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Barry Lynn, Ehud Gavze, Jimy Dudhia, David Gill, and Alexander Khain

Abstract

A new, computationally efficient Semi-Lagrangian advection (SLA) scheme was used to simulate an idealized supercell storm using WRF coupled with Spectral (bin) Microphysics (SBM). SLA was developed to make complicated microphysical schemes more computationally accessible to cloud resolving models. The SLA is a linear combination of Semi-Lagrangian schemes of the first and the second order. It has relatively low numerical diffusion, a high level of mass conservation accuracy, and preserves the sum of multiple advected variables. In addition to idealized tests, comparisons were made with standard WRF higher-order, non-linear advection schemes. Tests of the SLA were performed using different weighting coefficients of γ for the combination of the first and second order components. The results of SLA on grids of 1 km, 500 m, and 250 m agree well with those of the standard WRF advection schemes, with results most similar to simulations with 250 m grid spacing. At the same time, the advection CPU time required by the SLA was 2.2 to 3 times shorter than the WRF advection schemes. The speed-up occurred in part because of the utilization of the same advection matrix for the advection of all hydrometeor mass bins. The findings of this work support the hypothesis that cloud microphysical simulation is more sensitive to the choice of microphysics than to the choice of advection schemes, thereby justifying the use of computationally efficient lower order linear schemes.

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MINGTING LI, HUIJIE XUE, JUN WEI, LINLIN LIANG, ARNOLD L. GORDON, and SONG YANG

Abstract

The role of the Mindoro Strait-Sibutu passage pathway in influencing the Luzon Strait inflow to the South China Sea (SCS) and the SCS multi-layer circulation is investigated with a high-resolution (0.1° × 0.1°) regional ocean model. Significant changes are evident in the SCS upper layer circulation (250-900 m) by closing the Mindoro-Sibutu pathway in sensitivity experiments, as Luzon Strait transport is reduced by 75%, from −4.4 Sv to −1.2 Sv. Because of the vertical coupling between the upper and middle layers, closing the Mindoro-Sibutu pathway also weakens clockwise circulation in the middle layer (900–2150 m), but there is no significant change in the deep layer (below 2150 m). The Mindoro-Sibutu pathway is an important branch of the SCS throughflow into the Indonesian Seas. It is also the gateway for oceanic waves propagating clockwise around the Philippines Archipelago from the western Pacific Ocean into the eastern SCS, projecting El Niño-Southern Oscillation sea level signals to the SCS, impacting its interannual variations and multi-layer circulation. The results provide insights into the dynamics of how upstream and downstream passage throughflows are coupled to affect the general circulation in marginal seas.

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QiFeng Qian, XiaoJing Jia, Hai Lin, and Ruizhi Zhang

Abstract

In this study, four machine learning (ML) models (gradient boost decision tree (GBDT), light gradient boosting machine (LightGBM), categorical boosting (CatBoost) and extreme gradient boosting (XGBoost)) are used to perform seasonal forecasts for non-monsoonal winter precipitation over the Eurasian continent (30-60°N, 30-105°E) (NWPE). The seasonal forecast results from a traditional linear regression (LR) model and two dynamic models are compared. The ML and LR models are trained using the data for the period of 1979-2010, and then, these empirical models are used to perform the seasonal forecast of NWPE for 2011-2018. Our results show that the four ML models have reasonable seasonal forecast skills for the NWPE and clearly outperform the LR model. The ML models and the dynamic models have skillful forecasts for the NWPE over different regions. The ensemble means of the forecasts including the ML models and dynamic models show higher forecast skill for the NWEP than the ensemble mean of the dynamic-only models. The forecast skill of the ML models mainly benefits from a skillful forecast of the third empirical orthogonal function (EOF) mode (EOF3) of the NWPE, which has a good and consistent prediction among the ML models. Our results also illustrate that the sea ice over the Arctic in the previous autumn is the most important predictor in the ML models in forecasting the NWPE. This study suggests that ML models may be useful tools to help improve seasonal forecasts of the NWPE.

