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Abstract The 2023 Atlantic hurricane season was above normal, producing 20 named storms, 7 hurricanes, 3 major hurricanes, and seasonal accumulated cyclone energy that exceeded the 1991–2020 average. Hurricane Idalia was the most damaging hurricane of the year, making landfall as a Category 3 hurricane in Florida, resulting in eight direct fatalities and 3.6 billion U.S. dollars in damage. The above-normal 2023 hurricane season occurred during a strong El Niño event. El Niño events tend to be associated with increased vertical wind shear across the Caribbean and tropical Atlantic, yet vertical wind shear during the peak hurricane season months of August–October was well below normal. The primary driver of the above-normal season was likely record warm tropical Atlantic sea surface temperatures (SSTs), which effectively counteracted some of the canonical impacts of El Niño. The extremely warm tropical Atlantic and Caribbean were associated with weaker-than-normal trade winds driven by an anomalously weak subtropical ridge, resulting in a positive wind–evaporation–SST feedback. We tested atmospheric circulation sensitivity to SSTs in both the tropical and subtropical Pacific and the Atlantic using the atmospheric component of the Community Earth System Model, version 2.3. We found that the extremely warm Atlantic was the primary driver of the reduced vertical wind shear relative to other moderate/strong El Niño events. The concentrated warmth in the eastern tropical Pacific in August–October may have contributed to increased levels of vertical wind shear than if the warming had been more evenly spread across the eastern and central tropical Pacific. 

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. 

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. 

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. 

Abstract The parametric hurricane rainfall model (PHRaM), firstly introduced in 2007, has been widely used to forecast and quantify tropical-cyclone-induced rainfall (TC rainfall). The PHRaM is much more computationally efficient than global climate models, but PHRaM cannot be effectively utilized in the context of climate change because it does not have any parameters to capture the increase of tropospheric water vapor under the warming world. This study develops a new model that incorporates tropospheric water vapor to the PHRaM framework, named as the PHRaM with moisture (PHRaMM). The PHRaMM is trained to best fit the TC rainfall over the western North Pacific (WNP) unlike the PHRaM trained with the TCs over the continental US. The PHRaMM reliably simulates radial profile of TC rainfall and spatial distribution of accumulated rainfall during landfall in the present climate with the better prediction skills than existing statistical and operational numerical models. Using the PHRaMM, we evaluated the impacts of TC intensity and environmental moisture increase on TC rainfall change in a future climate. An increased TC intensity causes TC rainfall to increase in the inner-core region but to decrease in the outer region, whereas an increased environmental moisture causes the TC rainfall to increase over the entire TC area. According to the both effects of increased TC intensity and environmental moisture, the PHRaMM projected that the WNP TC rainfall could increase by 4.61–8.51% in the inner-core region and by 17.96–20.91% over the entire TC area under the 2-K warming scenario. 

This study analyzes an ensemble of numerical simulations of a heavy rainfall event east of Taiwan on 9 June 2020. Heavy rainfall was produced by quasi-stationary back-building mesoscale convective systems (MCS) associated with a mei-yu front. Global model forecast skill was poor in location and intensity of rainfall. The mesoscale ensemble showed liminal conditions between heavy rainfall or little to no rainfall. The two most accurate and two least accurate ensemble members are selected for analysis via validation against radar-estimated rainfall observations. All members feature moist soundings with low levels of free convection (LFC) and sufficient instability for deep convection. We find that stronger gradients in 100-m θe and θv in the most accurate members associated with a near-surface frontal boundary focus the lifting mechanism for deep, moist convection and enhanced rainfall. As the simulations progress, stronger southerly winds in the least accurate members advect drier mid-level air into the region of interest and shift the near-surface boundary further north and west. Analysis of the verification ensemble mean analysis reveals a strong near-surface frontal boundary similarly positioned as in the most accurate members and dry air aloft more similar to that in the least accurate members, suggesting that the positioning of the frontal boundary is more critical to accurately reproducing rainfall patterns and intensity in this case. The analyses suggest that subtle details in the simulation of frontal boundaries and mesoscale flow structures can lead to bifurcations in producing extreme or almost no rainfall. Implications for improved probabilistic forecasts of heavy rainfall events will be discussed. 

Remote sensing observational instruments are critical for better understanding and predicting severe weather. Observational data from such instruments, such as Doppler radar data, for example, are often processed for assimilation into numerical weather prediction models. As such instruments become more sophisticated, the amount of data to be processed grows and requires efficient variational analysis tools. Here we examine the code that implements the popular SAMURAI (Spline Analysis at Mesoscale Utilizing Radar and Aircraft Instrumentation) technique for estimating the atmospheric state for a given set of observations. We employ a number of techniques to significantly improve the code’s performance, including porting it to run on standard HPC clusters, analyzing and optimizing its single-node performance, implementing a more efficient nonlinear optimization method, and enabling the use of GPUs via OpenACC. Our efforts thus far have yielded more than 100x improvement over the original code on large test problems of interest to the community. 

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. 

Abstract Observations of the air vertical velocities ( w air ) in supercell updrafts are presented, including uncertainty estimates, from radiosonde GPS measurements in two supercells. These in situ observations were collected during the Colorado State University Convective Cloud Outflows and Updrafts Experiment (C 3 LOUD-Ex) in moderately unstable environments in Colorado and Wyoming. Based on the radiosonde accelerations, instances when the radiosonde balloon likely bursts within the updraft are determined, and adjustments are made to account for the subsequent reduction in radiosonde buoyancy. Before and after these adjustments, the maximum estimated w air values are 36.2 and 49.9 m s −1 , respectively. Radar data are used to contextualize the in situ observations and suggest that most of the radiosonde observations were located several kilometers away from the most intense vertical motions. Therefore, the radiosonde-based w air values presented likely underestimate the maximum values within these storms due to these sampling biases, as well as the impacts from hydrometeors, which are not accounted for. When possible, radiosonde-based w air values were compared to estimates from dual-Doppler methods and from parcel theory. When the radiosondes observed their highest w air values, dual-Doppler methods generally produced 15–20 m s −1 lower w air for the same location, which could be related to the differences in the observing systems’ resolutions. In situ observations within supercell updrafts, which have been limited in recent decades, can be used to improve our understanding and modeling of storm dynamics. This study provides new in situ observations, as well as methods and lessons that could be applied to future field campaigns.