Progress on the CTB project “Enhancing Operational Drought Monitoring and Prediction

Progress on the CTB project
“Enhancing Operational Drought Monitoring and Prediction
Products through Synthesis of NLDAS and CPPA Research
Results”
Eric F. Wood1, Dennis P. Lettenmaier2, Xing Yuan1, Lifeng Luo3,
YouLong Xia4, Michael Ek4, Kingtse Mo5
1Department
of Civil and Environmental Engineering, Princeton University
2Department of Civil and Environmental Engineering, University of Washington
3Department
of Geography, Michigan State University
4Environmental Modeling Center, NCEP/NWS/NOAA
5Climate Prediction Center, NCEP/NWS/NOAA
CTB PI Meeting,
Fort Worth TX, Oct 6, 2011
Summary of Project Tasks
Task 1
 Transition of the objective drought prediction system to NCEP
EMC.
 Upgrade the drought prediction system with the new CFSv2based forecasts.
Task 2
 Integration of forecast systems to provide objective drought
indices.
Task 3
 Data set unification.
Task 4
 Generation of drought index hindcasts and forecasts.
Task 5
 Assessment and verification studies.
Schematic illustration of the project’s seasonal hydrologic ensemble
prediction system being transferred to the CTB
Seasonal Climate Forecasts
(MME climate models, CPC outlook)
NLDAS
Drought
Monitoring
Component
MM-LSM
Hydrology
4
Task 1: Transition of the objective drought monitoring and
prediction system to NCEP EMC, and then to CTB.
• Work Done:
 The prediction component of the PU/UW, Drought
Monitor and Prediction System (DMAPS) was transferred
to NCEP/EMC at the beginning of the project, and has been
running in a quasi-operational setting since.
 The three prediction methods (CFS-based, CPC-based and
ESP-based) are implemented on EMC’s TEMPEST computer
system.
• Ongoing/Future Work:
 Evaluation and integration of CFSv2 into the system.
 Integration of all three drought prediction systems (i.e. PU,
UW, EMC) and its implementation on CTB system.
NLDAS Drought Forecast Analysis (Sept 2011)
http://www.emc.ncep.noaa.gov/mmb/nldas/forecast/TSM/perc/
6 months lead
1 month lead
CFSv1
CPC
ESP
Task 1+: Upgrade the drought prediction system with CFSv2
Percentage of positive RPSS for global monthly temperature and precipitation
anomaly
Task 1+: Upgrade the drought prediction system with CFSv2
VIC Off-line
(Control)
Soil moisture percentile
in Jan 2008
1 Month Lead
3 Month Lead
ESP
CFSv1
CFSv2
Princeton University
Task 2: Integration of systems to provide objective drought
indices (Lead PU)
• Issue:
 Real-time hydrological runs (needed for drought statistics
and the drought index) are based on different data sets,
which has a discernable impact on the drought assessment
(risk).
• Work Done:
 The uncertainties/differences among the three drought
monitoring systems (PU, UW and EMC) have been
analyzed in conjunction with Dr. Kingtse Mo (CPC).
• Ongoing/Future Work:
 Integration/unification of all three drought monitoring
systems (i.e. UW, EMC, PU) and its implementation on CTB
system with the assistance of EMC (YouLong Xia).
8
Uncertainties in North American Land Data Assimilation
Systems over the Contiguous United States. (Mo et al., 2011)
Uncertainties in SM
percentile and SRI-6
 The ensemble mean differences
between the two systems are large
over the western United States – in
some cases exceeding 20% for SM
and runoff percentile differences.
 These differences are too large for
drought classification.
Uncertainties mainly
prominent over 2002-08
Fig. 1: (a) RMS difference of SM percentiles for the experimental period (1979-2008) between the
control UW experiment, Exp(Fuw, Puw), and the control NCEP experiment, Exp(Fncep, Pncep). (b) Difference
of SM (c) same as (b), but for the real-time period (2002-2008); and (d)-(f) same as (a)-(c), but for SRI6.
Contour interval is 0.3.
(a) Uncertainties due to difference in
Precipitation forcings
(b) Uncertainties due to difference in
Temperature forcings
Fig. 2: Sources of uncertainties in SM percentile and SRI-6. (c) Uncertainties due to difference
 Uncertainties are mainly due to difference
in precipitation forcings.
 Post 2004 the number of stations reporting
data dropped.
