Scale-dependent uncertainties in global QPFs and QPEs from NWP model and satellite fields

Journal of Hydrometeorology

Volumn 11, Issue 1, 2010, Pages 139-155

  Lu, C ^a   Yuan, H ^b   Tollerud, E I ^c   Wang, N ^a  

^a Department of Environmental and Radiological Health Sciences   (United States)

^b UNIVERSITY OF COLORADO   (United States)

^c NOAA EARTH SYSTEM RESEARCH LABORATORY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

CLIMATE PREDICTION; CORRELATION; DECOMPOSITION ANALYSIS; ESTIMATION METHOD; NUMERICAL MODEL; PRECIPITATION (CLIMATOLOGY); RAINFALL; REMOTE SENSING; SATELLITE IMAGERY; UNCERTAINTY ANALYSIS; WEATHER FORECASTING;

MEDITERRANEAN REGION; NORTH AMERICA;

EID: 77953351130     PISSN: 1525755X     EISSN: None     Source Type: Journal    
DOI: 10.1175/2009JHM1164.1     Document Type: Article

References (39)

1. Adler, R. F., P. R. Keehn, and I. M. Hakkarinen, 1993: Estimation of monthly rainfall over Japan and surrounding waters from a combination of low-orbit microwave and geosynchronous IR data. J. Appl. Meteor., 32, 335-356.
2. Arakawa, A., 1993: Closure assumptions in the cumulus parameterization problem. The Representation of Cumulus Convection in Numerical Models of the Atmosphere, Meteor. Monogr., No. 46, Amer. Meteor. Soc., 1-15.
3. Arkin, P. A., and P. Xie, 1994: The Global Precipitation Climatology Project: First Algorithm Intercomparison Project. Bull. Amer. Meteor. Soc., 75, 401-419.
4. Beck, Ch., J. Grieser, and B. Rundolf, 2004: A new monthly precipitation climatology for the global land areas for the period 1951 to 2000 (in Dutch). DWD Rep., Klimastatusbericht 2004, 181-190. [Available online at http://www.dwd.de/en/FundE/Klima/KLIS/int/GPCC/GPCC.htm.]
5. Beylkin, G., and N. Saito, 1992: Wavelets, their autocorrelation functions, and multiresolution representation of signals. Intelligent Robots and Computer Vision XI:Biological NeuralNet, and 3D Methods, D. P. Casasent, Ed., International Society for Optical Engineering (SPIE Proceedings, Vol. 1862), 39-50.
6. Bousquet, O., C. A. Lin, and I. Zawadzki, 2006: Analysis of scale dependence of quantitative precipitation forecast verification: A case-study over the Mackenzie River basin. Quart. J. Roy. Meteor. Soc., 132, 2107-2125.
7. Casati, B., G. Ross, and D. B. Stephenson, 2004: A new intensityscale approach for the verification of spatial precipitation forecasts. Meteor. Appl., 11, 141-154.
8. Davis, C., B. Brown, and R. Bullock, 2006:Object-based verification of precipitation forecasts. Part I: Methodology and application to mesoscale rain areas. Mon. Wea. Rev., 134, 1772-1784.
9. Du, J., S. L. Mullen, and F. Sanders, 1997: Short-range ensemble forecasting of quantitative precipitation. Mon. Wea. Rev., 125, 2427-2459.
10. Ebert, E. E., and J. L. McBride, 2000: Verification of precipitation in weather systems: Determination of systematic errors. J. Hydrol., 239, 179-202.
11. Ebert, E. E., U. Damrath, W. Wergen, and M. E. Baldwin, 2003: The WGNE assessment of short-term quantitative precipitation forecasts. Bull. Amer. Meteor. Soc., 84, 481-492.
12. Ebert, E. E., J. E. Janowiak, and C. Kidd, 2007: Comparison of near-realtime precipitation estimates from satellite observations and numerical models. Bull. Amer. Meteor. Soc., 88, 47-64.
13. Gottschalck, J. G., J. Meng, M. Rodell, and P. Houser, 2005: Analysis of multiple precipitation products and preliminary assessment of their impact on global land data assimilation system land surface states. J. Hydrometeor., 6, 573-598.
14. Greene, J. S., and M. Morrissey, 2000: Validation and uncertainty analysis of satellite rainfall algorithms. Prof.Geogr., 52, 247-258.
15. Harris, D., M. Menabde, W. W. Seed, and G. L. Austin, 1996: Multifractal characterization of rain fields with a strong orographic influence. J. Geophys. Res., 101, 26 405-26 414.
16. Harris, D., E. Foufoula-Georgiou, K. K. Droegemeier, and J. J. Levit, 2001: Multifractal statistical properties of a high-resolution precipitation forecast. J. Hydrometeor., 2, 406-418.
17. Houghton, D. D., R. A. Petersen, and R. L. Wobus, 1993: Spatial resolution impacts on National Meteorological Center nested grid model simulations. Mon. Wea. Rev., 121, 1450-1466.
