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All sourcesNASA NTRSDOE OSTI
NASA NTRS2022

On the Effects of Cloud Water Content on Passive Microwave Snowfall Retrievals

The Bayesian passive microwave retrievals of snowfall often rely on mathematical matching of the observed vectors of brightness temperature with an a priori database of precipitation profiles and their corresponding brightness temperatures. Mathematical proximity does not necessarily lead to consistent retrievals due to limited information content of passive microwave observations. This paper defines imposter (genuine) vectors of brightness temperature as those that are mathematically close but physically inconsistent (consistent) and characterizes them through the Silhouette Coefficient (SC) analysis. The Neyman–Pearson (NP) hypothesis testing is used to separate the imposter and genuine brightness temperatures based on their associated values of cloud ice (IWP) and liquid water path (LWP), given by coincidences of CloudSat Profiling Radar (CPR) and the Global Precipitation Measurement (GPM) Microwave Imager (GMI). The study determines thresholds for IWP and LWP that allow optimal identification of imposter brightness temperatures of non-snowing and snowing clouds, which can mislead the passive microwave retrieval algorithms to falsely detect or miss the snowfall events. It is demonstrated that emission signal of supercooled liquid water in snowing clouds can lead to improved passive microwave retrieval of snowfall and conditioning the retrievals to the cloud IWP and LWP can result in marginal correction of the snowfall detection probability; however, reduce the probability of false alarm by 6%–8% over sea ice and open oceans.

Snowfall↗
NASA NTRS2019

Assessment of Soil Moisture Retrievals Using SMAP and ELBARA-III Measurements in a Tibetan Meadow Ecosystem

This study presents results evaluating retrievals of liquid water content for both thawed and frozen soils based on simultaneous SMAP and ELBARA-III measurements in a Tibetan meadow ecosystem. A good agreement is found between time series of the SMAP and ELBARA-III T(sub B(sup P)) measurements (R≥0.87). Differences noted between the two data sets are linked to distinct liquid water content variations found in the specific footprints, and the SMAP measurements are better explained by the in-situ liquid water content. Furthermore, the in-situ liquid water content shows better agreements with both SMAP and ELBARA-III measured T(sub B(sup P)) at horizontal polarization in the cold season, while the measurements at vertical polarization are more consistently varying with the liquid water content for the warm season. With the implementation of new vegetation and surface roughness parameterizations, the dynamics of in-situ liquid water content are better reproduced by corresponding retrievals for both thawed and frozen soils, leading to the reduction of the unbiased root mean-square error (ubRMSE) by more than 31 % in comparison to these retrievals using SMAP default parameterizations. Notably, the single channel algorithm configured with the new parameterizations using SMAP measured vertical polarization during the ascending overpass provides the best liquid water content retrievals with an ubRMSE of 0.035 cu m -cu m that outperforms the SMAP mission specification.

L-band microwave radiometry↗
NASA NTRS2002

Retrieval Of Cloud Pressure And Chlorophyll Content Using Raman Scattering In GOME Ultraviolet Spectra

Reliable cloud pressure estimates are needed for accurate retrieval of ozone and other trace gases using satellite-borne backscatter ultraviolet (buv) instruments such as the global ozone monitoring experiment (GOME). Cloud pressure can be derived from buv instruments by utilizing the properties of rotational-Raman scattering (RRS) and absorption by O2-O2. In this paper we estimate cloud pressure from GOME observations in the 355-400 nm spectral range using the concept of a Lambertian-equivalent reflectivity (LER) surface. GOME has full spectral coverage in this range at relatively high spectral resolution with a very high signal-to-noise ratio. This allows for much more accurate estimates of cloud pressure than were possible with its predecessors SBUV and TOMS. We also demonstrate the potential capability to retrieve chlorophyll content with full-spectral buv instruments. We compare our retrieved LER cloud pressure with cloud top pressures derived from the infrared ATSR instrument on the same satellite. The findings confirm results from previous studies that showed retrieved LER cloud pressures from buv observations are systematically higher than IR-derived cloud-top pressure. Simulations using Mie-scattering radiative transfer algorithms that include O2-O2 absorption and RRS show that these differences can be explained by increased photon path length within and below cloud.

Atlas, Robert↗
NASA NTRS2003

Retrieval of Ozone Column Content from Airborne Sun Photometer Measurements During SOLVE II: Comparison with SAGE III, POAM III,THOMAS and GOME Measurements

During the Second SAGE 111 Ozone Loss and Validation Experiment (SOLVE II), the 14- channel NASA Ames Airborne Trackmg Sunphotometer (AATS-14) was mounted on the NASA DC-8 and successfully measured spectra of total and aerosol optical depth (TOD and AOD) during the sunlit portions of eight science flights. Values of ozone column content above the aircraft have been derived from the AATS-14 data by using a linear least squares method. For each AATS-14 measured TOD spectrum, this method iteratively finds the ozone column content that yields the best match between measured and calculated TOD. The calculations assume the known Chappuis ozone band shape and a three-parameter AOD shape (quadratic in log-log space). Seven of the AATS-14 channels (each employing an interference filter with a nominal full-width at half maximum bandpass of -5 nm) are within the Chappuis band, with center wavelengths between 452.9 nm and 864.5 nm. One channel (604.4 nm) is near the peak, and three channels (499.4, 519.4 and 675.1 nm) have ozone absorption within 30-40% of that at the peak. For the typical DC-8 SOLVE II cruising altitudes of approx. 8-12 km and the background stratospheric aerosol conditions that prevailed during SOLVE 11, absorption of incoming solar radiation by ozone comprised a significant fraction of the aerosol-plus-ozone optical depth measured in the four AATS-14 channels centered between 499.4 and 675.1 nm. Typical AODs above the DC-8 ranged from 0.003-0.008 in these channels. For comparison, an ozone overburden of 0.3 atm-cm (300 DU) translates to ozone optical depths of 0.009,0.014, 0.041, and 0.012, respectively, at these same wavelengths. In this paper, we compare AATS-14 values of ozone column content with temporally and spatially near-coincident values derived from measurements acquired by the Stratospheric Aerosol and Gas Experiment III (SAGE III) and the Polar Ozone and Aerosol Measurement 111 (POAM III) satellite sensors. We also compare AATS-14 ozone retrievals during selected DC-8 latitudinal and longitudinal transects with total column ozone data acquired by the Total Ozone Mapping Spectrometer (TOMS) and the Global Ozone Monitoring Experiment (GOME) satellite sensors. To enable this comparison, the amount of ozone in the column below the aircraft is estimated by combining SAGE and/or POAM data with high resolution, fast response in-situ ozone measurements acquired during the DC-8 ascent at the start of each science flight.

Livingston, J.↗
NASA NTRS2007

Toward the characterization of upper tropospheric clouds using Atmospheric Infrared Sounder and Microwave Limb Sounder observations

We estimate the accuracy of cloud top altitude (Z) retrievals from the Atmospheric Infrared Sounder (AIRS) and Advanced Microwave Sounding Unit (AMSU) observing suite (ZA) on board the Earth Observing System Aqua platform. We compare ZA with coincident measurements of Z derived from the micropulse lidar and millimeter wave cloud radar at the Atmospheric Radiation Measurement (ARM) program sites of Nauru and Manus islands (ZARM) and the inferred Z from vertically resolved Microwave Limb Sounder (MLS) ice water content (IWC) retrievals. The mean difference in ZA minus ZARM plus or minus one standard deviation ranges from −2.2 to 1.6 km ± 1.0 to 4.2 km for all cases of AIRS effective cloud fraction (f A) > 0.15 at Manus Island using the cloud radar only. The range of mean values results from using different approaches to determine ZARM, day/night differences, and the magnitude of f A; the variation about the mean decreases for increasing values of f A. Analysis of ZARM from the micropulse lidar at Nauru Island for cases restricted to 0.05 ≤ f A ≤ 0.15 indicates a statistically significant improvement in ZA − ZARM over the cloud radar-derived values at Manus Island. In these cases the ZA − ZARM difference is −1.1 to 2.1 km ± 3.0 to 4.5 km. These results imply that the operational ZA is quantitatively useful for constraining cirrus altitude despite the nominal 45 km horizontal resolution. Mean differences of cloud top pressure (PCLD) inferred from coincident AIRS and MLS ice water content (IWC) retrievals depend upon the method of defining AIRS PCLD (as with the ARM comparisons) over the MLS spatial scale, the peak altitude and maximum value of MLS IWC, and f A. AIRS and MLS yield similar vertical frequency distributions when comparisons are limited to f A > 0.1 and IWC > 1.0 mg m−3. Therefore the agreement depends upon the opacity of the cloud, with decreased agreement for optically tenuous clouds. Further, the mean difference and standard deviation of AIRS and MLS PCLD are highly dependent on the MLS tangent altitude. For MLS tangent altitudes greater than 146 hPa, the strength of the limb technique, the disagreement becomes statistically significant. This implies that AIRS and MLS “agree” in a statistical sense at lower tangent altitudes and “disagree” at higher tangent altitudes. These results provide important insights on upper tropospheric cloudiness as observed by nadir-viewing AIRS and limb-viewing MLS.

Wu, Dong L.↗
DOE OSTI2021

Fast Local Spatial Verification for Feature-Agnostic Large-Scale Image Retrieval

Images from social media can reflect diverse viewpoints, heated arguments, and expressions of creativity, adding new complexity to retrieval tasks. Researchers working on Content-Based Image Retrieval (CBIR) have traditionally tuned their algorithms to match filtered results with user search intent. However, we are now bombarded with composite images of unknown origin, authenticity, and even meaning. With such uncertainty, users may not have an initial idea of what the search query results should look like. For instance, hidden people, spliced objects, and subtly altered scenes can be difficult for a user to detect initially in a meme image, but may contribute significantly to its composition. It is pertinent to design systems that retrieve images with these nuanced relationships in addition to providing more traditional results, such as duplicates and near-duplicates — and to do so with enough efficiency at large scale. In this work, we propose a new approach for spatial verification that aims at modeling object-level regions using image keypoints retrieved from an image index, which is then used to accurately weight small contributing objects within the results, without the need for costly object detection steps. We call this method the Objects in Scene to Objects in Scene (OS2OS) score, and it is optimized for fast matrix operations, which can run quickly on either CPUs or GPUs. It performs comparably to state-of-the-art methods on classic CBIR problems (Oxford 5K, Paris 6K, and Google-Landmarks), and outperforms them in emerging retrieval tasks such as image composite matching in the NIST MFC2018 dataset and meme-style imagery from Reddit.

42 ENGINEERING↗
NASA NTRS1986

Retrieving cloud ice water content and geometrical thickness from microwave and infrared radiometric observations

Techniques are presented and their application illustrated for analysis of remotely sensed data collected with an aircraft carrying a multispectral cloud radiometer and an advanced microwave moisture sounder. The instruments were used on NASA high altitude flights to perform cloud field experiments. Sample IR and microwave brightness temperature data are provided as functions of the ice water path and of the ice water content. Quantitative models are described for deriving the cloud ice (or liquid) water content and the cloud geometric thickness from the radiometric data.

