Storm Forecasting Research Articles

A novel deep learning approach for typhoon-induced storm surge modeling through efficient emulation of wind and pressure fields

Modeling typhoon-induced storm surges requires 10-m wind and sea level pressure fields as forcings, commonly obtained using parametric models or a fully dynamical simulation by numerical weather prediction (NWP) models. The parametric models are generally less accurate than the full-physics models of the NWP, but they are often preferred owing to their computational efficiency facilitating rapid uncertainty quantification. Here, we propose using a deep learning method based on generative adversarial networks (GAN) to translate the parametric model outputs into a more realistic atmospheric forcings structure resembling the NWP model results. Additionally, we introduce lead-lag parameters to incorporate a forecasting feature in our model. Thirty-four historical typhoon events from 1981 to 2012 are selected to train the GAN, followed by storm surge simulations for the four most recent events. The proposed method efficiently transforms the parametric model into realistic forcing fields by a standard desktop computer within a few seconds. The results show that the storm surge model accuracy with forcings generated by GAN is comparable to that of the NWP model and outperforms the parametric model. Our novel GAN model offers an alternative for rapid storm forecasting and can potentially combine varied data, such as those from satellite images, to improve the forecasts further.

Improving Tropical Cyclogenesis Forecasts of Hurricane Irma (2017) through the Assimilation of All-Sky Infrared Brightness Temperatures

Abstract The assimilation of satellite all-sky infrared (IR) brightness temperatures (BTs) has been shown in previous studies to improve intensity forecasts of tropical cyclones. In this study, we examine whether assimilating all-sky IR BTs can also potentially improve tropical cyclogenesis forecasts by improving the pregenesis cloud and moisture fields. By using an ensemble-based data assimilation system, we show that the assimilation of upper-tropospheric water vapor channel BTs observed by the Meteosat-10 SEVIRI instrument two days before the formation of a tropical depression improves the genesis forecast of Hurricane Irma (2017), a classic Cape Verde storm, by up to 24 h while also capturing its later rapid intensification in deterministic forecasts. In an experiment that withholds the assimilation of all-sky IR BTs, the assimilation of conventional observations from the Global Telecommunications System (GTS) leads to the premature genesis of Hurricane Irma by at least 24 h. This premature genesis is shown to result from an overestimation of the spatial coverage of deep convection within the African easterly wave (AEW) from which Irma eventually forms. The gross overestimation of deep convection without all-sky IR BTs is accompanied by higher column saturation fraction, stronger low-level convergence, and the earlier spinup of a low-level meso-β-scale vortex within the AEW that ultimately becomes Hurricane Irma. Through its adjustment to the initial moisture and cloud conditions, the assimilation of all-sky IR BTs leads to a more realistic convective evolution in forecasts and ultimately a more realistic timing of genesis. Significance Statement Every year hurricanes impact the lives of thousands of people living along the eastern coast of the United States. Many of these storms originate from tropical disturbances that exit the west coast of Africa. To give the public more warning time ahead of these storms, it is important to improve the forecasts of their formation. This study uses a system developed at The Pennsylvania State University to incorporate satellite observations into forecasts of a classic Cape Verde storm, Hurricane Irma (2017), two days before it formed. By using satellite-collected radiances, we improve the timing of its formation by up to 24 h due to a better representation of the mesoscale tropical disturbance from which it originated.

A semi-Lagrangian method for detecting and tracking deep convective clouds in geostationary satellite observations

