In this study, we evaluate the ability of the High-Resolution Rapid Refresh (HRRR) model to forecast cloud characteristics through comparison of observed and simulated satellite brightness temperatures (BTs) and radar reflectivity during different weather phenomena in December 2021: the Mayfield, Kentucky, tornado on 11 December, a heavy snow event in Minnesota from 10 to 11 December, and the Midwest derecho on 15 December. This is done to illustrate the importance of examining model accuracy across a range of weather phenomena. Observation and forecast objects were created using the Method for Object-Based Diagnostic Evaluation (MODE). HRRR accurately depicted the spatial displacements between observation cloud (defined using BTs) and radar reflectivity objects, namely, the centers of cloud objects are to the east of the radar objects for the tornado and derecho events, and generally west of the radar objects for the snow event. However, HRRR had higher (less intense) simulated BTs and higher (more intense) radar reflectivity than the observations for the tornado event. Simulated radar reflectivity is higher and BTs are lower than the observations during the middle of the snow event. Also, simulated radar reflectivity is higher and BTs are lower than the observations during the derecho event. Of the three weather events, the HRRR forecasts are most accurate for the snow event, based on the object-based threat score, followed by the derecho and tornado events. The tornado event has lower accuracy because matches between paired simulated and observation objects are worse than for the snow event, with less similarity in size forecast objects and greater distance between paired object centers. Significance Statement The purpose of this study is to assess the accuracy of forecast cloud and radar objects, defined using simulated satellite brightness temperatures and radar reflectivity, from the High-Resolution Rapid Refresh (HRRR) model. This assessment was conducted for a tornado, snow, and derecho event from December 2021. Results from these three events indicate that the HRRR model accurately represents the observed displacement between the center of cloud and radar objects for the tornado and derecho events, and is the most accurate overall for the snow event.

Accurate, reliable estimates of tropical cyclone (TC) intensity are a crucial element in the warning and forecast process worldwide, and for the better part of 50 years, estimates made from geostationary satellite observations have been indispensable to forecasters for this purpose. One such method, the Advanced Dvorak Technique (ADT), was used to develop analog ensemble (AnEn) techniques that provide more precise estimates of TC intensity with instant access to information on the reliability of the estimate. The resulting methods, ADT-AnEn and ADT-based Error Analog Ensemble (ADTE-AnEn), were trained and tested using seventeen years of historical ADT intensity estimates using k-fold cross-validation with 10 folds. Using only two predictors, ADT-estimated current intensity (maximum wind speed) and TC center latitude, both AnEn techniques produced significant reductions in mean absolute error and bias for all TC intensity classes in the North Atlantic and for most intensity classes in the Eastern Pacific. The ADTE-AnEn performed better for extreme intensities in both basins (significantly so in the Eastern Pacific) and will be incorporated in the University of Wisconsin’s Cooperative Institute for Meteorological Satellite Studies (UW-CIMSS) workflow for further testing during operations in 2021.

In recent years, atmospheric numerical modeling frameworks and satellite observing systems have both undergone significant advances. While these developments offer considerable potential for improving forecasts of high-impact weather events such as tropical cyclones (TC), much work remains to be done regarding the targeted processing and optimal use of observations now becoming available with high spatiotemporal resolution. Using the 2019 version of NCEP’s HWRF model, we explore several different strategies for the assimilation of TC-scale, high-density atmospheric motion vectors (AMVs) derived from the new-generation GOES-R series of geostationary satellites. Using 2017’s Atlantic Hurricane Irma as a case study, we examine the HWRF forecast impacts of observation pre-processing, including thinning and adjustments to observation errors. It is demonstrated that enhanced vortex-scale GOES-16 AMVs contribute to notable improvements in HWRF track forecast error compared to a baseline control experiment that does not incorporate the high-density AMVs. Impacts on TC intensity and structure (i.e., wind radii) forecast errors are less robust, but results from the optimization experiments suggest that further work (both with regard to data assimilation strategies and advancements in the methods themselves) should lead to improvements in these forecast variables as well.

The performance of the Hurricane Weather Research and Forecasting (HWRF) Model Rapid Intensification Analog Ensemble (RI-AnEn) is evaluated for real-time forecasts made during the National Oceanic and Atmospheric Administration (NOAA)’s 2018 Hurricane Forecast Improvement Program (HFIP) demonstration. Using a variety of assessment tools (Brier skill score, reliability diagrams, ROC curves, ROC skill scores), RI-AnEn is shown to perform competitively compared to both the deterministic HWRF and current operational probabilistic RI forecast aids. The assessment is extended to include forecasts from the 2017 HFIP demonstration and shows that RI-AnEn is the only model with significant RI forecast skill at all lead times in the Atlantic and eastern Pacific basins. Though RI-AnEn is overconfident in its RI forecasts, it is generally well calibrated for all lead times. Furthermore, significance testing indicates that for the 2017–18 Atlantic and eastern Pacific sample, RI-AnEn is more skillful than HWRF at all lead times and better than most of the other probabilistic guidance at 48 and 72 h. ROC curves reveal that RI-AnEn offers a good combination of sensitivity and specificity, performing comparably to SHIPS-RII at all lead times in both basins. With respect to specific high-impact cases from the 2018 Atlantic season, performance of RI-AnEn ranges from excellent (Hurricane Michael) to poor (Hurricane Florence). The multiyear assessment and results for two high-impact case studies from 2018 indicate that, while promising, RI-AnEn requires further work to refine its performance as well as to accurately situate its effectiveness relative to other RI forecasts aids.

