Biases in the Prediction of Convective Storm Characteristics with a Convection Allowing Ensemble

The severity, duration, and spatial extent of thunderstorm impacts is related to convective storm mode. This study assesses the skill of the High Resolution Rapid Refresh Ensemble (HRRR-E) and its deterministic counterpart (HRRRv4) at predicting convective mode and storm macrophysical properties using 35 convective events observed during the 2020 warm season across the eastern U.S. Seven cases were selected from each of five subjectively-determined convective organization modes: tropical cyclones, mesoscale convective systems (MCSs), quasi-linear convective systems, clusters, and cellular convection. These storm events were assessed using an object-based approach to identify convective storms and determine their individual size. Averaged across all 35 cases, both the HRRR-E and HRRRv4 predicted storm areas were generally larger than observed, with this bias being a function of storm lifetime and convective mode. Both modeling systems also under-predicted the rapid increase in storm counts during the initiation period, particularly for the smaller-scale storm modes. Interestingly, performance of the HRRRv4 differed from that of the HRRR-E, with the HRRRv4 generally having a larger bias in total storm area than the HRRR-E due to HRRRv4 predicting up to 66% more storm objects than the HRRR-E. The HRRR-E accurately predicted the convective mode 65% of the time, with complete misses being very rare (<5% of the time overall). However, an evaluation of rank histograms across all 35 cases revealed that the HRRR-E tended to be under-dispersive when predicting storm size for all but the MCS mode.

Should Reversible Convective Inhibition be Used when Determining the Inflow Layer of a Convective Storm?

Convective inhibition (CIN) is one of the parameters used by forecasters to determine the inflow layer of a convective storm, but little work has examined the best way to compute CIN. One decision that must be made is whether to lift parcels following a pseudoadiabat (removing hydrometeors as the parcel ascends) or reversible moist adiabat (retaining hydrometeors). To determine which option is best, idealized simulations of ordinary convection are examined using a variety of base states with different reversible CIN values for parcels originating in the lowest 500 m. Parcel trajectories suggest that ascent over the lowest few kilometers, where CIN is typically accumulated, is best conceptualized as a reversible moist adiabatic process instead of a pseudoadiabatic process. Most inflow layers do not contain parcels with substantial reversible CIN, despite these parcels possessing ample convective available potential energy and minimal pseudoadiabatic CIN. If a stronger initiation method is used, or hydrometeor loading is ignored, simulations can ingest more parcels with large amounts of reversible CIN. These results suggest that reversible CIN, not pseudoadiabatic CIN, is the physically relevant way to compute CIN and that forecasters may benefit from examining reversible CIN instead of pseudoadiabatic CIN when determining the inflow layer.

The Evolution of Hail Production in Simulated Supercell Storms

Hailstorms pose a significant socioeconomic risk, necessitating detailed assessments of how the hail threat changes throughout their lifetimes. Hail production involves the favorable juxtaposition of ingredients, but how storm evolution affects these ingredients is unknown, limiting understanding of how hail production evolves. Unfortunately, neither surface hail reports nor radar-based swath estimates have adequate resolution or details needed to assess evolving hail production. Instead, we use a novel approach of coupling a detailed hail trajectory model to idealized convective storm simulations to better understand storm evolution’s influence on hail production. Hail production varies substantially throughout storms’ mature phases: maximum sizes vary by a factor of two, and the concentration of severe hail more than fivefold during 45-60-min periods. This variability arises from changes in updraft properties, which come from (i) changes in low-level convergence, and (ii) internal storm dynamics, including anticyclonic vortex shedding/storm splitting, and the response of the updraft’s airflow and supercooled liquid water content to these events. Hodograph shape strongly affects such behaviors. Straighter hodographs lead to more prolific hail production through wider updrafts and weaker mesocyclones, and a periodicity in hail size metrics associated with anticyclonic vortex shedding and/or storm splitting. In contrast, a curved hodograph (favorable for tornadoes) led to a storm with a stronger but more compact updraft, which occasionally produced giant (10-cm) hail, but that was a less-prolific severe hail producer overall. Unless storms are adequately sampled throughout their lifecycles, snapshots from ground reports will insufficiently resolve the true nature of hail production.

