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Xian Xiao, Juanzhen Sun, Xiushu Qie, Zhuming Ying, Lei Ji, Mingxuan Chen, and Lina Zhang

Abstract

A proof-of-concept method for the assimilation of total lightning observations in the 4DVAR framework is proposed and implemented into the Variational Doppler Radar Analysis System (VDRAS). Its performance is evaluated for the very short-term precipitation forecasts of a localized convective event over northeastern China. The lightning DA scheme assimilated pseudo-observations for vertical velocity fields derived from observed total lightning rates and statistically computed vertical velocity profile from VDRAS analysis data. To reduce representative errors of the derived vertical velocity, a distance-weighted horizontal interpolation is applied to the input data prior to the DA. The case study reveals that although 0–2-h precipitation nowcasts are improved by assimilating lightning data alone compared to CTRL (no radar or lightning) and RAD (radar only), better results are obtained when the lightning data are assimilated with radar data simultaneously. The assimilation of both data sources results in improved dynamical consistency with enhanced updraft and latent heat as well as improved moisture distributions. Additional experiments are conducted to evaluate the sensitivity of the combined DA scheme to varied vertical velocity profiles, radii of horizontal interpolation, binning time intervals, and relationships used to estimate the maximum vertical velocity from lightning flash rates. It is shown that the scheme is robust to these variations with both radar and lightning assimilated data.

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Joshua Hartigan, Robert A. Warren, Joshua S. Soderholm, and Harald Richter

Abstract

The central east coast of Australia is frequently impacted by large hail and damaging winds associated with severe convective storms, with individual events recording damages exceeding AUD 1 billion. These storms present a significant challenge for forecasting because of their development in seemingly marginal environments. They often have been observed to intensify upon approaching the coast, with case studies and climatological analyses indicating that interactions with the sea breeze are key to this process. The relative importance of the additional lifting and vorticity along the sea-breeze front in comparison with the change to a cooler, moister air mass with stronger low-level shear behind the front has yet to be investigated. Here, the role of the sea-breeze air mass is isolated using idealized numerical simulations of storms developing in a horizontally homogeneous environment. The base-state substitution (BSS) modeling technique is utilized to introduce the sea-breeze air mass following initial storm development. Relative to a simulation without BSS, the storm is longer lived and more intense, ultimately developing supercell characteristics including increased updraft rotation, deviant motion to the left of the mean wind vector, and a strong reflectivity gradient on the inflow edge. Separately simulating the changes in the thermodynamic and wind fields reveals that the enhanced storm longevity and intensity are primarily due to the latter. The change in the low-level environmental winds slows gust-front propagation, allowing the storm to continue to ingest warm, potentially buoyant environmental air. At the same time, increased low-level shear promotes the development of persistent updraft rotation that causes the storm to make a transition from a multicell to a supercell.

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Simon Veldkamp, Kirien Whan, Sjoerd Dirksen, and Maurice Schmeits

Abstract

Current statistical post-processing methods for probabilistic weather forecasting are not capable of using full spatial patterns from the numerical weather prediction (NWP) model. In this paper we incorporate spatial wind speed information by using convolutional neural networks (CNNs) and obtain probabilistic wind speed forecasts in the Netherlands for 48 hours ahead, based on KNMI’s deterministic Harmonie-Arome NWP model. The probabilistic forecasts from the CNNs are shown to have higher Brier skill scores for medium to higher wind speeds, as well as a better continuous ranked probability score (CRPS) and logarithmic score, than the forecasts from fully connected neural networks and quantile regression forests. As a secondary result, we have compared the CNNs using 3 different density estimation methods (quantized softmax (QS), kernel mixture networks, and fitting a truncated normal distribution), and found the probabilistic forecasts based on the QS method to be best.

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Yunji Zhang, David J. Stensrud, and Eugene E. Clothiaux

Abstract

Recent studies have demonstrated advances in the analysis and prediction of severe thunderstorms and other weather hazards by assimilating infrared (IR) all-sky radiances into numerical weather prediction models using advanced ensemble-based techniques. It remains an open question how many of these advances are due to improvements in the radiance observations themselves, especially when compared with radiance observations from preceding satellite imagers. This study investigates the improvements gained by assimilation of IR all-sky radiances from the Advanced Baseline Imager (ABI) on board GOES-16 compared to those from its predecessor imager. Results show that all aspects of the improvements in ABI compared with its predecessor imager—finer spatial resolution, shorter scanning intervals, and more channels covering a wider range of the spectrum—contribute to more accurate ensemble analyses and forecasts of the targeted severe thunderstorm event, but in different ways. The clear-sky regions within the assimilated all-sky radiance fields have a particularly beneficial influence on the moisture fields. Results also show that assimilating different IR channels can lead to oppositely signed increments in the moisture fields, a by-product of inaccurate covariances at large distances resulting from sampling errors. These findings pose both challenges and opportunities in identifying appropriate vertical localizations and IR channel combinations to produce the best possible analyses in support of severe weather forecasting.

