A case study of GOES-15 imager bias characterization with a numerical weather prediction model
Lu REN
A case study of GOES-15 imager bias characterization with a numerical weather prediction model
The infrared imager onboard the Geostationary Operational Environmental Satellite 15 (GOES-15) provides temporally continuous observations over a limited spatial domain. To quantify bias of the GOES-15 imager, observations from four infrared channels (2, 3, 4, and 6) are compared with simulations from the numerical weather prediction model and radiative transfer model. One-day clear-sky infrared observations from the GOES-15 imager over an oceanic domain during nighttime are selected. Two datasets, Global Forecast System (GFS) analysis and ERA-Interim reanalysis, are used as inputs to the radiative transfer model. The results show that magnitudes of biases for the GOES-15 surface channels are approximately 1 K using two datasets, whereas the magnitude of bias for the GOES-15 water vapor channel can reach 5.5 K using the GFS dataset and 2.5 K using the ERA dataset. The GOES-15 surface channels show positive dependencies on scene temperature, whereas the water vapor channel has a weak dependence on scene temperature. The strong dependence of bias on sensor zenith angle for the GOES-15 water vapor channel using GFS analysis implies large biases might exist in GFS water vapor profiles.
data assimilation / NWP / GOES imager / bias
[1] |
Auligné T, McNally A P, Dee D P (2007). Adaptive bias correction for satellite data in a numerical weather prediction system. Q J R Meteorol Soc, 133(624): 631–642
CrossRef
Google scholar
|
[2] |
Capderou M (2005). Satellites: Orbits and Missions. Berlin: Springer, 364
|
[3] |
Chen Y, Han Y, van Delst P, Weng F (2013). Assessment of shortwave infrared sea surface reflection and nonlocal thermodynamic equilibrium effects in the community radiative transfer model using IASI data. J Atmos Ocean Technol, 30(9): 2152–2160
CrossRef
Google scholar
|
[4] |
Cox C, Munk W (1954). Measurements of the roughness of the sea surface from photographs of the sun’s glitter. J Opt Soc Am, 44(11): 838–850
CrossRef
Google scholar
|
[5] |
Da C (2015). Preliminary Assessment of the Advanced Himawari Imager (AHI) Measurement onboard Himawari-8 Geostationary Satellite. Remote Sens Lett, 6(8): 637–646
CrossRef
Google scholar
|
[6] |
Da C, Zou X (2014). An introduction to GOES imager data. Advances in Met S&T,
CrossRef
Google scholar
|
[7] |
Dee D P (2005). Bias and data assimilation. Q J R Meteorol Soc, 13 1(613): 3323–3343
CrossRef
Google scholar
|
[8] |
Han Y, Weng F, Liu Q, van Delst P (2007). A fast radiative transfer model for SSMIS upper atmosphere sounding channels. J Geophys Res, 112(D11): 121
CrossRef
Google scholar
|
[9] |
Hewison T J, Wu X, Yu F, Tahara Y, Hu X, Kim D, Koenig M (2013). GSICS inter-calibration of infrared channels of geostationary imagers using Metop/IASI. IEEE Trans Geosci Rem Sens, 51(3): 1160–1170
CrossRef
Google scholar
|
[10] |
Köpken C, Kelly G, Thépaut J N (2004). Assimilation of m<?Pub Caret?>eteosat radiance data within the 4D‐Var system at ECMWF: assimilation experiments and forecast impact. Q J R Meteorol Soc, 130(601): 2277–2292
CrossRef
Google scholar
|
[11] |
Menzel P, Schmetz J, Nieman S, van de Berg L, Gaertner V, Schmit T (1993). Intercomparison of the Operational Calibration of GOES-7 and Meteosat-3/4. US Department of Commerce, National Oceanic and Atmospheric Administration, National Environmental Satellite, Data, and Information Service
|
[12] |
Qin Z, Zou X, Weng F (2013). Evaluating added benefits of assimilating GOES imager radiance data in GSI for coastal QPFs. Mon Weather Rev, 141(1): 75–92
CrossRef
Google scholar
|
[13] |
Schmit T, Gunshor M, Fu G, Rink T, Bah K (2012). GOES-R Advanced Baseline Imager (ABI) algorithm theoretical basis document for cloud and moisture imagery product (CMIP), Version 3.0
|
[14] |
Su X, Derber J C, Jung J A, Tahara Y (2003). The usage of GOES imager clear sky brightness temperatures in the NCEP global data assimilation system. Preprints, 12th Conf. On Satellite Meteorology and Oceanography, Long Beach, CA, American Meteorological Society
|
[15] |
Szyndel M D E, Kelly G, Thépaut J N (2005). Evaluation of potential benefit of assimilation of SEVIRI water vapour radiance data from meteosat‐8 into global numerical weather prediction analyses. Atmos Sci Lett, 6(2): 105–111
CrossRef
Google scholar
|
[16] |
Weinreb M, Jamieson M, Fulton N, Chen Y, Johnson J X, Bremer J, Smith C, Baucom J (1997). Operational calibration of geostationary operational environmental satellite-8 and-9 imagers and sounders. Appl Opt, 36(27): 6895–6904
CrossRef
Google scholar
|
/
〈 | 〉 |