Monodisperse Aerosol Generator for Field Calibration of Atmospheric Measurement Instrumentation
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47 OTHER INSTRUMENTATION↗
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Chaos, Consternation and CALIPSO Calibration: New Strategies for Calibrating the CALIOP 1064 nm Channel
The very low signal-to-noise ratios of the 1064 nm CALIOP molecular backscatter signal make it effectively impossible to employ the "clear air" normalization technique typically used to calibrate elastic back-scatter lidars. The CALIPSO mission has thus chosen to cross-calibrate their 1064 nm measurements with respect to the 532 nm data using the two-wavelength backscatter from cirrus clouds. In this paper we discuss several known issues in the version 3 CALIOP 1064 nm calibration procedure, and describe the strategies that will be employed in the version 4 data release to surmount these problems.
Vaughan, Mark↗
Multi-scale integration of satellite remote sensing improves characterization of dry-season green-up in an Amazon tropical evergreen forest
In tropical forests, leaf phenology-particularly the pronounced dry-season green-up-strongly regulates biogeochemical cycles of carbon and water fluxes. However, uncertainties remain in the understanding of tropical forest leaf phenology at different spatial scales. Phenocams accurately characterize leaf phenology at the crown and ecosystem scales but are limited to a few sites and time spans of a few years. Time-series satellite observations might fill this gap, but the commonly used satellites (e.g. MODIS, Landsat and Sentinel-2) have resolutions too coarse to characterize single crowns. To resolve this observational challenge, we used the PlanetScope constellation with a 3m resolution and near daily nadir-view coverage. We first developed a rigorous method to cross-calibrate PlanetScope surface reflectance using daily BRDF-adjusted MODIS as the reference. We then used linear spectral unmixing of calibrated PlanetScope to obtain dry-season change in the fractional cover of green vegetation (GV) and non-photosynthetic vegetation (NPV) at the PlanetScope pixel level. We used the Central Amazon Tapajos National Forest k67 site, as all necessary data (from field to phenocam and satellite observations) was available. For this proof of concept, we chose a set of 22 dates of PlanetScope measurements in 2018 and 16 in 2019, all from the six drier months of the year to provide the highest possible cloud-free temporal resolution. Our results show that MODIS-calibrated dry-season PlanetScope data (1) accurately assessed seasonal changes in ecosystem-scale and crown-scale spectral reflectance; (2) detected an increase in ecosystem-scale GV fraction (and a decrease in NPV fraction) from June to November of both years, consistent with local phenocam observations with R 2 around 0.8; and (3) monitored large seasonal trend variability in crown-scale NPV fraction. Finally, our results highlight the potential of integrating multi-scale satellite observations to extend fine-scale leaf phenology monitoring beyond the spatial limits of phenocams.
54 ENVIRONMENTAL SCIENCES↗
Advanced Optical Diagnostics for Ice Crystal Cloud Measurements in the NASA Glenn Propulsion Systems Laboratory
A light extinction tomography technique has been developed to monitor ice water clouds upstream of a direct connected engine in the Propulsion Systems Laboratory (PSL) at NASA Glenn Research Center (GRC). The system consists of 60 laser diodes with sheet generating optics and 120 detectors mounted around a 36-inch diameter ring. The sources are pulsed sequentially while the detectors acquire line-of-sight extinction data for each laser pulse. Using computed tomography algorithms, the extinction data are analyzed to produce a plot of the relative water content in the measurement plane. To target the low-spatial-frequency nature of ice water clouds, unique tomography algorithms were developed using filtered back-projection methods and direct inversion methods that use Gaussian basis functions. With the availability of a priori knowledge of the mean droplet size and the total water content at some point in the measurement plane, the tomography system can provide near real-time in-situ quantitative full-field total water content data at a measurement plane approximately 5 feet upstream of the engine inlet. Results from ice crystal clouds in the PSL are presented. In addition to the optical tomography technique, laser sheet imaging has also been applied in the PSL to provide planar ice cloud uniformity and relative water content data during facility calibration before the tomography system was available and also as validation data for the tomography system. A comparison between the laser sheet system and light extinction tomography resulting data are also presented. Very good agreement of imaged intensity and water content is demonstrated for both techniques. Also, comparative studies between the two techniques show excellent agreement in calculation of bulk total water content averaged over the center of the pipe.
