Bayesian aerosol retrieval algorithm for MODIS AOD retrieval over land

NASA Astrophysics Data System (ADS)

Lipponen, Antti; Mielonen, Tero; Pitkänen, Mikko R. A.; Levy, Robert C.; Sawyer, Virginia R.; Romakkaniemi, Sami; Kolehmainen, Ville; Arola, Antti

2018-03-01

We have developed a Bayesian aerosol retrieval (BAR) algorithm for the retrieval of aerosol optical depth (AOD) over land from the Moderate Resolution Imaging Spectroradiometer (MODIS). In the BAR algorithm, we simultaneously retrieve all dark land pixels in a granule, utilize spatial correlation models for the unknown aerosol parameters, use a statistical prior model for the surface reflectance, and take into account the uncertainties due to fixed aerosol models. The retrieved parameters are total AOD at 0.55 µm, fine-mode fraction (FMF), and surface reflectances at four different wavelengths (0.47, 0.55, 0.64, and 2.1 µm). The accuracy of the new algorithm is evaluated by comparing the AOD retrievals to Aerosol Robotic Network (AERONET) AOD. The results show that the BAR significantly improves the accuracy of AOD retrievals over the operational Dark Target (DT) algorithm. A reduction of about 29 % in the AOD root mean square error and decrease of about 80 % in the median bias of AOD were found globally when the BAR was used instead of the DT algorithm. Furthermore, the fraction of AOD retrievals inside the ±(0.05+15 %) expected error envelope increased from 55 to 76 %. In addition to retrieving the values of AOD, FMF, and surface reflectance, the BAR also gives pixel-level posterior uncertainty estimates for the retrieved parameters. The BAR algorithm always results in physical, non-negative AOD values, and the average computation time for a single granule was less than a minute on a modern personal computer.

System engineering approach to GPM retrieval algorithms

DOE Office of Scientific and Technical Information (OSTI.GOV)

Rose, C. R.; Chandrasekar, V.

2004-01-01

System engineering principles and methods are very useful in large-scale complex systems for developing the engineering requirements from end-user needs. Integrating research into system engineering is a challenging task. The proposed Global Precipitation Mission (GPM) satellite will use a dual-wavelength precipitation radar to measure and map global precipitation with unprecedented accuracy, resolution and areal coverage. The satellite vehicle, precipitation radars, retrieval algorithms, and ground validation (GV) functions are all critical subsystems of the overall GPM system and each contributes to the success of the mission. Errors in the radar measurements and models can adversely affect the retrieved output values. Groundmore » validation (GV) systems are intended to provide timely feedback to the satellite and retrieval algorithms based on measured data. These GV sites will consist of radars and DSD measurement systems and also have intrinsic constraints. One of the retrieval algorithms being studied for use with GPM is the dual-wavelength DSD algorithm that does not use the surface reference technique (SRT). The underlying microphysics of precipitation structures and drop-size distributions (DSDs) dictate the types of models and retrieval algorithms that can be used to estimate precipitation. Many types of dual-wavelength algorithms have been studied. Meneghini (2002) analyzed the performance of single-pass dual-wavelength surface-reference-technique (SRT) based algorithms. Mardiana (2003) demonstrated that a dual-wavelength retrieval algorithm could be successfully used without the use of the SRT. It uses an iterative approach based on measured reflectivities at both wavelengths and complex microphysical models to estimate both No and Do at each range bin. More recently, Liao (2004) proposed a solution to the Do ambiguity problem in rain within the dual-wavelength algorithm and showed a possible melting layer model based on stratified spheres. With the No and Do calculated at each bin, the rain rate can then be calculated based on a suitable rain-rate model. This paper develops a system engineering interface to the retrieval algorithms while remaining cognizant of system engineering issues so that it can be used to bridge the divide between algorithm physics an d overall mission requirements. Additionally, in line with the systems approach, a methodology is developed such that the measurement requirements pass through the retrieval model and other subsystems and manifest themselves as measurement and other system constraints. A systems model has been developed for the retrieval algorithm that can be evaluated through system-analysis tools such as MATLAB/Simulink.« less

Improved Methodology for Surface and Atmospheric Soundings, Error Estimates, and Quality Control Procedures: the AIRS Science Team Version-6 Retrieval Algorithm

NASA Technical Reports Server (NTRS)

Susskind, Joel; Blaisdell, John; Iredell, Lena

2014-01-01

The AIRS Science Team Version-6 AIRS/AMSU retrieval algorithm is now operational at the Goddard DISC. AIRS Version-6 level-2 products are generated near real-time at the Goddard DISC and all level-2 and level-3 products are available starting from September 2002. This paper describes some of the significant improvements in retrieval methodology contained in the Version-6 retrieval algorithm compared to that previously used in Version-5. In particular, the AIRS Science Team made major improvements with regard to the algorithms used to 1) derive surface skin temperature and surface spectral emissivity; 2) generate the initial state used to start the cloud clearing and retrieval procedures; and 3) derive error estimates and use them for Quality Control. Significant improvements have also been made in the generation of cloud parameters. In addition to the basic AIRS/AMSU mode, Version-6 also operates in an AIRS Only (AO) mode which produces results almost as good as those of the full AIRS/AMSU mode. This paper also demonstrates the improvements of some AIRS Version-6 and Version-6 AO products compared to those obtained using Version-5.

On the Simulation of Sea States with High Significant Wave Height for the Validation of Parameter Retrieval Algorithms for Future Altimetry Missions

NASA Astrophysics Data System (ADS)

Kuschenerus, Mieke; Cullen, Robert

2016-08-01

To ensure reliability and precision of wave height estimates for future satellite altimetry missions such as Sentinel 6, reliable parameter retrieval algorithms that can extract significant wave heights up to 20 m have to be established. The retrieved parameters, i.e. the retrieval methods need to be validated extensively on a wide range of possible significant wave heights. Although current missions require wave height retrievals up to 20 m, there is little evidence of systematic validation of parameter retrieval methods for sea states with wave heights above 10 m. This paper provides a definition of a set of simulated sea states with significant wave height up to 20 m, that allow simulation of radar altimeter response echoes for extreme sea states in SAR and low resolution mode. The simulated radar responses are used to derive significant wave height estimates, which can be compared with the initial models, allowing precision estimations of the applied parameter retrieval methods. Thus we establish a validation method for significant wave height retrieval for sea states causing high significant wave heights, to allow improved understanding and planning of future satellite altimetry mission validation.

Neural Network-Based Retrieval of Surface and Root Zone Soil Moisture using Multi-Frequency Remotely-Sensed Observations

NASA Astrophysics Data System (ADS)

Hamed Alemohammad, Seyed; Kolassa, Jana; Prigent, Catherine; Aires, Filipe; Gentine, Pierre

2017-04-01

Knowledge of root zone soil moisture is essential in studying plant's response to different stress conditions since plant photosynthetic activity and transpiration rate are constrained by the water available through their roots. Current global root zone soil moisture estimates are based on either outputs from physical models constrained by observations, or assimilation of remotely-sensed microwave-based surface soil moisture estimates with physical model outputs. However, quality of these estimates are limited by the accuracy of the model representations of physical processes (such as radiative transfer, infiltration, percolation, and evapotranspiration) as well as errors in the estimates of the surface parameters. Additionally, statistical approaches provide an alternative efficient platform to develop root zone soil moisture retrieval algorithms from remotely-sensed observations. In this study, we present a new neural network based retrieval algorithm to estimate surface and root zone soil moisture from passive microwave observations of SMAP satellite (L-band) and AMSR2 instrument (X-band). SMAP early morning observations are ideal for surface soil moisture retrieval. AMSR2 mid-night observations are used here as an indicator of plant hydraulic properties that are related to root zone soil moisture. The combined observations from SMAP and AMSR2 together with other ancillary observations including the Solar-Induced Fluorescence (SIF) estimates from GOME-2 instrument provide necessary information to estimate surface and root zone soil moisture. The algorithm is applied to observations from the first 18 months of SMAP mission and retrievals are validated against in-situ observations and other global datasets.

An algorithm for estimating aerosol optical depth from HIMAWARI-8 data over Ocean

NASA Astrophysics Data System (ADS)

Lee, Kwon Ho

2016-04-01

The paper presents currently developing algorithm for aerosol detection and retrieval over ocean for the next generation geostationary satellite, HIMAWARI-8. Enhanced geostationary remote sensing observations are now enables for aerosol retrieval of dust, smoke, and ash, which began a new era of geostationary aerosol observations. Sixteen channels of the Advanced HIMAWARI Imager (AHI) onboard HIMAWARI-8 offer capabilities for aerosol remote sensing similar to those currently provided by the Moderate Resolution Imaging Spectroradiometer (MODIS). Aerosols were estimated in detection processing from visible and infrared channel radiances, and in retrieval processing using the inversion-optimization of satellite-observed radiances with those calculated from radiative transfer model. The retrievals are performed operationally every ten minutes for pixel sizes of ~8 km. The algorithm currently under development uses a multichannel approach to estimate the effective radius, aerosol optical depth (AOD) simultaneously. The instantaneous retrieved AOD is evaluated by the MODIS level 2 operational aerosol products (C006), and the daily retrieved AOD was compared with ground-based measurements from the AERONET databases. The results show that the detection of aerosol and estimated AOD are in good agreement with the MODIS data and ground measurements with a correlation coefficient of ˜0.90 and a bias of 4%. These results suggest that the proposed method applied to the HIMAWARI-8 satellite data can accurately estimate continuous AOD. Acknowledgments This work was supported by "Development of Geostationary Meteorological Satellite Ground Segment(NMSC-2014-01)" program funded by National Meteorological Satellite Centre(NMSC) of Korea Meteorological Administration(KMA).

A retrieval algorithm of hydrometer profile for submillimeter-wave radiometer

NASA Astrophysics Data System (ADS)

Liu, Yuli; Buehler, Stefan; Liu, Heguang

2017-04-01

Vertical profiles of particle microphysics perform vital functions for the estimation of climatic feedback. This paper proposes a new algorithm to retrieve the profile of the parameters of the hydrometeor(i.e., ice, snow, rain, liquid cloud, graupel) based on passive submillimeter-wave measurements. These parameters include water content and particle size. The first part of the algorithm builds the database and retrieves the integrated quantities. Database is built up by Atmospheric Radiative Transfer Simulator(ARTS), which uses atmosphere data to simulate the corresponding brightness temperature. Neural network, trained by the precalculated database, is developed to retrieve the water path for each type of particles. The second part of the algorithm analyses the statistical relationship between water path and vertical parameters profiles. Based on the strong dependence existing between vertical layers in the profiles, Principal Component Analysis(PCA) technique is applied. The third part of the algorithm uses the forward model explicitly to retrieve the hydrometeor profiles. Cost function is calculated in each iteration, and Differential Evolution(DE) algorithm is used to adjust the parameter values during the evolutionary process. The performance of this algorithm is planning to be verified for both simulation database and measurement data, by retrieving profiles in comparison with the initial one. Results show that this algorithm has the ability to retrieve the hydrometeor profiles efficiently. The combination of ARTS and optimization algorithm can get much better results than the commonly used database approach. Meanwhile, the concept that ARTS can be used explicitly in the retrieval process shows great potential in providing solution to other retrieval problems.

Estimating effective roughness parameters of the L-MEB model for soil moisture retrieval using passive microwave observations from SMAPVEX12

USDA-ARS?s Scientific Manuscript database

Although there have been efforts to improve existing soil moisture retrieval algorithms, the ability to estimate soil moisture from passive microwave observations is still hampered by problems in accurately modeling the observed microwave signal. This paper focuses on the estimation of effective sur...

Evaluation of Improvements to the TRMM Microwave Rain Algorithm

NASA Technical Reports Server (NTRS)

Yang, Song; Olson, Williams S.; Smith, Eric A.; Kummerow, Christian

2002-01-01

Improvements made to the Version 5 TRMM passive microwave rain retrieval algorithm (2A-12) are evaluated using independent data. Surface rain rate estimates from the Version 5 TRMM TMI (2A-12), PR (2A-25) and TMI/PR Combined (2B-31) algorithms and ground-based radar estimates for selected coincident subset datasets in 1998 over Melbourne and Kwajalein show varying degrees of agreement. The surface rain rates are then classified into convective and stratiform rain types over ocean, land, and coastal areas for more detailed comparisons to the ground radar measurements. These comparisons lead to a better understanding of the relative performances of the current TRMM rain algorithms. For example, at Melbourne more than 80% of the radar-derived rainfall is classified as convective rain. Convective rain from the TRMM rain algorithms is less than that from ground radar measurements, while TRMM stratiform rain is much greater. Rain area coverage from 2A-12 is also in reasonable agreement with ground radar measurements, with about 25% more over ocean and 25% less over land and coastal areas. Retrieved rain rates from the improved (Version 6) 2A-12 algorithm will be compared to 2A-25, 2B-31, and ground-based radar measurements to evaluate the impact of improvements to 2A-12 in Version 6. An important improvement to the Version 6 2A-12 algorithm is the retrieval of Q1/Q2 (latent heating/drying) profiles in addition to the surface rain rate and hydrometeor profiles. In order to ascertain the credibility of the new products, retrieved Q1/Q2 profiles are compared to independent ground-based estimates. Analyses of dual-Doppler radar data in conjunction with coincident rawinsonde data yield estimates of the vertical distributions of diabatic heating/drying at high horizontal resolution for selected cases over the Kwajalein and LBA field sites. The estimated vertical heating/drying structures appear to be reasonable. Comparisons of Q1/Q2 profiles from Version 6 2A-12 and the ground-based estimates are in progress. Retrieved Q1/Q2 structures will also be compared to MM5 hurricane simulations for selected cases. The results of these intercomparisons will be presented at the conference.

Retrieval of volcanic ash height from satellite-based infrared measurements

NASA Astrophysics Data System (ADS)

Zhu, Lin; Li, Jun; Zhao, Yingying; Gong, He; Li, Wenjie

2017-05-01

A new algorithm for retrieving volcanic ash cloud height from satellite-based measurements is presented. This algorithm, which was developed in preparation for China's next-generation meteorological satellite (FY-4), is based on volcanic ash microphysical property simulation and statistical optimal estimation theory. The MSG satellite's main payload, a 12-channel Spinning Enhanced Visible and Infrared Imager, was used as proxy data to test this new algorithm. A series of eruptions of Iceland's Eyjafjallajökull volcano during April to May 2010 and the Puyehue-Cordón Caulle volcanic complex eruption in the Chilean Andes on 16 June 2011 were selected as two typical cases for evaluating the algorithm under various meteorological backgrounds. Independent volcanic ash simulation training samples and satellite-based Cloud-Aerosol Lidar with Orthogonal Polarization data were used as validation data. It is demonstrated that the statistically based volcanic ash height algorithm is able to rapidly retrieve volcanic ash heights, globally. The retrieved ash heights show comparable accuracy with both independent training data and the lidar measurements, which is consistent with previous studies. However, under complicated background, with multilayers in vertical scale, underlying stratus clouds tend to have detrimental effects on the final retrieval accuracy. This is an unresolved problem, like many other previously published methods using passive satellite sensors. Compared with previous studies, the FY-4 ash height algorithm is independent of simultaneous atmospheric profiles, providing a flexible way to estimate volcanic ash height using passive satellite infrared measurements.

Cross-validation of two liquid water path retrieval algorithms applied to ground-based microwave radiation measurements by RPG-HATPRO instrument

NASA Astrophysics Data System (ADS)

Kostsov, Vladimir; Ionov, Dmitry; Biryukov, Egor; Zaitsev, Nikita

2017-04-01

A built-in operational regression algorithm (REA) of liquid water path (LWP) retrieval supplied by the manufacturer of the RPG-HATPRO microwave radiometer has been compared to a so-called physical algorithm (PHA) based on the inversion of the radiative transfer equation. The comparison has been performed for different scenarios of microwave observations by the RPG-HATPRO instrument that has been operating at St.Petersburg University since June 2012. The data for the scenarios have been collected within the time period December 2012 - December 2014. The estimations of bias and random error for both REA and PHA have been obtained. Special attention has been paid to the analysis of the quality of the LWP retrievals during and after rain events that have been detected by the built-in rain sensor. The estimation has been done of the time period after a rain event when the retrieval quality has to be considered as insufficient.

Description and Sensitivity Analysis of the SOLSE/LORE-2 and SAGE III Limb Scattering Ozone Retrieval Algorithms

NASA Technical Reports Server (NTRS)

Loughman, R.; Flittner, D.; Herman, B.; Bhartia, P.; Hilsenrath, E.; McPeters, R.; Rault, D.

2002-01-01

The SOLSE (Shuttle Ozone Limb Sounding Experiment) and LORE (Limb Ozone Retrieval Experiment) instruments are scheduled for reflight on Space Shuttle flight STS-107 in July 2002. In addition, the SAGE III (Stratospheric Aerosol and Gas Experiment) instrument will begin to make limb scattering measurements during Spring 2002. The optimal estimation technique is used to analyze visible and ultraviolet limb scattered radiances and produce a retrieved ozone profile. The algorithm used to analyze data from the initial flight of the SOLSE/LORE instruments (on Space Shuttle flight STS-87 in November 1997) forms the basis of the current algorithms, with expansion to take advantage of the increased multispectral information provided by SOLSE/LORE-2 and SAGE III. We also present detailed sensitivity analysis for these ozone retrieval algorithms. The primary source of ozone retrieval error is tangent height misregistration (i.e., instrument pointing error), which is relevant throughout the altitude range of interest, and can produce retrieval errors on the order of 10-20 percent due to a tangent height registration error of 0.5 km at the tangent point. Other significant sources of error are sensitivity to stratospheric aerosol and sensitivity to error in the a priori ozone estimate (given assumed instrument signal-to-noise = 200). These can produce errors up to 10 percent for the ozone retrieval at altitudes less than 20 km, but produce little error above that level.

Broadband Phase Retrieval for Image-Based Wavefront Sensing

NASA Technical Reports Server (NTRS)

Dean, Bruce H.

2007-01-01

A focus-diverse phase-retrieval algorithm has been shown to perform adequately for the purpose of image-based wavefront sensing when (1) broadband light (typically spanning the visible spectrum) is used in forming the images by use of an optical system under test and (2) the assumption of monochromaticity is applied to the broadband image data. Heretofore, it had been assumed that in order to obtain adequate performance, it is necessary to use narrowband or monochromatic light. Some background information, including definitions of terms and a brief description of pertinent aspects of image-based phase retrieval, is prerequisite to a meaningful summary of the present development. Phase retrieval is a general term used in optics to denote estimation of optical imperfections or aberrations of an optical system under test. The term image-based wavefront sensing refers to a general class of algorithms that recover optical phase information, and phase-retrieval algorithms constitute a subset of this class. In phase retrieval, one utilizes the measured response of the optical system under test to produce a phase estimate. The optical response of the system is defined as the image of a point-source object, which could be a star or a laboratory point source. The phase-retrieval problem is characterized as image-based in the sense that a charge-coupled-device camera, preferably of scientific imaging quality, is used to collect image data where the optical system would normally form an image. In a variant of phase retrieval, denoted phase-diverse phase retrieval [which can include focus-diverse phase retrieval (in which various defocus planes are used)], an additional known aberration (or an equivalent diversity function) is superimposed as an aid in estimating unknown aberrations by use of an image-based wavefront-sensing algorithm. Image-based phase-retrieval differs from such other wavefront-sensing methods, such as interferometry, shearing interferometry, curvature wavefront sensing, and Shack-Hartmann sensing, all of which entail disadvantages in comparison with image-based methods. The main disadvantages of these non-image based methods are complexity of test equipment and the need for a wavefront reference.

Cross Validation of Rain Drop Size Distribution between GPM and Ground Based Polarmetric radar

NASA Astrophysics Data System (ADS)

Chandra, C. V.; Biswas, S.; Le, M.; Chen, H.

2017-12-01

Dual-frequency precipitation radar (DPR) on board the Global Precipitation Measurement (GPM) core satellite has reflectivity measurements at two independent frequencies, Ku- and Ka- band. Dual-frequency retrieval algorithms have been developed traditionally through forward, backward, and recursive approaches. However, these algorithms suffer from "dual-value" problem when they retrieve medium volume diameter from dual-frequency ratio (DFR) in rain region. To this end, a hybrid method has been proposed to perform raindrop size distribution (DSD) retrieval for GPM using a linear constraint of DSD along rain profile to avoid "dual-value" problem (Le and Chandrasekar, 2015). In the current GPM level 2 algorithm (Iguchi et al. 2017- Algorithm Theoretical Basis Document) the Solver module retrieves a vertical profile of drop size distributionn from dual-frequency observations and path integrated attenuations. The algorithm details can be found in Seto et al. (2013) . On the other hand, ground based polarimetric radars have been used for a long time to estimate drop size distributions (e.g., Gorgucci et al. 2002 ). In addition, coincident GPM and ground based observations have been cross validated using careful overpass analysis. In this paper, we perform cross validation on raindrop size distribution retrieval from three sources, namely the hybrid method, the standard products from the solver module and DSD retrievals from ground polarimetric radars. The results are presented from two NEXRAD radars located in Dallas -Fort Worth, Texas (i.e., KFWS radar) and Melbourne, Florida (i.e., KMLB radar). The results demonstrate the ability of DPR observations to produce DSD estimates, which can be used subsequently to generate global DSD maps. References: Seto, S., T. Iguchi, T. Oki, 2013: The basic performance of a precipitation retrieval algorithm for the Global Precipitation Measurement mission's single/dual-frequency radar measurements. IEEE Transactions on Geoscience and Remote Sensing, 51(12), 5239-5251. Gorgucci, E., Chandrasekar, V., Bringi, V. N., and Scarchilli, G.: Estimation of Raindrop Size Distribution Parameters from Polarimetric Radar Measurements, J. Atmos. Sci., 59, 2373-2384, doi:10.1175/1520-0469(2002)0592.0.CO;2, 2002.

Validating precision estimates in horizontal wind measurements from a Doppler lidar

DOE PAGES

Newsom, Rob K.; Brewer, W. Alan; Wilczak, James M.; ...

2017-03-30

Results from a recent field campaign are used to assess the accuracy of wind speed and direction precision estimates produced by a Doppler lidar wind retrieval algorithm. The algorithm, which is based on the traditional velocity-azimuth-display (VAD) technique, estimates the wind speed and direction measurement precision using standard error propagation techniques, assuming the input data (i.e., radial velocities) to be contaminated by random, zero-mean, errors. For this study, the lidar was configured to execute an 8-beam plan-position-indicator (PPI) scan once every 12 min during the 6-week deployment period. Several wind retrieval trials were conducted using different schemes for estimating themore » precision in the radial velocity measurements. Here, the resulting wind speed and direction precision estimates were compared to differences in wind speed and direction between the VAD algorithm and sonic anemometer measurements taken on a nearby 300 m tower.« less

The potential of LIRIC to validate the vertical profiles of the aerosol mass concentration estimated by an air quality model

NASA Astrophysics Data System (ADS)

Siomos, Nikolaos; Filoglou, Maria; Poupkou, Anastasia; Liora, Natalia; Dimopoulos, Spyros; Melas, Dimitris; Chaikovsky, Anatoli; Balis, Dimitris

2015-04-01

Vertical profiles of the aerosol mass concentration derived by a retrieval algorithm that uses combined sunphotometer and LIDAR data (LIRIC) were used in order to validate the mass concentration profiles estimated by the air quality model CAMx. LIDAR and CIMEL measurements of the Laboratory of Atmospheric Physics of the Aristotle University of Thessaloniki were used for this validation.The aerosol mass concentration profiles of the fine and coarse mode derived by CAMx were compared with the respective profiles derived by the retrieval algorithm. For the coarse mode particles, forecasts of the Saharan dust transportation model BSC-DREAM8bV2 were also taken into account. Each of the retrieval algorithm's profiles were matched to the models' profile with the best agreement within a time window of four hours before and after the central measurement. OPAC, a software than can provide optical properties of aerosol mixtures, was also employed in order to calculate the angstrom exponent and the lidar ratio values for 355nm and 532nm for each of the model's profiles aiming in a comparison with the angstrom exponent and the lidar ratio values derived by the retrieval algorithm for each measurement. The comparisons between the fine mode aerosol concentration profiles resulted in a good agreement between CAMx and the retrieval algorithm, with the vertical mean bias error never exceeding 7 μgr/m3. Concerning the aerosol coarse mode concentration profiles both CAMx and BSC-DREAM8bV2 values are severely underestimated, although, in cases of Saharan dust transportation events there is an agreement between the profiles of BSC-DREAM8bV2 model and the retrieval algorithm.

Long-term analysis of aerosol optical depth over Northeast Asia using a satellite-based measurement: MI Yonsei Aerosol Retrieval Algorithm (YAER)

NASA Astrophysics Data System (ADS)

Kim, Mijin; Kim, Jhoon; Yoon, Jongmin; Chung, Chu-Yong; Chung, Sung-Rae

2017-04-01

In 2010, the Korean geostationary earth orbit (GEO) satellite, the Communication, Ocean, and Meteorological Satellite (COMS), was launched including the Meteorological Imager (MI). The MI measures atmospheric condition over Northeast Asia (NEA) using a single visible channel centered at 0.675 μm and four IR channels at 3.75, 6.75, 10.8, 12.0 μm. The visible measurement can also be utilized for the retrieval of aerosol optical properties (AOPs). Since the GEO satellite measurement has an advantage for continuous monitoring of AOPs, we can analyze the spatiotemporal variation of the aerosol using the MI observations over NEA. Therefore, we developed an algorithm to retrieve aerosol optical depth (AOD) using the visible observation of MI, and named as MI Yonsei Aerosol Retrieval Algorithm (YAER). In this study, we investigated the accuracy of MI YAER AOD by comparing the values with the long-term products of AERONET sun-photometer. The result showed that the MI AODs were significantly overestimated than the AERONET values over bright surface in low AOD case. Because the MI visible channel centered at red color range, contribution of aerosol signal to the measured reflectance is relatively lower than the surface contribution. Therefore, the AOD error in low AOD case over bright surface can be a fundamental limitation of the algorithm. Meanwhile, an assumption of background aerosol optical depth (BAOD) could result in the retrieval uncertainty, also. To estimate the surface reflectance by considering polluted air condition over the NEA, we estimated the BAOD from the MODIS dark target (DT) aerosol products by pixel. The satellite-based AOD retrieval, however, largely depends on the accuracy of the surface reflectance estimation especially in low AOD case, and thus, the BAOD could include the uncertainty in surface reflectance estimation of the satellite-based retrieval. Therefore, we re-estimated the BAOD using the ground-based sun-photometer measurement, and investigated the effects of the BAOD assumption. The satellite-based BAOD was significantly higher than the ground-based value over urban area, and thus, resulted in the underestimation of surface reflectance and the overestimation of AOD. The error analysis of the MI AOD also showed sensitivity to cloud contamination, clearly. Therefore, improvements of cloud masking process in the developed single channel MI algorithm as well as the modification of the surface reflectance estimation will be required for the future study.

Retrieval of Aerosol Microphysical Properties from AERONET Photo-Polarimetric Measurements. 2: A New Research Algorithm and Case Demonstration

NASA Technical Reports Server (NTRS)

Xu, Xiaoguang; Wang, Jun; Zeng, Jing; Spurr, Robert; Liu, Xiong; Dubovik, Oleg; Li, Li; Li, Zhengqiang; Mishchenko, Michael I.; Siniuk, Aliaksandr;

Characterization and error analysis of an operational retrieval algorithm for estimating column ozone and aerosol properties from ground-based ultra-violet irradiance measurements

NASA Astrophysics Data System (ADS)

Taylor, Thomas E.; L'Ecuyer, Tristan; Slusser, James; Stephens, Graeme; Krotkov, Nick; Davis, John; Goering, Christian

2005-08-01

Extensive sensitivity and error characteristics of a recently developed optimal estimation retrieval algorithm which simultaneously determines aerosol optical depth (AOD), aerosol single scatter albedo (SSA) and total ozone column (TOC) from ultra-violet irradiances are described. The algorithm inverts measured diffuse and direct irradiances at 7 channels in the UV spectral range obtained from the United States Department of Agriculture's (USDA) UV-B Monitoring and Research Program's (UVMRP) network of 33 ground-based UV-MFRSR instruments to produce aerosol optical properties and TOC at all seven wavelengths. Sensitivity studies of the Tropospheric Ultra-violet/Visible (TUV) radiative transfer model performed for various operating modes (Delta-Eddington versus n-stream Discrete Ordinate) over domains of AOD, SSA, TOC, asymmetry parameter and surface albedo show that the solutions are well constrained. Realistic input error budgets and diagnostic and error outputs from the retrieval are analyzed to demonstrate the atmospheric conditions under which the retrieval provides useful and significant results. After optimizing the algorithm for the USDA site in Panther Junction, Texas the retrieval algorithm was run on a cloud screened set of irradiance measurements for the month of May 2003. Comparisons to independently derived AOD's are favorable with root mean square (RMS) differences of about 3% to 7% at 300nm and less than 1% at 368nm, on May 12 and 22, 2003. This retrieval method will be used to build an aerosol climatology and provide ground-truthing of satellite measurements by running it operationally on the USDA UV network database.

Iterative-Transform Phase Retrieval Using Adaptive Diversity

NASA Technical Reports Server (NTRS)

Dean, Bruce H.

2007-01-01

A phase-diverse iterative-transform phase-retrieval algorithm enables high spatial-frequency, high-dynamic-range, image-based wavefront sensing. [The terms phase-diverse, phase retrieval, image-based, and wavefront sensing are defined in the first of the two immediately preceding articles, Broadband Phase Retrieval for Image-Based Wavefront Sensing (GSC-14899-1).] As described below, no prior phase-retrieval algorithm has offered both high dynamic range and the capability to recover high spatial-frequency components. Each of the previously developed image-based phase-retrieval techniques can be classified into one of two categories: iterative transform or parametric. Among the modifications of the original iterative-transform approach has been the introduction of a defocus diversity function (also defined in the cited companion article). Modifications of the original parametric approach have included minimizing alternative objective functions as well as implementing a variety of nonlinear optimization methods. The iterative-transform approach offers the advantage of ability to recover low, middle, and high spatial frequencies, but has disadvantage of having a limited dynamic range to one wavelength or less. In contrast, parametric phase retrieval offers the advantage of high dynamic range, but is poorly suited for recovering higher spatial frequency aberrations. The present phase-diverse iterative transform phase-retrieval algorithm offers both the high-spatial-frequency capability of the iterative-transform approach and the high dynamic range of parametric phase-recovery techniques. In implementation, this is a focus-diverse iterative-transform phaseretrieval algorithm that incorporates an adaptive diversity function, which makes it possible to avoid phase unwrapping while preserving high-spatial-frequency recovery. The algorithm includes an inner and an outer loop (see figure). An initial estimate of phase is used to start the algorithm on the inner loop, wherein multiple intensity images are processed, each using a different defocus value. The processing is done by an iterative-transform method, yielding individual phase estimates corresponding to each image of the defocus-diversity data set. These individual phase estimates are combined in a weighted average to form a new phase estimate, which serves as the initial phase estimate for either the next iteration of the iterative-transform method or, if the maximum number of iterations has been reached, for the next several steps, which constitute the outerloop portion of the algorithm. The details of the next several steps must be omitted here for the sake of brevity. The overall effect of these steps is to adaptively update the diversity defocus values according to recovery of global defocus in the phase estimate. Aberration recovery varies with differing amounts as the amount of diversity defocus is updated in each image; thus, feedback is incorporated into the recovery process. This process is iterated until the global defocus error is driven to zero during the recovery process. The amplitude of aberration may far exceed one wavelength after completion of the inner-loop portion of the algorithm, and the classical iterative transform method does not, by itself, enable recovery of multi-wavelength aberrations. Hence, in the absence of a means of off-loading the multi-wavelength portion of the aberration, the algorithm would produce a wrapped phase map. However, a special aberration-fitting procedure can be applied to the wrapped phase data to transfer at least some portion of the multi-wavelength aberration to the diversity function, wherein the data are treated as known phase values. In this way, a multiwavelength aberration can be recovered incrementally by successively applying the aberration-fitting procedure to intermediate wrapped phase maps. During recovery, as more of the aberration is transferred to the diversity function following successive iterations around the ter loop, the estimated phase ceases to wrap in places where the aberration values become incorporated as part of the diversity function. As a result, as the aberration content is transferred to the diversity function, the phase estimate resembles that of a reference flat.

Ice surface temperature retrieval from AVHRR, ATSR, and passive microwave satellite data: Algorithm development and application

NASA Technical Reports Server (NTRS)

Key, Jeff; Maslanik, James; Steffen, Konrad

1995-01-01

During the second phase project year we have made progress in the development and refinement of surface temperature retrieval algorithms and in product generation. More specifically, we have accomplished the following: (1) acquired a new advanced very high resolution radiometer (AVHRR) data set for the Beaufort Sea area spanning an entire year; (2) acquired additional along-track scanning radiometer(ATSR) data for the Arctic and Antarctic now totalling over eight months; (3) refined our AVHRR Arctic and Antarctic ice surface temperature (IST) retrieval algorithm, including work specific to Greenland; (4) developed ATSR retrieval algorithms for the Arctic and Antarctic, including work specific to Greenland; (5) developed cloud masking procedures for both AVHRR and ATSR; (6) generated a two-week bi-polar global area coverage (GAC) set of composite images from which IST is being estimated; (7) investigated the effects of clouds and the atmosphere on passive microwave 'surface' temperature retrieval algorithms; and (8) generated surface temperatures for the Beaufort Sea data set, both from AVHRR and special sensor microwave imager (SSM/I).

Enhanced Deep Blue Aerosol Retrieval Algorithm: The Second Generation

NASA Technical Reports Server (NTRS)

Hsu, N. C.; Jeong, M.-J.; Bettenhausen, C.; Sayer, A. M.; Hansell, R.; Seftor, C. S.; Huang, J.; Tsay, S.-C.

2013-01-01

The aerosol products retrieved using the MODIS collection 5.1 Deep Blue algorithm have provided useful information about aerosol properties over bright-reflecting land surfaces, such as desert, semi-arid, and urban regions. However, many components of the C5.1 retrieval algorithm needed to be improved; for example, the use of a static surface database to estimate surface reflectances. This is particularly important over regions of mixed vegetated and non- vegetated surfaces, which may undergo strong seasonal changes in land cover. In order to address this issue, we develop a hybrid approach, which takes advantage of the combination of pre-calculated surface reflectance database and normalized difference vegetation index in determining the surface reflectance for aerosol retrievals. As a result, the spatial coverage of aerosol data generated by the enhanced Deep Blue algorithm has been extended from the arid and semi-arid regions to the entire land areas.

Phase Retrieval Using a Genetic Algorithm on the Systematic Image-Based Optical Alignment Testbed

NASA Technical Reports Server (NTRS)

Taylor, Jaime R.

2003-01-01

NASA s Marshall Space Flight Center s Systematic Image-Based Optical Alignment (SIBOA) Testbed was developed to test phase retrieval algorithms and hardware techniques. Individuals working with the facility developed the idea of implementing phase retrieval by breaking the determination of the tip/tilt of each mirror apart from the piston motion (or translation) of each mirror. Presented in this report is an algorithm that determines the optimal phase correction associated only with the piston motion of the mirrors. A description of the Phase Retrieval problem is first presented. The Systematic Image-Based Optical Alignment (SIBOA) Testbeb is then described. A Discrete Fourier Transform (DFT) is necessary to transfer the incoming wavefront (or estimate of phase error) into the spatial frequency domain to compare it with the image. A method for reducing the DFT to seven scalar/matrix multiplications is presented. A genetic algorithm is then used to search for the phase error. The results of this new algorithm on a test problem are presented.

Estimating vertical profiles of water-cloud droplet effective radius from SWIR satellite measurements via a statistical model derived from CloudSat observations

NASA Astrophysics Data System (ADS)

Nagao, T. M.; Murakami, H.; Nakajima, T. Y.

2017-12-01

This study proposes an algorithm to estimate vertical profiles of cloud droplet effective radius (CDER-VP) for water clouds from shortwave infrared (SWIR) measurements of Himawari-8/AHI via a statistical model of CDER-VP derived from CloudSat observation. Several similar algorithms in previous studies utilize a spectral radiance matching on the assumption of simultaneous observations of CloudSat and Aqua/MODIS. However, our algorithm does not assume simultaneous observations with CloudSat. First, in advance, a database (DB) of CDER-VP is prepared by the following procedure: TOA radiances at 0.65, 2.3 and 10.4-μm bands of the AHI are simulated using CDER-VP and cloud optical depth vertical profile (COD-VP) contained in the CloudSat 2B-CWC-RVOD and 2B-TAU products. Cloud optical thickness (COT), Column-CDER and cloud top height (CTH) are retrieved from the simulated radiances using a traditional retrieval algorithm with vertically homogeneous cloud model (1-SWIR VHC method). The CDER-VP is added to the DB by using the COT and Column-CDER retrievals as a key of the DB. Then by using principal component (PC) analysis, up to three PC vectors of the CDER-VPs in the DB are extracted. Next, the algorithm retrieves CDER-VP from actual AHI measurements by the following procedure: First, COT, Column-CDER and CTH are retrieved from TOA radiances at 0.65, 2.3 and 10.4-μm bands of the AHI using by 1-SWIR VHC method. Then, the PC vectors of CDER-VP is fetched from the DB using the COT and Column-CDER retrievals as the key of the DB. Finally, using coefficients of the PC vectors of CDER-VP as variables for retrieval, CDER-VP, COT and CTH are retrieved from TOA radiances at 0.65, 1.6, 2.3, 3.9 and 10.4-μm bands of the AHI based on optimal estimation method with iterative radiative transfer calculation. The simulation result showed the CDER-VP retrieval errors were almost smaller than 3 - 4 μm. The CDER retrieval errors at the cloud base were almost larger than the others (e.g. CDER at cloud top), especially when COT and CDER was large. The tendency can be explained by less sensitivities of SWIRs to CDER at cloud base. Additionally, as a case study, this study will attempt to apply the algorithm to the AHI's high-frequency observations, and to interpret the time series of the CDER-VP retrievals in terms of temporal evolution of water clouds.

Cloud retrievals from satellite data using optimal estimation: evaluation and application to ATSR

NASA Astrophysics Data System (ADS)

Poulsen, C. A.; Siddans, R.; Thomas, G. E.; Sayer, A. M.; Grainger, R. G.; Campmany, E.; Dean, S. M.; Arnold, C.; Watts, P. D.

2012-08-01

Clouds play an important role in balancing the Earth's radiation budget. Hence, it is vital that cloud climatologies are produced that quantify cloud macro and micro physical parameters and the associated uncertainty. In this paper, we present an algorithm ORAC (Oxford-RAL retrieval of Aerosol and Cloud) which is based on fitting a physically consistent cloud model to satellite observations simultaneously from the visible to the mid-infrared, thereby ensuring that the resulting cloud properties provide both a good representation of the short-wave and long-wave radiative effects of the observed cloud. The advantages of the optimal estimation method are that it enables rigorous error propagation and the inclusion of all measurements and any a priori information and associated errors in a rigorous mathematical framework. The algorithm provides a measure of the consistency between retrieval representation of cloud and satellite radiances. The cloud parameters retrieved are the cloud top pressure, cloud optical depth, cloud effective radius, cloud fraction and cloud phase. The algorithm can be applied to most visible/infrared satellite instruments. In this paper, we demonstrate the applicability to the Along-Track Scanning Radiometers ATSR-2 and AATSR. Examples of applying the algorithm to ATSR-2 flight data are presented and the sensitivity of the retrievals assessed, in particular the algorithm is evaluated for a number of simulated single-layer and multi-layer conditions. The algorithm was found to perform well for single-layer cloud except when the cloud was very thin; i.e., less than 1 optical depths. For the multi-layer cloud, the algorithm was robust except when the upper ice cloud layer is less than five optical depths. In these cases the retrieved cloud top pressure and cloud effective radius become a weighted average of the 2 layers. The sum of optical depth of multi-layer cloud is retrieved well until the cloud becomes thick, greater than 50 optical depths, where the cloud begins to saturate. The cost proved a good indicator of multi-layer scenarios. Both the retrieval cost and the error need to be considered together in order to evaluate the quality of the retrieval. This algorithm in the configuration described here has been applied to both ATSR-2 and AATSR visible and infrared measurements in the context of the GRAPE (Global Retrieval and cloud Product Evaluation) project to produce a 14 yr consistent record for climate research.

Improved Soundings and Error Estimates using AIRS/AMSU Data

NASA Technical Reports Server (NTRS)

Susskind, Joel

2006-01-01

AIRS was launched on EOS Aqua on May 4, 2002, together with AMSU A and HSB, to form a next generation polar orbiting infrared and microwave atmospheric sounding system. The primary products of AIRS/AMSU are twice daily global fields of atmospheric temperature-humidity profiles, ozone profiles, sea/land surface skin temperature, and cloud related parameters including OLR. The sounding goals of AIRS are to produce 1 km tropospheric layer mean temperatures with an rms error of 1 K, and layer precipitable water with an rms error of 20 percent, in cases with up to 80 percent effective cloud cover. The basic theory used to analyze AIRS/AMSU/HSB data in the presence of clouds, called the at-launch algorithm, and a post-launch algorithm which differed only in the minor details from the at-launch algorithm, have been described previously. The post-launch algorithm, referred to as AIRS Version 4.0, has been used by the Goddard DAAC to analyze and distribute AIRS retrieval products. In this paper we show progress made toward the AIRS Version 5.0 algorithm which will be used by the Goddard DAAC starting late in 2006. A new methodology has been developed to provide accurate case by case error estimates for retrieved geophysical parameters and for the channel by channel cloud cleared radiances used to derive the geophysical parameters from the AIRS/AMSU observations. These error estimates are in turn used for quality control of the derived geophysical parameters and clear column radiances. Improvements made to the retrieval algorithm since Version 4.0 are described as well as results comparing Version 5.0 retrieval accuracy and spatial coverage with those obtained using Version 4.0.

Rainfall Estimates from the TMI and the SSM/I

NASA Technical Reports Server (NTRS)

Hong, Ye; Kummerow, Christian D.; Olson, William S.; Viltard, Nicolas

1999-01-01

The Tropical Rainfall Measuring Mission (TRMM), which is a joint Japan-U.S. Earth observing satellite, has been successfully launched from Japan on November 27, 1997. The main purpose of the TRMM is to measure quantitatively rainfall over the tropics for the research of climate and weather. One of three rainfall measuring instruments abroad the TRMM is the high resolution TRMM Microwave Imager (TMI). The TMI instrument is essentially the copy of the SSM/I with a dual-polarized pair of 10.7 GHz channels added to increase the dynamic range of rainfall estimates. In addition, the 21.3 GHz water vapor absorption channel is designed in the TMI as opposed to the 22.235 GHz in the SSM/I to avoid saturation in the tropics. This paper will present instantaneous rain rates estimated from the coincident TMI and SSM/I observations. The algorithm for estimating instantaneous rainfall rates from both sensors is the Goddard Profiling algorithm (Gprof). The Gprof algorithm is a physically based, multichannel rainfall retrieval algorithm, The algorithm is very portable and can be used for various sensors with different channels and resolutions. The comparison of rain rates estimated from TMI and SSM/I on the same rain regions will be performed. The results from the comparison and the insight of tile retrieval algorithm will be given.

Genetic Algorithm Phase Retrieval for the Systematic Image-Based Optical Alignment Testbed

NASA Technical Reports Server (NTRS)

Taylor, Jaime; Rakoczy, John; Steincamp, James

2003-01-01

Phase retrieval requires calculation of the real-valued phase of the pupil fimction from the image intensity distribution and characteristics of an optical system. Genetic 'algorithms were used to solve two one-dimensional phase retrieval problem. A GA successfully estimated the coefficients of a polynomial expansion of the phase when the number of coefficients was correctly specified. A GA also successfully estimated the multiple p h e s of a segmented optical system analogous to the seven-mirror Systematic Image-Based Optical Alignment (SIBOA) testbed located at NASA s Marshall Space Flight Center. The SIBOA testbed was developed to investigate phase retrieval techniques. Tiphilt and piston motions of the mirrors accomplish phase corrections. A constant phase over each mirror can be achieved by an independent tip/tilt correction: the phase Conection term can then be factored out of the Discrete Fourier Tranform (DFT), greatly reducing computations.

Phase retrieval based wavefront sensing experimental implementation and wavefront sensing accuracy calibration

NASA Astrophysics Data System (ADS)

Mao, Heng; Wang, Xiao; Zhao, Dazun

2009-05-01

As a wavefront sensing (WFS) tool, Baseline algorithm, which is classified as the iterative-transform algorithm of phase retrieval, estimates the phase distribution at pupil from some known PSFs at defocus planes. By using multiple phase diversities and appropriate phase unwrapping methods, this algorithm can accomplish reliable unique solution and high dynamic phase measurement. In the paper, a Baseline algorithm based wavefront sensing experiment with modification of phase unwrapping has been implemented, and corresponding Graphical User Interfaces (GUI) software has also been given. The adaptability and repeatability of Baseline algorithm have been validated in experiments. Moreover, referring to the ZYGO interferometric results, the WFS accuracy of this algorithm has been exactly calibrated.

Evaluation of Skin Temperatures Retrieved from GOES-8

NASA Technical Reports Server (NTRS)

Suggs, Ronnie, J.; Jedlovec, G. J.; Lapenta, W. M.; Haines, S. L.

2000-01-01

Skin temperatures derived from geostationary satellites have the potential of providing the temporal and spatial resolution needed for model assimilation. To adequately assess the potential improvements in numerical model forecasts that can be made by assimilating satellite data, an estimate of the accuracy of the skin temperature product is necessary. A particular skin temperature algorithm, the Physical Split Window Technique, that uses the longwave infrared channels of the GOES Imager has shown promise in recent model assimilation studies to provide land surface temperatures with reasonable accuracy. A comparison of retrieved GOES-8 skin temperatures from this algorithm with in situ measurements is presented. Various retrieval algorithm issues are addressed including surface emissivity

Development and application of a probability distribution retrieval scheme to the remote sensing of clouds and precipitation

NASA Astrophysics Data System (ADS)

McKague, Darren Shawn

2001-12-01

The statistical properties of clouds and precipitation on a global scale are important to our understanding of climate. Inversion methods exist to retrieve the needed cloud and precipitation properties from satellite data pixel-by-pixel that can then be summarized over large data sets to obtain the desired statistics. These methods can be quite computationally expensive, and typically don't provide errors on the statistics. A new method is developed to directly retrieve probability distributions of parameters from the distribution of measured radiances. The method also provides estimates of the errors on the retrieved distributions. The method can retrieve joint distributions of parameters that allows for the study of the connection between parameters. A forward radiative transfer model creates a mapping from retrieval parameter space to radiance space. A Monte Carlo procedure uses the mapping to transform probability density from the observed radiance histogram to a two- dimensional retrieval property probability distribution function (PDF). An estimate of the uncertainty in the retrieved PDF is calculated from random realizations of the radiance to retrieval parameter PDF transformation given the uncertainty of the observed radiances, the radiance PDF, the forward radiative transfer, the finite number of prior state vectors, and the non-unique mapping to retrieval parameter space. The retrieval method is also applied to the remote sensing of precipitation from SSM/I microwave data. A method of stochastically generating hydrometeor fields based on the fields from a numerical cloud model is used to create the precipitation parameter radiance space transformation. The impact of vertical and horizontal variability within the hydrometeor fields has a significant impact on algorithm performance. Beamfilling factors are computed from the simulated hydrometeor fields. The beamfilling factors vary quite a bit depending upon the horizontal structure of the rain. The algorithm is applied to SSM/I images from the eastern tropical Pacific and is compared to PDFs of rain rate computed using pixel-by-pixel retrievals from Wilheit and from Liu and Curry. Differences exist between the three methods, but good general agreement is seen between the PDF retrieval algorithm and the algorithm of Liu and Curry. (Abstract shortened by UMI.)

