In this presentation we describe the application of a previously developed technique that is now being used to correct the daytime polarization calibration of the CALIPSO lidar. The technique leverages the fact that the solar radiation background signals from dense cirrus clouds are largely unpolarized due to the internal multiple reflections within the non-spherical ice particles and the multiple scattering that occurs among these particles. Therefore, the ratio of polarization components of the cirrus background signals provides a good estimate for the polarization gain ratio (PGR) of the lidar. However, in the visible and ultraviolet regime, the molecular contribution is too large to be ignored, and thus corrections must be applied to account for the highly polarizing characteristics of the molecular scattering. This presentation describes the theory and implementation of the molecular scattering correction.
Surface air pressure is the most important atmospheric variable for atmospheric dynamics. It is regularly measured by in-situ meteorological sensors, and there are no operational capabilities that could remotely sense the pressure over the globe. The poor spatiotemporal coverage of this dynamically crucial variable is a significant observational gap in weather predictions. To improve forecasts of severe weather conditions, especially the intensity and track of tropical storms, large spatial coverage and frequent sampling of surface barometry are critically needed for numerical weather forecast models. Recent development in remote sensing techniques provides a great hope of atmospheric barometry in large spatiotemporal scales.
Currently, NASA Langley Research Center tries to use the concept of Differential-absorption Barometric Radar (DiBAR) working at the 50-56 GHz O2 absorption bands to fill the observational gap. The numerical simulation shows that with this DiBAR remote sensing system, the uncertainty in instantaneous radar surface air pressure estimates can be as low as ~1 mb. Prototype instrumentation and its related laboratory, ground and airborne experiments indicate that satellite DiBAR remote sensing systems will obtain needed air pressure observations and meet or exceed the science requirements for surface air pressure fields. Observational system simulation experiments (OSSEs) for space DiBAR performance based on the existing DiBAR technology and capability show substantial improvements in tropical storm predictions, not only for the typhoon track and position but also for the typhoon intensity. Satellite DiBAR measurements will provide an unprecedented level of the prediction and knowledge on global extreme weather conditions.
A space multi-frequency differential oxygen absorption radar system will fill the gap in the global observations of atmospheric air pressure, increase our knowledge in the dynamics, and significantly improve weather, especially severe weather such as typhoon and hurricane, predictions. Advanced tropical storm forecasts are expected with the studied capability. The development of the DiBAR system and associated OSSE results will be presented.
We are investigating the potential of the “vortex” laser beam to provide additional information of natural scenes from aircraft and space-based lidars. This type of beam has a spatial wavefront with a helical twist that creates an optical singularity on axis, and carries orbital angular momentum. We will report on preliminary results for differences in Rayleigh-Mie scattering, and scattering from rough surfaces, and plans for future studies.
Lidar remote sensing based on visible wavelength is one of the only way to penetrate the water surface and to obtain range resolved information of the ocean surface mixed layer at the synoptic scale. Accurate measurement of the mixed layer properties is important for ocean weather forecast and to assist the optimal deployment of military assets. Turbulence within the mixed layer also plays an important role in climate variability as it also influences ocean heat storage and algae photosynthesis (Sverdrup 1953, Behrenfeld 2010).
As of today, mixed layer depth changes are represented in the models through various parameterizations constrained mostly by surface properties like wind speed, surface salinity and sea surface temperature. However, cooling by wind and rain can create strong gradients (0.5C) of temperature between the submillimeter surface layer and the subsurface layer (Soloviev and Lukas, 1997) which will manifest itself as a low temperature bias in the observations.
Temperature and salinity profiles are typically used to characterize the mixed layer variability (de Boyer Montégut et al. 2004) and are both key components of turbulence characterization (Hou 2009). Recently, several research groups have been investigating ocean temperature profiling with laser remote sensing based either on Brillouin (Fry 2012, Rudolf and Walther 2014) or Raman scattering (Artlett and Pask 2015, Lednev et al. 2016). It is the continuity of promising research that started decades ago (Leonard et al. 1979, Guagliardo and Dufilho 1980, Hirschberg et al. 1984) and can benefit from the current state of laser and detector technology.
One aspect of this research that has not been overlooked (Artlett and Pask 2012) but has yet to be revisited is the impact of temperature on vibrational Raman polarization (Chang and Young, 1972).
The TURBulence Ocean Lidar is an experimental system, aimed at characterizing underwater turbulence by examining various Stokes parameters. Its multispectral capability in both emission (based on an optical parametric oscillator) and detection (optical filters) provide flexibility to measure the polarization signature of both elastic and inelastic scattering.