Abstract. Automated methods for the detection and tracking of deep convective clouds in geostationary satellite imagery have a vital role in both the forecasting of severe storms and research into their behaviour. Studying the interactions and feedbacks between multiple deep convective clouds (DCC), however, poses a challenge for existing algorithms due to the necessary compromise between false detection and missed detection errors. We utilise an optical flow method to determine the motion of deep convective clouds in GOES-16 ABI imagery in order to construct a semi-Lagrangian framework for the motion of the cloud field, independently of the detection and tracking of cloud objects. The semi-Lagrangian framework allows severe storms to be simultaneously detected and tracked in both spatial and temporal dimensions. For the purpose of this framework we have developed a novel Lagrangian convolution method and a number of novel implementations of morphological image operations that account for the motion of observed objects. These novel methods allow the accurate extension of computer vision techniques to the temporal domain for moving objects such as DCCs. By combining this framework with existing methods for detecting DCCs (including detection of growing cores through cloud top cooling and detection of anvil clouds using brightness temperature), we show that the novel framework enables reductions in errors due to both false and missed detections compared to any of the individual methods, reducing the need to compromise when compared with existing frameworks. The novel framework enables the continuous tracking of anvil clouds associated with detected deep convection after convective activity has stopped, enabling the study of the entire life cycle of DCCs and their associated anvils. Furthermore, we expect this framework to be applicable to a wide range of cases including the detection and tracking of low-level clouds and other atmospheric phenomena. In addition, this framework may be used to combine observations from multiple sources, including satellite observations, weather radar and reanalysis model data.

The orbit schemes to monitor Martian dust storms: Benefits to China’s future Mars missions

<p indent="0mm">Mars is most often the focus of planetary explorations because it promises to have signs of extraterrestrial life. China’s first Mars mission, called Tianwen-1, was launched on 22 May 2021 and was successful in “orbiting, landing and roving” the planet and exploring its environment. Following this success, China planned to carry out another Mars mission, known as Tianwen-3, to collect and return samples. However, dust storms posed a significant threat. Such activity on Mars has an evident pattern of seasonal variation, frequently occurring during spring and summer in the southern hemisphere. Under certain conditions, this dust activity can develop into a global storm, lasting several months, and can strongly perturb the upper atmosphere, which is crucial for the aerocapture of probes. Even regional storms can modify local visibility, temperature, wind, and the density of the lower and middle atmosphere, which impact parachutes, navigational imaging, and other decent-entry-landing and take-off probe processes. Indeed, the tiniest grains of dust pose a threat to human soft tissues and are harmful to the surface probes’ mechanisms and filters. Solar panels, cameras, etc., are also adversely impacted by dust deposition, which peaks during the fallout of a global dust storm. Thus, forecasting dust storms is invaluable for reducing or avoiding damage to these probes. However, the physical complexity of dust storms on Mars is not fully understood, making accurate forecasts of dust storms, and particularly their onset, challenging. To aid future Mars missions such as the Tianwen-3, we propose that China establish a network to monitor Martian dust storms and atmospheric weather that is based on observations of Mars from a spacecraft constellation. This constellation consists of three spacecraft in an equatorial synchronous orbit with an altitude of about <sc>17000 km</sc> and one spacecraft in a large-elliptic polar orbit whose perigee altitude is about <sc>150 km.</sc> The constellation with “four-point integration” can form a monitoring and alert network that provides simultaneous, continuous, and global-scale observations of the Martian atmosphere and surface. As an innovative platform for space-based science, this network has many advantages for Mars exploration, because it can monitor Martian dust storms and geomorphic changes on the surface, as well as the space environment and atmospheric weather. It is also beneficial for spacecraft relaying communication from probes on the Martian surface, allowing continuous data to be uplinked. Finally, the constellation is an ideal platform for on-orbit computing, delivering computer resources to surface assets, and global navigation of Mars.

A Regional Ionospheric Storm Forecasting Method Using a Deep Learning Algorithm: LSTM

AbstractAn ionospheric storm forecasting method was proposed using a deep learning algorithm, LSTM (long short‐term memory). We used the perturbation index to denote the level of an ionospheric storm, deduced from foF2 data, and helped to remove most of the local time and seasonal variations in the ionosphere. In constructing the model, a number of correlated factors were used as inputs, including the properties of coronal mass ejections, solar flare bursts, interplanetary conditions, and geomagnetic and ionospheric states, and the output was whether an ionospheric storm occurred locally in the next 24 hr. Data sets from 2007 to 2014 were used to train the model, and those from 2015 to 2016 were used for validation. The results showed that the model behaved well in most events. The mean precision rate, recall rate, accuracy, and F1 score of the model were 71.7%, 59.7%, 92.7%, and 65.0% in northern China and 78.9%, 56.3%, 96.3% and 65.0% in southern China, respectively. The LSTM forecasting model performed better than other models such as persistence, multiple‐layer perceptron and support vector machine models. Case studies also showed good performance during geomagnetic storms of different strengths. We believe that this model can be beneficial for functional ionospheric storm operation.

