development of the ncep unified global coupled system (ugcs… · 2016-11-17 · development of the...

DEVELOPMENT OF THE NCEP UNIFIED GLOBAL COUPLED SYSTEM (UGCS) FOR WEATHER

AND CLIMATE PREDICTION

Xingren Wu, Suranjana Saha, Jiande Wang, Jack Woollen, Patrick Tripp, Malaquias Pena, Shrinivas Moorthi, David Behringer,

Partha Bhattacharjee, Jun Wang, Arun Chawla, Avichal Mehra, Mike Ek, Jiarui Dong, Xu Li, Sarah Lu, Daryl Kleist, Steve Penny

1

“A good prediction system incorporates

both a good data assimilation (DA)

system and a good forecast model.”

Coupled Data Assimilation and Forecasting System

2

3

A Historical Perspective

ONE DAY OF CFS Analysis or Reanalysis

12Z GDAS 18Z GDAS 0Z GDAS

9-hr coupled T574L64 forecast guess (GFS + MOM4 + SeaIce+ Noah)

12Z GODAS

0Z GLDAS

6Z GDAS

18Z GODAS 0Z GODAS 6Z GODAS

4

R2 (1997) CDASv2 (2011)

Vertical coordinate Sigma Sigma/pressure

Spectral resolution T62 T574

Horizontal resolution ~210 km ~27 km

Vertical layers 28 64

Top level pressure ~3 hPa 0.266 hPa

Layers above 100 hPa ~7 ~24

Layers below 850 hPa ~6 ~13

Lowest layer thickness ~40 m ~20 m

Analysis scheme SSI GSI

Satellite data NESDIS temperature

retrievals (2 satellites)

Radiances

(all satellites)

Bob Kistler, EMC

5

GODAS – 3DVAR

Version

Changing from a version based on MOM V3 to one based on MOM4p0d

As was the case with MOM4p0d, the code has been completely rewritten from Fortran 77 to Fortran 90.

Domain and Resolution

Changing to a global domain.

Increasing resolution to match the MOM4p0d configuration used in the CFS: 1/2ox1/2o (1/4o within 10o of the equator); 40 Z-levels.

Functionality

Adding the ability to compile the 3DVAR analysis in either an executable combining the analysis with MOM or an executable containing only the analysis. The latter formulation is used with the CFS where it reads the forecast from a restart file produced by the coupled CFS, does the analysis, and updates the restart file.

Adding relaxation of surface temperature and salinity to observed fields under the control of the 3DVAR analysis.

Data

The data sets that can be assimilated remain unchanged from the current operational GODAS: XBTs, tropical moorings (TAO, TRITON, PIRATA, RAMA), Argo floats, CTDs), altimetry (JASON-x).

Dave Behringer, EMC

6

Global Land Data Assimilation System (GLDAS)

• GLDAS (running Noah LSM under NASA/Land Information System) forced with

CFSv2/GDAS atmospheric data assimilation output and blended precipitation in a semi-

coupled mode, versus no GLDAS in CFSv1, where CFSv2/GLDAS ingested into

CFSv2/GDAS once every 24-hours.

• In CFSv2/GLDAS, blended precipitation a function of satellite (CMAP; heaviest weight in

tropics), surface gauge (heaviest in middle latitudes) and GDAS (modeled; high latitude), vs

use of model precipitation comparison with CMAP product and corresponding adjustment to

soil moisture in CFSv1.

• Snow cycled in CFSv2/GLDAS if model within 0.5x to 2.0x of the observed value (IMS

snow cover, and AFWA snow depth products), else adjusted to 0.5 or 2.0 of observed value.

IMS snow cover AFWA snow depth GDAS-CMAP precip Gauge locations

Mike Ek, EMC

7

The NCEP Climate Forecast System

Reanalysis

Suranjana Saha, Shrinivas Moorthi, Hua-Lu Pan,

Xingren Wu, Jiande Wang, Sudhir Nadiga,

Patrick Tripp, Robert Kistler, John Woollen,

David Behringer, Haixia Liu, Diane Stokes,

Robert Grumbine, George Gayno, Jun Wang,

Yu-Tai Hou, Hui-ya Chuang, Hann-Ming H.

