In this study, the fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model (MM5) with three-dimensional variational data assimilation (3DVAR) is utilized to investigate influences of GPS occultation refractivity on simulations of typhoons past Taiwan. Two recent cases were simulated, including Typhoon Nari in September 2001 and Typhoon Nakri in July 2002. The GPS observation data are taken from the Challenging Minisatellite Payload for Geophysical Research and Application (CHAMP) and Satélite de Aplicaciones Científicas-C (SAC-C) satellites that provide several retrieved refractivity profiles in the simulated domain near the initialization time. Through 3DVAR, the observed refractivity can be quickly ingested into the model initial conditions to recover the information over the ocean. The initial moisture increments from ingested GPS refractivity soundings exhibit a maximum magnitude of about 1.5 g kg−1 associated with temperature increments of generally less than 0.2°C. The differences between the model local refractivity and the observed refractivity are less than 3% with a maximum magnitude of about 10 units. Pronounced increments from an occultation point are found within an influential radius of 500–600 km only. For the simulation without the assimilation of GPS refractivity (the no-GPS run), the simulated Typhoon Nari coherently moves southwestward toward Taiwan early in the simulation but then exhibits a westward track along the northwest of Taiwan after landfall. With GPS refractivity assimilated, the simulated westward track in the no-GPS run is closer to the west coast. During landfall, the cloud convection associated with the intense vortex core encounters the Central Mountain Range (CMR) and produces torrential rainfalls along its northwestern slope. Both the GPS run and the no-GPS run capture the observed feature of very intense rainfall over the southwestern slope base of the CMR later in the simulation, while the intensity as well as the track is improved in the GPS run. In the other case (Nakri), the simulated rainfall distributions, in general, are similar for both the GPS run and the no-GPS run; however, the GPS run exhibits a more pronounced low to the southeast of Taiwan, which results in more intense rainfall in the northeast of Taiwan as observed. Both GPS runs for Nari and Nakri show improved skills in 24-h accumulated rainfall prediction, in particular, at later stages, as supported by higher threat scores and smaller root-mean-square errors against observations over the island. This positive impact can be attributed largely to the fact that the accumulative effects from assimilation of initial GPS refractivity soundings are instrumental to model performance. A cycling 3DVAR scheme is also explored in the simulation for Nari to investigate the impact of complementary NASA Quick Scatterometer (QuikSCAT) near-surface wind observations on model prediction. When such observed near-surface wind is assimilated into reinitialization at a later integration time, the track prediction is further improved and thus the prediction for accumulated rainfall is improved as well.

This study investigates the Global Navigation Satellite System (GNSS) radio occultation (RO) data from FORMOSAT-7/COSMIC-2 (FS7/C2), which provides considerably more and deeper profiles at lower latitudes than those from the former FORMOSAT-3/COSMIC (FS3/C). The statistical analysis of six-month RO data shows that the rate of penetration depth below 1 km height within ±45° latitudes can reach 80% for FS7/C2, significantly higher than 40% for FS3/C. For verification, FS7/C2 RO data are compared with the observations from chartered missions that provided aircraft dropsondes and on-board radiosondes, with closer observation times and distances from the oceanic RO occultation over the South China Sea and near a typhoon circulation region. The collocated comparisons indicate that FS7/C2 RO data are reliable, with small deviations from the ground-truth observations. The RO profiles are compared with collocated radiosondes, RO data from other missions, global analyses of ERA5 and National Centers for Environmental Prediction (NCEP) final (FNL), and satellite retrievals of NOAA Unique Combined Atmospheric Processing System (NCAPS). The comparisons exhibit consistent vertical variations, showing absolute mean differences and standard deviations of temperature profiles less than 0.5 °C and 1.5 °C, respectively, and deviations of water vapor pressure within 2 hPa in the lower troposphere. From the latitudinal distributions of mean difference and standard deviation (STD), the intertropical convergence zone (ITCZ) is evidentially shown in the comparisons, especially for the NUCAPS, which shows a larger deviation in moisture when compared to FS7/C2 RO data. The sensitivity of data collocation in time departure and spatial distance among different datasets are presented in this study as well.

Precipitable water (PW) retrievals from FORMOSAT‐3/Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC) Global Positioning System (GPS) radio occultation (RO) measurements were analyzed and compared with those derived from Special Sensor Microwave/Imager (SSM/I) and Advanced Microwave Scanning Radiometer for Earth Observation System (AMSR‐E) during the El Niño‐Southern Oscillation (ENSO) events from 2007 to 2011. For the three ENSO events in 2007–2011, monthly mean binned COSMIC PW results are in a very high correlation (up to 0.98) with those of SSM/I and AMSR‐E over the ocean, generally with root‐mean‐square differences less than 4 mm. PW retrievals from the three satellites are also of similar latitudinal variations. However, the PW is slightly underestimated by GPS RO, in particular, in the tropical regions. This underestimate may be caused partially by the fact that not all RO measurements can reach the surface. Inter‐satellite PW anomaly comparisons for the winter months in the ENSO events, with respect to those during the neutral (non‐ENSO) months, show consistent ENSO signals with major PW anomaly near the central Pacific in the warm event and near the Indonesian region and east of Australia in the two cold events. However, the 2007/2008 La Niña is somewhat less correlated for COSMIC with AMSR‐E and SSM/I. For the stronger 2010/2011 La Niña, their PW anomalies are in higher correlations of about 0.8.

Radio occultation (RO) observations of precipitable water (PW) from the Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC) are compared with ground‐based Global Positioning System (GPS) PW for 6 years (2007–2012). From comparisons of more than 120,000 collocated samples, differences in PW between RO and GPS exhibit a standard deviation of less than 3 mm with a global correlation of 0.96 to 0.97. The monthly mean RO PW is also compared to the National Centers for Environmental Prediction (NCEP) reanalyses. PWs over land for RO, GPS, and reanalyses are highly correlated in each hemisphere. We also used the RO data to explore the variation of the monthly mean PW in four regions (Tibetan Plateau, central Australia, Amazon Basin, and central Africa) where ground stations are few. This study highlights the characteristics of these PW climatologies, suggesting that the RO data are useful complements to global reanalyses and other data sets.

Conventional soundings are rather limited over the western North Pacific and can be largely compensated by GNSS radio occultation (RO) data. We utilize the GSI hybrid assimilation system to assimilate RO data and the multiresolution global model (MPAS) to investigate the RO data impact on prediction of Typhoon Nepartak that passed over southern Taiwan in 2016. In this study, the performances of assimilation with local RO refractivity and bending angle operators are compared for the assimilation analysis and typhoon forecast. Assimilations with both RO data have shown similar and comparable temperature and moisture increments after cycling assimilation and largely reduce the RMSEs of the forecast without RO data assimilation at later times. The forecast results at 60–15-km resolution show that RO data assimilation largely improves the typhoon track prediction compared to that without RO data assimilation, and assimilation with bending angle has better performances than assimilation with refractivity, in particular for wind forecast. The improvement in the forecasted track is mainly due to the improved simulation for the translation of the typhoon. Diagnostics of wavenumber-1 potential vorticity (PV) tendency budget indicates that the northwestward typhoon translation dominated by PV horizontal advection is slowed down by the southward tendency induced by the stronger differential diabatic heating south of the typhoon center for bending-angle assimilation. Simulations with the enhanced resolution of 3 km in the region of the storm track show further improvements in both typhoon track and intensity prediction with RO data assimilation. Positive RO impacts on track prediction are also illustrated for two other typhoons using the MPAS-GSI system.