Error model for the assimilation of cloud-affected infrared satellite observations in an ensemble data assimilation system

Quarterly Journal of the Royal Meteorological Society

Volumn 142, Issue 697, 2016, Pages 1797-1808

  Harnisch, F ^a   Weissmann, M ^a   Perianez A ^b,c,d  

^a UNIVERSITY OF MUNICH   (Germany)

^b DEUTSCHER WETTERDIENST   (Germany)

^c RIKEN   (Japan)

^d UNIVERSITY OF READING   (United Kingdom)

Author keywords

Cloud affected observations; Ensemble data assimilation; MSG SEVIRI; Non Gaussian behaviour; Observation error

Indexed keywords

ERRORS; GAUSSIAN DISTRIBUTION; GAUSSIAN NOISE (ELECTRONIC); IMAGE ENHANCEMENT; LUMINANCE; TEMPERATURE;

BRIGHTNESS TEMPERATURES; ENSEMBLE DATA ASSIMILATION; INFRARED BRIGHTNESS TEMPERATURE; INFRARED SATELLITES; METEOSAT SECOND GENERATION SPINNING ENHANCED VISIBLE AND INFRARED IMAGER (SEVIRI) (MSG); MSG SEVIRI; NON-GAUSSIAN; OBSERVATION ERRORS;

ERROR STATISTICS;

BRIGHTNESS TEMPERATURE; CLOUD; DATA ASSIMILATION; ENSEMBLE FORECASTING; ERROR ANALYSIS; METEOSAT; SEVIRI;

EID: 84964331192     PISSN: 00359009     EISSN: 1477870X     Source Type: Journal    
DOI: 10.1002/qj.2776     Document Type: Article

References (43)

