Zhaoxia Pu, Ph.D. Associate Professor, Atmospheric Sciences
Graduate Director, Atmospheric Sciences
University of Utah
135 S 1460 East Rm 819 (WBB)
Salt Lake City, Ut 84112-0110
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.
Assimilation of satellite data in improving numerical simulations of tropical cyclones:
progress, challenge and development (Book Section), 2009
Tracking and verification of East Atlantic tropical cyclone genesis in NCEP global
ensemble: Case studies during NASA African monsoon multi-disciplinary analyses (Journal
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
Sensitivity of numerical simulation of early rapid intensification of Hurricane Emily
(2005) to cumulus parameterization schemes in different model horizontal resolutions
(Journal Article), 2009
MODIS/Terra Observed Snow Cover Over the Tibet Plateau: Distribution, Variation and
Possible Connection with the East Asian Summer Monsoon (EASM) (Journal Article),
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
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
Applications of data assimilation in climate modeling: a perspective from regional
climate studies over western China . (Journal Article), 2007
MODIS/Terra observed seasonal variations of snow cover over the Tibet Plateau (Journal
The organization of vertical motion in asymmetric hurricane ---- Bonnie (1998). (Journal
Mesoscale assimilation of TMI data with 4DVAR: Sensitivity study (Journal Article),
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)
Improving Hurricane Forecasts [details]
Accurate forecast of hurricanes can save lives and reduce serious economic losses.
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
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
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
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