Innovations in Winter Storm Forecasting and Decision Support Services

Abstract Winter storms are disruptive to society and the economy, and they often cause significant injuries and deaths. Innovations in winter storm forecasting have occurred across the value chain over the past two decades, from physical understanding, to observations, to model forecasts, to postprocessing, to forecaster knowledge and interpretation, to products and services, and ultimately to decision support. These innovations enable more accurate and consistent forecasts, which are increasingly being translated into actionable information for decision-makers. This paper reviews the current state of winter storm forecasting in the context of the U.S. National Weather Service operations and describes a potential future state. Given predictability limitations, a key challenge of winter storm forecasting has been characterizing uncertainty and communicating the forecast in ways that are understandable and useful to decision-makers. To address this challenge, particular focus is placed on establishing a probabilistic framework, with probabilistic hazard information serving as a foundation for winter storm decision support services. The framework is guided by social science research to ensure effective communication of risk to meet users’ needs. Solutions to gaps impeding progress in winter storm forecasting are highlighted, including better understanding of mesoscale phenomenon, the need for better ensemble calibration, a rigorous and consistent database of observed impacts, and linking multiparameter probabilities (e.g., probability of intense snowfall rates at rush hour) with users’ information needs and decisions.

Human or Machine? How Much Difference in Understanding and Trust Does a Human Element Make in Storm Forecasts?

The past two Atlantic hurricane seasons have been among the most active in history. In the hurricane-prone southeast, broadcast meteorologists and emergency managers want their communities to both understand and trust the severe weather alerts they send out. This study examined how college students, a vulnerable population, view those messages. Results show students believed they understood, and actually did understand, messages with both human and graphics more than human only or graphics only. The human-graphics combination was also the most trusted of the three options. These findings suggest that not only is the human element important in emergency messages but students are also seeking out local television and meteorologists for their weather information more than the internet.

Performance of the WRF Model for the Forecasting of the V-Shaped Storm Recorded on 11–12 November 2019 in the Eastern Sicily

During the autumn season, Sicily is often affected by severe weather events, such as self-healing storms called V-shaped storms. These phenomena cause significant total rainfall quantities in short time intervals in localized spatial areas. In this framework, this study analyzes the meteorological event recorded on 11–12 November 2019 in Sicily (southern Italy), using the Weather Research and Forecasting (WRF) model with a horizontal spatial grid resolution of 3 km. It is important to note that, in this event, the most significant rainfall accumulations were recorded in eastern Sicily. In particular, the weather station of Linguaglossa North Etna (Catania) recorded a total rainfall of 293.6 mm. The precipitation forecasting provided by the WRF model simulation has been compared with the data recorded by the meteorological stations located in Sicily. In addition, a further simulation was carried out using the Four-Dimensional Data Assimilation (FDDA) technique to improve the model capability in the event reproduction. In this regard, in order to evaluate which approach provides the best performance (with or without FDDA), the Root Mean Square Error (RMSE) and dichotomous indexes (Accuracy, Threat Score, BIAS, Probability of Detection, and False Alarm Rate) were calculated.

A multi-site tide level prediction model based on graph convolutional recurrent networks

Predicting regional tide levels is vital for engineering and catastrophe avoidance along the shore. Data-driven method is capable of fast prediction of tide levels. However, current data-driven algorithms only make predictions for individual tide stations, rather than aiming to a regional network system of tide stations. This paper proposes a model based on graph convolutional recurrent networks to predict tidal levels at regional multiple tide stations. The model captures spatial and temporal features from historical tide level and meteorological data. Future tidal levels for multiple tide stations are the model's output. In this work, 48-year historical data from five tidal stations in Pearl River Delta were utilized for model training and evaluation. The results show that: (1) The model outperforms five commonly used baseline models in terms of evaluation metrics RMSE and MAE, and is able to predict future tide levels at multiple tide stations; (2) Short-term forecasts (1 and 3 h) are more accurate than long-term forecasts (12 h); (3) The model retains a high degree of accuracy for short-term predictions and satisfactory accuracy for long-term prediction during typhoons. The method provides a new instrument for regional prediction of tide levels and forecasting of storm surges.