Improved Temperature Sounding and Quality Control Methodology Using AIRS/AMSU Data: The AIRS Science Team Version 5 Retrieval Algorithm

NASA Technical Reports Server (NTRS)

Susskind, Joel; Blaisdell, John M.; Iredell, Lena; Keita, Fricky

2009-01-01

This paper describes the AIRS Science Team Version 5 retrieval algorithm in terms of its three most significant improvements over the methodology used in the AIRS Science Team Version 4 retrieval algorithm. Improved physics in Version 5 allows for use of AIRS clear column radiances in the entire 4.3 micron CO2 absorption band in the retrieval of temperature profiles T(p) during both day and night. Tropospheric sounding 15 micron CO2 observations are now used primarily in the generation of clear column radiances .R(sub i) for all channels. This new approach allows for the generation of more accurate values of .R(sub i) and T(p) under most cloud conditions. Secondly, Version 5 contains a new methodology to provide accurate case-by-case error estimates for retrieved geophysical parameters and for channel-by-channel clear column radiances. Thresholds of these error estimates are used in a new approach for Quality Control. Finally, Version 5 also contains for the first time an approach to provide AIRS soundings in partially cloudy conditions that does not require use of any microwave data. This new AIRS Only sounding methodology, referred to as AIRS Version 5 AO, was developed as a backup to AIRS Version 5 should the AMSU-A instrument fail. Results are shown comparing the relative performance of the AIRS Version 4, Version 5, and Version 5 AO for the single day, January 25, 2003. The Goddard DISC is now generating and distributing products derived using the AIRS Science Team Version 5 retrieval algorithm. This paper also described the Quality Control flags contained in the DISC AIRS/AMSU retrieval products and their intended use for scientific research purposes.

Preparations for Global Precipitation Measurement(GPM)Ground Validation

NASA Technical Reports Server (NTRS)

Bidwell, S. W.; Bibyk, I. K.; Duming, J. F.; Everett, D. F.; Smith, E. A.; Wolff, D. B.

2004-01-01

The Global Precipitation Measurement (GPM) program is an international partnership led by the National Aeronautics and Space Administration (NASA) and the Japan Aerospace Exploration Agency (JAXA). GPM will improve climate, weather, and hydro-meterorological forecasts through more frequent and more accurate measurement of precipitation across the globe. This paper describes the concept and the preparations for Ground Validation within the GPM program. Ground Validation (GV) plays a critical role in the program by investigating and quantitatively assessing the errors within the satellite retrievals. These quantitative estimates of retrieval errors will assist the scientific community by bounding the errors within their research products. The two fundamental requirements of the GPM Ground Validation program are: (1) error characterization of the precipitation retrievals and (2) continual improvement of the satellite retrieval algorithms. These two driving requirements determine the measurements, instrumentation, and location for ground observations. This paper describes GV plans for estimating the systematic and random components of retrieval error and for characterizing the spatial and temporal structure of the error. This paper describes the GPM program for algorithm improvement in which error models are developed and experimentally explored to uncover the physical causes of errors within the retrievals. GPM will ensure that information gained through Ground Validation is applied to future improvements in the spaceborne retrieval algorithms. This paper discusses the potential locations for validation measurement and research, the anticipated contributions of GPM's international partners, and the interaction of Ground Validation with other GPM program elements.

An enhanced TIMESAT algorithm for estimating vegetation phenology metrics from MODIS data

USGS Publications Warehouse

Tan, B.; Morisette, J.T.; Wolfe, R.E.; Gao, F.; Ederer, G.A.; Nightingale, J.; Pedelty, J.A.

2011-01-01

An enhanced TIMESAT algorithm was developed for retrieving vegetation phenology metrics from 250 m and 500 m spatial resolution Moderate Resolution Imaging Spectroradiometer (MODIS) vegetation indexes (VI) over North America. MODIS VI data were pre-processed using snow-cover and land surface temperature data, and temporally smoothed with the enhanced TIMESAT algorithm. An objective third derivative test was applied to define key phenology dates and retrieve a set of phenology metrics. This algorithm has been applied to two MODIS VIs: Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI). In this paper, we describe the algorithm and use EVI as an example to compare three sets of TIMESAT algorithm/MODIS VI combinations: a) original TIMESAT algorithm with original MODIS VI, b) original TIMESAT algorithm with pre-processed MODIS VI, and c) enhanced TIMESAT and pre-processed MODIS VI. All retrievals were compared with ground phenology observations, some made available through the National Phenology Network. Our results show that for MODIS data in middle to high latitude regions, snow and land surface temperature information is critical in retrieving phenology metrics from satellite observations. The results also show that the enhanced TIMESAT algorithm can better accommodate growing season start and end dates that vary significantly from year to year. The TIMESAT algorithm improvements contribute to more spatial coverage and more accurate retrievals of the phenology metrics. Among three sets of TIMESAT/MODIS VI combinations, the start of the growing season metric predicted by the enhanced TIMESAT algorithm using pre-processed MODIS VIs has the best associations with ground observed vegetation greenup dates. ?? 2010 IEEE.

An Enhanced TIMESAT Algorithm for Estimating Vegetation Phenology Metrics from MODIS Data

NASA Technical Reports Server (NTRS)

Tan, Bin; Morisette, Jeffrey T.; Wolfe, Robert E.; Gao, Feng; Ederer, Gregory A.; Nightingale, Joanne; Pedelty, Jeffrey A.

2012-01-01

An enhanced TIMESAT algorithm was developed for retrieving vegetation phenology metrics from 250 m and 500 m spatial resolution Moderate Resolution Imaging Spectroradiometer (MODIS) vegetation indexes (VI) over North America. MODIS VI data were pre-processed using snow-cover and land surface temperature data, and temporally smoothed with the enhanced TIMESAT algorithm. An objective third derivative test was applied to define key phenology dates and retrieve a set of phenology metrics. This algorithm has been applied to two MODIS VIs: Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI). In this paper, we describe the algorithm and use EVI as an example to compare three sets of TIMESAT algorithm/MODIS VI combinations: a) original TIMESAT algorithm with original MODIS VI, b) original TIMESAT algorithm with pre-processed MODIS VI, and c) enhanced TIMESAT and pre-processed MODIS VI. All retrievals were compared with ground phenology observations, some made available through the National Phenology Network. Our results show that for MODIS data in middle to high latitude regions, snow and land surface temperature information is critical in retrieving phenology metrics from satellite observations. The results also show that the enhanced TIMESAT algorithm can better accommodate growing season start and end dates that vary significantly from year to year. The TIMESAT algorithm improvements contribute to more spatial coverage and more accurate retrievals of the phenology metrics. Among three sets of TIMESAT/MODIS VI combinations, the start of the growing season metric predicted by the enhanced TIMESAT algorithm using pre-processed MODIS VIs has the best associations with ground observed vegetation greenup dates.

EM Bias-Correction for Ice Thickness and Surface Roughness Retrievals over Rough Deformed Sea Ice

NASA Astrophysics Data System (ADS)

Li, L.; Gaiser, P. W.; Allard, R.; Posey, P. G.; Hebert, D. A.; Richter-Menge, J.; Polashenski, C. M.

2016-12-01

The very rough ridge sea ice accounts for significant percentage of total ice areas and even larger percentage of total volume. The commonly used Radar altimeter surface detection techniques are empirical in nature and work well only over level/smooth sea ice. Rough sea ice surfaces can modify the return waveforms, resulting in significant Electromagnetic (EM) bias in the estimated surface elevations, and thus large errors in the ice thickness retrievals. To understand and quantify such sea ice surface roughness effects, a combined EM rough surface and volume scattering model was developed to simulate radar returns from the rough sea ice `layer cake' structure. A waveform matching technique was also developed to fit observed waveforms to a physically-based waveform model and subsequently correct the roughness induced EM bias in the estimated freeboard. This new EM Bias Corrected (EMBC) algorithm was able to better retrieve surface elevations and estimate the surface roughness parameter simultaneously. In situ data from multi-instrument airborne and ground campaigns were used to validate the ice thickness and surface roughness retrievals. For the surface roughness retrievals, we applied this EMBC algorithm to co-incident LiDAR/Radar measurements collected during a Cryosat-2 under-flight by the NASA IceBridge missions. Results show that not only does the waveform model fit very well to the measured radar waveform, but also the roughness parameters derived independently from the LiDAR and radar data agree very well for both level and deformed sea ice. For sea ice thickness retrievals, validation based on in-situ data from the coordinated CRREL/NRL field campaign demonstrates that the physically-based EMBC algorithm performs fundamentally better than the empirical algorithm over very rough deformed sea ice, suggesting that sea ice surface roughness effects can be modeled and corrected based solely on the radar return waveforms.

Performance of the Falling Snow Retrieval Algorithms for the Global Precipitation Measurement (GPM) Mission

NASA Technical Reports Server (NTRS)

Skofronick-Jackson, Gail; Munchak, Stephen J.; Ringerud, Sarah

2016-01-01

Retrievals of falling snow from space represent an important data set for understanding the Earth's atmospheric, hydrological, and energy cycles, especially during climate change. Estimates of falling snow must be captured to obtain the true global precipitation water cycle, snowfall accumulations are required for hydrological studies, and without knowledge of the frozen particles in clouds one cannot adequately understand the energy and radiation budgets. While satellite-based remote sensing provides global coverage of falling snow events, the science is relatively new and retrievals are still undergoing development with challenges remaining). This work reports on the development and testing of retrieval algorithms for the Global Precipitation Measurement (GPM) mission Core Satellite, launched February 2014.

Bias adjustment of infrared-based rainfall estimation using Passive Microwave satellite rainfall data

NASA Astrophysics Data System (ADS)

Karbalaee, Negar; Hsu, Kuolin; Sorooshian, Soroosh; Braithwaite, Dan

2017-04-01

This study explores using Passive Microwave (PMW) rainfall estimation for spatial and temporal adjustment of Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Cloud Classification System (PERSIANN-CCS). The PERSIANN-CCS algorithm collects information from infrared images to estimate rainfall. PERSIANN-CCS is one of the algorithms used in the Integrated Multisatellite Retrievals for GPM (Global Precipitation Mission) estimation for the time period PMW rainfall estimations are limited or not available. Continued improvement of PERSIANN-CCS will support Integrated Multisatellite Retrievals for GPM for current as well as retrospective estimations of global precipitation. This study takes advantage of the high spatial and temporal resolution of GEO-based PERSIANN-CCS estimation and the more effective, but lower sample frequency, PMW estimation. The Probability Matching Method (PMM) was used to adjust the rainfall distribution of GEO-based PERSIANN-CCS toward that of PMW rainfall estimation. The results show that a significant improvement of global PERSIANN-CCS rainfall estimation is obtained.

Extended capture range for focus-diverse phase retrieval in segmented aperture systems using geometrical optics.

PubMed

Jurling, Alden S; Fienup, James R

2014-03-01

Extending previous work by Thurman on wavefront sensing for segmented-aperture systems, we developed an algorithm for estimating segment tips and tilts from multiple point spread functions in different defocused planes. We also developed methods for overcoming two common modes for stagnation in nonlinear optimization-based phase retrieval algorithms for segmented systems. We showed that when used together, these methods largely solve the capture range problem in focus-diverse phase retrieval for segmented systems with large tips and tilts. Monte Carlo simulations produced a rate of success better than 98% for the combined approach.

Retrieval of Ice Cloud Properties Using an Optimal Estimation Algorithm and MODIS Infrared Observations: 2. Retrieval Evaluation

NASA Technical Reports Server (NTRS)

Wang, Chenxi; Platnick, Steven; Zhang, Zhibo; Meyer, Kerry; Wind, Galina; Yang, Ping

2016-01-01

An infrared-based optimal estimation (OE-IR) algorithm for retrieving ice cloud properties is evaluated. Specifically, the implementation of the algorithm with MODerate resolution Imaging Spectroradiometer (MODIS) observations is assessed in comparison with the operational retrieval products from MODIS on the Aqua satellite (MYD06), Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP), and the Imaging Infrared Radiometer (IIR); the latter two instruments fly on the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) satellite in the Afternoon Constellation (A-Train) with Aqua. The results show that OE-IR cloud optical thickness (tau) and effective radius (r(sub eff)) retrievals perform best for ice clouds having 0.5 < tau< 7 and r(sub eff) < 50microns. For global ice clouds, the averaged retrieval uncertainties of tau and r(sub eff) are 19% and 33%, respectively. For optically thick ice clouds with tau larger than 10, however, the tau and r(sub eff) retrieval uncertainties can exceed 30% and 50%, respectively. For ice cloud top height (h), the averaged global uncertainty is 0.48km. Relatively large h uncertainty (e.g., > 1km) occurs for tau < 0.5. Analysis of 1month of the OE-IR retrievals shows large tau and r(sub eff) uncertainties in storm track regions and the southern oceans where convective clouds are frequently observed, as well as in high-latitude regions where temperature differences between the surface and cloud top are more ambiguous. Generally, comparisons between the OE-IR and the operational products show consistent tau and h retrievals. However, obvious differences between the OE-IR and the MODIS Collection 6 r(sub eff) are found.

Verification, improvement and application of aerosol optical depths in China Part 1: Inter-comparison of NPP-VIIRS and Aqua-MODIS

NASA Astrophysics Data System (ADS)

Wei, Jing; Sun, Lin; Huang, Bo; Bilal, Muhammad; Zhang, Zhaoyang; Wang, Lunche

2018-02-01

The objective of this study is to evaluate typical aerosol optical depth (AOD) products in China, which experienced seriously increasing atmospheric particulate pollution. For this, the Aqua-MODerate resolution Imaging Spectroradiometer (MODIS) AOD products (MYD04) at 10 km spatial resolution and Visible Infrared Imaging Radiometer Suite (VIIRS) Environmental Data Record (EDR) AOD product at 6 km resolution for different Quality Flags (QF) are obtained for validation against AErosol RObotic NETwork (AERONET) AOD measurements during 2013-2016. Results show that VIIRS EDR similarly Dark Target (DT) and MODIS DT algorithms perform worse with only 45.36% and 45.59% of the retrievals (QF = 3) falling within the Expected Error (EE, ±(0.05 + 15%)) compared to the Deep Blue (DB) algorithm (69.25%, QF ≥ 2). The DT retrievals perform poorly over the Beijing-Tianjin-Hebei (BTH) and Yangtze-River-Delta (YRD) regions, which significantly overestimate the AOD observations, but the performance is better over the Pearl-River-Delta (PRD) region than DB retrievals, which seriously under-estimate the AOD loadings. It is not surprising that the DT algorithm performs better over vegetated areas, while the DB algorithm performs better over bright areas mainly depends on the accuracy of surface reflectance estimation over different land use types. In general, the sensitivity of aerosol to apparent reflectance reduces by about 34% with an increasing surface reflectance by 0.01. Moreover, VIIRS EDR and MODIS DT algorithms perform overall better in the winter as 64.53% and 72.22% of the retrievals are within the EE but with less retrievals. However, the DB algorithm performs worst (57.17%) in summer mainly affected by the vegetation growth but there are overall high accuracies with more than 62% of the collections falling within the EE in other three seasons. Results suggest that the quality assurance process can help improve the overall data quality for MYD04 DB retrievals, but it is not always true for VIIRS EDR and MYD04 DT AOD retrievals.

GOSAT CO2 retrieval results using TANSO-CAI aerosol information over East Asia

NASA Astrophysics Data System (ADS)

KIM, M.; Kim, W.; Jung, Y.; Lee, S.; Kim, J.; Lee, H.; Boesch, H.; Goo, T. Y.

2015-12-01

In the satellite remote sensing of CO2, incorrect aerosol information could induce large errors as previous studies suggested. Many factors, such as, aerosol type, wavelength dependency of AOD, aerosol polarization effect and etc. have been main error sources. Due to these aerosol effects, large number of data retrieved are screened out in quality control, or retrieval errors tend to increase if not screened out, especially in East Asia where aerosol concentrations are fairly high. To reduce these aerosol induced errors, a CO2 retrieval algorithm using the simultaneous TANSO-CAI aerosol information is developed. This algorithm adopts AOD and aerosol type information as a priori information from the CAI aerosol retrieval algorithm. The CO2 retrieval algorithm based on optimal estimation method and VLIDORT, a vector discrete ordinate radiative transfer model. The CO2 algorithm, developed with various state vectors to find accurate CO2 concentration, shows reasonable results when compared with other dataset. This study concentrates on the validation of retrieved results with the ground-based TCCON measurements in East Asia and the comparison with the previous retrieval from ACOS, NIES, and UoL. Although, the retrieved CO2 concentration is lower than previous results by ppm's, it shows similar trend and high correlation with previous results. Retrieved data and TCCON measurements data are compared at three stations of Tsukuba, Saga, Anmyeondo in East Asia, with the collocation criteria of ±2°in latitude/longitude and ±1 hours of GOSAT passing time. Compared results also show similar trend with good correlation. Based on the TCCON comparison results, bias correction equation is calculated and applied to the East Asia data.

Direct retrieval of ocean surface evaporation and latent heat flux from the spacebased observations

NASA Technical Reports Server (NTRS)

Liu, W. T.; Tang, W.

2000-01-01

The Tropical Rain Measuring Mission (TRMM) provides the opportunity to improve the spacebased estimation of evaporation. An algorithm for retrieving evaporation directly from the radiances observed by the TRMM Microwave Imager and its validation results are described.

Validation and Uncertainty Estimates for MODIS Collection 6 "Deep Blue" Aerosol Data

NASA Technical Reports Server (NTRS)

Sayer, A. M.; Hsu, N. C.; Bettenhausen, C.; Jeong, M.-J.

2013-01-01

The "Deep Blue" aerosol optical depth (AOD) retrieval algorithm was introduced in Collection 5 of the Moderate Resolution Imaging Spectroradiometer (MODIS) product suite, and complemented the existing "Dark Target" land and ocean algorithms by retrieving AOD over bright arid land surfaces, such as deserts. The forthcoming Collection 6 of MODIS products will include a "second generation" Deep Blue algorithm, expanding coverage to all cloud-free and snow-free land surfaces. The Deep Blue dataset will also provide an estimate of the absolute uncertainty on AOD at 550 nm for each retrieval. This study describes the validation of Deep Blue Collection 6 AOD at 550 nm (Tau(sub M)) from MODIS Aqua against Aerosol Robotic Network (AERONET) data from 60 sites to quantify these uncertainties. The highest quality (denoted quality assurance flag value 3) data are shown to have an absolute uncertainty of approximately (0.086+0.56Tau(sub M))/AMF, where AMF is the geometric air mass factor. For a typical AMF of 2.8, this is approximately 0.03+0.20Tau(sub M), comparable in quality to other satellite AOD datasets. Regional variability of retrieval performance and comparisons against Collection 5 results are also discussed.

A method to combine spaceborne radar and radiometric observations of precipitation

NASA Astrophysics Data System (ADS)

Munchak, Stephen Joseph

This dissertation describes the development and application of a combined radar-radiometer rainfall retrieval algorithm for the Tropical Rainfall Measuring Mission (TRMM) satellite. A retrieval framework based upon optimal estimation theory is proposed wherein three parameters describing the raindrop size distribution (DSD), ice particle size distribution (PSD), and cloud water path (cLWP) are retrieved for each radar profile. The retrieved rainfall rate is found to be strongly sensitive to the a priori constraints in DSD and cLWP; thus, these parameters are tuned to match polarimetric radar estimates of rainfall near Kwajalein, Republic of Marshall Islands. An independent validation against gauge-tuned radar rainfall estimates at Melbourne, FL shows agreement within 2% which exceeds previous algorithms' ability to match rainfall at these two sites. The algorithm is then applied to two years of TRMM data over oceans to determine the sources of DSD variability. Three correlated sets of variables representing storm dynamics, background environment, and cloud microphysics are found to account for approximately 50% of the variability in the absolute and reflectivity-normalized median drop size. Structures of radar reflectivity are also identified and related to drop size, with these relationships being confirmed by ground-based polarimetric radar data from the North American Monsoon Experiment (NAME). Regional patterns of DSD and the sources of variability identified herein are also shown to be consistent with previous work documenting regional DSD properties. In particular, mid-latitude regions and tropical regions near land tend to have larger drops for a given reflectivity, whereas the smallest drops are found in the eastern Pacific Intertropical Convergence Zone. Due to properties of the DSD and rain water/cloud water partitioning that change with column water vapor, it is shown that increases in water vapor in a global warming scenario could lead to slight (1%) underestimates of a rainfall trends by radar but larger overestimates (5%) by radiometer algorithms. Further analyses are performed to compare tropical oceanic mean rainfall rates between the combined algorithm and other sources. The combined algorithm is 15% higher than the version 6 of the 2A25 radar-only algorithm and 6.6% higher than the Global Precipitation Climatology Project (GPCP) estimate for the same time-space domain. Despite being higher than these two sources, the combined total is not inconsistent with estimates of the other components of the energy budget given their uncertainties.

NASA Team 2 Sea Ice Concentration Algorithm Retrieval Uncertainty

NASA Technical Reports Server (NTRS)

Brucker, Ludovic; Cavalieri, Donald J.; Markus, Thorsten; Ivanoff, Alvaro

2014-01-01

Satellite microwave radiometers are widely used to estimate sea ice cover properties (concentration, extent, and area) through the use of sea ice concentration (IC) algorithms. Rare are the algorithms providing associated IC uncertainty estimates. Algorithm uncertainty estimates are needed to assess accurately global and regional trends in IC (and thus extent and area), and to improve sea ice predictions on seasonal to interannual timescales using data assimilation approaches. This paper presents a method to provide relative IC uncertainty estimates using the enhanced NASA Team (NT2) IC algorithm. The proposed approach takes advantage of the NT2 calculations and solely relies on the brightness temperatures (TBs) used as input. NT2 IC and its associated relative uncertainty are obtained for both the Northern and Southern Hemispheres using the Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E) TB. NT2 IC relative uncertainties estimated on a footprint-by-footprint swath-by-swath basis were averaged daily over each 12.5-km grid cell of the polar stereographic grid. For both hemispheres and throughout the year, the NT2 relative uncertainty is less than 5%. In the Southern Hemisphere, it is low in the interior ice pack, and it increases in the marginal ice zone up to 5%. In the Northern Hemisphere, areas with high uncertainties are also found in the high IC area of the Central Arctic. Retrieval uncertainties are greater in areas corresponding to NT2 ice types associated with deep snow and new ice. Seasonal variations in uncertainty show larger values in summer as a result of melt conditions and greater atmospheric contributions. Our analysis also includes an evaluation of the NT2 algorithm sensitivity to AMSR-E sensor noise. There is a 60% probability that the IC does not change (to within the computed retrieval precision of 1%) due to sensor noise, and the cumulated probability shows that there is a 90% chance that the IC varies by less than +/-3%. We also examined the daily IC variability, which is dominated by sea ice drift and ice formation/melt. Daily IC variability is the highest, year round, in the MIZ (often up to 20%, locally 30%). The temporal and spatial distributions of the retrieval uncertainties and the daily IC variability is expected to be useful for algorithm intercomparisons, climate trend assessments, and possibly IC assimilation in models.

Bayesian Retrieval of Complete Posterior PDFs of Oceanic Rain Rate From Microwave Observations

NASA Technical Reports Server (NTRS)

Chiu, J. Christine; Petty, Grant W.

2005-01-01

This paper presents a new Bayesian algorithm for retrieving surface rain rate from Tropical Rainfall Measurements Mission (TRMM) Microwave Imager (TMI) over the ocean, along with validations against estimates from the TRMM Precipitation Radar (PR). The Bayesian approach offers a rigorous basis for optimally combining multichannel observations with prior knowledge. While other rain rate algorithms have been published that are based at least partly on Bayesian reasoning, this is believed to be the first self-contained algorithm that fully exploits Bayes Theorem to yield not just a single rain rate, but rather a continuous posterior probability distribution of rain rate. To advance our understanding of theoretical benefits of the Bayesian approach, we have conducted sensitivity analyses based on two synthetic datasets for which the true conditional and prior distribution are known. Results demonstrate that even when the prior and conditional likelihoods are specified perfectly, biased retrievals may occur at high rain rates. This bias is not the result of a defect of the Bayesian formalism but rather represents the expected outcome when the physical constraint imposed by the radiometric observations is weak, due to saturation effects. It is also suggested that the choice of the estimators and the prior information are both crucial to the retrieval. In addition, the performance of our Bayesian algorithm is found to be comparable to that of other benchmark algorithms in real-world applications, while having the additional advantage of providing a complete continuous posterior probability distribution of surface rain rate.

Cloud, Aerosol, and Volcanic Ash Retrievals Using ASTR and SLSTR with ORAC

NASA Astrophysics Data System (ADS)

McGarragh, Gregory; Poulsen, Caroline; Povey, Adam; Thomas, Gareth; Christensen, Matt; Sus, Oliver; Schlundt, Cornelia; Stapelberg, Stefan; Stengel, Martin; Grainger, Don

2015-12-01

The Optimal Retrieval of Aerosol and Cloud (ORAC) is a generalized optimal estimation system that retrieves cloud, aerosol and volcanic ash parameters using satellite imager measurements in the visible to infrared. Use of the same algorithm for different sensors and parameters leads to consistency that facilitates inter-comparison and interaction studies. ORAC currently supports ATSR, AVHRR, MODIS and SEVIRI. In this proceeding we discuss the ORAC retrieval algorithm applied to ATSR data including the retrieval methodology, the forward model, uncertainty characterization and discrimination/classification techniques. Application of ORAC to SLSTR data is discussed including the additional features that SLSTR provides relative to the ATSR heritage. The ORAC level 2 and level 3 results are discussed and an application of level 3 results to the study of cloud/aerosol interactions is presented.

[The progress in retrieving land surface temperature based on thermal infrared and microwave remote sensing technologies].

PubMed

Zhang, Jia-Hua; Li, Xin; Yao, Feng-Mei; Li, Xian-Hua

2009-08-01

Land surface temperature (LST) is an important parameter in the study on the exchange of substance and energy between land surface and air for the land surface physics process at regional and global scales. Many applications of satellites remotely sensed data must provide exact and quantificational LST, such as drought, high temperature, forest fire, earthquake, hydrology and the vegetation monitor, and the models of global circulation and regional climate also need LST as input parameter. Therefore, the retrieval of LST using remote sensing technology becomes one of the key tasks in quantificational remote sensing study. Normally, in the spectrum bands, the thermal infrared (TIR, 3-15 microm) and microwave bands (1 mm-1 m) are important for retrieval of the LST. In the present paper, firstly, several methods for estimating the LST on the basis of thermal infrared (TIR) remote sensing were synthetically reviewed, i. e., the LST measured with an ground-base infrared thermometer, the LST retrieval from mono-window algorithm (MWA), single-channel algorithm (SCA), split-window techniques (SWT) and multi-channels algorithm(MCA), single-channel & multi-angle algorithm and multi-channels algorithm & multi-angle algorithm, and retrieval method of land surface component temperature using thermal infrared remotely sensed satellite observation. Secondly, the study status of land surface emissivity (epsilon) was presented. Thirdly, in order to retrieve LST for all weather conditions, microwave remotely sensed data, instead of thermal infrared data, have been developed recently, and the LST retrieval method from passive microwave remotely sensed data was also introduced. Finally, the main merits and shortcomings of different kinds of LST retrieval methods were discussed, respectively.

Evaluation and Application of Satellite-Based Latent Heating Profile Estimation Methods

NASA Technical Reports Server (NTRS)

Olson, William S.; Grecu, Mircea; Yang, Song; Tao, Wei-Kuo

2004-01-01

In recent years, methods for estimating atmospheric latent heating vertical structure from both passive and active microwave remote sensing have matured to the point where quantitative evaluation of these methods is the next logical step. Two approaches for heating algorithm evaluation are proposed: First, application of heating algorithms to synthetic data, based upon cloud-resolving model simulations, can be used to test the internal consistency of heating estimates in the absence of systematic errors in physical assumptions. Second, comparisons of satellite-retrieved vertical heating structures to independent ground-based estimates, such as rawinsonde-derived analyses of heating, provide an additional test. The two approaches are complementary, since systematic errors in heating indicated by the second approach may be confirmed by the first. A passive microwave and combined passive/active microwave heating retrieval algorithm are evaluated using the described approaches. In general, the passive microwave algorithm heating profile estimates are subject to biases due to the limited vertical heating structure information contained in the passive microwave observations. These biases may be partly overcome by including more environment-specific a priori information into the algorithm s database of candidate solution profiles. The combined passive/active microwave algorithm utilizes the much higher-resolution vertical structure information provided by spaceborne radar data to produce less biased estimates; however, the global spatio-temporal sampling by spaceborne radar is limited. In the present study, the passive/active microwave algorithm is used to construct a more physically-consistent and environment-specific set of candidate solution profiles for the passive microwave algorithm and to help evaluate errors in the passive algorithm s heating estimates. Although satellite estimates of latent heating are based upon instantaneous, footprint- scale data, suppression of random errors requires averaging to at least half-degree resolution. Analysis of mesoscale and larger space-time scale phenomena based upon passive and passive/active microwave heating estimates from TRMM, SSMI, and AMSR data will be presented at the conference.

Validation of ERS-1 environmental data products

NASA Technical Reports Server (NTRS)

Goodberlet, Mark A.; Swift, Calvin T.; Wilkerson, John C.

1994-01-01

Evaluation of the launch-version algorithms used by the European Space Agency (ESA) to derive wind field and ocean wave estimates from measurements of sensors aboard the European Remote Sensing satellite, ERS-1, has been accomplished through comparison of the derived parameters with coincident measurements made by 24 open ocean buoys maintained by the National Oceanic and Atmospheric Administration). During the period from November 1, 1991 through February 28, 1992, data bases with 577 and 485 pairs of coincident sensor/buoy wind and wave measurements were collected for the Active Microwave Instrument (AMI) and Radar Altimeter (RA) respectively. Based on these data, algorithm retrieval accuracy is estimated to be plus or minus 4 m/s for AMI wind speed, plus or minus 3 m/s for RA wind speed and plus or minus 0.6 m for RA wave height. After removing 180 degree ambiguity errors, the AMI wind direction retrieval accuracy was estimated at plus or minus 28 degrees. All of the ERS-1 wind and wave retrievals are relatively unbiased. These results should be viewed as interim since improved algorithms are under development. As final versions are implemented, additional assessments should be conducted to complete the validation.

Evaluation of chlorophyll-a retrieval algorithms based on MERIS bands for optically varying eutrophic inland lakes.

PubMed

Lyu, Heng; Li, Xiaojun; Wang, Yannan; Jin, Qi; Cao, Kai; Wang, Qiao; Li, Yunmei

2015-10-15

Fourteen field campaigns were conducted in five inland lakes during different seasons between 2006 and 2013, and a total of 398 water samples with varying optical characteristics were collected. The characteristics were analyzed based on remote sensing reflectance, and an automatic cluster two-step method was applied for water classification. The inland waters could be clustered into three types, which we labeled water types I, II and III. From water types I to III, the effect of the phytoplankton on the optical characteristics gradually decreased. Four chlorophyll-a retrieval algorithms for Case II water, a two-band, three-band, four-band and SCI (Synthetic Chlorophyll Index) algorithm were evaluated for three water types based on the MERIS bands. Different MERIS bands were used for the three water types in each of the four algorithms. The four algorithms had different levels of retrieval accuracy for each water type, and no single algorithm could be successfully applied to all water types. For water types I and III, the three-band algorithm performed the best, while the four-band algorithm had the highest retrieval accuracy for water type II. However, the three-band algorithm is preferable to the two-band algorithm for turbid eutrophic inland waters. The SCI algorithm is recommended for highly turbid water with a higher concentration of total suspended solids. Our research indicates that the chlorophyll-a concentration retrieval by remote sensing for optically contrasted inland water requires a specific algorithm that is based on the optical characteristics of inland water bodies to obtain higher estimation accuracy. Copyright © 2015 Elsevier B.V. All rights reserved.

Improved Atmospheric Soundings and Error Estimates from Analysis of AIRS/AMSU Data

NASA Technical Reports Server (NTRS)

Susskind, Joel

2007-01-01

The AIRS Science Team Version 5.0 retrieval algorithm became operational at the Goddard DAAC in July 2007 generating near real-time products from analysis of AIRS/AMSU sounding data. This algorithm contains many significant theoretical advances over the AIRS Science Team Version 4.0 retrieval algorithm used previously. Three very significant developments of Version 5 are: 1) the development and implementation of an improved Radiative Transfer Algorithm (RTA) which allows for accurate treatment of non-Local Thermodynamic Equilibrium (non-LTE) effects on shortwave sounding channels; 2) the development of methodology to obtain very accurate case by case product error estimates which are in turn used for quality control; and 3) development of an accurate AIRS only cloud clearing and retrieval system. These theoretical improvements taken together enabled a new methodology to be developed which further improves soundings in partially cloudy conditions, without the need for microwave observations in the cloud clearing step as has been done previously. In this methodology, longwave C02 channel observations in the spectral region 700 cm-' to 750 cm-' are used exclusively for cloud clearing purposes, while shortwave C02 channels in the spectral region 2195 cm-' to 2395 cm-' are used for temperature sounding purposes. The new methodology for improved error estimates and their use in quality control is described briefly and results are shown indicative of their accuracy. Results are also shown of forecast impact experiments assimilating AIRS Version 5.0 retrieval products in the Goddard GEOS 5 Data Assimilation System using different quality control thresholds.

A 1DVAR-based snowfall rate retrieval algorithm for passive microwave radiometers

NASA Astrophysics Data System (ADS)

Meng, Huan; Dong, Jun; Ferraro, Ralph; Yan, Banghua; Zhao, Limin; Kongoli, Cezar; Wang, Nai-Yu; Zavodsky, Bradley

2017-06-01

Snowfall rate retrieval from spaceborne passive microwave (PMW) radiometers has gained momentum in recent years. PMW can be so utilized because of its ability to sense in-cloud precipitation. A physically based, overland snowfall rate (SFR) algorithm has been developed using measurements from the Advanced Microwave Sounding Unit-A/Microwave Humidity Sounder sensor pair and the Advanced Technology Microwave Sounder. Currently, these instruments are aboard five polar-orbiting satellites, namely, NOAA-18, NOAA-19, Metop-A, Metop-B, and Suomi-NPP. The SFR algorithm relies on a separate snowfall detection algorithm that is composed of a satellite-based statistical model and a set of numerical weather prediction model-based filters. There are four components in the SFR algorithm itself: cloud properties retrieval, computation of ice particle terminal velocity, ice water content adjustment, and the determination of snowfall rate. The retrieval of cloud properties is the foundation of the algorithm and is accomplished using a one-dimensional variational (1DVAR) model. An existing model is adopted to derive ice particle terminal velocity. Since no measurement of cloud ice distribution is available when SFR is retrieved in near real time, such distribution is implicitly assumed by deriving an empirical function that adjusts retrieved SFR toward radar snowfall estimates. Finally, SFR is determined numerically from a complex integral. The algorithm has been validated against both radar and ground observations of snowfall events from the contiguous United States with satisfactory results. Currently, the SFR product is operationally generated at the National Oceanic and Atmospheric Administration and can be obtained from that organization.

Surface Soil Moisture Retrieval Using SSM/I and Its Comparison with ESTAR: A Case Study Over a Grassland Region

NASA Technical Reports Server (NTRS)

Jackson, T.; Hsu, A. Y.; ONeill, P. E.

1999-01-01

This study extends a previous investigation on estimating surface soil moisture using the Special Sensor Microwave/Imager (SSM/I) over a grassland region. Although SSM/I is not optimal for soil moisture retrieval, it can under some conditions provide information. Rigorous analyses over land have been difficult due to the lack of good validation data sets. A scientific objective of the Southern Great Plains 1997 (SGP97) Hydrology Experiment was to investigate whether the retrieval algorithms for surface soil moisture developed at higher spatial resolution using truck-and aircraft-based passive microwave sensors can be extended to the coarser resolutions expected from satellite platform. With the data collected for the SGP97, the objective of this study is to compare the surface soil moisture estimated from the SSM/I data with those retrieved from the L-band Electronically Scanned Thinned Array Radiometer (ESTAR) data, the core sensor for the experiment, using the same retrieval algorithm. The results indicated that an error of estimate of 7.81% could be achieved with SSM/I data as contrasted to 2.82% with ESTAR data over three intensive sampling areas of different vegetation regimes. It confirms the results of previous study that SSM/I data can be used to retrieve surface soil moisture information at a regional scale under certain conditions.

Development and comparisons of wind retrieval algorithms for small unmanned aerial systems

NASA Astrophysics Data System (ADS)

Bonin, T. A.; Chilson, P. B.; Zielke, B. S.; Klein, P. M.; Leeman, J. R.

2012-12-01

Recently, there has been an increase in use of Unmanned Aerial Systems (UASs) as platforms for conducting fundamental and applied research in the lower atmosphere due to their relatively low cost and ability to collect samples with high spatial and temporal resolution. Concurrent with this development comes the need for accurate instrumentation and measurement methods suitable for small meteorological UASs. Moreover, the instrumentation to be integrated into such platforms must be small and lightweight. Whereas thermodynamic variables can be easily measured using well aspirated sensors onboard, it is much more challenging to accurately measure the wind with a UAS. Several algorithms have been developed that incorporate GPS observations as a means of estimating the horizontal wind vector, with each algorithm exhibiting its own particular strengths and weaknesses. In the present study, the performance of three such GPS-based wind-retrieval algorithms has been investigated and compared with wind estimates from rawinsonde and sodar observations. Each of the algorithms considered agreed well with the wind measurements from sounding and sodar data. Through the integration of UAS-retrieved profiles of thermodynamic and kinematic parameters, one can investigate the static and dynamic stability of the atmosphere and relate them to the state of the boundary layer across a variety of times and locations, which might be difficult to access using conventional instrumentation.

Comparison and application of wind retrieval algorithms for small unmanned aerial systems

NASA Astrophysics Data System (ADS)

Bonin, T. A.; Chilson, P. B.; Zielke, B. S.; Klein, P. M.; Leeman, J. R.

2013-07-01

Recently, there has been an increase in use of Unmanned Aerial Systems (UASs) as platforms for conducting fundamental and applied research in the lower atmosphere due to their relatively low cost and ability to collect samples with high spatial and temporal resolution. Concurrent with this development comes the need for accurate instrumentation and measurement methods suitable for small meteorological UASs. Moreover, the instrumentation to be integrated into such platforms must be small and lightweight. Whereas thermodynamic variables can be easily measured using well-aspirated sensors onboard, it is much more challenging to accurately measure the wind with a UAS. Several algorithms have been developed that incorporate GPS observations as a means of estimating the horizontal wind vector, with each algorithm exhibiting its own particular strengths and weaknesses. In the present study, the performance of three such GPS-based wind-retrieval algorithms has been investigated and compared with wind estimates from rawinsonde and sodar observations. Each of the algorithms considered agreed well with the wind measurements from sounding and sodar data. Through the integration of UAS-retrieved profiles of thermodynamic and kinematic parameters, one can investigate the static and dynamic stability of the atmosphere and relate them to the state of the boundary layer across a variety of times and locations, which might be difficult to access using conventional instrumentation.

Triple-frequency radar retrievals of snowfall properties from the OLYMPEX field campaign

NASA Astrophysics Data System (ADS)

Leinonen, J. S.; Lebsock, M. D.; Sy, O. O.; Tanelli, S.

2017-12-01

Retrieval of snowfall properties with radar is subject to significant errors arising from the uncertainties in the size and structure of snowflakes. Recent modeling and theoretical studies have shown that multi-frequency radars can potentially constrain the microphysical properties and thus reduce the uncertainties in the retrieved snow water content. So far, there have only been limited efforts to leverage the theoretical advances in actual snowfall retrievals. In this study, we have implemented an algorithm that retrieves the snowfall properties from triple-frequency radar data using the radar scattering properties from a combination of snowflake scattering databases, which were derived using numerical scattering methods. Snowflake number concentration, characteristic size and density are derived using a combination of optimal estimation and Kalman smoothing; the snow water content and other bulk properties are then derived from these. The retrieval framework is probabilistic and thus naturally provides error estimates for the retrieved quantities. We tested the retrieval algorithm using data from the APR3 airborne radar flown onboard the NASA DC-8 aircraft during the Olympic Mountain Experiment (OLYMPEX) in late 2015. We demonstrated consistent retrieval of snow properties and smooth transition from single- and dual-frequency retrievals to using all three frequencies simultaneously. The error analysis shows that the retrieval accuracy is improved when additional frequencies are introduced. We also compare the findings to in situ measurements of snow properties as well as measurements by polarimetric ground-based radar.

Comparison of snow depth retrieval algorithm in Northeastern China based on AMSR2 and FY3B-MWRI data

NASA Astrophysics Data System (ADS)

Fan, Xintong; Gu, Lingjia; Ren, Ruizhi; Zhou, Tingting

2017-09-01

Snow accumulation has a very important influence on the natural environment and human activities. Meanwhile, improving the estimation accuracy of passive microwave snow depth (SD) retrieval is a hotspot currently. Northeastern China is a typical snow study area including many different land cover types, such as forest, grassland and farmland. Especially, there is relatively stable snow accumulation in January every year. The brightness temperatures which are observed by the Advanced Microwave Scanning Radiometer 2 (AMSR2) on GCOM-W1 and FengYun3B Microwave Radiation Imager (FY3B-MWRI) in the same period in 2013 are selected as the study data in the research. The results of snow depth retrieval using AMSR2 standard algorithm and Jiang's FY operational algorithm are compared in the research. Moreover, to validate the accuracy of the two algorithms, the retrieval results are compared with the SD data observed at the national meteorological stations in Northeastern China. Furthermore, the retrieval SD is also compared with AMSR2 and FY standard SD products, respectively. The root mean square errors (RMSE) results using AMSR2 standard algorithms and FY operational algorithm are close in the forest surface, which are 6.33cm and 6.28cm, respectively. However, The FY operational algorithm shows a better result than the AMSR2 standard algorithms in the grassland and farmland surface. The RMSE results using FY operational algorithm in the grassland and farmland surface are 2.44cm and 6.13cm, respectively.

Ten Years of Cloud Optical and Microphysical Retrievals from MODIS

NASA Technical Reports Server (NTRS)

Platnick, Steven; King, Michael D.; Wind, Galina; Hubanks, Paul; Arnold, G. Thomas; Amarasinghe, Nandana

2010-01-01

The MODIS cloud optical properties algorithm (MOD06/MYD06 for Terra and Aqua MODIS, respectively) has undergone extensive improvements and enhancements since the launch of Terra. These changes have included: improvements in the cloud thermodynamic phase algorithm; substantial changes in the ice cloud light scattering look up tables (LUTs); a clear-sky restoral algorithm for flagging heavy aerosol and sunglint; greatly improved spectral surface albedo maps, including the spectral albedo of snow by ecosystem; inclusion of pixel-level uncertainty estimates for cloud optical thickness, effective radius, and water path derived for three error sources that includes the sensitivity of the retrievals to solar and viewing geometries. To improve overall retrieval quality, we have also implemented cloud edge removal and partly cloudy detection (using MOD35 cloud mask 250m tests), added a supplementary cloud optical thickness and effective radius algorithm over snow and sea ice surfaces and over the ocean, which enables comparison with the "standard" 2.1 11m effective radius retrieval, and added a multi-layer cloud detection algorithm. We will discuss the status of the MOD06 algorithm and show examples of pixellevel (Level-2) cloud retrievals for selected data granules, as well as gridded (Level-3) statistics, notably monthly means and histograms (lD and 2D, with the latter giving correlations between cloud optical thickness and effective radius, and other cloud product pairs).

Missing texture reconstruction method based on error reduction algorithm using Fourier transform magnitude estimation scheme.

PubMed

Ogawa, Takahiro; Haseyama, Miki

2013-03-01

A missing texture reconstruction method based on an error reduction (ER) algorithm, including a novel estimation scheme of Fourier transform magnitudes is presented in this brief. In our method, Fourier transform magnitude is estimated for a target patch including missing areas, and the missing intensities are estimated by retrieving its phase based on the ER algorithm. Specifically, by monitoring errors converged in the ER algorithm, known patches whose Fourier transform magnitudes are similar to that of the target patch are selected from the target image. In the second approach, the Fourier transform magnitude of the target patch is estimated from those of the selected known patches and their corresponding errors. Consequently, by using the ER algorithm, we can estimate both the Fourier transform magnitudes and phases to reconstruct the missing areas.

Microwave Observations of Precipitation and the Atmosphere

NASA Technical Reports Server (NTRS)

Staelin, David H.; Rosenkranz, Philip W.

2004-01-01

This research effort had three elements devoted to improving satellite-derived passive microwave retrievals of precipitation rate: morphological rain-rate retrievals, warm rain retrievals, and extension of a study of geostationary satellite options. The morphological precipitation-rate retrieval method uses for the first time the morphological character of the observed storm microwave spectra. The basic concept involves: 1) retrieval of point rainfall rates using current algorithms, 2) using spatial feature vectors of the observations over segmented multi-pixel storms to estimate the integrated rainfall rate for that storm (cu m/s), and 3) normalization of the point rain-rate retrievals to ensure consistency with the storm-wide retrieval. This work is ongoing, but two key steps have been completed: development of a segmentation algorithm for defining spatial regions corresponding to single storms for purposes of estimation, and reduction of some of the data from NAST-M that will be used to support this research going forward. The warm rain retrieval method involved extension of Aquai/AIRS/AMSU/HSB algorithmic work on cloud water retrievals. The central concept involves the fact that passive microwave cloud water retrievals over approx. 0.4 mm are very likely associated with precipitation. Since glaciated precipitation is generally detected quite successfully using scattering signatures evident in the surface-blind 54- and 183-GHz bands, this new method complements the first by permitting precipitation retrievals of non-glaciated events. The method is most successful over ocean, but has detected non-glaciated convective cells over land, perhaps in their early formative stages. This work will require additional exploration and validation prior to publication. Passive microwave instrument configurations for use in geostationary orbit were studied. They employ parabolic reflectors between 2 and 4 meters in diameter, and frequencies up to approx.430 GHz; this corresponds to nadir spot diameters as small as 10 km.

Regarding retrievals of methane in the atmosphere from IASI/Metop spectra and their comparison with ground-based FTIR measurements data

NASA Astrophysics Data System (ADS)

Khamatnurova, M. Yu.; Gribanov, K. G.; Zakharov, V. I.; Rokotyan, N. V.; Imasu, R.

2017-11-01

The algorithm for atmospheric methane distribution retrieval in atmosphere from IASI spectra has been developed. The feasibility of Levenberg-Marquardt method for atmospheric methane total column amount retrieval from the spectra measured by IASI/METOP modified for the case of lack of a priori covariance matrices for methane vertical profiles is studied in this paper. Method and algorithm were implemented into software package together with iterative estimation of a posteriori covariance matrices and averaging kernels for each individual retrieval. This allows retrieval quality selection using the properties of both types of matrices. Methane (XCH4) retrieval by Levenberg-Marquardt method from IASI/METOP spectra is presented in this work. NCEP/NCAR reanalysis data provided by ESRL (NOAA, Boulder, USA) were taken as initial guess. Surface temperature, air temperature and humidity vertical profiles are retrieved before methane vertical profile retrieval. The data retrieved from ground-based measurements at the Ural Atmospheric Station and data of L2/IASI standard product were used for the verification of the method and results of methane retrieval from IASI/METOP spectra.

Validation of Suomi-NPP VIIRS sea ice concentration with very high-resolution satellite and airborne camera imagery

NASA Astrophysics Data System (ADS)

Baldwin, Daniel; Tschudi, Mark; Pacifici, Fabio; Liu, Yinghui

2017-08-01

Two independent VIIRS-based Sea Ice Concentration (SIC) products are validated against SIC as estimated from Very High Spatial Resolution Imagery for several VIIRS overpasses. The 375 m resolution VIIRS SIC from the Interface Data Processing Segment (IDPS) SIC algorithm is compared against estimates made from 2 m DigitalGlobe (DG) WorldView-2 imagery and also against estimates created from 10 cm Digital Mapping System (DMS) camera imagery. The 750 m VIIRS SIC from the Enterprise SIC algorithm is compared against DG imagery. The IDPS vs. DG comparisons reveal that, due to algorithm issues, many of the IDPS SIC retrievals were falsely assigned ice-free values when the pixel was clearly over ice. These false values increased the validation bias and RMS statistics. The IDPS vs. DMS comparisons were largely over ice-covered regions and did not demonstrate the false retrieval issue. The validation results show that products from both the IDPS and Enterprise algorithms were within or very close to the 10% accuracy (bias) specifications in both the non-melting and melting conditions, but only products from the Enterprise algorithm met the 25% specifications for the uncertainty (RMS).

Use of GOES, SSM/I, TRMM Satellite Measurements Estimating Water Budget Variations in Gulf of Mexico - Caribbean Sea Basins

NASA Technical Reports Server (NTRS)

Smith, Eric A.

