Assimilating spaceborne lidar dust extinction can improve dust forecasts

Atmospheric mineral dust has a rich tri-dimensional spatial and temporal structure that is poorly constrained in forecasts and analyses when only column-integrated aerosol optical depth (AOD) is assimilated. At present, this is the case of most operational global aerosol assimilation products. Aerosol vertical distributions obtained from spaceborne lidars can be assimilated in aerosol models, but questions about the extent of their benefit upon analyses and forecasts along with their consistency with AOD assimilation remain unresolved. Our study thoroughly explores the added value of assimilating spaceborne vertical dust profiles, with and without the joint assimilation of dust optical depth (DOD). We also discuss the consistency in the assimilation of both sources of information and analyse the role of the smaller footprint of the spaceborne lidar profiles in the results. To that end, we have performed data assimilation experiments using dedicated dust observations for a period of 2 months over northern Africa, the Middle East, and Europe. We assimilate DOD derived from the Visible Infrared Imaging Radiometer Suite (VIIRS) on board Suomi National Polar-Orbiting Partnership (SUOMI-NPP) Deep Blue and for the first time Cloud-Aerosol Lidar with Orthogonal Polarisation (CALIOP)-based LIdar climatology of Vertical Aerosol Structure for space-based lidar simulation studies (LIVAS) pure-dust extinction coefficient profiles on an aerosol model. The evaluation is performed against independent ground-based DOD derived from AErosol RObotic NETwork (AERONET) Sun photometers and ground-based lidar dust extinction profiles from the Cyprus Clouds Aerosol and Rain Experiment (CyCARE) and PREparatory: does dust TriboElectrification affect our ClimaTe (Pre-TECT) field campaigns. Jointly assimilating LIVAS and Deep Blue data reduces the root mean square error (RMSE) in the DOD by 39 % and in the dust extinction coefficient by 65 % compared to a control simulation that excludes assimilation. We show that the assimilation of dust extinction coefficient profiles provides a strong added value to the analyses and forecasts. When only Deep Blue data are assimilated, the RMSE in the DOD is reduced further, by 42 %. However, when only LIVAS data are assimilated, the RMSE in the dust extinction coefficient decreases by 72 %, the largest improvement across experiments. We also show that the assimilation of dust extinction profiles yields better skill scores than the assimilation of DOD under an equivalent sensor footprint. Our results demonstrate the strong potential of future lidar space missions to improve desert dust forecasts, particularly if they foresee a depolarization lidar channel to allow discrimination of desert dust from other aerosol types.

Optimization of Aeolus' aerosol optical properties by maximum-likelihood estimation

The European Space Agency (ESA) Earth Explorer Mission Aeolus was launched in August 2018, carrying the first Doppler wind lidar in space. Its primary payload, the Atmospheric LAser Doppler INstrument (ALADIN), is an ultraviolet (UV) high-spectral-resolution lidar (HSRL) measuring atmospheric backscatter from air molecules and particles in two separate channels. The primary mission product is globally distributed line-of-sight wind profile observations in the troposphere and lower stratosphere. Atmospheric optical properties are provided as a spin-off product. Being an HSRL, Aeolus is able to independently measure the particle extinction coefficients, co-polarized particle backscatter coefficients and the co-polarized lidar ratio (the cross-polarized return signal is not measured). This way, the retrieval is independent of a priori lidar ratio information. The optical properties are retrieved using the standard correct algorithm (SCA), which is an algebraic inversion scheme and therefore sensitive to measurement noise. In this work, we reformulate the SCA into a physically constrained maximum-likelihood estimation (MLE) problem and demonstrate a predominantly positive impact and considerable noise suppression capabilities. These improvements originate from the use of all available information by the MLE in conjunction with the expected physical bounds concerning positivity and the expected range of the lidar ratio. To consolidate and to illustrate the improvements, the new MLE algorithm is evaluated against the SCA on end-to-end simulations of two homogeneous scenes and for real Aeolus data collocated with measurements by a ground-based lidar and the Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) satellite. The largest improvements were seen in the retrieval precision of the extinction coefficients and lidar ratio ranging up to 1 order of magnitude or more in some cases due to effective noise dampening. In real data cases, the increased precision of MLE with respect to the SCA is demonstrated by increased horizontal homogeneity and better agreement with the ground truth, though proper uncertainty estimation of MLE results is challenged by the constraints, and the accuracy of MLE and SCA retrievals can depend on calibration errors, which have not been considered.

