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Mike Hobbins Colorado Basin River Forecast Center ( CBRFC ), NWS-NOAA Salt Lake City, UT

Forecasting evaporative demand across the conterminous US: ET information for river forecasting. Mike Hobbins Colorado Basin River Forecast Center ( CBRFC ), NWS-NOAA Salt Lake City, UT mike.hobbins@noaa.gov. Two goals of CBRFC ET project.

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Mike Hobbins Colorado Basin River Forecast Center ( CBRFC ), NWS-NOAA Salt Lake City, UT

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  1. Forecasting evaporative demand across the conterminous US: ET information for river forecasting Mike Hobbins Colorado Basin River Forecast Center (CBRFC), NWS-NOAA Salt Lake City, UT mike.hobbins@noaa.gov

  2. Two goals of CBRFCET project • 1. Improve streamflow forecasts with dynamic E0 • Raise CBRFC’s streamflow forecast skill at daily operational and seasonal time-scales; • Replace the current, static evaporative driver of the Sac-SMA model with a physically based, accurate, temporally dynamic E0. • 2. Forecasting ETrc across NWS Western Region • Provide forecasts of reference crop ET (ETrc) that are scientifically sound, web-disseminated, fine-resolution, accurate, and daily-to-weekly; • Develop a 30-year climatology to add value to these forecasts. • ET = actual evapotranspiration • E0 = evaporative demand • Epan = pan evaporation • ETrc = reference crop ET

  3. E0reanalyses and forecasts Model drivers • Reanalysis:North American Land Data Assimilation System (NLDAS) • T, Air temperature at 2-m elevation • q, Specific humidity at 2 m • Rd, Down-welling SW radiation • Ld, Down-welling LW radiation • Pa, Station pressure • U10, Wind speed at 10 m • Hourly time-step • 0.125-deg (~12 km) resolution • Jan 1, 1980, to Dec 31, 2009 • Forecast: National Digital Forecast Database (NDFD) • T, Air temperature at 2 m • Tdew, Dewpoint at 2 m • U2, Wind speed at 2 m • ECA, Effective cloud amount • Hourly, 3-hourly, or 6-hourly time-steps • 2.5-km / 5-km resolution HRAP grid • Forecast verification: Real-Time Mesocscale Analysis (RTMA) Climatologic annual ETrc (mm), 1980-2009 Forecast daily ETrc (mm), May 3, 2010

  4. E0reanalyses and forecasts E0 drivers & models Users Uses water supply • seasonal Colorado Basin River Forecast Center Sac-SMA model streamflow • daily, • seasonal flood Epan NDFD NWP forecasts • daily recreation ET = f(E0) E0 modeling • reanalyses verified against climatology • forecasts verified wrt observations reservoir operations • climatology verified wrt observations • forecast unbiased wrt climatology • integrated into existing modeling framework • daily-weekly NLDAS reanalyses US Bureau of Reclamation scheduling inter-state water deliveries • seasonal • physically based • recognized models • implementable within existing operational frameworks irrigation scheduling agriculture • daily-weekly ETrc municipal demand analyses municipalities • temporally dynamic • spatially distributed • low latency • forecast-able • spatio-temporal resolution • verifiable and unbiased wrt observations • seasonal US Drought Monitor drought monitoring • daily-weekly • (latency issue) • (no explicit E0 input)

  5. Dynamic evaporative demand across CBFC: DRGC2H test-basin Mean annual Epan, 1980 - 2009 Epan across DRGC2H, 1980 - 2009 (& 1983) mean, max, min daily Epan (1980-2009) 1983 daily Epan current, static E0 PenPan equation of synthetic pan evaporation Modifies Penman equation to replicate the enhanced characterization of radiative and advective dynamics of evaporation pans. Streamflow at DRGC2H, 1983 Skill-test of simulation at DRGC2H, 1980 - 2009 • weighted combination of radiative and advective drivers. • synthesizes monthly Epan observations well. • Epan = synthetic pan evaporation • λ = latent heat of vaporization • U2 = 2-m wind speed • fq(U2) = vapor transfer function or “wind function” • esat = saturated vapor pressure • ea = actual vapor pressure • Δ = desat/dT at air temperature • aP = ratio of effective surface areas for heat and water-vapor transfer in a pan • γ = psychrometric constant • Qn = net available energy for Epan

