Improving SeasonalRange Hydrometeorological Predictions: the Atmospheric Perspective

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Improving Seasonal-Range Hydrometeorological
Predictions the Atmospheric Perspective
prepared for 2006 Terrestrial Hydromet. Workshop,
16-18 Nov 2006
NOAA Earth System Research Laboratory

• Tom Hamill
• NOAA Earth System Research Lab, Physical Sciences
  Div.
• tom.hamill_at_noaa.gov

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MotivationCourtesy of Andy Wood and Dennis
Lettenmaier abstract

• Despite the passage of almost 50 years since
  the development of the first computerized
  hydrologic simulation models, and over 30 years
  since the development of hydrologic ensemble
  forecast methods, the prevalent method used for
  forecasting seasonal streamflow in the western
  U.S. is still regression of spring and summer
  streamflow volume on spring snowpack and/or the
  previous winters accumulated precipitation, the
  method of choice for almost a century. A recent
  retrospective analysis has shown that the skill
  of the forecasts have not improved in the last 40
  years, despite large investments in science and
  technology related to the monitoring and
  assessment of the land surface.

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Questions (and depressing answers)

• Whats the state of seasonal atmospheric
  prediction? (not great prediction is tougher
  than diagnostics)
• What are the potential sources of improved
  atmospheric predictive ability? (few good ones
  besides ENSO, and they dont come around very
  often)
• How can we exploit whatever little predictability
  there is? (its gonna cost you).

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text discussion of seasonal forecasts
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text discussion of seasonal forecasts
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• THE MAIN FACTORS WHICH USUALLY INFLUENCE
• SEASONAL CLIMATE INCLUDE
• 1) El Niño and La Niña - which together make up
  El Niño / Southern Oscillation or ENSO
• 2) Trends - approximated by the difference
  between the most recent 10-year mean of
  temperature or 15-year mean of precipitation for
  a given location and time of year and the 30-year
  climatology period (currently 1971-2000) (climate
  change signal)
• 3) Tropical 30-60-day oscillation - which may
  affect climate variability within a season (i.e.,
  MJO)
• 4) The North Atlantic Oscillation (NAO) and the
  Pacific North American (PNA) patterns - which
  affect the temperature anomaly pattern,
  especially during the cold seasons
• 5) Persistently dry or wet soils in the summer
  and snow and ice cover anomalies in the winter.
• 6) An objective consolidation (called Con in
  the text) of The OCN, CCA, SMLR and CFS forecasts
  is used as a first-guess in preparing the
  forecast maps.

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Sources of potential predictive ability ENSO
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Altered position of jet in El Niño vs. La Niña
El Niño
La Niña
from Shapiro et al. (2001)
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El Niño causes shifts in extreme-event frequency
c/o Randy Dole and Klaus Wolter, NOAA/ERSL/PSD
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El Niño and hurricane activity
inactive hurricane season
El Niño is one of many large-scale predictors of
western-Atlantic tropical cyclone
activity. ENSO forces increased vertical wind
shear in trades.
active
Blake and Gray (2004)
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Global warming trend skill relative to assumed
stationary climatology
How will hydro models work with
non-stationary time series? Are factors like
infiltration going to change dramatically over
time?
Jones and Mann (2004)
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Sources of potential predictive ability the
Madden-Julian Oscillation
orange-yellow are cold cold tops
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MJO the optimistic perspective (Ferranti et al.
1990)
relax tropics to analyzed state control relax
tropics to persisted initial condition
ECMWF T42 forecasts in times of active MJO test
skill of perfect tropical forcing vs. existing
model. However (1) small sample size, (2) were
not yet anywhere near perfect tropical
prediction, (3) MJO active only small percentage
of time.
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91-day running mean of RMM12 RMM22
long periods with no significant MJO activity
?
from Wheelers http//www.bom.gov.au/bmrc/clfor/cf
staff/matw/maproom/RMM/ts.PCvar91drm.gif
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Predictive ability from land-state anomalies?
Case 2004 European heat wave (dry soils
preceding that spring)
1-2 month response to observed SST forcing
(warmer than avg).
response to dry initial root layer (note EMCWF
model diminishes amplitude of seasonal soil
moisture perturbations, so had to force dry soils
in model)
response to dry soil over all layers
Ferranti and Viterbo (2006)
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Numerical climate prediction The optimistic
perspective
but ACC insensitive to bias errors
from Shukla et al., BAMS (2000)
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ECMWF 2-4 month 2-m temperature skill
not much skill outside of ENSO region
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ECMWF 2-4 month 2-m precipitation skill
egads, even worse .
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Our current state-of-the-artNOAA Climate
Prediction Centers Seasonal Forecasts
Nov-Dec-Jan
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Temperature,a yearsworth
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Precipitation,a yearsworth
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Skill via the RankedProbabilitySkill Score15
- 45 day forecasts
courtesy W. Ebusuzaki, NCEP
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Numerical climate prediction approaches

• Multi-model ensembles
• Single-models ensembles corrected by hindcasts
• Hybrid of the two
• (wont talk about dynamical downscaling)

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Statistical correction using hindcasts, or
reforecasts Share some examples from week-2
forecasting.
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NCEP GFS reforecast

• Reforecast definition a data set of
  retrospective numerical forecasts using the same
  model as is used to generate real-time forecasts.
• Model T62L28 NCEP GFS, circa 1998
• Initial States NCEP-NCAR Reanalysis II plus 7
  /- bred modes.
• Duration 15 days runs every day at 00Z from
  19781101 to now. (http//www.cdc.noaa.gov/people/j
  effrey.s.whitaker/refcst/week2).
• Data Selected fields (winds, hgt, temp on 5
  press levels, precip, t2m, u10m, v10m, pwat,
  prmsl, rh700, heating). NCEP/NCAR reanalysis
  verifying fields included (Web form to download
  at http//www.cdc.noaa.gov/reforecast).

