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

The fidelity of NCEP-CFS Seasonal Hindcasts over Nordeste

The predictability of February-March-April seasonal rainfall over Nordeste (Northeast Brazil) is examined from the suite of CFS hindcasts. Nordeste from past forced AGCM studies is found to be one of the most promising areas of climate prediction as its variability is strongly dictated by the slowly varying SSTA over the eastern equatorial Pacific Ocean and the tropical Atlantic Ocean. The rainfall variability over Nordeste is intricately related to SST variations in both the equatorial Pacific Ocean and the tropical Atlantic Ocean and their subtle interactions. We propose to examine if such a conclusion can be drawn from a coupled climate model which has relatively high skill in predicting SSTA over the equatorial Pacific region. Furthermore, the analysis will also provide a hindcast skill evaluation over the tropical Atlantic region.Pages: 585-58

Monitoring the Indian Summer Monsoon Evolution at the Granularity of the Indian Meteorological Sub-divisions using Remotely Sensed Rainfall Products

We make use of satellite-based rainfall products from the Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA) to objectively define local onset and demise of the Indian Summer Monsoon (ISM) at the spatial resolution of the meteorological subdivisions defined by the Indian Meteorological Department (IMD). These meteorological sub-divisions are the operational spatial scales for official forecasts issued by the IMD. Therefore, there is a direct practical utility to target these spatial scales for monitoring the evolution of the ISM. We find that the diagnosis of the climatological onset and demise dates and its variations from the TMPA product is quite similar to the rain gauge based analysis of the IMD, despite the differences in the duration of the two datasets. This study shows that the onset date variations of the ISM have a significant impact on the variations of the seasonal length and seasonal rainfall anomalies in many of the meteorological sub-divisions: for example, the early or later onset of the ISM is associated with longer and wetter or shorter and drier ISM seasons, respectively. It is shown that TMPA dataset (and therefore its follow up Global Precipitation Measurement (GPM) Integrated Multi-satellite Retrievals for GPM (IMERG)) could be usefully adopted for monitoring the onset of the ISM and therefore extend its use to anticipate the potential anomalies of the seasonal length and seasonal rainfall anomalies of the ISM in many of the Indian meteorological sub-divisions

Datasets from Regional Atmospheric Model Simulations and Regional Coupled Ocean-Atmospheric Model Simulations over India

These are the datasets from the climate simulations by using Regional Spectral Model coupled with Regional Ocean Modeling System (CRSM) and standalone Regional Spectral Model (URSM). The both the CRSM and URSM is configured at 20-km resolution and covers Indian region. The integration is carried out for a period of 20 years from January 1, 1986 through December 31, 2005. The simulation is forced by CCSM4 climate model participated in CMIP5

Influence of Intraseasonal Variability on the Development of Monsoon Depressions

Previous studies showed that the activity of monsoon low-pressure systems (LPS), which produce a large fraction of the South Asian monsoon's total rainfall, is modulated by intraseasonal variability. Using satellite-derived products and atmospheric reanalyzes, this study examines how the boreal summer intraseasonal oscillation (ISO) separately modulates the occurrence of weaker LPS (lows) and stronger LPS (depressions). It is found that the genesis of lows is insensitive to ISO phase, while depressions exhibit a strong preference for genesis during the phase that is convectively active over the northern Indian Ocean. Essentially, development of LPS into depressions depends upon the timing of genesis of the initial disturbance. Evidence is presented supporting the hypothesis that the development of lows into depressions is fostered by large-scale atmospheric conditions governed by the ISO. Results also suggest that while lows have no preference for forming over ocean compared to land, depressions mostly form over ocean

Estimating the Onset and Demise of the Wet Season

These Python scripts generate the onset and demise of the wet season over different parts of the world using GPM-IMERG (12-hour latency) products