The Mount Everest plume in winter

Mt. Everest’s summit pyramid is the highest obstacle on earth to the wintertime jet-stream winds. Downwind, in its wake, a visible plume often forms. The meteorology and composition of the plume are unknown. Accordingly, we observed real-time images from a geosynchronous meteorological satellite from November 2020 through March 2021 to identify plumes and collect the corresponding meteorological data. We used the data with a basic meteorological model to show the plumes formed when sufficiently moist air was drawn into the wake. We conclude the plumes were composed initially of either cloud droplets or ice particles depending on the temperature. One plume was observed to glaciate downwind. Thus, Everest plumes may be a source of snowfall formed insitu. The plumes, however, were not composed of resuspended snow.

Evaluation of WRF-CHIMERE coupled models for the simulation of PM_2.5 in large East African urban conurbations

Urban conurbations of East Africa are affected by harmful levels of air pollution. The paucity of local air quality networks and the absence of capacity to forecast air quality make it difficult to quantify the real level of air pollution in this area. The chemistry-transport model CHIMERE has been coupled with the meteorological model WRF and used to simulate hourly concentrations of Particulate Matter PM2.5 for three East African urban conurbations: Addis Ababa in Ethiopia, Nairobi in Kenya and Kampala in Uganda. Two existing emission inventories were combined to test the performance of CHIMERE as an air quality tool for a target monthly period of 2017 and the results compared against observed data from urban and rural sites. The results show that the model is able to reproduce hourly and daily temporal variability of aerosol concentrations close to observations both in urban and rural environments. CHIMERE’s performance as a tool for managing air quality was also assessed. The analysis demonstrated that despite the absence of high-resolution data and up-to-date biogenic and anthropogenic emissions, the model was able to reproduce 66–99 % of the daily PM2.5 exceedances above the WHO 24-hour mean PM2.5 guideline (25 µg m−3) in the three cities. An analysis of the 24-hour mean levels of PM2.5 was also carried out for 17 constituencies in the vicinity of Nairobi. This showed that 47 % of the constituencies in the area exhibited a low air quality index for PM2.5 in the unhealthy category for human health exposing between 10000 to 30000 people/km2 to harmful levels of air contamination.

HydroPy (v1.0): a new global hydrology model written in Python

Global hydrological models (GHMs) are a useful tool in the assessment of the land surface water balance. They are used to further the understanding of interactions between water balance components and their past evolution as well as potential future development under various scenarios. While GHMs have been part of the hydrologist's toolbox for several decades, the models are continuously being developed. In our study, we present the HydroPy model, a revised version of an established GHM, the Max Planck Institute for Meteorology's Hydrology Model (MPI-HM). Being rewritten in Python, the new model requires much less effort in maintenance, and due to its flexible infrastructure, new processes can be easily implemented. Besides providing a thorough documentation of the processes currently implemented in HydroPy, we demonstrate the skill of the model in simulating the land surface water balance. We find that evapotranspiration is reproduced realistically for the majority of the land surface but is underestimated in the tropics. The simulated river discharge correlates well with observations. Biases are evident for the annual accumulated discharge; however, they can – at least to some extent – be attributed to discrepancies between the meteorological model forcing data and the observations. Finally, we show that HydroPy performs very similarly to MPI-HM and thus conclude the successful transition from MPI-HM to HydroPy.

Modellierung von Regenereignissen in urbanen Gebieten mit dem hochauflösenden, numerischen Modell MITRAS

