Selected Papers (Last Updated: 3/2017; see CV for more complete and up-to-date list)


  1. Simpson, M., Wikle, C.K. and S.H. Holan, 2017: Adaptively-tuned particle swarm optimization with application to spatial design. STAT, in press.
  2. McDermott, P.L., and C.K. Wikle, 2016: A model-based approach for analog spatio-temporal dynamic forecasting. Environmetrics, 27: 70--82. doi: 10.1002/env.2374.
  3. Crawford, W.J., Smith, P.J., Milliff, R.F., Fiechter, J., Wikle, C.K., Edwards, C.A., and A.M. Moore, 2016: Weak constraint four-dimensional variational data assimilation in a model of the California Current System, Advances in Statistical Climatology, Meteorology and Oceanography, 2, 171--192.
  4. Gladish, D.W., Kuhnert, P.M., Pagendam, D.E., Wikle, C.K., Bartley, R., Searle, R.D., Ellis, R.J., Dougall, C., Turner, R.D.R., Lewis, S.E., Bainbridge, Z.T., and J.E. Brodie, 2016: Spatio-temporal assimilation of modelled catchment loads with monitoring data in the Great Barrier Reef, The Annals of Applied Statistics, 10, 1590--1618.
  5. Bradley, J.R., Wikle, C.K., and Holan, S.H., 2016: Regionalization of multiscale spatial processes using a criterion for spatial aggregation error. Journal of the Royal Statistical Society, Series B, doi:10.1111/rssb.12179.
  6. Bradley, J.R., Wikle, C.K., and Holan, S.H., 2016: Bayesian spatial change of support for count-valued survey data. Journal of the American Statistical Association, 111, 472--487.
  7. Yang, W.H., Holan, S.H., and Wikle, C.K., 2016: Bayesian lattice filters for time-varying autoregression and time-frequency analysis. Bayesian Analysis, 11, 977--1003.
  8. {\bf Wikle, C.K.}, Leeds, W.B., and M.B. Hooten, 2016: Models for ecological models: Ocean primary productivity. CHANCE, 29: 23--30.
  9. Katzfuss, M., Stroud, J.R., and C.K. Wikle, 2016: Understanding the ensemble Kalman filter. The American Statistician, 70:4, 350--357, DOI:10.1080/00031305.2016.1141709.
  10. Rota, C.R., Wikle, C.K., Kays, R.W., Forrester, T.D., McShea, W.J., Parsons, A.W., and Millspaugh, J.J., 2016: A two-species occupancy model accommodating simultaneous spatial and interspecific dependence. Ecology, 97, 48--53.
  11. Bradley, J.R., Holan, S.H., and Wikle, C.K., 2015: Multivariate spatio-temporal models for high-dimensional areal data with application to longitudinal employer-household dynamics. Annals of Applied Statistics, 9: 1761--1791.
  12. Quick, H., Holan, S.H., Wikle, C.K., and Reiter, J.P., 2015: Bayesian marked point process modeling for generating fully synthetic public use data with point-referenced geography. Spatial Statistics, 14: 439--451.
  13. Bradley, J.R., Wikle, C.K., and Holan, S.H., 2015: Spatio-temporal change of support with application to American Community Survey multi-year period estimates. STAT, 4: 255--270.
  14. Quick, H., Holan, S.H., and Wikle, C.K, 2015: Zeros and ones: A case for suppressing zeros in sensitive count data with an application to stroke mortality. STAT, 4: 227--234.
  15. Ryan, M., Bradley, J.R., Oswald, T., Wikle, C.K., and Holan, S.H., 2015: An analysis of bullying and suicide in the United States using a non-Gaussian multivariate spatial model. Proceedings of The National Conference On Undergraduate Research (NCUR), [Refereed Conference Proceedings], 155--161.
  16. Porter, A.T., Holan, S.H., and Wikle, C.K., 2015: Bayesian Semiparametric Hierarchical Empirical Likelihood Spatial Models. Journal of Statistical Planning and Inference, 165: 78--90.
  17. Porter, A.T., Wikle, C.K., and Holan, S.H., 2015: Small Area Estimation via Multivariate Fay-Herriot Models With Latent Spatial Dependence. Australian and New Zealand Journal of Statistics, 57: 15--29.
  18. Porter, A.T., Holan, S.H., and Wikle, C.K., 2015: Multivariate Spatial Hierarchical Bayesian Empirical Likelihood Methods for Small Area Estimation. STAT, 4: 108--116.