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Christopher J. Nowotarski, Justin Spotts, Roger Edwards, Scott Overpeck, and Gary R. Woodall

Abstract

Tropical cyclone tornadoes pose a unique challenge to warning forecasters given their often marginal environments and radar attributes. In late August 2017 Hurricane Harvey made landfall on the Texas coast and produced 52 tornadoes over a record-breaking seven consecutive days. To improve warning efforts, this case study of Harvey’s tornadoes includes an event overview as well as a comparison of near-cell environments and radar attributes between tornadic and nontornadic warned cells. Our results suggest that significant differences existed in both the near-cell environments and radar attributes, particularly rotational velocity, between tornadic cells and false alarms. For many environmental variables and radar attributes, differences were enhanced when only tornadoes associated with a tornado debris signature were considered. Our results highlight the potential of improving warning skill further and reducing false alarms by increasing rotational velocity warning thresholds, refining the use of near-storm environment information, and focusing warning efforts on cells likely to produce the most impactful tornadoes.

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Juliana Dias, Stefan N. Tulich, Maria Gehne, and George N. Kiladis

Abstract

A set of 30-day reforecast experiments, focused on the Northern Hemisphere (NH) cool season (November–March), is performed to quantify the remote impacts of tropical forecast errors on the National Centers for Environmental Prediction (NCEP) global forecast system (GFS). The approach is to nudge the model towards reanalyses in the tropics and then measure the change in skill at higher latitudes as function of lead time. In agreement with previous analogous studies, results show that midlatitude predictions tend to be improved in association with reducing tropical forecast errors during weeks 2-4, particularly over the North Pacific and western North America, where gains in subseasonal precipitation anomaly pattern correlations are substantial. It is also found that tropical nudging is more effective at improving NH subseasonal predictions in cases where skill is relatively low in the control reforecast, whereas it tends to improve less cases that are already relatively skillful. By testing various tropical nudging configurations, it is shown that tropical circulation errors play a primary role in the remote modulation of predictive skill. A time dependent analysis suggests a roughly one week lag between a decrease in tropical errors and an increase in NH predictive skill. A combined tropical nudging and conditional skill analysis indicates that improved Madden Julian Oscillation (MJO) predictions throughout its lifecycle could improve weeks 3-4 NH precipitation predictions.

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Yanni Zhao, Rensheng Chen, Chuntan Han, Lei Wang, Shuhai Guo, and Junfeng Liu

Abstract

A precipitation observation network was constructed in a high-altitude area of the Qilian Mountains in Northwest China, which contained 23 sets of instruments. Because 21 sets of instruments were surrounded by protective fences, a precipitation intercomparison experiment was carried out at the highest station (4651 m) of the observation network with the same configuration to study the impact of wind on precipitation measurements under this configuration and to develop a correction method suitable for the entire network. The 30-min measured precipitation from June 2018 to October 2019 was corrected by transfer functions provided by Kochendorfer et al. (2017b), and their parameters were recalibrated with a local dataset. The results showed that the transfer functions fitted to the local dataset had a better performance than those using original parameters. Because of the influence of the experimental configuration on wind speed and direction, the root mean square error (RMSE) corrected with the original parameters increased by an average of 86%, but the RMSE adjusted by the new transfer functions decreased by 9%. Moreover, the resultant biases using new transfer functions were close to 0. The new coefficients of snowfall were derived by local measurements and applied to datasets from other sites within the Qilian Mountains observation network to evaluate their performances. The corrected snowfall at 21 stations increased by an average of 32.6 mm, and the relative precipitation increment ranged from 6% to 26%. This method can be used to correct snowfall in the observation network under the non-standard site configuration.