Wu, M.-L. C.↗
NASA NTRSDate not supplied

Test Results for the SEA Ice Crystal Detector (ICD) under SLD Conditions at the NASA IRT

The Science Engineering Associates hot-wire Ice Crystal Detector was tested under Appendix C and Supercooled Large Drop Conditions at the NASA Icing Research Tunnel, with the purpose of comparing the measurements to the tunnel LWC calibration, and to test the performance of the mixed-phase retrievals of the probe. The liquid water content measured by the total water content sensor of the probe agreed well with the tunnel calibration over a wide range of MVDs, not unexpectedly since the tunnel was calibrated with a similar Multiwire hot-wire probe. A scale factor difference of about 7% was attributed to the lack of an efficiency correction for the Ice Crystal Detector at smaller median volume diameters, and typical sample area uncertainties of liquid water content devices. In addition, the simultaneous equations that are used to retrieve liquid and ice water contents were tested under liquid and glaciated conditions. Using constant ice and water efficiencies for each wire, the equations were found to produce a large and false ice water content retrieval in large median volume diameter liquid cases. By applying a liquid efficiency to the liquid water content sensor that varied with median volume diameter, retrievals were improved such that false ice water content was less than ±10% of the total water content out to the 460 μm median volume diameter limit of the testing. The probe was also tested in glaciated conditions by freezing out a small median volume diameter spray. Using constant ice efficiencies for the two sensors provided from aircraft measurements, the retrieved ice water content was about 40% higher than expected for the case presented, likely due to the ice efficiencies that were used from aircraft measurements of natural clouds being too low for the tunnel’s glaciated sprays. Application of ice efficiencies that vary with particle size and potentially other parameters will be explored for future improvement of retrievals.

Air Craft Icing↗
NASA NTRS2021

Test Results for the SEA Ice Crystal Detector (ICD) under SLD Conditions at the NASA IRT

The Science Engineering Associates hot-wire Ice Crystal Detector was tested under Appendix C and Supercooled Large Drop Conditions at the NASA Icing Research Tunnel, with the purpose of comparing the measurements to the tunnel LWC calibration, and to test the performance of the mixed-phase retrievals of the probe. The liquid water content measured by the total water content sensor of the probe agreed well with the tunnel calibration over a wide range of MVDs, not unexpectedly since the tunnel was calibrated with a similar Multiwire hot-wire probe. A scale factor difference of about 7% was attributed to the lack of an efficiency correction for the Ice Crystal Detector at smaller median volume diameters, and typical sample area uncertainties of liquid water content devices. In addition, the simultaneous equations that are used to retrieve liquid and ice water contents were tested under liquid and glaciated conditions. Using constant ice and water efficiencies for each wire, the equations were found to produce a large and false ice water content retrieval in large median volume diameter liquid cases. By applying a liquid efficiency to the liquid water content sensor that varied with median volume diameter, retrievals were improved such that false ice water content was less than ±10% of the total water content out to the 460 μm median volume diameter limit of the testing. The probe was also tested in glaciated conditions by freezing out a small median volume diameter spray. Using constant ice efficiencies for the two sensors provided from aircraft measurements, the retrieved ice water content was about 40% higher than expected for the case presented, likely due to the ice efficiencies that were used from aircraft measurements of natural clouds being too low for the tunnel’s glaciated sprays. Application of ice efficiencies that vary with particle size and potentially other parameters will be explored for future improvement of retrievals.

o Aircraft Icing↗
NASA NTRS1994

Using Induction to Refine Information Retrieval Strategies

Conceptual information retrieval systems use structured document indices, domain knowledge and a set of heuristic retrieval strategies to match user queries with a set of indices describing the document's content. Such retrieval strategies increase the set of relevant documents retrieved (increase recall), but at the expense of returning additional irrelevant documents (decrease precision). Usually in conceptual information retrieval systems this tradeoff is managed by hand and with difficulty. This paper discusses ways of managing this tradeoff by the application of standard induction algorithms to refine the retrieval strategies in an engineering design domain. We gathered examples of query/retrieval pairs during the system's operation using feedback from a user on the retrieved information. We then fed these examples to the induction algorithm and generated decision trees that refine the existing set of retrieval strategies. We found that (1) induction improved the precision on a set of queries generated by another user, without a significant loss in recall, and (2) in an interactive mode, the decision trees pointed out flaws in the retrieval and indexing knowledge and suggested ways to refine the retrieval strategies.

