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We have performed an analysis of reprocessed GPS/MET data spanning 1995–1997 generated by the COSMIC Data Analysis and Archive Center (CDAAC) in 2007. CDAAC developed modified dual-frequency processing methods for the encrypted data (anti-spoofing (AS)-on) during 1995–1997. We compared the CDAAC data set to the Modern-Era Retrospective Analysis for Research and Applications-2 (MERRA-2) reanalysis, separately for AS-on and AS-off, focusing on the altitude range 10–30 km. MERRA-2 did not assimilate GPS/MET data in the period 1995–1997. To gain insight into the CDAAC data set, we developed a single-frequency data set for GPS/MET , which is unaffected by the presence of encryption. We find excellent agreement between the more limited single-frequency data set and the CDAAC data set: the bias between these two data sets is consistently less than 0.25 % in refractivity, whether or not AS is on. Given the different techniques applied between the CDAAC and the new data set presented here (designated JPL), agreement suggests that the CDAAC AS-on processing and the single-frequency processing are not biased in an aggregate sense greater than 0.25 % in refractivity, which corresponds approximately to a temperature bias less than 0.5 K. Since the profiles contained in the new single-frequency data set are not a subset of the CDAAC profiles, the combination of the CDAAC data set, consisting of 9579 profiles, and the new single-frequency data set, consisting of 4729 profiles, yields a total number of 11 531 unique profiles from combining the JPL and CDAAC data sets. All numbers are after quality control has been applied by the respective processing activities. [ABSTRACT FROM AUTHOR]

Conditions in the ionosphere have become increasingly important to forecast, since more and more spaceborne and ground-based technological systems rely on ionospheric weather. Here we explore the feasibility of ionospheric forecasts with the current generation of physics-based models. In particular, we focus on total electron content (TEC) predictions using the Global Ionosphere-Thermosphere Model (GITM). Simulations are configured in a forecast mode and performed for four typical high-speed-stream events during 2007–2012. The simulated TECs are quantified through a metric, which divides the globe into a number of local regions and robustly differentiates between quiet and disturbed periods. Proposed forecast products are hourly global maps color-coded by the TEC disturbance level of each local region. To assess the forecasts, we compare the simulated TEC disturbances with global TEC maps derived from Global Positioning System (GPS) satellite observations. The forecast performance is found to be merely acceptable, with a large number of regions where the observed variations are not captured by the simulations. Examples of model-data agreements and disagreements are investigated in detail, aiming to understand the model behavior and improve future forecasts. For one event, we identify two adjacent regions with similar TEC observations but significant differences in how local chemistry versus plasma transport contribute to electron density changes in the simulation. Suggestions for further analysis are described.

The statistical characterization of the relationship between thermosphere-ionosphere anomalies and space weather events, also referred to as space weather anomalies, such as solar coronal mass ejections (CMEs) and geomagnetic storms, is a crucial component in the development of a forecast capability for thermosphere-ionosphere disturbances. This manuscript presents a systematic statistical approach for analyzing historical ionosphere and space weather observations to derive a quantitative characterization of the relationships between the thermosphere-ionosphere anomalies and space weather anomalies. Based on 2 years of historical data, our analysis reveals the complex nature of the relationship between space weather disturbances and ionospheric responses.

Interest in forecasting space weather in the thermosphere and ionosphere (T-I) led to a community workshop held at NASA’s Jet Propulsion Laboratory in October, 2014. The workshop focus was “Scientific Challenges in Thermosphere-Ionosphere Forecasting” to emphasize that forecasting presumes a sufficiently advanced state of scientific knowledge, yet one that is still evolving. The purpose of the workshop, and this topical issue that arose from the workshop, was to discuss research frontiers that will lead to improved space weather forecasts. Three areas are discussed in some detail in this paper: (1) the role of lower atmosphere forcing in the response of the T-I to geomagnetic disturbances; (2) the significant deposition of energy at polar latitudes during geomagnetic disturbances; and (3) recent developments in understanding the propagation of coronal mass ejections through the heliosphere and prospects for forecasting the north-south component of the interplanetary magnetic field (IMF) using observations at the Lagrangian L5 point. We describe other research presented at the workshop that appears in the topical issue. The possibility of establishing a “positive feedback loop” where improved scientific knowledge leads to improved forecasts is described (Siscoe 2006, Space Weather, 4, S01003; Mannucci 2012, Space Weather, 10, S07003).

We analyze the energy budget of the ionosphere-thermosphere (IT) system during two High-Speed Streams (HSSs) on 22–31 January, 2007 (in the descending phase of solar cycle 23) and 25 April–2 May, 2011 (in the ascending phase of solar cycle 24) to understand typical features, similarities, and differences in magnetosphere-ionosphere-thermosphere (IT) coupling during HSS geomagnetic activity. We focus on the solar wind energy input into the magnetosphere (by using coupling functions) and energy partitioning within the IT system during these intervals. The Joule heating is estimated empirically. Hemispheric power is estimated based on satellite measurements. We utilize observations from TIMED/SABER (Thermosphere-Ionosphere-Mesosphere Energetics and Dynamics/Sounding of the Atmosphere using Broadband Emission Radiometry) to estimate nitric oxide (NO) and carbon dioxide (CO2) cooling emission fluxes. We perform a detailed modeling study of these two similar HSS events with the Global Ionosphere-Thermosphere Model (GITM) and different external driving inputs to understand the IT response and to address how well the model reproduces the energy transport. GITM is run in a mode with forecastable inputs. It is shown that the model captures the main features of the energy coupling, but underestimates NO cooling and auroral heating in high latitudes. Lower thermospheric forcing at 100 km altitude is important for correct energy balance of the IT system. We discuss challenges for a physics-based general forecasting approach in modeling the energy budget of moderate IT storms caused by HSSs.