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DOE OSTI2026

Performance of reanalysis and mesoscale models off the coast of Hawai'i

The eastern Hawai'i coast in the United States is characterized by considerable wind resource fuelled by persistent trade winds, making it an important area for energy research. The need is strong for reanalyses and higher-resolution regional simulations where observations have been historically limited, such as Hawai'i's offshore environments. However, studies using offshore observations in other parts of the world have shown that significant errors can occur in reanalyses and wind datasets, which can lead to inaccurate estimates of wind energy generation, payback periods, and extreme weather risks at project locations. The degree of such errors is influenced by a number of factors, including spatial resolution and the handling of processes within the planetary boundary layer (PBL). In this work, we provide a wind resource characterization from year-long lidar buoy measurements off the eastern coast of O'ahu, Hawai'i, an environment previously unobserved at the rotor level, and use the characterization to evaluate the performance of two simulation datasets. The O'ahu deployment location is meteorologically unique and less complex than land-based wind resource characterizations, being strongly characterized by trade winds with minimal land–atmosphere interaction influences. Despite the unique and fairly consistent meteorological conditions, we hypothesize that distinct simulation datasets will exhibit diverse ranges of errors similar to those that have been seen for other offshore locations. We find the European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis version 5 (ERA5) to strongly underestimate observed wind speeds at the O'ahu location (bias = −1.54 m s −1 at a height of 140 m above sea level), while a regional Weather Research and Forecasting Model (WRF) simulation produced by the University of Hawai'i (UH-WRF) provides a significantly smaller wind speed bias (−0.25 m s −1 ), highlighting the value of running regional, higher-resolution simulations. The large bias noted for ERA5 is driven by significant underestimation of fast wind speeds (>9 m s −1 ), which the study site is largely characterized by, along with discontinuities in the ERA5 diurnal cycle. We also speculate that the relative sparsity of observations for data assimilation in this remote part of the world could influence the performance of ERA5 and that challenges with characterizing island effects could impact the performance of both datasets.

17 WIND ENERGY↗

NASA NTRS1979

Water vapor differential absorption lidar development and evaluation

A ground-based differential absorption lidar (DIAL) system is described which has been developed for vertical range-resolved measurements of water vapor. The laser transmitter consists of a ruby-pumped dye laser, which is operated on a water vapor absorption line at 724.372 nm. Part of the ruby laser output is transmitted simultaneously with the dye laser output to determine atmospheric scattering and attenuation characteristics. The dye and ruby laser backscattered light is collected by a 0.5-m diam telescope, optically separated in the receiver package, and independently detected using photomultiplier tubes. Measurements of vertical water vapor concentration profiles using the DIAL system at night are discussed, and comparisons are made between the water vapor DIAL measurements and data obtained from locally launched rawinsondes. Agreement between these measurements was found to be within the uncertainty of the rawinsonde data to an altitude of 3 km. Theoretical simulations of this measurement were found to give reasonably accurate predictions of the random error of the DIAL measurements. Confidence in these calculations will permit the design of aircraft and Shuttle DIAL systems and experiments using simulation results as the basis for defining lidar system performance requirements

Browell, E. V.↗

NASA NTRS2021

Assimilation of Lidar Planetary Boundary Layer Height Observations

Lidar backscatter and wind retrievals of the planetary boundary layer height (PBLH) are assimilated into 22 hourly forecasts from the NASA Unified - Weather and Research Forecast (NU-WRF) model during the Plains Elevated Convection Convection at Night (PECAN) campaign on July 11, 2015 in Greensburg, Kansas, using error statistics collected from the model profiles to compute the necessary covariance matrices. Two separate forecast runs using different PBL physics schemes were employed, and comparisons with 6 independent radiosonde profiles were made for each run. Both of the forecast runs accurately predicted the PBLH and the state variable profiles within the planetary boundary layer during the early morning, and the assimilation had a small impact during this time. In the late afternoon, the forecast runs showed decreased accuracy as the convective boundary layer developed. However, assimilation of the Doppler lidar PBLH observations were found to improve the temperature and V velocity profiles relative to independent radiosonde profiles. Water vapor was overcorrected, leading to increased differences with independent data. Errors in the U velocity were made slightly larger. The computed forecast error covariances between the PBLH and state variables were found to rise in the late afternoon, leading to the larger improvements in the afternoon. This work represents the first effort to assimilate PBLH into forecast states using ensemble methods.