Baudin, Catherine↗
NASA NTRSDate not supplied

Detection of Drizzle and Light Snowfall Over the Southern Ocean Using the GPM Combined Algorithm

Following the 2014 launch of the Global Precipitation Measurement Mission (GPM), an unprecedented combination of coincident active and passive microwave observations are available for state of the art precipitation retrieval. The GPM Combined Algorithm forms the backbone of this effort, optimizing geophysical variables for agreement with the multi-spectral information content. Combined retrievals are then utilized, along with a radiative transfer model, as a database applied for retrievals across a constellation of passive microwave radiometers of varying frequencies and viewing geometries. By keeping such retrievals related through the transfer standard of the combined algorithm, level 3 products such as the Integrated Multi-satellitE Retrievals for GPM (IMERG) are able to provide consistent global products for users at the higher temporal resolution required for hydrological applications on the global scale. In the current version of the combined product, precipitation retrievals are carried out only in the presence of a signal from the active radar. As a result, light precipitation and drizzle below the threshold of radar sensitivity are not included in any of the products down the chain from the constellation to IMERG. In this work, the effects of enhancing the retrievals with an optimal estimation-type (OE) water vapor and cloud retrieval using the passive observations are explored over the Southern Ocean on a regional scale. Non-convergence of the OE in areas with no detectable radar signal is used to identify areas with high probability of light precipitation and drizzle. Microphysical scale characterization of the light precipitation will be explored using information derived from the higher sensitivity CloudSat mission along with model information to identify and associate the related atmospheric state and dynamics. This is a physically-based approach requiring radiometric consistency with all available multi-spectral observations. Retrieved drizzle can then be included in the constellation databases continuing across all scales through to the level 3 products. The technique, successfully demonstrated for the Southern Ocean, can be easily adapted for light precipitation retrieval over other areas and surfaces leading to a global climatology of light precipitation.

Sarah Rinegerud↗
NASA NTRS1983

Retrieval of clear sky moisture profiles using the 183 GHz water vapor line

A technique for retrieving vertical moisture profiles from downlooking radiometric measurements of atmospheric radiation near the 183 GHz water vapor line is described. A simulation experiment utilizing this retrieval technique and temperature and moisture profiles from tropical radiosonde stations has been carried out. Assuming the atmospheric temperature profile is known to 2 K rms (state-of-the-art with present remote sensors) and the instrument noise is 1-2 K rms brightness temperature (achievable with current technology), the humidity between the 700 and 1000 mb pressure levels was retrieved to within about 20 percent of true value over ocean and within about 45 percent of true value over land. The precipitable water content was retrieved to within about 10 percent rms of true value over the ocean and within about 45 percent rms of true value over land.

Kakar, R. K.↗
NASA NTRS1981

Regression techniques for oceanographic parameter retrieval using space-borne microwave radiometry

Variations of conventional multiple regression techniques are applied to the problem of remote sensing of oceanographic parameters from space. The techniques are specifically adapted to the scanning multichannel microwave radiometer (SMRR) launched on the Seasat and Nimbus 7 satellites to determine ocean surface temperature, wind speed, and atmospheric water content. The retrievals are studied primarily from a theoretical viewpoint, to illustrate the retrieval error structure, the relative importances of different radiometer channels, and the tradeoffs between spatial resolution and retrieval accuracy. Comparisons between regressions using simulated and actual SMMR data are discussed; they show similar behavior.

Hofer, R.↗
NASA NTRS2014

MLS and CALIOP Cloud Ice Measurements in the Upper Troposphere: A Constraint from Microwave on Cloud Microphysics

