Gap-free 16-year (2005-2020) sub-diurnal surface meteorological observations across Florida

The rather unique sub-tropical, flat, peninsular region of Florida is subject to a unique climate with extreme weather events across the year that impacts agriculture, public health, and management of natural resources. Meteorological data at high temporal resolutions especially in the tropical latitudes are essential to understand diurnal and semi-diurnal variations of climate, which are considered to be the fundamental modes of climate variations of our Earth system. However, many meteorological datasets contain gaps that limit their use for validation of models and further detailed observational analysis. The objective of this paper is to apply a set of data gap filling strategies to develop a gap-free dataset with 15-minute observations for the sub-tropical region of Florida. Using data from the Florida Automated Weather Network (FAWN), methods of linear interpolation, trend continuation, reference to external sources, and nearest station substitution were applied to fill in the data gaps depending on the extent of the gap. The outcome of this study provides continuous, publicly accessible surface meteorological observations for 30 FAWN stations at 15-minute intervals for the years 2005-2020.Comment: 16 pages, 8 figures, 3 table

Downscaling the South American Monsoon Seasonal Climate

Novel techniques of dynamic downscaling are presented in the context of the South American Monsoon. It is shown that considerable improvement from a relatively coarse AGCM with rather poor skill in simulating the South American monsoon variability can be gained from adopting some bias correcting procedures while downscaling to higher resolution regional climate models.Pages: 583-58

Coupled Climate Simulation of the South American Monsoon

Using the newly formulated coupled climate model of the Center for Ocean-Land-Atmosphere Studies, we look at the seasonal to interannual variations of the South American monsoon and its relationship to ENSO and tropical Atlantic Variability.Pages: 1131-113

Data from coupled RSM simulations

The Regional Spectral Model (RSM; atmospheric component) coupled with Regional Ocean Modeling System (ROMS; oceanic component) is used in this study as the Regional Coupled Model(RCM) (H. Li & Misra, 2014; Misra, Mishra, & Bhardwaj, 2017; Misra et al., 2018). The RCM performs reasonably well in capturing the mean seasonal rainfall over the Indian region as well as the subseasonal variability (Misra et al., 2017, 2018). The circulation patterns associated with the active-break cycle over India are well-simulated in the model. The RSM was first introduced in Juang and Kanamitsu (1994) and several modifications have been made to the model since then (Glazer & Misra, 2018; Kanamitsu, Yoshimura, Yhang, & Hong, 2010; Misra et al., 2018). It has 28 terrain following sigma vertical coordinates. The ROMS has 30 vertical sigma levels on a horizontal staggered Arakawa C grid (Shchepetkin & McWilliams, 2005). In this experiment, a 10 km grid resolution is used and the grids are identical for both the RSM and ROMS. This enables direct exchange of fluxes between atmospheric and oceanic components without the need for interpolation. The coupling between ROMS and RSM occurs at three hour interval. Flux correction is not applied to the integration. The domain of integration is specified over the Indian region as shown in Misra et al. (2018) (roughly 4N-37N, 50E-101E). Lateral boundary conditions for the RSM are prescribed by the National Centers for Environmental Prediction{Department of Energy global atmospheric reanalysis (Kanamitsu et al., 2002) every six hours. The ROMS boundary forcings are from Simple Ocean Data Assimilation version 2.2.4 (SODA v2.2.4), global oceanic analysis (Carton & Giese, 2008) prescribed at a monthly interval. The integration is carried out for a period of 10 years from January 1, 1986 through December 31, 1995