scholarly journals Spectroscopic imaging of sub-kilometer spatial structure in lower-tropospheric water vapor

2021 ◽  
Vol 14 (4) ◽  
pp. 2827-2840
Author(s):  
David R. Thompson ◽  
Brian H. Kahn ◽  
Philip G. Brodrick ◽  
Matthew D. Lebsock ◽  
Mark Richardson ◽  
...  
Keyword(s):  
Water Vapor ◽  
Spatial Variability ◽  
Imaging Spectroscopy ◽  
Lower Troposphere ◽  
Airborne Lidar ◽  
Order Structure ◽  
Scaling Properties ◽  
Airborne Imaging ◽  
Shortwave Infrared ◽  
Airborne Lidar Data

Abstract. The subgrid spatial variability of water vapor is an important geophysical parameter for modeling tropical convention and cloud processes in atmospheric models. This study maps sub-kilometer spatial structures in total atmospheric column water vapor with visible to shortwave infrared (VSWIR) imaging spectroscopy. We describe our inversion approach and validate its accuracy with coincident measurements by airborne imaging spectrometers and the AERONET ground-based observation network. Next, data from NASA's AVIRIS-NG spectrometer enable the highest-resolution measurement to date of water vapor's spatial variability and scaling properties. We find second-order structure function scaling exponents consistent with prior studies of convective atmospheres. Airborne lidar data show that this total column measurement provides information about variability in the lower troposphere. We conclude by discussing the implications of these measurements and paths toward future campaigns to build upon these results.

scholarly journals Spectroscopic Imaging of Sub-Kilometer Spatial Structure in Lower Tropospheric Water Vapor

2020 ◽  
Author(s):  
David R. Thompson ◽  
Brian H. Kahn ◽  
Philip G. Brodrick ◽  
Matthew D. Lebsock ◽  
Mark Richardson ◽  
...  
Keyword(s):  
Water Vapor ◽  
Spatial Variability ◽  
General Circulation ◽  
Imaging Spectroscopy ◽  
General Circulation Models ◽  
Order Structure ◽  
Scaling Properties ◽  
Observation Network ◽  
Airborne Imaging ◽  
Shortwave Infrared

Abstract. Understanding the subgrid spatial variability of water vapor is important for parameterizing and simulating cloud processes in General Circulation Models (GCMs). This study maps sub-kilometer spatial structures in total atmospheric column water vapor with Visible to Shortwave Infrared (VSWIR) imaging spectroscopy. We describe our inversion approach and validate its accuracy with coincident measurements by airborne imaging spectrometers and the AERONET ground-based observation network. Next, data from NASA’s AVIRIS-NG spectrometer enables the highest resolution measurement to date of water vapor’s spatial variability and scaling properties. We find second order structure function scaling exponents consistent with prior studies of convective atmospheres. Finally, we conclude by discussing the implications of these measurements and paths toward future campaigns to build upon these results.

scholarly journals Structure Function Analysis of Water Vapor Simulated with a Convection-Permitting Model and Comparison to Airborne Lidar Observations

2017 ◽  
Vol 74 (4) ◽  
pp. 1201-1210
Author(s):  
Tobias Selz ◽  
Lucas Fischer ◽  
George C. Craig
Keyword(s):  
Water Vapor ◽  
Structure Function ◽  
Airborne Lidar ◽  
Sensitivity Analyses ◽  
Small Scale ◽  
Limited Area ◽  
Air Mass ◽  
Scaling Properties ◽  
Air Masses ◽  
The Impact

Abstract The spatial scale dependence of midlatitude water vapor variability in the high-resolution limited-area model COSMO is evaluated using diagnostics of scaling behavior. Past analysis of airborne lidar measurements showed that structure function scaling exponents depend on the corresponding airmass characteristics, and that a classification of the troposphere into convective and nonconvective layers led to significantly different power-law behaviors for each of these two regimes. In particular, scaling properties in the convective air mass were characterized by rough and highly intermittent data series, whereas the nonconvective regime was dominated by smoother structures with weaker small-scale variability. This study finds similar results in a model simulation with an even more pronounced distinction between the two air masses. Quantitative scaling diagnostics agree well with measurements in the nonconvective air mass, whereas in the convective air mass the simulation shows a much higher intermittency. Sensitivity analyses were performed using the model data to assess the impact of limitations of the observational dataset, which indicate that analyses of lidar data most likely underestimated the intermittency in convective air masses due to the small samples from single flight tracks, which led to a bias when data with poor fits were rejected. Though the quantitative estimation of intermittency remains uncertain for convective air masses, the ability of the model to capture the dominant weather regime dependence of water vapor scaling properties is encouraging.

Mapping methane point emissions with imaging spectroscopy satellite missions

2021 ◽  
Author(s):  
Luis Guanter ◽  
Itziar Irakulis-Loitxate ◽  
Elena Sánchez-García ◽  
Javier Gorroño ◽  
Yongguang Zhang ◽  
...  
Keyword(s):  
Imaging Spectroscopy ◽  
Electromagnetic Spectrum ◽  
Spectral Window ◽  
Remote Sensing Technique ◽  
The Earth ◽  
Spatial Coverage ◽  
Airborne Imaging ◽  
Emission Detection ◽  
Shortwave Infrared ◽  
Spectral Channels

<p>Imaging spectroscopy, also known as hyperspectral imaging, is a remote sensing technique in which images of the solar radiation reflected by the Earth are produced in hundreds of spectral channels between the visible and the shortwave infrared part of the electromagnetic spectrum (roughly 400–2500 nm). The 2100-2450 nm spectral window can be used for methane retrievals, as it has been demonstrated over the last years with airborne imaging  spectrometers, and very recently also with space-based instruments. Satellite-based hyperspectral images are acquired with a typical spatial sampling for satellite data of 30 m, a spatial coverage between 30x30 and 60x60 km per scene, and a spectral sampling of 10 nm. In this work, we will present an overview of the state-of-the-art of methane mapping with imaging spectroscopy missions. We will review the characteristics of the available missions, the main retrieval approaches, and will present examples of methane emission detection from a number of missions and locations around the Earth.</p><p> </p>

scholarly journals Tropospheric Water Vapor Transport as Determined from Airborne Lidar Measurements

2010 ◽  
Vol 27 (12) ◽  
pp. 2017-2030 ◽  
Author(s):  
Andreas Schäfler ◽  
Andreas Dörnbrack ◽  
Christoph Kiemle ◽  
Stephan Rahm ◽  
Martin Wirth
Keyword(s):  
Water Vapor ◽  
Wind Speed ◽  
Moisture Transport ◽  
Lower Troposphere ◽  
Airborne Lidar ◽  
Vapor Transport ◽  
Specific Humidity ◽  
Water Vapor Transport ◽  
Horizontal Wind ◽  
Warm Sector

Abstract The first collocated measurements during THORPEX (The Observing System Research and Predictability Experiment) regional campaign in Europe in 2007 were performed by a novel four-wavelength differential absorption lidar and a scanning 2-μm Doppler wind lidar on board the research aircraft Falcon of the Deutsches Zentrum für Luft- und Raumfahrt (DLR). One mission that was characterized by exceptionally high data coverage (47% for the specific humidity q and 63% for the horizontal wind speed υh) was selected to calculate the advective transport of atmospheric moisture qυh along a 1600-km section in the warm sector of an extratropical cyclone. The observations are compared with special 1-hourly model data calculated by the ECMWF integrated forecast system. Along the cross section, the model underestimates the wind speed on average by −2.8% (−0.6 m s−1) and overestimates the moisture at dry layers and in the boundary layer, which results in a wet bias of 17.1% (0.2 g kg−1). Nevertheless, the ECMWF model reproduces quantitatively the horizontally averaged moisture transport in the warm sector. There, the superposition of high low-level humidity and the increasing wind velocities with height resulted in a deep tropospheric layer of enhanced water vapor transport qυh. The observed moisture transport is variable and possesses a maximum of qυh = 130 g kg−1 m s−1 in the lower troposphere. The pathways of the moisture transport from southwest via several branches of different geographical origin are identified by Lagrangian trajectories and by high values of the vertically averaged tropospheric moisture transport.

scholarly journals Intercomparison of Water Vapor Data Measured with Lidar during IHOP_2002. Part II: Airborne-to-Airborne Systems

2007 ◽  
Vol 24 (1) ◽  
pp. 22-39 ◽  
Author(s):  
Andreas Behrendt ◽  
Volker Wulfmeyer ◽  
Thorsten Schaberl ◽  
Hans-Stefan Bauer ◽  
Christoph Kiemle ◽  
...  
Keyword(s):  
Water Vapor ◽  
Lower Troposphere ◽  
Formation Flight ◽  
Airborne Lidar ◽  
Raman Lidar ◽  
Atmospheric Variability ◽  
Height Range ◽  
Field Campaigns ◽  
Langley Research Center ◽  
Better Than

Abstract The dataset of the International H2O Project (IHOP_2002) gives the first opportunity for direct intercomparisons of airborne water vapor lidar systems and allows very important conclusions to be drawn for future field campaigns. Three airborne differential absorption lidar (DIAL) systems were operated simultaneously during some IHOP_2002 missions: the DIAL of Deutsches Zentrum für Luft- und Raumfahrt (DLR), the Lidar Atmospheric Sensing Experiment (LASE) of the National Aeronautics and Space Administration (NASA) Langley Research Center, and the Lidar Embarque pour l’etude des Aerosols et des Nuages de l’interaction Dynamique Rayonnement et du cycle de l’Eau (LEANDRE II) of the Centre National de la Recherche Scientifique (CNRS). Data of one formation flight with DLR DIAL and LEANDRE II were investigated, which consists of 54 independent profiles of the two instruments measured with 10-s temporal average. For the height range of 1.14–1.64 km above sea level, a bias of (−0.41 ± 0.16) g kg−1 or −7.9% ± 3.1% was found for DLR DIAL compared to LEANDRE II (LEANDRE II drier) as well as root-mean-square (RMS) deviations of (0.87 ± 0.18) g kg−1 or 16.9% ± 3.5%. With these results, relative bias values of −9.3%, −1.5%, +2.7%, and +8.1% result for LEANDRE II, DLR DIAL, the scanning Raman lidar (SRL), and LASE, respectively, using the mutual bias values determined in Part I for the latter three sensors. From the three possible profile-to-profile intercomparisons between DLR DIAL and LASE, one case cannot provide information on the system performances due to very large inhomogeneity of the atmospheric water vapor field, while one of the two remaining two cases showed a difference of −4.6% in the height range of 1.4–3.0 km and the other of −25% in 1.3–3.8 km (in both cases DLR DIAL was drier than LASE). The airborne-to-airborne comparisons showed that if airborne water vapor lidars are to be validated down to an accuracy of better than 5% in the lower troposphere, the atmospheric variability of water vapor has to be taken into account down to scales of less than a kilometer unless a sufficiently large number of intercomparison cases is available to derive statistically solid biases and RMS deviations. In conclusion, the overall biases between the water vapor data of all three airborne lidar systems operated during IHOP_2002 are smaller than 10% in the present stage of data evaluation, which confirms the previous estimates of the instrumental accuracies for all the systems.

Données LiDAR aériennes: pertinence de l'interprétation visuelle pour la foresterie

2017 ◽  
Vol 168 (3) ◽  
pp. 127-133
Author(s):  
Matthew Parkan
Keyword(s):  
Cross Sections ◽  
Forest Edge ◽  
Airborne Lidar ◽  
Visual Interpretation ◽  
Individual Tree ◽  
Small Areas ◽  
Interactive 3D ◽  
The Individual ◽  
Airborne Lidar Data ◽  
Stem Position

Airborne LiDAR data: relevance of visual interpretation for forestry Airborne LiDAR surveys are particularly well adapted to map, study and manage large forest extents. Products derived from this technology are increasingly used by managers to establish a general diagnosis of the condition of forests. Less common is the use of these products to conduct detailed analyses on small areas; for example creating detailed reference maps like inventories or timber marking to support field operations. In this context, the use of direct visual interpretation is interesting, because it is much easier to implement than automatic algorithms and allows a quick and reliable identification of zonal (e.g. forest edge, deciduous/persistent ratio), structural (stratification) and point (e.g. tree/stem position and height) features. This article examines three important points which determine the relevance of visual interpretation: acquisition parameters, interactive representation and identification of forest characteristics. It is shown that the use of thematic color maps within interactive 3D point cloud and/or cross-sections makes it possible to establish (for all strata) detailed and accurate maps of a parcel at the individual tree scale.

scholarly journals Cloth simulation-based construction of pit-free canopy height models from airborne LiDAR data

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Wuming Zhang ◽  
Shangshu Cai ◽  
Xinlian Liang ◽  
Jie Shao ◽  
Ronghai Hu ◽  
...  
Keyword(s):  
Tree Height ◽  
Point Clouds ◽  
Airborne Lidar ◽  
Canopy Height ◽  
Lidar Data ◽  
Cloth Simulation ◽  
Simulation Based ◽  
Maximum Tree Height ◽  
Airborne Lidar Data ◽  
Better Than

Abstract Background The universal occurrence of randomly distributed dark holes (i.e., data pits appearing within the tree crown) in LiDAR-derived canopy height models (CHMs) negatively affects the accuracy of extracted forest inventory parameters. Methods We develop an algorithm based on cloth simulation for constructing a pit-free CHM. Results The proposed algorithm effectively fills data pits of various sizes whilst preserving canopy details. Our pit-free CHMs derived from point clouds at different proportions of data pits are remarkably better than those constructed using other algorithms, as evidenced by the lowest average root mean square error (0.4981 m) between the reference CHMs and the constructed pit-free CHMs. Moreover, our pit-free CHMs show the best performance overall in terms of maximum tree height estimation (average bias = 0.9674 m). Conclusion The proposed algorithm can be adopted when working with different quality LiDAR data and shows high potential in forestry applications.

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