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Chenyue Zhang
,
Ming Xue
,
Kefeng Zhu
, and
Xiaoding Yu

Abstract

A climatology of significant tornadoes [SIGTOR, tornadoes rated (E)F2+ on the (enhanced) Fujita scale] within China and in three subregions, including northern, central, and southern China, is first presented for the period 1980–2016. In total, 129 SIGTOR are recorded in China, with an average of 3.5 per year. The tornado inflow environments of the south-central and southeast regions of the United States (USC and USSE) are compared with those of China and its subregions based on sounding-derived parameters including shear, storm-relative helicity, convective available potential energy (CAPE), lifting condensation level (LCL), etc. Soundings are extracted from the ERA5 reanalysis dataset. The results confirm that the SIGTOR in USSE are characterized by high shear, low CAPE, and low LCL whereas those in USC are characterized by moderate-to-high shear, high CAPE, and high LCL. The thermodynamic conditions of tornadic cases are favorable for China, with moderate-to-high CAPE and low-to-moderate LCL, but their kinematic conditions are much less favorable than in the United States, a fact that is believed to be primarily responsible for the lower tornado frequency and intensity in China. The high CAPE in China is due mostly to high humidity. For three subregions in China, the central China cases account for 60% of total samples, and its environmental features are similar to those of China. The average shear with northern China cases is stronger than that with the other two subregions, and the midlayer is relatively dry. The southern China SIGTOR have the most conducive humidity conditions, but the CAPE and shear there are the lowest. The northern, central, and southern China environments can be considered as representative of midlatitude, subtropical, and tropical regions.

Significance Statement

We document the climatological characteristics of significant tornadoes (SIGTOR) within China and compare the inflow environments of SIGTOR in China and its subregions with those in the U.S. central and southeastern regions. The availability of hourly high-resolution ERA5 data makes the environments based on extracted proximity soundings much more accurate than possible with earlier reanalyses. The environmental characteristics show systematic differences in the tornado environments of different regions of China and the United States and suggest different roles played by thermodynamic and kinematic conditions for tornado formation. Overall, the environmental differences are consistent with the resulting frequency and intensities of SIGTOR. The findings are helpful toward improving tornado forecasting and warning or even understanding of potential impacts of climate change on SIGTOR, especially in China, where such studies are rarer.

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José C. Fernández-Alvarez
,
Marta Vázquez
,
Albenis Pérez-Alarcón
,
Raquel Nieto
, and
Luis Gimeno

Abstract

Moisture transport and changes in the source–sink relationship play a vital role in the atmospheric branch of the hydrological cycle. Lagrangian approaches have emerged as the dominant tool to account for estimations of moisture sources and sinks; those that use the FLEXPART model fed by ERA-Interim reanalysis are most commonly used. With the release of the higher spatial resolution ERA5, it is crucial to compare the representation of moisture sources and sinks using the FLEXPART Lagrangian model with different resolutions in the input data, as well as its version for WRF-ARW input data, the FLEXPART-WRF. In this study, we compare this model for 2014 and moisture sources for the Iberian Peninsula and moisture sinks of North Atlantic and Mediterranean. For comparison criteria, we considered FLEXPARTv9.0 outputs forced by ERA-Interim reanalysis as “control” values. It is concluded that FLEXPARTv10.3 forced with ERA5 data at various horizontal resolutions (0.5° and 1°) represents moisture source and sink zones as represented forced by ERA-Interim (1°). In addition, the version fed with the dynamic downscaling WRF-ARW outputs (∼20 km), previously forced with ERA5, also represents these patterns accurately, allowing this tool to be used in future investigations at higher resolutions and for regional domains.

Significance Statement

The FLEXPART dispersion model forced with ERA5 reanalysis data at various resolutions represents moisture source and sink zones compared to when it is forced by ERA-Interim. When the Weather Research and Forecasting Model is used to dynamically downscale ERA5, FLEXPART-WRF can also represent moisture sources and sinks, allowing this tool to be used in future investigations requiring higher resolution and regional domains and on regions with a predominance of complex orography due to its ability to represent local moisture transport.

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Erik Crosman
,
Aaron M. Ward
,
Stephen W. Bieda III
,
T. Todd Lindley
,
Mike Gittinger
,
Sandip Pal
, and
Hemanth Vepuri

Abstract

While numerous collaborations exist between the atmospheric sciences research community and the US National Weather Service (NWS), collaborative research field studies between undergraduate (UG) students at universities and the NWS are less common. The SCORCHER (Summertime Canyon Observations and Research to Characterize Heat Extreme Regimes) study was an UG student-driven research field campaign conducted in Palo Duro Canyon State Park, Texas, USA, during the Summer of 2021. The SCORCHER campaign was mainly aimed at improving our basic scientific understanding of extreme heat, public safety and forecasting applications, and creating an empowering UG educational field research experience. This “in-box” article highlights the collaborative study design, execution, and lessons learned.

