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Title: Extreme Weather Events and their Probabilistic Prediction By NCEP Ensemble Forecast System


1
Extreme Weather Events and their Probabilistic
PredictionBy NCEP Ensemble Forecast System
  • Yuejian Zhu
  • Environmental Modeling Center
  • National Centers for Environmental Prediction
  • NWS/NOAA/USA
  • Contribution to 2005 Annual Meeting of CMS
  • September 15-17, Zhengzhou, China

2
Dedicated to International Symposium of
Rainstorms, Inundation and Disaster
Mitigation And To commemorate the 30-year
anniversary of the August 1975 flooding
rainfall and inundation that occurred in Henan
Province
3
Significant modern floods around the world-
Records from encyclopedia
  • Flooding in Mumbai India in July 2005 left over
    700 dead.
  • The 2002 European flood was a flooding disaster
    that affected many states including Czech
    Republic, Germany and Poland. Historical cities
    like Prague and Dresden were partly flooded. In
    Germany the so called "Jahrhundertflut" (flood of
    century) caused a 22,6 billion Euro damage.
  • In 1975 a freak typhoon destroyed over sixty dams
    in China's Henan Province, killing over 200,000
    people. (see Banqiao Dam)
  • The 1931 Huang He flood caused between 800,000
    and 4,000,000 deaths in China, one of a series of
    disastrous floods on the Huang He.

4
758 flood and Banqiao Reservoir Dam
The Banqiao Reservoir Dam (Chinese ??????
Pinyin Banqiáo Shuikù Dàbà) and Shimantan
Reservoir Dam (Chinese ??????? Pinyin
Shímàntan Shuikù Dàbà) are among 62 dams in
Zhumadian Prefecture of China's Henan Province
that failed catastrophically in 1975 during a
freak typhoon. Approximately 26,000 people died
from flooding and another 145,000 died during
subsequent epidemics. In addition, about
5,960,000 buildings collapsed.
5
NCEP/NCAR Reanalysis at 2.5 degree resolution
1975/08/04
6
NCEP/NCAR Reanalysis at 2.5 degree resolution
1975/08/05
7
NCEP/NCAR Reanalysis at 2.5 degree resolution
1975/08/06
8
NCEP/NCAR Reanalysis at 2.5 degree resolution
1975/08/07
9
NCEP/NCAR Reanalysis at 2.5 degree resolution
1975/08/08
10
CONTENTS
  • Introduction.
  • Definition of Extreme Events.
  • Nature of Extreme Events.
  • Forecasting Extremes.
  • Verifying Forecasts for Extremes.
  • Use of Extreme Forecasts.
  • Summary and Discussion.
  • Deterministic or probabilistic?
  • High predictable system.
  • Economic values and uncertainties.
  • Numerical production!!!
  • Acknowledgments.

11
Introduction
  • 1. Extreme Weather Events
  • . Unusual, Unexpected, rare weather
    events.
  • . Cost loss of lives, properties,
    equipments and etc.
  • . Forecast ( may be difficulty or may be
    not ) ?
  • . Alarms to users ( Such as Watch, Warning
    and etc... )
  • . Early decision, and early protection!!!
  • . Widely social impacts.
  • . Always use updated forecast information
  • 2. Probabilistic Forecasts
  • . Ensemble model based forecasts.
  • . Forecast In terms of probability or
    possibility.
  • . Wide coverage of the weather events from
    probabilistic
  • sense. Include extreme weather events.
  • . Many variables ( temperature,
    precipitation, wind and etc. ).

12
Definition of Extreme Events
  • 1. Climatological extremes
  • . Based on climatological distributions.
  • . The tails ( 5 or less ) of the
    climatological distribution.
  • . Considering a particular meteorological
    variable.
  • . Considering a specific time and place.
  • 2. Forecast extremes
  • . Similar to climatological extremes.
  • . Different range and values of
    distribution.
  • . Narrow band than climatology.
  • . Conditional climatological sense.
  • 3. User specific extremes
  • . User defined extreme (not climatology, not
    forecasting ).
  • . For particular user, in particular area
    and in time period
  • . Sensitivity to particular meteorological
    element.
  • . The combination of the temporal/spatial.

13
Nature of Extreme Events
  • 1. Physical system.
  • . The same for extreme and non-extreme
    events.
  • . Different from phase space of system.
  • . Near the edge of the distribution.
  • . Small scale system in generally.
  • 2. Nonlinear process.
  • . Play a crucial role to define the "edge".
  • . Creating additional uncertainty.
  • . Model's limitation to predict extreme by
    nonlinear process.
  • 3. Combination of many factors
  • . Snow covers, cloud covers.
  • . Minimum temperature, and maximum
    temperature.
  • . Combined high temperature and high
    humidity heat index.
  • . Wind speed, combined cold temperature and
    wind sheer.
  • . Precipitation amount and concentration.
  • . Time, location and etc...

