Evaluation of two common source estimation measurement strategies using large-eddy simulation of plume dispersion under neutral atmospheric conditions

This study uses large-eddy simulations (LESs) to evaluate two widely used observational techniques that estimate point source emissions. We evaluate the use of car measurements perpendicular to the wind direction and the commonly used Other Test Method 33A (OTM 33A). The LES study simulates a plume from a point source released into a stationary, homogeneous, and neutral atmospheric surface layer over flat terrain. This choice is motivated by our ambition to validate the observational methods under controlled conditions where they are expected to perform well since the sources of uncertainties are minimized. Three plumes with different release heights were sampled in a manner that mimics sampling according to car transects and the stationary OTM 33A. Subsequently, source strength estimates are compared to the true source strength used in the simulation. Standard deviations of the estimated source strengths decay proportionally to the inverse of the square root of the number of averaged transects, showing statistical independence of individual samples. The analysis shows that for the car transect measurements at least 15 repeated measurement series need to be averaged to obtain a source strength within 40 % of the true source strength. For the OTM 33A analysis, which recommends measurements within 200 m of the source, the estimates of source strengths have similar values close to the source, which is caused by insufficient dispersion of the plume by turbulent mixing close to the source. Additionally, the derived source strength is substantially overestimated with OTM 33A. This overestimation is driven by the proposed OTM 33A dispersion coefficients, which are too large for this specific case. This suggests that the conditions under which the OTM 33A dispersion constants were derived were likely influenced by motions with length scales beyond the scale of the surface layer. Lastly, our simulations indicate that, due to wind-shear effects, the position of the time-averaged centerline of the plumes may differ from the plume emission height. This mismatch can be an additional source of error if a Gaussian plume model (GPM) is used to interpret the measurement. In the case of the car transect measurements, a correct source estimate then requires an adjustment of the source height in the GPM.</p

Relation between convective rainfall properties and antecedent soil moisture heterogeneity conditions in North Africa

Recent observational studies have demonstrated the relevance of soil moisture heterogeneity and the associated thermally-induced circulation on deep convection and rainfall triggering. However, whether this dynamical mechanism further influences rainfall propertiessuch as rain volume or timinghas yet to be confirmed by observational data. Here, we analyze 10 years of satellite-based sub-daily soil moisture and precipitation records and explore the potential of strong spatial gradients in morning soil moisture to influence the properties of afternoon rainfall in the North African region, at the 100-km scale. We find that the convective rain systems that form over locally drier soils and anomalously strong soil moisture gradients have a tendency to initiate earlier in the afternoon; they also yield lower volumes of rain, weaker intensity and lower spatial variability. The strongest sensitivity to antecedent soil conditions is identified for the timing of the rain onset; it is found to be correlated with the magnitude of the soil moisture gradient. Further analysis shows that the early initiation of rainfall over dry soils and strong surface gradients yet requires the presence of a very moist boundary layer on that day. Our findings agree well with the expected effects of thermally-induced circulation on rainfall properties suggested by theoretical studies and point to the potential of locally drier and heterogeneous soils to influence convective rainfall development. The systematic nature of the identified effect of soil moisture state on the onset time of rainstorms in the region is of particular relevance and may help foster research on rainfall predictability

Observed Patterns of Surface Solar Irradiance under Cloudy and Clear-sky Conditions

Surface solar irradiance varies on scales down to seconds or meters mainly due to clouds, but also via moisture structures in the atmospheric boundary layer. The highly variable nature of irradiance is not resolved by most atmospheric models, yet heterogeneity in surface irradiance impacts the overlying cloud field through feedback with the land surface. Atmospheric model resolution and radiative transfer calculations are simplified, necessary due to high computational demands, but the development of fast models capable of accurately resolving irradiance variability is limited by our understanding of cloud-driven solar irradiance variability. Spatial and spectrally resolving observational datasets of solar irradiance at such high resolution are rare, but they are required for characterizing observed variability, understanding the mechanisms, and model validation. In 2021, we deployed a spatial network of low-cost radiometers at the FESSTVaL (Germany) and LIAISE (Spain) field campaigns, specifically to gather data on cloud-driven surface patterns of irradiance, including spectral effects, with the aim to address this gap in observations and understanding. This work discusses the measurement strategies at both campaigns, the performance and calibration of these radiometers, analysis techniques to construct spatial patterns despite limited network size, and our interpretation of these observations. We find that cumulus, altocumulus, and cirrus clouds generate large spatiotemporal variability in irradiance, but through different mechanisms, and with spatial scales of patterns ranging from 50 m to 30 km. Under clear-sky conditions, solar irradiance varies significantly in water vapour absorption bands at the minute scale, due to local and regional variability in atmospheric moisture.Comment: Submitted to Quarterly Journal of the Royal Meteorological Societ

