A new approach for near-real-time monitoring of atmospheric stability, atmospheric water vapor and liquid water

Presentación realizada en la 3rd European Nowcasting Conference, celebrada en la sede central de AEMET en Madrid del 24 al 26 de abril de 2019

Towards the profiling of the atmospheric boundary layer at European scale—introducing the COST Action PROBE

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The Second ARM Training and Science Application Event : Training the Next Generation of Atmospheric Scientists

Non peer reviewe

On methods for assessment of the influence and impact of observations in convection-permitting numerical weather prediction

In numerical weather prediction (NWP), a large number of observations are used to create initial conditions for weather forecasting through a process known as data assimilation. An assessment of the value of these observations for NWP can guide us in the design of future observation networks, help us to identify problems with the assimilation system, and allow us to assess changes to the assimilation system. However, the assessment can be challenging in convection-permitting NWP. First, the strong nonlinearity in the forecast model limits the methods available for the assessment. Second, convection-permitting NWP typically uses a limited area model and provides short forecasts, giving problems with verification and our ability to gather sufficient statistics. Third, convection-permitting NWP often makes use of novel observations, which can be difficult to simulate in an observing system simulation experiment (OSSE). We compare methods that can be used to assess the value of observations in convection-permitting NWP and discuss operational considerations when using these methods. We focus on their applicability to ensemble forecasting systems, as these systems are becoming increasingly dominant for convection-permitting NWP. We also identify several future research directions: comparison of forecast validation using analyses and observations, the effect of ensemble size on assessing the value of observations, flow-dependent covariance localization, and generation and validation of the nature run in an OSSE.Comment: 35 page

Current challenges and future directions in data assimilation and reanalysis

The first Joint WCRP1-WWRP2 Symposium on Data Assimilation and Reanalysis took place on13-17 September 2021, and it was organized in conjunction with the ECMWF Annual Seminaron observations. The last WCRP/WWRP-organized meetings were held separately for data assimilation and reanalysis in 2017 (Buizza et al. 2018; Cardinali et al. 2019). Since then, commonchallenges and new emerging topics have increased the need to bring these communities together toexchange new ideas and findings. Thus, a symposium involving the aforementioned communitieswas jointly organized by DWD3, HErZ4, WCRP, WWRP, and the ECMWF annual seminar. Majorgoals were to increase diversity, provide early career scientists with opportunities to present theirwork and extend their professional network, and bridge gaps between the various communities.The online format allowed more than 500 participants from over 50 countries to meet in avirtual setting, using the gathertown 5 platform as the central tool to access the meeting. A virtualconference center was created where people could freely move around and talk to other close-byparticipants. A lobby served as the main hub and it connected the poster halls and the conferencerooms for the oral presentations and the ECMWF seminar talks. The feedback from the participantswas overwhelmingly positive.Scientifically, the meeting offered opportunities to bring together the communities of Earth systemdata assimilation, reanalysis and observations to identify current challenges, seek opportunitiesfor collaboration, and strategic planning on more integrated systems for the longer term. Thecontributions totalled 140 oral and over 150 poster presentations covering a large variety oftopics with increased interest in Earth system approaches, machine learning and increased spatial resolutions. Key findings of the symposium and the ECMWF annual seminar are summarized insection 2. Section 3 highlights the common and emerging challenges of these communities.Fil: Valmassoi, Arianna. Hans-ertel-centre For Weather Research; Alemania. Institut Fur Geowissenschaften ; Universitaet Bonn;Fil: Keller, Jan D.. Deutscher Wetterdienst; AlemaniaFil: Kleist, Daryl T.. National Ocean And Atmospheric Administration; Estados UnidosFil: English, Stephen. European Center For Medium Range Weather Forecasting; Reino UnidoFil: Ahrens, Bodo. Goethe Universitat Frankfurt; AlemaniaFil: Ďurán, Ivan Bašták. Goethe Universitat Frankfurt; AlemaniaFil: Bauernschubert, Elisabeth. Deutscher Wetterdienst; AlemaniaFil: Bosilovich, Michael G.. National Aeronautics and Space Administration; Estados UnidosFil: Fujiwara, Masatomo. Hokkaido University; JapónFil: Hersbach, Hans. European Center For Medium Range Weather Forecasting; Reino UnidoFil: Lei, Lili. Nanjing University; ChinaFil: Löhnert, Ulrich. University Of Cologne; AlemaniaFil: Mamnun, Nabir. Helmholtz Centre for Environmental Research; AlemaniaFil: Martin, Cory R.. German Research Centre for Geosciences; AlemaniaFil: Moore, Andrew. California State University; Estados UnidosFil: Niermann, Deborah. Deutscher Wetterdienst; AlemaniaFil: Ruiz, Juan Jose. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Centro de Investigaciones del Mar y la Atmósfera. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Centro de Investigaciones del Mar y la Atmósfera; ArgentinaFil: Scheck, Leonhard. Deutscher Wetterdienst; Alemani

