HR: 14:30h
AN: A53E-06    [Abstracts]
TI: Developing the Local 3-Month Precipitation Outlook
AU: * Meyers, J C
EM: jenna.meyers@noaa.gov
AF: NOAA National Weather Service, 125 S. State St. Rm#1311, Salt Lake City, UT 84138, United States
AU: Timofeyeva, M
EM: marina.timofeyeva@noaa.gov
AF: UCAR / NOAA NWS, 1325 Eastwest Hwy SSMC2 room 13370, Silver Spring, MD 20910, United States
AU: Unger, D A
EM: david.unger@noaa.gov
AF: Climate Prediction Center NOAA/NWS/NCEP/CPC, 5200 Auth Road, Camp Springs, MD 20746,
AU: Comrie, A C
EM: comrie@arizona.edu
AF: Dept. Geography and Regional Development, University of Arizona, 414 Harvill Building Box#2, Tucson, AZ 85721,
AB: In 2007, NOAA's National Weather Service (NWS) introduced the Local 3 Month Temperature Outlook (L3MTO), which downscales the NWS's probabilistic outlooks for the average 3-month temperature to a local station. Creating a local precipitation outlook is more complex than for temperature because of the higher spatial and temporal variability of precipitation. These characteristics of the observation data (1) make it difficult to fit a single probability distribution to data over a large spatial area and (2) may lead to questionable predictability even in the case of an adequate distribution fit. 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 by setting the intercept to zero to account for the fact that precipitation is a discrete variable and is bounded at zero. Such modification, in theory, should increase the standard error of predictions, because as fewer parameters are estimated the degrees of freedom increase. Therefore, the L3MPO methodology has been evaluated by a verification analysis that utilizes L3MPO hind-casts, which are created using the archived forecast data from CPC's national outlooks for 232 sites in the western U.S. The Modified Heidke Skill Score (MHSS) at 75 percent confidence level is used as verification in this assessment of long-term forecast goodness. The MHSS was computed for each station using 11 years (1994-2005) and all leads for individual target 3-month periods: e.g. Jan-Mar, Feb-Apr, etc. The stations were then stratified by two criteria: 1) existence of potential predictability and 2) conditions favoring assumption of Normal distribution. Overall, there are about 60 percent of stations that show forecast improvement over the use of the 1971-2000 climatology. Forecasts were poor for only 10 percent of stations 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 to linear regression will be used. The alternative methodology makes use of a regression model with a normal-quantile transformation of the data. The transformation includes the use of the 1971-2000 climatological underlying distribution (Normal, Lognormal or Gamma) expressed as normal quantiles. The prediction is made in the units of the normal quantiles that are translated to metric units using the climatology distribution. The advantage of using this method avoids the problems associated with the asymmetric properties of precipitation distribution. However, this methodology also has a disadvantage because of its inability to adjust for the most recent trends, which might play an important role in forecasting precipitation in a changing climate.
DE: 3238 Prediction (3245, 4263)
DE: 3245 Probabilistic forecasting (3238)
DE: 4263 Ocean predictability and prediction (3238)
SC: Atmospheric Sciences [A]
MN: 2007 Fall Meeting