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Hsiao-Chun Lin
,
Juanzhen Sun
,
Tammy M. Weckwerth
,
Everette Joseph
, and
Junkyung Kay

Abstract

The New York State Mesonet (NYSM) has provided continuous in situ and remote sensing observations near the surface and within the lower troposphere since 2017. The dense observing network can capture the evolution of mesoscale motions with high temporal and spatial resolution. The objective of this study was to investigate whether the assimilation of NYSM observations into numerical weather prediction models could be beneficial for improving model analysis and short-term weather prediction. The study was conducted using a convective event that occurred in New York on 21 June 2021. A line of severe thunderstorms developed, decayed, and then reintensified as it propagated eastward across the state. Several data assimilation (DA) experiments were conducted to investigate the impact of NYSM data using the operational DA system Gridpoint Statistical Interpolation with rapid update cycles. The assimilated datasets included National Centers for Environmental Prediction Automated Data Processing global upper-air and surface observations, NYSM surface observations, Doppler lidar wind retrievals, and microwave radiometer (MWR) thermodynamic retrievals at NYSM profiler sites. In comparison with the control experiment that assimilated only conventional data, the timing and location of the convection reintensification was significantly improved by assimilating NYSM data, especially the Doppler lidar wind data. Our analysis indicated that the improvement could be attributed to improved simulation of the Mohawk–Hudson Convergence. We also found that the MWR DA resulted in degraded forecasts, likely due to large errors in the MWR temperature retrievals. Overall, this case study suggested the positive impact of assimilating NYSM surface and profiler data on forecasting summertime severe weather.

Open access
M. Andrés-Carcasona
,
M. Soria
,
E. García-Melendo
, and
A. Miró

Abstract

Robert’s rising thermal bubble (RRTB) is a benchmark case used to assess atmospheric models. In this paper, RRTB is further studied both using an analytical and a numerical approach, improving to a greater extent the qualitative description found in the literature. The theoretical framework used is that of buoyant thermals and scaling theory that together are able to predict part of the expected behavior of the bubble as it rises and, therefore, can be used to further validate the simulations. For the numerical experiments, we simulate both a two-dimensional and three-dimensional RRTB using a variety of convection schemes under the Boussinesq approximation and with a higher resolution. While the results are in agreement with those presented by previous authors on the same benchmark and also with the theoretical framework established, we add the quantitative measures to validate the underlying physics of the numerical model. Our results also show that, due to its completely chaotic nature when confined in a 2D plane, RRTB becomes a very challenging candidate to be used as a benchmark if only compared in a qualitative way, and when the 3D bubble is simulated, the shape changes significantly.

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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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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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Chong Wang
and
Xiaofeng Li

Abstract

This article developed a deep learning (DL) model for estimating tropical cyclone (TC) 34-, 50-, and 64-kt (1 kt ≈ 0.51 m s−1) wind radii in four quadrants from infrared images in the global ocean. We collected 63 675 TC images from 2004 to 2016 and divided them into three periods (2004–12, 2013–14, and 2015–16) for model training, validation, and testing. First, four DL-based radius estimation models were developed to estimate the TC wind radius for each of the four quadrants. Then, the entire original images and the one-quarter-quadrant subimages were included in the model training for each quadrant. Last, we modified the mean absolute error (MAE) loss function in these DL-based models to reduce the side effect of an unbalanced distribution of wind radii and developed an asymmetric TC wind radius estimation model globally. The comparison of model results with the best-track data of TCs shows that the MAEs of 34-kt wind radius are 18.8, 19.5, 18.6, and 18.8 n mi (1 n mi = 1.852 km) for the northeast, southeast, southwest, and northwest quadrants, respectively. The MAEs of 50-kt wind radius are 11.3, 11.3, 11.1, and 10.8 n mi, respectively, and the MAEs of 64-kt wind radius are 8.9, 9.9, 9.2, and 8.7 n mi, respectively. These results represent a 12.1%–35.5% improvement over existing methods in the literature. In addition, the DL-based models were interpreted with two deep visualization toolboxes. The results indicate that the TC eye, cloud, and TC spiral structure are the main factors that affect the model performance.

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Tong Ren
,
Ping Yang
,
Kevin Garrett
,
Yingtao Ma
,
Jiachen Ding
, and
James Coy

Abstract

The satellite observational data assimilation community requires consistent hydrometer descriptions—including mass–size relation and particle size distribution—to be used in both the forecast model and observation operator. We develop a microphysics-scheme-consistent snow and graupel single-scattering property database to meet this requirement. In this database, snowflakes are modeled as a mixture of small column and large aggregated ice particles, the mixing ratios of which may be adjusted to satisfy a given mass–size relation. Snow single-scattering properties are computed for four different mass–size relations. Subsequently, the snow description in the Thompson microphysics scheme is used as an example to demonstrate how microphysics-scheme-consistent snow bulk optical properties are derived. The Thompson-scheme-consistent snow bulk optical properties are added to the Community Radiative Transfer Model (CRTM), version 2.4.0. With CloudSat Cloud Profiling Radar (CPR) snow and liquid precipitation retrievals as the inputs, CRTM simulations are performed over global oceans and compared with four collocated Global Precipitation Measurement (GPM) Microwave Imager (GMI) high-frequency channel observations. The CRTM simulated brightness temperatures show agreement with the GMI observed brightness temperatures in cases of light-to-moderate precipitation over extratropical and polar ice-free oceans, with root-mean-square errors of 4.3, 13.0, 1.8, and 3.3 K in the 166-GHz (vertical polarization), 166-GHz (horizontal polarization), 183 ± 3-GHz (vertical polarization), and 183 ± 7-GHz (vertical polarization) channels, respectively. The result demonstrates the potential of using the newly developed microphysics-scheme-consistent snow optical parameterization in data assimilation applications.

