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Neelesh Rampal
,
Andrew Lorrey
, and
Nicolas Fauchereau

Abstract

Weather regimes (WRs), also known as synoptic types, are defined as recurrent patterns that have been used to categorize variability in atmospheric circulation. However, defining the optimal number of patterns can often be arbitrary, and there are common shortcomings when oversimplifying a wide range of synoptic conditions and weather outcomes. We build on previous work that has defined regional WRs and objectively ascribe an optimal number of once-daily weather patterns for Aotearoa New Zealand (ANZ) using affinity propagation combined with K-means clustering. Nine primary WRs for ANZ were classified based on once-daily geopotential height spatial patterns, but these patterns still retained a wide degree of spatial variability. Subsidiary clusters were subsequently defined within each primary WR by applying affinity propagation and K-means clustering to reveal the largest within-cluster differences based on joint daily temperature and precipitation anomalies. Up to three subsidiary patterns in each of the primary regimes were revealed, with a total of 21 unique daily patterns emerging from the two-tier classification. Subsidiary WRs reveal subtle differences in the location and intensity of regional-scale pressure anomalies, pressure gradients, and wind flow over both main islands that lead to large differences in surface weather anomalies. Impacts of atmospheric variability related to each subsidiary WR are exemplified by different spatial outcomes for rainfall and temperature (including intensity of anomalies) at regional and subregional levels. The approach presented in this study has utility for enhancing prediction of weather outcomes, including extreme weather, and can also be applied more widely over a range of time scales to improve understanding of weather and climate linkages.

Open access
Lige Cao
,
Xinrong Wu
,
Guijun Han
,
Wei Li
,
Xiaobo Wu
,
Haowen Wu
,
Chaoliang Li
,
Yundong Li
, and
Gongfu Zhou

Abstract

To effectively reduce model bias and improve assimilation quality, we adopt a hybrid adaptive approach of ensemble adjustment Kalman filter (EAKF) and multigrid analysis (MGA), called EAKF-MGA, to implement parameter optimization as follows. For each assimilation cycle, observations are used to adjust the prior ensembles of both state variables and parameters using the EAKF without inflation. Then, the MGA is adaptively triggered to extract multiscale information from the observational residual to innovate the ensemble mean of the state once again. Results of biased twin experiments consisting of a barotropic spectral model and idealized observation systems show that the proposed EAKF-MGA is insensitive to state variance inflation and localization during the parameter optimization process, compared with the EAKF with adaptive inflation. We also find that computational efficiency is another important advantage of the EAKF-MGA for both state estimation and parameter estimation since extremely small ensemble size is allowed, while the EAKF with adaptive inflation does not work anymore. In essence, the EAKF-MGA is designed to estimate and correct systematic errors jointly with model’s state variables. Through alleviating biases, including the model bias caused by the biased parameter and the analysis bias resulting from the sampling noise given the limited ensemble size, it can be guaranteed that the analysis in the EAKF-MGA will be proceeded onward with the standard assumption of the unbiased model background field in modern data assimilation theory to be met.

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Yaping Wang
,
Nusrat Yussouf
,
Christopher A. Kerr
,
Derek R. Stratman
, and
Brian C. Matilla

Abstract

An experimental Warn-on-Forecast System (WoFS) ensemble data assimilation (DA) and prediction system at 1-km grid spacing is developed and tested using two landfalling tropical cyclone (TC) events, one springtime severe thunderstorm event, and one summertime flash flood event. To evaluate the impact of DA at 1-km grid spacing, two experiments are conducted. One experiment, namely, the WoFS-1km, generates 3-h ensemble forecasts from the 1-km WoFS analyses while another experiment, namely, the Downscaled-1km, generates 3-h ensemble forecasts from downscaled 3-km analyses. With 1-km DA, the two landfalling TC events and the summertime event show some improvement in predicting high reflectivity, while the springtime event performs worse. Meanwhile, WoFS-1km is slightly better at predicting heavier precipitation (>20 mm h−1) with lower bias. However, heavy precipitation spatial placement error is only mitigated in one TC event and the summertime event with 1-km DA but is neutral or worse in the other two events. Object-based verification for rotation objects indicates that WoFS-1km performs better in one of the TC events, but worse in the springtime event with lower probability of detection and higher false alarm ratio due to fewer strong rotation objects being generated. The forecast skill of WoFS-1km for the springtime event is degraded mainly because the convective cores do not sufficiently develop as the forecast advances. The conditional benefits from 1-km DA in this study highlights the need for evaluation of a larger sample of convective storm cases and further development of the system.