In this study, bias-corrected temperature and moisture retrievals from the Atmospheric Emitted Radiance Interferometer (AERI) were assimilated using the Data Assimilation Research Testbed ensemble adjustment Kalman filter to assess their impact on Weather Research and Forecasting model analyses and forecasts of a severe convective weather (SCW) event that occurred on 18–19 May 2017. Relative to a control experiment that assimilated conventional observations only, the AERI assimilation experiment produced analyses that were better fit to surface temperature and moisture observations and which displayed sharper depiction of surface boundaries (cold front, dry line) known to be important in the initiation and development of SCW. Forecasts initiated from the AERI analyses also exhibited improved performance compared to the control forecasts using several metrics, including neighborhood maximum ensemble probabilities (NMEP) and fractions skill scores (FSS) computed using simulated and observed radar reflectivity factor. Though model analyses were impacted in a broader area around the AERI network, forecast improvements were generally confined to the relatively small area of the computational domain located downwind of the small cluster of AERI observing sites. A larger network would increase the spatial coverage of “downwind areas” and provide increased sampling of the lower atmosphere during both active and quiescent periods. This would in turn offer the potential for larger and more consistent improvements in model analyses and, in turn, improved short-range ensemble forecasts. Forecast improvements found during this and other recent studies provide motivation to develop a nationwide network of boundary layer profiling sensors.

A feed forward neural network (FFNN) is developed for tropical cyclone (TC) intensity prediction, where intensity is defined as the maximum 1-min average 10-m wind speed. This deep learning model incorporates a real-time operational estimate of the current intensity and predictors derived from Hurricane Weather Research and Forecasting (HWRF; 2017 version) Model forecasts. The FFNN model is developed with the operational constraint of being restricted to 6-h-old HWRF data. Best track intensity data are used for observational verification. The forecast training data are from 2014 to 2016 HWRF reforecast data and cover a wide variety of TCs from both the Atlantic and eastern Pacific Ocean basins. Cross validation shows that the FFNN increasingly outperforms the operational observation-adjusted HWRF (HWFI) in terms of mean absolute error (MAE) at forecast lead times from 3 to 57 h. Out-of-sample testing on real-time data from 2017 shows the HWFI produces lower MAE than the FFNN at lead times of 24 h or less and similar MAEs at later lead times. On the other hand, the 2017 data indicate significant potential for the FFNN in the prediction of rapid intensification (RI), with RI defined here as an intensification of at least 30 kt (1 kt approximate to 0.51 m s(-1)) in a 24-h period. The FFNN produces 4 times the number of hits in HWFI for RI. While the FFNN has more false alarms than the HWFI, Brier skill scores show that, in the Atlantic, the FFNN has significantly greater skill than the HWFI and probabilistic Statistical Hurricane Intensity Prediction System RI index.

It is well known that global numerical model analyses and forecasts benefit from the routine assimilation of atmospheric motion vectors (AMVs) derived from meteorological satellites. Recent studies have also shown that the assimilation of enhanced (spatial and temporal) AMVs can benefit research-mode regional model forecasts of tropical cyclone track and intensity. In this study, the impact of direct assimilation of enhanced (higher resolution) AMV datasets in the NCEP operational Hurricane Weather Research and Forecasting Model (HWRF) system is investigated. Forecasts of Atlantic tropical cyclone track and intensity are examined for impact by inclusion of enhanced AMVs via direct data assimilation. Experiments are conducted for AMVs derived using two methodologies ("HERITAGE'' and "GOES-R''), and also for varying levels of quality control in order to assess and inform the optimization of the AMV assimilation process. Results are presented for three selected Atlantic tropical cyclone events and compared to Control forecasts without the enhanced AMVs as well as the corresponding operational HWRF forecasts. The findings indicate that the direct assimilation of high-resolution AMVs has an overall modest positive impact on HWRF forecasts, but the impact magnitudes are dependent on the 1) availability of rapid scan imagery used to produce the AMVs, 2) AMV derivation approach, 3) level of quality control employed in the assimilation, and 4) vortex initialization procedure (including the degree to which unbalanced states are allowed to enter the model analyses).

