Transcript Evaluation of Daily precipitation from Coupled Model

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Anthony DeAngelis
EVALUATION OF DAILY
PRECIPITATION FROM COUPLED
MODEL INTERCOMPARISON PROJECT
PHASE III (CMIP3) MODELS
Outline
 Introduction
 Data, Models, and Methodology
 Results
 Spatial Comparisons over United States
 Analysis of Resolution
 Ranking of Model Performance
 Conclusions
 Future Directions
Importance of Precipitation
 Agriculture, water resources, power, etc.
 Extreme Precipitation
 Flooding takes 140 lives in the United States each year
(USGS 2006).
 Observational evidence of increases in the frequency
and intensity of extreme precipitation throughout the
world over 20th century (e.g., Groisman et al. 2005)
 Model projections of future increases in heavy
precipitation in response to increasing greenhouse
gases (e.g., Pall et al. 2007)
 Quantification of future changes in precipitation
relies on model simulations
How well do models simulate
precipitation?
 IPCC AR4 –Fairly realistic mean precipitation by
ensemble of PCMDI coupled models
Mean Precipitation 1980-1999
CMAP
Observations 
Multi-model mean
of AOGCMs 
[IPCC AR4, Ch 8, Fig. 8.5]
How well do models simulate
precipitation?
 IPCC AR4 –Fairly realistic mean precipitation by
ensemble of PCMDI coupled models
 Sun et al. 2007 –Overestimation of light precipitation and
underestimation of heavy precipitation by CMIP3 models
Sun et al. (2007) Figure 1
observations
model average
How well do models simulate
precipitation?
 IPCC AR4 –Fairly realistic mean precipitation by
ensemble of PCMDI coupled models
 Sun et al. (2007) –Overestimation of light precipitation
and underestimation of heavy precipitation by CMIP3
models
 Kiktev et al. 2003 – HadAM3 has little skill in simulating
precipitation trends over 1950-1995
 Higher resolution models perform better
 Iorio et al. 2004 – NCAR CCM3 – mean and extreme precipitation
 Kimoto et al. 2005 – MIROC 3.0 – extreme precipitation
 Models with embedded cloud resolving models or certain
convective parameterizations perform better for
extreme precipitation (Iorio et al. 2004, Emori et al. 2005)
What did I do?
 Compared 20th century simulations from
CMIP3 models with observations over the
contiguous United States
 Looked at differences in spatial pattern of
precipitation characteristics for individual models
 Used a longer and consistent time period for
comparison (1961-1998) than previous studies
 Compared two gridded observational datasets
 Assessed the role of resolution on model
performance for all models collectively
Observational Data and Climate
Models
 Observations
 Climate Prediction Center’s Daily United States
Unified Precipitation (CPC) -0.25° x 0.25° lon-lat
(1948-1998) [Higgins et al. 2007]
 David Robinson’s daily gridded precipitation (DAVR) 1.0° x 1.0° lon-lat (1900-2003) [Dyer and Mote 2006]
 Climate Models
 20th century simulations – forced with observed
atmospheric composition
 18 CMIP3 models with daily precipitation from 19612000 and a standard (non 360 day) calendar
 One ensemble member for each model
 Meehl et al. (2007)
CMIP3 Models Used
Model #
Modeling Group
Country
Model ID
Spatial Resolution
(approximate - lon x lat)
