the contribution of soil moisture information to forecast skill: two studies randal koster and...
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The Contribution of Soil Moisture Information
to Forecast Skill: Two Studies
Randal Koster and Sarith MahanamaGlobal Modeling and Assimilation Office, NASA/GSFC
Ben LivnehDept. of Civil and Env. Engineering, U. Washington
With contributions from the GLACE-2 team, Dennis Lettenmaier, Rolf Reichle, and Qing Liu
Direct questions to: [email protected]
Long-term question:
To what extent might hydrological prediction benefit from satellite-based soil moisture data (e.g., from SMAP or SMOS)?
This talk:
Describe two recent studies quantifying the benefits to prediction of model-based soil moisture data (produced with observed met. data); make inferences regarding satellite data.
Study #1Subseasonal air temperature and
precipitation forecasts
GLACE-2: A quantification of the impact of realistic soil moisture initialization on the prediction of precipitation and air temperature at subseasonal timescales.
GLACE-2:Experiment Overview
Perform ensembles of retrospective
seasonal forecasts
Initialize land stateswith “observations”,
using GSWP approach
Prescribed, observed SSTs or the use of a coupled ocean
model
Initialize atmosphere with “observations”, via
reanalysis
Evaluate P, T forecasts against
observations
Series 1: In a subseasonal forecast system (GCM),
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GLACE-2:Experiment Overview
Perform ensembles of retrospective
seasonal forecasts
Initialize land stateswith “observations”,
using GSWP approach
Prescribed, observed SSTs or the use of a coupled ocean
model
Initialize atmosphere with “observations”, via
reanalysis
Evaluate P, T forecasts against
observations
Series 2: In a subseasonal forecast system (GCM),
“Randomize” land
initialization!
5
GLACE-2:Experiment Overview
Step 3: Compare skill in two sets of forecasts; isolate contribution of realistic land initialization.
Forecast skill,Series 1
Forecast skill, Series 2
Forecast skill due to land initialization
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Examine 60 independent subseasonal forecasts during JJA (10 ensemble members each) 600 2-month simulations.
Participant List
Group/Model Points of Contact
1. NASA/GSFC (USA): GMAO seasonal forecast system (old and new)
2. COLA (USA): COLA GCM, NCAR/CAM GCM
3. Princeton (USA): NCEP GCM
4. IACS (Switzerland): ECHAM GCM
5. KNMI (Netherlands): ECMWF
6. ECMWF
7. GFDL (USA): GFDL system
8. U. Gothenburg (Sweden): NCAR
9. CCSR/NIES/FRCGC (Japan): CCSR GCM
10. FSU/COAPS
11. CCCma
# models
S. Seneviratne, E. Davin
E. Wood, L. Luo
P. Dirmeyer, Z. Guo
R. Koster, S. Mahanama2
B. van den Hurk
T. Gordon
J.-H. Jeong
T. Yamada
2
1
1
1
1
1
1
13 models
1 G. Balsamo, F. Doblas-Reyes
M. Boisserie1
1 B. Merryfield
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Temperature forecasts: Increase in skill (r2) during JJA due to land initialization(Multi-model results, conditioned on strength of local initial soil moisture anomaly)
Extreme tercilesall dates
Extreme quintiles
Extreme deciles
16-30 days
31-45 days
46-60 days
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Forecast skill: r2 with land ICs vs r2 w/o land ICs
Dates for conditioning vary w/location
Study #2Seasonal streamflow prediction
A quantification, using multiple land models, of the degree to which soil moisture and snow initialization contribute to streamflow forecast skill at seasonal timescales.
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Experiment:
1. Perform multi-decadal offline simulation covering CONUS, using observations-based meteorological data. Determine streamflows in various basins for MAMJJ and compare against (naturalized) streamflow observations.
2. Repeat, but doing forecasts: Simulate MAMJJ streamflow knowing only soil moisture and snow conditions on January 1. (Use climatological met forcing for January – July.) Compare forecasts to observations. (Not a synthetic study!)
