FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4283699647> ?p ?o ?g. }

• W4283699647 endingPage "1382" @default.
• W4283699647 startingPage "1365" @default.
• W4283699647 abstract "Abstract Conventional statistical postprocessing techniques offer limited ability to improve the skills of probabilistic guidance for heavy precipitation. This paper introduces two artificial neural network (ANN)-based, geographically aware, and computationally efficient postprocessing schemes, namely, the ANN-multiclass (ANN-Mclass) and the ANN–censored, shifted gamma distribution (ANN-CSGD). Both schemes are implemented to postprocess Global Ensemble Forecast System (GEFS) forecasts to produce probabilistic quantitative precipitation forecasts (PQPFs) over the contiguous United States (CONUS) using a short (60 days), rolling training window. The performances of these schemes are assessed through a set of hindcast experiments, wherein postprocessed 24-h PQPFs from the two ANN schemes were compared against those produced using the benchmark quantile mapping algorithm for lead times ranging from 1 to 8 days. Outcomes of the hindcast experiments show that ANN schemes overall outperform the benchmark as well as the raw forecast over the CONUS in predicting probability of precipitation over a range of thresholds. The relative performance varies among geographic regions, with the two ANN schemes broadly improving upon quantile mapping over the central, south, and southeast, and slightly underperforming along the Pacific coast where skills of raw forecasts are the highest. Between the two schemes, the hybrid ANN-CSGD outperforms at higher rainfall thresholds (i.e., >50 mm day −1 ), though the outperformance comes at a slight expense of sharpness and spatial specificity. Collectively, these results confirm the ability of the ANN algorithms to produce skillful PQPFs with a limited training window and point to the prowess of the hybrid scheme for calibrating PQPFs for rare-to-extreme rainfall events." @default.
• W4283699647 created "2022-06-30" @default.
• W4283699647 creator A5042943987 @default.
• W4283699647 creator A5069325704 @default.
• W4283699647 creator A5071773009 @default.
• W4283699647 creator A5075733431 @default.
• W4283699647 creator A5077068160 @default.
• W4283699647 date "2022-09-01" @default.
• W4283699647 modified "2023-10-16" @default.
• W4283699647 title "Improving Probabilistic Quantitative Precipitation Forecasts Using Short Training Data through Artificial Neural Networks" @default.
• W4283699647 cites W1482914888 @default.
• W4283699647 cites W1951366072 @default.
• W4283699647 cites W1966043936 @default.
• W4283699647 cites W1966334841 @default.
• W4283699647 cites W1984113680 @default.
• W4283699647 cites W1989872370 @default.
• W4283699647 cites W1992962241 @default.
• W4283699647 cites W2013765062 @default.
• W4283699647 cites W2047323835 @default.
• W4283699647 cites W2047634553 @default.
• W4283699647 cites W2049113840 @default.
• W4283699647 cites W2063838379 @default.
• W4283699647 cites W2073241381 @default.
• W4283699647 cites W2120933360 @default.
• W4283699647 cites W2128469217 @default.
• W4283699647 cites W2128580816 @default.
• W4283699647 cites W2146193817 @default.
• W4283699647 cites W2153900817 @default.
• W4283699647 cites W2162852329 @default.
• W4283699647 cites W2172654556 @default.
• W4283699647 cites W2329659416 @default.
• W4283699647 cites W2481365208 @default.
• W4283699647 cites W2596546901 @default.
• W4283699647 cites W2612648799 @default.
• W4283699647 cites W2745823364 @default.
• W4283699647 cites W2801493395 @default.
• W4283699647 cites W2803408063 @default.
• W4283699647 cites W2806888261 @default.
• W4283699647 cites W2897127991 @default.
• W4283699647 cites W2970178550 @default.
• W4283699647 cites W2980462578 @default.
• W4283699647 cites W3042212860 @default.
• W4283699647 cites W3046652087 @default.
• W4283699647 cites W3073871350 @default.
• W4283699647 cites W3101340403 @default.
• W4283699647 cites W3108051726 @default.
• W4283699647 cites W3121536461 @default.
• W4283699647 cites W3140878669 @default.
• W4283699647 cites W3143949042 @default.
• W4283699647 cites W3177326293 @default.
• W4283699647 cites W3206359964 @default.
• W4283699647 cites W4200309788 @default.
• W4283699647 cites W4226405133 @default.
• W4283699647 doi "https://doi.org/10.1175/jhm-d-22-0021.1" @default.
• W4283699647 hasPublicationYear "2022" @default.
• W4283699647 type Work @default.
• W4283699647 citedByCount "0" @default.
• W4283699647 crossrefType "journal-article" @default.
• W4283699647 hasAuthorship W4283699647A5042943987 @default.
• W4283699647 hasAuthorship W4283699647A5069325704 @default.
• W4283699647 hasAuthorship W4283699647A5071773009 @default.
• W4283699647 hasAuthorship W4283699647A5075733431 @default.
• W4283699647 hasAuthorship W4283699647A5077068160 @default.
• W4283699647 hasConcept C105795698 @default.
• W4283699647 hasConcept C107054158 @default.
• W4283699647 hasConcept C118671147 @default.
• W4283699647 hasConcept C119857082 @default.
• W4283699647 hasConcept C121332964 @default.
• W4283699647 hasConcept C13280743 @default.
• W4283699647 hasConcept C140178040 @default.
• W4283699647 hasConcept C153294291 @default.
• W4283699647 hasConcept C154945302 @default.
• W4283699647 hasConcept C159985019 @default.
• W4283699647 hasConcept C170061395 @default.
• W4283699647 hasConcept C185798385 @default.
• W4283699647 hasConcept C192562407 @default.
• W4283699647 hasConcept C204323151 @default.
• W4283699647 hasConcept C205649164 @default.
• W4283699647 hasConcept C2777211547 @default.
• W4283699647 hasConcept C33923547 @default.
• W4283699647 hasConcept C41008148 @default.
• W4283699647 hasConcept C49937458 @default.
• W4283699647 hasConcept C50644808 @default.
• W4283699647 hasConcept C83002819 @default.
• W4283699647 hasConceptScore W4283699647C105795698 @default.
• W4283699647 hasConceptScore W4283699647C107054158 @default.
• W4283699647 hasConceptScore W4283699647C118671147 @default.
• W4283699647 hasConceptScore W4283699647C119857082 @default.
• W4283699647 hasConceptScore W4283699647C121332964 @default.
• W4283699647 hasConceptScore W4283699647C13280743 @default.
• W4283699647 hasConceptScore W4283699647C140178040 @default.
• W4283699647 hasConceptScore W4283699647C153294291 @default.
• W4283699647 hasConceptScore W4283699647C154945302 @default.
• W4283699647 hasConceptScore W4283699647C159985019 @default.
• W4283699647 hasConceptScore W4283699647C170061395 @default.
• W4283699647 hasConceptScore W4283699647C185798385 @default.
• W4283699647 hasConceptScore W4283699647C192562407 @default.
• W4283699647 hasConceptScore W4283699647C204323151 @default.

• next