14
Forecasting Extremes
  • 1. Procedures
  • . No specific tools or procedures in
    generally.
  • . The same process for non-extreme.
  • . Improving the model forecast accuracy.
  • . Increase model's predictability to
    extreme.
  • . Experiences of forecasters ( sometime ???
    )
  • 2. Methods
  • . Probabilistic forecast, such as PQPF.
  • . In sense of probability by ensemble or
    single forecast.
  • . Particular process for specific extreme
    events (possible).
  • . Multi-methods ( relative measure of
    predictability - RMOP).
  • ensemble spread

15
Verifying Forecasts for Extremes
  • 1. Systematic forecast behavior
  • . Systematic model errors ( model biases,
    need remove ).
  • . Model forecast is more likely to give
    less extreme.
  • or moderate extreme.
  • . Good example for precipitation forecast.
  • Over-forecast for less, under-forecast
    for extreme amount
  • 2. Control .vs. ensemble forecasts
  • . Single/deterministic forecast ( more
    difficulty to predict extreme )
  • . Ensemble mean/medium forecast.
  • is better for climatological extreme
    events.
  • . Ensemble based probabilistic forecast
  • is a best approach to extreme.

16
Verifying Forecasts for Extremes (Continues)
  • 3. RMS error
  • . In general sense, large RMS error for
    extreme
  • events, but not necessary
  • . Persistence circulation forecast
    (Zolton,1992).
  • . Surface temp. forecast (Ziehmann,2000)
  • . Need more case to verify
  • 4. Error in categorical forecasts
  • . Climate forecast, normal forecast and
    extreme forecast.
  • . Brier scores, Brier skill scores for
    climate and ensemble.
  • 5. Error in probabilistic forecasts
  • . Hitting rate, false alarm.
  • . ROC, information content, economic values.
  • . Reliability, rank probability skill scores
    (RPSS).
  • . Brier scores for ensemble precipitation
    forecast.
  • . Extreme forecast has a better scores.

17
Use of Extreme Forecasts
  • 1. Probabilistic forecast
  • . Numerical model based probabilistic
    forecast.
  • such as ensemble forecast.
  • . Statistical model based probabilistic
    forecast.
  • . Convert single forecast to probabilistic
    forecast based on
  • climatological information.
  • . Reliability, calibrated reliability (by
    remove bias).
  • 2. Forecast skills
  • . No long term skill to refer to extreme
    events.
  • . Leading time dependence.
  • . Seasonal dependence.
  • . Optimize user behavior by analysis
  • . Considering the ratio of cost/loss and
    tolerance.

18
Summary and Discussion
  • 1. Observed extreme weather events
  • . Mostly large impact to society, natural
    and etc..
  • 2. Model forecast ability
  • . Limitation to predict extreme events.
  • . Properly interpret probabilistic forecast.
  • . RMS error is poorer than near normal
    forecast
  • . Probabilistic/categorical evaluation is
    better for extreme.
  • 3. Suggestions
  • . Enhance the model predictability for
    nonlinear process.
  • . Using probabilistic approach.
  • . Reducing model's systematic error/bias.
  • . Establishing/improving evaluation system.

19
Deterministic or Probabilistic ?
Any prediction for the future is uncertainty Is
for-cast! No deterministic answer! Must be
probabilistic!
20
Clear sky, no precipitation
21
20mm/24hrs (0)
Precipitation
2mm/24hrs (30)
22
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23
Current Hurricane Forecast
24
High predictable heavy precipitation event
GFS ENS
February 12-13 1997 (Southern Louisiana flooding)
Location and intensity
GFS made a very good forecast, But Ensemble made
a excellent forecast.
25
HIGHLY PREDICTABLE HEAVY PRECIPITATION EVENT
(20010313)
26
High Predictable Heavy Precipitation Events
(20010113)
Ensemble-based precipitation forecasts gave
relatively high probability values for the half
and one inch thresholds for the 24-hr period
ending 031312 for the Gulf states with 1 through
8 days lead time. The corresponding observed
precipitation amounts indicate that the forecasts
were rather successful. The high predictability
in precipitation was associated with high
confidence (and well verifying) forecasts for 500
hPa height. The cut-off low over the SW US that
allowed Pacific air to reach the Gulf of Maxico
at low latitudes (over and south of Baja CA) was
well predicted, with high confidence, at various
lead times (see, for example, at 4, 7, and even
at 10 days). Red colors in these charts over the
cut-off low correspond to an area associated with
high predictability.
27
RMOP
28
DETERMINSTIC/PROBABILISTIC FORECASTQPF .vs. PQPF
  • Northern California State Christmas-New Year
    flooding
  • Winter storm last more than 10 days
  • Total precipitation amount exceeding 660mm over
    the huge area
  • The homes of 100,000 residents who has been
    evacuated.
  • Some stranded residents has to be rescued by
    helicopter.
  • Caused a lot of damages include road, bridge and
    resident houses.