2004-01-01

This study presents results from a multi-satellite/multi-sensor retrieval system designed to obtain the atmospheric water budget over the open ocean. A combination of 3ourly-sampled monthly datasets derived from the GOES-8 5-channel Imager, the TRMM TMI radiometer, and the DMSP 7-channel passive microwave radiometers (SSM/I) have been acquired for the combined Gulf of Mexico-Caribbean Sea basin. Whereas the methodology has been tested over this basin, the retrieval system is designed for portability to any open-ocean region. Algorithm modules using the different datasets to retrieve individual geophysical parameters needed in the water budget equation are designed in a manner that takes advantage of the high temporal resolution of the GOES-8 measurements, as well as the physical relationships inherent to the TRMM and SSM/I passive microwave measurements in conjunction with water vapor, cloud liquid water, and rainfall. The methodology consists of retrieving the precipitation, surface evaporation, and vapor-cloud water storage terms in the atmospheric water balance equation from satellite techniques, with the water vapor advection term being obtained as the residue needed for balance. Thus, the intent is to develop a purely satellite-based method for obtaining the full set of terms in the atmospheric water budget equation without requiring in situ sounding information on the wind profile. The algorithm is validated by cross-checking all the algorithm components through multiple- algorithm retrieval intercomparisons. A further check on the validation is obtained by directly comparing water vapor transports into the targeted basin diagnosed from the satellite algorithms to those obtained observationally from a network of land-based upper air stations that nearly uniformly surround the basin, although it is fair to say that these checks are more effective m identifying problems in estimating vapor transports from a leaky operational radiosonde network than in verifying the transport estimates determined from the satellite algorithm system Total columnar atmospheric water budget results are presented for an extended annual cycle consisting of the months of October-97, January-98, April-98, July-98,October-98, and January 1999. These results are used to emphasize the changing relationship in E-P, as well as in the varying roles of storage and advection in balancing E-P both on daily and monthly time scales and on localized and basin space scales. Results from the algorithm-to-algorithm intercomparisons are also presented in the context of sensitivity testing to help understand the intrinsic uncertainties in evaluating the water budget terms by an all-satellite algorithm approach.

Retrieved Vertical Profiles of Latent Heat Release Using TRMM Rainfall Products

NASA Technical Reports Server (NTRS)

Tao, W.-K.; Lang, S.; Olson, W. S.; Meneghini, R.; Yang, S.; Simpson, J.; Kummerow, C.; Smith, E.

2000-01-01

This paper represents the first attempt to use TRMM rainfall information to estimate the four dimensional latent heating structure over the global tropics for February 1998. The mean latent heating profiles over six oceanic regions (TOGA COARE IFA, Central Pacific, S. Pacific Convergence Zone, East Pacific, Indian Ocean and Atlantic Ocean) and three continental regions (S. America, Central Africa and Australia) are estimated and studied. The heating profiles obtained from the results of diagnostic budget studies over a broad range of geographic locations are used to provide comparisons and indirect validation for the heating algorithm estimated heating profiles. Three different latent heating algorithms, the Goddard Convective-Stratiform (CSH) heating, the Goddard Profiling (GPROF) heating, and the Hydrometeor heating (HH) are used and their results are intercompared. The horizontal distribution or patterns of latent heat release from the three different heating retrieval methods are quite similar. They all can identify the areas of major convective activity (i.e., a well defined ITCZ in the Pacific, a distinct SPCZ) in the global tropics. The magnitude of their estimated latent heating release is also not in bad agreement with each other and with those determined from diagnostic budget studies. However, the major difference among these three heating retrieval algorithms is the altitude of the maximum heating level. The CSH algorithm estimated heating profiles only show one maximum heating level, and the level varies between convective activity from various geographic locations. These features are in good agreement with diagnostic budget studies. By contrast, two maximum heating levels were found using the GPROF heating and HH algorithms. The latent heating profiles estimated from all three methods can not show cooling between active convective events. We also examined the impact of different TMI (Multi-channel Passive Microwave Sensor) and PR (Precipitation Radar) rainfall information on latent heating structures.

Review of TRMM/GPM Rainfall Algorithm Validation

NASA Technical Reports Server (NTRS)

Smith, Eric A.

2004-01-01

A review is presented concerning current progress on evaluation and validation of standard Tropical Rainfall Measuring Mission (TRMM) precipitation retrieval algorithms and the prospects for implementing an improved validation research program for the next generation Global Precipitation Measurement (GPM) Mission. All standard TRMM algorithms are physical in design, and are thus based on fundamental principles of microwave radiative transfer and its interaction with semi-detailed cloud microphysical constituents. They are evaluated for consistency and degree of equivalence with one another, as well as intercompared to radar-retrieved rainfall at TRMM's four main ground validation sites. Similarities and differences are interpreted in the context of the radiative and microphysical assumptions underpinning the algorithms. Results indicate that the current accuracies of the TRMM Version 6 algorithms are approximately 15% at zonal-averaged / monthly scales with precisions of approximately 25% for full resolution / instantaneous rain rate estimates (i.e., level 2 retrievals). Strengths and weaknesses of the TRMM validation approach are summarized. Because the dew of convergence of level 2 TRMM algorithms is being used as a guide for setting validation requirements for the GPM mission, it is important that the GPM algorithm validation program be improved to ensure concomitant improvement in the standard GPM retrieval algorithms. An overview of the GPM Mission's validation plan is provided including a description of a new type of physical validation model using an analytic 3-dimensional radiative transfer model.

Impact of spatial resolution on cirrus infrared satellite retrievals in the presence of cloud heterogeneity

NASA Astrophysics Data System (ADS)

Fauchez, T.; Platnick, S. E.; Meyer, K.; Zhang, Z.; Cornet, C.; Szczap, F.; Dubuisson, P.

2015-12-01

Cirrus clouds are an important part of the Earth radiation budget but an accurate assessment of their role remains highly uncertain. Cirrus optical properties such as Cloud Optical Thickness (COT) and ice crystal effective particle size are often retrieved with a combination of Visible/Near InfraRed (VNIR) and ShortWave-InfraRed (SWIR) reflectance channels. Alternatively, Thermal InfraRed (TIR) techniques, such as the Split Window Technique (SWT), have demonstrated better accuracy for thin cirrus effective radius retrievals with small effective radii. However, current global operational algorithms for both retrieval methods assume that cloudy pixels are horizontally homogeneous (Plane Parallel Approximation (PPA)) and independent (Independent Pixel Approximation (IPA)). The impact of these approximations on ice cloud retrievals needs to be understood and, as far as possible, corrected. Horizontal heterogeneity effects in the TIR spectrum are mainly dominated by the PPA bias that primarily depends on the COT subpixel heterogeneity; for solar reflectance channels, in addition to the PPA bias, the IPA can lead to significant retrieval errors due to a significant photon horizontal transport between cloudy columns, as well as brightening and shadowing effects that are more difficult to quantify. Furthermore TIR retrievals techniques have demonstrated better retrieval accuracy for thin cirrus having small effective radii over solar reflectance techniques. The TIR range is thus particularly relevant in order to characterize, as accurately as possible, thin cirrus clouds. Heterogeneity effects in the TIR are evaluated as a function of spatial resolution in order to estimate the optimal spatial resolution for TIR retrieval applications. These investigations are performed using a cirrus 3D cloud generator (3DCloud), a 3D radiative transfer code (3DMCPOL), and two retrieval algorithms, namely the operational MODIS retrieval algorithm (MOD06) and a research-level SWT algorithm.

Wind Retrievals under Rain for Passive Satellite Microwave Radiometers and its Applications to Hurricane Tracking

NASA Technical Reports Server (NTRS)

Meissner, Thomas; Wentz, Frank J.

2008-01-01

We have developed an algorithm that retrieves wind speed under rain using C-hand and X-band channels of passive microwave satellite radiometers. The spectral difference of the brightness temperature signals due to wind or rain allows to find channel combinations that are sufficiently sensitive to wind speed but little or not sensitive to rain. We &ve trained a statistical algorithm that applies under hurricane conditions and is able to measure wind speeds in hurricanes to an estimated accuracy of about 2 m/s. We have also developed a global algorithm, that is less accurate but can be applied under all conditions. Its estimated accuracy is between 2 and 5 mls, depending on wind speed and rain rate. We also extend the wind speed region in our model for the wind induced sea surface emissivity from currently 20 m/s to 40 mls. The data indicate that the signal starts to saturate above 30 mls. Finally, we make an assessment of the performance of wind direction retrievals from polarimetric radiometers as function of wind speed and rain rate

An incremental DPMM-based method for trajectory clustering, modeling, and retrieval.

PubMed

Hu, Weiming; Li, Xi; Tian, Guodong; Maybank, Stephen; Zhang, Zhongfei

2013-05-01

Trajectory analysis is the basis for many applications, such as indexing of motion events in videos, activity recognition, and surveillance. In this paper, the Dirichlet process mixture model (DPMM) is applied to trajectory clustering, modeling, and retrieval. We propose an incremental version of a DPMM-based clustering algorithm and apply it to cluster trajectories. An appropriate number of trajectory clusters is determined automatically. When trajectories belonging to new clusters arrive, the new clusters can be identified online and added to the model without any retraining using the previous data. A time-sensitive Dirichlet process mixture model (tDPMM) is applied to each trajectory cluster for learning the trajectory pattern which represents the time-series characteristics of the trajectories in the cluster. Then, a parameterized index is constructed for each cluster. A novel likelihood estimation algorithm for the tDPMM is proposed, and a trajectory-based video retrieval model is developed. The tDPMM-based probabilistic matching method and the DPMM-based model growing method are combined to make the retrieval model scalable and adaptable. Experimental comparisons with state-of-the-art algorithms demonstrate the effectiveness of our algorithm.

Global Scale Simultaneous Retrieval of Smoothened Vegetation Optical Depth and Surface Roughness Parameter using AMSR-E X-band Observations

NASA Astrophysics Data System (ADS)

Lanka, Karthikeyan; Pan, Ming; Konings, Alexandra; Piles, María; D, Nagesh Kumar; Wood, Eric

2017-04-01

Traditionally, passive microwave retrieval algorithms such as Land Parameter Retrieval Model (LPRM) estimate simultaneously soil moisture and Vegetation Optical Depth (VOD) using brightness temperature (Tb) data. The algorithm requires a surface roughness parameter which - despite implications - is generally assumed to be constant at global scale. Due to inherent noise in the satellite data and retrieval algorithm, the VOD retrievals are usually observed to be highly fluctuating at daily scale which may not occur in reality. Such noisy VOD retrievals along with spatially invariable roughness parameter may affect the quality of soil moisture retrievals. The current work aims to smoothen the VOD retrievals (with an assumption that VOD remains constant over a period of time) and simultaneously generate, for the first time, global surface roughness map using multiple descending X-band Tb observations of AMSR-E. The methodology utilizes Tb values under a moving-time-window-setup to estimate concurrently the soil moisture of each day and a constant VOD in the window. Prior to this step, surface roughness parameter is estimated using the complete time series of Tb record. Upon carrying out the necessary sensitivity analysis, the smoothened VOD along with soil moisture retrievals is generated for the 10-year duration of AMSR-E (2002-2011) with a 7-day moving window using the LPRM framework. The spatial patterns of resulted global VOD maps are in coherence with vegetation biomass and climate conditions. The VOD results also exhibit a smoothening effect in terms of lower values of standard deviation. This is also evident from time series comparison of VOD and LPRM VOD retrievals without optimization over moving windows at several grid locations across the globe. The global surface roughness map also exhibited spatial patterns that are strongly influenced by topography and land use conditions. Some of the noticeable features include high roughness over mountainous regions and heavily vegetated tropical rainforests, low roughness in desert areas and moderate roughness value over higher latitudes. The new datasets of VOD and surface roughness can help improving the quality of soil moisture retrievals. Also, the methodology proposed is generic by nature and can be implemented over currently operating AMSR2, SMOS, and SMAP soil moisture missions.

Generating Global Leaf Area Index from Landsat: Algorithm Formulation and Demonstration

NASA Technical Reports Server (NTRS)

Ganguly, Sangram; Nemani, Ramakrishna R.; Zhang, Gong; Hashimoto, Hirofumi; Milesi, Cristina; Michaelis, Andrew; Wang, Weile; Votava, Petr; Samanta, Arindam; Melton, Forrest;

SWIM: A Semi-Analytical Ocean Color Inversion Algorithm for Optically Shallow Waters

NASA Technical Reports Server (NTRS)

McKinna, Lachlan I. W.; Werdell, P. Jeremy; Fearns, Peter R. C. S.; Weeks, Scarla J.; Reichstetter, Martina; Franz, Bryan A.; Bailey, Sean W.; Shea, Donald M.; Feldman, Gene C.

2014-01-01

In clear shallow waters, light that is transmitted downward through the water column can reflect off the sea floor and thereby influence the water-leaving radiance signal. This effect can confound contemporary ocean color algorithms designed for deep waters where the seafloor has little or no effect on the water-leaving radiance. Thus, inappropriate use of deep water ocean color algorithms in optically shallow regions can lead to inaccurate retrievals of inherent optical properties (IOPs) and therefore have a detrimental impact on IOP-based estimates of marine parameters, including chlorophyll-a and the diffuse attenuation coefficient. In order to improve IOP retrievals in optically shallow regions, a semi-analytical inversion algorithm, the Shallow Water Inversion Model (SWIM), has been developed. Unlike established ocean color algorithms, SWIM considers both the water column depth and the benthic albedo. A radiative transfer study was conducted that demonstrated how SWIM and two contemporary ocean color algorithms, the Generalized Inherent Optical Properties algorithm (GIOP) and Quasi-Analytical Algorithm (QAA), performed in optically deep and shallow scenarios. The results showed that SWIM performed well, whilst both GIOP and QAA showed distinct positive bias in IOP retrievals in optically shallow waters. The SWIM algorithm was also applied to a test region: the Great Barrier Reef, Australia. Using a single test scene and time series data collected by NASA's MODIS-Aqua sensor (2002-2013), a comparison of IOPs retrieved by SWIM, GIOP and QAA was conducted.

Retrieval of tropospheric aerosol properties over land from visible and near-infrared spectral reflectance: Application over Maryland

NASA Astrophysics Data System (ADS)

Levy, Robert Carroll

Aerosols are major components of the Earth's global climate system, affecting the radiation budget and cloud processes of the atmosphere. When located near the surface, high concentrations lead to lowered visibility, increased health problems and generally reduced quality of life for the human population. Over the United States mid-Atlantic region, aerosol pollution is a problem mainly during the summer. Satellites, such as the MODerate Imaging Spectrometer (MODIS), from their vantage point above the atmosphere, provide unprecedented coverage of global and regional aerosols over land. During MODIS' eight-year operation, exhaustive data validation and analyses have shown how the algorithm should be improved. This dissertation describes the development of the 'second-generation' operational algorithm for retrieval of global tropospheric aerosol properties over dark land surfaces, from MODIS-observed spectral reflectance. New understanding about global aerosol properties, land surface reflectance characteristics, and radiative transfer properties were learned in the process. This new operational algorithm performs a simultaneous inversion of reflectance in two visible channels (0.47 and 0.66 mum) and one shortwave infrared channel (2.12 mum), thereby having increased sensitivity to coarse aerosol. Inversion of the three channels retrieves the aerosol optical depth (tau) at 0.55 mum, the percentage of non-dust (fine model) aerosol (eta) and the surface reflectance. This algorithm is applied globally, and retrieves tau that is highly correlated (y = 0.02 + 1.0x, R=0.9) with ground-based sunphotometer measurements. The new algorithm estimates the global, over-land, long-term averaged tau ˜ 0.21, a 25% reduction from previous MODIS estimates. This leads to reducing estimates of global, non-desert, over-land aerosol direct radiative effect (all aerosols) by 1.7 W·m-2 (0.5 W·m-2 over the entire globe), which significantly impacts assessment of aerosol direct radiative forcing (contribution from anthropogenic aerosols only). Over the U.S. mid-Atlantic region, validated retrievals of tau (an integrated column property) can help to estimate surface PM2.5 concentration, a monitored criteria air quality property. The 3-dimensional aerosol loading in the region is characterized using aircraft measurements and the Community Multi-scale Air Quality Model (CMAQ) model, leading to some convergence of observed quantities and modeled processes.

Global Precipitation Measurement (GPM) Core Observatory Falling Snow Estimates

NASA Astrophysics Data System (ADS)

Skofronick Jackson, G.; Kulie, M.; Milani, L.; Munchak, S. J.; Wood, N.; Levizzani, V.

2017-12-01

Retrievals of falling snow from space represent an important data set for understanding and linking the Earth's atmospheric, hydrological, and energy cycles. Estimates of falling snow must be captured to obtain the true global precipitation water cycle, snowfall accumulations are required for hydrological studies, and without knowledge of the frozen particles in clouds one cannot adequately understand the energy and radiation budgets. This work focuses on comparing the first stable falling snow retrieval products (released May 2017) for the Global Precipitation Measurement (GPM) Core Observatory (GPM-CO), which was launched February 2014, and carries both an active dual frequency (Ku- and Ka-band) precipitation radar (DPR) and a passive microwave radiometer (GPM Microwave Imager-GMI). Five separate GPM-CO falling snow retrieval algorithm products are analyzed including those from DPR Matched (Ka+Ku) Scan, DPR Normal Scan (Ku), DPR High Sensitivity Scan (Ka), combined DPR+GMI, and GMI. While satellite-based remote sensing provides global coverage of falling snow events, the science is relatively new, the different on-orbit instruments don't capture all snow rates equally, and retrieval algorithms differ. Thus a detailed comparison among the GPM-CO products elucidates advantages and disadvantages of the retrievals. GPM and CloudSat global snowfall evaluation exercises are natural investigative pathways to explore, but caution must be undertaken when analyzing these datasets for comparative purposes. This work includes outlining the challenges associated with comparing GPM-CO to CloudSat satellite snow estimates due to the different sampling, algorithms, and instrument capabilities. We will highlight some factors and assumptions that can be altered or statistically normalized and applied in an effort to make comparisons between GPM and CloudSat global satellite falling snow products as equitable as possible.

CDRD and PNPR passive microwave precipitation retrieval algorithms: verification study over Africa and Southern Atlantic

NASA Astrophysics Data System (ADS)

Panegrossi, Giulia; Casella, Daniele; Cinzia Marra, Anna; Petracca, Marco; Sanò, Paolo; Dietrich, Stefano

2015-04-01

The ongoing NASA/JAXA Global Precipitation Measurement mission (GPM) requires the full exploitation of the complete constellation of passive microwave (PMW) radiometers orbiting around the globe for global precipitation monitoring. In this context the coherence of the estimates of precipitation using different passive microwave radiometers is a crucial need. We have developed two different passive microwave precipitation retrieval algorithms: one is the Cloud Dynamics Radiation Database algorithm (CDRD), a physically ¬based Bayesian algorithm for conically scanning radiometers (i.e., DMSP SSMIS); the other one is the Passive microwave Neural network Precipitation Retrieval (PNPR) algorithm for cross¬-track scanning radiometers (i.e., NOAA and MetOp¬A/B AMSU-¬A/MHS, and NPP Suomi ATMS). The algorithms, originally created for application over Europe and the Mediterranean basin, and used operationally within the EUMETSAT Satellite Application Facility on Support to Operational Hydrology and Water Management (H-SAF, http://hsaf.meteoam.it), have been recently modified and extended to Africa and Southern Atlantic for application to the MSG full disk area. The two algorithms are based on the same physical foundation, i.e., the same cloud-radiation model simulations as a priori information in the Bayesian solver and as training dataset in the neural network approach, and they also use similar procedures for identification of frozen background surface, detection of snowfall, and determination of a pixel based quality index of the surface precipitation retrievals. In addition, similar procedures for the screening of not ¬precipitating pixels are used. A novel algorithm for the detection of precipitation in tropical/sub-tropical areas has been developed. The precipitation detection algorithm shows a small rate of false alarms (also over arid/desert regions), a superior detection capability in comparison with other widely used screening algorithms, and it is applicable to all available PMW radiometers in the GPM constellation of satellites (including NPP Suomi ATMS, and GMI). Three years of SSMIS and AMSU/MHS data have been considered to carry out a verification study over Africa of the retrievals from the CDRD and PNPR algorithms. The precipitation products from the TRMM ¬Precipitation radar (PR) (TRMM product 2A25 and 2A23) have been used as ground truth. The results of this study aimed at assessing the accuracy of the precipitation retrievals in different climatic regions and precipitation regimes will be presented. Particular emphasis will be given to the analysis of the level of coherence of the precipitation estimates and patterns between the two algorithms exploiting different radiometers. Recent developments aimed at the full exploitation of the GPM constellation of satellites for optimal precipitation/drought monitoring will be also presented.

Assessment of diverse algorithms applied on MODIS Aqua and Terra data over land surfaces in Europe

NASA Astrophysics Data System (ADS)

Glantz, P.; Tesche, M.

2012-04-01

Beside an increase of greenhouse gases (e.g., carbon dioxide, methane and nitrous oxide) human activities (for instance fossil fuel and biomass burning) have lead to perturbation of the atmospheric content of aerosol particles. Aerosols exhibits high spatial and temporal variability in the atmosphere. Therefore, aerosol investigation for climate research and environmental control require the identification of source regions, their strength and aerosol type, which can be retrieved based on space-borne observations. The aim of the present study is to validate and evaluate AOT (aerosol optical thickness) and Ångström exponent, obtained with the SAER (Satellite AErosol Retrieval) algorithm for MODIS (MODerate resolution Imaging Spectroradiometer) Aqua and Terra calibrated level 1 data (1 km horizontal resolution at ground), against AERONET (AErosol RObotic NETwork) observations and MODIS Collection 5 (c005) standard product retrievals (10 km), respectively, over land surfaces in Europe for the seasons; early spring (period 1), mid spring (period 2) and summer (period 3). For several of the cases analyzed here the Aqua and Terra satellites passed the investigation area twice during a day. Thus, beside a variation in the sun elevation the satellite aerosol retrievals have also on a daily basis been performed with a significant variation in the satellite-viewing geometry. An inter-comparison of the two algorithms has also been performed. The validation with AERONET shows that the MODIS c005 retrieved AOT is, for the wavelengths 0.469 and 0.500 nm, on the whole within the expected uncertainty for one standard deviation of the MODIS retrievals over Europe (Δτ = ±0.05 ± 0.15τ). The SAER estimated AOT for the wavelength 0.443 nm also agree reasonable well with AERONET. Thus, the majority of the SAER AOT values are within the MODIS expected uncertainty range, although somewhat larger RMSD (root mean square deviation) occurs compared to the results obtained with the MODIS c005 algorithm. The discrepancy between SAERand AERONET AOT is, however, substantially larger for the wavelength 488 nm, which means that several of the AOT values are without the MODIS expected uncertainty range. Both algorithms are unable to estimate Ångström exponent accurately, although the MODIS c005 algorithm performs a better job. Based on the inter-comparison of the SAER and MODIS c005 algorithms it was found here that the former estimation of AOT is for values up to 1on the whole within the expected uncertainties for one standard deviation of the MODIS retrievals, considering both Aqua and Terra and periods 1 and 3. The latter also occurs for Aqua and period 2, while then for AOT values lower than 0.5. The present algorithms were, beside aerosols emitted from clean sources and continental sources in Europe, also applied with favor on aerosol particles transported from agricultural fires in Russia and Ukraine. The latter events were associated with high aerosol loadings, although probably with similar single scattering albedo as the days classified as clean. We also present observations performed with space borne and ground-based lidars in the area investigated. From the latter platforms the vertical distribution of aerosol extinction in the atmosphere can be measured. This study suggests that the present satellite retrievals of AOT, particularly obtained with the MODIS c005 algorithm, will, in combination with the lidar measurements, be very useful in validation of regional and climate models over Europe.

The performance of the new enhanced-resolution satellite passive microwave dataset applied for snow water equivalent estimation

NASA Astrophysics Data System (ADS)

Pan, J.; Durand, M. T.; Jiang, L.; Liu, D.

2017-12-01

The newly-processed NASA MEaSures Calibrated Enhanced-Resolution Brightness Temperature (CETB) reconstructed using antenna measurement response function (MRF) is considered to have significantly improved fine-resolution measurements with better georegistration for time-series observations and equivalent field of view (FOV) for frequencies with the same monomial spatial resolution. We are looking forward to its potential for the global snow observing purposes, and therefore aim to test its performance for characterizing snow properties, especially the snow water equivalent (SWE) in large areas. In this research, two candidate SWE algorithms will be tested in China for the years between 2005 to 2010 using the reprocessed TB from the Advanced Microwave Scanning Radiometer for EOS (AMSR-E), with the results to be evaluated using the daily snow depth measurements at over 700 national synoptic stations. One of the algorithms is the SWE retrieval algorithm used for the FengYun (FY) - 3 Microwave Radiation Imager. This algorithm uses the multi-channel TB to calculate SWE for three major snow regions in China, with the coefficients adapted for different land cover types. The second algorithm is the newly-established Bayesian Algorithm for SWE Estimation with Passive Microwave measurements (BASE-PM). This algorithm uses the physically-based snow radiative transfer model to find the histogram of most-likely snow property that matches the multi-frequency TB from 10.65 to 90 GHz. It provides a rough estimation of snow depth and grain size at the same time and showed a 30 mm SWE RMS error using the ground radiometer measurements at Sodankyla. This study will be the first attempt to test it spatially for satellite. The use of this algorithm benefits from the high resolution and the spatial consistency between frequencies embedded in the new dataset. This research will answer three questions. First, to what extent can CETB increase the heterogeneity in the mapped SWE? Second, will the SWE estimation error statistics be improved using this high-resolution dataset? Third, how will the SWE retrieval accuracy be improved using CETB and the new SWE retrieval techniques?

An operational retrieval algorithm for determining aerosol optical properties in the ultraviolet

NASA Astrophysics Data System (ADS)

Taylor, Thomas E.; L'Ecuyer, Tristan S.; Slusser, James R.; Stephens, Graeme L.; Goering, Christian D.

2008-02-01

This paper describes a number of practical considerations concerning the optimization and operational implementation of an algorithm used to characterize the optical properties of aerosols across part of the ultraviolet (UV) spectrum. The algorithm estimates values of aerosol optical depth (AOD) and aerosol single scattering albedo (SSA) at seven wavelengths in the UV, as well as total column ozone (TOC) and wavelength-independent asymmetry factor (g) using direct and diffuse irradiances measured with a UV multifilter rotating shadowband radiometer (UV-MFRSR). A novel method for cloud screening the irradiance data set is introduced, as well as several improvements and optimizations to the retrieval scheme which yield a more realistic physical model for the inversion and increase the efficiency of the algorithm. Introduction of a wavelength-dependent retrieval error budget generated from rigorous forward model analysis as well as broadened covariances on the a priori values of AOD, SSA and g and tightened covariances of TOC allows sufficient retrieval sensitivity and resolution to obtain unique solutions of aerosol optical properties as demonstrated by synthetic retrievals. Analysis of a cloud screened data set (May 2003) from Panther Junction, Texas, demonstrates that the algorithm produces realistic values of the optical properties that compare favorably with pseudo-independent methods for AOD, TOC and calculated Ångstrom exponents. Retrieval errors of all parameters (except TOC) are shown to be negatively correlated to AOD, while the Shannon information content is positively correlated, indicating that retrieval skill improves with increasing atmospheric turbidity. When implemented operationally on more than thirty instruments in the Ultraviolet Monitoring and Research Program's (UVMRP) network, this retrieval algorithm will provide a comprehensive and internally consistent climatology of ground-based aerosol properties in the UV spectral range that can be used for both validation of satellite measurements as well as regional aerosol and ultraviolet transmission studies.

Simultaneous reconstruction of temperature distribution and radiative properties in participating media using a hybrid LSQR-PSO algorithm

NASA Astrophysics Data System (ADS)

Niu, Chun-Yang; Qi, Hong; Huang, Xing; Ruan, Li-Ming; Wang, Wei; Tan, He-Ping

2015-11-01

A hybrid least-square QR decomposition (LSQR)-particle swarm optimization (LSQR-PSO) algorithm was developed to estimate the three-dimensional (3D) temperature distributions and absorption coefficients simultaneously. The outgoing radiative intensities at the boundary surface of the absorbing media were simulated by the line-of-sight (LOS) method, which served as the input for the inverse analysis. The retrieval results showed that the 3D temperature distributions of the participating media with known radiative properties could be retrieved accurately using the LSQR algorithm, even with noisy data. For the participating media with unknown radiative properties, the 3D temperature distributions and absorption coefficients could be retrieved accurately using the LSQR-PSO algorithm even with measurement errors. It was also found that the temperature field could be estimated more accurately than the absorption coefficients. In order to gain insight into the effects on the accuracy of temperature distribution reconstruction, the selection of the detection direction and the angle between two detection directions was also analyzed. Project supported by the Major National Scientific Instruments and Equipment Development Special Foundation of China (Grant No. 51327803), the National Natural Science Foundation of China (Grant No. 51476043), and the Fund of Tianjin Key Laboratory of Civil Aircraft Airworthiness and Maintenance in Civil Aviation University of China.

Collection Fusion Using Bayesian Estimation of a Linear Regression Model in Image Databases on the Web.

ERIC Educational Resources Information Center

Kim, Deok-Hwan; Chung, Chin-Wan

2003-01-01

Discusses the collection fusion problem of image databases, concerned with retrieving relevant images by content based retrieval from image databases distributed on the Web. Focuses on a metaserver which selects image databases supporting similarity measures and proposes a new algorithm which exploits a probabilistic technique using Bayesian…

Remote Sensing of Cloud Top Height from SEVIRI: Analysis of Eleven Current Retrieval Algorithms

NASA Technical Reports Server (NTRS)

Hamann, U.; Walther, A.; Baum, B.; Bennartz, R.; Bugliaro, L.; Derrien, M.; Francis, P. N.; Heidinger, A.; Joro, S.; Kniffka, A.;

A novel algorithm for laser self-mixing sensors used with the Kalman filter to measure displacement

NASA Astrophysics Data System (ADS)

Sun, Hui; Liu, Ji-Gou

2018-07-01

This paper proposes a simple and effective method for estimating the feedback level factor C in a self-mixing interferometric sensor. It is used with a Kalman filter to retrieve the displacement. Without the complicated and onerous calculation process of the general C estimation method, a final equation is obtained. Thus, the estimation of C only involves a few simple calculations. It successfully retrieves the sinusoidal and aleatory displacement by means of simulated self-mixing signals in both weak and moderate feedback regimes. To deal with the errors resulting from noise and estimate bias of C and to further improve the retrieval precision, a Kalman filter is employed following the general phase unwrapping method. The simulation and experiment results show that the retrieved displacement using the C obtained with the proposed method is comparable to the joint estimation of C and α. Besides, the Kalman filter can significantly decrease measurement errors, especially the error caused by incorrectly locating the peak and valley positions of the signal.

Improvements to the swath-level near-surface atmospheric state parameter retrievals within the NRL Ocean Surface Flux System (NFLUX)

NASA Astrophysics Data System (ADS)

May, J. C.; Rowley, C. D.; Meyer, H.

2017-12-01

The Naval Research Laboratory (NRL) Ocean Surface Flux System (NFLUX) is an end-to-end data processing and assimilation system used to provide near-real-time satellite-based surface heat flux fields over the global ocean. The first component of NFLUX produces near-real-time swath-level estimates of surface state parameters and downwelling radiative fluxes. The focus here will be on the satellite swath-level state parameter retrievals, namely surface air temperature, surface specific humidity, and surface scalar wind speed over the ocean. Swath-level state parameter retrievals are produced from satellite sensor data records (SDRs) from four passive microwave sensors onboard 10 platforms: the Special Sensor Microwave Imager/Sounder (SSMIS) sensor onboard the DMSP F16, F17, and F18 platforms; the Advanced Microwave Sounding Unit-A (AMSU-A) sensor onboard the NOAA-15, NOAA-18, NOAA-19, Metop-A, and Metop-B platforms; the Advanced Technology Microwave Sounder (ATMS) sensor onboard the S-NPP platform; and the Advanced Microwave Scannin Radiometer 2 (AMSR2) sensor onboard the GCOM-W1 platform. The satellite SDRs are translated into state parameter estimates using multiple polynomial regression algorithms. The coefficients to the algorithms are obtained using a bootstrapping technique with all available brightness temperature channels for a given sensor, in addition to a SST field. For each retrieved parameter for each sensor-platform combination, unique algorithms are developed for ascending and descending orbits, as well as clear vs cloudy conditions. Each of the sensors produces surface air temperature and surface specific humidity retrievals. The SSMIS and AMSR2 sensors also produce surface scalar wind speed retrievals. Improvement is seen in the SSMIS retrievals when separate algorithms are used for the even and odd scans, with the odd scans performing better than the even scans. Currently, NFLUX treats all SSMIS scans as even scans. Additional improvement in all of the surface retrievals comes from using a 3-hourly SST field, as opposed to a daily SST field.

Maximum likelihood phase-retrieval algorithm: applications.

PubMed

Nahrstedt, D A; Southwell, W H

1984-12-01

The maximum likelihood estimator approach is shown to be effective in determining the wave front aberration in systems involving laser and flow field diagnostics and optical testing. The robustness of the algorithm enables convergence even in cases of severe wave front error and real, nonsymmetrical, obscured amplitude distributions.

Evaluation of MODIS aerosol optical depth for semi­-arid environments in complex terrain

NASA Astrophysics Data System (ADS)

Holmes, H.; Loria Salazar, S. M.; Panorska, A. K.; Arnott, W. P.; Barnard, J.

2015-12-01

The use of satellite remote sensing to estimate spatially resolved ground level air pollutant concentrations is increasing due to advancements in remote sensing technology and the limited number of surface observations. Satellite retrievals provide global, spatiotemporal air quality information and are used to track plumes, estimate human exposures, model emissions, and determine sources (i.e., natural versus anthropogenic) in regulatory applications. Ground level PM2.5 concentrations can be estimated using columnar aerosol optical depth (AOD) from MODIS, where the satellite retrieval serves as a spatial surrogate to simulate surface PM2.5 gradients. The spatial statistical models and MODIS AOD retrieval algorithms have been evaluated for the dark, vegetated eastern US, while the semi-arid western US continues to be an understudied region with associated complexity due to heterogeneous emissions, smoke from wildfires, and complex terrain. The objective of this work is to evaluate the uncertainty of MODIS AOD retrievals by comparing with columnar AOD and surface PM2.5 measurements from AERONET and EPA networks. Data is analyzed from multiple stations in California and Nevada for three years where four major wildfires occurred. Results indicate that MODIS retrievals fail to estimate column-integrated aerosol pollution in the summer months. This is further investigated by quantifying the statistical relationships between MODIS AOD, AERONET AOD, and surface PM2.5 concentrations. Data analysis indicates that the distribution of MODIS AOD is significantly (p<0.05) different than AERONET AOD. Further, using the results of distributional and association analysis the impacts of MODIS AOD uncertainties on the spatial gradients are evaluated. Additionally, the relationships between these uncertainties and physical parameters in the retrieval algorithm (e.g., surface reflectance, Ångström Extinction Exponent) are discussed.

Using Ground-Based Measurements and Retrievals to Validate Satellite Data

NASA Technical Reports Server (NTRS)

Dong, Xiquan

2002-01-01

The proposed research is to use the DOE ARM ground-based measurements and retrievals as the ground-truth references for validating satellite cloud results and retrieving algorithms. This validation effort includes four different ways: (1) cloud properties on different satellites, therefore different sensors, TRMM VIRS and TERRA MODIS; (2) cloud properties at different climatic regions, such as DOE ARM SGP, NSA, and TWP sites; (3) different cloud types, low and high level cloud properties; and (4) day and night retrieving algorithms. Validation of satellite-retrieved cloud properties is very difficult and a long-term effort because of significant spatial and temporal differences between the surface and satellite observing platforms. The ground-based measurements and retrievals, only carefully analyzed and validated, can provide a baseline for estimating errors in the satellite products. Even though the validation effort is so difficult, a significant progress has been made during the proposed study period, and the major accomplishments are summarized in the follow.

Phytoplankton pigment concentrations in the Middle Atlantic Bight - Comparison of ship determinations and CZCS estimates. [Coastal Zone Color Scanner

NASA Technical Reports Server (NTRS)

Gordon, H. R.; Brown, J. W.; Clark, D. K.; Brown, O. B.; Evans, R. H.; Broenkow, W. W.

1983-01-01

The processing algorithms used for relating the apparent color of the ocean observed with the Coastal-Zone Color Scanner on Nimbus-7 to the concentration of phytoplankton pigments (principally the pigment responsible for photosynthesis, chlorophyll-a) are developed and discussed in detail. These algorithms are applied to the shelf and slope waters of the Middle Atlantic Bight and also to Sargasso Sea waters. In all, four images are examined, and the resulting pigment concentrations are compared to continuous measurements made along ship tracks. The results suggest that over the 0.08-1.5 mg/cu m range, the error in the retrieved pigment concentration is of the order of 30-40% for a variety of atmospheric turbidities. In three direct comparisons between ship-measured and satellite-retrieved values of the water-leaving radiance, the atmospheric correction algorithm retrieved the water-leaving radiance with an average error of about 10%. This atmospheric correction algorithm does not require any surface measurements for its application.

Algorithm theoretical baseline for formaldehyde retrievals from S5P TROPOMI and from the QA4ECV project

NASA Astrophysics Data System (ADS)

De Smedt, Isabelle; Theys, Nicolas; Yu, Huan; Danckaert, Thomas; Lerot, Christophe; Compernolle, Steven; Van Roozendael, Michel; Richter, Andreas; Hilboll, Andreas; Peters, Enno; Pedergnana, Mattia; Loyola, Diego; Beirle, Steffen; Wagner, Thomas; Eskes, Henk; van Geffen, Jos; Folkert Boersma, Klaas; Veefkind, Pepijn

2018-04-01

On board the Copernicus Sentinel-5 Precursor (S5P) platform, the TROPOspheric Monitoring Instrument (TROPOMI) is a double-channel, nadir-viewing grating spectrometer measuring solar back-scattered earthshine radiances in the ultraviolet, visible, near-infrared, and shortwave infrared with global daily coverage. In the ultraviolet range, its spectral resolution and radiometric performance are equivalent to those of its predecessor OMI, but its horizontal resolution at true nadir is improved by an order of magnitude. This paper introduces the formaldehyde (HCHO) tropospheric vertical column retrieval algorithm implemented in the S5P operational processor and comprehensively describes its various retrieval steps. Furthermore, algorithmic improvements developed in the framework of the EU FP7-project QA4ECV are described for future updates of the processor. Detailed error estimates are discussed in the light of Copernicus user requirements and needs for validation are highlighted. Finally, verification results based on the application of the algorithm to OMI measurements are presented, demonstrating the performances expected for TROPOMI.

Variability in Tropospheric Ozone over China Derived from Assimilated GOME-2 Ozone Profiles

NASA Astrophysics Data System (ADS)

van Peet, J. C. A.; van der A, R. J.; Kelder, H. M.

2016-08-01

A tropospheric ozone dataset is derived from assimilated GOME-2 ozone profiles for 2008. Ozone profiles are retrieved with the OPERA algorithm, using the optimal estimation method. The retrievals are done on a spatial resolution of 160×160 km on 16 layers ranging from the surface up to 0.01 hPa. By using the averaging kernels in the data assimilation, the algorithm maintains the high resolution vertical structures of the model, while being constrained by observations with a lower vertical resolution.

The performance of Yonsei CArbon Retrieval (YCAR) algorithm with improved aerosol information using GOSAT measurements over East Asia

NASA Astrophysics Data System (ADS)

Jung, Y.; Kim, J.; Kim, W.; Boesch, H.; Yoshida, Y.; Cho, C.; Lee, H.; Goo, T. Y.

2016-12-01

The Greenhouse Gases Observing SATellite (GOSAT) is the first satellite dedicated to measure atmospheric CO2 concentrations from space that can able to improve our knowledge about carbon cycle. Several studies have performed to develop the CO2 retrieval algorithms using GOSAT measurements, but limitations in spatial coverage and uncertainties due to aerosols and thin cirrus clouds are still remained as a problem for monitoring CO2 concentration globally. In this study, we develop the Yonsei CArbon Retrieval (YCAR) algorithm based on optimal estimation method to retrieve the column-averaged dry-air mole fraction of carbon dioxide (XCO2) with optimized a priori CO2 profiles and aerosol models over East Asia. In previous studies, the aerosol optical properties (AOP) and the aerosol top height used to cause significant errors in retrieved XCO2 up to 2.5 ppm. Since this bias comes from a rough assumption of aerosol information in the forward model used in CO2 retrieval process, the YCAR algorithm improves the process to take into account AOPs as well as aerosol vertical distribution; total AOD and the fine mode fraction (FMF) are obtained from the ground-based measurements closely located, and other parameters are obtained from a priori information. Comparing to ground-based XCO2 measurements, the YCAR XCO2 product has a bias of 0.59±0.48 ppm and 2.16±0.87 ppm at Saga and Tsukuba sites, respectively, showing lower biases and higher correlations rather than the GOSAT standard products. These results reveal that considering better aerosol information can improve the accuracy of CO2 retrieval algorithm and provide more useful XCO2 information with reduced uncertainties.

A passive microwave technique for estimating rainfall and vertical structure information from space. Part 1: Algorithm description

NASA Technical Reports Server (NTRS)

Kummerow, Christian; Giglio, Louis

1994-01-01

This paper describes a multichannel physical approach for retrieving rainfall and vertical structure information from satellite-based passive microwave observations. The algorithm makes use of statistical inversion techniques based upon theoretically calculated relations between rainfall rates and brightness temperatures. Potential errors introduced into the theoretical calculations by the unknown vertical distribution of hydrometeors are overcome by explicity accounting for diverse hydrometeor profiles. This is accomplished by allowing for a number of different vertical distributions in the theoretical brightness temperature calculations and requiring consistency between the observed and calculated brightness temperatures. This paper will focus primarily on the theoretical aspects of the retrieval algorithm, which includes a procedure used to account for inhomogeneities of the rainfall within the satellite field of view as well as a detailed description of the algorithm as it is applied over both ocean and land surfaces. The residual error between observed and calculated brightness temperatures is found to be an important quantity in assessing the uniqueness of the solution. It is further found that the residual error is a meaningful quantity that can be used to derive expected accuracies from this retrieval technique. Examples comparing the retrieved results as well as the detailed analysis of the algorithm performance under various circumstances are the subject of a companion paper.

Genetic Algorithm Phase Retrieval for the Systematic Image-Based Optical Alignment Testbed

NASA Technical Reports Server (NTRS)

Rakoczy, John; Steincamp, James; Taylor, Jaime

2003-01-01

A reduced surrogate, one point crossover genetic algorithm with random rank-based selection was used successfully to estimate the multiple phases of a segmented optical system modeled on the seven-mirror Systematic Image-Based Optical Alignment testbed located at NASA's Marshall Space Flight Center.

Directional effects on NDVI and LAI retrievals from MODIS: A case study in Brazil with soybean

NASA Astrophysics Data System (ADS)

Breunig, Fábio Marcelo; Galvão, Lênio Soares; Formaggio, Antônio Roberto; Epiphanio, José Carlos Neves

2011-02-01

The Moderate Resolution Imaging Spectroradiometer (MODIS) is largely used to estimate Leaf Area Index (LAI) using radiative transfer modeling (the "main" algorithm). When this algorithm fails for a pixel, which frequently occurs over Brazilian soybean areas, an empirical model (the "backup" algorithm) based on the relationship between the Normalized Difference Vegetation Index (NDVI) and LAI is utilized. The objective of this study is to evaluate directional effects on NDVI and subsequent LAI estimates using global (biome 3) and local empirical models, as a function of the soybean development in two growing seasons (2004-2005 and 2005-2006). The local model was derived from the pixels that had LAI values retrieved from the main algorithm. In order to keep the reproductive stage for a given cultivar as a constant factor while varying the viewing geometry, pairs of MODIS images acquired in close dates from opposite directions (backscattering and forward scattering) were selected. Linear regression relationships between the NDVI values calculated from these two directions were evaluated for different view angles (0-25°; 25-45°; 45-60°) and development stages (<45; 45-90; >90 days after planting). Impacts on LAI retrievals were analyzed. Results showed higher reflectance values in backscattering direction due to the predominance of sunlit soybean canopy components towards the sensor and higher NDVI values in forward scattering direction due to stronger shadow effects in the red waveband. NDVI differences between the two directions were statistically significant for view angles larger than 25°. The main algorithm for LAI estimation failed in the two growing seasons with gradual crop development. As a result, up to 94% of the pixels had LAI values calculated from the backup algorithm at the peak of canopy closure. Most of the pixels selected to compose the 8-day MODIS LAI product came from the forward scattering view because it displayed larger LAI values than the backscattering. Directional effects on the subsequent LAI retrievals were stronger at the peak of the soybean development (NDVI values between 0.70 and 0.85). When the global empirical model was used, LAI differences up to 3.2 for consecutive days and opposite viewing directions were observed. Such differences were reduced to values up to 1.5 with the local model. Because of the predominance of LAI retrievals from the MODIS backup algorithm during the Brazilian soybean development, care is necessary if one considers using these data in agronomic growing/yield models.

Phase Retrieval from Modulus Using Homeomorphic Signal Processing and the Complex Cepstrum: An Algorithm for Lightning Protection Systems

DOE Office of Scientific and Technical Information (OSTI.GOV)

Clark, G A

2004-06-08

In general, the Phase Retrieval from Modulus problem is very difficult. In this report, we solve the difficult, but somewhat more tractable case in which we constrain the solution to a minimum phase reconstruction. We exploit the real-and imaginary part sufficiency properties of the Fourier and Hilbert Transforms of causal sequences to develop an algorithm for reconstructing spectral phase given only spectral modulus. The algorithm uses homeomorphic signal processing methods with the complex cepstrum. The formal problem of interest is: Given measurements of only the modulus {vert_bar}H(k){vert_bar} (no phase) of the Discrete Fourier Transform (DFT) of a real, finite-length, stable,more » causal time domain signal h(n), compute a minimum phase reconstruction {cflx h}(n) of the signal. Then compute the phase of {cflx h}(n) using a DFT, and exploit the result as an estimate of the phase of h(n). The development of the algorithm is quite involved, but the final algorithm and its implementation are very simple. This work was motivated by a Phase Retrieval from Modulus Problem that arose in LLNL Defense Sciences Engineering Division (DSED) projects in lightning protection for buildings. The measurements are limited to modulus-only spectra from a spectrum analyzer. However, it is desired to perform system identification on the building to compute impulse responses and transfer functions that describe the amount of lightning energy that will be transferred from the outside of the building to the inside. This calculation requires knowledge of the entire signals (both modulus and phase). The algorithm and software described in this report are proposed as an approach to phase retrieval that can be used for programmatic needs. This report presents a brief tutorial description of the mathematical problem and the derivation of the phase retrieval algorithm. The efficacy of the theory is demonstrated using simulated signals that meet the assumptions of the algorithm. We see that for the noiseless case, the reconstructions are extremely accurate. When moderate to heavy simulated white Gaussian noise was added, the algorithm performance remained reasonably robust, especially in the low frequency part of the spectrum, which is the part of most interest for lightning protection. Limitations of the algorithm include the following: (1) It does not account for noise in the given spectral modulus. Fortunately, the lightning protection signals of interest generally have a reasonably high signal-to-noise ratio (SNR). (2) The DFT length N must be even and larger than the length of the nonzero part of the measured signals. These constraints are simple to meet in practice. (3) Regardless of the properties of the actual signal h(n), the phase retrieval results are constrained to have the minimum phase property. In most problems of practical interest, these assumptions are very reasonable and probably valid. They are reasonable assumptions for Lightning Protection applications. Proposed future work includes (a) Evaluating the efficacy of the algorithm with real Lightning Protection signals from programmatic applications, (b) Performing a more rigorous analysis of noise effects, (c) Using the algorithm along with advanced system identification algorithms to estimate impulse responses and transfer functions, (d) Developing algorithms to deal with measured partial (truncated) spectral moduli, and (e) R & D of phase retrieval algorithms that specifically deal with general (not necessarily minimum phase) signals, and noisy spectral moduli.« less

Retrieval of Atmospheric Water Vapor Profiles from the Special Sensor Microwave TEMPERATURE-2

NASA Astrophysics Data System (ADS)

Al-Khalaf, Abdulrahman Khal

1995-01-01

Radiometric measurements from the Special Sensor Microwave/Temperature-2 (SSM/T-2) instrument are used to retrieve atmospheric water vapor profiles over ocean, land, coast, and ice/snow backgrounds. These measurements are used to retrieve vertical distribution of integrated water vapor (IWV) and total integrated water vapor (TIWV) using a physical algorithm. The algorithm infers the presence of cloud at a given height from super-saturation of the retrieved humidity at that height then the algorithm estimate the cloud liquid water content. Retrievals of IWV over five different layers are validated against available ground truth such as global radiosondes and ECMWF analyses. Over ocean, the retrieved total integrated water vapor (TIWV) and IWV close to the surface compare quite well, with those from radiosonde observations and the European Center for Medium Range Weather Forecasts (ECMWF) analyses. However, comparisons to radiosonde results are better than (ECMWF) analyses. TIWV root mean square (RMS) difference was 5.95 mm and TWV RMS difference for the lowest layer (SFC-850 mb) was 2.8 mm for radiosonde comparisons. Water vapor retrieval over land is less accurate than over ocean due to the low contrast between the surface and the atmosphere near the surface; therefore, land retrievals are more reliable at layers above 700 mb. However, TIWV and IWV at all layers compare appropriately with ground truth. Over coastal areas the agreement between retrieved water vapor profiles and ground truth is quite good for both TIWV and IWV for the five layers. The natural variability and large variations in the surface emissivity over ice and snow fields leads toward poor results. Clouds degrade retrievals over land and coast, improve the retrievals a little over ocean, and improve dramatically over snow/ice. Examples of retrieved relative humidity profiles were shown to illustrate the algorithm performance for the actual profile retrieval. The overall features of the retrieved profiles compared well with those from radiosonde data and ECMWF analyses. However, due to the limited number of channels, the retrieved profiles generally do not reproduce the fine details when a rapid change in relative humidity versus height was observed.

