Zhaoxia Pu, Ph.D.

Zhaoxia Pu

Zhaoxia Pu, Ph.D. Associate Professor, Atmospheric Sciences

Graduate Director, Atmospheric Sciences

Atmospheric Sciences
University of Utah
135 S 1460 East Rm 819 (WBB)
Salt Lake City, Ut 84112-0110

Office: 712 WBB
Office Phone: (801) 585-3864
Email: zhaoxia.pu@utah.edu
My Website: www.inscc.utah.edu/~pu/

Degrees:
1997 Ph.D. Meteorology/Atmospheric Sciences Lanzhou University

 

Prof. Pu obtained her Ph.D. in Meteorology in 1997, soon after her Ph.D. research (1993-1996) at National Centers for Environmental Prediction (NCEP)'s Environmental Modeling Center (EMC) in Washington, DC. She then continued her research at NCEP as a UCAR/NCEP postdoctoral fellow in 1997 and 1998. During 1999-2004, Dr. Pu worked at NASA Goddard Space Flight Center (NASA/GSFC) as a research scientist. She joined the faculty of the Department of Atmospheric Sciences, University of Utah in 2004.

Selected Publications:

Atmospheric modeling, data assimilation and predictability (Translated from E. Kalnay s book in English)  (Book), 2005
[ISBN]  [citation] 

Assimilation of satellite data in improving numerical simulations of tropical cyclones: progress, challenge and development  (Book Section), 2009
[citation] 

Impact of stochastic convection on ensemble forecasts of tropical cyclone development  (Journal Article), 2011
[citation]  [text]

Characteristics and numerical simulations of extremely high atmospheric boundary layer heights over a arid region in northwest of China.  (Journal Article), 2011
[citation]  [text]

Four-dimensional assimilation of multi-time wind profiles over a single station and numerical simulation of warm season convections observed during IHOP_2002.  (Journal Article), 2011
[citation]  [text]

Tracking and verification of East Atlantic tropical cyclone genesis in NCEP global ensemble: Case studies during NASA African monsoon multi-disciplinary analyses  (Journal Article), 2010
[citation]  [text]

An Observing System Simulation Experiment (OSSE) to assess the impact of Doppler wind lidar (DWL) measurements on the numerical simulation of a tropical cyclone  (Journal Article), 2010
[citation]  [text]

Beating the uncertainties: Ensemble forecasting and ensemble based data assimilation (review article)  (Journal Article), 2010
[citation]  [text]

Impact of airborne Doppler Wind Lidar data on numerical simulation of a tropical cyclone  (Journal Article), 2010
[citation]  [abstract]  [text]

Validation of AIRS temperature and moisture profiles over tropical oceans and their impact on numerical simulations of tropical cyclones  (Journal Article), 2010
[citation]  [text]

Diagnosis of the initial and forecast errors in the numerical simulation of the rapid intensification of Hurricane Emily (2005)  (Journal Article), 2009
[citation]  [text]

Ensemble-based Kalman filters in strongly nonlinear dynamics  (Journal Article), 2009
[citation]  [text]

Impact of airborne Doppler radar data assimilation on the numerical simulation of intensity changes of Hurricane Dennis near a landfall  (Journal Article), 2009
[citation]  [text]

Sensitivity of numerical simulation of early rapid intensification of Hurricane Emily (2005) to cumulus parameterization schemes in different model horizontal resolutions  (Journal Article), 2009
[citation]  [text]

Impact of aircraft dropsonde and satellite wind data on the numerical simulation of two landfalling tropical storms during TCSP  (Journal Article), 2008
[citation]  [text]

MODIS/Terra Observed Snow Cover Over the Tibet Plateau: Distribution, Variation and Possible Connection with the East Asian Summer Monsoon (EASM)  (Journal Article), 2008
[citation] 

Runoff-denoted drought index and its relationship to the yields of spring wheat in the arid area of Hexi corridor, Northwest China  (Journal Article), 2008
[citation] 

Sensitivity of numerical simulations of the early rapid intensification of Hurricane Emily (2005) to cumulus parameterization schemes in different model grid resolutions  (Journal Article), 2008
[citation]  [text]

Applications of data assimilation in climate modeling: a perspective from regional climate studies over western China .  (Journal Article), 2007
[citation] 

MODIS/Terra observed seasonal variations of snow cover over the Tibet Plateau  (Journal Article), 2007
[citation] 

The organization of vertical motion in asymmetric hurricane ---- Bonnie (1998).  (Journal Article), 2006
[citation] 

Mesoscale assimilation of TMI data with 4DVAR: Sensitivity study  (Journal Article), 2004
[citation] 

Research Statement

Numerical modeling, data assimilation and predictability of high-impact weather systems.

My major research theme is to combine numerical computer models and observational data for improved analysis, forecasting and model parameters. My current research focuses on advanced methodologies for atmospheric data assimilation, with a emphasis on improving high-impact weather forecasting (e.g., hurricane, winter storms, heavy rain, high winds). I am also interested in studying the social and economic impacts of these high-impact weather systems.