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Shifeng Hao, Xiaopeng Cui, and Jianping Huang

Abstract

The square conservative exponential integral method (SCEIM) is proposed for transport problems on the sphere. The method is a combination of the square conservation algorithm and the exponential integral method. The main emphasis in the development of SCEIM is on conservation, positive-definite, and reversibility as well as achieving comparable accuracy to other published schemes. The most significant advantage of SCEIM is to change the forward model to the backward model by setting a negative time step, and the backward model can be used to solve the inverse problem. Moreover, the polar problem is significantly improved by using a simple effective central skip-point difference scheme without major penalty on the overall effectiveness of SCEIM. To demonstrate the effectiveness and generality of the SCEIM, this method is evaluated by standard cosine bell tests and deformational flow tests. The numerical results show that SCEIM is a time-convergence method as well as a grid-convergence method, and has a strong shape-preserving ability. In the tests of the inverse problem, the sharp fronts are successfully regressed back into their initial weak fronts and the cosine bells move against the wind direction and return to the initial position with high accuracy. The numerical results of forward simulations are compared with those of published schemes, the total mass conservation, and error norms are competitive in term of accuracy.

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T. Connor Nelson, James Marquis, Adam Varble, and Katja Friedrich

Abstract

The Remote Sensing of Electrification, Lightning, and Mesoscale/Microscale Processes with Adaptive Ground Observations (RELAMPAGO) and Cloud, Aerosol, and Complex Terrain Interactions (CACTI) projects deployed a high-spatiotemporal-resolution radiosonde network to examine environments supporting deep convection in the complex terrain of central Argentina. This study aims to characterize atmospheric profiles most representative of the near-cloud environment (in time and space) to identify the mesoscale ingredients affecting storm initiation and growth. Spatiotemporal autocorrelation analysis of the soundings reveals that there is considerable environmental heterogeneity, with boundary layer thermodynamic and kinematic fields becoming statistically uncorrelated on scales of 1–2 h and 30 km. Using this as guidance, we examine a variety of environmental parameters derived from soundings collected within close proximity (30 km in space and 30 min in time) of 44 events over 9 days where the atmosphere either: 1) supported the initiation of sustained precipitating convection, 2) yielded weak and short-lived precipitating convection, or 3) produced no precipitating convection in disagreement with numerical forecasts from convection-allowing models (i.e., Null events). There are large statistical differences between the Null event environments and those supporting any convective precipitation. Null event profiles contained larger convective available potential energy, but had low free-tropospheric relative humidity, higher freezing levels, and evidence of limited horizontal convergence near the terrain at low levels that likely suppressed deep convective growth. We also present evidence from the radiosonde and satellite measurements that flow–terrain interactions may yield gravity wave activity that affects CI outcome.

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Joshua B. Wadler, Jun A. Zhang, Robert F. Rogers, Benjamin Jaimes, and Lynn K. Shay

Abstract

The spatial and temporal variation in multiscale structures during the rapid intensification of Hurricane Michael (2018) are explored using a coupled atmospheric–oceanic dataset obtained from NOAA WP-3D and G-IV aircraft missions. During Michael’s early life cycle, the importance of ocean structure is studied to explore how the storm intensified despite experiencing moderate vertical shear. Michael maintained a fairly symmetric precipitation distribution and resisted lateral mixing of dry environmental air into the circulation upshear. The storm also interacted with an oceanic eddy field leading to cross-storm sea surface temperature (SST) gradients of ~2.5°C. This led to the highest enthalpy fluxes occurring left of shear, favoring the sustainment of updrafts into the upshear quadrants and a quick recovery from low-entropy downdraft air. Later in the life cycle, Michael interacted with more uniform and higher SSTs that were greater than 28°C, while vertical shear imposed asymmetries in Michael’s secondary circulation and distribution of entropy. Midlevel (~4–8 km) outflow downshear, a feature characteristic of hurricanes in shear, transported high-entropy air from the eyewall region outward. This outflow created a cap that reduced entrainment across the boundary layer top, protecting it from dry midtropospheric air out to large radii (i.e., >100 km), and allowing for rapid energy increases from air–sea enthalpy fluxes. Upshear, low-level (~0.5–2 km) outflow transported high-entropy air outward, which aided boundary layer recovery from low-entropy downdraft air. This study underscores the importance of simultaneously measuring atmospheric and oceanographic parameters to understand tropical cyclone structure during rapid intensification.

