Showing papers on "Radiometer published in 2019"

Intercomparison in spatial distributions and temporal trends derived from multi-source satellite aerosol products

Abstract: . Satellite-derived aerosol products provide long-term and large-scale observations for analysing aerosol distributions and variations, climate-scale aerosol simulations, and aerosol–climate interactions. Therefore, a better understanding of the consistencies and differences among multiple aerosol products is important. The objective of this study is to compare 11 global monthly aerosol optical depth (AOD) products, which are the European Space Agency Climate Change Initiative (ESA-CCI) Advanced Along-Track Scanning Radiometer (AATSR), Advanced Very High Resolution Radiometer (AVHRR), Multi-angle Imaging SpectroRadiometer (MISR), Moderate Resolution Imaging Spectroradiometer (MODIS), Sea-viewing Wide Field-of-view Sensor (SeaWiFS), Visible Infrared Imaging Radiometer (VIIRS), and POLarization and Directionality of the Earth's Reflectance (POLDER) products. AErosol RObotic NEtwork (AERONET) Version 3 Level 2.0 monthly measurements at 308 sites around the world are selected for comparison. Our results illustrate that the spatial distributions and temporal variations of most aerosol products are highly consistent globally but exhibit certain differences on regional and site scales. In general, the AATSR Dual View (ADV) and SeaWiFS products show the lowest spatial coverage with numerous missing values, while the MODIS products can cover most areas (average of 87 %) of the world. The best performance is observed in September–October–November (SON) and the worst is in June–July–August (JJA). All the products perform unsatisfactorily over northern Africa and Middle East, southern and eastern Asia, and their coastal areas due to the influence from surface brightness and human activities. In general, the MODIS products show the best agreement with the AERONET-based AOD values on different spatial scales among all the products. Furthermore, all aerosol products can capture the correct aerosol trends at most cases, especially in areas where aerosols change significantly. The MODIS products perform best in capturing the global temporal variations in aerosols. These results provide a reference for users to select appropriate aerosol products for their particular studies.

Global wind speed and wave height extremes derived from long-duration satellite records

Abstract: The application of extreme-value analysis to long-duration (30 year) global altimeter and radiometer datasets is considered. In contrast to previous extreme-value analyses of satellite data...

Enhanced-Resolution SMAP Brightness Temperature Image Products

Abstract: The NASA-sponsored Calibrated Passive Micro- wave Daily Equal-Area Scalable Earth Grid 2.0 Brightness Temperature (CETB) Earth System Data Record Project team has generated a multisensor, multidecadal time series of high-resolution radiometer products designed to support climate studies. This project uses image reconstruction techniques to generate conventional and enhanced-resolution daily brightness temperature images on a standard set of map projections. Sensors included in CETB are the Aqua Advanced Microwave Scanning Radiometer–Earth Observing System (AMSR-E), Scanning Multichannel Microwave Radiometer, and all Special Sensor Microwave/Imager and Special Sensor Microwave Imager/Sounder radiometers. These span frequencies between 6 and 89 GHz. This paper considers the issues of adding the L-band (1.6 GHz) Soil Moisture Active Passive (SMAP) radiometer measurements to the CETB climate record, with emphasis on optimizing the reconstruction to provide the highest possible spatial resolution at the lowest noise level. SMAP radiometer reconstruction on SMAP-standard grids is also considered. Simulation is used to optimize the reconstruction, and the results confirmed using actual data. A comparison of the performance of the Backus–Gilbert approach and the radiometer form of the Scatterometer Image Reconstruction algorithm is provided. These are compared to the conventional drop-in-the-bucket gridded imaging.

