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Songjiang Feng
,
Yan Tan
,
Junfeng Kang
,
Quanjia Zhong
,
Yanjie Li
, and
Ruiqiang Ding

Abstract

In this study, the extreme gradient boosting (XGBoost) algorithm is used to correct tropical cyclone (TC) intensity in ensemble forecast data from the Typhoon Ensemble Data Assimilation and Prediction System (TEDAPS) at the Shanghai Typhoon Institute (STI), China Meteorological Administration (CMA). Results show that the forecast accuracy of TC intensity may be improved substantially using the XGBoost algorithm, especially when compared with a simple ensemble average of all members in the ensemble forecast [as depicted by the ensemble average (EnsAve) algorithm in this study]. The forecast errors for maximum wind speed (MWS) and minimum sea level pressure (MSLP) have been reduced by a significant margin, ranging from 6.3% to 18.4% for MWS and from 4% to 14.9% for MSLP, respectively. The performance of the XGBoost algorithm is overall better than that of the EnsAve algorithm, although there are a few samples when it is worse. The bias analysis shows that TEDAPS underpredicts the MWS and overpredicts the MSLP, meaning that the TEDAPS underestimates TC intensity. However, the XGBoost algorithm can reduce the bias to improve the forecast accuracy of TC intensity. Specifically, it achieves a reduction of over 20% in forecast errors for both the MWS and MSLP of typhoons compared to the EnsAve algorithm, indicating the XGBoost algorithm’s particular advantage in forecasting intense TCs. These results indicate that the TC intensity forecast can be substantially improved using the XGBoost algorithm, relative to the EnsAve algorithm.

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Lei Wang
,
Qiying Chen
,
Ning Jiang
,
Jianglin Hu
, and
Guoqiang Xu

Abstract

It is known that the southwest vortex (SWV) is an important weather system that may induce severe weather. The southward deviation of an SWV track forecasted by the Global Assimilation and Prediction System of the China Meteorological Administration (CMA-GFS) is systematically diagnosed in this study. The southward shift of the SWV is directly attributed to the deviation of the steering flow caused by the weak forecast of the upper-level trough. According to the diagnosis of potential tendency, the underestimation of the initial vorticity advection forecasted by CMA-GFS dominates the weak development of the upper-level trough. The underestimation of the vorticity advection is eventually sourced to the weak geostrophic wind caused by the weak initial meridional and zonal gradients of the midlevel height in front of the trough. The assimilation process on the initial field of the CMA-GFS acts a negative effect on forecasting this SWV track. It weakens the π field at midmodel level, resulting in the weak midlevel height gradient in front of the trough. A verified numerical experiment initialized by a more reasonable field is carried out and the southward shift of the SWV is obviously modified. This study suggests that a reasonable analysis field is crucial for the accurate forecast of the SWV track.

Significance Statement

The important impact of initial field deviation in key regions on the forecast in the late period is highlighted. A systematic diagnosis process for identifying and addressing forecast issues on SWV track is proposed. This research provides a comprehensive approach for diagnosing the forecast deviation associated with SWV track.

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Zhaohui Wang
,
Alexander D. Fraser
,
Phil Reid
,
Richard Coleman
, and
Siobhan O’Farrell

Abstract

Although operational weather forecasting centers are increasingly using global coupled atmosphere–ocean–ice models to replace atmosphere-only models for short- and medium-range (10 day) weather forecasting, the influence of sea ice on such forecasting has yet to be fully quantified, especially in the Southern Ocean. To address this gap, a polar-specific version of the Weather Research and Forecasting Model is implemented with a circumpolar Antarctic domain to investigate the impact of daily updates of sea ice concentration on short- and medium- range weather forecasting. A statistically significant improvement in near-surface atmospheric temperature and humidity is shown from +24 to +192 h when updating the daily sea ice concentration in the model. The forecast skill improvements for 2-m temperature and dewpoint temperature are enhanced from June to September, which is the period of late sea ice advance. Regionally, model improvement is shown to occur in most sea ice regions, although the improvement is strongest in the Ross Sea and Weddell Sea sectors. The surface heat budget also shows remarkable improvement in outgoing radiative heat fluxes and both sensible and latent heat fluxes. This idealized research demonstrates the nonnegligible effect of including more accurate time-varying sea ice concentration in numerical weather forecasting.