Andrew Tangborn↗

NASA NTRS2019

Application of High-Dimensional Fuzzy K-Means Cluster Analysis to CALIOP/CALIPSO Version 4.1 Cloud-Aerosol Discrimination

This study applies fuzzy k-means (FKM) cluster analyses to a subset of the parameters reported in the CALIPSO lidar level 2 data products in order to classify the layers detected as either clouds or aerosols. The results obtained are used to assess the reliability of the cloud–aerosol discrimination (CAD) scores reported in the version 4.1 release of the CALIPSO data products. FKM is an unsupervised learning algorithm, whereas the CALIPSO operational CAD algorithm (COCA) takes a highly supervised approach. Despite these substantial computational and architectural differences, our statistical analyses show that the FKM classifications agree with the COCA classifications for more than 94 % of the cases in the troposphere. This high degree of similarity is achieved because the lidar-measured signatures of the majority of the clouds and the aerosols are naturally distinct, and hence objective methods can independently and effectively separate the two classes in most cases. Classification differences most often occur in complex scenes (e.g., evaporating water cloud filaments embedded in dense aerosol) or when observing diffuse features that occur only intermittently (e.g., volcanic ash in the tropical tropopause layer). The two methods examined in this study establish overall classification correctness boundaries due to their differing algorithm uncertainties. In addition to comparing the outputs from the two algorithms, analysis of sampling, data training, performance measurements, fuzzy linear discriminants, defuzzification, error propagation, and key parameters in feature type discrimination with the FKM method are further discussed in order to better understand the utility and limits of the application of clustering algorithms to space lidar measurements. In general, we find that both FKM and COCA classification uncertainties are only minimally affected by noise in the CALIPSO measurements, though both algorithms can be challenged by especially complex scenes containing mixtures of discrete layer types. Our analysis results show that attenuated backscatter and color ratio are the driving factors that separate water clouds from aerosols; backscatter intensity, depolarization, and mid-layer altitude are most useful in discriminating between aerosols and ice clouds; and the joint distribution of backscatter intensity and depolarization ratio is critically important for distinguishing ice clouds from water clouds.

Zeng, Shan↗

DOE OSTI2020

Leveraging 3D CAD and Virtual Reality in Design of the Calcine Disposition Project - 20512

Fluor Idaho's Calcine Disposition Project (CDP) is leveraging the power of three-dimensional (3D) visualization, light detection and ranging (Lidar), and virtual reality to improve designs and worker safety. With the use of computer-aided design (CAD) modeling software, the CDP has created 3D models of the facility and systems to aid in the design, development, and operation of its retrieval and transfer system. This by itself is nothing new and has been done in industry for years. The CDP project however is taking this a few steps farther with the use of Lidar and virtual reality software. The CRP is tasked with removing a radioactive granular material from stainless steel bins located in concrete vaults that were constructed in the late 1950's and early 1960's. While construction drawings are available, it is not certain that the drawings are as-built or how accurate they truly are. Part of the project requires precise placement of equipment on to bins around multiple pipe, electrical lines, and bin stiffeners. Relying solely on the existing drawings and models created from these drawings is highly risky and prone to failure. The first step to determine the precise location and orientation of all obstructions in the vault the project will use a Lidar system to scan the vault structure and precisely locate all vault and bin components. Once this information is obtained, the data will be integrated into the CAD model. The model will then be verified, and a true as-built model developed. Precise component location, size, and orientation will be used for final design and placement of retrieval components. The second step will be to take the verified 3D model and scan data and import it into virtual reality software creating a virtual world. While some work on the project requires all work to be completed remotely due to high radiation fields, some areas have low enough radiation levels that personnel can enter and perform work. With the use of a virtual reality headset, operators and technicians will be able to enter our virtual world and become familiar with the surroundings and perform work prior to ever entering the radiation environment. This training is expected to pay dividends by improving worker efficiency, reducing errors, and improving confidence that the work can be performed as expected. It will play a key aspect in as low as reasonably achievable (ALARA) principles. An additional aspect in using the Lidar scans and virtual environment, is that it will give engineers and managers an opportunity to revisit the facility, especially radiation areas, at any time to obtain design information and measurements that may be costly or impossible to obtain once the facility transitions into radiation operations. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

NASA NTRS2003

Lidar Measurements of Wind, Moisture and Boundary Layer Evolution in a Dryline During IHOP2002