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WHY?
850 hPa temperature bias for a grid point in
the central U.S.
Spread of yearly bias estimates from
31-day running mean F - O Note the spread is
often larger than the bias, especially for long
leads.
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Calibrating ensemble forecasts raw, bias
correction, logistic regression
(these are more skillful than NCEP/CPC
human-synthesized forecasts)
Hamill et al. (2004)
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Multi-model reforecasts
Whitaker and Vitart (2006)
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Reforecasts and statistical downscaling
Downscaling using PRISM / Mountain Mapper
technology (C. Daly. Oregon St., NOAA RFCs,
OHD)
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Recent OR-WA floods, 3-6 day forecast
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Some Questions

• Do we really understand the atmospheric responses
  to boundary forcings other than El Niño? Are
  interactions of various boundary forcings linear
  or nonlinear?
• Are existing dynamical hydrologic techniques
  (ensemble streamflow models with resampled time
  series drawn from unconditional or conditional
  climatologies) applicable with a non-stationary
  climate?
• Are (post-processed) data from numerical climate
  predictions a useful input to seasonal hydrologic
  forecast models? Will they ever be?
• What forecast products are going to be the most
  useful for hydrologic predictions (surely, not
  tercile probabilities)?
• Is dynamical downscaling (regional climate
  modeling, or RCM) a viable approach? Must there
  be global-model skill in order to see skill from
  RCMs?

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References
Hamill, T. M., J. S. Whitaker, and X. Wei, 2003
Ensemble re-forecasting improving medium-range
forecast skill using retrospective forecasts.
Mon. Wea. Rev., 132, 1434-1447.
http//www.cdc.noaa.gov/people/tom.hamill/reforeca
st_mwr.pdf Hamill, T. M., J. S. Whitaker, and
S. L. Mullen, 2005 Reforecasts, an important
dataset for improving weather predictions. Bull.
Amer. Meteor. Soc., 87, 33-46. http//www.cdc.noaa
.gov/people/tom.hamill/refcst_bams.pdf
Whitaker, J. S, F. Vitart, and X. Wei, 2006
Improving week two forecasts with multi-model
re-forecast ensembles. Mon. Wea. Rev., 134,
2279-2284. http//www.cdc.noaa.gov/people/jeffrey.
s.whitaker/Manuscripts/multimodel.pdf Hamill,
T. M., and J. S. Whitaker, 2006 Probabilistic
quantitative precipitation forecasts based on
reforecast analogs theory and application. Mon.
Wea. Rev., in press. http//www.cdc.noaa.gov/peopl
e/tom.hamill/reforecast_analog_v2.pdf Hamill,
T. M., and J. Juras, 2006 Measuring forecast
skill is it real skill or is it the varying
climatology? Quart. J. Royal Meteor. Soc., in
press. http//www.cdc.noaa.gov/people/tom.hamill/s
kill_overforecast_QJ_v2.pdf Wilks, D. S., and
T. M. Hamill, 2006 Comparison of ensemble-MOS
methods using GFS reforecasts. Mon. Wea. Rev., in
press. http//www.cdc.noaa.gov/people/tom.hamill/W
ilksHamill_emos.pdf Hamill, T. M. and J. S.
Whitaker, 2006 White Paper. Producing
high-skill probabilistic forecasts
using reforecasts implementing the National
Research Council vision. Available at
http//www.cdc.noaa.gov/people/tom.hamill/whitepap
er_reforecast.pdf .
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ECMWF ROC Area, 2-m Temp

• gt 0.5 indicates
• skill in this bias-insensitive measure
• In N-Hem
• extratropics,
• skill primarily
• from PNA
• region.
• Prominent
• ENSO signal

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ECMWF ROC Area, Precipitation
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ECMWF model details

• HOPE (Hamburg Ocean Primitive Equation model)
  version 2
• Ocean data assimilation univariate temperature
  Optimum Interpolation (OI) carried out on
  overlapping sub-domains of the model horizontal
  grid.
• ECMWF IFS (Integrated Forecast System) model
  version 23r4.
• Bias correction of mean using hindcasts.

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surface temperature
precipitation
(these are more skillful than NCEP/CPC
human-synthesized forecasts)
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Analog high-resolution precipitation forecast
calibration technique
(actually run with 10 to 75 analogs)
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Verified over 25 years of forecasts skill
scores use conventional method of calculation
which may overestimate skill (Hamill and Juras
2006).
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Calibrating T2m CRPSS