<p>Durch den globalen Klimawandel verändert sich der Niederschlag und vielerorts wird eine Zunahme von Starkregenereignissen festgestellt. Gleichzeitig werden in urbanen Gebieten durch den steigenden Flächenbedarf zusätzlich Flächen versiegelt. Diese beiden Effekte zusammen erhöhen den Oberflächenabfluss, was zu einem Anstieg des Risikos lokaler Überflutungen im urbanen Raum führt. Somit steigt der Bedarf an verschiedenen Anpassungsszenarien für Städte an den veränderten Niederschlag.</p> <p>Der Anstieg der Rechenkapazitäten erlaubt mittlerweile hochauflösende Modellsimulationen (<10 m horizontale Auflösung) unter Berücksichtigung von meteorologischen Prozessen, wie den Stadteinflüssen auf die Wolken- und Niederschlagsdynamik. Damit kann der Einfluss von verschiedenen Anpassungsszenarien im urbanen Raum auf die im Stadtgebiet zwischen und auf Gebäuden auftretende Niederschlagsmenge mit einem numerischen Modell quantifiziert werden.</p> <p>Aufgrund der Größe eines hochaufgelösten Modellgebietes passiert ein Regenereignis das Modellgebiet in kurzer Zeit, ohne dass sich Wolken bei einer advektiven Situation im Modellgebiet voll entwickeln können. Daher werden die mit dem mikroskaligen, hindernissauflösenden Transport- und Strömungsmodell MITRAS (Salim et al. 2019) durchgeführten Modellrechnungen mit Informationen über Regenereignisse, die sich bereits außerhalb des Modellgebietes bilden, angetrieben (Nudging Methode). Dazu werden in MITRAS die Daten eines X-Band Radars mit einer 100 m räumlichen und 30 s zeitlichen Auflösung verwendet.</p> <p>In diesem Beitrag wird der Nudging-Ansatz beschrieben und die Ergebnisse der Evaluierung mit verschiedenen synoptischen Grundbedingungen für ein Testgebiet in Hamburg präsentiert.</p> <p> </p> <p>Salim M.H, Schlünzen K.H., Grawe D., Boettcher M., Gierisch A.M.U., Fock B.H. (2018): The microscale obstacle-resolving meteorological model MITRAS v2.0: model theory. Geosci. Model Dev., 11, 3427–3445, https://doi.org/10.5194/gmd-11-3427-2018.</p> <p> </p> <p><em>Beitrag zum Exzellenzcluster „CLICCS - Klima, Klimawandel und Gesellschaft“, Beitrag zu dem Centrum für Erdsystemforschung und Nachhaltigkeit (CEN) an der Universität Hamburg</em></p>

Simulation of karst floods with a hydrological model improved by meteorological model coupling

Karst basins are prone to rapid flooding because of their geomorphic complexity and exposed karst landforms with low infiltration rates. Accordingly, simulating and forecasting floods in karst regions can provide important technical support for local flood control. The study area, the Liujiang karst river basin, is the most well-developed karst area in South China, and its many mountainous areas lack rainfall gauges, limiting the availability of precipitation information. Quantitative precipitation forecast (QPF) from the Weather Research and Forecasting model (WRF) and quantitative precipitation estimation (QPE) from remote sensing information by an artificial neural network cloud classification system (PERSIANN-CCS) can offer reliable precipitation estimates. Here, the distributed Karst-Liuxihe (KL) model was successfully developed from the terrestrial Liuxihe model, as reflected in improvements to its underground structure and confluence algorithm. Compared with other karst distributed models, the KL model has a relatively simple structure and small modeling data requirements, which are advantageous for flood prediction in karst areas lacking hydrogeological data. Our flood process simulation results suggested that the KL model agrees well with observations and outperforms the Liuxihe model. The average Nash coefficient, correlation coefficient, and water balance coefficient increased by 0.24, 0.19, and 0.20, respectively, and the average flood process error, flood peak error, and peak time error decreased by 13%, 11%, and 2 hours, respectively. Coupling the WRF model and PERSIANN-CCS with the KL model yielded a good performance in karst flood simulation and prediction. Notably, coupling the WRF and KL models effectively predicted the karst flood processes and provided flood prediction results with a lead time of 96 hours, which is important for flood warning and control.

The Use of Geoinformatics in Coastal Atmospheric Transport Phenomena: The Athens Experiment

Coastal environment, an area where abrupt changes occur between land and sea, significantly affects the quality of life of a high portion of the Earth’s population. Therefore, the wide range of phenomena observed in coastal areas need to be assessed reliably regarding both data sets and methods applied. In particular, the study of coastal atmospheric transport phenomena which affect a variety of activities in coastal areas, using modeling techniques, demand accurate estimations of a range of meteorological and climatological variables related to the planetary boundary layer. However, the accuracy of such estimations is not obvious. Geoinformatics is able to fill this gap and provide the framework for the design, processing and implementation of accurate geo-databases. This paper aims to highlight the role of geoinformatics in the context of coastal meteorology and climatology. More precisely, it aims to reveal the effect on the performance of a Mesoscale Meteorological Model when a new scheme regarding the input surface parameters is developed using satellite data and application of Geographical Information Systems. The development of the proposed scheme is described and evaluated using the coastal Metropolitan Area of Athens, Greece as a case study. The results indicate a general improvement in the model performance based on the statistical evaluations of three meteorological parameters (temperature, wind speed and wind direction) using four appropriate indicators. The best performance was observed for temperature, then for wind direction and finally for wind speed. The necessity of the proposed new scheme is further discussed.