Cloud Properties and Radiative Heating Rates for TWP

DOE Data Explorer

Comstock, Jennifer

2013-11-07

A cloud properties and radiative heating rates dataset is presented where cloud properties retrieved using lidar and radar observations are input into a radiative transfer model to compute radiative fluxes and heating rates at three ARM sites located in the Tropical Western Pacific (TWP) region. The cloud properties retrieval is a conditional retrieval that applies various retrieval techniques depending on the available data, that is if lidar, radar or both instruments detect cloud. This Combined Remote Sensor Retrieval Algorithm (CombRet) produces vertical profiles of liquid or ice water content (LWC or IWC), droplet effective radius (re), ice crystal generalized effective size (Dge), cloud phase, and cloud boundaries. The algorithm was compared with 3 other independent algorithms to help estimate the uncertainty in the cloud properties, fluxes, and heating rates (Comstock et al. 2013). The dataset is provided at 2 min temporal and 90 m vertical resolution. The current dataset is applied to time periods when the MMCR (Millimeter Cloud Radar) version of the ARSCL (Active Remotely-Sensed Cloud Locations) Value Added Product (VAP) is available. The MERGESONDE VAP is utilized where temperature and humidity profiles are required. Future additions to this dataset will utilize the new KAZR instrument and its associated VAPs.

Optimal estimation for global ground-level fine particulate matter concentrations

NASA Astrophysics Data System (ADS)

Donkelaar, Aaron; Martin, Randall V.; Spurr, Robert J. D.; Drury, Easan; Remer, Lorraine A.; Levy, Robert C.; Wang, Jun

2013-06-01

We develop an optimal estimation (OE) algorithm based on top-of-atmosphere reflectances observed by the MODIS satellite instrument to retrieve near-surface fine particulate matter (PM2.5). The GEOS-Chem chemical transport model is used to provide prior information for the Aerosol Optical Depth (AOD) retrieval and to relate total column AOD to PM2.5. We adjust the shape of the GEOS-Chem relative vertical extinction profiles by comparison with lidar retrievals from the CALIOP satellite instrument. Surface reflectance relationships used in the OE algorithm are indexed by land type. Error quantities needed for this OE algorithm are inferred by comparison with AOD observations taken by a worldwide network of sun photometers (AERONET) and extended globally based upon aerosol speciation and cross correlation for simulated values, and upon land type for observational values. Significant agreement in PM2.5 is found over North America for 2005 (slope = 0.89; r = 0.82; 1-σ error = 1 µg/m3 + 27%), with improved coverage and correlation relative to previous work for the same region and time period, although certain subregions, such as the San Joaquin Valley of California are better represented by previous estimates. Independently derived error estimates of the OE PM2.5 values at in situ locations over North America (of ±(2.5 µg/m3 + 31%) and Europe of ±(3.5 µg/m3 + 30%) are corroborated by comparison with in situ observations, although globally (error estimates of ±(3.0 µg/m3 + 35%), may be underestimated. Global population-weighted PM2.5 at 50% relative humidity is estimated as 27.8 µg/m3 at 0.1° × 0.1° resolution.

Recent Improvements in Retrieving Near-Surface Air Temperature and Humidity Using Microwave Remote Sensing

NASA Technical Reports Server (NTRS)

Roberts, J. Brent

2010-01-01

Detailed studies of the energy and water cycles require accurate estimation of the turbulent fluxes of moisture and heat across the atmosphere-ocean interface at regional to basin scale. Providing estimates of these latent and sensible heat fluxes over the global ocean necessitates the use of satellite or reanalysis-based estimates of near surface variables. Recent studies have shown that errors in the surface (10 meter)estimates of humidity and temperature are currently the largest sources of uncertainty in the production of turbulent fluxes from satellite observations. Therefore, emphasis has been placed on reducing the systematic errors in the retrieval of these parameters from microwave radiometers. This study discusses recent improvements in the retrieval of air temperature and humidity through improvements in the choice of algorithms (linear vs. nonlinear) and the choice of microwave sensors. Particular focus is placed on improvements using a neural network approach with a single sensor (Special Sensor Microwave/Imager) and the use of combined sensors from the NASA AQUA satellite platform. The latter algorithm utilizes the unique sampling available on AQUA from the Advanced Microwave Scanning Radiometer (AMSR-E) and the Advanced Microwave Sounding Unit (AMSU-A). Current estimates of uncertainty in the near-surface humidity and temperature from single and multi-sensor approaches are discussed and used to estimate errors in the turbulent fluxes.

Retrieval of ice cloud properties using an optimal estimation algorithm and MODIS infrared observations: 1. Forward model, error analysis, and information content

NASA Astrophysics Data System (ADS)

Wang, Chenxi; Platnick, Steven; Zhang, Zhibo; Meyer, Kerry; Yang, Ping

2016-05-01

An optimal estimation (OE) retrieval method is developed to infer three ice cloud properties simultaneously: optical thickness (τ), effective radius (reff), and cloud top height (h). This method is based on a fast radiative transfer (RT) model and infrared (IR) observations from the MODerate resolution Imaging Spectroradiometer (MODIS). This study conducts thorough error and information content analyses to understand the error propagation and performance of retrievals from various MODIS band combinations under different cloud/atmosphere states. Specifically, the algorithm takes into account four error sources: measurement uncertainty, fast RT model uncertainty, uncertainties in ancillary data sets (e.g., atmospheric state), and assumed ice crystal habit uncertainties. It is found that the ancillary and ice crystal habit error sources dominate the MODIS IR retrieval uncertainty and cannot be ignored. The information content analysis shows that for a given ice cloud, the use of four MODIS IR observations is sufficient to retrieve the three cloud properties. However, the selection of MODIS IR bands that provide the most information and their order of importance varies with both the ice cloud properties and the ambient atmospheric and the surface states. As a result, this study suggests the inclusion of all MODIS IR bands in practice since little a priori information is available.

Retrieval of ice cloud properties using an optimal estimation algorithm and MODIS infrared observations. Part I: Forward model, error analysis, and information content.

PubMed

Wang, Chenxi; Platnick, Steven; Zhang, Zhibo; Meyer, Kerry; Yang, Ping

2016-05-27

An optimal estimation (OE) retrieval method is developed to infer three ice cloud properties simultaneously: optical thickness ( τ ), effective radius ( r eff ), and cloud-top height ( h ). This method is based on a fast radiative transfer (RT) model and infrared (IR) observations from the MODerate resolution Imaging Spectroradiometer (MODIS). This study conducts thorough error and information content analyses to understand the error propagation and performance of retrievals from various MODIS band combinations under different cloud/atmosphere states. Specifically, the algorithm takes into account four error sources: measurement uncertainty, fast RT model uncertainty, uncertainties in ancillary datasets (e.g., atmospheric state), and assumed ice crystal habit uncertainties. It is found that the ancillary and ice crystal habit error sources dominate the MODIS IR retrieval uncertainty and cannot be ignored. The information content analysis shows that, for a given ice cloud, the use of four MODIS IR observations is sufficient to retrieve the three cloud properties. However, the selection of MODIS IR bands that provide the most information and their order of importance varies with both the ice cloud properties and the ambient atmospheric and the surface states. As a result, this study suggests the inclusion of all MODIS IR bands in practice since little a priori information is available.

Evaluation of the OMI Cloud Pressures Derived from Rotational Raman Scattering by Comparisons with other Satellite Data and Radiative Transfer Simulations

NASA Technical Reports Server (NTRS)

Vasilkov, Alexander; Joiner, Joanna; Spurr, Robert; Bhartia, Pawan K.; Levelt, Pieternel; Stephens, Graeme

2009-01-01

In this paper we examine differences between cloud pressures retrieved from the Ozone Monitoring Instrument (OMI) using the ultraviolet rotational Raman scattering (RRS) algorithm and those from the thermal infrared (IR) Aqua/MODIS. Several cloud data sets are currently being used in OMI trace gas retrieval algorithms including climatologies based on IR measurements and simultaneous cloud parameters derived from OMI. From a validation perspective, it is important to understand the OMI retrieved cloud parameters and how they differ with those derived from the IR. To this end, we perform radiative transfer calculations to simulate the effects of different geophysical conditions on the OMI RRS cloud pressure retrievals. We also quantify errors related to the use of the Mixed Lambert-Equivalent Reflectivity (MLER) concept as currently implemented of the OMI algorithms. Using properties from the Cloudsat radar and MODIS, we show that radiative transfer calculations support the following: (1) The MLER model is adequate for single-layer optically thick, geometrically thin clouds, but can produce significant errors in estimated cloud pressure for optically thin clouds. (2) In a two-layer cloud, the RRS algorithm may retrieve a cloud pressure that is either between the two cloud decks or even beneath the top of the lower cloud deck because of scattering between the cloud layers; the retrieved pressure depends upon the viewing geometry and the optical depth of the upper cloud deck. (3) Absorbing aerosol in and above a cloud can produce significant errors in the retrieved cloud pressure. (4) The retrieved RRS effective pressure for a deep convective cloud will be significantly higher than the physical cloud top pressure derived with thermal IR.

A Simple and Universal Aerosol Retrieval Algorithm for Landsat Series Images Over Complex Surfaces

NASA Astrophysics Data System (ADS)

Wei, Jing; Huang, Bo; Sun, Lin; Zhang, Zhaoyang; Wang, Lunche; Bilal, Muhammad

2017-12-01

Operational aerosol optical depth (AOD) products are available at coarse spatial resolutions from several to tens of kilometers. These resolutions limit the application of these products for monitoring atmospheric pollutants at the city level. Therefore, a simple, universal, and high-resolution (30 m) Landsat aerosol retrieval algorithm over complex urban surfaces is developed. The surface reflectance is estimated from a combination of top of atmosphere reflectance at short-wave infrared (2.22 μm) and Landsat 4-7 surface reflectance climate data records over densely vegetated areas and bright areas. The aerosol type is determined using the historical aerosol optical properties derived from the local urban Aerosol Robotic Network (AERONET) site (Beijing). AERONET ground-based sun photometer AOD measurements from five sites located in urban and rural areas are obtained to validate the AOD retrievals. Terra MODerate resolution Imaging Spectrometer Collection (C) 6 AOD products (MOD04) including the dark target (DT), the deep blue (DB), and the combined DT and DB (DT&DB) retrievals at 10 km spatial resolution are obtained for comparison purposes. Validation results show that the Landsat AOD retrievals at a 30 m resolution are well correlated with the AERONET AOD measurements (R2 = 0.932) and that approximately 77.46% of the retrievals fall within the expected error with a low mean absolute error of 0.090 and a root-mean-square error of 0.126. Comparison results show that Landsat AOD retrievals are overall better and less biased than MOD04 AOD products, indicating that the new algorithm is robust and performs well in AOD retrieval over complex surfaces. The new algorithm can provide continuous and detailed spatial distributions of AOD during both low and high aerosol loadings.

Phase-Retrieval Uncertainty Estimation and Algorithm Comparison for the JWST-ISIM Test Campaign

NASA Technical Reports Server (NTRS)

Aronstein, David L.; Smith, J. Scott

2016-01-01

Phase retrieval, the process of determining the exitpupil wavefront of an optical instrument from image-plane intensity measurements, is the baseline methodology for characterizing the wavefront for the suite of science instruments (SIs) in the Integrated Science Instrument Module (ISIM) for the James Webb Space Telescope (JWST). JWST is a large, infrared space telescope with a 6.5-meter diameter primary mirror. JWST is currently NASA's flagship mission and will be the premier space observatory of the next decade. ISIM contains four optical benches with nine unique instruments, including redundancies. ISIM was characterized at the Goddard Space Flight Center (GSFC) in Greenbelt, MD in a series of cryogenic vacuum tests using a telescope simulator. During these tests, phase-retrieval algorithms were used to characterize the instruments. The objective of this paper is to describe the Monte-Carlo simulations that were used to establish uncertainties (i.e., error bars) for the wavefronts of the various instruments in ISIM. Multiple retrieval algorithms were used in the analysis of ISIM phase-retrieval focus-sweep data, including an iterativetransform algorithm and a nonlinear optimization algorithm. These algorithms emphasize the recovery of numerous optical parameters, including low-order wavefront composition described by Zernike polynomial terms and high-order wavefront described by a point-by-point map, location of instrument best focus, focal ratio, exit-pupil amplitude, the morphology of any extended object, and optical jitter. The secondary objective of this paper is to report on the relative accuracies of these algorithms for the ISIM instrument tests, and a comparison of their computational complexity and their performance on central and graphical processing unit clusters. From a phase-retrieval perspective, the ISIM test campaign includes a variety of source illumination bandwidths, various image-plane sampling criteria above and below the Nyquist- Shannon critical sampling value, various extended object sizes, and several other impactful effects.

Direct estimation of land surface albedo from VIIRS data: Algorithm improvement and preliminary validation

NASA Astrophysics Data System (ADS)

Wang, Dongdong; Liang, Shunlin; He, Tao; Yu, Yunyue

2013-11-01

surface albedo (LSA), part of the Visible Infrared Imaging Radiometer Suite (VIIRS) surface albedo environmental data record (EDR), is an essential variable regulating shortwave energy exchange between the land surface and the atmosphere. Two sub-algorithms, the dark pixel sub-algorithm (DPSA) and the bright pixel sub-algorithm (BPSA), were proposed for retrieving LSA from VIIRS data. The BPSA estimates LSA directly from VIIRS top-of-atmosphere (TOA) reflectance through simulation of atmospheric radiative transfer. Several changes have been made to improve the BPSA since the deployment of VIIRS. A database of the Moderate Resolution Imaging Spectroradiometer (MODIS) bidirectional reflectance distribution function (BRDF) is collected and converted to bidirectional reflectance at VIIRS bands. The converted reflectance is then used as input to the atmospheric radiative transfer model to generate a look-up table (LUT) of regression coefficients with consideration of surface BRDF. Before its implementation in the operational system, the new BPSA is tested on the local infrastructure. The incorporation of the surface BRDF improves the accuracy of LSA estimation and reduces the temporal variation of LSA over stable surfaces. VIIRS LSA retrievals agree well with the MODIS albedo products. Comparison with field measurements at seven Surface Radiation (SURFRAD) Network sites shows that VIIRS LSA retrieved from the LUT with surface BRDF has an R2 value of 0.80 and root mean square error of 0.049, better than MODIS albedo products. The VIIRS results have a slight negative bias of 0.004, whereas the MODIS albedo is underestimated with a larger negative bias of 0.026.

Remote sensing estimation of colored dissolved organic matter (CDOM) in optically shallow waters

NASA Astrophysics Data System (ADS)

Li, Jiwei; Yu, Qian; Tian, Yong Q.; Becker, Brian L.

2017-06-01

It is not well understood how bottom reflectance of optically shallow waters affects the algorithm performance of colored dissolved organic matters (CDOM) retrieval. This study proposes a new algorithm that considers bottom reflectance in estimating CDOM absorption from optically shallow inland or coastal waters. The field sampling was conducted during four research cruises within the Saginaw River, Kawkawlin River and Saginaw Bay of Lake Huron. A stratified field sampling campaign collected water samples, determined the depth at each sampling location and measured optical properties. The sampled CDOM absorption at 440 nm broadly ranged from 0.12 to 8.46 m-1. Field sample analysis revealed that bottom reflectance does significantly change water apparent optical properties. We developed a CDOM retrieval algorithm (Shallow water Bio-Optical Properties algorithm, SBOP) that effectively reduces uncertainty by considering bottom reflectance in shallow waters. By incorporating the bottom contribution in upwelling radiances, the SBOP algorithm was able to explain 74% of the variance of CDOM values (RMSE = 0.22 and R2 = 0.74). The bottom effect index (BEI) was introduced to efficiently separate optically shallow and optically deep waters. Based on the BEI, an adaptive approach was proposed that references the amount of bottom effect in order to identify the most suitable algorithm (optically shallow water algorithm [SBOP] or optically deep water algorithm [QAA-CDOM]) to improve CDOM estimation (RMSE = 0.22 and R2 = 0.81). Our results potentially help to advance the capability of remote sensing in monitoring carbon pools at the land-water interface.

A comparison between two algorithms for the retrieval of soil moisture using AMSR-E data

USDA-ARS?s Scientific Manuscript database

A comparison between two algorithms for estimating soil moisture with microwave satellite data was carried out by using the datasets collected on the four Agricultural Research Service (ARS) watershed sites in the US from 2002 to 2009. These sites collectively represent a wide range of ground condit...

Enhancement and evaluation of an algorithm for atmospheric profiling continuity from Aqua to Suomi-NPP

NASA Astrophysics Data System (ADS)

Lipton, A.; Moncet, J. L.; Payne, V.; Lynch, R.; Polonsky, I. N.

2017-12-01

We will present recent results from an algorithm for producing climate-quality atmospheric profiling earth system data records (ESDRs) for application to data from hyperspectral sounding instruments, including the Atmospheric InfraRed Sounder (AIRS) on EOS Aqua and the Cross-track Infrared Sounder (CrIS) on Suomi-NPP, along with their companion microwave sounders, AMSU and ATMS, respectively. The ESDR algorithm uses an optimal estimation approach and the implementation has a flexible, modular software structure to support experimentation and collaboration. Data record continuity benefits from the fact that the same algorithm can be applied to different sensors, simply by providing suitable configuration and data files. Developments to be presented include the impact of a radiance-based pre-classification method for the atmospheric background. In addition to improving retrieval performance, pre-classification has the potential to reduce the sensitivity of the retrievals to the climatological data from which the background estimate and its error covariance are derived. We will also discuss evaluation of a method for mitigating the effect of clouds on the radiances, and enhancements of the radiative transfer forward model.

GOCI Yonsei aerosol retrieval version 2 products: an improved algorithm and error analysis with uncertainty estimation from 5-year validation over East Asia

NASA Astrophysics Data System (ADS)

Choi, Myungje; Kim, Jhoon; Lee, Jaehwa; Kim, Mijin; Park, Young-Je; Holben, Brent; Eck, Thomas F.; Li, Zhengqiang; Song, Chul H.

2018-01-01

The Geostationary Ocean Color Imager (GOCI) Yonsei aerosol retrieval (YAER) version 1 algorithm was developed to retrieve hourly aerosol optical depth at 550 nm (AOD) and other subsidiary aerosol optical properties over East Asia. The GOCI YAER AOD had accuracy comparable to ground-based and other satellite-based observations but still had errors because of uncertainties in surface reflectance and simple cloud masking. In addition, near-real-time (NRT) processing was not possible because a monthly database for each year encompassing the day of retrieval was required for the determination of surface reflectance. This study describes the improved GOCI YAER algorithm version 2 (V2) for NRT processing with improved accuracy based on updates to the cloud-masking and surface-reflectance calculations using a multi-year Rayleigh-corrected reflectance and wind speed database, and inversion channels for surface conditions. The improved GOCI AOD τG is closer to that of the Moderate Resolution Imaging Spectroradiometer (MODIS) and Visible Infrared Imaging Radiometer Suite (VIIRS) AOD than was the case for AOD from the YAER V1 algorithm. The V2 τG has a lower median bias and higher ratio within the MODIS expected error range (0.60 for land and 0.71 for ocean) compared with V1 (0.49 for land and 0.62 for ocean) in a validation test against Aerosol Robotic Network (AERONET) AOD τA from 2011 to 2016. A validation using the Sun-Sky Radiometer Observation Network (SONET) over China shows similar results. The bias of error (τG - τA) is within -0.1 and 0.1, and it is a function of AERONET AOD and Ångström exponent (AE), scattering angle, normalized difference vegetation index (NDVI), cloud fraction and homogeneity of retrieved AOD, and observation time, month, and year. In addition, the diagnostic and prognostic expected error (PEE) of τG are estimated. The estimated PEE of GOCI V2 AOD is well correlated with the actual error over East Asia, and the GOCI V2 AOD over South Korea has a higher ratio within PEE than that over China and Japan.

A New Retrieval Algorithm for OMI NO2: Tropospheric Results and Comparisons with Measurements and Models

NASA Technical Reports Server (NTRS)

Swartz, W. H.; Bucesla, E. J.; Lamsal, L. N.; Celarier, E. A.; Krotkov, N. A.; Bhartia, P, K,; Strahan, S. E.; Gleason, J. F.; Herman, J.; Pickering, K.

2012-01-01

Nitrogen oxides (NOx =NO+NO2) are important atmospheric trace constituents that impact tropospheric air pollution chemistry and air quality. We have developed a new NASA algorithm for the retrieval of stratospheric and tropospheric NO2 vertical column densities using measurements from the nadir-viewing Ozone Monitoring Instrument (OMI) on NASA's Aura satellite. The new products rely on an improved approach to stratospheric NO2 column estimation and stratosphere-troposphere separation and a new monthly NO2 climatology based on the NASA Global Modeling Initiative chemistry-transport model. The retrieval does not rely on daily model profiles, minimizing the influence of a priori information. We evaluate the retrieved tropospheric NO2 columns using surface in situ (e.g., AQS/EPA), ground-based (e.g., DOAS), and airborne measurements (e.g., DISCOVER-AQ). The new, improved OMI tropospheric NO2 product is available at high spatial resolution for the years 200S-present. We believe that this product is valuable for the evaluation of chemistry-transport models, examining the spatial and temporal patterns of NOx emissions, constraining top-down NOx inventories, and for the estimation of NOx lifetimes.

New approach to the retrieval of AOD and its uncertainty from MISR observations over dark water

NASA Astrophysics Data System (ADS)

Witek, Marcin L.; Garay, Michael J.; Diner, David J.; Bull, Michael A.; Seidel, Felix C.

2018-01-01

A new method for retrieving aerosol optical depth (AOD) and its uncertainty from Multi-angle Imaging SpectroRadiometer (MISR) observations over dark water is outlined. MISR's aerosol retrieval algorithm calculates cost functions between observed and pre-simulated radiances for a range of AODs (from 0.0 to 3.0) and a prescribed set of aerosol mixtures. The previous version 22 (V22) operational algorithm considered only the AOD that minimized the cost function for each aerosol mixture and then used a combination of these values to compute the final, best estimate AOD and associated uncertainty. The new approach considers the entire range of cost functions associated with each aerosol mixture. The uncertainty of the reported AOD depends on a combination of (a) the absolute values of the cost functions for each aerosol mixture, (b) the widths of the cost function distributions as a function of AOD, and (c) the spread of the cost function distributions among the ensemble of mixtures. A key benefit of the new approach is that, unlike the V22 algorithm, it does not rely on empirical thresholds imposed on the cost function to determine the success or failure of a particular mixture. Furthermore, a new aerosol retrieval confidence index (ARCI) is established that can be used to screen high-AOD retrieval blunders caused by cloud contamination or other factors. Requiring ARCI ≥ 0.15 as a condition for retrieval success is supported through statistical analysis and outperforms the thresholds used in the V22 algorithm. The described changes to the MISR dark water algorithm will become operational in the new MISR aerosol product (V23), planned for release in 2017.

New Approach to the Retrieval of AOD and its Uncertainty from MISR Observations Over Dark Water

NASA Astrophysics Data System (ADS)

Witek, M. L.; Garay, M. J.; Diner, D. J.; Bull, M. A.; Seidel, F.

2017-12-01

A new method for retrieving aerosol optical depth (AOD) and its uncertainty from Multi-angle Imaging SpectroRadiometer (MISR) observations over dark water is outlined. MISR's aerosol retrieval algorithm calculates cost functions between observed and pre-simulated radiances for a range of AODs (from 0.0 to 3.0) and a prescribed set of aerosol mixtures. The previous Version 22 (V22) operational algorithm considered only the AOD that minimized the cost function for each aerosol mixture, then used a combination of these values to compute the final, "best estimate" AOD and associated uncertainty. The new approach considers the entire range of cost functions associated with each aerosol mixture. The uncertainty of the reported AOD depends on a combination of a) the absolute values of the cost functions for each aerosol mixture, b) the widths of the cost function distributions as a function of AOD, and c) the spread of the cost function distributions among the ensemble of mixtures. A key benefit of the new approach is that, unlike the V22 algorithm, it does not rely on arbitrary thresholds imposed on the cost function to determine the success or failure of a particular mixture. Furthermore, a new Aerosol Retrieval Confidence Index (ARCI) is established that can be used to screen high-AOD retrieval blunders caused by cloud contamination or other factors. Requiring ARCI≥0.15 as a condition for retrieval success is supported through statistical analysis and outperforms the thresholds used in the V22 algorithm. The described changes to the MISR dark water algorithm will become operational in the new MISR aerosol product (V23), planned for release in 2017.

Application of randomly oriented spheroids for retrieval of dust particle parameters from multiwavelength lidar measurements

NASA Astrophysics Data System (ADS)

Veselovskii, I.; Dubovik, O.; Kolgotin, A.; Lapyonok, T.; di Girolamo, P.; Summa, D.; Whiteman, D. N.; Mishchenko, M.; Tanré, D.

2010-11-01

Multiwavelength (MW) Raman lidars have demonstrated their potential to profile particle parameters; however, until now, the physical models used in retrieval algorithms for processing MW lidar data have been predominantly based on the Mie theory. This approach is applicable to the modeling of light scattering by spherically symmetric particles only and does not adequately reproduce the scattering by generally nonspherical desert dust particles. Here we present an algorithm based on a model of randomly oriented spheroids for the inversion of multiwavelength lidar data. The aerosols are modeled as a mixture of two aerosol components: one composed only of spherical and the second composed of nonspherical particles. The nonspherical component is an ensemble of randomly oriented spheroids with size-independent shape distribution. This approach has been integrated into an algorithm retrieving aerosol properties from the observations with a Raman lidar based on a tripled Nd:YAG laser. Such a lidar provides three backscattering coefficients, two extinction coefficients, and the particle depolarization ratio at a single or multiple wavelengths. Simulations were performed for a bimodal particle size distribution typical of desert dust particles. The uncertainty of the retrieved particle surface, volume concentration, and effective radius for 10% measurement errors is estimated to be below 30%. We show that if the effect of particle nonsphericity is not accounted for, the errors in the retrieved aerosol parameters increase notably. The algorithm was tested with experimental data from a Saharan dust outbreak episode, measured with the BASIL multiwavelength Raman lidar in August 2007. The vertical profiles of particle parameters as well as the particle size distributions at different heights were retrieved. It was shown that the algorithm developed provided substantially reasonable results consistent with the available independent information about the observed aerosol event.

Investigating the Use of a Simplified Aerosol Parameterization in Space-Based XCO2 Retrievals from OCO-2

NASA Astrophysics Data System (ADS)

Nelson, R. R.; O'Dell, C.

2017-12-01

The primary goal of OCO-2 is to use hyperspectral measurements of reflected near-infrared sunlight to retrieve the column-averaged dry-air mole fraction of carbon dioxide (XCO2) with high accuracy. This is only possible for measurements of scenes nearly free of optically thick clouds and aerosols. As some cloud or aerosol contamination will always be present, the OCO-2 retrieval algorithm includes clouds and aerosols as retrieved properties in its state vector. Information content analyses demonstrate that there are only 2-6 pieces of information about aerosols in the OCO-2 radiances. However, the upcoming OCO-2 algorithm (B8) attempts to retrieve 9 aerosol parameters; this over-fitting can hinder convergence and produce multiple solutions. In this work, we develop a simplified cloud and aerosol parameterization that intelligently reduces the number of retrieved parameters to 5 by only retrieving information about two aerosol layers: a lower tropospheric layer and an upper tropospheric / stratospheric layer. We retrieve the optical depth of each layer and the height of the lower tropospheric layer. Each of these layers contains a mixture of fine and coarse mode aerosol. In comparisons between OCO-2 XCO2 estimates and validation sources including TCCON, this scheme performs about as well as the more complicated OCO-2 retrieval algorithm, but has the potential benefits of more interpretable aerosol results, faster convergence, less nonlinearity, and greater throughput. We also investigate the dependence of our results on the optical properties of the fine and coarse mode aerosol types, such as their effective radii and the environmental relative humidity.

Monthly-Diurnal Water Budget Variability Over Gulf of Mexico-Caribbean Sea Basin from Satellite Observations

NASA Technical Reports Server (NTRS)

Smith, E. A.; Santos, P.

2006-01-01

This study presents results from a multi-satellite/multi-sensor retrieval system design d to obtain the atmospheric water budget over the open ocean. A combination of hourly-sampled monthly datasets derived from the GOES-8 5-channel Imager, the TRMM TMI radiometer, and the DMSP 7-channel passive microwave radiometers (SSM/I) have been acquired for the combined Gulf of Mexico-Caribbean Sea basin. Whereas the methodology has been tested over this basin, the retrieval system is designed for portability to any open-ocean region. Algorithm modules using the different datasets to retrieve individual geophysical parameters needed in the water budget equation are designed in a manner that takes advantage of the high temporal resolution of the GOES-8 measurements, as well as the physical relationships inherent to the TRMM and SSM/I passive microwave measurements in conjunction with water vapor, cloud liquid water, and rainfall. The methodology consists of retrieving the precipitation, surface evaporation, and vapor-cloud water storage terms in the atmospheric water balance equation from satellite techniques, with the water vapor advection term being obtained as the residue needed for balance. Thus, the intent is to develop a purely satellite-based method for obtaining the full set of terms in the atmospheric water budget equation without requiring in situ sounding information on the wind profile. The algorithm is validated by cross-checking all the algorithm components through multiple-algorithm retrieval intercomparisons. A further check on the validation is obtained by directly comparing water vapor transports into the targeted basin diagnosed from the satellite algorithms to those obtained observationally from a network of land-based upper air stations that nearly uniformly surround the basin, although it is fair to say that these checks are more effective in identifying problems in estimating vapor transports from a "leaky" operational radiosonde network than in verifying the transport estimates determined from the satellite algorithm system. Total columnar atmospheric water budget results are presented for an extended annual cycle consisting of the months of October-97, January-98, April-98, July-98,October-98, and January- 1999. These results are used to emphasize the changing relationship in E-P, as well as in the varying roles of storage and advection in balancing E-P both on daily and monthly time scales and on localized and basin space scales. Results from the algorithm-to-algorithm intercomparisons are also presented in the context of sensitivity testing to help understand the intrinsic uncertainties in evaluating the water budget terms by an all-satellite algorithm approach.

Retrieving the Height of Smoke and Dust Aerosols by Synergistic Use of VIIRS, OMPS, and CALIOP Observations

NASA Technical Reports Server (NTRS)

Lee, Jaehwa; Hsu, N. Christina; Bettenhausen, Corey; Sayer, Andrew M.; Seftor, Colin J.; Jeong, Myeong-Jae

2015-01-01

Aerosol Single scattering albedo and Height Estimation (ASHE) algorithm was first introduced in Jeong and Hsu (2008) to provide aerosol layer height as well as single scattering albedo (SSA) for biomass burning smoke aerosols. One of the advantages of this algorithm was that the aerosol layer height can be retrieved over broad areas, which had not been available from lidar observations only. The algorithm utilized aerosol properties from three different satellite sensors, i.e., aerosol optical depth (AOD) and Ångström exponent (AE) from Moderate Resolution Imaging Spectroradiometer (MODIS), UV aerosol index (UVAI) from Ozone Monitoring Instrument (OMI), and aerosol layer height from Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP). Here, we extend the application of the algorithm to Visible Infrared Imaging Radiometer Suite (VIIRS) and Ozone Mapping and Profiler Suite (OMPS) data. We also now include dust layers as well as smoke. Other updates include improvements in retrieving the AOD of nonspherical dust from VIIRS, better determination of the aerosol layer height from CALIOP, and more realistic input aerosol profiles in the forward model for better accuracy.

Global Contrast Based Salient Region Detection.

PubMed

Cheng, Ming-Ming; Mitra, Niloy J; Huang, Xiaolei; Torr, Philip H S; Hu, Shi-Min

2015-03-01

Automatic estimation of salient object regions across images, without any prior assumption or knowledge of the contents of the corresponding scenes, enhances many computer vision and computer graphics applications. We introduce a regional contrast based salient object detection algorithm, which simultaneously evaluates global contrast differences and spatial weighted coherence scores. The proposed algorithm is simple, efficient, naturally multi-scale, and produces full-resolution, high-quality saliency maps. These saliency maps are further used to initialize a novel iterative version of GrabCut, namely SaliencyCut, for high quality unsupervised salient object segmentation. We extensively evaluated our algorithm using traditional salient object detection datasets, as well as a more challenging Internet image dataset. Our experimental results demonstrate that our algorithm consistently outperforms 15 existing salient object detection and segmentation methods, yielding higher precision and better recall rates. We also show that our algorithm can be used to efficiently extract salient object masks from Internet images, enabling effective sketch-based image retrieval (SBIR) via simple shape comparisons. Despite such noisy internet images, where the saliency regions are ambiguous, our saliency guided image retrieval achieves a superior retrieval rate compared with state-of-the-art SBIR methods, and additionally provides important target object region information.

Satellite Sampling and Retrieval Errors in Regional Monthly Rain Estimates from TMI AMSR-E, SSM/I, AMSU-B and the TRMM PR

NASA Technical Reports Server (NTRS)

Fisher, Brad; Wolff, David B.

2010-01-01

Passive and active microwave rain sensors onboard earth-orbiting satellites estimate monthly rainfall from the instantaneous rain statistics collected during satellite overpasses. It is well known that climate-scale rain estimates from meteorological satellites incur sampling errors resulting from the process of discrete temporal sampling and statistical averaging. Sampling and retrieval errors ultimately become entangled in the estimation of the mean monthly rain rate. The sampling component of the error budget effectively introduces statistical noise into climate-scale rain estimates that obscure the error component associated with the instantaneous rain retrieval. Estimating the accuracy of the retrievals on monthly scales therefore necessitates a decomposition of the total error budget into sampling and retrieval error quantities. This paper presents results from a statistical evaluation of the sampling and retrieval errors for five different space-borne rain sensors on board nine orbiting satellites. Using an error decomposition methodology developed by one of the authors, sampling and retrieval errors were estimated at 0.25 resolution within 150 km of ground-based weather radars located at Kwajalein, Marshall Islands and Melbourne, Florida. Error and bias statistics were calculated according to the land, ocean and coast classifications of the surface terrain mask developed for the Goddard Profiling (GPROF) rain algorithm. Variations in the comparative error statistics are attributed to various factors related to differences in the swath geometry of each rain sensor, the orbital and instrument characteristics of the satellite and the regional climatology. The most significant result from this study found that each of the satellites incurred negative longterm oceanic retrieval biases of 10 to 30%.

Estimating the vegetation canopy height using micro-pulse photon-counting LiDAR data.

PubMed

Nie, Sheng; Wang, Cheng; Xi, Xiaohuan; Luo, Shezhou; Li, Guoyuan; Tian, Jinyan; Wang, Hongtao

2018-05-14

The upcoming space-borne LiDAR satellite Ice, Cloud and land Elevation Satellite-2 (ICESat-2) is scheduled to launch in 2018. Different from the waveform LiDAR system onboard the ICESat, ICESat-2 will use a micro-pulse photon-counting LiDAR system. Thus new data processing algorithms are required to retrieve vegetation canopy height from photon-counting LiDAR data. The objective of this paper is to develop and validate an automated approach for better estimating vegetation canopy height. The new proposed method consists of three key steps: 1) filtering out the noise photons by an effective noise removal algorithm based on localized statistical analysis; 2) separating ground returns from canopy returns using an iterative photon classification algorithm, and then determining ground surface; 3) generating canopy-top surface and calculating vegetation canopy height based on canopy-top and ground surfaces. This automatic vegetation height estimation approach was tested to the simulated ICESat-2 data produced from Sigma Space LiDAR data and Multiple Altimeter Beam Experimental LiDAR (MABEL) data, and the retrieved vegetation canopy heights were validated by canopy height models (CHMs) derived from airborne discrete-return LiDAR data. Results indicated that the estimated vegetation canopy heights have a relatively strong correlation with the reference vegetation heights derived from airborne discrete-return LiDAR data (R 2 and RMSE values ranging from 0.639 to 0.810 and 4.08 m to 4.56 m respectively). This means our new proposed approach is appropriate for retrieving vegetation canopy height from micro-pulse photon-counting LiDAR data.

Estimating Cloud optical thickness from SEVIRI, for air quality research, by implementing a semi-analytical cloud retrieval algorithm

NASA Astrophysics Data System (ADS)

Pandey, Praveen; De Ridder, Koen; van Looy, Stijn; van Lipzig, Nicole

2010-05-01

Clouds play an important role in Earth's climate system. As they affect radiation hence photolysis rate coefficients (ozone formation),they also affect the air quality at the surface of the earth. Thus, a satellite remote sensing technique is used to retrieve the cloud properties for air quality research. The geostationary satellite, Meteosat Second Generation (MSG) has onboard, the Spinning Enhanced Visible and Infrared Imager (SEVIRI). The channels in the wavelength 0.6 µm and 1.64 µm are used to retrieve cloud optical thickness (COT). The study domain is over Europe covering a region between 35°N-70°N and 5°W-30°E, centred over Belgium. The steps involved in pre-processing the EUMETSAT level 1.5 images are described, which includes, acquisition of digital count number, radiometric conversion using offsets and slopes, estimation of radiance and calculation of reflectance. The Sun-earth-satellite geometry also plays an important role. A semi-analytical cloud retrieval algorithm (Kokhanovsky et al., 2003) is implemented for the estimation of COT. This approach doesn't involve the conventional look-up table approach, hence it makes the retrieval independent of numerical radiative transfer solutions. The semi-analytical algorithm is implemented on a monthly dataset of SEVIRI level 1.5 images. Minimum reflectance in the visible channel, at each pixel, during the month is accounted as the surface albedo of the pixel. Thus, monthly variation of COT over the study domain is prepared. The result so obtained, is compared with the COT products of Satellite Application Facility on Climate Monitoring (CM SAF). Henceforth, an approach to assimilate the COT for air quality research is presented. Address of corresponding author: Praveen Pandey, VITO- Flemish Institute for Technological Research, Boeretang 200, B 2400, Mol, Belgium E-mail: praveen.pandey@vito.be

Vertical Profiles of Latent Heat Release and Their Retrieval for TOGA COARE Convective Systems Using a Cloud Resolving Model, SSM/I, and Ship-borne Radar Data

NASA Technical Reports Server (NTRS)

Tao, Wei-Kuo; Lang, S.; Simpson, J.; Olson, W. S.; Johnson, D.; Ferrier, B.; Kummerow, C.; Adler, R.

1999-01-01

Latent heating profiles associated with three (TOGA COARE) Tropical Ocean and Global Atmosphere Coupled Ocean Atmosphere Response Experiment active convective episodes (December 10-17 1992; December 19-27 1992; and February 9-13 1993) are examined using the Goddard Cumulus Ensemble (GCE) Model and retrieved by using the Goddard Convective and Stratiform Heating (CSH) algorithm . The following sources of rainfall information are input into the CSH algorithm: Special Sensor Microwave Imager (SSM/1), Radar and the GCE model. Diagnostically determined latent heating profiles calculated using 6 hourly soundings are used for validation. The GCE model simulated rainfall and latent heating profiles are in excellent agreement with those estimated by soundings. In addition, the typical convective and stratiform heating structures (or shapes) are well captured by the GCE model. Radar measured rainfall is smaller than that both estimated by the GCE model and SSM/I in all three different COARE IFA periods. SSM/I derived rainfall is more than the GCE model simulated for the December 19-27 and February 9-13 periods, but is in excellent agreement with the GCE model for the December 10-17 period. The GCE model estimated stratiform amount is about 50% for December 19-27, 42% for December 11-17 and 56% for the February 9-13 case. These results are consistent with large-scale analyses. The accurate estimates of stratiform amount is needed for good latent heating retrieval. A higher (lower) percentage of stratiform rain can imply a maximum heating rate at a higher (lower) altitude. The GCE model always simulates more stratiform rain (10 to 20%) than the radar for all three convective episodes. SSM/I derived stratiform amount is about 37% for December 19-27, 48% for December 11-17 and 41% for the February 9-13 case. Temporal variability of CSH algorithm retrieved latent heating profiles using either GCE model simulated or radar estimated rainfall and stratiform amount is in good agreement with that diagnostically determined for all three periods. However, less rainfall and a smaller stratiform percentage estimated by radar resulted in a weaker (underestimated) latent heating profile and a lower maximum latent heating level compared to those determined diagnostically. Rainfall information from SSM/I can not retrieve individual convective events due to poor temporal sampling. Nevertheless, this study suggests that a good 4r, rainfall retrieval from SSM/I for a convective event always leads to a good latent heating retrieval. Sensitivity testing has been performed and the results indicate that the SSM/I derived time averaged stratiform amount may be underestimated for December 19-27. Time averaged heating profiles derived from SSM/I, however, are not in bad agreement with those derived by soundings for the December 10-17 convective period. The heating retrievals may be more accurate for longer time scales provided there is no bias in the sampling.

Estimation of Multiple Parameters over Vegetated Surfaces by Integrating Optical-Thermal Remote Sensing Observations

NASA Astrophysics Data System (ADS)

Ma, H.

2016-12-01

Land surface parameters from remote sensing observations are critical in monitoring and modeling of global climate change and biogeochemical cycles. Current methods for estimating land surface parameters are generally parameter-specific algorithms and are based on instantaneous physical models, which result in spatial, temporal and physical inconsistencies in current global products. Besides, optical and Thermal Infrared (TIR) remote sensing observations are usually separated to use based on different models , and the Middle InfraRed (MIR) observations have received little attention due to the complexity of the radiometric signal that mixes both reflected and emitted fluxes. In this paper, we proposed a unified algorithm for simultaneously retrieving a total of seven land surface parameters, including Leaf Area Index (LAI), Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), land surface albedo, Land Surface Temperature (LST), surface emissivity, downward and upward longwave radiation, by exploiting remote sensing observations from visible to TIR domain based on a common physical Radiative Transfer (RT) model and a data assimilation framework. The coupled PROSPECT-VISIR and 4SAIL RT model were used for canopy reflectance modeling. At first, LAI was estimated using a data assimilation method that combines MODIS daily reflectance observation and a phenology model. The estimated LAI values were then input into the RT model to simulate surface spectral emissivity and surface albedo. Besides, the background albedo and the transmittance of solar radiation, and the canopy albedo were also calculated to produce FAPAR. Once the spectral emissivity of seven MODIS MIR to TIR bands were retrieved, LST can be estimated from the atmospheric corrected surface radiance by exploiting an optimization method. At last, the upward longwave radiation were estimated using the retrieved LST, broadband emissivity (converted from spectral emissivity) and the downward longwave radiation (modeled by MODTRAN). These seven parameters were validated over several representative sites with different biome type, and compared with MODIS and GLASS product. Results showed that this unified inversion algorithm can retrieve temporally complete and physical consistent land surface parameters with high accuracy.

Retrieval of radiative and microphysical properties of clouds from multispectral infrared measurements

NASA Astrophysics Data System (ADS)

Iwabuchi, Hironobu; Saito, Masanori; Tokoro, Yuka; Putri, Nurfiena Sagita; Sekiguchi, Miho

2016-12-01

Satellite remote sensing of the macroscopic, microphysical, and optical properties of clouds are useful for studying spatial and temporal variations of clouds at various scales and constraining cloud physical processes in climate and weather prediction models. Instead of using separate independent algorithms for different cloud properties, a unified, optimal estimation-based cloud retrieval algorithm is developed and applied to moderate resolution imaging spectroradiometer (MODIS) observations using ten thermal infrared bands. The model considers sensor configurations, background surface and atmospheric profile, and microphysical and optical models of ice and liquid cloud particles and radiative transfer in a plane-parallel, multilayered atmosphere. Measurement and model errors are thoroughly quantified from direct comparisons of clear-sky observations over the ocean with model calculations. Performance tests by retrieval simulations show that ice cloud properties are retrieved with high accuracy when cloud optical thickness (COT) is between 0.1 and 10. Cloud-top pressure is inferred with uncertainty lower than 10 % when COT is larger than 0.3. Applying the method to a tropical cloud system and comparing the results with the MODIS Collection 6 cloud product shows good agreement for ice cloud optical thickness when COT is less than about 5. Cloud-top height agrees well with estimates obtained by the CO2 slicing method used in the MODIS product. The present algorithm can detect optically thin parts at the edges of high clouds well in comparison with the MODIS product, in which these parts are recognized as low clouds by the infrared window method. The cloud thermodynamic phase in the present algorithm is constrained by cloud-top temperature, which tends not to produce results with an ice cloud that is too warm and liquid cloud that is too cold.

Intercomparison of snow depth retrievals over Arctic sea ice from radar data acquired by Operation IceBridge

NASA Astrophysics Data System (ADS)

Kwok, Ron; Kurtz, Nathan T.; Brucker, Ludovic; Ivanoff, Alvaro; Newman, Thomas; Farrell, Sinead L.; King, Joshua; Howell, Stephen; Webster, Melinda A.; Paden, John; Leuschen, Carl; MacGregor, Joseph A.; Richter-Menge, Jacqueline; Harbeck, Jeremy; Tschudi, Mark

2017-11-01

Since 2009, the ultra-wideband snow radar on Operation IceBridge (OIB; a NASA airborne mission to survey the polar ice covers) has acquired data in annual campaigns conducted during the Arctic and Antarctic springs. Progressive improvements in radar hardware and data processing methodologies have led to improved data quality for subsequent retrieval of snow depth. Existing retrieval algorithms differ in the way the air-snow (a-s) and snow-ice (s-i) interfaces are detected and localized in the radar returns and in how the system limitations are addressed (e.g., noise, resolution). In 2014, the Snow Thickness On Sea Ice Working Group (STOSIWG) was formed and tasked with investigating how radar data quality affects snow depth retrievals and how retrievals from the various algorithms differ. The goal is to understand the limitations of the estimates and to produce a well-documented, long-term record that can be used for understanding broader changes in the Arctic climate system. Here, we assess five retrieval algorithms by comparisons with field measurements from two ground-based campaigns, including the BRomine, Ozone, and Mercury EXperiment (BROMEX) at Barrow, Alaska; a field program by Environment and Climate Change Canada at Eureka, Nunavut; and available climatology and snowfall from ERA-Interim reanalysis. The aim is to examine available algorithms and to use the assessment results to inform the development of future approaches. We present results from these assessments and highlight key considerations for the production of a long-term, calibrated geophysical record of springtime snow thickness over Arctic sea ice.

The TOMS V9 Algorithm for OMPS Nadir Mapper Total Ozone: An Enhanced Design That Ensures Data Continuity

NASA Astrophysics Data System (ADS)

Haffner, D. P.; McPeters, R. D.; Bhartia, P. K.; Labow, G. J.

