Statistical properties of global precipitation in the NCEP GFS model and TMPA observations for data assimilation

Monthly Weather Review

Volumn 144, Issue 2, 2016, Pages 663-679

  Lien, Guo Yuan ^a,b   Kalnay, Eugenia ^a   Miyoshi, Takemasa ^a,b,c   Huffman, George J ^d  

^a University of Maryland   (United States)

^b RIKEN   (Japan)

^c JAPAN AGENCY FOR MARINE EARTH SCIENCE AND TECHNOLOGY   (Japan)

^d NASA GODDARD SPACE FLIGHT CENTER   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

GAUSSIAN DISTRIBUTION; PRECIPITATION (METEOROLOGY);

DATA ASSIMILATION; GAUSSIAN TRANSFORMATION; GLOBAL FORECAST SYSTEMS; GLOBAL PRECIPITATION; MARINE STRATOCUMULUS; PRACTICAL SOLUTIONS; SATELLITE PRECIPITATION; STATISTICAL PROPERTIES;

FORECASTING;

COMPUTER SIMULATION; DATA ASSIMILATION; ERROR ANALYSIS; ERROR CORRECTION; MODEL TEST; NUMERICAL MODEL; PRECIPITATION (CLIMATOLOGY); WEATHER FORECASTING;

EID: 84959291679     PISSN: 00270644     EISSN: 15200493     Source Type: Journal    
DOI: 10.1175/MWR-D-15-0150.1     Document Type: Article

References (43)