Research Keywords, Regions of Interest and Languages:

Keywords: Atmospheric Sciences (4); Data Assimilation (3); Weather Prediction or Forecasting (5); Chaos and Predictability; Numerical modeling (6); Computer Simulation or Modeling (13); Data Analysis (7); Ensemble forecasting; Nonlinear Dynamics (2); Wind Climatology
Languages: Chinese (28); English (131)

Research Projects

Improving Hurricane Forecasts [details]

Accurate forecast of hurricanes can save lives and reduce serious economic losses. However,
hurricane forecast, especially its intensity forecast, remains a challenging problem in modern numerical
weather prediction. NASA EOS satellites provide useful data sources to improving hurricane forecasts
and enhancing our understanding in hurricane intensification. Studies by Dr. Pu demonstrated that
assimilation of TRMM, QuikSCAT and other satellite data into high resolution numerical model have
resulted in significant improvement in hurricane forecasts.

Project Grants:The impact of Aqua satellite multi-sensor data on predicting and understanding hurricane intensity change: NASA 2007

Global Wind Mesurements for Weather and Climate [details]

Measurement of global wind profiles is recognized as a primary unmet observational requirement
for understanding atmospheric dynamics and improving weather forecasts. NASA has classified
tropospheric wind profiling as high-priority science and invested in wind profiling instrument
development efforts. In addition to satellite-based space wind lidar measurements, a high altitude airborne
system flown on UAV or other advanced platforms has been supported for studying mesoscale
atmospheric systems.
The principal objective of this proposal is to explore the configuration of the future NASA space
and airborne wind lidar missions to advance our fundamental understanding of the seasonal cycle of
global wind field and predicting high impact weather systems. We propose a highly collaborative research
effort to assess the minimum requirements of Doppler lidar wind measurements to fulfill the needs for 1)
seasonal climate studies and 2) analysis and forecasting of mesoscale high-impact weather systems.

Project Grants:Targeted Doppler wind lidar observations for seasonal climate studies and high-impact weather forecasting: NASA 2008

Mountain Terrain Atmospheric Modeling [details]

This project is part of MATERHORN program.
We will study the predictability at mesoscale, in particular, the error growth (i.e., the sensitivity to initial conditions at various lead times), and develop meaningful measures of skill relative to appropriate conditional climatologies (i.e., the skill of capturing specific phenomena when they are supposed to appear; e.g., turbulence generation when a Richardson number criterion is satisfied). Sensitivities to input properties and boundary conditions will be investigated. Data assimilation studies will be conducted, and different techniques (e.g., 4DVAR, ensemble Kalman filtering, 3DVAR) will be compared.
Project Web Site:www.nd.edu/~dynamics/materhorn/

Surface data assimilation [details]

Effective incorporation of single-level observations, especially those of air temperature near the earth's surface, to accurately determine modeled initial atmospheric conditions represents a major challenge in numerical weather prediction. The exact reasons for this difficulty remain unclear, though inadequate representation of the diurnal cycle is thought to play an important role. This is particularly true in regions of complex terrain, where sharp variations of elevation and corresponding surface temperature (which are imperfectly resolved by coarse model grids) may lead to large differences between model "first guess" fields and inserted local observations. This challenge stands in the way of capitalizing fully on the bounty of new observations coming from expanded surface observing networks. The research supported here focuses on the use of observing system simulation experiments (OSSEs) in conjunction with the Weather Research and Forecasting (WRF) model to address this problem. OSSEs will be used to supply synthetic observations at sites where actual observations are being made. After incorporation of known, representative errors these synthesized observations will in turn be assimilated using both traditional variational (3DVAR) and more modern (but computationally expensive) Ensemble Kalman filter (EnKF) techniques. Ensuing model forecasts will be compared with actual observations at these selected sites to quantify the impact of such errors as well as assess methods for their reduction. The goals of this effort are thus to (1) identify and understand fundamental problems interfering with the inclusion of surface observations in weather forecast models, and (2) design and conduct numerical experiments to overcome these obstacles and thereby improve forecast accuracy.

The intellectual merit of this work centers upon identification of leading sources of weather forecast errors and design of methods for their mitigation. Broader impacts of this work will include: significant improvements to the community-based WRF model; more complete and efficient utilization of data emerging from a growing array of surface observational networks; and the education of a graduate student under supervision of a PI from an underrepresented group.

Project Grants:On Variable Terrains and Diurnal Variations in Surface Data Assimilation : NSF 2008

Western Pacific Tropical Cyclones [details]

The objective of this proposed study is to investigate large-scale environmental conditions, mesoscale phenomena and small scale convective bursts as well as their interactions that are responsible for TC formation and intensity changes. Specific areas include 1) Characterize the intensity of convection over the western Pacific oceans from radar, aircraft and satellite data; 2) Derive an accurate mesoscale environment of convective systems through the assimilation of satellite, radar, lidar and in-situ data; 3) Evaluate the quality of the global forecast system (e.g., NCEP and NOGAPS ensembles) for accurate TC analyses and forecasts; 4) Understand the environmental factors that determine tropical cyclone formation and rapid intensification.

Project Grants:Envrionmental Conditions of Western Pacific Tropical Cyclones: TPARC 2008

Courses I Teach

ATMOS 3110 Introduction to Atmospheric Science
ATMOS 5110 Dynamic Meteorology (ATMOS 5100)
ATMOS 6130 Numerical Weather Prediction

Awards

2010 Certificate of Appreciation - National Aeronautics and Space Administration

2010 Fellow - Royal Meteorological Society

2006 Early Career Scientist Assembly Visiting Research Award - NCAR

2000 Outstanding Achievement Award - Code 912, Laboratory for Atmospheres, NASA Goddard Space Flight Center

1993 1st prize for science and technology advancement and outstanding young meteorologist - Chinese Meteorological Society, Gansu Chapter