Metrics for the evaluation of warm convective cloud fields in a large-eddy simulation with Meteosat images

The representation of warm convective clouds in atmospheric models and satellite observations can considerably deviate from each other partly due to different spatial resolutions. This study aims to establish appropriate metrics to evaluate high-resolution simulations of convective clouds by the ICON Large-Eddy Model (ICON-LEM) with observations from Meteosat SEVIRI over Germany. The time series and frequency distributions of convective cloud fraction and liquid water path (LWP) are analyzed. Furthermore, the study focuses on size distributions and decorrelation scales of warm convective cloud fields. The investigated metrics possess a pronounced sensitivity to the apparent spatial resolution. At the fine spatial scale, the simulations show higher occurrence frequencies of large LWP values and a factor of two to four smaller convective cloud fractions. Coarse-graining of simulated fields to the optical resolution of Meteosat essentially removes the differences between the observed and simulated metrics. The distribution of simulated cloud sizes compares well with the observations and can be represented by a power law, with a moderate resolution sensitivity. A lower limit of cloud sizes is identified, which is 8–10 times the native grid resolution of the model. This likely marks the effective model resolution beyond which the scaling behaviour of considered metrics is not reliable, implying that a further increase in spatial resolution would be desirable to better resolve cloud processes below 1 km. It is finally shown that ICON-LEM is consistent with spatio-temporal decorrelation scales observed with Meteosat having values of 30 min and 7 km, if transferred to the true optical satellite resolution. However, the simulated Lagrangian decorrelation times drop to 10 min at 1 km resolution, a scale covered by the upcoming generation of geostationary satellites

Supernova explosions and the birth of neutron stars

We report here on recent progress in understanding the birth conditions of neutron stars and the way how supernovae explode. More sophisticated numerical models have led to the discovery of new phenomena in the supernova core, for example a generic hydrodynamic instability of the stagnant supernova shock against low-mode nonradial deformation and the excitation of gravity-wave activity in the surface and core of the nascent neutron star. Both can have supportive or decisive influence on the inauguration of the explosion, the former by improving the conditions for energy deposition by neutrino heating in the postshock gas, the latter by supplying the developing blast with a flux of acoustic power that adds to the energy transfer by neutrinos. While recent two-dimensional models suggest that the neutrino-driven mechanism may be viable for stars from about 8 solar masses to at least 15 solar masses, acoustic energy input has been advocated as an alternative if neutrino heating fails. Magnetohydrodynamic effects constitute another way to trigger explosions in connection with the collapse of sufficiently rapidly rotating stellar cores, perhaps linked to the birth of magnetars. The global explosion asymmetries seen in the recent simulations offer an explanation of even the highest measured kick velocities of young neutron stars.Comment: 10 pages, 8 figures, 19 ps files; to be published in Proc. of Conf. "40 Years of Pulsars: Millisecond Pulsars, Magnetars, and More", August 12-17, 2007, McGill Univ., Montreal, Canada; high-resolution images can be obtained upon request; incorrect panel in fig.8 replace

Assimilating visible and infrared radiances in idealized simulations of deep convection

International audienceCloud-affected radiances from geostationary satellite sensors provide the first area-wide observable signal of convection with high spatial resolution in the range of kilometers and high temporal resolution in the range of minutes. However, these observations are not yet assimilated in operational convection-resolving weather prediction models as the rapid, nonlinear evolution of clouds makes the assimilation of related observations very challenging. To address these challenges, we investigate the assimilation of satellite radiances from visible and infrared channels in idealized observing system simulation experiments (OSSEs) for a day with summertime deep convection in central Europe. This constitutes the first study assimilating a combination of all-sky observations from infrared and visible satellite channels, and the experiments provide the opportunity to test various assimilation settings in an environment where the observation forward operator and the numerical model exhibit no systematic errors. The experiments provide insights into appropriate settings for the assimilation of cloud-affected satellite radiances in an ensemble data assimilation system and demonstrate the potential of these observations for convective-scale weather prediction. Both infrared and visible radiances individually lead to an overall forecast improvement, but best results are achieved with a combination of both observation types that provide complementary information on atmospheric clouds. This combination strongly improves the forecast of precipitation and other quantities throughout the whole range of 8-h lead time