Bencic, Timothy J.↗
CLARREO Pathfinder as a SI-traceable Reference for Satellite Intercalibration
The Climate Absolute Radiance and Refractivity Observatory (CLARREO) Pathfinder (CPF) consists of an Earthviewing reflected solar (RS) spectrometer that will measure the Earth-reflected solar radiation from International Space Station with an SI-traceable radiometric uncertainty of 0.3% (1-sigma). The high-accuracy CPF measurements will provide an in-orbit reference for intercalibrating other spaceflight RS instruments. The CPF intercalibration team has been tasked to develop a state-of-the-art approach to calibrate the shortwave channel (300-5000 nm) of the Clouds and the Earth’s Radiant Energy System (CERES) instrument and the reflective solar bands (RSB) of the Visible Infrared Imaging Radiometer Suite (VIIRS) instrument, both onboard the NOAA-20 satellite, against the CPF benchmark measurements. The aimed intercalibration methodology uncertainty for both the target instruments is also 0.3%. To meet this stringent intercalibration accuracy, the CPF team has developed methods for mitigating the impacts of spatial, spectral, and angular differences between the intercalibration footprints from the CPF and target instruments. To further alleviate uncertainty, the CPF team will employ Polarization Distribution Models (PDMs) to characterize the polarization state of the Earth-reflected radiance as a function of the intercalibration footprint scene type, solar and viewing geometry, and wavelength. The PDMs will assist in identifying low-polarized scene radiances for meticulously intercalibrating the polarization sensitive VIIRS instrument against the significantly-less polarization-sensitive CPF instrument. This paper will highlight the CPF mission overview, the details of the CPF intercalibration approach, and additional outcomes of the CPF intercalibration studies that may benefit the broader remote sensing community.community.
Hyperspectral↗
Performance of the Lidar Design and Data Algorithms for the GLAS Global Cloud and Aerosol Measurements
The Geoscience Laser Altimeter System (GLAS) launched in early 2003 is the first polar orbiting satellite lidar. The instrument design includes high performance observations of the distribution and optical scattering cross sections of atmospheric clouds and aerosol. The backscatter lidar operates at two wavelengths, 532 and 1064 nm. For the atmospheric cloud and aerosol measurements, the 532 nm channel was designed for ultra high efficiency with solid state photon counting detectors and etalon filtering. Data processing algorithms were developed to calibrate and normalize the signals and produce global scale data products of the height distribution of cloud and aerosol layers and their optical depths and particulate scattering cross sections up to the limit of optical attenuation. The paper will concentrate on the effectiveness and limitations of the lidar channel design and data product algorithms. Both atmospheric receiver channels meet and exceed their design goals. Geiger Mode Avalanche Photodiode modules are used for the 532 nm signal. The operational experience is that some signal artifacts and non-linearity require correction in data processing. As with all photon counting detectors, a pulse-pile-up calibration is an important aspect of the measurement. Additional signal corrections were found to be necessary relating to correction of a saturation signal-run-on effect and also for daytime data, a small range dependent variation in the responsivity. It was possible to correct for these signal errors in data processing and achieve the requirement to accurately profile aerosol and cloud cross section down to 10-7 llm-sr. The analysis procedure employs a precise calibration against molecular scattering in the mid-stratosphere. The 1064 nm channel detection employs a high-speed analog APD for surface and atmospheric measurements where the detection sensitivity is limited by detector noise and is over an order of magnitude less than at 532 nm. A unique feature of the GLAS is a full acquisition of the surface return pulse, which has important application to the atmospheric transmission retrieval.
