Andrea Molod Global Modeling and Assimilation Office [email protected]With: Haydee Salmun, Frank Buonaiuto, K. Wisniewska (student) - Hunter College of CUNY Localized Characterization of Winter Storms in the New York Metropolitan Area and Statistical Prediction of Associated Storm Surge NASA Goddard Space Flight Center GLOBAL MODELING AND ASSIMILATION OFFICE
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Andrea Molod Global Modeling and Assimilation Office [email protected] With: Haydee Salmun, Frank Buonaiuto, K. Wisniewska (student) - Hunter College.
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With: Haydee Salmun, Frank Buonaiuto, K. Wisniewska (student) - Hunter College of CUNY
Localized Characterization of Winter Storms in the New York
Metropolitan Area and Statistical Prediction of Associated Storm
Surge
NASA Goddard Space Flight CenterGLOBAL MODELING AND ASSIMILATION OFFICE
Outline
• Context and motivation
• Identifying and characterizing winter storms
• Storm impacts - beach erosion and storm surge
• Statistical prediction of surge
East Coast Cool-weather Storms (ECCS) – storms that impact the northeast
• “Nor’easter” (“Hatteras Bomb”)
• “Alberta Clipper”
• “Gulf Low”
• “Colorado Low”
• “Chatanooga Choo Choo”
• ~1000 km dia.
• warm and cold fronts
• major weather producers
East Coast
FL/Bahamas
Gulf
Alberta
Colorado
Nevada
Regions of Cyclogenesis (Davis and FitzGerald, 2004)
Context and Motivation
Feb 27 – March 7, 1717 –series of stormsJanuary 28-? ,1772 - 3 ft in Baltimore – (Washington-Jefferson Storm)February 2-4, 1886March 27-28, 1891 March 15-18, 1892 February 8, 1899 February 11-13, 1899February 16-18, 1900December 22-23, 1908January 27-28, 1922 - (Knickerbocher Storm)January 29-30, 1930December 17, 1932February 7, 1936March 28-29, 19421950 Nov. 25–2718–19 Mar 1956 28,588 32.814–17 Feb 1958 126,004 53.818–21 Mar 1958 62,103 40.72–5 Mar 1960 133,734 53.911–13 Dec 1960 74,528 48.018–21 Jan 1961 62,260 43.02–5 Feb 1961 112,171 50.3March 5-7, 1962 11–14 Jan 1964 110,258 48.829–31 Jan 1966 122,452 23.823–25 Dec 1966 83,389 18.15–7 Feb 1967 50,896 44.88–10 Feb 1969 66,440 31.222–28 Feb 1969 48,370 10.325–28 Dec 1969 131,351 25.018–20 Feb 1972 140,869 24.51977Jan. 28–29
Knickerbocker Snowstorm (NOAA Photo Library).
Kocin and Uccellini 2004: Estimated area (X 103 mi2) and population (in millions, from the 1999 census) affected by snowfall accumulations of 10 in. (25 cm) and greater
19–21 Jan 1978 161,583 50.95–7 Feb 1978 120,490 47.617–19 Feb 1979 56,923 31.56–7 Apr 1982 76,839 22.510–12 Feb 1983 111,129 51.421–23 Jan 1987 132,772 34.925–26 Jan 1987 38,008 11.522–23 Feb 1987 28,276 16.6November 11, 198712–14 Mar 1993 212,594 59.98–12 Feb 1994 54,951 39.02–4 Feb 1995 97,971 29.96–8 Jan 1996 137,918 56.631 Mar–1 Apr 1997 32,021 13.024–26 Jan 2000 59,567 19.730–31 Dec 2000 56,484 28.0February 15-18, 2003 Feb 11-13, 2006 Dec 16-20 2009Feb 5-10 2010 Feb 9-10 2010
Context and Motivation
Characterization of East Coast Cool-Weather Storms
Beach erosion: Tiana, Shinnecock, East Hampton, LI.
Identification and Characterization of ECCSs
Station # Location Record Length
44004 38.48 N 70.43 W 1977-2007
44025 40.25 N 73.17 W 1991-2007
44017 40.69 N 72.05 W 2002-2007
• Compute multi-year average and standard deviation of sea level pressure.
• Storm is identified as an event when the pressure is less than two standard deviations below the mean, the minimum lasts for more than 4 hours and is separated from another event by at least 20 hours.
•Exclude tropical storms from record using data from National Hurricane Center lists of hurricanes and tropical storms. Confirm storm event using daily weather maps
• Classify storms - 3 levels of intensity - using pressure tendencies.