Gap Filling of High-Resolution Soil Moisture for SMAP/Sentinel-1: A Two-Layer Machine Learning-Based Framework

Abstract: As the most recent 3 km soil moisture product from the Soil Moisture Active Passive (SMAP) mission, the SMAP/Sentinel-1 L2_SM_SP product has a unique capability to provide global-scale 3 km soil moisture estimates through the fusion of radar and radiometer microwave observations. The spatial and temporal availability of this high-resolution soil moisture product depends on concurrent radar and radiometer observations which is significantly restricted by the narrow swath and low revisit schedule of the Sentinel-1 radars. To address this issue, this paper presents a novel two-layer machine learning-based framework which predicts the brightness temperature and subsequently the soil moisture at gap areas. The proposed method is able to gap-fill soil moisture satisfactorily at areas where the radiometer observations are available while the radar observations are missing. We find that incorporating historical radar backscatter measurements (30-day average) into the machine learning framework boosts its predictive performance. The effectiveness of the two-layer framework is validated against regional hold-out SMAP/Sentinel-1 3 km soil moisture estimates at four study areas with distinct climate regimes. Results indicate that our proposed method is able to reconstruct 3 km soil moisture at gap areas with higher Pearson correlation coefficient (47%/35%/20%/80% improvement of mean R, at Arizona/Oklahoma/Iowa/Arkansas) and lower unbiased Root Mean Square Error (20%/10%/7%/26% improvement of mean ubRMSE) when compared to the SMAP 33 km soil moisture product. Additional validation against airborne data and in-situ data from soil moisture networks is also satisfactory.

Estimating Snow Depth on Arctic Sea Ice using Satellite Microwave Radiometry and a Neural Network

Abstract: . Snow lying on top of sea ice plays an important role in the radiation budget because of its high albedo and the Arctic freshwater budget, and it influences the Arctic climate: it is a fundamental climate variable. Importantly, accurate snow depth products are required to convert satellite altimeter measurements of ice freeboard to sea ice thickness (SIT). Due to the harsh environment and challenging accessibility, in situ measurements of snow depth are sparse. The quasi-synoptic frequent repeat coverage provided by satellite measurements offers the best approach to regularly monitor snow depth on sea ice. A number of algorithms are based on satellite microwave radiometry measurements and simple empirical relationships. Reducing their uncertainty remains a major challenge. A High Priority Candidate Mission called the Copernicus Imaging Microwave Radiometer (CIMR) is now being studied at the European Space Agency. CIMR proposes a conically scanning radiometer having a swath >1900 km and including channels at 1.4, 6.9, 10.65, 18.7 and 36.5 GHz on the same platform. It will fly in a high-inclination dawn–dusk orbit coordinated with the MetOp-SG(B). As part of the preparation for the CIMR mission, we explore a new approach to retrieve snow depth on sea ice from multi-frequency satellite microwave radiometer measurements using a neural network approach. Neural networks have proven to reach high accuracies in other domains and excel in handling complex, non-linear relationships. We propose one neural network that only relies on AMSR2 channel brightness temperature data input and another one using both AMSR2 and SMOS data as input. We evaluate our results from the neural network approach using airborne snow depth measurements from Operation IceBridge (OIB) campaigns and compare them to products from three other established snow depth algorithms. We show that both our neural networks outperform the other algorithms in terms of accuracy, when compared to the OIB data and we demonstrate that plausible results are obtained even outside the algorithm training period and area. We then convert CryoSat freeboard measurements to SIT using different snow products including the snow depth from our networks. We confirm that a more accurate snow depth product derived using our neural networks leads to more accurate estimates of SIT, when compared to the SIT measured by a laser altimeter at the OIB campaign. Our network with additional SMOS input yields even higher accuracies, but has the disadvantage of a larger “hole at the pole”. Our neural network approaches are applicable over the whole Arctic, capturing first-year ice and multi-year ice conditions throughout winter. Once the networks are designed and trained, they are fast and easy to use. The combined AMSR2 + SMOS neural network is particularly important as a precursor demonstration for the Copernicus CIMR candidate mission highlighting the benefit of CIMR.

