Application of High-Resolution Regional Climate Model Product in Climate and Weather Research

Accurate regional and local scale information about seasonal climate variability and its impact on water availability is important in many practical applications like agriculture, water resource planning, long term decision making etc. Presently, the primary source for real-time seasonal climate for...

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Bibliographic Details
Main Author: Pal, Sujan
Other Authors: Castro, Christopher L., Ritchie, Elizabeth, Chang, Hsin-I, Galarneau, Thomas J.
Format: Thesis
Language:English
Published: The University of Arizona. 2017
Subjects:
Online Access:http://hdl.handle.net/10150/624093
Description
Summary:Accurate regional and local scale information about seasonal climate variability and its impact on water availability is important in many practical applications like agriculture, water resource planning, long term decision making etc. Presently, the primary source for real-time seasonal climate forecast comes from the CPC within the NOAA-NCEP which uses its model forecast component (CFSv2) of North American Multi-Model Ensemble (NMME). But it has been observed that in comparison to the cool season, the level of skill in warm season seasonal forecasts of precipitation produced by the NMME is much lower (Kirtman et al. 2014) due to the poor climatological representation of warm season convective precipitation. To fully realise the potential in improving warm season seasonal forecasts using a dynamical modeling approach requires dynamical downscaling of NMME models to better improve their representation of convective precipitation at a convective-permitting (3km) grid. A decade-long CFSR (the reanalysis product of CFS) data is dynamically downscaled using WRF to demonstrate the value added of convective permitting modeling in the representation of mean and extreme warm season precipitation over the Southwest United States. The study shows evidence that the use of regional model adds value to the reanalyses in terms to better special and temporal representation which is also consistent with previous studies and appears to be an important initial step towards seasonal to subseasonal (S2S) forecasting. Empirical observations show that the structure and size of tropical cyclones (TCs) have dramatic impacts at landfall, including wind damage and storm surge. A better understanding of how the large-scale environment affects TC size and size change might be helpful in the predictions of the TC environment to infer how the TC size might change close to landfall. This study investigates the influence of environmental factors on TC size expansions using numerical simulations. Two periods of size change are investigated one in Hurricane Katrina (2005) as it moved through the Gulf of Mexico and one in Igor (2010) as it begins to undergo extratropical transition. Size changes are evaluated using the North Atlantic Hurricane Database second generation (HURDAT2) data set, which contains the maximum radial extent of the 64-, 50- and 34-kt wind in four quadrants. The average 34-kt wind radius (R34) is used as an indicator of the size of the TC. For the purposes of this study, the environment of a TC is investigated if the wind field either expanded or contracted in size at least 15 n mi radially in a 12-hour period. The regional model used is WRF-ARW. The results found from the simulation of Hurricane Katrina support previous results that increased surface fluxes and higher moisture availability is conducive to TC wind expansion and that as the moisture is depleted, the expansion of the wind field is no longer supported. In the case of Hurricane Igor, the influences of the midlatitude westerlies was evident in the increasing deep vertical wind shear, which is known to be detrimental to TC structure and intensity when strong enough.