  19. Wildhaber, M.L., Wikle, C.K., Moran, E.H., Anderson, C.J., Franz, K.J., and R. Dey, 2015: Hierarchical, stochastic modelling of large river ecosystems and fish growth across spatio-temporal scales and climate models: Missouri River sturgeon example. Geological Society, London, Special Publications, 408, doi:10.1144/SP408.11.
  20. Wildhaber, M.L., Dey, R., Wikle, C.K., Anderson, C.J., Moran, E.H., and K.J. Franz, 2015: A stochastic bioenergetics model based approach to translating large river flow and temperature in to fish population responses: the pallid sturgeon example. Geological Society, London, Special Publications, 408, doi:10.1144/SP408.10.
  21. Wu, G., Holan, S.H., Nilon, C.H., and Wikle, C.K., 2015: Bayesian Binomial Mixture Models for Estimating Abundance in Ecological Monitoring Studies. Annals of Applied Statistics, 9: 1--26.
  22. Yang, W.-H., Wikle, C.K., Holan, S.H., Myers, D.B., and K.A. Sudduth, 2015: Bayesian analysis of spatially-dependent functional responses with spatially-dependent multi-dimensional functional predictors. Statistica Sinica, 25, 205--223
  23. Wikle, C.K., 2014: Modern perspectives on statistics for spatio-temporal data. WIRES Computational Statistics, 7, 86—98.
  24. Porter, A.T., Holan, S.H., Wikle, C.K., and Cressie, N., 2014: Spatial Fay-Herriot Models for Small Area Estimation With Functional Covariates. Spa tial Statistics, 10:27-42.
  25. Wikle, C.K., Holan, S.H., Sudduth, K.A., and D.B. Myers, 2014: Soil property estimation and design for agroecosystem management using hierarchical geo spatial functional data models, Journal of the Indian Society of Agricultural Statistics, 68: 203--216.
  26. Gladish, D.L., C.K. Wikle, 2014: Physically-motivated parameter reduction in reduced rank quadratic nonlinear dynamic spatio-temporal models. Enviro nmetrics, 25, 230-244.
  27. Gladish, D., Wikle, C.K., and Holan, S., 2014. Covariate-based cepstral parameterizations for time-varying spatial error covariances. Environmetrics , 25, 69-83.
  28. Pagendam, D.E., Kuhnert, P.M., Leeds, W.B., Wikle, C.K., Bartley, R., and E.E. Peterson, 2014: Assimilating catchment processes with monitoring data to estimate sediment loads to the Great Barrier Reef. Environmetrics, 25, 214--229.
  29. Song, Y., Li, Y., Bates, B., and C.K. Wikle, 2014. A Bayesian hierarchical downscaling model for southwest western Australia rainfall. Journal of th e Royal Statistical Society, Series C, DOI: 10.1111/rssc.12055.
  30. Dobricic, S., Wikle, C.K., Milliff, R.F., Pinardi, N. and L.M. Berliner, 2014: Assimilation of oceanographic observations with estimates of vertical b ackground error covariances by a Bayesian hierarchical model. Quarterly Journal of the Royal Meteorological Society, DOI: 10.1002/qj.2348.
  31. Milliff, R.F., Fiechter, J., Leeds, W.B., Herbei, R., {\bf Wikle, C.K.}, Hooten, M.B., Moore, A.M., Powell, T.M., and J.L. Brown, 2013. Uncertainty man agement in coupled physical-biological lower-trophic level ocean ecosystem models. Oceanography, 26, 98--115.
  32. Wikle, C.K., Milliff, R.F., Herbei, R., and W.B. Leeds, 2013: Modern statistical methods in oceanography: A hierarchical perspective. Statistical S cience, 28, 466-486. DOI: 10.1214/13-STS436
  33. Yang, W.-H., Wikle, C.K., Holan, S.H., and M.L. Wildhaber, 2013: Ecological prediction with nonlinear multivariate time-frequency functional data model s. Journal of Agricultural, Biological and Environmental Statistics, 18,450-474.
  34. Wu, G., Holan, S.H., and C.K. Wikle, 2013: Hierarchical Bayesian spatio-temporal Conway-Maxwell Poisson models with dynamic dispersion. Journal of Agricultural, Biological and Environmental Statistics, 18, 335-356.