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Xiaoran Zhuang, Ming Xue, Jinzhong Min, Zhiming Kang, Naigeng Wu, and Fanyou Kong

Abstract

Error growth is investigated based on convection-allowing ensemble forecasts starting from 0000 UTC for 14 active convection events over central to eastern U.S. regions from spring 2018. The analysis domain is divided into the NW, NE, SE and SW quadrants (subregions). Total difference energy and its decompositions are used to measure and analyze error growth at and across scales. Special attention is paid to the dominant types of convection with respect to their forcing mechanisms in the four subregions and the associated difference in precipitation diurnal cycles. The discussions on the average behaviors of error growth in each region are supplemented by 4 representative cases. Results show that the meso-γ-scale error growth is directly linked to precipitation diurnal cycle while meso-α-scale error growth has strong link to large scale forcing. Upscale error growth is evident in all regions/cases but up-amplitude growth within own scale plays different roles in different regions/cases.

When large-scale flow is important (as in the NE region), precipitation is strongly modulated by the large-scale forcing and becomes more organized with time, and upscale transfer of forecast error is stronger. On the other hand, when local instability plays more dominant roles (as in the SE region), precipitation is overall least organized and has the weakest diurnal variations. Its associated errors at the γ– and β-scale can reach their peaks sooner and meso-α-scale error tends to rely more on growth of error with its own scale. Small-scale forecast errors are directly impacted by convective activities and have short response time to convection while increasingly larger scale errors have longer response times and delayed phase within the diurnal cycle.

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P.L Houtekamer, Bin He, Dominik Jacques, Ron McTaggart-Cowan, Leo Separovic, Paul A. Vaillancourt, Ayrton Zadra, and Xingxiu Deng

Abstract

An important step in an Ensemble Kalman Filter (EnKF) algorithm is the integration of an ensemble of short-range forecasts with a Numerical Weather Prediction (NWP) model. A multi-physics approach is used in the Canadian global EnKF system. This paper explores whether the many integrations with different versions of the model physics can be used to obtain more accurate and more reliable probability distributions for the model parameters. Some model parameters have a continuous range of possible values. Other parameters are categorical and act as switches between different parametrizations. In an evolutionary algorithm, the member configurations that contribute most to the quality of the ensemble are duplicated, while adding a small perturbation, at the expense of configurations that perform poorly. The evolutionary algorithm is being used in the migration of the EnKF to a new version of the Canadian NWP model with upgraded physics. The quality of configurations is measured with both a deterministic and an ensemble score, using the observations assimilated in the EnKF system. When using the ensemble score in the evaluation, the algorithm is shown to be able to converge to non-Gaussian distributions. However, for several model parameters, there is not enough information to arrive at improved distributions. The optimized system features slight reductions in biases for radiance measurements that are sensitive to humidity. Modest improvements are also seen in medium-range ensemble forecasts.

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Scott D. Rudlosky and Katrina S. Virts

Abstract

Two Geostationary Lightning Mappers (GLMs) now observe spatial and temporal lightning distributions over a vast region. The GOES-16 GLM covers most land areas in the Western Hemisphere, and detects ~4 times as much lightning as the GOES-17 GLM. Although the continents dominate the lightning distributions year round, each season exhibits widespread lightning over parts of the Atlantic Ocean and within three broad regions over the Pacific. These oceanic regions demonstrate the key role convective organization plays in producing larger, longer lasting, and more energetic flashes observed by both GLMs over the oceans. Texture within the flash densities reveals a close relationship with the underlying topography, underscored by the complex diurnal cycles observed along coastlines and in mountainous regions. GLM information beyond flash frequency provides additional insights into storm mode and evolution. For example, over the Sierra Madre Occidental, time series reveal initially small, short-duration GLM flashes growing larger and longer as storms grow upscale. These mesoscale convective systems often transition offshore, contributing to an average flash area maxima over the eastern Pacific. Data quality improves during the study period with tuning of the ground system software. GLM artifacts due to solar intrusion and Sun glint greatly diminish following the blooming filter installation, and the second-level threshold filter reduces false events along particular subarray boundaries (i.e., bar artifacts). Analysis of the overlap region reveals a pronounced north-south line near 103° W, with the GOES-East (-West) GLM detecting more flashes to the east (west) of this line.

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