An Analysis of the 3 May 2020 Low-Predictability Derecho Using a Convection-Allowing MPAS Ensemble

This study analyzes the low short-range predictability of the 3 May 2020 derecho using a 40-member convection-allowing Model for Prediction Across Scales (MPAS) ensemble. Elevated storms formed in south-central Kansas late at night and evolved into a progressive mesoscale convective system (MCS) during the morning while moving across southern Missouri and northern Arkansas, and affected western and middle Tennessee and southern Kentucky in the afternoon. The convective initiation (CI) in south-central Kansas, the organization of a dominant bow echo MCS and the MCS maintenance over Tennessee were identified as the three main predictability issues. These issues were explored using three MPAS ensemble members, observations and the Rapid Refresh analyses. The MPAS members were classified as successful or unsuccessful with regard to each predictability issue. CI in south-central Kansas was sensitive to the temperature and dewpoint profiles in low levels, which were associated with greater elevated thermodynamic instability and lower level of free convection in the successful member. The subsequent organization of a dominant bowing MCS was well predicted by the member that had more widespread convection in the early stages and no detrimental interaction with other simulated convective systems. Lastly, the inability of MPAS ensemble members to predict the MCS maintenance over western and middle Tennessee was linked to a dry bias in low levels and much lower thermodynamic instability ahead of the MCS compared to observations. This case demonstrates the challenges in operational forecasting of warm-season derecho-producing progressive MCSs, particularly when ensemble numerical weather prediction guidance solutions differ considerably.

Inter-Zone Differences of Convective Development in a Convection Outbreak Event over Southeastern Coast of China: An Observational Analysis

Among the densely-populated coastal areas of China, the southeastern coast has received less attention in convective development despite having been suffering from significantly increasing thunderstorm activities. The convective complexity under such a region with extremely complex underlying and convective conditions deserves in-depth observational surveys. This present study examined a high-impact convection outbreak event with over 40 hail reports in the southeastern coast of China on 6 May 2020 by focusing on contrasting the convective development (from convective initiation to supercell occurrences) among three adjacent convection-active zones (north (N), middle (M), and south (S)). The areas from N to S featured overall flatter terrain, higher levels of free convection, lower relative humidity, larger convective inhibition, more convective available potential energy, and greater vertical wind shears. With these mesoscale environmental variations, distinct inter-zone differences in the convective development were observed with the region’s surveillance radar network and the Himawari-8 geostationary satellite. Convection initiated in succession from N to S and began with more warm-rain processes in N and M and more ice-phase processes in S. The subsequent convection underwent more vigorous vertical growth from N to S. The extremely deep convection in S was characterized by the considerably strong precipitation above the freezing level, echo tops of up to 18 km, and a great amount of deep (even overshooting) and thick convective clouds with significant cloud-top glaciation. Horizontal anvil expansion in convective clouds was uniquely apparent over S. From N to S, more pronounced mesocyclone and weak-echo region signatures indicated high risks of severe supercell hailstorms. These results demonstrate the strong linkage between the occurrence likelihood of severe convection and associated weather (such as supercells and hailstones) and the early-stage convective development that can be well-captured by high-resolution observations and may facilitate fine-scale convection nowcasting.

Multi-Scale Object-Based Probabilistic Forecast Evaluation of WRF-Based CAM Ensemble Configurations

Convection-allowing model (CAM) ensembles contain a distinctive ability to predict convective initiation location, mode, and morphology. Previous studies on CAM ensemble verification have primarily used neighborhood-based methods. A recently introduced object-based probabilistic (OBPROB) framework provides an alternative and novel framework in which to re-evaluate aspects of optimal CAM ensemble design with an emphasis on ensemble storm mode and morphology prediction. Herein, we adopt and extend the OBPROB method in conjunction with a traditional neighborhood-based method to evaluate forecasts of four differently configured 10-member CAM ensembles. The configurations include two single-model/single-physics, a single-model/multi-physics, and a multi-model/multi-physics configuration. Both OBPROB and neighborhood frameworks show that ensembles with more diverse member-to-member designs improve probabilistic forecasts over single-model/single-physics designs through greater sampling of different aspects of forecast uncertainties. Individual case studies are evaluated to reveal the distinct forecast features responsible for the systematic results identified from the different frameworks. Neighborhood verification, even at high reflectivity thresholds, is primarily impacted by mesoscale locations of convective and stratiform precipitation across scales. In contrast, the OBPROB verification explicitly focuses on convective precipitation only and is sensitive to the morphology of similarly located storms.