 Although the NCEP uses all available
station reports each day (about 60008000), the UW system relies on a smaller
number of stations (about 2400), which
however have a more consistent
climatology, and are more likely to report
on most days.
in Rad. and Humid. forcings
10
Number of ACIS stations reported valid data during
1979-2004
Precipitation
Tmax
Tmin
11
Number of ACIS stations (which report valid data)
Issues with stations data download in real-time
(post-2004)
12
Task 3: Data set unification (Lead UW)
• Issue:
 Real-time hydrological runs (needed for initial conditions) are
based on different data sets, which has a discernable impact on
the hydrologic forecasts.
• Work Done:
 Maurer et al., (2002) data set has been developed for the period
of 1915-2010 (August) at 1/16 degree resolution for the CONUS.
The data set has been tested against the previous version (i.e.
Maurer et al., 2002).
• Ongoing/Future Work:
 Development of 1915-present data set at 1/8 deg, using index
station method for CONUS domain is underway.
 Extension of NARR data set (possibly to as early as 1915) that
adjusts station-based methods to be consistent with NARR in
the overlap period, and to use the extended data set for
purposes of estimating the probabilities associated with drought
indices. This work will be undertaken in collaboration with Dr
Kingste Mo (CPC).
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Extension and Spatial Refinement of a Long-Term Hydrologically Based14
Dataset of Land Surface Fluxes and States for CONUS (Livneh et al., 2011)
(a)
(b)
Fig. 3: (a) Precipitation (b) Tmax comparison between Livneh et al., (2011) and
Maurer et al. (2002) data set for the selected grid cells.
15
(c)
(d)
Fig. 3: (c) Tmin (d) Wind speed comparison between Livneh et al., (2011) and
Maurer et al. (2002) data set for the selected grid cells.
.
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Task 5: Assessment and verification studies (Lead UW)
• Work Done:
 The assessment of the relative controls of initial hydrologic
conditions and atmospheric forcings on seasonal
hydrologic and drought prediction skill (over the CONUS)
has been performed.
 Comparison of skill between CFSv1, CFSv2 and ESP for river
discharge (eastern US, being extended to CONUS)
 Assessment of skill in predicting on-set , continuation and
recovery of drought over the SE NIDIS Testbed
(cont)
17
Task 5: Assessment and verification studies (Lead UW)
• Ongoing/Future Work:
 The evaluation and verification of the forecast skill of the
multi-model drought products in collaboration with our
partners at EMC and CPC.
 Metrics currently being used to assess the Princeton
seasonal hydrologic forecast system include ranked
probability scores, brier score and root mean square error,
with a focus on drought over the NIDIS testbeds.
Soil Moisture, Snow, and Seasonal Streamflow Forecasts in the
United States (Mahanama et al., 2011)
(a) EXP1: Initial SM and snow known
(b) EXP2: Only initial snow known
(c) EXP3: Only initial SM known
Skill (r2) vs observations
Fig. 4: Skill (r2) of multi-model ensemble 3-month streamflow forecasts at 0-month lead for
four start dates (columns) and the three experiments (rows). Gray shading indicates that
skill levels are not significant at the 95% level.
18
Seasonal hydrologic prediction in the United States: understanding the19
role of initial hydrologic conditions and seasonal climate forecast skill
(Shukla and Lettenmaier, 2011)
0
1
2
3
4
5
6
IHCs dominate
Atmospheric
forcings dominate
Fig. 5: Plot of the maximum lead (in months) where RMSE Ratio [RMSE(ESP)/RMSE(revESP)]
is less than 1, for cumulative runoff forecasts, initialized on the beginning of each month.
Month-1 Forecasts
Feb
May
Aug
Nov
ESP
Correlation with
Predicted Runoff for
the first two months
over Eastern US
CFSv1
CFSv2
Feb
0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8
Cold season is better than warm
season.
Skill decreases dramatically in
the second month.
Due to the effects of initial
conditions, CFSv1 and CFSv2 have
more obvious improvement for
month-2.
