Using QuikSCAT in the added value assessment of dynamically downscaled wind speed

Abstract

ABSTRACT:Hindcasts with reanalysis-driven regional climate models (RCMs) are a common tool to assess weather statistics (i.e. climate) and recent changes and trends. A remote sensing-based method to investigate the added value of surface marine RCM wind speed is introduced. The capability of the dynamical downscaling approach (with spectral nudging applied) to add value to the reanalysis wind speed forcing is assessed by the comparison with QuikSCAT Level 2B 12.5 km (L2B12) swath data in European waters for 2000-2007. Co-location criteria are within 0.1°and 0.06°in longitudinal and latitudinal distance from RCM grid points and within 10 min. In the wind speed range, QuikSCAT L2B12 is reliably reproducing (3-20 m s −1 ), dynamically downscaled wind speed does not show an added value in 'open ocean' areas. However, in coastal areas with complex topography, the regional models show an added value, especially around Iceland and the Iberian Peninsula and in the Mediterranean, Baltic and Irish Seas, validating the findings of previous in situ data-based studies on the added value. Strong interseasonal differences exist, in winter enhanced cyclonic and meso-cyclonic activity increases the potential of dynamical downscaling. In winter time, the added value is more pronounced around Iceland and Greenland, south of Iceland and within the Gulf of Lyon/Mistral region. Summarizing the presented method can be easily applied for other ocean areas, making QuikSCAT a valuable tool to identify marine regions where dynamical downscaling adds value to surface marine wind speed. A detailed comparison of 10 m winds from the National Centres of Environmental Prediction (NCEP)/National Centre for Atmospheric Research (NCAR) and the newer NCEP/DOE-II reanalyses is presented in the annex, motivating the use of the NCEP/NCAR reanalysis in the added value assessment.

A regional climate model hindcast for Siberia: analysis of snow water equivalent

“…These assessments primarily concentrate on temperature, precipitation, sea level pressure, wind or mesoscale atmospheric circulation systems. More realistic detail compared to driving reanalyses was achieved on regional scales, e.g., in cases of temperature with complex orography (Prömmel et al, 2010), orographically induced wind systems (Winterfeldt et al, 2010) or North Atlantic polar lows and East Asian typhoons (Feser and von Storch, 2008;Zahn and von Storch, 2008).…”

Projected Change—Models and Methodology

“…10.10. Winterfeldt et al (2011) analysed AV in dynamically downscaled wind speed fields. They used the Brier skill score (BSS) to detect the AV of the regionally modelled (with spectrally nudged-REMO) wind in comparison with the global reanalysis (NCEP).…”

“…Figure 10.13 presents the SLP and 10 m wind speed fields filtered with a digital bandpass filter that allows better Fig. 10.11 Brier skill score using QuikSCAT level 2B12 as the source of ground-truth data, global reanalysis (NCEP reanalysis) as the reference forecast, and a regional model (spectrally nudged-REMO) as the forecast, after Winterfeldt et al (2011) presentation of phenomena at the 200-600 km spatial scale. The community land model (CLM) was able to identify the polar low along the Norwegian coast, although it was still too coarse to describe fine detail.…”

Field significance of performance measures in the context of regional climate model evaluation. Part 2: precipitation

“…Duffy et al 2006;Feser 2006;Sotillo et al 2006;Buonomo et al 2007;Sanchez-Gomez et al 2009;Prömmel et al 2010;Di Luca et al 2012;Kendon et al 2012;Cardoso et al 2013;Chan et al 2013;Pearson et al 2015Torma et al 2015, which affects the fidelity of spatial analysis of added value or wholly precludes it. Winterfeldt and Weisse (2009) and Vautard et al (2013) apply few relative metrics only for individual locations, and Winterfeldt et al (2011) and Dosio et al (2015) on a gridpoint basis.…”

“…Others estimate statistical significance (grid)point-wise, but take neither multiplicity nor spatial autocorrelation of test statistics into account (e.g. Duffy et al 2006;Buonomo et al 2007;Winterfeldt and Weisse 2009;Winterfeldt et al 2011;Chan et al 2013;Katragkou et al 2015).…”