Spinhirne, James D.↗
AMPR CAMP2Ex Calibrated and Quality-Controlled Dataset Level 2B, Revision B
Data were acquired by the Advanced Microwave Precipitation Radiometer (AMPR) during the Cloud, Aerosol and Monsoon Processes Philippines Experiment (CAMP2Ex) field campaign in August-October of 2019. These files include the Level 2B calibrated, corrected, and geo-referenced brightness temperature for the four AMPR-observed frequencies (10, 19, 37, 85 GHz). These data are archived in a self-describing, Climate and Forecasting (CF) 1.6-compliant Version 4 Network Common Data Format (netCDF4) format.Python software has been developed for reading, plotting, and providing some additional analysis capabilities. This software is available from: https://github.com/nasa/pyampr.The AMPR instrument is explained in more detail here: https://weather.msfc.nasa.gov/ampr/.These data have been determined to be viable for publishable scientific research, and alsoshould be useful for generating quicklooks or understanding what happened during a flight. Note: AMPRis not expected to provide useful data during significant aircraft maneuvers
Timothy Lang↗
A Machine Learning Approach to Determine Surface Radiative Fluxes based on CERES Observations
The Clouds and Earth’s Radiant Energy System (CERES) projects provides satellite-based observations of the radiative fluxes and clouds systems. CERES climate quality data products typically take several months of calibration and validation before release to the public. An alternative data product, Fast Longwave and Shortwave radiative Flux (FLASHFlux), was created to provide data to the applied sciences and educational users. FLASHFlux provides Top-of-Atmosphere radiative fluxes, Clouds properties, and parameterized surface radiative fluxes within four days for footprint (Level 2) data. We investigate the use of Artificial Neural Network (ANN) using MODerate resolution Imaging Spectroradiometer (MODIS) derived clouds properties and meteorology from the Global Assimilation and Meteorology Office (GMAO) scaled to the CERES footprint from the CERES Clouds Radiative Swath (CRS) data product to compute surface radiative fluxes. We test ANN produce fluxes against surface fluxes produced from the Fu-Liou model used in CRS and the Langley Parameterized Shortwave Algorithm (LPSA) and Langley Parameterized Longwave Algorithm (LPLA) used in FLASHFlux. We also validated each model with ground-based observations. Furthermore, we investigate Leave-One-Feature-Out Importance (LOFO) to evaluate the significance of each feature in our training and provide insight for future models. Advances in machine learning, along with increases in computational capabilities and available data allow us to estimate effects of unresolved processes in our climate without direct modeling. This work evaluates the ability to create accurate data-driven models to supplement or replace current models that estimate surface radiative fluxes.
Climatology↗
Io's sodium cloud
The first two-dimensional images of the source region of Io's neutral sodium cloud have been acquired by ground-based observation. Observed asymmetries in its spatial brightness distribution provide new evidence that the cloud is supplied by sodium that is ejected nonisotropically from Io or its atmosphere. Complementary, high-time-resolution, calibrated image sequences that give the first comprehensive picture of the variations of the fainter regions of the cloud extending more than 100,000 kilometers from Io were also obtained. These data demonstrate that the cloud exhibits a persistent systematic behavior coupled with Io's orbital position, a distinct 'east-west orbital asymmetry', a variety of spatial morphologies, and true temporal changes. The geometric stability of the sodium source is also indicated. Isolation of the cloud's temporal changes constitutes an important milestone toward its utilization as a long-term probe of Io and the inner Jovian magnetosphere.
Goldberg, B. A.↗
MOSAiC Radar b1 Processing: Corrections, Calibrations, and Processing Report
The U.S. Department of Energy’s (DOE) Atmospheric Radiation Measurement (ARM) user facility deployed many instruments on board a German ice breaker, the Research Vessel (RV) Polarstern, for one year from October 2019 to October 2020. The purpose of the Multidisciplinary Drifting Observatory for the Study of Arctic Climate (MOSAiC) field campaign was to study the decline in the sea-ice pack around the North Pole, and what factors may be at play. After the campaign ended, efforts were undertaken to provide a calibrated radar data set for future studies. MOSAiC presented new challenges to this process, as existing methodologies often were not applicable for the frozen environment with little to no ground clutter, and concurrent engineering updates or calibrations could not be accomplished once the ship set off. Like the previous Cloud, Aerosol, and Complex Terrain Interactions (CACTI) and Cold-air Outbreaks in the Marine Boundary Layer Experiment (COMBLE) documentation (Hardin et al. 2020, Matthews et al. 2023), the data correction and calibration process is known in ARM as creating a “b1” datastream. This means that the radar datastreams have been well characterized to the best possible quality. This report will detail the status of the raw “a1” level data sets during the MOSAiC campaign, the corrections that were applied to create the “b1” data files, and the details of the applied methods.