Evaluating Latent-Heat-Nudging Schemes and Radar forward Operator Settings for a Convective Summer Period over Germany Using the ICON-KENDA System

Radar data assimilation has been operational at the Deutscher Wetterdienst for several years and is essential for generating accurate precipitation forecasts. The current work attempts to further enhance the radar data assimilation by improving the latent heat nudging (LHN) scheme and by reducing the observation error (OE) caused by the representation error of the efficient modular volume radar operator (EMVORADO). First of all, a series of hindcasts for a one-month convective period over Germany are performed. Compared with radar reflectivity and satellite observations, it is found that the LHN scheme that implicitly adjusts temperature performs better, and the beam broadening effect and the choice of the scattering schemes in EMVORADO are important. Moreover, the Mie scheme with the new parameterization to reduce the brightband effect not only proves to be the best in hindcasts but also that it results in the smallest standard deviations and the shortest horizontal correlation length scales of the OE in data assimilation experiments

Irradiance and cloud optical properties from solar photovoltaic systems

Solar photovoltaic power output is modulated by atmospheric aerosols and clouds and thus contains valuable information on the optical properties of the atmosphere. As a ground-based data source with high spatiotemporal resolution it has great potential to complement other ground-based solar irradiance measurements as well as those of weather models and satellites, thus leading to an improved characterisation of global horizontal irradiance. In this work several algorithms are presented that can retrieve global tilted and horizontal irradiance and atmospheric optical properties from solar photovoltaic data and/or pyranometer measurements. Specifically, the aerosol (cloud) optical depth is inferred during clear sky (completely overcast) conditions. The method is tested on data from two measurement campaigns that took place in Allgäu, Germany in autumn 2018 and summer 2019, and the results are compared with local pyranometer measurements as well as satellite and weather model data. Using power data measured at 1 Hz and averaged to 1 minute resolution, the hourly global horizontal irradiance is extracted with a mean bias error compared to concurrent pyranometer measurements of 11.45 W m−2, averaged over the two campaigns, whereas for the retrieval using coarser 15 minute power data the mean bias error is 16.39 W m−2. During completely overcast periods the cloud optical depth is extracted from photovoltaic power using a lookup table method based on a one-dimensional radiative transfer simulation, and the results are compared to both satellite retrievals as well as data from the COSMO weather model. Potential applications of this approach for extracting cloud optical properties are discussed, as well as certain limitations, such as the representation of 3D radiative effects that occur under broken cloud conditions. In principle this method could provide an unprecedented amount of ground-based data on both irradiance and optical properties of the atmosphere, as long as the required photovoltaic power data are available and are properly pre-screened to remove unwanted artefacts in the signal. Possible solutions to this problem are discussed in the context of future work

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 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

Assimilating visible satellite images for convective-scale numerical weather prediction: A case-study

Satellite images in the visible spectral range contain high-resolution cloud information, but have not been assimilated directly before. This paper presents a case-study on the assimilation of visible Meteosat SEVIRI images in a convective-scale data assimilation system based on a local ensemble transform Kalman filter (LETKF) in a near-operational set-up. For this purpose, a fast look-up table-based forward operator is used to generated synthetic satellite images from the model state. Single-observation experiments show that the assimilation of visible reflectances improves cloud cover under most conditions and often reduces temperature and humidity errors. In cycled experiments for two summer days with convective precipitation, the assimilation strongly reduces the errors of cloud cover and improves the precipitation forecast. While these results are promising, several issues are identified that limit the efficacy of the assimilation process. First, the linearity assumption of the LETKF can lead to errors as reflectance is a nonlinear function of the model state. Second, errors can arise from the fact that visible reflectances alone are ambiguous and only weakly sensitive to the water phase and cloud-top height. And lastly, it is not obvious how to localise vertical covariances as visible reflectances are sensitive to clouds at all heights. For the latter reason, no vertical localisation was used in this study. To investigate the robustness of the results, the horizontal localisation scale, the assigned observation error and the spatial density of observations were varied in sensitivity experiments. The best results were obtained for an observation error close to the Desroziers estimate. High observation density combined with small localisation radii resulted in the smallest 1 hr forecast error. These settings were also beneficial for 3 hr forecasts, but forecasts at that lead time were less sensitive to the observation density and the localisation scale