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Evan S. Bentley, Richard L. Thompson, Barry R. Bowers, Justin G. Gibbs, and Steven E. Nelson

Abstract

Previous work has considered tornado occurrence with respect to radar data, both WSR-88D and mobile research radars, and a few studies have examined techniques to potentially improve tornado warning performance. To date, though, there has been little work focusing on systematic, large-sample evaluation of National Weather Service (NWS) tornado warnings with respect to radar-observable quantities and the near-storm environment. In this work, three full years (2016–18) of NWS tornado warnings across the contiguous United States were examined, in conjunction with supporting data in the few minutes preceding warning issuance, or tornado formation in the case of missed events. The investigation herein examines WSR-88D and Storm Prediction Center (SPC) mesoanalysis data associated with these tornado warnings with comparisons made to the current Warning Decision Training Division (WDTD) guidance. Combining low-level rotational velocity and the significant tornado parameter (STP), as used in prior work, shows promise as a means to estimate tornado warning performance, as well as relative changes in performance as criteria thresholds vary. For example, low-level rotational velocity peaking in excess of 30 kt (15 m s−1), in a near-storm environment, which is not prohibitive for tornadoes (STP > 0), results in an increased probability of detection and reduced false alarms compared to observed NWS tornado warning metrics. Tornado warning false alarms can also be reduced through limiting warnings with weak (<30 kt), broad (>1 n mi; 1 n mi = 1.852 km) circulations in a poor (STP = 0) environment, careful elimination of velocity data artifacts like sidelobe contamination, and through greater scrutiny of human-based tornado reports in otherwise questionable scenarios.

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Bo Pang, Adam A. Scaife, Riyu Lu, and Rongcai Ren

Abstract

This study investigates the stratosphere–troposphere coupling associated with the Scandinavian (SCA) pattern in boreal winter. The results indicate that the SCA impacts stratospheric circulation but that its positive and negative phases have different effects. The positive phase of the SCA (SCA+) pattern is restricted to the troposphere, but the negative phase (SCA) extends to the upper stratosphere. The asymmetry between phases is also visible in the lead–lag evolution of the stratosphere and troposphere. Prominent stratospheric anomalies are found to be intensified following SCA+ events, but prior to SCA events. Further analysis reveals that the responses are associated with upward propagation of planetary waves, especially wavenumber 1, which is asymmetric between SCA phases. The wave amplitudes in the stratosphere, originating from the troposphere, are enhanced after the SCA+ events and before the SCA events. Furthermore, the anomalous planetary wave activity can be understood through its interference with climatological stationary waves. Constructive wave interference is accompanied by clear upward propagation in the SCA+ events, while destructive interference suppresses stratospheric waves in the SCA events. Our results also reveal that the SCA+ events are more likely to be followed by sudden stratospheric warming (SSW) events, because of the deceleration of stratospheric westerlies following the SCA+ events.

Open access
Robert M. Banta, Yelena L. Pichugina, Lisa S. Darby, W. Alan Brewer, Joseph B. Olson, Jaymes S. Kenyon, S. Baidar, S. G. Benjamin, H. J. S. Fernando, K. O. Lantz, J. K. Lundquist, B. J. McCarty, T. Marke, S. P. Sandberg, J. Sharp, W. J. Shaw, D. D. Turner, J. M. Wilczak, R. Worsnop, and M. T. Stoelinga

Abstract

Complex-terrain locations often have repeatable near-surface wind patterns, such as synoptic gap flows and local thermally forced flows. An example is the Columbia River Valley in east-central Oregon–Washington, a significant wind energy generation region and the site of the Second Wind Forecast Improvement Project (WFIP2). Data from three Doppler lidars deployed during WFIP2 define and characterize summertime wind regimes and their large-scale contexts, and provide insight into NWP model errors by examining differences in the ability of a model [NOAA’s High-Resolution Rapid Refresh (HRRR version 1)] to forecast wind speed profiles for different flow regimes. Seven regimes were identified based on daily time series of the lidar-measured rotor-layer winds, which then suggested two broad categories. First, in three of the regimes the primary dynamic forcing was the large-scale pressure gradient. Second, in two other regimes the dominant forcing was the diurnal heating-cooling cycle (regional sea-breeze-type dynamics), including the marine intrusion previously described, which generates strong nocturnal winds over the region. For the large-scale pressure gradient regimes, HRRR had wind speed biases of ~1 m s−1 and RMSEs of 2–3 m s−1. Errors were much larger for the thermally forced regimes, owing to the premature demise of the strong nocturnal flow in HRRR. Thus, the more dominant the role of surface heating in generating the flow, the larger the errors. Major errors could result from surface heating of the atmosphere, boundary layer responses to that heating, and associated terrain interactions. Measurement/modeling research programs should be designed to determine which of these modeled processes produce the largest errors, so those processes can be improved and errors reduced.

