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With varying tangential winds and combinations of stratiform and convective clouds, tropical cyclones (TCs) can be difficult to accurately portray when mosaicking data from ground-based radars. This study utilizes the Dual-frequency Precipitation Radar (DPR) from the Global Precipitation Measurement Mission (GPM) satellite to evaluate reflectivity obtained using four sampling methods of Weather Surveillance Radar 1988-Doppler data, including ground radars (GRs) in the GPM ground validation network and three mosaics, specifically the Multi-Radar/Multi-Sensor System plus two we created by retaining the maximum value in each grid cell (MAX) and using a distance-weighted function (DW). We analyzed Hurricane Laura (2020), with a strong gradient in tangential winds, and Tropical Storm Isaias (2020), where more stratiform precipitation was present. Differences between DPR and GR reflectivity were larger compared to previous studies that did not focus on TCs. Retaining the maximum value produced higher values than other sampling methods, and these values were closest to DPR. However, some MAX values were too high when DPR time offsets were greater than 120 s. The MAX method produces a more consistent match to DPR than the other mosaics when reflectivity is <35 dBZ. However, even MAX values are 3–4 dBZ lower than DPR in higher-reflectivity regions where gradients are stronger and features change quickly. The DW and MRMS mosaics produced values that were similar to one another but lower than DPR and MAX values. 

{"Abstract":["This is software and data to support the manuscript "Variations in Tropical Cyclone Size and Rainfall Patterns based on Synoptic-Scale Moisture Environments in the North Atlantic," which we are submitting to the journal, Journal of Geophysical Research Atmospheres.The MIT license applies to all source code and scripts published in this dataset.The software includes all code that is necessary to follow and evaluate the work. Public datasets include (1) the Atlantic hurricane database HURDAT2 (https://www.nhc.noaa.gov/data/#hurdat), (2) NASA’s Global Precipitation Measurement IMERG final precipitation (https://catalog.data.gov/dataset/gpm-imerg-final-precipitation-l3-half-hourly-0-1-degree-x-0-1-degree-v07-gpm-3imerghh-at-g), (3) the Tropical Cyclone Extended Best Track Dataset (https://rammb2.cira.colostate.edu/research/tropical-cyclones/tc_extended_best_track_dataset/), (4) the European Centre for Medium-Range Weather Forecasts (ECMWF) atmospheric reanalysis (ERA5) (https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era5), and (5) the Statistical Hurricane Intensity Prediction Scheme (SHIPS) dataset (https://rammb.cira.colostate.edu/research/tropical_cyclones/ships/data/). We are also including four datasets generated by the code that will be helpful in evaluating the work. Lastly, we used the eofs software package, a python package for computing empirical orthogonal functions (EOFs), available publicly here: https://doi.org/10.5334/jors.122.All figures and tables in the manuscript are generated using Python, ArcGIS Pro, and GraphPad/Prism 10 Software:ArcGIS Pro used to make Figures 5GraphPad/Prism 10 Software used to make box plots in Figures 6-9Python used to make Figures 1-4, 10-11, and Tables 1-5Public Datasets:HURDAT2: Landsea, C. and Beven, J., 2019: The revised Atlantic hurricane database (HURDAT2). March 2022, https://www.aoml.noaa.gov/hrd/hurdat/hurdat2-format.pdfIMERG:NASA EarthData: GPM IMERG Final Precipitation L3 Half Hourly 0.1 degree x 0.1 degree V06. 9 December 2024, https://catalog.data.gov/dataset/gpm-imerg-final-precipitation-l3-half-hourly-0-1-degree-x-0-1-degree-v07-gpm-3imerghh-at-g. Note that this dataset is not longer publicly available, as it has been replaced with IMERG version 7: https://disc.gsfc.nasa.gov/datasets/GPM_3IMERGHH_07/summary?keywords="IMERG final"Extended Best Track:Regional and Mesoscale Meteorology Branch, 2022: The Tropical Cyclone Extended Best Track Dataset (EBTRK). March 2022, https://rammb2.cira.colostate.edu/research/tropical-cyclones/tc_extended_best_track_dataset/ERA5: Guillory, A. (2022). ERA5. Ecmwf [Dataset]. https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era5. (Accessed March 2, 2023). Also: Hersbach, H., and Coauthors, 2020: The ERA5 global reanalysis. Quarterly Journal of the Royal Meteorological Society, 146, 1999–2049, https://doi.org/10.1002/qj.3803SHIPS:Ships Predictor Files - Colorado State University (2022). Statistical Tropical Cyclone Intensity Forecast Technique Development. https://rammb.cira.colostate.edu/research/tropical_cyclones/ships/data/ships_predictor_file_2022.pdf. Also: DeMaria, M., and J. Kaplan, 1994: A Statistical Hurricane Intensity Prediction Scheme (SHIPS) for the Atlantic Basin. Weather and Forecasting, 9, 209–220, https://doi.org/10.1175/1520-0434(1994)009<0209:ASHIPS>2.0.CO;2.Public Software: Dawson, A., 2016: eofs: A Library for EOF Analysis of Meteorological, Oceanographic, and Climate Data. JORS, 4, 14, https://doi.org/10.5334/jors.122.van der Walt, S., Schönberger, J. L., Nunez-Iglesias, J., Boulogne, F., Warner, J. D., Yager, N., et al. (2014). Scikit-image: Image processing in Python [Software]. PeerJ, 2, e453. https://doi.org/10.7717/peerj.453"]} 

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 Because geostationary satellite (Geo) imagery provides a high temporal resolution window into tropical cyclone (TC) behavior, we investigate the viability of its application to short-term probabilistic forecasts of TC convective structure to subsequently predict TC intensity. Here, we present a prototype model that is trained solely on two inputs: Geo infrared imagery leading up to the synoptic time of interest and intensity estimates up to 6 h prior to that time. To estimate future TC structure, we compute cloud-top temperature radial profiles from infrared imagery and then simulate the evolution of an ensemble of those profiles over the subsequent 12 h by applying a deep autoregressive generative model (PixelSNAIL). To forecast TC intensities at hours 6 and 12, we input operational intensity estimates up to the current time (0 h) and simulated future radial profiles up to +12 h into a “nowcasting” convolutional neural network. We limit our inputs to demonstrate the viability of our approach and to enable quantification of value added by the observed and simulated future radial profiles beyond operational intensity estimates alone. Our prototype model achieves a marginally higher error than the National Hurricane Center’s official forecasts despite excluding environmental factors, such as vertical wind shear and sea surface temperature. We also demonstrate that it is possible to reasonably predict short-term evolution of TC convective structure via radial profiles from Geo infrared imagery, resulting in interpretable structural forecasts that may be valuable for TC operational guidance. Significance Statement This work presents a new method of short-term probabilistic forecasting for tropical cyclone (TC) convective structure and intensity using infrared geostationary satellite observations. Our prototype model’s performance indicates that there is some value in observed and simulated future cloud-top temperature radial profiles for short-term intensity forecasting. The nonlinear nature of machine learning tools can pose an interpretation challenge, but structural forecasts produced by our model can be directly evaluated and, thus, may offer helpful guidance to forecasters regarding short-term TC evolution. Since forecasters are time limited in producing each advisory package despite a growing wealth of satellite observations, a tool that captures recent TC convective evolution and potential future changes may support their assessment of TC behavior in crafting their forecasts. 

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.