1
Bjerknes Centre for Climate Research
Norway
BCCR BCM 2.0
2.81° x 2.81°
2
Canadian Centre for Climate Modelling & Analysis
Canada
CCCMA CGCM 3.1 T47
3.75° x 3.75°
3
Canadian Centre for Climate Modelling & Analysis
Canada
CCCMA CGCM 3.1 T63
2.81° x 2.81°
4
Centre National de Recherches Météorologiques
France
CNRM CM 3
2.81° x 2.81°
5
CSIRO Atmospheric Research
Australia
CSIRO MK 3.0
1.88° x 1.88°
6
CSIRO Atmospheric Research
Australia
CSIRO MK 3.5
1.88° x 1.88°
7
Geophysical Fluid Dynamics Laboratory
USA
GFDL CM 2.0
2.50° x 2.00°
8
Geophysical Fluid Dynamics Laboratory
USA
GFDL CM 2.1
2.50° x 2.00°
9
Goddard Institute for Space Studies
USA
GISS AOM
4.00° x 3.00°
10
Goddard Institute for Space Studies
USA
GISS E H
5.00° x 3.91°
11
Goddard Institute for Space Studies
USA
GISS E R
5.00° x 3.91°
12
Institute of Atmospheric Physics
China
IAP FGOALS 1.0 G
2.81° x 3.00°
13
Institute for Numerical Mathematics
Russia
INM CM 3.0
5.00° x 4.00°
14
Center for Climate System Research, National Institute for
Environmental Studies, and Frontier Research Center for
Global Change
Japan
MIROC 3.2 MEDRES
2.81° x 2.81°
15
Max Planck Institute for Meteorology
Germany
MPI ECHAM 5
1.88° x 1.88°
16
Meteorological Research Institute
Japan
MRI CGCM 2.3.2
2.81° x 2.81°
17
National Center for Atmospheric Research
USA
NCAR CCSM 3.0
1.41° x 1.41°
18
National Center for Atmospheric Research
USA
NCAR PCM 1
2.81° x 2.81°
More information: http://www-pcmdi.llnl.gov/ipcc/model_documentation/ipcc_model_documentation.php
Spatial Comparisons
 Linear re-gridding to 2.5° x 2.5° lon-lat
 Typical model resolution that is fine enough to resolve
the coastlines
 Precipitation Quantities for 1961-1998
 Mean
 Frequency of wet days (precip. ≥ 0.254 mm/day)
 Standard deviation for wet days divided by mean for
wet days – precipitation variability
 99th percentile for all days
 Generalized extreme value normalized scale
parameter for yearly maximum daily precipitation
distribution – extreme precipitation variability
Mean Precipitation 1961-1998 (mm/day)
Improper terrain
representation?
Convective
parameterizations?
[Iorio et al. 2004]
Agreement with IPCC AR4
Frequency of Wet Days 1961-1998 (days/year)
Normalized Standard Deviation for Wet Days 1961-1998 (dimensionless)
Could be related to
too many wet days
99th Percentile for All Days 1961-1998 (mm/day)
Convective
parameterizations
again?
Example Generalized Extreme Value (GEV) Distribution
Representative of New Jersey in Observations
location parametercenter of distribution
(45 mm/day)
I plot
scale/location
(0.2 in this case)
scale parameterspread of distribution
(9 mm/day)
GEV Normalized Scale Parameter for Yearly Maximum 1961-1998 (dimensionless)
Not enough variability of
precipitation extremes
GEV Normalized Scale Parameter for Yearly Maximum 1961-1998 (dimensionless)
Does Spatial Resolution Make
a Difference?
 Linear re-gridding to 5.0° x 4.0° lon-lat
 Error- root mean square of absolute difference between
each model and observations average (Iorio et al. 2004)
 Plot error against finite grid equivalent resolution (# of
global grid cells)
 Fit least squares linear regression to error vs. resolution plot
Error vs. Resolution Results
