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  • important points:
    Arun Kumar works for the NCEP Environmental Modeling Center, Climate Modeling Branch and runs the global spectral model to produce forecasts.
    this study period and region are important because there is an evolving drought that we would like to use as a test situation for the method’s skill.
    this approach was previously applied to the East Coast during Summer 2000, when there was an evolving drought in that region – a paper evaluating the project is currently submitted to JGR-Atmospheres.
  • important point(s):
    the approach attempts to make use of forecast skill from 2 sources:
    better understanding of synoptic scale teleconnections and the effects of persistence in SSTs on regional climate, as reproduced in coupled ocean-atmosphere models;
    the macroscale hydrologic model yields an improved ability to model the persistence in hydrologic states at the regional scale (more compatible with climate model scales than prior hydrologic modeling).
    Climate forecasts with monthly and seasonal horizons are now operationally available, and if they can be translated to streamflow, then they may be useful for water management.
  • important point(s):
    the approach attempts to make use of forecast skill from 2 sources:
    better understanding of synoptic scale teleconnections and the effects of persistence in SSTs on regional climate, as reproduced in coupled ocean-atmosphere models;
    the macroscale hydrologic model yields an improved ability to model the persistence in hydrologic states at the regional scale (more compatible with climate model scales than prior hydrologic modeling).
    Climate forecasts with monthly and seasonal horizons are now operationally available, and if they can be translated to streamflow, then they may be useful for water management.
  • important point(s):
    the overall forecasting approach involves using forecast model (the global spectral model) T & P output at a coarse timestep & scale as hydrologic model input at a finer timestep and scale.
    to make a hydrologic forecast, you need a transformation of the forecasts that first overcomes climate model bias and the scale differences, then simulates the water balance.
    also, GSM is really run at very fine timestep (~5-15 minutes) but only the monthly anomalies are archived for our use. most of the signal is at the monthly scale, however, so this is acceptable.
  • important point(s):
    GSM forecasts take the form of monthly ensembles of length 6 months
    we get them early in each month for a start date of the following month.
    the climatology ensemble enables us to define the climate model bias and correct it
    climatology ensembles run out 6 months just like the forecasts, but use observed rather than predicted tropical Pacific SSTs
    also:
    210 ensembles for GSM climatology are derived from observed SSTs in each year of the 21 year climatology period (1979-1999) combined with 10 initial atmospheric conditions for each year
    GSM is at T42 spatial resolution, but moving to T62 soon (resolution improvement of about 1/3)
  • important point(s):
    VIC is a water & energy balance with some subgrid scale approximations for vegetation, elevation and soil dynamics, and has a crude routing that works as a post processor.
    VIC has been applied to a number of continental scale river basins around the world and is well documented in the literature.
  • important point(s):
    we’re modeling most of the US at 1/8 degree now with the VIC model, but we are performing this forecasting exercise in the Columbia River basin. The plusses show the grid of the numerical weather prediction (forecasting) model that we used (GSM), and the ¼ degree hydrology model resolution can just be discerned in the figure. 24 climate model grid points were used, and 1,668 VIC model cells. We’ve aggregate the VIC model to ¼ degree from 1/8 degree in the Columbia River basin to speed the forecast runs.
  • important point(s):
    this is our approach to turning GSM forecasts into VIC input: first, bias correction:
    1. using the climatologies of the observed precip & temperature to define parallel distributions (for each month in the forecast), we translate each met. value in the GSM ensembles to a quantile value, then retrieve the met. variable value for that quantile from the observed distribution. (at the ends of the empirical distributions, we use fitted theoretical ones if needed).
    then downscaling:
    2. that was all done at the GSM scale. then we interpolate the anomalies to the to the VIC resolution (nothing fancy here).
    then we impose the daily pattern by resampling the historic VIC forcings (P&T for each month taken from the same year to preserve correlations), and then scaling monthly avg. temp and shifting month. tot. P to reproduce the forecast anomalies. after all, when you sample at random, the daily pattern you get won’t have the monthly anomaly you need for the forecast signal.
    the bias correction step is critical, as the next 2 plots will show.
  • important point(s):
    here we see the biases from a spatial view -- note the large temperature bias in raw GSM forcing (for July), and in precip over the eastern edge of the Ohio Basin (as an example). In the third column of images, the biases have been removed by our method, so they should look like the first column (observed) – and they do.