3. Repeat, knowing only snow conditions on January 1.
4. Repeat, knowing only soil moisture conditions on January 1.
MAMJJ Streamflow Forecast Skill (r2)
a. CTRL: Forcings, initial snow, initial SM known (not true forecasts) b. Exp1: Initial snow, initial SM known
c. Exp2: Initial snow known d. Exp3: Initial SM known
belongs to 5 ( also 1&2) belongs to 2 (also 1)
Skill of model simulation of MAMJJ streamflow given: -- Realistic January 1 initial
conditions -- “Perfect” prediction of
forcing during forecast period
Skill (r2)
Skill of model simulation of MAMJJ streamflow given: -- Realistic January 1 initial
conditions -- No skill in prediction of
forcing during forecast period (use
climatology)
MAMJJ Streamflow Forecast Skill (r2)
a. CTRL: Forcings, initial snow, initial SM known (not true forecasts) b. Exp1: Initial snow, initial SM known
c. Exp2: Initial snow known d. Exp3: Initial SM known
belongs to 5 ( also 1&2) belongs to 2 (also 1)
Skill (r2)
“Snow initialization only” test: snow important toward northwest of study area.
“Soil moisture initialization only” test: SM more important toward southeast of study area.
Oct. 1 initialization
Jan. 1 initialization
Apr. 1 initialization
July 1 initialization
Synthetic study results: Lead, in months, for which some significant (95% confidence level) streamflow forecast skill is obtained from soil moisture initialization.
0 1 22 3 4 5 6 87 109Number of Lead Months
Soil moisture initialized
Snow initialized
These two studies show that accurate soil moisture initialization can lead to improvements in:
subseasonal air temperature forecastsseasonal streamflow forecasts
What are the implications for satellite-derived soil moisture data?
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In the global GLACE-2 analysis, the skill levels obtained are clearly connected to the accuracy of the soil moisture initialization, indicating that improved soil moisture estimates can lead to improved forecasts.
Land-Derived Skill (r2) for Air Temperature Forecasts
Rain-gauge density(# gauges /
2ox2.5o grid cell)
Measure of Underlying Model “Predictability”Surrogate for soil moisture accuracy
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In the global GLACE-2 analysis, the skill levels obtained are clearly connected to the accuracy of the soil moisture initialization, indicating that improved soil moisture estimates can lead to improved forecasts.
Land-Derived Skill (r2) for Air Temperature Forecasts
Rain-gauge density(# gauges /
2ox2.5o grid cell)
Measure of Underlying Model “Predictability”
SMAP or SMOS would effectively increase the ordinates of the dots…
… suggesting that we’d get more skill for these locations with SMAP or SMOS data
SMAP data coverage
Rain gauge density: a reasonable surrogate metric for the accuracy of soil moisture initial conditions in a forecast
SMAP data will be available over most of the world (the white areas), allowing first-order increases in soil moisture accuracy in many regions. (Of course, GPM will help, as well…)
Rain gauge density
GLACE-2 cutoff
Rain gauge density
Even in regions of high rain gauge density, data assimilation studies* with AMSR-based and SMMR-based soil moisture data show that satellite-based products improve the estimation of soil moisture over that obtained with the rainfall forcing alone. SMAP and SMOS will improve over AMSR and SMMR, providing even higher accuracy than indicated here.
*Reichle et al., JGR, 112, 2007 Reichle and Koster, GRL, 32, 2005 Liu et al., Journal Hydromet., submitted.
From Liu et al. (submitted)
Skill (r)
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Summary
Accurate soil moisture initialization (as derived from met forcing, particularly precipitation) does contribute significantly to skill in temperature and streamflow forecasts.(Koster et al., GRL, 2010; Koster et al., Nature Geosci., 2010)
Forecasts are indeed found to improve with the accuracy of the soil moisture initialization.
Given AMSR & SMMR experience, SMAP & SMOS should contribute significantly to the accuracy of initialization.
Inference: SMAP & SMOS should contribute to forecast skill.
Obvious next challenge: quantify these contributions!