Significance Statement

The purpose of this study is to understand how a more realistic Antarctic sea ice field may influence the skill of short- and medium-range weather forecasts. Many operational atmospheric numerical weather prediction (NWP) models use a static forecast field through the time frame of the model’s forecast—often 3–10 days. In this study, we updated the sea ice concentration field daily and compared the forecast outcomes with those from model runs using a static sea ice concentration field. We found the forecast skill of near-surface temperature and humidity show the most significant improvements in our idealized experiments. This indicates the importance of incorporating improved dynamic sea ice representation in Antarctic short- to medium-range operational weather forecasting.

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Clifford Mass
,
David Ovens
,
John Christy
, and
Robert Conrick

Abstract

An unprecedented heat wave occurred over the Pacific Northwest and southwest Canada on 25–30 June 2021, resulting in all-time temperature records that greatly exceeded previous record maximum temperatures. The impacts were substantial, including several hundred deaths, thousands of hospitalizations, a major wildfire in Lytton, British Columbia, Canada, and severe damage to regional vegetation. Several factors came together to produce this extreme event: a record-breaking midtropospheric ridge over British Columbia in the optimal location, record-breaking midtropospheric temperatures, strong subsidence in the lower atmosphere, low-level easterly flow that produced downslope warming on regional terrain and the removal of cooler marine air, an approaching low-level trough that enhanced downslope flow, the occurrence at a time of maximum insolation, and drier-than-normal soil moisture. It is shown that all-time-record temperatures have not become more frequent and that annual high temperatures only increased at the rate of baseline global warming. Although anthropogenic warming may have contributed as much as 1°C to the event, there is little evidence of further amplification from increasing greenhouse gases. Weather forecasts were excellent for this event, with highly accurate predictions of the extreme temperatures.

Significance Statement

This paper describes the atmospheric evolution that produced an extreme heat wave over the Pacific Northwest during June 2021 and puts this event into historical perspective.

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Quan Nguyen
and
Chanh Kieu

Abstract

Exploring new techniques to improve the prediction of tropical cyclone (TC) formation is essential for operational practice. Using convolutional neural networks, this study shows that deep learning can provide a promising capability for predicting TC formation from a given set of large-scale environments at certain forecast lead times. Specifically, two common deep-learning architectures including the residual net (ResNet) and UNet are used to examine TC formation in the Pacific Ocean. With a set of large-scale environments extracted from the NCEP–NCAR reanalysis during 2008–21 as input and the TC labels obtained from the best track data, we show that both ResNet and UNet reach their maximum forecast skill at the 12–18-h forecast lead time. Moreover, both architectures perform best when using a large domain covering most of the Pacific Ocean for input data, as compared to a smaller subdomain in the western Pacific. Given its ability to provide additional information about TC formation location, UNet performs generally worse than ResNet across the accuracy metrics. The deep learning approach in this study presents an alternative way to predict TC formation beyond the traditional vortex-tracking methods in the current numerical weather prediction.

Significance Statement

This study presents a new approach for predicting tropical cyclone (TC) formation based on deep learning (DL). Using two common DL architectures in visualization research and a set of large-scale environments in the Pacific Ocean extracted from the reanalysis data, we show that DL has an optimal capability of predicting TC formation at the 12–18-h lead time. Examining the DL performance for different domain sizes shows that the use of a large domain size for input data can help capture some far-field information needed for predicting TCG. The DL approach in this study demonstrates an alternative way to predict or detect TC formation beyond the traditional vortex-tracking methods used in the current numerical weather prediction.

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David S. Richardson
,
Hannah L. Cloke
,
John A. Methven
, and
Florian Pappenberger

Abstract

We investigate the run-to-run consistency (jumpiness) of ensemble forecasts of tropical cyclone tracks from three global centers: ECMWF, the Met Office, and NCEP. We use a divergence function to quantify the change in cross-track position between consecutive ensemble forecasts initialized at 12-h intervals. Results for the 2019–21 North Atlantic hurricane season show that the jumpiness varied substantially between cases and centers, with no common cause across the different ensemble systems. Recent upgrades to the Met Office and NCEP ensembles reduced their overall jumpiness to match that of the ECMWF ensemble. The average divergence over the set of cases provides an objective measure of the expected change in cross-track position from one forecast to the next. For example, a user should expect on average that the ensemble mean position will change by around 80–90 km in the cross-track direction between a forecast for 120 h ahead and the updated forecast made 12 h later for the same valid time. This quantitative information can support users’ decision-making, for example, in deciding whether to act now or wait for the next forecast. We did not find any link between jumpiness and skill, indicating that users should not rely on the consistency between successive forecasts as a measure of confidence. Instead, we suggest that users should use ensemble spread and probabilistic information to assess forecast uncertainty, and consider multimodel combinations to reduce the effects of jumpiness.

