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““Precipitable Water Values Precipitable Water Values Associated with Recent Flood Associated with Recent Flood
Events in Southeast Texas”Events in Southeast Texas”
Paul LewisPaul Lewis
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Outline For Today• Few introductory itemsFew introductory items
– What is PW?What is PW?
– Heavy rainfall forecasting methodsHeavy rainfall forecasting methods
– SE Texas “recent” heavy rainfall eventsSE Texas “recent” heavy rainfall events
• A study of two events utilizing a PW statistical A study of two events utilizing a PW statistical analysis method (from WFO Rapid City, SD)analysis method (from WFO Rapid City, SD)
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What is PW? – From the AMS “Glossary of Meteorology”http://amsglossary.allenpress.com/glossary
• ““The total precipitable water is that contained in a column The total precipitable water is that contained in a column of unit cross section extending all of the way from the of unit cross section extending all of the way from the earth's surface to the ‘top’ of the atmosphere”earth's surface to the ‘top’ of the atmosphere”
• Utilizing inches & millimeters in this presentationUtilizing inches & millimeters in this presentation
– “W” is total precipitable water vapor– “g” is acceleration of gravity– “x(p)” is the mixing ratio at pressure “p”– “p1” & “p2” define a pressure layer
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A Word of Caution• Rainfall in convective events often exceed PWRainfall in convective events often exceed PW
→ Convergence of water vapor is frequently quite largeConvergence of water vapor is frequently quite large
• The AMS “Glossary of Meteorology” states:The AMS “Glossary of Meteorology” states:““Nevertheless, there is general correlation between Nevertheless, there is general correlation between precipitation amounts in given storms and the precipitation amounts in given storms and the precipitable water vapor of the air masses involved precipitable water vapor of the air masses involved in those storms.”in those storms.”
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Heavy Rainfall Forecasting• ““No one method can be utilized by itself without No one method can be utilized by itself without
consideration of all other patterns” (T.W. Funk)consideration of all other patterns” (T.W. Funk)
– Implies that Implies that pattern and event recognition is requiredpattern and event recognition is required
• For this presentation, cases with heavy rainfallFor this presentation, cases with heavy rainfall
• Note: High PW can occur without rainfallNote: High PW can occur without rainfall
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Using 2SD and PW MAX – An Empirical Observation• 95% of the values lie within ±2SD of the mean value (Bunkers, WFO UNR)
→This implies that PW +2SD is a fairly rare event (Bunkers, WFO UNR)
→ Thus, PW MAX would be a very rare event
See: http://www.crh.noaa.gov/unr/?n=pw
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Other Items to Keep in Mind• Other heavy rainfall forecasting rules of thumb could be considered
– PW & 70% Thickness saturation (T.W. Funk 1991)– Flash Flood Decision Tree (HGX/SIL)– Ingredients-Based Methodology (Doswell et al 1996)
• Upper air sounding data records have systematic observational errors in humidity (Wang and Zhang 2007)– Includes sensor errors and external factors, some due to human error
• Sounding data time and spatial gaps can be filled with GPS data– GPS data can contain errors but can be more reliable than model data
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SE Texas Heavy Rainfall Events
“Droughts are broken by floods”
• Data selection – A headache!
• Utilizing a 24-hour calendar day lower limit of 4 inches gives dozens of examples at just the official Houston observing sites since 1900
9Courtesy of the NOAA Regional Climate Centers – http://xmacis.rcc-acis.org/HGX/
77 cases of 4-inch or greater rainfall in a 24-hour period
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Recent (10-Year) SE Texas Heavy Rainfall Events
• Major events do not always cover the entire CWA– Plus, events may be missed by the official observing sites
• Rainfall data from just the official Houston observing site presents a large number of cases
11Courtesy of the NOAA Regional Climate Centers – http://xmacis.rcc-acis.org/HGX/
12Courtesy of the NOAA Regional Climate Centers – http://xmacis.rcc-acis.org/HGX/
14 cases of 4-inch or greater rainfall
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For this presentation:
– June 2001 – TS Allison (no GPS data available)
– Two 2009 events show GPS data importance:
• April 17
• April 18
• Rain band & core rain events• PW statistical method corresponded
well to the heaviest rainfall locations
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Tropical Storm Allison – June 2001
Clear Creek flooding at FM-528
Buffalo Bayou flooding near downtown Houston
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PW (inches) for 04 – 10 June 2001CRP LCH SHV FWD
MAX / +2 SD 2.65 / 2.25 2.55 / 2.25 2.45 / 2.15 2.45 / 1.90
6/4 – 00Z / 12Z 1.48/ 1.41 1.81 / 1.91 1.39 / 1.51 1.24 / 1.24
6/5 – 00Z / 12Z 1.56 / 1.70 1.70 / 2.06 1.44 / 1.65 1.30 / 1.43
6/6 – 00Z / 12Z 1.89 / 1.62 2.20 / 2.16 2.07 / 2.17 1.42 / 1.47
6/7 – 00Z / 12Z 1.73 / 1.72 1.86 / 2.64 2.09 / 2.08 1.65 / 1.94
6/8 – 00Z / 12Z 2.03 / 1.87 1.87 / 2.38 2.03 / 2.02 1.76 / 1.74
6/9 – 00Z / 12Z 2.06 / 1.97 2.09 / 2.39 MM / 2.02 1.65 / 1.51
6/10 – 00Z / 12Z 1.77 / 1.60 2.28 / 2.31 1.75 / 1.42 1.10 / 1.30
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KHGX Storm Total Precipitation
Allison Event 4 – 10 June 2001
Houston Area Rainfall•24 hour total Saturday 9 June•5-day total 4 – 9 June 2001
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17 & 18 April 2009 Events• PW from CRP (2SD=1.75) and LCH (2SD=1.85)• GPS data available• Note. . .
→ PW at upper air sites low (limitations in time/space)
→ GPS data filled in the time and space gaps
→ PW Statistical Method indicated good flood potential
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mm: 35 37 39 41 43 45 47 49 51 53 55inches: 1.38 1.46 1.54 1.61 1.69 1.77 1.85 1.93 2.01 2.09 2.17
7:15 AM CDT17 April 2009
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mm: 35 37 39 41 43 45 47 49 51 53 55inches: 1.38 1.46 1.54 1.61 1.69 1.77 1.85 1.93 2.01 2.09 2.17
10:15 AM CDT17 April 2009
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mm: 35 37 39 41 43 45 47 49 51 53 55inches: 1.38 1.46 1.54 1.61 1.69 1.77 1.85 1.93 2.01 2.09 2.17
1:15 PM CDT17 April 2009
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mm: 35 37 39 41 43 45 47 49 51 53 55inches: 1.38 1.46 1.54 1.61 1.69 1.77 1.85 1.93 2.01 2.09 2.17
4:15 PM CDT17 April 2009
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mm: 35 37 39 41 43 45 47 49 51 53 55inches: 1.38 1.46 1.54 1.61 1.69 1.77 1.85 1.93 2.01 2.09 2.17
7:15 PM CDT17 April 2009
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mm: 35 37 39 41 43 45 47 49 51 53 55inches: 1.38 1.46 1.54 1.61 1.69 1.77 1.85 1.93 2.01 2.09 2.17
7:15 AM CDT18 April 2009
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mm: 35 37 39 41 43 45 47 49 51 53 55inches: 1.38 1.46 1.54 1.61 1.69 1.77 1.85 1.93 2.01 2.09 2.17
10:15 AM CDT18 April 2009
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mm: 35 37 39 41 43 45 47 49 51 53 55inches: 1.38 1.46 1.54 1.61 1.69 1.77 1.85 1.93 2.01 2.09 2.17
1:15 PM CDT18 April 2009
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mm: 35 37 39 41 43 45 47 49 51 53 55inches: 1.38 1.46 1.54 1.61 1.69 1.77 1.85 1.93 2.01 2.09 2.17
3:15 PM CDT18 April 2009
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mm: 35 37 39 41 43 45 47 49 51 53 55inches: 1.38 1.46 1.54 1.61 1.69 1.77 1.85 1.93 2.01 2.09 2.17
3:45 PM CDT18 April 2009
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mm: 35 37 39 41 43 45 47 49 51 53 55inches: 1.38 1.46 1.54 1.61 1.69 1.77 1.85 1.93 2.01 2.09 2.17
4:15 PM CDT18 April 2009
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mm: 35 37 39 41 43 45 47 49 51 53 55inches: 1.38 1.46 1.54 1.61 1.69 1.77 1.85 1.93 2.01 2.09 2.17
7:15 PM CDT18 April 2009
17 & 18 April 2009
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Date Time (UTC) LCH/CRP 2SD PW LCH CRP GPS PW Range
4/17 12Z 1.75 / 1.85 0.82 1.28 1.38 to 1.77
4/17 15Z 1.75 / 1.85 — — 1.38 to 1.69
4/17 18Z 1.75 / 1.85 — — 1.50 to 1.89
4/17 21Z 1.75 / 1.85 — — 1.57 to 1.85
4/18 00Z 1.75 / 1.85 0.78 1.22 1.38 to 1.81
4/18 12Z 1.75 / 1.85 1.01 1.06 1.38 to 1.69
4/18 15Z 1.75 / 1.85 — — 1.38 to 1.97
4/18 18Z 1.75 / 1.85 — — 1.54 to 1.93
4/18 20Z – 21Z 1.75 / 1.85 — — 1.65 to 2.09
4/19 00Z 1.75 / 1.85 1.64 1.17 1.57 to 1.85
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Observed Rainfall
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Observed Rainfall
My home
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Conclusion• The PW Statistical Method can give a general idea of the The PW Statistical Method can give a general idea of the potentialpotential for for
a heavy rainfall eventa heavy rainfall event– However, remember that pattern and event recognition is requiredHowever, remember that pattern and event recognition is required
• A PW value close to 2SD above normal is a fairly rare eventA PW value close to 2SD above normal is a fairly rare event– A PW value near the PW MAX is a very rare eventA PW value near the PW MAX is a very rare event
• The 12Z and 00Z upper air sounding network is limitedThe 12Z and 00Z upper air sounding network is limited– GPS data can be utilized to fill in the PW picture both on the temporal and GPS data can be utilized to fill in the PW picture both on the temporal and
spatial scalesspatial scales
Research and Data Sources• GPS data – GPS data – http://www.unidata.ucar.edu/data/suominet/gif/http://www.unidata.ucar.edu/data/suominet/gif/• PW study graphs – PW study graphs – http://www.crh.noaa.gov/unr/?n=pw http://www.crh.noaa.gov/unr/?n=pw • Rainfall dataRainfall data
– http://xmacis.rcc-acis.org/HGX/http://xmacis.rcc-acis.org/HGX/– http://hurricane.ncdc.noaa.gov/pls/plhas/has.dsselecthttp://hurricane.ncdc.noaa.gov/pls/plhas/has.dsselect
• Radiosonde dataRadiosonde data– Wang, J., and Zhang, L., 2007: “Systematic Errors in Global Radiosonde Precipitable Wang, J., and Zhang, L., 2007: “Systematic Errors in Global Radiosonde Precipitable
Water Data from Comparisons with Ground-Based GPS Measurements,” Water Data from Comparisons with Ground-Based GPS Measurements,” Journal of Journal of ClimateClimate, Volume 21, pp 2218–2238, Volume 21, pp 2218–2238
• Upper air dataUpper air data– http://vortex.plymouth.edu/get_raob-u.htmlhttp://vortex.plymouth.edu/get_raob-u.html– http://weather.uwyo.edu/upperair/sounding.htmlhttp://weather.uwyo.edu/upperair/sounding.html
• Flash Flood StudiesFlash Flood Studies– Doswell, C.A., Brooks, H.E., and Maddox, R.A., 1996: “Flash Flood Forecasting: An Doswell, C.A., Brooks, H.E., and Maddox, R.A., 1996: “Flash Flood Forecasting: An
Ingredients-Based Methodology,” Ingredients-Based Methodology,” Weather and ForecastingWeather and Forecasting, Volume 11, pp 560–581, Volume 11, pp 560–581– Funk, T. W., 1991: “Forecasting Techniques,” Weather and Forecasting, Volume 6, Funk, T. W., 1991: “Forecasting Techniques,” Weather and Forecasting, Volume 6,
pp 548–564pp 548–56434
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