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Nicholas E. Clark, Sandip Pal, and Temple R. Lee

Abstract

Despite many observational studies on the atmospheric boundary layer (ABL) depth (zi) variability across time scales (e.g., diurnal, seasonal, annual, and decadal), zi variability before, during, and after frontal passages over land, or simply zi variability as a function of weather patterns, has remained relatively unexplored. In this study, we provide an empirical framework using 5-years (2014-2018) of daytime rawinsonde observations and surface analyses over 18 central and southeast US sites to report zi variability across frontal boundaries. By providing systematic observations of front-relative contrasts in zi (i.e., zi differences between warm and cold sectors, Δzi = zWarm i − zCold i) and boundary-layer moisture (i.e., ABL-q) regimes in summer and winter, we propose a new paradigm to study zi changes across cold front boundaries. For most cases, we found deeper zi over the warm sector than the cold sector in both summer and winter, though with significant site-to-site variability in Δzi. Additionally, our results show a positive ΔqABL (i.e., frontal contrasts in ABL-q) in summer and winter, supporting what is typically observed in mid-latitude cyclones. We found that a front-relative ΔqABL of 1 g kg−1 often yielded at least a 100 m Δzi across the frontal boundary in both summer and winter. This work provides a synoptic-scale basis for zi variability and establishes a foundation for model verification to examine the impact of airmass exchange associated with advection on zi. This work will advance our understanding of ABL processes in synoptic environments and help unravel sources of front-relative zi variability.

Open access
Logan R. Bundy, Vittorio A. Gensini, and Mark S. Russo

Abstract

This study used corn insurance data as a proxy for agricultural loss to better inform producers and decision makers about resilience and mitigation. Building on previous research examining crop losses based on weather and climate perils, updates to the peril climatology, identification of peril hot-spots, and the quantification of annual trends using inflation-adjusted indemnities for corn was performed over the period 1989–2020. Normalization techniques in loss cost and acreage loss at county-level spatial resolution were also calculated. Indemnity data showed drought and excess moisture as the two costliest and most frequent perils for corn in the U.S., though, changes in the socioeconomic landscape and frequency of extreme weather events in the recent decade have led to significant increases in corn indemnities for drought, heat, excess moisture, flood, hail, excess wind, and cold wet weather. Normalized losses also displayed significant trends, but were dependent on the cause of loss and amount of spatial aggregation. Perhaps most notable were the documented robust increases in corn losses associated with excess moisture, especially considering future projections for increased mid- and end-of-century extreme precipitation. Subtle decreasing trends in drought, hail, freeze/frost, and flood loss cost over the study period indicates hedging taking place to protect against these perils, especially in corn acreage outside the Corn Belt in high-risk production zones. The use of crop insurance as a proxy for agricultural loss highlights the importance for quantifying spatiotemporal trends by informing targeted adaption to certain hazards and operational management decisions.

Open access
James H. Stagge and Kyungmin Sung

Abstract

The Standardized Precipitation Index (SPI) measures meteorological drought relative to historical climatology by normalizing accumulated precipitation. Longer record lengths improve parameter estimates, but these longer records may include signals of anthropogenic climate change and multi-decadal natural climate fluctuations. Historically, climate non-stationarity has either been ignored or incorporated into the SPI using a quasi-stationary reference period, such as the WMO 30-year period. This study introduces and evaluates a novel non-stationary SPI model based on Bayesian splines, designed to both improve parameter estimates for stationary climates and to explicitly incorporate non-stationarity. Using synthetically generated precipitation, this study directly compares the proposed Bayesian SPI model to existing SPI approaches based on Maximum Likelihood Estimation (MLE) for stationary and non-stationary climates. The proposed model not only reproduced the performance of existing SPI models, but improved upon them in several key areas: reducing parameter uncertainty and noise, simultaneously modeling the likelihood of zero and positive precipitation, and capturing non-linear trends and seasonal shifts across all parameters. Further, the fully Bayesian approach ensures all parameters have uncertainty estimates, including zero precipitation likelihood. The study notes that the zero precipitation parameter is too sensitive and could be improved in future iterations. The study concludes with an application of the proposed Bayesian Non-Stationary SPI (NSPI) model for 9 gauges across a range of hydroclimate zones in the U.S. Results of this experiment show the model is stable and reproduces non-stationary patterns identified in prior studies, while also indicating new findings, particularly for the shape and zero precipitation parameters.

Open access
Yu Shu, Jisong Sun, and Jin Chenlu

Abstract

The mesoscale vortex (MV) is an important rain-producing system. In this study, the reanalysis data and satellite precipitation products are used to classify MVs into three categories: mesoscale convective vortex (MCV), mesoscale stratiform vortex (MSV), and mesoscale dry vortex (MDV). Then, these three categories of midlevel MVs in China from 2007 to 2016 are investigated. A total of 21 053 MVs are obtained. Most MVs form in the northwest of parent convection, and 45% of MVs generate secondary convection. The Tibetan Plateau is the main MV source region. Steered by the westerlies, MVs mainly move eastward. MCV is active in summer, MDV in winter, and MSV in spring and autumn. MCV diurnal variations are closely related to local topography, and MDVs mainly form around midnight. Composite analyses show that MCVs form near the high-value center of convective available potential energy at the development stage of parent convection. The composite MCV forms near the low pressure trough and the thermal ridge at 500 hPa, and a low-level jet exists to the south of the MCV center. At the initiation and maturity stages of MCV, strong convergence and divergence respectively exist at low levels and 400 hPa. The vortex circulation mainly locates near 500 hPa. Above the vortex is a warm core associated with the latent heat release, and below is a cold anomaly related to the cold pool. In the downshear region, there is strong low-level convergence and ascending motion, higher humidity, and greater latent heat release, which favor the formation of secondary convection.

