Articles | Volume 10, issue 9
https://doi.org/10.5194/gmd-10-3425-2017
https://doi.org/10.5194/gmd-10-3425-2017
Development and technical paper
 | 
19 Sep 2017
Development and technical paper |  | 19 Sep 2017

Analysis of errors introduced by geographic coordinate systems on weather numeric prediction modeling

Yanni Cao, Guido Cervone, Zachary Barkley, Thomas Lauvaux, Aijun Deng, and Alan Taylor

Related authors

Quantifying methane emissions from natural gas production in north-eastern Pennsylvania
Zachary R. Barkley, Thomas Lauvaux, Kenneth J. Davis, Aijun Deng, Natasha L. Miles, Scott J. Richardson, Yanni Cao, Colm Sweeney, Anna Karion, MacKenzie Smith, Eric A. Kort, Stefan Schwietzke, Thomas Murphy, Guido Cervone, Douglas Martins, and Joannes D. Maasakkers
Atmos. Chem. Phys., 17, 13941–13966, https://doi.org/10.5194/acp-17-13941-2017,https://doi.org/10.5194/acp-17-13941-2017, 2017
Short summary

Related subject area

Atmospheric sciences
SLUCM+BEM (v1.0): a simple parameterisation for dynamic anthropogenic heat and electricity consumption in WRF-Urban (v4.3.2)
Yuya Takane, Yukihiro Kikegawa, Ko Nakajima, and Hiroyuki Kusaka
Geosci. Model Dev., 17, 8639–8664, https://doi.org/10.5194/gmd-17-8639-2024,https://doi.org/10.5194/gmd-17-8639-2024, 2024
Short summary
NAQPMS-PDAF v2.0: a novel hybrid nonlinear data assimilation system for improved simulation of PM2.5 chemical components
Hongyi Li, Ting Yang, Lars Nerger, Dawei Zhang, Di Zhang, Guigang Tang, Haibo Wang, Yele Sun, Pingqing Fu, Hang Su, and Zifa Wang
Geosci. Model Dev., 17, 8495–8519, https://doi.org/10.5194/gmd-17-8495-2024,https://doi.org/10.5194/gmd-17-8495-2024, 2024
Short summary
Source-specific bias correction of US background and anthropogenic ozone modeled in CMAQ
T. Nash Skipper, Christian Hogrefe, Barron H. Henderson, Rohit Mathur, Kristen M. Foley, and Armistead G. Russell
Geosci. Model Dev., 17, 8373–8397, https://doi.org/10.5194/gmd-17-8373-2024,https://doi.org/10.5194/gmd-17-8373-2024, 2024
Short summary
Observational operator for fair model evaluation with ground NO2 measurements
Li Fang, Jianbing Jin, Arjo Segers, Ke Li, Ji Xia, Wei Han, Baojie Li, Hai Xiang Lin, Lei Zhu, Song Liu, and Hong Liao
Geosci. Model Dev., 17, 8267–8282, https://doi.org/10.5194/gmd-17-8267-2024,https://doi.org/10.5194/gmd-17-8267-2024, 2024
Short summary
Valid time shifting ensemble Kalman filter (VTS-EnKF) for dust storm forecasting
Mijie Pang, Jianbing Jin, Arjo Segers, Huiya Jiang, Wei Han, Batjargal Buyantogtokh, Ji Xia, Li Fang, Jiandong Li, Hai Xiang Lin, and Hong Liao
Geosci. Model Dev., 17, 8223–8242, https://doi.org/10.5194/gmd-17-8223-2024,https://doi.org/10.5194/gmd-17-8223-2024, 2024
Short summary

Cited articles

Barkley, Z. R., Lauvaux, T., Davis, K. J., Deng, A., Cao, Y., Sweeney, C., Martins, D., Miles, N. L., Richardson, S. J., Murphy, T., Cervone, G., Karion, A., Schwietzke, S., Smith, M., Kort, E. A., and Maasakkers, J. D.: Quantifying methane emissions from natural gas production in northeastern Pennsylvania, Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2017-200, in review, 2017.
Bugayevskiy, L. M. and Snyder, J.: Map projections: A reference manual, CRC Press, Philadelphia, USA, 1995.
Chen, F. and Dudhia, J.: Coupling an advanced land surface-hydrology model with the Penn State-NCAR MM5 modeling system. Part I: Model implementation and sensitivity, Mon. Weather Rev., 129, 569–585, 2001.
Cao, Y. and Cervone, G.: WRF processing, available at: https://github.com/yannicao/wrf_reprojection, last access: 22 July 2017.
David, C. H., Gochis, D. J., Maidment, D. R., Yu, W., Yates, D. N., and Yang, Z.-L.: Using NHDPlus as the Land Base for the Noah-distributed Model, Transactions in GIS, 13, 363–377, 2009.
Download
Short summary
This research investigates the role and importance of reprojecting geographic information system layers used by weather numerical models as input by performing sensitivity studies of greenhouse gas transport and dispersion in northeastern Pennsylvania. To bridge the gap between geographic information system data and atmospheric models, this study presents an innovative approach by creating R code to automatically generate model input from geographic data and analyze the model output.