Meteorological Analysis (CMA) Data along the Northern Gulf Coast between April 2015 and December 2016
No. of Downloads:
No. of Files:
Gulf of Mexico Research Initiative (GoMRI)
Consortium for Oil Spill Exposure Pathways in Coastal River-Dominated Ecosystems (CONCORDE)
Mississippi State University / Geosystems Research Institute
2015-04-01 to 2016-12-31
Atmospheric Forcing, Meteorological Analysis
A gridded hourly database at 0.01 degree spacing for the northern Gulf of Mexico, the CONCORDE Meteorological Analysis (CMA), was developed from April 2015 to December 2016. It combines 4 data sources to create 13 meteorological parameters in NetCDF format. Data sources include: the Real-Time Mesoscale Analysis (RTMA), the North American Mesoscale Forecast System (NAM), the Next Generation Weather Radar (NEXRAD), and the Advanced Very High Resolution Radiometer (AVHRR). These data sources provide 2-meter temperature, relative humidity, surface pressure, 10-meter u and v wind components, sea surface temperature, cloud fraction, downward shortwave radiation flux, downward longwave radiation flux and one hour precipitation total. Additional parameters including the surface u and v momentum stresses, and sensible heat flux, were calculated using the Florida State University’s Coupled Ocean-Atmosphere Response Experiment (COARE) algorithm.
Patrick Fitzpatrick and Yee Lau. 2019. Meteorological Analysis (CMA) Data along the Northern Gulf Coast between April 2015 and December 2016. Distributed by: Gulf of Mexico Research Initiative Information and Data Cooperative (GRIIDC), Harte Research Institute, Texas A&M University–Corpus Christi. doi:10.7266/N72F7KHX
The necessity of high-resolution atmospheric forcing for the ocean model development motivated the creation of a long-term dataset known as the CONCORDE Meteorological Analysis (CMA).
Data Parameters and Units:
Latitude (decimal degrees), longitude (decimal degrees), time (seconds since 1970-1-1 00:00:00), 2-meter temperature (Celsius), relative humidity (rha, Percentage), surface pressure (mb), 10-meter u and v wind components (m/s), sea surface temperature (sst, Celsius), cloud fraction (decimal), downward shortwave radiation flux (DSWRF, Watts/m2), downward longwave radiation flux (DLWRF, Watts/m2), one hour precipitation total (precip1Hr, mm/hr), surface u and v momentum stresses (N/m2), and sensible heat flux (Watts/m2).
The necessity of high-resolution atmospheric forcing for the ocean model development motivated the creation of a long-term dataset known as the CONCORDE Meteorological Analysis (CMA). CMA consists of hourly reanalysis of 2-m temperature, relative humidity, surface pressure, 10-m u and v wind components, sea surface temperature, cloud fraction, downward shortwave radiation flux, downward longwave radiation flux, 1-h precipitation total, surface u and v momentum stresses, and sensible heat flux at 0.01° grid spacing. The surface momentum and thermodynamic fields are processed from the Real-Time Mesoscale Analysis (RTMA). RTMA (De Pondeca et al. 2011) is a 2.5-km 2DVAR product which uses the High Resolution Rapid Refresh (HRRR) model forecast (Benjamin et al. 2016) for the background field, adjusted with surface observations with a non-isotropic recursive filter (Purser et al. 2003). The RTMA data assimilation procedure matches observed surface data, captures mesoscale and diurnal patterns, and provides a coherent field that best matches the geographically-related features. CMA’s radiation parameters and total cloud cover percentage are from North American Mesoscale Forecast System (NAM) forecast fields. The Next Generation Weather Radar Level-III provides the hourly precipitation totals on a 0.24-km by 1° grid based on a dual polarization Quantitative Precipitation Estimate (QPE) algorithm. Radar data from top of each hour are used from Slidell, LA or Mobile, AL are used based on closer proximity to the analysis grid point, then interpolated by nearest neighbor. The Advanced Very High Resolution Radiometer provides daily sea surface temperature in 1.09 km resolution. Quality control is applied and a 10-day running mean was used. The Florida State University’s Coupled Ocean-Atmosphere Response Experiment flux algorithm calculates the sensible heat flux and surface momentum stresses. All data is either bilinearly interpolated or uses near-neighbor. The CMA dataset begins in April 2015 and covers through December 2016.
Provenance and Historical References:
This dataset was initially accepted on 2019-02-11 and was updated on 2019-02-28. The values in the variables "surfaceVMomentumStress" and "surfaceUMomentumStress" were modified to correct an error in which the output was always absolute values. Benjamin, S.G., Weygandt, S.S., Brown, J.M., Hu, M., Alexander, C.R., Smirnova, T.G., Olson, J.B., James, E.P., Dowell, D.C., Grell, G.A., Lin, H., Peckham, S.E., Smith, T.L., Moninger, W.R., & Kenyon, J.S. (2016). A North American Hourly Assimilation and Model Forecast Cycle: The Rapid Refresh. Mon. Wea. Rev., 144, 1669-1694 doi: 10.1175/MWR-D-15-0242.1 De Pondeca, M.S., Manikin, G.S., DiMego, G., Benjamin, S.G., Parrish, D.F., Purser, R.J., Wu, W., Horel, J.D., Myrick, D.T., Lin, Y., Aune, R.M., Keyser, D., Colman, B., Mann, G., & Vavra, J. (2011). The Real-Time Mesoscale Analysis at NOAA's National Centers for Environmental Prediction: Current Status and Development. Wea. Forecasting, 26, 593-612 doi: 10.1175/WAF-D-10-05037.1 Purser, R.J., Wu, W., Parrish, D.F., & Roberts, N.M. (2003). Numerical Aspects of the Application of Recursive Filters to Variational Statistical Analysis, Part I: Spatially Homogeneous and Isotropic Gaussian Covariances. Mon. Wea. Rev., 131, 1524-1535 doi: 10.1175//1520-0493(2003)131<1524:NAOTAO>2.0.CO;2
Parra, S. M., Sanial, V., Boyette, A. D., Cambazoglu, M. K., Soto, I. M., Greer, A. T., … Dinniman, M. S. (2020). Bonnet Carré Spillway freshwater transport and corresponding biochemical properties in the Mississippi Bight. Continental Shelf Research, 199, 104114. doi:10.1016/j.csr.2020.104114
Greer, A. T., Boyette, A. D., Cruz, V. J., Cambazoglu, M. K., Dzwonkowski, B., Chiaverano, L. M., … Wiggert, J. D. (2020). Contrasting fine‐scale distributional patterns of zooplankton driven by the formation of a diatom‐dominated thin layer. Limnology and Oceanography. doi:10.1002/lno.11450