GEO4PALM v1.1: an open-source geospatial data processing toolkit for the PALM model system

A geospatial data processing tool, GEO4PALM, has been developed to generate geospatial static input for the Parallelized Large-Eddy Simulation (PALM) model system. PALM is a community-driven large-eddy simulation model for atmospheric and environmental research. Throughout PALM\u27s 20-year development, research interests have been increasing in its application to realistic conditions, especially for urban areas. For such applications, geospatial static input is essential. Although abundant geospatial data are accessible worldwide, geospatial data availability and quality are highly variable and inconsistent. Currently, the geospatial static input generation tools in the PALM community heavily rely on users for data acquisition and pre-processing. New PALM users face large obstacles, including significant time commitments, to gain the knowledge needed to be able to pre-process geospatial data for PALM. Expertise beyond atmospheric and environmental research is frequently needed to understand the data sets required by PALM. Here, we present GEO4PALM, which is a free and open-source tool. GEO4PALM helps users generate PALM static input files with a simple, homogenised, and standardised process. GEO4PALM is compatible with geospatial data obtained from any source, provided that the data sets comply with standard geo-information formats. Users can either provide existing geospatial data sets or use the embedded data interfaces to download geo-information data from free online sources for any global geographic area of interest. All online data sets incorporated in GEO4PALM are globally available, with several data sets having the finest resolution of 1 m. In addition, GEO4PALM provides a graphical user interface (GUI) for PALM domain configuration and visualisation. Two application examples demonstrate successful PALM simulations driven by geospatial input generated by GEO4PALM using different geospatial data sources for Berlin, Germany, and Ōtautahi / Christchurch, New Zealand. GEO4PALM provides an easy and efficient way for PALM users to configure and conduct PALM simulations for applications and investigations such as urban heat island effects, air pollution dispersion, renewable energy resourcing, and weather-related hazard forecasting. The wide applicability of GEO4PALM makes PALM more accessible to a wider user base in the scientific community

Investigating multiscale meteorological controls and impact of soil moisture heterogeneity on radiation fog in complex terrain using semi-idealised simulations

Coupled surface–atmosphere high-resolution mesoscale simulations were carried out to understand meteorological processes involved in the radiation fog life cycle in a city surrounded by complex terrain. The controls of mesoscale meteorology and microscale soil moisture heterogeneity on fog were investigated using case studies for the city of ¯Otautahi / Christchurch, New Zealand. Numerical model simulations from the synop- tic to microscale were carried out using the Weather Research and Forecasting (WRF) model and the Parallelised Large-Eddy Simulation Model (PALM). Heterogeneous soil moisture, land use, and topography were included. The spatial heterogeneity of soil moisture was derived using Landsat 8 satellite imagery and ground-based me- teorological observations. Nine semi-idealised simulations were carried out under identical meteorological conditions. One contained homogeneous soil moisture of about 0.31 m$^3$ m$^{−3}$, with two other simulations of halved and doubled soil moisture to demonstrate the range of soil moisture impact. Another contained heterogeneous soil moisture derived from Landsat 8 imagery. For the other five simulations, the soil moisture heterogeneity magnitudes were amplified following the observed spatial distribution to aid our understanding of the impact of soil moisture heterogeneity. Analysis using pseudo-process diagrams and accumulated latent heat flux shows significant spatial heterogeneity of processes involved in the simulated fog. Our results showed that soil mois- ture heterogeneity did not significantly change the general spatial structure of near-surface fog occurrence, even when the heterogeneity signal was amplified and/or when the soil moisture was halved and doubled. However, compared to homogeneous soil moisture, spatial heterogeneity in soil moisture can lead to changes in fog duration. These changes can be more than 50 min, although they are not directly correlated with spatial variations in soil moisture. The simulations showed that the mesoscale (10 to 200 km) meteorology controls the location of fog occurrence, while soil moisture heterogeneity alters fog duration at the microscale on the order of 100 m to 1 km. Our results highlight the importance of including soil moisture heterogeneity for accurate spatiotemporal fog forecasting

A Machine Learning Based Downscaling Approach to Produce High Spatio-Temporal Resolution Land Surface Temperature of the Antarctic Dry Valleys from MODIS Data