54 ENVIRONMENTAL SCIENCES↗
TPSAS-NF1676L-19788-DND
The calibration of the historical AVHRR visible channels has always been hindered by the degrading NOAA satellite orbits. The complete AVHRR record spans over three decades making it useful for cloud, aerosol, and land use climate studies. Studies that monitor long-term changes in these properties require climate quality calibration. The AVHRR sensors do not have any onboard visible calibration, unlike the IR channels. Many AVHRR post-launch calibration methods have been published. Some of these are based on the pseudo invariant targets such as desert and polar ice and others employ direct calibration transfer from a well-calibrated sensor such as MODIS. Most historical studies only employed only one method and reconciling calibration differences from multiple studies was difficult. This study employs multiple calibration approaches, which are then merged according to their individual uncertainty. Since the visible spectral response functions are similar across AVHRR sensor and that the morning and afternoon orbits usually degrade in the same manner, an afternoon and morning orbiter during the MODIS era is chosen as the reference AVHRR sensor. The reference AVHRR sensor is inter-calibrated with the Aqua-MODIS Collection 6 standard using simultaneous nadir overpass (SNO) radiance pairs. Three pseudo invariant target approaches are utilized, deserts, polar ice and deep convective clouds. These sites are characterized using reference AVHRR reflectances as a function of solar zenith angle. The multiple calibration approach is then validated with non-reference AVHRR sensor during the MODIS era, using AVHRR and MODIS SNO measurements. For historical AVHRR satellite having consistent calibration across invariant targets then validates the approach. This calibration effort is in sponsored by the NOAA CDR program to provide climate quality visible calibration coefficients to the remote sensing community. Each of the calibration approaches are unique and when utilized in tandem can accurately calibrate the AVHRR record. AVHRR calibration methods cannot rely on repeatable annual orbits for calibration model development. Inconsistent results prompt code and invariant target examinations that cannot be determined using only single method approaches. Results from this study will be presented at the meeting.
David Doelling↗
Effects of Tunable Data Compression on Geophysical Products Retrieved from Surface Radar Observations with Applications to Spaceborne Meteorological Radars
This paper presents results and analyses of applying an international space data compression standard to weather radar measurements that can easily span 8 orders of magnitude and typically require a large storage capacity as well as significant bandwidth for transmission. By varying the degree of the data compression, we analyzed the non-linear response of models that relate measured radar reflectivity and/or Doppler spectra to the moments and properties of the particle size distribution characterizing clouds and precipitation. Preliminary results for the meteorologically important phenomena of clouds and light rain indicate that for a 0.5 dB calibration uncertainty, typical for the ground-based pulsed-Doppler 94 GHz (or 3.2 mm, W-band) weather radar used as a proxy for spaceborne radar in this study, a lossless compression ratio of only 1.2 is achievable. However, further analyses of the non-linear response of various models of rainfall rate, liquid water content and median volume diameter show that a lossy data compression ratio exceeding 15 is realizable. The exploratory analyses presented are relevant to future satellite missions, where the transmission bandwidth is premium and storage requirements of vast volumes of data, potentially problematic.
cloud properties↗
The 27-28 October 1986 FIRE IFO Cirrus Case Study: Cloud Optical Properties Determined by High Spectral Resolution Lidar
During the First ISCCP Region Experiment (FIRE) cirrus intensive field observation (IFO) the High Spectral Resolution Lidar was operated from a roof top site on the University of Wisconsin-Madison campus. Because the HSRL technique separately measures the molecular and cloud particle backscatter components of the lidar return, the optical thickness is determined independent of particle backscatter. This is accomplished by comparing the known molecular density distribution to the observed decrease in molecular backscatter signal with altitude. The particle to molecular backscatter ratio yields calibrated measurements of backscatter cross sections that can be plotted ro reveal cloud morphology without distortion due to attenuation. Changes in cloud particle size, shape, and phase affect the backscatter to extinction ratio (backscatter-phase function). The HSRL independently measures cloud particle backscatter phase function. This paper presents a quantitative analysis of the HSRL cirrus cloud data acquired over an approximate 33 hour period of continuous near zenith observations. Correlations between small scale wind structure and cirrus cloud morphology have been observed. These correlations can bias the range averaging inherent in wind profiling lidars of modest vertical resolution, leading to increased measurement errors at cirrus altitudes. Extended periods of low intensity backscatter were noted between more strongly organized cirrus cloud activity. Optical thicknesses ranging from 0.01-1.4, backscatter phase functions between 0.02-0.065 sr (exp -1) and backscatter cross sections spanning 4 orders of magnitude were observed. the altitude relationship between cloud top and bottom boundaries and the cloud optical center altitude was dependent on the type of formation observed Cirrus features were observed with characteristic wind drift estimated horizontal sizes of 5-400 km. The clouds frequently exhibited cellular structure with vertical to horizontal dimension ratios of 1:5-1:1.
