An Hourly Assimilation–Forecast Cycle: The RUC

Benjamin, Stanley G.; Dévényi, D.; Weygandt, Stephen S.; Brundage, Kevin J.; Brown, John M.; Grell, Georg; Kim, Dong-Soo; Schwartz, Barry E.; Smirnova, Tatiana G.; Smith, Ted; Manikin, Geoffrey S.

doi:10.1175/1520-0493(2004)132<0495:ahactr>2.0.co;2

Cited by 516 publications

(327 citation statements)

References 44 publications

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“…However, if the tracks are degraded too much, then any metric will appear to have value in demonstrating tracking skill. For this verification process, AALTO tracks were intentionally degraded by using random prioritization and increasing the motion history from 30 min to one day, causing AALTO to almost exclusively use the initial motion estimate, in this case based on the RUC model (Benjamin et al, 2004) storm motion vectors. Both prior art and tests of AALTO demonstrate the need for global optimization in order to maximize tracking performance.…”

Section: Comparison To Scit Tracksmentioning

confidence: 99%

The advanced algorithm for tracking objects (AALTO)

Limpert

Houston

Lock

2015

Meteorological Applications

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ABSTRACT:The advanced algorithm for the tracking of objects (AALTO) constructs tracks from objects, such as thunderstorms or mesocyclones, detected by multiple weather radars at irregular time intervals. It is important to have high accuracy in tracking thunderstorms to generate skilful forecasts and high-quality climatologies and, fundamentally, to ensure that any derived product from tracks captures only that particular storm, and in its entirety. AALTO incorporates many of the best practices of existing tracking algorithms and techniques employed by meteorologists in constructing tracks. AALTO differs from existing algorithms designed to track meteorological phenomena that manifest in radar data in the following ways: (1) AALTO is designed to track objects from multiple radars, enabling analysis over a larger domain than if a single radar was used; (2) improved tracking is realized through improved initial motion estimates and directional thresholding and (3) AALTO looks both at the track history and at the subsequent possible positions along a track when constructing the best possible tracks, mimicking the approach that would be taken by a human meteorologist. Verification was done using metrics that were objectively determined to distinguish between good and degraded tracks; a description of the approach to determine the appropriate metrics is presented. An overview of the AALTO tracking procedure and an example case are presented in this study.

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Section: Comparison To Scit Tracksmentioning

confidence: 99%

The advanced algorithm for tracking objects (AALTO)

Limpert

Houston

Lock

2015

Meteorological Applications

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“…In this research, we chose different schemes from four physical processes, which produce 40 different combinations. The multi-scheme includes three microphysics (MP) process schemes (Lin scheme [40], WRF single moment (WSM) three-class simple ice scheme [41], WSM six-class scheme [42]), four land surface (SFC) schemes (thermal diffusion scheme (from Mesoscale Model 5), unified Noah land-surface model [43], the Rapid Update Cycle (RUC) land-surface model [44], and Pleim-Xu scheme [45]), three cumulus (CU) schemes (Kain-Fritsch [46], Betts-Miller [47], and Grell-Devenyi [48]), and two planet boundary layer (PBL) schemes (the Yonsei University [49], and the Mellor-Yamada-Janjić [50]). The Table A1 in the appendix lists the combination of physical schemes for each ensemble member.…”

Section: Ensemble Member Productionmentioning

confidence: 99%

A Long-Term Wind Speed Ensemble Forecasting System with Weather Adapted Correction

Chu

Wang

et al. 2016

Energies

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Abstract:Wind forecasting is critical in the wind power industry, yet forecasting errors often exist. In order to effectively correct the forecasting error, this study develops a weather adapted bias correction scheme on the basis of an average bias-correction method, which considers the deviation of estimated biases associated with the difference in weather type within each unit of the statistical sample. This method is tested by an ensemble forecasting system based on the Weather Research and Forecasting (WRF) model. This system provides high resolution wind speed deterministic forecasts using 40 members generated by initial perturbations and multi-physical schemes. The forecasting system outputs 28-52 h predictions with a temporal resolution of 15 min, and is evaluated against collocated anemometer towers observations at six wind fields located on the east coast of China. Results show that the information contained in weather types produces an improvement in the forecast bias correction.

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“…The equations for reflectivity due to graupel are the same as Equation 5 and Equation 6 with the substitution of ρ g for ρ s , q g for q s , and also using N 0 for graupel in Equation 6.…”

Section: Radar Reflectivity Estimationmentioning

confidence: 99%

“…The numerical weather data sets employed for the initial implementation were produced by the High-Resolution Rapid Refresh (HRRR) model as described by [5], [6], and [7]. The HRRR model is a storm-resolving model which assimilates a multitude of observations.…”

Section: Introductionmentioning

confidence: 99%

Creating a realistic weather environment for motion-based piloted flight simulation

Daniels

Schaffner

Evans

et al. 2012

2012 IEEE/AIAA 31st Digital Avionics Systems Conference (DASC)

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A flight simulation environment is being enhanced to facilitate experiments that evaluate research prototypes of advanced onboard weather radar, hazard / integrity monitoring (HIM), and integrated alerting and notification (IAN) concepts in adverse weather conditions. The simulation environment uses weather data based on real weather events to support operational scenarios in a terminal area.A simulated atmospheric environment was realized by using numerical weather data sets. These were produced from the High-Resolution Rapid Refresh (HRRR) model hosted and run by the National Oceanic and Atmospheric Administration (NOAA). To align with the planned flight simulation experiment requirements, several HRRR data sets were acquired courtesy of NOAA. These data sets coincided with severe weather events at the Memphis International Airport (MEM) in Memphis, TN. In addition, representative flight tracks for approaches and departures at MEM were generated and used to develop and test simulations of (1) The simulation includes a weather radar display that provides weather and turbulence modes, derived from the modeled weather along the flight track. The radar capabilities and the pilots controls simulate current-generation commercial weather radar systems. Appropriate data-linked weather advisories (e.g., SIGMET) were derived from the HRRR weather models and provided to the pilot consistent with NextGen concepts of use for Aeronautical Information Service (AIS) and Meteorological (MET) data link products.The net result of this simulation development was the creation of an environment that supports investigations of new flight deck information systems, methods for incorporation of better weather information, and pilot interface and operational improvements for better aviation safety. This research is part of a larger effort at NASA to study the impact of the growing complexity of operations, information, and systems on crew decision-making and response effectiveness; and then to recommend methods for improving future designs.

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An Hourly Assimilation–Forecast Cycle: The RUC

Cited by 516 publications

References 44 publications

The advanced algorithm for tracking objects (AALTO)

The advanced algorithm for tracking objects (AALTO)

A Long-Term Wind Speed Ensemble Forecasting System with Weather Adapted Correction

Creating a realistic weather environment for motion-based piloted flight simulation

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