1. Auligné T, McNally A, Dee D. 2007. Adaptive bias correction for satellite data in a numerical weather prediction system. Q. J. R. Meteorol. Soc. 133: 631-642.
2. Baldauf M, Seifert A, Förstner J, Majewski D, Raschendorfer M, Reinhardt T. 2011. Operational convective-scale numerical weather prediction with the COSMO Model: Description and sensitivities. Mon. Weather Rev. 139: 3887-3905.
3. Bauer P, Geer AJ, Lopez P, Salmond D. 2010. Direct 4D-Var assimilation of all-sky radiances. Part I: Implementation. Q. J. R. Meteorol. Soc. 136: 1868-1885.
4. Bauer P, Auligné T, Bell W, Geer A, Guidard V, Heilliette S, Kazumori M, Kim MJ, Liu EHC, McNally AP, Macpherson B, Okamoto K, Renshaw R, Riishøjgaard LP. 2011a. Satellite cloud and precipitation assimilation at operational NWP centres. Q. J. R. Meteorol. Soc. 137: 1934-1951.
5. Bauer P, Ohring G, Kummerow C, Auligne T. 2011b. Assimilating satellite observations of clouds and precipitation into NWP models. Bull. Am. Meteorol. Soc. 92: ES25-ES28.
6. Bierdel L, Friederichs P, Bentzien S. 2012. Spatial kinetic energy spectra in the convection-permitting limited-area NWP model COSMO-DE. Meteorol. Z. 21: 245-258.
7. Böhme T, Stapelberg S, Akkermans T, Crewell S, Fischer J, Reinhardt T, Seifert A, Selbach C, Van Lipzig N. 2011. Long-term evaluation of COSMO forecasting using combined observational data of the GOP period. Meteorol. Z. 20: 119-132.
8. Chambon P, Meunier LF, Guillaume F, Piriou JM, Roca R, Mahfouf JF. 2015. Investigating the impact of the water-vapour sounding observations from SAPHIR on board Megha-Tropiques for the ARPEGE global model. Q. J. R. Meteorol. Soc. 141: 1769-1779.
9. Chevallier F, Lopez P, Tompkins AM, Janisková M, Moreau E. 2004. The capability of 4D-Var systems to assimilate cloud-affected satellite infrared radiances. Q. J. R. Meteorol. Soc. 130: 917-932.
10. Cintineo R, Otkin JA, Xue M, Kong F. 2014. Evaluating the performance of planetary boundary layer and cloud microphysical parameterization schemes in convection-permitting ensemble forecasts using synthetic GOES-13 satellite observations. Mon. Weather Rev. 142: 163-182.
11. Dee D. 2005. Bias and data assimilation. Q. J. R. Meteorol. Soc. 131: 3323-3344.
12. Eikenberg S, Köhler C, Seifert A, Crewell S. 2015. How microphysical choices affect simulated infrared brightness temperatures. Atmos. Res. 156: 67-79.
13. English S, McNally T, Bormann N, Salonen K, Matricardi M, Horanyi A, Rennie M, Janisková M, Michele SD, Geer A, Tomaso ED, Cardinali C, de Rosnay P, Sabater JM, Bonavita M, Albergel C, Engelen R, Thépaut JN. 2013. Impact of Satellite Data, Technical Memorandum 711. ECMWF Reading, UK.
14. Errico RM, Bauer P, Mahfouf JF. 2007. Issues regarding the assimilation of cloud and precipitation data. J. Atmos. Sci. 64: 3785-3798.
15. Geer AJ, Baordo F. 2014. Improved scattering radiative transfer for frozen hydrometeors at microwave frequencies. Atmos. Meas. Tech. 6: 1839-1860, doi: 10.5194/amt-7-1839-2014.
16. Geer AJ, Bauer P. 2011. Observation errors in all-sky data assimilation. Q. J. R. Meteorol. Soc. 137: 2024-2037, doi: 10.1002/qj.830.
17. Geer AJ, Bauer P, Lopez P. 2010. Direct 4D-Var assimilation of all-sky radiances. Part II: Assessment. Q. J. R. Meteorol. Soc. 136: 1886-1905.
18. Geer AJ, Baordo F, Bormann N, English S. 2014. All-sky Assimilation of Microwave Humidity Sounders, Technical Memorandum 741. ECMWF Reading, UK.
19. Harnisch F, Keil C. 2015. Initial conditions for convective-scale ensemble forecasting provided by ensemble data assimilation. Mon. Weather Rev. 143: 1583-1600.
20. Hocking J, Rayer P, Saunders R, Matricardi M, Geer A, Brunet P. 2011. 'RTTOV v10 Users Guide', NWC SAF report. EUMETSAT: Darmstadt, Germany.
21. Hunt BR, Kostelich EJ, Szunyogh I. 2007. Efficient data assimilation for spatiotemporal chaos: A local ensemble transform Kalman filter. Physica D 230: 112-126.
