A more accurate representation of ice‐phase processes in numerical models necessitates an enhanced understanding of ice‐particle microphysics and their respective formation conditions. Prior in situ ...measurements have noted distinctive ice‐particle shape characteristics associated with different cloud systems and geographical locations. The recent advancement in ice‐particle backscattering theories enables a more comprehensive exploration of the geographical distribution and seasonal dependence of ice‐particle shape categories than ever before. This exploration is being undertaken for the first time using space‐borne lidar measurements. Distinct geographical preferences were observed for five different ice‐particle categories. Bullets/rosettes were the most common, followed by Voronois, which were especially prevalent in high‐level tropical clouds, and 2D columns, which were commonly found in mid‐ and low‐level clouds. Droxtals were primarily observed in high‐level subtropical regions. The global distribution of ice‐particle types provides valuable insights into the physical processes related to ice cloud particle shape formation, cloud duration, and radiative impacts.
Plain Language Summary
An enhanced understanding of ice‐particle microphysics associated with different cloud systems and geographical locations is expected to improve the representation of ice clouds in numerical models for better future climate predictions. For such purpose, space‐borne lidar observations have been intensively used to characterize the global distribution of cloud phases, as well as ice particle types. Latest theoretical studies have indicated the possibility of further decomposing these ice particle types into more specific ice‐particle category information, but had not been applied to actual global observation data. In this work, the geographical distribution of five ice‐particle categories was derived based on the theoretical studies using spaceborne lidar data for the first time. It was found that different ice‐particle categories had a unique geographical preference for occurrence, along with seasonal and height‐dependent characteristics. The global distribution of ice‐particle categories obtained in the present study is expected to be useful for understanding the physical processes related to ice‐particle shape formation and the ice‐particle terminal velocity characteristics relevant to cloud duration.
Key Points
For the first time, spaceborne lidar was used to study meticulously the height‐resolved global distribution of five ice‐particle categories
Each cloud‐particle type displayed a unique geographical preference for occurrence, along with seasonal and height‐dependent characteristics
The global data on ice‐particle types and particle size from radar‐lidar synergies promises to be valuable for future cloud modeling studies
Properties of Coherent Structures over Paris Cheliotis, Ioannis; Dieudonné, Elsa; Delbarre, Hervé ...
Journal of applied meteorology and climatology,
11/2021, Letnik:
60, Številka:
11
Journal Article
Recenzirano
Odprti dostop
The studies related to the coherent structures in the atmosphere, using Doppler wind lidar observations, so far have relied on the manual detection and classification of the structures in the lidar ...images, making this process time-consuming. We developed an automated classification that is based on texture analysis parameters and the quadratic discriminant analysis algorithm for the detection of medium-to-large fluctuations and coherent structures recorded by single Doppler wind lidar quasi-horizontal scans. The algorithm classified a training dataset of 150 cases into four types of patterns, namely, streaks (narrow stripes), rolls (wide stripes), thermals (enclosed areas), and “others” (impossible to classify), with 91% accuracy. Subsequently, we applied the trained algorithm to a dataset of 4577 lidar scans recorded in Paris, atop a 75-m tower for a 2-month period (September–October 2014). The current study assesses the quality of the classification by examining the physical properties of the classified cases. The results show a realistic classification of the data: with rolls and thermals cases mostly classified concurrently with a well-developed atmospheric boundary layer and the streaks cases associated with nocturnal low-level jets events. Furthermore, rolls and streaks cases were mostly observed under moderate or high wind conditions. The detailed analysis of a 4-day period reveals the transition between the types. The analysis of the space spectra in the direction transverse to the mean wind, during these four days, revealed streak spacing of 200–400 m and roll sizes, as observed in the lower level of the mixed layer, of approximately 1 km.
Wind lidars can be used on wind turbines to monitor the inflow for power curve verification and for control purposes. In these applications, the lidar is most often placed on the nacelle behind the ...rotating blades, which occasionally intercept the line-of-sight measurements, resulting in decreased data availability or biased wind measurements. Distinguishing the wind from the blade signals is challenging for continuous-wave Doppler lidar observations. Here, we present a method that provides a more effective filtering than a typical filter relying on the strength of the backscattered signal. The method proposed is based on modelling the radial speed contribution generated by the wind turbine blades, and we present the results of a case study using a scanning wind lidar installed on the nacelle of an 850 kW wind turbine. We show that using the methodology proposed, we can optimize the identification of wind measurements, and thus, the data reliability of wind-turbine-mounted continuous-wave Doppler lidars is enhanced. Furthermore, the method is useful also for assessing the location and the alignment of a nacelle wind lidar in relation to a wind turbine’s rotor, which improves the accuracy of the inflow data and allows for a more efficient monitoring of the performance of a wind turbine.