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Masaki Toda, Masahiro Watanabe, and Masakazu Yoshimori

Abstract

Modeling studies have shown that the surface air temperature (SAT) increase in response to an increase in the atmospheric CO2 concentration is larger over land than over ocean. This so-called land–ocean warming contrast, ϕ, defined as the land-mean SAT change divided by the ocean-mean SAT change, is a striking feature of global warming. Small heat capacity over land is unlikely to be the sole cause because the land–ocean warming contrast is found in the equilibrium state of CO2 doubling experiments. Several different mechanisms have been proposed to explain the land–ocean warming contrast, but a comprehensive understanding has not yet been obtained. In Part I of this study, we propose a framework to diagnose ϕ based on energy budgets at the top of atmosphere and for the atmosphere, which enables the decomposition of contributions from effective radiative forcing (ERF), climate feedback, heat capacity, and atmospheric energy transport anomaly to ϕ. Using this framework, we analyzed the SAT response to an abrupt CO2 quadrupling using 15 Coupled Model Intercomparison Project phase 6 (CMIP6) Earth system models. In the near-equilibrium state (years 121–150), ϕ is 1.49 ± 0.11, which is primarily induced by the land–ocean difference in ERF and heat capacity. We found that contributions from ERF, feedback, and energy transport anomaly tend to cancel each other, leading to a small intermodel spread of ϕ compared to the large spread of individual components. In the equilibrium state without heat capacity contribution, ERF and energy transport anomaly are the major contributors to ϕ, which shows a weak negative correlation with the equilibrium climate sensitivity.

Open access
Jason M. English, David D. Turner, Trevor I. Alcott, William R. Moninger, Janice L. Bytheway, Robert Cifelli, and Melinda Marquis

Abstract

Improved forecasts of atmospheric river (AR) events, which provide up to half the annual precipitation in California, may reduce impacts to water supply, lives, and property. We evaluate quantitative precipitation forecasts (QPF) from the High-Resolution Rapid Refresh model version 3 (HRRRv3) and version 4 (HRRRv4) for five AR events that occurred in February–March 2019 and compare them to quantitative precipitation estimates (QPE) from Stage IV and Mesonet products. Both HRRR versions forecast spatial patterns of precipitation reasonably well, but are drier than QPE products in the Bay Area and wetter in the Sierra Nevada range. The HRRR dry bias in the Bay Area may be related to biases in the model temperature profile, while integrated water vapor (IWV), wind speed, and wind direction compare reasonably well. In the Sierra Nevada range, QPE and QPF agree well at temperatures above freezing. Below freezing, the discrepancies are due in part to errors in the QPE products, which are known to underestimate frozen precipitation in mountainous terrain. HRRR frozen QPF accuracy is difficult to quantify, but the model does have wind speed and wind direction biases near the Sierra Nevada range. HRRRv4 is overall more accurate than HRRRv3, likely due to data assimilation improvements, and possibly physics improvements. Applying a neighborhood maximum method impacted performance metrics, but did not alter general conclusions, suggesting closest gridbox evaluations may be adequate for these types of events. Improvements to QPF in the Bay Area and QPE/QPF in the Sierra Nevada range would be particularly useful to provide better understanding of AR events.

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Luke J. LeBel, Brian H. Tang, and Ross A. Lazear

Abstract

The complex terrain at the intersection of the Mohawk and Hudson valleys of New York has an impact on the development and evolution of severe convection in the region. Specifically, previous research has concluded that terrain-channeled flow in the Mohawk and Hudson valleys likely contributes to increased low-level wind shear and instability in the valleys during severe weather events such as the historic 31 May 1998 event that produced a strong (F3) tornado in Mechanicville, New York. The goal of this study is to further examine the impact of terrain channeling on severe convection by analyzing a high-resolution WRF Model simulation of the 31 May 1998 event. Results from the simulation suggest that terrain-channeled flow resulted in the localized formation of an enhanced low-level moisture gradient, resembling a dryline, at the intersection of the Mohawk and Hudson valleys. East of this boundary, the environment was characterized by stronger low-level wind shear and greater low-level moisture and instability, increasing tornadogenesis potential. A simulated supercell intensified after crossing the boundary, as the larger instability and streamwise vorticity of the low-level inflow was ingested into the supercell updraft. These results suggest that terrain can have a key role in producing mesoscale inhomogeneities that impact the evolution of severe convection. Recognition of these terrain-induced boundaries may help in anticipating where the risk of severe weather may be locally enhanced.

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Marvin Kähnert, Harald Sodemann, Wim C. de Rooy, and Teresa M. Valkonen

Abstract

Forecasts of marine cold air outbreaks critically rely on the interplay of multiple parameterization schemes to represent subgrid-scale processes, including shallow convection, turbulence, and microphysics. Even though such an interplay has been recognized to contribute to forecast uncertainty, a quantification of this interplay is still missing. Here, we investigate the tendencies of temperature and specific humidity contributed by individual parameterization schemes in the operational weather prediction model AROME-Arctic. From a case study of an extensive marine cold air outbreak over the Nordic seas, we find that the type of planetary boundary layer assigned by the model algorithm modulates the contribution of individual schemes and affects the interactions between different schemes. In addition, we demonstrate the sensitivity of these interactions to an increase or decrease in the strength of the parameterized shallow convection. The individual tendencies from several parameterizations can thereby compensate each other, sometimes resulting in a small residual. In some instances this residual remains nearly unchanged between the sensitivity experiments, even though some individual tendencies differ by up to an order of magnitude. Using the individual tendency output, we can characterize the subgrid-scale as well as grid-scale responses of the model and trace them back to their underlying causes. We thereby highlight the utility of individual tendency output for understanding process-related differences between model runs with varying physical configurations and for the continued development of numerical weather prediction models.

