Studying Anomalous Discrepancies between MODIS and CALIOP Cloud Observations CyberTraining: Big Data + High-Performance Computing + Atmospheric Sciences
| dc.contributor.author | Abraham, Christine | |
| dc.contributor.author | Norman, Olivia | |
| dc.contributor.author | Shepherd, Erick | |
| dc.contributor.author | Zheng, Jianyu | |
| dc.contributor.author | Zhang, Zhibo | |
| dc.date.accessioned | 2021-03-31T18:34:53Z | |
| dc.date.available | 2021-03-31T18:34:53Z | |
| dc.date.issued | 2020 | |
| dc.description.abstract | When examining collocated data from the A-Train satellite constellation, there are a notable number of clouds that CALIOP identifies as transparent but MODIS paradoxically reports as having a high cloud optical thickness (COT), therefore implying that the cloud is opaque. We refer to these as ”anomalous transparent clouds”. Our team is investigating two hypotheses in an effort to explain the occurrence of these anomalies. The first hypothesis is that the anomalies could be MODIS COT retrieval errors due to the misclassification of high albedo surfaces, such as snow and sea ice, as clouds. The other hypothesis is that the anomalies could be clouds which are misidentified as having a high COT due to 3D radiative effects. The former hypothesis was tested by collocating the single-layer cloud anomalies that were over water with NSIDC AMSRE sea ice observations using a k-nearest neighbors (k-NN) algorithm. We determined that around 50% of such anomalies occur over areas with high sea ice concentrations (95-100%). Further research is required to account for the other half of the anomalies that showed no correlation with high albedo surfaces. We have taken preliminary steps toward exploring whether the cloud 3D radiative effects hypothesis might explain the remaining anomalies. | en_US |
| dc.description.sponsorship | This work is supported by the grant “CyberTraining: DSE: Cross-Training of Researchers in Computing, Applied Mathematics and Atmospheric Sciences using Advanced Cyberinfrastructure Resources” from the National Science Foundation (grant no. OAC–1730250). The hardware used in the computational studies is part of the UMBC High Performance Computing Facility (HPCF). The facility is supported by the U.S. National Science Foundation through the MRI program (grant nos. CNS–0821258, CNS–1228778, and OAC–1726023) and the SCREMS program (grant no. DMS–0821311), with additional substantial support from the University of Maryland, Baltimore County (UMBC). See hpcf.umbc.edu for more information on HPCF and the projects using its resources. | en_US |
| dc.description.uri | http://hpcf-files.umbc.edu/research/papers/CT2020Team2.pdf | en_US |
| dc.format.extent | 11 pages | en_US |
| dc.genre | technical reports | en_US |
| dc.identifier | doi:10.13016/m2wegr-famv | |
| dc.identifier.citation | Abraham, Christine; Norman, Olivia; Shepherd, Erick; Zheng, Jianyu; Zhang, Zhibo; Studying Anomalous Discrepancies between MODIS and CALIOP Cloud Observations CyberTraining: Big Data + High-Performance Computing + Atmospheric Sciences; http://hpcf-files.umbc.edu/research/papers/CT2020Team2.pdf | en_US |
| dc.identifier.uri | http://hdl.handle.net/11603/21269 | |
| dc.language.iso | en_US | en_US |
| dc.publisher | UMBC HPCF | en_US |
| dc.relation.isAvailableAt | The University of Maryland, Baltimore County (UMBC) | |
| dc.relation.ispartof | UMBC Information Systems Department Collection | |
| dc.relation.ispartof | UMBC Physics Department | |
| dc.relation.ispartof | UMBC Faculty Collection | |
| dc.relation.ispartof | UMBC Student Collection | |
| dc.relation.ispartofseries | HPCF;2020–12 | |
| dc.rights | This item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author. | |
| dc.subject | UMBC High Performance Computing Facility (HPCF) | |
| dc.title | Studying Anomalous Discrepancies between MODIS and CALIOP Cloud Observations CyberTraining: Big Data + High-Performance Computing + Atmospheric Sciences | en_US |
| dc.type | Text | en_US |