  • important point(s):
    the first hurdle in making forecasts is to overcome the regional biases. this and the next slide show the bias -- first for one GSM cell, where you can see plotted the observed forcings vs. the GSM raw climatology forcings (for the climatology period ‘79-’99). biases are so large in temperature that a hydrologic model would be blown out of the water.
    this is from one set (May) of climatology & forecast ensembles back on the East Coast
  • important point(s):
    the first hurdle in making forecasts is to overcome the regional biases. this and the next slide show the bias -- first for one GSM cell, where you can see plotted the observed forcings vs. the GSM raw climatology forcings (for the climatology period ‘79-’99). biases are so large in temperature that a hydrologic model would be blown out of the water.
    this is from one set (May) of climatology & forecast ensembles back on the East Coast
  • important point(s):
    after bias correcting and downscaling the climate model forecasts, the procedure for producing hydrologic forecasts is as follows:
    we spin up the hydrologic model to the start of the forecast using observed met. data (from 2 sources: NCDC cooperator stations through 3-4 months before the start of the forecasts, then LDAS 1/8 degree gridded forcings thereafter).
    The GSM forecasts comprise 2 sets of ensembles, one for climatology and one for the forecast. The climatology ensemble yields a distribution of the conditions we’ve seen over the period 1979-1999, while the forecast ensemble yields the distribution of the conditions we might see for the next 6 months. Although the climatology ensemble is nominally unbiased against a simulated climatology based on observed met. data (rather than bias-corrected, downscaled GSM met. forcings), we compare the forecast and GSM climatology so that any unforeseen biases (resulting, perhaps, from the downscaling method) occur in both climatology and forecast. Eventually this cautionary step may be eliminated, and we’ll compare directly to the simulated observed climatology.
    at the end of the spin-up period and one month before month 1 (out of 6) of the forecasts, we save the hydrologic model state. The state is then used for initializing the forecast runs. Through the first month, the model runs on observed data to the last date possible, then switches to the forecast data. Usually, we process the observed forcings up through the 15th to 25th of this initialization month, then the forecast forcing data carries the run forward for the remaining days in the month, and throughout the following 6 month forecast period. Note, the state files used for the climatology runs correspond to the spin-up associated with the particular year (out of 1979-1999) from which the climatology ensemble member is drawn.
    the spin-up period captures the antecedent land surface hydrologic conditions for the forecast period: in the Columbia basin, the primary field of interest is snow water equivalent.
    forecast products are spatial (distributed soil moisture, runoff, snowpack (swe), etc.), and spatial runoff + baseflow is routed to produce streamflow at specific points, the inflow nodes for a management model, perhaps.
  • important point(s):
    This set of plots shows the initial conditions (starting state, approximately) for snowpack in the basin, in comparison to 2001 SNOTEL data, and the 1988 and 1977 simulated conditions.
    The small circles in the plots at left contain the SNOTEL swe estimates for 2001, march 15. There are many more stations, but I just plotted a couple dozen.
    The background in the plots at left is the simulated snow water equivalence, for comparison to SNOTEL. In the top plot, it looks like 1977 is a little lower than current SNOTEL. In the middle one, it looks like 1988 is a closer match, and in 2001, it looks like we undersimulate SWE a bit in some locations, compared to SNOTEL (see the sites in northern Idaho).
    On the right are the ratios of 1977 to 2001 and 1998 to 2001, which confirm that the 2001 simulation shows deeper snowpack than 1977, somewhat nearer to 1988 (another very low year, mind you).
    One observation about the discrepancies between SNOTEL and the simulation is worth making: The VIC grid cells represent areas of about 150 km squared (1/8 degree) and 625 km squared (at ¼ degree), whereas the SNOTEL data are points, so we don’t expect them to match up perfectly. The VIC model, though, adds an estimate of the spatial distribution of the snowpack that is only possible in a more limited way from the point SNOTEL data – so the combination of the two has the potential to yield improved estimates of basinwide snowpack than would be possible without the distributed hydrologic model. Not to mention, retrospective comparisons are possible to years before the SNOTEL network existed, such as 1977…
    We could also show soil moisture and runoff starting states, but the snowpack is most critical in this basin.
