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Henry Santer
,
Jonathan Poterjoy
, and
Joshua McCurry

Abstract

Estimating and predicting the state of the atmosphere is a probabilistic problem for which an ensemble modeling approach often is taken to represent uncertainty in the system. Common methods for examining uncertainty and assessing performance for ensembles emphasize pointwise statistics or marginal distributions. However, these methods lose specific information about individual ensemble members. This paper explores contour band depth (cBD), a method of analyzing uncertainty in terms of contours of scalar fields. cBD is fully nonparametric and induces an ordering on ensemble members that leads to box-and-whisker-plot-type visualizations of uncertainty for two-dimensional data. By applying cBD to synthetic ensembles, we demonstrate that it provides enhanced information about the spatial structure of ensemble uncertainty. We also find that the usefulness of the cBD analysis depends on the presence of multiple modes and multiple scales in the ensemble of contours. Finally, we apply cBD to compare various convection-permitting forecasts from different ensemble prediction systems and find that the value it provides in real-world applications compared to standard analysis methods exhibits clear limitations. In some cases, contour boxplots can provide deeper insight into differences in spatial characteristics between the different ensemble forecasts. Nevertheless, identification of outliers using cBD is not always intuitive, and the method can be especially challenging to implement for flow that exhibits multiple spatial scales (e.g., discrete convective cells embedded within a mesoscale weather system).

Significance Statement

Predictions of Earth’s atmosphere inherently come with some degree of uncertainty owing to incomplete observations and the chaotic nature of the system. Understanding that uncertainty is critical when drawing scientific conclusions or making policy decisions from model predictions. In this study, we explore a method for describing model uncertainty when the quantities of interest are well represented by contours. The method yields a quantitative visualization of uncertainty in both the location and the shape of contours to an extent that is not possible with standard uncertainty quantification methods and may eventually prove useful for the development of more robust techniques for evaluating and validating numerical weather models.

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Ken-Chung Ko
,
Huang-Hsiung Hsu
, and
Jyun-Hong Liu

Abstract

This study examined the impact of northward- and westward-propagating summertime intraseasonal oscillations (ISOs) on submonthly wave patterns and tropical cyclones (TCs) in the subtropical western North Pacific. In the ISO westerly phase, submonthly wave patterns associated with the northward-propagating ISO appeared to be more energetic and most of the corresponding TCs maintained their wind speed for a relatively long period. Perturbation kinetic energy exhibited a stronger maximum in the ISO northward mode than in the westward mode. The analysis of barotropic conversion in the ISO northward mode revealed that an increase in barotropic conversion can be attributed to a strong association between the perturbation zonal wind component and the background flow. Therefore, submonthly wave patterns moving in a direction similar to that of the northward-propagating ISO continuously extracted energy from the background flow to the south of the submonthly base region. However, in the westward mode, the ISO propagating in a direction almost perpendicular to the submonthly wave pattern tracks not only altered the direction of the wave pattern but also created a background environment that was detached from submonthly perturbations. Thus, the background flow transferred less energy to submonthly wave patterns, resulting in shorter TC durations in the ISO westward mode than in the northward mode.

Significance Statement

In this study, we focused on the northward and westward ISO propagation routes in the subtropical western North Pacific to investigate their impact on the submonthly wave pattern and TCs. This is important because the ISO propagating behavior can change the background flow for the submonthly wave pattern. The results showed that the northward ISO tended to enhance the wave pattern through strengthening the background component of the barotropic conversion. TCs associated with submonthly wave patterns tended to maintain their intensity longer in the ISO northward mode. The wave pattern associated with the westward-propagating ISO remained weaker.

