Land and Hydrology modeling inNCEP Weather and Climate

Prediction Models

Ken Mitchell

Environmental Modeling CenterNational Centers for Environmental Prediction

NCEP: Where America's Climate and Weather Services Begin

NOAA Science Advisory BoardNovember 6, 2001

OVERARCHING THEMES

• Linkages between weather, water, and climate predictions

• Multiple disciplines of meteorology, hydrology, oceanography

• Dynamical model predictions at multiple time and space scales

• Fundamental importance of vast computer power

• Ensemble prediction and probabilistic forecasts

• An emerging community modeling approach

• Central role of data assimilation for atmosphere, land, ocean

• Partnerships to infuse science and technology into operations

NCEP Dynamical Model Prediction Suite

1 - Ocean seasonal forecast model: coupled ocean/atmosphere -- predict tropical Pacific SST (issued monthly out to 11 months) -- GFDL MOM ocean model (order 50-150 km resolution)2 - Atmospheric seasonal forecast model (SFM): coupled atmosphere/land -- predict global atmospheric state (issued monthly out to 7 months) -- NCEP global climate model (order 200 km resolution)3 - Atmospheric medium-range forecast model (MRF): coupled atmosphere/land -- predict global atmospheric state (issued daily out to 15 days) -- NCEP global medium-range model (order 75 km resolution)4 - Atmospheric short-range forecast model (ETA): coupled atmosphere/land -- predict N. American atmospheric state (4 times daily out to 2.5 to 3.5 days) -- NCEP regional short-range model (order 22 km resolution)5 - Atmospheric nested short-range forecast model (N-ETA): coupled atmos/land -- predict regional atmospheric state, (4 times daily out to 2 days) -- NCEP regional short-range forecast model (order 10 km resolution)6 - Hurricane forecast model (GFDL) and RUC model (FSL).

Community Weather and Climate Models:Community Weather and Climate Models:NCEP partnershipsNCEP partnerships

1 - Community global weather and climate model • Earth System Model framework • Atmosphere, Ocean, and Land modules • Partners: NCEP, NASA, NCAR, GFDL, DOE, MIT, others

2 - Community Weather Research and Forecast (WRF) regional model • Ultra-high 1-km spatial resolution for national/regional domains • Resolve individual thunderstorm cells • Partners: NCEP, NCAR, FSL, AFWA, others

Relocatable Eta “Nests”

High-resolution regional Eta forecasts "nested" inside of the parent Eta (22km/50 lev resolution)

Six nested domains are being run.

Hawaii and Puerto Rico twice per day

Alaska, East, West, Central U.S. once per day

Output available on NCEP ftp server

Homeland Defense Initiative

NCEP Eta weather model

is linked with NOAA/ARL radiological dispersion model known as HYSPLIT.

This linked pair of models is executed at ultra-high spatial resolution of 4-km over any of the domains at right.

High Performance Computer

• Ingests over 3.9 million observation reports daily • Produces 100 gigabytes of information daily• NCEP transmits 174,314 products each day• Second most powerful weather supercomputer in the world !

– ( 46X Faster than the Cray C90, 3.0 tflops peak performance )

• 584 nodes with four 375 MHz CPUs each (2336 processors)

IBM SP at Bowie Computer Center in Bowie, MD

NCEP Central Operations, Oct 2001

IBM SUPERCOMPUTER: Operational December 1999Follow-on NCEP Modeling Advances

1 - Seasonal Forecast Model: Nov 01 -- transitioned to operations on IBM (from former demo platform) -- first formally operational seasonal forecast system in the world2 - Global Medium-Range Model: Jan 00 -- resolution increase from 105-km/28-layers to 75-km/42-layers3 - Eta Regional Short-Range Model: -- increase resolution from 33-km/45-layers to 22-km/50-layers: Sep 00 -- increase resolution from 22-km/50-layers to 12-km/60-layers: Nov 01

Also improvements in several NCEP models in cloud microphysics, precipitation assimilation, ingest of new radar and satellite observations, improved land surface physics, new tropical cyclone initialization. Next Slide: Example of impact of Eta Model resolution increase from22-km to 12-km on a heavy convective precipitation event in Texas.

Impact of Resolution on Model Precipitation Forecast22 km Operational Meso Eta 10 km Experimental “Threats” Meso Eta

2.2” 4.3”Better locationBetter intensity

7.0”

5.7”

Observed

Eta model-based 24-hour forecast valid 19 May 01; 24HR PQPF OF .25”

Hydrological Applications Need Probabilistic Precipitation Forecasts

Schematicexample ofensemblepredictions (used forderivationof probabilisticforecasts andmeasures ofpredictability).

