Ken MitchellRongqian Yang, Jesse Meng, Youlong Xia, Zoltan Toth, Dingchen Hou

NCEP Environmental Modeling Center

Fourth Southwest Hydrometeorology SymposiumU. Arizona, Tucson, AZ, 20-21 September 2007

Collaborative Drought Monitoring and Seasonal Prediction in CPPA: Support to NIDIS

Partnering CPPA PIs:Eric Wood – Princeton U.,

Dennis Lettenmaier – U. Washington , Brian Cosgrove – NASA/GSFC/HSB,

Kingtse Mo – NCEP/CPCHuug van den Dool – NCEP/CPC

Pedro Restrepo – NWS/OHD

CPPA Partners in this Effort:• Ken Mitchell: NCEP/EMC

– Youlong Xia, Helin Wei, Jesse Meng, Rongqian Yang• Eric Wood: Princeton U.

– Lifeng Luo, Justin Sheffield• Dennis Lettenmaier: U. Washington

– Andy Wood, Ted Bohn• Brian Cosgrove: NASA/GSFC Hydro Sci Branc Branch

– Christa Peters-Lidard, Chuck Alonge, Matt Rodell, S. Kumar• Kingtse Mo: NCEP/CPC

– Wanru Wu, Muthuvel Chelliah • Huug Van den Dool: NCEP/CPC

– Yun Fan• Pedro Restrepo: NWS/OHD

– John Schaake, DJ Seo• Zoltan Toth: NCEP/EMC

– Dingchen Hou

CPPA: Climate Prediction Program for the Americas(Predecessor programs: GCIP and GAPP)

CPPA Science Objectives:

• Improve the understanding and model simulation of ocean, atmosphere and land-surface processes

• Determine the predictability of climate variations on intra-seasonal to interannual time scale

• Advance NOAA’s operational climate forecasts, monitoring, and analysis systems

• Develop climate-based hydrologic forecasting capabilities and decision support tools for water resource applications.

PACS

Outline of This Presentation• CPPA: Climate Prediction Program for the Americas

– Seasonal forecast scale is current emphasis• Two strategic approaches (objective, reproducible, retrospective)

– Coupled prediction models (and their 4DDA/analysis)– Uncoupled prediction models (and their 4DDA/analysis)

• Coupled Monitoring & Prediction (coupled atmosphere-land)– Global Models & Analysis:

• GFS (NCEP Global Forecast System): medium-range• CFS ( NCEP Climate Forecast System): seasonal-range

– Regional Models & Analysis• NARR (North American Regional Reanalysis): with realtime extension• RCMs (Regional Climate Models)

• Uncoupled Monitoring & Prediction (land component only)– Motivation: Downscaling, bias correction, multiple models– National focus (but with global potential)– NLDAS: N. American Land Data Assimilation System

• Climate Test Bed: NCEP-NCPO Partnership– Achieve future upgrades and NOAA operations for all above

Two Strategic Approaches to Hydrologic Prediction:A) Coupled B) Uncoupled

Atmospheric Model(GCM or RCM)

Land Surface Model

River Routing Model

Runoff

Precipitation

Land Surface Models:Noah, VIC, Mosaic, SAC

Bias-correctedPrecipitationForecasts (ensemble)

Runoff

River Routing Model

Stream FlowStream Flow

Post Processor:Downscaling &Bias Correction)

precipitation

Post Processor Post processor

Final ProductsFinal Product

Fluxes

Both approaches should be executed in ensemble mode.

Drought Variables to Monitor and PredictOn several time scales: weeks to seasonal

(energy demand, agriculture, fire risk, water resource, river commerce)

• Precipitation anomalies– weeks, months, seasonal, annual

• Temperature anomalies– weeks, months, seasonal

• Humidity anomalies– weeks, months, seasonal

• Surface evaporation anomalies– weeks, months, seasonal

• Soil Moisture anomalies– months, seasonal, interannual– vertical profiles

• Snowpack anomalies– months, seasonal, interannual

• Runoff and stream/river discharge anomalies– months, seasonal, interannual– OHD emphasis

Shorter Time Scales

Longer Time Scales

Drought / HydrologicalAnalysis/Monitoring

• Coupled Reanalysis & Monitoring (NCEP: EMC & CPC)– NARR: N. American Regional Reanalysis (includes precip assimilation)

• 32-km, 3-hourly, Jan 1979 – present• Eta Data Assimilation System (EDAS) of 2001 is frozen and executed for 28 years