We will present the characteristics of TURBOL and several results from our laboratory and field experiments with an emphasis on temperature profiling capabilities based on vibrational Raman polarization. We will also present other directions of research related to this activity.
We have developed a new aerosol retrieval technique based on combing high-resolution A band spectra with lidar profiles. Our goal is the development of a technique to retrieve aerosol absorption, one of the critical parameters affecting the global radiation budget and one which is currently poorly constrained by satellite measurements. Our approach relies on two key factors: 1) the use of high spectral resolution (17,000:1) measurements which resolve the Aband line structure, and 2) the use of co-located lidar profile measurements to constrain the vertical distribution of scatterers in the forward model. The algorithm has been developed to be applied to observations from the CALIPSO and OCO-2 satellites, flying in formation as part of the A-train constellation. We describe the approach and present simulated retrievals to illustrate performance potential.
In the past few years, we have demonstrated how the surface return measured by the active
instruments onboard CloudSat and CALIPSO could be used to retrieve the optical depth and backscatter
phase function (lidar ratio) of aerosols and ice clouds. This methodology lead to the development of a
data fusion product publicly available at the ICARE archive center using the Synergized Optical Depth of
Aerosols and Ice Clouds (SODA & ICE) algorithm1. This algorithm, also allowing to derive ocean surface
wind speed, has been extended to include dense cloud surface return to analyze aerosol and cloud
properties above such clouds.
This low level data fusion of CALIPSO and CloudSat ocean surface echoes has been used by several
researchers to explore different research paths. Among them, we can cite:
• A new characterization of the lidar ratio of cirrus clouds2
• The analysis of the precipitable water and development of a new Millimeter-Wave Propagation
Model for the W-Band observations (EMPIRIMA3)
• The analysis of the lidar ratio of sea-spray aerosols4, and of Aerosol multilayer lidar ratio and
extinction5
• A contribution to the retrieval of the subsurface particulate backscatter coefficients of
phytoplankton particles6
In this paper, we present the main features of SODA & ICE, summarizing some of the results obtained.
This low level data fusion of CALIPSO and CloudSat ocean surface echoes has been used by several
researchers to explore different research paths. Among them, we can cite:
A new characterization of the lidar ratio of cirrus clouds2
The analysis of the precipitable water and development of a new Millimeter-Wave Propagation
Model for the W-Band observations (EMPIRIMA3)
The analysis of the lidar ratio of sea-spray aerosols4, and of Aerosol multilayer lidar ratio and
extinction5
A contribution to the retrieval of the subsurface particulate backscatter coefficients of
phytoplankton particles6
In this paper, we present the main features of SODA & ICE, summarizing some of the results obtained.
A relationship between depolarization ratio and surface concentration of particulate organic carbon (POC) is developed
from the NASA SeaWiFS Bio-optical Archive and Storage System (SeaBASS) in situ measurements and the Cloud-
Aerosol Lidar with Orthogonal Polarization (CALIOP) active lidar measurements. This relationship provides an
algorithm for estimating global POC from satellite or airborne polarization lidar measurements. Application of this
relationship to CALIOP data indicates that the estimates of POC ranges from about 3.3 mg/m3 within the South Pacific
Subtropical Gyre to 1.2×103 mg/m3 in the area near land are in good agreement with Moderate Resolution Imaging
spectroradiometer (MODIS) POC products. Our results present depolarization ratio as a valuable tool for evaluating
global POC predictions in ocean ecosystem. The application of the algorithm to a 7-year of CALIOP depolarization ratio
mean values revealed patters of seasonal and interannual variability of POC. By comparing the results averaged over the
entire study region and entire season for each year separately, we found that the lowest POC occurred in 2013 and the
highest POC occurred in 2008.
We assess the accuracy of land surface elevation retrieved from the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) mission through comparisons with the U.S. Geological Survey National Elevation Dataset (NED), Shuttle Radar Topography Mission (SRTM), and the altimetry product from the Geoscience Laser Altimeter System onboard the Ice, Cloud, and Land Elevation Satellite (ICESat). The vertical accuracy of the CALIPSO-derived land surface elevation was tested against these three datasets for about 16 million lidar shots over the continental United States. The results show that the CALIPSO-derived elevation was highly correlated with the elevation result from the NED, SRTM, and ICESat datasets. The overall absolute vertical accuracies of the CALIPSO-derived land surface elevation expressed as the root mean square error (RMSE) are 5.58 and 5.90 m when compared with the SRTM and NED results, respectively. Lower accuracy of the CALIPSO-derived land surface elevation was achieved by comparison with the ICESat results (8.35-m RMSE), primarily due to the several kilometers distance between the CALIPSO and ICESat ground footprints. The results show that the variability in terrain, vegetation, canopy, and footprint size can all influence comparisons between the CALIPSO-derived elevation and the results obtained from NED, SRTM, and ICESat datasets.
