Presentation on theme: "The Moisture-Stability Index (MSI) and its Application to Flash Flood Forecasting in the Hawaiian Islands Kevin Kodama and Robert Ballard NOAA/NWS Honolulu."— Presentation transcript:
The Moisture-Stability Index (MSI) and its Application to Flash Flood Forecasting in the Hawaiian Islands Kevin Kodama and Robert Ballard NOAA/NWS Honolulu
Outline Flash flooding in Hawaii Ingredients for flash flooding and heavy rainfall Moisture-Stability Index (MSI) MethodologyResults Summary – Application to Operations
Flash Flooding in Hawaii One of the main natural hazards in the Hawaiian Islands Responsible for most of the direct weather-related deaths in HI Feb-Mar 2006, Statewide –7 deaths, 28 events Oct 2004, Manoa –8.7/5-hrs, $100 Mil Nov 2000, Big Is –37/24-hrs (22/6-hrs) –$70 Mil
Flash Flood Warnings and Watches Flash flood warnings: Issued by NWS when flooding poses a threat to life and property and occurs within 6- hrs of cause. –Hawaii: Often less than 1-hr and sometimes less than 30- minutes of heavy rain onset –Little to no time for preparation, mainly reaction Flash flood watches: Issued by NWS when there is a possibility of flash flooding with 48-hrs. –More uncertainty but sufficient time for meaningful preparations –Preposition equipment –Lean forward posture by emergency management –Challenge to improve both accuracy and lead time
Ingredients for Flash Flooding In most cases, Hawaii flash flooding due to heavy rainfall –Rare cases: dam failure –No cases: ice jams Thus, ingredients needed are... –Rainfall of sufficient intensity and duration –Necessary hydrologic factors (antecedent moisture, basin characteristics, etc.)
Ingredients for Heavy Rainfall Moisture, always present, but... –Deep moist layer is better –Greater low level moisture more unstable Upward Motion –Provided by airmass instability –Also assisted by orographic forcing High Precipitation Efficiency –More water vapor converted to precipitation
The Moisture-Stability Index (MSI) Developed by Bob Ballard, HFO Science & Ops Officer Forecasting by anomalies Uses standardized anomalies (N) of 500 and 700 hPa temperatures and precipitable water –500 hPa: Diagnose instability aloft –700 hPa: Diagnose temp near trade wind inversion level –PW: Diagnose moisture availability Anomalies combined to produce MSI MSI = N pw - N N 700 –Negative values of 500 and 700 mb anomalies contribute positively –i.e., Cooler than normal temps increase MSI
MSI Scale Near normal: -1.5 to +1.5 Positive MSI scale –Slightly wetter than normal: +1.6 to +2.5 –Enhanced showers: +2.6 to +3.5 –Towering Cu: +3.6 to +4.5 –Thunderstorms: +4.6 to +6.5 –Wow!: >+6.5 Negative MSI scale –Slightly drier: -1.6 to -2.5 –Dry and stable: -2.6 to -4.5 –HI Visitor Bureau Wx!: <= -4.6
Study Methodology Is MSI correlated with rainfall intensity? Compare maximum rain rates from automated gage network with MSI from nearest sounding. –Lihue, Kauai sounding: 36 gages on Kauai & Oahu –Hilo, Hawaii sounding: 28 gages on Maui & Big Island –Molokai & Lanai not included Only 3 gages Which sounding?
Study Methodology cont. Rain rates obtained from gage alarms sent to WFO –15-minute sampling period –Converted to in/hr rate Maximum rain rate from all alarms on island compared to MSI from closest sounding Alarms from 06Z to 18Z vs. 12Z sounding MSI Alarms from 18Z to 06Z vs. 00Z sounding MSI 474 alarm vs. MSI data points
MSI vs Rainfall Intensity
Most intense rates have high MSI –Max rate >=3/hr, then MSI >= +3.0 (22 of 28 cases) MSI < -1.0 then all cases <3/hr max rate Many cases of high MSI but low rain –Intense rain core missed gage?
Summary Looked at utility of Moisture-Stability Index (MSI) for flash flood operations in Hawaii MSI assesses heavy rainfall potential by analyzing anomalies of 700 & 500 mb temperature and total precipitable water Study compared MSI with the max rain rate data from an automated rain gage network Moisture-Stability Index is related to max rain intensity –Max rate >=3/hr, then MSI >= +3.0 (22 of 28 cases) –MSI < -1.0 then all cases < 3/hr (and fewer alarms) Application to operations –Calculate MSI from model fields for longer range guidance –Incorporate into Gridded Forecast Editor (GFE)
MSI as GFE Guidance MSI calculated in GFE out to 10 days for GFS, ECMWF, NOGAPS models Forecasters can use MSI fields for improved situational awareness of FF potential and use it as guidance for flash flood watches
Further Study Incorporate radar data into analysis –Improved spatial representation Verification statistics of model-derived MSI –Any useful bias adjustments?