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Steven M. Lazarus, Jason Chiappa, Hadley Besing, Michael E. Splitt, and Jeremy A. Riousset

Abstract

The meteorological characteristics associated with thunderstorm top turbulence and tropical cyclone (TC) gigantic jets (GJ) are investigated. Using reanalysis data and observations, the large-scale environment and storm top structure of three GJ-producing TCs are compared to three non-GJ oceanic thunderstorms observed via low-light camera. Evidence of gravity wave breaking is manifest in the IR satellite images with cold ring and enhanced-V signatures prevalent in TCs Hilda and Harvey and embedded warm spots in the Dorian and Null storms. Statistics from an additional six less prodigious GJ environments are also included as a baseline. Distinguishing features of the TC GJ environment include higher tropopause, colder brightness temperatures, more stable lower stratosphere/distinct tropopause and reduced tropopause penetration. These factors support enhanced gravity wave (GW) breaking near the cloud top (overshoot). The advantage of a higher tropopause is that both electrical conductivity and GW breaking increase with altitude and thus act in tandem to promote charge dilution by increasing the rate at which the screening layer forms as well as enhancing the storm top mixing. The roles of the upper level ambient flow and shear are less certain. Environments with significant upper tropospheric shear may compensate for a lower tropopause by reducing the height of the critical layer which would also promote more intense GW breaking and turbulence near the cloud top.

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Robert Conrick, Clifford F. Mass, Joseph P. Boomgard-Zagrodnik, and David Ovens

Abstract

During late summer 2020, large wildfires over the Pacific Northwest produced dense smoke that impacted the region for an extended period. During this period of poor air quality, persistent low-level cloud coverage was poorly forecast by operational numerical weather prediction models, which dissipated clouds too quickly or produced insufficient cloud coverage extent. This deficiency raises questions about the influence of wildfire smoke on low-level clouds in the marine environment of the Pacific Northwest.

This paper investigates the effects of wildfire smoke on the properties of low-level clouds, including their formation, microphysical properties, and dissipation. A case study from 12-14 September 2020 is used as a testbed to evaluate the impact of wildfire smoke on such clouds. Observations from satellites and surface observing sites, coupled with mesoscale model simulations, are applied to understand the influence of wildfire smoke during this event. Results indicate that the presence of thick smoke over Washington led to decreased temperatures in the lower troposphere which enhanced low-level cloud coverage, with smoke particles altering the microphysical structure of clouds to favor high concentrations of small droplets. Thermodynamic changes due to smoke are found to be the primary driver of enhanced cloud lifetime during these events, with microphysical changes to clouds as a secondary contributing factor. However, both the thermodynamic and microphysical effects are necessary to produce a realistic simulation.

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Olivia VanBuskirk, Paulina Ćwik, Renee A. McPherson, Heather Lazrus, Elinor Martin, Charles Kuster, and Esther Mullens

Abstract

Heavy precipitation events and their associated flooding can have major impacts on communities and stakeholders. There is a lack of knowledge, however, about how stakeholders make decisions at the sub-seasonal to seasonal (S2S) timescales (i.e., two weeks to three months). To understand how decisions are made and S2S predictions are or can be used, the project team for “Prediction of Rainfall Extremes at Sub-seasonal to Seasonal Periods” (PRES2iP) conducted a two-day workshop in Norman, Oklahoma, during July 2018. The workshop engaged 21 professionals from environmental management and public safety communities across the contiguous United States in activities to understand their needs for S2S predictions of potential extended heavy precipitation events. Discussions and role-playing activities aimed to identify how workshop participants manage uncertainty and define extreme precipitation, the timescales over which they make key decisions, and the types of products they use currently. This collaboration with stakeholders has been an integral part of PRES2iP research and has aimed to foster actionable science. The PRES2iP team is using the information produced from this workshop to inform the development of predictive models for extended heavy precipitation events and to collaboratively design new forecast products with our stakeholders, empowering them to make more-informed decisions about potential extreme precipitation events.

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Martin Hurtado

Abstract

In a previous work, a weather radar algorithm with low computational cost has been developed to estimate the background noise power from the data collected at each radial. The algorithm consists of a sequence of steps designed to identify signal-free range volumes which are subsequently used to estimate the noise power. In this paper, we derive compact-closed form expressions to replace the numerical formulations used in the first two steps of the algorithm proposed in the original paper. The goal is to facilitate efficient implementation of the algorithm.

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