KEYWORDS: Data processing, Commercial off the shelf technology, Field programmable gate arrays, Data archive systems, Digital signal processing, Algorithm development, Signal processing, Carbon dioxide, Aerosols, LIDAR
A new development of on-board data processing platform has been in progress at NASA Langley Research Center since April, 2012, and the overall review of such work is presented in this paper. The project is called High-Speed On-Board Data Processing for Science Instruments (HOPS) and focuses on a high-speed scalable data processing platform for three particular National Research Council’s Decadal Survey missions such as Active Sensing of CO2 Emissions over Nights, Days, and Seasons (ASCENDS), Aerosol-Cloud-Ecosystems (ACE), and Doppler Aerosol Wind Lidar (DAWN) 3-D Winds. HOPS utilizes advanced general purpose computing with Field Programmable Gate Array (FPGA) based algorithm implementation techniques. The significance of HOPS is to enable high speed on-board data processing for current and future science missions with its reconfigurable and scalable data processing platform. A single HOPS processing board is expected to provide approximately 66 times faster data processing speed for ASCENDS, more than 70% reduction in both power and weight, and about two orders of cost reduction compared to the state-of-the-art (SOA) on-board data processing system. Such benchmark predictions are based on the data when HOPS was originally proposed in August, 2011. The details of these improvement measures are also presented. The two facets of HOPS development are identifying the most computationally intensive algorithm segments of each mission and implementing them in a FPGA-based data processing board. A general introduction of such facets is also the purpose of this paper.
The Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP), an instrument on the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO), was operated as an atmospheric lidar system to study the impact of clouds and aerosols on the Earth’s radiation budget and climate. This paper discusses the receiver transient response of the CALIOP instrument, which is useful for getting a reliable attenuated backscatter profile from CALIOP data products. The noise tail effect (slow decaying rate) of PMT and broadening effect of the
low-pass filter are both considered in modeling of the receiver transient response. An analytical expression of the CALIOP transient response function was obtained by the least square fitting of lidar measurements from land surfaces.
The primary objective of the atmospheric profiling lidar aboard Cloud-Aerosol Lidar and Infrared Pathfinder Satellite
Observations (CALIPSO) mission launched in April 2006 has been studying the climate impact of clouds and aerosols in
the atmosphere. However, CALIPSO lidar also collects information about other components of the Earth’s ecosystem,
such as polar ice sheets. The purpose of this study is to propose a new technique to provide high resolution of polar ice
sheet surface elevation from CALIPSO single shot lidar measurements (70 m spot size). The new technique relies on an
empirical relationship between the peak signal ratio and the distance between the surface and the peak signal range bin
center to achieve high altimetry resolution. The ice sheet surface elevation results in the region of Greenland and
Antarctic compare very well with the Ice, Cloud and land Elevation Satellite (ICESat) laser altimetry measurements. The
comparisons suggest that the obtained CALIPSO ice sheet surface elevation by the new technique is accurate to within 1
m. Based on the new technique, the preliminary data product of along-track topography retrieved from the CALIPSO
lidar measurements is available to the altimetry community for evaluation.
We have developed a Vector Radiative Transfer (VRT) code for coupled atmosphere and ocean systems based
on the successive order of scattering (SOS) method. In order to achieve efficiency and maintain accuracy, the
scattering matrix is expanded in terms of the Wigner d functions and the delta fit or delta-M technique is used
to truncate the commonly-present large forward scattering peak. To further improve the accuracy of the SOS
code, we have implemented the analytical first order scattering treatment using the exact scattering matrix of
the medium in the SOS code. The expansion and truncation techniques are kept for higher order scattering. The
exact first order scattering correction was originally published by Nakajima and Takana.1 A new contribution of
this work is to account for the exact secondary light scattering caused by the light reflected by and transmitted
through the rough air-sea interface.
KEYWORDS: LIDAR, Pulsed laser operation, Data modeling, Super resolution, Linear filtering, Profiling, Backscatter, Sensors, Receivers, Field emission displays
CALIPSO satellite has been making global lidar measurements since June 2006 and its lidar, CALIOP, will likely be the
only lidar in space during the next several years. Laser altimetry data from space and aircraft-based atmospheric profiling
lidars, such as CALIOP, have not been widely used in the community due to their limited vertical sampling resolution
(30 meter) and broad laser pulse width (20 ns). This study intends to improve the CALIPSO laser altimetry data quality
and provide a highly accurate altimetry data product to the laser altimetry community.
