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.

Abstract. 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.

In radar observations of hydrometeors, the 0°C isotherm in the atmosphere (i.e., the freezing level) usually appears as a region of enhanced reflectivity. This region is known as the bright band (BB). In this study, observations over 12 months from a vertically pointing 35-GHz radar and a collocated disdrometer at the Natural Environment Research Council (NERC) Facility for Atmospheric and Radio Research (NFARR) are used to identify and compare microphysical differences between BB and non-brightband (NBB) periods. From these observations, the relationship between radar reflectivity Z and rainfall intensity R is found to be Z = 772R0.57 for BB periods and Z = 108R0.99 for NBB periods. Additionally, the brightband strength (BBS) was calculated using a novel method derived from the Michelson contrast equation in an attempt to explain the observed variability in BB precipitation. A series of Z–R relationships are computed with respect to BBS. The coefficients increase with increasing BBS from 227 to 926, while the exponents decrease with increasing BBS from 0.85 to 0.38. The results also indicate that NBB periods identified in the presence of a 0°C isotherm in other studies may be misclassified due to their inability to identify weak brightband periods. As such, it is hypothesized that NBB periods are solely due to warm rain processes.

Abstract. Mapping Air Pollution eMissions (MAPM) is a 2-year project whose goal is to develop a method to infer particulate matter (PM) emissions maps from in situ PM concentration measurements. Central to the functionality of MAPM is an inverse model. The input of the inverse model includes a spatially distributed prior emissions estimate and PM measurement time series from instruments distributed across the desired domain. In this proof-of-concept study, we describe the construction of this inverse model, the mathematics underlying the retrieval of the resultant posterior PM emissions maps, the way in which uncertainties are traced through the MAPM processing chain, and plans for future developments. To demonstrate the capability of the inverse model developed for MAPM, we use the PM2.5 measurements obtained during a dedicated winter field campaign in Christchurch, New Zealand, in 2019 to infer PM2.5 emissions maps on a city scale. The results indicate a systematic overestimation in the prior emissions for Christchurch of at least 40 %–60 %, which is consistent with some of the underlying assumptions used in the composition of the bottom-up emissions map used as the prior, highlighting the uncertainties in bottom-up approaches for estimating PM2.5 emissions maps.

Abstract. 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 which requires a licence agreement for operational use or academic research (http://www.cosmo-model.org/). This paper describes and validates the new free and open-source WRF4PALM tools (available on https://github.com/dongqi-DQ/WRF4PALM). 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.

Abstract. MAPM (Mapping Air Pollution eMissions) is a two-year project whose goal is to develop a method to infer particulate matter (PM) emissions maps from in situ PM concentration measurements. Central to the functionality of MAPM is an inverse model. The input of the inverse model includes a spatially-distributed prior emissions estimate and PM measurement time series from instruments distributed across the desired domain. Here we describe the construction of this inverse model, the mathematics underlying the retrieval of the resultant posterior PM emissions maps, the way in which uncertainties are traced through the MAPM processing chain, and plans for future development of the processing chain. To demonstrate the capability of the inverse model developed for MAPM, we use the PM2.5 measurements obtained during a dedicated winter field campaign in Christchurch, New Zealand, in 2019 to infer PM2.5 emissions maps on city scale. The results indicate a systematic overestimation in the prior emissions for Christchurch of at least 40–60 %, which is consistent with some of the underlying assumptions used in the composition of the bottom-up emissions map used as the prior, highlighting the uncertainties in bottom-up approaches for estimating PM2.5 emissions maps. The paper also presents the results of two sets of observing system simulation experiments (OSSEs) that explore how measurement uncertainties affect the computation of the derived emissions maps, and the extent to which using emissions maps from one day as the prior for the next day improves the ability of the inversion system to characterize the emissions sources. We find in the first case that a smaller number of high-accuracy instruments performs significantly better than a higher number of low-accuracy instruments. In the second case, the results are ultimately inconclusive, showing the need for further investigations that are beyond the scope of this study.

