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Sipra Biswas
,
Kallol Sarkar
, and
Tapan Kumar Das

Abstract

Being situated in the estuary of the flood-dominated Hooghly River system, the macrotidal Indian Sundarban Delta (ISD) has become one of the most complex, dynamic and rapidly changing landforms on the earth’s surface. To study horizontal areal shifting of shoreline and its impact on mangrove-cover in the region, United State Geological Survey (USGS)-satellite data of 1980, 1990, 2000, 2010 and 2021 were used. Remote sensing and GIS techniques were employed in the investigation. Simultaneous prograding and retrograding shoreline shifting was distinguished almost in all the parts, though sediment-starved eastern and macrotidally more active southern lobes experienced dominantly retreating shift, and sediment-engorged western lobe demonstrated to be more dynamic. Net areal change over north-south tracks followed the trend of decreasing accretion to increasing erosion while going from west to east, whereas that over west-east tracks followed the trend of exponentially increasing erosion while going from north to south. Overall accretion of ∼91 sq. km in the ISD accounted for augmentation of sparse vegetation of ∼13 sq. km, whereas, ∼243 sq. km erosion called for depletion of sparse & moderate vegetation of ∼18 & ∼174 sq. km respectively over the 41-year period. Various oceanographic and riparian forces and actions, episodic natural events etc. vis-a-vis several anthropogenic interventions— all together contributed to such changes. The findings may help the coastal environmentalists, professionals, planners, decision-makers and implementers in formulating and taking up of suitable strategic measures for integrated and effective coastal zone management in this estuarine wetland-forest.

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Chong-Chi Tong
,
Ming Xue
,
Chengsi Liu
,
Jingyao Luo
, and
Youngsun Jung

Abstract

To improve the representation of all relevant scales in initial conditions for large-domain convection-allowing models, a new multi-scale ensemble Kalman filter (MEnKF) algorithm is developed and implemented within the GSI data assimilation framework coupled with the FV3 limited area model. The algorithm utilizes ensemble background error covariances filtered to match the observations assimilated. This is realized in a sequential manner: 1) When assimilating coarse-resolution observations such as radiosondes, ensemble background perturbations are filtered to remove scales smaller than those the observations can represent, along with relatively large horizontal localization radii to ensure low-noise and balanced analysis increments. 2) The resulting ensemble analyses from the first step then serve as the background to assimilate denser observations such as radar data with smaller localization radii. Several passes can be taken to assimilate all observations. In this paper, vertically increasing horizontal filter scales are used when assimilating rawinsonde and surface observations together while radar data are assimilated in the second step.

The algorithm is evaluated through six convective storm cases during May 2021, with cycled assimilation of either conventional data only or with additional radar reflectivity followed by 24-h ensemble forecasts. Overall, positive impacts of the MEnKF on forecasts are obtained regardless of reflectivity data; its advantage over the single-scale EnKF is most significant in surface humidity and temperature forecasts up to at least 12 hours. More accurate hourly precipitation forecasts with MEnKF can last up to 24 hours for light rain. Furthermore, MEnKF forecasts higher ensemble probabilities for the observed hazardous events.

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Andrew L. Stewart
,
Yan Wang
,
Aviv Solodoch
,
Ru Chen
, and
James C. McWilliams

Abstract

Eastern Boundary Upwelling Systems (EBUSs) host equatorward wind-driven near-surface currents overlying poleward subsurface undercurrents. Various previous theories for these undercurrents have emphasized the role of poleward alongshore pressure gradient forces (APF). Energetic mesoscale variability may also serve to accelerate undercurrents via mesoscale stirring of the potential vorticity gradient imposed by the continental slope. However, it remains unclear whether this eddy rectification mechanism contributes substantially to driving poleward undercurrents in EBUS. This study isolates the influence of eddy rectification on undercurrents via a suite of idealized simulations forced either by alongshore winds, with or without an APF, or by randomly-generated mesoscale eddies. It is found that the simulations develop undercurrents with strengths comparable to those found in nature in both wind-forced and randomly forced experiments. Analysis of the momentum budget reveals that the along-isobath undercurrent flow is accelerated by isopycnal advective eddy momentum fluxes and the APF, and retarded by frictional drag. The undercurrent acceleration may manifest as eddy momentum fluxes or as topographic form stress depending on the coordinate system used to compute the momentum budget, which reconciles these findings with previous work that linked eddy acceleration of the undercurrent to topographic form stress. The leading-order momentum balance motivates a scaling for the strength of the undercurrent that explains most of the variance across the simulations. These findings indicate that eddy rectification is of comparable importance to the APF in driving poleward undercurrents in EBUSs, and motivate further work to diagnose this effect in high-resolution models and observations, and to parameterize it in coarse-resolution ocean/climate models.