In this study, a super-resolution laser altimetry technique has been proposed to provide improved lidar altimetry from a
profiling lidar with relatively broad pulse width and slow sampling rate. Application of the technique to CALIPSO data
leads to highly accurate CALIPSO land surface elevation measurements. The surface elevations will be derived from
near 5-year CALIPSO global observations. The CALIPSO surface elevation results in Northern America derived by the
new technique agree with the National Elevation Database (NED) high resolution elevation maps and a comparison
suggests that the accuracy of the new CALIPSO land surface elevation measurements is better than 1 meter.
The Topographic Mapping Flash Lidar (TMFL) developed at Ball Aerospace combines a pushbroom format transmitter
at 1064 nm with a flash focal plane receiver. The wide 20 degree field of view of the instrument enables broad swath
coverage from a single laser pulse without the need for a scanning mechanism. These features make the TMFL design
particularly well-suited for space flight. TMFL has been demonstrated during an airborne flight where data were
gathered over a forest plot to measure tree waveforms. Topographic maps were assembled of river beds and geologic
areas of high relief. The TMFL has also been used to observe multiple-scattering phenomena in clouds by illuminating a
steam plume from the aircraft above. Signal was recorded off-axis from the illuminated laser line by as much as 1
degree. The TMFL study of multiple-scattering is valuable as it provides a unique way to significantly improve the
calibration of measured backscatter for space lidars. Lidar backscatter was also measured from water surface and was
shown to correlate with models of water surface roughness.
Aerosols and clouds play important roles in Earth's climate system but uncertainties over their interactions and their
effects on the Earth energy budget limit our understanding of the climate system and our ability to model it. The
CALIPSO satellite was developed to provide new capabilities to observe aerosol and cloud from space and to reduce
these uncertainties. CALIPSO carries the first polarization-sensitive lidar to fly in space, which has now provided a
four-year record of global aerosol and cloud profiles. This paper briefly summarizes the status of the CALIPSO mission,
describes some of the results from CALIPSO, and presents highlights of recent improvements in data products.
The Wide Field Camera (WFC) is one of three instruments in the CALIPSO science payload, with the other two being
the Cloud-Aerosol LIdar with Orthogonal Polarization (CALIOP) and the Infrared Imaging Radiometer (IIR). The
WFC is a narrow-band, push-broom imager that provides continuous high-spatial-resolution imagery during the daylight
segments of the orbit over a swath centered on the CALIOP footprint. The instantaneous field of view of each WFC
pixel is approximately 125 m × 125 m when projected on the Earth's surface from an orbit altitude of 705 km. The
spectral band of the WFC, with a center wavelength of 645 nm and a FWHM bandwidth of 50 nm, is designed to match
the Aqua MODIS instrument's channel 1. The primary WFC Level 1 products are radiance and reflectance registered to
an Earth-based grid centered on the CALIOP ground track. "First light" WFC images were acquired on 18 May 2006
and routine data acquisition began in early June 2006. An initial science assessment of the WFC on-orbit performance
was conducted based on analysis of the first twelve months of flight data. Comparisons of the WFC measurements with
the well-calibrated Aqua MODIS channel 1 data were performed to evaluate the on-orbit radiometric performance of the
WFC. Overall agreement is excellent, especially over bright deep convective clouds where the WFC measurements
agree to within a few percent of MODIS. This paper provides a summary of our overall assessment of the on-orbit
radiometric performance of the WFC.
To quantify the radiative forcing of ice clouds, we need to fully understand the optical and microphysical properties of these clouds. This paper reports on some preliminary results associated with the optical properties of ice crystals within ice clouds and the effect of ice crystal habit on the retrieval of ice cloud properties from use of the infrared spectrum. Furthermore, various cloud parameters retrieved from the atmospheric infrared sounder (AIRS) data are also reported.
In the area of aerosol remote sensing, one of the more noteworthy points of the last decade has been the realization that dust and smoke can be sensed from space over land and ocean by utilizing observations of scattered ultraviolet light [Torres, et al. 1998]. The spectral contrast ratio available from the Total Ozone Mapping Spectrometer (TOMS) backscatter ultraviolet (buv) data does provide a wealth of qualitative information, such as the ability to track the global dispersion of dust and smoke from regional sources. Quantitative information, e.g. total optical depth, single scattering albedo, however, is more difficult to extract from buv data. Assumptions must be made concerning various parameters that influence buv observations, e.g. the height of the aerosol layer, surface albedo, aerosol size distribution and index of refraction. While the necessity of assumptions is due in part to the availability of only two wavelengths from historical TOMS data, these assumptions may not truly be needed for future sensors. We examine what can be gained from making measurements of polarization in addition to those of radiance (as is currently done by TOMS and its successor the Ozone Measuring Instrument, OMI, on EOS-AURA) in the TOMS spectral coverage range free from ozone absorption (340-380 nm). Measurements of the degree of linear polarization and the plane of polarization with an uncertainty of less than 0.005 would help to determine the aerosol layer height to within less than 1 km. Multi-angle measurements would also help to better utilize the polarization data by defining the particle effective radius.