Variability in the convective boundary layer moisture, wind and temperature fields and their importance in the forecasting and understanding of storms have been discussed in the literature. These variations have been reported in relation to frontal zones, stationary boundaries and during horizontal convective rolls. While all three vary substantially in the convective boundary layer, moisture poses a particular challenge. Moisture or water vapor concentration (expressed as a mass mixing ratio, g/kg), is conserved in all meteorological processes except condensation and evaporation. The water vapor mixing ratio often remains distinct across an air -mass boundary even when the temperature difference is indistinct. These properties make it an ideal choice in visualizing and understanding many of the atmosphere's dynamic features. However, it also presents a unique measurement challenge because water vapor content can vary by more than three orders of magnitude in the troposphere. Characterization of the 3D-distribution of water vapor is also difficult as water vapor observations can suffer from large sampling errors and substantial variability both in the vertical and horizontal. This study presents groundbased measurements of wind, boundary layer structure and water vapor mixing ratio measurements observed by three co-located lidars. This presentation will focus on the evolution and variability of moisture and wind in the boundary layer during a dry line event that occurred on 22 May 2002. These data sets and analyses are unique in that they combine simultaneous measurements of wind, moisture and CBL structure to study the detailed thermal variability in and around clear air updrafts during a dryline event. It will quantify the variation caused by, in and around buoyant plumes and across a dryline. The data presented here were collected in the panhandle of Oklahoma as part of the International BO Project (IHOP-2002), a field experiment that took place over the Southern Great Plains (SGP) of the United States from 13 May to 30 June 2002. The chief goal of IHOP-2002 is to improve characterization of the four-dimensional (4-D) distribution of water vapor and its application to improving the understanding and prediction of convection

Demoz, Belay↗

NASA NTRS1992

Lidar measurements of cloud extinction coefficient distribution and its forward scattering phase function according to multiply scattered lidar returns

The study of the inversion algorithm for the single scatter lidar equation, for quantitative determination of cloud (or aerosol) optical properties, has received much attention over the last thirty years. Some of the difficulties associated with the solution of this equation are not yet solved. One problem is that a single scatter lidar equation has two unknowns. Because of this, the determination of the far-end boundary value, in the case of Klett's algorithm, is a problem if the atmosphere is optically inhomogeneous. Another difficulty concerns multiple scattering. There is a large error in the extinction distribution solution, in many cases, if only the single scattering component is considered, while neglecting the multiple scattering component. However, the use of multiple scattering in the remote sensing of aerosol or cloud optical properties is promising. In our early study, an inversion method for simultaneous determination of the cloud (or aerosol) Extinction Coefficient Distribution (ECD) and its Forward Scattering Phase Function (FSPF) was proposed according to multiply scattered lidar returns with two fields of view for the receiver. The method is based on a parameterized multiple scatter lidar equation. This paper is devoted to further numerical tests and an experimental study of lidar measurements of cloud ECD and FSPF using this method.

Qiu, Jinhuan↗

NASA NTRS2004

Use of Probability Distribution Functions for Discriminating Between Cloud and Aerosol in Lidar Backscatter Data

In this paper we describe the algorithm hat will be used during the upcoming Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) mission for discriminating between clouds and aerosols detected in two wavelength backscatter lidar profiles. We first analyze single-test and multiple-test classification approaches based on one-dimensional and multiple-dimensional probability density functions (PDFs) in the context of a two-class feature identification scheme. From these studies we derive an operational algorithm based on a set of 3-dimensional probability distribution functions characteristic of clouds and aerosols. A dataset acquired by the Cloud Physics Lidar (CPL) is used to test the algorithm. Comparisons are conducted between the CALIPSO algorithm results and the CPL data product. The results obtained show generally good agreement between the two methods. However, of a total of 228,264 layers analyzed, approximately 5.7% are classified as different types by the CALIPSO and CPL algorithm. This disparity is shown to be due largely to the misclassification of clouds as aerosols by the CPL algorithm. The use of 3-dimensional PDFs in the CALIPSO algorithm is found to significantly reduce this type of error. Dust presents a special case. Because the intrinsic scattering properties of dust layers can be very similar to those of clouds, additional algorithm testing was performed using an optically dense layer of Saharan dust measured during the Lidar In-space Technology Experiment (LITE). In general, the method is shown to distinguish reliably between dust layers and clouds. The relatively few erroneous classifications occurred most often in the LITE data, in those regions of the Saharan dust layer where the optical thickness was the highest.

