FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4317233487> ?p ?o ?g. }

• W4317233487 abstract "<strong class=journal-contentHeaderColor>Abstract.</strong> Downward longwave radiation (DLR) affects energy exchange between the land surface and the atmosphere, and plays an important role in weather forecasting, agricultural activities, and the development of climate models. Because DLR is seldom observed at conventional radiation stations, numerous empirical parameterizations have been presented to estimate DLR from screen-level meteorological variables. The reliability and representativeness of parameterization depend on the coefficients regressed from the simultaneous observations of DLR and meteorological variables. Only a few previous studies have attempted to build parameterizations over regions in China such as the Tibetan Plateau and East China. In this study, a long-term (2011–2022) hourly dataset of DLR and meteorological elements, obtained from seven stations of the China Baseline Surface Radiation Network, was used to recalculate the coefficients of the Brunt and Weng models, and to develop a new model. Results showed that the mean bias error (MBE) and relative MBE (rMBE) between the measured clear-sky DLR and that estimated using the Brunt, Weng, and new models were −4.3, −5.1, and 3.7 W m⁻² and −1.5 %, −1.8 %, and 1.3 %, respectively. The root mean squared errors (RMSEs) where in the range of 13.8–14.3 W m⁻² and the relative RMSEs (rRMSEs) were approximately 5.0 %. The MBEs (rMBEs) of the Brunt, Weng, and new models under all-sky conditions were −2.8 W m⁻² (−1.0 %), −6.1 W m⁻² (−2.1 %), and −1.5 W m⁻² (−0.5 %), respectively. The RMSE (rRMSE) of the parameterization models in retrieving all-sky DLR was ~17.5 W m⁻² (~6.1 %). Therefore, the models are considered suitable for retrieval of DLR over China." @default.
• W4317233487 created "2023-01-18" @default.
• W4317233487 date "2023-01-18" @default.
• W4317233487 modified "2023-10-17" @default.
• W4317233487 title "Comment on acp-2022-794" @default.
• W4317233487 doi "https://doi.org/10.5194/acp-2022-794-rc1" @default.
• W4317233487 hasPublicationYear "2023" @default.
• W4317233487 type Work @default.
• W4317233487 citedByCount "0" @default.
• W4317233487 crossrefType "peer-review" @default.
• W4317233487 hasBestOaLocation W43172334871 @default.
• W4317233487 hasConcept C121332964 @default.
• W4317233487 hasConcept C153294291 @default.
• W4317233487 hasConcept C205649164 @default.
• W4317233487 hasConcept C33923547 @default.
• W4317233487 hasConcept C39432304 @default.
• W4317233487 hasConcept C65440619 @default.
• W4317233487 hasConcept C73329638 @default.
• W4317233487 hasConcept C91586092 @default.
• W4317233487 hasConceptScore W4317233487C121332964 @default.
• W4317233487 hasConceptScore W4317233487C153294291 @default.
• W4317233487 hasConceptScore W4317233487C205649164 @default.
• W4317233487 hasConceptScore W4317233487C33923547 @default.
• W4317233487 hasConceptScore W4317233487C39432304 @default.
• W4317233487 hasConceptScore W4317233487C65440619 @default.
• W4317233487 hasConceptScore W4317233487C73329638 @default.
• W4317233487 hasConceptScore W4317233487C91586092 @default.
• W4317233487 hasLocation W43172334871 @default.
• W4317233487 hasOpenAccess W4317233487 @default.
• W4317233487 hasPrimaryLocation W43172334871 @default.
• W4317233487 hasRelatedWork W1995391060 @default.
• W4317233487 hasRelatedWork W2024095558 @default.
• W4317233487 hasRelatedWork W2046970602 @default.
• W4317233487 hasRelatedWork W2074286478 @default.
• W4317233487 hasRelatedWork W2089020205 @default.
• W4317233487 hasRelatedWork W2365757842 @default.
• W4317233487 hasRelatedWork W2558847511 @default.
• W4317233487 hasRelatedWork W2748952813 @default.
• W4317233487 hasRelatedWork W2899084033 @default.
• W4317233487 hasRelatedWork W3023536432 @default.
• W4317233487 isParatext "false" @default.
• W4317233487 isRetracted "false" @default.
• W4317233487 workType "peer-review" @default.