A case study of GOES-15 imager bias characterization with a numerical weather prediction model

doi:10.1007/s11707-016-0579-y

Front. Earth Sci.

2016, Vol. 10

Issue (3) : 409-418 https://doi.org/10.1007/s11707-016-0579-y

RESEARCH ARTICLE

A case study of GOES-15 imager bias characterization with a numerical weather prediction model

Lu REN(

)

Department of Climate and Space Sciences and Engineering, University of Michigan, MI 48109, USA

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Abstract

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.

Keywords data assimilation NWP GOES imager bias

Corresponding Author(s): Lu REN

Just Accepted Date: 16 March 2016 Online First Date: 13 April 2016 Issue Date: 20 June 2016

Cite this article:

Lu REN. A case study of GOES-15 imager bias characterization with a numerical weather prediction model[J]. Front. Earth Sci., 2016, 10(3): 409-418.

URL:

https://academic.hep.com.cn/fesci/EN/10.1007/s11707-016-0579-y
https://academic.hep.com.cn/fesci/EN/Y2016/V10/I3/409

Fig.1 The maximum coverage (dashed) and the coverage with the pixel distortion index less than 3 (solid) of GOES-15 imager in 2012. The sub-satellite point is at 135°W.

Fig.2 Weighting functions (WFs) of the GOES-15 imager channels at 3.9 µm (black solid), 6.5 µm (black dashed), 10.7 µm (gray solid), and 13.3 µm (gray dashed), calculated by the Community Radiative Transfer Model (CRTM) based on the US standard atmosphere.

Fig.3 Cloud fraction based on MODIS Level 2 cloud products at (a) 0600UTC, June 23, 2012, and (b) 0605UTC, June 23, 2012. The corresponding pixels with f^cld<10% are respectively shown in (c) and (d).

Fig.4 The observed brightness temperature of GOES-15 imager channels 2, 3, 4, and 6 over the same region covered by MODIS in Fig. 3(a).

Fig.5 The observed brightness temperature of GOES-15 imager channels 2, 3, 4, and 6 over the same region covered by MODIS in Fig. 3(b).

Fig.6 The histogram of the collocated GOES-15 Imager clear-sky pixels with different sensor zenith angles (48,642 pixels total).

Fig.7 The bias (solid) and standard deviation (dashed) of ( T b o b s − T b s i m ) (Unit: K) for GOES-15 channels 2, 3, 4, and 6 when using the GFS analysis (black) or the ERA-Interim reanalysis (grey) as the background.

Fig.8 The histogram of ( T b o b s − T b s i m ) (Unit: K) for GOES-15 channels 2, 3, 4, and 6 if using the GFS analysis (black) or the ERA-Interim reanalysis (grey) as the background.

Fig.9 The dependence of ( T b o b s − T b s i m ) on observed brightness temperature (Unit: K) for GOES-15 channels 2, 3, 4, and 6 when using the GFS analysis as the background.

Fig.10 The dependence of ( T b o b s − T b s i m ) on observed brightness temperature (Unit: K) for GOES-15 channels 2, 3, 4, and 6 when using the ERA-Interim reanalysis as the background.

Fig.11 The dependence of ( T b o b s − T b s i m ) on sensor zenith angle (Unit: degree) for GOES-15 channels 2, 3, 4, and 6 when using the GFS analysis as the background. Colors show the number of observations.

Fig.12 The dependence of ( T b o b s − T b s i m ) on sensor zenith angle (Unit: degree) for GOES-15 channels 2, 3, 4, and 6 when using the ERA-Interim reanalysis as the background. Colors show the number of observations.

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 https://doi.org/10.1002/qj.56
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 https://doi.org/10.1175/JTECH-D-12-00267.1
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 https://doi.org/10.1364/JOSA.44.000838
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 https://doi.org/10.1080/2150704X.2015.1066522
6	Da C, Zou X (2014). An introduction to GOES imager data. Advances in Met S&T, https://doi.org/10.3969/j.issn.2095-1973.2014.04.009
7	Dee D P (2005). Bias and data assimilation. Q J R Meteorol Soc, 13 1(613): 3323–3343 https://doi.org/10.1256/qj.05.137
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 https://doi.org/10.1029/2006JD008208
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 https://doi.org/10.1109/TGRS.2013.2238544
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 https://doi.org/10.1256/qj.02.230
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 https://doi.org/10.1175/MWR-D-12-00079.1
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 https://doi.org/10.1002/asl.98
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 https://doi.org/10.1364/AO.36.006895

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