A "wind rose" diagram is the most common way of displaying wind
data, and can be measured in a “speed distribution” or a “frequency
distribution". Wind roses can be a yearly average, or can be made
for specific seasons; some even include air temperature
information.
While weather data from software tools can provide a basic
understanding of wind patterns, the best way to get accurate data
is to perform real measurements at the site itself.
Climate data, including wind patterns, mostly comes from airports.
It is often the case that the wind patterns measured at the airport
are very different than the wind patterns of nearby sites. However,
by understanding basic concepts of air movements, you can adjust
the wind data to better suit the site location and simulate more
accurate scenarios. When wind data is collected at airports, it is
typically measured at 10 m (30 ft) above ground. Consider this and
your terrain when designing with winds at a pedestrian level.
. The modern wind rose used by meteorologists gives the
percentage of the time the wind blows from each direction during
the observation period; it sometimes shows the strengths of these
winds and the percentage of the time calm air or light winds are
observed.
A wind rose gives a very succinct but information-laden view of
how wind speed and direction are typically distributed at a
particular location. Presented in a circular format, the wind rose
shows the frequency of winds blowing FROM particular directions.
The length of each "spoke" around the circle is related to the
frequency of time that the wind blows from a particular direction.
Each concentric circle represents a different frequency, emanating
from zero at the center to increasing frequencies at the outer
circles. The wind roses shown here contain additional information,
in that each spoke is broken down into discrete frequency
categories that show the percentage of time that winds blow from a
particular direction and at certain speed ranges. All wind roses
shown here use 16 cardinal directions, such as north (N), NNE, NE,
etc.
An example is shown here. It is the wind rose for Chennai, India
based on three months of hourly wind data (all hours of the day).
This rose shows that the winds at Chennai during the period blow
from the southeast much of the time. In fact, the 3 spokes around
the southeast direction (ESE, SE and SSE) comprise 39% of all
hourly wind directions. This is quickly calculated by taking the
sum of the frequencies of each of these directions (13+16+10=39%).
This also shows that the wind rarely blows from the northwest.
These wind roses also provide details on speeds from different
directions. Examining winds from the southeast (the longest spoke)
one can determine that approximately 1% of the time the wind blows
from the southeast at speeds between 3.6 and 7.2 kilometer per
hour. Similarly, on this spoke it can be calculated that winds blow
from the southeast at speeds between 7.2 and 10.8 km/hr about 3% of
the time, at speeds between 10.8 and 18 km/hr about 7% of the time,
between 18 and 28 km/hr about 5% of the time.
Please note the legend at the bottom of the wind rose that gives
the speed categories and their associated colors.
The legend at the bottom gives additional information such as the
unit (km/hr), the average wind speed for the overall hours (in this
case 12.8 km/hr), and percentage of time that the winds are calm
(0%), and the years and month and hours of data on which each rose
was constructed.
To calculate the typical amount of time that the wind blows from a
particular direction and certain speeds just multiply the
respective frequency by the appropriate amount of time.
===== User Story =====
A Climate Data Manager requires functionality
to effectively visualize climate data in graphical form and export
the result or download the data used.
This funtionality will allow A Climate Data User to explore
graphically the climate data stored in the database and
export the result in different format or download the climate data
used.
The Climate Data Manager requires
functionality to allow data management staff and End-Users
to:
• generate a wide variety of tabular reports using the climate
data stored in the database
• generate a large variety of graphs using graphical
representation of climate data or other data generated from them
using for example statistical techniques. (e.g:
windrose)
• generate different maps over selected areas (e.g: country)
using spatial statistical analysis including topography
• display various media stored in the database such as video
and photos (e.g: station photos)
• export/print the generated graphs/maps in different
formats
• download the climate data/processed data used to produce the
reports (tabular, graphs or maps) into different formats
• conduct an integrated search of the climate database or the
observations metadata catalogue or the discovery metadata catalogue
or the provenance metadata catalogue using different search
categories and parameters.
• allow data management staff to modify/correct a database
observations record by linking the graphical visualisation to the
climate database where possible and appropriate (e.g: from a time
series plot); and keep the changes.
•
•
This component will need to ensure:
• that the integrity of the Climate Database and climate
data is protected; and
• keep metrics relating to the overall status of the data rescue
activity.
===== end =====
===== Data Rescue User Story =====
A Climate Data Manager requires functionality to digitally capture
historical climate data and associated climate observations
metadata and to enter this information into the Climate
Database.
This information is required to ensure that long term climate
patterns can be understood and that historical climate data can be
used to help quality assure new climate observations.
Historical climate data comes in many forms, from paper
observation forms, field books, station records through to
microfiche and a range of graphical forms such as a thermograph or
Campbell-Stokes sunshine recorder.
The Climate Data Manager requires functionality to allow data
management staff to:
• scan and store images of the historical climate data forms
etc;
• capture the data stored on these forms using a variety of methods including optical character recognition, chart digitisation and key entry, as appropriate for each form;
• create and modify customisable forms that can be adjusted to suite the form being digitised with fields validated for content at the time of data entry;
• undertake key entry, with data validation and automatic calculation of required summaries and values as appropriate;
• support data key entry of the same form by multiple key entry staff and flag differences for the climate data manager to resolve;
• record the context under which the climate observations were made using the <u>'Manage Climate Metadata'</u> component;
• record the provenance of the climate record and climate observations metadata digitised, including establishing a link to the scanned image of the original paper media etc;
• allow data rescue staff to view and validate both the original form, together with the values entered into the <u>Climate Database</u> for that form; and
• The Climate Data Manager will require an over-ride to allow a supervisor to corret errors as appropriate, recording the provenance of such changes.
This component will need to ensure:
• that the integrity of the Climate Database and climate
data is protected;
• that only authorised staff can undertake Climate Data
Rescue activities;
• authorised data entry staff can only modify records that have
been captured via the Data Rescue component, and not modify
other records in the Climate Database; and
• keep metrics relating to the overall status of the data rescue
activity.
===== end =====
A Climate Data Manager requires functionality to ingest climate
data into the Climate Database and when required to extract that
data.
Data Ingests operate in accordance with defined Business Rules
and may be run either manually, or as scheduled tasks with support
for automated, self recovery.
Each data ingest has functionality to: