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The Single Field-of-view (SFOV) Sounder Atmospheric Product (SiFSAP) retrieval algorithm has been developed to address the need to retrieve high spatial resolution atmospheric data products from hyper-spectral sounders and ensure the radiometric consistency between the retrieved properties and measured spectral radiances. It is based on an integrated optimal estimation inversion scheme that processes data from the satellite based synergistic microwave (MW) and infrared (IR) spectral measurements from advanced sounders. The retrieval system utilizes the principal component radiative transfer model (PCRTM) which performs radiative transfer calculations monochromatically and includes accurate cloud scattering simulations. SiFSAP includes temperature, water vapor, surface skin temperature and emissivity, cloud height and microphysical properties, and concentrations of essential trace gases for each SFOV at a native instrument spatial resolution. Error estimations are provided based on a rigorous analysis for uncertainty propagation from the Top-Of-Atmosphere (TOA) spectral radiances to the retrieved geophysical properties. As a comparison, the spatial resolution for the traditional hyper-spectral sounder retrieval products is much coarser than the native resolution of the instruments due to common use of the ‘cloud clearing’ technique to compensate for the lack of cloud scattering simulation in the forward model. The degraded spatial resolution in traditional cloud-clearing sounder retrieval products limits their applications for capturing meteorological or climate signals at finer spatial scales. Moreover, rigorous uncertainty propagation estimation needed for long-term climate trend studies cannot be given due to the lack of direct radiative transfer relationships between the observed TOA radiances and the retrieved geophysical properties. With advantages of the higher spatial resolution, the simultaneous retrieval of atmospheric, cloud, and surface properties using all available spectral information, and the establishment of ‘radiance closure’ in the sounder spectral measurements, the SiFSAP provides additional information needed for various weather and climate studies and applications using sounding observations. This paper gives an overview of SiFSAP retrieval algorithm, and assessment of SiFSAP atmospheric temperature, water vapor, clouds, and surface products derived from the Cross-track Infrared Sounder (CrIS) and Advanced Technology Microwave Sounder (ATMS) data.

Climate products are typically derived by performing spatial and temporal averaging of level-2 products. It is a time-consuming process to generate level-2 data products since modern hyperspectral satellite sensors have millions of observations each day with thousands of spectral channels for each observation. Additionally, differences in level-2 retrieval algorithms for different satellite sensors can lead to errors in the climate products. We have developed a Climate Fingerprinting Sounder Product (ClimFiSP), which is derived from spatiotemporally averaged level-1 hyperspectral radiances directly. The ClimFiSP algorithm uses consistent radiative kernels and a robust spectral fingerprinting method. It provides accurate data climate data fusion products from multiple satellite sensors. It eliminates or reduces the errors due to inconsistent L2 algorithms. We have applied this method to both AIRS and CrIS (on SNPP and on NOAA 20) data and generated two decades climate data records for atmospheric temperature, water vapor, cloud, trace gases, and surface skin temperature. The ClimFiSP are being transitioned to NASA data centers for routine generations level-3 products.

Australia’s unprecedented fire disasters at the end of 2019 to early 2020 emitted huge amounts of carbon monoxide (CO) and fire aerosol particles to the atmosphere, particularly during the Pyrocumulonimbus (pyroCb) outbreak that occurred in southeast Australia between 29 December 2019 and 4 January 2020. It was estimated that at least 18 pyroCbs were generated during this episode, and some of them injected ice, smoke, and biomass burning gases above the local tropopause. An unprecedented abundance of H2O and CO in the stratosphere, and the displacement of background ozone (O3) and N2O from rapid ascent of air from the troposphere and lower stratosphere were found from satellite observations. Some other studies also found that the fire emissions and their long-range transport resulted in stratospheric aerosol, temperature, and O3 anomalies after the 2020 Australian bushfires and altered the Antarctic ozone and vortex, posing great impact to local air qality and climate change. This study will focus on the thermodynamic state of atmosphere associated with these pyroCbs, and its impact on the change of the cloud properties and trace gases during this unprecedented Australia fires, mainly based on a new single Field of View (SFOV) Sounder Atmospheric Products (SiFSAP) and TROPOMI. SiFSAP was developed by NASA using the Cross-track Infrared Sounder (CrIS) and Advanced Technology Microwave Sounder (ATMS) onboard SNPP and JPSS-1, and will soon be available to the public at NASA DAAC. Since this product has a spatial resolution of 15 km at nadir, which is better than most global weather and climate models and other current operational sounding products, a process-oriented analysis of the dynamic transport of CO and fire plumes during this unprecedented fire disasters will be made in this study. Based on a Principal Component Radiative Transfer Model (PCRTM) and an optimized estimation retrieval algorithm, a simultaneously retrieval is made using the whole spectral information measured by CrIS, and the derived SiFSAP include temperature, water vapor, trace gases (such as O3, CO2, CO, CH4 and N2O), cloud properties and surface properties. Use of ATMS together with CrIS allows SiFSAP to get accurate retrieval products under thick pyroCb conditions. An algorithm to detect pyroCb based on the hyperspectral infrared sounder spectrum from CrIS will be developed and verified. In addition to SiFSAP sounding products, CO, O3, NO2 from TROPOMI and O3 from OMPS will be used. The wind fields from the NASA’s Modern-Era Retrospective Analysis for Research and Applications Version-2 (MERRA-2) and ERA5 will be used to characterize the transport, and the SiFSAP temperature and water vapor profiles within and around pyroCbs will be compared with MERRA-2 and ERA5 products.

Aerosols emitted from wildfires are becoming one of the main sources of poor air quality in the US mainland. Their extinction in UVB (wavelength range 280–315 nm) is difficult to be retrieved using simple lidar techniques because of the impact of O3 absorption and lacking information of lidar ratio at those wavelengths. The 2018 Long Island Sound Tropospheric Ozone Study (LISTOS) campaign in the New York City region allowed the characterization of lidar ratio for UVB aerosol retrieval. An algorithm for the aerosol extinction retrieval out of the Langley Mobile Ozone Lidar (LMOL) was used in conjunction with the NASA Langley High Altitude Lidar Observatory (HALO) 532 nm aerosol extinction product. This approach requires assuming 2 parameters, the lidar ratio at 292 nm and the Ångström Exponent (AE) between 532 nm and 292 nm. The objective of this work is to determine these two parameters and assess the retrieval error caused by improper assumption of lidar ratio. This work also accomplishes the first know 292 nm aerosol product inter-comparison between HALO and Tropospheric Ozone Lidar Network (TOLNet) ozone lidar. HALO results were compared with the aerosol data retrieved from the 292 nm band from LMOL with different approximations of the lidar ratio and the AE to determine optimal parameters. Using optimized parameters, the LMOL aerosol extinction can be retrieved with a 10 % accuracy up to 3 km. This work highlights the importance of the lidar ratio and AE in the retrieval and validation of 292 nm aerosol profiles obtained from UV-lidar. Errors arise from approaches that utilize a random priori lidar ratio and AE assumption. The lidar ratios at 292 nm determined in this work will also improve our understanding of the UVB optical properties of aerosol in the lower troposphere affected by transported wildfire emission.

Air pollution associated with wildfire smoke transport during the summer can significantly affect ozone (O3) and particulate matter (PM) concentrations, even in heavily populated areas like New York City (NYC). Here, we use observations from aircraft, ground-based lidar, in-situ analyzers and satellite to study and assess wildfire smoke transport, vertical distribution, optical properties, and potential impact on air quality in the NYC urban and coastal areas during the summer 2018 Long Island Sound Tropospheric Ozone Study (LISTOS). We investigate an episode of dense smoke transported and mixed into the planetary boundary layer (PBL) on August 15–17, 2018. The horizontal advection of the smoke is shown to be characterized with the prevailing northwest winds in the PBL (velocity > 10 m/s) based on Doppler wind lidar measurements. The wildfire sources and smoke transport paths from the northwest US/Canada to northeast US are identified from the NOAA hazard mapping system (HMS) fires and smoke product and NOAA-HYbrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) backward trajectory analysis. The smoke particles are distinguished from the urban aerosols by showing larger lidar-ratio (70-sr at 532-nm) and smaller depolarization ratio (0.02) at 1064-nm using the NASA High Altitude Lidar Observatory (HALO) airborne high-spectral resolution lidar (HSRL) measurements. The extinction-related angstrom exponents in the near-infrared (IR at 1020–1640 nm) and Ultraviolet (UV at 340–440 nm) from NASA-Aerosol Robotic Network (AERONET) product show a reverse variation trend along the smoke loadings, and their absolute differences indicate strong correlation with the smoke-Aerosol Optical Depth (AOD) (R > 0.94). We show that the aloft smoke plumes can contribute as much as 60–70% to the column AOD and that concurrent high-loadings of O3, carbon monoxide (CO), and black carbon (BC) were found in the elevated smoke layers from the University of Maryland (UMD) aircraft in-situ observations. Meanwhile, the surface PM2.5 (PM with diameter ≤ 2.5 μm), organic carbon (OC) and CO measurements show coincident and sharp increase (e.g., PM2.5 from 5 μg/m3 before the plume intrusion to ~30 μg/m3) with the onset of the plume intrusions into the PBL along with hourly O3 exceedances in the NYC region. We further evaluate the NOAA-National Air Quality Forecasting Capability (NAQFC) model PBL-height, PM2.5, and O3 with the observations and demonstrate good consistency near the ground during the convective PBL period, but significant bias at other times. The aloft smoke layers are sometimes missed by the model.