2015-12-01

The TOMS V9 total ozone algorithm will be applied to the OMPS Nadir Mapper instrument to supersede the exisiting V8.6 data product in operational processing and re-processing for public release. Becuase the quality of the V8.6 data is already quite high, enchancements in V9 are mainly with information provided by the retrieval and simplifcations to the algorithm. The design of the V9 algorithm has been influenced by improvements both in our knowledge of atmospheric effects, such as those of clouds made possible by studies with OMI, and also limitations in the V8 algorithms applied to both OMI and OMPS. But the namesake instruments of the TOMS algorithm are substantially more limited in their spectral and noise characterisitics, and a requirement of our algorithm is to also apply the algorithm to these discrete band spectrometers which date back to 1978. To achieve continuity for all these instruments, the TOMS V9 algorithm continues to use radiances in discrete bands, but now uses Rodgers optimal estimation to retrieve a coarse profile and provide uncertainties for each retrieval. The algorithm remains capable of achieving high accuracy results with a small number of discrete wavelengths, and in extreme cases, such as unusual profile shapes and high solar zenith angles, the quality of the retrievals is improved. Despite the intended design to use limited wavlenegths, the algorithm can also utilitze additional wavelengths from hyperspectral sensors like OMPS to augment the retreival's error detection and information content; for example SO2 detection and correction of Ring effect on atmospheric radiances. We discuss these and other aspects of the V9 algorithm as it will be applied to OMPS, and will mention potential improvements which aim to take advantage of a synergy with OMPS Limb Profiler and Nadir Mapper to further improve the quality of total ozone from the OMPS instrument.

Validation of MODIS-derived bidirectional reflectivity retrieval algorithm in mid-infrared channel with field measurements.

PubMed

Tang, Bo-Hui; Wu, Hua-; Li, Zhao-Liang; Nerry, Françoise

2012-07-30

This work addressed the validation of the MODIS-derived bidirectional reflectivity retrieval algorithm in mid-infrared (MIR) channel, proposed by Tang and Li [Int. J. Remote Sens. 29, 4907 (2008)], with ground-measured data, which were collected from a field campaign that took place in June 2004 at the ONERA (Office National d'Etudes et de Recherches Aérospatiales) center of Fauga-Mauzac, on the PIRRENE (Programme Interdisciplinaire de Recherche sur la Radiométrie en Environnement Extérieur) experiment site [Opt. Express 15, 12464 (2007)]. The leaving-surface spectral radiances measured by a BOMEM (MR250 Series) Fourier transform interferometer were used to calculate the ground brightness temperatures with the combination of the inversion of the Planck function and the spectral response functions of MODIS channels 22 and 23, and then to estimate the ground brightness temperature without the contribution of the solar direct beam and the bidirectional reflectivity by using Tang and Li's proposed algorithm. On the other hand, the simultaneously measured atmospheric profiles were used to obtain the atmospheric parameters and then to calculate the ground brightness temperature without the contribution of the solar direct beam, based on the atmospheric radiative transfer equation in the MIR region. Comparison of those two kinds of brightness temperature obtained by two different methods indicated that the Root Mean Square Error (RMSE) between the brightness temperatures estimated respectively using Tang and Li's algorithm and the atmospheric radiative transfer equation is 1.94 K. In addition, comparison of the hemispherical-directional reflectances derived by Tang and Li's algorithm with those obtained from the field measurements showed that the RMSE is 0.011, which indicates that Tang and Li's algorithm is feasible to retrieve the bidirectional reflectivity in MIR channel from MODIS data.

Least Square Approach for Estimating of Land Surface Temperature from LANDSAT-8 Satellite Data Using Radiative Transfer Equation

NASA Astrophysics Data System (ADS)

Jouybari-Moghaddam, Y.; Saradjian, M. R.; Forati, A. M.

2017-09-01

Land Surface Temperature (LST) is one of the significant variables measured by remotely sensed data, and it is applied in many environmental and Geoscience studies. The main aim of this study is to develop an algorithm to retrieve the LST from Landsat-8 satellite data using Radiative Transfer Equation (RTE). However, LST can be retrieved from RTE, but, since the RTE has two unknown parameters including LST and surface emissivity, estimating LST from RTE is an under the determined problem. In this study, in order to solve this problem, an approach is proposed an equation set includes two RTE based on Landsat-8 thermal bands (i.e.: band 10 and 11) and two additional equations based on the relation between the Normalized Difference Vegetation Index (NDVI) and emissivity of Landsat-8 thermal bands by using simulated data for Landsat-8 bands. The iterative least square approach was used for solving the equation set. The LST derived from proposed algorithm is evaluated by the simulated dataset, built up by MODTRAN. The result shows the Root Mean Squared Error (RMSE) is less than 1.18°K. Therefore; the proposed algorithm can be a suitable and robust method to retrieve the LST from Landsat-8 satellite data.

Development of GK-2A cloud optical and microphysical properties retrieval algorithm

NASA Astrophysics Data System (ADS)

Yang, Y.; Yum, S. S.; Um, J.

2017-12-01

Cloud and aerosol radiative forcing is known to be one of the the largest uncertainties in climate change prediction. To reduce this uncertainty, remote sensing observation of cloud radiative and microphysical properties have been used since 1970s and the corresponding remote sensing techniques and instruments have been developed. As a part of such effort, Geo-KOMPSAT-2A (Geostationary Korea Multi-Purpose Satellite-2A, GK-2A) will be launched in 2018. On the GK-2A, the Advanced Meteorological Imager (AMI) is primary instrument which have 3 visible, 3 near-infrared, and 10 infrared channels. To retrieve optical and microphysical properties of clouds using AMI measurements, the preliminary version of new cloud retrieval algorithm for GK-2A was developed and several validation tests were conducted. This algorithm retrieves cloud optical thickness (COT), cloud effective radius (CER), liquid water path (LWP), and ice water path (IWP), so we named this algorithm as Daytime Cloud Optical thickness, Effective radius and liquid and ice Water path (DCOEW). The DCOEW uses cloud reflectance at visible and near-infrared channels as input data. An optimal estimation (OE) approach that requires appropriate a-priori values and measurement error information is used to retrieve COT and CER. LWP and IWP are calculated using empirical relationships between COT/CER and cloud water path that were determined previously. To validate retrieved cloud properties, we compared DCOEW output data with other operational satellite data. For COT and CER validation, we used two different data sets. To compare algorithms that use cloud reflectance at visible and near-IR channels as input data, MODIS MYD06 cloud product was selected. For the validation with cloud products that are based on microwave measurements, COT(2B-TAU)/CER(2C-ICE) data retrieved from CloudSat cloud profiling radar (W-band, 94 GHz) was used. For cloud water path validation, AMSR-2 Level-3 Cloud liquid water data was used. Detailed results will be shown at the conference.

Global Precipitation Measurement (GPM) Ground Validation: Plans and Preparations

NASA Technical Reports Server (NTRS)

Schwaller, M.; Bidwell, S.; Durning, F. J.; Smith, E.

2004-01-01

The Global Precipitation Measurement (GPM) program is an international partnership led by the National Aeronautics and Space Administration (NASA) and the Japan Aerospace Exploration Agency (JAXA). GPM will improve climate, weather, and hydro-meteorological forecasts through more frequent and more accurate measurement of precipitation across the globe. This paper describes the concept, the planning, and the preparations for Ground Validation within the GPM program. Ground Validation (GV) plays an important role in the program by investigating and quantitatively assessing the errors within the satellite retrievals. These quantitative estimates of retrieval errors will assist the scientific community by bounding the errors within their research products. The two fundamental requirements of the GPM Ground Validation program are: (1) error characterization of the precipitation retrievals and (2) continual improvement of the satellite retrieval algorithms. These two driving requirements determine the measurements, instrumentation, and location for ground observations. This paper outlines GV plans for estimating the systematic and random components of retrieval error and for characterizing the spatial p d temporal structure of the error and plans for algorithm improvement in which error models are developed and experimentally explored to uncover the physical causes of errors within the retrievals. This paper discusses NASA locations for GV measurements as well as anticipated locations from international GPM partners. NASA's primary locations for validation measurements are an oceanic site at Kwajalein Atoll in the Republic of the Marshall Islands and a continental site in north-central Oklahoma at the U.S. Department of Energy's Atmospheric Radiation Measurement Program site.

Integrating uncertainty propagation in GNSS radio occultation retrieval: from excess phase to atmospheric bending angle profiles

NASA Astrophysics Data System (ADS)

Schwarz, Jakob; Kirchengast, Gottfried; Schwaerz, Marc

2018-05-01

Global Navigation Satellite System (GNSS) radio occultation (RO) observations are highly accurate, long-term stable data sets and are globally available as a continuous record from 2001. Essential climate variables for the thermodynamic state of the free atmosphere - such as pressure, temperature, and tropospheric water vapor profiles (involving background information) - can be derived from these records, which therefore have the potential to serve as climate benchmark data. However, to exploit this potential, atmospheric profile retrievals need to be very accurate and the remaining uncertainties quantified and traced throughout the retrieval chain from raw observations to essential climate variables. The new Reference Occultation Processing System (rOPS) at the Wegener Center aims to deliver such an accurate RO retrieval chain with integrated uncertainty propagation. Here we introduce and demonstrate the algorithms implemented in the rOPS for uncertainty propagation from excess phase to atmospheric bending angle profiles, for estimated systematic and random uncertainties, including vertical error correlations and resolution estimates. We estimated systematic uncertainty profiles with the same operators as used for the basic state profiles retrieval. The random uncertainty is traced through covariance propagation and validated using Monte Carlo ensemble methods. The algorithm performance is demonstrated using test day ensembles of simulated data as well as real RO event data from the satellite missions CHAllenging Minisatellite Payload (CHAMP); Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC); and Meteorological Operational Satellite A (MetOp). The results of the Monte Carlo validation show that our covariance propagation delivers correct uncertainty quantification from excess phase to bending angle profiles. The results from the real RO event ensembles demonstrate that the new uncertainty estimation chain performs robustly. Together with the other parts of the rOPS processing chain this part is thus ready to provide integrated uncertainty propagation through the whole RO retrieval chain for the benefit of climate monitoring and other applications.

Improvements in Night-Time Low Cloud Detection and MODIS-Style Cloud Optical Properties from MSG SEVIRI

NASA Technical Reports Server (NTRS)

Wind, Galina (Gala); Platnick, Steven; Riedi, Jerome

2011-01-01

The MODIS cloud optical properties algorithm (MOD06IMYD06 for Terra and Aqua MODIS, respectively) slated for production in Data Collection 6 has been adapted to execute using available channels on MSG SEVIRI. Available MODIS-style retrievals include IR Window-derived cloud top properties, using the new Collection 6 cloud top properties algorithm, cloud optical thickness from VISINIR bands, cloud effective radius from 1.6 and 3.7Jlm and cloud ice/water path. We also provide pixel-level uncertainty estimate for successful retrievals. It was found that at nighttime the SEVIRI cloud mask tends to report unnaturally low cloud fraction for marine stratocumulus clouds. A correction algorithm that improves detection of such clouds has been developed. We will discuss the improvements to nighttime low cloud detection for SEVIRI and show examples and comparisons with MODIS and CALIPSO. We will also show examples of MODIS-style pixel-level (Level-2) cloud retrievals for SEVIRI with comparisons to MODIS.

Highlights of TOMS Version 9 Total Ozone Algorithm

NASA Technical Reports Server (NTRS)

Bhartia, Pawan; Haffner, David

2012-01-01

The fundamental basis of TOMS total ozone algorithm was developed some 45 years ago by Dave and Mateer. It was designed to estimate total ozone from satellite measurements of the backscattered UV radiances at few discrete wavelengths in the Huggins ozone absorption band (310-340 nm). Over the years, as the need for higher accuracy in measuring total ozone from space has increased, several improvements to the basic algorithms have been made. They include: better correction for the effects of aerosols and clouds, an improved method to account for the variation in shape of ozone profiles with season, latitude, and total ozone, and a multi-wavelength correction for remaining profile shape errors. These improvements have made it possible to retrieve total ozone with just 3 spectral channels of moderate spectral resolution (approx. 1 nm) with accuracy comparable to state-of-the-art spectral fitting algorithms like DOAS that require high spectral resolution measurements at large number of wavelengths. One of the deficiencies of the TOMS algorithm has been that it doesn't provide an error estimate. This is a particular problem in high latitudes when the profile shape errors become significant and vary with latitude, season, total ozone, and instrument viewing geometry. The primary objective of the TOMS V9 algorithm is to account for these effects in estimating the error bars. This is done by a straightforward implementation of the Rodgers optimum estimation method using a priori ozone profiles and their error covariances matrices constructed using Aura MLS and ozonesonde data. The algorithm produces a vertical ozone profile that contains 1-2.5 pieces of information (degrees of freedom of signal) depending upon solar zenith angle (SZA). The profile is integrated to obtain the total column. We provide information that shows the altitude range in which the profile is best determined by the measurements. One can use this information in data assimilation and analysis. A side benefit of this algorithm is that it is considerably simpler than the present algorithm that uses a database of 1512 profiles to retrieve total ozone. These profiles are tedious to construct and modify. Though conceptually similar to the SBUV V8 algorithm that was developed about a decade ago, the SBUV and TOMS V9 algorithms differ in detail. The TOMS algorithm uses 3 wavelengths to retrieve the profile while the SBUV algorithm uses 6-9 wavelengths, so TOMS provides less profile information. However both algorithms have comparable total ozone information and TOMS V9 can be easily adapted to use additional wavelengths from instruments like GOME, OMI and OMPS to provide better profile information at smaller SZAs. The other significant difference between the two algorithms is that while the SBUV algorithm has been optimized for deriving monthly zonal means by making an appropriate choice of the a priori error covariance matrix, the TOMS algorithm has been optimized for tracking short-term variability using month and latitude dependent covariance matrices.

Retrieval of ice cloud properties using an optimal estimation algorithm and MODIS infrared observations. Part I: Forward model, error analysis, and information content

PubMed Central

Wang, Chenxi; Platnick, Steven; Zhang, Zhibo; Meyer, Kerry; Yang, Ping

2018-01-01

An optimal estimation (OE) retrieval method is developed to infer three ice cloud properties simultaneously: optical thickness (τ), effective radius (reff), and cloud-top height (h). This method is based on a fast radiative transfer (RT) model and infrared (IR) observations from the MODerate resolution Imaging Spectroradiometer (MODIS). This study conducts thorough error and information content analyses to understand the error propagation and performance of retrievals from various MODIS band combinations under different cloud/atmosphere states. Specifically, the algorithm takes into account four error sources: measurement uncertainty, fast RT model uncertainty, uncertainties in ancillary datasets (e.g., atmospheric state), and assumed ice crystal habit uncertainties. It is found that the ancillary and ice crystal habit error sources dominate the MODIS IR retrieval uncertainty and cannot be ignored. The information content analysis shows that, for a given ice cloud, the use of four MODIS IR observations is sufficient to retrieve the three cloud properties. However, the selection of MODIS IR bands that provide the most information and their order of importance varies with both the ice cloud properties and the ambient atmospheric and the surface states. As a result, this study suggests the inclusion of all MODIS IR bands in practice since little a priori information is available. PMID:29707470

Retrieval of Ice Cloud Properties Using an Optimal Estimation Algorithm and MODIS Infrared Observations. Part I: Forward Model, Error Analysis, and Information Content

NASA Technical Reports Server (NTRS)

Wang, Chenxi; Platnick, Steven; Zhang, Zhibo; Meyer, Kerry; Yang, Ping

2016-01-01

An optimal estimation (OE) retrieval method is developed to infer three ice cloud properties simultaneously: optical thickness (tau), effective radius (r(sub eff)), and cloud-top height (h). This method is based on a fast radiative transfer (RT) model and infrared (IR) observations from the MODerate resolution Imaging Spectroradiometer (MODIS). This study conducts thorough error and information content analyses to understand the error propagation and performance of retrievals from various MODIS band combinations under different cloud/atmosphere states. Specifically, the algorithm takes into account four error sources: measurement uncertainty, fast RT model uncertainty, uncertainties in ancillary datasets (e.g., atmospheric state), and assumed ice crystal habit uncertainties. It is found that the ancillary and ice crystal habit error sources dominate the MODIS IR retrieval uncertainty and cannot be ignored. The information content analysis shows that, for a given ice cloud, the use of four MODIS IR observations is sufficient to retrieve the three cloud properties. However, the selection of MODIS IR bands that provide the most information and their order of importance varies with both the ice cloud properties and the ambient atmospheric and the surface states. As a result, this study suggests the inclusion of all MODIS IR bands in practice since little a priori information is available.

Retrieval of Ice Cloud Properties Using an Optimal Estimation Algorithm and MODIS Infrared Observations. Part I: Forward Model, Error Analysis, and Information Content

NASA Technical Reports Server (NTRS)

Wang, Chenxi; Platnick, Steven; Zhang, Zhibo; Meyer, Kerry; Yang, Ping

2016-01-01

An optimal estimation (OE) retrieval method is developed to infer three ice cloud properties simultaneously: optical thickness (tau), effective radius (r(sub eff)), and cloud top height (h). This method is based on a fast radiative transfer (RT) model and infrared (IR) observations from the MODerate resolution Imaging Spectroradiometer (MODIS). This study conducts thorough error and information content analyses to understand the error propagation and performance of retrievals from various MODIS band combinations under different cloud/atmosphere states. Specifically, the algorithm takes into account four error sources: measurement uncertainty, fast RT model uncertainty, uncertainties in ancillary data sets (e.g., atmospheric state), and assumed ice crystal habit uncertainties. It is found that the ancillary and ice crystal habit error sources dominate the MODIS IR retrieval uncertainty and cannot be ignored. The information content analysis shows that for a given ice cloud, the use of four MODIS IR observations is sufficient to retrieve the three cloud properties. However, the selection of MODIS IR bands that provide the most information and their order of importance varies with both the ice cloud properties and the ambient atmospheric and the surface states. As a result, this study suggests the inclusion of all MODIS IR bands in practice since little a priori information is available.

Recent Progress in Development of SWOT River Discharge Algorithms

NASA Astrophysics Data System (ADS)

Pavelsky, Tamlin M.; Andreadis, Konstantinos; Biancamaria, Sylvian; Durand, Michael; Moller, Dewlyn; Rodriguez, Enersto; Smith, Laurence C.

2013-09-01

The Surface Water and Ocean Topography (SWOT) Mission is a satellite mission under joint development by NASA and CNES. The mission will use interferometric synthetic aperture radar technology to continuously map, for the first time, water surface elevations and water surface extents in rivers, lakes, and oceans at high spatial resolutions. Among the primary goals of SWOT is the accurate retrieval of river discharge directly from SWOT measurements. Although it is central to the SWOT mission, discharge retrieval represents a substantial challenge due to uncertainties in SWOT measurements and because traditional discharge algorithms are not optimized for SWOT-like measurements. However, recent work suggests that SWOT may also have unique strengths that can be exploited to yield accurate estimates of discharge. A NASA-sponsored workshop convened June 18-20, 2012 at the University of North Carolina focused on progress and challenges in developing SWOT-specific discharge algorithms. Workshop participants agreed that the only viable approach to discharge estimation will be based on a slope-area scaling method such as Manning's equation, but modified slightly to reflect the fact that SWOT will estimate reach-averaged rather than cross- sectional discharge. While SWOT will provide direct measurements of some key parameters such as width and slope, others such as baseflow depth and channel roughness must be estimated. Fortunately, recent progress has suggested several algorithms that may allow the simultaneous estimation of these quantities from SWOT observations by using multitemporal observations over several adjacent reaches. However, these algorithms will require validation, which will require the collection of new field measurements, airborne imagery from AirSWOT (a SWOT analogue), and compilation of global datasets of channel roughness, river width, and other relevant variables.

Snowfall Rate Retrieval using NPP ATMS Passive Microwave Measurements

NASA Technical Reports Server (NTRS)

Meng, Huan; Ferraro, Ralph; Kongoli, Cezar; Wang, Nai-Yu; Dong, Jun; Zavodsky, Bradley; Yan, Banghua; Zhao, Limin

2014-01-01

Passive microwave measurements at certain high frequencies are sensitive to the scattering effect of snow particles and can be utilized to retrieve snowfall properties. Some of the microwave sensors with snowfall sensitive channels are Advanced Microwave Sounding Unit (AMSU), Microwave Humidity Sounder (MHS) and Advance Technology Microwave Sounder (ATMS). ATMS is the follow-on sensor to AMSU and MHS. Currently, an AMSU and MHS based land snowfall rate (SFR) product is running operationally at NOAA/NESDIS. Based on the AMSU/MHS SFR, an ATMS SFR algorithm has been developed recently. The algorithm performs retrieval in three steps: snowfall detection, retrieval of cloud properties, and estimation of snow particle terminal velocity and snowfall rate. The snowfall detection component utilizes principal component analysis and a logistic regression model. The model employs a combination of temperature and water vapor sounding channels to detect the scattering signal from falling snow and derive the probability of snowfall (Kongoli et al., 2014). In addition, a set of NWP model based filters is also employed to improve the accuracy of snowfall detection. Cloud properties are retrieved using an inversion method with an iteration algorithm and a two-stream radiative transfer model (Yan et al., 2008). A method developed by Heymsfield and Westbrook (2010) is adopted to calculate snow particle terminal velocity. Finally, snowfall rate is computed by numerically solving a complex integral. The ATMS SFR product is validated against radar and gauge snowfall data and shows that the ATMS algorithm outperforms the AMSU/MHS SFR.

What is the Uncertainty in MODIS Aerosol Optical Depth in the Vicinity of Clouds?

NASA Technical Reports Server (NTRS)

Patadia, Falguni; Levy, Rob; Mattoo, Shana

2017-01-01

MODIS dark-target (DT) algorithm retrieves aerosol optical depth (AOD) using a Look Up Table (LUT) approach. Global comparison of AOD (Collection 6 ) with ground-based sun photometer gives an Estimated Error (EE) of +/-(0.04 + 10%) over ocean. However, EE does not represent per-retrieval uncertainty. For retrievals that are biased high compared to AERONET, here we aim to closely examine the contribution of biases due to presence of clouds and per-pixel retrieval uncertainty. We have characterized AOD uncertainty at 550 nm, due to standard deviation of reflectance in 10 km retrieval region, uncertainty related to gas (H2O, O3) absorption, surface albedo, and aerosol models. The uncertainty in retrieved AOD seems to lie within the estimated over ocean error envelope of +/-(0.03+10%). Regions between broken clouds tend to have higher uncertainty. Compared to C6 AOD, a retrieval omitting observations in the vicinity of clouds (< or = 1 km) is biased by about +/- 0.05. For homogeneous aerosol distribution, clear sky retrievals show near zero bias. Close look at per-pixel reflectance histograms suggests retrieval possibility using median reflectance values.

An Algorithm For Climate-Quality Atmospheric Profiling Continuity From EOS Aqua To Suomi-NPP

NASA Astrophysics Data System (ADS)

Moncet, J. L.

2015-12-01

We will present results from an algorithm that is being developed to produce climate-quality atmospheric profiling earth system data records (ESDRs) for application to hyperspectral sounding instrument data from Suomi-NPP, EOS Aqua, and other spacecraft. The current focus is on data from the S-NPP Cross-track Infrared Sounder (CrIS) and Advanced Technology Microwave Sounder (ATMS) instruments as well as the Atmospheric InfraRed Sounder (AIRS) on EOS Aqua. The algorithm development at Atmospheric and Environmental Research (AER) has common heritage with the optimal estimation (OE) algorithm operationally processing S-NPP data in the Interface Data Processing Segment (IDPS), but the ESDR algorithm has a flexible, modular software structure to support experimentation and collaboration and has several features adapted to the climate orientation of ESDRs. Data record continuity benefits from the fact that the same algorithm can be applied to different sensors, simply by providing suitable configuration and data files. The radiative transfer component uses an enhanced version of optimal spectral sampling (OSS) with updated spectroscopy, treatment of emission that is not in local thermodynamic equilibrium (non-LTE), efficiency gains with "global" optimal sampling over all channels, and support for channel selection. The algorithm is designed for adaptive treatment of clouds, with capability to apply "cloud clearing" or simultaneous cloud parameter retrieval, depending on conditions. We will present retrieval results demonstrating the impact of a new capability to perform the retrievals on sigma or hybrid vertical grid (as opposed to a fixed pressure grid), which particularly affects profile accuracy over land with variable terrain height and with sharp vertical structure near the surface. In addition, we will show impacts of alternative treatments of regularization of the inversion. While OE algorithms typically implement regularization by using background estimates from climatological or numerical forecast data, those sources are problematic for climate applications due to the imprint of biases from past climate analyses or from model error.

Water, Energy, and Carbon with Artificial Neural Networks (WECANN): A statistically-based estimate of global surface turbulent fluxes and gross primary productivity using solar-induced fluorescence.

PubMed

Alemohammad, Seyed Hamed; Fang, Bin; Konings, Alexandra G; Aires, Filipe; Green, Julia K; Kolassa, Jana; Miralles, Diego; Prigent, Catherine; Gentine, Pierre

2017-01-01

A new global estimate of surface turbulent fluxes, latent heat flux (LE) and sensible heat flux (H), and gross primary production (GPP) is developed using a machine learning approach informed by novel remotely sensed Solar-Induced Fluorescence (SIF) and other radiative and meteorological variables. This is the first study to jointly retrieve LE, H and GPP using SIF observations. The approach uses an artificial neural network (ANN) with a target dataset generated from three independent data sources, weighted based on triple collocation (TC) algorithm. The new retrieval, named Water, Energy, and Carbon with Artificial Neural Networks (WECANN), provides estimates of LE, H and GPP from 2007 to 2015 at 1° × 1° spatial resolution and on monthly time resolution. The quality of ANN training is assessed using the target data, and the WECANN retrievals are evaluated using eddy covariance tower estimates from FLUXNET network across various climates and conditions. When compared to eddy covariance estimates, WECANN typically outperforms other products, particularly for sensible and latent heat fluxes. Analysing WECANN retrievals across three extreme drought and heatwave events demonstrates the capability of the retrievals in capturing the extent of these events. Uncertainty estimates of the retrievals are analysed and the inter-annual variability in average global and regional fluxes show the impact of distinct climatic events - such as the 2015 El Niño - on surface turbulent fluxes and GPP.

Water, Energy, and Carbon with Artificial Neural Networks (WECANN): a statistically based estimate of global surface turbulent fluxes and gross primary productivity using solar-induced fluorescence

NASA Astrophysics Data System (ADS)

Hamed Alemohammad, Seyed; Fang, Bin; Konings, Alexandra G.; Aires, Filipe; Green, Julia K.; Kolassa, Jana; Miralles, Diego; Prigent, Catherine; Gentine, Pierre

2017-09-01

A new global estimate of surface turbulent fluxes, latent heat flux (LE) and sensible heat flux (H), and gross primary production (GPP) is developed using a machine learning approach informed by novel remotely sensed solar-induced fluorescence (SIF) and other radiative and meteorological variables. This is the first study to jointly retrieve LE, H, and GPP using SIF observations. The approach uses an artificial neural network (ANN) with a target dataset generated from three independent data sources, weighted based on a triple collocation (TC) algorithm. The new retrieval, named Water, Energy, and Carbon with Artificial Neural Networks (WECANN), provides estimates of LE, H, and GPP from 2007 to 2015 at 1° × 1° spatial resolution and at monthly time resolution. The quality of ANN training is assessed using the target data, and the WECANN retrievals are evaluated using eddy covariance tower estimates from the FLUXNET network across various climates and conditions. When compared to eddy covariance estimates, WECANN typically outperforms other products, particularly for sensible and latent heat fluxes. Analyzing WECANN retrievals across three extreme drought and heat wave events demonstrates the capability of the retrievals to capture the extent of these events. Uncertainty estimates of the retrievals are analyzed and the interannual variability in average global and regional fluxes shows the impact of distinct climatic events - such as the 2015 El Niño - on surface turbulent fluxes and GPP.

Connecting Satellite-Based Precipitation Estimates to Users

NASA Technical Reports Server (NTRS)

Huffman, George J.; Bolvin, David T.; Nelkin, Eric

2018-01-01

Beginning in 1997, the Merged Precipitation Group at NASA Goddard has distributed gridded global precipitation products built by combining satellite and surface gauge data. This started with the Global Precipitation Climatology Project (GPCP), then the Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA), and recently the Integrated Multi-satellitE Retrievals for the Global Precipitation Measurement (GPM) mission (IMERG). This 20+-year (and on-going) activity has yielded an important set of insights and lessons learned for making state-of-the-art precipitation data accessible to the diverse communities of users. Merged-data products critically depend on the input sensors and the retrieval algorithms providing accurate, reliable estimates, but it is also important to provide ancillary information that helps users determine suitability for their application. We typically provide fields of estimated random error, and recently reintroduced the quality index concept at user request. Also at user request we have added a (diagnostic) field of estimated precipitation phase. Over time, increasingly more ancillary fields have been introduced for intermediate products that give expert users insight into the detailed performance of the combination algorithm, such as individual merged microwave and microwave-calibrated infrared estimates, the contributing microwave sensor types, and the relative influence of the infrared estimate.

Orbiting Carbon Observatory-2 (OCO-2) cloud screening algorithms: validation against collocated MODIS and CALIOP data

NASA Astrophysics Data System (ADS)

Taylor, Thomas E.; O'Dell, Christopher W.; Frankenberg, Christian; Partain, Philip T.; Cronk, Heather Q.; Savtchenko, Andrey; Nelson, Robert R.; Rosenthal, Emily J.; Chang, Albert Y.; Fisher, Brenden; Osterman, Gregory B.; Pollock, Randy H.; Crisp, David; Eldering, Annmarie; Gunson, Michael R.

2016-03-01

The objective of the National Aeronautics and Space Administration's (NASA) Orbiting Carbon Observatory-2 (OCO-2) mission is to retrieve the column-averaged carbon dioxide (CO2) dry air mole fraction (XCO2) from satellite measurements of reflected sunlight in the near-infrared. These estimates can be biased by clouds and aerosols, i.e., contamination, within the instrument's field of view. Screening of the most contaminated soundings minimizes unnecessary calls to the computationally expensive Level 2 (L2) XCO2 retrieval algorithm. Hence, robust cloud screening methods have been an important focus of the OCO-2 algorithm development team. Two distinct, computationally inexpensive cloud screening algorithms have been developed for this application. The A-Band Preprocessor (ABP) retrieves the surface pressure using measurements in the 0.76 µm O2 A band, neglecting scattering by clouds and aerosols, which introduce photon path-length differences that can cause large deviations between the expected and retrieved surface pressure. The Iterative Maximum A Posteriori (IMAP) Differential Optical Absorption Spectroscopy (DOAS) Preprocessor (IDP) retrieves independent estimates of the CO2 and H2O column abundances using observations taken at 1.61 µm (weak CO2 band) and 2.06 µm (strong CO2 band), while neglecting atmospheric scattering. The CO2 and H2O column abundances retrieved in these two spectral regions differ significantly in the presence of cloud and scattering aerosols. The combination of these two algorithms, which are sensitive to different features in the spectra, provides the basis for cloud screening of the OCO-2 data set.To validate the OCO-2 cloud screening approach, collocated measurements from NASA's Moderate Resolution Imaging Spectrometer (MODIS), aboard the Aqua platform, were compared to results from the two OCO-2 cloud screening algorithms. With tuning of algorithmic threshold parameters that allows for processing of ≃ 20-25 % of all OCO-2 soundings, agreement between the OCO-2 and MODIS cloud screening methods is found to be ≃ 85 % over four 16-day orbit repeat cycles in both the winter (December) and spring (April-May) for OCO-2 nadir-land, glint-land and glint-water observations.No major, systematic, spatial or temporal dependencies were found, although slight differences in the seasonal data sets do exist and validation is more problematic with increasing solar zenith angle and when surfaces are covered in snow and ice and have complex topography. To further analyze the performance of the cloud screening algorithms, an initial comparison of OCO-2 observations was made to collocated measurements from the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) aboard the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO). These comparisons highlight the strength of the OCO-2 cloud screening algorithms in identifying high, thin clouds but suggest some difficulty in identifying some clouds near the surface, even when the optical thicknesses are greater than 1.

New approaches to removing cloud shadows and evaluating the 380 nm surface reflectance for improved aerosol optical thickness retrievals from the GOSAT/TANSO-Cloud and Aerosol Imager

NASA Astrophysics Data System (ADS)

Fukuda, Satoru; Nakajima, Teruyuki; Takenaka, Hideaki; Higurashi, Akiko; Kikuchi, Nobuyuki; Nakajima, Takashi Y.; Ishida, Haruma

2013-12-01

satellite aerosol retrieval algorithm was developed to utilize a near-ultraviolet band of the Greenhouse gases Observing SATellite/Thermal And Near infrared Sensor for carbon Observation (GOSAT/TANSO)-Cloud and Aerosol Imager (CAI). At near-ultraviolet wavelengths, the surface reflectance over land is smaller than that at visible wavelengths. Therefore, it is thought possible to reduce retrieval error by using the near-ultraviolet spectral region. In the present study, we first developed a cloud shadow detection algorithm that uses first and second minimum reflectances of 380 nm and 680 nm based on the difference in Rayleigh scattering contribution for these two bands. Then, we developed a new surface reflectance correction algorithm, the modified Kaufman method, which uses minimum reflectance data at 680 nm and the NDVI to estimate the surface reflectance at 380 nm. This algorithm was found to be particularly effective at reducing the aerosol effect remaining in the 380 nm minimum reflectance; this effect has previously proven difficult to remove owing to the infrequent sampling rate associated with the three-day recursion period of GOSAT and the narrow CAI swath of 1000 km. Finally, we applied these two algorithms to retrieve aerosol optical thicknesses over a land area. Our results exhibited better agreement with sun-sky radiometer observations than results obtained using a simple surface reflectance correction technique using minimum radiances.

Sequential reconstruction of driving-forces from nonlinear nonstationary dynamics

NASA Astrophysics Data System (ADS)

Güntürkün, Ulaş

2010-07-01

This paper describes a functional analysis-based method for the estimation of driving-forces from nonlinear dynamic systems. The driving-forces account for the perturbation inputs induced by the external environment or the secular variations in the internal variables of the system. The proposed algorithm is applicable to the problems for which there is too little or no prior knowledge to build a rigorous mathematical model of the unknown dynamics. We derive the estimator conditioned on the differentiability of the unknown system’s mapping, and smoothness of the driving-force. The proposed algorithm is an adaptive sequential realization of the blind prediction error method, where the basic idea is to predict the observables, and retrieve the driving-force from the prediction error. Our realization of this idea is embodied by predicting the observables one-step into the future using a bank of echo state networks (ESN) in an online fashion, and then extracting the raw estimates from the prediction error and smoothing these estimates in two adaptive filtering stages. The adaptive nature of the algorithm enables to retrieve both slowly and rapidly varying driving-forces accurately, which are illustrated by simulations. Logistic and Moran-Ricker maps are studied in controlled experiments, exemplifying chaotic state and stochastic measurement models. The algorithm is also applied to the estimation of a driving-force from another nonlinear dynamic system that is stochastic in both state and measurement equations. The results are judged by the posterior Cramer-Rao lower bounds. The method is finally put into test on a real-world application; extracting sun’s magnetic flux from the sunspot time series.

Retrieval of volcanic SO2 from HIRS/2 using optimal estimation

NASA Astrophysics Data System (ADS)

Miles, Georgina M.; Siddans, Richard; Grainger, Roy G.; Prata, Alfred J.; Fisher, Bradford; Krotkov, Nickolay

2017-07-01

We present an optimal-estimation (OE) retrieval scheme for stratospheric sulfur dioxide from the High-Resolution Infrared Radiation Sounder 2 (HIRS/2) instruments on the NOAA and MetOp platforms, an infrared radiometer that has been operational since 1979. This algorithm is an improvement upon a previous method based on channel brightness temperature differences, which demonstrated the potential for monitoring volcanic SO2 using HIRS/2. The Prata method is fast but of limited accuracy. This algorithm uses an optimal-estimation retrieval approach yielding increased accuracy for only moderate computational cost. This is principally achieved by fitting the column water vapour and accounting for its interference in the retrieval of SO2. A cloud and aerosol model is used to evaluate the sensitivity of the scheme to the presence of ash and water/ice cloud. This identifies that cloud or ash above 6 km limits the accuracy of the water vapour fit, increasing the error in the SO2 estimate. Cloud top height is also retrieved. The scheme is applied to a case study event, the 1991 eruption of Cerro Hudson in Chile. The total erupted mass of SO2 is estimated to be 2300 kT ± 600 kT. This confirms it as one of the largest events since the 1991 eruption of Pinatubo, and of comparable scale to the Northern Hemisphere eruption of Kasatochi in 2008. This retrieval method yields a minimum mass per unit area detection limit of 3 DU, which is slightly less than that for the Total Ozone Mapping Spectrometer (TOMS), the only other instrument capable of monitoring SO2 from 1979 to 1996. We show an initial comparison to TOMS for part of this eruption, with broadly consistent results. Operating in the infrared (IR), HIRS has the advantage of being able to measure both during the day and at night, and there have frequently been multiple HIRS instruments operated simultaneously for better than daily sampling. If applied to all data from the series of past and future HIRS instruments, this method presents the opportunity to produce a comprehensive and consistent volcanic SO2 time series spanning over 40 years.

Passive remote sensing of altitude and optical depth of dust plumes using the oxygen A and B bands: First results from EPIC/DSCOVR at Lagrange-1 point

NASA Astrophysics Data System (ADS)

Xu, Xiaoguang; Wang, Jun; Wang, Yi; Zeng, Jing; Torres, Omar; Yang, Yuekui; Marshak, Alexander; Reid, Jeffrey; Miller, Steve

2017-07-01

We presented an algorithm for inferring aerosol layer height (ALH) and optical depth (AOD) over ocean surface from radiances in oxygen A and B bands measured by the Earth Polychromatic Imaging Camera (EPIC) on the Deep Space Climate Observatory (DSCOVR) orbiting at Lagrangian-1 point. The algorithm was applied to EPIC imagery of a 2 day dust outbreak over the North Atlantic Ocean. Retrieved ALHs and AODs were evaluated against counterparts observed by Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP), Moderate Resolution Imaging Spectroradiometer, and Aerosol Robotic Network. The comparisons showed 71.5% of EPIC-retrieved ALHs were within ±0.5 km of those determined from CALIOP and 74.4% of EPIC AOD retrievals fell within a ± (0.1 + 10%) envelope of MODIS retrievals. This study demonstrates the potential of EPIC measurements for retrieving global aerosol height multiple times daily, which are essential for evaluating aerosol profile simulated in climate models and for better estimating aerosol radiative effects.

Estimation of global snow cover using passive microwave data

NASA Astrophysics Data System (ADS)

Chang, Alfred T. C.; Kelly, Richard E.; Foster, James L.; Hall, Dorothy K.

2003-04-01

This paper describes an approach to estimate global snow cover using satellite passive microwave data. Snow cover is detected using the high frequency scattering signal from natural microwave radiation, which is observed by passive microwave instruments. Developed for the retrieval of global snow depth and snow water equivalent using Advanced Microwave Scanning Radiometer EOS (AMSR-E), the algorithm uses passive microwave radiation along with a microwave emission model and a snow grain growth model to estimate snow depth. The microwave emission model is based on the Dense Media Radiative Transfer (DMRT) model that uses the quasi-crystalline approach and sticky particle theory to predict the brightness temperature from a single layered snowpack. The grain growth model is a generic single layer model based on an empirical approach to predict snow grain size evolution with time. Gridding to the 25 km EASE-grid projection, a daily record of Special Sensor Microwave Imager (SSM/I) snow depth estimates was generated for December 2000 to March 2001. The estimates are tested using ground measurements from two continental-scale river catchments (Nelson River and the Ob River in Russia). This regional-scale testing of the algorithm shows that for passive microwave estimates, the average daily snow depth retrieval standard error between estimated and measured snow depths ranges from 0 cm to 40 cm of point observations. Bias characteristics are different for each basin. A fraction of the error is related to uncertainties about the grain growth initialization states and uncertainties about grain size changes through the winter season that directly affect the parameterization of the snow depth estimation in the DMRT model. Also, the algorithm does not include a correction for forest cover and this effect is clearly observed in the retrieval. Finally, error is also related to scale differences between in situ ground measurements and area-integrated satellite estimates. With AMSR-E data, improvements to snow depth and water equivalent estimates are expected since AMSR-E will have twice the spatial resolution of the SSM/I and will be able to characterize better the subnivean snow environment from an expanded range of microwave frequencies.

Data Assimilation Experiments using Quality Controlled AIRS Version 5 Temperature Soundings

NASA Technical Reports Server (NTRS)

SUsskind, Joel

2008-01-01

The AIRS Science Team Version 5 retrieval algorithm has been finalized and is now operational at the Goddard DAAC in the processing (and reprocessing) of all AIRS data. The AIRS Science Team Version 5 retrieval algorithm contains two significant improvements over Version 4: 1) Improved physics allows for use of AIRS observations in the entire 4.3 pm C02 absorption band in the retrieval of temperature profile T(p) during both day and night. Tropospheric sounding 15 pm C02 observations are now used primarily in the generation of cloud cleared radiances Ri. This approach allows for the generation of accurate values of Ri and T(p) under most cloud conditions. 2) Another very significant improvement in Version 5 is the ability to generate accurate case-by-case, level-by-level error estimates for the atmospheric temperature profile, as well as for channel-by- channel error estimates for Ri. These error estimates are used for quality control of the retrieved products. We have conducted forecast impact experiments assimilating AIRS temperature profiles with different levels of quality control using the NASA GEOS-5 data assimilation system. Assimilation of quality controlled T(p) resulted in significantly improved forecast skill compared to that obtained from analyses obtained when all data used operationally by NCEP, except for AIRS data, is assimilated. We also conducted an experiment assimilating AIRS radiances uncontaminated by clouds, as done Operationally by ECMWF and NCEP. Forecasts resulting from assimilated AIRS radiances were of poorer quality than those obtained assimilating AIRS temperatures.

Phase retrieval from intensity-only data by relative entropy minimization.

PubMed

Deming, Ross W

2007-11-01

A recursive algorithm, which appears to be new, is presented for estimating the amplitude and phase of a wave field from intensity-only measurements on two or more scan planes at different axial positions. The problem is framed as a nonlinear optimization, in which the angular spectrum of the complex field model is adjusted in order to minimize the relative entropy, or Kullback-Leibler divergence, between the measured and reconstructed intensities. The most common approach to this so-called phase retrieval problem is a variation of the well-known Gerchberg-Saxton algorithm devised by Misell (J. Phys. D6, L6, 1973), which is efficient and extremely simple to implement. The new algorithm has a computational structure that is very similar to Misell's approach, despite the fundamental difference in the optimization criteria used for each. Based upon results from noisy simulated data, the new algorithm appears to be more robust than Misell's approach and to produce better results from low signal-to-noise ratio data. The convergence of the new algorithm is examined.

GPM Precipitation Estimates over the Walnut Gulch Experimental Watershed/LTAR site in Southeastern Arizona

NASA Astrophysics Data System (ADS)

Goodrich, D. C.; Tan, J.; Petersen, W. A.; Unkrich, C. C.; Demaria, E. M.; Hazenberg, P.; Lakshmi, V.

2017-12-01

Precipitation profiles from the GPM Core Observatory Dual-frequency Precipitation Radar (DPR) form part of the a priori database used in GPM Goddard Profiling (GPROF) algorithm passive microwave radiometer retrievals of rainfall. The GPROF retrievals are in turn used as high quality precipitation estimates in gridded products such as IMERG. Due to the variability in and high surface emissivity of land surfaces, GPROF performs precipitation retrievals as a function of surface classes. As such, different surface types may possess different error characteristics, especially over arid regions where high quality ground measurements are often lacking. Importantly, the emissive properties of land also result in GPROF rainfall estimates being driven primarily by the higher frequency radiometer channels (e.g., > 89 GHz) where precipitation signals are most sensitive to coupling between the ice-phase and rainfall production. In this study, we evaluate the rainfall estimates from the Ku channel of the DPR as well as GPROF estimates from various passive microwave sensors. Our evaluation is conducted at the level of individual satellite pixels (5 to 15 km in diameter), against a dense network of weighing rain gauges (90 in 150 km2) in the USDA-ARS Walnut Gulch Experimental Watershed and Long-Term Agroecosystem Research (LTAR) site in southeastern Arizona. The multiple gauges in each satellite pixel and precise accumulation about the overpass time allow a spatially and temporally representative comparison between the satellite estimates and ground reference. Over Walnut Gulch, both the Ku and GPROF estimates are challenged to delineate between rain and no-rain. Probabilities of detection are relatively high, but false alarm ratios are also high. The rain intensities possess a negative bias across nearly all sensors. It is likely that storm types, arid conditions and the highly variable precipitation regime present a challenge to both rainfall retrieval algorithms. An array of ground-based sensors is being deployed during the 2017 monsoon season to better understand possible reasons for this discrepancy.

Atmospheric correction of SeaWiFS ocean color imagery in the presence of absorbing aerosols off the Indian coast using a neuro-variational method

NASA Astrophysics Data System (ADS)

Brajard, J.; Moulin, C.; Thiria, S.

2008-10-01

This paper presents a comparison of the atmospheric correction accuracy between the standard sea-viewing wide field-of-view sensor (SeaWiFS) algorithm and the NeuroVaria algorithm for the ocean off the Indian coast in March 1999. NeuroVaria is a general method developed to retrieve aerosol optical properties and water-leaving reflectances for all types of aerosols, including absorbing ones. It has been applied to SeaWiFS images of March 1999, during an episode of transport of absorbing aerosols coming from pollutant sources in India. Water-leaving reflectances and aerosol optical thickness estimated by the two methods were extracted along a transect across the aerosol plume for three days. The comparison showed that NeuroVaria allows the retrieval of oceanic properties in the presence of absorbing aerosols with a better spatial and temporal stability than the standard SeaWiFS algorithm. NeuroVaria was then applied to the available SeaWiFS images over a two-week period. NeuroVaria algorithm retrieves ocean products for a larger number of pixels than the standard one and eliminates most of the discontinuities and artifacts associated with the standard algorithm in presence of absorbing aerosols.

Next Generation Aura-OMI SO2 Retrieval Algorithm: Introduction and Implementation Status

NASA Technical Reports Server (NTRS)

Li, Can; Joiner, Joanna; Krotkov, Nickolay A.; Bhartia, Pawan K.

2014-01-01

We introduce our next generation algorithm to retrieve SO2 using radiance measurements from the Aura Ozone Monitoring Instrument (OMI). We employ a principal component analysis technique to analyze OMI radiance spectral in 310.5-340 nm acquired over regions with no significant SO2. The resulting principal components (PCs) capture radiance variability caused by both physical processes (e.g., Rayleigh and Raman scattering, and ozone absorption) and measurement artifacts, enabling us to account for these various interferences in SO2 retrievals. By fitting these PCs along with SO2 Jacobians calculated with a radiative transfer model to OMI-measured radiance spectra, we directly estimate SO2 vertical column density in one step. As compared with the previous generation operational OMSO2 PBL (Planetary Boundary Layer) SO2 product, our new algorithm greatly reduces unphysical biases and decreases the noise by a factor of two, providing greater sensitivity to anthropogenic emissions. The new algorithm is fast, eliminates the need for instrument-specific radiance correction schemes, and can be easily adapted to other sensors. These attributes make it a promising technique for producing long-term, consistent SO2 records for air quality and climate research. We have operationally implemented this new algorithm on OMI SIPS for producing the new generation standard OMI SO2 products.

