Evaluating daytime planetary boundary-layer height estimations resolved by both active and passive remote sensing instruments during the CHEESEHEAD19 field campaign

During the Chequamegon Heterogeneous Ecosystem Energy-balance Study Enabled by a High-density Extensive Array of Detectors 2019 (CHEESEHEAD19) field campaign, held in the summer of 2019 in northern Wisconsin, U.S.A., active and passive ground-based remote sensing instruments were deployed to understand the response of the planetary boundary layer to heterogeneous land surface forcing. These instruments include Radar Wind Profilers, Microwave Radiometers, Atmospheric Emitted Radiance Interferometers, Ceilometers, High Spectral Resolution Lidars, Doppler Lidars, and Collaborative Lower Atmospheric Modelling Profiling Systems that combine several of these instruments. In this study, these ground-based remote sensing instruments are used to estimate the height of the daytime planetary boundary layer, and their performance is compared against independent boundary-layer depth estimates obtained from radiosondes launched as part of the field campaign. The impact of clouds (in particular boundary layer clouds) on boundary-layer depth is also investigated. We found that while overall all instruments are able to provide reasonable boundary-layer depth estimates, each of them shows strengths and weaknesses under certain conditions. For example, Radar Wind Profilers perform well during cloud free conditions, and Microwave Radiometers and Atmospheric Emitted Radiance Interferometers have a very good agreement during all conditions, but are limited by the smoothness of the retrieved thermodynamic profiles. The estimates from Ceilometers and High Spectral Resolution Lidars can be hindered by the presence of elevated aerosol layers or clouds, and the multi-instrument retrieval from the Collaborative Lower Atmospheric Modelling Profiling Systems can be constricted to a limited height range in low aerosol conditions.

Overview: Fusion of radar polarimetry and numerical atmospheric modelling towards an improved understanding of cloud and precipitation processes

Cloud and precipitation processes are still a main source of uncertainties in numerical weather prediction and climate change projections. The Priority Programme “Polarimetric Radar Observations meet Atmospheric Modelling (PROM)”, funded by the German Research Foundation (Deutsche Forschungsgemeinschaft, DFG), is guided by the hypothesis that many uncertainties relate to the lack of observations suitable to challenge the representation of cloud and precipitation processes in atmospheric models. Such observations can, however, at present be provided by the recently installed dual-polarization C-band weather radar network of the German national meteorological service in synergy with cloud radars and other instruments at German supersites and similar national networks increasingly available worldwide. While polarimetric radars potentially provide valuable in-cloud information on hydrometeor type, quantity, and microphysical cloud and precipitation processes, and atmospheric models employ increasingly complex microphysical modules, considerable knowledge gaps still exist in the interpretation of the observations and in the optimal microphysics model process formulations. PROM is a coordinated interdisciplinary effort to increase the use of polarimetric radar observations in data assimilation, which requires a thorough evaluation and improvement of parameterizations of moist processes in atmospheric models. As an overview article of the inter-journal special issue “Fusion of radar polarimetry and numerical atmospheric modelling towards an improved understanding of cloud and precipitation processes”, this article outlines the knowledge achieved in PROM during the past 2 years and gives perspectives for the next 4 years.

Rapoc: the Rosseland and Planck opacity converter. A user-friendly and fast opacity program for Python

<p>We present a novel code that converts the widely-used wavelength-dependent opacities of gaseous species into Rosseland and Planck mean opacities (RPMs). RAPOC (Rosseland and Planck Opacity Converter) is a straightforward and efficient Python code that makes use of ExoMol and DACE data as well as any other user-defined data, provided that it is within the correct format. Furthermore, RAPOC has the useful ability of rapidly interpolating between discrete data points, therefore allowing for a complete incorporation in atmospheric models. </p> <p>Whereas RPMs should not be used as a replacement for more rigorous opacity analyses, they have certain benefits. For example, RPMs  allow  one  to  use  Grey  or  semi-Grey  models  when  analysing  gaseous environments;  which  are  simpler,  have  exact  solutions,  and  can  be  used  as benchmarks  for  more  rigorous  approaches. By incorporating the pressure and temperature dependence of RPMs, RAPOC provides a more complex treatment of the mean opacities than what is sometimes used within the literature, notably assuming constant values or adopting simple analytic formulations.  We report  examples  of RAPOC opacities  that  are  incorporated  into  a  semi-Grey  model  to produce the temperature profile of HD 209458 b that is then compared to the realisations of the more rigorous POSEIDON code.</p> <p>The RAPOC code will provide the exoplanetary community a new tool for atmospheric modelling. For a quick installation in one's machinery, the “pip install rapoc” command can be used.</p>

Overview: Fusion of Radar Polarimetry and Numerical Atmospheric Modelling Towards an Improved Understanding of Cloud and Precipitation Processes