Juang, Joe Sela, Mark Iredell, Russ Treadon,

Daryl Kleist, Paul Van Delst, Dennis Keyser,

John Derber, Michael Ek, Jesse Meng, Helin

Wei, Rongqian Yang, Stephen Lord, Huug van

den Dool, Arun Kumar, Wanqiu Wang, Craig

Long, Muthuvel Chelliah, Yan Xue, Boyin

Huang, Jae-Kyung Schemm, Wesley Ebisuzaki,

Roger Lin, Pingping Xie, Mingyue Chen,

Shuntai Zhou, Wayne Higgins, Cheng-Zhi Zou,

Quanhua Liu, Yong Chen, Yong Han, Lidia

Cucurull, Richard W. Reynolds, Glenn Rutledge,

Mitch Goldberg

Bulletin of the American Meteorological Society

Volume 91, Issue 8, pp 1015-1057. doi:

10.1175/2010BAMS3001.1

TOWARDS BUILDING A PROTOTYPE

OF THE NEXT GENERATION

UNIFIED GLOBAL COUPLED

ANALYSIS AND FORECAST SYSTEM

AT NCEP (UGCS)

8

Atmosphere, Land, Ocean, Seaice, Wave, Chemistry, Ionosphere

Data Assimilation cycle

Analysis frequency Hourly

Forecast length 9 hours

Resolution 10 km

Ensemble members 100

Testing regime 3 years (last 3 years)

Upgrade frequency 1 year

UNIFIED GLOBAL COUPLED SYSTEM

DATA ASSIMILATION

9


Weather Forecasts

Forecast length 10 days

Resolution 10 km

Ensemble members 10/day

Testing regime 3 years (last 3 years)



WEATHER FORECASTS

10


Sub-seasonal forecasts

Forecast length 6 weeks

Resolution 30 km

Ensemble members 20/day

Testing regime 20+ years (1999-present)



SUB-SEASONAL FORECASTS

11


Seasonal Forecasts

Forecast length ~1 year

Resolution 50 km

Ensemble members 40 (lagged)

Testing regime 40+ years (1979-present)



SEASONAL FORECASTS

12


Predictions for all spatial and temporal scales will be ensemble

based.

There will be a continuous process of making coupled Reanalysis

and Reforecasts for every implementation (weather, sub-seasonal

and seasonal)

Since the resolution of all parts of the system is usually increased

with every new implementation (in proportion to the increased

computing power currently available), we need to explore cheaper

cloud computing and storage options for making these

reanalysis/reforecasts.


NEW PARADIGM

13

Whole

Atm

osphere

Model

NGGPS Unified Global Coupled Model

Application

=

Ensemble

+

Reanalysis

+

Reforecast

NGGPS

Unified Global Coupled Model

“GFS” “GEFS” “CFS”

Actionable

weather

Week 1

through 4-6

Seasonal &

annual

1 y 2 y 4 y Update cycle

3 y 1999 - present 1979 - present Reanalysis

6h 6h 6h cycling

WCOSS WCOSS WCOSS where 14

NGGPS Planned Components

Atmospheric Components

NEMS/ESMF

Atm Dycore

(FV3)

Wave

(WW3) Sea Ice

(CICE/SIS2/KISS)

Aerosols

(GOCART)

Ocean

(HYCOM/MOM)

Land Surface

(NOAH)

Atm Physics

(GFS)

Atm DA

(GSI)

15

• Atmosphere: Hybrid 4D-EnVAR approach using a 80-member coupled

forecast and analysis ensemble, with Semi-Lagrangian dynamics, and 128

levels in the vertical hybrid sigma/pressure coordinates. Will test with the

new GFDL FV3 dynamic core when it is ready.

