AJCC  Vol.4 No.1 , March 2015
Rainy/Non-Rainy Day Pattern Analysis for North Carolina
Abstract: Trends in rainy/non-rainy days are investigated using the Mann-Kendall non-parametric test at 249 weather station sites of North Carolina, United States. Sen-Slope method has been applied to predict the trend magnitude. Inverse distance weighing interpolation technique is adopted to represent the spatial distribution of trend magnitude across the North Carolina. Quality controlled daily precipitation data sets from 1950 to 2009 have been used to analyze. The double-mass curve and autocorrelation were adopted to analyze the precipitation time series of each station to check the consistency and homogeneity. Standard Precipitation Index (SPI) has also been discussed for the study area. It is found in North Carolina that a number of rainy day trends are increasing both spatially and temporally. Eastern part of North Carolina shows the significant increasing rainy day trends. Trend significance has been checked at 1% and 5% significance level. Recent decades show the high SPI in both the extent of wetness and dryness.
Cite this paper: Islam, A. (2015) Rainy/Non-Rainy Day Pattern Analysis for North Carolina. American Journal of Climate Change, 4, 1-8. doi: 10.4236/ajcc.2015.41001.

[1]   GIPCC (2007) Climate Change 2007: The Physical Science Basis, Summary for Policymakers. In: Solomon, S., et al., Eds., Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC), Cambridge Univ Press, Cambridge, 237-336.

[2]   Karl, T.R. and Knight, R.W. (1998) Secular Trends of Precipitation Amount, Frequency, and Intensity in the United States. Bulletin of the American Meteorological Society, 79, 231-241.<0231:STOPAF>2.0.CO;2

[3]   Kunkel, K., Andsager, K., Liang, X., Arritt, R., Takle, E., Gutowski, W. and Pan, Z. (2002) Observations and Regional Climate Model Simulations of Heavy Precipitation Events and Seasonal Anomalies: A Comparison. Journal of Hydrometeorology, 3, 322-334.<0322:OARCMS>2.0.CO;2

[4]   Small, D., Islam, S. and Vogel, R.M. (2006) Trends in Precipitation and Streamflow in the Eastern US. Paradox or Perception? Geophysical Research Letters, 33, Article ID: L03403.

[5]   Keim, B.D. and Fischer, M.R. (2005) Are There Spurious Precipitation Trends in the United States Climate Division Database? Geophysical Research Letters, 32, Article ID: L04702.

[6]   Sayemuzzaman, M. and Jha, M. (2014) Seasonal and Annual Precipitation Time Series Trend Analysis in North Carolina, United States. Atmospheric Research, 137, 183-194.

[7]   Robinson, P. (2005) North Carolina Weather and Climate. University of North Carolina Press in Association with the State Climate Office of North Carolina. Ryan Boyles, Graphics.

[8]   GorjiSefidmazgi, M., Sayemuzzaman, M., Homaifar, A., Jha, M.K. and Liess, S. (2014) Trend Analysis Using Non-Stationary Time Series Clustering Based on the Finite Element Method. Nonlinear Processes in Geophysics, 21, 605-615.

[9]   USDA-ARS (2014) Agricultural Research Service, United States Department of Agriculture.

[10]   Sayemuzzaman, M., Jha, M.K. and Mekonnen, A. (2014) Spatio-Temporal Long-Term (1950-2009) Temperature Trend Analysis in North Carolina, United States. Theoretical and Applied Climatology, 116, 3-4.

[11]   Peterson, T.C., Easterling, D.R., Karl, T.R., Groisman, P.Y., Nicholis, N., Plummer, N., Torok, S., Auer, I., Boehm, R., Gullett, D., Vincent, L., Heino, R., Tuomenvirta, H., Mestre, O., Szentimrey, T., Salinger, J., Førland, E., Hanssen-Bauer, I., Alexandersson, H., Jones, P. and Parker, D. (1998) Homogeneity Adjustments of in Situ Atmospheric Climate Data: A Review. International Journal of Climatology, 18, 1493-1517.<1493::

[12]   Sayemuzzaman, M. and Jha, M.K. (2014) Modeling of Future Land Cover Land Use Change in North Carolina Using Markov Chain and Cellular Automata Model. American Journal of Engineering and Applied Science, 7, 295-306.

[13]   Gocic, M. and Trajkovic, S. (2013) Analysis of Changes in Meteorological Variables Using Mann-Kendall and Sen’s Slope Estimator Statistical Tests in Serbia. Global and Planetary Change, 100, 172-182.

[14]   Chang, S.Y. and Sayemuzzaman, M. (2014) Using Unscented Kalman Filter in Subsurface Contaminant Transport Models. Journal of Environmental Informatics, 23, 14-22.

[15]   Mann, H.B. (1945) Non-Parametric Tests against Trend. Econometrica, 13, 245-259.

[16]   Kendall, M.G. (1975) Rank Correlation Measures. Charles Griffin, London.

[17]   Sen, P.K. (1968) Estimates of the Regression Coefficient Based on Kendall’s Tau. Journal of the American Statistical Association, 63, 1379-1389.

[18]   Fan, J.Q. and Yao, Q.W. (2003) Nonlinear Time Series: Nonparametric and Parametric Methods (Springer Series in Statistics). Springer-Verlag, New York.

[19]   Sayemuzzaman, M., Jha, M.K., Mekonnen, A. and Schimmel, K.A. (2014) Subseasonal Climate Variability for North Carolina, United States. Atmospheric Research, 145, 69-79.

[20]   Sayemuzzaman, M. (2010) Using Unscented Kalman Filter in Subsurface Contaminant Transport Models. Master’s Thesis, Energy and Environmental System Department, North Carolina A & T State University, Greensboro.

[21]   Sayemuzzaman, M. and Jha, M.K. (2013) Monthly Time Series Trend Analysis of Temperature and Precipitation in North Carolina. Proceedings of the AGU Fall Meeting, San Francisco, 9-13 December 2013, 1-8.

[22]   Salas, J.D., Delleur, J.W., Yevjevich, V.M. and Lane, W.L. (1980) Applied Modeling of Hydrologic Time Series. Water Resources Publications, Littleton.

[23]   Sayemuzzaman, M. (2014) Spatio-Temporal Trends of Climate Variability in North Carolina. Ph.D. Dissertation, North Carolina A & T State University, Greensboro.

[24]   McKee, T.B., Nolan, J.D. and Kliest, J. (1993) The Relationship of Drought Frequency and Duration of Time Scales. Proceedings of the 8th Conference on Applied Climatology, Anaheim, 17-22 January 1993, 1-6.