Grund, C. J.↗
Mapping tall shrub biomass in Alaska at landscape scale using structure-from-motion photogrammetry and lidar
Warming in arctic and boreal regions is increasing shrub cover and biomass. In southcentral Alaska, willow (Salix spp.) and alder (Alnus spp.) shrubs grow taller than many tree species and account for a substantial proportion of aboveground biomass, yet they are not individually measured as part of the operational Forest Inventory and Analysis (FIA) Program. The goal of this research was to test methods for landscape-scale mapping of tall shrub biomass in upper montane and subalpine environments using FIA-type plot measurements (n = 51) and predictor variables from imagery-based structure-from-motion (SfM) and airborne lidar. Specifically, we compared biomass models constructed from imagery acquired by unmanned aerial vehicle (UAV; ~1.7 cm pixels), imagery from the NASA Goddard's Lidar, Hyperspectral, and Thermal Airborne Imager (G-LiHT; ~3.1 cm pixels), and concomitant G-LiHT small-footprint lidar. Tall shrub biomass was most accurately predicted at 5 m resolution (R^2 = 0.81, RMSE = 1.09 kg/sq. m) using G-LiHT SfM color and structure variables. Lidar-only models had lower precision (R^2 = 0.74, RMSE = 1.26 kg/sq. m), possibly due to reduced model information content from variable multicollinearity or lower data density. Separate models for upper montane zones with trees and shrubs and subalpine zones with only shrubs were always chosen over single models based on minimization of Akaike's Information Criterion, indicating the need for variable sets robust to overhanging tree canopy. Decreasing point density from UAV (5000–8000 pts./sq. m) to the G-LiHT SfM point cloud (500–2000 pts./sq. m) had little impact on model fit, suggesting that high-resolution airborne imagery can extend SfM approaches well beyond line-of-sight restrictions for UAV platforms. Overall, our results confirmed that SfM from high-resolution imagery is a viable approach to estimate shrub biomass in the boreal region, especially when an existing lidar terrain model and local field calibration data are available to quantify uncertainty in the SfM point cloud and landscape-scale estimates of shrub biomass.
Michael Alonzo↗
TPSAS-NF1676L-10737-DND
The Clouds and the Earth's Radiant Energy System (CERES) project provides the community a quality climate TOA and surface fluxes and cloud properties and are key elements in evaluating the ability of climate models to predict both past and future climate states. One important aspect of successful climate monitoring is satellite calibration. The CERES instantaneous broadband calibration radiance stability is 0.1% per year, whereas the accuracy of the longwave (LW) and shortwave (SW) is better than 0.5% and 1% respectively. CERES uses the 3-hourly GEO fluxes and clouds to provide diurnally averaged daily and monthly TOA flux means from the CERES measurements onboard the Terra (10:30 AM local equator crossing time) and Aqua (13:30 PM) sun-synchronous satellites. The GEO radiances are calibrated against MODIS as a reference to provide consistent GEO retrieved cloud properties and derived broadband fluxes. These fluxes are then carefully normalized against the well-calibrated CERES fluxes to ensure climate quality fluxes, free of GEO artifacts. With the upcoming release of Edition3 products, CERES has just recalibrated all the CERES instruments to ensure that both Terra and Aqua fluxes are on the same radiometric scale and the removal of all known instrument trends. CERES will also recalibrate all GEO satellites using the entire GEO time record. CERES employs collocated, coincident ray-matched GEO/MODIS radiances as its primary technique, calibrating GEO radiances beginning in the year 2000. The advantage of the ray-matching technique is that it provides a full dynamic range of radiances to check the linearity of the GEO visible instrument. CERES uses deep convective clouds and invariant desert sites to validate the primary technique. All three methods can assess the stability of the GEO visible instrument. Examples of the CERES GEO calibration techniques and validation efforts will be shown at the conference. Special emphasis will be given to the newly incorporated desert site methodology.