EnsembleForecasting:

Example of 8-day PQPF:Probabilistic Quantitative Precipitation Forecast

(calibrated to remove bias)

NWS Office of Hydrologic Development (OHD):Ensemble Forecast Strategy for Water Management

Realisticsoil moisturein coupledland-atmosphereclimate modelsimproves seasonalpredictability ofprecipitation inthe warm season

Improving Weather and Climate Prediction:Becoming a Complete Earth System Endeavor

1 - ATMOSPHERE: troposphere, stratosphere- initial conditions require atmosphere data assimilation

2 - OCEAN: deep ocean, seas, coastal ocean, sea ice- initial conditions require ocean data assimilation

3 - LAND: soil, snowpack, vegetation, runoff- initial conditions require land data assimilation

Improving weather and climate prediction bybringing together meteorologists and hydrologistsin:

(A) coupled land-atmosphere modeling(B) land data assimilation(C) water resource applications of weather and climate forecasts

GAPP: GEWEX Americas Prediction Project

(NOAA Office of Global Programs)

The GAPP model development strategy

WATERAND

ENERGYBUDGETS

IN SITUANDGIS

REMOTESENSING

SATELLITEAND

SURFACE

DATA SOURCES

GENERALCIRCULATION

MODELS

BASIN-SCALEHYDROLOGIC

MODELS

MESOSCALEATMOSPHERIC

MODELS

RETRIEVALS QUALITYASSURANCE

IMPROVEDCOUPLEDCLIMATEMODELS

REGIONALWATER

ASSESSMENT

SITE SPECIFIC NONSITE SPECIFIC TRANSPORTABILITY

FIELD AND ANALYTICAL STUDIES

GCIPDATABASE

MODELDEVELOPMENT

GAPP-strategy for research infusion to operations

DATABASEDEVELOPMENT

ROUTINE ENHANCED

EOPRETROSPECTIVE

Standard ResolutionMeteorological

Nonstandard ResolutionMeteorological

PrecipitationRadiation and CloudsWindWater Vapor

HydrologicalRunoffSoil MoistureEvaporationSnow (water equivalent)

Geographical

Vegetation

Field Campaign(s)

OBSERVATIONS

MODEL DEVELOPMENT

OperationalPath

ResearchPath

Evaluate Current Models (GCM and Meso)

Implement Upgraded Surface Hydrology

Improve Cloud Parameterizations

Algorithm DevelopmentData Analysis

Data Assimilation

Intercompare Surface Hydro- logical Models

Scale Analysis

Improve Atmos- pheric Models

Develop New Macroscale Hydrologic Models

Evaluate and Improve Coupled Models

Collaborators

*UCAR/Visiting Scientist Program

John SchaakeVictor KorenQingyun Duan

NWS/OHD

Tilden MeyersNOAA/ARL

Fei ChenNCAR

Wayne HigginsNCEP/CPC

Eric WoodPrinceton Univ.

Dan TarpleyNOAA/NESDIS

Bruce Ramsay

Jerry Wegiel

George GaynoBrian Moore

AFWA

Soroosh SorooshianJames ShuttleworthHoshin Gupta

Univ. Arizona

Dennis LettenmaierUniv. Washington

Ken MitchellMichael Ek*Dag Lohmann*

NCEP/EMC

Rachel PinkerHugo Berbery

Univ. Maryland

Ken CrawfordUniv. Oklahoma

AtmosphericResearch

Alan Betts

GAPP GCIP

Paul HouserNASA/GSFC

NCEP Eta model forecast during July 1998:Texas/Oklahoma drought, 24-hour forecast valid 00Z 27 July 1998

In late July 1998, after nearly twomonths of self-cyclingthe land states in theEDAS, the Eta modelsuccessfully capturedthe extremely dry soilmoisture (upper left)and warm soil temps(upper right) over theTexas/Oklahomaregion, yieldingforecasts of high 2-mair temps (lowerleft) and deep, dry,hot boundary layersthat verified wellagainst raobs (e.g.,at Norman, OK –lower right).

air temperature (2-meter) Norman, OK sonde(obs=solid, model=dashed)

soil moisture availability (1-m) soil temperature (5-cm)

Eta model Oct 2001 end-of-month soil moisture statesand monthly observed total precipitation.

Monthly OBSERVED PRECIPITATION accumulationsInterannual variability of North American Monsoon - interior Southwest

totalprecipitation

(mm)

departurefrom

normal(mm)

drymoist

Monthly observed precipitation accumulation based on 0.25 deg lat/lon gridded analysis of dailytotal gage-only data (Higgins, R.W., W. Shi and E. Yarosh, 2000: Improved United States PrecipitationQuality Control System and Analysis. NCEP/Climate Prediction Center ATLAS No. 7, 40 pp).

