Two different statistical downscaling techniques are tested for a forecasting procedure of the Local 3 Month Precipitation Outlook (L3MPO). The first technique being tested is the methodology used for the L3MTO that (1) applies a linear regression to identify the statistical relationship between a station parameter and its corresponding forecast region and (2) adjusts the regression parameters to the most recent trends at the station. We modified the original L3MTO linear regression methodology to account for the fact that precipitation is a discrete variable, by treating it as a continuous variable being bounded at zero. By setting the intercept to zero (one parametric regression model) in theory, should increase the standard error of predictions because the degree of freedom increases, due to the fact that fewer parameters are being estimated. The second methodology makes use of a regression model with a normal-quantile transformation of the data. The transformation includes the use of the climatological underlying distribution (Normal, Lognormal or Gamma) expressed as normal quantiles. The advantage of using this method avoids the problems associated with the asymmetric properties of precipitation distribution. However, this methodology also has an identified disadvantage of the current method's inability to adjust for the most recent trends, which might play an important role in forecasting precipitation in a changing climate.
In order to determine the best methodology for creating the L3MPO, we are partnering with NOAA's Climate Prediction Center (CPC) and Earth System Research Laboratory (ESRL), Cooperative Institute for Research in Environmental Sciences (CIRES) , and National Center for Atmospheric Research (NCAR) to help evaluate the effectiveness and limitations of the methods, as well as investigate other downscaling approaches for the entire US. Currently, one type of verification analysis has been completed that utilizes hind-casts to assess long-term forecast goodness on both methodologies for the Western US. Overall, there are about 60% of stations nationwide that show forecast improvement over the use of the climatology reference period. Forecasts were poor for only 10% of stations that are within the areas with existing potential predictability, whose data did not allow for the assumption of a Normal distribution. The forecast for such stations might improve if an alternative method is utilized.