predictability, forecastability, and observability
TRANSCRIPT
Predictability, forecastability, and observability
NRL Probabilistic-prediction Research Office
1
Predictability• The rate (or factor) of divergence of nearby
trajectories (norm dependent) • An intrinsic, yet unknowable, system property• The aim is to learn about the system dynamics
by asymptoting towards the system’s intrinsic predictability.
• Ultimate aim is to attempt to increase forecast skill
• Inherently probabilistic
A spectrum of -abilities
• Predictability– How trajectories of the true system diverge
• Model predictability– How trajectories of a given model diverge
• Forecastability– How a model trajectory diverges from a true
system trajectory
ForecastError
Time
Predictability
Forecastability
Back story• Aerosols are an important parameter for Navy
operations.• Aerosol estimation and prediction is a hard problem
fraught with uncertainties.• The NRL Probabilistic-prediction Research Office aims
to advance the estimation, communication, and use of METOC uncertainty for improved science and improved decision making.
• Got to talking with Jeff and Doug about predictability and uncertainty, but quickly learned that there are extreme observational challenges as well.
Observability• The ability to estimate the system state through
indirect observations– Predictability studies can tell us how close our initial
conditions need to be to the “true” state in order to produce useful forecasts
– Observability speaks to constraining the initial state to the necessary level
• what we need to observe • with what accuracy• with what spatial distribution • with what frequency
Indirect: sparse observationsAverage AOD correlation length scales (km) for a single 12hr forecast
Indirect: correlated variables
COAMPS: Correlation between AOD at a point and 10m dust concentration
Battlespace on Demand
Tier 3 – the Decision Layer• Options / Courses of Action• Quantify Risk• Asset Allocation / Timing
Tier 2 – the Performance Layer
Tier 1 – the (forecast)EnvironmentLayer
Initial and Boundary ConditionsSatellitesSatellites
Fleet DataFleet Data
40nm
100 nm
SSN AreaUPDATEDCONOPs
1800 sq nm
MPA Station #1UPDATEDCONOPs
4200 sq nm
MPA Station #2UPDATEDCONOPs
4000 sq nm
100 nm
28-00N
29-40N
60nm
30 nm
074-45W 072-50W
Province “B”
Province “A”“A” “A”
Updated CONOPs
Cumulative Probability of Detecting Both Threat Subs
0.0%
10.0%
20.0%
30.0%
40.0%
50.0%
60.0%
70.0%
80.0%
90.0%
100.0%
0 5 10 15 20 25 30 35 40
Hour
Cum
ulat
ive
Prob
abili
ty
Updated CONOPsUpdated EnvironmentOriginal CONOPsUpdated EnvironmentOriginal CONOPsHistorical Environment
Opportunities
Battlespace on Demand
Tier 3 – the Decision Layer• Options / Courses of Action• Quantify Risk• Asset Allocation / Timing
Tier 2 – the Performance Layer
Tier 1 – the (forecast)EnvironmentLayer
Initial and Boundary ConditionsSatellitesSatellites
Fleet DataFleet Data
40nm
100 nm
SSN AreaUPDATEDCONOPs
1800 sq nm
MPA Station #1UPDATEDCONOPs
4200 sq nm
MPA Station #2UPDATEDCONOPs
4000 sq nm
100 nm
28-00N
29-40N
60nm
30 nm
074-45W 072-50W
Province “B”
Province “A”“A” “A”
Updated CONOPs
Cumulative Probability of Detecting Both Threat Subs
0.0%
10.0%
20.0%
30.0%
40.0%
50.0%
60.0%
70.0%
80.0%
90.0%
100.0%
0 5 10 15 20 25 30 35 40
Hour
Cum
ulat
ive
Prob
abili
ty
Updated CONOPsUpdated EnvironmentOriginal CONOPsUpdated EnvironmentOriginal CONOPsHistorical Environment
• What about T2 and T3 in the context of civilian aerosols?
• T2 examples• Air quality• Volcanic ash impacts
• T3 examples• Advisories• Policy
Global Aerosol Community?
Field Experiments
Climate Satellite Products
Ground Networks
Operational Satellite Products
Tier 3:Safety of navigation/operationsAir quality mitigation/permittingClimate change mitigation/adaptation
Tier 2:PM2.5/PM10Plume locationsRadiative forcingTier 3:Operational forecasts models Long term re-analysesClimate model predictions
Same as DoD construct: many data providers, models, required performance metrics. Ultimately, need to aid many customers
Questions• Science
– What types of observing systems or networks should be put in place to best advance the science and/or forecast products?
– What are the relevant prediction problems and norms?
• Culture– Does the aerosol community really care about “broader impacts”,
or are the basic science questions motivation enough?
• Strategy– How far should the aerosol community move in the T2/T3
direction? Is throwing your product over the fence good enough, or do you need stronger T2/T3 links to justify and motivate your T0/T1 efforts?