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Experimental Real-time Seasonal Hydrologic Forecasting
Andrew WoodDennis Lettenmaier
University of Washington
Arun KumarNCEP/EMC/CMB
presented:
JISAO weekly seminarSeattle, WA Nov 13, 2001
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)
Topics Today
1. Approach2. Columbia River basin (summer 2001) application3. East Coast (summer 2000) application4. Related work5. Comments
climate model forecastmeteorological outputs
• ~1.9 degree resolution (T62)• monthly total P, avg T
Use 3 step approach: 1) statistical bias correction 2) downscaling3) hydrologic simulation
General Approach
hydrologic model inputs
streamflow, soil moisture,snowpack,runoff• 1/8-1/4 degree resolution
• daily P, Tmin, Tmax
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
Models: VIC Hydrologic Model
domain slide
Example Flow Routing Network
One Way Coupling of GSM and VIC models
a) bias correction: climate model climatology observed climatologyb) 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 1Probability
Te
mp
era
ture
TGSM
TOBS
GSM Regional Bias:a spatial example
Bias is removed at the monthly GSM-scale from the meteorological forecasts
(so 3rd column ~= 1st column)
GSM Regional Bias:
one cell example
For sample cell located over Ohio River basin, biases in monthly Ptot & Tavg are significant!
GSM Regional Bias:
one cell example
Bias: Developing a Correction
-5
5
15
25
35
Mon 1 Mon 2 Mon 3 Mon 4 Mon 5 Mon 6
deg
C
-5
5
15
25
35
Mon 1 Mon 2 Mon 3 Mon 4 Mon 5 Mon 6
deg
C
-5
5
15
25
35
Mon 1 Mon 2 Mon 3 Mon 4 Mon 5 Mon 6
deg
C
20 member forecast ensemble
-5
5
15
25
35
Mon 1 Mon 2 Mon 3 Mon 4 Mon 5 Mon 6
deg
C
-5
5
15
25
35
Mon 1 Mon 2 Mon 3 Mon 4 Mon 5 Mon 6
deg
C
from 1979 SSTsfrom 1980 SSTs
from 1981 SSTs
from 1999 SSTs
from current SSTs
(21 sets)10 member climatology ensembles
Bias: Developing a Correction
10
15
20
25
30
0 0.2 0.4 0.6 0.8 1
percentile (wrt 1979-99)
deg
C
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
deg
C
-5
5
15
25
35
Mon 1 Mon 2 Mon 3 Mon 4 Mon 5 Mon 6
deg
C
-5
5
15
25
35
Mon 1 Mon 2 Mon 3 Mon 4 Mon 5 Mon 6
deg
C
-5
5
15
25
35
Mon 1 Mon 2 Mon 3 Mon 4 Mon 5 Mon 6
deg
C
1979 SSTsetc.
from 1999SSTs
10 member climatology ens.
* for each month, each GSM grid cell and variable
*
Bias: Applying a Correction
Note: we apply correction to both forecast ensembleand climatology ensemble itself, for later use
Bias-Correction: Spatial Perspective
shown1 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
deg
C
bias-corrected
-5
5
15
25
35
Mon 1 Mon 2 Mon 3 Mon 4 Mon 5 Mon 6
deg
C
Bias: Spatial Perspectiveexpress as anomaly
-8
-4
0
4
8
Mon 1 Mon 2 Mon 3 Mon 4 Mon 5 Mon 6
deg
C
bias-corrected
-5
5
15
25
35
Mon 1 Mon 2 Mon 3 Mon 4 Mon 5 Mon 6
deg
C
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
deg
C
-8
-4
0
4
8
Mon 1 Mon 2 Mon 3 Mon 4 Mon 5 Mon 6
deg
C
Downscaling: step 2 adds spatial VIC-scale variability to smooth anomaly field
mean fields
anomaly
note:month m, m = 1-6ens e, e = 1-20
VIC-scale monthly forecast
-5
5
15
25
35
Mon1
Mon2
Mon3
Mon4
Mon5
Mon6
de
g C
Lastly, temporal disaggregation…
VIC-scale monthly forecast
Lastly, temporal disaggregation…
VIC-scale monthly forecast
-5
5
15
25
35
Mon1
Mon2
Mon3
Mon4
Mon5
Mon6
deg
C
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?
