Wheat yield variability will increase in the future due to the projected increase in extreme weather events and long-term climate change effects. Currently, regional agricultural statistics are used to monitor wheat yield. Remotely sensed vegetation indices have a higher spatio-temporal resolution and could give more insight into crop yield. In this paper, we (i) evaluate the possibility to use Normalized Difference Vegetation Index (NDVI) time series to estimate wheat yield in Latvia and (ii) determine which weather variables impact wheat yield changes using both ALARO-0 and REMO Regional Climate Models (RCM) output. The integral from NDVI series (aNDVI) for winter and spring wheat fields is used as a predictor to model regional wheat yield from 2014 to 2018. A correlation analysis between weather variables, wheat yield and aNDVI was used to elucidate which weather variables impact wheat yield changes in Latvia. Our results indicate that high temperatures in June for spring wheat and in July for winter wheat had a negative correlation with yield. A linear regression yield model explained 71% of the variability with a residual standard error of 0.55 Mg/ha. When RCM data were added as predictor variables to the wheat yield empirical model a random forest approach resulted in better results compared to a linear regression approach, the explained variance increased up to 97% and the residual standard error decreased to 0.17 Mg/ha. We conclude that NDVI time series and RCM output enabled regional crop yield and weather impact monitoring at higher spatio-temporal resolutions than regional statistics.

This study analyses long‐term changes in drought indices (Standardised Precipitation Index—SPI, Standardised Precipitation–Evapotranspiration Index—SPEI) at 1 and 3 months scales at 182 stations in 11 central and eastern European countries during 1949–2018. For comparative purposes, the necessary atmospheric evaporative demand (AED) to obtain SPEI was calculated using two methods, Hargreaves‐Samani (SPEIH) and Penman‐Monteith (SPEIP). The results show some relevant changes and tendencies in the drought indices. Statistically significant increase in SPI and SPEI during the cold season (November–March), reflecting precipitation increase, was found in the northern part of the study region, in Estonia, Latvia, Lithuania, northern Belarus and northern Poland. In the rest of study domain, a weak and mostly insignificant decrease prevailed in winter. Summer season (June–August) is characterized by changes in the opposite sign. An increase was observed in the north, while a clear decrease in SPEI, reflecting a drying trend, was typical for the southern regions: the Czech Republic, Slovakia, Hungary, Romania, Moldova and southern Poland. A general drying tendency revealed also in April, which was statistically significant over a wide area in the Czech Republic and Poland. Increasing trends in SPI and SPEI for September and October were detected in Romania, Moldova and Hungary. The use of SPEI instead of SPI generally enhances drying trends.

The paper studies spatial wind energy flow distribution in Latvia based on wind speed measurements carried out at an altitude of 10 m over a period of two years, from 2015 to 2016. The measurements, with 1 min increments, were carried out using certified measuring instruments installed at 22 observation stations of the Latvian National Hydrometeorological and Climatological Service of the Latvian Environment, Geology and Meteorology Centre (LEGMC). The models of the spatial distribution of averaged wind speed and wind energy density were developed using the method of spatial interpolation based on the historical measurement results and presented in the form of colour contour maps with a 1x1 km resolution. The paper also provides the results of wind speed spatial distribution modelling using a climatological reanalysis ERA5 at the altitudes of 10, 54, 100 and 136 m with a 31x31 km resolution. The analysis includes the comparison of actual wind speed measurement results with the outcomes of ERA5 modelling for meteorological observation stations in Ainazi, Daugavpils, Priekuli, Saldus and Ventspils.

-Thunderstorms are the most hazardous meteorological phenomena in Latvia in the summer season, and the assessment of their characteristics is essential for the development of an effective national climate and weather prediction service. However, the complex nature of convective processes sets specific limitations to their observation, analysis and forecasting. Therefore, the aim of this study is to analyse thunderstorm features associated with severe thunderstorms observed in weather radar and satellite data in Latvia over the period 2006-2015. The obtained results confirm the applicability of the selected thunderstorm features for thunderstorm nowcasting and analysis in Latvia. The most frequent features observed on days with thunderstorm were maximum radar reflectivities exceeding 50 dBZ and the occurrence of overshooting tops and tilted updrafts, while the occurrence of gravity waves, V-shaped storm structures and small ice particles have been found to be useful indicators of increased thunderstorm severity potential.

Abstract. To allow for climate impact studies on human and natural systems high-resolution climate information is needed. Over some parts of the world plenty of regional climate simulations have been carried out, while in other regions hardly any high-resolution climate information is available. This publication aims at addressing one of these regional gaps by presenting an evaluation study for two regional climate models (RCMs) (REMO and ALARO-0) at a horizontal resolution of 0.22° (25 km) over Central Asia. The output of the ERA-Interim driven RCMs is compared with different observational datasets over the 1980–2017 period. The choice of the observational dataset has an impact on the scores but in general one can conclude that both models reproduce reasonably well the spatial patterns for temperature and precipitation. The evaluation of minimum and maximum temperature demonstrates that both models underestimate the daily temperature range. More detailed studies of the annual cycle over subregions should be carried out to reveal whether this is due to an incorrect simulation in cloud cover, atmospheric circulation or heat and moisture fluxes. In general, the REMO model scores better for temperature whereas the ALARO-0 model prevails for precipitation. This publication demonstrates that the REMO and ALARO-0 RCMs can be used to perform climate projections over Central Asia and that the produced climate data can be applied in impact modelling.