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Tao Sun
,
Juanzhen Sun
,
Yaodeng Chen
, and
Haiqin Chen

Abstract

This study presents a multiscale four-dimensional variational data assimilation (MS-4DVar) scheme that aims to assimilate multiscale information from conventional and radar observations. The MS-4DVar scheme separately assimilates conventional and radar data in different outer loop iterations of an incremental 4DVar with varied resolutions in the tangent linear and adjoint models (TLM/ADM) and time window lengths in the 4DVar. The MS-4DVar scheme was evaluated through a series of single observation tests and several cycled assimilation and forecasting experiments for a real squall-line case. Our results indicated that different TLM/ADM resolutions and time window lengths applied to the conventional and radar observations improved the multiscale analysis. In addition, the MS-4DVar scheme was more efficient than the common 4DVar because of the low-resolution TLM/ADM used for conventional data and the shortened time window length for radar data. Verification of the squall-line forecasts suggested that the MS-4DVar scheme improved the hourly accumulated precipitation and radar reflectivity forecast skills and reduced the forecast errors of both large-scale environmental and convective-scale states. Further diagnosis revealed that the improvement of precipitation forecast skill was attributable to the stronger cold pool, deeper saturated water vapor layer, and stronger updraft of the simulated squall-line system, as well as a more favorable convective environment.

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Zhaoyang Huo
,
Yubao Liu
,
Yueqin Shi
,
Baojun Chen
,
Hang Fan
, and
Yang Li

Abstract

A summer convective precipitation case, occurring in eastern China on 16–17 July 2020, is selected to investigate the impact of joint assimilation of ground-based profiling platforms and weather radars on forecasting convective storms using observational system simulation experiments (OSSEs). The simulated profiling platforms include the Doppler wind lidar (DWL), a wind profiler (WP), and a microwave radiometer (MWR). Results show that joint assimilation of WP and radar data produces a better analysis of convective dynamical structure than joint assimilation of DWL and radar data, since WP detects deeper layer winds. Joint assimilation of MWR and radar data enables rapid adjustment of temperature and humidity and thus, avoids the potential errors introduced by the latent heat term of the radar diabatic initialization in the early stage. Profiling observations in a horizontal spacing of 80 km provide fewer benefits for convective forecasting, while reducing the spacing to 40 km can dramatically improve model analysis and forecasts. Joint assimilation of multiple profiling observations in a 20-km horizontal spacing with radar data exhibits a beneficial synergistic effect and mitigates “the ramp-down issue” during the forecast stage. Assimilating profiling observations with an update interval less than 30 min does not have as pronounced an effect on convective forecasts as horizontal spacing. Furthermore, assimilating profiling observations at a 20-km horizontal spacing can obtain accurate mesoscale background environment and forecast storms with an ability comparable to radar data assimilation. This work emphasizes the need to consider implementing a joint mesoscale detection system that incorporates weather radars and profiling observations for leveraging convective storm forecasting.

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Sharanya J. Majumdar
,
Linus Magnusson
,
Peter Bechtold
,
Jean Raymond Bidlot
, and
James D. Doyle

Abstract

Structure and intensity forecasts of 19 tropical cyclones (TCs) during the 2020 Atlantic hurricane season are investigated using two NWP systems. An experimental 4-km global ECMWF model (EC4) with upgraded moist physics is compared with a 9-km version (EC9) to evaluate the influence of resolution. EC4 is then benchmarked against the 4-km regional COAMPS–Tropical Cyclones (COAMPS-TC) system (CO4) to compare systems with similar resolutions. EC4 produced stronger TCs than EC9, with a >30% reduction of the maximum wind speed bias in EC4, resulting in lower forecast errors. However, both ECMWF predictions struggled to intensify initially weak TCs, and the radius of maximum winds (RMW) was often too large. In contrast, CO4 had lower biases in central pressure, maximum wind speed, and RMW. Regardless, minimal statistical differences between CO4 and EC4 intensity errors were found for ≥36-h forecasts. Rapid intensification cases yielded especially large intensity errors. CO4 produced superior forecasts of RMW, together with an excellent pressure–wind relationship. Differences in the results are due to contrasting physics and initialization schemes. ECMWF uses global data assimilation with no special treatment of TCs, whereas COAMPS-TC constructs a vortex for TCs with initial intensity ≥55 kt (∼28 m s−1) based on data provided by forecasters. Two additional ECMWF experiments were conducted. The first yielded improvements when the drag coefficient was reduced at high wind speeds, thereby weakening the coupling between the low-level winds and the surface. The second produced overly intense TCs when explicit deep convection was used, due to unrealistic mid–upper-tropospheric heating.