Estimating the snow depth, the snow–ice interface temperature, and the effective temperature of Arctic sea ice using Advanced Microwave Scanning Radiometer 2 and ice mass balance buoy data

Abstract: . Mapping sea ice concentration (SIC) and understanding sea ice properties and variability is important, especially today with the recent Arctic sea ice decline. Moreover, accurate estimation of the sea ice effective temperature ( Teff ) at 50 GHz is needed for atmospheric sounding applications over sea ice and for noise reduction in SIC estimates. At low microwave frequencies, the sensitivity to the atmosphere is low, and it is possible to derive sea ice parameters due to the penetration of microwaves in the snow and ice layers. In this study, we propose simple algorithms to derive the snow depth, the snow–ice interface temperature ( TSnow−Ice ) and the Teff of Arctic sea ice from microwave brightness temperatures (TBs). This is achieved using the Round Robin Data Package of the ESA sea ice CCI project, which contains TBs from the Advanced Microwave Scanning Radiometer 2 (AMSR2) collocated with measurements from ice mass balance buoys (IMBs) and the NASA Operation Ice Bridge (OIB) airborne campaigns over the Arctic sea ice. The snow depth over sea ice is estimated with an error of 5.1 cm, using a multilinear regression with the TBs at 6, 18, and 36 V. The TSnow−Ice is retrieved using a linear regression as a function of the snow depth and the TBs at 10 or 6 V. The root mean square errors (RMSEs) obtained are 2.87 and 2.90 K respectively, with 10 and 6 V TBs. The Teff at microwave frequencies between 6 and 89 GHz is expressed as a function of TSnow−Ice using data from a thermodynamical model combined with the Microwave Emission Model of Layered Snowpacks. Teff is estimated from the TSnow−Ice with a RMSE of less than 1 K.

Land Surface Temperature Retrieval from Sentinel-3A Sea and Land Surface Temperature Radiometer, Using a Split-Window Algorithm

Abstract: Land surface temperature (LST) is a crucial parameter in the interaction between the ground and the atmosphere. The Sentinel-3A Sea and Land Surface Temperature Radiometer (SLSTR) provides global daily coverage of day and night observation in the wavelength range of 0.55 to 12.0 μm. LST retrieved from SLSTR is expected to be widely used in different fields of earth surface monitoring. This study aimed to develop a split-window (SW) algorithm to estimate LST from two-channel thermal infrared (TIR) and one-channel middle infrared (MIR) images of SLSTR observation. On the basis of the conventional SW algorithm, using two TIR channels for the daytime observation, the MIR data, with a higher atmospheric transmittance and a lower sensitivity to land surface emissivity, were further used to develop a modified SW algorithm for the nighttime observation. To improve the retrieval accuracy, the algorithm coefficients were obtained in different subranges, according to the view zenith angle, column water vapor, and brightness temperature. The proposed algorithm can theoretically estimate LST with an error lower than 1 K on average. The algorithm was applied to northern China and southern UK, and the retrieved LST captured the surface features for both daytime and nighttime. Finally, ground validation was conducted over seven sites (four in the USA and three in China). Results showed that LST could be estimated with an error mostly within 1.5 to 2.5 K from the algorithm, and the error of the nighttime algorithm involved with MIR data was about 0.5 K lower than the daytime algorithm.

The Pan-and-Tilt Hyperspectral Radiometer system (PANTHYR) for autonomous satellite validation measurements—prototype design and testing