• Ocean/Seaice: GFDL MOM5.1/MOM6 and/or HYCOM for the ocean and

CICE/SIS2/KISS for sea-ice coupling, using the NEMS coupler.

• Aerosols: Inline GOCART for aerosol coupling.

• Waves: WAVEWATCH III for wave coupling.

• Land: Noah Land Model for land coupling.

• Ionosphere: Inline Whole Atmosphere Model (WAM) –up to 600km.

PROOF OF CONCEPT

16

Coupled Model

Ensemble Forecast

NEMS

OC

EAN

SEA

-IC

E

WA

VE

LAN

D

AER

O

ATM

OS

Ensemble Analysis (N Members)

OUTPUT

Coupled Ensemble Forecast (N members)

INPUT

Coupled Model

Ensemble Forecast

NEMS

OC

EAN

SEA-IC

E

WA

VE

LAN

D

AER

O

ATM

OS

NCEP Coupled Hybrid Data Assimilation and Forecast System

17

NEMS

• The NOAA Environmental Modeling System is being built to unify

operational systems under a single framework, in order to more

easily share common structures/components and to expedite

interoperability.

• The first two systems under NEMS (NAM/NMMB and GOCART)

have been implemented into NCEP operations with others to follow

in the next few years, such as the GFS, etc.

• The NUOPC (National Unified Operational Prediction Capability,

NOAA, Navy and Air Force) layer will be used to make

collaboration with other groups less difficult, when

building/coupling modeling systems.

• Incorporation of a NUOPC physics driver can help standardize the

often complex connections to physics packages, thereby enhancing

their portability. Mark Iredell, EMC 18

Atmospheric Data Assimilation Upgrades

Daryl Kleist, UMD and NCEP

• Develop prototype, Hybrid 4D-EnVAR data assimilation

system using the GSI and a NEMS-based GSM at SL

T574/L64 resolution with a 80 member ensemble.

• Test and validate system using UGCS coupled model as

background for atmospheric DA. Determine impacts of each

domain for atmospheric prediction and DA.

• Prototype system will be initial NEMS-based code set for

atmospheric component of NGGPS and CFSv3 development.

System will generate more accurate background field for the

atmosphere and other domains.

19

Atmospheric Model Physics Upgrades

CONVECTION

• Improve the representation of small-scale phenomena by implementing a scale-

aware PDF-based subgrid-scale (SGS) turbulence and cloudiness scheme replacing

the boundary layer turbulence, the shallow convection, and the cloud fraction

schemes in the GFS and CFS.

• Improve the treatment of deep convection by introducing a unified

parameterization that scales continuously between the simulation of individual

clouds when and where the grid spacing is sufficiently fine and the behavior of a

conventional parameterization of deep convection when and where the grid spacing

is coarse.

• Improve the representation of the interactions of clouds, radiation, and

microphysics by using the additional information provided by the PDF-based SGS

cloud scheme.

• Alternatively, upgrade the clouds and boundary layer parameterization, using the

approach of moist Eddy Diffusion and Massflux (EDMF) approach by considering

several microphysics schemes available in WRF package and a simple pdf based

clouds and improved scale aware convection by extending SAS. S. Moorthi, EMC

20

Atmospheric Model Physics Upgrades

RADIATION AND OTHER:

• Improve cloud radiation interaction with advanced radiation

parameterization based on the McICA-RRTMG from AER Inc. with

interactive aerosol (both direct and indirect) affects.

• Include radiative effects due to additional (other than CO2) green-house

gases from near real-time observations.

• Improve the representation of aerosol-cloud-radiation interaction in the

model. Implement a double-moment cloud microphysics scheme and a

multimodal aerosol model. Develop an interface to link cloud properties and

aerosol physiochemical properties, consistent coupling of cloud micro and

PDF-based macro physics, and the modification of RRTM to support the

new cloud-aerosol package.