22. Jones TA, Otkin JA, Stensrud DJ, Knopfmeier K. 2013. Assimilation of satellite infrared radiances and Doppler radar observations during a cool season Observing System Simulation Experiment. Mon. Weather Rev. 141: 3273-3299.
23. Kazumori M, Geer A, English S. 2016. Effects of all-sky assimilation of GCOM-W/AMSR2 radiances in the ECMWF numerical weather prediction system. Q. J. R. Meteorol. Soc. 142: 721-737, doi: 10.1002/qj.2669.
24. Kostka PM, Weissmann M, Buras R, Mayer B, Stiller O. 2014. Observation operator for visible and near-infrared satellite reflectances. J. Atmos. Oceanic Technol. 31: 1216-1233.
25. Lange H, Janjić T. 2016. Assimilation of mode-S EHS aircraft observations in COSMO-KENDA. Mon. Weather Rev., doi: 10.1175/MWR-D-15-0112.1.
26. Lorenc AC. 1986. Analysis methods for numerical weather prediction. Q. J. R. Meteorol. Soc. 112: 1177-1194.
27. McNally A. 2002. A note on the occurrence of cloud in meteorologically sensitive areas and the implications for advanced infrared sounders. Q. J. R. Meteorol. Soc. 128: 2551-2556.
28. McNally A. 2009. The direct assimilation of cloud-affected satellite infrared radiances in the ECMWF 4D-Var. Q. J. R. Meteorol. Soc. 135: 1214-1229.
29. Matricardi M. 2005. The Inclusion of Aerosols and Clouds in RTIASI, the ECMWF Fast Radiative Transfer Model for the Infrared Atmospheric Sounding Interferometer, Technical Memorandum 474. ECMWF Reading, UK.
30. Okamoto K. 2013. Assimilation of overcast cloudy infrared radiances of the geostationary MTSAT-1R imager. Q. J. R. Meteorol. Soc. 139: 715-730.
31. Okamoto K, McNally AP, Bell W. 2014. Progress towards the assimilation of all-sky infrared radiances: An evaluation of cloud effects. Q. J. R. Meteorol. Soc. 140: 1603-1614.
32. Otkin JA. 2012. Assimilation of water vapor sensitive infrared brightness-temperature observations during a high impact weather event. J. Geophys. Res. 117: D19, doi: 10.1029/2012JD017568.
33. Pangaud T, Fourrie N, Guidard V, Dahoui M, Rabier F. 2009. Assimilation of AIRS radiances affected by mid-to low-level clouds. Mon. Weather Rev. 137: 4276-4292.
34. Pavelin E, English S, Eyre J. 2008. The assimilation of cloud-affected infrared satellite radiances for numerical weather prediction. Q. J. R. Meteorol. Soc. 134: 737-749.
35. Schmetz J, Pili P, Tjemkes S, Just D, Kerkmann J, Rota S, Ratier A. 2002. An introduction to Meteosat second generation (MSG). Bull. Am. Meteorol. Soc. 83: 977-992.
36. Schomburg A, Schraff C, Potthast R. 2015. A concept for the assimilation of satellite cloud information in an Ensemble Kalman Filter: Single-observation experiments. Q. J. R. Meteorol. Soc. 141: 893-908, doi: 10.1002/qj.2407.
37. Schraff C, Reich H, Rhodin A, Schomburg A, Stephan K, Periáñez A, Potthast R. 2016. Kilometre-Scale Ensemble Data Assimilation for the COSMO model (KENDA). Q. J. R. Meteorol. Soc., doi: 10.1002/qj.2748.
38. Sommer M, Weissmann M. 2014. Observation impact in a convective-scale localized ensemble transform Kalman filter. Q. J. R. Meteorol. Soc. 140: 2672-2679.
39. Stengel M, Lindskog M, Undén P, Gustafsson N. 2013. The impact of cloud-affected IR radiances on forecast accuracy of a limited-area NWP model. Q.J.R. Meteorol. Soc. 139: 2081-2096.
40. Stengel M, Undén P, Lindskog M, Dahlgren P, Gustafsson N, Bennartz R. 2009. Assimilation of SEVIRI infrared radiances with HIRLAM 4D-Var. Q. J. R. Meteorol. Soc. 135: 2100-2109.
41. Vidot J, Baran AJ, Brunel P. 2015. A new ice cloud parameterization for infrared radiative transfer simulation of cloudy radiances: Evaluation and optimization with IIR observations and ice cloud profile retrieval products. J. Geophys. Res. Atmos. 120: 6937-6951, doi: 10.1002/2015JD023462.
42. Weissmann M, Göber M, Hohenegger C, Janjic T, Keller J, Ohlwein C, Seifert A, Trömel S, Ulbrich T, Wapler K, Bollmeyer C, Deneke H. 2014. Initial phase of the Hans-Ertel Centre for Weather Research-A virtual centre at the interface of basic and applied weather and climate. Meteorol. Z. 23: 193-208.
43. Wyser K. 1998. The effective radius in ice clouds. J. Clim. 11: 1793-1802.