We studied ionization effects produced by solar energetic particles (SEP) in the northern polar ionosphere and atmosphere during December 2006 (local night). Height profiles of specific ionization produced by the SEP in the polar cap were calculated using POES data on energetic particles. The SEP ionization dynamics reveals three intensifications at heights from 50 to 100 km in 7 to 8, 13 and 14 December. At the same time a 3‐D tomography of electron content (EC) in the ionosphere and upper atmosphere was performed on the base of COSMIC/FORMOSAT‐3 sounding data. We found pretty close temporal and spatial patterns for the EC enhancements and for the SEP ionization. The discrepancies might be attributed to three effects: unaccounted contribution of low‐energy electrons to the ionization of lower ionosphere, fast growth of electron losses with the depth in atmosphere, and strong contribution from storm‐time ionospheric disturbances.

This study investigates the impact of assimilating Formosat-7/COSMIC-II (FS7/C2) radio occultation (RO) refractivity data on predicting the heavy rainfall event that occurred in Taiwan on August 13, 2019. This event was characterized by heavy rainfall over the coastal region of central and southwestern Taiwan. Our investigation is performed using the Weather Research and Forecasting-Local Ensemble Transform Kalman Filter. Generally, assimilating the RO data increases the amount of moisture over the northern South China Sea (SCS) and the Pearl River area in southern China. It was expected that assimilating the RO data would improve low-level moisture analysis, given that more RO data are available for the lower atmosphere compared to those from Formosat-3/COSMIC-I. However, our results show that the experiment that does not include the RO data below 3 km facilitates better rainfall prediction over Taiwan in terms of the intensity and location of heavy rainfall. This heavy rainfall event can be attributed to moisture transport from the Pearl River area, where the RO data at the altitude of 3–5 km provide effective moisture enhancement to deepen the high-moisture layer. The experiment using the local spectral width (LSW) to conduct the quality control (QC) also helps improve rainfall prediction. However, such an LSW-based QC procedure tends to reject significant amounts of RO data 3 km above the land. Based on this case study, our results show that the QC procedure brings a larger impact to rainfall prediction than counterparts that adjust the observation error variance. A sophisticated QC procedure should be developed to optimize the impact of low-level RO data.

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A regional hybrid gain data assimilation (HGDA) system is newly developed using Weather Research and Forecasting model (WRF). The WRF-HGDA augments an ensemble-based Kalman filter (WRF-LETKF) with information from the variational analysis system (WRF-3DVAR) by combining their gain matrices. The performance of WRF-HGDA is evaluated by assimilating the GNSS radio occultation (RO) observations from the FORMOSAT-3/COSMIC (FS3/C) and the FORMOSAT-7/COSMIC2 (FS7/C2) under an Observing System Simulation Experiment (OSSE) framework. The results demonstrate that the variational correction improves the WRF-LETKF, with the equal-weighted WRF-HGDA outperforming its component DA systems in the moisture and wind fields when only conventional observations are assimilated. Assimilating additional RO data from FS7/C2 further improves the WRF-LETKF and WRF-HGDA systems. Although the variational correction for the mid-level temperature causes degradation in the WRF-HGDA analysis, this can be alleviated by adjusting the combination weight toinclude more flow-dependent information in WRF-HGDA at these levels. Further tuning of the static background error covariance used in WRF-3DVAR also brings some improvement in the WRF-HGDA wind analysis. Our results imply that a well-tuned variational system is critical for the accuracy of the regional HGDA

Studies have shown that assimilating the radio occultation (RO) observations, including those from the FORMOSAT-3/COSMIC (constellation observing systems for meteorology, ionosphere, and climate) (FS3-C), provides positive impacts on tropical cyclone (TC) forecasts. The FS3-C's successor, the FORMOSAT-7/COSMIC-2 (FS7-C2), provides denser spatial data coverage over the Tropics and Subtropics, where severe weather systems often occur. This study investigates the impact of FS7-C2 refractivity profiles on the prediction of TC genesis. A quick observing system simulation experiment is conducted for the period when Hurricanes Helene and Gordon (2006) occurred over the North Atlantic Ocean using a regional ensemble data assimilation system. Though assimilating FS3-C or FS7-C2 ROs successfully reproduces Helene's development, assimilating FS7-C2 ROs better captures the genesis and development of Gordon with abundant moisture and vorticity in Gordon's core region, providing conditions favorable for the development of deep convection. A minimum area-mean total precipitable water vapor of 54 mm, as well as the existence of mid-level cyclonic vorticity (e.g., 500 hPa), at the storm core region in the initial condition is required for forecasting Gordon's genesis. Also, the assimilation of FS7-C2 ROs in our experiments reduces the 500 hPa geopotential error by 22% and improves probabilistic quantitative precipitation forecast compared with assimilating FS3-C ROs. Two sensitivity tests are conducted to evaluate the impact of low-level negatively biased FS7-C2 RO profiles and the removal of FS7-C2 data below 3 km on Gordon's genesis. The former test does not degrade Gordon's genesis forecast skills due to a dipole error correlation between the background ROs and the moisture field over an observed RO profile near Gordon. The latter test does degrade Gordon's forecast skills but is still better than the assimilation of FS3-C ROs since the features of low-level moisture and mid-level vorticity are preserved to some extent.