GSM forecast and climatology ensembles
-5
5
15
25
35
Mon 1 Mon 2 Mon 3 Mon 4 Mon 5 Mon 6
deg
C
-5
5
15
25
35
Mon 1 Mon 2 Mon 3 Mon 4 Mon 5 Mon 6
deg
C
-5
5
15
25
35
Mon 1 Mon 2 Mon 3 Mon 4 Mon 5 Mon 6
deg
C
20 member forecast ensemble
-5
5
15
25
35
Mon 1 Mon 2 Mon 3 Mon 4 Mon 5 Mon 6
deg
C
-5
5
15
25
35
Mon 1 Mon 2 Mon 3 Mon 4 Mon 5 Mon 6
deg
C
from 1979 SSTsfrom 1980 SSTs
from 1981 SSTs
from 1999 SSTs
from current SSTs
(21 sets)10 member climatology ensembles
GSM climatology: use #2
-5
5
15
25
35
Mon 1 Mon 2 Mon 3 Mon 4 Mon 5 Mon 6
deg
C
-5
5
15
25
35
Mon 1 Mon 2 Mon 3 Mon 4 Mon 5 Mon 6
deg
C
-5
5
15
25
35
Mon 1 Mon 2 Mon 3 Mon 4 Mon 5 Mon 6
deg
C
sample: 21 member climatology ensemble
-5
5
15
25
35
Mon 1 Mon 2 Mon 3 Mon 4 Mon 5 Mon 6
deg
C
-5
5
15
25
35
Mon 1 Mon 2 Mon 3 Mon 4 Mon 5 Mon 6
deg
C
from 1979 SSTsetc.
from 1999SSTs
10 member climatology ens. (21 sets)
GSM climatology: use #2
-5
5
15
25
35
Mon 1 Mon 2 Mon 3 Mon 4 Mon 5 Mon 6
deg
C
sample: 21 member climatology ensemble
-5
5
15
25
35
Mon 1 Mon 2 Mon 3 Mon 4 Mon 5 Mon 6
deg
C
-5
5
15
25
35
Mon 1 Mon 2 Mon 3 Mon 4 Mon 5 Mon 6
deg
C
-5
5
15
25
35
Mon 1 Mon 2 Mon 3 Mon 4 Mon 5 Mon 6
deg
C
-5
5
15
25
35
Mon 1 Mon 2 Mon 3 Mon 4 Mon 5 Mon 6
deg
C
from 1979 SSTsetc.
from 1999SSTs
10 member climatology ens. (21 sets)
-5
5
15
25
35
Mon 1 Mon 2 Mon 3 Mon 4 Mon 5 Mon 6
deg
C
20 member forecast ens.
Simulations
Forecast Productsstreamflow soil moisture
runoffsnowpack
VIC model spin-upVIC 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
Columbia River Application
CRB
Initial Conditions
late-May SWE &water balance
CRB
Initial Conditions
(percentiles)
CRB: May forecastobservedforecast
forecastmedians
CRB: May forecast
hindcast“observed”
forecast
forecast medians
CRB May forecasthindcast “observed”forecast
forecastmedians
CRB May forecast
basin avg. soil moisture
CRB May Forecast
Streamflow
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
CRBMay Forecast
cumulative flow averages
forecastmedians
East Coast Application
Model forecasting domain
East Coast spin-up period
East Coast spin-up period
East Coast spin-up period
East Coast spin-up period
East Coast hindcast
East Coast hindcast
East Coast hindcast
East Coast hindcast
East Coast
Apr ’00 forecast for May-Jun-Jul
forecast median shown as percentile of climatology ensemble
East Coast
May ’00 forecast for Jun-Jul-Aug
East Coast
Jun ’00 forecast for Jul-Aug-Sep
ENSO extreme pseudo-forecast evaluation
perfect-SST forecasts from Nov. 97
Related Applications
Related: Yakima R. Mesocale Model Downscaling (RCM @ ½ to VIC @ 1/8)
Related:
PCM-based climate change scenarios
Related:
PCM-based climate change scenarios
Related:
PCM-based climate change scenarios
Related:PCM-based climate change scenarios
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
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”
END