GNSS storm nowcasting demonstrator for Bulgaria

&lt;p&gt;GNSS is an established atmospheric monitoring technique delivering water vapour data in near-real time with latency 90 minutes for operational Numerical Weather Prediction in Europe within the EGVAP service. However, nowadays with advancement of GNSS processing the quality of real-time GNSS tropospheric products is well comparable to near-real time solution and in addition they can be provided in a temporal resolution of 5 minutes and low latency, suitable for severe weather nowcasting. The aim of the project is to exploit the added value of GNSS tropospheric product for nowcasting of convective storm by building demonstrators in support of public weather and hail suppression services in Bulgaria. In Bulgaria &amp;#160;thunderstorms and hail events are &amp;#160;occur between May and September with a peak in July. The convective Storm Demonstrator (Storm Demo) is based on GNSS tropospheric products and Instability Indices to derive site specific threshold values integrated and updated in real-time on a publicly accessible geoportal. The demonstrator targets development of service centered at GNSS products for two regions with hail suppression operations namely Northwestern and Central Bulgaria.&lt;span&gt;&amp;#160; &lt;/span&gt;As a part of the Storm Demo real-time PPP processing will be conducted with G-Nut software for the first time in Southeast Europe for the hail suppression season May-September 2021. Evaluation of the real-time products will be performed using reprocessed GNSS tropospheric products.&lt;span&gt;&amp;#160; &lt;/span&gt;The added value of the high temporal resolution of the GNSS tropospheric products will be investigated for selected storm cases.&lt;span&gt;&amp;#160; &lt;/span&gt;This service will be unique in Europe and will serve as a prototype for real-time provision of GNSS products for storm nowcasting.&lt;span&gt;&amp;#160;&lt;/span&gt;&lt;/p&gt;

Climatology of Convective Storms in Estonia from Radar Data and Severe Convective Environments

Data from the C-band weather radar located in central Estonia in conjunction with the latest reanalysis of the European Centre for Medium-Range Weather Forecasts (ECMWF), ERA5, and Nordic Lightning Information System (NORDLIS) lightning location system data are used to investigate the climatology of convective storms for nine summer periods (2010–2019, 2017 excluded). First, an automated 35-dBZ reflectivity threshold-based storm area detection algorithm is used to derive initial individual convective cells from the base level radar reflectivity. Those detected cells are used as a basis combined with convective available potential energy (CAPE) values from ERA5 reanalysis to find thresholds for a severe convective storm in Estonia. A severe convective storm is defined as an area with radar reflectivity at least 51 dBZ and CAPE at least 80 J/kg. Verification of those severe convective storm areas with lightning data reveals a good correlation on various temporal scales from hourly to yearly distributions. The probability of a severe convective storm day in the study area during the summer period is 45%, and the probability of a thunderstorm day is 54%. Jenkinson Collison’ circulation types are calculated from ERA5 reanalysis to find the probability of a severe convective storm depending on the circulation direction and the representativeness of the investigated period by comparing it against 1979–2019. The prevailing airflow direction is from SW and W, whereas the probability of the convective storm to be severe is in the case of SE and S airflow. Finally, the spatial distribution of the severe convective storms shows that the yearly mean number of severe convective days for the 100 km2 grid cell is mostly between 3 and 8 in the distance up to 150 km from radar. Severe convective storms are most frequent in W and SW parts of continental Estonia.

Assessing the influence of complex terrain on severe convective environments in northeastern Alabama

Storms crossing complex terrain can potentially encounter rapidly changing convective environments. However, our understanding of terrain-induced variability in convective stormenvironments remains limited. HRRR data are used to create climatologies of popular convective storm forecasting parameters for different wind regimes. Self-organizing maps (SOMs) are used to generate six different low-level wind regimes, characterized by different wind directions, for which popular instability and vertical wind shear parameters are averaged. The climatologies show that both instability and vertical wind shear are highly variable in regions of complex terrain, and that the spatial distributions of perturbations relative to the terrain are dependent on the low-level wind direction. Idealized simulations are used to investigate the origins of some of the perturbations seen in the SOM climatologies. The idealized simulations replicate many of the features in the SOM climatologies, which facilitates analysis of their dynamical origins. Terrain influences are greatest when winds are approximately perpendicular to the terrain. In such cases, a standing wave can develop in the lee, leading to an increase in low-level wind speed and a reduction in vertical wind shear with the valley lee of the plateau. Additionally, CAPE tends to be decreased and LCL heights are increased in the lee of the terrain where relative humidity within the boundary layer is locally decreased.

A Novel Convective Storm Location Prediction Model Based on Machine Learning Methods

A weather radar is a frequently used device in remote sensing to identify meteorological phenomena using electromagnetic waves. It can observe atmospheric conditions in a wide area with a remarkably high spatiotemporal resolution, and its observation results are useful to meteorological research and services. Recent research on data analysis using radar data has concentrated on applying machine learning techniques to solve complicated problems, including quality control, quantitative precipitation estimation, and convective storm prediction. Convective storms, which consist of heavy rains and winds, are closely related to real-life and cause significant loss of life and property. This paper proposes a novel approach utilizing the given convective storms’ temporal properties based on machine learning models to predict future locations. The experimental results showed that the machine learning-based prediction models are capable of nowcasting future locations of convective storms with a slight difference.