Improved Determination of Surface and Atmospheric Temperatures Using Only Shortwave AIRS Channels: The AIRS Version 6 Retrieval Algorithm

NASA Technical Reports Server (NTRS)

Susskind, Joel; Blaisdell, John; Iredell, Lena

2010-01-01

AIRS was launched on EOS Aqua on May 4, 2002 together with ASMU-A and HSB to form a next generation polar orbiting infrared and microwave atmosphere sounding system (Pagano et al 2003). The theoretical approach used to analyze AIRS/AMSU/HSB data in the presence of clouds in the AIRS Science Team Version 3 at-launch algorithm, and that used in the Version 4 post-launch algorithm, have been published previously. Significant theoretical and practical improvements have been made in the analysis of AIRS/AMSU data since the Version 4 algorithm. Most of these have already been incorporated in the AIRS Science Team Version 5 algorithm (Susskind et al 2010), now being used operationally at the Goddard DISC. The AIRS Version 5 retrieval algorithm contains three significant improvements over Version 4. Improved physics in Version 5 allowed for use of AIRS clear column radiances (R(sub i)) in the entire 4.3 micron CO2 absorption band in the retrieval of temperature profiles T(p) during both day and night. Tropospheric sounding 15 micron CO2 observations were used primarily in the generation of clear column radiances (R(sub i)) for all channels. This new approach allowed for the generation of accurate Quality Controlled values of R(sub i) and T(p) under more stressing cloud conditions. Secondly, Version 5 contained a new methodology to provide accurate case-by-case error estimates for retrieved geophysical parameters and for channel-by-channel clear column radiances. Thresholds of these error estimates are used in a new approach for Quality Control. Finally, Version 5 contained for the first time an approach to provide AIRS soundings in partially cloudy conditions that does not require use of any microwave data. This new AIRS Only sounding methodology was developed as a backup to AIRS Version 5 should the AMSU-A instrument fail. Susskind et al 2010 shows that Version 5 AIRS Only sounding are only slightly degraded from the AIRS/AMSU soundings, even at large fractional cloud cover.

GOCI Yonsei aerosol retrieval version 2 aerosol products: improved algorithm description and error analysis with uncertainty estimation from 5-year validation over East Asia

NASA Astrophysics Data System (ADS)

Choi, M.; Kim, J.; Lee, J.; KIM, M.; Park, Y. J.; Holben, B. N.; Eck, T. F.; Li, Z.; Song, C. H.

2017-12-01

The Geostationary Ocean Color Imager (GOCI) Yonsei aerosol retrieval (YAER) version 1 algorithm was developed for retrieving hourly aerosol optical depth at 550 nm (AOD) and other subsidiary aerosol optical properties over East Asia. The GOCI YAER AOD showed comparable accuracy compared to ground-based and other satellite-based observations, but still had errors due to uncertainties in surface reflectance and simple cloud masking. Also, it was not capable of near-real-time (NRT) processing because it required a monthly database of each year encompassing the day of retrieval for the determination of surface reflectance. This study describes the improvement of GOCI YAER algorithm to the version 2 (V2) for NRT processing with improved accuracy from the modification of cloud masking, surface reflectance determination using multi-year Rayleigh corrected reflectance and wind speed database, and inversion channels per surface conditions. Therefore, the improved GOCI AOD ( ) is similar with those of Moderate Resolution Imaging Spectroradiometer (MODIS) and Visible Infrared Imaging Radiometer Suite (VIIRS) AOD compared to V1 of the YAER algorithm. The shows reduced median bias and increased ratio within range (i.e. absolute expected error range of MODIS AOD) compared to V1 in the validation results using Aerosol Robotic Network (AERONET) AOD ( ) from 2011 to 2016. The validation using the Sun-Sky Radiometer Observation Network (SONET) over China also shows similar results. The bias of error ( is within -0.1 and 0.1 range as a function of AERONET AOD and AE, scattering angle, NDVI, cloud fraction and homogeneity of retrieved AOD, observation time, month, and year. Also, the diagnostic and prognostic expected error (DEE and PEE, respectively) of are estimated. The estimated multiple PEE of GOCI V2 AOD is well matched with actual error over East Asia, and the GOCI V2 AOD over Korea shows higher ratio within PEE compared to over China and Japan. Hourly AOD products based on the improved GOCI YAER AOD could contribute to better understandings of aerosols in terms of long-term climate changes and short-term air quality monitoring and forecasting perspectives over East Asia, especially rapid diurnal variation and transboundary transport.

Data Assimilation Experiments Using Quality Controlled AIRS Version 5 Temperature Soundings

NASA Technical Reports Server (NTRS)

Susskind, Joel

2009-01-01

The AIRS Science Team Version 5 retrieval algorithm has been finalized and is now operational at the Goddard DAAC in the processing (and reprocessing) of all AIRS data. The AIRS Science Team Version 5 retrieval algorithm contains a number of significant improvements over Version 4. Two very significant improvements are described briefly below. 1) The AIRS Science Team Radiative Transfer Algorithm (RTA) has now been upgraded to accurately account for effects of non-local thermodynamic equilibrium on the AIRS observations. This allows for use of AIRS observations in the entire 4.3 micron CO2 absorption band in the retrieval algorithm during both day and night. Following theoretical considerations, tropospheric temperature profile information is obtained almost exclusively from clear column radiances in the 4.3 micron CO2 band in the AIRS Version 5 temperature profile retrieval step. These clear column radiances are a derived product that are indicative of radiances AIRS channels would have seen if the field of view were completely clear. Clear column radiances for all channels are determined using tropospheric sounding 15 micron CO2 observations. This approach allows for the generation of accurate values of clear column radiances and T(p) under most cloud conditions. 2) Another very significant improvement in Version 5 is the ability to generate accurate case-by-case, level-by-level error estimates for the atmospheric temperature profile, as well as for channel-by-channel clear column radiances. These error estimates are used for quality control of the retrieved products. Based on error estimate thresholds, each temperature profiles is assigned a characteristic pressure, pg, down to which the profile is characterized as good for use for data assimilation purposes. We have conducted forecast impact experiments assimilating AIRS quality controlled temperature profiles using the NASA GEOS-5 data assimilation system, consisting of the NCEP GSI analysis coupled with the NASA FVGCM, at a spatial resolution of 0.5 deg by 0.5 deg. Assimilation of Quality Controlled AIRS temperature profiles down to pg resulted in significantly improved forecast skill compared to that obtained from experiments when all data used operationally by NCEP, except for AIRS data, is assimilated. These forecasts were also significantly better than to those obtained when AIRS radiances (rather than temperature profiles) are assimilated, which is the way AIRS data is used operationally by NCEP and ECMWF.

A-Train Aerosol Observations Preliminary Comparisons with AeroCom Models and Pathways to Observationally Based All-Sky Estimates

NASA Technical Reports Server (NTRS)

Redemann, J.; Livingston, J.; Shinozuka, Y.; Kacenelenbogen, M.; Russell, P.; LeBlanc, S.; Vaughan, M.; Ferrare, R.; Hostetler, C.; Rogers, R.;

Simultaneous polarimeter retrievals of microphysical aerosol and ocean color parameters from the "MAPP" algorithm with comparison to high-spectral-resolution lidar aerosol and ocean products.

PubMed

Stamnes, S; Hostetler, C; Ferrare, R; Burton, S; Liu, X; Hair, J; Hu, Y; Wasilewski, A; Martin, W; van Diedenhoven, B; Chowdhary, J; Cetinić, I; Berg, L K; Stamnes, K; Cairns, B

2018-04-01

We present an optimal-estimation-based retrieval framework, the microphysical aerosol properties from polarimetry (MAPP) algorithm, designed for simultaneous retrieval of aerosol microphysical properties and ocean color bio-optical parameters using multi-angular total and polarized radiances. Polarimetric measurements from the airborne NASA Research Scanning Polarimeter (RSP) were inverted by MAPP to produce atmosphere and ocean products. The RSP MAPP results are compared with co-incident lidar measurements made by the NASA High-Spectral-Resolution Lidar HSRL-1 and HSRL-2 instruments. Comparisons are made of the aerosol optical depth (AOD) at 355 and 532 nm, lidar column-averaged measurements of the aerosol lidar ratio and Ångstrøm exponent, and lidar ocean measurements of the particulate hemispherical backscatter coefficient and the diffuse attenuation coefficient. The measurements were collected during the 2012 Two-Column Aerosol Project (TCAP) campaign and the 2014 Ship-Aircraft Bio-Optical Research (SABOR) campaign. For the SABOR campaign, 73% RSP MAPP retrievals fall within ±0.04 AOD at 532 nm as measured by HSRL-1, with an R value of 0.933 and root-mean-square deviation of 0.0372. For the TCAP campaign, 53% of RSP MAPP retrievals are within 0.04 AOD as measured by HSRL-2, with an R value of 0.927 and root-mean-square deviation of 0.0673. Comparisons with HSRL-2 AOD at 355 nm during TCAP result in an R value of 0.959 and a root-mean-square deviation of 0.0694. The RSP retrievals using the MAPP optimal estimation framework represent a key milestone on the path to a combined lidar + polarimeter retrieval using both HSRL and RSP measurements.

Thin ice clouds in the Arctic: cloud optical depth and particle size retrieved from ground-based thermal infrared radiometry

DOE Office of Scientific and Technical Information (OSTI.GOV)

Blanchard, Yann; Royer, Alain; O'Neill, Norman T.

Multiband downwelling thermal measurements of zenith sky radiance, along with cloud boundary heights, were used in a retrieval algorithm to estimate cloud optical depth and effective particle diameter of thin ice clouds in the Canadian High Arctic. Ground-based thermal infrared (IR) radiances for 150 semitransparent ice clouds cases were acquired at the Polar Environment Atmospheric Research Laboratory (PEARL) in Eureka, Nunavut, Canada (80° N, 86° W). We analyzed and quantified the sensitivity of downwelling thermal radiance to several cloud parameters including optical depth, effective particle diameter and shape, water vapor content, cloud geometric thickness and cloud base altitude. A lookupmore » table retrieval method was used to successfully extract, through an optimal estimation method, cloud optical depth up to a maximum value of 2.6 and to separate thin ice clouds into two classes: (1) TIC1 clouds characterized by small crystals (effective particle diameter ≤ 30 µm), and (2) TIC2 clouds characterized by large ice crystals (effective particle diameter > 30 µm). The retrieval technique was validated using data from the Arctic High Spectral Resolution Lidar (AHSRL) and Millimeter Wave Cloud Radar (MMCR). Inversions were performed over three polar winters and results showed a significant correlation ( R 2 = 0.95) for cloud optical depth retrievals and an overall accuracy of 83 % for the classification of TIC1 and TIC2 clouds. A partial validation relative to an algorithm based on high spectral resolution downwelling IR radiance measurements between 8 and 21µm was also performed. It confirms the robustness of the optical depth retrieval and the fact that the broadband thermal radiometer retrieval was sensitive to small particle (TIC1) sizes.« less

Thin ice clouds in the Arctic: cloud optical depth and particle size retrieved from ground-based thermal infrared radiometry

NASA Astrophysics Data System (ADS)

Blanchard, Yann; Royer, Alain; O'Neill, Norman T.; Turner, David D.; Eloranta, Edwin W.

2017-06-01

Multiband downwelling thermal measurements of zenith sky radiance, along with cloud boundary heights, were used in a retrieval algorithm to estimate cloud optical depth and effective particle diameter of thin ice clouds in the Canadian High Arctic. Ground-based thermal infrared (IR) radiances for 150 semitransparent ice clouds cases were acquired at the Polar Environment Atmospheric Research Laboratory (PEARL) in Eureka, Nunavut, Canada (80° N, 86° W). We analyzed and quantified the sensitivity of downwelling thermal radiance to several cloud parameters including optical depth, effective particle diameter and shape, water vapor content, cloud geometric thickness and cloud base altitude. A lookup table retrieval method was used to successfully extract, through an optimal estimation method, cloud optical depth up to a maximum value of 2.6 and to separate thin ice clouds into two classes: (1) TIC1 clouds characterized by small crystals (effective particle diameter ≤ 30 µm), and (2) TIC2 clouds characterized by large ice crystals (effective particle diameter > 30 µm). The retrieval technique was validated using data from the Arctic High Spectral Resolution Lidar (AHSRL) and Millimeter Wave Cloud Radar (MMCR). Inversions were performed over three polar winters and results showed a significant correlation (R2 = 0.95) for cloud optical depth retrievals and an overall accuracy of 83 % for the classification of TIC1 and TIC2 clouds. A partial validation relative to an algorithm based on high spectral resolution downwelling IR radiance measurements between 8 and 21 µm was also performed. It confirms the robustness of the optical depth retrieval and the fact that the broadband thermal radiometer retrieval was sensitive to small particle (TIC1) sizes.

Thin ice clouds in the Arctic: cloud optical depth and particle size retrieved from ground-based thermal infrared radiometry

DOE PAGES

Blanchard, Yann; Royer, Alain; O'Neill, Norman T.; ...

2017-06-09

Multiband downwelling thermal measurements of zenith sky radiance, along with cloud boundary heights, were used in a retrieval algorithm to estimate cloud optical depth and effective particle diameter of thin ice clouds in the Canadian High Arctic. Ground-based thermal infrared (IR) radiances for 150 semitransparent ice clouds cases were acquired at the Polar Environment Atmospheric Research Laboratory (PEARL) in Eureka, Nunavut, Canada (80° N, 86° W). We analyzed and quantified the sensitivity of downwelling thermal radiance to several cloud parameters including optical depth, effective particle diameter and shape, water vapor content, cloud geometric thickness and cloud base altitude. A lookupmore » table retrieval method was used to successfully extract, through an optimal estimation method, cloud optical depth up to a maximum value of 2.6 and to separate thin ice clouds into two classes: (1) TIC1 clouds characterized by small crystals (effective particle diameter ≤ 30 µm), and (2) TIC2 clouds characterized by large ice crystals (effective particle diameter > 30 µm). The retrieval technique was validated using data from the Arctic High Spectral Resolution Lidar (AHSRL) and Millimeter Wave Cloud Radar (MMCR). Inversions were performed over three polar winters and results showed a significant correlation ( R 2 = 0.95) for cloud optical depth retrievals and an overall accuracy of 83 % for the classification of TIC1 and TIC2 clouds. A partial validation relative to an algorithm based on high spectral resolution downwelling IR radiance measurements between 8 and 21µm was also performed. It confirms the robustness of the optical depth retrieval and the fact that the broadband thermal radiometer retrieval was sensitive to small particle (TIC1) sizes.« less

Simultaneous polarimeter retrievals of microphysical aerosol and ocean color parameters from the “MAPP” algorithm with comparison to high-spectral-resolution lidar aerosol and ocean products

DOE Office of Scientific and Technical Information (OSTI.GOV)

Stamnes, S.; Hostetler, C.; Ferrare, R.

We present an optimal estimation based retrieval framework, the Microphysical Aerosol Properties from Polarimetry (MAPP) algorithm, designed for simultaneous retrieval of aerosol microphysical properties and ocean color bio-optical parameters using multi-angular polarized radiances. Polarimetric measurements from the airborne NASA Research Scanning Polarimeter (RSP) were inverted by MAPP to produce atmosphere and ocean products. The RSP MAPP results are compared with co-incident lidar measurements made by the NASA High Spectral Resolution Lidar HSRL-1 and HSRL-2 instruments. Comparisons are made of the aerosol optical depth (AOD) at 355, 532, and 1064 nm, lidar column-averaged measurements of the aerosol lidar ratio and Ã…ngstrømmore » exponent, and lidar ocean measurements of the particulate hemispherical backscatter coefficient and the diffuse attenuation coefficient. The measurements were collected during the 2012 Two-Column Aerosol Project (TCAP) campaign and the 2014 Ship-Aircraft Bio- Optical Research (SABOR) campaign. For the SABOR campaign, 71% RSP MAPP retrievals fall within 0.04 AOD at 532 nm as measured by HSRL-1, with an R value of 0.925 and root-mean-square deviation of 0.04. For the TCAP campaign, 55% of RSP MAPP retrievals are within 0.04 AOD as measured by HSRL-2, with an R value of 0.925 and root-mean-square deviation of 0.07. Comparisons with HSRL-2 AOD at 355 nm during TCAP result in an R value of 0.96 and a root-mean-square deviation of also 0.07. The RSP retrievals using the MAPP optimal estimation framework represent a key milestone on the path to a combined lidar+polarimeter retrieval using both HSRL and RSP measurements.« less

Single-footprint retrievals of temperature, water vapor and cloud properties from AIRS

NASA Astrophysics Data System (ADS)

Irion, Fredrick W.; Kahn, Brian H.; Schreier, Mathias M.; Fetzer, Eric J.; Fishbein, Evan; Fu, Dejian; Kalmus, Peter; Wilson, R. Chris; Wong, Sun; Yue, Qing

2018-02-01

Single-footprint Atmospheric Infrared Sounder spectra are used in an optimal estimation-based algorithm (AIRS-OE) for simultaneous retrieval of atmospheric temperature, water vapor, surface temperature, cloud-top temperature, effective cloud optical depth and effective cloud particle radius. In a departure from currently operational AIRS retrievals (AIRS V6), cloud scattering and absorption are in the radiative transfer forward model and AIRS single-footprint thermal infrared data are used directly rather than cloud-cleared spectra (which are calculated using nine adjacent AIRS infrared footprints). Coincident MODIS cloud data are used for cloud a priori data. Using single-footprint spectra improves the horizontal resolution of the AIRS retrieval from ˜ 45 to ˜ 13.5 km at nadir, but as microwave data are not used, the retrieval is not made at altitudes below thick clouds. An outline of the AIRS-OE retrieval procedure and information content analysis is presented. Initial comparisons of AIRS-OE to AIRS V6 results show increased horizontal detail in the water vapor and relative humidity fields in the free troposphere above the clouds. Initial comparisons of temperature, water vapor and relative humidity profiles with coincident radiosondes show good agreement. Future improvements to the retrieval algorithm, and to the forward model in particular, are discussed.

An Algorithm for Retrieving Land Surface Temperatures Using VIIRS Data in Combination with Multi-Sensors

PubMed Central

Xia, Lang; Mao, Kebiao; Ma, Ying; Zhao, Fen; Jiang, Lipeng; Shen, Xinyi; Qin, Zhihao

2014-01-01

A practical algorithm was proposed to retrieve land surface temperature (LST) from Visible Infrared Imager Radiometer Suite (VIIRS) data in mid-latitude regions. The key parameter transmittance is generally computed from water vapor content, while water vapor channel is absent in VIIRS data. In order to overcome this shortcoming, the water vapor content was obtained from Moderate Resolution Imaging Spectroradiometer (MODIS) data in this study. The analyses on the estimation errors of vapor content and emissivity indicate that when the water vapor errors are within the range of ±0.5 g/cm2, the mean retrieval error of the present algorithm is 0.634 K; while the land surface emissivity errors range from −0.005 to +0.005, the mean retrieval error is less than 1.0 K. Validation with the standard atmospheric simulation shows the average LST retrieval error for the twenty-three land types is 0.734 K, with a standard deviation value of 0.575 K. The comparison between the ground station LST data indicates the retrieval mean accuracy is −0.395 K, and the standard deviation value is 1.490 K in the regions with vegetation and water cover. Besides, the retrieval results of the test data have also been compared with the results measured by the National Oceanic and Atmospheric Administration (NOAA) VIIRS LST products, and the results indicate that 82.63% of the difference values are within the range of −1 to 1 K, and 17.37% of the difference values are within the range of ±2 to ±1 K. In a conclusion, with the advantages of multi-sensors taken fully exploited, more accurate results can be achieved in the retrieval of land surface temperature. PMID:25397919

The Community Cloud retrieval for CLimate (CC4CL) - Part 2: The optimal estimation approach

NASA Astrophysics Data System (ADS)

McGarragh, Gregory R.; Poulsen, Caroline A.; Thomas, Gareth E.; Povey, Adam C.; Sus, Oliver; Stapelberg, Stefan; Schlundt, Cornelia; Proud, Simon; Christensen, Matthew W.; Stengel, Martin; Hollmann, Rainer; Grainger, Roy G.

2018-06-01

The Community Cloud retrieval for Climate (CC4CL) is a cloud property retrieval system for satellite-based multispectral imagers and is an important component of the Cloud Climate Change Initiative (Cloud_cci) project. In this paper we discuss the optimal estimation retrieval of cloud optical thickness, effective radius and cloud top pressure based on the Optimal Retrieval of Aerosol and Cloud (ORAC) algorithm. Key to this method is the forward model, which includes the clear-sky model, the liquid water and ice cloud models, the surface model including a bidirectional reflectance distribution function (BRDF), and the "fast" radiative transfer solution (which includes a multiple scattering treatment). All of these components and their assumptions and limitations will be discussed in detail. The forward model provides the accuracy appropriate for our retrieval method. The errors are comparable to the instrument noise for cloud optical thicknesses greater than 10. At optical thicknesses less than 10 modeling errors become more significant. The retrieval method is then presented describing optimal estimation in general, the nonlinear inversion method employed, measurement and a priori inputs, the propagation of input uncertainties and the calculation of subsidiary quantities that are derived from the retrieval results. An evaluation of the retrieval was performed using measurements simulated with noise levels appropriate for the MODIS instrument. Results show errors less than 10 % for cloud optical thicknesses greater than 10. Results for clouds of optical thicknesses less than 10 have errors up to 20 %.

The influence of sea fog inhomogeneity on its microphysical characteristics retrieval

NASA Astrophysics Data System (ADS)

Hao, Zengzhou; Pan, Delu; Gong, Fang; He, Xianqiang

2008-10-01

A study on the effect of sea fog inhomogeneity on its microphysical parameters retrieval is presented. On the condition that the average liquid water content is linear vertically and the power spectrum spectral index sets 2.0, we generate a 3D sea fog fields by controlling the total liquid water contents greater than 0.04g/m3 based on the iterative method for generating scaling log-normal random field with an energy spectrum and a fragmentized cloud algorithm. Based on the fog field, the radiance at the wavelengths of 0.67 and 1.64 μm are simulated with 3D radiative transfer model SHDOM, and then the fog optical thickness and effective particle radius are simultaneously retrieved using the generic look-up-table AVHRR cloud algorithm. By comparing those fog optical thickness and effective particle radius, the influence of sea fog inhomogeneity on its properties retrieval is discussed. It exhibits the system bias when inferring sea fog physical properties from satellite measurements based on the assumption of plane parallel homogeneous atmosphere. And the bias depends on the solar zenith angel. The optical thickness is overrated while the effective particle radius is under-estimated at two solar zenith angle 30° and 60°. Those results show that it is necessary for sea fog true characteristics retrieval to develop a new algorithm using the 3D radiative transfer.

Orbiting Carbon Observatory-2 (OCO-2) cloud screening algorithms; validation against collocated MODIS and CALIOP data

NASA Astrophysics Data System (ADS)

Taylor, T. E.; O'Dell, C. W.; Frankenberg, C.; Partain, P.; Cronk, H. Q.; Savtchenko, A.; Nelson, R. R.; Rosenthal, E. J.; Chang, A. Y.; Fisher, B.; Osterman, G.; Pollock, R. H.; Crisp, D.; Eldering, A.; Gunson, M. R.

2015-12-01

The objective of the National Aeronautics and Space Administration's (NASA) Orbiting Carbon Observatory-2 (OCO-2) mission is to retrieve the column-averaged carbon dioxide (CO2) dry air mole fraction (XCO2) from satellite measurements of reflected sunlight in the near-infrared. These estimates can be biased by clouds and aerosols within the instrument's field of view (FOV). Screening of the most contaminated soundings minimizes unnecessary calls to the computationally expensive Level 2 (L2) XCO2 retrieval algorithm. Hence, robust cloud screening methods have been an important focus of the OCO-2 algorithm development team. Two distinct, computationally inexpensive cloud screening algorithms have been developed for this application. The A-Band Preprocessor (ABP) retrieves the surface pressure using measurements in the 0.76 μm O2 A-band, neglecting scattering by clouds and aerosols, which introduce photon path-length (PPL) differences that can cause large deviations between the expected and retrieved surface pressure. The Iterative Maximum A-Posteriori (IMAP) Differential Optical Absorption Spectroscopy (DOAS) Preprocessor (IDP) retrieves independent estimates of the CO2 and H2O column abundances using observations taken at 1.61 μm (weak CO2 band) and 2.06 μm (strong CO2 band), while neglecting atmospheric scattering. The CO2 and H2O column abundances retrieved in these two spectral regions differ significantly in the presence of cloud and scattering aerosols. The combination of these two algorithms, which key off of different features in the spectra, provides the basis for cloud screening of the OCO-2 data set. To validate the OCO-2 cloud screening approach, collocated measurements from NASA's Moderate Resolution Imaging Spectrometer (MODIS), aboard the Aqua platform, were compared to results from the two OCO-2 cloud screening algorithms. With tuning to allow throughputs of ≃ 30 %, agreement between the OCO-2 and MODIS cloud screening methods is found to be ≃ 85 % over four 16-day orbit repeat cycles in both the winter (December) and spring (April-May) for OCO-2 nadir-land, glint-land and glint-water observations. No major, systematic, spatial or temporal dependencies were found, although slight differences in the seasonal data sets do exist and validation is more problematic with increasing solar zenith angle and when surfaces are covered in snow and ice and have complex topography. To further analyze the performance of the cloud screening algorithms, an initial comparison of OCO-2 observations was made to collocated measurements from the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) aboard the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO). These comparisons highlight the strength of the OCO-2 cloud screening algorithms in identifying high, thin clouds but suggest some difficulty in identifying some clouds near the surface, even when the optical thicknesses are greater than 1.

Foundations for statistical-physical precipitation retrieval from passive microwave satellite measurements. I - Brightness-temperature properties of a time-dependent cloud-radiation model

NASA Technical Reports Server (NTRS)

Smith, Eric A.; Mugnai, Alberto; Cooper, Harry J.; Tripoli, Gregory J.; Xiang, Xuwu

1992-01-01

The relationship between emerging microwave brightness temperatures (T(B)s) and vertically distributed mixtures of liquid and frozen hydrometeors was investigated, using a cloud-radiation model, in order to establish the framework for a hybrid statistical-physical rainfall retrieval algorithm. Although strong relationships were found between the T(B) values and various rain parameters, these correlations are misleading in that the T(B)s are largely controlled by fluctuations in the ice-particle mixing ratios, which in turn are highly correlated to fluctuations in liquid-particle mixing ratios. However, the empirically based T(B)-rain-rate (T(B)-RR) algorithms can still be used as tools for estimating precipitation if the hydrometeor profiles used for T(B)-RR algorithms are not specified in an ad hoc fashion.

Retrieving the Height of Smoke and Dust Aerosols by Synergistic Use of Multiple Satellite Sensors

NASA Technical Reports Server (NTRS)

Lee, Jaehwa; Hsu, N. Christina; Bettenhausen, Corey; Sayer, Andrew M.; Seftor, Colin J.; Jeong, Myeong-Jae

2016-01-01

The Aerosol Single scattering albedo and Height Estimation (ASHE) algorithm was first introduced in Jeong and Hsu (2008) to provide aerosol layer height and single scattering albedo (SSA) for biomass burning smoke aerosols. By using multiple satellite sensors synergistically, ASHE can provide the height information over much broader areas than lidar observations alone. The complete ASHE algorithm uses aerosol data from MODIS or VIIRS, OMI or OMPS, and CALIOP. A simplified algorithm also exists that does not require CALIOP data as long as the SSA of the aerosol layer is provided by another source. Several updates have recently been made: inclusion of dust layers in the retrieval process, better determination of the input aerosol layer height from CALIOP, improvement in aerosol optical depth (AOD) for nonspherical dust, development of quality assurance (QA) procedure, etc.

Retrieving Baseflow from SWOT Mission

NASA Astrophysics Data System (ADS)

Baratelli, F.; Flipo, N.; Biancamaria, S.; Rivière, A.

2017-12-01

The quantification of aquifer contribution to river discharge is of primary importance to evaluate the impact of climatic and anthropogenic stresses on the availability of water resources. Several baseflow estimation methods require river discharge measurements, which can be difficult to obtain at high spatio-temporal resolution for large scale basins. The SWOT satellite mission will provide discharge estimations for large rivers (50 - 100 m wide) even in remote basins. The frequency of these estimations depends on the position and ranges from zero to four values in the 21-days satellite cycle. This work aims at answering the following question: can baseflow be estimated from SWOT observations during the mission lifetime? An algorithm based on hydrograph separation by Chapman's filter was developed to automatically estimate the baseflow in a river network at regional or larger scale (> 10000 km2). The algorithm was first applied using the discharge time series simulated at daily time step by a coupled hydrological-hydrogeological model to obtain the reference baseflow estimations. The same algorithm is then forced with discharge time series sampled at SWOT observation frequency. The methodology was applied to the Seine River basin (65000 km2, France). The results show that the average baseflow is estimated with good accuracy for all the reaches which are observed at least once per cycle (relative bias less than 4%). The time evolution of baseflow is also rather well retrieved, with a Nash coefficient which is more than 0.7 for 94% of the network length. This work provides new potential for the SWOT mission in terms of global hydrological analysis.

Predicting ozone profile shape from satellite UV spectra

NASA Astrophysics Data System (ADS)

Xu, Jian; Loyola, Diego; Romahn, Fabian; Doicu, Adrian

2017-04-01

Identifying ozone profile shape is a critical yet challenging job for the accurate reconstruction of vertical distributions of atmospheric ozone that is relevant to climate change and air quality. Motivated by the need to develop an approach to reliably and efficiently estimate vertical information of ozone and inspired by the success of machine learning techniques, this work proposes a new algorithm for deriving ozone profile shapes from ultraviolet (UV) absorption spectra that are recorded by satellite instruments, e.g. GOME series and the future Sentinel missions. The proposed algorithm formulates this particular inverse problem in a classification framework rather than a conventional inversion one and places an emphasis on effectively characterizing various profile shapes based on machine learning techniques. Furthermore, a comparison of the ozone profiles from real GOME-2 data estimated by our algorithm and the classical retrieval algorithm (Optimal Estimation Method) is performed.

Semi-analytical Model for Estimating Absorption Coefficients of Optically Active Constituents in Coastal Waters

NASA Astrophysics Data System (ADS)

Wang, D.; Cui, Y.

2015-12-01

The objectives of this paper are to validate the applicability of a multi-band quasi-analytical algorithm (QAA) in retrieval absorption coefficients of optically active constituents in turbid coastal waters, and to further improve the model using a proposed semi-analytical model (SAA). The ap(531) and ag(531) semi-analytically derived using SAA model are quite different from the retrievals procedures of QAA model that ap(531) and ag(531) are semi-analytically derived from the empirical retrievals results of a(531) and a(551). The two models are calibrated and evaluated against datasets taken from 19 independent cruises in West Florida Shelf in 1999-2003, provided by SeaBASS. The results indicate that the SAA model produces a superior performance to QAA model in absorption retrieval. Using of the SAA model in retrieving absorption coefficients of optically active constituents from West Florida Shelf decreases the random uncertainty of estimation by >23.05% from the QAA model. This study demonstrates the potential of the SAA model in absorption coefficients of optically active constituents estimating even in turbid coastal waters. Keywords: Remote sensing; Coastal Water; Absorption Coefficient; Semi-analytical Model

Improved Algorithms for Accurate Retrieval of UV - Visible Diffuse Attenuation Coefficients in Optically Complex, Inshore Waters

NASA Technical Reports Server (NTRS)

Cao, Fang; Fichot, Cedric G.; Hooker, Stanford B.; Miller, William L.

2014-01-01

Photochemical processes driven by high-energy ultraviolet radiation (UVR) in inshore, estuarine, and coastal waters play an important role in global bio geochemical cycles and biological systems. A key to modeling photochemical processes in these optically complex waters is an accurate description of the vertical distribution of UVR in the water column which can be obtained using the diffuse attenuation coefficients of down welling irradiance (Kd()). The Sea UV Sea UVc algorithms (Fichot et al., 2008) can accurately retrieve Kd ( 320, 340, 380,412, 443 and 490 nm) in oceanic and coastal waters using multispectral remote sensing reflectances (Rrs(), Sea WiFS bands). However, SeaUVSeaUVc algorithms are currently not optimized for use in optically complex, inshore waters, where they tend to severely underestimate Kd(). Here, a new training data set of optical properties collected in optically complex, inshore waters was used to re-parameterize the published SeaUVSeaUVc algorithms, resulting in improved Kd() retrievals for turbid, estuarine waters. Although the updated SeaUVSeaUVc algorithms perform best in optically complex waters, the published SeaUVSeaUVc models still perform well in most coastal and oceanic waters. Therefore, we propose a composite set of SeaUVSeaUVc algorithms, optimized for Kd() retrieval in almost all marine systems, ranging from oceanic to inshore waters. The composite algorithm set can retrieve Kd from ocean color with good accuracy across this wide range of water types (e.g., within 13 mean relative error for Kd(340)). A validation step using three independent, in situ data sets indicates that the composite SeaUVSeaUVc can generate accurate Kd values from 320 490 nm using satellite imagery on a global scale. Taking advantage of the inherent benefits of our statistical methods, we pooled the validation data with the training set, obtaining an optimized composite model for estimating Kd() in UV wavelengths for almost all marine waters. This optimized composite set of SeaUVSeaUVc algorithms will provide the optical community with improved ability to quantify the role of solar UV radiation in photochemical and photobiological processes in the ocean.

Regional trace gas monitoring simplified - A linear retrieval scheme for carbon monoxide from hyperspectral soundings

NASA Astrophysics Data System (ADS)

Smith, N.; Huang, A.; Weisz, E.; Annegarn, H. J.

2011-12-01

The Fast Linear Inversion Trace gas System (FLITS) is designed to retrieve tropospheric total column trace gas densities [molec.cm-2] from space-borne hyperspectral infrared soundings. The objective to develop a new retrieval scheme was motivated by the need for near real-time air quality monitoring at high spatial resolution. We present a case study of FLITS carbon monoxide (CO) retrievals from daytime (descending orbit) Infrared Atmospheric Sounding Interferometer (IASI) measurements that have a 0.5 cm-1 spectral resolution and 12 km footprint at nadir. The standard Level 2 IASI CO retrieval product (COL2) is available in near real-time but is spatially averaged over 2 x 2 pixels, or 50 x 50 km, and thus more suitable for global analysis. The study region is Southern Africa (south of the equator) for the period 28-31 August 2008. An atmospheric background estimate is obtained from a chemical transport model, emissivity from regional measurements and surface temperature (ST) from space-borne retrievals. The CO background error is set to 10%. FLITS retrieves CO by assuming a simple linear relationship between the IASI measurements and background estimate of the atmosphere and surface parameters. This differs from the COL2 algorithm that treats CO retrieval as a moderately non-linear problem. When compared to COL2, the FLITS retrievals display similar trends in distribution and transport of CO over time with the advantage of an improved spatial resolution (single-pixel). The value of the averaging kernel (A) is consistently above 0.5 and indicates that FLITS retrievals have a stable dependence on the measurement. This stability is achieved through careful channel selection in the strongest CO absorption lines (2050-2225 cm-1) and joint retrieval with skin temperature (IASI sensitivity to CO is highly correlated with ST), thus no spatial averaging is necessary. We conclude that the simplicity and stability of FLITS make it useful first as a research tool, i.e. the algorithm is easy to understand and computationally simple enough to run on most desktop computers, and second, as an operational tool that can calculate near real-time CO retrievals at instrument resolution for regional monitoring.

Algorithms for radiative transfer simulations for aerosol retrieval

NASA Astrophysics Data System (ADS)

Mukai, Sonoyo; Sano, Itaru; Nakata, Makiko

2012-11-01

Aerosol retrieval work from satellite data, i.e. aerosol remote sensing, is divided into three parts as: satellite data analysis, aerosol modeling and multiple light scattering calculation in the atmosphere model which is called radiative transfer simulation. The aerosol model is compiled from the accumulated measurements during more than ten years provided with the world wide aerosol monitoring network (AERONET). The radiative transfer simulations take Rayleigh scattering by molecules and Mie scattering by aerosols in the atmosphere, and reflection by the Earth surface into account. Thus the aerosol properties are estimated by comparing satellite measurements with the numerical values of radiation simulations in the Earth-atmosphere-surface model. It is reasonable to consider that the precise simulation of multiple light-scattering processes is necessary, and needs a long computational time especially in an optically thick atmosphere model. Therefore efficient algorithms for radiative transfer problems are indispensable to retrieve aerosols from space.

Estimation of leaf water contents from mid- and thermal infrared spectra by coupling genetic algorithm and partial least squares regression

NASA Astrophysics Data System (ADS)

Arshad, Muhammad; Ullah, Saleem; Khurshid, Khurram; Ali, Asad

2017-10-01

Leaf Water Content (LWC) is an essential constituent of plant leaves that determines vegetation heath and its productivity. An accurate and on-time measurement of water content is crucial for planning irrigation, forecasting drought and predicting woodland fire. The retrieval of LWC from Visible to Shortwave Infrared (VSWIR: 0.4-2.5 μm) has been extensively investigated but little has been done in the Mid and Thermal Infrared (MIR and TIR: 2.50 -14.0 μm), windows of electromagnetic spectrum. This study is mainly focused on retrieval of LWC from Mid and Thermal Infrared, using Genetic Algorithm integrated with Partial Least Square Regression (PLSR). Genetic Algorithm fused with PLSR selects spectral wavebands with high predictive performance i.e., yields high adjusted-R2 and low RMSE. In our case, GA-PLSR selected eight variables (bands) and yielded highly accurate models with adjusted-R2 of 0.93 and RMSEcv equal to 7.1 %. The study also demonstrated that MIR is more sensitive to the variation in LWC as compared to TIR. However, the combined use of MIR and TIR spectra enhances the predictive performance in retrieval of LWC. The integration of Genetic Algorithm and PLSR, not only increases the estimation precision by selecting the most sensitive spectral bands but also helps in identifying the important spectral regions for quantifying water stresses in vegetation. The findings of this study will allow the future space missions (like HyspIRI) to position wavebands at sensitive regions for characterizing vegetation stresses.

A Method to Retrieve Rainfall Rate Over Land from TRMM Microwave Imager Observations

NASA Technical Reports Server (NTRS)

Prabhakara, C.; Iacovazzi, R., Jr.; Yoo, J.-M.; Lau, William K. M. (Technical Monitor)

2002-01-01

Over tropical land regions, rain rate maxima in mesoscale convective systems revealed by the Precipitation Radar (PR) flown on the Tropical Rainfall Measuring Mission (TRMM) satellite are found to correspond to thunderstorms, i.e., Cbs. These Cbs are reflected as minima in the 85 GHz brightness temperature, T85, observed by the TRMM Microwave Imager (TMI) radiometer. Because the magnitude of TMI observations do not discriminate satisfactorily convective and stratiform rain, we developed here a different TMI discrimination method. In this method, two types of Cbs, strong and weak, are inferred from the Laplacian of T85 at minima. Then, to retrieve rain rate, where T85 is less than 270 K, a weak (background) rain rate is deduced using T85 observations. Furthermore, over a circular area of 10 km radius centered at the location of each T85 minimum, an additional Cb component of rain rate is added to the background rain rate. This Cb component of rain rate is estimated with the help of (T19-T37) and T85 observations. Initially, our algorithm is calibrated with the PR rain rate measurements from 20 MCS rain events. After calibration, this method is applied to TMI data taken from several tropical land regions. With the help of the PR observations, we show that the spatial distribution and intensity of rain rate over land estimated from our algorithm are better than those given by the current TMI-Version-5 Algorithm. For this reason, our algorithm may be used to improve the current state of rain retrievals on land.

Development, Validation, and Potential Enhancements to the Second-Generation Operational Aerosol Product at the National Environmental Satellite, Data, and Information Service of the National Oceanic and Atmospheric Administration

NASA Technical Reports Server (NTRS)

Stowe, Larry L.; Ignatov, Alexander M.; Singh, Ramdas R.

1997-01-01

A revised (phase 2) single-channel algorithm for aerosol optical thickness, tau(sup A)(sub SAT), retrieval over oceans from radiances in channel 1 (0.63 microns) of the Advanced Very High Resolution Radiometer (AVHRR) has been implemented at the National Oceanic and Atmospheric Administration's National Environmental Satellite Data and Information Service for the NOAA 14 satellite launched December 30, 1994. It is based on careful validation of its operational predecessor (phase 1 algorithm), implemented for NOAA 14 in 1989. Both algorithms scale the upward satellite radiances in cloud-free conditions to aerosol optical thickness using an updated radiative transfer model of the ocean and atmosphere. Application of the phase 2 algorithm to three matchup Sun-photometer and satellite data sets, one with NOAA 9 in 1988 and two with NOAA 11 in 1989 and 1991, respectively, show systematic error is less than 10%, with a random error of sigma(sub tau) approx. equal 0.04. First results of tau(sup A)(sub SAT) retrievals from NOAA 14 using the phase 2 algorithm, and from checking its internal consistency, are presented. The potential two-channel (phase 3) algorithm for the retrieval of an aerosol size parameter, such as the Junge size distribution exponent, by adding either channel 2 (0.83 microns) from the current AVHRR instrument, or a 1.6-microns channel to be available on the Tropical Rainfall Measurement Mission and the NOAA-KLM satellites by 1997 is under investigation. The possibility of using this additional information in the retrieval of a more accurate estimate of aerosol optical thickness is being explored.

Quantifying the Climate-Scale Accuracy of Satellite Cloud Retrievals

NASA Astrophysics Data System (ADS)

Roberts, Y.; Wielicki, B. A.; Sun-Mack, S.; Minnis, P.; Liang, L.; Di Girolamo, L.

2014-12-01

Instrument calibration and cloud retrieval algorithms have been developed to minimize retrieval errors on small scales. However, measurement uncertainties and assumptions within retrieval algorithms at the pixel level may alias into decadal-scale trends of cloud properties. We first, therefore, quantify how instrument calibration changes could alias into cloud property trends. For a perfect observing system the climate trend accuracy is limited only by the natural variability of the climate variable. Alternatively, for an actual observing system, the climate trend accuracy is additionally limited by the measurement uncertainty. Drifts in calibration over time may therefore be disguised as a true climate trend. We impose absolute calibration changes to MODIS spectral reflectance used as input to the CERES Cloud Property Retrieval System (CPRS) and run the modified MODIS reflectance through the CPRS to determine the sensitivity of cloud properties to calibration changes. We then use these changes to determine the impact of instrument calibration changes on trend uncertainty in reflected solar cloud properties. Secondly, we quantify how much cloud retrieval algorithm assumptions alias into cloud optical retrieval trends by starting with the largest of these biases: the plane-parallel assumption in cloud optical thickness (τC) retrievals. First, we collect liquid water cloud fields obtained from Multi-angle Imaging Spectroradiometer (MISR) measurements to construct realistic probability distribution functions (PDFs) of 3D cloud anisotropy (a measure of the degree to which clouds depart from plane-parallel) for different ISCCP cloud types. Next, we will conduct a theoretical study with dynamically simulated cloud fields and a 3D radiative transfer model to determine the relationship between 3D cloud anisotropy and 3D τC bias for each cloud type. Combining these results provides distributions of 3D τC bias by cloud type. Finally, we will estimate the change in frequency of occurrence of cloud types between two decades and will have the information needed to calculate the total change in 3D optical thickness bias between two decades. If we uncover aliases in this study, the results will motivate the development and rigorous testing of climate specific cloud retrieval algorithms.

Towards better understanding of high-mountain cryosphere changes using GPM data: A Joint Snowfall and Snow-cover Passive Microwave Retrieval Algorithm

NASA Astrophysics Data System (ADS)

Ebtehaj, A.; Foufoula-Georgiou, E.

2016-12-01

Scientific evidence suggests that the duration and frequency of snowfall and the extent of snow cover are rapidly declining under global warming. Both precipitation and snow cover scatter the upwelling surface microwave emission and decrease the observed high-frequency brightness temperatures. The mixture of these two scattering signals is amongst the largest sources of ambiguities and errors in passive microwave retrievals of both precipitation and snow-cover. The dual frequency radar and the high-frequency radiometer on board the GPM satellite provide a unique opportunity to improve passive retrievals of precipitation and snow-cover physical properties and fill the gaps in our understating of their variability in view of climate change. Recently, a new Bayesian rainfall retrieval algorithm (called ShARP) was developed using modern approximation methods and shown to yield improvements against other algorithms in retrieval of rainfall over radiometrically complex land surfaces. However, ShARP uses a large database of input rainfall and output brightness temperatures, which might be undersampled. Furthermore, it is not capable to discriminate between solid and liquid phase of precipitation and specifically discriminate the background snow-cover emission and its contamination effects on the retrievals. We address these problems by extending it to a new Bayesian land-atmosphere retrieval framework (ShARP-L) that allows joint retrievals of atmospheric constituents and land surface physical properties. Using modern sparse approximation techniques, the database is reduced to atomic microwave signatures in a family of compact class consistent dictionaries. These dictionaries can efficiently represent the entire database and allow us to discriminate between different land-atmosphere states. First the algorithm makes use of the dictionaries to detect the phase of the precipitation and type of the land-cover and then it estimates the physical properties of precipitation and snow cover using an extended version of the Dantzig Selector, which is robust to non-Gaussian and correlated geophysical noise. Promising results are presented in retrievals of snowfall and snow-cover over coastal orographic features of North America's Coast Range and South America's Andes.

Actual daily evapotranspiration estimated from MERIS and AATSR data over the Chinese Loess Plateau

NASA Astrophysics Data System (ADS)

Liu, R.; Wen, J.; Wang, X.; Wang, L.; Tian, H.; Zhang, T. T.; Shi, X. K.; Zhang, J. H.; Lu, Sh. N.

2009-02-01

The Loess Plateau is located in north of China and has a significant impact on the climate and ecosystem evolvement over the East Asian continent. Based on the land surface energy balance theory, the potential of using Medium Resolution Imaging Spectrometer (onboard sensor of the Environmental Satellite) remote sensing data on 7, 11 and 27 June 2005 is explored. The "split-window" algorithm is used to retrieve surface temperature from the Advanced the Along-Track Scanning Radiometer, another onboard senor of the Environmental Satellite. Then the near surface net radiation, sensible heat flux and soil heat flux are estimated by using the developed algorithm. We introduce a simple algorithm to predict the heat flux partitioning between the soil and vegetation. Combining the sunshine hours, air temperature, sunshine duration and wind speed measured by weather stations, a model for estimating daily ET is proposed. The instantaneous ET is also converted to daily value. Comparison of latent heats flux retrieved by remote sensing data with ground observation from eddy covariance flux system during Loess Plateau land surface process field Experiment, the maximum and minimum error of this approach are 10.96% and 4.80% respectively, the cause of the bias is also explored and discussed.

Development of water level estimation algorithms using SARAL/Altika dataset and validation over the Ukai reservoir, India

NASA Astrophysics Data System (ADS)

Chander, Shard; Ganguly, Debojyoti

2017-01-01

Water level was estimated, using AltiKa radar altimeter onboard the SARAL satellite, over the Ukai reservoir using modified algorithms specifically for inland water bodies. The methodology was based on waveform classification, waveform retracking, and dedicated inland range corrections algorithms. The 40-Hz waveforms were classified based on linear discriminant analysis and Bayesian classifier. Waveforms were retracked using Brown, Ice-2, threshold, and offset center of gravity methods. Retracking algorithms were implemented on full waveform and subwaveforms (only one leading edge) for estimating the improvement in the retrieved range. European Centre for Medium-Range Weather Forecasts (ECMWF) operational, ECMWF re-analysis pressure fields, and global ionosphere maps were used to exactly estimate the range corrections. The microwave and optical images were used for estimating the extent of the water body and altimeter track location. Four global positioning system (GPS) field trips were conducted on same day as the SARAL pass using two dual frequency GPS. One GPS was mounted close to the dam in static mode and the other was used on a moving vehicle within the reservoir in Kinematic mode. In situ gauge dataset was provided by the Ukai dam authority for the time period January 1972 to March 2015. The altimeter retrieved water level results were then validated with the GPS survey and in situ gauge dataset. With good selection of virtual station (waveform classification, back scattering coefficient), Ice-2 retracker and subwaveform retracker both work better with an overall root-mean-square error <15 cm. The results support that the AltiKa dataset, due to a smaller foot-print and sharp trailing edge of the Ka-band waveform, can be utilized for more accurate water level information over inland water bodies.

Dynamic statistical optimization of GNSS radio occultation bending angles: advanced algorithm and performance analysis

NASA Astrophysics Data System (ADS)

Li, Y.; Kirchengast, G.; Scherllin-Pirscher, B.; Norman, R.; Yuan, Y. B.; Fritzer, J.; Schwaerz, M.; Zhang, K.