<p>Spring frost has been recognized as the most harmful weather hazard for agriculture. One way to fight it back is using a wind machine - a 6-m diameter blowing fan atop a 10-m mast. It rotates on itself in approximately 4min30s and blows a slightly positive air using the strength of the nocturnal thermal inversion to mix cold air near the ground with warmer air above. Previous studies have focused on the protection area of the wind machine under different weather conditions or propeller designs. However, while weather conditions are undergone and field measurements are sparse, effects like the topography, the synergy between devices, or the addition of a burner are hard to catch and separate and are, therefore, not yet well understood.</p> <p>In this study, we present field measurements dedicated to the future calibration of a computational fluid dynamics model (PALM) involving an actuator disk to simulate a wind machine operating during radiative frost conditions. This numerical model will aim to understand better such tower's external effects, for which field measurements are challenging to implement.</p> <p>To characterize the jet of the propeller at the onset, vertical profiles were measured with a 3D sonic anemometer at high frequency (100Hz) 10 m away from the wind machine every meter between 3 and 15 m heights. Mass flow and momentum rates of about 500m3/s and 5000N were deduced for some different designs of wind machines.</p> <p>To characterize how the jet interacts with the ground regarding the distance from its source, 2D sonic anemometers were placed in a row in front of the wind machine. Results highlight three different zones where the jet behaved distinctively:</p> <ul> <li>A dead zone, where the jet passed over the ground (0 to 30 m away from the WM);</li> <li>An impact zone where the jet directly hit the ground with maximum velocity (40 to 60m away from the WM);</li> <li>A spreading zone where the protection mixing was due to eddies spreading in the inter-rows and breaking into smaller eddies in contact with posts and vine plants (70m away from the WM and beyond). As the distance from the machine increased, the jet velocity decreased before vanishing.</li> </ul> <p>From these results about the onset condition and development of the jet, it will be possible to tune the rotating actuator disk to reproduce with PALM (an LES meteorological-oriented modeling system) an acceptable behavior of the flows (gust and weather interacting with the ground) despite several simplifications of the underlying physics.</p> <p>While calibrations are still ongoing, the first results are encouraging, whether it be on a wind machine in a free environment or with the reproduction of a radiative night situation. The primary analysis will focus on the animation of state variables to assist in analyzing statistical results on field measurements. As little knowledge is available about the combined use of a burner with a wind machine, several strategies for a burner location will be tested in order to initiate a research topic that the authors believe is currently unexplored.</p>

Abstract. Coupled surface-atmosphere high-resolution simulations were carried out to understand radiation fog development and persistence 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 Christchurch, New Zealand. Numerical model simulations from the synoptic to micro- scale 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 (https://www.usgs.gov/landsat-missions/landsat-8, last access: 10 October 2022) satellite imagery and ground-based meteorological observations. Eight simulations were carried out under identical meteorological conditions. One contained homogeneous soil moisture and one contained heterogeneous soil moisture derived from Landsat 8 imagery. For the other six simulations, the soil moisture heterogeneity magnitudes were amplified following the observed spatial distribution to aid our understanding of the impact of soil moisture heterogeneity. Our results showed that soil moisture heterogeneity did not significantly change the general spatial structure of near-surface fog occurrence, even when amplified. However, compared to homogeneous soil moisture, spatial heterogeneity in soil moisture leads to significant changes in radiation fog duration. The resulting changes in fog duration can be more than 50 minutes, although such changes are not directly correlated with spatial variations in soil moisture. The simulations showed that the mesoscale (104 to 2 × 105 m) meteorology controls the location of fog occurrence, while soil moisture heterogeneity alters fog duration at the microscale (10−2 to 103 m). Our results highlight the importance of including soil moisture heterogeneity for accurate spatiotemporal fog forecasting.