Liu, Zhaoyan↗

NASA NTRS2002

Lidar Measurements of Wind, Moisture, and Boundary Layer Evolution in a Dry Line during 1HOP 2002

Variability in the convective boundary layer moisture, wind and temperature fields and their importance in the forecasting and understanding of storms have been discussed in the literature. These . variations have been reported in relation to frontal zones, stationary boundaries and during horizontal convective rolls. While all three vary substantially in the convective boundary layer, moisture poses a particular challenge. Moisture or water vapor concentration (expressed as a mass mixing ratio, g/kg), is conserved in all meteorological processes except condensation and evaporation. The water vapor mixing ratio often remains distinct across an air-mass boundary even when the temperature difference is indistinct. These properties make it an ideal choice in visualizing and understanding many of the atmosphere's dynamic features. However, it also presents a unique measurement challenge because water vapor content can vary by more than three orders of magnitude in the troposphere. Characterization of the 3D-distribution of water vapor is also difficult as water vapor observations can suffer from large sampling errors and substantial variability both in the vertical and horizontal. This study presents ground-based measurements of wind, boundary layer structure and water vapor mixing ratio measurements observed by three co-located lidars. This presentation will focus on the evolution and variability of moisture and wind in the boundary layer during a dry line event that occurred on 22 May 2002. These data sets and analyses are unique in that they combine simultaneous measurements of wind, moisture and CBL structure to study the detailed thermal variability in and around clear air updrafts during a dryline event. It will quantify the variation caused by, in and around buoyant plumes and across a dryline. The data presented here were collected in the panhandle of Oklahoma as part of the International H2O Project (MOP-2002), a field experiment that took place over the Southern Great Plains (SGP) of the United States from 13 May to 30 June 2002. The chief goal of MOP-2002 is to improve characterization of the four-dimensional (4-D) distribution of water vapor and its application to improving the understanding and prediction of convection

Demoz, Belay↗

NASA NTRS1980

DIAL with heterodyne detection including speckle noise: Aircraft/shuttle measurements of O3, H2O, and NH3 with pulsed tunable CO2 lasers

Atmospheric trace constituent measurements with higher vertical resolution than attainable with passive radiometers are discussed. Infrared differential absorption lidar (DIAL), which depends on Mie scattering from aerosols, has special advantages for tropospheric and lower stratospheric applications and has great potential importance for measurements from shuttle and aircraft. Differential absorption lidar data reduction involves comparing large amplitude signals which have small differences. The accuracy of the trace constituent concentration inferred from DIAL measurements depends strongly on the errors in determining the amplitude of the signals. Thus, the commonly used SNR expression (signal divided by noise in the absence of signal) is not adequate to describe DIAL measurement accuracy and must be replaced by an expression which includes the random coherent (speckle) noise within the signal. A comprehensive DIAL computer algorithm is modified to include heterodyne detection and speckle noise. Examples for monitoring vertical distributions of O3, H2O, and NH3 using a ground-, aircraft-, or shuttle-based pulsed tunable CO2 laser DIAL system are given.

Brockman, P.↗

NASA NTRS2018

Performance Analysis of Airborne Photon-Counting Lidar Data in Preparation for the ICESat-2 Mission

Two airborne photon-counting laser altimeters have been deployed in direct support of National Aeronautics and Space Adminsitration (NASA's) upcoming Ice, Cloud, and land Elevation Satellite-2 (ICESat-2) mission. Multiple Altimeter Beam Experimental Lidar (MABEL) was developed specifically for ICESat-2 testing and development. MABEL data are used to simulate key aspects of the ICESat-2 measurement strategy and are critical to the development of the algorithms for geophysical data-product generation. Slope Imaging Multipolarization Photon-counting Lidar (SIMPL) is a NASA Goddard Space Flight Center instrument that has also been deployed in support of ICESat-2 performance discovery. Both instruments are photon-counting, small footprint laser altimeters that sample in both the 532- and 1064-nm wavelengths. And both instruments serve as a proxy for ICESat-2 operational performance and error assessment and a basis for the development of potential validation strategies. This paper provides an overview of how data from MABEL and SIMPL overflights have specifically provided the foundation for understanding the quality of ICESat-2 data and how we can plan to evaluate ICESat-2 products through comparison with other modalities of lidar data and/or groundtruth locations using ground fiducials.