July 1999 July 2000

Eta model monthly-mean 2-m (C) air

temperature vs obs:interior Southwest

interiorSouthwest

Eta modelend-of-month

2nd layervolumetric

soil moisturerelativelymoist

Eta forecast hour00 2412 36 48

obs

Eta

29 C

16

23

30 33 C

16

24

32

Eta forecast hour00 2412 36 48

obs

Eta

July 1999 July 2000

relativelydry

NORTH AMERICAN MONSOONEta model captures interannual variability of daytime

maximum temperature and model soil moisture

NOAH LAND-SURFACE MODEL UPGRADEScold season processes (Koren

et al 1999) - patchy snowcover - frozen soil (new state variable) - snow density/snow depth (new state variable) bare soil evaporation refinements - parameterize upper sfc crust cap on evapsoil heat flux - new soil thermal conductivity (Peters-Lidard et al 1998) - under snowpack (Lunardini, 1981) - vegetation reduction of thermal cond. (Peters-Lidard et al 1997)surface characterization- maximum snow albedo database (Robinson & Kukla 1985)- dynamic thermal roughness length refinements vegetation - deeper rooting depth in forests - canopy resistance refinements

NOAH LSM tested in various land-modelintercomparison projects, e.g. PILPS, GSWP,and (near-future) DMIP.

NOAH LSM soil thermodynamics tested successfully by OHDin Sacramento soil water accounting model (SAC-SWA).Example below: observed air temperature (top), and observed (white) and modelled (red) soil temperature at 20 cm, 40 cm, and 80 cm at Valdai, Russia (Oct 71-Sep 72).

NOAH LSM performs well in U. Arizonamulti-criteria objective calibration system.

NOAH LSM chosen as one of several land modelsto be used in their NSF Science and Tech Center.

Major Recent Multi-Institution InitiativeLDAS: Land Data Assimilation System

GOAL: provide soil moisture/temperature initial conditionsSuperior to present EDAS

METHOD: drive stand-alone uncoupled land-surface modelswith observed gage/radar precipitation and satellite-derivedsurface solar insolation. - bypass atmospheric model precipitation and radiation biases

ADDITIONS: assimilate satellite-derived soil moisture andskin temperature - test land-data assimilation tools (e.g. adjoint models,variational methods, Kalman filtering, surface emissivity models)

Real-time National LDAS Demonstration(Hosted by NCEP/EMC; Partners: NWS/OHD, NESDIS/ORA, NASA/GSFC,

Princeton U., U. Washington, Rutgers U., U. Maryland, others)

• U.S. domain at 1/8 deg.• executed from Apr 1999 to present (ongoing) - hosted on NCEP SGI-platform - website hosted at NASA/GSFC - retrospective runs back to Sep 1996 nearly complete (NASA-provided forcing)

• hourly surface forcing - precipitation, incoming solar, downward longwave, air temperature, humidity, wind speed

• 4 land-models executing in parallel - NOAH (NCEP), VIC (Princeton), MOSAIC (NASA), SAC-SWA(NWS-OHD) - 15 to 60 minute time steps

• common outputs - soil moisture & temperature, snowpack, surface fluxes of water and energy, snowmelt, runoff

• common streamflow connectivity and routing model - provided by Univ. Washington and Princeton

NWS/OHD provided soil classes on LDAS domain

Soils classification map as derived by NWS/OHD for the LDAS project for the 1/8-deg LDAS grid, using the 11-layer, 16-texture 1-km Penn StateSTATSGO data base (D. Miller, GAPP PI) and 5-minute ARS FAO data.

Soil TextureClassifications: 1 Sand 2 Loamy sand 3 Sandy loam 4 Silt loam 5 Silt 6 Loam 7 Sandy clay loam 8 Silty clay loam 9 Clay loam10 Sandy clay11 Silty clay12 Clay13 Organic materials14 Water15 Bedrock16 Other

NOAH LSM 4-layer soil moisture from National-LDAS(valid 29 Feb 2000, 12Z)

The 188 basins chosen for LDAS-project streamflow validation using therespective USGS streamflow gaging stations. (Example time series for the twolabeled basins are shown in next figure.)

USGS gage

No. 01503000

USGS gageNo. 01631000

NOAH LSM LDAS streamflow validation at two USGS gaging stations: UpperSusquehanna (station No. 01503000; 42.0353N - 75.8033W) and South ForkShenandoah (station No. 01631000; 38.9139N - 78.2111W) for the period 21 Juneto 08 December 2000.

Future directions:

• Propagate/unify NOAH LSM in all NCEPglobal and regional, weather and climate models

• Extend national LDAS demonstration to global domain - in collaboration with NASA/GSFC (now underway)

• Demonstrate impact of land-surface initial conditions andphysics on seasonal climate predictability - especially warm season

• Demonstrate impact of regional climate models on seasonalclimate predictability

Issues for NOAA’s Science Advisory BoardIssues for NOAA’s Science Advisory Board

1 - Expand research into methods to account for weather andclimate model forecast precipitation biases in hydrologicalmodels and water resource applications.

2 - Increase emphasis on improving forecast model physicalparameterizations of precipitation and water cycle processes:cloud microphysics, deep convection, cloud-radiation feedbacks,PBL fluxes and land-surface processes.

3 - “Stay the course” on developing community weather andclimate models, and the linkages between weather and climateprediction.

4 - Sustain momentum towards aggressive expansion ofsupercomputer power and mass-storage systems.