  Coronal holes (CH) are regions of open magnetic fields that appear as dark
areas in the solar corona due to their low density and temperature compared to
the surrounding quiet corona. To date, accurate identification and segmentation
of CHs has been a difficult task due to their comparable intensity to local
quiet Sun regions. Current segmentation methods typically rely on the use of
single EUV passband and magnetogram images to extract CH information. Here, the
Coronal Hole Identification via Multi-thermal Emission Recognition Algorithm
(CHIMERA) is described, which analyses multi-thermal images from the
Atmospheric Image Assembly (AIA) onboard the Solar Dynamics Observatory (SDO)
to segment coronal hole boundaries by their intensity ratio across three
passbands (171 \AA, 193 \AA, and 211 \AA). The algorithm allows accurate
extraction of CH boundaries and many of their properties, such as area,
position, latitudinal and longitudinal width, and magnetic polarity of
segmented CHs. From these properties, a clear linear relationship was
identified between the duration of geomagnetic storms and coronal hole areas.
CHIMERA can therefore form the basis of more accurate forecasting of the start
and duration of geomagnetic storms.