  35. Karpman, D., Ferreira, M.A.R., and C.K. Wikle, 2013: A point process model for tornado report climatology. STAT, 2, 1-8. DOI: 10.1002/sta4.14< /li>
  36. Leeds, W.B., Wikle, C.K., Fiechter, J., Brown, J., and Milliff, R.F., 2013: Modeling 3-D spatio-temporal biogeochemical processes with a forest of 1-D computer model emulators. Environmetrics, 24, 1-12.
  37. Oleson, J.J. and C.K. Wikle, 2013: Predicting infectious disease outbreak risk via migratory waterfowl vectors. Journal of Applied Statistics, 4 0, 656-673.
  38. Fiechter, J., Herbei, R., Leeds, W.B., Brown, J., Milliff, R., Wikle, C.K., Powell, T., and A. Moore, 2013: A Bayesian parameter estimation method appl ied to a marine ecosystem model for the coastal Gulf of Alaska. Ecological Modeling, 258, 122-133.
  39. Leeds, W.B., Wikle, C.K., and J. Fiechter, 2013: Emulator-assisted reduced-rank ecological data assimilation for multivariate dynamical spatio-tempor al processes. Statistical Methodology, doi:10.1016/j.statmet.2012.11.004.
  40. Holan, S.H., Yang, W.-H., Matteson, D.S., and C.K. Wikle, 2012: An approach for identifying and predicting economic recessions in real-time using time-frequency functional models. Invited discussion paper in Applied Stochastic Models in Business and Industry, 28, 485-499. DOI: 10.1002/asmb.1954.
  41. Holan, S.H., Yang, W.-H., Matteson, D.S., and C.K. Wikle, 2012: Rejoinder - An approach for identifying and predicting economic recessions in real-tim e using time-frequency functional models. Invited discussion paper in Applied Stochastic Models in Business and Industry, 28, 504-505.
  42. Leeds, W.B. and C.K. Wikle, 2012: Science-based parameterizations for dynamical spatio-temporal models. WIREs Computational Statistics, 4, 554- 560.
  43. Micheas, A.C., Wikle, C.K., Larsen, D.R., 2012: Random set modelling of three-dimensional objects in a hierarchical Bayesian context. Journal of St atistical Computation and Simulation. DOI:10.1080/00949655.2012.696647
  44. Wikle, C.K., 2011: A hierarchical Bayesian arms race model. International J ournal of Conflict and Reconciliation, 1, no. 1.
  45. Hooten, M.B., Leeds, W.B., Fiechter, J. and C.K. Wikle, 2011: Assessing first-or der emulator inference for physical parameters in nonlinear mechanistic models. Journal of Agricultural, Biological, and Ecological Statistics, 16, 475-494.
  46. Arab, A., Holan, S.H., Wikle, C.K., and M.L. Wildhaber, 2011: Semiparametric bivariate zero-inflated Poisson models with application to studies of abu ndance for multiple species. Environmetrics, in press.
  47. Milliff, R.F., Bonazzi, A., Wikle, C.K., Pinardi, N., and L.M. Berliner, 2011: Ocean ensemble forecasting, Part I: Ensemble Mediterranean winds from a Bayesian hierarchical model. Quarterly Journa l of the Royal Meteorological Society Early view: DOI:10.1002/qj.767.
  48. Pinardi, N., Bonazzi, A., Dobricic, S., Milliff, R.F., Wikle, C.K., and L.M. Berliner, 2011: Ocean ensemble forecasting, Part II: Mediterranean forecast system response. Quarterly Journal of the Roy al Meteorological Society. DOI:10.1002/qj.816.
  49. Wikle, C.K. and S.H. Holan, 2011: Polynomial nonlinear spatio-tempor al integro-difference equation models Journal of Time Series Analysis. 32, 339-350; DOI: 10.1111//j.1467-9892.2011.00729.x
  50. Wikle, C.K., 2011: A hierarchical Bayesian arms race model The Internat ional Journal of Conflict & Reconciliation . 1 , no. 1.
  51. Wikle, C.K. and M.B. Hooten, 2010: A general science-based framewor k for spatio-temporal dynamical models. Invited discussion paper for Test. 19, 417-451
  52. Holan, S.H., Wikle, C.K., Sullivan-Beckers, L.E., and R.B. Cocroft, 2010: Modeling complex phenotypes: generalized linear models using spectrogram predictors of animal communication signals. Biometrics. 66, 914-924.