Current uncertainties in the role of aerosols and clouds in the Earth's climate system limit our abilities to model the climate system and predict climate change. These limitations are due primarily to difficulties of adequately measuring aerosols and clouds on a global scale. The A-train satellites (Aqua, CALIPSO, CloudSat, PARASOL, and Aura) will provide an unprecedented opportunity to address these uncertainties. The various active and passive sensors of the A-train will use a variety of measurement techniques to provide comprehensive observations of the multi-dimensional properties of clouds and aerosols. However, to fully achieve the potential of this ensemble requires a robust data analysis framework to optimally and efficiently map these individual measurements into a comprehensive set of cloud and aerosol physical properties. In this work we introduce the Multi-Instrument Data Analysis and Synthesis (MIDAS) project, whose goal is to develop a suite of physically sound and computationally efficient algorithms that will combine active and passive remote sensing data in order to produce improved assessments of aerosol and cloud radiative and microphysical properties. These algorithms include (a) the development of an intelligent feature detection algorithm that combines inputs from both active and passive sensors, and (b) identifying recognizable multi-instrument signatures related to aerosol and cloud type derived from clusters of image pixels and the associated vertical profile information. Classification of these signatures will lead to the automated identification of aerosol and cloud types. Testing of these new algorithms is done using currently existing and readily available active and passive measurements from the Cloud Physics Lidar and the MODIS Airborne Simulator, which simulate, respectively, the CALIPSO and MODIS A-train instruments.
The Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) satellite will be launched in April of 2005, and will make continuous measurements of the Earth's atmosphere for the following three years. Retrieving the spatial and optical properties of clouds and aerosols from the CALIPSO lidar backscatter data will be confronted by a number of difficulties that are not faced in the analysis of ground-based data. Among these are the very large distance from the target, the high speed at which the satellite traverses the ground track, and the ensuing low signal-to-noise ratios that result from the mass and power restrictions imposed on space-based platforms. In this work we describe an integrated analysis scheme that employs a nested, multi-grid averaging technique designed to optimize tradeoffs between spatial resolution and signal-to-noise ratio. We present an overview of the three fundamental retrieval algorithms (boundary location, feature classification, and optical properties analysis), and illustrate their interconnections using data product examples that include feature top and base altitudes, feature type (i.e., cloud or aerosol), and layer optical depths.
Ice clouds have been identified as one of the most uncertain components in atmospheric research. In recent years, the atmospheric radiative transfer and remote sensing community has made a concerted effort to improve the characterization of cirrus clouds. A number of airborne and balloon-borne observations have demonstrated that cirrus clouds are essentially composed of nonspherical ice crystals with various habits (or shapes) and sizes. In this paper, we report on some recent progresses towards the computation of the single-scattering properties nonspherical ice crystals and the relevant applications to remote sensing and radiative transfer simulations. Specifically, we have developed a database of the optical properties of ice crystals at the infrared wavelengths. In conjunction with the application of the scattering database, we also developed a fast infrared transfer model under cirrus cloudy condition, which is applied to the retrieval of ice clouds from satellite-based infrared measurements.
Monitoring aviation safety hazards, such as icing conditions, and retrieving cloud physical properties for climate modeling studies requires cloud thermodynamic phase (water/ice) discrimination.
Polarization information from lidar measurement provide such information. Depolarization of lidar backscattering indicates that the scattering cloud particles are non-spherical (i.e., ice clouds). For space based lidar measurements, backscatter from water cloud particles is also depolarized because of multiple scattering. Thus cloud water/ice discrimination is not straight-forward. An alternative method which is less sensitive to multiple scattering is proposed in this study. The new approach is based on the fact that
there are big differences in P44 (an element of the scattering phase matrix) at 180° between spherical and non-spherical particles. When the incident beam is left-hand-circularly polarized, backscattering by a nonspherical particle is also left handed. Circular component of backscattering by a spherical particle is right-handed for left-hand-circularly polarized incident beam.
Monte Carlo simulations with full Stokes vector indicate that multiple scattering does not affect the sphere/non-sphere determination with this new circular polarization approach.
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