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Alfredo Peña
,
Jeffrey D. Mirocha
, and
M. Paul van der Laan

Abstract

Wind-farm parameterizations in weather models can be used to predict both the power output and farm effects on the flow; however, their correctness has not been thoroughly assessed. We evaluate the wind-farm parameterization of the Weather Research and Forecasting Model with large-eddy simulations (LES) of the wake performed with the same model. We study the impact on the velocity and turbulence kinetic energy (TKE) of inflow velocity, roughness, resolution, number of turbines (one or two), and inversion height and strength. We compare the mesoscale with the LES by spatially averaging the LES within areas correspondent to the mesoscale horizontal spacing: one covering the turbine area and two downwind. We find an excellent agreement of the velocity within the turbine area between the two types of simulations. However, within the same area, we find the largest TKE discrepancies because in mesoscale simulations, the turbine-added TKE has to be highest at the turbine position to be advected downwind. Within the downwind areas, differences between velocities increase as the wake recovers faster in the LES, whereas for the TKE both types of simulations show similar levels. From the various configurations, the impact of inversion height and strength is small for these heights and inversion levels. The highest impact for the one-turbine simulations appears under the low-speed case due to the higher thrust, whereas the impact of resolution is low for the large-eddy simulations but high for the mesoscale simulations. Our findings demonstrate that higher-fidelity simulations are needed to validate wind-farm parameterizations.

Open access
Wansuo Duan
,
Junjie Ma
, and
Stéphane Vannitsem

Abstract

In this paper, a new nonlinear forcing singular vector (NFSV) approach is proposed to provide mutually independent optimally combined modes of initial perturbations and model perturbations (C-NFSVs) in ensemble forecasts. The C-NFSVs are a group of optimally growing structures that take into account the impact of the interaction between the initial errors and the model errors effectively, generalizing the original NFSV for simulations of the impact of the model errors. The C-NFSVs method is tested in the context of the Lorenz-96 model to demonstrate its potential to improve ensemble forecast skills. This method is compared with the orthogonal conditional nonlinear optimal perturbations (O-CNOPs) method for estimating only the initial uncertainties and the orthogonal NFSVs (O-NFSVs) for estimating only the model uncertainties. The results demonstrate that when both the initial perturbations and model perturbations are introduced in the forecasting system, the C-NFSVs are much more capable of achieving higher ensemble forecasting skills. The use of a deep learning approach as a remedy for the expensive computational costs of the C-NFSVs is evaluated. The results show that learning the impact of the C-NFSVs on the ensemble provides a useful and efficient alternative for the operational implementation of C-NFSVs in forecasting suites dealing with the combined effects of the initial errors and the model errors.

Significance Statement

A new ensemble forecasting method for dealing with combined effects of initial errors and model errors, i.e., the C-NFSVs, is proposed, which is an extension of the NFSV approach for simulating the model error effects in ensemble forecasts. The C-NFSVs provide mutually independent optimally combined modes of initial perturbations and model perturbations. This new method is tested for generating ensemble forecasts in the context of the Lorenz-96 model, and there are indications that the optimally growing structures may provide reliable ensemble forecasts. Furthermore, it is found that a hybrid dynamical–deep learning approach could be a potential avenue for real-time ensemble forecasting systems when perturbations combine the impact of the initial and the model errors.

