Attch_2_SOO_FINAL_CALL_003_dtd_20190212.docx

Statement of Objectives (SOO)

Topic 3 a): Solar, Solar Wind and Ionospheric Effects: D-region Absorption Models and Associated Solar Drivers and Tools Dated 12 Feb 19

1.0 Scope

The scope of this call is to improve both the absorption calculations in regional ionospheric models and the solar inputs they require. In particular, AFRL would like: (1) a real-time non-deviative absorption model as well as a demonstration of the utility of potential real-time data sources and a comprehensive validation plan; (2) a solar flare D-region absorption model. To develop and improve solar drivers for ionospheric models, the following tools are also desired: (3) observation-based global solar coronal hole probability maps; (4) machine learning-based daily solar indices forecasts; (5) forecasts of solar soft X-ray fluxes.

2.0 Performance Objectives

2.1 Overall Objective:

As radio waves propagate through the ionosphere, there are power losses associated with deviative absorption, non-deviative absorption, and spreading. Non-deviative absorption quantifies losses due to collisions between neutral and charged particles and mostly occurs in the D-region and somewhat less in the E-region of the ionosphere. While the community standard non-deviative absorption climatology is based on an established f0E dependence, there is a strong desire to include real-time data assimilative non-deviative absorption models in simulations of High Frequency (HF, 3-30 MHz) skywave propagation. In addition, it is well known that solar flares cause increased absorption of HF radiation through enhanced D-region plasma densities, which is not captured in climatology models. A model of this solar flare-driven absorption is desired.

To support both regional ionospheric models and non-deviative absorption calculations, this call also includes the development and improvement of solar drivers. Over the past decade, AFRL has developed the Air Force Data Assimilative Photospheric Flux Transport (ADAPT) and Solar Indices Forecasting Tool (SIFT) models. To generate nowcasts and 1- to 7-day forecasts of the global solar magnetic field, ADAPT utilizes publicly available data and incorporates magnetic field flux transport with rigorous data assimilation methods when merging new observations. While the original intent was to use the output of ADAPT as an improved driver of solar wind models, over the years other uses for ADAPT outputs have emerged. Of particular interest for ionospheric models is SIFT, which utilizes the Earth-side magnetic field generated by ADAPT to estimate future values for solar indices, e.g., F10.7 and sunspot number (SSN) using simple algorithms. ADAPT maps and SIFT forecasts are publically available through the National Solar Observatory. AFRL would like to advance solar data analysis tools that support ADAPT development as well as incorporate ADAPT output into solar forecasting models with the intent to improve forecasts.

2.2 Technical Objectives:

a) Real-time Ionospheric Non-deviative Absorption Model

· Offerors should provide a demonstration of the utility of including data assimilation into a non-deviative ionospheric absorption model.

· A comprehensive validation and verification plan should be provided.

· If the necessary data are not available currently in real-time, offerors should define the requirements for real-time data.

· The real-time model should cover latitudes below 55° latitude. The output of the model should be dB loss per km on a 3D grid through the D- and E-region with sufficient vertical resolution to resolve the peak of absorption.

b) Solar Flare D-region Absorption Model

· Develop a model of D-region absorption due to solar flares. The model should depend, at a minimum, on frequency, solar zenith angle, and solar soft X-ray flux.

· The model should be validated against observations, covering a range of solar flare magnitudes from M2.0 to >X5.0.

· The flare D-region absorption model should cover latitudes below 55°. The output of the model should calculate the loss along an arbitrary slant path in the ionosphere.

c) Observation-based Global Solar Coronal Hole Probability Maps

· Develop an algorithm that identifies coronal holes at all latitudes in solar images and provides a measurement of the uncertainty in that detection. Different detection techniques may be used for polar and equatorial coronal holes. Methods for uncertainty determination can be based on, but not limited to, multiple input sources or temporal coherence.

· In addition to the algorithm, a database of coronal hole probability maps should be provided for the last decade using publically available data sources. Each grid point in the map should indicate the probability that the grid point is within a coronal hole region. Sub-degree spatial resolution and multiple maps per day are desired.

· Validation of the algorithm should be shown for multiple years.

d) Machine Learning Techniques to Improve SIFT Forecasts

· Develop a machine-learning algorithm that uses the ensemble of ADAPT forecast global magnetic maps to forecast with uncertainties the daily values of solar indices, including but not limited to F10.7 and SSN. The algorithm should provide multi-day forecasts.

· Offerors should demonstrate the performance of their forecasts for at least four 6-month periods over a range of solar activity levels

e) Forecasting Soft X-ray Time Series

· Develop an algorithm to forecast the solar soft X-ray flux in the GOES XRS-B (0.1-0.8 nm) band with at least 15-minute temporal resolution out to several hours using publically available data. The algorithm should provide estimates of uncertainties in forecasts. Use of ADAPT output is encouraged but also not required.

· The effects of small flares should be included. Inclusion of the effects of moderate and large solar flares in the forecast is desired but not required.

· Forecasts should be validated for at least four 6-month periods over a range of solar activity levels.

SOO to call 003 FA9453-19-S-0001 1