Cloud and precipitation processes are still the main source of uncertainties in numerical weather prediction and climate change projections. The Priority Program "Polarimetric Radar Observations meet Atmospheric Modelling (PROM)", funded by the German Research Foundation (Deutsche Forschungsgemeinschaft, DFG), is guided by the hypothesis, that many uncertainties relate to the lack of observations suitable to challenge the representation of cloud and precipitation processes in atmospheric models. Such observations can, however, nowadays be provided e.g. by the recently installed dual-polarization C band weather radar network of the German national meteorological service in synergy with cloud radars and other instruments at German supersites and similar national networks increasingly available worldwide. While polarimetric radars potentially provide valuable in-cloud information e.g. on hydrometeor type, quantity, and microphysical cloud and precipitation processes, and atmospheric models employ increasingly higher moment microphysical modules, still considerable knowledge gaps exist in the interpretation of the observations and large uncertainties in the optimal microphysics model process formulations. PROM is a coordinated interdisciplinary effort to intensify the use of polarimetric radar observations in data assimilation, which requires a thorough evaluation and improvement of parametrizations of moist processes in atmospheric models. As an overview article of the inter-journal special issue "Fusion of radar polarimetry and numerical atmospheric modelling towards an improved understanding of cloud and precipitation processes", it outlines the knowledge achieved in PROM during the past two years and gives perspectives for the next four years.

Preliminary Tests on the Sensitivity of the FORAIR_IT Air Quality Forecasting System to Different Meteorological Drivers

Since 2017, the operational high-resolution air quality forecasting system FORAIR_IT, developed and maintained by the Italian National Agency for New Technologies, Energy and Sustainable Economic Development, has been providing three-day forecasts of concentrations of atmospheric pollutants over Europe and Italy, on a daily basis, with high spatial resolution (20 km on Europe, 4 km on Italy). The system is based on the Atmospheric Modelling System of the National Integrated Assessment Model for Italy (AMS-MINNI), which is a national modelling system evaluated in several studies across Italy and Europe. AMS-MINNI, in its forecasting setup, is presently a candidate model for the Copernicus Atmosphere Monitoring Service’s regional production, dedicated to European-scale ensemble model forecasts of air quality. In order to improve the quality of the meteorological input into the chemical transport model component of FORAIR_IT, several tests were carried out on daily forecasts of NO2 and O3 concentrations for January and August 2019 (representative of the meteorological seasons of winter and summer, respectively). The aim was to evaluate the sensitivity to the meteorological input in NO2 and O3 concentration forecasting. More specifically, the Weather Research and Forecasting model (WRF) was tested to potentially improve the meteorological driver with respect to the Regional Atmospheric Modelling System (RAMS), which is currently embedded in FORAIR_IT. In this work, the WRF chain is run in several setups, changing the parameterization of several micrometeorological variables (snow, mixing height, albedo, roughness length, soil heat flux + friction velocity, Monin–Obukhov length), with the main objective being to take advantage of WRF’s consistent physics in the calculation of both mesoscale variables and micrometeorological parameters for air quality simulations. Daily forecast concentrations produced by the different meteorological model configurations are compared to the available measured concentrations, showing the general good performance of WRF-driven results, even if performance skills are different according to the single meteorological configuration and to the pollutant type. WRF-driven forecasts clearly improve the model reproduction of the temporal variability of concentrations, while the bias of O3 is higher than in the RAMS-driven configuration. The results suggest that we should keep testing WRF configurations, with the objective of obtaining a robust improvement in forecast concentrations with respect to RAMS-driven forecasts.

SIMULAÇÃO NUMÉRICA DO DESASTRE DE ORIGEM NATURAL OCORRIDO EM JANEIRO DE 2011 NO MUNICÍPIO DE NOVA FRIBURGO, RJ, UTILIZANDO O MODELO BRAMS

O conceito de desastre de origem natural é definido como o produto de um evento de ordem natural inerente à dinâmica da Terra associadas à vulnerabilidade de uma região. Esta vulnerabilidade pode estar associada a fatores naturais, antrópicos ou a uma combinação destes. O ano de 2011 foi marcado pela ocorrência de um desastre de origem natural ocorrido na Região Serrana do Rio de Janeiro (RSRJ) resultando em cerca de 900 mortos e 300 mil pessoas atingidas. Na cidade de Nova Friburgo foram 420 vítimas fatais e cerca de 180 mil pessoas atingidas direta e indiretamente pelo desastre natural O presente artigo analisa o ambiente sinótico e de mesoescala que culminou no desastre natural na cidade de Nova Friburgo. Utilizou-se o modelo atmosférico Brazilian Regional Atmospheric Modelling System (BRAMS) para simular o comportamento da atmosfera no período de 11 a 14 de janeiro de 2011. Verificou-se a partir das imagens do satélite GOES-12, canal Temperatura Realçada, e das simulações do BRAMS, que as chuvas intensas que provocaram os desastres na RSRJ foram geradas pelo deslocamento de uma frente fria que se acoplou a Zona de Convergência de Umidade, organizando a Zona de Convergência do Atlântico Sul.