Removal of Systematic Model Bias on a Model Grid
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Removal of Systematic Model Bias on a Model Grid
Removal of Systematic Model Bias on a Model GridClifford F. Mass1, Jeffrey Baars, Garrett Wedam, Eric Grimit, and Richard Steed, Department of Atmosph Removal of Systematic Model Bias on a Model Gridheric SciencesUniversity of WashingtonSeattle, Washington 98195Submitted toWeather and Forecasting390521 Corresponding AuthorProfessor Clifford F. MassDepartment of Atmospheric SciencesBox 351640University of WashingtonSeattle. Washington 98195cliff@atmos.washington.edu1AbstractAll numerical forecas Removal of Systematic Model Bias on a Model Gridt models possess systematic biases. Attempts to reduce such biases at individual station using simple statistical correction have met some success. HoRemoval of Systematic Model Bias on a Model Grid
wever, an acute need exists for a bias reduction method that works on the entire model grid. Such a method should be viable in complex terrain, in locRemoval of Systematic Model Bias on a Model GridClifford F. Mass1, Jeffrey Baars, Garrett Wedam, Eric Grimit, and Richard Steed, Department of Atmosph Removal of Systematic Model Bias on a Model Gridt exist. This paper describes a systematic bias removal scheme for forecast grids at the surface that IS applicable to a wide range of regions and parameters.Using observational data and model forecasts for a one-year period over the Pacific Northwest, a method was developed to bias correct gridded Removal of Systematic Model Bias on a Model Grid2-m temperature and 2-m dew point forecasts. The method calculates bias at observing locations and uses these biases to estimate bias on the model griRemoval of Systematic Model Bias on a Model Grid
d, specifically, grid points are matched with nearby stations that have similar land use and elevation, and by only applying observations with similarRemoval of Systematic Model Bias on a Model GridClifford F. Mass1, Jeffrey Baars, Garrett Wedam, Eric Grimit, and Richard Steed, Department of Atmosph Removal of Systematic Model Bias on a Model Gridhe bias correction method reduces bias substantially, particularly for periods when biases are large. Adaptations to weather regime changes are made within a period of days, and the method essentially "shuts off when model biases are small. In the future, this approach will be extended to additional Removal of Systematic Model Bias on a Model Grid variables.21 IntroductionVirtually all weather prediction models possess substantial systematic bias, errors that are relatively stable over days, weRemoval of Systematic Model Bias on a Model Grid
eks, or longer. Such biases occur at all levels but are normally largest at the surface where deficiencies in model physics and surface specificationsRemoval of Systematic Model Bias on a Model GridClifford F. Mass1, Jeffrey Baars, Garrett Wedam, Eric Grimit, and Richard Steed, Department of Atmosph Removal of Systematic Model Bias on a Model Gridasting systems (see Figure 1 for an example for the MM5).In the U.S., the removal of systematic bias is only attempted operationally at observation sites as a byproduct of applying Model Output Statistics (MOS) as a forecast post-processing step (Glahn and Lowry 1972). In fact, it has been suggested Removal of Systematic Model Bias on a Model Grid by some (e.g.. Neilley and Hanson 2004) that bias removal is the most important contribution of MOS and might be completed in a more economical way.Removal of Systematic Model Bias on a Model Grid
As noted in Baars and Mass (2005), although MOS reduces average forecast bias over extended periods, for shorter intervals of days to weeks, MOS forecRemoval of Systematic Model Bias on a Model GridClifford F. Mass1, Jeffrey Baars, Garrett Wedam, Eric Grimit, and Richard Steed, Department of Atmosph Removal of Systematic Model Bias on a Model GridOS is usually incapable of compensating for such transient model failures and produces surface temperature forecasts that are too warm. MOS also requires an extended developmental period (usually at least two years), which is problematic when a model is experiencing continuous improvement. One appro Removal of Systematic Model Bias on a Model Gridach to reducing a consistent, but short-term, bias in MOS is updatable MOS (UMOS) as developed at the Canadian Meteorological Center (Wilson and ValleRemoval of Systematic Model Bias on a Model Grid
e 2002). The method proposed in this paper is related to updateable MOS but extends it in new ways.3It has become increasingly apparent that bias remoRemoval of Systematic Model Bias on a Model GridClifford F. Mass1, Jeffrey Baars, Garrett Wedam, Eric Grimit, and Richard Steed, Department of Atmosph Removal of Systematic Model Bias on a Model Gridnteractive Forecast Preparation System (IFPS). a graphical forecast preparation and dissemination system in which forecasters input and manipulate model forecast grids before they are distributed in various forms (Ruth 2002, Glahn and Ruth 2003). Systematic model biases need to be removed from these Removal of Systematic Model Bias on a Model Grid grids, and it is a poor use of limited human resources to have forecasters manually removing model biases if an objective system could do so. AdditioRemoval of Systematic Model Bias on a Model Grid
