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The receiver operating characteristic (ROC) and the area under the curve (AUC), initially developed for signal processing and psychology, are a test for assessing the performance of a binary classification problem at varying threshold values. The ion density (Ni) observed by FORMOSAT‐7/COSMIC‐2 is used to study traveling ionospheric disturbances (TIDs) triggered by the 15 January 2022 Tonga volcanic eruption. We examine parameters of Ni, differential Ni, and standard deviation Ni (STD_Ni) in January 2022, simulate TID wavefronts traveling with various speeds from 10 to 1,000 m/s, and apply the ROC curve to globally identify the significance of TIDs in STD_Ni triggered by the volcanic eruption. ROC and AUC results show that in addition to TIDs related to tsunami/tropospheric Lamb waves and a series of fast‐moving waves with propagation speeds of 180–350 and 450–600 m/s, respectively, those long‐lasting low‐speeds less than 70 m/s and high‐speeds about 690, 860, and 990 m/s meet 95% statistical significance, which confirms TIDs being detected. These show that the FORMOSAT‐7/COSMIC‐2 ion density can be used to globally detect various TIDs triggered by the Tonga volcanic eruption. The ROC test results also show a potential use case for detecting other geophysical signals in future applications. Plain Language Summary: Large‐scale natural phenomena such as volcanoes and tsunami induce prominent traveling atmospheric disturbances (TADs) which also simultaneously and locally influence the above ionosphere activating prominent traveling ionospheric disturbances (TIDs). To assess the significance of such TIDs, we treat the TIDs as a series of signals, and the receiver operating characteristic (ROC) and the area under the curve (AUC), developed to address binary classification problems, are used to evaluate these signals at varying threshold values. The 15 January 2022 Tonga volcanic eruption triggers a series of TADs, TIDs, and tsunami waves propagating around the globe. Measurements of the ion density (Ni) by IVM (ion velocity meter) onboard FORMOSAT‐7/COSMIC‐2 (F7C2) are used to identify TIDs triggered by the volcanic eruption. We examine TIDs of F7C2 Ni, differential Ni, and standard deviation (STD) of Ni in January 2022, and find that TIDs associated with the tsunami waves, tropospheric Lamb waves, and high‐speed waves within 450–600 m/s triggered by the Tonga volcanic eruption on the globe are very prominent on the eruption day. In addition, TIDs with long‐lasting low‐speeds less than 70 m/s and high‐speeds about 690, 860, and 990 m/s can be identified in the ROC curves and AUC indices. Key Points: The FORMOSAT‐7/COSMIC‐2 ion density globally observes various traveling ionospheric disturbances triggered by the Tonga volcanic eruptionThe receiver operating characteristic (ROC) test can detect traveling ionospheric disturbances and potentially other geophysical signalsDisturbances with low‐speeds of 10–70 m/s, tsunami, tropospheric Lamb wave, and high‐speeds of 490/690/860/990 m/s are identified [ABSTRACT FROM AUTHOR]

Low Earth orbit (LEO) radio occultation|radio occultations (RO) constellations can provide global electron density profiles (EDPs) to better specify and forecast the ionosphere‐thermosphere (I‐T) system. To inform future RO constellation design, this study uses comprehensive Observing System Simulation Experiments (OSSEs) to assess the ionospheric specification impact of assimilating synthetic EDPs into a coupled I‐T model. These OSSEs use 10 different sets of RO constellation configurations containing 6 or 12 LEO satellites with base orbit parameter combinations of 520 or 800 km altitude, and 24° or 72° inclination. The OSSEs are performed using the Ensemble Adjustment Kalman Filter implemented in the data assimilation (DA) Research Testbed and the Thermosphere‐Ionosphere‐Electrodynamics General Circulation Model (TIEGCM). A different I‐T model is used for the nature run, the Whole Atmosphere Model‐Ionosphere Plasmasphere Electrodynamics (WAM‐IPE), to simulate the period of interest is the St. Patrick's Day storm on March 13–18, 2015. Errors from models and EDP retrieval are realistically accounted for in this study through distinct I‐T models and by retrieving synthetic EDPs through an extension Abel inversion algorithm. OSSE assessment, using multiple metrics, finds that greater EDP spatial coverage leading to improved specification at altitudes 300 km and above, with the 520 km altitude constellations performing best due to yielding the highest observation counts. A potential performance limit is suggested with two 6‐satellite constellations. Lastly, close examination of Abel inversion error impacts highlights major EDP limitations at altitudes below 200 km and dayside equatorial regions with large horizontal gradients and low electron density magnitudes. Plain Language Summary: The upper atmosphere, the region above 100 km altitude, is strongly influenced by space weather events that can negatively impact ground and space‐based technologies. These technologies include communication and navigation systems impacted by radio wave propagation through high altitudes plasma, a region called the ionosphere. Developing observing systems that provide global monitoring of the ionosphere is a critical need for understanding and forecasting space weather changes, such as radio occultations (RO) that provide plasma observations using global positioning radio signals. In this study, we evaluate these hypothetical RO observing systems in simulated experiments using data assimilation (DA), an approach that integrates synthetic observations into a physics‐based model. We find that increased observational coverage corresponds to better estimated plasma states, and that lower orbit altitude constellations yield higher observation counts. This study comprehensively incorporates model and observation errors to more realistically represent real‐world conditions. One limitation of RO data is highlighted in regions near the equator and at lower altitudes (below 250 km) where there is a breakdown in assumptions for observation retrieval. This study illustrates the clear operational benefits of these plasma observations, informing the future observing system design and aiding their use for space weather forecasting. Key Points: OSSE study assessing hypothetical radio occultation (RO) constellations, the first to comprehensively account for forecast model and Abel inversion errorsThe RO constellation with low‐ and high‐inclination orbits at 520 km altitude performs the best with the highest observation countsUncharacterized Abel inversion errors and poorly retrieved low plasma density limit assimilation impact on the equatorial ionosphere [ABSTRACT FROM AUTHOR]