2015-08-01

We introduce a new dynamic statistical optimization algorithm to initialize ionosphere-corrected bending angles of Global Navigation Satellite System (GNSS)-based radio occultation (RO) measurements. The new algorithm estimates background and observation error covariance matrices with geographically varying uncertainty profiles and realistic global-mean correlation matrices. The error covariance matrices estimated by the new approach are more accurate and realistic than in simplified existing approaches and can therefore be used in statistical optimization to provide optimal bending angle profiles for high-altitude initialization of the subsequent Abel transform retrieval of refractivity. The new algorithm is evaluated against the existing Wegener Center Occultation Processing System version 5.6 (OPSv5.6) algorithm, using simulated data on two test days from January and July 2008 and real observed CHAllenging Minisatellite Payload (CHAMP) and Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC) measurements from the complete months of January and July 2008. The following is achieved for the new method's performance compared to OPSv5.6: (1) significant reduction of random errors (standard deviations) of optimized bending angles down to about half of their size or more; (2) reduction of the systematic differences in optimized bending angles for simulated MetOp data; (3) improved retrieval of refractivity and temperature profiles; and (4) realistically estimated global-mean correlation matrices and realistic uncertainty fields for the background and observations. Overall the results indicate high suitability for employing the new dynamic approach in the processing of long-term RO data into a reference climate record, leading to well-characterized and high-quality atmospheric profiles over the entire stratosphere.

An effective inversion algorithm for retrieving bimodal aerosol particle size distribution from spectral extinction data

NASA Astrophysics Data System (ADS)

He, Zhenzong; Qi, Hong; Yao, Yuchen; Ruan, Liming

2014-12-01

The Ant Colony Optimization algorithm based on the probability density function (PDF-ACO) is applied to estimate the bimodal aerosol particle size distribution (PSD). The direct problem is solved by the modified Anomalous Diffraction Approximation (ADA, as an approximation for optically large and soft spheres, i.e., χ⪢1 and |m-1|⪡1) and the Beer-Lambert law. First, a popular bimodal aerosol PSD and three other bimodal PSDs are retrieved in the dependent model by the multi-wavelength extinction technique. All the results reveal that the PDF-ACO algorithm can be used as an effective technique to investigate the bimodal PSD. Then, the Johnson's SB (J-SB) function and the modified beta (M-β) function are employed as the general distribution function to retrieve the bimodal PSDs under the independent model. Finally, the J-SB and M-β functions are applied to recover actual measurement aerosol PSDs over Beijing and Shanghai obtained from the aerosol robotic network (AERONET). The numerical simulation and experimental results demonstrate that these two general functions, especially the J-SB function, can be used as a versatile distribution function to retrieve the bimodal aerosol PSD when no priori information about the PSD is available.

A Parameterized Inversion Model for Soil Moisture and Biomass from Polarimetric Backscattering Coefficients

NASA Technical Reports Server (NTRS)

Truong-Loi, My-Linh; Saatchi, Sassan; Jaruwatanadilok, Sermsak

2012-01-01

A semi-empirical algorithm for the retrieval of soil moisture, root mean square (RMS) height and biomass from polarimetric SAR data is explained and analyzed in this paper. The algorithm is a simplification of the distorted Born model. It takes into account the physical scattering phenomenon and has three major components: volume, double-bounce and surface. This simplified model uses the three backscattering coefficients ( sigma HH, sigma HV and sigma vv) at low-frequency (P-band). The inversion process uses the Levenberg-Marquardt non-linear least-squares method to estimate the structural parameters. The estimation process is entirely explained in this paper, from initialization of the unknowns to retrievals. A sensitivity analysis is also done where the initial values in the inversion process are varying randomly. The results show that the inversion process is not really sensitive to initial values and a major part of the retrievals has a root-mean-square error lower than 5% for soil moisture, 24 Mg/ha for biomass and 0.49 cm for roughness, considering a soil moisture of 40%, roughness equal to 3cm and biomass varying from 0 to 500 Mg/ha with a mean of 161 Mg/ha

A novel image retrieval algorithm based on PHOG and LSH

NASA Astrophysics Data System (ADS)

Wu, Hongliang; Wu, Weimin; Peng, Jiajin; Zhang, Junyuan

2017-08-01

PHOG can describe the local shape of the image and its relationship between the spaces. The using of PHOG algorithm to extract image features in image recognition and retrieval and other aspects have achieved good results. In recent years, locality sensitive hashing (LSH) algorithm has been superior to large-scale data in solving near-nearest neighbor problems compared with traditional algorithms. This paper presents a novel image retrieval algorithm based on PHOG and LSH. First, we use PHOG to extract the feature vector of the image, then use L different LSH hash table to reduce the dimension of PHOG texture to index values and map to different bucket, and finally extract the corresponding value of the image in the bucket for second image retrieval using Manhattan distance. This algorithm can adapt to the massive image retrieval, which ensures the high accuracy of the image retrieval and reduces the time complexity of the retrieval. This algorithm is of great significance.

Improving Forecast Skill by Assimilation of Quality Controlled AIRS Version 5 Temperature Soundings

NASA Technical Reports Server (NTRS)

Susskind, Joel; Reale, Oreste

2009-01-01

The AIRS Science Team Version 5 retrieval algorithm has been finalized and is now operational at the Goddard DAAC in the processing (and reprocessing) of all AIRS data. The AIRS Science Team Version 5 retrieval algorithm contains two significant improvements over Version 4: 1) Improved physics allows for use of AIRS observations in the entire 4.3 micron CO2 absorption band in the retrieval of temperature profile T(p) during both day and night. Tropospheric sounding 15 micron CO2 observations are now used primarily in the generation of cloud cleared radiances R(sub i). This approach allows for the generation of accurate values of R(sub i) and T(p) under most cloud conditions. 2) Another very significant improvement in Version 5 is the ability to generate accurate case-by-case, level-by-level error estimates for the atmospheric temperature profile, as well as for channel-by-channel error estimates for R(sub i). These error estimates are used for Quality Control of the retrieved products. We have conducted forecast impact experiments assimilating AIRS temperature profiles with different levels of Quality Control using the NASA GEOS-5 data assimilation system. Assimilation of Quality Controlled T(p) resulted in significantly improved forecast skill compared to that obtained from analyses obtained when all data used operationally by NCEP, except for AIRS data, is assimilated. We also conducted an experiment assimilating AIRS radiances uncontaminated by clouds, as done operationally by ECMWF and NCEP. Forecast resulting from assimilated AIRS radiances were of poorer quality than those obtained assimilating AIRS temperatures.

Improvements for retrieval of cloud droplet size by the POLDER instrument

NASA Astrophysics Data System (ADS)

Shang, H.; Husi, L.; Bréon, F. M.; Ma, R.; Chen, L.; Wang, Z.

2017-12-01

The principles of cloud droplet size retrieval via Polarization and Directionality of the Earth's Reflectance (POLDER) requires that clouds be horizontally homogeneous. The retrieval is performed by combining all measurements from an area of 150 km × 150 km to compensate for POLDER's insufficient directional sampling. Using POLDER-like data simulated with the RT3 model, we investigate the impact of cloud horizontal inhomogeneity and directional sampling on the retrieval and analyze which spatial resolution is potentially accessible from the measurements. Case studies show that the sub-grid-scale variability in droplet effective radius (CDR) can significantly reduce valid retrievals and introduce small biases to the CDR ( 1.5µm) and effective variance (EV) estimates. Nevertheless, the sub-grid-scale variations in EV and cloud optical thickness (COT) only influence the EV retrievals and not the CDR estimate. In the directional sampling cases studied, the retrieval using limited observations is accurate and is largely free of random noise. Several improvements have been made to the original POLDER droplet size retrieval. For example, measurements in the primary rainbow region (137-145°) are used to ensure retrievals of large droplet (>15 µm) and to reduce the uncertainties caused by cloud heterogeneity. A premium resoltion of 0.8° is determined by considering successful retrievals and cloud horizontal homogeneity. The improved algorithm is applied to measurements of POLDER in 2008, and we further compared our retrievals with cloud effective radii estimations of Moderate Resolution Imaging Spectroradiometer (MODIS). The results indicate that in global scale, the cloud effective radii and effective variance is larger in the central ocean than inland and coast areas. Over heavy polluted regions, the cloud droplets has small effective radii and narraw distribution due to the influence of aerosol particles.

Application of Artificial Neural Networks to the Development of Improved Multi-Sensor Retrievals of Near-Surface Air Temperature and Humidity Over Ocean

NASA Technical Reports Server (NTRS)

Roberts, J. Brent; Robertson, Franklin R.; Clayson, Carol Anne

2012-01-01

Improved estimates of near-surface air temperature and air humidity are critical to the development of more accurate turbulent surface heat fluxes over the ocean. Recent progress in retrieving these parameters has been made through the application of artificial neural networks (ANN) and the use of multi-sensor passive microwave observations. Details are provided on the development of an improved retrieval algorithm that applies the nonlinear statistical ANN methodology to a set of observations from the Advanced Microwave Scanning Radiometer (AMSR-E) and the Advanced Microwave Sounding Unit (AMSU-A) that are currently available from the NASA AQUA satellite platform. Statistical inversion techniques require an adequate training dataset to properly capture embedded physical relationships. The development of multiple training datasets containing only in-situ observations, only synthetic observations produced using the Community Radiative Transfer Model (CRTM), or a mixture of each is discussed. An intercomparison of results using each training dataset is provided to highlight the relative advantages and disadvantages of each methodology. Particular emphasis will be placed on the development of retrievals in cloudy versus clear-sky conditions. Near-surface air temperature and humidity retrievals using the multi-sensor ANN algorithms are compared to previous linear and non-linear retrieval schemes.

An operational MODIS aerosol retrieval algorithm at high spatial resolution, and its application over a complex urban region

NASA Astrophysics Data System (ADS)

Wong, Man Sing; Nichol, Janet E.; Lee, Kwon Ho

2011-03-01

Aerosol retrieval algorithms for the MODerate Resolution Imaging Spectroradiometer (MODIS) have been developed to estimate aerosol and microphysical properties of the atmosphere, which help to address aerosol climatic issues at global scale. However, higher spatial resolution aerosol products for urban areas have not been well-researched mainly due to the difficulty of differentiating aerosols from bright surfaces in urban areas. Here, an aerosol retrieval algorithm using the MODIS 500-m resolution bands is described, to retrieve aerosol properties over Hong Kong and the Pearl River Delta region. The rationale of our technique is to first estimate the aerosol reflectances by decomposing the top-of-atmosphere reflectances from surface reflectances and Rayleigh path reflectances. For the determination of surface reflectances, a Minimum Reflectance Technique (MRT) is used, and MRT images are computed for different seasons. For conversion of aerosol reflectance to aerosol optical thickness (AOT), comprehensive Look Up Tables specific to the local region are constructed, which consider aerosol properties and sun-viewing geometry in the radiative transfer calculations. Four local aerosol types, namely coastal urban, polluted urban, dust, and heavy pollution, were derived using cluster analysis on 3 years of AERONET measurements in Hong Kong. The resulting 500 m AOT images were found to be highly correlated with ground measurements from the AERONET (r2 = 0.767) and Microtops II sunphotometers (r2 = 0.760) in Hong Kong. This study further demonstrates the application of the fine resolution AOT images for monitoring inter-urban and intra-urban aerosol distributions and the influence of trans-boundary flows. These applications include characterization of spatial patterns of AOT within the city, and detection of regional biomass burning sources.

Water, Energy, and Carbon with Artificial Neural Networks (WECANN): a statistically based estimate of global surface turbulent fluxes and gross primary productivity using solar-induced fluorescence

DOE Office of Scientific and Technical Information (OSTI.GOV)

Alemohammad, Seyed Hamed; Fang, Bin; Konings, Alexandra G.

A new global estimate of surface turbulent fluxes, latent heat flux (LE) and sensible heat flux ( H), and gross primary production (GPP) is developed using a machine learning approach informed by novel remotely sensed solar-induced fluorescence (SIF) and other radiative and meteorological variables. This is the first study to jointly retrieve LE, H, and GPP using SIF observations. The approach uses an artificial neural network (ANN) with a target dataset generated from three independent data sources, weighted based on a triple collocation (TC) algorithm. The new retrieval, named Water, Energy, and Carbon with Artificial Neural Networks (WECANN), provides estimatesmore » of LE, H, and GPP from 2007 to 2015 at 1° × 1° spatial resolution and at monthly time resolution. The quality of ANN training is assessed using the target data, and the WECANN retrievals are evaluated using eddy covariance tower estimates from the FLUXNET network across various climates and conditions. When compared to eddy covariance estimates, WECANN typically outperforms other products, particularly for sensible and latent heat fluxes. Analyzing WECANN retrievals across three extreme drought and heat wave events demonstrates the capability of the retrievals to capture the extent of these events. Uncertainty estimates of the retrievals are analyzed and the interannual variability in average global and regional fluxes shows the impact of distinct climatic events – such as the 2015 El Niño – on surface turbulent fluxes and GPP.« less

Water, Energy, and Carbon with Artificial Neural Networks (WECANN): a statistically based estimate of global surface turbulent fluxes and gross primary productivity using solar-induced fluorescence

DOE PAGES

Alemohammad, Seyed Hamed; Fang, Bin; Konings, Alexandra G.; ...

2017-09-20

A new global estimate of surface turbulent fluxes, latent heat flux (LE) and sensible heat flux ( H), and gross primary production (GPP) is developed using a machine learning approach informed by novel remotely sensed solar-induced fluorescence (SIF) and other radiative and meteorological variables. This is the first study to jointly retrieve LE, H, and GPP using SIF observations. The approach uses an artificial neural network (ANN) with a target dataset generated from three independent data sources, weighted based on a triple collocation (TC) algorithm. The new retrieval, named Water, Energy, and Carbon with Artificial Neural Networks (WECANN), provides estimatesmore » of LE, H, and GPP from 2007 to 2015 at 1° × 1° spatial resolution and at monthly time resolution. The quality of ANN training is assessed using the target data, and the WECANN retrievals are evaluated using eddy covariance tower estimates from the FLUXNET network across various climates and conditions. When compared to eddy covariance estimates, WECANN typically outperforms other products, particularly for sensible and latent heat fluxes. Analyzing WECANN retrievals across three extreme drought and heat wave events demonstrates the capability of the retrievals to capture the extent of these events. Uncertainty estimates of the retrievals are analyzed and the interannual variability in average global and regional fluxes shows the impact of distinct climatic events – such as the 2015 El Niño – on surface turbulent fluxes and GPP.« less

Evaluation and application of an algorithm for atmospheric profiling continuity from Aqua to Suomi-NPP

NASA Astrophysics Data System (ADS)

Lipton, A.; Moncet, J. L.; Lynch, R.; Payne, V.; Alvarado, M. J.

2016-12-01

We will present results from an algorithm that is being developed to produce climate-quality atmospheric profiling earth system data records (ESDRs) for application to data from hyperspectral sounding instruments, including the Atmospheric InfraRed Sounder (AIRS) on EOS Aqua and the Cross-track Infrared Sounder (CrIS) on Suomi-NPP, along with their companion microwave sounders, AMSU and ATMS, respectively. The ESDR algorithm uses an optimal estimation approach and the implementation has a flexible, modular software structure to support experimentation and collaboration. Data record continuity benefits from the fact that the same algorithm can be applied to different sensors, simply by providing suitable configuration and data files. For analysis of satellite profiles over multi-decade periods, a concern is that the algorithm could respond inadequately to climate change if it uses a static background as a retrieval constraint, leading to retrievals that underestimate secular changes over extended periods of time and become biased toward an outdated climatology. We assessed the ability of our algorithm to respond appropriately to changes in temperature and water vapor profiles associated with climate change and, in particular, on the impact of using a climatological background in retrievals when the climatology is not static. We simulated a scenario wherein our algorithm processes 30 years of data from CrIS and ATMS (CrIMSS) with a static background based on data from the start of the 30-year period. We performed simulations using products from Coupled Model Intercomparison Project 5 (CMIP5), and in particular the "representative concentration pathways" midrange emissions (RCP4.5) scenario from the GISS-E2-R model. We will present results indicating that regularization using empirical orthogonal functions (EOFs) from a 30-year outdated covariance had a negligible effect on results. For temperature, the secular change is represented with high fidelity with the CrIMSS retrievals. For water vapor, an outdated background adds distortion to the secular moistening trend in the troposphere only above 300 mb, where the sensor information content is less than at lower levels. We will also present results illustrating the consistency between retrievals from near-simultaneous AIRS and CrIMSS measurements.

A MODIS-based begetation index climatology

USDA-ARS?s Scientific Manuscript database

Passive microwave soil moisture algorithms must account for vegetation attenuation of the signal in the retrieval process. One approach to accounting for vegetation is to use vegetation indices such as the Normalized Difference Vegetation Index (NDVI) to estimate the vegetation optical depth. The pa...

Estimating surface soil moisture from SMAP observations using a Neural Network technique.

PubMed

Kolassa, J; Reichle, R H; Liu, Q; Alemohammad, S H; Gentine, P; Aida, K; Asanuma, J; Bircher, S; Caldwell, T; Colliander, A; Cosh, M; Collins, C Holifield; Jackson, T J; Martínez-Fernández, J; McNairn, H; Pacheco, A; Thibeault, M; Walker, J P

2018-01-01

A Neural Network (NN) algorithm was developed to estimate global surface soil moisture for April 2015 to March 2017 with a 2-3 day repeat frequency using passive microwave observations from the Soil Moisture Active Passive (SMAP) satellite, surface soil temperatures from the NASA Goddard Earth Observing System Model version 5 (GEOS-5) land modeling system, and Moderate Resolution Imaging Spectroradiometer-based vegetation water content. The NN was trained on GEOS-5 soil moisture target data, making the NN estimates consistent with the GEOS-5 climatology, such that they may ultimately be assimilated into this model without further bias correction. Evaluated against in situ soil moisture measurements, the average unbiased root mean square error (ubRMSE), correlation and anomaly correlation of the NN retrievals were 0.037 m 3 m -3 , 0.70 and 0.66, respectively, against SMAP core validation site measurements and 0.026 m 3 m -3 , 0.58 and 0.48, respectively, against International Soil Moisture Network (ISMN) measurements. At the core validation sites, the NN retrievals have a significantly higher skill than the GEOS-5 model estimates and a slightly lower correlation skill than the SMAP Level-2 Passive (L2P) product. The feasibility of the NN method was reflected by a lower ubRMSE compared to the L2P retrievals as well as a higher skill when ancillary parameters in physically-based retrievals were uncertain. Against ISMN measurements, the skill of the two retrieval products was more comparable. A triple collocation analysis against Advanced Microwave Scanning Radiometer 2 (AMSR2) and Advanced Scatterometer (ASCAT) soil moisture retrievals showed that the NN and L2P retrieval errors have a similar spatial distribution, but the NN retrieval errors are generally lower in densely vegetated regions and transition zones.

A level 2 wind speed retrieval algorithm for the CYGNSS mission

NASA Astrophysics Data System (ADS)

Clarizia, Maria Paola; Ruf, Christopher; O'Brien, Andrew; Gleason, Scott

2014-05-01

The NASA EV-2 Cyclone Global Navigation Satellite System (CYGNSS) is a spaceborne mission focused on tropical cyclone (TC) inner core process studies. CYGNSS consists of a constellation of 8 microsatellites, which will measure ocean surface wind speed in all precipitating conditions, including those experienced in the TC eyewall, and with sufficient frequency to resolve genesis and rapid intensification. It does so through the use of an innovative remote sensing technique, known as Global Navigation Satellite System-Reflectometry, or GNSS-R. GNSS-R uses signals of opportunity from navigation constellations (e.g. GPS, GLONASS, Galileo), scattered by the surface of the ocean, to retrieve the surface wind speed. The dense space-time sampling capabilities, the ability of L-band signals to penetrate well through rain, and the possibility of simple, low-cost/low-power GNSS receivers, make GNSS-R ideal for the CYGNSS goals. Here we present an overview of a Level 2 (L2) wind speed retrieval algorithm, which would be particularly suitable for CYGNSS, and could be used to estimate winds from GNSS-R in general. The approach makes use of two different observables computed from 1-second Level 2a (L2a) delay-Doppler Maps (DDMs) of radar cross section. The first observable is called Delay-Doppler Map Average (DDMA), and it's the averaged radar cross section over a delay-Doppler window around the DDM peak (i.e. the specular reflection point coordinate in delay and Doppler). The second is called the Leading Edge Slope (LES), and it's the leading edge of the Integrated Delay Waveform (IDW), obtained by integrating the DDM along the Doppler dimension. The observables are calculated over a limited range of delays and Doppler frequencies, to comply with baseline spatial resolution requirements for the retrieved winds, which in the case of CYGNSS is 25 km x 25 km. If the observable from the 1-second DDM corresponds to a resolution higher than the specified one, time-averaging between consecutive observables is also applied, to reduce further the noise in the observables. The observables are correlated with wind speed, allowing one to develop an empirical Geophysical Model Function (GMF) that relates the observable value to the ground truth matchup winds, using a training dataset. The empirical GMF can then be used to estimate the winds from a generic dataset of observables, independent from the training one. In addition to that, the degree of decorrelation existing between winds retrieved from DDMA and from LES leads to the development of a Minimum Variance (MV) estimator, which provides improved wind estimates compared to those from DDMA or LES alone. The retrieval algorithm is applied in this study to GNSS-R synthetic data simulated using an End-to-End Simulator (E2ES) developed for CYGNSS, and using the true wind speeds that constitute the input to the simulations, as the ground-truth matchups. The performances of the retrieval algorithm will be presented in the form of Root Mean Square (RMS) error between the true and retrieved winds, highlighting that, for those specular points acquired with high enough gain of the receiver antenna, the RMS error meets the CYGNSS requirements on the wind speed uncertainty, which must be the greatest between 2 m/s or 10% of the measured wind.

The AMSR2 Satellite-based Microwave Snow Algorithm (SMSA) to estimate regional to global snow depth and snow water equivalent

NASA Astrophysics Data System (ADS)

Kelly, R. E. J.; Saberi, N.; Li, Q.

2017-12-01

With moderate to high spatial resolution (<1 km) regional to global snow water equivalent (SWE) observation approaches yet to be fully scoped and developed, the long-term satellite passive microwave record remains an important tool for cryosphere-climate diagnostics. A new satellite microwave remote sensing approach is described for estimating snow depth (SD) and snow water equivalent (SWE). The algorithm, called the Satellite-based Microwave Snow Algorithm (SMSA), uses Advanced Microwave Scanning Radiometer - 2 (AMSR2) observations aboard the Global Change Observation Mission - Water mission launched by the Japan Aerospace Exploration Agency in 2012. The approach is unique since it leverages observed brightness temperatures (Tb) with static ancillary data to parameterize a physically-based retrieval without requiring parameter constraints from in situ snow depth observations or historical snow depth climatology. After screening snow from non-snow surface targets (water bodies [including freeze/thaw state], rainfall, high altitude plateau regions [e.g. Tibetan plateau]), moderate and shallow snow depths are estimated by minimizing the difference between Dense Media Radiative Transfer model estimates (Tsang et al., 2000; Picard et al., 2011) and AMSR2 Tb observations to retrieve SWE and SD. Parameterization of the model combines a parsimonious snow grain size and density approach originally developed by Kelly et al. (2003). Evaluation of the SMSA performance is achieved using in situ snow depth data from a variety of standard and experiment data sources. Results presented from winter seasons 2012-13 to 2016-17 illustrate the improved performance of the new approach in comparison with the baseline AMSR2 algorithm estimates and approach the performance of the model assimilation-based approach of GlobSnow. Given the variation in estimation power of SWE by different land surface/climate models and selected satellite-derived passive microwave approaches, SMSA provides SWE estimates that are independent of real or near real-time in situ and model data.

Retrieval of NO2 stratospheric profiles from ground-based zenith-sky uv-visible measurements at 60°N

NASA Astrophysics Data System (ADS)

Hendrick, F.; van Roozendael, M.; Lambert, J.-C.; Fayt, C.; Hermans, C.; de Mazière, M.

2003-04-01

Nitrogen dioxide (NO_2) plays an important role in controlling ozone abundances in the stratosphere, either directly through the NOx (NO+NO_2) catalytic cycle, either indirectly by reaction with the radical ClO to form the reservoir species ClONO_2. In this presentation, NO_2 stratospheric profiles are retrieved from ground-based UV-visible NO_2 slant column abundances measured since 1998 at the complementary NDSC station of Harestua (Norway, 60^oN). The retrieval algorithm is based on the Rodgers optimal estimation inversion method and a forward model consisting in the IASB-BIRA stacked box photochemical model PSCBOX coupled to the radiative transfer package UVspec/DISORT. This algorithm has been applied to a set of about 50 sunrises and sunsets for which spatially and temporally coincident NO_2 measurements made by the HALOE (Halogen Occultation Experiment) instrument on board the Upper Atmosphere Research Satellite (UARS) are available. The consistency between retrieved and HALOE profiles is discussed in term of the different seasonal conditions investigated which are spring with and without chlorine activation, summer, and fall.

Comparison of SMOS and SMAP Soil Moisture Retrieval Approaches Using Tower-based Radiometer Data over a Vineyard Field

NASA Technical Reports Server (NTRS)

Miernecki, Maciej; Wigneron, Jean-Pierre; Lopez-Baeza, Ernesto; Kerr, Yann; DeJeu, Richard; DeLannoy, Gabielle J. M.; Jackson, Tom J.; O'Neill, Peggy E.; Shwank, Mike; Moran, Roberto Fernandez;

An Algorithm for the Retrieval of Droplet Number Concentration and Geometrical Thickness of Stratiform Marine Boundary Layer Clouds Applied to MODIS Radiometric Observations.

NASA Astrophysics Data System (ADS)

Schüller, Lothar; Bennartz, Ralf; Fischer, Jürgen; Brenguier, Jean-Louis

2005-01-01

Algorithms are now currently used for the retrieval of cloud optical thickness and droplet effective radius from multispectral radiance measurements. This paper extends their application to the retrieval of cloud droplet number concentration, cloud geometrical thickness, and liquid water path in shallow convective clouds, using an algorithm that was previously tested with airborne measurements of cloud radiances and validated against in situ measurements of the same clouds. The retrieval is based on a stratified cloud model of liquid water content and droplet spectrum. Radiance measurements in visible and near-infrared channels of the Moderate Resolution Imaging Spectroradiometer (MODIS), which is operated from the NASA platforms Terra and Aqua, are analyzed. Because of uncertainties in the simulation of the continental surface reflectance, the algorithm is presently limited to the monitoring of the microphysical structure of boundary layer clouds over the ocean. Two MODIS scenes of extended cloud fields over the North Atlantic Ocean trade wind region are processed. A transport and dispersion model (the Hybrid Single-Particle Lagrangian Integrated Trajectory Model, HYSPLIT4) is also used to characterize the origin of the air masses and hence their aerosol regimes. One cloud field formed in an air mass that was advected from southern Europe and North Africa. It shows high values of the droplet concentration when compared with the second cloud system, which developed in a more pristine environment. The more pristine case also exhibits a higher geometrical thickness and, thus, liquid water path, which counterbalances the expected cloud albedo increase of the polluted case. Estimates of cloud liquid water path are then compared with retrievals from the Special Sensor Microwave Imager (SSM/I). SSM/I-derived liquid water paths are in good agreement with the MODIS-derived values.

Variable Sampling Mapping

NASA Technical Reports Server (NTRS)

Smith, Jeffrey, S.; Aronstein, David L.; Dean, Bruce H.; Lyon, Richard G.

2012-01-01

The performance of an optical system (for example, a telescope) is limited by the misalignments and manufacturing imperfections of the optical elements in the system. The impact of these misalignments and imperfections can be quantified by the phase variations imparted on light traveling through the system. Phase retrieval is a methodology for determining these variations. Phase retrieval uses images taken with the optical system and using a light source of known shape and characteristics. Unlike interferometric methods, which require an optical reference for comparison, and unlike Shack-Hartmann wavefront sensors that require special optical hardware at the optical system's exit pupil, phase retrieval is an in situ, image-based method for determining the phase variations of light at the system s exit pupil. Phase retrieval can be used both as an optical metrology tool (during fabrication of optical surfaces and assembly of optical systems) and as a sensor used in active, closed-loop control of an optical system, to optimize performance. One class of phase-retrieval algorithms is the iterative transform algorithm (ITA). ITAs estimate the phase variations by iteratively enforcing known constraints in the exit pupil and at the detector, determined from modeled or measured data. The Variable Sampling Mapping (VSM) technique is a new method for enforcing these constraints in ITAs. VSM is an open framework for addressing a wide range of issues that have previously been considered detrimental to high-accuracy phase retrieval, including undersampled images, broadband illumination, images taken at or near best focus, chromatic aberrations, jitter or vibration of the optical system or detector, and dead or noisy detector pixels. The VSM is a model-to-data mapping procedure. In VSM, fully sampled electric fields at multiple wavelengths are modeled inside the phase-retrieval algorithm, and then these fields are mapped to intensities on the light detector, using the properties of the detector and optical system, for comparison with measured data. Ultimately, this model-to-data mapping procedure enables a more robust and accurate way of incorporating the exit-pupil and image detector constraints, which are fundamental to the general class of ITA phase retrieval algorithms.

Evaluating Machine Learning Regression Algorithms for Operational Retrieval of Biophysical Parameters: Opportunities for Sentinel

NASA Astrophysics Data System (ADS)

Verrelst, Jochem; Rivera, J. P.; Alonso, L.; Guanter, L.; Moreno, J.

2012-04-01

ESA’s upcoming satellites Sentinel-2 (S2) and Sentinel-3 (S3) aim to ensure continuity for Landsat 5/7, SPOT- 5, SPOT-Vegetation and Envisat MERIS observations by providing superspectral images of high spatial and temporal resolution. S2 and S3 will deliver near real-time operational products with a high accuracy for land monitoring. This unprecedented data availability leads to an urgent need for developing robust and accurate retrieval methods. Machine learning regression algorithms could be powerful candidates for the estimation of biophysical parameters from satellite reflectance measurements because of their ability to perform adaptive, nonlinear data fitting. By using data from the ESA-led field campaign SPARC (Barrax, Spain), it was recently found [1] that Gaussian processes regression (GPR) outperformed competitive machine learning algorithms such as neural networks, support vector regression) and kernel ridge regression both in terms of accuracy and computational speed. For various Sentinel configurations (S2-10m, S2- 20m, S2-60m and S3-300m) three important biophysical parameters were estimated: leaf chlorophyll content (Chl), leaf area index (LAI) and fractional vegetation cover (FVC). GPR was the only method that reached the 10% precision required by end users in the estimation of Chl. In view of implementing the regressor into operational monitoring applications, here the portability of locally trained GPR models to other images was evaluated. The associated confidence maps proved to be a good indicator for evaluating the robustness of the trained models. Consistent retrievals were obtained across the different images, particularly over agricultural sites. To make the method suitable for operational use, however, the poorer confidences over bare soil areas suggest that the training dataset should be expanded with inputs from various land cover types.

Application of phase-diverse phase retrieval to wavefront sensing in non-connected complicated pupil optics

NASA Astrophysics Data System (ADS)

Mao, Heng; Wang, Xiao; Zhao, Dazun

2007-07-01

Baseline algorithm, as a tool in wavefront sensing (WFS), incorporates the phase-diverse phase retrieval (PDPR) method with hybrid-unwrapping approach to ensure a unique pupil phase estimate with high WFS accuracy even in the case of high dynamic range aberration, as long as the pupil shape is of a convex set. However, for a complicated pupil, such as that in obstructed pupil optics, the said unwrapping approach would fail owing to the fake values at points located in obstructed areas of the pupil. Thus a modified unwrapping approach that can minimize the negative effects of the obstructed areas is proposed. Simulations have shown the validity of this unwrapping approach when it is embedded in Baseline algorithm.

Absolute magnitude calibration using trigonometric parallax - Incomplete, spectroscopic samples

NASA Technical Reports Server (NTRS)

Ratnatunga, Kavan U.; Casertano, Stefano

1991-01-01

A new numerical algorithm is used to calibrate the absolute magnitude of spectroscopically selected stars from their observed trigonometric parallax. This procedure, based on maximum-likelihood estimation, can retrieve unbiased estimates of the intrinsic absolute magnitude and its dispersion even from incomplete samples suffering from selection biases in apparent magnitude and color. It can also make full use of low accuracy and negative parallaxes and incorporate censorship on reported parallax values. Accurate error estimates are derived for each of the fitted parameters. The algorithm allows an a posteriori check of whether the fitted model gives a good representation of the observations. The procedure is described in general and applied to both real and simulated data.

Validation of UARS Microwave Limb Sounder Ozone Measurements

NASA Technical Reports Server (NTRS)

Froidevaux, L.; Read, W. G.; Lungu, T. A.; Cofield, R. E.; Fishbein, E. F.; Flower, D. A.; Jarnot, R. F.; Ridenoure, B. P.; Shippony, Z.; Waters, J. W.;

First Evaluation of Rainfall Derived from Commercial Microwave Links in São Paulo, Brazil

NASA Astrophysics Data System (ADS)

Uijlenhoet, R.; Rios Gaona, M. F.; Overeem, A.; Leijnse, H.; Raupach, T.

2017-12-01

Rainfall estimation from commercial microwave link (CML) networks has gained a lot of attention from the hydrometeorological community in the last decade. Path-averaged rainfall intensities can be retrieved from the signal attenuation between cell phone towers. Such a technique offers rainfall retrievals at high spatiotemporal resolutions. High spatiotemporal rainfall measurements are highly important for urban hydrology, given the often deadly impact of flash floods to society. This study evaluates CML rainfall retrievals for a subtropical climate. Rainfall estimation for subtropical climates is highly relevant, since many countries with few surface rainfall observations are located in such areas. The evaluation is done for the Brazilian city of São Paulo. RAINLINK (the open-source algorithm) retrieves rainfall intensities from attenuation measurements. We evaluated CMLs in the São Paulo metropolitan area for 81 days between October 2014 and January 2015. The evaluation was done against a dense automatic gauge network. High correlations (>0.9) and low biases ( 30%) are obtained, especially for short CMLs.

Known plaintext attack on double random phase encoding using fingerprint as key and a method for avoiding the attack.

PubMed

Tashima, Hideaki; Takeda, Masafumi; Suzuki, Hiroyuki; Obi, Takashi; Yamaguchi, Masahiro; Ohyama, Nagaaki

2010-06-21

We have shown that the application of double random phase encoding (DRPE) to biometrics enables the use of biometrics as cipher keys for binary data encryption. However, DRPE is reported to be vulnerable to known-plaintext attacks (KPAs) using a phase recovery algorithm. In this study, we investigated the vulnerability of DRPE using fingerprints as cipher keys to the KPAs. By means of computational experiments, we estimated the encryption key and restored the fingerprint image using the estimated key. Further, we propose a method for avoiding the KPA on the DRPE that employs the phase retrieval algorithm. The proposed method makes the amplitude component of the encrypted image constant in order to prevent the amplitude component of the encrypted image from being used as a clue for phase retrieval. Computational experiments showed that the proposed method not only avoids revealing the cipher key and the fingerprint but also serves as a sufficiently accurate verification system.

The Ozone Mapping and Profiler Suite (OMPS) Limb Profiler (LP) Version 1 aerosol extinction retrieval algorithm: theoretical basis

NASA Astrophysics Data System (ADS)

Loughman, Robert; Bhartia, Pawan K.; Chen, Zhong; Xu, Philippe; Nyaku, Ernest; Taha, Ghassan

2018-05-01

The theoretical basis of the Ozone Mapping and Profiler Suite (OMPS) Limb Profiler (LP) Version 1 aerosol extinction retrieval algorithm is presented. The algorithm uses an assumed bimodal lognormal aerosol size distribution to retrieve aerosol extinction profiles at 675 nm from OMPS LP radiance measurements. A first-guess aerosol extinction profile is updated by iteration using the Chahine nonlinear relaxation method, based on comparisons between the measured radiance profile at 675 nm and the radiance profile calculated by the Gauss-Seidel limb-scattering (GSLS) radiative transfer model for a spherical-shell atmosphere. This algorithm is discussed in the context of previous limb-scattering aerosol extinction retrieval algorithms, and the most significant error sources are enumerated. The retrieval algorithm is limited primarily by uncertainty about the aerosol phase function. Horizontal variations in aerosol extinction, which violate the spherical-shell atmosphere assumed in the version 1 algorithm, may also limit the quality of the retrieved aerosol extinction profiles significantly.

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

NASA Technical Reports Server (NTRS)

Atlas, Robert (Technical Monitor); Joiner, Joanna; Vasikov, Alexander; Flittner, David; Gleason, James; Bhartia, P. K.

2002-01-01

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.

A variational technique to estimate snowfall rate from coincident radar, snowflake, and fall-speed observations

NASA Astrophysics Data System (ADS)

Cooper, Steven J.; Wood, Norman B.; L'Ecuyer, Tristan S.

2017-07-01

Estimates of snowfall rate as derived from radar reflectivities alone are non-unique. Different combinations of snowflake microphysical properties and particle fall speeds can conspire to produce nearly identical snowfall rates for given radar reflectivity signatures. Such ambiguities can result in retrieval uncertainties on the order of 100-200 % for individual events. Here, we use observations of particle size distribution (PSD), fall speed, and snowflake habit from the Multi-Angle Snowflake Camera (MASC) to constrain estimates of snowfall derived from Ka-band ARM zenith radar (KAZR) measurements at the Atmospheric Radiation Measurement (ARM) North Slope Alaska (NSA) Climate Research Facility site at Barrow. MASC measurements of microphysical properties with uncertainties are introduced into a modified form of the optimal-estimation CloudSat snowfall algorithm (2C-SNOW-PROFILE) via the a priori guess and variance terms. Use of the MASC fall speed, MASC PSD, and CloudSat snow particle model as base assumptions resulted in retrieved total accumulations with a -18 % difference relative to nearby National Weather Service (NWS) observations over five snow events. The average error was 36 % for the individual events. Use of different but reasonable combinations of retrieval assumptions resulted in estimated snowfall accumulations with differences ranging from -64 to +122 % for the same storm events. Retrieved snowfall rates were particularly sensitive to assumed fall speed and habit, suggesting that in situ measurements can help to constrain key snowfall retrieval uncertainties. More accurate knowledge of these properties dependent upon location and meteorological conditions should help refine and improve ground- and space-based radar estimates of snowfall.

Merging Sounder and Imager Data for Improved Cloud Depiction on SNPP and JPSS.

NASA Astrophysics Data System (ADS)

Heidinger, A. K.; Holz, R.; Li, Y.; Platnick, S. E.; Wanzong, S.

2017-12-01

Under the NOAA GOES-R Algorithm Working Group (AWG) Program, NOAA supports the development of an Infrared (IR) Optimal Estimation (OE) Cloud Height Algorithm (ACHA). ACHA is an enterprise solution that supports many geostationary and polar orbiting imager sensors. ACHA is operational at NOAA on SNPP VIIRS and has been adopted as the cloud height algorithm for the NASA NPP Atmospheric Suite of products. Being an OE algorithm, ACHA is flexible and capable of using additional observations and constraints. We have modified ACHA to use sounder (CriS) observations to improve the cloud detection, typing and height estimation. Specifically, these improvements include retrievals in multi-layer scenarios and improved performance in polar regions. This presentation will describe the process for merging VIIRS and CrIS and a demonstration of the improvements.

Combined Radar-Radiometer Surface Soil Moisture and Roughness Estimation

NASA Technical Reports Server (NTRS)

Akbar, Ruzbeh; Cosh, Michael H.; O'Neill, Peggy E.; Entekhabi, Dara; Moghaddam, Mahta

2017-01-01

A robust physics-based combined radar-radiometer, or Active-Passive, surface soil moisture and roughness estimation methodology is presented. Soil moisture and roughness retrieval is performed via optimization, i.e., minimization, of a joint objective function which constrains similar resolution radar and radiometer observations simultaneously. A data-driven and noise-dependent regularization term has also been developed to automatically regularize and balance corresponding radar and radiometer contributions to achieve optimal soil moisture retrievals. It is shown that in order to compensate for measurement and observation noise, as well as forward model inaccuracies, in combined radar-radiometer estimation surface roughness can be considered a free parameter. Extensive Monte-Carlo numerical simulations and assessment using field data have been performed to both evaluate the algorithms performance and to demonstrate soil moisture estimation. Unbiased root mean squared errors (RMSE) range from 0.18 to 0.03 cm3cm3 for two different land cover types of corn and soybean. In summary, in the context of soil moisture retrieval, the importance of consistent forward emission and scattering development is discussed and presented.

An analysis of errors in special sensor microwave imager evaporation estimates over the global oceans

NASA Technical Reports Server (NTRS)

Esbensen, S. K.; Chelton, D. B.; Vickers, D.; Sun, J.

1993-01-01

The method proposed by Liu (1984) is used to estimate monthly averaged evaporation over the global oceans from 1 yr of special sensor microwave imager (SDSM/I) data. Intercomparisons involving SSM/I and in situ data are made over a wide range of oceanic conditions during August 1987 and February 1988 to determine the source of errors in the evaporation estimates. The most significant spatially coherent evaporation errors are found to come from estimates of near-surface specific humidity, q. Systematic discrepancies of over 2 g/kg are found in the tropics, as well as in the middle and high latitudes. The q errors are partitioned into contributions from the parameterization of q in terms of the columnar water vapor, i.e., the Liu q/W relationship, and from the retrieval algorithm for W. The effects of W retrieval errors are found to be smaller over most of the global oceans and due primarily to the implicitly assumed vertical structures of temperature and specific humidity on which the physically based SSM/I retrievals of W are based.

Combined Radar-Radiometer Surface Soil Moisture and Roughness Estimation.

PubMed

Akbar, Ruzbeh; Cosh, Michael H; O'Neill, Peggy E; Entekhabi, Dara; Moghaddam, Mahta

2017-07-01

A robust physics-based combined radar-radiometer, or Active-Passive, surface soil moisture and roughness estimation methodology is presented. Soil moisture and roughness retrieval is performed via optimization, i.e., minimization, of a joint objective function which constrains similar resolution radar and radiometer observations simultaneously. A data-driven and noise-dependent regularization term has also been developed to automatically regularize and balance corresponding radar and radiometer contributions to achieve optimal soil moisture retrievals. It is shown that in order to compensate for measurement and observation noise, as well as forward model inaccuracies, in combined radar-radiometer estimation surface roughness can be considered a free parameter. Extensive Monte-Carlo numerical simulations and assessment using field data have been performed to both evaluate the algorithm's performance and to demonstrate soil moisture estimation. Unbiased root mean squared errors (RMSE) range from 0.18 to 0.03 cm3/cm3 for two different land cover types of corn and soybean. In summary, in the context of soil moisture retrieval, the importance of consistent forward emission and scattering development is discussed and presented.

Pulse retrieval algorithm for interferometric frequency-resolved optical gating based on differential evolution.

PubMed

Hyyti, Janne; Escoto, Esmerando; Steinmeyer, Günter

2017-10-01

A novel algorithm for the ultrashort laser pulse characterization method of interferometric frequency-resolved optical gating (iFROG) is presented. Based on a genetic method, namely, differential evolution, the algorithm can exploit all available information of an iFROG measurement to retrieve the complex electric field of a pulse. The retrieval is subjected to a series of numerical tests to prove the robustness of the algorithm against experimental artifacts and noise. These tests show that the integrated error-correction mechanisms of the iFROG method can be successfully used to remove the effect from timing errors and spectrally varying efficiency in the detection. Moreover, the accuracy and noise resilience of the new algorithm are shown to outperform retrieval based on the generalized projections algorithm, which is widely used as the standard method in FROG retrieval. The differential evolution algorithm is further validated with experimental data, measured with unamplified three-cycle pulses from a mode-locked Ti:sapphire laser. Additionally introducing group delay dispersion in the beam path, the retrieval results show excellent agreement with independent measurements with a commercial pulse measurement device based on spectral phase interferometry for direct electric-field retrieval. Further experimental tests with strongly attenuated pulses indicate resilience of differential-evolution-based retrieval against massive measurement noise.

Inverse estimation of the spheroidal particle size distribution using Ant Colony Optimization algorithms in multispectral extinction technique

NASA Astrophysics Data System (ADS)

He, Zhenzong; Qi, Hong; Wang, Yuqing; Ruan, Liming

2014-10-01

Four improved Ant Colony Optimization (ACO) algorithms, i.e. the probability density function based ACO (PDF-ACO) algorithm, the Region ACO (RACO) algorithm, Stochastic ACO (SACO) algorithm and Homogeneous ACO (HACO) algorithm, are employed to estimate the particle size distribution (PSD) of the spheroidal particles. The direct problems are solved by the extended Anomalous Diffraction Approximation (ADA) and the Lambert-Beer law. Three commonly used monomodal distribution functions i.e. the Rosin-Rammer (R-R) distribution function, the normal (N-N) distribution function, and the logarithmic normal (L-N) distribution function are estimated under dependent model. The influence of random measurement errors on the inverse results is also investigated. All the results reveal that the PDF-ACO algorithm is more accurate than the other three ACO algorithms and can be used as an effective technique to investigate the PSD of the spheroidal particles. Furthermore, the Johnson's SB (J-SB) function and the modified beta (M-β) function are employed as the general distribution functions to retrieve the PSD of spheroidal particles using PDF-ACO algorithm. The investigation shows a reasonable agreement between the original distribution function and the general distribution function when only considering the variety of the length of the rotational semi-axis.

Early Examples from the Integrated Multi-Satellite Retrievals for GPM (IMERG)

NASA Astrophysics Data System (ADS)

Huffman, George; Bolvin, David; Braithwaite, Daniel; Hsu, Kuolin; Joyce, Robert; Kidd, Christopher; Sorooshian, Soroosh; Xie, Pingping

2014-05-01

The U.S. GPM Science Team's Day-1 algorithm for computing combined precipitation estimates as part of GPM is the Integrated Multi-satellitE Retrievals for GPM (IMERG). The goal is to compute the best time series of (nearly) global precipitation from "all" precipitation-relevant satellites and global surface precipitation gauge analyses. IMERG is being developed as a unified U.S. algorithm drawing on strengths in the three contributing groups, whose previous work includes: 1) the TRMM Multi-satellite Precipitation Analysis (TMPA); 2) the CPC Morphing algorithm with Kalman Filtering (K-CMORPH); and 3) the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks using a Cloud Classification System (PERSIANN-CCS). We review the IMERG design and development, plans for testing, and current status. Some of the lessons learned in running and reprocessing the previous data sets include the importance of quality-controlling input data sets, strategies for coping with transitions in the various input data sets, and practical approaches to retrospective analysis of multiple output products (namely the real- and post-real-time data streams). IMERG output will be illustrated using early test data, including the variety of supporting fields, such as the merged-microwave and infrared estimates, and the precipitation type. We end by considering recent changes in input data specifications, the transition from TRMM-based calibration to GPM-based, and further "Day 2" development.

Quantifying Uncertainties in Mass-Dimensional Relationships Through a Comparison Between CloudSat and SPartICus Reflectivity Factors

NASA Astrophysics Data System (ADS)

Mascio, J.; Mace, G. G.

2015-12-01

CloudSat and CALIPSO, two of the satellites in the A-Train constellation, use algorithms to calculate the scattering properties of small cloud particles, such as the T-matrix method. Ice clouds (i.e. cirrus) cause problems with these cloud property retrieval algorithms because of their variability in ice mass as a function of particle size. Assumptions regarding the microphysical properties, such as mass-dimensional (m-D) relationships, are often necessary in retrieval algorithms for simplification, but these assumptions create uncertainties of their own. Therefore, ice cloud property retrieval uncertainties can be substantial and are often not well known. To investigate these uncertainties, reflectivity factors measured by CloudSat are compared to those calculated from particle size distributions (PSDs) to which different m-D relationships are applied. These PSDs are from data collected in situ during three flights of the Small Particles in Cirrus (SPartICus) campaign. We find that no specific habit emerges as preferred and instead we conclude that the microphysical characteristics of ice crystal populations tend to be distributed over a continuum and, therefore, cannot be categorized easily. To quantify the uncertainties in the mass-dimensional relationships, an optimal estimation inversion was run to retrieve the m-D relationship per SPartICus flight, as well as to calculate uncertainties of the m-D power law.

Using microwave observations to estimate land surface temperature during cloudy conditions

USDA-ARS?s Scientific Manuscript database

Land surface temperature (LST), a key ingredient for physically-based retrieval algorithms of hydrological states and fluxes, remains a poorly constrained parameter for global scale studies. The main two observational methods to remotely measure T are based on thermal infrared (TIR) observations and...

