  Several techniques have been used to generate weather forecast texts. In this
paper, case based reasoning (CBR) is proposed for weather forecast text
generation because similar weather conditions occur over time and should have
similar forecast texts. CBR-METEO, a system for generating weather forecast
texts was developed using a generic framework (jCOLIBRI) which provides modules
for the standard components of the CBR architecture. The advantage in a CBR
approach is that systems can be built in minimal time with far less human
effort after initial consultation with experts. The approach depends heavily on
the goodness of the retrieval and revision components of the CBR process. We
evaluated CBRMETEO with NIST, an automated metric which has been shown to
correlate well with human judgements for this domain. The system shows
comparable performance with other NLG systems that perform the same task.