Pixel-CRN: A New Machine Learning Approach for Convective Storm Nowcasting

The short-term convective storm forecasting (i.e., nowcasting) mainly rely on weather radar which can resolve the 3D structure of convective storms. With the rapid development of numerical models, modern models can produce 3D reanalysis data which gives atmospheric background information of convective storms. Current deep learning nowcasting models use only 2D radar images for nowcasting and often require massive historical data for training. But it may not be operationally feasible to collect long-term radar data to train a new model. Hence, how to establish a nowcasting model using only a small dataset has become an important issue. In addition, existing models do not effectively use the state-of-the-art model reanalysis data, which is a shortcoming of these models. To tackle these problems, this article develops a pixel-wise convolutional recurrent neural network (Pixel-CRN) for precipitation nowcasting. It has three key designs: (1) Through a concise pixel-wise sampling and oversampling technique, Pixel-CRN can be trained using only a small dataset. (2) In spatial learning, Pixel-CRN embeds a spatial convolution subnet into the recurrent unit, which can input raw 3D radar and model reanalysis data, thus valuable atmospheric background information can be learned to assist nowcasting. (3) In spatiotemporal learning, according to the information bottleneck principle, Pixel-CRN builds a heterogeneous encoder-decoder structure to squeeze multi-channel 3D input data into latent space, and recurrently generates 30 and 60 min nowcasts. Compared with existing deep learning nowcasting methods, the experimental results show that Pixel-CRN can provide skillful results with a rather small training dataset.

A multi-objective framework to select numerical options in air quality prediction models: A case study on dust storm modeling

Numerical weather prediction models are very important tools in predicting severe weather phenomena such as dust storms. However, the prediction accuracy in these models depends on the options considered in the modeling. In this study, a multi-objective framework is presented to determine the optimal options of the weather research forecasting with chemistry (WRF-Chem) model. For this purpose, a severe dust storm that occurred in the center of Iran is considered and the effect of 10 options including grid (computational domain size, modeling start time, horizontal, vertical and temporal resolution), physical (initial conditions, boundary layer and land surface schemes) and chemical options (dust emission schemes and dust source functions) are investigated. In general, the results showed that the WRF-Chem model has a high ability to model dust storms, but its results depend on the options considered in the modeling. Evaluation of grid options showed that inappropriate selection of domain size and modeling start time can lead to the failure in dust storm forecasting. Also, the land surface scheme has the greatest impact on dust concentration among the physical options. In addition, chemical options have the greatest impact on the dust storm forecasting as well. Based on the proposed multi-objective framework, the optimal options for dust storm modeling were determined. The proposed approach is comprehensive and can be used for other atmospheric/air quality modeling.

Impact of Assimilating High‐Resolution Atmospheric Motion Vectors on Convective Scale Short‐Term Forecasts: 3. Experiments With Radar Reflectivity and Radial Velocity

AbstractBased on the idealized supercell and real case studies in Part I and II, the purpose of this subsequent study is to further investigate the impact of assimilating Geostationary Operational Environmental Satellites‐16 (GOES‐16) derived atmospheric motion vectors (AMVs) in addition to WSR_88D Doppler radar observations on convective scale numerical weather prediction. Five high‐impact weather events that occurred in spring 2018 and 2019 are analyzed using the National Severe Storms Laboratory three‐dimensional variational data assimilation (DA) system. Four types of experiments are implemented and compared: (a) the control experiment (NoDA) without assimilating any observation, (b) the radar DA experiment (RAD), (c) the GOES‐16 AMV DA experiment (AMV), and (d) the experiment assimilating AMVs together with radar data (AMV_RAD). Score metrics aggregated over all cases indicate that AMV_RAD performs slightly better than RAD in 0–3 hr reflectivity and precipitation forecasts especially at higher thresholds, suggesting the added value of GOES‐16 AMVs on radar data. Detailed case examinations also show that AMV_RAD generally exhibits slightly more skillful storm forecast in terms of the areal coverage, storm mode, and storm orientations, owing to improvements in the analysis of boundary locations and localized enhanced divergence signatures. In spite of encouraging objective and subjective evaluation results, AMV_RAD has difficulty in adjusting the moisture gradient associated with dryline and tends to underpredict the associated weak discrete storms.

