Geoscience Reference
In-Depth Information
7. Conclusion
The objective of this contribution was to provide a broad overview of the use of radar and
radar networks for the provision of severe weather warnings and to very briefly describe
historical legacies and current practice. The target audience are those NHMS' who might be
contemplating developing or enhancing such a service. Weather radar clearly plays a central
role in this application. Not discussed are important applications such as nowcasting
precipitation, quantitiative precipitation estimation, wind retrieval, data assimilation for
numerical weather prediction, etc. It also does not address the convective initiation aspects
(Roberts et al, 2006; Sun et al, 1991; Sun and Crook, 1994). For a reliable warning service,
design, infrastructure (reliable power and telecommunications), support and maintenance
are critical and were not discussed in this contribution. These are major considerations but
out of scope for this contribution.
The level and nature of the service will be determined by both meteorological and non-
meteorological factors. The prevalence of severe weather, climatology and a defining event
determine the impact, the exposure and the opportunity to develop a warning service.
Socio-economic factors, risk persona, as well as the organizational structure, are particularly
important in the design and expectations for the radar processing, visualization and
dissemination systems. This contribution provided a short global survey of radar based
systems to illustrate the commonality but also the differences in implementation. One
solution does not fit all. Underlying these systems is the forecast process and it is
emphasized that they all rely on human expertise in the decision-making process and so the
human-machince mix is a critical item. This will drive the expertise and therefore the
training requirements for the severe weather analyst.
This contribution highlighted the use of automation in the production of guidance products.
Some systems rely on very little automation and totally rely on manual interpretation. All
systems, except one, default to this mode. One of most highly automated systems is CARDS
(Canada). Automation is necessary because of the need for look at details for warning
preparation purposes while maintaining situational awareness in the situation where one
forecaster is responsible for about ten radars. It processes radar data for identifying and
ranking thunderstorm cells and features. It also creates highly processed image products to
streamline and to guide the decision-making process. It still relies on human decision-
making for the final preparation of the warning. KONRAD is the only system that produces
totally automated products. However, it could be argued that these products are directed to
“sophisticated users” for their specific planning and decision-making purposes and not
warning purposes.
Given the limited space and time, all radar processing systems were inadequately described.
There is room for improvement in describing all aspects of the processing chain from better
algorithms (e.g. hail, hook echoes; Lemon, 1998; Wang et al, 2011) to advanced concepts
where thermodynamic diagnostic fields, useful for understanding, are retrieved (Sun et al,
1991; Sun and Crook, 1994). Through the description of specific innovative aspects of
individual systems, and since there are commonalities amongst them, the intent was to
provide the reader with an overview of the capabilities of all the systems. There is fine work
being done elsewhere that is not represented; to name a few, Italy, Switzerland and Finland.
Another glaring oversight is the lack of description of systems by manufacturers. Some even