To monitor environmental and biological processes, Land Surface Temperature (LST) is a central variable, which is highly variable in space and time. This particularly applies to the Antarctic Dry Valleys, which host an ecosystem highly adapted to the extreme conditions in this cold desert. To predict possible climate induced changes on the Dry Valley ecosystem, high spatial and temporal resolution environmental variables are needed. Thus we enhanced the spatial resolution of the MODIS satellite LST product that is sensed sub-daily at a 1 km spatial resolution to a 30 m spatial resolution. We employed machine learning models that are trained using Landsat 8 thermal infrared data from 2013 to 2019 as a reference to predict LST at 30 m resolution. For the downscaling procedure, terrain derived variables and information on the soil type as well as the solar insolation were used as potential predictors in addition to MODIS LST. The trained model can be applied to all available MODIS scenes from 1999 onward to develop a 30 m resolution LST product of the Antarctic Dry Valleys. A spatio-temporal validation revealed an R2 of 0.78 and a RMSE of 3.32 ∘C. The downscaled LST will provide a valuable surface climate data set for various research applications, such as species distribution modeling, climate model evaluation, and the basis for the development of further relevant environmental information such as the surface moisture distribution

The role of helicity and fire–atmosphere turbulent energy transport in potential wildfire behaviour

Background. Understanding near-surface fire–atmosphere interactions at turbulence scale is fundamental for predicting fire spread behaviour. Aims. This study aims to investigate the fire–atmosphere interaction and the accompanying energy transport processes within the convective boundary layer. Methods. Three groups of large eddy simulations representing common ranges of convective boundary layer conditions and fire intensities were used to examine how ambient buoyancy-induced atmospheric turbulence impacts fire region energy transport. Key results. In a relatively weak convective boundary layer, the fire-induced buoyancy force could impose substantial changes to the near-surface atmospheric turbulence and cause an anticorrelation of the helicity between the ambient atmosphere and the fire-induced flow. Fire-induced impact became much smaller in a stronger convective environment, with ambient atmospheric flow maintaining coherent structures across the fire heating region. A highefficiency heat transport zone above the fire line was found in all fire cases. The work also found counter-gradient transport zones of both momentum and heat in fire cases in the weak convective boundary layer group. Conclusions. We conclude that fire region energy transport can be affected by convective boundary layer conditions. Implications. Ambient atmospheric turbulence can impact fire behaviour through the energy transport process. The counter-gradient transport might also indicate the existence of strong buoyancy-induced mixing processes

Wrf4palm v1.0: A mesoscale dynamical driver for the microscale PALM model system 6.0

A set of Python-based tools, WRF4PALM, has been developed for offline nesting of the PALM model system 6.0 into the Weather Research and Forecasting (WRF) modelling system. Time-dependent boundary conditions of the atmosphere are critical for accurate representation of microscale meteorological dynamics in high-resolution real-data simulations. WRF4PALM generates initial and boundary conditions from WRF outputs to provide time-varying meteorological forcing for PALM. The WRF model has been used across the atmospheric science community for a broad range of multidisciplinary applications. The PALM model system 6.0 is a turbulence-resolving large-eddy simulation model with an additional Reynolds-averaged Navier–Stokes (RANS) mode for atmospheric and oceanic boundary layer studies at microscale (Maronga et al., 2020). Currently PALM has the capability to ingest output from the regional scale Consortium for Small-scale Modelling (COSMO) atmospheric prediction model. However, COSMO is not an open source model and requires a licence agreement for operational use or academic research (http://www.cosmo-model.org/, last access: 23 April 2021). This paper describes and validates the new free and open-source WRF4PALM tools (available at https://github.com/dongqi-DQ/WRF4PALM, last access: 23 April 2021). Two case studies using WRF4PALM are presented for Christchurch, New Zealand, which demonstrate successful PALM simulations driven by meteorological forcing from WRF outputs. The WRF4PALM tools presented here can potentially be used for micro- and mesoscale studies worldwide, for example in boundary layer studies, air pollution dispersion modelling, wildfire emissions and spread, urban weather forecasting, and agricultural meteorology

The surface energy balance during foehn events at Joyce Glacier, McMurdo Dry Valleys, Antarctica

