Choosing between post-processing precipitation forecasts or chaining several uncertainty quantification tools in hydrological forecasting systems

This study aims to decipher the interactions of a precipitation post-processor and several other tools for uncertainty quantification implemented in a hydrometeorological forecasting chain. We make use of four hydrometeorological forecasting systems that differ by how uncertainties are estimated and propagated. They consider the following sources of uncertainty: system A, forcing, system B, forcing and initial conditions, system C, forcing and model structure, and system D, forcing, initial conditions, and model structure. For each system's configuration, we investigate the reliability and accuracy of post-processed precipitation forecasts in order to evaluate their ability to improve streamflow forecasts for up to 7 d of forecast horizon. The evaluation is carried out across 30 catchments in the province of Quebec (Canada) and over the 2011–2016 period. Results are compared using a multicriteria approach, and the analysis is performed as a function of lead time and catchment size. The results indicate that the precipitation post-processor resulted in large improvements in the quality of forecasts with regard to the raw precipitation forecasts. This was especially the case when evaluating relative bias and reliability. However, its effectiveness in terms of improving the quality of hydrological forecasts varied according to the configuration of the forecasting system, the forecast attribute, the forecast lead time, and the catchment size. The combination of the precipitation post-processor and the quantification of uncertainty from initial conditions showed the best results. When all sources of uncertainty were quantified, the contribution of the precipitation post-processor to provide better streamflow forecasts was not remarkable, and in some cases, it even deteriorated the overall performance of the hydrometeorological forecasting system. Our study provides an in-depth investigation of how improvements brought by a precipitation post-processor to the quality of the inputs to a hydrological forecasting model can be cancelled along the forecasting chain, depending on how the hydrometeorological forecasting system is configured and on how the other sources of hydrological forecasting uncertainty (initial conditions and model structure) are considered and accounted for. This has implications for the choices users might make when designing new or enhancing existing hydrometeorological ensemble forecasting systems.

Quantification of methane emissions from hotspots and during COVID-19 using a global atmospheric inversion

Concentrations of atmospheric methane (CH4), the second most important greenhouse gas, continue to grow. In recent years this growth rate has increased further (2020: +14.7 ppb), the cause of which remains largely unknown. Here, we demonstrate a high-resolution (~80 km), short-window (24-hour) 4D-Var global inversion system based on the ECMWF Integrated Forecasting System (IFS) and newly available satellite observations. The largest national disagreement found between prior (63.1 Tg yr−1) and posterior (59.8 Tg yr−1) CH4 emissions is from China, mainly attributed to the energy sector. Emissions estimated form our global system agree well with previous basin-wide regional studies and point source specific studies. Emission events (leaks/blowouts) >10 t hr−1 were detected, but without accurate prior uncertainty information, were not well quantified. Our results suggest that global anthropogenic CH4 emissions for 2020 were 5.7 Tg yr−1 (+1.6 %) higher than for 2019, mainly attributed to the energy and agricultural sectors. Regionally, the largest 2020 increases were seen from China (+2.6 Tg yr−1, 4.3 %), with smaller increases from India (+0.8 Tg yr−1, 2.2 %) and Indonesia (+0.3 Tg yr−1, 2.6 %). Results show the rise in emissions, and subsequent atmospheric growth, would have occurred with or without the COVID-19 slowdown. During the onset of the global slowdown (March–April, 2020) energy sector CH4 emissions from China increased; however, during later months (May–June, 2020) emissions decreased below expected pre-slowdown levels. The accumulated impact of the slowdown on CH4 emissions from March–June 2020 is found to be small. Changes in atmospheric chemistry, not investigated here, may have contributed to the observed growth in 2020. Future work aims to develop the global IFS inversion system and to extend the 4D-Var window-length using a hybrid ensemble-variational method.

The Black Sea Physics Analysis and Forecasting System within the Framework of the Copernicus Marine Service

This work describes the design, implementation and validation of the Black Sea physics analysis and forecasting system, developed by the Black Sea Physics production unit within the Black Sea Monitoring and Forecasting Center as part of the Copernicus Marine Environment and Monitoring Service. The system provides analyses and forecasts of the temperature, salinity, sea surface height, mixed layer depth and currents for the whole Black Sea basin, excluding the Azov Sea, and has been operational since 2016. The system is composed of the NEMO (v 3.4) numerical model and an OceanVar scheme, which brings together real time observations (in-situ temperature and salinity profiles, sea level anomaly and sea surface temperature satellite data). An operational quality assessment framework is used to evaluate the accuracy of the products which set the basic standards for the future upgrades, highlighting the strengths and weaknesses of the model and the observing system in the Black Sea.

Role of clusters in development of the digital ecosystem of the agro-industrial complex

The main trends in agriculture over the past few years have been export orientation and digitalization. Since 2019, the departmental project “Digital Agriculture” has been implemented. In 2021, a draft order of the government of the Russian Federation “On the Strategic Direction in the Field of Digital Transformation of the Agro-Industrial and Fishery Complex of the Russian Federation until 2030” was developed, which provides for development of a national online platform for promoting the agricultural products and the launch of a modeling and forecasting system for the agro-industrial complex. Currently, the beneficiaries of digitalization are large agricultural holdings and the IT industry (manufacturers of sensors and software developers for machinery and equipment, software companies, fertilizer producers and telecom operators). In order for small and medium-sized farms that do not have sufficient resources for end-to-end digitalization of production and business processes to be able to join the innovation race, it is necessary to look for a format of interaction in which competition and cooperation are organically combined. The article discusses one of such forms of association – clusters; explores their potential role in development of platform network architectures; the similarities and differences of cluster and digital industrial systems are analyzed. It is concluded that the flexibility and openness of cluster structures allows actors to find their functional areas and niches and organically fit into the created digital ecosystem of the agro-industrial complex.