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Preliminary results: NARR-driven E p , E pan , and ET rc. North American Regional Reanalysis ( NARR) driven daily Ep, Epan, ETrc. drivers: DPT2m, 2-meter temperature [K] TMP2m, 2-meter dew point temperature [K] UGRD10m & VGRD10m, 10-meter U & V winds [m/sec]
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Preliminary results: NARR-driven Ep, Epan, and ETrc
North American Regional Reanalysis (NARR) driven daily Ep, Epan, ETrc • drivers: • DPT2m, 2-meter temperature [K] • TMP2m, 2-meter dew point temperature [K] • UGRD10m & VGRD10m, 10-meter U & V winds [m/sec] • PRESsfc, surface pressure [Pa] • DLWRFsfc, surface downward longwave radiation flux [W/m2] • ULWRFsfc, surface upward longwave radiation flux [W/m2] • DSWRFsfc, surface downward shortwave radiation flux [W/m2] • USWRFsfc, surface upward shortwave radiation flux [W/m2] • GFLUXsfc, surface ground heat flux [W/m2] • outputs: • Epan, PenPan • Ep, Penman • Ep, Penman-Monteith • Ep, Hamon • ETrc, Kimberly Penman • ETrc, Hargreaves • ETrc, Penman-Monteith • five headwater basins: • BPPA3 – Black Point of Pines, AZ, Salt-Verde • GBYC2 – Lake Granby, CO, Upper Colorado • TCFC2 – Tenmile Creek, CO, Upper Colorado • DRGC2 – Durango, CO, San Juan • WBRW4 – Green River at Warren Bridge, WY, Upper Green • 1/8-degree (~32 km at mid-coverage latitudes) • coverage over entire North American continent • daily, Jan 1, 1979 • to Dec 31, 2009
Comparing climatological seasonalities – DRGC2H Apply pan coefficient, k
Ep, Epan, and ETrc vs. SAC-SMA flow-prediction errors – DRGC2H
Ep, Epan, and ETrc vs. SAC-SMA flow-prediction errors – BPPA3H
Preliminary results: Monsoon - Phoenix BPPA3
Preliminary results: Monsoon - Tucson BPPA3 UPBA3
Preliminary results: Monsoon - Albuquerque DRGC2 BFFU1
Conclusions from NARR-derived Ep, Epan, ETrc • Flux / model choice: • Ep from Epan (PenPan): • provides closest daily match to current SAC-SMA evaporative driver (i.e., climatological observed Epan). • may not require any recalibration of basins for implementation. • data inputs more extensive, but identical to what RFCs and WFOs will use to drive upcoming ETrc forecasts. • encodes all physical drivers. • Of the four flux/models examined, ETrc Hargreaves provides worst match to Epan, almost an order of magnitude lower.
Conclusions from NARR-derived Ep, Epan, ETrc • Southern basin monsoons: • Of all fluxes, Epan bears strongest relation to Prcp over monsoon • Parameterization improvement: • Can more finely calibrate SW, LW = f(Sky) parameterization • Issues: • Need RTMA data set. • Flow estimation errors: • Ep-relation to differs across basins and within years • need objective skill test: • daily (operational), • water supply seasonal, • headwaters vs. forecast groups.
Next steps: Western Region ETrc forecasts RTMA, Aug 2006 – pres. NARR, Jan 1979 – pres. • Project goals: • provide end-users with web-disseminated, fine-resolution, accurate, daily/weekly forecasts of ETrc across NWS Western Region domain, • add value to the daily/weekly ETrc forecasts by generatting and comparing to a 30-year climatology. T(t) Tdew(t) SW(t) LW(t) U2(t) T(t) Tdew(t) Sky(t) U2(t) dataset development ETrc(t) ETrc(t) T(forecast) Tdew(forecast) Sky(forecast) U2(forecast) ETrc(t) statistical analysis WFO daily operations climatology development ETrc(climo) ETrc(forecast) Value-added ETrc(forecast) dissemination to end-users www publication
Next steps: CBRFC water supply / streamflow forecast improvement RTMA NARR Conduct reanalyses of streamflow using various Ep/ETrc data-series Motivation: improve water supply forecsts Goals: time-series of streamflows of greater skill: - at a water-supply time-scale - at a daily time-scale Skill scores: - matching Epan observations? - reforecasting streamflow? T(t) Tdew(t) SW(t) LW(t) U2(t) T(t) Tdew(t) Sky(t) U2(t) dataset development Ep(t, flux) Ep(t, flux) Prcp(t, basin) T(t, basin) PE(t, basin, flux) SAC-SMA model hydrologic reanalysis Qsim(t, basin, flux) Qobs(t, basin) statistical analysis verification and model selection SS(basin, flux) MODEL(basin ??) implementation in NWSRFS
Next steps: Scientific output • Paper 1: • Improving river forecasting skills using dynamic representation of evaporative demand: • - Ep/ETrc options • - skill scores • - daily • - seasonal • Paper 2: • Examining trends in evaporative demand across the conterminous US: • - quantifying trends • - describing spatial distribution • - decomposing trends to drivers