Using Real-Time Weather Data from an Unmanned Aircraft System to Support the Advanced Research Version of the Weather Research and Forecast Model

Passner, Jeffrey E.; Kirby, S. H.; Jameson, Terry

doi:10.21236/ada561959

Cited by 6 publications

(2 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The weather engine of the system is the 3.9 version of the open source, community developed Weather Research and Forecasting (WRF) model (Skamarock et al, 2008), since it is flexibly scalable, proved to be usable for (even UAS) aviation meteorology purposes (Passner et al, 2009), and capable to assimilate UAS observed weather data (Passner et al, 2012;Jonassen et al, 2012;Reen and Dumais, 2018) besides other applications from its broad range of functionalities. WRF has multiple choices of parameter options.…”

Section: Introductionmentioning

confidence: 99%

Weather model fine-tuning with software container-based simulation platform

Lovas¹,

Kardos²,

Gyöngyösi³

et al. 2019

Időjárás

View full text Add to dashboard Cite

Abstract⎯ Fine-tuning of a weather model requires immense computational resources, however, such capacities are usually available on non-homogeneous IT platforms. In addition, development and operational application are typically performed on different, heterogeneous systems (from laptops to dedicated HPC servers or cloud computing environments). To manage scalability and platform independent portability, a new layersupporting state-of-the-art software container technology and batch processing -has been introduced. Encouraged by prior successful benchmark tests of the WRF model, the effect of model setup has been investigated over 10 different cases, tested on 30 different configurations. Including different parameterizations, the results of 300 different runs can be compared in a uniform database, yielding a sufficiently wide pool of samples in order to obtain the configuration of the modeling system optimal to the scope of our research, based on a relatively objective selection method. Continuously expanding database of near real-time preliminary outputs gives the opportunity for run-time steering of the experiments. This research currently benefits the development of an aviation meteorological support system, in the meanwhile, our contributions could be applied in an even wider aspect, either from the applicability of big data technology point of view, or with respect to the given best practice model setup.

show abstract

Section: Introductionmentioning

confidence: 99%

Weather model fine-tuning with software container-based simulation platform

Lovas¹,

Kardos²,

Gyöngyösi³

et al. 2019

Időjárás

View full text Add to dashboard Cite

show abstract

“…One advantage of TAMDAR over standard AMDAR observations is the presence of TAMDAR sensors on smaller aircraft that fly into a larger number of airports and cruise at a lower altitude; this allows for better observational coverage at lower altitudes [4]. A modified TAMDAR sensor package has been applied to an unmanned aerial system (UAS [5]). UAS weather observations could significantly enhance observing capabilities, especially in environments where observation density is very low.…”

Section: Introductionmentioning

confidence: 99%

Assimilation of Aircraft Observations in High-Resolution Mesoscale Modeling

Reen

Dumais

2018

Advances in Meteorology

View full text Add to dashboard Cite

Aircraft-based observations are a promising source of above-surface observations for assimilation into mesoscale model simulations. The Tropospheric Airborne Meteorological Data Reporting (TAMDAR) observations have potential advantages over some other aircraft observations including the presence of water vapor observations. The impact of assimilating TAMDAR observations via observation nudging in 1 km horizontal grid spacing Weather Research and Forecasting model simulations is evaluated using five cases centered over California. Overall, the impact of assimilating the observations is mixed, with the layer with the greatest benefit being above the surface in the lowest 1000 m above ground level and the variable showing the most consistent benefit being temperature. Varying the nudging configuration demonstrates the sensitivity of the results to details of the assimilation, but does not clearly demonstrate the superiority of a specific configuration.

show abstract

Impact of Assimilation of Radiosonde and UAV Observations from the Southern Ocean in the Polar WRF Model

et al. 2020

View full text Add to dashboard Cite

Weather forecasting in the Southern Ocean and Antarctica is a challenge above all due to the rarity of observations to be assimilated in numerical weather prediction (NWP) models. As observations are expensive and logistically challenging, it is important to evaluate the benefit that additional observations could bring to NWP. Atmospheric soundings applying unmanned aerial vehicles (UAVs) have a large potential to supplement conventional radiosonde sounding observations. Here, we applied UAV and radiosonde sounding observations from an RV Polarstern cruise in the ice-covered Weddell Sea in austral winter 2013 to evaluate the impact of their assimilation in the Polar version of the Weather Research and Forecasting (Polar WRF) model. Our experiments revealed small to moderate impacts of radiosonde and UAV data assimilation. In any case, the assimilation of sounding data from both radiosondes and UAVs improved the analyses of air temperature, wind speed, and humidity at the observation site for most of the time. Further, the impact on the results of 5day-long Polar WRF experiments was often felt over distances of at least 300 km from the observation site. All experiments succeeded in capturing the main features of the evolution of near-surface variables, but the effects of data assimilation varied between different cases. Due to the limited vertical extent of the UAV observations, the impact of their assimilation was limited to the lowermost 1−2-km layer, and assimilation of radiosonde data was more beneficial for modeled sea level pressure and near-surface wind speed.

show abstract

Using Real-Time Weather Data from an Unmanned Aircraft System to Support the Advanced Research Version of the Weather Research and Forecast Model

Cited by 6 publications

References 5 publications

Weather model fine-tuning with software container-based simulation platform

Weather model fine-tuning with software container-based simulation platform

Assimilation of Aircraft Observations in High-Resolution Mesoscale Modeling

Impact of Assimilation of Radiosonde and UAV Observations from the Southern Ocean in the Polar WRF Model

Contact Info

Product

Resources

About