Abstract: This paper describes a system, named “pan-and-tilt hyperspectral radiometer system” (PANTHYR) that is designed for autonomous measurement of hyperspectral water reflectance. The system is suitable for deployment in diverse locations (including offshore platforms) for the validation of water reflectance derived from any satellite mission with visible and/or near-infrared spectral bands (400–900 nm). Key user requirements include reliable autonomous operation at remote sites without grid power or cabled internet and only limited maintenance (1–2 times per year), flexible zenith and azimuth pointing, modularity to adapt to future evolution of components and different sites (power, data transmission, and mounting possibilities), and moderate hardware acquisition cost. PANTHYR consists of two commercial off-the-shelf (COTS) hyperspectral radiometers, mounted on a COTS pan-and-tilt pointing system, controlled by a single-board-computer and associated custom-designed electronics which provide power, pointing instructions, and data archiving and transmission. The variable zenith pointing improves protection of sensors which are parked downward when not measuring, and it allows for use of a single radiance sensor for both sky and water viewing. The latter gives cost reduction for radiometer purchase, as well as reduction of uncertainties associated with radiometer spectral and radiometric differences for comparable two-radiance-sensor systems. The system is designed so that hardware and software upgrades or changes are easy to implement. In this paper, the system design requirements and choices are described, including details of the electronics, hardware, and software. A prototype test on the Acqua Alta Oceanographic Tower (near Venice, Italy) is described, including comparison of the PANTHYR system data with two other established systems: the multispectral autonomous AERONET-OC data and a manually deployed three-sensor hyperspectral system. The test established that high-quality hyperspectral data for water reflectance can be acquired autonomously with this system. Lessons learned from the prototype testing are described, and the future perspectives for the hardware and software development are outlined.

Arctic Sea Ice Classification Using Microwave Scatterometer and Radiometer Data During 2002–2017

Abstract: Temporal and spatial variation of sea ice type in the Arctic is an indicator of regional and global change. Arctic sea ice can be classified into two major categories: multiyear ice (MYI) and first-year ice. In this paper, classification method based on machine learning is established and applied to produce daily sea ice classification data set during the winter (November–April) from 2002 to 2017 using active microwave data from QuikSCAT and Advanced Scatterometer as well as passive microwave data from Advanced Microwave Scanning Radiometer for EOS, Special Sensor Microwave Imager/Sounder, and Advanced Microwave Scanning Radiometer 2 radiometer. First, the open water area is flagged out using brightness temperature (Tb) from the passive microwave sensor. Then, K-means algorithm is applied to identify the clusters of the two ice types in the Tb/backscatter parameter space and finally assign pixels to each class. Two optimization methods based on the movement of MYI and marginal ice zone are used to correct the misclassification of MYI. The results have shown a decrease of MYI in winter from 2002 to 2017, especially in 2008 and 2013 with a remarkable recovery in 2014. The classifications are consistent with results by visual interpretation from synthetic aperture radar images in the Canadian Arctic Archipelago with overall classification accuracy over 93%. Comparison with classifications from previous studies and products shows that our method could reflect more differences in MYI declining trend interannually and less anomalous fluctuations in certain years.

Parameter Optimization of a Discrete Scattering Model by Integration of Global Sensitivity Analysis Using SMAP Active and Passive Observations

Abstract: Active and passive microwave signatures respond differently to the land surface and provide complementary information on the characteristics of the observed scenes The objective of this paper is to explore the synergy of active radar and passive radiometer observations at the same spatial scale to constrain a discrete radiative transfer model, the Tor Vergata (TVG) model, to gain insights into the microwave scattering and emission mechanisms over grasslands The TVG model can simultaneously simulate the backscattering coefficient and emissivity with a set of input parameters To calibrate this model, in situ soil moisture and temperature data collected from the Maqu area in the northeastern region of the Tibetan Plateau, interpolated leaf area index (LAI) data from the Moderate Resolution Imaging Spectroradiometer LAI eight-day products, and concurrent and coincident Soil Moisture Active Passive (SMAP) radar and radiometer observations are used Because this model needs numerous input parameters to be driven, the extended Fourier amplitude sensitivity test is first applied to conduct global sensitivity analysis (GSA) to select the sensitive and insensitive parameters Only the most sensitive parameters are defined as free variables, to separately calibrate the active-only model (TVG-A), the passive-only model (TVG-P), and the active and passive combined model (TVG-AP) The accuracy of the calibrated models is evaluated by comparing the SMAP observations and the model simulations The results show that TVG-AP can well reproduce the backscattering coefficient and brightness temperature, with correlation coefficients of 087, 089, 078, and 043 and root-mean-square errors of 049 dB, 052 dB, 720 K, and 1047 K for $\sigma _{\mathrm{ HH}}^{o} $ , $\sigma _{\mathrm{ VV}}^{o} $ , $T_{\mathrm {BH}}$ , and $T_{\mathrm {BV}}$ , respectively In contrast, TVG-A and TVG-P can only accurately model the backscattering coefficient and brightness temperature, respectively Without any modifications of the calibrated parameters, the error metrics computed from the validation data are slightly worse than those of the calibration data These results demonstrate the feasibility of the synergistic use of SMAP active radar and passive radiometer observations under the unified framework of a physical model In addition, the results demonstrate the necessity and effectiveness of applying GSA in model optimization It is expected that these findings can contribute to the development of model-based soil moisture retrieval methods using active and passive microwave remote sensing data

