Use case: winter maintenance of highways infrastructure - decision support for de-icing

This use case is a variation on the generic operational forecasting use case described here: http://external.opengeospatial.org/twiki_public/pub/MetOceanDWG/MetOceanUseCases/National-Weather-Service-operational-forecasting-for-severe-weather-warning-service.rtf

The difference for this use case is the focus on delivery of service to the transport sector rather than public & emergency responder communities.

1) Observation collection:
<as generic operational forecasting use case> plus ...

Observations are collected every 5- to 10-minutes from road-side sensors distributed through transport network (example: http://www.vaisala.com/weather/products/rosa.html).

Observed phenomena include:
-- road surface temperature and condition
-- road surface chemical condition
-- road surface ice / water layer thickness
-- visibility
-- wind-speed & direction
-- air temperature
-- humidity
-- dew point
-- precipitation
-- camera imagery

The road-side observations are distributed under license. The specific permissions relating to the license is dependent on the jurisdiction of the maintenance organization. For example, some regions are maintained & operated on behalf of local government organization (usually through some sourcing arrangement) thus having a more flexible approach to exploitation of the observed phenomena, whilst other regions operate using a commercial bureau service that licenses the local government organization to use the observations. In the latter case, all rights pertaining to the exploitation of the observations are retained by the bureau service provider.

Working in partnership with the local government organizations responsible for highway maintenance, the forecasting centre establishes appropriate licenses to exploit the road-side sensor observations within their operational forecasting process. In the case of an observation originating from a bureau service, the license will not normally permit further onward distribution of the observation.

2) Data assimilation & merge-back
<as generic operational forecasting use case> plus ... inclusion of observations from the road-side sensor network.

3) Global weather prediction
<as generic operational forecasting use case>

4) Mesoscale weather prediction
<as generic operational forecasting use case>

5) Probabilistic forecasting
<as generic operational forecasting use case>

6) Nowcasting
<as generic operational forecasting use case>

7) Site-specific forecasting & statistical correction
As a result of the suite of numerical models routinely executed, the forecasting centre provides a 2 km resolution model output with predominant focus extending from surface conditions to the top of the boundary layer [http://en.wikipedia.org/wiki/Planetary_boundary_layer]. The operational schedule provides the 2 km model output as follows:
-- on 6-hourly cycles (4-time daily), model output at 1-hour temporal resolution to T+48 (2-days ahead)
-- on hourly cycles, model output at 1-hour temporal resolution to T+6 (six-hours ahead)

These numerical weather forecast assets provide rapidly updated information for predicting the state of the road-transport network.

Each highway maintenance organization has detailed information describing the road network they manage. Prior to the beginning of the winter season, the forecasting centre will have identified a set of locations within the transport network [there may be a use case in this analysis activity?] of each organization it is contract to deliver services to. Each location is considered representative of a segment of the transport network. Information about the weather and road-surface conditions at these locations will provide the highways maintenance decision maker with the ability to infer the conditions over the remainder of the segment the specific site is intended to represent.

The forecasting centre configures their operational modeling suite to execute a 'site-specific' forecast at each of these locations. This analysis further fine tunes the prediction by using ancillary information pertinent to that specific location in combination with the high-resolution 2 km model output. The site-specific forecast will include probabilistic parameters based on statistics derived from the ensemble. 

The forecast centre will also execute a road-surface model at each of these locations to predict the road-surface conditions.

The site-specific weather and road-surface conditions will be executed on the same schedule as the 2 km model; four-times daily at hourly resolutions to T+48 hours and every hour at hourly resolution to T+6 hours.

Where the site-specific forecast locations co-incide with the location of a road-side sensor, the forecast weather and road-state can be 'verified' (compared) to the observed conditions. This is an important measure of forecast quality, but also enables the forecast centre to develop a statistical corrections to manage systematic biases for given locations in given weather situations.

8) Service delivery to highway maintenance decision makers
Operational forecasters using semi-automated decision-support tools combine the 2 km model forecast and the site-specific weather & road-condition forecasts with information about the road network topology. Computer algorithms are used to generate 'first-guess' proposals that the forecasters are then able to modify. Because the operational forecasters understand the decision-making process, they are able to provide timely advice and alerts to the decision-makers about what time to undertake the gritting operations (grit too early and rain may wash the material away leaving roads unprotected) and which segments of the transport network need to be treated, thus maintaining safe traveling conditions.

The service is delivered to the high-way maintenance decision makers using a web-application, supplemented with direct telephone contact with an operational forecaster where necessary. To maintain the confidence of the user-community, the web-application must provide updates of the observed conditions as they unfold so that the decision makers are aware on any divergence with the forecast. However, the license restrictions on some of the observation records prevents the forecasting centre including this information within the web-application. The data presented within the web-application must be filtered according the terms and conditions of the license.

Where the observations data license prohibits usage, or there are no sensors in-situ, the forecasting centre provides 'synthetic observations' (clearly labelled) derived from the latest forecast analysis (i.e. T+0). Whilst not an actual observation, the physics of the forecasting models make these synthetic observations a reasonable substitute.