Identification of ECCSs - Method
40
20
0
-20
-40
90 95 ‘00 ‘05
90 95 ‘00 ‘05
40
20
0
-20
-40
NDBC 44004 – 195 ECCSs ‘91-’07
NDBC 44025 – 206 ECCSs ’91-’07
Time Series of Pressure Anomaly
Possible Storm Events - Tropical or Extratropical:
Tropical eliminated from National Hurricane Center data
Identification of ECCSs - Method
--> average of 11 - 12 storms per year - very good agreement with previous work (Hirsch, et al., 2001)
NDBC Station 44004
Storm Class Pressure Tendency Storm Count ‘91-’07 Storm Count ‘77-’07
Level 1 PT < 0.34 mb/hr 14 25
Level 2 0.34 ≤ PT <1.30 mb/hr 156 251
Level 3 PT ≥ 1.30 mb/hr 23 41
NDBC Station 44025
Storm Class Pressure Tendency Storm Count ‘91-’07
Level 1 PT < 0.31 mb/hr 17
Level 2 0.31 ≤ PT < 1.16 mb/hr 159
Level 3 PT ≥ 1.16 mb/hr 32
Categorize according to “how quickly the low deepens”, (Pressure Tendency)
• Compute mean pressure tendency and standard deviation
• Three storm intensity classes – based on terciles of standard deviation
Characterization of ECCSs – Intensity Scale
Typical ECCSs of Different Intensity Classes Approximate location of NBDC Sta.
44025 is indicated by a X on the maps
Level 3Level 2Level 1
xxxxxx
Similar annual distribution of storm counts - max in Jan, min in summer months
Differences between interannual distributions (more variability at 44004) – record too short at 44025 to characterize ENSO or NAO related variability
a)
d)c)
b)
Station 44025
Station 44004
Annual and Interannual Variability
Mean ECCS Characteristics for NDBC Station 44004 – Period 1977-2007
Relationship between intensity and surge is robust
Statistical Models based on obs and/or relationships derived from simplified theory:
• Tancredo (1958) - Regression equation relating significant wave height and storm surge, significant wave heights derived from simplified theory that relate wave heights to winds.
• Harris (1962) - regression equation based on a “meteorological factor”, chosen based on linearized two-dimensional hydrodynamic equations.
• Pore et al. (1974) - Regression equation (for New York) based on atmospheric pressure at eight grid points with time lags ranging from 0 to 6 hours.
• DeGaetano (2008) - extreme surge events based on storm strength (criterion based on meteorological conditions)
Dynamical models:
• ET-SURGE - NOAA’s forecast model for extratropical storm surge (Ji et al., 2010). Operational forecasts include a simple bias correction.
• ADCIRC - Advance Circulation Model for Coastal Ocean Hydrodynamics of Luettich et al. (1992). Uses steady state, barotropic equations, grid designed for simulation of flooding during storm events (Colle et al., 2008). Run at SUNYSB.
• ECOM - Estuarine Coastal and Ocean Model of Blumberg and Mellor (1987). Run by the New York Harbor Observing and Prediction System.
Found for predictions of “storm maximum storm surge”:
• SSMAX based linearly on significant wave height alone is statistically indistinguishable from any other combination at both buoys • Using data from the closer buoy (44025) produces a statistically better prediction of SSMAX.
; (meters)
with RMS error 0.145 m.
SSMAX = “storm maximum” storm surge, is the maximum value of storm surge reached during the storm period
H = storm composite significant wave height, is the average of the ‘top third’ largest significant wave heights during the storm
0412.01961.0 44025 −= HSSMAXTheBattery
Statistical Model
-> Evaluate predictive capability of this statistical model
Retrospective forecasts (NAM-WRF & NOMADS) of sea level pressure at location of NDBC Station 44025 for the period Feb 2005 - Dec 2008
Retrospective forecasts of significant wave heights from NOAA’s WAVEWATCH IIITM output at location of NDBC Station 44025
NOAA ET-SURGE standard forecast of surge values at The Battery, and NOAA operationalforecasts
Observed storm surge calculated from water level data at The Battery for the period 1959 – 2008 obtained from NOAA
Results based on 41 stormsNDBC Station 44025
The Battery
Locations of The Battery and NDBC station 44025
Statistical Model - Data used for Evaluation
Sea Level Pressure at Buoy 44025List of StormsThreshold algorithm
to identify storms
Wind, wave data at Buoy 44025 Compute storm
composite values
Storm surge (computed) at The Battery
Perform regression
analysis
Statistical Model for Storm-max Surge at The Battery
NOAA ET-SURGE predictions are from direct (archived) output from ET-SURGE.
