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Experimental Real-time Seasonal Hydrologic Forecasting Andrew Wood Dennis Lettenmaier University of Washington Arun Kumar NCEP/EMC/CMB presented: JISAO.

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Presentation on theme: "Experimental Real-time Seasonal Hydrologic Forecasting Andrew Wood Dennis Lettenmaier University of Washington Arun Kumar NCEP/EMC/CMB presented: JISAO."— Presentation transcript:

1 Experimental Real-time Seasonal Hydrologic Forecasting Andrew Wood Dennis Lettenmaier University of Washington Arun Kumar NCEP/EMC/CMB presented: JISAO weekly seminar Seattle, WANov 13, 2001

2 Overview Research Objective: To produce monthly to seasonal snowpack, streamflow, runoff & soil moisture forecasts for continental scale river basins Underlying rationale/motivation: 1.Global numerical weather prediction / climate models (e.g. GSM) take advantage of SST – atmosphere teleconnections 2.Hydrologic models add soil-moisture – streamflow influence (persistence)

3 Topics Today 1.Approach 2.Columbia River basin (summer 2001) application 3.East Coast (summer 2000) application 4.Related work 5.Comments

4 climate model forecast meteorological outputs ~1.9 degree resolution (T62) monthly total P, avg T Use 3 step approach:1) statistical bias correction 2) downscaling 3) hydrologic simulation General Approach  hydrologic model inputs  streamflow, soil moisture, snowpack, runoff 1/8-1/4 degree resolution daily P, Tmin, Tmax

5 Models: Global Spectral Model (GSM) ensemble forecasts from NCEP/EMC forecast ensembles available near beginning of each month, extend 6 months beginning in following month each month: 210 ensemble members define GSM climatology for monthly Ptot & Tavg 20 ensemble members define GSM forecast

6 Models: VIC Hydrologic Model

7 domain slide Example Flow Routing Network

8 One Way Coupling of GSM and VIC models a) bias correction: climate model climatology  observed climatology b) spatial interpolation: GSM (1.8-1.9 deg.)  VIC (1/8 deg) c) temporal disaggregation (via resampling of observed patterns): monthly  daily a. b. c. T GSM T OBS

9 GSM Regional Bias: a spatial example Bias is removed at the monthly GSM-scale from the meteorological forecasts (so 3 rd column ~= 1 st column)

10 GSM Regional Bias: one cell example For sample cell located over Ohio River basin, biases in monthly Ptot & Tavg are significant!

11 GSM Regional Bias: one cell example

12 Bias: Developing a Correction 20 member forecast ensemble from 1979 SSTs from 1980 SSTs from 1981 SSTs from 1999 SSTs from current SSTs (21 sets)10 member climatology ensembles

13 Bias: Developing a Correction GSM Observed July Tavg, for 1 GSM cell 1979 SSTs etc. from 1999 SSTs 10 member climatology ens. * for each month, each GSM grid cell and variable *

14 Bias: Applying a Correction Note: we apply correction to both forecast ensemble and climatology ensemble itself, for later use

15 Bias-Correction: Spatial Perspective shown 1 month, 1 variable (T), 1 ens-member raw GSM outputbias-corrected

16 Bias: Spatial Perspective express as anomaly bias-corrected

17 Downscaling: step 1 is interpolation (bias corrected) anomalyanomaly at VIC scale

18 Downscaling: step 2 adds spatial VIC-scale variability to smooth anomaly field mean fields anomaly note: month m, m = 1-6 ens e, e = 1-20 VIC-scale monthly forecast

19 Lastly, temporal disaggregation… VIC-scale monthly forecast

20 Lastly, temporal disaggregation… VIC-scale monthly forecast

21 Downscaling Test 1.Start with GSM-scale monthly observed met data for 21 years 2.Downscale into a daily VIC-scale timeseries 3.Force hydrology model to produce streamflow 4.Is observed streamflow reproduced?

22 GSM forecast and climatology ensembles 20 member forecast ensemble from 1979 SSTs from 1980 SSTs from 1981 SSTs from 1999 SSTs from current SSTs (21 sets)10 member climatology ensembles

23 GSM climatology: use #2 sample: 21 member climatology ensemble from 1979 SSTs etc. from 1999 SSTs 10 member climatology ens. (21 sets)

24 GSM climatology: use #2 sample: 21 member climatology ensemble from 1979 SSTs etc. from 1999 SSTs 10 member climatology ens. (21 sets) 20 member forecast ens.

25 Simulations Forecast Products streamflow soil moisture runoff snowpack VIC model spin-up VIC forecast ensemble climate forecast information (from GSM) VIC climatology ensemble 1-2 years back start of month 0end of month 6 NCDC met. station obs. up to 2-4 months from current LDAS/other met. forcings for remaining spin-up data sources A B C

26 Columbia River Application

27 CRB Initial Conditions late-May SWE & water balance

28 CRB Initial Conditions (percentiles)

29 CRB: May forecast observed forecast medians

30 CRB: May forecast hindcast “observed” forecast forecast medians

31 CRB May forecast hindcast “observed” forecast medians

32 CRB May forecast basin avg. soil moisture

33 CRB May Forecast Streamflow

34 CRB: sequential streamflow forecasts hindcast climatologies forecasts ensemble medians

35 CRB May Forecast cumulative flow averages forecast medians

36 East Coast Application

37 Model forecasting domain

38 East Coast spin-up period

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42 East Coast hindcast

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46 East Coast Apr ’00 forecast for May-Jun-Jul forecast median shown as percentile of climatology ensemble

47 East Coast May ’00 forecast for Jun-Jul-Aug

48 East Coast Jun ’00 forecast for Jul-Aug-Sep

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50 ENSO extreme pseudo-forecast evaluation perfect-SST forecasts from Nov. 97

51 Related Applications

52 Related:Yakima R. Mesocale Model Downscaling (RCM @ ½ to VIC @ 1/8)

53 Related: PCM-based climate change scenarios

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57 Summary Comments  climate-hydrology forecast model system has potential  can also try other ensemble forecast models/methods  can also try other bias-correction/downscaling approaches  critical needs  access to quality met data during spinup period  ability to demonstrate / assess skill quantitatively  perfect-SST (“AMIP-type”) hindcast ensembles a start, but really need a long term retrospective forecast set

58 Summary Comments  climate-hydrology forecast model system has potential  can also try other ensemble forecast models/methods  can also try other bias-correction/downscaling approaches  critical needs  access to quality met data during spinup period  ability to demonstrate / assess skill quantitatively  perfect-SST (“AMIP-type”) hindcast ensembles a start, but really need a long term retrospective forecast set  2 of me:  one for research  one for “operations”

59 END


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