Consistent clouds, convection, radiation and microphysics interactions

• Enhance the parameterization of gravity wave drag, particularly the non-

stationary, propagating type. As shown by ECMWF, this is important for the

proper simulation of QBO in the stratosphere. Yu-Tai Hou, Sarah Lu 21

• Hybrid Method:

The Hybrid-Gain method of Penny (2014)

• EnKF Component:

The Local Ensemble Transform Kalman Filter (LETKF)

developed by Hunt et al. (2007) at the University of Maryland (UMD)

• Variational Component:

NCEP’s operational 3DVar used in the Global Ocean Data

Assimilation System (GODAS) described by Derber and Rosati

(1989) and Behringer (2007)

Penny, S.G., 2014: The Hybrid Local Ensemble Transform Kalman Filter. Mon. Wea. Rev., 142, 2139–2149. doi: http://dx.doi.org/ 10.1175/MWR-D-13-00131.1

Hunt, B.R., E.J. Kostelich, and I. Szunyogh, 2007: Efficient Data Assimilation for Spatiotemporal Chaos: A Local Ensemble Transform Kalman Filter. Physica D, 230, 112-126.

Derber, J. D., and A. Rosati, 1989: A global oceanic data assimilation system. J. Phys. Oceanogr., 19, 1333–1347. Behringer, D. W., 2007: The Global Ocean Data Assimilation System at NCEP. Preprints, 11th Symp. on Integrated Observing and Assimilation Systems for Atmosphere, Oceans and Land Surface, San Antonio, TX, Amer. Meteor. Soc., 14–18.

Hybrid GODAS

22 Steve Penny, UMD and EMC

http://dx.doi.org/

http://dx.doi.org/

Hybrid 3DVar/LETKF GODAS

• The following slide shows the results from an 8-year Reanalysis using temperature and salinity profile data as used in the CFSR.

• All experiment conditions are identical except for the DA system (3DVar vs. Hybrid)

• The Hybrid-GODAS eliminates growing biases in temperature and salinity, and gives an overall reduction in RMSD and BIAS.

• This ocean data assimilation approach is also being tested with the HYCOM ocean model

Steve Penny, UMD and EMC 23

Hybrid-GODAS

The reduction in RMS

deviations for temperature

and salinity are fairly

uniform throughout the

upper ocean

Temperature

3DVar-GODAS

Salinity Historical Reanalysis, 1991-1999

RM

SD (

ºC)

BIA

S (º

C)

De

pth

(m

)

Growth in temperature and

salinity biases using 3DVar are

eliminated by the Hybrid

The magnitude of all biases are

generally reduced

RMS Deviations are reduced

uniformly by the hybrid, for

both temperature and salinity

(solid line = 3 mo. moving ave.

dashed line = linear regression)

Steve Penny, UMD 24

25

NSST (Near-Surface Sea Temperature)

0),0(),(),0(' tTtTtT cc

),(),()(),( ''

0 tzTtzTtTtzT cwf

fT

],0[ wzz

Mixed Layer

Thermocline

z

)(zT

Deeper

Ocean

)0()/1()( ''

www TzzzT

)0()/1()( ''

ccc TzzT

Diurnal Warming Profile

Skin Layer Cooling Profile

'

wT

z

'

cT

z

wz )(' zTw

)(' zTc

c

),,(),(),( ' tzTtzTtzT wf )1(~ mmOc

T mixf TT

)5(~ mOzw

c

wz

),(),(),( ''' tzTtzTtzT cw

The ocean model does not produce an actual SST. The NSST determines a T-Profile just below the sea surface, where the vertical thermal structure is due to diurnal thermocline layer warming and thermal skin layer cooling.

