FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4289205874> ?p ?o ?g. }

• W4289205874 endingPage "3631" @default.
• W4289205874 startingPage "3631" @default.
• W4289205874 abstract "Precipitation type is a key parameter used for better retrieval of precipitation characteristics as well as to understand the cloud–convection–precipitation coupling processes. Ice crystals and water droplets inherently exhibit different characteristics in different precipitation regimes (e.g., convection, stratiform), which reflect on satellite remote sensing measurements that help us distinguish them. The Global Precipitation Measurement (GPM) Core Observatory’s microwave imager (GMI) and dual-frequency precipitation radar (DPR) together provide ample information on global precipitation characteristics. As an active sensor, the DPR provides an accurate precipitation type assignment, while passive sensors such as the GMI are traditionally only used for empirical understanding of precipitation regimes. Using collocated precipitation type flags from the DPR as the “truth”, this paper employs machine learning (ML) models to train and test the predictability and accuracy of using passive GMI-only observations together with ancillary information from a reanalysis and GMI surface emissivity retrieval products. Out of six ML models, four simple ones (support vector machine, neural network, random forest, and gradient boosting) and the 1-D convolutional neural network (CNN) model are identified to produce 90–94% prediction accuracy globally for five types of precipitation (convective, stratiform, mixture, no precipitation, and other precipitation), which is much more robust than previous similar effort. One novelty of this work is to introduce data augmentation (subsampling and bootstrapping) to handle extremely unbalanced samples in each category. A careful evaluation of the impact matrices demonstrates that the polarization difference (PD), brightness temperature (Tc) and surface emissivity at high-frequency channels dominate the decision process, which is consistent with the physical understanding of polarized microwave radiative transfer over different surface types, as well as in snow and liquid clouds with different microphysical properties. Furthermore, the view-angle dependency artifact that the DPR’s precipitation flag bears with does not propagate into the conical-viewing GMI retrievals. This work provides a new and promising way for future physics-based ML retrieval algorithm development." @default.
• W4289205874 created "2022-08-01" @default.
• W4289205874 creator A5003917889 @default.
• W4289205874 creator A5009740524 @default.
• W4289205874 creator A5021699021 @default.
• W4289205874 creator A5037092171 @default.
• W4289205874 creator A5058750873 @default.
• W4289205874 creator A5061240396 @default.
• W4289205874 creator A5065732280 @default.
• W4289205874 creator A5073692611 @default.
• W4289205874 creator A5077262472 @default.
• W4289205874 creator A5080825791 @default.
• W4289205874 date "2022-07-29" @default.
• W4289205874 modified "2023-10-15" @default.
• W4289205874 title "A Comprehensive Machine Learning Study to Classify Precipitation Type over Land from Global Precipitation Measurement Microwave Imager (GPM-GMI) Measurements" @default.
• W4289205874 cites W1678356000 @default.
• W4289205874 cites W1941659294 @default.
• W4289205874 cites W1977412301 @default.
• W4289205874 cites W1986666017 @default.
• W4289205874 cites W2033877673 @default.
• W4289205874 cites W2035201210 @default.
• W4289205874 cites W2049271465 @default.
• W4289205874 cites W2087854442 @default.
• W4289205874 cites W2102406168 @default.
• W4289205874 cites W2111724319 @default.
• W4289205874 cites W2117190680 @default.
• W4289205874 cites W2129018774 @default.
• W4289205874 cites W2176872904 @default.
• W4289205874 cites W2490420619 @default.
• W4289205874 cites W2512425440 @default.
• W4289205874 cites W2521727227 @default.
• W4289205874 cites W2764025886 @default.
• W4289205874 cites W2766437349 @default.
• W4289205874 cites W2767638814 @default.
• W4289205874 cites W2794403946 @default.
• W4289205874 cites W2938652620 @default.
• W4289205874 cites W2972808069 @default.
• W4289205874 cites W2987994251 @default.
• W4289205874 cites W2995280114 @default.
• W4289205874 cites W3005414175 @default.
• W4289205874 cites W3009517970 @default.
• W4289205874 cites W3023696930 @default.
• W4289205874 cites W3110738151 @default.
• W4289205874 cites W3131139589 @default.
• W4289205874 cites W3167321938 @default.
• W4289205874 cites W3169741373 @default.
• W4289205874 cites W3181514878 @default.
• W4289205874 cites W3185550056 @default.
• W4289205874 cites W4211154273 @default.
• W4289205874 cites W4212883601 @default.
• W4289205874 cites W4213315021 @default.
• W4289205874 cites W4225717283 @default.
• W4289205874 cites W4254405095 @default.
• W4289205874 cites W4280565794 @default.
• W4289205874 cites W919401192 @default.
• W4289205874 doi "https://doi.org/10.3390/rs14153631" @default.
• W4289205874 hasPublicationYear "2022" @default.
• W4289205874 type Work @default.
• W4289205874 citedByCount "1" @default.
• W4289205874 countsByYear W42892058742023 @default.
• W4289205874 crossrefType "journal-article" @default.
• W4289205874 hasAuthorship W4289205874A5003917889 @default.
• W4289205874 hasAuthorship W4289205874A5009740524 @default.
• W4289205874 hasAuthorship W4289205874A5021699021 @default.
• W4289205874 hasAuthorship W4289205874A5037092171 @default.
• W4289205874 hasAuthorship W4289205874A5058750873 @default.
• W4289205874 hasAuthorship W4289205874A5061240396 @default.
• W4289205874 hasAuthorship W4289205874A5065732280 @default.
• W4289205874 hasAuthorship W4289205874A5073692611 @default.
• W4289205874 hasAuthorship W4289205874A5077262472 @default.
• W4289205874 hasAuthorship W4289205874A5080825791 @default.
• W4289205874 hasBestOaLocation W42892058741 @default.
• W4289205874 hasConcept C107054158 @default.
• W4289205874 hasConcept C121332964 @default.
• W4289205874 hasConcept C127313418 @default.
• W4289205874 hasConcept C153294291 @default.
• W4289205874 hasConcept C154945302 @default.
• W4289205874 hasConcept C29278236 @default.
• W4289205874 hasConcept C39432304 @default.
• W4289205874 hasConcept C41008148 @default.
• W4289205874 hasConcept C44838205 @default.
• W4289205874 hasConcept C50644808 @default.
• W4289205874 hasConcept C53802167 @default.
• W4289205874 hasConcept C62649853 @default.
• W4289205874 hasConcept C76155785 @default.
• W4289205874 hasConceptScore W4289205874C107054158 @default.
• W4289205874 hasConceptScore W4289205874C121332964 @default.
• W4289205874 hasConceptScore W4289205874C127313418 @default.
• W4289205874 hasConceptScore W4289205874C153294291 @default.
• W4289205874 hasConceptScore W4289205874C154945302 @default.
• W4289205874 hasConceptScore W4289205874C29278236 @default.
• W4289205874 hasConceptScore W4289205874C39432304 @default.
• W4289205874 hasConceptScore W4289205874C41008148 @default.
• W4289205874 hasConceptScore W4289205874C44838205 @default.
• W4289205874 hasConceptScore W4289205874C50644808 @default.
• W4289205874 hasConceptScore W4289205874C53802167 @default.
• W4289205874 hasConceptScore W4289205874C62649853 @default.
• W4289205874 hasConceptScore W4289205874C76155785 @default.

• next