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Milind Sharma
,
Robin L. Tanamachi
, and
Eric C. Bruning

Abstract

The dual-polarization radar characteristics of severe storms are commonly used as indicators to estimate the size and intensity of deep convective updrafts. In this study, we track rapid fluctuations in updraft intensity and size by objectively identifying polarimetric fingerprints such as Z DR and K DP columns, which serve as proxies for mixed-phase updraft strength. We quantify the volume of Z DR and K DP columns to evaluate their utility in diagnosing temporal variability in lightning flash characteristics. Specifically, we analyze three severe storms that developed in environments with low-to-moderate instability and strong 0–6-km wind shear in northern Alabama during the 2016–17 VORTEX-Southeast field campaign. In these three cases (a tornadic supercell embedded in stratiform precipitation, a nontornadic supercell, and a supercell embedded within a quasi-linear convective system), we find that the volume of the K DP columns exhibits a stronger correlation with the total flash rate. The higher covariability of the K DP column volume with the total flash rate suggests that the overall electrification and precipitation microphysics were dominated by cold cloud processes. The lower covariability with the Z DR column volume indicates the presence of nonsteady updrafts or a less prominent role of warm rain processes in graupel growth and subsequent electrification. Furthermore, we observe that the majority of cloud-to-ground (CG) lightning strikes a carried negative charge to the ground. In contrast to findings from a tornadic supercell over the Great Plains, lightning flash initiations in the Alabama storms primarily occurred outside the footprint of the Z DR and K DP column objects.

Significance Statement

This study quantifies the correlation between mixed-phase updraft intensity and total lightning flash rate in three severe storms in northern Alabama. In the absence of direct updraft velocity measurements, we use polarimetric signatures, such as Z DR and K DP columns, as proxies for updraft strength. Our analysis of polarimetric radar and lightning mapping array data reveals that the lightning flash rate is more highly correlated with the K DP column volume than with the Z DR column volume in all three storms examined. This contrasts with previous findings in storms over the central Great Plains, where the Z DR column volume showed higher covariability with flash rate. Interestingly, lightning initiation in the Alabama storms mainly occurred outside the Z DR and K DP column areas, contrary to previous findings.

Open access
Francesco De Martin
,
Silvio Davolio
,
Mario Marcello Miglietta
, and
Vincenzo Levizzani

Abstract

The Po Valley in northern Italy is a hotspot for tornadoes in Europe in spite of being surrounded by two mountain ridges: the Alps in the north and the Apennines in the southwest. The research focuses on the case study of 19 September 2021, when seven tornadoes (four of them rated as F2) developed in the Po Valley in a few hours. The event was analyzed using observations and numerical simulations with the convection-permitting Modello Locale in Hybrid Coordinates (MOLOCH) model. Observations show that during the event in the Po Valley, there were two surface boundaries that created a triple point: an outflow boundary generated by convection triggered in the Alpine foothills and a dryline generated by downslope winds from the Apennines, while warm and moist air advected westward from the Adriatic Sea east (ahead) of the boundaries. Tornadoes developed about 20 km northeast of the triple point. Numerical simulations with 500-m grid spacing suggest that the development of supercells and drylines in the Po Valley was sensitive to the elevation of the Apennines. Simulated vertical profiles show that the best combination of instability and wind shear for the development of tornadoes was attained within a narrow area located ahead of the dryline. A conceptual model for the development of tornadoes in the Po Valley is proposed, and the differences between tornado environments over a flat terrain and over a region with complex terrain are discussed.

Significance Statement

The Po Valley is a highly populated area where some of the most violent tornadoes in Europe have developed. We investigated a tornado outbreak that occurred on 19 September 2021 in this region, in order to identify its main environmental characteristics. High-resolution numerical simulations revealed that values of instability and wind shear were compatible with the development of several tornadoes only in a narrow area close to the intersection of two surface boundaries (a triple point). Moreover, the atmospheric environment during the tornado outbreak was strongly influenced by the presence of mountain ridges surrounding the plain. We have summarized our results in a conceptual model that can potentially be used for forecasting applications.