 Statistically significant improvement in the
frequency of wet days with higher resolution
= model average
CMIP3 Models Used
Model #
Modeling Group
Country
Model ID
Spatial Resolution
(approximate - lon x lat)
1
Bjerknes Centre for Climate Research
Norway
BCCR BCM 2.0
2.81° x 2.81°
2
Canadian Centre for Climate Modelling & Analysis
Canada
CCCMA CGCM 3.1 T47
3.75° x 3.75°
3
Canadian Centre for Climate Modelling & Analysis
Canada
CCCMA CGCM 3.1 T63
2.81° x 2.81°
4
Centre National de Recherches Météorologiques
France
CNRM CM 3
2.81° x 2.81°
5
CSIRO Atmospheric Research
Australia
CSIRO MK 3.0
1.88° x 1.88°
6
CSIRO Atmospheric Research
Australia
CSIRO MK 3.5
1.88° x 1.88°
7
Geophysical Fluid Dynamics Laboratory
USA
GFDL CM 2.0
2.50° x 2.00°
8
Geophysical Fluid Dynamics Laboratory
USA
GFDL CM 2.1
2.50° x 2.00°
9
Goddard Institute for Space Studies
USA
GISS AOM
4.00° x 3.00°
10
Goddard Institute for Space Studies
USA
GISS E H
5.00° x 3.91°
11
Goddard Institute for Space Studies
USA
GISS E R
5.00° x 3.91°
12
Institute of Atmospheric Physics
China
IAP FGOALS 1.0 G
2.81° x 3.00°
13
Institute for Numerical Mathematics
Russia
INM CM 3.0
5.00° x 4.00°
14
Center for Climate System Research, National Institute for
Environmental Studies, and Frontier Research Center for
Global Change
Japan
MIROC 3.2 MEDRES
2.81° x 2.81°
15
Max Planck Institute for Meteorology
Germany
MPI ECHAM 5
1.88° x 1.88°
16
Meteorological Research Institute
Japan
MRI CGCM 2.3.2
2.81° x 2.81°
17
National Center for Atmospheric Research
USA
NCAR CCSM 3.0
1.41° x 1.41°
18
National Center for Atmospheric Research
USA
NCAR PCM 1
2.81° x 2.81°
More information: http://www-pcmdi.llnl.gov/ipcc/model_documentation/ipcc_model_documentation.php
Error vs. Resolution Results
 Statistically significant improvement in the
frequency of wet days with higher resolution
 All other quantities showed decreasing error
with higher resolution, but the linear fit was
not statistically significant
 All quantities showed low percentage of
model error variability explained by the linear
fit (r2)
Other potential reasons for
variability in model error
 Different vertical resolutions
 Different grid types (e.g., spectral resolution




vs. finite grid)
Different cloud and convective
parameterizations
Different microphysics schemes
Different ocean components
Different radiation schemes
Ranking of Model Performance
 Ratio: Root mean square error for each model
divided by the average root mean square
error for all models for each precipitation
quantity
 Eliminates biases from quantities with different
units (e.g., mean precipitation, frequency of wet
days)
 Take average of ratio over precipitation
quantities for each model and rank them
CMIP3 Model Ranking
All Precipitation Quantities
Model Rank
1. MPI ECHAM 5
2. CSIRO MK 3.5
3. MRI CGCM 2.3.2
4. GFDL CM 2.1
5. NCAR CCSM 3.0
6. Model Average
7. CCCMA CGCM 3.1 T63
8. MIROC 3.2 MEDRES
9. CCCMA CGCM 3.1 T47
10. GFDL CM 2.0
11. INM CM 3.0
12. CSIRO MK 3.0
13. BCCR BCM 2.0
14. CNRM CM 3
15. NCAR PCM 1
16. IAP FGOALS 1.0 G
17. GISS E H
18. GISS AOM
19. GISS E R
Average Error
0.6408
0.7559
0.7666
0.7954
0.7987
0.8155
0.8457
0.8647
0.8886
0.8982
0.9161
0.9222
0.9712
1.0737
1.1117
1.1613
1.3447
1.3743
