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Szymon PorĘba, Mateusz Taszarek, and Zbigniew Ustrnul

Abstract

The relationship between convective parameters derived from ERA5 and cloud-to-ground (CG) lightning flashes from the PERUN network in Poland was evaluated. All flashes detected between 2002 and 2019 were divided into intensity categories based on a peak 1-min CG lightning flash rate and were collocated with proximal profiles from ERA5 to assess their climatological variability. Thunderstorms in Poland are the most frequent in July, between 1400 and 1600 UTC and over the southeastern parts of the country. The highest median of most unstable convective available potential energy (MUCAPE) for CG lightning flash events is from June to August, between 1400 and 1600 UTC (around 900 J kg−1), whereas patterns in 0–6-km wind shear [deep-layer shear (DLS)] are reversed, with the highest median values during winter and night (around 25 m s−1). The best overlap of MUCAPE and DLS (MUWMAXSHEAR parameter) is in July–August, typically between 1400 and 2000 UTC with median values of around 850 m2 s−2. Thunderstorms in Poland are the most frequent in MUCAPE below 1000 J kg−1, and DLS between 8 and 15 m s−1. Along with increasing MUCAPE and DLS, peak CG lightning flash rates increase as well. Compared to DLS, MUCAPE is a more important parameter in forecasting any lightning activity, but when these two are combined together (MUWMAXSHEAR) they are more reliable in distinguishing between thunderstorms producing small and high CG lightning flash rates. Our results also indicate that higher CG lightning flash rates result in thunderstorms more frequently associated with severe weather reports (hail, tornado, wind).

Significance Statement

Each year severe thunderstorms produce considerable material losses and lead to deaths across central Europe; thus, a better understanding of local storm climatologies and their accompanying environments is important for operational forecasters, emergency managers, and risk estimation. In this research we address this issue by analyzing 18 years of lightning intensity data and collocated atmospheric environments. Thunderstorms in Poland are the most frequent in July between 1400 and 1600 UTC and form typically in environments with low atmospheric instability and moderate vertical shear of the horizontal wind. The probability for storms producing intense lightning increases when both of these environmental parameters reach higher values.

Open access
David C. Dowell, Curtis R. Alexander, Eric P. James, Stephen S. Weygandt, Stanley G. Benjamin, Geoffrey S. Manikin, Benjamin T. Blake, John M. Brown, Joseph B. Olson, Ming Hu, Tatiana G. Smirnova, Terra Ladwig, Jaymes S. Kenyon, Ravan Ahmadov, David D. Turner, Jeffrey D. Duda, and Trevor I. Alcott

Abstract

The High-Resolution Rapid Refresh (HRRR) is a convection-allowing implementation of the Advanced Research version of the Weather Research and Forecasting (WRF-ARW) Model with hourly data assimilation that covers the conterminous United States and Alaska and runs in real time at the NOAA/National Centers for Environmental Prediction (NCEP). Implemented operationally at NOAA/NCEP in 2014, the HRRR features 3-km horizontal grid spacing and frequent forecasts (hourly for CONUS and 3-hourly for Alaska). HRRR initialization is designed for optimal short-range forecast skill with a particular focus on the evolution of precipitating systems. Key components of the initialization are radar-reflectivity data assimilation, hybrid ensemble-variational assimilation of conventional weather observations, and a cloud analysis to initialize stratiform cloud layers. From this initial state, HRRR forecasts are produced out to 18 h every hour, and out to 48 h every 6 h, with boundary conditions provided by the Rapid Refresh system. Between 2014 and 2020, HRRR development was focused on reducing model bias errors and improving forecast realism and accuracy. Improved representation of the planetary boundary layer, subgrid-scale clouds, and land surface contributed extensively to overall HRRR improvements. The final version of the HRRR (HRRRv4), implemented in late 2020, also features hybrid data assimilation using flow-dependent covariances from a 3-km, 36-member ensemble (“HRRRDAS”) with explicit convective storms. HRRRv4 also includes prediction of wildfire smoke plumes. The HRRR provides a baseline capability for evaluating NOAA’s next-generation Rapid Refresh Forecast System, now under development.

Significance Statement

NOAA’s operational hourly updating, convection-allowing model, the High-Resolution Rapid Refresh (HRRR), is a key tool for short-range weather forecasting and situational awareness. Improvements in assimilation of weather observations, as well as in physics parameterizations, have led to improvements in simulated radar reflectivity and quantitative precipitation forecasts since the initial implementation of HRRR in September 2014. Other targeted development has focused on improved representation of the diurnal cycle of the planetary boundary layer, resulting in improved near-surface temperature and humidity forecasts. Additional physics and data assimilation changes have led to improved treatment of the development and erosion of low-level clouds, including subgrid-scale clouds. The final version of HRRR features storm-scale ensemble data assimilation and explicit prediction of wildfire smoke plumes.

