R2O | Research to Operations

In the weather enterprise, there are numerous service organizations. The services that these organizations provide vary, but generally involve analyzing, interpreting, and, in many cases, forecasting meteorological and environmental parameters. Service organizations not only exist in the private sector, but in the public sector as well. The National Weather Service and National Environmental Satellite, Data, and Information Service are large service organizations in the federal government. Service is in their name. Service organizations in scientific fields, like meteorology, can struggle to find an internal identity because, when it comes to core service improvement, there is a tension between focusing on development to better services (e.g., physical science research-based enhancements) and improvements to the delivery of those services (i.e., the nature of the communication). While rarely is a service organization in a science-related enterprise completely devoid of science, bud...

DRIP (data rich, information poor) has been used for decades to describe the ineffective or absent use of data that an organization or enterprise collects daily to inform important decisions involving certain conditions that could impact the mission of that entity. Breaking the curse of DRIP involves collecting, storing, organizing, and accessing data so that it can be converted into information. That information must subsequently be archived, analyzed, and ultimately used to create a DAIR (data and information rich) environment. Gleaning information from data is challenging, especially if the data is collected for a purpose other than converting it to information, or the information sought requires combining multiple data sources. Correcting for DRIP postures requires both technical and scientific acumen. In science, data is collected in search of information. In many cases, research is conducted to provide information from data. The R2O cycle seeks to transition the new research info...

As new data sources and the amount of unique data collected continues to expand, subjected only to technical limitations, scientific enterprises, with the weather enterprise no exception, are prone to human limitations to interpret and apply additional data to scientific questions and operational challenges. Therefore, in order to assess the value of observations or research byproducts from different sources but of the same type, it is necessary to understand how the data is used, and how often it is used in comparison to “adjacent” data. That is, the value of observational data and data conveyed in the form of research byproducts is higher if it is routinely used in scientific and/or operational pursuits and that use impacts the scientific and/or operational result. This concept should strategically drive research to operations initiatives. For example, in the weather enterprise, there are multiple sources for total precipitable water (TPW) observations. TPW is a measure of the spec...

In the field of meteorology, recent discussion has centered on whether current and evolving technologies have the capacity to push human intervention to the margins of weather forecast and warning functions. This is a loaded question, and one that has consequences for R2O and its role in improving weather services. However, the consideration and debate surrounding the transition of weather predictions from a predominantly human role to a computer capability must include a discussion of how the intersection of boundaries internal to the field and its organizations, and technology, brought the weather enterprise to its current operating state. The United States National Weather Service has long maintained a workforce of operational meteorologists that are responsible for interrogating meteorological data and formulating predictions intended for public dissemination. Over time, the amount of meteorological data has grown. There are more observations, more precise observations, and more ...

In November 2015, Google shared the dynamics of their successful teams : psychological safety, dependability, structure and clarity, meaning of work, and impact of work. Google found that the individual backgrounds of the members of the team mattered less than how the team interpreted their mission relative to the organization, and ultimately, how they behaved. Evidence clearly indicated that psychological safety was the most important. That is, trust between team members is essential in overcoming personal insecurities and the prospect of embarrassment. I have found that it is easy to remember Google’s five dynamics with the word ‘prism’, where each letter represents the first letter of one of the five dynamics (and you change dependability to its close synonym, reliability). A prism is also a great metaphor for a team, because high-functioning teams succeed at ideation and execution based on an initial thread or concept, much like a prism disperses light from a single white band to ...