A quantitative precipitation estimate (QPE) provides basic information for the modelling of many kinds of hydro-meteorological processes, e.g., as input to rainfall-runoff models for flash flood forecasting. Weather radar observations are crucial in order to meet the requirements, because of their very high temporal and spatial resolution. Other sources of precipitation data, such as telemetric rain gauges and satellite observations, are also included in the QPE. All of the used data are characterized by different temporal and spatial error structures. Therefore, a combination of the data should be based on quality information quantitatively determined for each input to take advantage of a particular source of precipitation measurement. The presented work on multi-source QPE, being implemented as the RainGRS system, has been carried out in the Polish national meteorological and hydrological service for new nowcasting and hydrological platforms in Poland. For each of the three data sources, different quality algorithms have been designed: (i) rain gauge data is quality controlled and, on this basis, spatial interpolation and estimation of quality field is performed, (ii) radar data are quality controlled by RADVOL-QC software that corrects errors identified in the data and characterizes its final quality, (iii) NWC SAF (Satellite Application Facility on support to Nowcasting and Very Short Range Forecasting) products for both visible and infrared channels are combined and the relevant quality field is determined from empirical relationships that are based on analyses of the product performance. Subsequently, the quality-based QPE is generated with a 1-km spatial resolution every 10 minutes (corresponding to radar data). The basis for the combination is a conditional merging technique that is enhanced by involving detailed quality information that is assigned to individual input data. The validation of the RainGRS estimates was performed taking account of season and kind of precipitation.

Climate change on the globe has been manifested over the past few decades by, among other things, an increase in the frequency of extreme weather events, such as droughts. Drought affects millions of people around the world every year. Its effects usually appear after a long period of rainfall deficit. Based on climate projections, it is emphasized that water scarcity will be one of the most important problems in the future. Due to the importance of the increasing problem of drought, the analysis of this phenomenon in Poland was undertaken in the context of agriculture. The aim of this study was to assess the course of drought in the vegetation period during years 2001-2020 for Poland based on the Selyaninov’s coefficient (HTC). As shown in this study, the western and central parts of the country, dominated by arable land, are particularly vulnerable to drought. Due to the cyclic nature of periods classified by HTC as dry, it can be concluded that the problem of precipitation deficit for crops in Poland could get worse.

The purpose of agrometeorological services conducted by various institutions around the world is to support decisions in the field of planning individual farmer works and agrotechnical treatments so as to fully enable the use of the prevailing weather and climatic conditions. However, the not always sufficient spatial distribution of ground measuring stations limits the possibility of the precise determination of meteorological conditions and the state of vegetation in a specific location. The solution may be the simultaneous use of both ground and satellite data, which can improve and enhance the final agrometeorological products. This paper presents examples of the use of meteorological products combining classical ground measurement and data from meteorological radars and satellites, applied in an agrometeorological service provided by the Institute of Meteorology and Water Management in Poland. Selected examples cover Wielkopolskie Province, which is a primarily agricultural region. An analysis of the course of the soil moisture index and HTC as well as differences in the PEI spatial distribution from ground and satellite data for the extremely dry growing season of 2018 are presented. The authors tried to demonstrate that combining data available from different sources may be a necessary condition for modern agriculture in the conditions of climate change.

This paper presents the evolution of the mesoscale convection system as seen on satellite images during all stages: pre-convection, initiation, and maturity. The evolution of any atmospheric phenomenon can be monitored effectively only when the data available have adequate temporal and spatial...

CONTEXT Crop yield forecasting is essential for agricultural planning, food security assessment, and climate change adaptation. In Poland and other regions of Central Europe, interannual variability in early-season meteorological conditions is a key driver of yield variability, yet its systematic use in regional-scale forecasting models remains limited. OBJECTIVE This study aims to quantify the influence of early-season meteorological anomalies on yields of nine key field crops in Poland and to assess the potential for generating yield forecasts several months before harvest using satellite and reanalysis data. METHODS Linear regression models were developed using anomalies of 2-metre temperature, precipitation, reference evapotranspiration and root-zone soil moisture derived from ERA5 reanalysis and EUMETSAT H-SAF and LSA-SAF satellite products. Models were trained separately for each crop and each of the 16 Polish voivodeships using data from 2004 to 2022. A rolling time-based cross-validation scheme was applied, complemented by an independent hold-out validation for 2019–2022. RESULTS AND CONCLUSIONS Positive temperature anomalies during January–April were consistently associated with higher yields across all winter cereals, while soil moisture deficits in the upper root zone (SM1 and SM2 layers) represented the primary limiting factor. Reference evapotranspiration anomalies in May–June provided a proxy signal for radiation and atmospheric demand conditions. Preliminary forecasts were feasible up to four months before harvest for winter cereals and up to three months for spring cereals. Independent validation for the Opolskie voivodeship yielded Pearson correlation coefficients of 0.6–0.8 for most crops, with maize showing lower agreement (r ≈ 0.45). SIGNIFICANCE The proposed anomaly-based framework provides interpretable, timely and operationally relevant yield forecasts for regional agricultural planning under increasing climate variability. Its transparency and reliance on freely available satellite and reanalysis products make it suitable for implementation in early-warning systems across Central Europe.