Effects of aerosols on the forecasting of Mei-yu frontal storms over the Yangtze–Huai River valley

The evolutions of Mei-yu frontal storms during the Mei-yu season in 2018 were simulated in the Weather Research and Forecasting with Chemistry (WRF/Chem) model to investigate the effects of aerosols on the prediction of clouds and precipitation. Two groups of daily launched forecast experiments were conducted with a duration of 17 days, by turning the chemistry–aerosol module on (WRF/Chem) and off (WRF), and additional cloud droplet number concentration (Nd) experiments were perturbed in WRF. Comparison between the two groups of experiments suggested that activating the chemistry–aerosol module improved the forecast skill for precipitation exceeding 5 mm/h during the Mei-yu season, while had no positive forecast skill on precipitation exceeding 0.1 mm/h in terms of both traditional and neighborhood spatial verifications. In contrast to the uniform Nd distribution adopted in WRF, the inhomogeneous Nd distribution in WRF/Chem agreed better with in-situ aircraft observations collected during the field campaign. As Nd increases in WRF experiments, the warm clouds decreased and deep convective clouds significantly increased with more convective updraft mass flux and net heat budget. Scavenging in Mei-yu frontal storms generally reduced the median Nd in the WRF/Chem experiment. The fewer and larger cloud particles in WRF/Chem promoted the conversion of cloud to rain and the accretion of cloud by rain, thereby leading to invigoration of warm clouds and precipitation efficiency, which explains the improved forecast skill for precipitation exceeding 5 mm/h. Enhanced warm-rain processes caused fewer cloud particles for further ascent and ultimately resulted in suppression of deep convective clouds. The preliminary findings in this study highlights the potential significance of incorporating chemistry–aerosol schemes in the operational forecasting of Mei-yu rainfall.

Hurricane Sally (2020) Shifts the Ocean Thermal Structure across the Inner Core during Rapid Intensification over the Shelf

Abstract Prediction of rapid intensification in tropical cyclones prior to landfall is a major societal issue. While air–sea interactions are clearly linked to storm intensity, the connections between the underlying thermal conditions over continental shelves and rapid intensification are limited. Here, an exceptional set of in situ and satellite data are used to identify spatial heterogeneity in sea surface temperatures across the inner core of Hurricane Sally (2020), a storm that rapidly intensified over the shelf. A leftward shift in the region of maximum cooling was observed as the hurricane transited from the open gulf to the shelf. This shift was generated, in part, by the surface heat flux in conjunction with the along- and across-shelf transport of heat from storm-generated coastal circulation. The spatial differences in the sea surface temperatures were large enough to potentially influence rapid intensification processes suggesting that coastal thermal features need to be accounted for to improve storm forecasting as well as to better understand how climate change will modify interactions between tropical cyclones and the coastal ocean. Significance Statement The connections between the underlying thermal energy in the ocean that powers tropical cyclones and rapid intensification of storms over continental shelves are limited. An exceptional set of data collected in the field as well as from space with satellites was used to identify spatial variations in sea surface temperatures across the inner core of Hurricane Sally (2020), a storm that rapidly intensified over the shelf. The spatial differences were due to the heat loss from the surface of the ocean as well as heat transport by shelf currents. The spatial differences were large enough to potentially influence how quickly storms can intensify, suggesting that coastal thermal features need to be accounted for to improve storm forecasting.

