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7/23/2019 Water Forum2 Yang

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Developing a High-Resolution

Texas Water and ClimatePrediction Model

1Water Forum II on Texas Drought and Beyond, Austin, Texas, 22-23 October, 2012

Zong-Liang Yang

(512) 471-3824

[email protected]


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Hyperresolution global land surface

modeling: Meeting a grand challenge

for monitoring Earths terrestrial water

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Hyperresolution global land surface

modeling: Meeting a grand challenge

for monitoring Earths terrestrial water

An opinion paper by Wood, E. F., et al., 2011, WRR, 47, W05301.

A grand challenge to the community

Hyperresolution: O(1 km) globally; O(100 m) continental scales

Need for hyperresolution: global food production; waterresources sustainability; flood, drought, and climate changeprediction

Six major challenges: surfacesubsurface interactions due to finescale topography and vegetation;

landatmospheric interactions; soil moisture & evapotranspiration;

inclusion of water quality as part of the biogeochemical cycle; representation of human impacts from water management;

utilizing massively parallel computer systems in solving 109unknowns; and

developing the required in situ and remote sensing global data sets.

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Hyperresolution regional land surface

modeling for Texas

An opinion presentation proposed today at Water Forum II.

A grand challenge to the community

Hyperresolution: O(100 m) CONUS; O(10 m) Texas

Need for hyperresolution: Texas food production; waterresources sustainability; flood, drought, and climate changeprediction

Six major challenges: surfacesubsurface interactions due to finescale topography and vegetation;

landatmospheric interactions; soil moisture & evapotranspiration;

inclusion of water quality as part of the biogeochemical cycle;

representation of human impacts from water management; utilizing massively parallel computer systems in solving 109unknowns; and

developing the required in situ and remote sensing data sets.

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How to obtain hyperresolution weather

data for Texas?

O(10 m) is not feasible; but

O(1 km) is possible.

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The grid resolution of regional and global models hasreduced hugely, and will continue to do so

e.g. 4 km resolution Weather Research and Forecasting (WRF)Model

4 km Topography

e.g. increasing spatial resolution in Global Models

EarthSimulator

www.nec.co.jp

www.nssl.noaa.gov/wrf/

3.5 km cloud resolution

N Atlantic ocean eddies

Revolution in ModelingShuttleworth (2011)


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High Performance Computing Petascale [O(1015)] Computing Architectures

Massively parallel supercomputers (104105multi-core processors)

Worlds Fastest Supercomputer in 2011

579.4trillion math operations per second (TFlops)

3936 nodes, 62976 core processors

Texas Advanced Computing Center, UT-Austin

Ranger

5/19/2011

Worlds Fastest Supercomputer in 2013

10peta math operations per second (PFlops)

500,000 processors

Texas Advanced Computing Center, UT-Austin

Stampede

January 2013


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8Yuan (2011); Shukla (2009)


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While climate projections at O(100

year) timescales are useful, water

resource planning also needs climate

predictions at O(10 day) to O(1 year)time scales!

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Step1: Seasonal Climate

Forecasts

Yuan (2011); Shukla (2009)


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Step 2: Seasonal Climate

Dynamic Downscaling

Yuan and Liang (2011)

GRL


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Dynamic Downscaling with Bias

Correction Improves the PDF of Daily

Maximum Temperature in Summer

Daily maximum temperature

Temperature bins (oC)

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Probability(%)

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WRF_NNRP

WRF_CAM

WRF_CAMbc_ave

WRF_CAMbc_std

Black:WRF simulation drivenby NNRP

Green:

WRF simulation drivenby GCM output withoutbias correction

Blue:With mean value biascorrection

Red:With mean value andvariance biascorrections

The PDF is computed over the central US region(4050N, 10085W) at 60-kmresolution Xu and Yang (2012)


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Xing Yuan & Eric Wood, Princeton University


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UT has world-class expertise

in Understanding and modeling terrestrial

hydrological processes & global water cycle

Land Model for Climate Prediction

Land Model for Weather Forecasts Mapping geospatial datasets

Observing the global water cycle

High Performance Computing Lonestar

Ranger

Stampede


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NCAR Community Land Model (CLM4)for Climate Models in 2010

Co-Chairs: David Lawrence (NCAR), Zong-Liang Yang (Univ of Texas at Austin)


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Collaborators: Yang, Niu (UT), Chen (NCAR), Ek (NCEP/NOAA), and others

Noah-MPLand Model for Weather

Forecasts

A new paradigm in land-surfacehydrological modeling

In a broad sense,o Multi-parameterization Multi-physics Multi-

hypothesis

A modular & powerful framework foro Diagnosing differences

o Identifying structural errors

o Improving understandingo Enhancing data/model fusion and data assimilation

o Facilitating ensemble forecasts and uncertaintyquantification

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Niu et al. (2011); Yang et al. (2011)

G it R d Cli t E i t


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Gravity Recovery and Climate Experiment

(GRACE)

10 years of missionoperation (Tapley etal., 2004)

First-time global dataof gravity (~100 km,monthly to 10-day)

Unprecedentedaccuracy of mass

variations

Allowing a betterunderstanding of theglobal water cycle


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Center for Integrated EarthSystem Science

http://www.jsg.utexas.edu/ciess

Formed inAugust 2011

Director: Liang Yang Associate Director:

David Maidment

A cooperative

effort between


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Summary

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CIESS was formed to integrate UTs

expertisein earth system science andcollaborate with national/internationalcommunitiesfor the betterment of society.

As high-resolution seasonal to decadalclimate and hydrologic forecasts areemerging as a new paradigm for modelingand prediction research, CIESS is positioned

to develop an integrated atmosphereland-surfaceriver network modeling system,applicable to Texas for water resourceapplications.

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