  53. Hooten, M.B. and C.K. Wikle, 2010: Statistical agent-based mode ls for discrete spatio-temporal systems. Journal of the American Statistical Association, 105, 236-248.
  54. Micheas, A., and C.K. Wikle, 2009: A Bayesian hierarchical non-ove rlapping random disc growth model. Journal of the American Statistical Association, 104, 274-283.
  55. Sheng, Y., and C.K. Wikle, 2009: Bayesian IRT models incorporating ge neral and specific abilities. Behaviormetrika, 36, 27-48.
  56. Hooten, M.B., Wikle, C.K., Sheriff, S., and J. Rushin, 2009: Opti mal spatio-temporal hybrid sampling designs for monitoring ecological structure. Journal of Vegetation Science, 20, 639-649.
  57. Cressie, N., Calder, K., Clark, J., VerHoef, J., and C.K. Wikle, 2009: Accounting for uncertainty in ecological analysis: the strengths and limitations of hierarchical statistical modeling. Ecological Applications<\i>, 19, 553-570.
  58. Malmberg, A., Arellano, A., Edwards, D.P., Flyer, N., Nychka, D., and C.K. Wikle, 2008: Interpolating fields of carbon monoxide data using a hybrid statistical-physical model. The Annals of Applied Statistics, 2, 1231--1248.
  59. Arab, A., Wildhaber, M., Wikle, C.K., and C.N. Gentry, 2008: Zero-inf lated modelling of fish catch per unit area resulting from multiple gears: Application to channel catfish and shovelnose sturgeon in the Missouri River. North American Journal of Fisheries Management, 28, 1044--1058.
  60. Sheng, Y., and C.K. Wikle, 2008: Bayesian multidimensional IRT mod els with a hierarchical structure. Educational and Psychological Measurement, 68, 413-430.
  61. Hooten, M. B. and C.K. Wikle, 2007: A Hierarchical Bayesian non-l inear spatio-temporal model for the spread of invasive species with application to the Eurasian Collared-Dove. Environmental and Ecological Statis tics, 15, 59-70.
  62. Micheas, A.C., Fox, N.I., Lack, S.A., and C.K. Wikle, 2007: Cell id entification and verification of QPF ensembles using shape analysis techniques, Journal of Hydrology, 343, 105-116.
  63. Song, Y., Wikle, C.K., Anderson, C.J., and S.A. Lack, 2007: Bayesian estimation of stochastic parameterizations in a numerical weather forecasting model, Monthly Weather Review. 135, 4045-4059.
  64. Hooten, M.B., Wikle, C.K., Dorazio, R.M., and J.A. Royle, 2007: Hiera rchical matrix models for characterizing invasions, Biometrics, 63, 558-567.
  65. Anderson, C.J., Wikle, C.K., Zhou, Q., and J.A. Royle, 2007: Population Influences on Tornado Reports in the United States. Weather and Forecasting, 22, 571-579.
  66. Sheng, Y., and C.K. Wikle, 2007: Comparing multi-unidimensional and unidimensional item response theory models, Educational and Psychological Measurement, 67, 899-919.
  67. He, H.S., Dey, D.C., Fan, X., Hooten, M.B., Kabrick, J.M., Wikle, C.K., and Z. Fan, 2007: Mapping pre-European settlement vegetation u sing a hierarchical Bayesian model, Plant Ecology, 191, 85-94.
  68. Wikle, C.K., and L. M. Berliner, 2007: A Bayesian tutorial for data assi milation, Physica D, 230, 1-16.
  69. Berliner, L.M. and C.K. Wikle, 2007: Approximate importance sampling Mon te Carlo for data assimilation, Physica D, 230, 37-49.
  70. Hooten, M.B. and C.K. Wikle, 2007: Shifts in the spatio-temporal g rowth dynamics of shortleaf pine. Environmental and Ecological Statistics, 14, 207-227.
  71. Xu, K., and C.K. Wikle, 2007: E-M algorithm implementation of effi cient spatio-temporal dynamical models. Journal of Statistical Planning and Inference, 137, 567-588.
  72. Wikle, C.K. and Hooten, M.B. (2006). Hierarchical Bayesian Spatio-Tem poral Models for Population Spread. In Applications of Computational Statistics in the Environmental Sciences: Hierarchical Bayes and MCMC Methods. J.S. Clark and A. Gelfand (eds). Oxford University Press. 145-169
  73. Xu, K., Wikle, C.K., and N. Fox (2005). A kernel based spatio temporal dynamical model for nowcasting radar precipitation. Journal of the American Statistical Association, 100, 1133-1144.