An analog ensemble (AnEn) technique is applied to the prediction of tropical cyclone (TC) intensity (i.e., maximum 1-min averaged 10-m wind speed). The AnEn is an inexpensive, naturally calibrated ensemble prediction of TC intensity derived from a training dataset of deterministic Hurricane Weather Research and Forecasting (HWRF; 2015 version) Model forecasts. In this implementation of the AnEn, a set of analog forecasts is generated by searching an HWRF archive for forecasts sharing key features with the current HWRF forecast. The forecast training period spans 2011–15. The similarity of a current forecast with past forecasts is estimated using predictors derived from the HWRF reforecasts that capture thermodynamic and kinematic properties of a TC’s environment and its inner core. Additionally, the value of adding a multimodel intensity consensus forecast as an AnEn predictor is examined. Once analogs are identified, the verifying intensity observations corresponding to each analog HWRF forecast are used to produce the AnEn intensity prediction. In this work, the AnEn is developed for both the eastern Pacific and Atlantic Ocean basins. The AnEn’s performance with respect to mean absolute error (MAE) is compared with the raw HWRF output, the official National Hurricane Center (NHC) forecast, and other top-performing NHC models. Also, probabilistic intensity forecasts are compared with a quantile mapping model based on the HWRF’s intensity forecast. In terms of MAE, the AnEn outperforms HWRF in the eastern Pacific at all lead times examined and up to 24-h lead time in the Atlantic. Also, unlike traditional dynamical ensembles, the AnEn produces an excellent spread–skill relationship.

In this study, cycled forecast experiments were performed to assess the ability of different cloud microphysics and cumulus parameterization schemes in the Hurricane Weather Research and Forecasting (HWRF) Model to accurately simulate the evolution of the cloud and moisture fields during the entire life cycle of Hurricane Edouard (2014). The forecast accuracy for each model configuration was evaluated through comparison of observed and simulated Geostationary Operational Environmental Satellite-13 (GOES-13) infrared brightness temperatures and satellite-derived tropical cyclone intensity estimates computed using the advanced Dvorak technique (ADT). Overall, the analysis revealed a large moist bias in the mid-and upper troposphere during the entire forecast period that was at least partially due to a moist bias in the initialization datasets but was also affected by the microphysics and cumulus parameterization schemes. Large differences occurred in the azimuthal brightness temperature distributions, with two of the microphysics schemes producing hurricane eyes that were much larger and clearer than observed, especially for later forecast hours. Comparisons to the forecast 10-m wind speeds showed reasonable agreement (correlations between 0.58 and 0.74) between the surface-based intensities and the ADT intensity estimates inferred via cloud patterns in the upper troposphere. It was also found that model configurations that had the smallest differences between the ADT and surface-based intensities had the most accurate track and intensity forecasts. Last, the cloud microphysics schemes had the largest impact on the forecast accuracy.

The potential usefulness of spaceborne Doppler radar as a tropical cyclone observing tool is assessed by conducting a high-resolution simulation of an intense hurricane and generating synthetic observations of reflectivity and radial velocity. The ground-based velocity track display (GBVTD) technique is used to process the radial velocity observations and generate retrievals of meteorologically relevant metrics such as the maximum wind (MW), radius of maximum wind (RMW), and radius of 64-kt wind (R64). Results indicate that the performance of the retrieved metrics compares favorably with the current state-of-the-art satellite methods for intensity estimation and somewhat better than current methods for structure (i.e., wind radii).

Recently, a number of investigations have been made that point to the robust effectiveness of the Ensemble Kalman Filter (EnKF) in convective-scale data assimilation. These studies have focused on the assimilation of ground-based Doppler radar observations (i.e. radial velocity and reflectivity). The present study differs from these investigations in two important ways. First, in anticipation of future satellite technology, the impact of assimilating spaceborne Doppler-retrieved vertical velocity is examined; second, the potential for the EnKF to provide an alternative to instrument-based microphysical retrievals is investigated. It is shown that the RMS errors of the analyzed fields produced by assimilation of vertical velocity alone are in general better than those obtained in previous studies: in most cases assimilation of vertical velocity alone leads to analyses with small errors (e.g. <1 ms-1 for velocity components) after only 3 or 4 assimilation cycles. The microphysical fields are notable exceptions, exhibiting lower errors when observations of reflectivity are assimilated together with observations of vertical velocity, likely a result of the closer relationship between reflectivity and the microphysical fields themselves. It is also shown that the spatial distribution of the error estimates improves (i.e. approaches the true errors) as more assimilation cycles are carried out, which could be a significant advantage of EnKF model-based retrievals.

Records are presented from nine barographs located in a small area close to the path of a tornado. The pressure profile in the range from 720 to 2,300 feet from the path of the tornado center as determined from the barograph records was found to be in good agreement with calculations based on a simple model consisting of a frictionless vortex with in-flow.