MABEL↗

DOE OSTI2020

Evaluating the WFIP2 updates to the HRRR model using scanning Doppler lidar measurements in the complex terrain of the Columbia River Basin

The wind-energy (WE) industry relies on numerical weather prediction (NWP) forecast models as foundational or base models for many purposes, including wind-resource assessment and wind-power forecasting. During the Second Wind Forecast Improvement Project (WFIP2) in the Columbia River Basin of Oregon and Washington, a significant effort was made to improve NWP forecasts through focused model development, to include experimental refinements to the High Resolution Rapid Refresh (HRRR) model physics and horizontal grid spacing. In this study, the performance of an experimental version of HRRR that includes these refinements is tested against a control version, which corresponds to that of the operational HRRR run by National Oceanic and Atmospheric Administration/National Centers for Environmental Protection at the outset of WFIP2. Furthermore, the effects of horizontal grid resolution were also tested by comparing wind forecasts from the HRRR (with 3-km grid spacing) with those from a finer-resolution HRRR nest with 750-m grid spacing. Model forecasts are validated against accurate wind-profile measurements by three scanning, pulsed Doppler lidars at sites separated by a total distance of 71 km. Model skill and improvements in model skill, attributable to physics refinements and improved horizontal grid resolution, varied by season, by site, and during periods of atmospheric phenomena relevant to WE. In general, model errors were the largest below 150 m above ground level (AGL). Experimental HRRR refinements tended to reduce the mean absolute error (MAE) and other error metrics for many conditions, but degradation in skill (increased MAE) was noted below 150 m AGL at the two lowest-elevation sites at night. Finer resolution was found to produce the most significant reductions in the error metrics.

17 WIND ENERGY↗

NASA NTRS1992

Observation of Stratospheric Ozone with NIES Lidar System in Tsukuba, Japan

Lidars are expected to play important roles in an international monitoring network of the stratosphere such as the Network for the Detection of Stratospheric Change (NDSC). The National Institute for Environmental Studies (NIES) in Tsukuba constructed an ozone lidar system in March 1988 and started observation in August 1988. The lidar system has a 2-m telescope and injection locked XeCl and XeF excimer lasers which can measure ozone profiles (15-45 km) and temperature profiles (30-80 km). From December 1991, lidar observations have been carried out in which the second Stokes line of the stimulated Raman scattering of a KrF laser has been used. Ozone profiles obtained with the NIES lidar system are compared with the data provided by the SAGE II satellite sensor. Results showed good agreement for the individual and the zonal mean profiles. Variations of ozone with various time scales at each altitude can be studied using the data obtained with the NIES ozone lidar system. Seasonal variations are easily found at 20 km, 30 km, and 35 km, which are qualitatively understood as a result of dynamical and photochemical effects. Systematic errors of ozone profiles due to the Pinatubo stratospheric aerosols have been detected using multi-wavelength observation.

Nakane, H.↗

NASA NTRS1998

Vertical Distribution of Saharan Dust from the Goddard Three Dimensional Transport Model: Comparison with LITE Data

Spatial distributions of Saharan dust are simulated by the Goddard 3D chemistry transport model driven by meteorological fields from the Goddard Earth Observing System Data Assimilation System, GEOS-2 DAS. An aerosol module accounts for gravitational settling and the aerosol source function is based on surface moisture content and wind speed. The simulated aerosol fields are compared with Total Ozone Mapping Spectrometer (TOMS) by P. Ginoux in this session. The main focus is how ventilation of the assimilation winds will impact the simulated aerosol vertical distribution. Excessive ventilation would transport too much aerosol in the upper troposphere and lead to errors in the long range transport as well as errors in any radiative forcing calculations derived from the simulated aerosol fields. Simulated aerosol fields using GEOS-1 DAS wind fields for September 1994 will be compared with lidar observations during the Lidar In-space Technology Experiment (LITE). The lidar return signal clearly delineates the vertical extent and horizontal variability of the aerosol layer allowing rigorous comparison with the simulated fields. A second focus is the simulation of aerosol scavenging due to moist processes. Three-dimensional precipitation fields can be derived from two possible sources: Assimilated precipitation fields from GEOS-DAS or fields obtained from the Special Sensor Microwave/imager (SSM/1). Of interest is the sensitivity of the modeled aerosol fields to the different precipitation fields driving the moist scavenging module.