Open access
Eric P. James, Curtis R. Alexander, David C. Dowell, Stephen S. Weygandt, Stanley G. Benjamin, Geoffrey S. Manikin, John M. Brown, Joseph B. Olson, Ming Hu, Tatiana G. Smirnova, Terra Ladwig, Jaymes S. Kenyon, and David D. Turner

Abstract

The High-Resolution Rapid Refresh (HRRR) is a convection-allowing implementation of the Advanced Research version of the Weather Research and Forecast (WRF-ARW) Model that covers the conterminous United States and Alaska and runs hourly (for CONUS; every 3 h for Alaska) in real time at the National Centers for Environmental Prediction. The high-resolution forecasts support a variety of user applications including aviation, renewable energy, and prediction of many forms of severe weather. In this second of two articles, forecast performance is documented for a wide variety of forecast variables and across HRRR versions. HRRR performance varies across geographical domain, season, and time of day depending on both prevalence of particular meteorological phenomena and the availability of both conventional and nonconventional observations. Station-based verification of surface weather forecasts (2-m temperature and dewpoint temperature, 10-m winds, visibility, and cloud ceiling) highlights the ability of the HRRR to represent daily planetary boundary layer evolution and the development of convective and stratiform cloud systems, while gridded verification of simulated composite radar reflectivity and quantitative precipitation forecasts reveals HRRR predictive skill for summer and winter precipitation systems. Significant improvements in performance for specific forecast problems are documented for the upgrade versions of the HRRR (HRRRv2, v3, and v4) implemented in 2016, 2018, and 2020, respectively. Development of the HRRR model data assimilation and physics paves the way for future progress with operational convective-scale modeling.

Significance Statement

NOAA’s operational hourly updating convection-allowing model, the High-Resolution Rapid Refresh (HRRR), is a key tool for short-range weather forecasting and situational awareness. Improvements in assimilation of weather observations, as well as in physics parameterizations, has led to improvements in simulated radar reflectivity and quantitative precipitation forecasts since the initial implementation of HRRR in September 2014. Other targeted development has focused on improved representation of the diurnal cycle of the planetary boundary layer, resulting in improved near-surface temperature and humidity forecasts. Additional physics and data assimilation changes have led to improved treatment of the development and erosion of low-level clouds, including subgrid-scale clouds. The final version of HRRR features storm-scale ensemble data assimilation and explicit prediction of wildfire smoke plumes.

Open access
Zuohao Cao, Stéphane Bélair, and Da-Lin Zhang

Abstract

A short-range regional, two-way coupled atmosphere–ocean–ice model has been recently developed in an attempt to improve, among other things, quantitative precipitation forecasts (QPFs) over southern Ontario, Canada, by incorporating air–lake interaction over the Great Lakes region. Here, we attempt to 1) assess the impact of the air–lake coupling on daily QPFs, as verified against the Canadian Precipitation Analysis and independent observations, over southern Ontario during the period of June 2016–May 2017; and 2) diagnose major physical processes governing the QPF differences between the coupled and uncoupled models by relating precipitation to those processes at the air–water interface and above. Results indicate that the coupled model tends to reduce the area-averaged and monthly averaged daily QPF biases and standard deviations in 5 months of October, November, and December 2016, and April and May 2017, but increase and deteriorate precipitation biases during the summer months. Most of the deteriorations occur during the daytime, while improvements are observed during the nighttime (in 7 of 12 months). During the daytime, slight improvements appear in 2 months. A further diagnosis indicates that the daily QPF differences between the two models are highly correlated with the differences of their sensible and latent heat fluxes. The maximum (minimum) difference of sensible (latent) heat flux in August 2016 (December 2016) is in phase with the maximum (minimum) difference of the two-model daily QPFs. The daily QPF differences in the other months are also controlled by the differences of vertically integrated water vapor flux convergence, and surface temperature.

Open access
Victoria A. Lang, Teresa J. Turner, Brandon R. Selbig, Austin R. Harris, and Jonathan D. W. Kahl

Abstract

Wind gusts present challenges to operational meteorologists, both to forecast accurately and also to verify. Strong wind gusts can damage structures and create costly risks for diverse industrial sectors. The meteorologically stratified gust factor (MSGF) model incorporates site-specific gust factors (the ratio of peak wind gust to mean wind speed) with wind speed and direction forecast guidance. The MSGF model has previously been shown to be a viable operational tool that exhibits skill (improvement over climatology) in forecasting peak wind gusts. This study assesses the performance characteristics of the MSGF model by evaluating peak gust predictions during several types of gust-producing weather phenomena. Peak wind gusts were prepared and verified for seven specific weather conditions over an 8-yr period at 16 sites across the United States. When coupled with two forms of model output statistics (MOS) wind guidance, the MSGF model generally shows skill in predicting peak wind gusts at forecast projections ranging from 6 to 72 h. The model performed best during high pressure and nocturnal conditions and was also skillful during conditions involving snow. The model did not perform well during the “rain with thunder” weather type. The MSGF model is a viable tool for the operational prediction of peak gusts for most gust-producing weather types.

