The Safety Analysis and Risk Mitigation Process  Section 3  Service  Functionality  Failure  Condition/Hazard  Environment /  System State  Effect  Worst Credible  Severity/Rating  Weather  Adverse  weather  information  (Adverse  weather  includes wind  shear,  thunderstorms,  icing, IMC, etc.)  Loss of function  All  Adverse  weather  information  reported as  unavailable  Minimal  Flight crew  inconvenience  Malfunction: failure to detect  All  Adverse  weather not  reported  Major  Significant  reduction in  safety margin  Malfunction:  false detection  All  Adverse  weather falsely  reported  Minimal  Flight crew  inconvenience  3.5.4.3.1  Likelihood versus Frequency  3.5.4.3  Determining Likelihood  3.5.4.3.2  What to Consider When Defining Likelihood  2. For guidance on how to design and conduct modeling in support of safety risk analyses, refer to Air Traffic Safety Oversight Service Safety Oversight Circular 07-05A,  Guidance on Safety Risk Modeling and Simulation of Hazards and Mitigations . 47 3.5_SMSM_201904  Originally published April 2019  Uncontrolled copy when downloaded Frequency and Modeling  Likelihood  is defined as the estimated probability or frequency, in quantitative or qualitative  terms, of a hazard’s effect or outcome. More specifically, the concept of likelihood can be  separated into two components: likelihood/probability and frequency. Frequency  is how often a  given effect occurs; it is a known value determined (for example) by monitoring a hazard and its  effects to identify initial, current, or residual risk (see  Section 4.3.1 and Section 4.3.4). Conversely, likelihood is an expression of the probability of a hazard’s effects occurring (i.e., a  rate of how often a given effect is expected to occur), which is used to estimate initial and  predicted residual risk. Provide a rationale for likelihood estimations in the HAW. Frequency is sometimes used to help estimate likelihood, but historical data do not always  represent future conditions. Historical frequency may be zero for a given procedure, but that  does not mean that the future likelihood is also zero. For example, a facility may conduct a  procedure that has unreported incidents that could lead to an undesirable outcome, such as a  loss of separation or a collision. Likewise, a facility may not have encountered the scenario or  system state that exposes the more severe outcome. Consider all potential effects that are  derived from indicators of the operation in all credible scenarios. This practice is required to  challenge the philosophy of, “It has not happened in the past, so it will not happen in the future.”  When possible, use modeling to examine the effects of hazards that are too rare to have  significant historical statistical data available. 2 If modeling is required and data are available,  the risk assessment should be based on statistical or observational data (e.g., radar tracks). Where there are insufficient data to construct statistical risk assessments, input from Subject  Matter Experts (SMEs) can be used. This means that if the true rate of a particular type of  operation is unknown, it can be estimated using expert judgment. It is important to note that  complex proposed NAS changes, such as changes to separation standards, require quantitative  data to support the associated risk analysis.