Significance Statement

Forecasting the tracks of tropical cyclones is essential to mitigate their impacts on society. Numerical weather prediction models provide valuable guidance, but occasionally there is a large jump in the predicted track from one run to the next. This jumpiness complicates the creation and communication of consistent forecast advisories and early warnings. In this work we aim to better understand forecast jumpiness and we provide practical information to forecasters to help them better use the model guidance. We show that the jumpiest cases are different for different modeling centers, that recent model upgrades have reduced forecast jumpiness, and that there is not a strong link between jumpiness and forecast skill.

Open access
Trevor A. Campbell
,
Gary M. Lackmann
,
Maria J. Molina
, and
Matthew D. Parker

Abstract

Severe convection occurring in high-shear, low-CAPE (HSLC) environments is a common cool-season threat in the southeastern United States. Previous studies of HSLC convection document the increased operational challenges that these environments present compared to their high-CAPE counterparts, corresponding to higher false-alarm ratios and lower probability of detection for severe watches and warnings. These environments can exhibit rapid destabilization in the hours prior to convection, sometimes associated with the release of potential instability. Here, we use self-organizing maps (SOMs) to objectively identify environmental patterns accompanying HSLC cool-season severe events and associate them with variations in severe weather frequency and distribution. Large-scale patterns exhibit modest variation within the HSLC subclass, featuring strong surface cyclones accompanied by vigorous upper-tropospheric troughs and northward-extending regions of instability, consistent with prior studies. In most patterns, severe weather occurs immediately ahead of a cold front. Other convective ingredients, such as lower-tropospheric vertical wind shear, near-surface equivalent potential temperature (θe ) advection, and the release of potential instability, varied more significantly across patterns. No single variable used to train SOMs consistently demonstrated differences in the distribution of severe weather occurrence across patterns. Comparison of SOMs based on upper and lower quartiles of severe occurrence demonstrated that the release of potential instability was most consistently associated with higher-impact events in comparison to other convective ingredients. Overall, we find that previously developed HSLC composite parameters reasonably identify high-impact HSLC events.

Significance Statement

Even when atmospheric instability is not optimal for severe convective storms, in some situations they can still occur, presenting increased challenges to forecasters. These marginal environments may occur at night or during the cool season, when people are less attuned to severe weather threats. Here, we use a sorting algorithm to classify different weather patterns accompanying such storms, and we distinguish which specific patterns and weather system features are most strongly associated with severe storms. Our goals are to increase situational awareness for forecasters and to improve understanding of the processes leading to severe convection in marginal environments.

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Sarah M. Griffin
,
Anthony Wimmers
, and
Christopher S. Velden

Abstract

This study details a two-method, machine learning approach to predict current and short-term intensity change in global tropical cyclones (TCs), “D-MINT” and “D-PRINT.” D-MINT and D-PRINT use infrared imagery and environmental scalar predictors, while D-MINT also employs microwave imagery. Results show that current TC intensity estimates from D-MINT and D-PRINT are more skillful than three established intensity estimation methods routinely used by operational forecasters for North Atlantic and eastern and western North Pacific TCs. Short-term intensity predictions are validated against five operational deterministic guidances at 6-, 12-, 18-, and 24-h lead times. D-MINT and D-PRINT are less skillful than NHC and consensus TC intensity predictions in North Atlantic and eastern North Pacific TCs, but are more skillful than the other guidances for at least half of the lead times. In western North Pacific, north Indian Ocean, and Southern Hemisphere TCs, D-MINT is more skillful than the JTWC and other individual TC intensity forecasts for over half of the lead times. When probabilistically predicting TC rapid intensification (RI), D-MINT is more skillful in North Atlantic and western North Pacific TCs than three operationally used RI guidances, but less skillful for yes–no RI forecasts. In addition, this work demonstrates the importance of microwave imagery, as D-MINT is more skillful than D-PRINT. Since D-MINT and D-PRINT are convolutional neural network models interrogating two-dimensional structures within TC satellite imagery, this study also demonstrates that those features can yield better short-term predictions than existing scalar statistics of satellite imagery in operational models. Finally, a diagnostics tool is revealed to aid the attribution of the D-MINT/D-PRINT intensity predictions.