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Xiaoming Shi
and
Yueya Wang

Abstract

Convection-permitting resolutions, which refer to kilometer-scale horizontal grid spacings, have become increasingly popular in regional numerical weather prediction and climate studies. However, this resolution range is in the gray zone for the simulation of convection, where conventional cumulus convection and subgrid-scale (SGS) turbulence parameterizations are inadequate for such grid spacings due to invalid assumptions and simplifications. Recent studies demonstrated that the magnitudes of SGS fluxes of momentum and scalars are comparable to those of resolved fluxes at convection-permitting resolutions and that horizontal SGS components are as important as the vertical SGS component. Thus, it appears necessary to adapt available schemes to model the SGS effects of convective motions for the gray zone. Here, we investigated the efficacy of separately parameterizing the vertical and horizontal SGS effects in improving the convection-permitting simulation of Typhoon Vicente (2012). To represent the vertical SGS turbulence effect, we evaluated the Grell-3, Tiedtke, and multiscale Kain–Fritsch (MSKF) schemes in the Weather Research and Forecasting (WRF) Model; the MSKF scheme is scale adaptive, whereas the other two are conventional cumulus schemes. For horizontal SGS turbulence, we evaluated the effects of the traditional Smagorinsky scheme and our newly developed reconstruction and nonlinear anisotropy (RNA) model, which models not only downgradient diffusion but also backscatter. We found that the simulation combining the MSKF and RNA schemes exhibits the best skill in predicting precipitation, especially rainfall extremes. The advantages are rooted in the MSKF scheme’s scale-awareness and parameterized cloud–radiation feedback and in the backscatter-enabling capability of the RNA model.

Significance Statement

Operational numerical weather prediction and some climate simulations have approached kilometer-scale horizontal resolutions, called convection-permitting resolutions. However, details of convective storms are not well represented at these resolutions, and small-scale fluid motions can potentially impact the overall simulation performance. In practice, the effects of such unresolved turbulent eddies were once neglected. We suggest representing these effects in the vertical and horizontal directions with an adaptive cumulus convection parameterization and an advanced turbulence model, respectively, which significantly improve the simulation of tropical cyclones. This framework allows us to adapt convection schemes developed by the mesoscale modeling community and turbulence schemes studied by large-eddy simulation groups for representing three-dimensional turbulence in the convection-permitting regime.

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Sijia Zhang
,
Zhaoming Liang
,
Donghai Wang
, and
Guixing Chen

Abstract

A local long-lived convective system developed at midnight over inland South China, producing record-breaking rainfall in Guangzhou on 7 May 2017. This study examines the physical processes responsible for nocturnal convection initiation (CI) and growth. Observational analyses show that the CI occurs in the warm sector under weakly forced synoptic conditions at 500 hPa, while moderate but nocturnally enhanced low-level southeasterlies with a mesoscale moist tongue at 925 hPa intrude inland from the northern South China Sea. Convection-permitting model results show that mesoscale low-level convergence and increased moisture at the leading edge of the southeasterlies are favorable for CI dynamically and thermodynamically. Local ascent and potential instability are further enhanced by orographic lifting and warm moist air from the urban surface, respectively, which trigger convection in northern Guangzhou. The mesoscale moist tongue of southeasterly flows then meets convectively generated outflows, thereby maintaining strong updrafts and continuously triggering back-building convective cells in eastern Guangzhou. Sensitivity tests are conducted to estimate the relative roles of ambient southeasterly moist tongue and urban thermal effects. The southeasterly moist tongue provides moisture that is crucial for CI, while warm moist air from the urban surface is lifted at the leading edge of the southeasterlies and locally facilitates convection. Therefore, the mesoscale processes of lifting and moistening due to nocturnal southeasterlies and their strong interaction with the local factors (orographic lifting, urban heating, and cold-pool-related ascent) provide the sustained lifting and instability crucial for triggering the local long-lived convective systems. The multiscale processes shed light on the understanding of the nocturnal warm-sector heavy rainfall inland.

Open access
Chenbin Xue
,
Xinyong Shen
,
Zhiying Ding
,
Naigeng Wu
,
Yizhi Zhang
,
Xian Chen
, and
Chunyan Guo

Abstract

This study investigates the organizational modes of convective storms and associated severe weather in spring and summer (March–August) of 2015–19 over southern China. These storms are classified into three major organizational structures (cellular, linear, and nonlinear), including 10 dominant morphologies. In general, cellular systems are most frequent, followed by linear systems. Convective storms are common in spring, increasing markedly from April to June, and peak in June. Convective storm cases are usually longer lived in spring, while shorter lived in summer. They also present pronounced diurnal variations, with a primary peak in the afternoon and several secondary peaks during the night to the morning. Approximately 79.7% of initial convection clearly exhibits a dominant eastward movement, with a faster moving speed in spring. Convective storms frequently evolve among organizational modes during their life spans. Linear systems produce the most severe weather observations, in which convective lines with trailing stratiform rain are most prolific. Bow echoes are most efficient in producing severe weather events among all systems, despite their rare occurrences. In spring, lines with parallel stratiform rain are abundant producers of severe wind events, ranking the second highest probability. In summer, embedded lines produce the second largest proportion of intense rainfall events, whereas lines with leading stratiform rain are most efficient in generating extremely intense rainfall and thus pose a distinct flooding threat. Broken lines produce the largest proportion of severe weather events among cellular storms. In contrast, nonlinear systems possess the weakest capability to produce severe weather events.