An Assessment of Convective Initiation Nowcasting Algorithm within 0-60 Minutes using Himawari-8 Satellite

Convective cloud monitoring since its growth stage primarily related to location and time of the first convective cloud initiated, called convective initiation (CI), could be the primary key in providing an earlier heavy rainfall event prediction. This study aimed to assess the accuracy and lead time of CI nowcasting using Satellite Convection Analysis and Tracking (SATCAST) algorithm in predicting the CI event within 0-60 minutes over Surabaya and surrounding area using Himawari-8 satellite during June-July-August (JJA) period in 2018. Three main processes used in this study were cloud masking, cloud object tracking, and CI nowcasting. Twelve interest fields were utilized as predictors based on six bands of Himawari-8 satellite, which represented cloud physics attributes such as cloud-top height, glaciation, or cooling rate. The verification was conducted by comparing CI prediction to CI location and time based on Surabaya weather radar within the next 0-60 minutes. The algorithm resulted that the prediction could achieve 87.3% of accuracy from the 3449 cloud objects. The prediction had POD, FAR, and CSI scores of 57.1%, 52.2%, and 35.2%, respectively. The 32.3 minutes of averaged lead time prediction indicated that CI nowcasting could detect growing cumulus about 30 minutes prior to the CI event.

Influences of environmental relative humidity and horizontal scale of sub-cloud ascent on deep convective initiation

This study examines two factors impacting initiation of moist deep convection: free tropospheric environmental relative humidity (ϕE) and horizon scale of sub-cloud ascent (Rsub), the latter exerting a dominant control on cumulus cloud width. A simple theoretical model is used to formulate a “scale selection” hypothesis: that a minimum Rsub is required for moist convection to go deep, and that this minimum scale decreases with increasing ϕE. Specifically, the ratio of to saturation deficit (1–ϕE) must exceed a certain threshold value that depends on cloud-layer environmental lapse rate. Idealized, large-eddy simulations of moist convection forced by horizontally-varying surface fluxes show strong sensitivity of maximum cumulus height to both ϕE and Rsub consistent with the hypothesis. Increasing Rsub by only 300-400 m can lead to a large increase (> 5 km) in cloud height. A passive tracer analysis shows that the bulk fractional entrainment rate decreases rapidly with Rsub but depends little on ϕE. However, buoyancy dilution increases as either Rsub or ϕE decreases; buoyancy above the level of free convection is rapidly depleted in dry environments when Rsub is small. While deep convective initiation occurs with an increase in relative humidity of the near environment from moistening by earlier convection, the importance of this moisture preconditioning is inconclusive as it is accompanied by an increase in Rsub. Overall, it is concluded that small changes to Rsub driven by external forcing or by convection itself could be a dominant regulator of deep convective initiation.

On the Localized Extreme Rainfall over the Great Bay Area in South China with Complex Topography and Strong UHI Effects

In this study, high-resolution surface and radar observations are used to analyze 24 localized extreme hourly rainfall (EXHR, > 60mm/h) events with strong urban heat island (UHI) effects over the Great Bay Area (GBA) in South China during 2011-2016 warm seasons. Quasi-idealized, convection-permitting ensemble simulations driven by diurnally varying lateral boundary conditions, which are extracted from the composite global analysis of 3-5 June 2013, are then conducted with a multi-layer urban canopy model to unravel the influences of the UHI and various surface properties nearby on the EXHR generation in a complex geographical environment with sea-land contrast, topography, and vegetation variation. Results show that EXHR is mostly distributed over the urban agglomeration and within about 40 km on its downwind side, and produced during the afternoon-to-evening hours by short-lived meso-γ to β-scale storms. On the EXHR days, the GBA is featured by a weak-gradient environment with abundant moisture, and a weak southwesterly flow prevailing in the boundary layer (BL). The UHI effects lead to the development of a deep mixed layer with “warm bubbles” over the urban agglomeration, in which the lower-BL convergence and BL-top divergence is developed, assisting in convective initiation. Such urban BL processes and associated convective development with moisture supply by the synoptic low-level southwesterly flow are enhanced by orographically increased horizontal winds and sea breezes under the influence of the herringbone coastline, thereby increasing the inhomogeneity and intensity of rainfall production over the “Π-shaped” urban clusters. Vegetation variations are not found to be an important factor in determining the EXHR production over the region.

