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Mario Hrastinski
,
Ján Mašek
, and
Ana Šljivić

Abstract

In this paper, we present the implementation and evaluate the impact of the new roughness length configuration in the ALARO canonical model configuration of the ALADIN system at the edge of the orographic gravity wave drag gray zone. As an essential input for turbulence parameterization, the roughness length affects the near-surface turbulent fluxes and the screen-level interpolation of meteorological parameters. We utilize GMTED2010 and ECOCLIMAP-II databases to derive orographic and vegetation components of the effective roughness length and introduce tuning parameters enabling us to optimize predicted near-surface turbulent momentum fluxes and 10-m wind speed. Based on sensitivity tests, we (i) prove the necessity of tuning the roughness length fields, (ii) considerably reduce the RMSE of near-surface turbulent momentum fluxes (6%–7%) and 10-m wind speed for different groups of stations (3%–10%), and (iii) identify the tree height as the most influential input parameter in our computational domain. The RMSE decomposition indicates that the improvement of 10-m wind speed mostly comes from a decrease in the random error and bias of the mean. The variability is slightly underestimated, thus reducing the model accuracy for wind speeds above the 95th percentile but at an acceptable level. We explain that roughness length tuning also compensates for the missing roughness sublayer correction in our system. Finally, we show that, although the impact of the orographic gravity wave drag scheme at a horizontal mesh size of 1.8 km is generally small, it is still beneficial for capturing some finer features observed in atmospheric soundings.

Significance Statement

Aiming to improve the 10-m wind speed forecast without sacrificing the accuracy of turbulent momentum fluxes in the kilometric resolution numerical weather prediction model, we derived new roughness length fields from high-resolution physiography databases. Therein, we proved the importance of tuning the input orography and vegetation fields and, depending on the time of day and year, reduced the root-mean-square error of 10-m wind speed by 3%–10%. Further, we demonstrated that orographic gravity wave drag parameterization is still needed to predict finer details seen in wind profiles from atmospheric soundings. Finally, we discussed the related simplifications in our model and their implications and proposed steps toward a more consistent and complete treatment of the near-surface turbulence.

Restricted access
Xubin Zhang
and
Jingshan Li

Abstract

In this study, downscaling, ensemble data assimilation, time lagging, and their combination were used to generate initial condition (IC) perturbations for 12-h convection-permitting ensemble forecasting for heavy-rainfall events over South China during the rainy season in 2013–20. These events were classified as weak- and strong-forcing cases based on synoptic-scale forcing during the presummer rainy season and as landfalling tropical cyclone (TC) cases. This study investigated the impacts of various IC perturbation methods on multiscale characteristics of perturbations and the forecast performance for both nonprecipitation and precipitation variables. These perturbation methods represented different source IC uncertainties and thus differed in multiscale characteristics of perturbations in vertical structures, horizontal distributions, and time evolution. The combination of various IC perturbation methods evidently increased perturbations or spreads of precipitation in both magnitude and location and thus improved the forecast-error estimation. Such an improvement was most and least evident for TC cases during the early and late forecasts, respectively, and was more evident for strong- than weak-forcing cases beyond 6 h. The combination of various IC perturbation methods generally improved both the ensemble-mean and probabilistic forecasts with case-dependent improvements. For heavy rainfall forecasting, 1–6-h improvements were most prominent for TC cases in terms of discrimination and accuracy, while 7–12-h improvements were least prominent for weak-forcing cases in terms of reliability and accuracy. In particular, the improvements in predicting weak-forcing cases increased with spatial errors. In contrast, for strong-forcing cases, the improvements were least and most prominent before and beyond 6 h, respectively.

Significance Statement

Precipitation forecasting for heavy-rainfall events over South China in the rainy season is still challenging due to large uncertainties. Convection-permitting ensemble forecasting is expected to address such uncertainties to improve forecasts of heavy rainfall. However, it is not yet clear how to optimally design convection-permitting ensembles by implementing perturbations in initial conditions (ICs). This study investigates the impacts of various IC perturbation methods on convection-permitting ensemble forecasting over South China in the rainy season. Various IC perturbation methods show discrepant multiscale characteristics of perturbations, which generally complement each other when these perturbations are combined. The added values of combining various IC perturbation methods in forecasting are confirmed for most variables. However, such values are case dependent, with the largest values for tropical cyclone cases during the early forecasts and for the presummer rainy season cases with strong synoptic-scale forcing during late forecasts. Thus, it is still essential to further improve the combination of various types of IC perturbation methods.