  • important point(s):
    The streamflow results are shown for The Columbia River at the Dalles as two ensembles (climatology and forecast) plotted alongside each other, for each forecast month.
    The clear message from this plot is that the forecast (red plusses) distributions are clustered significantly below the climatology distributions, to a diminishing extent at the end of the forecast period (when runoff is dominated more by precipitation than snowmelt).
    Note, however, that the climatology period doesn’t include the recent end point, 1977, so if it did, the distributions might extend further down with respect to the forecast ensembles – not enough to change the significance of the results, however.
  • important point(s):
    The streamflow results are shown for The Columbia River at the Dalles as two ensembles (climatology and forecast) plotted alongside each other, for each forecast month.
    The clear message from this plot is that the forecast (red plusses) distributions are clustered significantly below the climatology distributions, to a diminishing extent at the end of the forecast period (when runoff is dominated more by precipitation than snowmelt).
    Note, however, that the climatology period doesn’t include the recent end point, 1977, so if it did, the distributions might extend further down with respect to the forecast ensembles – not enough to change the significance of the results, however.
  • important point(s):
    plotted against the 1961-1990 streamflow climatology used by other agencies, the forecast ensemble medians for average flow in the first 3 months of the forecast period (top) and over all six months (bottom) end up between the 1977 and 1988 runoff averages.
    Given the uncertainties in the snow pack estimation used as initial conditions for the forecast runs, particularly the fact that our estimation is somewhat less dire than that of the NRCS (putting initial snowpack below their estimates for 1977), these results could be looked at as conservative – that is, 2001 runoff could actually be lower than we are showing here.
  • important point(s):
    we’re modeling most of the US at 1/8 degree now with the VIC model, but we are performing this forecasting exercise in the Columbia River basin. The plusses show the grid of the numerical weather prediction (forecasting) model that we used (GSM), and the ¼ degree hydrology model resolution can just be discerned in the figure. 24 climate model grid points were used, and 1,668 VIC model cells. We’ve aggregate the VIC model to ¼ degree from 1/8 degree in the Columbia River basin to speed the forecast runs.
  • important point(s):
    This set of plots shows the initial conditions (starting state, approximately) for snowpack in the basin, in comparison to 2001 SNOTEL data, and the 1988 and 1977 simulated conditions.
    The small circles in the plots at left contain the SNOTEL swe estimates for 2001, march 15. There are many more stations, but I just plotted a couple dozen.
    The background in the plots at left is the simulated snow water equivalence, for comparison to SNOTEL. In the top plot, it looks like 1977 is a little lower than current SNOTEL. In the middle one, it looks like 1988 is a closer match, and in 2001, it looks like we undersimulate SWE a bit in some locations, compared to SNOTEL (see the sites in northern Idaho).
    On the right are the ratios of 1977 to 2001 and 1998 to 2001, which confirm that the 2001 simulation shows deeper snowpack than 1977, somewhat nearer to 1988 (another very low year, mind you).
    One observation about the discrepancies between SNOTEL and the simulation is worth making: The VIC grid cells represent areas of about 150 km squared (1/8 degree) and 625 km squared (at ¼ degree), whereas the SNOTEL data are points, so we don’t expect them to match up perfectly. The VIC model, though, adds an estimate of the spatial distribution of the snowpack that is only possible in a more limited way from the point SNOTEL data – so the combination of the two has the potential to yield improved estimates of basinwide snowpack than would be possible without the distributed hydrologic model. Not to mention, retrospective comparisons are possible to years before the SNOTEL network existed, such as 1977…
    We could also show soil moisture and runoff starting states, but the snowpack is most critical in this basin.
  • Presentation

    1. 1. Experimental Real-time Seasonal Hydrologic Forecasting Andrew Wood Dennis Lettenmaier University of Washington Arun Kumar NCEP/EMC/CMB presented: JISAO weekly seminar Seattle, WA Nov 13, 2001
    2. 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. 3. Topics Today 1. Approach 2. Columbia River basin (summer 2001) application 3. East Coast (summer 2000) application 4. Related work 5. Comments
    4. 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. 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. 6. Models: VIC Hydrologic Model
    7. 7. domain slide Example Flow Routing Network
    8. 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. 0 5 10 15 20 25 30 0 1 Probability Temperature TGSM TOBS
    9. 9. GSM Regional Bias: a spatial example Bias is removed at the monthly GSM-scale from the meteorological forecasts (so 3rd column ~= 1st column)
    10. 10. GSM Regional Bias: one cell example For sample cell located over Ohio River basin, biases in monthly Ptot & Tavg are significant!
