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David Kristovich
Open access
David Kristovich
Open access
Brent Knutson, Wenbo Tang, and Pak Wai Chan

Abstract

The operational light detection and ranging (lidar) data from the Hong Kong International Airport (HKIA) in China are assimilated in the six-nest, high-resolution Weather Research and Forecasting (WRF) Model. The existing radar data assimilation schemes in the WRF data assimilation (WRFDA) package have been adapted to accommodate the high temporal frequency and spatial resolution of the lidar observations. The weather data are then used to produce Lagrangian coherent structures to detect atmospheric hazards for flights. The coherent structures obtained from the various datasets are contrasted against flight data measured on aircraft. It is found that both WRF and WRFDA produce coherent structures that are more distinguishable than those obtained from two-dimensional retrieval, which may improve the detection of true wind shear hazards.

Restricted access
Andrew D. Magee and Anthony S. Kiem

Abstract

Catastrophic impacts associated with tropical cyclone (TC) activity mean that the accurate and timely provision of TC outlooks are important to people, places, and numerous sectors in Australia and beyond. In this study, we apply a Poisson regression statistical framework to predict TC counts in the Australian region (AR; 5°–40°S, 90°–160°E) and its four subregions. We test 10 unique covariate models, each using different representations of the influence of El Niño–Southern Oscillation (ENSO), Indian Ocean dipole (IOD), and southern annular mode (SAM) and use an automated covariate selection algorithm to select the optimum combination of predictors. The performance of preseason TC count outlooks generated between April and October for the AR TC season (November–April) and in-season TC count outlooks generated between November and January for the remaining AR TC season are tested. Results demonstrate that skillful TC count outlooks can be generated in April (i.e., 7 months prior to the start of the AR TC season), with Pearson correlation coefficient values between r = 0.59 and 0.78 and covariates explaining between 35% and 60% of the variance in TC counts. The dependence of models on indices representing Indian Ocean sea surface temperature highlights the importance of the Indian Ocean for TC occurrence in this region. Importantly, generating rolling monthly preseason and in-season outlooks for the AR TC season enables the continuous refinement of expected TC counts in a given season.

Open access
Domingo Muñoz-Esparza, Robert D. Sharman, and Wiebke Deierling

Abstract

We explore the use of machine learning (ML) techniques, namely, regression trees (RT), for the purpose of aviation turbulence forecasting at upper levels [20–45 kft (~6–14 km) in altitude]. In particular, we develop a series of RT-based algorithms that include random forests (RF) and gradient-boosted regression trees (GBRT) methods. Numerical weather prediction model prognostic variables and derived turbulence diagnostics based on 6-h forecasts from the 3-km High-Resolution Rapid Refresh model are used as features to train these data-driven models. Training and evaluation are based on turbulence estimates of eddy dissipation rate (EDR) obtained from automated in situ aircraft reports. Our baseline RF model, consisting of 100 trees with 30 layers of maximum depth, significantly reduces forecast errors for EDR < 0.1 m2/3 s−1 (which corresponds roughly to null and light turbulence) when compared with a simple regression model, increasing the probability of detection and in turn reducing the number of false alarms. Model complexity reduction via GBRT and feature-relevance analyses is performed, indicating that considerable execution speedups can be achieved while maintaining the model’s predictive skill. Overall, the ML models exhibit enhanced performance in discriminating the EDR forecast among the light, moderate, and severe turbulence categories. In addition, these artificial intelligence techniques significantly simplify the generation of new NWP and grid-spacing specific turbulence forecast products.

Open access
Natalie P. Thomas, Michael G. Bosilovich, Allison B. Marquardt Collow, Randal D. Koster, Siegfried D. Schubert, Amin Dezfuli, and Sarith P. Mahanama

Abstract

Heat waves are extreme climate events that have the potential to cause immense stress on human health, agriculture, and energy systems, so understanding the processes leading to their onset is crucial. There is no single accepted definition for heat waves, but they are generally described as a sustained amount of time over which temperature exceeds a local threshold. Multiple different temperature variables are potentially relevant, because high values of both daily maximum and minimum temperatures can be detrimental to human health. In this study, we focus explicitly on the different mechanisms associated with summertime heat waves manifested during daytime hours versus nighttime hours over the contiguous United States. Heat waves are examined using the National Aeronautics and Space Administration Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2). Over 1980–2018, the increase in the number of heat-wave days per summer was generally stronger for nighttime heat-wave days than for daytime heat-wave days, with localized regions of significant positive trends. Processes linked with daytime and nighttime heat waves are identified through composite analysis of precipitation, soil moisture, clouds, humidity, and fluxes of heat and moisture. Daytime heat waves are associated with dry conditions, reduced cloud cover, and increased sensible heating. Mechanisms leading to nighttime heat waves differ regionally across the United States, but they are typically associated with increased clouds, humidity, and/or low-level temperature advection. In the midwestern United States, enhanced moisture is transported from the Gulf of Mexico during nighttime heat waves.