Open access
Julian O’Grady
,
Hamish Ramsay
,
Kathleen McInnes
, and
Rebecca Gregory

Abstract

Hazard studies based on thousands of synthetic tropical cyclone (TC) events require a validated model representation of the surface wind field. Here, we assess three different TC parametric vortex models with input from four along-track parameter studies of the TC size and shape, based on statistical formulation of the relationships to observed TC intensity, geographic location, and forward transition speed. The 12 model combinations are compared to in situ 10-min observed surface mean wind speeds for 10 TCs that made landfall over Queensland, Australia, which occurred over the period 2006–17. Empirical wind reduction factors to reduce gradient winds to the surface are recalculated for the more recent TCs at both offshore (ocean, small islands, reefs, and moorings) and onshore (land) locations. To improve the wind comparisons over ocean and land, a secondary reduction factor was developed based on an inland decay function. Pearson correlations for the unadjusted modeled peak wind speed from 118 instances of a TC passing a weather station sit between a range of 0.57 and 0.65 for the 12 model combinations. Using the secondary reduction factor based on the inland decay function increases the range of correlation to 0.74–0.81. Based on the assessment of the instances of peak surface wind speed correlations, bias, and root-mean-square error, along with the correlation 48 h around the peak, the top-ranked performing model combination for the region was an along-track parameter study with a double-vortex model, both previously tested for the South Pacific basin.

Significance Statement

When assessing tropical cyclone hazards, users are presented with several simplified parametric models to describe the surface wind field of tropical cyclones. These parametric models are used routinely for risk assessment of cyclonic winds, as well as for input to surge and wave models used in coastal hazard assessments. Differences between the models include the formulation of the parametric cyclone model, the way winds above the boundary layer are specified at the surface and along-track parameters that describe the cyclones’ size and shape. Of the 12 model combinations investigated in this study, the top-ranked performing model combination for the region was an along-track parameter equation with a double-vortex model, which were both tested previously for the South Pacific basin. Analysis is performed to show unadjusted modeled winds overestimate observed 10-min surface winds over the ocean by around 13% (median) and over land by around 73.9% (median), which is resolved in this study with a secondary empirical wind reduction factor. These findings will support future modeling of tropical cyclone winds for multiple applications, including regional risk assessment and coastal hazard studies.

Open access
Nicolas Bruneau
,
Thomas Loridan
,
Nic Hannah
,
Eugene Dubossarsky
,
Mathis Joffrain
, and
John Knaff

Abstract

While tropical cyclone (TC) risk is a global concern, high regional differences exist in the quality of available data. This paper introduces InCyc, a globally consistent database of high-resolution full-physics simulations of historical cyclones. InCyc is designed to facilitate analysis of TC wind risk across basins and is made available to research institutions. We illustrate the value of this database with a case study focused on key wind risk parameters, namely, the location and intensity of peak winds for the North Atlantic and western North Pacific basins. A novel approach based on random forest algorithms is introduced to predict the full distribution of these TC wind risk parameters. Based on a leave-one-storm-out evaluation, the analysis of the predictions shows how this innovative approach compares to other parametric models commonly used for wind risk assessment. We finally discuss why capturing the full distribution of variability is crucial as well as the broader use in the context of TC risk assessment systems (i.e., “catastrophe models”).

Open access
Yang Li
,
Yubao Liu
,
Yueqin Shi
,
Baojun Chen
,
Fanhui Zeng
,
Zhaoyang Huo
, and
Hang Fan

Abstract

Convective initiation (CI) nowcasting is crucial for reducing loss of human life and property caused by severe convective weather. A novel deep learning method based on the U-Net model (named as CIUnet) was developed for forecasting CI during the warm season with eight interest fields of Himawari-8 Advanced Himawari Imager (AHI) and terrain height. The results showed that the CIUnet model produced probability forecasts of CI occurrence location and time with probability of detection (POD) at 93.3% ± 0.3% and false alarm ratio (FAR) at 18.3% ± 0.4% at a lead time of 30 min. Sensitivity and permutation importance experiments on the input fields of the CIUnet model revealed that the differences in brightness temperature for spectral channels were more critical for CI nowcasts than the original infrared channel brightness temperatures. The brightness temperature difference between band 10 (7.3 μm) and band 13 (10.4 μm), which represents the cloud-top height relative to the lower troposphere, is identified as the most important input fields for CI nowcasting. The tri-spectral brightness temperature difference (TTD), which represents cloud-top glaciation, is ranked the second and it significantly reduced the FAR of the CI forecast. Using terrain heights as an extra input feature improved the POD, but slightly overestimated CI over complex terrain. In addition, a layer-wise relevance propagation (LRP) analyses was performed, and confirmed that the CIUnet model can effectively identify the crucial regions and features of the input fields for accurate CI prediction. Therefore, both permutation importance experiments and LPR analyses are useful for improving the CIUnet model and advancing the understanding of CI mechanisms.

