Space Weather Center (SWC) at Utah State University has created a team to deploy and obtain radiation effective dose rate data from dosimeters flown on commercial aircraft. The objective is to improve the accuracy of radiation dose and dose rate estimates for commercial aviation flight crews. There are two general sources of radiation exposure for flight crews: (1) the ever-present, background galactic cosmic rays (GCR), which originate outside the solar system, and (2) the solar energetic particle (SEP) events (or solar cosmic rays), which are associated with solar flares and coronal mass ejections lasting for several hours to days with widely varying intensity. The Automated Radiation Measurements for Aviation Safety (ARMAS) project is making substantial progress, currently implementing dosimeters flown in commercial aircraft to provide and improve sample data collected for the Nowcast of Atmospheric Ionizing Radiation for Aviation Safety (NAIRAS) estimates. We report on the results of our flights and the calibration of the dosimeters

Resolving Ionospheric E-region Modeling Challenges: The Solar Photon Flux Dependence

The EVE instrument of the NASA Solar Dynamics Observatory (SDO) provides for the first time EUV and XUV measurements of the solar irradiance that adequately define the major source of ionization of the atmosphere. In our study we modeled the E-region of the ionosphere and analyzed how it is affected by the solar irradiance data obtained by EVE and contrast this with the S2000 Solar Irradiance model, used previously. The ionosphere has two major layers, the E-layer at 100 km, and the F-layer at 300 km. The difference in solar irradiances are small except at some wavelength bands, it is these differences that lead to a better understanding of the physical/chemical processes of the E-region. Observations of the ionospheric layers is best achieved using incoherent scatter radars (ISR). We have compared our model with ISR data available from Arecibo Puerto Rico in an effort to understand how specific solar irradiance wavelength bands affect the E-region. This study focuses on two specific wavelength bands 0.1-15 nm and 91-103 nm. Both are responsible for E-region production, but in quite different manners

Science through Machine Learning: Quantification of Poststorm Thermospheric Cooling

Machine learning (ML) is often viewed as a black-box regression technique that is unable to provide considerable scientific insight. ML models are universal function approximators and - if used correctly - can provide scientific information related to the ground-truth dataset used for fitting. A benefit to ML over parametric models is that there are no predefined basis functions limiting the phenomena that can be modeled. In this work, we develop ML models on three datasets: the Space Environment Technologies (SET) High Accuracy Satellite Drag Model (HASDM) density database, a spatiotemporally matched dataset of outputs from the Jacchia-Bowman 2008 Empirical Thermospheric Density Model (JB2008), and an accelerometer-derived density dataset from CHAllenging Minisatellite Payload (CHAMP). These ML models are compared to the Naval Research Laboratory Mass Spectrometer and Incoherent Scatter radar (NRLMSIS 2.0) model to study the presence of post-storm cooling in the middle-thermosphere. We find that both NRLMSIS 2.0 and JB2008-ML do not account for post-storm cooling and consequently perform poorly in periods following strong geomagnetic storms (e.g. the 2003 Halloween storms). Conversely, HASDM-ML and CHAMP-ML do show evidence of post-storm cooling indicating that this phenomenon is present in the original datasets. Results show that density reductions up to 40% can occur 1--3 days post-storm depending on location and the strength of the storm

Using Data Assimilation to Understand the Systematic Errors in CHAMP Accelerometer-Derived Neutral Mass Density Data

Accelerometer-derived neutral mass density (NMD) is an important quantity that describes the variability of the upper atmosphere. NMD is widely used to calibrate and validate some models used for satellite orbit determination and prediction. Quantifying the true NMD is nearly impossible due to, among others, the lack of simultaneous in-situ measurements to cross-validate and the incomplete characterization of the uncertainties of these NMD products. Using multiple data assimilation (DA) experiments and robust statistical techniques, this study investigates the error distribution of three different accelerometer-derived NMD products from the CHAMP satellite mission during a time period of low solar and geomagnetic activities. The strategies applied here may be useful and applicable to other space missions spanning over longer time periods. The results indicate considerable differences among the three CHAMP data sets and also show a pronounced latitudinal dependency for the estimated error distributions. On average, the error estimates for NMD vary in the range 6.5-15.6% of the signal. The results further demonstrate that DA considerably enhances the capability of the physical model as well as an excellent tool to assess data uncertainties

Using Data Assimilation to Understand the Systematic Errors in CHAMP Accelerometer-Derived Neutral Mass Density Data

Accelerometer-derived neutral mass density (NMD) is an important measurement of the variability in upper atmosphere and one of the widely used measurements to calibrate and validate models used for satellite orbit determination and prediction. Providing precise estimates of the true uncertainty of these NMD products is a challenging task but essential for the space weather and geodetic communities. Using multiple data assimilation (DA) experiments and robust statistical techniques, we investigate the uncertainty distribution of three different accelerometer-derived NMD products from the CHAMP satellite mission. Here, in three different DA experiments, we use an ensemble Kalman filter to drive a physics-based model with CHAMP in-situ electron density and temperature data as well as neutral wind estimates from an empirical model. Using a multi-model ensemble comprised of both physical and empirical models, we characterize the error variances among the different NMD products. Our results indicate considerable differences among the CHAMP data sets and also show a pronounced latitudinal dependency for the estimated error distributions. On average, the error estimates for NMD vary in the range 6.5–15.6% of the signal. Our experiments demonstrate that DA considerably enhances the capability of the physical model. We note that the generic strategies applied here may be useful and applicable to other space missions spanning over longer time periods