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1. Improving Short-Term QPF Using Geostationary Satellite All-Sky Infrared Radiances: Real-Time Ensemble Data Assimilation and Forecast during the PRECIP 2020 and 2021 Experiments

   https://doi.org/10.1175/WAF-D-22-0156.1  

   Zhang, Yunji; Chen, Xingchao; Bell, Michael M. (April 2023, Weather and Forecasting)

   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. 

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2. Evaluation of Experimental High-Resolution Model Forecasts of Tropical Cyclone Precipitation Using Object-Based Metrics

   https://doi.org/10.1175/WAF-D-22-0223.1  

   Stackhouse, Shakira D; Zick, Stephanie E; Matyas, Corene J; Wood, Kimberly M; Hazelton, Andrew T; Alaka, Ghassan J (October 2023, Weather and Forecasting)

   Abstract Tropical cyclone (TC) precipitation poses serious hazards including freshwater flooding. High-resolution hurricane models predict the location and intensity of TC rainfall, which can influence local evacuation and preparedness policies. This study evaluates 0–72-h precipitation forecasts from two experimental models, the Hurricane Analysis and Forecast System (HAFS) model and the basin-scale Hurricane Weather Research and Forecasting (HWRF-B) Model, for 2020 North Atlantic landfalling TCs. We use an object-based method that quantifies the shape and size of the forecast and observed precipitation. Precipitation objects are then compared for light, moderate, and heavy precipitation using spatial metrics (e.g., area, perimeter, elongation). Results show that both models forecast precipitation that is too connected, too close to the TC center, and too enclosed around the TC center. Collectively, these spatial biases suggest that the model forecasts are too intense even though there is a negative intensity bias for both models, indicating there may be an inconsistency between the precipitation configuration and the maximum sustained winds in the model forecasts. The HAFS model struggles with forecasting stratiform versus convective precipitation and with the representation of lighter (stratiform) precipitation during the first 6 h after initialization. No such spinup issues are seen in the HWRF-B forecasts, which instead exhibit systematic biases at all lead times and systematic issues across all rain-rate thresholds. Future work will investigate spinup issues in the HAFS model forecast and how the microphysics parameterization affects the representation of precipitation in both models. 

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3. How Frequently Does Rapid Intensification Occur after Rapid Contraction of the Radius of Maximum Wind in Tropical Cyclones over the North Atlantic and Eastern North Pacific?

   https://doi.org/10.1175/MWR-D-21-0322.1  

   Li, Yuanlong; Wang, Yuqing; Tan, Zhe-Min (July 2022, Monthly Weather Review)

   Abstract The phenomenon that rapid contraction (RC) of the radius of maximum wind (RMW) could precede rapid intensification (RI) in tropical cyclones (TCs) has been found in several previous studies, but it is still unclear how frequently and to what extent RC precedes RI in rapidly intensifying and contracting TCs in observations. In this study, the statistical relationship between RMW RC and TC RI is examined based on the extended best track dataset for the North Atlantic and eastern North Pacific during 1999–2019. Results show that for more than ∼65% of available TCs, the time of the peak contraction rate precedes the time of the peak intensification rate, on average, by ∼10–15 h. With the quantitatively defined RC and RI, results show that ∼50% TCs with RC experience RI, and TCs with larger intensity and smaller RMW and embedded in more favorable environmental conditions tend to experience RI more readily following an RC. Among those TCs with RC and RI, more than ∼65% involve the onset of RC preceding the onset of RI, on average, by ∼15–25 h. The preceding time tends to be longer with lower TC intensity and larger RMW and shows weak correlations with environmental conditions. The qualitative results are insensitive to the time interval for the calculation of intensification/contraction rates and the definition of RI. The results from this study can improve our understanding of TC structure and intensity changes. 

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4. Climatology of Tropical Cyclone Rainfall Magnitude at Different Landfalling Stages: An Emphasis on After-Landfall Rain

   https://doi.org/10.1175/JAMC-D-22-0055.1  

   Guzman, Oscar; Jiang, Haiyan (July 2023, Journal of Applied Meteorology and Climatology)

   Abstract Estimating the magnitude of tropical cyclone (TC) rainfall at different landfalling stages is an important aspect of the TC forecast that directly affects the level of response from emergency managers. In this study, a climatology of the TC rainfall magnitude as a function of the location of the TC centers within distance intervals from the coast and the percentage of the raining area over the land is presented on a global scale. A total of 1834 TCs in the period from 2000 until 2019 are analyzed using satellite information to characterize the precipitation magnitude, volumetric rain, rainfall area, and axial-symmetric properties within the proposed landfalling categories, with an emphasis on the postlandfall stages. We found that TCs experience rainfall maxima in regions adjacent to the coast when more than 50% of their rainfall area is over the water. TC rainfall is also analyzed over the entire TC extent and the portion over land. When the total extent is considered, rainfall intensity, volumetric rain, and rainfall area increase with wind speed intensity. However, once it is quantified over the land only, we found that rainfall intensity exhibits a nearly perfect inversely proportional relation with the increase in TC rainfall area. In addition, when a TC with life maximum intensity of a major hurricane makes landfall as a tropical depression or tropical storm, it usually produces the largest spatial extent and the highest volumetric rain. Significant StatementThis study aims to describe the cycle of tropical cyclone (TC) precipitation magnitude through a new approach that defines the landfall categories as a function of the percentage of the TC precipitating area over the land and ocean, along with the location of the TC centers within distance intervals from the coast. Our central hypothesis is that TC rainfall should exhibit distinct features in the long-term satellite time series for each of the proposed stages. We particularly focused on the overland events due to their effects on human activities, finding that the TCs that at some point of their life cycle reached major hurricane strength and made landfall as a tropical storm or tropical depression produced the highest volumetric rain over the land surface. This research also presents key observational evidence of the relationship between the rain rate, raining area, and volumetric rain for landfalling TCs. 

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5. Global Predictability of Marine Heatwave Induced Rapid Intensification of Tropical Cyclones

   https://doi.org/10.1029/2024EF004935  

   Radfar, Soheil; Foroumandi, Ehsan; Moftakhari, Hamed; Moradkhani, Hamid; Foltz, Gregory R; Sen_Gupta, Alex (December 2024, Earth's Future)

   Abstract Prediction of the rapid intensification (RI) of tropical cyclones (TCs) is crucial for improving disaster preparedness against storm hazards. These events can cause extensive damage to coastal areas if occurring close to landfall. Available models struggle to provide accurate RI estimates due to the complexity of underlying physical mechanisms. This study provides new insights into the prediction of a subset of rapidly intensifying TCs influenced by prolonged ocean warming events known as marine heatwaves (MHWs). MHWs could provide sufficient energy to supercharge TCs. Preconditioning by MHW led to RI of recent destructive TCs, Otis (2023), Doksuri (2023), and Ian (2022), with economic losses exceeding $150 billion. Here, we analyze the TC best track and sea surface temperature data from 1981 to 2023 to identify hotspot regions for compound events, where MHWs and RI of tropical cyclones occur concurrently or in succession. Building upon this, we propose an ensemble machine learning model for RI forecasting based on storm and MHW characteristics. This approach is particularly valuable as RI forecast errors are typically largest in favorable environments, such as those created by MHWs. Our study offers insight into predicting MHW TCs, which have been shown to be stronger TCs with potentially higher destructive power. Here, we show that using MHW predictors instead of the conventional method of using sea surface temperature reduces the false alarm rate by 30%. Overall, our findings contribute to coastal hazard risk awareness amidst unprecedented climate warming causing more frequent MHWs. 

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