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Lukas Strauss, Stefano Serafin, and Manfred Dorninger

Abstract

In this paper, a verification study of the skill and potential economic value of forecasts of ice accretion on wind turbines is presented. The phase of active ice formation on turbine blades has been associated with the strongest wind power production losses in cold climates; however, skillful icing forecasts could permit taking protective measures using anti-icing systems. Coarse- and high-resolution forecasts for the range up to day 3 from global (IFS and GFS) and limited-area (WRF) models are coupled to the Makkonen icing model. Surface and upper-air observations and icing measurements at turbine hub height at two wind farms in central Europe are used for model verification over two winters. Two case studies contrasting a correct and an incorrect forecast highlight the difficulty of correctly predicting individual icing events. A meaningful assessment of model skill is possible only after bias correction of icing-related parameters and selection of model-dependent optimal thresholds for ice growth rate. The skill of bias-corrected forecasts of freezing and humid conditions is virtually identical for all models. Hourly forecasts of active ice accretion generally show limited skill; however, results strongly suggest the superiority of high-resolution WRF forecasts relative to other model variants. Predictions of the occurrence of icing within a period of 6 h are found to have substantially better accuracy. Probabilistic forecasts of icing that are based on gridpoint neighborhood ensembles show slightly higher potential economic value than forecasts that are based on individual gridpoint values, in particular at low cost-loss ratios, that is, when anti-icing measures are comparatively inexpensive.

Restricted access
Timothy J. Cady, David A. Rahn, Nathaniel A. Brunsell, and Ward Lyles

Abstract

Impervious surfaces and buildings in the urban environment alter the radiative balance and surface energy exchange and can lead to warmer temperatures known as the urban heat island (UHI), which can increase heat-related illness and mortality. Continued urbanization and anthropogenic warming will enhance city temperatures worldwide, raising the need for viable mitigation strategies. Increasing green space throughout a city is a viable option to lessen the impacts of the UHI but can be difficult to implement. The potential impact of converting existing vacant lots in Kansas City, Missouri, to green spaces is explored with numerical simulations for three heat-wave events. Using data on vacant property and identifying places with a high fraction of impervious surfaces, the most suitable areas for converting vacant lots to green spaces is determined. Land-use/land-cover datasets are modified to simulate varying degrees of feasible conversion of urban to green spaces in these areas, and the local cooling effect using each strategy is compared with the unmodified simulation. Under more aggressive greening strategies, a mean local cooling impact of 0.5°–1.0°C is present within the focus area itself during the nighttime hours. Some additional cooling via the “park cool island” is possible downwind of the converted green spaces under the more aggressive scenarios. Although moderate and conservative strategies of conversion could still lead to other benefits, those strategies have little impact on cooling. Only an aggressive approach yields significant cooling.

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Eric P. James, Stanley G. Benjamin, and Brian D. Jamison

Abstract

Weather observations from commercial aircraft constitute an essential component of the global observing system and have been shown to be the most valuable observation source for short-range numerical weather prediction (NWP) systems over North America. However, the distribution of aircraft observations is highly irregular in space and time. In this study, we summarize the recent state of aircraft observation coverage over the globe and provide an updated quantification of its impact upon short-range NWP forecast skill. Aircraft observation coverage is most dense over the contiguous United States and Europe, with secondary maxima in East Asia and Australia/New Zealand. As of late November 2019, 665 airports around the world had at least one daily ascent or descent profile observation; 400 of these come from North American or European airports. Flight reductions related to the COVID-19 pandemic have led to a 75% reduction in aircraft observations globally as of late April 2020. A set of data denial experiments with the latest version of the Rapid Refresh NWP system for recent winter and summer periods quantifies the statistically significant positive forecast impacts of assimilating aircraft observations. A special additional experiment excluding approximately 80% of aircraft observations reveals a reduction in forecast skill for both summer and winter amounting to 30%–60% of the degradation seen when all aircraft observations are excluded. These results represent an approximate quantification of the NWP impact of COVID-19-related commercial flight reductions, demonstrating that regional NWP guidance is degraded as a result of the decreased number of aircraft observations.

Open access
Helene Birkelund Erlandsen, Kajsa M. Parding, Rasmus Benestad, Abdelkader Mezghani, and Marie Pontoppidan

Abstract

We used empirical–statistical downscaling in a pseudoreality context, in which both large-scale predictors and small-scale predictands were based on climate model results. The large-scale conditions were taken from a global climate model, and the small-scale conditions were taken from dynamical downscaling of the same global model with a convection-permitting regional climate model covering southern Norway. This hybrid downscaling approach, a “perfect model”–type experiment, provided 120 years of data under the CMIP5 high-emission scenario. Ample calibration samples made rigorous testing possible, enabling us to evaluate the effect of empirical–statistical model configurations and predictor choices and to assess the stationarity of the statistical models by investigating their sensitivity to different calibration intervals. The skill of the statistical models was evaluated in terms of their ability to reproduce the interannual correlation and long-term trends in seasonal 2-m temperature T 2m, wet-day frequency f w, and wet-day mean precipitation μ. We found that different 30-yr calibration intervals often resulted in differing statistical models, depending on the specific choice of years. The hybrid downscaling approach allowed us to emulate seasonal mean regional climate model output with a high spatial resolution (0.05° latitude and 0.1° longitude grid) for up to 100 GCM runs while circumventing the issue of short calibration time, and it provides a robust set of empirically downscaled GCM runs.

Open access