– GR-1: NCEP/NCAR Global Reanalysis 1• ~ 2.5 deg, 6-hourly, 1948-present

– GR-2: NCEP/DOE Global Reanalysis 2• ~2.5 deg, 6-hourly, 1979-present

– All 3 above have daily realtime extensions & frozen configurations– Other coupled global reanalysis (ECMWF, NASA, JMA)

• Uncoupled Land Reanalysis and Monitoring (“LDAS”)– By CPPA PI Partners– Uses observed precipitation analysis to force land surface– NLDAS: N. American Land Data Assimilation

• CONUS, usually 1/8th degree (U. Washington version covers Mexico)• 10-year, 28 year, and 50+ year versions• Multiple institutions with multiple land models

– NCEP/EMC, NCEP/CPC, NASA/GSFC, U. Washington Princeton U.)– GLDAS: Global Land Data Assimilation (NCEP, NASA/GSFC, USAF)

• NCEP: 1979 – present, about 1-deg resolution

(Note: Mostly NARR and NLDAS are featured in following frames on monitoring)

NLDAS: N. American Land Data Assimilation SystemEnsemble Monitoring Mode

NLDAS (top row): Plots of Root Zone Soil Moisture Anomalies09 April 2006

(shown as percentiles wrt 10-year NLDAS climatology: 1997-2006)

NLDAS – Noah LSM Output

NLDAS – Mosaic LSM Output

NDMC – Weekly Drought Monitor

CPC - Leaky Bucket LSM Output

NLDAS results above are:1) objective2) quantifiable3) reproducible (over decadal periods)4) can manifest short & long time scales -- e.g. different soil depths

Traditional Weekly U.S. Drought Monitorat right is subjective, not reproducible, andtends to reflect rather long time scales

Precipitation Anomaly (Monthly): Summer 07

d) P anom Aug 2007

June-July: -- Wet S-Plains -- Dry SE

August: -- Wet N-Plains -- Less dry SE

Weak Southwest monsoon

Next Four Frames from CPCNew Experimental Drought Monitor Page:

(PI Kingtse Mo)

http://www.cpc.ncep.noaa.gov/products/Drought/

Precipitation Anomaly (Weekly): Aug 071

2

3

4

5

August:

Erin (TS) 8/15 - 8/19 Dean (Cat. 5) 8/13 - 8/23 Felix (Cat. 5) 8/31 - 9/5

(From Climate Review)

Erin

Dean

Dean

Precipitation Anomalies at Long Time ScalesExp: Standard Precipitation Index (SPI) thru Aug 07

Total Column Soil Moisture Anomaly (mm): Aug 07

Ensemble Average of Left 4 Frames AUG 2007

NA (Coupled) NLDAS:

NLDAS: NLDAS:

(Uncoupled)

(Uncoupled) (Uncoupled)

Monthly Soil Moisture Trend:Change in soil moisture from Jul to Aug 07

August:

S-Plains – decrease

N-Plains – increase

SE – not much change

Monthly Total Column Soil Moisture Anomaly (Model by Model and 4-model Ensemble Mean)

Rerun of NCEP Realtime NLDAS for 10 Years

Noah Mosaic

SAC VIC

NLDAS:Multi-Model

Ensemble MeanAnomaly

July 2006:Large changesince last year

From NCEP Realtime NLDAS for 10 Years: soon extended to 28 years

NOAH

SAC

MOSAIC

VIC

EnsembleMean

March SWE Climatology (mm)

Drought / Hydrological

Prediction

• Medium-Range: Ensemble coupled GFS

– GEFS: Global Ensemble Forecast System

• about 60 GFS two-week forecasts run daily

GEFS Forecast – Precipitation

Verification 27Aug2007-02Sep2007

Week1 Forecast Made 26Aug2007

Week2 Forecast Made 19Aug2007

Large uncertainties over SE for week2 forecast, overlapping with large errors

GEFS Forecast – Soil MoistureWeek1 Forecast

Made 26Aug2007 Week2 Forecast

Made 19Aug2007 Verification

27Aug2007-02Sep2007

Errors corresponding to large uncertainties

Drought/Hydrological:Prediction

• Seasonal-Range: 2 methods– 1) Dynamical:

• Coupled: – Operational: Ensemble CFS (coupled Atmosphere/Ocean/Land model)

» GFS Atmosphere/Land model coupled to GFDL MOM3 Ocean Model

» about 60 CFS 9-month forecasts run each month» plus companion 22-year hindcast (1982-2003): 15 members executed from every month of 1982-2003

– Experimental: Regional Climate Models RCMs) forced by CFS» RCM seasonal forecast experiment now underway in CPPA

