0000000000180115
AUTHOR
Juris Seņņikovs
Impact of climate change on the timing of strawberry phenological processes in the Baltic States
Climate change has been shown to impact aspects of agriculture and phenology. This study aims to quantify changes in the timing of garden strawberry blooms and harvests in the Baltic States using Regional Climate Models (RCMs). First, parameters for a strawberry phenology model based on the growing degree day (GDD) methodology were determined. Growing degree days were calculated using a modified sine wave method that estimates the diurnal temperature cycle from the daily maximum and minimum temperature. Model parameters include the base temperature and the required cumulative GDD sum, estimated from phenological and meteorological observations in Latvia for the years 2010–2013 via iterative…
Verification of Numerical Weather Prediction Model Results for Energy Applications in Latvia
Abstract Wind power forecasting greatly relies on wind speed forecasts. Numerical Weather Prediction (NWP) models are a reliable source of meteorological forecasts and they can also be used in wind resource assessment. In this work we carry out the verification of wind speed results from the NWP model Weather Research and Forecast (WRF), grid resolution - 3 km. Results from 172 model runs in May and November 2013 are compared with meteorological observations in 24 stations in Latvia. The model usually predicts wind speed values that are larger than the observed and the diurnal cycle has a large impact on verification results. Verification results obtained by interpolating model results betw…
PCA analysis of wind direction climate in the baltic states
Wind direction is one of the fundamental parameters of weather. In this study we investigate the wind direction climate 10 m above surface level in the Baltic States (Estonia, Latvia, Lithuania). The analysis of wind direction over larger regions is usually hindered by the fact that wind direction is a circular variable, which means that averaged values are meaningless. Here we show how Principal Component Analysis (PCA) can be applied to give a large scale overview of typical wind direction patterns in the region. Here we apply PCA to both observational and reanalysis data. The most significant wind direction patterns are detected in both synoptic scale and mesoscale, and we attempt to lin…