My experience with STATMOS

Whitney Huang

Department of Statistics, Purdue University

Spatial Statistics Seminar

April 20, 2015

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Outline

1 About STATMOS

2 Statistical Climatology

3 STATMOS visits/projects

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What is STATMOS

NSF funded research network for statistical methods for atmosphericand oceanic Sciences

Allow collaboration within the network

Summer schools, research workshops, online seminar

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https://www.statmos.washington.edu/members.html

Summer schools

PastI Ten Lectures on Statistical Climatology, UW Seattle 2012I Visualization of Climate Data, NCAR Boulder 2013I Spatio-Temporal Statistics, Búzios, Brazil 2014

UpcomingI Data Assimilation, NCAR Boulder 2015

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https://www.statmos.washington.edu/?p=42https://www2.image.ucar.edu/event/vcd2013http://www.stat.washington.edu/peter/PASI/PASI_2014.htmlhttp://www.stat.osu.edu/~oksana/summer-school-in-data-assimilation-2015.html

Workshops

PastI Spatial Statistics Workshop and INLA Shortcourse, CSU Fort Collins

2013I BIRS workshop on Oceans and Climate variability, Banff, Alberta 2013I Workshop on Spatial Statistics, TAMU College Station 2015

UpcomingI Big Data in Environmental Science Workshop, UBC Vancouver, British

Columbia 2015I Workshop on high performance computing for spatial statistics, UM

Ann Arbor 2015

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http://www.stat.colostate.edu/statdepartment/statnews/SpatialWorkshop.htmlhttp://www.birs.ca/events/2013/5-day-workshops/13w5104http://www.stat.tamu.edu/workshop-on-spatial-statistics/http://www.pims.math.ca/scientific-event/150511-bdeshttps://www.statmos.washington.edu/?p=1506

Online seminar

“Climate model emulation and future climate simulation”, Prof.Michael Stein

“Bayesian Approaches to the Use of Computer Models ”, Prof. MarkBerliner

“Tales of Tail Dependence”, Prof. Dan Cooley

“Matern-based nonstationary cross-covariance models for globalprocesses”, Prof. Mikyoung Jun

“Evaluating predictive performance”, Dr. Michael Scheuerer

“Multi-resolution spatial methods for large data sets”, Dr. DougNychka

“Spatial statistics for environmental-health data”, Prof. MontseFuentes

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http://www.stat.osu.edu/~pfc/statmos-seminars/

Outline

1 About STATMOS

2 Statistical Climatology

3 STATMOS visits/projects

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Climate vs. weather

Climate is what you expect · · ·

Weather is what you get · · ·

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Observational data vs. data product

weather station observations (with homogenization and qualitycontrol)

Gridded data product: irregularly spaced observations are convertedinto values on a regular grid to make it easier to plot or compare tomodel output or other data products.

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Data product example: PRISM

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Model based data products: reanalysis datasets

Ingredients

xt : a representation of the 3-dimensional state of the atmosphere attime t

yt : the observational data record at time t

xt+1 = M(xt): a state-of-the art numerical weather prediction (NWP)model,that advances the state forward a few hours

An approximate sequential Bayes analysis

Forecast using model: xt+1 = M(xt) giving a prior distribution [xt+1]

Update information using yt : compute [xt+1|yt+1]

Repeat cycle for full period of analysis

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Climate models

Figure : Slide courtesy of Steve Sain

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Climate models

Figure : Slide courtesy of Steve Sain

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Climate models: GCM vs. RCM

General Circulation Model (GCM): a mathematical model of thegeneral circulation of a planetary atmosphere or ocean

GCMs are run at coarse spatial resolutions and are unable to resolveimportant sub-grid scale features

Regional Climate Model (RCM): a dynamical downscaling uses alimited-area, high-resolution model driven by boundary conditionsfrom a GCM to derive smaller-scale information

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Statistical Climatology and Spatial Statistics

Weather and climate are inherently spatio-temporal phenomena

Spatial interpolation for climate data

Changes in climate extremes

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Outline

1 About STATMOS

2 Statistical Climatology

3 STATMOS visits/projects

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University of Chicago (with Prof. Michael Stein)To determine how the temperature extremes may be affected byincreased atmospheric CO2 levels1000 years GCM equilibrium experiment with three CO2 levelsPropose a graphical tool for studying changes in extremes

+ + + + + + + + + + + + + + + +

+ + + + + + + + + + + + + + + +

+ + + + + + + + + + + + + + + +

+ + + + + + + + + + + + + + + +

+ + + + + + + + + + + + + + + +

+ + + + + + + + + + + + + + + ++

++

30° N

35° N

40° N

45° N

50° N

120° W 110° W 100° W 90° W 80° W 70° W

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Changes in return levels of warm temperature extremes0

12

34

56

Lon= 124

5

Cha

nge

in r

etur

n le

vel °

C

Return period

Summer Tmax 700 ppm vs. 289 ppm

lat= 46lat= 43lat= 39lat= 35lat= 32lat= 28mean

120 116 112 109 105 101 98 94 90 86 82 79 75 71 68

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Changes in return levels of cold temperature extremes0

24

68

1012

Lon= 124

100

Cha

nge

in r

etur

n le

vel °

C

Return period

Winter Tmin 700 ppm vs. 289 ppm

lat= 46lat= 43lat= 39lat= 35lat= 32lat= 28mean

120 116 112 109 105 101 98 94 90 86 82 79 75 71 68

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Changes in return levels of temperature extremes

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Argonne National Laboratory

Located near Lemont, IL, outside Chicago, two hours drive fromPurdue

Presented a seminar talk in the Environmental Science Division

Collaborating with Dr. Wang on a temperature extremes projectusing a very high resolution RCM run

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National Climatic Data Center

Located in Asheville, North Carolina

The largest active archive of weather data in the world

Great place to get different kinds of climate data

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National Center for Atmospheric Research

Located in Boulder, Colorado

Presented a seminar talk in the Institute for Mathematics Applied toGeosciences (IMAGe)

Just finished a two weeks visit hosting by Dr. Doug Nychka

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Log density estimation (with Dr. Doug Nychka)X : the variable of interest with density function f (x)Model: f (x) ∝ eg(x)The function estimation of g(x) is achieve by using a flexible class ofspline function

Daily Precip (cm)

Den

sity

0 5 10 15

0.0

0.5

1.0

1.5

−−−

GPDLogspline roughLogspline smooth

−2 −1 0 1 2

−10

−6

−2

02

Log daily precip

Log

dens

ity

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Thank you for your attention!

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About STATMOSStatistical ClimatologySTATMOS visits/projects