R. Bhatt↗
Investigating the Use of Deep Convective Clouds (DCCT) to Monitor On-orbit Performance of the Geostationary Lightning Mapper (GLM) using Lightning Imaging Sensor (LIS) Measurements
There is a need to monitor the on-orbit performance of the Geostationary Lightning Mapper (GLM) on the Geostationary Operational Environmental Satellite R (GOES-R) for changes in instrument calibration that will affect GLM's lightning detection efficiency. GLM has no onboard calibration so GLM background radiance observations (available every 2.5 min) of Deep Convective Clouds (DCCs) are investigated as invariant targets to monitor GLM performance. Observations from the Lightning Imaging Sensor (LIS) and the Visible and Infrared Scanner (VIRS) onboard the Tropical Rainfall Measuring Mission (TRMM) satellite are used as proxy datasets for GLM and ABI 11 m measurements.
Buechler, Dennis E.↗
Ocean Color Instrument Integration and Testing
This paper describes the plans, flows, key facilities, components and equipment necessary to fully integrate, functionally test, qualify and calibrate the Ocean Color Instrument (OCI) on the Plankton, Aerosols, Clouds, and oceans Ecosystem (PACE) observatory. PACE is currently in the design phase of mission development. It is scheduled to launch in 2022, extending and improving NASA's twenty-year record of satellite observations of global ocean biology, aerosols and clouds. PACE will advance the assessment of ocean health by measuring the distribution of phytoplankton, which are small plants and algae that sustain the marine food web. It will also continue systematic records of key atmospheric variables associated with air quality and the Earth's climate. PACE's primary sensor, the OCI, is a highly advanced optical spectrometer that will be used to measure properties of light over portions of the electromagnetic spectrum. It will enable continuous measurement of light at finer wavelength resolution than previous NASA satellite sensors, extending key system ocean color data records for climate studies. The color of the ocean is determined by the interaction of sunlight with substances or particles present in seawater such as chlorophyll. By monitoring global phytoplankton distribution and abundance with unprecedented detail, the OCI will contribute to a better understanding of the complex systems that drive ocean ecology and it's impacts on global fisheries. This paper will focus on the Integration and Test (I&T) activities for OCI while it is at the NASA Goddard Space Flight Center. The OCI integration consists of assembly and alignment of the rotating telescope, electronics box integration, fixed deck assembly integration, thermal systems integration and the final assembly and testing. This I&T phase will be followed by the OCI calibration and characterization, environmental tests which include electromagnetic interference (EMI)/electromagnetic compatibility (EMC), vibration with sine sweep, acoustics, shock, thermal balance, thermal vacuum, mass properties and center of gravity. This paper will briefly discuss OCI shipment and delivery to the spacecraft vendor for observatory level I&T as well as some launch preparation activities.
Petro, Susanna↗
Lidar cloud studies for FIRE and ECLIPS
Optical remote sensing measurements of cirrus cloud properties were collected by one airborne and four ground-based lidar systems over a 32 h period during this case study from the First ISCCP (International Satellite Cloud Climatology Program) Regional Experiment (FIRE) Intensive Field Observation (IFO) program. The lidar systems were variously equipped to collect linear depolarization, intrinsically calibrated backscatter, and Doppler velocity information. Data presented describe the temporal evolution and spatial distribution of cirrus clouds over an area encompassing southern and central Wisconsin. The cirrus cloud types include: dissipating subvisual and thin fibrous cirrus cloud bands, an isolated mesoscale uncinus complex (MUC), a large-scale deep cloud that developed into an organized cirrus structure within the lidar array, and a series of intensifying mesoscale cirrus cloud masses. Although the cirrus frequently developed in the vertical from particle fall-streaks emanating from generating regions at or near cloud tops, glaciating supercooled (-30 to -35 C) altocumulus clouds contributed to the production of ice mass at the base of the deep cirrus cloud, apparently even through riming, and other mechanisms involving evaporation, wave motions, and radiative effects are indicated. The generating regions ranged in scale from approximately 1.0 km cirrus uncinus cells, to organized MUC structures up to approximately 120 km across.
Sassen, Kenneth↗