  74. Wikle, C.K., and J.A. Royle, (2005). Dynamic Design of Ecolog ical Monitoring Networks for Non-Gaussian Spatio-Temporal Data. Environmetrics, 16, 507-522.
  75. Fox, N.I. and C.K. Wikle, 2005: A Bayesian quantitative precipitation nowcast scheme. Weather and Forecasting, 20, 264-275.
  76. Fox, N.I. and C.K. Wikle, 2005: Providing distributed forecasts of precipitation using a Bayesian nowcast scheme. Atmospheric Scie nce Letters, 6, 59-65.
  77. Wikle, C.K. and L.M. Berliner, (2005). Combining information a cross spatial scales. Technometrics, 47, 80-91.
  78. Cripps, E., Nott, D., Dunsmuir, W.T.M., and C.K. Wikle, 2005: Space-time Modelling of Sydney Harbour Winds. Australian and New Zea land Journal of Statistics. 47, 3-17.
  79. Royle, J.A., and C.K. Wikle, (2005). Efficient Statistical Mappi ng of Avian Count Data. Ecological and Environmental Statistics, 12, 225-243.
  80. Hoar, T.J., Milliff, R.F., Nychka, D., Wikle, C.K., and L.M. Berliner, (2003). Winds from a Bayesian hierarchical model: Computation fo r atmosphere-ocean research. Journal of Computational and Graphical Statistics, 4, 781-807.
  81. Hooten, M. B., Larsen, D.R., and C.K. Wikle, (2003). Predicting the spatia l distribution of ground flora on large domains using a hierarchical Bayesian model. Landscape Ecology, 18, 487-502.
  82. Wikle, C.K. and C.J. Anderson, (2003). Climatological analysis of torna do report counts using a hierarchical Bayesian spatio-temporal model. Journal of Geophysical Research-Atmospheres, 108(D24), 9005, doi:10 .1029/2002JD002806.
  83. Berliner, L.M., Milliff, R.F., and C.K. Wikle, (2003). Bayes ian hierarchical modeling of air-sea interaction. Journal of Geophysical Research - Oceans, 108(C4)
  84. Wikle, C.K., L.M. Berliner, and R.F. Milliff, (2003). Hierarchical Baye sian approach to boundary value problems with stochastic boundary conditions. Monthly Weather Review, 131, 1051-1062.
  85. Wikle, C.K., (2003). Hierarchical Bayesian models for predicting the spread of ecological processes. Ecology, 84, 1382-1394. Append ix.
  86. Wikle, C.K., (2003). Hierarchical models in environmental science . International Statistical Review, 71, 181-199.
  87. Wikle, C.K., (2002). A kernel-based spectral model for non-Gaussi an spatio-temporal processes. Statistical Modelling: An International Journal, 2, 299-314.
  88. Nychka, D., C.K. Wikle, and J.A. Royle, (2002). Multiresolution models for nonstationary spatial covariance functions. Statistical Modelling: An International Journal, 2, 315-331.
  89. Wikle, C.K. and J.A. Royle, (2004). Spatial statistical modelin g in biology. In Encyclopedia of Life Support Systems (EOLSS), Developed under the Auspices of the UNESCO, Eolss Publishers, Oxford ,UK, [http://www.eolss.net]
  90. Wikle, C.K., (2003). Spatio-temporal models in climatology. In Encyclopedia of Life Support Systems (EOLSS), Developed under the Auspices of the UNESCO, Eolss Publishers, Oxford ,UK, [http://www.eolss.net]
  91. Cressie, N. and C.K. Wikle, (2002). Space-time Kalman filter. Entry in Encyclopedia of Environmetrics, vol.4, eds. A.H. El-Shaarawi and W.W. Piegorsch. Wiley, New York, pp.2045-2049.
  92. Wikle, C.K., (2002). Spatial modeling of count data: A case study in m odelling breeding bird survey data on large spatial domains. In Spatial Cluster Modelling, A. Lawson and D. Denison, eds. Chapman and Hall, 199- 209.
  93. Wikle, C.K., (2001). A kernel-based spectral approach for spatio-temporal dyn amic models. Proceedings of the 1st Spanish Workshop on Spatio-Temporal Modelling of Environmental Processes (METMA), Benicassim, Castellon (Spa in), 28-31 October 2001, pp. 167-180.