25 Xu Li, EMC

Atmospheric FCST Model with NSST Model built in

FORECAST

Hybrid ENKF GSI with Tf analysis built in

ANALYSIS

Hybrid GODAS

Get and then for

the coupling with NSST T-Profile

and Oceanic model

T-Profile

Incorporation of the NSST into an air-sea coupled

data assimilation and prediction system

],[ an

f

an

a TX

Oceanic FCST Model

)( an

oX )( bg

oX

)](,[ ' zTX bgbg

a

SST(z) = SSTfnd + Tw’(z) - Tc’(z) T(z) = Tf (zw ) + T’(z)

)]([ zT bg

o

BG

IC

)]([ ' zT bg

),,( an

f

an

o

an

a TXX

bg

f

bg

o

bg

a TXX ,,

bg

fT bgSST

Observation innovation in Tf analysis

Get skin-depth dependent T(z) with and NSST T-Profile

)(' zT bg

bg

fT

oX : Oceanic state vector

aX : Atmospheric state vector

NSST algorithm is part of both the analysis and forecast system. In the analysis, the NSST provides an analysis of the the interfacial temperature (“SST”) in the hybrid ENKF GSI using satellite radiances. In the forecast, the NSST provides the interfacial temperature (SST) in the free, coupled forecast instead of the temperature at 5-meter depth that is predicted by the ocean model.

26

Proposed Upgrades to Land Surface

Methodology upgrades: NASA’s Land Information System (LIS)

integrates NOAA NCEP’s operational land model, high-resolution

satellite and observational data, and land DA tools (EnKF).

Global observed precipitation forcing from 0.5o to 0.25o resolution

(CPC daily, 1979-present).

Model upgrades: Noah 3.x, Noah-MP (e.g, dynamic vegetation,

explicit canopy, CO2-based photosynthesis, groundwater, multi-

layer snow pack, river routing).

J. Dong, M. Ek, H. Wei, J. Meng, EMC 27

Proposed Upgrades to Land Surface (cont.)

Land use dataset upgrades:

• Near-realtime GVF (global 16-km, 1981-present, and 25-year

climatology), land-use/vegetation (IGBP-MODIS, global 1km),

soil type (STATSGO-FAO, global 1km), MODIS snow-free

albedo (global 5km), maximum snow albedo (U. Arizona, global

5km).

• SMOPS soil moisture (AMSR-E (2002-2011), SMOS (2010-

present), ASCAT, AMSR2 (2012-present), SMAP (launched

2014).

• Snow cover: IMS (global 24km (1997-present) & 4km (2004-

present).

• Snow water equivalent: SMMR (1978-1987), SSM/I (1987-2007),

AMSR-E (2002-2011), AMSR2 (2012-present).

28 J. Dong, M. Ek, H. Wei, J. Meng, EMC

In

Aerosol Data Assimilation

Observations

Aerosol Optical Depth (satellite & in-situ)

Smoke Emissions (satellite)

INP

UT

INPUT OUTPUT

Dust Sea salt Sulfate

Black carbon Organic carbon

AOD data assimilation (EnKF/GSI)

29 Sarah Lu, SUNY Albany and EMC

The Goddard Chemistry Aerosol Radiation and Transport (GOCART) model

simulates major tropospheric aerosol components, including sulfate, dust, black

carbon (BC), organic carbon (OC), and sea-salt aerosols. GOCART considers

emission, wet removal and dry removal processes and simple sulfate chemistry.

• Model upgrades:

To introduce aerosol-cloud interaction (indirect effect) in the GFS

• Methodology upgrades

To use much higher resolutions for NGAC (NEMS GFS Aerosol

Component (NGAC) to couple with higher resolution of the GFS.

Upgrade the Hybrid DA system to enable assimilation of AOD

observations.

Proposed upgrades to data assimilation

and modeling of aerosols


• Emissions dataset upgrades:

Use satellite-based smoke emissions (MODIS fire radiative power) for

organic carbon, black carbon and sulfate.

• Data assimilation upgrades:

The AOD observations will be from MODIS (satellite) and AERONET

(in-situ).

Aerosol data assimilation is proposed for the EOS era (2002 to present).