1.8701
Mean, Frequency of Wet Days, and Normalized
Standard Deviation for Wet Days
Model Rank
1. MPI ECHAM 5
2. CSIRO MK 3.5
3. MRI CGCM 2.3.2
4. NCAR CCSM 3.0
5. Model Average
6. MIROC 3.2 MEDRES
7. GFDL CM 2.1
8. CCCMA CGCM 3.1 T63
9. CSIRO MK 3.0
10. CCCMA CGCM 3.1 T47
11. INM CM 3.0
12. BCCR BCM 2.0
13. GFDL CM 2.0
14. GISS E R
15. IAP FGOALS 1.0 G
16. NCAR PCM 1
17. CNRM CM 3
18. GISS E H
19. GISS AOM
Average Error
0.5783
0.6974
0.7251
0.7448
0.8621
0.8895
0.8901
0.9154
0.932
0.9477
0.9489
1.0024
1.0388
1.0968
1.215
1.243
1.272
1.3503
1.5125
99th Percentile and GEV Normalized Scale
Model Rank
1. GFDL CM 2.1
2. GFDL CM 2.0
3. MPI ECHAM 5
4. CCCMA CGCM 3.1 T63
5. Model Average
6. CNRM CM 3
7. CCCMA CGCM 3.1 T47
8. MIROC 3.2 MEDRES
9. MRI CGCM 2.3.2
10. CSIRO MK 3.5
11. INM CM 3.0
12. NCAR CCSM 3.0
13. CSIRO MK 3.0
14. NCAR PCM 1
15. BCCR BCM 2.0
16. IAP FGOALS 1.0 G
17. GISS AOM
18. GISS E H
19. GISS E R
Average Error
0.6535
0.6872
0.7346
0.7412
0.7457
0.7762
0.7999
0.8275
0.8288
0.8436
0.8670
0.8797
0.9075
0.9147
0.9243
1.0807
1.1671
1.3364
3.0301
More information: http://www-pcmdi.llnl.gov/ipcc/model_documentation/ipcc_model_documentation.php
CMIP3 Model Ranking
All Precipitation Quantities
Model Rank
1. MPI ECHAM 5
2. CSIRO MK 3.5
3. MRI CGCM 2.3.2
4. GFDL CM 2.1
5. NCAR CCSM 3.0
6. Model Average
7. CCCMA CGCM 3.1 T63
8. MIROC 3.2 MEDRES
9. CCCMA CGCM 3.1 T47
10. GFDL CM 2.0
11. INM CM 3.0
12. CSIRO MK 3.0
13. BCCR BCM 2.0
14. CNRM CM 3
15. NCAR PCM 1
16. IAP FGOALS 1.0 G
17. GISS E H
18. GISS AOM
19. GISS E R
Average Error
0.6408
0.7559
0.7666
0.7954
0.7987
0.8155
0.8457
0.8647
0.8886
0.8982
0.9161
0.9222
0.9712
1.0737
1.1117
1.1613
1.3447
1.3743
1.8701
Mean, Frequency of Wet Days, and Normalized
Standard Deviation for Wet Days
Model Rank
1. MPI ECHAM 5
2. CSIRO MK 3.5
3. MRI CGCM 2.3.2
4. NCAR CCSM 3.0
5. Model Average
6. MIROC 3.2 MEDRES
7. GFDL CM 2.1
8. CCCMA CGCM 3.1 T63
9. CSIRO MK 3.0
10. CCCMA CGCM 3.1 T47
11. INM CM 3.0
12. BCCR BCM 2.0
13. GFDL CM 2.0
14. GISS E R
15. IAP FGOALS 1.0 G
16. NCAR PCM 1
17. CNRM CM 3
18. GISS E H
19. GISS AOM
Average Error
0.5783
0.6974
0.7251
0.7448
0.8621
0.8895
0.8901
0.9154
0.932
0.9477
0.9489
1.0024
1.0388
1.0968
1.215
1.243
1.272
1.3503
1.5125
99th Percentile and GEV Normalized Scale
Model Rank
1. GFDL CM 2.1
2. GFDL CM 2.0
3. MPI ECHAM 5
4. CCCMA CGCM 3.1 T63
5. Model Average
6. CNRM CM 3
7. CCCMA CGCM 3.1 T47
8. MIROC 3.2 MEDRES
9. MRI CGCM 2.3.2
10. CSIRO MK 3.5
11. INM CM 3.0
12. NCAR CCSM 3.0
13. CSIRO MK 3.0
14. NCAR PCM 1
15. BCCR BCM 2.0
16. IAP FGOALS 1.0 G
17. GISS AOM
18. GISS E H
19. GISS E R
Average Error
0.6535
0.6872
0.7346
0.7412
0.7457
0.7762
0.7999
0.8275
0.8288
0.8436
0.8670
0.8797
0.9075
0.9147
0.9243
1.0807
1.1671
1.3364
3.0301
More information: http://www-pcmdi.llnl.gov/ipcc/model_documentation/ipcc_model_documentation.php
CMIP3 Model Ranking
All Precipitation Quantities
Model Rank
1. MPI ECHAM 5
2. CSIRO MK 3.5
3. MRI CGCM 2.3.2
4. GFDL CM 2.1
5. NCAR CCSM 3.0
6. Model Average
7. CCCMA CGCM 3.1 T63
8. MIROC 3.2 MEDRES
9. CCCMA CGCM 3.1 T47
10. GFDL CM 2.0
11. INM CM 3.0
12. CSIRO MK 3.0
13. BCCR BCM 2.0
14. CNRM CM 3
15. NCAR PCM 1
16. IAP FGOALS 1.0 G
17. GISS E H
18. GISS AOM
