The Anatomy of a Series of Cloud Bursts that Eclipsed the US Rainfall Record

A series of extreme cloudbursts occurred on 14 April 2018 over the northern slopes of the island of Kaua‘i. The storm inundated some areas with 1262 mm (∼50”) of rainfall in a 24-hr period, eclipsing the previous 24-hr US rainfall record of 1100 mm (42”) set in Texas in 1979. Three periods of intense rainfall are diagnosed through detailed analysis of National Weather Service operational and special data sets. On the synoptic scale, a slowly southeastward propagating trough aloft over a deep layer of low level moisture (>40 mm of total precipitable water) produced prolonged instability over Kaua‘i. Enhanced NE to E low level flow impacted Kaua‘i’s complex terrain, which includes steep north and eastward facing slopes and cirques. The resulting orographic lift initiated deep convection. The wind profile exhibited significant shear in the troposphere and streamwise vorticity within the convective storm inflow. Evidence suggests that large directional shear in the boundary layer, paired with enhanced orographic vertical motion, produced rotating updrafts within the convective storms. Mesoscale rotation is manifest in the radar data during the latter two periods and reflectivity cores are observed to propagate both to the left and to the right of the mean shear, which is characteristic of supercells. The observations suggest that the terrain configuration in combination with the windshear separates the area of updrafts from the downdraft section of the storm, resulting in almost continuous heavy rainfall over Waipā Garden.

Using Ensemble Data Assimilation to Explore the Environmental Controls on the Initiation and Predictability of Moist Convection

Atmospheric deep moist convection has emerged as one of the most challenging topics for numerical weather prediction, due to its chaotic process of development and multi-scale physical interactions. This study examines the dynamics and predictability of a weakly organized linear convective system using convection permitting EnKF analysis and forecasts with assimilating all-sky satellite radiances from a water vapor sensitive band of the Advanced Baseline Imager on GOES-16. The case chosen occurred over the Gulf of Mexico on 11 June 2017 during the NASA Convective Processes Experiment (CPEX) field campaign. Analysis of the water vapor and dynamic ensemble covariance structures revealed that meso-α (2000-200 km) and meso-β (200-20 km) scale initial features helped to constrain the general location of convection with a few hours of lead time, contributing to enhancing convective activity, but meso-γ (20-2 km) or even smaller scale features with less than 30-minute lead time were identified to be essential for capturing individual convective storms. The impacts of meso-α scale initial features on the prediction of particular individual convective cells were found to be classified into two regimes; in a relatively dry regime, the meso-α scale environment needs to be moist enough to support the development of the convection of interest, but in a relatively wet regime, a drier meso-α scale environment is preferable to suppress the surrounding convective activity. This study highlights the importance of high-resolution initialization of moisture fields for the prediction of a quasi-linear tropical convective system, as well as demonstrating the accuracy that may be necessary to predict convection exactly when and where it occurs.

Characteristics of hail hazard in South Africa based on satellite detection of convective storms

Accurate estimates of hail risk to fixed and mobile assets such as crops, infrastructure and vehicles are required for both insurance pricing and preventive measures. Here we present an event catalog to describe hail hazard in South Africa guided by 14 years of geostationary satellite observations of convective storms. Overshooting cloud tops have been detected, grouped and tracked to describe the spatio-temporal extent of potential hail events. It is found that hail events concentrate mainly in the southeast of the country, along the Highveld and the eastern slopes. Events are most frequent from mid-November through February and peak in the afternoon, between 13 and 17 UTC. Multivariate stochastic modeling of event properties yields an event catalog spanning 25 000 years, aiming to estimate, in combination with vulnerability and exposure data, hail damage for return periods of 200 years.

An Object-Based Method for Tracking Convective Storms in Convection Allowing Models

The steady-state assumption commonly used in object-based tracking algorithms may be insufficient to determine the right track when a convective storm goes through a complicated evolution. Such an issue is exacerbated by the relatively coarse output frequency of current convection allowing model (CAM) forecasts (e.g., hourly), giving rise to many spatially well resolved but temporally not well resolved storms that steady-state assumption could not account for. To reliably track simulated storms in CAM outputs, this study proposed an object-based method with two new features. First, the method explicitly estimated the probability of each probable track based on either its immediate past and future motion or a reliable “first-guess motion” derived from storm climatology or near-storm environmental variables. Second, object size was incorporated into the method to help identify temporally not well resolved storms and minimize false tracks derived for them. Parameters of the new features were independently derived from a storm evolution analysis using 2-min Multi-Radar Multi-Sensor (MRMS) data and hourly CAM forecasts produced by the University of Oklahoma (OU) Multiscale data Assimilation and Predictability Laboratory (MAP) from May 2019. The performance of the new method was demonstrated with hourly MRMS and CAM forecast examples from May 2018. A systematic evaluation of four severe weather events indicated 99% accuracy achieved for over 600 hourly MRMS tracks derived with the proposed tracking method.

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.