Photo from Washington Post
29
24 hours observation
GFS ENS
30
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31
Probabilistic Evaluation (cost-loss analysis)
  • Based on hit rate (HR) and false alarm (FA)
    analysis
  • .. Economic Value (EV) of forecasts

Ensemble forecast
Average 2-day advantage
Deterministic forecast
32
Example of cost-loss analysis (economic values)
  • Wind sheer damages the airplane
  • Un-protect airplane (loss)
  • 2-million dollars for each airplane
  • Protect airplane (cost)
  • 20,000 dollars for each airplane
  • For decision makers !!!
  • 1100 cost-loss ratio for this case
  • Probabilistic forecast and forecast reliability
  • Typhoon Mai-sha affected Beijing City
  • Un-protect (may loss)
  • Flooding, traffic and others.
  • Protect (definitely cost)
  • Activities will be cancelled
  • Labors cost
  • Others
  • Scientific decision ???
  • Anyone counts this ratio?

33
Probabilistic Evaluation (useful tools)
  • ... Small and large uncertainty.
  • 1 day (large uncertainty) 4 days (control)
    10-13 days (small uncertainty)

34
Ensemble based Probabilistic Products
  • Spaghatti diagram for uncertainty
  • Standard/normalized spread, mean
  • Relative measure of predictability (RMOP)
  • Probabilistic quantitative precipitation forecast
    (PQPF)
  • Probabilistic precipitation types
  • Calibrated PQPF
  • Hurricane tracks/strike probability
  • Anomaly forecasts
  • User specified
  • Calibration !!!

35
Ensemble Forecast for Uncertainty (1)
By Bill Bua
36
Ensemble Forecast for Uncertainty (2)
By Bill Bua
37
Ensemble Forecast for Uncertainty (3)
By Bill Bua
38
Ensemble Forecast for Uncertainty (4)
39
1. By using equal climatological bins
(e.g. 10 bins, each grid points)2. Counts of
ensemble members agree with ensemble mean, (same
bin)3. Construct n1 probabilities for n
ensemble members from (2).3. Regional (NH,
weighted) Normalized Accumulated Probabilities
(n1)4. Calculate RMOP based on (3), but 30-d
decaying average.5. Verification information
(blue numbers) historical average (reliability)
40
Ensemble mean
10 Climatological equally likely bins
Example of 1 grid point
10 ensemble forecasts
The value of ensemble members agree to ensemble
mean is 4/10 or 40 (probability) There are 10512
points ( values ) for global at 2.5 2.5 degree
resolution
10 ensemble members could construct 11
probabilities categories, such as 0/10 (0),
1/10(10), 2/10(20), 3/10(30), 4/10(40),
5/10(50), 6/10(60), 7/10(70), 8/10(80),
9/10(90), 10/10(100) Sum of each grid point for
above 11 probabilistic categories by area
weighted and normalized for global or specified
region Get 0/10 1/10 2/10 3/10
4/10 5/10 6/10 7/10 8/10 9/10
10/10 .029 .047 .077
.085 .100 .135 .116 .089 .081
.070 .177 sum of these 1.0
(1.007 here) 2.9 7.6 15.3 23.8
33.8 47.3 58.9 67.8 75.9 82.9 100
accumulated values There is 30-day decaying
average of above values ( last line ) in the
data-base and updated everyday. Assume these are
30-day decaying average values In this case,
point value is 4/10, RMOP value of this point is
33.8
41
China
42
General public for past 8 years
43
Specific request
44
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45
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46
(No Transcript)
47
Ensemble Based Hurricane Track Plots
Karl (09/18)
Frances (08/28)
48
Example of probabilistic forecast in terms of
climatology
49
ENSEMBLE 10-, 50- (MEDIAN) 90-PERCENTILE
FORECAST VALUES (BLACK CONTOURS) AND
CORRESPONDING CLIMATE PERCENTILES (SHADES OF
COLOR)
50
The pre-NWP forecast accuracy
  • A schematic illustration of the increase of
    RMSE with forecast time. The pre-NWP forecaster
    started from a persistence forecast which he
    skillfully extrapolated into the future,
    converging towards climate for longer ranges

A?2
persistence
A
meteorologist
  • The time unit can be anything from hours to
    days depending on the parameter (hours for
    clouds, days for temperature)

51
NWP more accurate - but also less
persistence
A?2
  • A good NWP model is able to simulate all
    atmospheric scales throughout the forecast. It
    has the same variance as the observations and the
    persistence forecasts, which yields an error
    saturation level 41 above the climate

worlds best NWP
A
meteorologist
52
The art of good forecasting
  • The way out of the dilemma
  • Combine the high accuracy of NWP in the
    short range with a filtering of the
    non-predictable scales for longer ranges
  • This can be done both with and without the EPS

A?2
persistence
worlds best NWP
A
meteorologist
modified NWP forecast
53
Acknowledgments
  • I benefited from discussions with Drs.
    Zoltan Toth, Richard Wobus and Hua-Lu Pan of
    EMC/NCEP/NOAA
  • We acknowledge the support and
    encouragement of Dr. Stephen Lord, Director of
    EMC/NCEP/NOAA.
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