Development of a geophysic model output statistics module for improving short‐term numerical wind predictions over complex sites

Abstract

Developed for short-term (0-48 h) wind power forecasting purposes, high-resolution meteorological forecasts for Eastern Canada are available from Environment Canada's Numerical Weather Prediction (NWP) model configured on a limited area (GEM-LAM). This paper uses 3 years of forecast data from this model for the region of North Cape (Prince Edward Island, Canada). Although the model resolution is relatively high (2.5 km), statistical analysis and site inspection reveal that the model does not have a sufficiently refined grid to properly represent the meteorological phenomena over this complex coastal site. To cope with such representation error, a generalized Geophysic Model Output Statistics (GMOS) module is developed and applied to reduce the forecast error of the NWP forecasts. GMOS differs from other Model Output Statistics (MOS) that are widely used by meteorological centres in the following aspects: (i) GMOS takes into account the surrounding geophysical parameters such as surface roughness, terrain height, etc., along with wind direction; (ii) GMOS can be directly applied for model output correction without any training.Compared with other methods, GMOS using a multiple grid point approach improves the GEM-LAM predictions root mean squared error by 1-5% for all time horizons and most meteorological conditions. Also, the topographic signature of the forecast error (uneven directional distribution of the forecast error related to the surface characteristics) due to misrepresentation issues is significantly reduced. The NWP forecasts combined with GMOS outperform the persistence model from a 2 h horizon, instead of 3 h using MOS. Finally, GMOS is applied and validated at two other sites located in New Brunswick, Canada. Similar improvements on the forecasts were observed, thus showing the general applicability of GMOS.

Wind-driven snow conditions control the occurrence of contemporary marginal mountain permafrost in the Chic-Choc Mountains, south-eastern Canada: a case study from Mont Jacques-Cartier

“…Strong winds rapidly redistribute new snow. There is no anemometer on Mont Jacques-Cartier but wind data were obtained from GEM-LAM with a 2.5 km horizontal uniform grid resolution for the period 2007-2010 (Bédard et al, 2013). This confirmed the windy character of the area, with hourlyaveraged wind speeds > 8 m s −1 20 % of the time and speeds > 10 m s −1 6 % of the time.…”

“…Some winter rainfall events occur each year, even on the highest summits, associated with brief periods of thaw under southerly flow conditions (Fortin and Hétu, 2009;Germain et al, 2009). The prevailing winds blow from the west and north-west according to the Limited Area version of the Canadian Global Environmental Multi-scale Model (GEM-LAM) for the period 2007-2010 (Bédard et al, 2013).…”

Near-Surface Wind Observation Impact on Forecasts: Temporal Propagation of the Analysis Increment

“…Thus, GMOS uses a set of geostatistical weights to relate the most representative grid points with the observation site. From Bédard et al (2013), the GMOS multipoint linear regression (H GMOS ), at the kth station, is formulated as follows:…”

“…Gross error and background checks are also performed on the training dataset because GMOS relies on the availability of good quality data to train its statistical coefficients (Bédard et al 2013(Bédard et al , 2015. Only the 5331 stations benefiting from a minimum of 2 months of data are considered because a GMOS operator based on a 2 3 2 gridpoint stencil requires a minimum of 2 months of training data (Bédard et al 2015).…”

“…Still, removing model biases prior to the assimilation of surface observations only improves the analysis and nowcasting capability as the forecast quickly returns toward the model's own bias (Ancell et al 2011;Ancell 2012). As an alternative to improve the model forecast representation in observation space, Bédard et al (2013) proposed a geostatistical observation operator based on a multiple gridpoint approach called geophysical model output statistics (GMOS). As discussed in Bédard et al (2015), this can alleviate issues associated with representativeness and systematic error.…”