• Uncoupled (Princeton U. and U. Washington): – Princeton U.: CFS land surface forcing is downscaled and bias-corrected

and then used to force high-res uncoupled VIC land-only hydrology models

– U. Washington: CPC official tercile forecasts of precipitation and temperature are downscaled to force high-res uncoupled VIC land-only hydrology models

– 2) Empirical: e.g Ensemble Schaake Shuffle

CFS Seasonal SST Forecast Skill:Correlation of forecast with observed SST over 1982-1983

Example below for December initial conditions

More examples at: http://www.cpc.ncep.noaa.gov/products/people/wwang/cfs_skills/

CFS Seasonal Precip Forecast Skill over CONUS:Correlation of forecast with observed precip over 22-year hindcast

Example below for December initial conditions

More examples at: http://www.cpc.ncep.noaa.gov/products/people/wwang/cfs_skills/

Example CFS Forecast for El’Nino Event: Winter 1983 SST Anomaly (JFM avg)

Observed (with respect to 5-year 2000-2004 observed

climatology)

CFS Predicted(with respect to CFS 5-year 2000-2004 model

climatology)

Predicted JFM Precipitation anomaly for Winter 1983 ENSO Ops CFS versus Experimental Eta RCM versus Observed

(in terms of mean monthly precipitation: mm)

Observed

ETA RCM CFS

Poor

Better Better

Eta Regional Climate Model Experiments Configuration of Eta RCM shown in previous frame:1) model domain (shown at bottom)2) model resolution: 32-km, 45-levels

3) winter forecasts: JFM (initial conditions from mid-to-late Dec, forecasts to end March)4) 7 members of Eta RCM and CFS forecasts for each winter of 1983, 2000-2004

5) 2000-2004: used to derive 5-year Eta RCM and CFS forecast climatology6) 1983: Eta RCM and CFS forecasts depicted as anomalies from 2000-2004 model

Remaining Frames areExamples of Uncoupled

Seasonal Prediction Approaches

The emphasis is on downscaling to higher resolution and correction of bias in coupled models precipitation forecasts before applying to multiple high-resolutionland surface models.

NLDAS: Uncoupled Prediction Mode

U. Washingtonfor Westside

Princeton U.for Eastside

2006

West-Wide(U. Washington)

East-Wide (Princeton U.)

U. Washington (UW) West-Wide and Princeton U. East-Wide Seasonal Forecast Systems

Princeton East-Wide Link: http://hydrology.princeton.edu/~luo/research/FORECAST/project.phpUW West-Wide Link: http://www.hydro.washington.edu/forecast/westwide

Streamflow Forecast Details for UWWest-Wide: An Example

Clicking the stream flow forecast map also accesses current basin-averaged conditionsObservation

Ensemble Mean

Uncertainty Range

Soil Moisture: 198805 Forecast(East-Wide System: An Example)

Lead time

Climatological Forecast

Observations

CFS-based Forecast

Multi-model Forecast(includes European models)

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Bondville, IL

GFS_LH

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Bondville, IL-2

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Grassland, AZ

GFS_LH

GDAS_LH

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Ft. Peck, MT

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Brookings, SD

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Black Hills, SD

GFS_LH

GDAS_LH

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NAM_LH

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OBS_LH-res

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Columbia, MO

GFS_LH

GDAS_LH

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NARR_LH

NAM_LH

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OBS_LH-res

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OBS_LH

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Chestnut Ridge, TN

GFS_LH

GDAS_LH

GLDAS_LH

NARR_LH

NAM_LH

NDAS_LH

NLDAS_LH

OBS_LH-res

OBS_LH-ef

OBS_LH

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Walker Branch, TN

GFS_LH

GDAS_LH

GLDAS_LH

NARR_LH

NAM_LH

NDAS_LH

NLDAS_LH

OBS_LH-res

OBS_LH-ef

OBS_LH

Modeled and Observed surface fluxes: at 9 ARL/ATDD sitesMonthly Mean Diurnal Cycle: May 2007

Conclusions• NCEP has developed and operationally implemented a

suite of coupled analysis and forecast systems applicable to hydrometeorological monitoring and prediction– Reanalysis: Global & Regional Reanalysis (realtime updates)– GEFS: Medium-Range Global Ensemble Forecast System– CFS: Seasonal-Range Climate Forecast System

• Under CPPA sponsorship, CPPA PIs are collaborating on developing and demonstrating new suites of uncoupled hydrometeorological monitoring & prediction systems– Downscaling Focus: plus bias-correction & multi land models– NLDAS monitoring mode (analysis and reanalysis)– NLDAS prediction mode

• Dynamical: forced with dynamical coupled global models• Empirical:

– RCMs: testing seasonal forecast skill of Regional Climate Models