Given that there is an increasing demand and use of numerical models to forecast
precipitation events, it is essential to advance in the use of different verification
methods to measure the quality of the forecasts with the evaluation of errors and
biases. The method for object-based diagnostic evaluation (MODE) is a spatial
verification method that identifies regions of interest, like precipitation, in the same
way that a human would do. This method defines objects in the forecast and
observation fields based on user-defined parameters. MODE was used to evaluate the
performance of 4-km hourly precipitation forecasts from the Weather and Research
Forecasting Model (WRF) over southern South America against the Global
Precipitation Measurement (GPM) derived product IMERG Final Run version
(IMERG-F). For a one month period, tests were performed to select the values
for threshold and the radius of convolution parameters adequate for 3 and 24 hour
accumulated precipitation. The whole verification period considered was 2017-2018
and furthermore, traditional verification statistics (eg, Probability of Detection,
False Alarm Ratio) were used. Additionally, 24-hour accumulated precipitation
forecasts from WRF were compared with those from the Global Forecast System
(GFS). This study proved that traditional verification methods allow objectively to
know the quality of precipitation forecasts. Conversely, object verification rather
than making a pointwise evaluation of hits and misses, identifies precipitation
patterns and compares attributes describing position, size and intensity of matched
forecasted and observed objects. Regarding the analyzed models, although WRF
and GFS present many surprises and false alarms, hit events present low errors
associated with location and intensity of precipitation.