Predictability, forecastability, and observability Jim Hansen [email protected] 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 Forecast Error Predictability Forecastability Time 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 observations Average 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 Cumulative Probability of Detecting Both Threat Subs 100.0% 90.0% Tier 3 – the Decision Layer 80.0% 70.0% 60.0% 074-45W 072-50W Updated CONOPs 100 nm 50.0% • Options / Courses of Action 30 nm 29-40N 40.0% Updated CONOPs “A” “A” Updated Environment Province “A” 30.0% Original CONOPs Cumulative Probability 20.0% Updated Environment 60 SSN Area MPA Station #1 nm UPDATED UPDATED CONOPs CONOPs Original CONOPs • Quantify Risk 1800 sq nm 4200 sq nm 10.0% Historical Environment Province “B” 100 nm 0.0% 0 5 10 15 20 25 30 35 40 Hour • Asset Allocation / Timing 40 MPA Station #2 UPDATED nm CONOPs 4000 sq nm 28-00N Tier 2 – the Performance Layer Tier 1 – the (forecast) Environment Layer Fleet Data Satellites Initial and Boundary Conditions Opportunities • What about T2 and T3 in Battlespace on Demand the context of civilian aerosols? Cumulative Probability of Detecting Both Threat Subs 100.0% 90.0% Tier 3 – the Decision Layer 80.0% 70.0% 60.0% 074-45W 072-50W Updated 100 nm 50.0% CONOPs • Options / Courses of Action 30 nm Updated CONOPs 29-40N 40.0% “A” “A” Updated Environment Province “A” 30.0% Original CONOPs Cumulative Probability 20.0% Updated Environment 60 nm SSN Area MPA Station #1 UPDATED UPDATED Original CONOPs CONOPs CONOPs • Quantify Risk 1800 sq nm 4200 sq nm 10.0% Historical Environment Province “B” 100 nm 0.0% 0 5 10 15 20 25 30 35 40 Hour • Asset Allocation / Timing 40 MPA Station #2 UPDATED • T2 examples nm CONOPs 4000 sq nm • Air quality 28-00N • Volcanic ash impacts Tier 2 – the • T3 examples Performance Layer • Advisories • Policy Tier 1 – the (forecast) Environment Layer Fleet Data Satellites Initial and Boundary Conditions Global Aerosol Community? Tier 3: Safety of navigation/operations Air quality mitigation/permitting Climate change mitigation/adaptation Tier 2: PM2.5/PM10 Plume locations Radiative forcing Tier 3: Operational forecasts models Long term re-analyses Climate model predictions Same as DoD construct: many data providers, models, required Field Climate Operational Ground performance metrics. Ultimately, Experiments Satellite Satellite Networks need to aid many customers Products Products 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? .