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Daphne S. LaDue
,
David Roueche
,
Frank Lombardo
, and
Lara Mayeux

Abstract

When a tornado strikes a permanent or mobile/manufactured home, occupants are at risk of injury and death from blunt force trauma caused by debris-loaded winds and failure of the structure. Mechanisms for these failures have been studied for the past few decades and identified common weaknesses in the structural load path. Also under study in recent decades, much has been learned about how people receive and understand warnings and determine how, when, and if they will shelter in advance. Recent research, for example, shows most people do not shelter until close to impact, after seeing, hearing, or feeling the approaching tornado. To advance beyond these innovations, a new, multi-disciplinary approach was fielded in nine Southeast U.S. tornadoes between 2019 and 2022. For each tornado, 1) wind engineering assessments documented near-surface wind fields, 2) structural engineering assessments documented the primary wind load path for each structure, and 3) social science interviews captured the survivor’s narrative and asked several follow-up questions to assure key items of interest were addressed in each interview. When possible, the team was multi-disciplinary during the interview, enabling survivors to ask questions and better understand their experiences. Most survivors became aware of the approaching tornado with at least a few minutes lead time and most were able to reach a place of refuge. Most survivors recalled sensory experiences during the tornado and about half could describe direction or temporal sequences of damage. A case study of the Cookeville, Tennessee, Tornado of 3 March 2020 illustrates the power of the integrated data assessment.

Open access
Free access
Jeffrey Anderson
,
Chris Riedel
,
Molly Wieringa
,
Fairuz Ishraque
,
Marlee Smith
, and
Helen Kershaw

Abstract

The uncertainty associated with many observed and modeled quantities of interest in Earth system prediction can be represented by mixed probability distributions that are neither discrete nor continuous. For instance, a forecast probability of precipitation can have a finite probability of zero precipitation, consistent with a discrete distribution. However, nonzero values are not discrete and are represented by a continuous distribution; the same is true for rainfall rate. Other examples include snow depth, sea ice concentration, amount of a tracer or the source rate of a tracer. Some Earth system model parameters may also have discrete or mixed distributions. Most ensemble data assimilation methods do not explicitly consider the possibility of mixed distributions. The Quantile Conserving Ensemble Filtering Framework (Anderson 2022, 2023) is extended to explicitly deal with discrete or mixed distributions. An example is given using bounded normal rank histogram probability distributions applied to observing system simulation experiments in a low-order tracer advection model. Analyses of tracer concentration and tracer source are shown to be improved when using the extended methods. A key feature of the resulting ensembles is that there can be ensemble members with duplicate values. An extension of the rank histogram diagnostic method to deal with potential duplicates shows that the ensemble distributions from the extended assimilation methods are more consistent with the truth.

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Jakob Boventer
,
Matteo Bramati
,
Vasileios Savvakis
,
Frank Beyrich
,
Markus Kayser
,
Andreas Platis
, and
Jens Bange