RAVAN: CubeSat Demonstration for Multi-Point Earth Radiation Budget Measurements

Abstract: The Radiometer Assessment using Vertically Aligned Nanotubes (RAVAN) 3U CubeSat mission is a pathfinder to demonstrate technologies for the measurement of Earth’s radiation budget, the quantification of which is critical for predicting the future course of climate change. A specific motivation is the need for lower-cost technology alternatives that could be used for multi-point constellation measurements of Earth outgoing radiation. RAVAN launched 11 November 2016, into a nearly 600-km, Sun-synchronous orbit, and collected data for over 20 months. RAVAN successfully demonstrates two key technologies. The first is the use of vertically aligned carbon nanotubes (VACNTs) as absorbers in broadband radiometers for measuring Earth’s outgoing radiation and the total solar irradiance. VACNT forests are arguably the blackest material known and have an extremely flat spectral response over a wide wavelength range, from the ultraviolet to the far infrared. As radiometer absorbers, they have greater sensitivity for a given time constant and are more compact than traditional cavity absorbers. The second technology demonstrated is a pair of gallium phase-change black body cells that are used as a stable reference to monitor the degradation of RAVAN’s radiometer sensors on orbit. Four radiometers (two VACNT, two cavity), the pair of gallium black bodies, and associated electronics are accommodated in the payload of an agile 3U CubeSat bus that allows for routine solar and deep-space attitude maneuvers, which are essential for calibrating the Earth irradiance measurements. The radiometers show excellent long-term stability over the course of the mission and a high correlation between the VACNT and cavity radiometer technologies. Short-term variability—at greater than the tenths-of-a-Watt/m2 needed for climate accuracy—is a challenge that remains, consistent with insufficient thermal knowledge and control on a 3U CubeSat. There are also VACNT–cavity biases of 3% and 6% in the Total and SW channels, respectively, which would have to be overcome in a future mission. Although one of the black bodies failed after four months, the other provided a repeatable standard for the duration of the project. We present representative measurements from the mission and demonstrate how the radiometer time series can be used to reconstruct outgoing radiation spatial information. Improvements to the technology and approach that would lead to better performance and greater accuracy in future missions are discussed.

ECE Diagnostic for the initial Operation of Wendelstein 7-X

Abstract: The ECE diagnostic at W7-X in its standard mode of operation measures in X2 mode polarization with a 32 channel radiometer in the frequency band around 140 GHz for central magnetic field 2.5T. The radiometer is calibrated by a noise source and the overall system absolutely calibrated by means of a hot-cold source placed outside the torus in front of a Gaussian telescope optics with identical geometry and transmission line as it is installed for the measurements in the plasma vessel. The system is supplemented with a 16 channel zoom device with 4 GHz span for higher frequency resolution at a suitable radial range and a Michelson interferometer for the characterization of higher harmonics sharing the same line of sight.