Significance Statement

Improved forecasts of tropical storms and hurricanes depend on advances in computer weather models. We tested an experimental high-resolution (4 km) version of the global ECMWF model against its 9-km counterpart to evaluate the influence of resolution on storm position and intensity. We also compared this with the 4-km U.S. Navy model, which is designed for tropical storms and hurricanes. Over a 3-month period during the active 2020 Atlantic hurricane season, we found that increasing the horizontal resolution improved intensity forecasts. The Navy model forecasts were superior for the radius of maximum winds and had lower intensity biases. Two additional experiments with the ECMWF model revealed the importance of simulating air–sea interaction in high winds and current challenges with explicitly simulating deep thunderstorm clouds in their system.

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Ryan A. Sobash
,
David John Gagne II
,
Charlie L. Becker
,
David Ahijevych
,
Gabrielle N. Gantos
, and
Craig S. Schwartz

Abstract

While convective storm mode is explicitly depicted in convection-allowing model (CAM) output, subjectively diagnosing mode in large volumes of CAM forecasts can be burdensome. In this work, four machine learning (ML) models were trained to probabilistically classify CAM storms into one of three modes: supercells, quasi-linear convective systems, and disorganized convection. The four ML models included a dense neural network (DNN), logistic regression (LR), a convolutional neural network (CNN), and semisupervised CNN–Gaussian mixture model (GMM). The DNN, CNN, and LR were trained with a set of hand-labeled CAM storms, while the semisupervised GMM used updraft helicity and storm size to generate clusters, which were then hand labeled. When evaluated using storms withheld from training, the four classifiers had similar ability to discriminate between modes, but the GMM had worse calibration. The DNN and LR had similar objective performance to the CNN, suggesting that CNN-based methods may not be needed for mode classification tasks. The mode classifications from all four classifiers successfully approximated the known climatology of modes in the United States, including a maximum in supercell occurrence in the U.S. Central Plains. Further, the modes also occurred in environments recognized to support the three different storm morphologies. Finally, storm mode provided useful information about hazard type, e.g., storm reports were most likely with supercells, further supporting the efficacy of the classifiers. Future applications, including the use of objective CAM mode classifications as a novel predictor in ML systems, could potentially lead to improved forecasts of convective hazards.

Significance Statement

Whether a thunderstorm produces hazards such as tornadoes, hail, or intense wind gusts is in part determined by whether the storm takes the form of a single cell or a line. Numerical forecasting models can now provide forecasts that depict this structure. We tested several automated algorithms to extract this information from forecast output using machine learning. All of the automated methods were able to distinguish between a set of three convective types, with the simple techniques providing similarly skilled classifications compared to the complex approaches. The automated classifications also successfully discriminated between thunderstorm hazards, potentially leading to new forecast tools and better forecasts of high-impact convective hazards.

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Stephen D. Eckermann
,
Cory A. Barton
, and
James F. Kelly

Abstract

The virtual temperature used to model moisture-modified tropospheric dynamics is generalized to include a new thermospheric component. The resulting hybrid virtual potential temperature (HVPT) transitions seamlessly with height, from moist virtual potential temperature (MVPT) in the troposphere, to potential temperature in the stratosphere and mesosphere, to thermospheric virtual potential temperature thereafter. For numerical weather prediction (NWP) models looking to extend into the thermosphere, but still heavily invested in retaining MVPT-based dynamical cores for tropospheric prediction, upgrading to HVPT allows the core to capture critical new aspects of variable composition thermospheric dynamics, while leaving the original MVPT-based tropospheric equations and numerics essentially untouched. In this way, HVPT augmentation can both simplify and streamline extension into the thermosphere at little computational cost beyond the inevitable need for more vertical layers and somewhat smaller time steps. To demonstrate, we upgrade the MVPT-based dynamical core of the Navy global NWP model to HVPT, then test its performance in forecasting analytical globally balanced states containing hot or rapidly heated thermospheres and height-varying gas constants. These tests confirm that HVPT augmentation offers an efficient and effective means of extending MVPT-based NWP models into the thermosphere to accelerate development of future ground-to-space NWP models supporting space weather applications. The related issues of variable gravitational acceleration and shallow-atmosphere approximations are also briefly discussed.

