Rapid Update with EnVar Direct Radar Reflectivity Data Assimilation for the NOAA Regional Convection-Allowing NMMB Model over the CONUS: System Description and Initial Experiment Results

This study first describes the extended Grid-Point Statistical Interpolation analysis system (GSI)-based ensemble-variational data assimilation (DA) system within the North American Mesoscale Rapid Refresh (NAMRR) system for the Nonhydrostatic Multiscale Model on the B grid (NMMB). Experiments were conducted to examine three critical aspects of data assimilation configuration in this system. Ten retrospective high-impact convective cases during the warm season of 2015–2016 were adopted for testing. A 10-member, 18 h ensemble forecast was launched for each experiment. Specifically, the experiment using horizontal (vertical) localization radii (Lr) of 300 km (0.55-scaled height measured in the nature log of pressure) overall had more skills than that of 500 km (1.1-scaled height) for conventional in-situ observation assimilation. Diagnostics suggest that the higher forecast skills could be attributed to applying smaller Lr in the boundary with large temperature and moisture gradients. For radar DA, the experiment was more skillful with horizontal (vertical) Lr of 15 km (1.1-scaled height) than that of 12 km (0.55-scaled height). Diagnostics suggest that the improved forecasts were achieved by using wider Lr to spread radar observations into unobserved areas more effectively. Slight forecast skill differences between the relaxation inflation factors of 95% and 65% are presented. The impact of varying inflation magnitudes primarily occurred in the upper-level spread.

Impact of Radar Reflectivity and Lightning Data Assimilation on the Rainfall Forecast and Predictability of a Summer Convective Thunderstorm in Southern Italy

Heavy and localized summer events are very hard to predict and, at the same time, potentially dangerous for people and properties. This paper focuses on an event occurred on 15 July 2020 in Palermo, the largest city of Sicily, causing about 120 mm of rainfall in 3 h. The aim is to investigate the event predictability and a potential way to improve the precipitation forecast. To reach this aim, lightning (LDA) and radar reflectivity data assimilation (RDA) was applied. LDA was able to trigger deep convection over Palermo, with high precision, whereas the RDA had a key role in the prediction of the amount of rainfall. The simultaneous assimilation of both data sources gave the best results. An alert for a moderate–intense forecast could have been issued one hour and a half before the storm developed over the city, even if predicting only half of the total rainfall. A satisfactory prediction of the amount of rainfall could have been issued at 14:30 UTC, when precipitation was already affecting the city. Although the study is centered on a single event, it highlights the need for rapidly updated forecast cycles with data assimilation at the local scale, for a better prediction of similar events.

Benthic Habitat Map of the Southern Adriatic Sea (Mediterranean Sea) from Object-Based Image Analysis of Multi-Source Acoustic Backscatter Data

A huge amount of seabed acoustic reflectivity data has been acquired from the east to the west side of the southern Adriatic Sea (Mediterranean Sea) in the last 18 years by CNR-ISMAR. These data have been used for geological, biological and habitat mapping purposes, but a single and consistent interpretation of them has never been carried out. Here, we aimed at coherently interpreting acoustic data images of the seafloor to produce a benthic habitat map of the southern Adriatic Sea showing the spatial distribution of substrates and biological communities within the basin. The methodology here applied consists of a semi-automated classification of acoustic reflectivity, bathymetry and bathymetric derivatives images through object-based image analysis (OBIA) performed by using the ArcGIS tool RSOBIA (Remote Sensing OBIA). This unsupervised image segmentation was carried out on each cruise dataset separately, then classified and validated through comparison with bottom samples, images, and prior knowledge of the study areas.

Development of Convective-Scale Static Background Error Covariance within GSI-Based Hybrid EnVar System for Direct Radar Reflectivity Data Assimilation