Restricted access
Daniel Veloso-Aguila
,
Kristen L. Rasmussen
, and
Eric D. Maloney

Abstract

A multiscale analysis of the environment supporting tornadoes in southeast South America (SESA) was conducted based on a self-constructed database of 74 reports. Composites of environmental and convective parameters from ERA5 were generated relative to tornado events. The distribution of the reported tornadoes maximizes over the Argentine plains, while events are rare close to the Andes and south of Sierras de Córdoba. Events are relatively common in all seasons except in winter. Proximity environment evolution shows enhanced instability, deep-layer vertical wind shear, storm-relative helicity, reduced convective inhibition, and a lowered lifting condensation level before or during the development of tornadic storms in SESA. No consistent signal in low-level wind shear is seen during tornado occurrence. However, a curved hodograph with counterclockwise rotation is present. The Significant Tornado Parameter (STP) is also maximized prior to tornadogenesis, most strongly associated with enhanced CAPE. Differences in the convective environment between tornadoes in SESA and the U.S. Great Plains are discussed. On the synoptic scale, tornado events are associated with a strong anomalous trough crossing the southern Andes that triggers lee cyclogenesis, subsequently enhancing the South American low-level jet (SALLJ) that increases moisture advection to support deep convection. This synoptic trough also enhances vertical shear that, along with enhanced instability, sustains organized convection capable of producing tornadic storms. At planetary scales, the tornadic environment is modulated by Rossby wave trains that appear to be forced by convection near northern Australia. Madden–Julian oscillation phase 3 preferentially occurs 1–2 weeks ahead of tornado occurrence.

Significance Statement

The main goal of this study is to describe what atmospheric conditions (from local to global scales) are present prior to and during tornadic storms impacting southeast South America (SESA). Increasing potential for deep convection, wind shear, and potential for rotating updrafts, as well as reducing convective inhibition and cloud-base height, are predominant a few hours before and during the events in connection to low-level northerly winds enhancing moisture transport to the region. Remote convective activity near northern Australia appears to influence large-scale atmospheric circulation that subsequently triggers convective storms supporting tornadogenesis 1–2 weeks later in SESA. Our findings highlight the importance of accounting for atmospheric processes occurring at different scales to understand and predict tornado occurrences.

Restricted access
Lauren E. Pounds
,
Conrad L. Ziegler
,
Rebecca D. Adams-Selin
, and
Michael I. Biggerstaff

Abstract

This study uses a new, unique dataset created by combining multi-Doppler radar wind and reflectivity analysis, diabatic Lagrangian analysis (DLA) retrievals of temperature and water substance, and a complex hail trajectory model to create millions of numerically simulated hail trajectories in the Kingfisher, Oklahoma, supercell on 29 May 2012. The DLA output variables are used to obtain a realistic, 4D depiction of the storm’s thermal and hydrometeor structure as required input to the detailed hail growth trajectory model. Hail embryos are initialized in the hail growth module every 3 min of the radar analysis period (2251–0000 UTC) to produce over 2.7 million hail trajectories. A spatial integration technique considering all trajectories is used to identify locations within the supercell where melted particles and subsevere and severe hailstones reside in their lowest and highest concentrations. It is found that hailstones are more likely to reside for longer periods closer to the downshear updraft within the midlevel mesocyclone in a region of decelerated midlevel mesocyclonic horizontal flow, termed the downshear deceleration zone (DDZ). Additionally, clusters of trajectories are analyzed using a trajectory clustering method. Trajectory clusters show there are many trajectory pathways that result in hailstones ≥ 4.5 cm, including trajectories that begin upshear of the updraft away from ideal growth conditions and trajectories that grow within the DDZ. There are also trajectory clusters with similar shapes that experience widely different environmental and hailstone characteristics along the trajectory.

Significance Statement

The purpose of this study is to understand how hail grew in a thunderstorm that was observed by numerous instruments. The observations were input into a hail trajectory model to simulate hail growth. We found a part of the storm near the updraft where hailstones could remain aloft longer and therefore grow larger. Most modeled severe hailstones were found in the storm in this region. However, we also found that there are many different pathways hailstones can take to become large, although there are still some common characteristics among the pathways.

Restricted access
Koji Terasaki
and
Takemasa Miyoshi

Abstract

Densely observed remote sensing data such as radars and satellites generally contain significant spatial error correlations. In data assimilation, the observation error covariance matrix is usually assumed to be diagonal, and the dense data are thinned or spatially averaged to compensate for neglecting the spatial observation error correlation. However, in theory, including the spatial observation error correlation in data assimilation can make better use of the dense data. This study performs perfect model observing system simulation experiments (OSSEs) using the nonhydrostatic icosahedral atmospheric model (NICAM) and the local ensemble transform Kalman filter (LETKF) to assess the impact of assimilating horizontally dense and error-correlated observations. The condition number of the observation error covariance matrix, defined as the ratio of the largest to smallest eigenvalues, is important for the numerical stability of the LETKF computation. A large condition number makes it difficult to compute the ensemble transform matrix correctly. Reducing the condition number by reconditioning is found effective for stable computation. The results show that including the horizontal observation error correlation with reconditioning makes the analysis more accurate but requires 6 times more computations than the case with the diagonal observation error covariance matrix.