1. Amezcua, J., and P. J. van Leeuwen, 2014: Gaussian anamorphosis in the analysis step of the EnKF: A joint state-variable/observation approach. Tellus, 66A, 23493, doi:10.3402/tellusa.v66.23493.
2. Bauer, P., G. Ohring, C. Kummerow, and T. Auligne, 2011: Assimilating satellite observations of clouds and precipitation into NWP models. Bull. Amer. Meteor. Soc., 92, ES25-ES28, doi:10.1175/2011BAMS3182.1.
3. Behrangi, A., M. Lebsock, S. Wong, and B. Lambrigtsen, 2012: On the quantification of oceanic rainfall using spaceborne sensors. J. Geophys. Res., 117, D20105, doi:10.1029/2012JD017979.
4. Bocquet, M., C. A. Pires, and L. Wu, 2010: Beyond Gaussian statistical modeling in geophysical data assimilation. Mon. Wea. Rev., 138, 2997-3023, doi:10.1175/2010MWR3164.1.
5. Chao, W. C., 2013: Catastrophe-concept-based cumulus parameterization:Correction of systematic errors in the precipitation diurnal cycle over land in a GCM. J. Atmos. Sci., 70, 3599-3614, doi:10.1175/JAS-D-13-022.1.
6. Davolio, S., and A. Buzzi, 2004: A nudging scheme for the assimilation of precipitation data into a mesoscale model. Wea. Forecasting, 19, 855-871, doi:10.1175/1520-0434(2004)019,0855:ANSFTA.2.0.CO;2.
7. Dee, D. P., 2005: Bias and data assimilation. Quart. J. Roy. Meteor. Soc., 131, 3323-3343, doi:10.1256/qj.05.137.
8. Derber, J. C., and W.-S. Wu, 1998: The use of TOVS cloud-cleared radiances in the NCEP SSI analysis system. Mon. Wea. Rev., 126, 2287-2299, doi:10.1175/1520-0493(1998)126,2287:TUOTCC.2.0.CO;2.
9. Errico, R. M., P. Bauer, and J.-F. Mahfouf, 2007: Issues regarding the assimilation of cloud and precipitation data. J. Atmos. Sci., 64, 3785-3798, doi:10.1175/2006JAS2044.1.
10. Fabry, F., and J. Sun, 2010: For how long should what data be assimilated for the mesoscale forecasting of convection and why? Part I: On the propagation of initial condition errors and their implications for data assimilation. Mon. Wea. Rev., 138, 242-255, doi:10.1175/2009MWR2883.1.
11. Falkovich, A., E. Kalnay, S. Lord, and M. B. Mathur, 2000: A new method of observed rainfall assimilation in forecast models. J. Appl. Meteor., 39, 1282-1298, doi:10.1175/1520-0450(2000)039,1282:ANMOOR.2.0.CO;2.
12. Guttman, N. B., 1999: Accepting the standardized precipitation index:Acalculation algorithm. J. Amer. Water Resour. Assoc., 35, 311-322, doi:10.1111/j.1752-1688.1999.tb03592.x.
13. Han, J., and H.-L. Pan, 2011: Revision of convection and vertical diffusion schemes in the NCEP Global Forecast System. Wea. Forecasting, 26, 520-533, doi:10.1175/WAF-D-10-05038.1.
14. Huffman, G. J., and Coauthors, 2007: The TRMM Multisatellite Precipitation Analysis (TMPA): Quasi-global, multiyear, combined-sensor precipitation estimates at fine scales. J. Hydrometeor., 8, 38-55, doi:10.1175/JHM560.1.
15. Huffman, G. J., R. Adler, D. Bolvin, and E. Nelkin, 2010: The TRMM Multi-Satellite Precipitation Analysis (TMPA). Satellite Rainfall Applications for Surface Hydrology, M. Gebremichael and F. Hossain, Eds., Springer, 3-22.
16. Huffman, G. J., E. F. Stocker, D. T. Bolvin, and E. J. Nelkin, 2012: TRMM Multisatellite Precipitation Analysis. TRMM_3B42, version 7, NASA Goddard Space Flight Center, accessed 25 July 2012. [Available online at http://mirador.gsfc.nasa.gov/collections/TRMM_3B42__007.shtml.]
17. Hunt, B. R., and Coauthors, 2004: Four-dimensional ensemble Kalman filtering. Tellus, 56A, 273-277, doi:10.1111/j.1600-0870.2004.00066.x.
18. Koizumi, K., Y. Ishikawa, and T. Tsuyuki, 2005: Assimilation of precipitation data to the JMA mesoscale model with a fourdimensional variational method and its impact on precipitation forecasts. SOLA, 1, 45-48, doi:10.2151/sola.2005-013.
19. Leon, D. C., Z. Wang, and D. Liu, 2008: Climatology of drizzle in marine boundary layer clouds based on 1 year of data from CloudSat and Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO). J. Geophys. Res., 113, D00A14, doi:10.1029/2008JD009835.
20. Lien, G.-Y., E. Kalnay, and T. Miyoshi, 2013: Effective assimilation of global precipitation: Simulation experiments. Tellus, 65A, 19915, doi:10.3402/tellusa.v65i0.19915.
21. Lien, G.-Y., T. Miyoshi, and E. Kalnay, 2016: Assimilation of TRMM Multisatellite Precipitation Analysis with a low-resolution NCEP Global Forecast System. Mon. Wea. Rev. 144, 643-661, doi:10.1175/MWR-D-15-0149.1.
22. Lopez, P., 2011: Direct 4D-Var assimilation of NCEP stage IV radar and gauge precipitation data at ECMWF. Mon. Wea. Rev., 139, 2098-2116, doi:10.1175/2010MWR3565.1.