Bayesian modeling of microwave radiometer calibration on the example of the Wendelstein 7-X electron cyclotron emission diagnostic.

Abstract: This paper reports about a novel approach to the absolute intensity calibration of an electron cyclotron emission (ECE) spectroscopy system. Typically, an ECE radiometer consists of tens of separated frequency channels corresponding to different plasma locations. An absolute calibration of the overall diagnostic including near plasma optics and transmission line is achieved with blackbody sources at LN2 temperature and room temperature via a hot/cold calibration mirror unit. As the thermal emission of the calibration source is typically a few thousand times lower than the receiver noise temperature, coherent averaging over several hours is required to get a sufficient signal to noise ratio. A forward model suitable for any radiometer calibration using the hot/cold method and a periodic switch between them has been developed and used to extract the voltage difference between the hot and cold temperature source via Bayesian analysis. In contrast to the classical analysis which evaluates only the reference temperatures, the forward model takes into account intermediate effective temperatures caused by the finite beam width and thus uses all available data optimally. This allows the evaluation of weak channels where a classical analysis would not be feasible, is statistically rigorous, and provides a measurement of the beam width. By using a variance scaling factor, a model sensitive adaptation of the absolute uncertainties can be implemented, which will be used for the combined diagnostic Bayesian modeling analysis.

Deep Learning for Precipitation Estimation from Satellite and Rain Gauges Measurements

Abstract: This paper proposes a multimodal and multi-task deep-learning model for instantaneous precipitation rate estimation. Using both thermal infrared satellite radiometer and automatic rain gauge measurements as input, our encoder–decoder convolutional neural network performs a multiscale analysis of these two modalities to estimate simultaneously the rainfall probability and the precipitation rate value. Precipitating pixels are detected with a Probability Of Detection (POD) of 0.75 and a False Alarm Ratio (FAR) of 0.3. Instantaneous precipitation rate is estimated with a Root Mean Squared Error (RMSE) of 1.6 mm/h.

Compact spectral irradiance monitor flight demonstration mission

Abstract: Accurate, long-term solar spectral irradiance (SSI) measurements are vital for interpreting how solar variability impacts Earth’s climate and for validating climate model sensitivities to spectrally varying solar forcing. The Compact Spectral Irradiance Monitor (CSIM) 6U CubeSat successfully launched on Dec. 3rd, 2018 as part of the SpaceX SSO-A: SmallSat Express Mission ultimately achieving a sun-synchronous 575 km orbit. CSIM brings new, emerging technology advancements to maturation by demonstrating the unique capabilities of a complete SSI mission with inherent low mass and compact design. The instrument is a compact, two-channel prism spectral radiometer incorporating Si, InGaAs, and extended InGaAs focal plane photodiodes to record the solar spectrum daily across a continuous wavelength region spanning 200 – 2800 nm (>97% of the total solar irradiance). A new, novel electrical substitution radiometer (ESR) using vertically aligned carbon-nanotube (VACNT) bolometers serves as an absolute detector for periodic on-orbit spectral calibration corrections. Pre-launch component level performance characterizations and final instrument end-to-end absolute calibration achieved low combined standard uncertainty (uc<0.5%) in irradiance. These calibrations were performed in the LASP Spectral Radiometer Facility (SRF), a comprehensive spectral irradiance calibration facility utilizing a tunable laser system tied to an SI-traceable cryogenic radiometer. On-orbit, optical degradation corrections to better than 0.05% / year uncertainty are achieved by comparing periodic, simultaneous solar measurements of the two CSIM channels operating with significantly different solar exposure duty cycles. Operational overlap of CSIM with existing SSI measurements validate concepts for maintaining critical long-term solar data records.