The McMurdo Dry Valleys (MDV) are a polar desert, where glacial melt is the main source of water to streams and the ecosystem. Summer air temperatures are typically close to zero, and therefore foehn events can have a large impact on the meltwater production. A 14-month record of automatic weather station (AWS) data on Joyce Glacier is used to force a 1D surface energy balance model to study the impact of foehn events on the energy balance. AWS data and output of the Antarctic Mesoscale Prediction System (AMPS) on a 1.7 km grid are used to detect foehn events at the AWS site. Foehn events at Joyce Glacier occur under the presence of cyclones over the Ross Sea. The location of Joyce Glacier on the leeward side of the Royal Society Range during these synoptic events causes foehn warming through isentropic drawdown. This mechanism differs from the foehn warming through gap flow that was earlier found for other regions in the MDV and highlights the complex interaction of synoptic flow with local topography of the MDV. Shortwave radiation is the primary control on melt at Joyce Glacier, and melt often occurs with subzero air temperatures. During foehn events, melt rates are enhanced, contributing to 23 % of the total annual melt. Foehn winds cause a switch from a diurnal stability regime in the atmospheric surface layer to a continuous energy input from sensible heat flux throughout the day. The sensible heating during foehn, through an increase in turbulent mixing resulting from gustier and warmer wind conditions, is largely compensated for by extra heat losses through sublimation. Melt rates are enhanced through an additional energy surplus from a reduced albedo during foehn.</p

Fog type classification using a modified Richardson number for Christchurch, New Zealand

Situated on a coastal plain between the Southern Alps and Banks Peninsula, Christchurch, New Zealand, experiences around 49 fog days every year. Given its complex topography, accurate fog forecasting is difficult at Christchurch International Airport (CHA). Climatological analysis of local fog events is an important first step to gain insight into the processes involved in the fog lifecycle. In this study, fog events were identified using 12 years of meteorological observations from an automatic weather station situated at CHA. A novel fog type classification method was developed using the modified Richardson number (MRi). The MRi fog type classification method assesses the local dynamic stability of a 1.25 m shallow layer of near-surface air. Here, the MRi is used as a quantitative index to classify advection fog, advection–radiation fog, and radiation fog. Vertical gradients of air temperature and wind speed were derived for prefog and fog periods, and a number of criteria were applied to the MRi for the fog type classification. The fog type classification results were examined in correspondence with the derived fog intensity, duration, diurnal and seasonal variability of frequency of occurrences, and synoptic and local wind flows. In agreement with other fog studies across the world, fog occurs most frequently during local winter and spring. Radiation fog is the predominant type of fog identified at CHA, and its formation and development usually coincide with the local drainage northwesterlies. This study is the first to use long-term observational data to investigate the fog climatology and typology at CHA in detail. The fog climatological characteristics presented in this study will serve as the basis of future fog studies in Christchurch. The presented MRi fog type classification method can potentially be used in fog characteristic studies worldwide

Antarctic atmospheric boundary layer observations with the Small Unmanned Meteorological Observer (SUMO)

Between&nbsp;January&nbsp;2012 and June&nbsp;2017 a small unmanned aerial system (sUAS), known as the Small Unmanned Meteorological Observer (SUMO), was used to observe the state of the atmospheric boundary layer in the Antarctic. During six Antarctic field campaigns, 116 SUMO flights were completed. These flights took place during all seasons over both permanent ice and ice-free locations on the Antarctic continent and over sea ice in the western Ross Sea. Sampling was completed during spiral ascent and descent flight paths that observed the temperature, humidity, pressure and wind up to 1000&thinsp;m above ground level and sampled the entire depth of the atmospheric boundary layer, as well as portions of the free atmosphere above the boundary layer. A wide variety of boundary layer states were observed, including very shallow, strongly stable conditions during the Antarctic winter and deep, convective conditions over ice-free locations in the summer. The Antarctic atmospheric boundary layer data collected by the SUMO sUAS, described in this paper, can be retrieved from the United States Antarctic Program Data Center (https://www.usap-dc.org, last access: 8&nbsp;March&nbsp;2021). The data for all flights conducted on the continent are available at https://doi.org/10.15784/601054 (Cassano, 2017), and data from the Ross Sea flights are available at https://doi.org/10.15784/601191 (Cassano, 2019). </div