A new technique is advanced to obtain tropospheric temperature profile using vibrational Raman (VR) and rotational Raman (RR) backscattering. The proposed technique can obtain tropospheric temperature and decrease retrieval errors caused by insufficient suppression of the Mie backscattering in the RR detection channels. The retrieval equations are obtained using VR and RR lidar equations. The feasibility of this technique is demonstrated by applying the algorithm to the Hampton University VR and RR lidar signals. Intercomparisons of these atmospheric temperature retrievals from the Hampton University lidar show good agreement with local radiosonde measurements. Moreover, the proposed technique is applied to a cloudy case and is further validated by showing the good agreements with radiosonde measurements of cloud temperature.

Aerosol retrieval using ozone lidars in the ultraviolet (UV) band is challenging but necessary for correcting aerosol interference in ozone retrieval and for studying the ozone-aerosol correlations. This study describes the aerosol retrieval algorithm for a tropospheric ozone lidar, quantifies the retrieval error budget, and intercompares the aerosol retrieval products at 299 nm with those at 532 nm from a high spectral resolution lidar (HSRL). After the cloud-contaminated data is filtered out, the aerosol backscatter or extinction coefficients at a 30-m and 10-min resolution retrieved by the ozone lidar are highly correlated with the HSRL products, with a coefficient of 0.95 suggesting that the ozone lidar can reliably measure aerosol structures with high spatio-temporal resolution when the signal-to-noise ratio is sufficient. The actual uncertainties of the aerosol retrieval from the ozone lidar generally agree with our theoretical analysis. The backscatter color ratio (backscatter-related exponent of wavelength dependence) linking the coincident data measured by the two instruments at 299 and 532 nm is 1.34 ± 0.11 while the Ångström (extinction-related) exponent is 1.49 ± 0.16 for a mixture of urban and fire smoke aerosols within the troposphere above Huntsville, AL, USA.

The Stratospheric Aerosol and Gas Experiment III (SAGE III, 2018) instrument was launched on 19 February 2017 from the NASA Kennedy Space Center and was integrated aboard the International Space Station (ISS). SAGE III-ISS has been providing ozone profile measurements since June 2017. This paper presents an early validation of the Level 2 solar and lunar occultation ozone data products using ground-based lidar and ozonesondes from Hohenpeißenberg and Lauder as well as satellite ozone vertical products from the Atmospheric Chemistry Experiment Fourier Transform Spectrometer (ACE-FTS) instrument. Average differences in the ozone concentration between SAGE III-ISS and Hohenpeißenberg lidar observations for 1 year are less than 10 % between 16 and 42 km and less than 5 % between 20 and 40 km. Hohenpeißenberg ozonesonde comparisons are mostly within 10 % between 18 and 30 km. The Lauder lidar comparison results are less than 10 % between 17 and 37 km, and the Lauder ozonesonde comparison results are less than 10 % between 19 and 31 km. The seasonal average differences in the ozone concentration between SAGE III-ISS and ACE-FTS are mostly less than 5 % between 20 and 45 km for both the Northern Hemisphere and Southern Hemisphere. All results from these comparisons show that the SAGE III-ISS ozone solar data compare well with correlative measurements throughout the stratosphere. With few comparisons available, the percentage difference between the SAGE III-ISS lunar ozone data and the ozonesonde data is less than 10 % between 19 and 27 km. The percentage difference between the SAGE III-ISS lunar ozone data and the ACE-FTS ozone data is less than 10 % between 20 and 40 km.