An aerosol vertical data assimilation system (NAQPMS-PDAF v1.0): development and application

Aerosol vertical stratification information is important for global climate and planetary boundary layer (PBL) stability, and no single method can obtain spatiotemporally continuous vertical profiles. This paper develops an online data assimilation (DA) framework for the Eulerian atmospheric chemistry-transport model (CTM) Nested Air Quality Prediction Model System (NAQPMS) with the Parallel Data Assimilation Framework (PDAF) as the NAQPMS-PDAF for the first time. Online coupling occurs via a memory-based approach with two-level parallelization, and the arrangement of state vectors during the filter is specifically designed. Scaling tests provide evidence that the NAQPMS-PDAF can make efficient use of parallel computational resources for up to 2.5 k processors with weak scaling efficiency up to 0.7. One-month-long aerosol extinction coefficient profiles measured by the ground-based lidar and the concurrent hourly surface PM2.5 are solely and simultaneously assimilated to investigate the performance and application of the DA system. The hourly analysis and subsequent one-hour simulation are validated through lidar and surface PM2.5 measurements assimilated and not assimilated. The results show that lidar DA can significantly improve the underestimation of aerosol loading, especially at a height of approximately 400 m in the free-running (FR) experiment, with the BIAS changing from −0.20 (−0.14) 1/km to −0.02 (−0.01) 1/km and correlation coefficients increasing from 0.33 (0.28) to 0.91 (0.53) averaged over sites with measurements assimilated (not assimilated). Compared with the FR experiment, simultaneously assimilating PM2.5 and lidar can have a more consistent pattern of aerosol vertical profiles with a combination of surface PM2.5 and lidar, independent extinction coefficients from the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP), and aerosol optical depth (AOD) from the Aerosol Robotic Network (AERONET). Lidar DA has a larger temporal impact than that in PM2.5 DA but has deficiencies in subsequent quantification on the surface PM2.5. The proposed NAQPMS-PDAF has great potential for further research on the impact of aerosol vertical distribution.

Locations for the best lidar view on mid-level and high clouds

<p>Mid-level altocumuls clouds (Ac) and high cirrus clouds (Ci) can be considered as natural observatories for studying cloud glaciation in the atmosphere. While their altitude makes them difficult to access with in-situ instruments, they can be conveniently observed from ground with active remote-sensing instruments such as lidar and radar. However, active remote sensing of Ac and Ci at visible wavelengths with lidar requires a clear line of sight between the instrument and the target cloud. It is therefore advisable to carefully assess potential locations for deploying ground-based lidar instruments in field experiments or for long-term observations that are focussed on mid-level or high clouds. Here, observations of clouds with two spaceborne lidars are used to assess where ground-based lidar measurements of mid- and upper level clouds are least affected by the light-attenuating effect of low-level clouds. It is found that cirrus can be best observed in the tropics, the Tibetan plateau, the western part of North America, the Atacama region, the southern tip of South America, Greenland, Antarctica, and parts of Western Europe. For the observation of altocumuls clouds, a ground-based lidar is best placed on Greenland, Antarctica, the western flank of the Andes and Rocky Mountains, the Amazon, central Asia, Siberia, Western Australia, or the southern half of Africa.</p>

Modelling the volcanic ash plume from Eyjafjallajökull eruption (May 2010) over Europe: evaluation of the benefit of source term improvements and of the assimilation of aerosol measurements

Numerical dispersion models are used operationally worldwide to mitigate the effect of volcanic ash on aviation. In order to improve the representation of the horizontal dispersion of ash plumes and of the 3D concentration of ash, a study was conducted using the MOCAGE model during the European Natural Airborne Disaster Information and Coordination System for Aviation (EUNADICS-AV) project. Source term modelling and assimilation of different data were investigated. A sensitivity study of source term formulation showed that a resolved source term, using the FPLUME plume rise model in MOCAGE, instead of a parameterised source term, induces a more realistic representation of the horizontal dispersion of the ash plume. The FPLUME simulation provides more concentrated and focused ash concentrations in the horizontal and the vertical dimensions than the other source term. The assimilation of Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol optical depth has an impact on the horizontal dispersion of the plume, but this effect is rather low and local compared to source term improvement. More promising results are obtained with the continuous assimilation of ground-based lidar profiles, which improves the vertical distribution of ash and helps in reaching realistic values of ash concentrations. Using this configuration, the effect of assimilation may last for several hours and it may propagate several hundred kilometres downstream of the lidar profiles.

Microphysical process of precipitating hydrometeors from warm-front mid-level stratiform clouds revealed by ground-based lidar observations

Mid-level stratiform precipitations during the passage of warm fronts were detailedly observed on two occasions (light and moderate rain) by a 355 nm polarization lidar and water vapor Raman lidar, both equipped with waterproof transparent roof windows. The hours-long precipitation streaks shown in the lidar signal (X) and volume depolarization ratio (δv) reveal some ubiquitous features of the microphysical process of precipitating hydrometeors. We find that for the light-rain case precipitation that reaches the surface begins as ice-phase-dominant hydrometeors that fall out of a shallow liquid cloud layer at altitudes above the 0 ∘C isotherm level, and the depolarization ratio magnitude of falling hydrometeors increases from the liquid-water values (δv<0.09) to the ice/snow values (δv>0.20) during the first 100–200 m of their descent. Subsequently, the falling hydrometeors yield a dense layer with an ice/snow bright band occurring above and a liquid-water bright band occurring below (separated by a lidar dark band) as a result of crossing the 0 ∘C level. The ice/snow bright band might be a manifestation of local hydrometeor accumulation. Most falling raindrops shrink or vanish in the liquid-water bright band due to evaporation, whereas a few large raindrops fall out of the layer. We also find that a prominent δv peak (0.10–0.40) always occurs at an altitude of approximately 0.6 km when precipitation reaches the surface, reflecting the collision–coalescence growth of falling large raindrops and their subsequent spontaneous breakup. The microphysical process (at ice-bright-band altitudes and below) of moderate rain resembles that of the light-rain case, but more large-sized hydrometeors are involved.