A convective-scale static background-error covariance (BEC) matrix is further developed to include the capability of direct reflectivity assimilation and evaluated within the GSI-based 3-dimensional variational (3DVar) and hybrid ensemble-variational (EnVar) methods. Specific developments are summarized as follows: 1) Control variables (CVs) are extended to include reflectivity, vertical velocity, and all hydrometeor types. Various horizontal momentum and moisture CV options are included. 2) Cross-correlations between all CVs are established. 3) A storm intensity-dependent binning method is adopted to separately calculate static error matrices for clear-air and storms with varying intensities. The resultant static BEC matrices are simultaneously applied at proper locations guided by the observed reflectivity. 4) The EnVar is extended to adaptively incorporate static BECs based on the quality of ensemble covariances.Evaluation and examination of the new static BECs are first performed on the 8 May 2003 Oklahoma City supercell. Detailed diagnostics and 3DVar examinations suggest zonal/meridian winds and pseudo-relative humidity are selected horizontal momentum and moisture CVs for direct reflectivity assimilation, respectively; inclusion of cross-correlations favors to spinup and maintain the analyzed storms; application of binning improves characteristics and persistence of the simulated storm. Relative to an experiment using the full ensemble BECs (Exp-PureEnVar), incorporating static BECs in hybrid EnVar reduces spinup time and better analyzes reflectivity distributions while the background ensemble is deficient in sampling errors. Compared to both pure 3DVar and Exp-PureEnVar, hybrid EnVar better predicts reflectivity distributions and better maintains strong mesocyclone. Further examination through the 20 May 2013 Oklahoma supercells confirms these results and additionally demonstrates the effectiveness of adaptive hybridization.

A seven-year climatology of the initiation, decay and morphology of severe convective storms during the warm season over North China

The present work established a 7-year climatology of the initiation, decay, and morphology of severe convective storms (SCSs) during the warm seasons (May–September) of 2011–2018 (except 2014) over North China. This was achieved by using severe weather reports, precipitation observations, and composite Doppler radar reflectivity data. A total of 371 SCSs were identified. SCSs primarily initiated around noon with the highest frequency over the high terrain of Mount Taihang, and they mostly decayed over the plains at night. The storm morphologies were classified into three types of cellular storms (individual cells, clusters of cells, and broken lines), six types of linear systems (convective lines with no stratiform, with trailing stratiform, leading stratiform, parallel stratiform, embedded lines, and bow echoes), and nonlinear systems. Three types of severe convective weather, namely, short-duration heavy rainfall, hail, and thunderstorm high winds associated with these morphologies were investigated. Nonlinear systems were the most frequent morphology, followed by clusters of cells. Convective lines with trailing stratiform were the most frequent linear morphology. A total of 1,429 morphology samples from the 371 SCSs were found to be responsible for 15,966 severe convective weather reports. Linear (nonlinear) systems produced the most short-duration heavy rainfall (hail and thunderstorm high wind) reports. Bow echos were most efficient in producing both short-duration heavy rainfall and thunderstorm high wind reports whereas broken lines had the highest efficiency for hail production. The results in the present study are helpful for local forecasters to better anticipate the storm types and associated hazardous weather.

The 3D Neural Network for Improving Radar-Rainfall Estimation in Monsoon Climate

The reflectivity (Z)—rain rate (R) model has not been tested on single polarization radar for estimating monsoon rainfall in Southeast Asia, despite its widespread use for estimating heterogeneous rainfall. The artificial neural network (ANN) regression has been applied to the radar reflectivity data to estimate monsoon rainfall using parametric Z-R models. The 10-min reflectivity data recorded in Kota Bahru radar station (in Malaysia) and hourly rain record in nearby 58 gauge stations during 2013–2015 were used. The three-dimensional nearest neighbor interpolation with altitude correction was applied for pixel matching. The non-linear Levenberg Marquardt (LM) regression, integrated with ANN regression minimized the spatiotemporal variability of the proposed Z-R model. Results showed an improvement in the statistical indicator, when LM and ANN overestimated (6.6%) and underestimated (4.4%), respectively, the mean total rainfall. For all rainfall categories, the ANN model has a positive efficiency ratio of >0.2.

Electrical conductivity of warm dense silica from double-shock experiments

Understanding materials behaviour under extreme thermodynamic conditions is fundamental in many branches of science, including High-Energy-Density physics, fusion research, material and planetary science. Silica (SiO2) is of primary importance as a key component of rocky planets’ mantles. Dynamic compression is the most promising approach to explore molten silicates under extreme conditions. Although most experimental studies are restricted to the Hugoniot curve, a wider range of conditions must be reached to distill temperature and pressure effects. Here we present direct measurements of equation of state and two-colour reflectivity of double-shocked α-quartz on a large ensemble of thermodynamic conditions, which were until now unexplored. Combining experimental reflectivity data with numerical simulations we determine the electrical conductivity. The latter is almost constant with pressure while highly dependent on temperature, which is consistent with simulations results. Based on our findings, we conclude that dynamo processes are likely in Super-Earths’ mantles.