The HD(CP)² Observational Prototype Experiment (HOPE) – an overview

The HD(CP)2 Observational Prototype Experiment (HOPE) was performed as a major 2-month field experiment in Jülich, Germany, in April and May 2013, followed by a smaller campaign in Melpitz, Germany, in September 2013. HOPE has been designed to provide an observational dataset for a critical evaluation of the new German community atmospheric icosahedral non-hydrostatic (ICON) model at the scale of the model simulations and further to provide information on land-surface–atmospheric boundary layer exchange, cloud and precipitation processes, as well as sub-grid variability and microphysical properties that are subject to parameterizations. HOPE focuses on the onset of clouds and precipitation in the convective atmospheric boundary layer. This paper summarizes the instrument set-ups, the intensive observation periods, and example results from both campaigns. HOPE-Jülich instrumentation included a radio sounding station, 4 Doppler lidars, 4 Raman lidars (3 of them provide temperature, 3 of them water vapour, and all of them particle backscatter data), 1 water vapour differential absorption lidar, 3 cloud radars, 5 microwave radiometers, 3 rain radars, 6 sky imagers, 99 pyranometers, and 5 sun photometers operated at different sites, some of them in synergy. The HOPE-Melpitz campaign combined ground-based remote sensing of aerosols and clouds with helicopter- and balloon-based in situ observations in the atmospheric column and at the surface. HOPE provided an unprecedented collection of atmospheric dynamical, thermodynamical, and micro- and macrophysical properties of aerosols, clouds, and precipitation with high spatial and temporal resolution within a cube of approximately 10  ×  10  ×  10 km3. HOPE data will significantly contribute to our understanding of boundary layer dynamics and the formation of clouds and precipitation. The datasets have been made available through a dedicated data portal. First applications of HOPE data for model evaluation have shown a general agreement between observed and modelled boundary layer height, turbulence characteristics, and cloud coverage, but they also point to significant differences that deserve further investigations from both the observational and the modelling perspective

The Added Value of Large-Eddy and Storm-Resolving Models for Simulating Clouds and Precipitation

More than one hundred days were simulated over very large domains with fine (0.156 km to 2.5 km) grid spacing for realistic conditions to test the hypothesis that storm (kilometer) and large-eddy (hectometer) resolving simulations would provide an improved representation of clouds and precipitation in atmospheric simulations. At scales that resolve convective storms (storm-resolving for short), the vertical velocity variance becomes resolved and a better physical basis is achieved for representing clouds and precipitation. Similarly to past studies we found an improved representation of precipitation at kilometer scales, as compared to models with parameterized convection. The main precipitation features (location, diurnal cycle and spatial propagation) are well captured already at kilometer scales, and refining resolution to hectometer scales does not substantially change the simulations in these respects. It does, however, lead to a reduction in the precipitation on the time-scales considered – most notably over the ocean in the tropics. Changes in the distribution of precipitation, with less frequent extremes are also found in simulations incorporating hectometer scales. Hectometer scales appear to be more important for the representation of clouds, and make it possible to capture many important aspects of the cloud field, from the vertical distribution of cloud cover, to the distribution of cloud sizes, and to the diel (daily) cycle. Qualitative improvements, particularly in the ability to differentiate cumulus from stratiform clouds, are seen when one reduces the grid spacing from kilometer to hectometer scales. At the hectometer scale new challenges arise, but the similarity of observed and simulated scales, and the more direct connection between the circulation and the unconstrained degrees of freedom make these challenges less daunting. This quality, combined with already improved simulation as compared to more parameterized models, underpins our conviction that the use and further development of storm-resolving models offers exciting opportunities for advancing understanding of climate and climate change