Prediction of Energy Dissipation Rates for Aviation Turbulence. Part II: Nowcasting Convective and Nonconvective TurbulenceSource: Journal of Applied Meteorology and Climatology:;2017:;volume( 056 ):;issue: 002::page 339DOI: 10.1175/JAMC-D-16-0312.1Publisher: American Meteorological Society
Abstract: n addition to turbulence forecasts, which can be used for strategic planning for turbulence avoidance, short-term nowcasts can augment longer-term forecasts by providing much more timely and accurate turbulence locations for real-time tactical avoidance of turbulence hazards, especially those related to short-lived convection. This paper describes a turbulence-nowcasting algorithm that combines recent short-term turbulence forecasts with all currently available direct turbulence observations and inferences of turbulence from other sources. Building upon the need to provide forecasts that are aircraft independent, the nowcasts provide estimates of an atmospheric metric of turbulence, namely, the energy dissipation rate to the one-third power (EDR). Some observations directly provide EDR, such as in situ observations from select commercial aircraft and ground-based radar algorithm output, whereas others must be translated to EDR. A strategy is provided for mapping turbulence observations, such as pilot reports (PIREPs), and inferences from other relevant observational data sources, such as observed surface wind gusts, into EDR. These remapped observation values can then be combined with short-term turbulence forecasts and other convective diagnostics of turbulence to provide a turbulence nowcast of EDR in the national airspace. Case studies are provided to illustrate the algorithm procedure and benefits. The EDR nowcasts are compared with aircraft in situ EDR observations and PIREPs converted to EDR to obtain metrics of statistical performance. It is shown by one common performance metric, the area under the relative operating characteristic curve, that the turbulence nowcasts with assimilated observations considerably outperform the corresponding turbulence forecasts.
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| contributor author | Pearson, J. M. | |
| contributor author | Sharman, R. D. | |
| date accessioned | 2017-06-09T16:51:43Z | |
| date available | 2017-06-09T16:51:43Z | |
| date copyright | 2017/02/01 | |
| date issued | 2017 | |
| identifier issn | 1558-8424 | |
| identifier other | ams-75451.pdf | |
| identifier uri | http://onlinelibrary.yabesh.ir/handle/yetl/4217788 | |
| description abstract | n addition to turbulence forecasts, which can be used for strategic planning for turbulence avoidance, short-term nowcasts can augment longer-term forecasts by providing much more timely and accurate turbulence locations for real-time tactical avoidance of turbulence hazards, especially those related to short-lived convection. This paper describes a turbulence-nowcasting algorithm that combines recent short-term turbulence forecasts with all currently available direct turbulence observations and inferences of turbulence from other sources. Building upon the need to provide forecasts that are aircraft independent, the nowcasts provide estimates of an atmospheric metric of turbulence, namely, the energy dissipation rate to the one-third power (EDR). Some observations directly provide EDR, such as in situ observations from select commercial aircraft and ground-based radar algorithm output, whereas others must be translated to EDR. A strategy is provided for mapping turbulence observations, such as pilot reports (PIREPs), and inferences from other relevant observational data sources, such as observed surface wind gusts, into EDR. These remapped observation values can then be combined with short-term turbulence forecasts and other convective diagnostics of turbulence to provide a turbulence nowcast of EDR in the national airspace. Case studies are provided to illustrate the algorithm procedure and benefits. The EDR nowcasts are compared with aircraft in situ EDR observations and PIREPs converted to EDR to obtain metrics of statistical performance. It is shown by one common performance metric, the area under the relative operating characteristic curve, that the turbulence nowcasts with assimilated observations considerably outperform the corresponding turbulence forecasts. | |
| publisher | American Meteorological Society | |
| title | Prediction of Energy Dissipation Rates for Aviation Turbulence. Part II: Nowcasting Convective and Nonconvective Turbulence | |
| type | Journal Paper | |
| journal volume | 56 | |
| journal issue | 2 | |
| journal title | Journal of Applied Meteorology and Climatology | |
| identifier doi | 10.1175/JAMC-D-16-0312.1 | |
| journal fristpage | 339 | |
| journal lastpage | 351 | |
| tree | Journal of Applied Meteorology and Climatology:;2017:;volume( 056 ):;issue: 002 | |
| contenttype | Fulltext |