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Abstract Taiwan regularly receives extreme rainfall due to seasonal mei-yu fronts that are modified by Taiwan’s complex topography. One such case occurred between 1 and 3 June 2017 when a mei-yu front contributed to flooding and landslides from over 600 mm of rainfall in 12 h near the Taipei basin, and over 1500 mm of rainfall in 2 days near the Central Mountain Range (CMR). This mei-yu event is simulated using the Weather Research and Forecasting (WRF) Model with halved terrain as a sensitivity test to investigate the orographic mechanisms that modify the intensity, duration, and location of extreme rainfall. The reduction in WRF terrain height produced a decrease in rainfall duration and accumulation in northern Taiwan and a decrease in rainfall duration, intensity, and accumulation over the CMR. The reductions in northern Taiwan are linked to a weaker orographic barrier jet resulting from a lowered terrain height. The reductions in rainfall intensity and duration over the CMR are partially explained by a lack of orographic enhancements to mei-yu frontal convergence near the terrain. A prominent feature missing with the reduced terrain is a redirection of postfrontal westerly winds attributed to orographic deformation, i.e., the redirection of flow due to upstream topography. Orographically deforming winds converge with prefrontal flow to maintain the mei-yu front. In both regions, the decrease in mei-yu front propagation speed is linked to increased rainfall duration. These orographic features will be further explored using observations captured during the 2022 Prediction of Rainfall Extremes Campaign in the Pacific (PRECIP) field campaign. Significance StatementThis study examines the impact of terrain on rainfall intensity, duration, and location. A mei-yu front, an East Asian weather front known for producing heavy, long-lasting rainfall, was simulated for an extreme rain event in Taiwan with mountain heights halved as a sensitivity test. Reducing terrain decreased rainfall duration in northern and central Taiwan. Decreases in rainfall duration for both regions is attributed to increased mei-yu front propagation speed. This increase in northern Taiwan is attributed to a weakened barrier jet, a low-level jet induced by flow blocked by the steep mountains of Taiwan. A unique finding of this work is a change in winds north of the front controlling movement of the front near the mountains in central Taiwan. 

Abstract The interaction of airflow with complex terrain has the potential to significantly amplify extreme precipitation events and modify the structure and intensity of precipitating cloud systems. However, understanding and forecasting such events is challenging, in part due to the scarcity of direct in situ measurements. Doppler radar can provide the capability to monitor extreme rainfall events over land, but our understanding of airflow modulated by orographic interactions remains limited. The SAMURAI software is a three-dimensional variational data assimilation (3DVAR) technique that uses the finite element approach to retrieve kinematic and thermodynamic fields. The analysis has high fidelity to observations when retrieving flows over a flat surface, but the capability of imposing topography as a boundary constraint is not previously implemented. Here, we implement the immersed boundary method (IBM) as pseudo-observations at their native coordinates in SAMURAI to represent the topographic forcing and surface impermeability. In this technique, neither data interpolation onto a Cartesian grid nor explicit physical constraint integration during the cost function minimization is needed. Furthermore, the physical constraints are treated as pseudo-observations, offering the flexibility to adjust the strength of the boundary condition. A series of observing simulation sensitivity experiments (OSSEs) using a full-physics model and radar emulator simulating rainfall from Typhoon Chanthu (2021) over Taiwan are conducted to evaluate the retrieval accuracy and parameter settings. The OSSE results show that the strength of the IBM constraints can impact the overall wind retrievals. Analysis from real radar observations further demonstrates that the improved retrieval technique can advance scientific analyses for the underlying dynamics of orographic precipitation using radar observations. 

Abstract Polarimetric coastal radar data are used to compare the rainfall characteristics of Hurricanes Harvey (2017) and Florence (2018). Intense rainfall was an infrequent yet important contributor to the total rainfall in Harvey, but its relative contribution varied spatially. The total rainfall over land maximized near the coast over Beaumont, TX, due to intense convection resulting from prolonged onshore flow downshear from the circulation center. Overall, polarimetric radar observations in Harvey show a dominance of high concentrations of small‐to‐medium drops, consistent with prior tropical cyclone studies. The microphysical characteristics were spatially and temporally inhomogeneous however, with larger drops more frequent on 27 August and higher number concentrations more frequent on 28 and 30 August. The polarimetric variables and raindrop characteristics observed during Florence share broad similarities to Harvey, but had reduced variability, fewer observations of stronger reflectivity and differential reflectivity, and a lower frequency of high number concentrations and medium‐sized drops. The radar data indicate Florence had reduced coverage of stronger convection compared to Harvey. We hypothesize that differences in storm motion, intensity decay rates, and vertical wind shear produce the distinct precipitation structures and microphysical differences seen in Harvey and Florence. 