The Influence of Thermal Properties and Canopy- Intercepted Snow on Passive Microwave Transmissivity of a Scots Pine

Abstract: While many microwave studies related to tree emission have been undertaken, a few have considered the effect of phenological change on the emission from coniferous trees. The permittivity of vegetation tissue is known to be influenced by water content, while the water content and phase is sensitive to temperature in particular at temperatures below freezing. In addition to temperature, canopy-intercepted snow might also modify the tree emission and transmissivity in the microwave range. In this paper, a season-long experiment was designed to quantify the effect of snow accumulation and temperature on the observed microwave transmissivity from tree. A ground-based, upward-pointing multifrequency radiometer was used to monitor the microwave emissivity of a single coniferous tree at a site in Northern Finland. Radiometer measurements were combined with measurements of the canopy-intercepted snow cover and tree skin temperature. This paper presents two important findings. First, the tree transmissivity was strongly correlated with tree skin temperature under subzero temperature conditions, but uncorrelated with skin temperature changes above freezing. Second, although the tree transmissivity was slightly affected by the snow accumulation on the tree canopy, the overall influence on tree emission was statistically insignificant in this paper.

Mid-infrared laser heterodyne radiometer (LHR) based on a 3.53 μm room-temperature interband cascade laser.

Abstract: The performance of an interband cascade laser based laser heterodyne radiometer (LHR) is demonstrated in ground-based solar occultation mode. High-resolution (0.0033 cm-1) transmission spectra near 3.53 μm were obtained for simultaneous atmospheric observations of H2O and CH4. Combined with the preprocessed measurement data (acquired at Hefei, China, on June 21th 2016), an optimal estimation method based retrieval algorithm is developed for data retrieval and error analysis. By considering the corrected atmospheric parameters, vertical profiles of H2O and CH4 are retrieved. Finally, the measured total column abundance and XCH4 were calculated to be 1.87 ± 0.02 ppm and 1.88 ± 0.02 ppm, respectively. The interband cascade laser-based laser heterodyne radiometer that is demonstrated in this manuscript has high potential for use in the development of compact, robust, and unattended LHR for spacecraft, airborne or ground-based atmospheric sensing.

Remote Sensing of Sea Ice Thickness and Salinity With 0.5–2 GHz Microwave Radiometry

Abstract: An ultrawideband radiometer was used to measure microwave brightness temperature spectra over Arctic sea ice in the Lincoln Sea near the north coast of Greenland. Spectra over the range of 0.5–2 GHz were compared to thermal infrared images collected during the airborne campaign and also compared to nearly concurrent Sentinel-1 C-band synthetic aperture radar (SAR) data. Based on those comparisons, spectral signatures were associated with thick multiyear ice and thin ice. A radiative transfer (RT) model consisting of a homogeneous slab of sea ice bounded by sea water and air was then used to invert the spectra for sea ice thickness and salinity. Inferred thicknesses were consistent with ice thickness climatology for ice floes in the Lincoln Sea. Salinities are higher than expected which may be a consequence of neglecting surface and volume scattering contributions in the models.

Analysis of the Global Microwave Polarization Data of Clouds

Abstract: Information about the characteristics of ice particles in clouds is necessary for improving our understanding of the states, processes, and subsequent modeling of clouds and precipitation for numerical weather prediction and climate analysis. Two NASA passive microwave radiometers, the satellite-borne Global Precipitation Measurement (GPM) Microwave Imager (GMI) and the aircraft-borne Conical Scanning Millimeter-Wave Imaging Radiometer (CoSMIR), measure vertically and horizontally polarized microwaves emitted by clouds (including precipitating particles) and Earth’s surface below. In this paper, GMI (or CoSMIR) data are analyzed with CloudSat (or aircraft-borne radar) data to find polarized difference (PD) signals not affected by the surface, thereby obtaining the information on ice particles. Statistical analysis of 4 years of GMI and CloudSat data, for the first time, reveals that optically thick clouds contribute positively to GMI PD at 166GHz over all the latitudes and their positive magnitude of 166-GHz GMI PD varies little with latitude. This result suggests that horizontally oriented ice crystals in thick clouds are common from the tropics to high latitudes, which contrasts the result of Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) that horizontally oriented ice crystals are rare in optically thin ice clouds.