Lidar transforms how we map ecosystems, but its prospect for measuring ecosystem dynamics is limited by practical factors, such as variation in lidar acquisition and lack of ground data. To address ...practical use of multitemporal lidar for forest and carbon monitoring, we conducted airborne lidar surveys four times from 2002 to 2012 over a region in Scotland, and combined the repeat lidar data with field inventories to map tree growth, biomass dynamics, and carbon change. Our analyses emphasized both individual tree detection and area-based, grid-level approaches. Lidar-detected heights of individual trees correlated well with field values, but with noticeable underestimation biases (r=0.94, bias=−1.5m, n=598) due to the increased probability of missing treetops as pulse density decreases. If not corrected for such biases, lidar provided unrealistic or wrong estimates of tree growth unless laser sampling rates were high enough (e.g., >7points/m2). Upon correction, lidar could detect sub-annual tree growth (p-value<0.05). At grid levels, forest biomass density was reliably estimated from area-based lidar metrics by both Random Forests (RF) and a linear functional model (r>0.86, RMSEcv<21Mg/ha), irrespective of laser sampling rates. But RF constantly overfit the data, often with poorer predictions. The better generality of the linear model was further confirmed by its transferability—fitted for one year but applicable to other years—a strength not possessed by RF but desired to alleviate the reliance on ground biomass data for model calibration. Resultant lidar maps of forest structure captured canopy dynamics and carbon flux at fine scales, consistent with growth histories and known disturbances. The entire 20-km2 study area sequestered carbon at a rate of 0.59±0.4MgC/ha/year. Overall, our study describes robust techniques well suited for multitemporal lidar analysis and affirms the utility and potential of repeat lidar data for resource monitoring and carbon management; however, the full potential cannot be attained without the support of accompanying field surveys or modeling efforts in enhancing stakeholders' trustworthiness of lidar-based inference.
Display omitted
•Four lidar surveys over 10years to track carbon dynamics and individual tree growth.•Lidar captured sub-annual tree growth only after correcting negative height biases.•Random Forest overfit biomass/C & was outcompeted by a mechanistic functional model.•Use of physics to inform biomass estimation boosts model generality & transferability.•Future use of repeat lidar is bright, supported by field data and satellite imagery.
Abstract
This paper describes a technique for estimating the liquid water content (LWC) and a characteristic particle diameter in stratocumulus clouds using radar and lidar observations. The ...uncertainty in LWC estimate from radar and lidar measurements is significantly reduced once the characteristic particle diameter is known. The technique is independent of the drop size distribution. It is applicable for a broad range of W-band reflectivity
Z
between −30 and 0 dB
Z
and all values of lidar backscatter
β
observations. No partitioning of cloud or drizzle is required on the basis of an arbitrary threshold of
Z
as in prior studies. A method for estimating droplet diameter and LWC was derived from the electromagnetic simulations of radar and lidar observations. In situ stratocumulus cloud and drizzle probe spectra were input to the electromagnetic simulation. The retrieved droplet diameter and LWC were validated using in situ measurements from the southeastern Pacific Ocean. The retrieval method was applied to radar and lidar measurements from the northeastern Pacific. Uncertainty in the retrieved droplet diameter and LWC that are due to the measurement errors in radar and lidar backscatter measurements are 7% and 14%, respectively. The retrieved LWC was validated using the concurrent G-band radiometer estimates of the liquid water path.
Abstract
Micropulse differential absorption lidars (MPD) for water vapor, temperature, and aerosol profiling have been developed, demonstrated, and are addressing the needs of the atmospheric science ...community for low-cost ground-based networkable instruments capable of long-term monitoring of the lower troposphere. The MPD instruments use a diode-laser-based (DLB) architecture that can easily be adapted for a wide range of applications. In this study, a DLB direct-detection Doppler lidar based on the current MPD architecture is modeled to better understand the efficacy of the instrument for vertical wind velocity measurements, with the long-term goal of incorporating these measurements into the current network of MPD instruments. The direct-detection Doppler lidar is based on a double-edge receiver that utilizes two Fabry–Pérot interferometers and a vertical velocity retrieval that requires the ancillary measurement of the backscatter ratio, which is the ratio of the total backscatter coefficient to the molecular backscatter coefficient. The modeling in this paper accounts for the major sources of error. It indicates that the vertical velocity can be retrieved with an error of less than 0.56 m s
−1
below 4 km with a 150-m range resolution and an averaging time of 5 min.
