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Taiga Tsukada
and
Takeshi Horinouchi

Abstract

Estimation of the radius of maximum wind (RMW) of tropical cyclone (TC) is helpful for the disaster prevention and mitigation. If RMWs are estimated from infrared (IR) imagery taken by geostationary meteorological satellites, their estimation is available densely in time, regardless of the ocean basin. Kossin et al. showed that when TCs have clear eyes, the eye radii estimated from IR images have a high correlation with the RMW estimated from aircraft reconnaissance. The regression of the former onto that latter was shown to have a mean absolute error (MAE) of 4.7 km. We revisit the IR-based RMW estimation by using C-band synthetic aperture radar (SAR) sea surface wind estimates. The criteria for selecting clear-eye cases are simplified. The MAE of the Kossin et al. method is found to be smaller than previously suggested: 3.1 km when the proposed relation is used and 2.7 km when the regression is revised with the SAR-measured RMWs. We further propose an improvement of the IR-based method to estimate the eye radii. The resultant MAE is shown to be 1.7 km, which indicates that the IR-based RMW estimation is more accurate than has been suggested. A strong correlation between eyewall slope and eye size is confirmed. We also investigated cloud features in the eye that may be closely related to RMW and wind structure around RMW. Potential applications of highly accurate RMW estimation are discussed.

Significance Statement

The radius of maximum wind (RMW) of tropical cyclone (TC) is an important factor for TC intensity estimation and disaster prevention. A previous study suggested that the RMWs of TCs with clear eyes can be estimated from geostationary satellite images at a mean absolute error (MAE) of 4.7 km. Here we improved the method, reducing the MAE by more than one-half. Since the method does not require aircraft or satellite in low Earth orbit, it helps TC monitoring at high frequency. The method can also improve initialization of models used to predict TC hazards and further our physical understanding and the climatology of the wind structures near the centers of TCs.

Open access
Clemente Lopez-Bravo
,
Claire L. Vincent
,
Yi Huang
, and
Todd P. Lane

Abstract

A West Sumatra squall line occurred on 10 January 2016, with a clear offshore propagation of convection. Satellite-derived products from Himawari-8 Advanced Himawari Imager and the Geostationary Cloud Algorithm Testbed Geocat are used to investigate the westward propagation of cloudiness from Sumatra to the Indian Ocean with a lifetime of 1.5 days. A convective mask based on deep convective cell detection and a cell-tracking algorithm are used to estimate the propagation speed of the cloud system. Two distinct mesoscale convective responses are identified: 1) a rapid development in South Sumatra is influenced by the convective environment over the Indian Ocean. The propagation speed is estimated to be ∼5 m s−1 within the first 200 km from the coast. This speed is consistent with density currents. In contrast, 2) the coupling to the inertia–gravity wave is only evident for the northwest of Sumatra with speeds of ∼12 m s−1. The analysis of brightness temperature from the 10.4-μm spectral band and cloud-top temperature showed that the lifetime of the squall line is approximately 30 h with a propagating distance of ∼1000 km. Retrieved cloud properties and tracking of the offshore propagation indicated that the cloud structure consisted of multiple types of cells, propagating as envelopes of convection, and revealed the influence of large-scale variability of the Indian Ocean. Filtered OLR anomalies, satellite-derived rainfall, moisture flux convergence, and background winds flow around Sumatra are used to explore the effects of Kelvin wave activity that likely influenced the lifetime of the squall line.

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Timothy H. Raupach
,
Joshua Soderholm
,
Alain Protat
, and
Steven C. Sherwood

Abstract

We evaluated the performance in Australia of proxies designed to identify atmospheric conditions prone to hail and severe storms. In a convection-resolving but short-duration simulation, proxies that use instability and wind shear thresholds overestimated the probability of hail occurring when compared to the estimated occurrence of surface graupel in the model, particularly in Australia’s tropical north. We used reanalysis data and the Australian Bureau of Meteorology severe storm archive to examine atmospheric conditions at times and locations when hailstorms, other storms, and no storms were reported between January 1979 and March 2021. In instability–shear space, the best discriminator between hail and no-storm times was found to vary predictably with melting-level height, allowing a new proxy to better represent latitudinal trends in atmospheric conditions. We found extra conditions that can be applied to the new proxy to efficiently reduce the number of false alarms. The new proxy outperforms the tested existing proxies for detection of hail-prone conditions in Australia.

Significance Statement

Hail proxies take a description of the atmosphere, such as its temperature, moisture content, and wind properties at various heights, and determine the likelihood of hail forming and hitting the ground. It is a difficult task prone to uncertainty, but in many locations there are no direct observations of hail, and in these places information from proxies is valuable. Existing proxies have a tendency to overestimate the probability of hail falling in the north of Australia. In this study we developed an updated proxy that uses information about the atmosphere’s melting-level height to refine its hail predictions. The new proxy outperforms other tested proxies for hail in Australia. Accurate hail proxies are important for assessment of past and future changes to hail hazard and risk.

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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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