Space Sciences Innovations Inc. is dedicated to advancing the scientific understanding of the atmosphere, space environment, and translating this into actionable information for our customers.
Our products provide great immediate and mid to long term benefits to NASA, Air Force and commercial satellite companies for its operational fleet of satellites. We serve our clients by providing research, development and analysis delivered in reports, databases and software solutions.
Company staff consists of experienced scientists and data analysts with background in Earth’s radiation belts research, data assimilation and data modeling, computational dynamics. Through externally funded research conducted by our in-house scientific staff, and often in collaboration with world-renowned scientists at academic and other research institutions, we have developed analytical tools to help measure and observe the properties of the environment and to translate these measurements into useful information to take action.
- We provide to commercial satellite companies that fly hosted payloads for the US military; it is important for the operators of these satellites to have timely knowledge of whether observed anomalies are caused by space weather effects, for the same reasons that the military needs this information.
- The current urgent trend in the satellite industry for miniaturization of the satellite hardware and use of the off the shelf components will make satellites more vulnerable to space weather. This is a growing market for the tools developed in this project and for our future products.
- Non-military commercial market has interest in our products, particularly satellite operators and manufacturers. Our tools can provide a deeper understanding of their spacecrafts’ vulnerabilities to space weather impacts, assisting in more robust design.
- For insurance market we provide for space weather impacts on satellites, which requires statistically supported identification of space weather impacts on satellites.
Radiation belts research
Today radiation trapped in the geomagnetic field, as well as solar energetic particles that can access the magnetosphere, forms critical constraints on the design and operations of satellite systems.
From the perspective of spacecraft design and operations, radiation belt models and the availability of real‐time and retrospective data at a specific GEO location are the most relevant for current‐day practical problems. Accurate models are requisite for making robust design decisions. Current radiation belt models were developed several decades ago and are well recognized to be deficient in many ways for contemporary uses.
But nothing better exists.
One of the objectives of the planned NASA Radiation Belt Storm Probes (RBSP) mission is to rectify the outdated modeling situation.
To optimally blend available observations with physics-based models, a number of recent radiation belt studies have adopted a data assimilation technique, which is a common tool for atmospheric sciences, meteorology, climate studies and engineering.
We use data assimilation with a radial diffusion model to reconstruct radiation belt fluxes for fixed values of the first and the second adiabatic invariants, and found peaks in electron phase space density which cannot be explained by the radial diffusion.
Particle fluxes in the outer radiation belts can show substantial variation in time, over scales ranging from a few minutes, such as during the sudden commencement phase of geomagnetic storms, to the years-long variations associated with the progression of the solar cycle. As the energetic particles comprising these belts can pose a hazard to human activity in space, considerable effort has gone into understanding both the source of these particles and the physics governing their dynamical behavior. Computationally tracking individual test particles in a model magnetosphere represents a very direct, physically-based approach to modeling storm-time radiation belt dynamics.
Computational fluid dynamics (CFD) is a branch of fluid mechanics that uses numerical analysis and data structures to analyze and solve problems that involve fluid flows. Computers are used to perform the calculations required to simulate the free-stream flow of the fluid, and the interaction of the fluid (liquids and gases) with surfaces defined by boundary conditions.
Ongoing research yields software that improves the accuracy and speed of complex simulation scenarios such as transonic or turbulent flows. Initial validation of such software is typically performed using experimental apparatus such as wind tunnels. In addition, previously performed analytical or empirical analysis of a particular problem can be used for comparison.
Analysis of multiple spacecraft observations is complicated by the fact that it is not clear how to extrapolate observations in space, pitch–angle and energy. Analysis is further complicated by the fact that observations are contaminated by observational errors, which are different for different instruments.
Data assimilation algorithms allow for a combination of model results and sparse measurements from different spacecraft with different noise levels *. The data assimilation algorithm first computes the difference between model forecasts and observations at the points of observations, and then combines different observations with the model forecast in an optimal way.
Overall, the Kalman filter technique provides a powerful framework to estimate the state of the system in a way that minimizes the mean squared errors. The sequential Kalman filter is applied during the update times. The numerical forecast is first verified against the new data, and then combined or blended with the data.
Near-Earth Space Environment
We will provide continuous reconstruction of fluxes at all energies, radial distances and pitch angles. Such reconstruction will be most important for the application as it will allow to trace satellites through the system and calculate fluencies along the satellite orbit. General reconstruction of fluxes will be most important for the research as it will allow to utilize various data sources and will provide a global and comprehensive picture of the evolution of fluxes.
While Kalman filter uses the previous measurements to predict the current state and is optimal for real time predictions, the Kalman smoother procedure allows to utilize measurements before a given epoch time in the past and after given epoch time to reconstruct the time history of the events that happened in the past.
Near-Earth Space Environment
The nonavailability of real‐time and retrospective data for the analysis of on‐orbit anomalies is another problem that has long existed. And it is a problem that will not be resolved solely by the new radiation belt missions. Only when commercial and other satellites fly diagnostic packages as a part of their payloads will anomaly‐resolving data be available.
Analysis of the co-variance matrix obtained during Phase II with the full 3D Kalman filter will allow to estimate the uncertainties of the reanalysis. Applicability Kalman smoothing, EnkF and 3D-Var for the long-term reanalysis.
Installation of real-time data assimilative forecast will provide a system for prediction of the radiation belts for the scientific community, government and industry stakeholders.