    11. 11. GSM Regional Bias: one cell example
    12. 12. Bias: Developing a Correction -5 5 15 25 35 Mon 1 Mon 2 Mon 3 Mon 4 Mon 5 Mon 6 degC -5 5 15 25 35 Mon 1 Mon 2 Mon 3 Mon 4 Mon 5 Mon 6 degC -5 5 15 25 35 Mon 1 Mon 2 Mon 3 Mon 4 Mon 5 Mon 6 degC 20 member forecast ensemble -5 5 15 25 35 Mon 1 Mon 2 Mon 3 Mon 4 Mon 5 Mon 6 degC -5 5 15 25 35 Mon 1 Mon 2 Mon 3 Mon 4 Mon 5 Mon 6 degC from 1979 SSTs from 1980 SSTs from 1981 SSTs from 1999 SSTs from current SSTs (21 sets)10 member climatology ensembles
    13. 13. Bias: Developing a Correction 10 15 20 25 30 0 0.2 0.4 0.6 0.8 1 percentile (wrt 1979-99) degC GSM Observed July Tavg, for 1 GSM cell -5 5 15 25 35 Mon 1 Mon 2 Mon 3 Mon 4 Mon 5 Mon 6 degC -5 5 15 25 35 Mon 1 Mon 2 Mon 3 Mon 4 Mon 5 Mon 6 degC -5 5 15 25 35 Mon 1 Mon 2 Mon 3 Mon 4 Mon 5 Mon 6 degC -5 5 15 25 35 Mon 1 Mon 2 Mon 3 Mon 4 Mon 5 Mon 6 degC 1979 SSTs etc. from 1999 SSTs 10 member climatology ens. * for each month, each GSM grid cell and variable *
    14. 14. Bias: Applying a Correction Note: we apply correction to both forecast ensemble and climatology ensemble itself, for later use
    15. 15. Bias-Correction: Spatial Perspective shown 1 month, 1 variable (T), 1 ens-member raw GSM output -5 5 15 25 35 Mon 1 Mon 2 Mon 3 Mon 4 Mon 5 Mon 6 degC bias-corrected -5 5 15 25 35 Mon 1 Mon 2 Mon 3 Mon 4 Mon 5 Mon 6 degC
    16. 16. Bias: Spatial Perspective express as anomaly -8 -4 0 4 8 Mon 1 Mon 2 Mon 3 Mon 4 Mon 5 Mon 6 degC bias-corrected -5 5 15 25 35 Mon 1 Mon 2 Mon 3 Mon 4 Mon 5 Mon 6 degC
    17. 17. Downscaling: step 1 is interpolation (bias corrected) anomaly anomaly at VIC scale -8 -4 0 4 8 Mon 1 Mon 2 Mon 3 Mon 4 Mon 5 Mon 6 degC -8 -4 0 4 8 Mon 1 Mon 2 Mon 3 Mon 4 Mon 5 Mon 6 degC
    18. 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. 19. -5 5 15 25 35 Mon 1 Mon 2 Mon 3 Mon 4 Mon 5 Mon 6 degC Lastly, temporal disaggregation… VIC-scale monthly forecast
    20. 20. Lastly, temporal disaggregation… VIC-scale monthly forecast -5 5 15 25 35 Mon 1 Mon 2 Mon 3 Mon 4 Mon 5 Mon 6 degC
    21. 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. 22. GSM forecast and climatology ensembles -5 5 15 25 35 Mon 1 Mon 2 Mon 3 Mon 4 Mon 5 Mon 6 degC -5 5 15 25 35 Mon 1 Mon 2 Mon 3 Mon 4 Mon 5 Mon 6 degC -5 5 15 25 35 Mon 1 Mon 2 Mon 3 Mon 4 Mon 5 Mon 6 degC 20 member forecast ensemble -5 5 15 25 35 Mon 1 Mon 2 Mon 3 Mon 4 Mon 5 Mon 6 degC -5 5 15 25 35 Mon 1 Mon 2 Mon 3 Mon 4 Mon 5 Mon 6 degC from 1979 SSTs from 1980 SSTs from 1981 SSTs from 1999 SSTs from current SSTs (21 sets)10 member climatology ensembles
    23. 23. GSM climatology: use #2 -5 5 15 25 35 Mon 1 Mon 2 Mon 3 Mon 4 Mon 5 Mon 6 degC -5 5 15 25 35 Mon 1 Mon 2 Mon 3 Mon 4 Mon 5 Mon 6 degC -5 5 15 25 35 Mon 1 Mon 2 Mon 3 Mon 4 Mon 5 Mon 6 degC sample: 21 member climatology ensemble -5 5 15 25 35 Mon 1 Mon 2 Mon 3 Mon 4 Mon 5 Mon 6 degC -5 5 15 25 35 Mon 1 Mon 2 Mon 3 Mon 4 Mon 5 Mon 6 degC from 1979 SSTs etc. from 1999 SSTs 10 member climatology ens. (21 sets)