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Jaynise M. Pérez Valentín
,
Harindra J. S. Fernando
,
G. S. Bhat
,
Hemantha W. Wijesekera
,
Jayesh Phadtare
, and
Edgar Gonzalez

Abstract

The relationship between eastward-propagating convective equatorial signals (CES) along the equatorial Indian Ocean (EIO) and the northward-propagating monsoon intraseasonal oscillations (MISOs) in the Bay of Bengal (BOB) was studied using observational datasets acquired during the 2018 and 2019 MISO-BOB field campaigns. Convective envelopes of MISOs originating from just south of the BOB were associated with both strong and weak eastward CES (average speed ∼6.4 m s−1). Strong CES contributed to ∼20% of the precipitation budget of BOB, and they spurred northward-propagating convective signals that matched the canonical speed of MISOs (1–2 m s−1). In contrast, weak CES contributed to ∼14% of the BOB precipitation budget, and they dissipated without significant northward propagation. Eastward-propagating intraseasonal oscillations (ISOs; period 30–60 days) and convectively coupled Kelvin waves (CCKWs; period 4–15 days) accounted for most precipitation variability across the EIO during the 2019 boreal summer as compared with that of 2018. An agreement could be noted between high moisture content in the midtroposphere and the active phases of CCKWs and ISOs for two observational locations in the BOB. Basin-scale thermodynamic conditions prior to the arrival of strong or weak CES revealed warmer or cooler sea surface temperatures, respectively. Flux measurements aboard a research vessel suggest that the evolution of MISOs associated with strong CES are signified by local enhanced air–sea interactions, in particular the supply of local moisture and sensible heat, which could enhance deep convection and further moisten the upper troposphere.

Significance Statement

Eastward-propagating convective signals along the equatorial Indian Ocean and their relationship to the northward-propagating spells of rainfall that lead to moisture variability in the Bay of Bengal are studied for the 2018 and 2019 southwest monsoon seasons using observational datasets acquired during field campaigns. Strong convective equatorial signals spurred northward-propagating convection, as compared with weak signals that dissipated without significant northward propagation. Wave spectral analysis showed CCKWs (period 4–15 days), and eastward ISOs (period 30–60 days) accounted for most of the precipitation variability, with the former dominating during the 2018 boreal summer. High moisture periods observed from radiosonde measurements show agreement with the active phases of CCKWs and ISOs.

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Andrew Brown
,
Andrew Dowdy
,
Todd P. Lane
, and
Stacey Hitchcock

Abstract

Severe winds associated with thunderstorms and convection are a hazard affecting key aspects of society, including emergency management and infrastructure design. Several studies around the world have shown that severe convective winds (SCWs) can occur due to several different processes, in a range of atmospheric environments, with significant regional and temporal variations. However, in eastern Australia, the types of SCWs and their variability have not been assessed outside of individual case studies. Here, a combination of reanalysis, lightning, radar, and station data are used to characterize a set of 36 SCW events in four locations in eastern Australia. These events are objectively chosen based on the strongest measured wind gusts from station data (greater than 25 m s−1) over a 14-yr period, with 6-hourly lightning data and a 30-dBZ radar reflectivity threshold used to infer moist convective processes. Radar data analysis suggests that these SCW events are produced by several different types of parent thunderstorms, with station observations suggesting a range of temporal characteristics for these different event types. A clustering algorithm applied to environmental data is used to suggest three dominant types of events, based on low-level moisture, low-level temperature lapse rate, and deep-layer mean wind speed and vertical shear. Based on the distribution of synoptic conditions and thunderstorm properties for each environmental cluster, it is suggested that these three event types correspond to the following: 1) shallow vertical transport of strong synoptic-scale winds to the surface, 2) downbursts driven by subcloud evaporation, and 3) intense thunderstorms including supercells.

Significance Statement

The purpose of this study is to better understand the different types of severe wind events in eastern Australia that are produced by convective storms. We looked at 36 historical cases in four locations and find that severe winds can be produced by very different classes of convective storms. We also suggest that there are three key types of atmospheric environment that are associated with events in this region. These environments vary in terms of the vertical structure of temperature, moisture, and wind speed above the surface. Understanding the different types of environments that lead to severe convective winds can help to reduce uncertainties in future climate projections for this region based on environmental changes.

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Yue Yang
and
Xuguang Wang

Abstract

The sensitivity of convection-allowing forecasts over the continental United States to radar reflectivity data assimilation (DA) frequency is explored within the Gridpoint Statistical Interpolation (GSI)-based ensemble–variational (EnVar) system. Experiments with reflectivity DA intervals of 60, 20, and 5 min (RAIN60, RAIN20, and RAIN5, respectively) are conducted using 10 diverse cases. Quantitative verification indicates that the degree of sensitivity depends on storm features during the radar DA period. Five developing storms show high sensitivity, whereas five mature or decaying storms do not. The 20-min interval is the most reliable given its best overall performance compared to the 5- and 60-min intervals. Diagnostics suggest that the differences in analyzed cold pools (ACPs) among RAIN60, RAIN20, and RAIN5 vary by storm features during the radar DA period. Such ACP differences result in different forecast skills. In the case where RAIN20 outperforms RAIN60 and the case where RAIN5 outperforms RAIN20, assimilation of reflectivity with a higher frequency commonly produces enhanced and widespread ACPs, promoting broader storms that match better with reality than a lower frequency. In the case where RAIN5 performs worse than RAIN20, the model imbalance of RAIN5 overwhelms information gain associated with frequent assimilation, producing overestimated and spuriously fast-moving ACPs. In the cases where little sensitivity to the reflectivity DA frequency is found, similar ACPs are produced.

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