This study utilized a radar echo extrapolation system, a high-resolution numerical model with radar data assimilation, and three blending schemes including a new empirical one, called the extrapolation adjusted by model prediction (ExAMP), to carry out 150 min reflectivity nowcasting experiments for various heavy rainfall events in Taiwan in 2019. ExAMP features full trust in the pattern of the extrapolated reflectivity with intensity adjustable by numerical model prediction. The spatial performance for two contrasting events shows that the ExAMP scheme outperforms the others for the more accurate prediction of both strengthening and weakening processes. The statistical skill for all the sampled events shows that the nowcasts by ExAMP and the extrapolation system obtain the lowest and second lowest root mean square errors at all the lead time, respectively. In terms of threat scores and bias scores above certain reflectivity thresholds, the ExAMP nowcast may have more grid points of misses for high reflectivity in comparison to extrapolation, but serious overestimation among the points of hits and false alarms is the least likely to happen with the new scheme. Moreover, the event type does not change the performance ranking of the five methods, all of which have the highest predictability for a typhoon event and the lowest for local thunderstorm events.

Operational monsoon moisture surveillance and severe weather prediction is essential for timely water resource management and disaster risk reduction. For these purposes, this study suggests a moisture indicator using the COSMIC-2/FORMOSAT-7 radio occultation (RO) observations and evaluates numerical model experiments with RO data assimilation. The RO data quality is validated by a comparison between sampled RO profiles and nearby radiosonde profiles around Taiwan prior to the experiments. The suggested moisture indicator accurately monitors daily moisture variations in the South China Sea and the Bay of Bengal throughout the 2020 monsoon rainy season. For the numerical model experiments, the statistics of 152 moisture and rainfall forecasts for the 2020 Meiyu season in Taiwan show a neutral to slightly positive impact brought by RO data assimilation. A forecast sample with the most significant improvement reveals that both thermodynamic and dynamic fields are appropriately adjusted by model integration posterior to data assimilation. The statistics of 17 track forecasts for typhoon Hagupit (2020) also show the positive effect of RO data assimilation. A forecast sample reveals that the member with RO data assimilation simulates better typhoon structure and intensity than the member without, and the effect can be larger and faster via multi-cycle RO data assimilation.

Numerical weather simulations and forecasts are highly sensitive to environmental conditions. This is especially true in Taiwan, an ocean-surrounded island, during its transition season. Atmospheric temperature and moisture profiles retrieved from spaceborne sounders provide essential environmental information in regions lacking in situ observations. However, infrared sounders are sensitive to clouds and may induce uncertainties related to cloud properties. The present study analyzed 1 year of soundings from the National Oceanic and Atmospheric Administration’s Unique Combined Atmospheric Processing System (NUCAPS) to investigate the effects of clouds on the retrievals. The results indicated that the retrieved temperature profiles over land and under clouds had greater uncertainty than over oceans or in clear skies. In addition, the moisture profiles often exhibited a bias against cloud-top pressure. Therefore, this study proposed an objective quality control and bias correction method based on cloud effects. Excluding temperature observations affected by clouds and those over land reduced the root mean square difference from 3.3 K to 1.3 K. The relative cloud-top pressure level was used to conduct water vapor bias correction, which achieved effective correction for dry bias in the retrieved moisture profiles. After appropriate constraint criteria were applied, the bias-corrected profiles demonstrated a reduction in moisture bias from −4% to nearly 0%. That is, we assimilated sounding and radiance data into the regional Weather Research and Forecasting model and evaluated their effects, and we discovered that the retrieved profiles and direct observations positively contributed to the forecast of a spring frontal system. However, experiments using objective-bias-corrected sounding data improved skill scores in precipitation forecasts compared with using original sounding data or radiance data under a standard global operational baseline bias correction.