Month-2 Forecasts
May
Aug
ESP
CFSv1
CFSv2
0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8
Nov
SE NIDIS, Seasonal Forecast Discrimination (month 1)
SON
1
Observed Low Flow
0.8
6 Month
0.3
Low
Avg
JJA
High
1
Observed Avg Flow
0.8
0.6
0.4
0.2
0
Low
Avg
JJA
Ranked Probability Score
Forecast Relative Frequency
0.4
0.2
0
High
1
Observed High Flow
Low
Avg
SON
High
0.8
0.6
0.6
0.4
0.4
0.2
0.2
0
0
1
0.2
Observed
4 MonthAvg Flow
0.5
0.8
0.4
0.3
High
0.8
0.3
Low
Avg
DJF
0.2
Observed Avg Flow
0.5
0.4
0.2
0
0
0.3
Low
1 Month
Avg
DJF
1
Observed High Flow
0.5
0.8
0.8
0.6
0.6
0.4
0.4
0
0.2
0.2
0.2
Low
Avg
High
Low
Avg
MAM
High
0
1
Observed Avg Flow
0.8
0.6
0.4
0.2
Low
Avg
MAM
High
0
1
Observed High Flow
0.8
0.4
0.3
0.2
0.2
High
Observed High Flow
0.4
0.6
0.4
High
0.6
0.2
0.4
0.2
0.8
0.4
0.6
0.4
3 Month
0.4
1
0.8
0.5
1
0.6
Avg
0.2Low
SON
2 Month
0.5
Observed Low Flow
0.6
0.3
DJF MAM JJA SON
DJF 0MAM JJA SON
0
Low
Avg
ESP
High
Low
CFSV1
Avg
0.4
0.2
0.2
High
CFSV2
Low
Avg
High
0
Forecast Relative Frequency
0.4
Observed Low Flow
5 Month
0.5
0.6
MAM
1
Observed Low Flow
0.8
DJF
1
Ranked Probability Score
JJA
1
• Reliability Diagram
29-Sep-2011 19821-200912 Low
– Given a forecast of low flow, how reliable is it?
0.25
0.1
4 Month
0.5
0.4
0.3
0.2
2 Month
0.5
0.5
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0.25
0.4
0.3
4 Month
0.2
3 Month
0.1
0.5
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0.4
0.3
0.2
1 Month 2 Month
0.25
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
3 Month
0.1
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9
10.1
Month
 Sharpness 
0.5
0.1
0.4
0.9
0.8
ting
0.7
ca s
e
r
0.6
e rfo
0.3
0.3
Und
0.5
0.4
ting
0.3
c as
e
r
0.2
0.2ve rfo
0.2
DJF MAM JJA SON DJF 0.1
MAM JJA SON O
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9
CFSV1
0.25
0.1
0.4
ESP
0.25
CFSV2
0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9
Forecast Probability
Observed Relative Frequency
0.2
Observed Relative Frequency
0.4
0.25
0.1
5 Month
0.5
0.3
5 Month
Ranked Probability Score
6 Month
6 Month
References and Project Publications
•
•
•
•
•
Livneh B., E.A. Rosenberg, V. Mishra, K.M. Andreadis, and D.P. Lettenmaier,
2011: Extension and Spatial Refinement of a Long-Term Hydrologically Based
Dataset of Land Surface Fluxes and States for the Conterminous United States,
J. Climate, (in preparation).
Maurer, E. P., A. W. Wood, J. C. Adam, D. P. Lettenmaier, and B. Nijssen, 2002: A
long-term hydrologically based dataset of land surface fluxes and states for the
conterminous United States. J. Climate, 15, 3237–3251.
Mo. K. C., L. Chen, S. Shukla, T. Bohn, and D. P. Lettenmaier, 2011: Uncertainties
in the North American Land Data Assimilation Systems over the Contiguous
United States. J. Hydrometeorol. (to be submitted).
Mahanama, S.P., B. Livneh, R.D. Koster, D.P. Lettenmaier, and R.H. Reichle,
2011: Soil Moisture, Snow, and Seasonal Streamflow Forecasts in the United
States. J. Hydrometeorol. (in review).
Shukla, S. and D. P. Lettenmaier, 2011: Seasonal hydrologic prediction in the
United States: understanding the role of initial hydrologic conditions and seasonal
climate forecast skill, Hydrol. Earth Syst. Sci. (in review).
•
Yoon, J-H, K Mo and E F Wood, 2011 Meteorological Drought Prediction based on the
Standardized Precipitation Index, J Hydromet. (in press).
•
Yuan Xing; Wood Eric F.; Luo Lifeng; Ming Pan. 2011. A first look at Climate
Forecast System version 2 (CFSv2) for hydrological seasonal
prediction,Geophys. Res. Letts. 38, Art. No. L13402, doi: 10.1029/2011GL047792
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