18. Hsu, K., X. Gao, S. Sorooshian, and H. V. Gupta, 1997: Precipitation estimation from remotely sensed information using artificial neural networks. J. Appl. Meteor., 36, 1176-1190.
19. Huffman, G. J., R. F. Adler, M. M. Morrisey, D. T. Bolvin, S. Curtine, R. Joyce, B. McGavock, and J. Susskind, 2001: Global precipitation at one-degree daily resolution from multisatellite observations. J. Hydrometeor., 2, 36-50.
20. Hwang, S., J. E. Schemm, A. G. Barnston, and W. Kwon, 2001: Long-lead seasonal forecast skill in far eastern Asia using canonical correlation analysis. J. Climate, 14, 3005-3016.
21. Joyce, R. J., J. E. Janowiak, P. A. Arkin, and P. Xie, 2004: CMORPH: A method that produces global precipitation estimates from passive microwave and infrared data at high spatial and temporal resolution. J. Hydrometeor., 5, 487-503.
22. Kanamitsu, M., 1989: Description of the NMC Global Data Assimilation and Forecast System. Wea. Forecasting, 4, 335-342.
23. Kirby, J. F., 2005: Which wavelet best reproduces the Fourier power spectrum? Comput. Geosci., 31, 846-864.
24. Kronland-Martinet, R., J. Morlet, and A. Grossman, 1987: Analysis of sound patterns through wavelet transforms. J. Pattern Recognit. Artif. Intell., 1, 273-302.
25. Lu, C., S. Koch, and N. Wang, 2005: Determination of temporal and spatial characteristics of gravity waves using cross-spectral analysis and wavelet transformation. J. Geophys. Res., 110, D01109, doi:10.1029/2004JD004906.
26. Meyers, S. D., B. G. Kelly, and J. J. O'Brien, 1993: An introduction to wavelet analysis in oceanography and meteorology: With application to the dispersion of Yanai waves. Mon. Wea. Rev., 121, 2858-2866.
27. Molinari, J., and M. Dudek, 1992: Parameterization of convective precipitation in mesoscale numerical models: A critical review. Mon. Wea. Rev., 120, 326-344.
28. Mullen, S. L., and R. Buizza, 2001: Quantitative precipitation forecast over the United States by the ECMWF ensemble prediction system. Mon. Wea. Rev., 129, 638-663.
29. NCEP, 2003: The GFS Atmospheric Model. NCEP Office Note 442, 14 pp. [Available online at http://www.emc.ncep.noaa.gov/officenotes/newernotes/on442.pdf.]
30. Pielke, R. A., 1984: Mesoscale Meteorological Modeling. Academic Press, 612 pp.
31. Raymond, D. J., 1993: Observational constraints on cumulus parameterizations. The Representation of Cumulus Convection in NumericalModels of the Atmosphere,Meteor.Monogr., No. 46, Amer. Meteor. Soc., 17-28.
32. Rogers, R. F., J. M. Fritsch, and W. C. Lambert, 2000: A simple technique for using radar in the dynamic initialization of a mesoscale model. Mon. Wea. Rev., 128, 2560-2574.
33. Sanders, F., 1986: Trends in skill of daily forecasts of temperature and precipitation, 1966-78. Bull. Amer. Meteor. Soc., 67, 763-769.
34. Schneider, U., T. Fuchs, A. Meyer-Christoffer, and B. Rudolf, 2008: Global precipitation analysis products of the GPCC. Global Precipitation Climatology Centre Rep., 12 pp. [Available online at http://gpcc.dwd.de.]
35. Skoien, J. O., G. Blö schl, and A. W. Western, 2003: Characteristic space scales and timescales in hydrology. Water Resour. Res., 39, 1304, doi:10.1029/2002WR001736.
36. Sorooshian, S., K.-L. Hsu, X. Gao, H. V. Gupta, B. Imam, and D. Braithwaite, 2000: Evaluation of PERSIANN system satellite-based estimates of tropical rainfall. Bull. Amer. Meteor. Soc., 81, 2035-2046.
37. Xiao, Q., X. Zou, and Y.-H. Kuo, 2000: Incorporating the SSM/I-derived precipitable water and rainfall rate into a numerical model:Acase study for the ERICA IOP-4 cyclone. Mon. Wea. Rev., 128, 87-108.
38. Xie, P., J. E. Janowaik, P. A. Arkin, R. F. Adler, A. Gruber, R. Ferraro, G. J. Huffman, and S. Curtis, 2003: GPCP pentad precipitation analyses: An experimental dataset based on gauge observations and satellite estimates. J. Climate, 16, 2197-2214.
39. Yuan, H., C. Lu, J. McGinley, P. Shultz, B. Jamison, L. Wharton, and C. Anderson, 2009: Evaluation of short-range quantitative precipitation forecasts from a time-lagged multimodel ensemble. Wea. Forecasting, 24, 18-38.