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Maxi Boettcher
,
Matthias Röthlisberger
,
Roman Attinger
,
Joëlle Rieder
, and
Heini Wernli

Abstract

Meteorological extremes on the seasonal time scale have received increased attention due to their relevance for society and economy. A recently developed approach to identify seasonal extremes is applied here to ERA5 reanalyses from 1950-2020 to identify hot and cold, wet and dry, and stormy and calm extreme seasons globally. The approach consists of (i) fitting a statistical model to seasonal mean values (of temperature, precipitation, and wind speed) at each grid point, (ii) selecting a local return period threshold above which seasonal mean values are deemed extreme, and (iii) forming spatially coherent extreme season objects. The paper introduces the ERA5 extreme season explorer, an open-accessweb-portal enabling researchers to visualise and download extreme season objects of any of the six types in their region of interest, for further investigating their underlying dynamics, statistical properties, and impacts. To illustrate the potential of our extreme season objects, we first discuss the top 10 cold winters in ERA5 globally and then focus on an unusual triple-compound extreme season in winter 1953/54 in Europe, which was simultaneously extremely cold, dry, and calm. We show that detailed analysis of weather system dynamics, including cyclones, blocks, jets, and Rossby waves, provides important insight into the processes leading to extreme seasons. In summary, this study presents for the first time a catalogue of objectively identified extreme seasons in the last decades, shows exemplarily how large-scale dynamics can lead to such seasons, and with the help of the explorer supports the community in accelerating research in this important area at the interface of weather and climate dynamics.

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Partha S. Bhattacharjee
,
Li Zhang
,
Barry Baker
,
Li Pan
,
Raffaele Montuoro
,
Georg A. Grell
, and
Jeffery T. McQueen

Abstract

The NWS/NCEP recently implemented a new global deterministic aerosol forecast model named the Global Ensemble Forecast Systems Aerosols (GEFS-Aerosols), which is based on the Finite Volume version 3 GFS (FV3GFS). It replaced the operational NOAA Environmental Modeling System (NEMS) GFS Aerosol Component version 2 (NGACv2), which was based on a global spectral model (GSM). GEFS-Aerosols uses aerosol modules from the GOCART previously integrated in the WRF Model with Chemistry (WRF-Chem), FENGSHA dust scheme, and several other updates. In this study, we have extensively evaluated aerosol optical depth (AOD) forecasts from GEFS-Aerosols against various observations over a timespan longer than one year (2019–20). The total AOD improvement (in terms of seasonal mean) in GEFS-Aerosols is about 40% compared to NGACv2 in the fall and winter season of 2019. In terms of aerosol species, the biggest improvement came from the enhanced representation of biomass burning aerosol species as GEFS-Aerosols is able to capture more fire events in southern Africa, South America, and Asia than its predecessor. Dust AODs reproduce the seasonal variation over Africa and the Middle East. We have found that correlation of total AOD over large regions of the globe remains consistent for forecast days 3–5. However, we have found that GEFS-Aerosols generates some systematic positive biases for organic carbon AOD near biomass burning regions and sulfate AOD over prediction over East Asia. The addition of a data assimilation capability to GEFS-Aerosols in the near future is expected to address these biases and provide a positive impact to aerosol forecasts by the model.

Significance Statement

The purpose of this study is to quantify improvements associated with the newly implemented global aerosol forecast model at NWS/NCEP. The monthly and seasonal variations of AOD forecasts of various aerosol regimes are overall consistent with the observations. Our results provide a guide to downstream regional air quality models like CMAQ that will use GEFS-Aerosols to provide lateral boundary conditions.

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Yibing Su
,
James A. Smith
, and
Gabriele Villarini

Abstract

The Lower Mississippi River has experienced a cluster of extreme floods during the past two decades. The Bonnet Carré spillway, which is located on the Mississippi River immediately upstream of New Orleans, has been operated 15 times since its completion in 1931, with 7 occurrences after 2008. In this study, we examine rainfall and atmospheric water balance components associated with Lower Mississippi River flooding in 2008, 2011, and 2015–19. We focus on multiple time scales—1, 3, 7, and 14 days—reflecting contributions from individual storm systems and the aggregate contributions from a sequence of storm systems. Atmospheric water balance variables—integrated water vapor flux (IVT) and precipitable water—are central to our assessment of the storm environment for Lower Mississippi flood events. We find anomalously large IVT corridors accompany the critical periods of heavy rainfall and are organized in southwest–northeast orientation over the Mississippi domain. Atmospheric rivers play an important role as agents of extremes in water vapor flux and rainfall. We conduct climatological analyses of IVT and precipitable water extremes across the four time scales using 40 years of North American Regional Reanalysis (NARR) fields from 1979 to 2018. We find significant increasing trends in both variables at all time scales. Increases in IVT especially cover large regions of the Mississippi domain. The findings point to increased vulnerability faced by the Mississippi flood control system in the current and future climate.

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Jia Wang
and
Minghua Zhang

Abstract

A constrained data assimilation (CDA) system based on the ensemble variational (EnVar) method and physical constraints of mass and water conservations is evaluated through three convective cases during the Midlatitude Continental Convective Clouds Experiment (MC3E) of the Atmospheric Radiation Measurement (ARM) program. Compared to the original data assimilation (ODA), the CDA is shown to perform better in the forecasted state variables and simulated precipitation. The CDA is also shown to greatly mitigate the loss of forecast skills in observation denial experiments when radar radial winds are withheld in the assimilation. Modifications to the algorithm and sensitivities of the CDA to the calculation of the time tendencies in the constraints are described.

