A scale-dependent blending scheme for WRFDA: impact on regional weather forecasting

Abstract

Abstract. Due to limitation of the domain size and limited observations used in regional data assimilation and forecasting systems, regional forecasts suffer a general deficiency in effectively representing large-scale features such as those in global analyses and forecasts. In this paper, a scaledependent blending scheme using a low-pass Raymond tangent implicit filter was implemented in the Data Assimilation system of the Weather Research and Forecasting model (WRFDA) to reintroduce large-scale weather features from global model analysis into the WRFDA analysis. The impact of the blending method on regional forecasts was assessed by conducting full cycle data assimilation and forecasting experiments for a 2-week-long period in September 2012.It is found that there are obvious large-scale forecast errors in the regional WRFDA system running in full cycle mode without the blending scheme. The scale-dependent blending scheme can efficiently reintroduce the large-scale information from National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS) analyses, and keep small-scale information from WRF analyses. The blending scheme is shown to reduce analysis and forecasting error of wind, temperature and humidity up to 24 h compared to the full cycle experiments without blending. It is also shown to increase precipitation prediction skills in the first 6 h forecasts.

Impact of assimilating GOES imager clear‐sky radiance with a rapid refresh assimilation system for convection‐permitting forecast over Mexico

“…All hybrid experiments produced separate, independent 12 km and 4 km analyses. At each analysis cycle, the background is modified with a blending scheme [Wang et al, 2014]. The first experiment (hereafter "CON") assimilated conventional observations from radiosondes, ships, Tropospheric Airborne Meteorological Data Reporting (TAMDAR),satellite-derived winds, land and oceanic surface stations in both outer and inner domains ( Figure 6), and the advanced microwave sounding unit-A (AMSU-A) on NOAA-15, 16, 18,…”

Improving the regional model forecasting of persistent severe rainfall over the Yangtze River Valley using the spectral nudging and update cycle methods: a case study

“…For small-scale changes, blending technique is an effective method to make efficient use of large-scale forecasts of the forcing fields, retain the small-scale features of regional model (Yang, 2005). Some previous studies revealed that the application of blending increases precipitation prediction skills (Wang et al, 2014a(Wang et al, , 2014bHsiao et al, 2015), however, these studies improved initial conditions (ICs) through assimilation and directed against the regional ensemble prediction within 3 days.…”

A Dynamic Blending Scheme to Mitigate Large‐Scale Bias in Regional Models

“…The method can improve the simulation of winds, temperature, and sea level pressure (Guidard & Fischer, 2008); reduce large position and magnitude errors in precipitation; and reduce spurious precipitation (Vendrasco et al, 2016). Digital filter blending blends low-pass-filtered GM fields with high-pass-filtered RM fields using a spatial digital filter (Gustafsson et al, 2018;Stappers & Barkmeijer, 2011;Wang et al, 2014;Yang, 2005aYang, , 2005b. The method has been shown to reduce the forecast bias in RMs at nearly all model levels (Eerola, 2013;Wang et al, 2014) and improve the accuracy of precipitation predictions for the continental United States (Wang et al, 2014).…”

“…Using sensitivity experiments, Gómez and Miguez-Macho (2017) found a rapid (slow) reduction in forecast bias at scales longer (smaller) than~1,000 km (i.e., the Rossby radius of deformation in the midlatitudes) and hence a fixed wavelength of 1,000 km was used. Since fixed cutoff wave numbers cannot account for vertical variations in scale (i.e., motions in the planetary boundary layer occur at smaller scales than those in the upper atmosphere), some studies simply withheld the blending process at lower model levels (e.g., lower than 850 hPa; Guidard and Fischer (2008) VB, AB Wave number 12 Vendrasco et al (2016) VB, AB 1°, according to the original resolution of GFS/FNL von Storch et al (2000) SN, AB Wave number 5 (3) in x (y) direction Feser and von Storch (2008) SN, AB 750 and 215 km in two domains Cha and Lee (2009) SN, AB 1000 (1100) km in x (y) direction, or wave number 6 (8) in x (y) direction Vincent and Hahmann (2015) SN, AB 250 km Yang (2005a) DF, AB~50 (~300) km in winter (summer) Yang (2005b) DF, FB~200 km Wang et al (2014) DF, AB, and FB 600 or 1200 km Hsiao et al (2015) DF, AB 1200 km…”