Using 3D turbulence-resolving simulations to understand the impact of surface properties on the energy balance of a debris-covered glacier

Debris-covered glaciers account for almost onefifth of the total glacier ice volume in High Mountain Asia; however, their contribution to the total glacier melt remains uncertain, and the drivers controlling this melt are still largely unknown. Debris influences the properties (e.g. albedo, thermal conductivity, roughness) of the glacier surface and thus the surface energy balance and glacier melt. In this study we have used sensitivity tests to assess the effect of surface properties of debris on the spatial distribution of micrometeorological variables such as wind fields, moisture and temperature. Subsequently we investigated how those surface properties drive the turbulent fluxes and eventually the conductive heat flux of a debris-covered glacier. We simulated a debris-covered glacier (Lirung Glacier, Nepal) at a 1m resolution with the MicroHH model, with boundary conditions retrieved from an automatic weather station (temperature, wind and specific humidity) and unmanned aerial vehicle flights (digital elevation map and surface temperature). The model was validated using eddy covariance data. A sensitivity analysis was then performed to provide insight into how heterogeneous surface variables control the glacier microclimate. Additionally, we show that ice cliffs are local melt hot spots and that turbulent fluxes and local heat advection amplify spatial heterogeneity on the surface. The high spatial variability of small-scale meteorological variables suggests that point-based station observations cannot be simply extrapolated to an entire glacier. These outcomes should be considered in future studies for a better estimation of glacier melt in High Mountain Asia.</p

Predicting atmospheric optical properties for radiative transfer computations using neural networks

The radiative transfer equations are well-known, but radiation parametrizations in atmospheric models are computationally expensive. A promising tool for accelerating parametrizations is the use of machine learning techniques. In this study, we develop a machine learning-based parametrization for the gaseous optical properties by training neural networks to emulate a modern radiation parameterization (RRTMGP). To minimize computational costs, we reduce the range of atmospheric conditions for which the neural networks are applicable and use machine-specific optimised BLAS functions to accelerate matrix computations. To generate training data, we use a set of randomly perturbed atmospheric profiles and calculate optical properties using RRTMGP. Predicted optical properties are highly accurate and the resulting radiative fluxes have average errors within \SI{0.5}{\flux} compared to RRTMGP. Our neural network-based gas optics parametrization is up to 4 times faster than RRTMGP, depending on the size of the neural networks. We further test the trade-off between speed and accuracy by training neural networks for the narrow range of atmospheric conditions of a single large-eddy simulation, so smaller and therefore faster networks can achieve a desired accuracy. We conclude that our machine learning-based parametrization can speed-up radiative transfer computations whilst retaining high accuracy.Comment: 13 pages,5 figures, submitted to Philosophical Transactions

A Probabilistic Bulk Model of Coupled Mixed Layer and Convection. Part I: Clear-Sky Case

A new bulk model of the convective boundary layer, the probabilistic bulk convection model (PBCM), is presented. Unlike prior bulk approaches that have modeled the mixed-layer-top buoyancy flux as a constant fraction of the surface buoyancy flux, PBCM implements a new mixed-layer-top entrainment closure based on the mass flux of updrafts overshooting the inversion. This mass flux is related to the variability of the surface state (potential temperature θ and specific humidity q) of an ensemble of updraft plumes. The authors evaluate the model against observed clear-sky weak and strong inversion cases and show that PBCM performs well. The height, state, and timing of the boundary layer growth are accurately reproduced. Sensitivity studies are performed highlighting the role of the main parameters (surface variances, lateral entrainment). The model is weakly sensitive to the exact specification of the variability at the surface and is most sensitive to the lateral entrainment of environmental air into the rising plumes. Apart from allowing time-dependent top-of-the-boundary-layer entrainment rates expressed in terms of surface properties, which can be observed in situ, PBCM naturally takes into account the transition to the shallow convection regime, as described in a companion paper. Thus, PBCM represents an important step toward a unified framework bridging parameterizations of mixed-layer entrainment velocity in both clear-sky and moist convective boundary layers