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Kevin A. Biernat
,
Daniel Keyser
, and
Lance F. Bosart

Abstract

The prediction of weather conditions in the Arctic is important to human activities in the Arctic. Arctic cyclones (ACs), which are extratropical cyclones that originate within the Arctic or move into the Arctic from lower latitudes, can be associated with hazardous weather conditions that may adversely affect human activities. The purpose of this study is to increase understanding of processes that influence the forecast skill of the synoptic-scale flow over the Arctic and of ACs. The 11-member NOAA Global Ensemble Forecast System (GEFS) reforecast dataset, version 2, is utilized to identify periods of low and high forecast skill of the synoptic-scale flow over the Arctic, hereinafter referred to as low-skill and high-skill periods, respectively, during the summers of 2007–17, and to evaluate the forecast skill of ACs during these respective periods. The ERA-Interim dataset is used to examine characteristics of the Arctic environment and characteristics of ACs during low-skill and high-skill periods. The Arctic environment tends to be characterized by more vigorous baroclinic processes and latent heating during low-skill periods relative to high-skill periods. ACs occur more frequently over much of the Arctic; tend to be stronger; and tend to be located in regions of larger lower-tropospheric baroclinicity, lower-to-midtropospheric Eady growth rate (EGR), and latent heating during low-skill periods relative to high-skill periods. ACs during low-skill periods that are characterized by low forecast skill of intensity tend to be relatively strong and tend to be located in regions of relatively large lower-tropospheric baroclinicity, lower-to-midtropospheric EGR, and latent heating.

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Junjie Dong
,
Luyu Sun
,
James A. Carton
, and
Stephen G. Penny

Abstract

This study extends initial work by Sun and Penny and Sun et al. to explore the inclusion of path information from surface drifters using an augmented-state Lagrangian data assimilation based on the local ensemble transform Kalman filter (LETKF-LaDA) with vertical localization to improve analysis of the ocean. The region of interest is the Gulf of Mexico during the passage of Hurricane Isaac in the summer of 2012. Results from experiments with a regional ocean model at eddy-permitting and eddy-resolving model resolutions are used to quantify improvements to the analysis of sea surface velocity, sea surface temperature, and sea surface height in a data assimilation system. The data assimilation system assimilates surface drifter positions, as well as vertical profiles of temperature and salinity. Data were used from drifters deployed as a part of the Grand Lagrangian Deployment beginning 20 July 2012. Comparison of experiment results shows that at both eddy-permitting and eddy-resolving horizontal resolutions Lagrangian assimilation of drifter positions significantly improves analysis of the ocean state responding to hurricane conditions. These results, which should be applicable to other tropical oceans such as the Bay of Bengal, open new avenues for estimating ocean initial conditions to improve tropical cyclone forecasting.

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Daniel Jellis
,
Kenneth P. Bowman
, and
Anita D. Rapp

Abstract

Deep convection that penetrates the tropopause, referred to here as overshooting convection, is capable of lifting tropospheric air well into the stratosphere. In addition to water, these overshoots also transport various chemical species, affecting chemistry and radiation in the stratosphere. It is not currently known, however, how much transport is a result of this mechanism. To better understand overshooting convection, this study aims to characterize the durations of overshooting events. To achieve this, radar data from the Next Generation Weather Radar (NEXRAD) network is composited onto a three-dimensional grid at 5-min intervals. Overshoots are identified by comparing echo-top heights with tropopause estimates derived from ERA5 reanalysis data. These overshoots are linked in space from one analysis time to the next to form tracks. This process is performed for 12 four-day sample windows in the months May–August of 2017–19. Track characteristics such as duration, overshoot area, tropopause-relative altitude, and column-maximum reflectivity are investigated. Positive correlations are found between track duration and other track characteristics. Integrated track volume is found as a product of the overshoot area, depth, and duration, and provides a measure of the potential stratospheric impact of each track. Short-lived tracks are observed to contribute the most total integrated volume when considering track duration, while tracks that overshoot by 2–3 km show the largest contribution when considering overshoot depth. A diurnal cycle is observed, with peak track initiation around 1600–1700 local time. Track-mean duration peaks a few hours earlier, while track-mean area and tropopause-relative height peak a few hours later.

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