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Aaron J. Hill
,
Russ S. Schumacher
, and
Israel L. Jirak

Abstract

Historical observations of severe weather and simulated severe weather environments (i.e., features) from the Global Ensemble Forecast System v12 (GEFSv12) Reforecast Dataset (GEFS/R) are used in conjunction to train and test random forest (RF) machine learning (ML) models to probabilistically forecast severe weather out to days 4–8. RFs are trained with ∼9 years of the GEFS/R and severe weather reports to establish statistical relationships. Feature engineering is briefly explored to examine alternative methods for gathering features around observed events, including simplifying features using spatial averaging and increasing the GEFS/R ensemble size with time lagging. Validated RF models are tested with ∼1.5 years of real-time forecast output from the operational GEFSv12 ensemble and are evaluated alongside expert human-generated outlooks from the Storm Prediction Center (SPC). Both RF-based forecasts and SPC outlooks are skillful with respect to climatology at days 4 and 5 with diminishing skill thereafter. The RF-based forecasts exhibit tendencies to slightly underforecast severe weather events, but they tend to be well-calibrated at lower probability thresholds. Spatially averaging predictors during RF training allows for prior-day thermodynamic and kinematic environments to generate skillful forecasts, while time lagging acts to expand the forecast areas, increasing resolution but decreasing overall skill. The results highlight the utility of ML-generated products to aid SPC forecast operations into the medium range.

Significance Statement

Medium-range severe weather forecasts generated from statistical models are explored here alongside operational forecasts from the Storm Prediction Center (SPC). Human forecasters at the SPC rely on traditional numerical weather prediction model output to make medium-range outlooks and statistical products that mimic operational forecasts can be used as guidance tools for forecasters. The statistical models relate simulated severe weather environments from a global weather model to historical records of severe weather and perform noticeably better than human-generated outlooks at shorter lead times (e.g., day 4 and 5) and are capable of capturing the general location of severe weather events 8 days in advance. The results highlight the value in these data-driven methods in supporting operational forecasting.

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Ryan D. Patmore
,
Paul R. Holland
,
Catherine A. Vreugdenhil
,
Adrian Jenkins
, and
John R. Taylor

Abstract

The ice shelf–ocean boundary current has an important control on heat delivery to the base of an ice shelf. Climate and regional models that include a representation of ice shelf cavities often use a coarse grid, and results have a strong dependence on resolution near the ice shelf–ocean interface. This study models the ice shelf–ocean boundary current with a nonhydrostatic z-level configuration at turbulence-permitting resolution (1 m). The z-level model performs well when compared against state-of-the-art large-eddy simulations, showing its capability in representing the correct physics. We show that theoretical results from a one-dimensional model with parameterized turbulence reproduce the z-level model results to a good degree, indicating possible utility as a turbulence closure. The one-dimensional model evolves to a state of marginal instability, and we use the z-level model to demonstrate how this is represented in three dimensions. Instabilities emerge that regulate the strength of the pycnocline and coexist with persistent Ekman rolls, which are identified prior to the flow becoming intermittently unstable. When resolution of the z-level model is degraded to understand the gridscale dependencies, the degradation is dominated by the established problem of excessive numerical diffusion. We show that at intermediate resolutions (2–4 m), the boundary layer structure can be partially recovered by tuning diffusivities. Last, we compare replacing prescribed melting with interactive melting that is dependent on the local ocean conditions. Interactive melting results in a feedback such that the system evolves more slowly, which is exaggerated at lower resolution.

Open access
Bin Zheng
,
Ailan Lin
, and
Yanyan Huang

Abstract

In this study, persistent rainfall (PR) over South China (SC) is divided into two types. One type occurs multiple times in succession (defined as multiple PR, MPR); another type represents isolated PR (IPR), for which no new PR occurs for 10 days after the previous PR. The spatio-temporal structures of the 10–30-day intraseasonal oscillations (ISOs) associated with the two types of PR are compared and analyzed. The results reveal that the low-level moisture and air temperature perturbations always have a leading phase relative to the anomalous precipitation. In addition, the positive low-level moisture tendency appears in the MPR ending phase, whereas that in the IPR is close to zero. This difference results in convective development after the MPR ending phase, though not after the IPR. The moisture budget shows that the difference in moisture tendency between MPR and IPR is mainly due to meridional advection, including advections by the mean meridional flow across the perturbation moisture gradient and by the perturbation meridional flow across the mean moisture gradient. For the former, the difference is attributed to the perturbation moisture gradients, while the mean moisture gradients are responsible for the difference of the latter. Furthermore, an essential cause of the difference is the influence of higher-latitude disturbances that affect the IPR more significantly than the MPR. Two associated mechanisms are proposed. One is the perturbation stacking effect, and the other is the effect of angular momentum conservation. By contrast, the low-level temperature anomalies are not the key factor causing the difference between MPR and IPR.

Open access