Record high solar irradiance in Western Europe during first COVID-19 lockdown largely due to unusual weather

Spring 2020 broke sunshine duration records across western Europe. The Netherlands recorded the highest surface irradiance since 1928, exceeding the previous extreme of 2011 by 13 %, and the diffuse fraction of the irradiance measured a record low percentage (38 %). The coinciding irradiance extreme and a reduction in anthropogenic pollution due to COVID-19 measures triggered the hypothesis that cleaner-than-usual air contributed to the record. Based on analyses of ground-based and satellite observations and experiments with a radiative transfer model, we estimate a 1.3 % (2.3 W m$^{-2}$) increase in surface irradiance with respect to the 2010-2019 mean due to a low median aerosol optical depth, and a 17.6 % (30.7 W m$^{-2}$) increase due to several exceptionally dry days and a very low cloud fraction overall. Our analyses show that the reduced aerosols and contrails due to the COVID-19 measures are far less important in the irradiance record than the dry and particularly cloud-free weather.Comment: 21 pages, 12 figures, submitted to Communications Earth and Environmen

Land–Atmosphere Interactions: The LoCo Perspective

Land–atmosphere (L-A) interactions are a main driver of Earth’s surface water and energy budgets; as such, they modulate near-surface climate, including clouds and precipitation, and can influence the persistence of extremes such as drought. Despite their importance, the representation of L-A interactions in weather and climate models remains poorly constrained, as they involve a complex set of processes that are difficult to observe in nature. In addition, a complete understanding of L-A processes requires interdisciplinary expertise and approaches that transcend traditional research paradigms and communities. To address these issues, the international Global Energy and Water Exchanges project (GEWEX) Global Land–Atmosphere System Study (GLASS) panel has supported “L-A coupling” as one of its core themes for well over a decade. Under this initiative, several successful land surface and global climate modeling projects have identified hot spots of L-A coupling and helped quantify the role of land surface states in weather and climate predictability. GLASS formed the Local Land–Atmosphere Coupling (LoCo) project and working group to examine L-A interactions at the process level, focusing on understanding and quantifying these processes in nature and evaluating them in models. LoCo has produced an array of L-A coupling metrics for different applications and scales and has motivated a growing number of young scientists from around the world. This article provides an overview of the LoCo effort, including metric and model applications, along with scientific and programmatic developments and challenges

Land-Atmosphere Interactions: The LoCo Perspective

Land-atmosphere (L-A) interactions are a main driver of Earth's surface water and energy budgets; as such, they modulate near-surface climate, including clouds and precipitation, and can influence the persistence of extremes such as drought. Despite their importance, the representation of L-A interactions in weather and climate models remains poorly constrained, as they involve a complex set of processes that are difficult to observe in nature. In addition, a complete understanding of L-A processes requires interdisciplinary expertise and approaches that transcend traditional research paradigms and communities. To address these issues, the international Global Energy and Water Exchanges project (GEWEX) Global Land-Atmosphere System Study (GLASS) panel has supported 'L-A coupling' as one of its core themes for well over a decade. Under this initiative, several successful land surface and global climate modeling projects have identified hotspots of L-A coupling and helped quantify the role of land surface states in weather and climate predictability. GLASS formed the Local L-A Coupling ('LoCo') project and working group to examine L-A interactions at the process level, focusing on understanding and quantifying these processes in nature and evaluating them in models. LoCo has produced an array of L-A coupling metrics for different applications and scales, and has motivated a growing number of young scientists from around the world. This article provides an overview of the LoCo effort, including metric and model applications, along with scientific and programmatic developments and challenges