Using the bootstrap method for a statistical significance test of differences between summary histograms

Monthly Weather Review

Volumn 134, Issue 5, 2006, Pages 1442-1453

  Xu, Kuan Man ^a  

^a NASA LANGLEY RESEARCH CENTER   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

METEOROLOGICAL INSTRUMENTS; PROBABILITY DISTRIBUTIONS; STATISTICAL TESTS; WEATHER SATELLITES;

BOOTSTRAP METHOD; CLOUD OBJECT; EUCLIDEAN DISTANCE; SATELLITE MEASUREMENTS; STATISTICAL DIFFERENCES; SUMMARY HISTOGRAMS;

CLOUDS;

BOOTSTRAPPING; HISTOGRAM; PROBABILITY; STATISTICAL ANALYSIS;

EID: 33744760549     PISSN: 00270644     EISSN: None     Source Type: Journal    
DOI: 10.1175/MWR3133.1     Document Type: Article

References (32)

1. Anderson, J. L., and W. F. Stern, 1996: Evaluating the potential predictive utility of ensemble forecasts. J. Climate, 9, 260-269.
2. Beersma, J. L., and T. Adri Buishand, 1999: A simple test for equility of variances in monthly climate data. J. Climate, 12, 1770-1779.
3. Bove, M. C., J. J. O'Brien, J. B. Eisner, C. W. Landsea, and X. Niu, 1998: Effect of El Niño on U.S. landfalling hurricanes, revisited. Bull. Amer. Meteor. Soc., 79, 2477-2482.
4. Briggs, W. M., and D. S. Wilks, 1996: Extension of the Climate Prediction Center long-lead temperature and precipitation forecasts. J. Climate, 9, 827-839.
5. Cha, S.-H., and S. N. Srihari, 2002: On measuring the distance between histograms. Pattern Recog., 35, 1355-1370.
6. Chu, P.-S., 2002: Large-scale circulation features associated with decadal variations of tropical cyclone activity over the central North Pacific. J. Climate, 15, 2678-2689.
7. Chu, P.-S., and J. Wang, 1997: Tropical cyclone occurrences in the vicinity of Hawaii: Are the differences between El Niño and non-El Niño years significant? J. Climate, 10, 2683-2689.
8. Dong, X., and G. G. Mace, 2003: Profiles of low-level stratus cloud microphysics deduced from ground-based measurements. J. Atmos. Oceanic Technol., 20, 42-53.
9. Duda, R. O., and P. E. Hart, 1973: Pattern Classification and Scene Analysis. 1st ed. Wiley, 482 pp.
10. Ebisuzaki, W., 1997: A method to estimate the statistical significance of a correlation when the data are serially correlated. J. Climate, 10, 2147-2153.
11. Efron, B., 1982: The Jacknife, the Bootstrap, and other Resampling Plans. J. W. Arrowsmith, 92 pp.
12. Efron, B., and R. Tibshirani, 1993: An Introduction to the Bootstrap. Chapman & Hall, 456 pp.
13. Eitzen, Z. A., and K.-M. Xu, 2005: A statistical comparison of deep convective cloud objects observed by an Earth Observing System satellite and simulated by a cloud-resolving model. J. Geophys. Res., 110, D15S14, doi:10.1029/2004JD005086.
14. Gille, S. T., 2004: Using Kolmogorov-Smirnov statistics to assess Jason, TOPEX and Poseidon altimeter measurements. Mar. Geod., 27, 47-58.
15. Gille, S. T., and S. G. Llewellyn Smith, 2000: Velocity probability density functions from altimetry. J. Phys. Oceangr., 30, 125-136.
16. Gong, X., and M. B. Richman, 1995: On the application of cluster analysis to growing season precipitation data in North America east of the Rockies. J. Climate, 8, 897-931.
17. Hamill, T. M., 1999: Hypothesis tests for evaluating numerical precipitation forecasts. Wea. Forecasting, 14, 155-167.
18. Kuiper, N. H., 1960: Testing concerning random points on a circle. Proc. Koninkl. Neder. Akad. van Wetenschappen., A63, 38-47.
19. Livezey, R. E., and W. Y. Chen, 1983: Statistical field significance and its determination by Monte Carlo techniques. Mon. Wea. Rev., 109, 120-126.
20. Livezey, R. E., M. Masutani, A. Leetmaa, H. Rui, M. Ji, and A. Kumar, 1997: Teleconnective response of the Pacific-North American region atmosphere to large central equatorial Pacific SST anomalies. J. Climate, 10, 1787-1820.
21. Luo, Y., S. K. Krueger, G. G. Mace, and K.-M. Xu, 2003: Cirrus cloud properties from a cloud-resolving model simulation compared to cloud radar observations. J. Atmos. Sci., 60, 510-525.
22. Matusita, K., 1955: Decision rules based on distance for problems of fit, two samples and estimation. Ann. Math. Stat., 26, 631-641.
23. McFarquhar, G. M., 2004: A new representation of collision-induced breakup of raindrops and its implications for the shapes of raindrop size distribution. J. Atmos. Sci., 61, 777-794.
24. Sardeshmukh, P. D., G. P. Compo, and C. Penland, 2000: Change of probability associated with El Niño. J. Climate, 13, 4268-4286.
25. Sengupta, M., E. E. Clothiaux, and T. P. Ackerman, 2004: Climatology of warm boundary-layer clouds at the ARM SGP site and their comparison to models. J. Climate, 17, 4760-4782.
26. Wielicki, B. A., and R. M. Welch, 1986: Cumulus cloud properties derived using Landsat satellite data. J. Climate Appl. Meteor., 25, 261-276.
27. Wielicki, B. A., B. R. Barkstrom, E. F. Harrison, R. B. Lee III, G. L. Smith, and J. E. Cooper, 1996: Clouds and the Earth's Radiant Energy System (CERES): An Earth Observing System Experiment. Bull. Amer. Meteor. Soc., 77, 853-868.
28. Wilks, D. S., 1995: Statistical Methods in the Atmospheric Sciences: An Introduction. Academic Press, 464 pp.
29. Wilks, D. S., 1996: Statistical significance of long-range "optimal climate normal" temperature and precipitation forecasts. J. Climate, 9, 827-839.
30. Wilks, D. S., 1997: Resampling hypothesis tests for autocorrelated fields. J. Climate, 10, 65-82.
31. Xu, K.-M., T. Wong, B. A. Wielicki, L. Parker, and Z. A. Eitzen, 2005: Statistical analyses of satellite cloud object data from CERES. Part I: Methodology and preliminary results of 1998 El Niño/2000 La Niña. J. Climate, 18, 2497-2514.
32. Zwiers, F. W., 1990: The effect of serial correlation on statistical inferences made with the resampling procedures. J. Climate, 3, 1452-1461.