Abstract

One of the most widely used systems for wind speed and direction observations at meteorological sites is based on Doppler Wind LiDAR (DWL) technology. The wind vector derivation strategies of these instruments rely on the assumption of stationary and homogeneous horizontal wind, which is often not the case over heterogeneous terrain. This study focuses on the validation of two DWL systems, operated by the German Weather Service (DWD) and installed at the boundary layer field site Falkenberg (Lindenberg, Germany), with respect to measurements from a small, fixed-wing uncrewed aircraft system (UAS) of type MASC-3. A wind vector intercomparison at an altitude range from 100 to 500 m between DWL and UAS was performed, after a quality control of the aircraft’s data accuracy against a cup anemometer and wind vane mounted on a meteorological mast also operating at the location. Both DWL systems exhibit an overall root mean square difference in wind vector retrieval of less than 22% for wind speed and lower than 18° for wind direction. The enhancement or deterioration of these statistics is analyzed with respect to scanning height and atmospheric stability. The limitations of this type of validation approach are highlighted and accounted for in the analysis.

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Shihua Liu
,
Sihua Huang
,
Yanke Tan
,
Zhiping Wen
,
Xiaodan Chen
, and
Yuanyuan Guo

Abstract

Previous studies have pointed out that the tropical easterly jet (TEJ) core varies longitudinally or latitudinally. Whether there is a linkage between longitudinal and latitudinal variations of the TEJ core remains unclear. We found that, on the interannual time scale, the northward (southward) movement of the TEJ core is typically accompanied by a westward (eastward) shift, characterized by a noticeable northwest–southeast (NW–SE) displacement. This NW–SE shift is most evident in July. A locational index is defined to capture this shift by the difference of area-averaged 200-hPa zonal winds between the western Arabian Sea (AS) and the southern tip of the Indian Peninsula. Observations and numerical simulations demonstrated that the northwestward-shifted (southeastward-shifted) TEJ core is caused by the joint and individual influences from the enhanced (suppressed) convective activities over the eastern AS and suppressed (enhanced) convective activities over the northern Bay of Bengal–South China Sea (BOB–SCS). Enhanced (suppressed) convective activities over the eastern AS can induce upper-tropospheric divergence (convergence) and anticyclonic (cyclonic) circulations to the northwest of the convection, leading to anomalous easterly (westerly) over the western AS. The suppressed (enhanced) convective activities over the northern BOB–SCS can further facilitate the northwestward (southeastward) shift through inducing anomalous cyclonic (anticyclonic) circulation centering at the BOB and the associated anomalous westerly (easterly) over the southern tip of the Indian Peninsula. The NW–SE shift of the TEJ core may have an implication for the change in the area of the intense rainfall in South Asia.

Significance Statement

The purpose of this study is to explore the linkage between the zonal and meridional variations of the core of the tropical easterly jet (TEJ) and its underlying mechanisms. We found that the TEJ core features a pronounced northwest–southeast shift and this phenomenon only occurs in July. Thus, we defined a locational index to depict this unique characteristic and reveal its relationship with the anomalous convective activities over the eastern Arabian Sea and the northern Bay of Bengal–South China Sea. These results may help improve our understanding of the characteristics and mechanisms of the variations of the TEJ core.

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Free access
Da Fan
,
Steven J. Greybush
,
Eugene E. Clothiaux
, and
David John Gagne II

Abstract

Convective initiation (CI) nowcasting remains a challenging problem for both numerical weather prediction models and existing nowcasting algorithms. In this study, an object-based probabilistic deep learning model is developed to predict CI based on multichannel infrared GOES-16 satellite observations. The data come from patches surrounding potential CI events identified in Multi-Radar Multi-Sensor Doppler weather radar products over the Great Plains region from June and July 2020 and June 2021. An objective radar-based approach is used to identify these events. The deep learning model significantly outperforms the classical logistic model at lead times up to 1 hour, especially on the false alarm ratio. Through case studies, the deep learning model exhibits dependence on the characteristics of clouds and moisture at multiple altitudes. Model explanation further reveals that the contribution of features to model predictions is significantly dependent on the baseline, a reference point against which the prediction is compared. Under a moist baseline, moisture gradients in the lower and middle troposphere contribute most to correct CI forecasts. In contrast, under clear-sky baselines, correct CI forecasts are dominated by cloud-top features, including cloud-top glaciation, height, and cloud coverage. Our study demonstrates the advantage of using different baselines in further understanding model behavior and gaining scientific insights.

Open access