This study examines the consistency and microphysics assumptions among satellite ice water content (IWC) retrievals in the upper troposphere with collocated A-Train radiances from Microwave Limb Sounder (MLS) and lidar backscatters from Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP). For the cases in which IWC values are small (less than 10mg m(exp-23)), the cloud ice retrievals are constrained by both MLS 240- and 640- GHz radiances and CALIOP 532-nm backscatter beta(532). From the observed relationships between MLS cloud-induced radiance T(sub cir) and the CALIOP backscatter integrated gamma532 along the MLS line of sight, an empirical linear relation between cloud ice and the lidar backscatter is found: IWC/beta532=0.58+/-0.11. This lidar cloud ice relation is required to satisfy the cloud ice emission signals simultaneously observed at microwave frequencies, in which ice permittivity is relatively well known. This empirical relationship also produces IWC values that agree well with the CALIOP, version 3.0, retrieval at values, less than 10mg m(exp-3). Because the microphysics assumption is critical in satellite cloud ice retrievals, the agreement found in the IWC-beta532 relationships increase fidelity of the assumptions used by the lidar and microwave techniques for upper-tropospheric clouds.

ice water content↗
NASA NTRS2017

Information Theoretic Evaluation of Satellite Soil Moisture Retrievals

Microwave radiometry has a long legacy of providing estimates of remotely sensed near surfacesoil moisture measurements over continental and global scales. A consistent assessment of theerrors and uncertainties associated with these retrievals is important for their effective utilization in modeling, data assimilation and end-use application environments. This article presents an evaluationof soil moisture retrieval products from AMSR-E, ASCAT, SMOS, AMSR2 and SMAPinstruments using information theory-based metrics. These metrics rely on time series analysis ofsoil moisture retrievals for estimating the measurement error, level of randomness (entropy) andregularity (complexity) of the data. The results of the study indicate that the measurement errors inthe remote sensing retrievals are significantly larger than that of the ground soil moisture measurements.The SMAP retrievals, on the other hand, were found to have reduced errors (comparable to Preprint submitted to Remote Sensing of Environment October 1, 2017those of in-situ datasets), particularly over areas with moderate vegetation. The SMAP retrievals also demonstrate high information content relative to other retrieval products, with higher levelsof complexity and reduced entropy. Finally, a joint evaluation of the entropy and complexity ofremotely sensed soil moisture products indicates that the information content of the AMSR-E, ASCAT,SMOS and AMSR2 retrievals is low, whereas SMAP retrievals show better performance. The use of information theoretic assessments is effective in quantifying the required levels of improvements needed in the remote sensing soil moisture retrievals to enhance their utility and information content.

Information theory↗
NASA NTRS1994

Computation and use of the reflectivity at 3.75 micrometers from AVHRR thermal channels

Global study of land surface properties uses AVHRR channels 1 and 2, but channel 3 may be of interest, although its use requires preprocessing. It consists of both a reflective part and an emissive part, the former can be derived from T3, T4 and T5. Since the water vapor affects channel 3, its content is retrieved from the channel 4 and 5 using the split window technique. A formula of reflective part retrieval at 3.75 micrometers is tested in the case of sunglint observations where the emissivities of channels 4 and 5 can be set to the unity. The formula is adapted and validated to land surface using the FIFE-87 data set. Preliminary applications of the reflectance at 3.75 micrometers to the studies of surface properties retrieval, aerosol retrieval over land, and desertic aerosol retrieval, are addressed.

Roger, J. C.↗
NASA NTRS2012

Content Platforms Meet Data Storage, Retrieval Needs

Earth is under a constant barrage of information from space. Whether from satellites orbiting our planet, spacecraft circling Mars, or probes streaking toward the far reaches of the Solar System, NASA collects massive amounts of data from its spacefaring missions each day. NASA s Earth Observing System (EOS) satellites, for example, provide daily imagery and measurements of Earth s atmosphere, oceans, vegetation, and more. The Earth Observing System Data and Information System (EOSDIS) collects all of that science data and processes, archives, and distributes it to researchers around the globe; EOSDIS recently reached a total archive volume of 4.5 petabytes. Try to store that amount of information in your standard, four-drawer file cabinet, and you would need 90 million to get the job done. To manage the flood of information, NASA has explored technologies to efficiently collect, archive, and provide access to EOS data for scientists today and for years to come. One such technology is now providing similar capabilities to businesses and organizations worldwide.

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