Estimation of Vertical Fuel Layers in Tree Crowns Using High Density LiDAR Data

The accurate prediction and mitigation of wildfire behaviour relies on accurate estimations of forest canopy fuels. New techniques to collect LiDAR point clouds from remotely piloted aerial systems (RPAS) allow for the prediction of forest fuels at extremely fine scales. This study uses a new method to examine the ability of such point clouds to characterize the vertical arrangement and volume of crown fuels from within individual trees. This method uses the density and vertical arrangement of LiDAR points to automatically extract and measure the dimensions of each cluster of vertical fuel. The amount and dimensions of these extracted clusters were compared against manually measured clusters that were collected through the manual measurement of over 100 trees. This validation dataset was composed of manual point cloud measurements for all portions of living crown fuel for each tree. The point clouds used for this were ground-based LiDAR point clouds that were ~80 times denser than the RPAS LiDAR point clouds. Over 96% of the extracted clusters were successfully matched to a manually measured cluster, representing ~97% of the extracted volume. A smaller percentage of the manually measured clusters (~79%) were matched to an extracted cluster, although these represented ~99% of the total measured volume. The vertical arrangement and dimensions of the matched clusters corresponded strongly to one another, although the automated method generally overpredicted each cluster’s lower boundary. Tree-level volumes and crown width were, respectively, predicted with R-squared values of 0.9111 and 0.7984 and RMSE values of 44.36 m2 and 0.53 m. Weaker relationships were observed for tree-level metrics that relied on the extraction of lower crown features (live crown length, live crown base height, lowest live branch height). These metrics were predicted with R-squared values of 0.5568, 0.3120, and 0.2011 and RMSE values of 3.53 m, 3.55 m, and 3.66 m. Overall, this study highlights strengths and weaknesses of the developed method and the utility of RPAS LiDAR point clouds relative to ground-based point clouds.

Representative Construction Engineering Drawings Combining SLAM and Ground-Based LiDAR

Obtaining architectural engineering drawings is a crucial aspect of upgrading and repairing structures. Traditional elevation measuring is ineffective and results in a poor rate of restoration. The current building elevation measurement solutions based on 3D scanning technology all obtain building 3D point cloud data from a single type of laser scanner. These two methods can’t get both indoor and outdoor scenes at the same time. This paper presents a scanning strategy that combines SLAM with Ground-based LiDAR to solve this problem. The point cloud data for the building’s indoor and outdoor scenes are obtained independently, and the Ground-based LiDAR point cloud data is registered locally using the iterative closest point(ICP) algorithm. The SLAM point clouds and the Ground-based LiDAR point clouds are then registered as a whole to develop an overall model of the building using point constrained error equations. For various reasons, the building can be trimmed into a planar point cloud model depending on the application. Finally, engineering drawings for the construction of the building can be drawn. The method’s viability was demonstrated by using it in a 3D scanning project of a scenic site in Beijing. This technology improves model information interpretability, scanning efficiency, and provides powerful data assistance for building rehabilitation and repair. It is extremely important in the disciplines of urban planning, rehabilitation, and historic preservation. After performing a more optimal preprocessing, more than 90% classification accuracy was achieved across 18 low-power consumer devices for scenarios in which the in-band features-to-noise ratio (FNR) was very poor.

Estimation of Larch Growth at the Stem, Crown and Branch Levels Using Ground-based LiDAR Point Cloud

BackgroundTree growth is an important indicator of forest health and can reflect changes in forest structure. Traditional tree growth estimates use easy-to-measure parameters (e.g., tree height, diameter at breast height (DBH), and crown diameter) obtained via forest in situ measurements, which are labor-intensive and time-consuming to perform and cannot easily describe the changes throughout the whole growth period of a tree. The combination of Terrestrial Laser Scanning (TLS) and Quantitative Structure Modelling (QSM) can accurately estimate tree structural parameters nondestructively and has the potential to estimate tree growth. Therefore, this paper estimates tree growth according to the stem-, crown-, and branch-level attributes observed by ground-based LiDAR point clouds. Compared with conventional methods, this paper used tree height, DBH, stem volume, crown diameter, crown volume and first-order branch volume to estimate the growth of 55-year-old larch trees in Saihanba at the stem, crown and branch levels, respectively. ResultsThe experimental results showed that the absolute growth of the first-order branch volume was equivalent to that of the stems, which highlights the importance of branches in the study of tree growth. For 55-year-old larch, tree growth is mainly reflected in the growth of the crown, i.e., the growth of branches. Compared to one-dimensional parameters (tree height, DBH and crown diameter), the growth of three-dimensional parameters (crown, stem and first-order branch volumes) was more obvious. ConclusionsFor 55-year-old larch, three-dimensional tree parameters can more effectively describe tree growth, and the absolute growth of the first-order branch volume is close to the stem volume. In addition, it is necessary to estimate tree growth at different levels.