23. Lopez, P., 2013: Experimental 4D-Var assimilation of SYNOP rain gauge data at ECMWF. Mon. Wea. Rev., 141, 1527-1544, doi:10.1175/MWR-D-12-00024.1.
24. McKee, T. B., N. J. Doesken, and J. Kleist, 1993: The relationship of drought frequency and duration to time scales. Preprints, Eighth Conf. on Applied Climatology, Anaheim, CA, Amer. Meteor. Soc., 179-183.
25. Mesinger, F., and Coauthors, 2006: North American Regional Reanalysis. Bull. Amer. Meteor. Soc., 87, 343-360, doi:10.1175/BAMS-87-3-343.
26. Miyoshi, T., and S. Yamane, 2007: Local ensemble transform Kalman filtering with an AGCM at a T159/L48 resolution. Mon. Wea. Rev., 135, 3841-3861, doi:10.1175/2007MWR1873.1.
27. Ott, E., and Coauthors, 2004: A local ensemble Kalman filter for atmospheric data assimilation. Tellus, 56A, 415-428, doi:10.1111/j.1600-0870.2004.00076.x.
28. Pan, H.-L., and W.-S. Wu, 1995: Implementing amass flux convection parameterization package for the NMC medium-range forecast model. NMC Office Note 409, 43 pp. [Available online at http://www.lib.ncep.noaa.gov/ncepofficenotes/files/01408A42.pdf.]
29. Schöniger, A., W. Nowak, and H.-J. Hendricks Franssen, 2012: Parameter estimation by ensemble Kalman filters with transformed data: Approach and application to hydraulic tomography. Water Resour. Res., 48, W04502, doi:10.1029/2011WR010462.
30. Shige, S., S. Kida, H. Ashiwake, T. Kubota, and K. Aonashi, 2013: Improvement of TMI rain retrievals in mountainous areas. J. Appl. Meteor. Climatol., 52, 242-254, doi:10.1175/JAMC-D-12-074.1.
31. Simon, E., and L. Bertino, 2009: Application of the Gaussian anamorphosis to assimilation in a 3-D coupled physical-ecosystem model of the NorthAtlanticwith theEnKF:Atwin experiment. Ocean Sci., 5, 495-510, doi:10.5194/os-5-495-2009.
32. Simon, E., and L. Bertino, 2012: Gaussian anamorphosis extension of the DEnKF for combined state parameter estimation: Application to a 1D ocean ecosystem model. J. Mar. Syst., 89, 1-18, doi:10.1016/j.jmarsys.2011.07.007.
33. Tapiador, F. J., and Coauthors, 2012: Global precipitation measurement:Methods, datasets and applications. Atmos. Res., 104-105, 70-97, doi:10.1016/j.atmosres.2011.10.021.
34. Tsuyuki, T., 1996: Variational data assimilation in the tropics using precipitation data. Part II: 3D model. Mon. Wea. Rev., 124, 2545-2561, doi:10.1175/1520-0493(1996)124,2545:VDAITT.2.0.CO;2.
35. Tsuyuki, T., 1997: Variational data assimilation in the tropics using precipitation data. Part III: Assimilation of SSM/I precipitation rates. Mon. Wea. Rev., 125, 1447-1464, doi:10.1175/1520-0493(1997)125,1447:VDAITT.2.0.CO;2.
36. Tsuyuki, T., and T. Miyoshi, 2007: Recent progress of data assimilation methods in meteorology. J. Meteor. Soc. Japan, 85B, 331-361, doi:10.2151/jmsj.85B.331.
37. Ushio, T., and Coauthors, 2009: A Kalman filter approach to the Global Satellite Mapping of Precipitation (GSMaP) from combined passive microwave and infrared radiometric data. J. Meteor. Soc. Japan, 87A, 137-151, doi:10.2151/jmsj.87A.137.
38. vanZanten, M. C., B. Stevens, G. Vali, and D. H. Lenschow, 2005: Observations of drizzle in nocturnal marine stratocumulus. J. Atmos. Sci., 62, 88-106, doi:10.1175/JAS-3355.1.
39. Wackernagel, H., 2003: Multivariate Geostatistics. Springer, 408 pp.
40. Wen, M., S. Yang, A. Vintzileos, W. Higgins, and R. Zhang, 2012: Impacts of model resolutions and initial conditions on predictions of the Asian summer monsoon by the NCEP Climate Forecast System. Wea. Forecasting, 27, 629-646, doi:10.1175/WAF-D-11-00128.1.
41. Yussouf, N., E. R. Mansell, L. J. Wicker, D. M. Wheatley, and D. J. Stensrud, 2013: The ensemble Kalman filter analyses and forecasts of the 8 May 2003 Oklahoma City tornadic supercell storm using single- and double-moment microphysics schemes. Mon. Wea. Rev., 141, 3388-3412, doi:10.1175/MWR-D-12-00237.1.
42. Zhang, S. Q., M. Zupanski, A. Y. Hou, X. Lin, and S. H. Cheung, 2013: Assimilation of precipitation-affected radiances in a cloud-resolving WRF ensemble data assimilation system. Mon. Wea. Rev., 141, 754-772, doi:10.1175/MWR-D-12-00055.1.
43. Zupanski, D., S. Q. Zhang, M. Zupanski, A. Y. Hou, and S. H. Cheung, 2011: A prototype WRF-based ensemble data assimilation system for dynamically downscaling satellite precipitation observations. J. Hydrometeor., 12, 118-134, doi:10.1175/2010JHM1271.1.