Analyzing the turbulent planetary boundary layer by remote sensing systems: the Doppler wind lidar, aerosol elastic lidar and microwave radiometer

Abstract: . The planetary boundary layer (PBL) is the lowermost region of troposphere and is endowed with turbulent characteristics, which can have mechanical and/or thermodynamic origins. This behavior gives this layer great importance, mainly in studies about pollutant dispersion and weather forecasting. However, the instruments usually applied in studies of turbulence in the PBL have limitations in spatial resolution (anemometer towers) or temporal resolution (instrumentation aboard an aircraft). Ground-based remote sensing, both active and passive, offers an alternative for studying the PBL. In this study we show the capabilities of combining different remote sensing systems (microwave radiometer – MWR, Doppler lidar – DL – and elastic lidar – EL) for retrieving a detailed picture on the PBL turbulent features. The statistical moments of the high frequency distributions of the vertical wind velocity, derived from DL, and of the backscattered coefficient, derived from EL, are corrected by two methodologies, namely first lag correction and - 2 / 3 law correction. The corrected profiles, obtained from DL data, present small differences when compared with the uncorrected profiles, showing the low influence of noise and the viability of the proposed methodology. Concerning EL, in addition to analyzing the influence of noise, we explore the use of different wavelengths that usually include EL systems operated in extended networks, like the European Aerosol Research Lidar Network (EARLINET), Latin American Lidar Network (LALINET), NASA Micro-Pulse Lidar Network (MPLNET) or Skyradiometer Network (SKYNET). In this way we want to show the feasibility of extending the capability of existing monitoring networks without strong investments or changes in their measurements protocols. Two case studies were analyzed in detail, one corresponding to a well-defined PBL and another corresponding to a situation with presence of a Saharan dust lofted aerosol layer and clouds. In both cases we discuss results provided by the different instruments showing their complementarity and the precautions to be applied in the data interpretation. Our study shows that the use of EL at 532 nm requires a careful correction of the signal using the first lag time correction in order to get reliable turbulence information on the PBL.

A portable miniaturized laser heterodyne radiometer (mini‑LHR) for remote measurements of column CH 4 and CO 2 .

Abstract: We present the design of a portable version of our miniaturized laser heterodyne radiometer (mini-LHR) that simultaneously measures methane (CH4) and carbon dioxide (CO2) in the atmospheric column. The mini-LHR fits on a backpack frame, operates autonomously, and requires no infrastructure because it is powered by batteries charged by a folding 30 W solar panel. Similar to our earlier instruments, the mini-LHR is a passive laser heterodyne radiometer that operates by collecting sunlight that has undergone absorption by CH4 and CO2. Within the mini-LHR, sunlight is mixed with light from a distributive feedback (DFB) laser centered at approximately 1.64 μm where both gases have absorption features. The laser scans across these absorption features roughly every minute and the resulting beat signal is collected in the radio frequency (RF). Scans are averaged into half hour and hour data products and analyzed using the Planetary Spectrum Generator (PSG) retrieval to extract column mole fractions. Instrument performance is demonstrated through two deployments at significantly different sites in interior Alaska and Hawaii. The resolving power (λ/∆λ) is greater than 500,000 at 1.64 μm with precisions of better than 20 ppb and 1 ppm for CH4 and CO2, respectively. Because mini-LHR instruments are portable and can be co-located, they can be used to characterize bias between larger, stationary, column observing instruments. In addition, mini-LHRs can be deployed quickly to respond to transient events such as methane leaks or can be used for field studies targeting geographical regions.

The 2016 CEOS Infrared Radiometer Comparison: Part II: Laboratory Comparison of Radiation Thermometers

Abstract: To ensure confidence, measurements carried out by imaging radiometers mounted on satellites require robust validation using “fiducial quality” measurements of the same in situ parameter. Fo...