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Ken Mitchell Rongqian Yang, Jesse Meng, Youlong Xia, Zoltan Toth, Dingchen Hou

Fourth Southwest Hydrometeorology Symposium U. Arizona, Tucson, AZ, 20-21 September 2007. Ken Mitchell Rongqian Yang, Jesse Meng, Youlong Xia, Zoltan Toth, Dingchen Hou. NCEP Environmental Modeling Center. Collaborative Drought Monitoring and Seasonal Prediction in CPPA: Support to NIDIS.

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Ken Mitchell Rongqian Yang, Jesse Meng, Youlong Xia, Zoltan Toth, Dingchen Hou

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  1. Fourth Southwest Hydrometeorology Symposium U. Arizona, Tucson, AZ, 20-21 September 2007 Ken Mitchell Rongqian Yang, Jesse Meng, Youlong Xia, Zoltan Toth, Dingchen Hou NCEP Environmental Modeling Center 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/CPC Huug van den Dool – NCEP/CPC Pedro Restrepo – NWS/OHD

  2. 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

  3. 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

  4. 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

  5. Two Strategic Approaches to Hydrologic Prediction:A) CoupledB) Uncoupled precipitation Atmospheric Model (GCM or RCM) Bias-corrected Precipitation Forecasts (ensemble) Post Processor: Downscaling & Bias Correction) Precipitation Fluxes Land Surface Models: Noah, VIC, Mosaic, SAC Land Surface Model Runoff Runoff River Routing Model River Routing Model Stream Flow Stream Flow Post Processor Post processor Final Products Final Product Both approaches should be executed in ensemble mode.

  6. 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 evaporationanomalies • 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

  7. 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)

  8. NLDAS: N. American Land Data Assimilation System Ensemble Monitoring Mode

  9. NLDAS – Mosaic LSM Output NDMC – Weekly Drought Monitor NLDAS – Noah LSM Output NLDAS (top row): Plots of Root Zone Soil Moisture Anomalies09 April 2006 (shown as percentiles wrt 10-year NLDAS climatology: 1997-2006) CPC - Leaky Bucket LSM Output NLDAS results above are: 1) objective 2) quantifiable 3) reproducible (over decadal periods) 4) can manifest short & long time scales -- e.g. different soil depths Traditional Weekly U.S. Drought Monitor at right is subjective, not reproducible, and tends to reflect rather long time scales

  10. d) P anom Aug 2007 Precipitation Anomaly (Monthly): Summer 07 June-July: -- Wet S-Plains -- Dry SE August: -- Wet N-Plains -- Less dry SE Weak Southwest monsoon

  11. Next Four Frames from CPCNew Experimental Drought Monitor Page:(PI Kingtse Mo) http://www.cpc.ncep.noaa.gov/products/Drought/

  12. 1 3 5 2 4 Precipitation Anomaly (Weekly): Aug 07 Erin Dean Dean August: Erin (TS) 8/15 - 8/19 Dean (Cat. 5) 8/13 - 8/23 Felix (Cat. 5) 8/31 - 9/5 (From Climate Review)

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

  14. NA (Coupled) NLDAS: (Uncoupled) NLDAS: (Uncoupled) NLDAS: (Uncoupled) Total Column Soil Moisture Anomaly (mm): Aug 07 Ensemble Average of Left 4 Frames AUG 2007

  15. Monthly Soil Moisture Trend:Change in soil moisture from Jul to Aug 07 August: S-Plains – decrease N-Plains – increase SE – not much change

  16. 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 July 2006: Large change since last year NLDAS: Multi-Model Ensemble Mean Anomaly

  17. March SWE Climatology (mm) From NCEP Realtime NLDAS for 10 Years: soon extended to 28 years NOAH MOSAIC VIC SAC Ensemble Mean

  18. Drought / HydrologicalPrediction • Medium-Range: Ensemble coupled GFS • GEFS: Global Ensemble Forecast System • about 60 GFS two-week forecasts run daily

  19. GEFS Forecast – Precipitation Week2 Forecast Made 19Aug2007 Week1 Forecast Made 26Aug2007 Verification 27Aug2007-02Sep2007 Large uncertainties over SE for week2 forecast, overlapping with large errors

  20. GEFS Forecast – Soil Moisture Verification 27Aug2007-02Sep2007 Week2 Forecast Made 19Aug2007 Week1 Forecast Made 26Aug2007 Errors corresponding to large uncertainties

  21. 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

  22. CFS Seasonal SST Forecast Skill:Correlation of forecast with observed SST over 1982-1983Example below for December initial conditions More examples at: http://www.cpc.ncep.noaa.gov/products/people/wwang/cfs_skills/

  23. CFS Seasonal Precip Forecast Skill over CONUS:Correlation of forecast with observed precip over 22-year hindcastExample below for December initial conditions More examples at: http://www.cpc.ncep.noaa.gov/products/people/wwang/cfs_skills/

  24. 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)

  25. Predicted JFM Precipitation anomaly for Winter 1983 ENSO Ops CFS versus Experimental Eta RCM versus Observed(in terms of mean monthly precipitation: mm) ETA RCM CFS Poor Better Better Observed

  26. Eta Regional Climate Model Experiments Configuration of Eta RCM shown in previous frame: • model domain (shown at bottom) • 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 climatology • 6) 1983: Eta RCM and CFS forecasts depicted as anomalies from 2000-2004 model

  27. Remaining Frames areExamples of UncoupledSeasonal Prediction Approaches The emphasis is on downscaling to higher resolution and correction of bias in coupled models precipitation forecasts before applying to multiple high-resolution land surface models.

  28. NLDAS:UncoupledPrediction Mode Empirical Dynamical Princeton U. for Eastside U. Washington for Westside Ensembles

  29. U. Washington (UW) West-Wide and Princeton U. East-Wide Seasonal Forecast Systems 2006 Princeton East-Wide Link: http://hydrology.princeton.edu/~luo/research/FORECAST/project.php UW West-Wide Link:http://www.hydro.washington.edu/forecast/westwide West-Wide (U. Washington) East-Wide (Princeton U.)

  30. Ensemble Mean Clicking the stream flow forecast map also accesses current basin-averaged conditions Observation Uncertainty Range Streamflow Forecast Details for UWWest-Wide: An Example

  31. Observations Climatological Forecast CFS-based Forecast Multi-model Forecast (includes European models) Soil Moisture: 198805 Forecast(East-Wide System: An Example) Ohio Basin Lead time

  32. Modeled and Observed surface fluxes: at 9 ARL/ATDD sites Monthly Mean Diurnal Cycle: May 2007

  33. 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

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