Open access
Yingying Zhao, Matthew Newman, Antonietta Capotondi, Emanuele Di Lorenzo, and Daoxun Sun

Abstract

Teleconnections from the tropics energize variations of the North Pacific climate, but detailed diagnosis of this relationship has proven difficult. Simple univariate methods, such as regression on El Niño–Southern Oscillation (ENSO) indices, may be inadequate since the key dynamical processes involved—including ENSO diversity in the tropics, re-emergence of mixed layer thermal anomalies, and oceanic Rossby wave propagation in the North Pacific—have a variety of overlapping spatial and temporal scales. Here we use a multivariate linear inverse model to quantify tropical and extratropical multiscale dynamical contributions to North Pacific variability, in both observations and CMIP6 models. In observations, we find that the tropics are responsible for almost half of the seasonal variance, and almost three-quarters of the decadal variance, along the North American coast and within the Subtropical Front region northwest of Hawaii. SST anomalies that are generated by local dynamics within the northeast Pacific have much shorter time scales, consistent with transient weather forcing by Aleutian low anomalies. Variability within the Kuroshio–Oyashio Extension (KOE) region is considerably less impacted by the tropics, on all time scales. Consequently, without tropical forcing the dominant pattern of North Pacific variability would be a KOE pattern, rather than the Pacific decadal oscillation (PDO). In contrast to observations, most CMIP6 historical simulations produce North Pacific variability that maximizes in the KOE region, with amplitude significantly higher than observed. Correspondingly, the simulated North Pacific in all CMIP6 models is shown to be relatively insensitive to the tropics, with a dominant spatial pattern generally resembling the KOE pattern, not the PDO.

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Marianne Pietschnig, Abigail L. S. Swann, F. Hugo Lambert, and Geoffrey K. Vallis

Abstract

Future projections of precipitation change over tropical land are often enhanced by vegetation responses to CO2 forcing in Earth system models. Projected decreases in rainfall over the Amazon basin and increases over the Maritime Continent are both stronger when plant physiological changes are modeled than if these changes are neglected, but the reasons for this amplification remain unclear. The responses of vegetation to increasing CO2 levels are complex and uncertain, including possible decreases in stomatal conductance and increases in leaf area index due to CO2 fertilization. Our results from an idealized atmospheric general circulation model show that the amplification of rainfall changes occurs even when we use a simplified vegetation parameterization based solely on CO2-driven decreases in stomatal conductance, indicating that this mechanism plays a key role in complex model projections. Based on simulations with rectangular continents we find that reducing terrestrial evaporation to zero with increasing CO2 notably leads to enhanced rainfall over a narrow island. Strong heating and ascent over the island trigger moisture advection from the surrounding ocean. In contrast, over larger continents rainfall depends on continental evaporation. Simulations with two rectangular continents representing South America and Africa reveal that the stronger decrease in rainfall over the Amazon basin seen in Earth system models is due to a combination of local and remote effects, which are fundamentally connected to South America’s size and its location with respect to Africa. The response of tropical rainfall to changes in evapotranspiration is thus connected to size and configuration of the continents.

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Jing Zhang, Jie Feng, Hong Li, Yuejian Zhu, Xiefei Zhi, and Feng Zhang

Abstract

Operational and research applications generally use the consensus approach for forecasting the track and intensity of tropical cyclones (TCs) due to the spatial displacement of the TC location and structure in ensemble member forecasts. This approach simply averages the location and intensity information for TCs in individual ensemble members, which is distinct from the traditional pointwise arithmetic mean (AM) method for ensemble forecast fields. The consensus approach, despite having improved skills relative to the AM in predicting the TC intensity, cannot provide forecasts of the TC spatial structure. We introduced a unified TC ensemble mean forecast based on the feature-oriented mean (FM) method to overcome the inconsistency between the AM and consensus forecasts. FM spatially aligns the TC-related features in each ensemble field to their geographical mean positions before the amplitude of their features is averaged. We select 219 TC forecast samples during the summer of 2017 for an overall evaluation of the FM performance. The results show that the TC track consensus forecasts can differ from AM track forecasts by hundreds of kilometers at long lead times. AM also gives a systematic and statistically significant underestimation of the TC intensity compared with the consensus forecast. By contrast, FM has a very similar TC track and intensity forecast skill to the consensus approach. FM can also provide the corresponding ensemble mean forecasts of the TC spatial structure that are significantly more accurate than AM for the low- and upper-level circulation in TCs. The FM method has the potential to serve as a valuable unified ensemble mean approach for the TC prediction.

Open access