The NASA global‐scale observations of the limb and disk (GOLD) mission is a measurement opportunity to scan the far ultraviolet airglow at ∼134–162 nm over the American Hemisphere since October 2018. The FORMOSAT‐7/COSMIC‐2 (F7/C2) satellite mission has provided thousands of daily radio occultation soundings in the low‐ and mid‐latitude regions since July 2019. The nighttime OI–135.6 nm emission is mainly through radiative recombination, and the radiance is used to derive the peak electron density. Comparison with corresponding F7/C2 observations demonstrates good correlation in low‐latitudes, while is overestimated near mid‐latitudes in winter, induced by the photoelectrons emanating from magnetically conjugate Hemisphere. The machine learning technique Bagged Trees is implemented to develop an intensity to peak density model training from GOLD and F7/C2 observations. The validation demonstrates that Bagged Trees peak‐density has less influence from conjugate photoelectrons and indicates the power of machine learning techniques for geophysics data processing. Plain Language Summary: The global‐scale observations of the limb and disk (GOLD) mission can scan wide ranges of far‐ultraviolet airglow at ∼134–162 nm over the American Hemisphere, providing the intensity of the airglow radiance and related maximum electron density of the ionosphere. Meanwhile, the FORMOSAT‐7/COSMIC‐2 (F7/C2) satellite mission receives GNSS radio signal to do radio occultation soundings and provides more than 4,000 of vertical electron density profiles daily. This study examines the two observations from different sounding techniques and algorithms, which generally yield good agreements, except that in the winter Hemisphere, the GOLD images experience the twilight airglow excited by conjugate photoelectrons emanating during May, June, and July. Moreover, the machine learning technique Bagged Trees is implemented to develop an intensity to peak density model training from two satellite observations, and the validation shows that the machine learning technique can detect and reduce the influence of the conjugate photoelectrons. The results indicate that the model can improve and increase the accuracy of the application of GOLD intensity for retrieving the peak electron density of the ionosphere. Key Points: The F7/C2 NmF2 and global‐scale observations of the limb and disk (GOLD) 135.6 nm airglow peak density are consistent except in the mid‐latitude region in the winter HemisphereThe machine learning technique bagged tree is used to develop an intensity to NmF2 model based on GOLD and F7/C2 observationsThe machine learning model can reduce the overestimation of NmF2 influenced by the photoelectrons emanating from the opposite Hemisphere [ABSTRACT FROM AUTHOR]

The conversion of airglow intensity to volume emission rate (VER) is a common method for studying the ionosphere, but the contribution of the intensity conversion process to the uncertainty in estimated electron or ion density is significant. The Abel inversion is a commonly used method for retrieving VERs from vertical profiles of airglow intensities accumulated along the rays horizontally at the tangent point, but it requires that the intensities converge to zero at their uppermost height, which is often not the case due to observational limitations. In this study, we present a method for optimizing the retrieval of VER from satellite-measured airglow intensities using the techniques of deep learning and Abel inversion. This method can be applied to fill in unobserved or discontinuous observations in airglow intensity profiles with the Chapman function, allowing them to be used with the Abel inversion to determine VERs. We validate the method using limb 135.6 nm airglow emission intensity data from the NASA Global-scale Observations of the Limb and Disk (GOLD) mission. Our training process involves using three hidden layers with varying numbers of neurons, and we compare the performance of the best-performing deep learning models to Abel-transformed results from real-time observations. The combination of Abel inversion and deep learning has the potential to optimize the process of converting intensity to VER and improve the capacity for analyzing ionospheric observations. [ABSTRACT FROM AUTHOR]