FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4283731393> ?p ?o ?g. }

• W4283731393 endingPage "106324" @default.
• W4283731393 startingPage "106324" @default.
• W4283731393 abstract "Ensemble sensitivity analysis (ESA) uses sample statistics of ensemble forecasts to estimate relationships between forecast metrics and initial conditions. The ensemble sensitivity analysis is often considered as a simple univariate regression as it includes an approximation of analysis covariance matrix with corresponding diagonal elements. In this work, univariate ensemble sensitivity is extended to multivariate ensemble sensitivity that incorporates the contribution from the full covariance matrix in the sensitivity calculations. The performance of multivariate ensemble sensitivity over univariate is examined for meso- and convective scale ensemble forecasts of a heavy rainfall event that happened over the Chennai city in India in December 2015. The ensemble forecasts and analyses are generated using the Advanced Research - Weather Research and Forecasting (WRF) model Data Assimilation Research Testbed (DART) based Ensemble Kalman Filter (EnKF). Multivariate ensemble sensitivity shows organized sensitivity patterns, while the sensitivity values are found to be broadly distributed in univariate ensemble sensitivity. Both the methods are validated using a perturbed initial condition approach, and the results indicate that the multivariate ensemble sensitivity method is effective in predicting the forecast response closest to the actual model response compared to the univariate ensemble sensitivity. The impact of model error on sensitivity calculations is examined by generating a new set of ensembles that uses the Stochastic Kinetic Energy Backscatter Scheme (SKEBS). In the presence of added model error, the forecast response estimated by multivariate using SKEBS ensembles compares better with the actual response. It is found that the performance of the multivariate approach depends on the optimal choice of localization radius, and if insufficient localization is applied, the spurious long-distance correlation contaminates the performance of the multivariate ensemble sensitivity method. The impact of various forecast lead times on the univariate and multivariate ensemble sensitivity analysis indicates that responses using multivariate ensemble sensitivity are more accurate than univariate, especially at longer lead times when nonlinearity becomes significant. The performance of univariate and multivariate methods in convection-permitting scale is examined by using the high-resolution ensemble forecasts, and it is found that the multivariate sensitivity with localization substantially improves the estimates in finer scales." @default.
• W4283731393 created "2022-07-01" @default.
• W4283731393 creator A5000921887 @default.
• W4283731393 creator A5054692573 @default.
• W4283731393 date "2022-10-01" @default.
• W4283731393 modified "2023-09-23" @default.
• W4283731393 title "Multivariate ensemble sensitivity analysis applied for an extreme rainfall over Indian subcontinent" @default.
• W4283731393 cites W1515186042 @default.
• W4283731393 cites W1840917349 @default.
• W4283731393 cites W1967940070 @default.
• W4283731393 cites W1969981719 @default.
• W4283731393 cites W1983028605 @default.
• W4283731393 cites W1985801744 @default.
• W4283731393 cites W1987308763 @default.
• W4283731393 cites W1995262681 @default.
• W4283731393 cites W2018772114 @default.
• W4283731393 cites W2028979275 @default.
• W4283731393 cites W2030774493 @default.
• W4283731393 cites W2057636059 @default.
• W4283731393 cites W2083339292 @default.
• W4283731393 cites W2102420397 @default.
• W4283731393 cites W2102636427 @default.
• W4283731393 cites W2105458239 @default.
• W4283731393 cites W2119007429 @default.
• W4283731393 cites W2121735562 @default.
• W4283731393 cites W2132600099 @default.
• W4283731393 cites W2142794782 @default.
• W4283731393 cites W2146803308 @default.
• W4283731393 cites W2150026075 @default.
• W4283731393 cites W2177451271 @default.
• W4283731393 cites W2179584279 @default.
• W4283731393 cites W2179912439 @default.
• W4283731393 cites W2442587609 @default.
• W4283731393 cites W2509502773 @default.
• W4283731393 cites W2596512614 @default.
• W4283731393 cites W2773055204 @default.
• W4283731393 cites W2788901071 @default.
• W4283731393 cites W2788907976 @default.
• W4283731393 cites W2793186045 @default.
• W4283731393 cites W2799480366 @default.
• W4283731393 cites W2799815913 @default.
• W4283731393 cites W2799979825 @default.
• W4283731393 cites W2965320042 @default.
• W4283731393 cites W2988588679 @default.
• W4283731393 cites W2996674939 @default.
• W4283731393 cites W3125138811 @default.
• W4283731393 cites W4210928071 @default.
• W4283731393 doi "https://doi.org/10.1016/j.atmosres.2022.106324" @default.
• W4283731393 hasPublicationYear "2022" @default.
• W4283731393 type Work @default.
• W4283731393 citedByCount "0" @default.
• W4283731393 crossrefType "journal-article" @default.
• W4283731393 hasAuthorship W4283731393A5000921887 @default.
• W4283731393 hasAuthorship W4283731393A5054692573 @default.
• W4283731393 hasConcept C105795698 @default.
• W4283731393 hasConcept C119898033 @default.
• W4283731393 hasConcept C127413603 @default.
• W4283731393 hasConcept C153294291 @default.
• W4283731393 hasConcept C154945302 @default.
• W4283731393 hasConcept C157286648 @default.
• W4283731393 hasConcept C161584116 @default.
• W4283731393 hasConcept C178650346 @default.
• W4283731393 hasConcept C199163554 @default.
• W4283731393 hasConcept C205649164 @default.
• W4283731393 hasConcept C206833254 @default.
• W4283731393 hasConcept C21200559 @default.
• W4283731393 hasConcept C24326235 @default.
• W4283731393 hasConcept C24552861 @default.
• W4283731393 hasConcept C33923547 @default.
• W4283731393 hasConcept C41008148 @default.
• W4283731393 hasConcept C45942800 @default.
• W4283731393 hasConcept C79334102 @default.
• W4283731393 hasConceptScore W4283731393C105795698 @default.
• W4283731393 hasConceptScore W4283731393C119898033 @default.
• W4283731393 hasConceptScore W4283731393C127413603 @default.
• W4283731393 hasConceptScore W4283731393C153294291 @default.
• W4283731393 hasConceptScore W4283731393C154945302 @default.
• W4283731393 hasConceptScore W4283731393C157286648 @default.
• W4283731393 hasConceptScore W4283731393C161584116 @default.
• W4283731393 hasConceptScore W4283731393C178650346 @default.
• W4283731393 hasConceptScore W4283731393C199163554 @default.
• W4283731393 hasConceptScore W4283731393C205649164 @default.
• W4283731393 hasConceptScore W4283731393C206833254 @default.
• W4283731393 hasConceptScore W4283731393C21200559 @default.
• W4283731393 hasConceptScore W4283731393C24326235 @default.
• W4283731393 hasConceptScore W4283731393C24552861 @default.
• W4283731393 hasConceptScore W4283731393C33923547 @default.
• W4283731393 hasConceptScore W4283731393C41008148 @default.
• W4283731393 hasConceptScore W4283731393C45942800 @default.
• W4283731393 hasConceptScore W4283731393C79334102 @default.
• W4283731393 hasLocation W42837313931 @default.
• W4283731393 hasOpenAccess W4283731393 @default.
• W4283731393 hasPrimaryLocation W42837313931 @default.
• W4283731393 hasRelatedWork W1480636815 @default.
• W4283731393 hasRelatedWork W1965898538 @default.
• W4283731393 hasRelatedWork W1970821617 @default.
• W4283731393 hasRelatedWork W2044392877 @default.
• W4283731393 hasRelatedWork W2058589890 @default.

• next