Significance Statement

Under the influence of the East Asian summer monsoon, severe weather events produced by convective storms occur frequently in China, leading to serious natural disasters. Numerous studies have demonstrated that the morphologies of convective storms are helpful to improve our understanding and prediction of convective storms. However, fewer attempts have been made to examine the convective morphologies over southern China. We aim to reveal the general features of convective organizational modes (e.g., frequencies, durations, variations, etc.) and determine which particular types of severe weather are more or less likely to be associated with particular convective morphologies. These results are of benefit to local forecasters for better anticipating the storm types and issuing warnings for related hazardous weather.

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Ching-Yuang Huang
,
Jia-Yang Lin
,
William C. Skamarock
, and
Shu-Ya Chen

Abstract

A dynamical vortex initialization (DVI) scheme is implemented on unstructured meshes for the global model MPAS for typhoon forecasts. The DVI extracts the departure vortex within a specified radius of the vortex center and implants this vortex at the observed vortex location in continuously cycled 1-h integrations of the model. The cycling integration is stopped when either the simulated central sea level pressure or maximum wind speed of the typhoon has reached the value in the best track data, denoted as P-match or V-match, respectively. The DVI may spin up the initial vortex with a more contracting eyewall, but still keeping the same size of the outer vortex. Forecasts for 16 typhoons over the western North Pacific in 2015–20 are investigated. Predictions from the experiments with the 60–15-km variable-resolution MPAS mesh show that both P-match and V-match significantly improve the track forecasts, where V-match mostly requires less cycle runs than P-match. Cycling results with P-match or V-match are also dependent on the choice of physics suites within MPAS. Positive impacts are larger for V-match than P-match using the mesoscale reference physics suite, with significantly improved track forecasts and earlier intensity forecasts. Intensity differences resulting from the DVI have gradually decreased with forecast time, which are closely correlated to the differences in the averaged tropospheric potential vorticity of the inner vortex. The DVI with the 60–15–3-km variable-resolution mesh also works well and improves intensity forecasts. The DVI can also help produce asymmetric structures and spin up inner vortex cores for typhoons near high topography, which leads to improved intensity forecasts.

Open access
David J. Stensrud
,
George S. Young
, and
Matthew R. Kumjian

Abstract

Horizontal convective rolls (HCRs) with aspect ratios ≥ 5, called wide HCRs, are observed over land from WSR-88D radar reflectivity observations in clear air over central Oklahoma. Results indicate that wide HCRs are a natural part of the daily HCR life cycle, occurring most frequently from 1500 to 1700 UTC and from 2300 to 2400 UTC, with the HCRs having aspect ratios ∼ 3 during the rest of their lifetime. Wide HCRs are most likely to be observed from HCRs with lifetimes longer than 5 h. Results show that for HCRs lasting for more than 5 h, 12% have aspect ratios ≥ 5 during HCR formation, whereas 50% of have aspect ratios ≥ 5 at dissipation. An evaluation of radar observations from 50 cases of long-lived HCRs suggests the wide HCRs that occur in tandem with HCR formation early in the day develop in situ with a large aspect ratio. In contrast, the cases of wide HCRs that form late in the day most often appear to develop as specific HCR wavelengths are maintained while roll circulations with smaller wavelengths dissipate. These ephemeral wide HCRs over land deserve attention as the mechanisms leading to their formation are unclear.

Significance Statement

The atmospheric boundary layer extends from the ground up to a typical daytime height between 500 m and 3 km. Within this layer, the flow is often turbulent during the daytime, although there are common structures that help to organize the flow patterns. One of these structures is a field of horizontal counterrotating helical circulations, with parallel upwelling and downwelling zones. This study shows that the separation distance between these long parallel lines of upward and downward motion changes during the day and can be quite large when compared to the depth of the boundary layer, both early in the day and late in the day. Reasons for this behavior are unclear and deserve attention, as the boundary layer is where we spend our lives and has a large influence on our daily activities.

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