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This study performs numerical modeling for the climate of semi-arid regions by running a high-resolution atmospheric model constrained by large-scale climatic boundary conditions, a practice commonly called climate downscaling. These investigations focus especially on precipitation and temperature, quantities that are critical to life in semi-arid regions. Using the Weather Research

This study performs numerical modeling for the climate of semi-arid regions by running a high-resolution atmospheric model constrained by large-scale climatic boundary conditions, a practice commonly called climate downscaling. These investigations focus especially on precipitation and temperature, quantities that are critical to life in semi-arid regions. Using the Weather Research and Forecast (WRF) model, a non-hydrostatic geophysical fluid dynamical model with a full suite of physical parameterization, a series of numerical sensitivity experiments are conducted to test how the intensity and spatial/temporal distribution of precipitation change with grid resolution, time step size, the resolution of lower boundary topography and surface characteristics. Two regions, Arizona in U.S. and Aral Sea region in Central Asia, are chosen as the test-beds for the numerical experiments: The former for its complex terrain and the latter for the dramatic man-made changes in its lower boundary conditions (the shrinkage of Aral Sea). Sensitivity tests show that the parameterization schemes for rainfall are not resolution-independent, thus a refinement of resolution is no guarantee of a better result. But, simulations (at all resolutions) do capture the inter-annual variability of rainfall over Arizona. Nevertheless, temperature is simulated more accurately with refinement in resolution. Results show that both seasonal mean rainfall and frequency of extreme rainfall events increase with resolution. For Aral Sea, sensitivity tests indicate that while the shrinkage of Aral Sea has a dramatic impact on the precipitation over the confine of (former) Aral Sea itself, its effect on the precipitation over greater Central Asia is not necessarily greater than the inter-annual variability induced by the lateral boundary conditions in the model and large scale warming in the region. The numerical simulations in the study are cross validated with observations to address the realism of the regional climate model. The findings of this sensitivity study are useful for water resource management in semi-arid regions. Such high spatio-temporal resolution gridded-data can be used as an input for hydrological models for regions such as Arizona with complex terrain and sparse observations. Results from simulations of Aral Sea region are expected to contribute to ecosystems management for Central Asia.
ContributorsSharma, Ashish (Author) / Huang, Huei-Ping (Thesis advisor) / Adrian, Ronald (Committee member) / Herrmann, Marcus (Committee member) / Phelan, Patrick E. (Committee member) / Vivoni, Enrique (Committee member) / Arizona State University (Publisher)
Created2012
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Description
Earth-system models describe the interacting components of the climate system and

technological systems that affect society, such as communication infrastructures. Data

assimilation addresses the challenge of state specification by incorporating system

observations into the model estimates. In this research, a particular data

assimilation technique called the Local Ensemble Transform Kalman Filter (LETKF) is

applied

Earth-system models describe the interacting components of the climate system and

technological systems that affect society, such as communication infrastructures. Data

assimilation addresses the challenge of state specification by incorporating system

observations into the model estimates. In this research, a particular data

assimilation technique called the Local Ensemble Transform Kalman Filter (LETKF) is

applied to the ionosphere, which is a domain of practical interest due to its effects

on infrastructures that depend on satellite communication and remote sensing. This

dissertation consists of three main studies that propose strategies to improve space-

weather specification during ionospheric extreme events, but are generally applicable

to Earth-system models:

Topic I applies the LETKF to estimate ion density with an idealized model of

the ionosphere, given noisy synthetic observations of varying sparsity. Results show

that the LETKF yields accurate estimates of the ion density field and unobserved

components of neutral winds even when the observation density is spatially sparse

(2% of grid points) and there is large levels (40%) of Gaussian observation noise.

Topic II proposes a targeted observing strategy for data assimilation, which uses

the influence matrix diagnostic to target errors in chosen state variables. This

strategy is applied in observing system experiments, in which synthetic electron density

observations are assimilated with the LETKF into the Thermosphere-Ionosphere-

Electrodynamics Global Circulation Model (TIEGCM) during a geomagnetic storm.

Results show that assimilating targeted electron density observations yields on

average about 60%–80% reduction in electron density error within a 600 km radius of

the observed location, compared to 15% reduction obtained with randomly placed

vertical profiles.

Topic III proposes a methodology to account for systematic model bias arising

ifrom errors in parametrized solar and magnetospheric inputs. This strategy is ap-

plied with the TIEGCM during a geomagnetic storm, and is used to estimate the

spatiotemporal variations of bias in electron density predictions during the

transitionary phases of the geomagnetic storm. Results show that this strategy reduces

error in 1-hour predictions of electron density by about 35% and 30% in polar regions

during the main and relaxation phases of the geomagnetic storm, respectively.
ContributorsDurazo, Juan, Ph.D (Author) / Kostelich, Eric J. (Thesis advisor) / Mahalov, Alex (Thesis advisor) / Tang, Wenbo (Committee member) / Moustaoui, Mohamed (Committee member) / Platte, Rodrigo (Committee member) / Arizona State University (Publisher)
Created2018