Lim, Y. - K., Cocke, S., Shin, D. W., Schoof, J. T., LaRow, T. E., & O'Brien, J. J. (2010). Downscaling large-scale NCEP CFS to resolve fine-scale seasonal precipitation and extremes for the crop growing seasons over the southeastern United States.
Clim Dyn, 35(2-3), 449–471.
Misra, V., & Marx, L. (2009). The coupled seasonal hindcasts of the South American monsoon.
Int. J. Climatol., 29(8), 1101–1115.
Nielsen, E. R., Schumacher, R. S., & Keclik, A. M. (2016). The Effect of the Balcones Escarpment on Three Cases of Extreme Precipitation in Central Texas.
Mon. Wea. Rev., 144(1), 119–138.
Smedstad, O. M., Hurlburt, H. E., Metzger, E. J., Rhodes, R. C., Shriver, J. F., Wallcraft, A. J., et al. (2003). An operational Eddy resolving 1/16° global ocean nowcast/forecast system.
Journal of Marine Systems, 40-41, 341–361.
Solow, A. R., Adams, R. F., Bryant, K. J., Legler, D. M., O'Brien, J. J., McCarl, B. A., et al. (1998). The Value of Improved ENSO Prediction to U.S. Agriculture.
Climatic Change, 39(1), 47–60.
Srinivasan, A., Chassignet, E. P., Bertino, L., Brankart, J. M., Brasseur, P., Chin, T. M., et al. (2011). A comparison of sequential assimilation schemes for ocean prediction with the HYbrid Coordinate Ocean Model (HYCOM): Twin experiments with static forecast error covariances.
Ocean Modelling, 37(3-4), 85–111.
Todd, A., D. Dukhovskoy, M. Griffin. (2009). Effectiveness of the Keetch-Byram Drought Index toward the estimation of fires in Florida.
Agricultural and Forest Meteorology, , submitted.
Venugopal, T., Ali, M. M., Bourassa, M. A., Zheng, Y., Goni, G. J., Foltz, G. R., et al. (2018). Statistical Evidence for the Role of Southwestern Indian Ocean Heat Content in the Indian Summer Monsoon Rainfall.
Sci Rep, 8(1), 12092.
Abstract: This study examines the benefit of using Ocean Mean Temperature (OMT) to aid in the prediction of the sign of Indian Summer Monsoon Rainfall (ISMR) anomalies. This is a statistical examination, rather than a process study. The thermal energy needed for maintaining and intensifying hurricanes and monsoons comes from the upper ocean, not just from the thin layer represented by sea surface temperature (SST) alone. Here, we show that the southwestern Indian OMT down to the depth of the 26 degrees C isotherm during January-March is a better qualitative predictor of the ISMR than SST. The success rate in predicting above- or below-average ISMR is 80% for OMT compared to 60% for SST. Other January-March mean climate indices (e.g., NINO3.4, Indian Ocean Dipole Mode Index, El Nino Southern Oscillation Modoki Index) have less predictability (52%, 48%, and 56%, respectively) than OMT percentage deviation (PD) (80%). Thus, OMT PD in the southwestern Indian Ocean provides a better qualitative prediction of ISMR by the end of March and indicates whether the ISMR will be above or below the climatological mean value.
Weihs, R. (2016).
Surface and Atmospheric Boundary Layer Responses to Diurnal Variations of Sea Surface Temperature in an NWP Model. Ph.D. thesis, Florida State University, Tallahassee, FL.
Yatagai, A., Krishnamurti, T. N., Kumar, V., Mishra, A. K., & Simon, A. (2014). Use of APHRODITE Rain Gauge-Based Precipitation and TRMM 3B43 Products for Improving Asian Monsoon Seasonal Precipitation Forecasts by the Superensemble Method.
J. Climate, 27(3), 1062–1069.