Analysis of the environment of a severe hailstorm in Mendoza, Argentina during the RELAMPAGO-CACTI field campaign

<p>Storms in the Mendoza province, Argentina are known for frequently producing large and severe hail. The environmental conditions and strong interaction with topography there provide unique conditions for the initiation and intensification of severe storms. The RELAMPAGO (Remote sensing of Electrification, Lightning, And Mesoscale/microscale Processes with Adaptive Ground Observations) and CACTI (Clouds, Aerosols, and Complex Terrain Interactions) field campaigns were deployed between October 2018 to April 2019 over west-central Argentina, and have collected unprecedented Intensive Observation Periods (IOPs) in the region. During the IOP number 10 on November 26, 2018, a severe hailstorm developed and moved across the observational network in the Mendoza domain. 4-cm diameter hail was reported over multiple hailpad sites and with in-situ measurements. Several soundings, mobile and fixed radar observations, and surface observations are available for this case, along with 1-min GOES-16 ABI Mesoscale Domain Sector (MDS) data coverage.</p><p>High-temporal frequency soundings and surface observations collected prior to the convection initiation are analyzed, allowing a detailed description of the storm environment. Processes leading to convective initiation over the higher terrain include the development of the upslope flow associated with a mountain-plains circulation, the weakening and ascent of the nocturnal inversion owing to diurnal heating and mixed-layer growth, and upper-level cooling related to the advance of a shortwave trough. Once the storm initiates, it moves eastward towards the lower terrain, where the higher CAPE and deep-layer shear environment support the transition into a supercell. It is after this transition that the most severe hail at the surface is observed.</p>

Low-level buoyancy as a tool to understand boundary layer transitions

Advancements in remotely piloted aircraft systems (RPAS) introduced a new way to observe the atmospheric boundary layer (ABL). Adequate sampling of the lower atmosphere is key to improving numerical weather models and understanding fine-scale processes. The ABL’s sensitivity to changes in surface fluxes leads to rapid changes in thermodynamic variables. This study proposes using low-level buoyancy to characterize ABL transitions. Previously, buoyancy has been used as a bulk parameter to quantify stability. Higher resolution data from RPAS highlight buoyancy fluctuations. RPAS profiles from two field campaigns are used to assess the evolution of buoyancy under convective and stable boundary layers. Data from these campaigns included challenging events to forecast accurately, such as convective initiation and a low-level jet. Throughout the daily ABL transition, results show that the ABL height determined by the minimum in vertical buoyancy gradient agrees well with proven ABL height metrics, such as potential temperature gradient maxima. Moreover, in the cases presented, low-level buoyancy rapidly increases prior to convective initiation and rapidly decreases prior to the onset of a low-level jet. Low-level buoyancy is a function sensitive in space and time, and with further analysis could be used as a forecasting tool. This study expounds on the utility of buoyancy in the ABL and offers potential uses for future research.

Evaluating Convective Initiation in High-Resolution Numerical Weather Prediction Models Using GOES-16 Infrared Brightness Temperatures

The evolution of model-based cloud-top brightness temperatures (BT) associated with convective initiation (CI) is assessed for three bulk cloud microphysics schemes in the Weather Research and Forecasting Model. Using a composite-based analysis, cloud objects derived from high-resolution (500 m) model simulations are compared to 5-min GOES-16 imagery for a case study day located near the Alabama–Mississippi border. Observed and simulated cloud characteristics for clouds reaching CI are examined by utilizing infrared BTs commonly used in satellite-based CI nowcasting methods. The results demonstrate the ability of object-based verification methods with satellite observations to evaluate the evolution of model cloud characteristics, and the BT comparison provides insight into a known issue of model simulations producing too many convective cells reaching CI. The timing of CI from the different microphysical schemes is dependent on the production of ice in the upper levels of the cloud, which typically occurs near the time of maximum cloud growth. In particular, large differences in precipitation formation drive differences in the amount of cloud water able to reach upper layers of the cloud, which impacts cloud-top glaciation. Larger cloud mixing ratios are found in clouds with sustained growth leading to more cloud water lofted to the upper levels of the cloud and the formation of ice. Clouds unable to sustain growth lack the necessary cloud water needed to form ice and grow into cumulonimbus. Clouds with slower growth rates display similar BT trends as clouds exhibiting growth, which suggests that forecasting CI using geostationary satellites might require additional information beyond those derived at cloud top.