On 2019-02-14, at 1:00 UTC, a large increase of ozone density at 8 km altitude was detected above Hampton, Virginia using the NASA Langley Mobile Ozone Lidar, LMOL. Ozone levels above 70 ppbv were observed down to an altitude of 4.5 km up to 5 h after the start of the event. The NASA GEOS Composition Forecast (GEOS-CF) model was used to investigate the hypothesis of a stratospheric intrusion (SI). The agreement between the model and the observations confirmed the stratospheric origin of the airmass and highlighted the capabilities of GEOS-CF to simulate intrusions. In parallel, MicroPulse Lidar (MPL) observations indicated that depolarizing particulates high in the troposphere showed downward motions linked to the intrusion. Since these particulates are linked to cirrus clouds, it is hypothesised that the SI led to an ice-virga effect. This might suggest that particulate observations can exhibit patterns of stratospheric intrusions and can be used, in certain cases, as a signature of the events. These particulates, likely ice crystals, have opposite distribution gradients compared to O3 at their interface which could be explained by a non-mixing of stratospheric and tropospheric air-masses as well as destruction of O3 by ice crystal-induced processes. Model-data comparison shows that if that latter effect exists, it has small consequences for the observed case. This work shows the capabilities of the LMOL system to detect SI and to validate the vertical and temporal modeling of SI by GEOS-CF, as well as showing that signatures of SI could be detected by MPL.

We advance a novel retrieval technique that combines a Raman and multi-wavelength elastic backscattered signals to retrieve multi-wavelength lidar ratio profiles of aerosol. With profile of backscatter coefficients at 355 nm retrieved from elastic backscatter signal at 355 nm and Raman scattering signal at 387 nm, lidar ratio profiles can be calculated at 532 nm and 1064 nm from the elastic backscatter signals at these wavelengths, taking advantage that the 532 nm/355 nm and 1064 nm/355 nm backscatter ratios are generally approximately equal for two neighboring range bins. This technique has been tested using numerical simulations and applied to lidar measurements at the Hampton University, Hampton, Virginia.

An improved method that has the potential to improve the retrieval of aerosol optics properties (back- scatter/extinction coefficients) from elastic-Raman lidar data is presented. Aerosol backscatter coefficients can be retrieved by choosing the reference height at near-range rather than conventional far-range when the signal-to-noise ratios are low at the far-range or aloft aerosol layers and clouds appear there. Significant retrieval errors in aerosol backscatter coefficients caused by large uncertainties of the aerosol reference value at far-range can be reduced. To avoid the ill-posed retrievals of aerosol extinction from the conventional Raman method, the new method derives the aerosol extinction and lidar ratio with the constrained Fernald inversions by independent aerosol backscatter coefficients from above proposed method. The numerical simulations demonstrated that the proposed method pro- vides good accuracy and resolution of aerosol profile ret- rievals. And the method is also applied to elastic-Raman lidar measurements at the Hampton University, Hampton, Virginia.

Insufficient suppression of the elastic-scattering signal in the rotational Raman (RR) detection channels can result in a retrieval error particularly when the temperature of a thick cloud is measured using an RR lidar. To solve this problem, a technique is presented to obtain relative transmission factors for the two RR channels' thereby correcting for the influence of residual elastic-signal on the temperature retrieval. The feasibility of this technique is demonstrated by applying the algorithm to the Hampton University (HU) lidar measurements. Intercomparisons of these temperature retrievals from both water-phase and cirrus clouds show good agreement with radiosonde measurements.

A non-contact surface roughness measurement technique is put forward based on texture analysis of the digital laser speckle pattern coming from a measured grinding surface. The speckle pattern was captured by a digital camera while the grinding metal surface was illuminated by a laser. Then the surface roughness information immerged in the high frequency sub-band of the speckle pattern was extracted with the texture analysis of wavelet transform and Markov Random Fields model. Our research has illustrated that surface roughness Ra has a good monotonic relationship with the texture features of the speckle pattern. If this measuring system is calibrated with the surface standard samples roughness beforehand, the surface roughness actual value Ra may be obtained in the case of the same material surfaces ground at the same manufacture conditions.