  6. Goal 2: Forecasting ETrc across NWS Western Region Two sets of model drivers NDFD forecast surface, generated at NWS Weather Forecast Office NLDAS climatology surface, specific to date and tailored to local area +

  7. Goal 2: Forecasting ETrc across NWS Western Region • Product delivery Wind Temperature Dewpoint Sky cover • Input forecast grids • NDFD-derived • 2.5-km resolution • hourly WFO FORECAST + + + • ETrc climatology grids • NLDAS-derived • 0.125o resolution • CONUS-wide • daily / weekly • moving window / static weeks mean GFE script CBRFC variance minimum 90% exceedance median (50%) 10% exceedance maximum • ETrc forecast grid • 2.5-km resolution • daily/weekly outlook • ETrcpoint forecast • value-added • statistical context • spatial context WFOPRODUCT e.g., http://www.wrh.noaa.gov/sto/et

  8. Goal 2: Forecasting ETrc across NWS Western Region • Product delivery: Forecast ETrc “FRET” webpage • Operational status: • running at 12 NWS-WR WFOs: • soon at rest of NWS Western Region, • eventually CONUS-wide • experimental period ends 06/30/2011. FRET website for Sacramento, CA http://www.wrh.noaa.gov/forecast/evap/FRET/FRET.php?wfo=sto

  9. Goal 2: Forecasting ETrc across NWS Western Region Project status • Forecast • operations • real-time • daily/weekly • Climatology • Jan 1980 – Dec 2009 • high resolution • unbiased wrt forecasts ETrc(t) verification ETrc(forecast) statistical analysis Value-added ETrc(forecast) ETrc(climo) experimental www publication feedback from users www publication system spread

  10. Needs and issues: a summary Needs • consistency within/across data sources and a long-term climatology • that defines critical/canonical events • requires an improved climatology: • replacing low-res NLDASclimo with high-res. RTMA/NLDASclimo • eliminating bias between NLDASclimo and NDFD/RTMA data sources • verifying drivers • particularly forecast Rd from ECA • verifying E0 output: • Epan vs. pans • ETrc vs. CIMIS stations • E0 vs. MODIS-based E0 • mis-match between forecast and climatology modeling • spatial resolution • drivers • temporal extent • likely results in biases between climatology and forecasts • streamflow forecasting: • model: no sublimation mass flux • ETrc forecasting: • latency of ETrc observed drivers • lack of knowledge in ETrc forecast users • extend streamflow and ETrc forecasts to seasonal time-frame • resisting legacy T-based E0 formulations Issues

  11. Future work: Improving the climatologies ECA= Effective Cloud Amount • NLDAS • T • q • U10 • Rd • Ld • Pa • Hourly • 0.125-deg (~12 km) • also ~4 km (HRAP) Present • NDFD • T • Tdew • U2 • ECA • 3-, 6-, hourly • 2.5 → 5 km ~5 days in past Aug ‘06 + 1 day + 7 days Jan ‘79 biased wrtNDFD? climatology climatology forecast verifies NDFD serially inconsistent • RTMA • T • Tdew • U2 • ECA • Hourly • HRAP grid

  12. ET information for river forecasting Talks need to be relatively brief. Focus on the key points/issues/topics that will lead to discussions at the breakout sessions and development of the white paper.  15 minutes = 5-6 slides prognostic vs. diagnostic Essential Climate Variable (ECV) Global Climate Observing System (GCOS) 2012-2015 GEO Work Plan

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