Proposed upgrades to data assimilation

and modeling of aerosols (contd)


OUTPUT INPUT

Sea Ice

fraction &

thickness …

LETKF

Sea Ice Data Assimilation

Observations (remote & in-situ)

(real time or climatology)

IN

Sea Ice Data Assimilation

32 Xingren Wu, EMC

GFS

Ocean WAVEWATCH III

Qair

Rair

τair

SST

Uc

Uc

τdiff

τcoriol λ

Uref

Charnock

GFS model air-sea fluxes depend on sea state (roughness -> Charnock). WAVEWATCH III model forced by wind from GFS and currents from Ocean. Ocean model forced by heat flux, sea state dependent wind stress modified by growing or decaying wave fields and Coriolis-Stokes effect.

Wave Coupling

33

WAVEWATCH III

● Planned Upgrades

o Drive the wave model in a coupled mode with atmospheric

winds (for wave dependent boundary conditions in atmospheric

models)

o Include wave - ocean coupling (currents from ocean model to

wave model and wave induced langmuir mixing and Stokes

drift from wave model to ocean model)

o Data assimilation of significant wave heights to develop a wave

analysis

GSI approach

LETKF approach

o Planned sources of data

Spectral data from ocean buoys

Satellite data from altimeters

A. Chawla, H. Alves and H. Tolman, EMC

IMPROVE ANALYSIS COUPLING

The operational CFSR uses a coupled atmosphere-ocean-land-sea

ice forecast for the analysis background but the analysis is done

separately for each of the domains.

In the next reanalysis, the goal is to increase the coupling so that,

e.g., the ocean analysis influences the atmospheric analysis (and

vice versa). This will be achieved mainly by using a coupled

ensemble system to provide the background and the EnKF to

generate structure functions that extend across the sea-

atmosphere interface.

The same can be done for the atmosphere and land, because

assimilation of land data will be improved for soil temperature

and soil moisture content.

35

LETKF: Perform assimilation locally at each point

Courtesy: University of Maryland

The state estimate is updated at the

central grid red dot

All observations (purple diamonds)

within the local region are assimilated

LETKF Observation

operator

Model

ensemble analyses

ensemble forecasts

ensemble

“observations”

Observations

36

Coupled LETKF: Sharing of Departures

37

Atmos.

LETKF

Observation

operator

Coupled

Model

ensemble analyses

ensemble forecasts

ensemble

“observations”

Observations

Ocean

LETKF

Land

LETKF

Each grid point is solved independently, so by passing observation departures

(O-F) and model sub-state (land, ocean, atmosphere), one can solve LETKF

equations independently (on different grids if desired) and decide

(spatial/variable localization) how “strongly” to couple. For example, can pass

atmospheric O-F to ocean LETKF! Equivalent to “strong coupling”.

The way ahead

38

• work on building the CFSv3 repository in EMC subversion

• work on using Ricoto/ecflow for running the global parallel scripts in coupled mode

• work on coupled data assimilation

• work on improving the physics in the coupled system

• work on verification of the coupled system

Simple Test Harness for Scale-Aware Physics

• Weather (0-10 days)

• Sub-seasonal (11-45 days)

• Seasonal (2-6 months)

Weather coupled experiments

• T1534

• 10-day forecasts

• Initial conditions:

Summer July 2015

Winter January 2016

Hurricane period ?

siganl/sfcanl from operational/parallel runs

ocnanl from CFSv2 ocean initial states

Sub seasonal coupled experiments

• T574

• 45-day forecasts

• Initial conditions:

Strong MJO cases over the period from 1999-2015 (4-member

ensemble for each). Study onset/mature/decay phase.

siganl/sfcanl from CFSR

ocnanl from CFSR

Seasonal coupled experiments

• T126

• 6-month forecasts

• Initial conditions (over the period 1982-2015):

Strong El-Nino (warm) cases (4-member ensemble each)

Strong La-Nina (cold) cases ((4-member ensemble each)

Neutral ENSO cases (4-member ensemble each)

Summer start from May 15 for JJA (108 cases)

Winter start from Nov 15 for DJF (108 cases)

siganl/sfcanl from CFSR

ocnanl from CFSR.

43

THANK YOU!

development of the ncep unified global coupled system (ugcs… · 2016-11-17 · development of the...

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