Quantifying the Relationship Between Atmospheric River Origin Conditions and Landfall Temperature

AbstractThe temperature of landfalling atmospheric rivers (ARs) has direct implications for regional water resources. Compared to cool ARs, warm ARs can result in more surface runoff and flooding, less water availability via low snow accumulations and enhanced snowmelt, and greater challenges for storm forecasting and reservoir operations. Based on case studies, ARs with subtropical origin locations and warm origin conditions are assumed to be associated with warmer landfall temperatures—though, to date, this has not yet been demonstrated systematically. We analyze North Pacific ARs that made landfall along the West Coast of North America from 1980 to 2017. We find that ARs originating over the subtropical Pacific Ocean near Hawaii (“Pineapple Express”‐type ARs) are indeed 1.5°C warmer and 2 kg/m2 moister in winter than ARs originating elsewhere. We extend this analysis for the full study population of ARs by quantifying AR origin conditions, including the spatial distribution of origin temperature, moisture content, and integrated vapor transport (IVT). We use fixed effects multivariate regression to quantify the relative influence of the origin conditions on landfall temperature. Our regression models show that AR origins with lower latitudes, longer AR lifetimes, and stronger IVT are associated with warmer landfall temperatures and that ARs that start warm generally stay warm. We also find that the phase of the El Niño/Southern Oscillation does not exhibit a consistent relationship with AR landfall temperature. Overall, our results partially affirm—yet also complicate—common assumptions about the role of different origin conditions in landfalling AR temperature.

Distinguishing between Hodographs of Severe Hail and Tornadoes

Abstract Hodographs are valuable sources of pattern recognition in severe convective storm forecasting. Certain shapes are known to discriminate between single cell, multicell, and supercell storm organization. Various derived quantities such as storm-relative helicity (SRH) have been found to predict tornado potential and intensity. Over the years, collective research has established a conceptual model for tornadic hodographs (large and “looping,” with high SRH). However, considerably less attention has been given to constructing a similar conceptual model for hodographs of severe hail. This study explores how hodograph shape may differentiate between the environments of severe hail and tornadoes. While supercells are routinely assumed to carry the potential to produce all hazards, this is not always the case, and we explore why. The Storm Prediction Center (SPC) storm mode dataset is used to assess the environments of 8958 tornadoes and 7256 severe hail reports, produced by right- and left-moving supercells. Composite hodographs and indices to quantify wind shear are assessed for each hazard, and clear differences are found between the kinematic environments of hail-producing and tornadic supercells. The sensitivity of the hodograph to common thermodynamic variables was also examined, with buoyancy and moisture found to influence the shape associated with the hazards. The results suggest that differentiating between tornadic and hail-producing storms may be possible using properties of the hodograph alone. While anticipating hail size does not appear possible using only the hodograph, anticipating tornado intensity appears readily so. When coupled with buoyancy profiles, the hodograph may assist in differentiating between both hail size and tornado intensity.

A New Index to Describe the Response of Geomagnetic Disturbance to the Energy Injection from the Solar Wind

In this paper, we establish a new non-dimensional global geomagnetic disturbance index JpG by applying the spectral whitening method to the horizontal components of geomagnetic fields observed at eight ground-based stations distributed at low and middle latitudes during years 1998 to 2014. This index can describe the development of geomagnetic storms and its relationship with the Dst index has been verified, which gives a correlation coefficient (CC) of about 0.72. We also check the response of JpG to the arrival of upstream solar wind energy based on a proxy that the ring current injection term Q. The variation of JpG in course of geomagnetic storms is similar to the variation of Q, and the recorded minimum values of Q (Qmin) and JpG (JpGmin) for 30 great storms yields a relatively better CC of about 0.82. These results illustrate that JpG can effectively depict the storm evolution and is well related to the associated Q in amplitude, which provides an alternative means of geomagnetic storm forecasting. In addition, we note that the time difference between Qmin and JpGmin, as well as the time difference when JpG recovers from JpGmin to half and/or one-third of its value, are shorter than those of the corresponding Dst index. And especially, for multiple storms that occurred continuously on a short time scale, the recovery of the Dst index to a quiet period level can be affected by the following solar wind energy input, while the JpG index does not and exhibits independently.