Significance Statement

This study develops a method to predict current and short-term forecasts of tropical cyclone (TC) intensity using artificial intelligence. The resultant models use a convolutional neural network (CNN) that can identify two-dimensional features in satellite imagery that are indicative of TC intensity and future intensity change. The performance results indicate that in several TC basins, the CNN approach is generally more skillful than alternative satellite-based estimates of TC intensity as well as operational short-term forecasts of deterministic intensity change and of similar skill to probabilistic rapid intensification forecasts.

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Joseph A. Grim
,
James O. Pinto
, and
David C. Dowell

Abstract

This study provides a comparison of the operational HRRR version 4 and its eventual successor, the experimental Rapid Refresh Forecast System (RRFS) model (summer 2022 version), at predicting the evolution of convective storm characteristics during widespread convective events that occurred primarily over the eastern United States during summer 2022. In total 32 widespread convective events were selected using observations from the MRMS composite reflectivity, which includes an equal number of MCSs, quasi-linear convective systems (QLCSs), clusters, and cellular convection. Each storm system was assessed on four primary characteristics: total storm area, total storm count, storm area ratio (an indicator of mean storm size), and storm size distributions. It was found that the HRRR predictions of total storm area were comparable to MRMS, while the RRFS overpredicted total storm area by 40%–60% depending on forecast lead time. Both models tended to underpredict storm counts particularly during the storm initiation and growth period. This bias in storm counts originates early in the model runs (forecast hour 1) and propagates through the simulation in both models indicating that both miss storm initiation events and/or merge individual storm objects too quickly. Thus, both models end up with mean storm sizes that are much larger than observed (RRFS more so than HRRR). Additional analyses revealed that the storm area and individual storm biases were largest for the clusters and cellular convective modes. These results can serve as a benchmark for assessing future versions of RRFS and will aid model users in interpreting forecast guidance.

Open access
Roger Edwards
and
Richard L. Thompson

Abstract

On the local afternoon of 29 May 2012, a long-lived, right-moving (RM) supercell formed over northwestern Oklahoma and turned roughly southeastward. For >3 h, as it moved toward the Oklahoma City, Oklahoma, metro area, this supercell remained nontornadic and visually high-based, producing a nearly tornadic gustnado and a swath of significantly severe, sometimes giant hail up to 5 in. (12.7 cm) in diameter. Meanwhile, a left-moving (LM) supercell formed over southwestern Oklahoma about 100 mi (161 km) south-southwest of the RM storm, and moved northeastward, with a rear-flank gust front that became well defined on radar imagery as the LM storm approached southern and central parts of the metro. The authors, who had been observing the RM supercell in the field since genesis, surmised its potential future interaction with the LM storm’s trailing gust front about 1 h beforehand. We repositioned to near the gust front’s extrapolated collision point with the RM mesocyclone, in anticipation of maximized tornado potential, then witnessed a small tornado from the RM mesocyclone immediately following its interception of the boundary. Synchronized radar and photographic images of this remarkable sequence are presented and discussed in context of more recent findings on tornadic supercell–boundary interactions, with implications for operational utility.

Significance Statement

Supercells—well-organized, rotating thunderstorms mainly found in midlatitudes—commonly produce the largest hail, along with damaging gusts and most tornadoes. In radar imagery and photographs, we show the characteristics and merger of two supercell types: left-moving and right-moving, with respect to winds aloft. As the left-moving storm’s trailing gust front interacted with the right-mover’s mesocyclone, the latter strengthened quickly, soon producing a tornado. Observed evolution of these storms supports idealized numerical and conceptual models for supercell behavior and interactions with storm-scale boundaries, and may be useful in short-fused tornado forecast and warning operations.

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