Revisiting the Global Seasonal Snow Classification: An Updated Dataset for Earth System Applications

Twenty-five years ago, we published a global seasonal snow classification now widely used in snow research, physical geography, and as a mission planning tool for remote sensing snow studies. Performing the classification requires global datasets of air temperature, precipitation, and land-cover. When introduced in 1995, the finest resolution global datasets of these variables were on a 0.5° × 0.5° latitude-longitude grid (approximately 50 km). Here we revisit the snow classification system and, using new datasets and methods, present a revised classification on a 10-arcsecond × 10-arcsecond latitude-longitude grid (approximately 300 m). We downscaled 0.1° × 0.1° latitude-longitude (approximately 10 km) gridded meteorological climatologies (1981-2019, European Centre for Medium-Range Weather Forecasts [ECMWF] ReAnalysis, 5th Generation Land [ERA5-Land]) using MicroMet, a spatially distributed, high-resolution, micro-meteorological model. The resulting air temperature and precipitation datasets were combined with European Space Agency (ESA) Climate Change Initiative (CCI) GlobCover land-cover data (as a surrogate for wind speed) to produce the updated classification, which we have applied to all of Earth’s terrestrial areas. We describe this new, high-resolution snow classification dataset, highlight the improvements added to the classification system since its inception, and discuss the utility of the climatological snow classes at this much higher resolution. The snow class dataset (Global Seasonal-Snow Classification 2.0) and the tools used to develop the data are publicly available online at the National Snow and Ice Data Center (NSIDC).

Research and Optimization of Meteo-Particle Model for Wind Retrieval

As the aviation industry has entered a critical period of development, the demand for Automatic Dependent Surveillance Broadcast (ADS-B) technology is becoming increasingly urgent. Real-time detection of aviation wind field information and the early warning of wind field shear by atmospheric sounding system are two important factors related to the safe operation of aviation and airport. According to the advantages of ADS-B and Mode S data, this paper uses the Meteo-Particle (MP) model proposed by Sun et al., in their previous research to retrieve high-altitude wind field. Comparing the precision and accuracy of wind field retrieved results, and the optimization parameters of MP model suitable for meteorological model are further studied. To solve the problem of incomplete wind field coverage obtained by retrieval, an extrapolation algorithm of wind field is proposed. The results show that: (1) a comprehensive evaluation index is introduced, which can more effectively evaluate the comprehensive difference of wind field retrieval results in wind speed and direction. (2) The adaptability results of MP model in different periods and altitudes provide some reference for the research of other scholars. (3) The new parameter setting can improve the accuracy of the retrieved results, and the appropriate extrapolation of wind field fills in the blank part of aviation and meteorology.

On the cross-tropopause transport of water by tropical convective overshoots: a mesoscale modelling study constrained by in-situ observations during TRO-Pico field campaign in Brazil

Deep convection overshooting the lowermost stratosphere is well known for its role in the local stratospheric water vapour (WV) budget. While it is seldom the case, local enhancements of WV associated with stratospheric overshoots are often published. Nevertheless, one debatable topic prevails on the global impact of this event with respect to the temperature-driven dehydration of air parcels entering the stratosphere. As a first step, it is crucial to quantify their role at a local scale before assessing their impact at a large-scale in a meteorological model. It would lead to a forcing scheme for overshoots in the global models. This paper reports on the local enhancements of WV linked to stratospheric overshoots, observed during the TRO-Pico campaign conducted in March 2012 in Bauru, Brazil, using the BRAMS (Brazilian version of RAMS) mesoscale model. Since numerical simulation depends on the choice of several preferred parameters, each having its uncertainties, we vary the microphysics or the vertical resolution while simulating the overshoots. Thus, we produce a set of simulations illustrating the possible variations in representing the stratospheric overshoots. To resolve better the stratospheric hydration, we opt for simulations with the 800-m-horizontal-grid-point presentation. Next, we validate these simulations against the Bauru S-band radar echo tops and the TRO-Pico balloon-borne observations of WV and particles. Two of the three simulations' setups yield results compatible with the TRO-Pico observations. From these two simulations, we determine approximately 333 t to 2000 t of WV mass prevailing in the stratosphere due to an overshooting plume depending on the simulation setup. About 70 % of the ice mass remains between the 380 K to 385 K isentropic levels. The overshooting top comprises pristine ice and snow, while aggregates only play a role just above the tropopause. Interestingly, the horizontal cross-section of the overshooting top is about 450 km2 at 380 K isentrope, which is similar to the horizontal-grid-point resolution of a simulation that cannot compute overshoots explicitly. These results could establish a forcing scheme of overshooting hydration or dehydration in a large-scale simulation.