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Climate data support a suite of scientific and socioeconomic activities that can reinforce development gains and improve the lives of those most vulnerable to climate variability and change. Historical and current weather and climate observations are essential for many activities, including operational meteorology, identifying extreme events and assessing associated risks, developing climate-informed early warning systems, planning, and research. Rainfall is the most widely available and used climate variable. Thus, measurement of rainfall is crucial to society’s well-being. In general, measurements from ground meteorological stations managed by National Meteorological Agencies are the principal sources of rainfall data. The main strength of the station observations is that they are assumed to give the “true” measurements of rainfall. However, the distribution of the meteorological observation network over Africa is significantly inadequate, with declining numbers of stations and poor data quality. This problem is compounded by the fact that the distribution of existing stations is uneven, with most weather stations located in cities and towns along major roads. As a result, coverage tends to be worse in rural areas, where livelihoods may be most vulnerable to climate variability and change. This has resulted in critical gaps in the provision of climate services where it is needed the most. Space-based measurements from satellites are being used as a complement to or in place of ground observations. Satellite-derived precipitation estimates offer good spatial coverage and improved temporal and spatial resolution, as well as near-real-time availability. Moreover, a range of satellite rainfall products are freely available from many sources, and a couple of these products are available only for Africa. However, satellite rainfall products also suffer from many shortcomings that include accuracy, particularly at higher temporal resolutions; coarse spatial resolution; short time series; and temporal inhomogeneity due to varying inputs. This limits the use of the use these products for certain applications. Understanding satellite rainfall estimation errors is critical for deciding which products might be used for specific applications and requires rigorous evaluation of these products using ground observations. The challenge in Africa is lack of availability, accessibility, and quality of rain-gauge observations that could be used for this purpose. Despite these challenges, there have been some validation efforts over different parts of the continent. However, different and inconsistent approaches of validation have created challenges to using these evaluation results. A comprehensive validation of the main operational satellite products at a continental level is needed to overcome these challenges and make the best use of satellite rainfall products in different applications.

Very accurate long-term measurements of atmospheric CO2 concentrationsbi are needed to understand the role of human activities on the greenhouse effect, as well as the interactions between anthropogenic emissions and the natural carbon cycle. Knowledge of the carbon cycle has been acquired through the development describes the development of atmospheric measurement networks and methods for measuring CO2 in the atmosphere, including the measurement of CO2 in air bubbles extracted from ice cores, the emergence of precise in situ measurements at the beginning of the 1960s, and the operational networks now deployed in certain parts of the world. The surface network of atmospheric stations where CO2 is measured, either in air samples or by in situ instrumentation, made up of about 150 monitoring sites, supplemented by airborne measurements on board of research and commercial aircrafts, is coordinated by international projects aimed at guaranteeing a long-term measurement compatibility to within approximately 0.025‰ (0.1 ppm). This level of accuracy is necessary to characterize atmospheric signals such as the long-term trend, which has risen in 60 years from 1 to 2.2 ppm/year, but also the interannual, seasonal, and regional variations of CO2. These atmospheric signals provide unique information about natural biogeochemical cycles and their current disturbance. The additional measurement of radiocarbon in atmospheric CO2, as described in this article, also makes it possible to identify the contribution due to fossil fuel CO2 emissions. The logistics and metrological requirements of in situ measurements mean that the monitoring network only covers the globe very incompletely—hence the importance of satellite observations, which have been developing strongly since their emergence in the early 2000s. Recent space-based CO2 observations make it possible to measure the concentration of CO2 averaged in the atmospheric column with global coverage under cloud-free conditions, providing millions of measurements each year, with a precision that can now reach 1 ppm, thus 10 times less than in situ instrumentation. Similar measurements of total CO2 columns are also made by ground-based remote sensing instruments, at about 100 sites over the world. They provide important reference data to evaluate atmospheric CO2 measurements from satellites and, in combination with in situ measurements of vertical profiles, provide a transfer standard between the satellite measurements and ground-based in situ networks. This article provides an overview of CO2 monitoring programs and what they tell about large-scale biogeochemical change. The perspectives for the development of CO2 observations are important both for surface networks and for space-based observations, with the objective of moving toward the characterization of processes at increasingly fine spatial scales, in particular toward urban emissions.

Forecasting severe convective weather remains one of the most challenging tasks facing operational meteorology today, especially in the mid-latitudes, where severe convective storms occur most frequently and with the greatest impact. The forecast difficulties reflect, in part, the many different atmospheric processes of which severe thunderstorms are a by-product. These processes occur over a wide range of spatial and temporal scales, some of which are poorly understood and/or are inadequately sampled by observational networks. Therefore, anticipating the development and evolution of severe thunderstorms will likely remain an integral part of national and local forecasting efforts well into the future. Modern severe weather forecasting began in the 1940s, primarily employing the pattern recognition approach throughout the 1950s and 1960s. Substantial changes in forecast approaches did not come until much later, however, beginning in the 1980s. By the start of the new millennium, significant advances in the understanding of the physical mechanisms responsible for severe weather enabled forecasts of greater spatial and temporal detail. At the same time, technological advances made available model thermodynamic and wind profiles that supported probabilistic forecasts of severe weather threats. This article provides an updated overview of operational severe local storm forecasting, with emphasis on present-day understanding of the mesoscale processes responsible for severe convective storms, and the application of recent technological developments that have revolutionized some aspects of severe weather forecasting. The presentation, nevertheless, notes that increased understanding and enhanced computer sophistication are not a substitute for careful diagnosis of the current meteorological environment and an ingredients-based approach to anticipating changes in that environment; these techniques remain foundational to successful forecasts of tornadoes, large hail, damaging wind, and flash flooding.