In this paper, we illustrate a new, simple and complementary ground-based methodology to retrieve the vertically resolved atmospheric precipitation intensity through a synergy between measurements from the National Aeronautics and Space Administration (NASA) Micropulse Lidar network (MPLNET), an analytical model solution and ground-based disdrometer measurements. The presented results are obtained at two mid-latitude MPLNET permanent observational sites, located respectively at NASA Goddard Space Flight Center, USA, and at the Universitat Politècnica de Catalunya, Barcelona, Spain. The methodology is suitable to be applied to existing and/or future lidar/ceilometer networks with the main objective of either providing near real-time (3 h latency) rainfall intensity measurements and/or to validate satellite missions, especially for critical light precipitation (<3 mm h −1 ).

Long-term homogeneous climate data records (CDRs) are essential to diagnose changes in our climate, understand its variability, and assess and contextualize future climate projections (Cramer et al., 2018). Use of CDRs influenced by residual non-climatic factors may lead to incorrect conclusions about the changing state of the climate (Kivinen et al., 2017). Therefore, when CDRs are used it is highly desirable to:

Abstract. In this paper we present the automated software tool ELDA (EARLINET Lidar Data Analyzer) for the retrieval of profiles of optical particle properties from lidar signals. This tool is one of the calculus modules of the EARLINET Single Calculus Chain (SCC) which allows for the analysis of the data of many different lidar systems of EARLINET in an automated, unsupervised way. ELDA delivers profiles of particle extinction coefficients from Raman signals as well as profiles of particle backscatter coefficients from combinations of Raman and elastic signals or from elastic signals only. Those analyses start from pre-processed signals which have already been corrected for background, range dependency and hardware specific effects. An expert group reviewed all algorithms and solutions for critical calculus subsystems which are used within EARLINET with respect to their applicability for automated retrievals. Those methods have been implemented in ELDA. Since the software was designed in a modular way, it is possible to add new or alternative methods in future. Most of the implemented algorithms are well known and well documented, but some methods have especially been developed for ELDA, e.g., automated vertical smoothing and temporal averaging or the handling of effective vertical resolution in case of lidar ratio retrievals, or the merging of near-range and far-range products. The accuracy of the retrieved profiles was tested following the procedure of the EARLINET-ASOS algorithm inter-comparison exercise which is based on the analysis of synthetic signals. Mean deviations, mean relative deviations, and normalized root-mean-square deviations were calculated for all possible products and three height layers. In all cases, the deviations were clearly below the maximum allowed values according to the EARLINET quality requirements.

The uncertainty of important atmospheric parameters is a key factor for assessing the uncertainty of global change estimates given by numerical prediction models. One of the critical points of the uncertainty budget is related to the collocation mismatch in space and time among different observations. This is particularly important for vertical atmospheric profiles obtained by radiosondes or LIDAR.

In this paper we consider a statistical modelling approach to understand at which extent collocation uncertainty is related to environmental factors, height and distance between the trajectories. To do this we introduce a new statistical approach, based on the heteroskedastic functional regression (HFR) model which extends the standard functional regression approach and allows us a natural definition of uncertainty profiles. Moreover, using this modelling approach, a five-folded uncertainty decomposition is proposed. Eventually, the HFR approach is illustrated by the collocation uncertainty analysis of relative humidity from two stations involved in GCOS reference upper-air network (GRUAN)

Abstract. There are numerous networks and initiatives concerned with the non-satellite observing segment of Earth Observation. These are owned and operated by various entities and organisations often with different practices, norms, data policies etc. The Horizon 2020 project GAIA-CLIM is working to improve our collective ability to use an appropriate subset of these observations to rigorously characterise satellite observations. The first fundamental question is which observations from the mosaic of non-satellite observational capabilities are appropriate for such an application. This requires an assessment of the relevant, quantifiable aspects of the measurement series which are available. While fundamentally poor or incorrect measurements can be relatively easily identified, it is metrologically impossible to be sure that a measurement series is correct. Certain assessable aspects of the measurement series can, however, build confidence in their scientific maturity and appropriateness for given applications. These are aspects such as that it is well documented, well understood, representative, updated, publicly available, maintains rich metadata etc. Entities such as the Global Climate Observing System have suggested a hierarchy of networks whereby different subsets of the observational capabilities are assigned to different layers based upon such assessable aspects. Herein, we make a first attempt to formalise both such a system-of-systems networks concept and a means by which to, as objectively as possible, assess where in this framework different networks may reside. In this study, we concentrate upon networks measuring primarily a sub-set of the atmospheric Essential Climate Variables of interest to GAIA-CLIM activities. We show assessment results from our application of the guidance and how we plan to use this in downstream exemplary applications of the GAIA-CLIM project. However, the approach laid out could be more widely applicable. If broadly adopted, the system-of-systems approach will have potential benefits in guiding users to the most appropriate set of observations for their needs, and in highlighting to network owners and operators areas for potential improvement.