Open access
Yu-Chieng Liou
,
Tzu-Jui Chou
,
Yu-Ting Cheng
, and
Yung-Lin Teng

Abstract

This study presents a sequential procedure formulated by combining a multiple-Doppler radar wind synthesis technique with a thermodynamic retrieval method, which can be applied to retrieve the three-dimensional wind, pressure, temperature, rainwater mixing ratio, and moisture over complex terrain. The retrieved meteorological state variables are utilized to reinitialize a high-resolution numerical model, which then carries out time integration using four different microphysical (MP) schemes, including the Goddard Cumulus Ensemble (GCE), Morrison (MOR), WRF single-moment 6-class (WSM6), and WRF double-moment 6-class (WDM6) schemes. It is found that through this procedure, the short-term quantitative precipitation forecast (QPF) skill of a numerical model over mountainous areas can be significantly improved up to 6 h. The moisture field plays a crucial role in producing the correct rainfall forecast. Since no specific microphysical scheme outperforms the others, a combination of various rainfall scenarios forecasted by different MP schemes is suggested in order to provide a stable and reliable rainfall forecast. This work also demonstrates that, with the proposed approach, radar data from only two volume scans are sufficient to improve the rainfall forecasts. This is because the unobserved meteorological state variables are instantaneously retrieved and directly used to reinitialize the model, thereby the model spinup time can be effectively shortened.

Open access
Geir Evensen
,
Femke C. Vossepoel
, and
Peter Jan van Leeuwen

Abstract

This paper identifies and explains particular differences and properties of adjoint-free iterative ensemble methods initially developed for parameter estimation in petroleum models. The aim is to demonstrate the methods’ potential for sequential data assimilation in coupled and multiscale unstable dynamical systems. For this study, we have introduced a new nonlinear and coupled multiscale model based on two Kuramoto–Sivashinsky equations operating on different scales where a coupling term relaxes the two model variables toward each other. This model provides a convenient testbed for studying data assimilation in highly nonlinear and coupled multiscale systems. We show that the model coupling leads to cross covariance between the two models’ variables, allowing for a combined update of both models. The measurements of one model’s variable will also influence the other and contribute to a more consistent estimate. Second, the new model allows us to examine the properties of iterative ensemble smoothers and assimilation updates over finite-length assimilation windows. We discuss the impact of varying the assimilation windows’ length relative to the model’s predictability time scale. Furthermore, we show that iterative ensemble smoothers significantly improve the solution’s accuracy compared to the standard ensemble Kalman filter update. Results and discussion provide an enhanced understanding of the ensemble methods’ potential implementation and use in operational weather- and climate-prediction systems.

Open access
Koryu Yamamoto
,
Keita Iga
, and
Akira Yamazaki

Abstract

A cutoff low that covered central Europe in the middle of July 2021 brought heavy rainfall and severe flooding, resulting in more than 200 fatalities. This low was formed by a trough on 11 July and merged with another cutoff low around 12–13 July. Analysis of the energy budget and potential vorticity suggests that the main cutoff low was maintained through the merger with another cutoff low; this was the dominant contributor to maintenance of the main cutoff low around 12–13 July. The results of Lagrangian trajectory analyses support this conclusion. Analysis of diabatic PV modification during the merger indicates that radiation acts mainly to enhance the potential vorticity of the parcels when they move from another cutoff low into the main cutoff low, especially in the upper layer. However, that effect is not pronounced in the lower layer. These results demonstrate that cutoff lows can be maintained through a merger with another cutoff low and underline the need to consider diabatic processes when investigating mergers.

Significance Statement

This study examines an upper-tropospheric cyclone called a cutoff low, which caused a high-impact weather event over Europe in the middle of July 2021, and investigates its maintenance mechanism. This cutoff low merged with another, suggesting a contribution to the maintenance. Diabatic processes during the merger are also investigated. The results of this study suggest that not only do cyclone regions merge, but diabatic modification of the vortex structure can be seen when two cutoff lows merge, and the modification process may differ in different vertical layers of the cutoff low.