Significance Statement

It is important to effectively utilize observations in data assimilation for more accurate weather prediction. Spatially dense observations are known to have an error correlation that is ignored in the data assimilation. This study explores assimilating dense observations by explicitly including observation error correlations with an idealized experiment. The results shows that the analysis is improved by including the observation error correlations. Also, the condition number of the observation error covariance matrix is essential for stable computations.

Open access
Michael S. Fischer
,
Robert F. Rogers
,
Paul D. Reasor
, and
Jason P. Dunion

Abstract

This study uses a recently developed airborne Doppler radar database to explore how vortex misalignment is related to tropical cyclone (TC) precipitation structure and intensity change. It is found that for relatively weak TCs, defined here as storms with a peak 10-m wind of 65 kt (1 kt = 0.51 m s−1) or less, the magnitude of vortex tilt is closely linked to the rate of subsequent TC intensity change, especially over the next 12–36 h. In strong TCs, defined as storms with a peak 10-m wind greater than 65 kt, vortex tilt magnitude is only weakly correlated with TC intensity change. Based on these findings, this study focuses on how vortex tilt is related to TC precipitation structure and intensity change in weak TCs. To illustrate how the TC precipitation structure is related to the magnitude of vortex misalignment, weak TCs are divided into two groups: small-tilt and large-tilt TCs. In large-tilt TCs, storms display a relatively large radius of maximum wind, the precipitation structure is asymmetric, and convection occurs more frequently near the midtropospheric TC center than the lower-tropospheric TC center. Alternatively, small-tilt TCs exhibit a greater areal coverage of precipitation inward of a relatively small radius of maximum wind. Greater rates of TC intensification, including rapid intensification, are shown to occur preferentially for TCs with greater vertical alignment and storms in relatively favorable environments.

Significance Statement

Accurately predicting tropical cyclone (TC) intensity change is challenging. This is particularly true for storms that undergo rapid intensity changes. Recent numerical modeling studies have suggested that vortex vertical alignment commonly precedes the onset of rapid intensification; however, this consensus is not unanimous. Until now, there has not been a systematic observational analysis of the relationship between vortex misalignment and TC intensity change. This study addresses this gap using a recently developed airborne radar database. We show that the degree of vortex misalignment is a useful predictor for TC intensity change, but primarily for weak storms. In these cases, more aligned TCs exhibit precipitation patterns that favor greater intensification rates. Future work should explore the causes of changes in vortex alignment.

Restricted access
Yoshiki Fukutomi
and
Tetsuya Hiyama

Abstract

This study examined the dominant structure and characteristics of synoptic-scale (2–8-day periods) waves over northern Eurasia during 40 summer seasons (June–August, 1979–2018). The synoptic-scale wave patterns are isolated using an extended empirical orthogonal function (EEOF) analysis on the 300-hPa geopotential height anomalies, and a composite based on atmospheric circulation fields and gridded precipitation product. The wave patterns are classified into two types from two pairs of EEOF modes. These two different wave types are defined as the polar frontal (PF) mode and Arctic frontal (AF) mode, respectively. The PF-mode waves are initiated in the North Atlantic sector to the west of the British Isles. They propagate eastward across Siberia into the North Pacific, and produce precipitation mainly over the Eurasian polar frontal zone. The AF-mode wave train arcs along the climatological Arctic frontal zone (AFZ). The AF-mode waves originate near the Scandinavian Peninsula. Their eastward passage brings precipitation along the AFZ. The development of the synoptic-scale waves is reflected by unique background conditions over northern Eurasia. The lower-tropospheric baroclinicity in southern Siberia and central Asia favored the baroclinic growth of the PF-mode waves. The AF-mode waves are trapped in the well-organized baroclinic zone along the north coast of the Eurasian continent. The baroclinic zone is coupled with a band of large meridional gradient of potential vorticity in the upper troposphere, suggesting that this band acts as a waveguide for the AF-mode waves.

Significance Statement

This study examines the synoptic-scale waves in the 2–8-day range of time scales over northern Eurasia during summer. The synoptic-scale waves are categorized into two distinct types at different latitude bands by the EEOF analysis on the 300-hPa z anomalies. They are defined as polar frontal (PF) mode and Arctic frontal (AF) mode. Then the EEOF-based composite analysis is conducted to detect the large-scale circulation anomalies associated with the propagation of different types of synoptic-scale waves. The structure and characteristics are examined. The roles of the mean background conditions in the development and propagation of the respective types are discussed. The behavior of these wave disturbances as rain-producing weather systems is also examined.

Open access