Ensemble forecast for storm tide and resurgence from Tropical Cyclone Isaias

Ensemble-based probabilistic forecasting of storm surge is increasingly being used to provide metrics for emergency management decisions such as the near-worst case scenario. The Stevens Flood Advisory System is an ensemble prediction system used to forecast total water levels over a broad coastal region and street-scale flood levels for several New York Harbor (NYH) critical infrastructure sites. As a part of our continuous assessment of this system’s performance, we evaluate its prediction of storm tide and resurgence during Tropical Cyclone Isaias (2020), which tracked northward along the Pennsylvania/New Jersey border and caused the largest storm surge in NYH since Hurricane Sandy. Isaias specific track and speed generated an unusual flood event consisting of a storm surge, a blowout, then a significant resurgence that caused minor flooding. The analysis shows that the super-ensemble spread provided an equal or better estimate of uncertainties than sub-ensembles based only on any single meteorological forcing system. Because of ensemble averaging, the central forecast under-predicted peak water levels and the resurgence peak though these were predicted by some of the ensemble members. The impacts of errors in forecast storm arrival time and resolution-related biases in coarse global atmospheric models on the predictions are noted. A limited comparison for this single storm with the National Hurricane Center’s forecast show SFAS providing better accuracy and spread. Advantages and challenges of SFAS and other similar mid-latitude flood forecast systems are identified along with recommendations for analysis and improvement.

Dynamic ensemble visualizations to support understanding for uncertain trajectories.

When making decisions about uncertain spatial trajectories, such as storm forecasts, people rely on visualizations to support their understanding. Four experiments explored novel visualizations-dynamic ensembles. Nonexperts used visualizations to interpret probabilistic information about potential paths of a hurricane. Experiment 1 focused on global properties of the distribution, and showed dynamic ensembles imply a larger area at risk than traditional cones of uncertainty. Experiment 2 compared decisions with cones versus dynamic ensembles at specific individual locations. Dynamic ensembles offer more appreciation of risk outside the center of the distribution, and less abrupt in transitions from evacuation to nonevacuation choices. Experiment 3 compared decisions for dynamic ensembles versus static line ensembles. Similar evacuation rates across the two conditions suggest ensembles, rather than dynamics, are the more critical feature. Experiment 4 examined whether an additional dimension can be included in dynamic ensembles using color coding. Decisions reacted to this ancillary feature, with higher evacuation rates for locations threatened by more severe outcomes. Outcomes highlight the ability to systematically vary the level of risk communicated through the ensembles while also communicating the continuous nature of the risk. The overall findings show the viability of presenting uncertain spatial information using dynamic ensembles. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Testing the Feature Alignment Technique (FAT) in an Ensemble-Based Data Assimilation and Forecast System with Multiple-Storm Scenarios

Abstract Storm displacement errors can arise from a number of potential sources of error within a data assimilation (DA) and forecast system. Conversely, storm displacement errors can cause issues for storm-scale, ensemble-based systems using an ensemble Kalman filter (EnKF), such as NSSL’s Warn-on-Forecast System (WoFS). A previous study developed a fully grid-based feature alignment technique (FAT) to mitigate these phase errors and their impacts. However, that study developed and tested the FAT for single-storm cases. This study advances that work by implementing an object-based merging and matching technique into the FAT and tests the updated FAT in more complex scenarios of multiple storms. Ensemble-based experiments are conducted with and without the FAT for each of the scenarios. The experiments’ analyses and forecasts of storm-related fields are then evaluated using subjective and objective methods. Results from these idealized multiple-storm experiments continue to reveal the potential benefits of correcting storm displacement errors. For example, running the FAT even once can mitigate the “spinup” period experienced by the no-FAT experiments. The new results also show that running the FAT prior to every DA cycling step generally leads to more skillful forecasts at the smaller scales, especially in earlier-initialized forecasts. However, repeatedly running the FAT prior to every DA step can eventually lead to deterioration in analyses and forecasts. Potential solutions to this problem include using longer cycling intervals and running the FAT prior to DA less often. Additional ways to improve the FAT along with other results are presented and discussed. Significance Statement The purpose of this work is to explore the impact of correcting storm displacements on analyses and forecasts of storms using an ensemble-based data assimilation and forecast system in an idealized framework. Storm displacement errors are a common problem in current operational and experimental storm-scale forecast systems, so understanding their impact on these systems and providing a method to help mitigate them is important. Results from this study indicate that correcting storm displacement errors with the feature alignment technique can greatly improve analyses and forecasts in multiple-storm scenarios. Future work will focus on exploring the impact of correcting storm displacement errors in a real-data, storm-scale data assimilation and forecast system.