Open access
Free access
Douglas E. Pirhalla, Cameron C. Lee, Scott C. Sheridan, and Varis Ransibrahmanakul

Abstract

Anomalous sea levels along the mid-Atlantic and South Atlantic coasts of the United States are often linked to atmosphere–ocean dynamics, remote- and local-scale forcing, and other factors linked to cyclone passage, winds, waves, and storm surge. Herein, we examine sea level variability along the U.S. Atlantic coast through satellite altimeter and coastal tide gauge data within the context of synoptic-scale weather pattern forcing. Altimetry data, derived from sea level anomaly (SLA) data between 1993 and 2019, were compared with self-organizing map (SOM)-based atmospheric circulation and surface wind field categorizations to reveal spatiotemporal patterns and their interrelationships with high-water-level conditions at tide gauges. Regional elevated sea level patterns and variability were strongly associated with synergistic patterns of atmospheric circulation and wind. Recurring atmospheric patterns associated with high-tide flooding events and flood risk were identified, as were specific regional oceanographic variability patterns of SLA response. The incorporation of combined metrics of wind and circulation patterns further isolate atmospheric drivers of high-tide flood events and may have particular significance for predicting future flood events over multiple spatial and temporal scales.

Significance Statement

Mean sea level and minor to moderate coastal flood events, also called blue-sky or high-tide floods, are increasing along many U.S. coastlines. While the drivers of such events are numerous, here we identified key contributing weather patterns and environmental factors linked to increased risk of regional and local high-water conditions along the Atlantic coast. Our results indicate that the predictability of elevated sea levels and high-tide floods is highly dependent upon atmospheric drivers including wind and circulation patterns and, if applied in a tested modeling framework, may prove useful for predicting future floods at various time scales.

Open access
Free access
Robert S. Arthur, Timothy W. Juliano, Bianca Adler, Raghavendra Krishnamurthy, Julie K. Lundquist, Branko Kosović, and Pedro A. Jiménez

Abstract

Cold-air pools (CAPs), or stable atmospheric boundary layers that form within topographic basins, are associated with poor air quality, hazardous weather, and low wind energy output. Accurate prediction of CAP dynamics presents a challenge for mesoscale forecast models, in part because CAPs occur in regions of complex terrain, where traditional turbulence parameterizations may not be appropriate. This study examines the effects of the planetary boundary layer (PBL) scheme and horizontal diffusion treatment on CAP prediction in the Weather Research and Forecasting (WRF) model. Model runs with a one-dimensional (1D) PBL scheme and Smagorinsky-like horizontal diffusion are compared to runs that use a new three-dimensional (3D) PBL scheme to calculate turbulent fluxes. Simulations are completed in a nested configuration with 3 km/750 m horizontal grid spacing over a 10-day case study in the Columbia River basin, and results are compared to observations from the Second Wind Forecast Improvement Project. Using event-averaged error metrics, potential temperature and wind speed errors are shown to decrease both with increased horizontal grid resolution, and with improved treatment of horizontal diffusion over steep terrain. The 3D PBL scheme further reduces errors relative to a standard 1D PBL approach. Error reduction is accentuated during CAP erosion, when turbulent mixing plays a more dominant role in the dynamics. Lastly, the 3D PBL scheme is shown to reduce near-surface overestimates of turbulence kinetic energy during the CAP event. The sensitivity of turbulence predictions to the master length scale formulation in the 3D PBL parameterization is also explored.

Open access
Free access
Noah T. Plymale, Joshua E. Szekely, and Anna H. Rubinstein

Abstract

Atmospheric aerosols originating from natural and anthropogenic sources have important implications for modeling atmospheric phenomena, but aerosol conditions can change significantly and rapidly because of their dependence on local geography and atmospheric conditions. In this work, we applied a computational k-means clustering algorithm to a global set of data obtained from the Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2), to yield a set of 25 clusters that discriminate on the basis of land type, elevation, and atmospheric conditions to predict statistical aerosol optical depth (AOD) information. We considered different subsets of MERRA-2 data, consisting of all the data averaged over a single year (2016) as well as data averaged by meteorological season over a span of five years (2012–16), arriving at five separate sets of 25 clusters. We make the clustered AOD information available with decision trees, qualitative cluster descriptions, and color-coded cluster maps to assist in identifying which cluster to use in retrieving AOD information. The results of this analysis have applications in atmospheric modeling where knowledge of approximate or typical aerosol conditions is needed in lookup-table form without requiring access to large atmospheric databases or computationally intensive aerosol models; such applications could include quick-turnaround or large-volume analyses of atmospheric conditions required to inform decision-making that affects national security, such as in modeling remote sensing and estimating upper and lower bounds for visible and infrared photon transport.

Open access