nally, it would be surprising if subjective bias removal could be as skillful as automated approaches, considering the large amount of information necRemoval of Systematic Model Bias on a Model GridClifford F. Mass1, Jeffrey Baars, Garrett Wedam, Eric Grimit, and Richard Steed, Department of Atmosph Removal of Systematic Model Bias on a Model Gridalso needed for a wide range of applications from wind energy prediction and transportation to air quality modeling and military requirements, to name only a few. Finally, bias removal on forecast grids is an important post-processing step for ensemble prediction, since systematic bias is knowable a Removal of Systematic Model Bias on a Model Gridnd thus not a true source of forecast uncertainty. Thus, systematic model bias for each ensemble member should be removed as an initial step or the enRemoval of Systematic Model Bias on a Model Grid
semble variance will be inflated. Eckel and Mass (2005) demonstrated that a gridbased. 2-week. running-mean bias correction (BC) improved the forecastRemoval of Systematic Model Bias on a Model GridClifford F. Mass1, Jeffrey Baars, Garrett Wedam, Eric Grimit, and Richard Steed, Department of Atmosph Removal of Systematic Model Bias on a Model Gridlution through the removal of unrepresentative ensemble variance.The need for model bias removal has been discussed in a number of papers, with most limited to bias reduction at observation locations. Stensrud and Skindlov (1996)4found that model (MM4) 2-m temperature errors at observation locations Removal of Systematic Model Bias on a Model Grid over the southwest U.S. during summer could be considerably reduced using a simple bias correction (BC) scheme that removes the average bias over theRemoval of Systematic Model Bias on a Model Grid
study period. Stensrud and Yussouf (2003) applied a 7-day running-mean bias correction to each forecast of a 23-member ensemble system for 2-m temperRemoval of Systematic Model Bias on a Model GridClifford F. Mass1, Jeffrey Baars, Garrett Wedam, Eric Grimit, and Richard Steed, Department of Atmosph Removal of Systematic Model Bias on a Model Gridble to NGM MOS for temperature and superior for dew point. A Kalman filter approach was used to create diurnally varying forecast bias corrections fro 2-m temperatures at 240 sites in Norway (Homleid 1995). This approach removed much of the forecast bias when averaged over a month, although the stan Removal of Systematic Model Bias on a Model Griddard deviations of the differences between forecasts and observations remained nearly unchanged.Systematic bias removal on grids, as discussed in thisRemoval of Systematic Model Bias on a Model Grid
paper, has received less emphasis. As noted above. Eckel and Mass (2005) applied bias removal on MM5 forecast grids of an ensemble forecasting systemRemoval of Systematic Model Bias on a Model GridClifford F. Mass1, Jeffrey Baars, Garrett Wedam, Eric Grimit, and Richard Steed, Department of Atmosph Removal of Systematic Model Bias on a Model Gridalysis grids (RUC20 or the mean of operational analyses) as truth. The National Weather Service has recently developed a gridded MOS system that, like conventional MOS, reduces systematic bias (Dallavale and Glahn 2005). This system starts with MOS values at observation sites and then interpolates t Removal of Systematic Model Bias on a Model Gridhem to the model grid using a modified Cressman (1959) scheme that considers station and grid point elevations. In addition, surface type is considereRemoval of Systematic Model Bias on a Model Grid
d, with the interpolation only using land (water) data (MOS) points for land (water) grid points.5An optimal bias removal scheme for forecast grids shRemoval of Systematic Model Bias on a Model GridClifford F. Mass1, Jeffrey Baars, Garrett Wedam, Eric Grimit, and Richard Steed, Department of Atmosph Removal of Systematic Model Bias on a Model Gridhe higher resolutions (1-10 km) for which mesoscale models will be running in the near future. It should be capable of dealing with regions of sparse data, yet able to take advantage of higher data densities when they are available. It should be viable where gridded high-resolution analyses are not Removal of Systematic Model Bias on a Model Gridavailable or where long climatological records or long-term model forecast grid archives do not exist. Finally, it should be able to deal gracefully wRemoval of Systematic Model Bias on a Model Grid
ith regime changes, when model biases might change abruptly. This paper describes an attempt to create such a systematic bias removal scheme for forecRemoval of Systematic Model Bias on a Model GridClifford F. Mass1, Jeffrey Baars, Garrett Wedam, Eric Grimit, and Richard Steed, Department of Atmosph Removal of Systematic Model Bias on a Model Gridarch was tested on forecasts made by the Penn. State/NCAR Mesoscale Model Version 5 (MM5), which is run in real-time at the University of Washington (Mass et al. 2003). This modeling system uses 36 and 12 km grid spacing through 72 h. and a nested domain with 4-km grid spacing that is run out to 48 Removal of Systematic Model Bias on a Model Gridh. Using this system the 2-m temperature (T2) and 2-m dew point forecasts on a grid were corrected for The Modeling SystemGọi ngay
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