Significance Statement
Monitoring the temperature, relative humidity, and winds in the lower atmosphere is important for improving weather forecasting, particularly for severe weather such as thunderstorms. Cost-effective micropulse differential absorption lidar (MPD) instrumentation for continuous temperature and humidity monitoring has been developed and demonstrated, and its effects on weather forecasting are currently being evaluated. The modeling study described in this paper studies the feasibility of using a similar cost-effective MPD instrument architecture for monitoring vertical wind velocity in the lower atmosphere. Modeling indicates that wind velocities can be measured with less than 0.56 m s
−1
accuracy and demonstrates the feasibility of adding vertical wind velocity measurements to the MPD instruments.
The distribution of tropical cyclone (TC) eye cloud heights is documented for the first time using compact Raman lidar (CRL) measurements with high spatial resolution. These cloud heights act as ...tracers for low‐level vertical mixing in the eye region. Cloud height distributions using all available data from nine Atlantic TCs in 2021 and 2022 show significant vertical variance, dispelling the notion of a flat stratiform eye cloud deck. Eye cloud widths are multiscale, with shallow convective clouds dominating CRL returns. Data from Hurricane Sam (2021) highlight the evolution of shallow convective clouds in the TC eye and their associated temperature inversions. The frequent appearance of convective eye clouds, along with observed vertical wind fluctuations, suggests that vertical mixing from the boundary layer frequently occurs in the TC eye, even beneath strong inversions. This strong vertical mixing should be accurately portrayed by TC simulations and forecasts.
Plain Language Summary
While the temperature inversion and associated clouds within the tropical cyclone (TC) eye region are essential elements of storm structure, observing eye cloud heights remains a challenge, and the vertical distribution of these clouds is poorly constrained. This study documents TC eye cloud characteristics using a new aircraft‐based remote sensing technique for the first time, with a focus on cloud height distributions. Both case study and statistical approaches confirm that most of these clouds are convective in nature, not stratiform. The formation of shallow convective clouds within TC eyes is driven by shallow upward mixing as documented by aircraft flight level vertical wind measurements. Vertical mixing across the eye inversion layer changes inner core thermodynamics, highlights a potential pathway for intensity change, and emphasizes why this process must be captured in high resolution TC forecast models.
Key Points
New, aircraft‐based compact Raman lidar cloud, temperature, and water vapor measurements detail tropical cyclone inner core structure
Case studies and statistics demonstrate how shallow convective clouds with high spatial variability are the most common eye cloud type
Vertical velocity and cloud height analyses suggest that low level vertical mixing drives shallow convective eye cloud formation
Abstract To address the issue of invalid values in the measured wind speed data of the wind lidar, a data interpolation experiment was conducted by using the wind speed data outputted by the wind ...lidar as the base data source. Based on data preprocessing, the interpolation model was constructed by integrating MLP and the mean wind speed shear index. The accuracy of the interpolated wind speed data was evaluated. The experimental results show that the proposed interpolation method for wind lidar data achieved high accuracy, with a root mean square error of less than 0.75 m/s and an average relative error of less than 3.5%. This approach enhances the reliability and integrity of wind lidar data, providing a reference for the use of wind lidar data in wind resource assessment.
Full Waveform LiDAR for Adverse Weather Conditions Wallace, Andrew M.; Halimi, Abderrahim; Buller, Gerald S.
IEEE transactions on vehicular technology,
07/2020, Letnik:
69, Številka:
7
Journal Article
Recenzirano
Odprti dostop
We present and discuss the case for full waveform pixel and image acquisition and processing to enable LiDAR sensors to penetrate and reconstruct 3D surface maps through obscuring media. To that end, ...we review work on signal propagation, on scanning and arrayed sensors, on signal processing strategies for independent pixels and employing spatial context, on reducing complexity and accelerating processing by sensor design, algorithmic changes, compressed sensing, and parallel processing. We report several experimental studies on LiDAR imaging through complex media, and how these can inform the automotive LiDAR scenario. We conclude with a discussion of future development and potential for full waveform LiDAR (FWL).
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