Atmospheric aerosols have a significant impact on climate change through the scattering and absorption of incoming solar and outgoing thermal radiation. The aerosol optical properties can be directly measured using an elastic-Raman lidar. However, extraction of the extinction coefficient from the optical depth profile requires the use of numerical differentiation with these lidars, which suffers the random and systematic noises limitation and thereby reducing the detection sensitivity and accuracy. A new method to improve the quality of Raman lidar data processing is presented. Compared to the conventional method, the proposed method has the advantage, which can directly retrieve the aerosol extinction coefficients without numerical differentiation. Trial values of lidar ratio (from 10 to 90 sr with an increment of 1 sr) are applied to Fernald solution of the elastic lidar signals at 354.7 nm and all aerosol backscatter coefficients are obtained. The exact aerosol backscatter coefficients retrieved by combing elastic and Raman signals are used as constrain of these results of Fernald method to determine aerosol true lidar ratios as well as extinction coefficients. The numerical simulations demonstrated that the proposed method provides good accuracy and resolution of aerosol profile retrievals. And the method is also applied to elastic-Raman lidar measurements at the Hampton University, Hampton, Virginia.

Lidar backscatter signal resulting from laser light scattering from the aerosol and molecular in the atmosphere contains various information about the geometrical and physical properties of aerosol and molecular. The lidar backscatter signal can provide information about the planetary boundary layer (PBL) stratification by using aerosol as a tracer for convective and mixing processes. A PBL height and structure detecting technique based on the fractal dimension of three-wavelength backscatter signals is advanced. In this PBL height detecting technique, the three-wavelength backscatter signals are obtained by the Hampton University (HU, 37.02° N, 76.33° W) lidar. The fractal dimension was calculated using the three-wavelength lidar signals. The PBL heights obtained from fractal dimension of threewavelength lidar signals is compared with PBL heights obtained from the potential temperature profiles which are provided by NASA Langely Research Center (10 miles from HU). And results of the two methods agree well. Moreover, fractal dimension method can reduce the influence of the geometrical form factor on the PBL detecting to expand the detecting range of PBL and remove the effect of plume. Also, the fractal dimension method can show the PBL dynamics and the PBL evolution clearly.

Clouds play a key role in the climate system, for they can result in a warming or a cooling effect according to their characteristics and altitudes. Raman Lidars have been proven to be a very useful remote sensing tool to characterize cloud properties and locations. In this paper, cloud temperature and optical depth are obtained using rotational Raman (RR) and vibrational Raman techniques. Results of cloud temperature and optical depth (OD) observed by the Hampton University (HU) Rotational-Vibrational Raman Lidar are presented. The paper discusses the influence of cloud OD on temperature of the cloud base and top. From these measurements, the relation of low-altitude cloud OD and temperature is summarized. These analyses are unique in that they combine simultaneous measurements of these quantities that can lead to an improvement in the understanding of cloud radiation transfer and effects.

This paper presents coordinated ground-based observations by the NOAA-CREST Lidar Network (CLN) for profiling of aerosols, cloud, water vapor, and wind along the US east coast including Caribbean region at Puerto Rico. The instrumentation, methodology and observation capability are reviewed. The applications to continental and intercontinental-scale transport of smoke and dust plumes, and their large scale regional impact are discussed.

This letter describes a lidar retrieval technique that uses the transmittance ratio as a constraint to determine an average lidar ratio as well as extinction and backscatter coefficients of transparent cirrus clouds. The cloud transmittance ratio is directly obtained from two adjacent elastic lidar backscatter signals. The technique can be applied to cirrus measurements where neither the molecular scattering dominant signals above and below the cloud layer are found nor cloudfree reference profiles are available. The technique has been tested with simulated lidar signals and applied to backscatter lidar measurements at Hampton University, Hampton, Virginia.

The relationships between laser speckle pattern texture features of grinding surface and surface roughness are investigated. The laser speckle pattern texture images of surface roughness are taken by a simple setup. Gauss-Markov random field (GMRF) model is a kind of texture model method which can describe probability model of gather of texture and be able to capture the local contextual information in an image. Therefore, by modeling GMRF model the laser speckle texture images of grinding surface are analyzed. Feature vectors of GMRF are calculated with different neighbor sets. Texture features- Mean, Variance and Energy are extracted to characterize grinding surface roughness from GMRF feature vectors. The relationships curves are drawn between the texture features and surface roughness. The experiment results show that the surface roughness contained in the surface speckle pattern texture images has a good monotonic relationship with texture features of GMRF model. This method can extract the surface roughness of the object surface composed of the same material and machined by the same method as the standard specimen.