{% extends base_template %} {% import "macros.j2" as macros with context %} {% set report_name = report.report_parameters.name %} {% block body %} {% block report_title %}
This report of solar forecast accuracy was automatically generated using the Solar Forecast Arbiter. Please see our GitHub repository for known issues with the reports or to create a new issue.
{% endblock %} {% block download %}{% endblock %} {% block toc %}{{ cost.to_dict() | pretty_json }}
This report includes forecast and observation data available from {{ report.report_parameters.start }} to {{ report.report_parameters.end }}. {% endblock %} {% block obsandfx %}
The plots below show the realigned and resampled time series of observation and forecast data as well as the distribution of forecast vs observation data.
Controls to pan, zoom, and save the plot are shown on the right. Clicking on an item in the legend will hide/show it.
{% if includes_distribution %}For forecasts parameterized symmetrically around the 50th percentile, brighter colors indicate percentiles farther from the 50th percentile and darker colors indicate percentiles closer to the 50th percentile. For forecasts parameterized asymmetrically around the 50th percentile, brighter colors indicate smaller percentiles and darker colors indicate larger percentiles. Use the hover tool to determine the percentile.
{% endif %} {% endif %} {% if timeseries_prob_spec is defined %}The plot below shows probability vs. time for probabilistic forecasts with axis='x'.
{% endif %} {% endblock %} {% block datavalidation %}A table of metrics over the entire study period and figures for the selected categories are shown below. Metrics may be downloaded in CSV format by clicking here.
{{ macros.metric_table_fx_vert(report.raw_report.metrics, "total", report.report_parameters.metrics) }}{{ category_blurbs[category] }}
{% endif %}