Weaver, Clark J.↗

NASA NTRSDate not supplied

Improvements to GNSS Positioning in Challenging Environments by 3DMA Lidar Informed Selective Satellites Usage

The use of global navigation satellite systems (GNSS) for position estimation tends to yield poor results when operating inside of an urban canyon due to large obstructions (e.g., buildings) that disrupt signals as they travel from a satellite to a receiver resulting in a position estimate that may significantly fluctuate in magnitude and direction. Identifying and removing signals that are non-line-of-sight (NLOS) to the receiver and only using signals that are line-of-sight (LOS) can improve the estimated position. However, quickly and accurately determining the LOS status of each measurement can be challenging without additional information about the operating environment. Use of publicly available lidar data can be used to incorporate techniques, such as 3D-mapping-aided (3DMA), to estimate the LOS status of satellites and augment the position solution accordingly. To complicate the issue, the error on the GNSS position estimate in an urban canyon is often so large that is it not sufficient to use as an approximate location for LOS prediction. That is, at times the calculated GNSS solution is not representative of the true location and cannot be used to accurately predict which satellites are within LOS due to the difference in the physical geometry associated with the two locations. This paper explores the use of a GNSS/inertial fused position solution as the initial position estimate for predicting which satellites are within LOS in an urban environment and the impact that removal of predicted NLOS satellites has on the GNSS position solution.

GPS↗

NASA NTRS2021

Synthesizing Disparate LiDAR and Satellite Datasets through Deep Learning to Generate Wall-to-Wall Regional Inventories for the Complex, Mixed-Species Forests of the Eastern United States

Light detection and ranging (LiDAR) has become a commonly-used tool for generating remotely-sensed forest inventories. However, LiDAR-derived forest inventories have remained uncommon at a regional scale due to varying parameters among LiDAR data acquisitions and the availability of sufficient calibration data. Here, we present a model using a 3-D convolutional neural network (CNN), a form of deep learning capable of scanning a LiDAR point cloud, combined with coincident satellite data (spectral, phenology, and disturbance history). We compared this approach to traditional modeling used for making forest predictions from LiDAR data (height metrics and random forest) and found that the CNN had consistently lower uncertainty. We then applied the CNN to public data over six New England states in the USA, generating maps of 14 forest attributes at a 10 m resolution over 85% of the region. Aboveground biomass estimates produced a root mean square error of 36 Mg ha−1 (44%) and were within the 97.5% confidence of independent county-level estimates for 33 of 38 or 86.8% of the counties examined. CNN predictions for stem density and percentage of conifer attributes were moderately successful, while predictions for detailed species groupings were less successful. The approach shows promise for improving the prediction of forest attributes from regional LiDAR data and for combining disparate LiDAR datasets into a common framework for large-scale estimation.

Elias Ayrey↗

DOE OSTI2025

NREL ASSIST Barge / Thermodynamic retrievals TROPoe v0.19

This dataset contains daily files with thermodynamic profiles retrieved with the optimal estimation physical retrieval TROPoe v0.19 (Turner and Löhnert, 2014; Turner and Blumberg, 2019; Turner and Löhnert, 2021). The profiles are retrieved every 10 minutes from instantaneous observations from the NREL ASSIST-II (SN 12) infrared spectrometer. Observations are noise-filtered but not averaged in time to minimize errors due to non-uniform clouds. Additional input data in TROPoe are cloud base height from a collocated NREL Vaisala CL51 ceilometer (when available) or vertically staring Halo XR lidar, and surface temperature, relative humidity, and pressure from the collocated Oregon State University met tower. The full pipeline to run the retrieval is available at https://github.com/StefanoWind/TROPoe_processor. In addition to these temporally resolved input data, TROPoe requires an a priori dataset (prior) that provides mean climatological estimates of thermodynamic profiles and specifies how temperature and humidity covary with height as an input (for details see, e.g., Djalalova et al., 2022). The prior is a key component of the retrieval and provides a constraint on the ill-posed inversion problem. A monthly prior was computed from operational radiosonde launches at Upton, NY.

17 WIND ENERGY↗

NASA NTRS1990

Launch flexibility using NLP guidance and remote wind sensing

This paper examines the use of lidar wind measurements in the implementation of a guidance strategy for a nonlinear programming (NLP) launch guidance algorithm. The NLP algorithm uses B-spline command function representation for flexibility in the design of the guidance steering commands. Using this algorithm, the guidance system solves a two-point boundary value problem at each guidance update. The specification of different boundary value problems at each guidance update provides flexibility that can be used in the design of the guidance strategy. The algorithm can use lidar wind measurements for on pad guidance retargeting and for load limiting guidance steering commands. Examples presented in the paper use simulated wind updates to correct wind induced final orbit errors and to adjust the guidance steering commands to limit the product of the dynamic pressure and angle-of-attack for launch vehicle load alleviation.

Cramer, Evin J.↗