19. GISS E R
Average Error
0.6408
0.7559
0.7666
0.7954
0.7987
0.8155
0.8457
0.8647
0.8886
0.8982
0.9161
0.9222
0.9712
1.0737
1.1117
1.1613
1.3447
1.3743
1.8701
Mean, Frequency of Wet Days, and Normalized
Standard Deviation for Wet Days
Model Rank
1. MPI ECHAM 5
2. CSIRO MK 3.5
3. MRI CGCM 2.3.2
4. NCAR CCSM 3.0
5. Model Average
6. MIROC 3.2 MEDRES
7. GFDL CM 2.1
8. CCCMA CGCM 3.1 T63
9. CSIRO MK 3.0
10. CCCMA CGCM 3.1 T47
11. INM CM 3.0
12. BCCR BCM 2.0
13. GFDL CM 2.0
14. GISS E R
15. IAP FGOALS 1.0 G
16. NCAR PCM 1
17. CNRM CM 3
18. GISS E H
19. GISS AOM
Average Error
0.5783
0.6974
0.7251
0.7448
0.8621
0.8895
0.8901
0.9154
0.932
0.9477
0.9489
1.0024
1.0388
1.0968
1.215
1.243
1.272
1.3503
1.5125
99th Percentile and GEV Normalized Scale
Model Rank
1. GFDL CM 2.1
2. GFDL CM 2.0
3. MPI ECHAM 5
4. CCCMA CGCM 3.1 T63
5. Model Average
6. CNRM CM 3
7. CCCMA CGCM 3.1 T47
8. MIROC 3.2 MEDRES
9. MRI CGCM 2.3.2
10. CSIRO MK 3.5
11. INM CM 3.0
12. NCAR CCSM 3.0
13. CSIRO MK 3.0
14. NCAR PCM 1
15. BCCR BCM 2.0
16. IAP FGOALS 1.0 G
17. GISS AOM
18. GISS E H
19. GISS E R
Average Error
0.6535
0.6872
0.7346
0.7412
0.7457
0.7762
0.7999
0.8275
0.8288
0.8436
0.8670
0.8797
0.9075
0.9147
0.9243
1.0807
1.1671
1.3364
3.0301
More information: http://www-pcmdi.llnl.gov/ipcc/model_documentation/ipcc_model_documentation.php
CMIP3 Model Ranking
All Precipitation Quantities
Model Rank
1. MPI ECHAM 5
2. CSIRO MK 3.5
3. MRI CGCM 2.3.2
4. GFDL CM 2.1
5. NCAR CCSM 3.0
6. Model Average
7. CCCMA CGCM 3.1 T63
8. MIROC 3.2 MEDRES
9. CCCMA CGCM 3.1 T47
10. GFDL CM 2.0
11. INM CM 3.0
12. CSIRO MK 3.0
13. BCCR BCM 2.0
14. CNRM CM 3
15. NCAR PCM 1
16. IAP FGOALS 1.0 G
17. GISS E H
18. GISS AOM
19. GISS E R
Average Error
0.6408
0.7559
0.7666
0.7954
0.7987
0.8155
0.8457
0.8647
0.8886
0.8982
0.9161
0.9222
0.9712
1.0737
1.1117
1.1613
1.3447
1.3743
1.8701
Mean, Frequency of Wet Days, and Normalized
Standard Deviation for Wet Days
Model Rank
1. MPI ECHAM 5
2. CSIRO MK 3.5
3. MRI CGCM 2.3.2
4. NCAR CCSM 3.0
5. Model Average
6. MIROC 3.2 MEDRES
7. GFDL CM 2.1
8. CCCMA CGCM 3.1 T63
9. CSIRO MK 3.0
10. CCCMA CGCM 3.1 T47
11. INM CM 3.0
12. BCCR BCM 2.0
13. GFDL CM 2.0
14. GISS E R
15. IAP FGOALS 1.0 G
16. NCAR PCM 1
17. CNRM CM 3
18. GISS E H
19. GISS AOM
Average Error
0.5783
0.6974
0.7251
0.7448
0.8621
0.8895
0.8901
0.9154
0.932
0.9477
0.9489
1.0024
1.0388
1.0968
1.215
1.243
1.272
1.3503
1.5125
99th Percentile and GEV Normalized Scale
Model Rank
1. GFDL CM 2.1
2. GFDL CM 2.0
3. MPI ECHAM 5
4. CCCMA CGCM 3.1 T63
5. Model Average
6. CNRM CM 3
7. CCCMA CGCM 3.1 T47
8. MIROC 3.2 MEDRES
9. MRI CGCM 2.3.2
10. CSIRO MK 3.5
11. INM CM 3.0
12. NCAR CCSM 3.0
13. CSIRO MK 3.0
14. NCAR PCM 1
15. BCCR BCM 2.0
16. IAP FGOALS 1.0 G
17. GISS AOM
18. GISS E H
19. GISS E R
Average Error
0.6535
0.6872
0.7346
0.7412
0.7457
0.7762
0.7999
0.8275
0.8288
0.8436
0.8670
0.8797
0.9075
0.9147
0.9243
1.0807
1.1671
1.3364
3.0301
More information: http://www-pcmdi.llnl.gov/ipcc/model_documentation/ipcc_model_documentation.php
Conclusions
 CMIP3 models underestimate mean and extreme
precipitation amounts near the Gulf Coast
 Convective parameterizations (Iorio et al. 2004)
 CMIP3 models produce precipitation days too frequently,