NOAA’s operational forecast (NOAA ETANOM) consists of the ET-SURGE output and an error correction (not in archived data), computed as the 5-day running mean of the previous days’ errors of ET-SURGE output.
Variability in the anomaly correction is an indication of the variability in the error of ET-SURGE model output (range of anomaly: -0.13 m – 0.28 m)
• statistical forecast (STAT FCST)• NOAA ET-SURGE output (NOAA ET)• NOAA operational forecast (NOAA ETANOM)• observations at The Battery (OBS)
Statistical Model - Evaluation
* Values of OBS SSMAX are always positive
* SSMAX estimates using STAT FCST are always positive
* OBS SSMAX range: 0.1 m – 0.92 m
* Range of SSMAX from STAT FCST: 0.17 m – 0.83 m
* SSMAX from ET-SURGE model output can be negative (4 cases)
* SSMAX from ET-SURGE operational fcst negative only once
* Range of SSMAX from ET-SURGE model output: -0.28 m – 0.72 m
* Range of SSMAX from ET-SURGE operational fcst: -0.19 m – 0.84 m
Differences among the different estimates of SSMAX
* SSMAX from NOAA-ET is underpredicted by 0.25 m ( = 0.11 m)
* SSMAX from NOAA-ETANOM is underpredicted by 0.14 m ( = 0.11 m)
* Mean error of SSMAX from STAT FCST is 0.05 m ( = 0.15 m)
Lead Time
Statistic STAT – OBS NOAA ET – OBSNOAA ETANOM –
OBS
12-hourMean (m) 0.0534 -0.2477 -0.1459
STD 0.1591 0.1186 0.1151
24-hourMean (m) 0.0927 -0.2346 -0.121
STD 0.1597 0.1266 0.126
48-hourMean (m) 0.0418 -0.2713 -0.137
STD 0.1341 0.1346 0.1474
Statistical Model - Results of Evaluation
From statistical analysis for 12-hr lead time:
* error in NOAA-ET SSMAX is greater than the error in STAT FCST SSMAX at > 95% significance level
* error in SSMAX from NOAA-ETANOM is statistically indistinguishable from error in STAT FCST of SSMAX
Characterization of the STAT FCST error:
Can distinguish between error in STAT FCST due to failure of the regression relation and error in STAT FCST due to wave height forecast errors -- use standard deviation of the regression as the metric.
* STAT FCST tends to slightly underpredict or overpredict on average
•underpredictions --> errors in forecasted significant wave heights
•overpredictions --> failure of the regression relation
* NOAA forecasts, both with and without anomaly, tend to underpredict
Statistical Model - Results of Evaluation
In Conclusion: we established that the statistical method for predicting “storm-maximum” storm surge (SSMAX) is robust
in two thirds or more of the cases, using predicted values of wave height does not have a negative impact on the statistical estimate of SSMAX
evaluation of 12-, 24- and 48-hr lead time STAT FCST predictions showed it to outperform NOAA’s ET-SURGE model forecasts and be equivalent in skill to NOAA’s (bias-corrected) operational forecast
the lead time of NOAA’s operational forecast is limited due to the form of the bias correction – STAT FCST does not have this limitation and we propose that it could be used in conjunction with ET-SURGE forecasts.
Salmun, H., A. Molod, F. Buonaiuto, K. Wisniewska and K. Clarke (2009): East Coast Cool-weather Storms in the New York Metropolitan Region. Journal of Applied Meteorology and Climatology, 48, 11 2320-2330.
Salmun, H., A. Molod, K. Wisniewska and F. Buonaiuto (2010): Statistical Prediction of the Storm Surge Associated with Cool Weather Storms at The Battery, New York. Journal of Applied Meteorology and Climatology; In Press
Future Work
Expand spatial scope – how robust spatially is our regression?
• Evaluation of validity of regression coefficients for predicting SSMAX at The Battery using wave heights from other nearby buoys
• Evaluation of validity of regression coefficients for predicting SSMAX at other water level measurement locations
x
x
x approximate location of NDBC station 44025 approximate location of storm’s center
Typical sea level pressure fields from NASA’s MERRA re-analysis (~ 0.5° resolution) showing typical patterns of storms for which error in STAT FCST SSMAX are small (top – stronger storms with centers passing directly over area) and those for which errors are large (bottom – weaker storms, centers passing farther away
Future Work - Characterization of STAT FCST Error
Also: re-evaluate role of other predictors (we were surprised by what was NOT important as a predictor)