Assessment of the data assimilation framework for the Rapid Refresh Forecast System v0.1 and impacts on forecasts of convective storms

The Rapid Refresh Forecast System (RRFS) is currently under development and aims to replace the National Centers for Environmental Prediction (NCEP) operational suite of regional and convective scale modeling systems in the next upgrade. In order to achieve skillful forecasts comparable to the current operational suite, each component of the RRFS needs to be configured through exhaustive testing and evaluation. The current data assimilation component uses the Gridpoint Statistical Interpolation (GSI) system. In this study, various data assimilation algorithms and configurations in GSI are assessed for their impacts on RRFS analyses and forecasts of a squall line over Oklahoma on 4 May 2020. Results show that a baseline RRFS run without data assimilation is able to represent the observed convection, but with stronger cells and large location errors. With data assimilation, these errors are reduced, especially in the 4 and 6 h forecasts using 75 % of the ensemble background error covariance (BEC) and with the supersaturation removal function activated in GSI. Decreasing the vertical ensemble localization radius in the first 10 layers of the hybrid analysis results in overall less skillful forecasts. Convection and precipitation are overforecast in most forecast hours when using planetary boundary layer pseudo-observations, but the root mean square error and bias of the 2 h forecast of 2 m dew point temperature are reduced by 1.6 K during the afternoon hours. Lighter hourly accumulated precipitation is predicted better when using 100 % ensemble BEC in the first 4 h forecast, but heavier hourly accumulated precipitation is better predicted with 75 % ensemble BEC. Our results provide insight into current capabilities of the RRFS data assimilation system and identify configurations that should be considered as candidates for the first version of RRFS.

Spontaneous Aggregation of Convective Storms

Idealized simulations of the tropical atmosphere have predicted that clouds can spontaneously clump together in space, despite perfectly homogeneous settings. This phenomenon has been called self-aggregation, and it results in a state where a moist cloudy region with intense deep convective storms is surrounded by extremely dry subsiding air devoid of deep clouds. We review here the main findings from theoretical work and idealized models of this phenomenon, highlighting the physical processes believed to play a key role in convective self-aggregation. We also review the growing literature on the importance and implications of this phenomenon for the tropical atmosphere, notably, for the hydrological cycle and for precipitation extremes, in our current and in a warming climate. Expected final online publication date for the Annual Review of Fluid Mechanics, Volume 54 is January 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

A Model for the Relationship between Rainfall, GNSS-Derived Integrated Water Vapour, and CAPE in the Eastern Central Andes

Atmospheric water vapour content is a key variable that controls the development of deep convective storms and rainfall extremes over the central Andes. Direct measurements of water vapour are challenging; however, recent developments in microwave processing allow the use of phase delays from L-band radar to measure the water vapour content throughout the atmosphere: Global Navigation Satellite System (GNSS)-based integrated water vapour (IWV) monitoring shows promising results to measure vertically integrated water vapour at high temporal resolutions. Previous works also identified convective available potential energy (CAPE) as a key climatic variable for the formation of deep convective storms and rainfall in the central Andes. Our analysis relies on GNSS data from the Argentine Continuous Satellite Monitoring Network, Red Argentina de Monitoreo Satelital Continuo (RAMSAC) network from 1999 to 2013. CAPE is derived from version 2.0 of the ECMWF’s (European Centre for Medium-Range Weather Forecasts) Re-Analysis (ERA-interim) and rainfall from the TRMM (Tropical Rainfall Measuring Mission) product. In this study, we first analyse the rainfall characteristics of two GNSS-IWV stations by comparing their complementary cumulative distribution function (CCDF). Second, we separately derive the relation between rainfall vs. CAPE and GNSS-IWV. Based on our distribution fitting analysis, we observe an exponential relation of rainfall to GNSS-IWV. In contrast, we report a power-law relationship between the daily mean value of rainfall and CAPE at the GNSS-IWV station locations in the eastern central Andes that is close to the theoretical relationship based on parcel theory. Third, we generate a joint regression model through a multivariable regression analysis using CAPE and GNSS-IWV to explain the contribution of both variables in the presence of each other to extreme rainfall during the austral summer season. We found that rainfall can be characterised with a higher statistical significance for higher rainfall quantiles, e.g., the 0.9 quantile based on goodness-of-fit criterion for quantile regression. We observed different contributions of CAPE and GNSS-IWV to rainfall for each station for the 0.9 quantile. Fourth, we identify the temporal relation between extreme rainfall (the 90th, 95th, and 99th percentiles) and both GNSS-IWV and CAPE at 6 h time steps. We observed an increase before the rainfall event and at the time of peak rainfall—both for GNSS-integrated water vapour and CAPE. We show higher values of CAPE and GNSS-IWV for higher rainfall percentiles (99th and 95th percentiles) compared to the 90th percentile at a 6-h temporal scale. Based on our correlation analyses and the dynamics of the time series, we show that both GNSS-IWV and CAPE had comparable magnitudes, and we argue to consider both climatic variables when investigating their effect on rainfall extremes.

Reconstruction of the Radar Reflectivity of Convective Storms Based on Deep Learning and Himawari-8 Observations

Radar reflectivity (RR) greater than 35 dBZ usually indicates the presence of severe convective weather, which affects a variety of human activities, including aviation. However, RR data are scarce, especially in regions with poor radar coverage or substantial terrain obstructions. Fortunately, the radiance data of space-based satellites with universal coverage can be converted into a proxy field of RR. In this study, a convolutional neural network-based data-driven model is developed to convert the radiance data (infrared bands 07, 09, 13, 16, and 16–13) of Himawari-8 into the radar combined reflectivity factor (CREF). A weighted loss function is designed to solve the data imbalance problem due to the sparse convective pixels in the sample. The developed model demonstrates an overall reconstruction capability and performs well in terms of classification scores with 35 dBZ as the threshold. A five-channel input is more efficient in reconstructing the CREF than the commonly used one-channel input. In a case study of a convective event over North China in the summer using the test dataset, U-Net reproduces the location, shape and strength of the convective storm well. The present RR reconstruction technology based on deep learning and Himawari-8 radiance data is shown to be an efficient tool for producing high-resolution RR products, which are especially needed for regions without or with poor radar coverage.