|
Bastola, S., Misra, V., & Li, H. (2013). Seasonal Hydrological Forecasts for Watersheds over the Southeastern United States for the Boreal Summer and Fall Seasons. Earth Interact., 17(25), 1–22.
|
|
Nof, D., Jia, Y., Chassignet, E., & Bozec, A. (2011). Fast Wind-Induced Migration of Leddies in the South China Sea. J. Phys. Oceanogr., 41(9), 1683–1693.
|
|
Wei, J., Dirmeyer, P. A., Guo, Z., Zhang, L., & Misra, V. (2010). How Much Do Different Land Models Matter for Climate Simulation? Part I: Climatology and Variability. J. Climate, 23(11), 3120–3134.
|
|
Misra, V., & Dirmeyer, P. A. (2009). Air, Sea, and Land Interactions of the Continental U.S. Hydroclimate. J. Hydrometeor, 10(2), 353–373.
|
|
Perrie, W., Zhang, W., Bourassa, M., Shen, H., & Vachon, P. W. (2008). Impact of Satellite Winds on Marine Wind Simulations. Wea. Forecasting, 23(2), 290–303.
|
Devanas, A., & Stefanova, L. (2018). Statistical Prediction Of Waterspout Probability For The Florida Keys. Wea. Forecasting, 33, 389–410.
Abstract: A statistical model of waterspout probability was developed for wet-season (June–September) days over the Florida Keys. An analysis was performed on over 200 separate variables derived from Key West 1200 UTC daily wet-season soundings during the period 2006–14. These variables were separated into two subsets: days on which a waterspout was reported anywhere in the Florida Keys coastal waters and days on which no waterspouts were reported. Days on which waterspouts were reported were determined from the National Weather Service (NWS) Key West local storm reports. The sounding at Key West was used for this analysis since it was assumed to be representative of the atmospheric environment over the area evaluated in this study. The probability of a waterspout report day was modeled using multiple logistic regression with selected predictors obtained from the sounding variables. The final model containing eight separate variables was validated using repeated fivefold cross validation, and its performance was compared to that of an existing waterspout index used as a benchmark. The performance of the model was further validated in forecast mode using an independent verification wet-season dataset from 2015–16 that was not used to define or train the model. The eight-predictor model was found to produce a probability forecast with robust skill relative to climatology and superior to the benchmark waterspout index in both the cross validation and in the independent verification.
|
Misra, V., & Bhardwaj, A. (2019). Defining the Northeast Monsoon of India. Mon. Wea. Rev., 147(3), 791–807.
Abstract: This study introduces an objective definition for onset and demise of the Northeast Indian Monsoon (NEM). The definition is based on the land surface temperature analysis over the Indian subcontinent. It is diagnosed from the inflection points in the daily anomaly cumulative curve of the area-averaged surface temperature over the provinces of Andhra Pradesh, Rayalseema, and Tamil Nadu located in the southeastern part of India. Per this definition, the climatological onset and demise dates of the NEM season are 6 November and 13 March, respectively. The composite evolution of the seasonal cycle of 850hPa winds, surface wind stress, surface ocean currents, and upper ocean heat content suggest a seasonal shift around the time of the diagnosed onset and demise dates of the NEM season. The interannual variations indicate onset date variations have a larger impact than demise date variations on the seasonal length, seasonal anomalies of rainfall, and surface temperature of the NEM. Furthermore, it is shown that warm El Niño�Southern Oscillation (ENSO) episodes are associated with excess seasonal rainfall, warm seasonal land surface temperature anomalies, and reduced lengths of the NEM season. Likewise, cold ENSO episodes are likely to be related to seasonal deficit rainfall anomalies, cold land surface temperature anomalies, and increased lengths of the NEM season.
|
|
Smith, S. R., Briggs, K., Lopez, N., & Kourafalou, V. (2016). Applying Automated Underway Ship Observations to Numerical Model Evaluation. J. Atmos. Oceanic Technol., 33(3), 409–428.
|
Ansong, J. K., Arbic, B. K., Simmons, H. L., Alford, M. H., Buijsman, M. C., Timko, P. G., et al. (2018). Geographical Distribution of Diurnal and Semidiurnal Parametric Subharmonic Instability in a Global Ocean Circulation Model. J. Phys. Oceanogr., 48(6), 1409–1431.
Abstract: The evidence for, baroclinic energetics of, and geographic distribution of parametric subharmonic instability (PSI) arising from both diurnal and semidiurnal tides in a global ocean general circulation model is investigated using 1/12.5° and 1/25° simulations that are forced by both atmospheric analysis fields and the astronomical tidal potential. The paper examines whether PSI occurs in the model, and whether it accounts for a significant fraction of the tidal baroclinic energy loss. Using energy transfer calculations and bispectral analyses, evidence is found for PSI around the critical latitudes of the tides. The intensity of both diurnal and semidiurnal PSI in the simulations is greatest in the upper ocean, consistent with previous results from idealized simulations, and quickly drops off about 5° from the critical latitudes. The sign of energy transfer depends on location; the transfer is positive (from the tides to subharmonic waves) in some locations and negative in others. The net globally integrated energy transfer is positive in all simulations and is 0.5%�10% of the amount of energy required to close the baroclinic energy budget in the model. The net amount of energy transfer is about an order of magnitude larger in the 1/25° semidiurnal simulation than the 1/12.5° one, implying the dependence of the rate of energy transfer on model resolution.
|
|
Misra, V., Mishra, A., & Bhardwaj, A. (2018). Simulation of the Intraseasonal Variations of the Indian Summer Monsoon in a Regional Coupled Ocean-Atmosphere Model. J. Climate, 31(8), 3167–3185.
|