especially in the north and west
 Higher resolution models perform much better
 CMIP3 models have too little variability in all
precipitation and extreme precipitation in the northern
interior west
 The MPI ECHAM5 is the best, the model average is
better than the majority of individual models, and the
GISS models are the worst with 20th century precipitation
characteristics over the US
Future Directions
 Understand the reasons for differences in
model performance
 What makes the MPI ECHAM5 so good?
 Evaluate the ability of CMIP3 models to
simulate precipitation changes
 Time period used here is too short for a reliable
analysis
 Expand the evaluation of CMIP3 precipitation
to other regions
References







Dyer, J. L., and T. L. Mote, 2006: Spatial variability and patterns of snow depth over North
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Emori, S., A. Hasegawa, T. Suzuki, and K. Dairaku, 2005: Validation, parameterization
dependence and future projection of daily precipitation simulated with an atmospheric GCM,
Geophys. Res. Lett., 32, L06708, doi:10.1029/2004GL022306.
Groisman, P. Y., R. W. Knight, D. R. Easterling, T. R. Karl, G. C. Hegerl, and V. N. Razuvaev,
2005: Trends in intense precipitation in the climate record, J. Clim., 18, 1326-1350.
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Iorio, J. P., P. B. Duffy, B. Govindasamy, S. L. Thompson, M. Khairoutdinov, and D. Randall,
2004: Effects of model resolution and subgrid scale physics on the simulation of precipitation
in the continental United State, Clim. Dyn., 23, 243–258, doi:10.1007/s00382-004-0440-y.
Kiktev, D., D. M. H. Sexton, L. Alexander, and C. K. Folland, 2003: Comparison of modeled
and observed trends in indices of daily climate extremes, J. Clim., 16, 3560–3571.
Kimoto, M., N. Yasutomi, C. Yokoyama, and S. Emori, 2005: Projected changes in
precipitation characteristics near Japan under the global warming, Scientific Online Letters on
the Atmosphere, 1, 85–88, doi:10.2151/sola.2005-023.
References Continued

Meehl, G. A., C. Covey, T. Delworth, M. Latif, B. McAvaney, J. F. B. Mitchell, R. J. Stouffer,
and K. E. Taylor, 2007: The WCRP CMIP3 multi-model dataset: A new era in climate change
research, Bull. Amer. Meteor. Soc., 88, 1383-1394.

Pall, P., M. R. Allen, and D. A. Stone, 2007: Testing the Clausius-Clapeyron constraint on
changes in extreme precipitation under CO2 warming, Clim. Dyn., 28, 351-363.

Randall, D. A. and Coauthors, 2007: Climate Models and Their Evaluation. In: Climate
Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth
Assessment Report of the Intergovernmental Panel on Climate Change [Solomon, S., D. Qin,
M. Manning, Z. Chen, M. Marquis, K. B. Averyt, M. Tignor and H. L. Miller (eds.)].
Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA.

Sun, Y., S. Solomon, A. Dai, and R. W. Portmann, 2007: How often will it rain? J. Clim., 20,
4801-4818.

United States Department of the Interior, United States Geological Survey, 2006: Fact Sheet:
Flood Hazards- A National Threat. Available at http://pubs.usgs.gov/fs/2006/3026/.
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For more plots, see http://envsci.rutgers.edu/~toine379/extremeprecip/home