“Code Red” – Public Warning in Operation “Cast Lead” ISMOR 2009

1. “Code Red” –Public Warning in Operation “Cast Lead” ISMOR 2009 Lt.Col. Ami Mizrahi, M.Sc. Center for System Analysis Planning Division IDF

2. Background • In operation “Cast Lead” Hamas fired rockets on civilians • Protection based on • “Most Protected Room” • Concrete room / Inner room / Lower floors / Staircases • Alerting the population • Sensor constellation / Sirens

3. The Dilemma • Increase alert probability • Maximize P alert • Minimal distraction of civilian life under ongoing rocket attacks • Minimize P unnecessary alert • Analogous to Pfa and Pd

4. Protection Policy The “Blitz” on London Yom Kippur War Desert Storm 2nd Lebanon War Cast Lead Partial Alert “Stay in Shelters” Alert Everyone Focused Alert Halt Civilian Routine Psychological Impact 1940 1973 1991 2006 2009

5. Desert Storm 1991 • 39 Al-Hussein Missiles (SCUD) were launched at Israel • Israel was divided into 6 alert zones • “Sealed Room” • Gas Masks • Every missile caused ~1/3 of Israel’s population to be alerted (~2M)

6. 2nd Lebanon War 2006 • ~4,000 rockets were fired at Israel • Improved hit predictions • New sensors • More Public Warning Zones • On the average ~100K people were alerted

7. Prior to Operation “Cast Lead”2001-2008 • Experience gathered for 8 years 2001-2008 • A few rockets per day - 4000 rockets over the years • Order of magnitude increase in # of zones • Order of magnitude decrease in # of people in a zone • Improved hit prediction • Implement relevant alerting logic

8. Logic for Improvised Rockets

9. Operation “Cast Lead” Dec. 27th, 2008 - Jan. 17th, 2009 • During “Cast Lead” ~1000 Rockets and Mortars Fired • New threats - longer range rockets • Alerting logic problematic for longer range rockets

10. Longer Threats -> more Unnecessary Alerts

11. Longer Threats -> more Unnecessary Alerts

12. Logic Change Vector of launch Hit Area Prediction

13. What was done? • Empiricalestimation of hit prediction accuracy • Why empirical? • Non-standard rockets • Adaptation to theater (sensor combination, specific locations…) • Define MOEs • Palert • Number of people affected by the Unnecessary Alerts • Define new logic • Calibrate parameters • Test new logic on new cases (Validation) • Test stability of new logic • “Go to the decision makers”

14. Research Timeline ~ 3 Days ~ 1 Week ~ 3 Days ~ 1 Week Identify Problem Collect Data Change Code Test Stability & Validate Approval End of Operation

15. Research Difficulties • Data collection Vs. Real time crisis management • Hard to get data from rescue personnel • Rocket location • Low priority to locate rockets falling outside residential areas • Possible sample skewing • Numerous authorities • Military / Civilian - Police, Intel, Home Front Command, Our teams • Very Noisy Data • Limited accuracy of data • Cross-check the data. Go to the field. • Limited High Level Attention

16. Summary • Basic dilemma remains: • P alert <> P unnecessary alerts • Requirement for flexibility for the alert system • Alert Time <> Accuracy • Local optimizations based on scenario • Real time Alerting Zone Control • Active Defense (intercept) poses more questions: • Danger from Debris • OR During Hostilities • Improved Data Collection During Hostilities • Relevant research time