Open access
Xiangzhou Song
,
Xuehan Xie
,
Yunwei Yan
, and
Shang-Ping Xie

Abstract

Based on data collected from 14 buoys in the Gulf Stream, this study examines how hourly air–sea turbulent heat fluxes vary on subdaily time scales under different boundary layer stability conditions. The annual mean magnitudes of the subdaily variations in latent and sensible heat fluxes at all stations are 40 and 15 W m−2, respectively. Under near-neutral conditions, hourly fluctuations in air–sea humidity and temperature differences are the major drivers of subdaily variations in latent and sensible heat fluxes, respectively. When the boundary layer is stable, on the other hand, wind anomalies play a dominant role in shaping the subdaily variations in latent and sensible heat fluxes. In the context of a convectively unstable boundary layer, wind anomalies exert a strong controlling influence on subdaily variations in latent heat fluxes, whereas subdaily variations in sensible heat fluxes are equally determined by air–sea temperature difference and wind anomalies. The relative contributions by all physical quantities that affect subdaily variations in turbulent heat fluxes are further documented. For near-neutral and unstable boundary layers, the subdaily contributions are O(2) and O(1) W m−2 for latent and sensible heat fluxes, respectively, and they are less than O(1) W m−2 for turbulent heat fluxes under stable conditions.

Significance Statement

High-resolution buoy observations of air–sea variables in the Gulf Stream provide the opportunity to investigate the physical factors that determine subdaily variations in air–sea turbulent heat fluxes. This study addresses two key points. First, the observed subdaily amplitudes of heat fluxes are related to various processes, including wind fields and air–sea thermal effect differences. Second, the global sea surface heat budget is known to not be in near-zero balance and it ranges from several to tens of watts per square meter. Therefore, consideration of the relatively strong influence of subdaily variability in air–sea turbulent heat fluxes could provide a new strategy for solving the global heat budget balance problem.

Open access
Joël Stein
and
Fabien Stoop

Abstract

A procedure for evaluating the quality of probabilistic forecasts of binary events has been developed. This is based on a two-step procedure: pooling of forecasts on the one hand and observations on the other hand, on all the points of a neighborhood in order to obtain frequencies at the neighborhood length scale and then to calculate the Brier divergence for these neighborhood frequencies. This score allows the comparison of a probabilistic forecast and observations at the neighborhood length scale, and therefore, the rewarding of event forecasts shifted from the location of the observed event by a distance smaller than the neighborhood size. A new decomposition of this score generalizes that of the Brier score and allows the separation of the generalized resolution, reliability, and uncertainty terms. The neighborhood Brier divergence skill score (BDnSS) measures the performance of the probabilistic forecast against the sample climatology. BDnSS and its decomposition have been used for idealized and real cases in order to show the utility of neighborhoods when comparing at different scales the performances of ensemble forecasts between themselves or with deterministic forecasts or of deterministic forecasts between themselves.

Significance Statement

A pooling of forecasts on the one hand and observations on the other hand, on all the points of a neighborhood, is performed in order to obtain frequencies at the neighborhood scale. The Brier divergence is then calculated for these neighborhood frequencies to compare a probabilistic forecast and observations at the neighborhood scale. A new decomposition of this score generalizes that of the Brier score and allows the separation of the generalized resolution, reliability, and uncertainty terms. This uncertainty term is used to define the neighborhood Brier divergence skill score which is an alternative to the popular fractions skill score, with a more appropriate denominator.

Open access
Jingnan Wang
,
Xiaodong Wang
,
Jiping Guan
,
Lifeng Zhang
,
Tao Chang
, and
Wei Yu

