Browsing by Subject "Reanalysis"

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  • Räisänen, Jouni (2019)
    Energetics of interannual temperature variability in the years 1980-2016 is studied using two reanalysis data sets. Monthly temperature anomalies are decomposed to contributions from the net surface energy flux, atmospheric energy convergence minus storage (CONV), and processes that affect the top-of-the-atmosphere radiation balance. The analysis reveals a strong compensation between the net surface heat flux and CONV over the ice-free oceans, with the former driving the temperature variability over the tropical oceans and the latter at higher latitudes. CONV also makes a dominant contribution to temperature anomalies in the winter hemisphere extratopical continents. During the summer half-year and in the tropics, however, variations in cloudiness dominate the temperature variability over land, while the contribution of CONV is modest or even negative. The latter reflects the diffusion-like behaviour of short-term atmospheric variability, which acts to spread out the local, to a large extent cloud-induced temperature anomalies to larger areas. The ERA-Interim and MERRA2 reanalyses largely agree on the general energy budget features of interannual temperature variability, although substantial quantitative differences occur in some of the individual terms.
  • Urraca, Ruben; Huld, Thomas; Lindfors, Anders V.; Riihelä, Aku; Javier Martinez-de-Pison, Francisco; Sanz-Garcia, Andres (2018)
    Solar radiation databases used for simulating PV systems are typically selected according to their annual bias in global horizontal irradiance (G(H)) because this bias propagates proportionally to plane-of-array irradiance (G(POA)) and module power (P-DC). However, the bias may get amplified through the simulations due to the impact of deviations in estimated irradiance on parts of the modeling chain depending on irradiance. This study quantifies these effects at 39 European locations by comparing simulations using satellite-based (SARAH) and reanalysis (COSMO-REA6 and ERAS) databases against simulations using station measurements. SARAH showed a stable bias through the simulations producing the best Pp c predictions in Central and South Europe, whereas the bias of reanalyses got substantially amplified because their deviations vary with atmospheric transmissivity due to an incorrect prediction of clouds. However, SARAH worsened at the northern locations covered by the product (55-65 degrees N) underestimating both G(POA) and P-DC. On the contrary, ERAS not only covers latitudes above 65 degrees but it also obtained the least biased P-DC estimations between 55 and 65 degrees N, which supports its use as a complement of satellite-based databases in high latitudes. The most significant amplifications occurred through the transposition model ranging from +/- 1% up to +/- 6%. Their magnitude increased linearly with the inclination angle, and they are related to the incorrect estimation of beam and diffuse irradiance. The bias increased around + 1% in the PV module model because the PV conversion efficiency depends on irradiance directly, and indirectly via module temperature. The amplification of the bias was similar and occasionally greater than the bias in annual G(H), so databases with the smallest bias in G(H) may not always provide the least biased PV simulations.