  94. Wikle, C.K., Milliff, R.F., Nychka, D., and L.M. Berliner, (2001). S patiotemporal hierarchical Bayesian modeling: Tropical ocean surface winds. JASA, 96, 382-397.
  95. Berliner, L.M., Wikle, C.K. and N. Cressie, (2000). Long-lead p rediction of Pacific SSTs via Bayesian Dynamic Modeling. Journal of Climate, 13 , 3953-3968. Color Figures.
  96. Wikle, C.K. and N. Cressie, (2000). Space-time statistical modeling of environmental data. Quantifying Spatial Uncertainty in Natura l Resources, H.T. Mowrer, R.G. Congalton, eds. Ann Arbor Press, Chelsea, MI, 213-235.
  97. Wikle, C.K., (2000). Hierarchical Space-Time Dynamic Models. In Case Studies in Statistics and the Atmospheric Sciences, L.M Ber liner, D. Nychka, T. Hoar, eds., Springer-Verlag, New York, 45-64.
  98. Berliner, L.M., Wikle, C.K., and R.F. Milliff, (1999). Multiresolution wavelet analyses in hierarchical Bayesian turbulence models. Bayesian Inference in Wavelet Based Models, a Springer Lecture-Notes volume, P. Mueller and B. Vidakovic, eds., 341-359.
  99. Berliner, L.M., Royle, J.A., Wikle, C.K., and R.F. Milliff, (1999). Bayesian Methods in the Atmospheric Sciences. Bayesian Statistic s 6, J.M. Bernardo, J.O. Berger, A.P. Dawid and A.F.M. Smith (Eds.), Oxford University Press, 83-100.
  100. Wikle, C.K., Milliff, R.F., and W.G. Large, (1999). Surface wind variability on spatial scales from 1 to 1000 km observed during TOGA C OARE. Journal of Atmospheric Science, 56, 2222-2231.
  101. Wikle, C.K. and J.A. Royle, (1999). Space-time models and dynamic des ign of environmental monitoring networks. Journal of Agricultural, Biological, and Environmental Statistics, 4, 489-507.
  102. Wikle, C.K. and N. Cressie, (1999). A dimension reduced approach to space-time Kalman filtering. Biometrika, 86, 815-829.
  103. Cressie, N. and C.K. Wikle, (1998). The variance-based cross-variogram: You can add apples and oranges. Mathematical Geology, 30, 789-799.
  104. Wikle, C.K., Berliner, L.M., and N. Cressie, (1998). Hierarc hical Bayesian space-time models. Environmental and Ecological Statistics, 5, 117-154.
  105. Brown, T.J. Berliner, L.M., Wilks, D.S., Richman, M.B., and C.K. Wikle, (1998). Statistics education in the atmospheric sciences. Bu lletin of the American Meteorological Society, 80, 2087-2097.
  106. Cressie, N. and C.K. Wikle, (1998). Strategies for dynamic space-time statistical modeling: Discussion of 'The Kriged Kalman Filter' by Mardia et al. Test, 7, no. 2, 257-64.
  107. Royle, J.A., Berliner, L.M., Wikle, C.K., and R. Milliff, (1998). A hierarchical spatial model for constructing wind fields from scatte rometer data in the Labrador sea. Case Studies in Bayesian Statistics IV, Springer-Verlag, 367-382.
  108. Wikle, C.K. and R.A. Madden, (1997). Seasonal variation of upper tropospheric and lower stratospheric equatorial waves over the tropica l Pacific. Journal of Atmospheric Science, 54, 1895-1909.
  109. Chen, T.-C., Pfaendtner, J., J.-M. Chen, and C.K. Wikle (1996). Variability of the global precipitable water with a time scale of 90-15 0 days. Journal of Geophysical Research, 101, 9323-9332.
  110. Chen, T.-C., J.-M. Chen, and C.K. Wikle (1996). Interdecadal variation in U.S. Pacific coast precipitation over the past four decades < i>Bulletin of the American Meteorological Society, 77, 1197-1205.
  111. Wikle, C.K. and T.-C. Chen, (1996). On the semiannual variation in the northern hemisphere extratropical height field. Journal of Cl imate, 9, 2250-2258.
  112. Wikle, C.K., Sherman, P.J., and T.-C. Chen (1995). Identifying periodic components in atmospheric data using a family of minimum varian ce spectral estimators. Journal of Climate, 8, 2352-2363.