Abstract

The forecast uncertainty, particularly for precipitation, serves as a crucial indicator of the reliability of deterministic forecasts. Traditionally, forecast uncertainty is estimated by ensemble forecasting, which is computationally expensive since the forecast model is run multiple times with perturbations. Recently, deep learning methods have been explored to learn the statistical properties of ensemble prediction systems due to their low computational costs. However, accurately and effectively capturing the uncertainty information in precipitation forecasts remains challenging. In this study, we present a novel spatiotemporal transformer network (ST-TransNet) as an alternative approach to estimate uncertainty with ensemble spread and probabilistic forecasts, by learning from historical ensemble forecasts. ST-TransNet features a hierarchical structure for extracting multiscale features and incorporates a spatiotemporal transformer module with window-based attention to capture correlations in both spatial and temporal dimensions. Additionally, window-based attention can not only extract local precipitation patterns but also reduce computational costs. The proposed ST-TransNet is evaluated on the TIGGE ensemble forecast dataset and Global Precipitation Measurement (GPM) precipitation products. Results show that ST-TransNet outperforms both traditional and deep learning methods across various metrics. Case studies further demonstrate its ability to generate reasonable and accurate spread and probability forecasts from a single deterministic precipitation forecast. It demonstrates the capacity and efficiency of neural networks in estimating precipitation forecast uncertainty.

Open access
Joshua Chun Kwang Lee
,
Javier Amezcua
, and
Ross Noel Bannister

Abstract

Two aspects of ensemble localization for data assimilation are explored using the simplified nonhydrostatic ABC model in a tropical setting. The first aspect (i) is the ability to prescribe different localization length scales for different variables (variable-dependent localization). The second aspect (ii) is the ability to control (i.e., to knock out by localization) multivariate error covariances (selective multivariate localization). These aspects are explored in order to shed light on the cross-covariances that are important in the tropics and to help determine the most appropriate localization configuration for a tropical ensemble–variational (EnVar) data assimilation system. Two localization schemes are implemented within the EnVar framework to achieve (i) and (ii). One is called the isolated variable-dependent localization (IVDL) scheme and the other is called the symmetric variable-dependent localization (SVDL) scheme. Multicycle observation system simulation experiments are conducted using IVDL or SVDL mainly with a 100-member ensemble, although other ensemble sizes are studied (between 10 and 1000 members). The results reveal that selective multivariate localization can reduce the cycle-averaged root-mean-square error (RMSE) in the experiments when cross-covariances associated with hydrostatic balance are retained and when zonal wind/mass error cross-covariances are knocked out. When variable-dependent horizontal and vertical localization are incrementally introduced, the cycle-averaged RMSE is further reduced. Overall, the best performing experiment using both variable-dependent and selective multivariate localization leads to a 3%–4% reduction in cycle-averaged RMSE compared to the traditional EnVar experiment. These results may inform the possible improvements to existing tropical numerical weather prediction systems that use EnVar data assimilation.

Open access
Maziar Bani Shahabadi
and
Mark Buehner

Abstract

Cloud-affected microwave humidity sounding radiances were excluded from assimilation in the hybrid four-dimensional ensemble–variational (4D-EnVar) system of the Global Deterministic Prediction System (GDPS) at Environment and Climate Change Canada (ECCC). This was due to the inability of the current radiative transfer model to consider the scattering effect from frozen hydrometeors at these frequencies. In addition to upgrading the observation operator to RTTOV-SCATT, quality control, bias correction, and 4D-EnVar assimilation components are modified to perform all-sky assimilation of Microwave Humidity Sounder (MHS) channel 2–5 observations over ocean in the GDPS. The input profiles to RTTOV-SCATT are extended to include liquid cloud, ice cloud, and cloud fraction profiles for the simulation and assimilation of MHS observations over water. There is a maximum (35%) increase in the number of channel 2 assimilated MHS observations with smaller increases for channels 3–5 in the all-sky experiment compared to the clear-sky experiment, mostly because of newly assimilated cloud-affected observations. The standard deviation (stddev) of difference between the observed global positioning system radio occultation (GPSRO) refractivity observations and the corresponding simulated values using the background state was reduced in the lower troposphere below 9 km in the all-sky experiment. Verifications of forecasts against the radiosonde observations show statistically significant reductions of 1% in the stddev of error for geopotential height, temperature, and horizontal wind for the all-sky experiment between 72- and 120-h forecast ranges in the troposphere in the Northern Hemisphere domain. Verifications of forecasts against ECMWF analyses also show small improvements in the zonal mean of stddev of error for temperature and horizontal wind for the all-sky experiment between 72- and 120-h forecast ranges. This work was planned for operational implementation in the GDPS in fall 2023.

Open access