Significance Statement

Wind gusts are an important and potentially costly environmental hazard. Wind gusts affect many industrial sectors, including transportation, power generation, forestry, construction, and insurance, but predicting gusts remains a challenging component of weather forecasting. Recent studies have demonstrated that the meteorologically stratified gust factor (MSGF) model shows skill in predicting peak gusts. This study shows that the MSGF model is skillful at predicting peak gusts during specific types of gust-producing weather phenomena at forecast projections up to 72 h, providing further confirmation that the MSGF model is a viable tool for the operational prediction of peak gusts.

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

Abstract

This study develops a probabilistic model based on a convolutional neural network to predict rapid intensification (RI) in both North Atlantic and eastern North Pacific tropical cyclones (TCs). Coined “I-RI,” an advantage of using a convolutional neural network to predict RI is that it is designed to learn from spatial fields, like two-dimensional satellite imagery, as well as scalar features. The resulting model RI probability output is validated against two operational RI guidances—an empirical and a deterministic method—to assess skill at predicting RI over 12-, 24-, 36-, 48-, and 72-h lead times. Results indicate that in North Atlantic TCs, AI-RI is more skillful at predicting RI over 12- and 24-h lead times compared to both operational RI guidances. In eastern North Pacific TCs, AI-RI is more skillful than the empirical operational RI guidance at most RI thresholds, but less skillful than the deterministic RI guidance at all thresholds. For TCs north of 15°N, where the deterministic skill was lower, AI-RI was more skillful than the deterministic operational guidance for over half of the RI thresholds. It is also found that AI-RI struggles to reach the higher RI probabilities produced by both of the operational RI guidances in both basins. This work demonstrates that the two-dimensional structures within the satellite imagery of TCs and the evolution of these structures identified using the difference in satellite images, captured by a convolutional neural network, yield better 12–24-h indicators of RI than existing scalar assessments of satellite brightness temperature.

Significance Statement

The purpose of this study is to develop a method to predict tropical cyclone rapid intensification using artificial intelligence. The developed model uses a convolutional neural network, which can identify features in satellite imagery that are indicative of rapid intensification. The results suggest that, compared with current operational rapid intensification models, a convolutional neural network approach is generally more skillful at predicting rapid intensification.

Restricted access
Stephen S. Weygandt, Stanley G. Benjamin, Ming Hu, Curtis R. Alexander, Tatiana G. Smirnova, and Eric P. James

Abstract

A technique for model initialization using three-dimensional radar reflectivity data has been developed and applied within the NOAA 13-km Rapid Refresh (RAP) and 3-km High-Resolution Rapid Refresh (HRRR) regional forecast systems. This technique enabled the first assimilation of radar reflectivity data for operational NOAA forecast models, critical especially for more accurate short-range prediction of convective storms. For the RAP, the technique uses a diabatic digital filter initialization (DFI) procedure originally deployed to control initial inertial gravity wave noise. Within the forward-model integration portion of diabatic DFI, temperature tendencies obtained from the model cloud/precipitation processes are replaced by specified latent heating–based temperature tendencies derived from the three-dimensional radar reflectivity data, where available. To further refine initial conditions for the convection-allowing HRRR model, a similar procedure is used in the HRRR, but without DFI. Both of these procedures, together called the “Radar-LHI” (latent heating initialization) technique, have been essential for initialization of ongoing precipitation systems, especially convective systems, within all NOAA operational versions of the 13-km RAP and 3-km HRRR models extending through the latest implementation upgrade at NCEP in 2020. Application of the latent heat–derived temperature tendency induces a vertical circulation with low-level convergence and upper-level divergence in precipitation systems. Retrospective tests of the Radar-LHI technique show significant improvement in short-range (0–6 h) precipitation system forecasts, as revealed by reflectivity verification scores. Results presented document the impact on HRRR reflectivity forecasts of the radar reflectivity initialization technique applied to the RAP alone, HRRR alone, and both the RAP and HRRR.

Significance Statement

The large forecast uncertainty of convective situations, even at short lead times, coupled with the hazardous weather they produce, makes convective storm prediction one of the most significant short-range forecast challenges confronting the operational numerical weather prediction community. Prediction of heavy precipitation events also requires accurate initialization of precipitation systems. An innovative assimilation technique using radar reflectivity data to initialize NOAA operational weather prediction models is described. This technique, which uses latent heating specified from radar reflectivity (and can accommodate lightning data and other convection/precipitation indicators), was first implemented in 2009 at NOAA/NCEP and continues to be used in 2022 in the NCEP-operational RAP and HRRR models, making it a backbone of the NOAA rapidly updated numerical weather prediction capability.

Open access
Hyun-Sook Kim, Jessica Meixner, Biju Thomas, Brandon G. Reichl, Bin Liu, Avichal Mehra, and Alan Wallcraft

Abstract

In this research, we develop a three-way coupled prediction system to advance the realization of air–sea interaction processes. This study considers the sea-state-dependent momentum flux and nonlinear interactions between waves, winds, and ocean currents using the U.S. National Centers for Environmental Prediction’s operational Hurricane Weather Research and Forecasting (HWRF)-Hybrid Coordinate Ocean Model (HYCOM) coupled modeling system. Wave feedback is performed through the air–sea interaction module (ASIM) added to WAVEWATCH III (WW3), which employs the wave boundary layer to parameterize unresolved high-frequency tail spectra by using the mean flux profile constructed from the conservation of total momentum and wave energy. The atmospheric momentum flux is updated using the sea-state-dependent Charnock coefficient, wave-induced stress, and ocean surface currents before being passed to HYCOM. Wave coupling in HYCOM includes Coriolis–Stokes forcing to simulate wave–current interactions and to enhance mixing to account for Langmuir turbulence. The fully coupled system is tested for Hurricane Laura (2020). This paper examines the forecast skills of the individual component models by comparing simulations with observations. Without skill degradation of HYCOM and WW3, the three-way coupling method improves the track and intensity forecast skills by 5% each over those of HWRF-HYCOM coupling, and 27% and 17% over those of uncoupling, respectively. Importantly, this fully coupled system outperforms rapid intensification by reducing the intensification magnitude and matching the occurrence and duration. Overall, the forecast performance evaluated in the study establishes a baseline for the next-generation hurricane prediction system.

Significance Statement

This study is the documentation of the numerical advancement of tropical cyclone (TC) forecasting and the demonstration of the improvement of the TC intensity forecast. A key asset is the importance of wave coupling and inclusion of the nonlinear interactions in the air–sea interaction zone, and is to advance the current U.S. NCEP operational coupled hurricane modeling system. By assessing simulations for Hurricane Laura (2020), we demonstrate skill improvement of the storm structure, and intensity forecasts, especially for rapid intensification (RI) by correcting the timing and the magnitude of RI simulated by uncoupling and two-way coupling.

Restricted access
Kimberly L. Elmore, John T. Allen, and Alan E. Gerard

Abstract

The occurrence and properties of hail smaller than severe thresholds (diameter < 25 mm) are poorly understood. Prior climatological hail studies have predominantly focused on large or severe hail (diameter at least 25 mm or 1 in.). Through use of data from the Meteorological Phenomena Identification Near the Ground project, Storm Data, and the Community Collaborative Rain, Hail and Snow Network the occurrence and characteristics of both severe and sub-severe hail are explored. Spatial distributions of days with the different classes of hail are developed on an annual and seasonal basis for the period 2013–20. Annually, there are several hail-day maxima that do not follow the maxima of severe hail: the peak is broadly centered over Oklahoma (about 28 days yr−1). A secondary maximum exists over the Colorado Front Range (about 26 days yr−1), a third extends across northern Indiana from the southern tip of Lake Michigan (about 24 days yr−1 with hail), and a fourth area is centered over the corners of southwest North Carolina, northwest South Carolina, and the northeast tip of Georgia. Each of these maxima in hail days are driven by sub-severe hail. While similar patterns of severe hail have been previously documented, this is the first clear documentation of sub-severe hail patterns since the early 1990s. Analysis of the hail size distribution suggests that to capture the overall hail risk, each of the datasets provide a complimentary data source.

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Susan Rennie, Shaun Cooper, Peter Steinle, Gary Dietachmayer, Monika Krysta, Charmaine Franklin, Chris Bridge, Matthew Marshall, Yi Xiao, and Dean Sgarbossa

Abstract

The Australian Bureau of Meteorology recently upgraded its convection-allowing numerical weather prediction system, known as the Australian Community Climate and Earth System Simulator (ACCESS-C). ACCESS-C includes seven domains covering major population centers, nested inside the Bureau’s global NWP system. The upgrade included the introduction of data assimilation, with hourly cycling 4D-Var. With a much newer version of the Unified Model to provide the forecast, a range of storm attribute diagnostics to improve forecasting of severe weather events could be introduced. This paper details the configuration of the new version of ACCESS-C. Some verification compared with its predecessor (a downscaling system of comparable resolution) is presented. Of greater note is an exploration of the differences in the model characteristics between the new and old systems, which will affect how users interpret the outputs.

Restricted access