    24. 24. GSM climatology: use #2 -5 5 15 25 35 Mon 1 Mon 2 Mon 3 Mon 4 Mon 5 Mon 6 degC sample: 21 member climatology ensemble -5 5 15 25 35 Mon 1 Mon 2 Mon 3 Mon 4 Mon 5 Mon 6 degC -5 5 15 25 35 Mon 1 Mon 2 Mon 3 Mon 4 Mon 5 Mon 6 degC -5 5 15 25 35 Mon 1 Mon 2 Mon 3 Mon 4 Mon 5 Mon 6 degC -5 5 15 25 35 Mon 1 Mon 2 Mon 3 Mon 4 Mon 5 Mon 6 degC from 1979 SSTs etc. from 1999 SSTs 10 member climatology ens. (21 sets) -5 5 15 25 35 Mon 1 Mon 2 Mon 3 Mon 4 Mon 5 Mon 6 degC 20 member forecast ens.
    25. 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 0 end 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. 26. Columbia River Application
    27. 27. CRB Initial Conditions late-May SWE & water balance
    28. 28. CRB Initial Conditions (percentiles)
    29. 29. CRB: May forecast observedforecast forecast medians
    30. 30. CRB: May forecast hindcast “observed” forecast forecast medians
    31. 31. CRB May forecast hindcast “observed”forecast forecast medians
    32. 32. CRB May forecast basin avg. soil moisture
    33. 33. CRB May Forecast Streamflow
    34. 34. Forecasts of Columbia River Flow @ The Dalles, 2001 0 50000 100000 150000 200000 250000 300000 350000 400000 450000 500000 Apr May Jun Jul Aug Sep Oct Nov cfs Mar fcast Mar clim Apr fcast Apr clim May fcast May clim Hindcast CRB: sequential streamflow forecasts hindcast climatologies forecasts ensemble medians
    35. 35. CRB May Forecast cumulative flow averages forecast medians
    36. 36. East Coast Application
    37. 37. Model forecasting domain
    38. 38. East Coast spin-up period
    39. 39. East Coast spin-up period
    40. 40. East Coast spin-up period
    41. 41. East Coast spin-up period
    42. 42. East Coast hindcast
    43. 43. East Coast hindcast
    44. 44. East Coast hindcast
    45. 45. East Coast hindcast
    46. 46. East Coast Apr ’00 forecast for May-Jun-Jul forecast median shown as percentile of climatology ensemble
    47. 47. East Coast May ’00 forecast for Jun-Jul-Aug
    48. 48. East Coast Jun ’00 forecast for Jul-Aug- Sep
    49. 49. ENSO extreme pseudo-forecast evaluation perfect-SST forecasts from Nov. 97
    50. 50. Related Applications
    51. 51. Related: Yakima R. Mesocale Model Downscaling (RCM @ ½ to VIC @ 1/8)
    52. 52. Related: PCM-based climate change scenarios
    53. 53. Related: PCM-based climate change scenarios
    54. 54. Related: PCM-based climate change scenarios
    55. 55. Related: PCM-based climate change scenarios
    56. 56. 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
    57. 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  2 of me:  one for research  one for “operations”
    58. 58. END

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