Data from the Colorado State University (CSU) CHILL radar located near Greeley, Colorado during the Pre-CIP_2021 (Preparatory Rockies Experiment for the Campaign In the Pacific 2021) campaign in July and August 2021. CHILL is a dual wavelength S- and X-band, dual-polarization radar that conducted RHI and surveillance scans. 

Data from the Colorado State University (CSU) CHIVO radar located near Fort Collins, Colorado during the Pre-CIP_2021 (Preparatory Rockies Experiment for the Campaign In the Pacific 2021) campaign from May-August 2021. CHIVO is a C-band, dual-polarization radar that conducted RHI and surveillance scans. 

Abstract The Prediction of Rainfall Extremes Campaign In the Pacific (PRECIP) aims to improve our understanding of extreme rainfall processes in the East Asian summer monsoon. A convection-permitting ensemble-based data assimilation and forecast system (the PSU WRF-EnKF system) was run in real time in the summers of 2020–21 in advance of the 2022 field campaign, assimilating all-sky infrared (IR) radiances from the geostationary Himawari-8 and GOES-16 satellites, and providing 48-h ensemble forecasts every day for weather briefings and discussions. This is the first time that all-sky IR data assimilation has been performed in a real-time forecast system at a convection-permitting resolution for several seasons. Compared with retrospective forecasts that exclude all-sky IR radiances, rainfall predictions are statistically significantly improved out to at least 4–6 h for the real-time forecasts, which is comparable to the time scale of improvements gained from assimilating observations from the dense ground-based Doppler weather radars. The assimilation of all-sky IR radiances also reduced the forecast errors of large-scale environments and helped to maintain a more reasonable ensemble spread compared with the counterpart experiments that did not assimilate all-sky IR radiances. The results indicate strong potential for improving routine short-term quantitative precipitation forecasts using these high-spatiotemporal-resolution satellite observations in the future. Significance Statement During the summers of 2020/21, the PSU WRF-EnKF data assimilation and forecast system was run in real time in advance of the 2022 Prediction of Rainfall Extremes Campaign In the Pacific (PRECIP), assimilating all-sky (clear-sky and cloudy) infrared radiances from geostationary satellites into a numerical weather prediction model and providing ensemble forecasts. This study presents the first-of-its-kind systematic evaluation of the impacts of assimilating all-sky infrared radiances on short-term qualitative precipitation forecasts using multiyear, multiregion, real-time ensemble forecasts. Results suggest that rainfall forecasts are improved out to at least 4–6 h with the assimilation of all-sky infrared radiances, comparable to the influence of assimilating radar observations, with benefits in forecasting large-scale environments and representing atmospheric uncertainties as well. 

Data from the Colorado State University (CSU) Sea-Going Polarimetric (SEA-POL) radar that was deployed on Yonaguni (a Japanese island to the east of Taiwan) for the PRECIP (Prediction of Rainfall Extremes Campaign in the Pacific) campaign from June to August 2022. SEA-POL is a deployable C-band dual-polarization radar that conducted RHI and surveillance scans on 12-minute cycles. 

Data from the Colorado State University radiosonde systems that were deployed at Hsinchu, Taiwan and Yonaguni, Japan for the PRECIP campaign. Measurements include vertical profiles of temperature, moisture and winds. 

Data from the Parsivel disdrometer that was deployed on Yonaguni (a Japanese island to the east of Taiwan) for the PRECIP (Prediction of Rainfall Extremes Campaign in the Pacific) campaign from June to August 2022. It was deployed near the CSU SEA-POL dual-polarization radar to obtain drop-size distribution measurements in Mei-yu fronts, thunderstorms, and tropical cyclones.