Simultaneous determination of aerosol optical thickness and water-leaving radiance from multispectral measurements in coastal waters

NASA Astrophysics Data System (ADS)

Shi, Chong; Nakajima, Teruyuki

2018-03-01

Retrieval of aerosol optical properties and water-leaving radiance over ocean is challenging since the latter mostly accounts for ˜ 10 % of the satellite-observed signal and can be easily influenced by the atmospheric scattering. Such an effort would be more difficult in turbid coastal waters due to the existence of optically complex oceanic substances or high aerosol loading. In an effort to solve such problems, we present an optimization approach for the simultaneous determination of aerosol optical thickness (AOT) and normalized water-leaving radiance (nLw) from multispectral satellite measurements. In this algorithm, a coupled atmosphere-ocean radiative transfer model combined with a comprehensive bio-optical oceanic module is used to jointly simulate the satellite-observed reflectance at the top of atmosphere and water-leaving radiance just above the ocean surface. Then, an optimal estimation method is adopted to retrieve AOT and nLw iteratively. The algorithm is validated using Aerosol Robotic Network - Ocean Color (AERONET-OC) products selected from eight OC sites distributed over different waters, consisting of observations that covered glint and non-glint conditions from the Moderate Resolution Imaging Spectroradiometer (MODIS) instrument. Results show a good consistency between retrieved and in situ measurements at each site. It is demonstrated that more accurate AOTs are determined based on the simultaneous retrieval method, particularly in shorter wavelengths and sunglint conditions, where the averaged percentage difference (APD) of retrieved AOT is generally reduced by approximate 10 % in visible bands compared with those derived from the standard atmospheric correction (AC) scheme, since all the spectral measurements can be used jointly to increase the information content in the inversion of AOT, and the wind speed is also simultaneously retrieved to compensate the specular reflectance error estimated from the rough ocean surface model. For the retrieval of nLw, atmospheric overcorrection can be avoided in order to have a significant improvement of the inversion of nLw at 412 nm. Furthermore, generally better estimates of band ratios of nLw(443) / nLw(554) and nLw(488) / nLw(554) are obtained using the simultaneous retrieval approach with lower root mean square errors and relative differences than those derived from the standard AC approach in comparison to the AERONET-OC products, as well as the APD values of retrieved Chl which decreased by about 5 %. On the other hand, the standard AC scheme yields a more accurate retrieval of nLw at 488 nm, prompting a further optimization of the oceanic bio-optical module of the current model.

Satellite Estimation of Daily Land Surface Water Vapor Pressure Deficit from AMSR- E

NASA Astrophysics Data System (ADS)

Jones, L. A.; Kimball, J. S.; McDonald, K. C.; Chan, S. K.; Njoku, E. G.; Oechel, W. C.

2007-12-01

Vapor pressure deficit (VPD) is a key variable for monitoring land surface water and energy exchanges, and estimating plant water stress. Multi-frequency day/night brightness temperatures from the Advanced Microwave Scanning Radiometer on EOS Aqua (AMSR-E) were used to estimate daily minimum and average near surface (2 m) air temperatures across a North American boreal-Arctic transect. A simple method for determining daily mean VPD (Pa) from AMSR-E air temperature retrievals was developed and validated against observations across a regional network of eight study sites ranging from boreal grassland and forest to arctic tundra. The method assumes that the dew point and minimum daily air temperatures tend to equilibrate in areas with low night time temperatures and relatively moist conditions. This assumption was tested by comparing the VPD algorithm results derived from site daily temperature observations against results derived from AMSR-E retrieved temperatures alone. An error analysis was conducted to determine the amount of error introduced in VPD estimates given known levels of error in satellite retrieved temperatures. Results indicate that the assumption generally holds for the high latitude study sites except for arid locations in mid-summer. VPD estimates using the method with AMSR-E retrieved temperatures compare favorably with site observations. The method can be applied to land surface temperature retrievals from any sensor with day and night surface or near-surface thermal measurements and shows potential for inferring near-surface wetness conditions where dense vegetation may hinder surface soil moisture retrievals from low-frequency microwave sensors. This work was carried out at The University of Montana, at San Diego State University, and at the Jet Propulsion Laboratory, California Institute of Technology, under contract to the National Aeronautics and Space Administration.

Evaluation of MERIS products from Baltic Sea coastal waters rich in CDOM

NASA Astrophysics Data System (ADS)

Beltrán-Abaunza, J. M.; Kratzer, S.; Brockmann, C.

2013-11-01

In this study, retrievals of the medium resolution imaging spectrometer (MERIS) reflectances and water quality products using 4 different coastal processing algorithms freely available are assessed by comparison against sea-truthing data. The study is based on a pair-wise comparison using processor-dependent quality flags for the retrieval of valid common macro-pixels. This assessment is required in order to ensure the reliability of monitoring systems based on MERIS data, such as the Swedish coastal and lake monitoring system (http.vattenkvalitet.se). The results show that the pre-processing with the Improved Contrast between Ocean and Land (ICOL) processor, correcting for adjacency effects, improve the retrieval of spectral reflectance for all processors, Therefore, it is recommended that the ICOL processor should be applied when Baltic coastal waters are investigated. Chlorophyll was retrieved best using the FUB (Free University of Berlin) processing algorithm, although overestimations in the range 18-26.5%, dependent on the compared pairs, were obtained. At low chlorophyll concentrations (< 2.5 mg m-3), random errors dominated in the retrievals with the MEGS (MERIS ground segment processor) processor. The lowest bias and random errors were obtained with MEGS for suspended particulate matter, for which overestimations in te range of 8-16% were found. Only the FUB retrieved CDOM (Coloured Dissolved Organic Matter) correlate with in situ values. However, a large systematic underestimation appears in the estimates that nevertheless may be corrected for by using a~local correction factor. The MEGS has the potential to be used as an operational processing algorithm for the Himmerfjärden bay and adjacent areas, but it requires further improvement of the atmospheric correction for the blue bands and better definition at relatively low chlorophyll concentrations in presence of high CDOM attenuation.

Evaluation of MERIS products from Baltic Sea coastal waters rich in CDOM

NASA Astrophysics Data System (ADS)

Beltrán-Abaunza, J. M.; Kratzer, S.; Brockmann, C.

2014-05-01

In this study, retrievals of the medium resolution imaging spectrometer (MERIS) reflectances and water quality products using four different coastal processing algorithms freely available are assessed by comparison against sea-truthing data. The study is based on a pair-wise comparison using processor-dependent quality flags for the retrieval of valid common macro-pixels. This assessment is required in order to ensure the reliability of monitoring systems based on MERIS data, such as the Swedish coastal and lake monitoring system (http://vattenkvalitet.se). The results show that the pre-processing with the Improved Contrast between Ocean and Land (ICOL) processor, correcting for adjacency effects, improves the retrieval of spectral reflectance for all processors. Therefore, it is recommended that the ICOL processor should be applied when Baltic coastal waters are investigated. Chlorophyll was retrieved best using the FUB (Free University of Berlin) processing algorithm, although overestimations in the range 18-26.5%, dependent on the compared pairs, were obtained. At low chlorophyll concentrations (< 2.5 mg m-3), data dispersion dominated in the retrievals with the MEGS (MERIS ground segment processor) processor. The lowest bias and data dispersion were obtained with MEGS for suspended particulate matter, for which overestimations in the range of 8-16% were found. Only the FUB retrieved CDOM (coloured dissolved organic matter) correlate with in situ values. However, a large systematic underestimation appears in the estimates that nevertheless may be corrected for by using a local correction factor. The MEGS has the potential to be used as an operational processing algorithm for the Himmerfjärden bay and adjacent areas, but it requires further improvement of the atmospheric correction for the blue bands and better definition at relatively low chlorophyll concentrations in the presence of high CDOM attenuation.

Exploiting the synergy between SMAP and SMOS to improve brightness temperature simulations and soil moisture retrievals in arid regions

NASA Astrophysics Data System (ADS)

Ebrahimi, Mohsen; Alavipanah, Seyed Kazem; Hamzeh, Saeid; Amiraslani, Farshad; Neysani Samany, Najmeh; Wigneron, Jean-Pierre

2018-02-01

The objective of this study was to exploit the synergy between SMOS and SMAP based on vegetation optical depth (VOD) to improve brightness temperature (TB) simulations and land surface soil moisture (SM) retrievals in arid regions of the world. In the current operational algorithm of SMAP (level 2), vegetation water content (VWC) is considered as a proxy to compute VOD which is calculated by an empirical conversion function of NDVI. Avoiding the empirical estimation of VOD, the SMOS algorithm is used to retrieve simultaneously SM and VOD from TB observations. The present study attempted to improve SMAP TB simulations and SM retrievals by benefiting from the advantages of the SMOS (L-MEB) algorithm. This was achieved by using a synergy method based on replacing the default value of SMAP VOD with the retrieved value of VOD from the SMOS multi angular and bi-polarization observations of TB. The insitu SM measurements, used as reference SM in this study, were obtained from the International Soil Moisture Network (ISMN) over 180 stations located in arid regions of the world. Furthermore, four stations were randomly selected to analyze the temporal variations in VOD and SM. Results of the synergy method showed that the accuracy of the TB simulations and SM retrievals was respectively improved at 144 and 124 stations (out of a total of 180 stations) in terms of coefficient of determination (R2) and unbiased root mean squared error (UbRMSE). Analyzing the temporal variations in VOD showed that the SMOS VOD, conversely to the SMAP VOD, can better illustrate the presence of herbaceous plants and may be a better indicator of the seasonal changes in the vegetation density and biomass over the year.

Simultaneous aerosol/ocean products retrieved during the 2014 SABOR campaign using the NASA Research Scanning Polarimeter (RSP)

NASA Astrophysics Data System (ADS)

Stamnes, S.; Hostetler, C. A.; Ferrare, R. A.; Hair, J. W.; Burton, S. P.; Liu, X.; Hu, Y.; Stamnes, K. H.; Chowdhary, J.; Brian, C.

2017-12-01

The SABOR (Ship-Aircraft Bio-Optical Research) campaign was conducted during the summer of 2014, in the Atlantic Ocean, over the Chesapeake Bay and the eastern coastal region of the United States. The NASA GISS Research Scanning Polarimeter, a multi-angle, multi-spectral polarimeter measured the upwelling polarized radiances from a B200 aircraft. We present results from the new "MAPP" algorithm for RSP that is based on optimal estimation and that can retrieve simultaneous aerosol microphysical properties (including effective radius, single-scattering albedo, and real refractive index) and ocean color products using accurate radiative transfer and Mie calculations. The algorithm was applied to data collected during SABOR to retrieve aerosol microphysics and ocean products for all Aerosols-Above-Ocean (AAO) scenes. The RSP MAPP products are compared against collocated aerosol extinction and backscatter profiles collected by the NASA LaRC airborne High Spectral Resolution Lidar (HSRL-1), including lidar depth profiles of the ocean diffuse attenuation coefficient and the hemispherical backscatter coefficient.

Modeling and analysis of LWIR signature variability associated with 3D and BRDF effects

NASA Astrophysics Data System (ADS)

Adler-Golden, Steven; Less, David; Jin, Xuemin; Rynes, Peter

2016-05-01

Algorithms for retrieval of surface reflectance, emissivity or temperature from a spectral image almost always assume uniform illumination across the scene and horizontal surfaces with Lambertian reflectance. When these algorithms are used to process real 3-D scenes, the retrieved "apparent" values contain the strong, spatially dependent variations in illumination as well as surface bidirectional reflectance distribution function (BRDF) effects. This is especially problematic with horizontal or near-horizontal viewing, where many observed surfaces are vertical, and where horizontal surfaces can show strong specularity. The goals of this study are to characterize long-wavelength infrared (LWIR) signature variability in a HSI 3-D scene and develop practical methods for estimating the true surface values. We take advantage of synthetic near-horizontal imagery generated with the high-fidelity MultiService Electro-optic Signature (MuSES) model, and compare retrievals of temperature and directional-hemispherical reflectance using standard sky downwelling illumination and MuSES-based non-uniform environmental illumination.

Obtaining the Grobner Initialization for the Ground Flash Fraction Retrieval Algorithm

NASA Technical Reports Server (NTRS)

Solakiewicz, R.; Attele, R.; Koshak, W.

2011-01-01

At optical wavelengths and from the vantage point of space, the multiple scattering cloud medium obscures one's view and prevents one from easily determining what flashes strike the ground. However, recent investigations have made some progress examining the (easier, but still difficult) problem of estimating the ground flash fraction in a set of N flashes observed from space In the study by Koshak, a Bayesian inversion method was introduced for retrieving the fraction of ground flashes in a set of flashes observed from a (low earth orbiting or geostationary) satellite lightning imager. The method employed a constrained mixed exponential distribution model to describe the lightning optical measurements. To obtain the optimum model parameters, a scalar function of three variables (one of which is the ground flash fraction) was minimized by a numerical method. This method has formed the basis of a Ground Flash Fraction Retrieval Algorithm (GoFFRA) that is being tested as part of GOES-R GLM risk reduction.

Ground-Based Remote Retrievals of Cumulus Entrainment Rates

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wagner, Timothy J.; Turner, David D.; Berg, Larry K.

2013-07-26

While fractional entrainment rates for cumulus clouds have typically been derived from airborne observations, this limits the size and scope of available data sets. To increase the number of continental cumulus entrainment rate observations available for study, an algorithm for retrieving them from ground-based remote sensing observations has been developed. This algorithm, called the Entrainment Rate In Cumulus Algorithm (ERICA), uses the suite of instruments at the Southern Great Plains (SGP) site of the United States Department of Energy's Atmospheric Radiation Measurement (ARM) Climate Research Facility as inputs into a Gauss-Newton optimal estimation scheme, in which an assumed guess ofmore » the entrainment rate is iteratively adjusted through intercomparison of modeled liquid water path and cloud droplet effective radius to their observed counterparts. The forward model in this algorithm is the Explicit Mixing Parcel Model (EMPM), a cloud parcel model that treats entrainment as a series of discrete entrainment events. A quantified value for measurement uncertainty is also returned as part of the retrieval. Sensitivity testing and information content analysis demonstrate the robust nature of this method for retrieving accurate observations of the entrainment rate without the drawbacks of airborne sampling. Results from a test of ERICA on three months of shallow cumulus cloud events show significant variability of the entrainment rate of clouds in a single day and from one day to the next. The mean value of 1.06 km-¹ for the entrainment rate in this dataset corresponds well with prior observations and simulations of the entrainment rate in cumulus clouds.« less

Improved Surface Parameter Retrievals using AIRS/AMSU Data

NASA Technical Reports Server (NTRS)

Susskind, Joel; Blaisdell, John

2008-01-01

The AIRS Science Team Version 5.0 retrieval algorithm became operational at the Goddard DAAC in July 2007 generating near real-time products from analysis of AIRS/AMSU sounding data. This algorithm contains many significant theoretical advances over the AIRS Science Team Version 4.0 retrieval algorithm used previously. Two very significant developments of Version 5 are: 1) the development and implementation of an improved Radiative Transfer Algorithm (RTA) which allows for accurate treatment of non-Local Thermodynamic Equilibrium (non-LTE) effects on shortwave sounding channels; and 2) the development of methodology to obtain very accurate case by case product error estimates which are in turn used for quality control. These theoretical improvements taken together enabled a new methodology to be developed which further improves soundings in partially cloudy conditions. In this methodology, longwave C02 channel observations in the spectral region 700 cm(exp -1) to 750 cm(exp -1) are used exclusively for cloud clearing purposes, while shortwave C02 channels in the spectral region 2195 cm(exp -1) 2395 cm(exp -1) are used for temperature sounding purposes. This allows for accurate temperature soundings under more difficult cloud conditions. This paper further improves on the methodology used in Version 5 to derive surface skin temperature and surface spectral emissivity from AIRS/AMSU observations. Now, following the approach used to improve tropospheric temperature profiles, surface skin temperature is also derived using only shortwave window channels. This produces improved surface parameters, both day and night, compared to what was obtained in Version 5. These in turn result in improved boundary layer temperatures and retrieved total O3 burden.

The Next-Generation Goddard Convective-Stratiform Heating Algorithm: New Retrievals for Tropical and Extra-tropical Environments

NASA Astrophysics Data System (ADS)

Lang, S. E.; Tao, W. K.; Iguchi, T.

2017-12-01

The Goddard Convective-Stratiform Heating (or CSH) algorithm has been used to estimate cloud heating over the global Tropics using TRMM rainfall data and a set of look-up-tables (LUTs) derived from a series of multi-week cloud-resolving model (CRM) simulations using the Goddard Cumulus Ensemble model (GCE). These simulations link satellite observables (i.e., surface rainfall and stratiform fraction) with cloud heating profiles, which are not directly observable. However, with the launch of GPM in 2014, the range over which such algorithms can be applied has been extended from the Tropics into higher latitudes, including cold season and synoptic weather systems. In response, the CSH algorithm and its LUTs have been revised both to improve the retrievals in the Tropics as well as expand retrievals to higher latitudes. For the Tropics, the GCE simulations used to build the LUTs were upgraded using larger 2D model domains (512 vs 256 km) and a new, improved Goddard 4-ice scheme as well as expanded with additional cases (4 land and 6 ocean in total). The new tropical LUTs are also re-built using additional metrics. Besides surface type, conditional rain intensity and stratiform fraction, the new LUTs incorporate echo top heights and low-level (0-2 km) vertical reflectivity gradients. CSH retrievals in the Tropics based on the new LUTs show significant differences from previous iterations using TRMM data or the old LUT metrics. For the Extra-tropics, 6 NU-WRF simulations of synoptic events (3 East Coast and 3 West Coast), including snow, were used to build new extra-tropical CSH LUTs. The LUT metrics for the extra-tropics are based on radar characteristics and freezing level height. The extra-tropical retrievals are evaluated with a self-consistency check approach using the model heating as `truth,' and freezing level height is used to transition CSH retrievals from the Tropics to Extra-tropics. Retrieved zonal average heating structures in the Extra-tropics are presented and show distinct differences from those in the Tropics.

Cloud Properties of CERES-MODIS Edition 4 and CERES-VIIRS Edition 1

NASA Technical Reports Server (NTRS)

Sun-Mack, Sunny; Minnis, Patrick; Chang, Fu-Lung; Hong, Gang; Arduini, Robert; Chen, Yan; Trepte, Qing; Yost, Chris; Smith, Rita; Brown, Ricky;

Lidar-Radiometer Inversion Code (LIRIC) for the Retrieval of Vertical Aerosol Properties from Combined Lidar Radiometer Data: Development and Distribution in EARLINET

NASA Technical Reports Server (NTRS)

Chaikovsky, A.; Dubovik, O.; Holben, Brent N.; Bril, A.; Goloub, P.; Tanre, D.; Pappalardo, G.; Wandinger, U.; Chaikovskaya, L.; Denisov, S.;

Dissolved Organic Carbon along the Louisiana coast from MODIS and MERIS satellite data

NASA Astrophysics Data System (ADS)

Chaichi Tehrani, N.; D'Sa, E. J.

2012-12-01

Dissolved organic carbon (DOC) plays a critical role in the coastal and ocean carbon cycle. Hence, it is important to monitor and investigate its the distribution and fate in coastal waters. Since DOC cannot be measured directly through satellite remote sensors, chromophoric dissolved organic matter (CDOM) as an optically active fraction of DOC can be used as an alternative proxy to trace DOC concentrations. Here, satellite ocean color data from MODIS, MERIS, and field measurements of CDOM and DOC were used to develop and assess CDOM and DOC ocean color algorithms for coastal waters. To develop a CDOM retrieval algorithm, empirical relationships between CDOM absorption coefficient at 412 nm (aCDOM(412)) and reflectance ratios Rrs(488)/Rrs(555) for MODIS and Rrs(510)/Rrs(560) for MERIS were established. The performance of two CDOM empirical algorithms were evaluated for retrieval of (aCDOM(412)) from MODIS and MERIS in the northern Gulf of Mexico. Further, empirical algorithms were developed to estimate DOC concentration using the relationship between in situ aCDOM(412) and DOC, as well as using the newly developed CDOM empirical algorithms. Accordingly, our results revealed that DOC concentration was strongly correlated to aCDOM (412) for summer and spring-winter periods (r2 = 0.9 for both periods). Then, using the aCDOM(412)-Rrs and the aCDOM(412)-DOC relationships derived from field measurements, a relationship between DOC-Rrs was established for MODIS and MERIS data. The DOC empirical algorithms performed well as indicated by match-up comparisons between satellite estimates and field data (R2=0.52 and 0.58 for MODIS and MERIS for summer period, respectively). These algorithms were then used to examine DOC distribution along the Louisiana coast.

Satellite-based retrieval of particulate matter concentrations over the United Arab Emirates (UAE)

NASA Astrophysics Data System (ADS)

Zhao, Jun; Temimi, Marouane; Hareb, Fahad; Eibedingil, Iyasu

2016-04-01

In this study, an empirical algorithm was established to retrieve particulate matter (PM) concentrations (PM2.5 and PM10) using satellite-derived aerosol optical depth (AOD) over the United Arab Emirates (UAE). Validation of the proposed algorithm using ground truth data demonstrates its good accuracy. Time series of in situ measured PM concentrations between 2014 and 2015 showed high values in summer and low values in winter. Estimated and in situ measured PM concentrations were higher in 2015 than 2014. Remote sensing is an essential tool to reveal and back track the seasonality and inter-annual variations of PM concentrations and provide valuable information on the protection of human health and the response of air quality to anthropogenic activities and climate change.

Algorithms and sensitivity analyses for Stratospheric Aerosol and Gas Experiment II water vapor retrieval

NASA Technical Reports Server (NTRS)

Chu, W. P.; Chiou, E. W.; Larsen, J. C.; Thomason, L. W.; Rind, D.; Buglia, J. J.; Oltmans, S.; Mccormick, M. P.; Mcmaster, L. M.

1993-01-01

The operational inversion algorithm used for the retrieval of the water-vapor vertical profiles from the Stratospheric Aerosol and Gas Experiment II (SAGE II) occultation data is presented. Unlike the algorithm used for the retrieval of aerosol, O3, and NO2, the water-vapor retrieval algorithm accounts for the nonlinear relationship between the concentration versus the broad-band absorption characteristics of water vapor. Problems related to the accuracy of the computational scheme, the accuracy of the removal of other interfering species, and the expected uncertainty of the retrieved profile are examined. Results are presented on the error analysis of the SAGE II water vapor retrieval, indicating that the SAGE II instrument produced good quality water vapor data.

Monitoring carbon dioxide from space: Retrieval algorithm and flux inversion based on GOSAT data and using CarbonTracker-China

NASA Astrophysics Data System (ADS)

Yang, Dongxu; Zhang, Huifang; Liu, Yi; Chen, Baozhang; Cai, Zhaonan; Lü, Daren

2017-08-01

Monitoring atmospheric carbon dioxide (CO2) from space-borne state-of-the-art hyperspectral instruments can provide a high precision global dataset to improve carbon flux estimation and reduce the uncertainty of climate projection. Here, we introduce a carbon flux inversion system for estimating carbon flux with satellite measurements under the support of "The Strategic Priority Research Program of the Chinese Academy of Sciences—Climate Change: Carbon Budget and Relevant Issues". The carbon flux inversion system is composed of two separate parts: the Institute of Atmospheric Physics Carbon Dioxide Retrieval Algorithm for Satellite Remote Sensing (IAPCAS), and CarbonTracker-China (CT-China), developed at the Chinese Academy of Sciences. The Greenhouse gases Observing SATellite (GOSAT) measurements are used in the carbon flux inversion experiment. To improve the quality of the IAPCAS-GOSAT retrieval, we have developed a post-screening and bias correction method, resulting in 25%-30% of the data remaining after quality control. Based on these data, the seasonal variation of XCO2 (column-averaged CO2 dry-air mole fraction) is studied, and a strong relation with vegetation cover and population is identified. Then, the IAPCAS-GOSAT XCO2 product is used in carbon flux estimation by CT-China. The net ecosystem CO2 exchange is -0.34 Pg C yr-1 (±0.08 Pg C yr-1), with a large error reduction of 84%, which is a significant improvement on the error reduction when compared with in situ-only inversion.

Estimation of contour motion and deformation for nonrigid object tracking

NASA Astrophysics Data System (ADS)

Shao, Jie; Porikli, Fatih; Chellappa, Rama

2007-08-01

We present an algorithm for nonrigid contour tracking in heavily cluttered background scenes. Based on the properties of nonrigid contour movements, a sequential framework for estimating contour motion and deformation is proposed. We solve the nonrigid contour tracking problem by decomposing it into three subproblems: motion estimation, deformation estimation, and shape regulation. First, we employ a particle filter to estimate the global motion parameters of the affine transform between successive frames. Then we generate a probabilistic deformation map to deform the contour. To improve robustness, multiple cues are used for deformation probability estimation. Finally, we use a shape prior model to constrain the deformed contour. This enables us to retrieve the occluded parts of the contours and accurately track them while allowing shape changes specific to the given object types. Our experiments show that the proposed algorithm significantly improves the tracker performance.

Comparative Results of AIRS AMSU and CrIS/ATMS Retrievals Using a Scientifically Equivalent Retrieval Algorithm

NASA Technical Reports Server (NTRS)

Susskind, Joel; Kouvaris, Louis; Iredell, Lena

2016-01-01

The AIRS Science Team Version 6 retrieval algorithm is currently producing high quality level-3 Climate Data Records (CDRs) from AIRSAMSU which are critical for understanding climate processes. The AIRS Science Team is finalizing an improved Version-7 retrieval algorithm to reprocess all old and future AIRS data. AIRS CDRs should eventually cover the period September 2002 through at least 2020. CrISATMS is the only scheduled follow on to AIRSAMSU. The objective of this research is to prepare for generation of a long term CrISATMS level-3 data using a finalized retrieval algorithm that is scientifically equivalent to AIRSAMSU Version-7.

A variational technique to estimate snowfall rate from coincident radar, snowflake, and fall-speed observations

DOE PAGES

Cooper, Steven J.; Wood, Norman B.; L'Ecuyer, Tristan S.

2017-07-20

Estimates of snowfall rate as derived from radar reflectivities alone are non-unique. Different combinations of snowflake microphysical properties and particle fall speeds can conspire to produce nearly identical snowfall rates for given radar reflectivity signatures. Such ambiguities can result in retrieval uncertainties on the order of 100–200% for individual events. Here, we use observations of particle size distribution (PSD), fall speed, and snowflake habit from the Multi-Angle Snowflake Camera (MASC) to constrain estimates of snowfall derived from Ka-band ARM zenith radar (KAZR) measurements at the Atmospheric Radiation Measurement (ARM) North Slope Alaska (NSA) Climate Research Facility site at Barrow. MASCmore » measurements of microphysical properties with uncertainties are introduced into a modified form of the optimal-estimation CloudSat snowfall algorithm (2C-SNOW-PROFILE) via the a priori guess and variance terms. Use of the MASC fall speed, MASC PSD, and CloudSat snow particle model as base assumptions resulted in retrieved total accumulations with a -18% difference relative to nearby National Weather Service (NWS) observations over five snow events. The average error was 36% for the individual events. The use of different but reasonable combinations of retrieval assumptions resulted in estimated snowfall accumulations with differences ranging from -64 to +122% for the same storm events. Retrieved snowfall rates were particularly sensitive to assumed fall speed and habit, suggesting that in situ measurements can help to constrain key snowfall retrieval uncertainties. Furthermore, accurate knowledge of these properties dependent upon location and meteorological conditions should help refine and improve ground- and space-based radar estimates of snowfall.« less

A variational technique to estimate snowfall rate from coincident radar, snowflake, and fall-speed observations

DOE Office of Scientific and Technical Information (OSTI.GOV)

Cooper, Steven J.; Wood, Norman B.; L'Ecuyer, Tristan S.

Estimates of snowfall rate as derived from radar reflectivities alone are non-unique. Different combinations of snowflake microphysical properties and particle fall speeds can conspire to produce nearly identical snowfall rates for given radar reflectivity signatures. Such ambiguities can result in retrieval uncertainties on the order of 100–200% for individual events. Here, we use observations of particle size distribution (PSD), fall speed, and snowflake habit from the Multi-Angle Snowflake Camera (MASC) to constrain estimates of snowfall derived from Ka-band ARM zenith radar (KAZR) measurements at the Atmospheric Radiation Measurement (ARM) North Slope Alaska (NSA) Climate Research Facility site at Barrow. MASCmore » measurements of microphysical properties with uncertainties are introduced into a modified form of the optimal-estimation CloudSat snowfall algorithm (2C-SNOW-PROFILE) via the a priori guess and variance terms. Use of the MASC fall speed, MASC PSD, and CloudSat snow particle model as base assumptions resulted in retrieved total accumulations with a -18% difference relative to nearby National Weather Service (NWS) observations over five snow events. The average error was 36% for the individual events. The use of different but reasonable combinations of retrieval assumptions resulted in estimated snowfall accumulations with differences ranging from -64 to +122% for the same storm events. Retrieved snowfall rates were particularly sensitive to assumed fall speed and habit, suggesting that in situ measurements can help to constrain key snowfall retrieval uncertainties. Furthermore, accurate knowledge of these properties dependent upon location and meteorological conditions should help refine and improve ground- and space-based radar estimates of snowfall.« less

Comparisons of Instantaneous TRMM Ground Validation and Satellite Rain Rate Estimates at Different Spatial Scales

NASA Technical Reports Server (NTRS)

Wolff, David B.; Fisher, Brad L.

2007-01-01

This study provides a comprehensive inter-comparison of instantaneous rain estimates from the two rain sensors aboard the TRMM satellite with ground data from thee designated Ground Validation Sites: Kwajalein Atoll, Melbourne, Florida and Houston, Texas. The satellite rain retrievals utilize rain observations collected by the TRMM microwave imager (TMI) and the Precipitation Radar (PR) aboard the TRMM satellite. Three standard instantaneous rain products are the generated from the rain information retrieved from the satellite using the TMI, PR and Combined (COM) rain algorithms. The validation data set used in this study was obtained from instantaneous rain rates inferred from ground radars at each GV site. The first comparison used 0.5(sup 0) x 0.5(sup 0) gridded data obtained from the TRMM 3668 product, and similarly gridded GV data obtained from ground-based radars. The comparisons were made at the same spatial and temporal scales in order to eliminate sampling biases in our comparisons. An additional comparison was made by averaging rain rates for the PR, COM and GV estimates within each TMI footprint (approx. 150 square kilometers). For this analysis, unconditional mean rain rates from PR, COM and GV estimates were calculated within each TMI footprint that was observed within 100 km from the respective GV site (and also observed by the PR). This analysis used all the available matching data from the period 1999-2004, representing a sample size of over 50,000 footprints for each site. In the first analysis our results showed that all of the respective rain rate estimates agree well, with some exceptions. The more salient differences were associated with heavy rain events in which one or more of the algorithms failed to properly retrieve these extreme events. Also, it appears that there is a preferred mode of precipitation for TMI rain rates at or near 2 mm per hour over the ocean. This mode was noted over ocean areas of Melbourne, Florida and Kwajalein, Republic of the Marshall Islands, and is shown to exist in TRMM tropical-global ocean areas as well. Further research by algorithm developers is needed to explain or justify the seemingly errant observed probability distributions.

NASA GPM GV Science Implementation

NASA Technical Reports Server (NTRS)

Petersen, W. A.

2009-01-01

Pre-launch algorithm development & post-launch product evaluation: The GPM GV paradigm moves beyond traditional direct validation/comparison activities by incorporating improved algorithm physics & model applications (end-to-end validation) in the validation process. Three approaches: 1) National Network (surface): Operational networks to identify and resolve first order discrepancies (e.g., bias) between satellite and ground-based precipitation estimates. 2) Physical Process (vertical column): Cloud system and microphysical studies geared toward testing and refinement of physically-based retrieval algorithms. 3) Integrated (4-dimensional): Integration of satellite precipitation products into coupled prediction models to evaluate strengths/limitations of satellite precipitation producers.

Retrieval of atmospheric properties from hyper and multispectral imagery with the FLAASH atmospheric correction algorithm

NASA Astrophysics Data System (ADS)

Perkins, Timothy; Adler-Golden, Steven; Matthew, Michael; Berk, Alexander; Anderson, Gail; Gardner, James; Felde, Gerald

2005-10-01

Atmospheric Correction Algorithms (ACAs) are used in applications of remotely sensed Hyperspectral and Multispectral Imagery (HSI/MSI) to correct for atmospheric effects on measurements acquired by air and space-borne systems. The Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes (FLAASH) algorithm is a forward-model based ACA created for HSI and MSI instruments which operate in the visible through shortwave infrared (Vis-SWIR) spectral regime. Designed as a general-purpose, physics-based code for inverting at-sensor radiance measurements into surface reflectance, FLAASH provides a collection of spectral analysis and atmospheric retrieval methods including: a per-pixel vertical water vapor column estimate, determination of aerosol optical depth, estimation of scattering for compensation of adjacency effects, detection/characterization of clouds, and smoothing of spectral structure resulting from an imperfect atmospheric correction. To further improve the accuracy of the atmospheric correction process, FLAASH will also detect and compensate for sensor-introduced artifacts such as optical smile and wavelength mis-calibration. FLAASH relies on the MODTRANTM radiative transfer (RT) code as the physical basis behind its mathematical formulation, and has been developed in parallel with upgrades to MODTRAN in order to take advantage of the latest improvements in speed and accuracy. For example, the rapid, high fidelity multiple scattering (MS) option available in MODTRAN4 can greatly improve the accuracy of atmospheric retrievals over the 2-stream approximation. In this paper, advanced features available in FLAASH are described, including the principles and methods used to derive atmospheric parameters from HSI and MSI data. Results are presented from processing of Hyperion, AVIRIS, and LANDSAT data.

Validation of MODIS Dust Aerosol Retrieval and Development Ambient Dust Phase Function using PRIDE Data

NASA Technical Reports Server (NTRS)

Remer, Lorraine A.; Lau, William (Technical Monitor)

2002-01-01

The PRIDE data set of MODIS aerosol retrievals co-located with sunphotometer measurements provides the basis of MODIS validation in a dust environment. The sunphotometer measurements include AERONET automatic instruments, land-based Microtops instruments, ship-board Microtops instruments and the AATS-6 aboard the Navajo aircraft. Analysis of these data indicate that the MODIS retrieval is within pre-launch estimates of uncertainty within the spectral range of 600-900 nm. However, the MODIS algorithm consistently retrieves smaller particles than reality thus leading to incorrect spectral response outside of the 600-900 nm range and improper size information. Further analysis of MODIS retrievals in other dust environments shows the inconsistencies are due to nonspherical effects in the phase function. These data are used to develop an ambient phase function for dust aerosol to be used for remote sensing purposes.

New-Generation NASA Aura Ozone Monitoring Instrument (OMI) Volcanic SO2 Dataset: Algorithm Description, Initial Results, and Continuation with the Suomi-NPP Ozone Mapping and Profiler Suite (OMPS)

NASA Technical Reports Server (NTRS)

Li, Can; Krotkov, Nickolay A.; Carn, Simon; Zhang, Yan; Spurr, Robert J. D.; Joiner, Joanna

2017-01-01

Since the fall of 2004, the Ozone Monitoring Instrument (OMI) has been providing global monitoring of volcanic SO2 emissions, helping to understand their climate impacts and to mitigate aviation hazards. Here we introduce a new-generation OMI volcanic SO2 dataset based on a principal component analysis (PCA) retrieval technique. To reduce retrieval noise and artifacts as seen in the current operational linear fit (LF) algorithm, the new algorithm, OMSO2VOLCANO, uses characteristic features extracted directly from OMI radiances in the spectral fitting, thereby helping to minimize interferences from various geophysical processes (e.g., O3 absorption) and measurement details (e.g., wavelength shift). To solve the problem of low bias for large SO2 total columns in the LF product, the OMSO2VOLCANO algorithm employs a table lookup approach to estimate SO2 Jacobians (i.e., the instrument sensitivity to a perturbation in the SO2 column amount) and iteratively adjusts the spectral fitting window to exclude shorter wavelengths where the SO2 absorption signals are saturated. To first order, the effects of clouds and aerosols are accounted for using a simple Lambertian equivalent reflectivity approach. As with the LF algorithm, OMSO2VOLCANO provides total column retrievals based on a set of predefined SO2 profiles from the lower troposphere to the lower stratosphere, including a new profile peaked at 13 km for plumes in the upper troposphere. Examples given in this study indicate that the new dataset shows significant improvement over the LF product, with at least 50% reduction in retrieval noise over the remote Pacific. For large eruptions such as Kasatochi in 2008 (approximately 1700 kt total SO2/ and Sierra Negra in 2005 (greater than 1100DU maximum SO2), OMSO2VOLCANO generally agrees well with other algorithms that also utilize the full spectral content of satellite measurements, while the LF algorithm tends to underestimate SO2. We also demonstrate that, despite the coarser spatial and spectral resolution of the Suomi National Polar-orbiting Partnership (Suomi-NPP) Ozone Mapping and Profiler Suite (OMPS) instrument, application of the new PCA algorithm to OMPS data produces highly consistent retrievals between OMI and OMPS. The new PCA algorithm is therefore capable of continuing the volcanic SO2 data record well into the future using current and future hyperspectral UV satellite instruments.

SMOS/SMAP Synergy for SMAP Level 2 Soil Moisture Algorithm Evaluation

NASA Technical Reports Server (NTRS)

Bindlish, Rajat; Jackson, Thomas J.; Zhao, Tianjie; Cosh, Michael; Chan, Steven; O'Neill, Peggy; Njoku, Eni; Colliander, Andreas; Kerr, Yann

2011-01-01

Soil Moisture Active Passive (SMAP) satellite has been proposed to provide global measurements of soil moisture and land freeze/thaw state at 10 km and 3 km resolutions, respectively. SMAP would also provide a radiometer-only soil moisture product at 40-km spatial resolution. This product and the supporting brightness temperature observations are common to both SMAP and European Space Agency's Soil Moisture and Ocean Salinity (SMOS) mission. As a result, there are opportunities for synergies between the two missions. These include exploiting the data for calibration and validation and establishing longer term L-band brightness temperature and derived soil moisture products. In this investigation we will be using SMOS brightness temperature, ancillary data, and soil moisture products to develop and evaluate a candidate SMAP L2 passive soil moisture retrieval algorithm. This work will begin with evaluations based on the SMOS product grids and ancillary data sets and transition to those that will be used by SMAP. An important step in this analysis is reprocessing the multiple incidence angle observations provided by SMOS to a global brightness temperature product that simulates the constant 40 degree incidence angle observations that SMAP will provide. The reprocessed brightness temperature data provide a basis for evaluating different SMAP algorithm alternatives. Several algorithms are being considered for the SMAP radiometer-only soil moisture retrieval. In this first phase, we utilized only the Single Channel Algorithm (SCA), which is based on the radiative transfer equation and uses the channel that is most sensitive to soil moisture (H-pol). Brightness temperature is corrected sequentially for the effects of temperature, vegetation, roughness (dynamic ancillary data sets) and soil texture (static ancillary data set). European Centre for Medium-Range Weather Forecasts (ECMWF) estimates of soil temperature for the top layer (as provided as part of the SMOS ancillary data) were used to correct for surface temperature effects and to derive microwave emissivity. ECMWF data were also used for precipitation forecasts, presence of snow, and frozen ground. Vegetation options are described below. One year of soil moisture observations from a set of four watersheds in the U.S. were used to evaluate four different retrieval methodologies: (1) SMOS soil moisture estimates (version 400), (2) SeA soil moisture estimates using the SMOS/SMAP data with SMOS estimated vegetation optical depth, which is part of the SMOS level 2 product, (3) SeA soil moisture estimates using the SMOS/SMAP data and the MODIS-based vegetation climatology data, and (4) SeA soil moisture estimates using the SMOS/SMAP data and actual MODIS observations. The use of SMOS real-world global microwave observations and the analyses described here will help in the development and selection of different land surface parameters and ancillary observations needed for the SMAP soil moisture algorithms. These investigations will greatly improve the quality and reliability of this SMAP product at launch.

Soil Moisture Estimate Under Forest Using a Semi-Empirical Model at P-Band

NASA Technical Reports Server (NTRS)

Truong-Loi, My-Linh; Saatchi, Sassan; Jaruwatanadilok, Sermsak

2013-01-01

Here we present the result of a semi-empirical inversion model for soil moisture retrieval using the three backscattering coefficients: sigma(sub HH), sigma(sub VV) and sigma(sub HV). In this paper we focus on the soil moisture estimate and use the biomass as an ancillary parameter estimated automatically from the algorithm and used as a validation parameter, We will first remind the model analytical formulation. Then we will sow some results obtained with real SAR data and compare them to ground estimates.

Precipitation and Latent Heating Distributions from Satellite Passive Microwave Radiometry. Part 1; Method and Uncertainties

NASA Technical Reports Server (NTRS)

Olson, William S.; Kummerow, Christian D.; Yang, Song; Petty, Grant W.; Tao, Wei-Kuo; Bell, Thomas L.; Braun, Scott A.; Wang, Yansen; Lang, Stephen E.; Johnson, Daniel E.

2004-01-01

A revised Bayesian algorithm for estimating surface rain rate, convective rain proportion, and latent heating/drying profiles from satellite-borne passive microwave radiometer observations over ocean backgrounds is described. The algorithm searches a large database of cloud-radiative model simulations to find cloud profiles that are radiatively consistent with a given set of microwave radiance measurements. The properties of these radiatively consistent profiles are then composited to obtain best estimates of the observed properties. The revised algorithm is supported by an expanded and more physically consistent database of cloud-radiative model simulations. The algorithm also features a better quantification of the convective and non-convective contributions to total rainfall, a new geographic database, and an improved representation of background radiances in rain-free regions. Bias and random error estimates are derived from applications of the algorithm to synthetic radiance data, based upon a subset of cloud resolving model simulations, and from the Bayesian formulation itself. Synthetic rain rate and latent heating estimates exhibit a trend of high (low) bias for low (high) retrieved values. The Bayesian estimates of random error are propagated to represent errors at coarser time and space resolutions, based upon applications of the algorithm to TRMM Microwave Imager (TMI) data. Errors in instantaneous rain rate estimates at 0.5 deg resolution range from approximately 50% at 1 mm/h to 20% at 14 mm/h. These errors represent about 70-90% of the mean random deviation between collocated passive microwave and spaceborne radar rain rate estimates. The cumulative algorithm error in TMI estimates at monthly, 2.5 deg resolution is relatively small (less than 6% at 5 mm/day) compared to the random error due to infrequent satellite temporal sampling (8-35% at the same rain rate).

Optimal estimation retrievals of the atmospheric structure and composition of HD 189733b from secondary eclipse spectroscopy

NASA Astrophysics Data System (ADS)

Lee, J.-M.; Fletcher, L. N.; Irwin, P. G. J.

2012-02-01

Recent spectroscopic observations of transiting hot Jupiters have permitted the derivation of the thermal structure and molecular abundances of H2O, CO2, CO and CH4 in these extreme atmospheres. Here, for the first time, we apply the technique of optimal estimation to determine the thermal structure and composition of an exoplanet by solving the inverse problem. The development of a suite of radiative transfer and retrieval tools for exoplanet atmospheres is described, building upon a retrieval algorithm which is extensively used in the study of our own Solar system. First, we discuss the plausibility of detection of different molecules in the dayside atmosphere of HD 189733b and the best-fitting spectrum retrieved from all publicly available sets of secondary eclipse observations between 1.45 and 24 μm. Additionally, we use contribution functions to assess the vertical sensitivity of the emission spectrum to temperatures and molecular composition. Over the altitudes probed by the contribution functions, the retrieved thermal structure shows an isothermal upper atmosphere overlying a deeper adiabatic layer (temperature decreasing with altitude), which is consistent with previously reported dynamical and observational results. The formal uncertainties on retrieved parameters are estimated conservatively using an analysis of the cross-correlation functions and the degeneracy between different atmospheric properties. The formal solution of the inverse problem suggests that the uncertainties on retrieved parameters are larger than suggested in previous studies, and that the presence of CO and CH4 is only marginally supported by the available data. Nevertheless, by including as broad a wavelength range as possible in the retrieval, we demonstrate that available spectra of HD 189733b can constrain a family of potential solutions for the atmospheric structure.

Use of A-Train Aerosol Observations to Constrain Direct Aerosol Radiative Effects (DARE) Comparisons with Aerocom Models and Uncertainty Assessments

NASA Technical Reports Server (NTRS)

Redemann, J.; Shinozuka, Y.; Kacenelenbogen, M.; Segal-Rozenhaimer, M.; LeBlanc, S.; Vaughan, M.; Stier, P.; Schutgens, N.

2017-01-01

We describe a technique for combining multiple A-Train aerosol data sets, namely MODIS spectral AOD (aerosol optical depth), OMI AAOD (absorption aerosol optical depth) and CALIOP aerosol backscatter retrievals (hereafter referred to as MOC retrievals) to estimate full spectral sets of aerosol radiative properties, and ultimately to calculate the 3-D distribution of direct aerosol radiative effects (DARE). We present MOC results using almost two years of data collected in 2007 and 2008, and show comparisons of the aerosol radiative property estimates to collocated AERONET retrievals. Use of the MODIS Collection 6 AOD data derived with the dark target and deep blue algorithms has extended the coverage of the MOC retrievals towards higher latitudes. The MOC aerosol retrievals agree better with AERONET in terms of the single scattering albedo (ssa) at 441 nm than ssa calculated from OMI and MODIS data alone, indicating that CALIOP aerosol backscatter data contains information on aerosol absorption. We compare the spatio-temporal distribution of the MOC retrievals and MOC-based calculations of seasonal clear-sky DARE to values derived from four models that participated in the Phase II AeroCom model intercomparison initiative. Overall, the MOC-based calculations of clear-sky DARE at TOA over land are smaller (less negative) than previous model or observational estimates due to the inclusion of more absorbing aerosol retrievals over brighter surfaces, not previously available for observationally-based estimates of DARE. MOC-based DARE estimates at the surface over land and total (land and ocean) DARE estimates at TOA are in between previous model and observational results. Comparisons of seasonal aerosol property to AeroCom Phase II results show generally good agreement best agreement with forcing results at TOA is found with GMI-MerraV3. We discuss sampling issues that affect the comparisons and the major challenges in extending our clear-sky DARE results to all-sky conditions. We present estimates of clear-sky and all-sky DARE and show uncertainties that stem from the assumptions in the spatial extrapolation and accuracy of aerosol and cloud properties, in the diurnal evolution of these properties, and in the radiative transfer calculations.

Assimilation of concentration measurements for retrieving multiple point releases in atmosphere: A least-squares approach to inverse modelling

NASA Astrophysics Data System (ADS)

Singh, Sarvesh Kumar; Rani, Raj

2015-10-01

The study addresses the identification of multiple point sources, emitting the same tracer, from their limited set of merged concentration measurements. The identification, here, refers to the estimation of locations and strengths of a known number of simultaneous point releases. The source-receptor relationship is described in the framework of adjoint modelling by using an analytical Gaussian dispersion model. A least-squares minimization framework, free from an initialization of the release parameters (locations and strengths), is presented to estimate the release parameters. This utilizes the distributed source information observable from the given monitoring design and number of measurements. The technique leads to an exact retrieval of the true release parameters when measurements are noise free and exactly described by the dispersion model. The inversion algorithm is evaluated using the real data from multiple (two, three and four) releases conducted during Fusion Field Trials in September 2007 at Dugway Proving Ground, Utah. The release locations are retrieved, on average, within 25-45 m of the true sources with the distance from retrieved to true source ranging from 0 to 130 m. The release strengths are also estimated within a factor of three to the true release rates. The average deviations in retrieval of source locations are observed relatively large in two release trials in comparison to three and four release trials.

A Fast and Sensitive New Satellite SO2 Retrieval Algorithm based on Principal Component Analysis: Application to the Ozone Monitoring Instrument

NASA Technical Reports Server (NTRS)

Li, Can; Joiner, Joanna; Krotkov, A.; Bhartia, Pawan K.

2013-01-01

We describe a new algorithm to retrieve SO2 from satellite-measured hyperspectral radiances. We employ the principal component analysis technique in regions with no significant SO2 to capture radiance variability caused by both physical processes (e.g., Rayleigh and Raman scattering and ozone absorption) and measurement artifacts. We use the resulting principal components and SO2 Jacobians calculated with a radiative transfer model to directly estimate SO2 vertical column density in one step. Application to the Ozone Monitoring Instrument (OMI) radiance spectra in 310.5-340 nm demonstrates that this approach can greatly reduce biases in the operational OMI product and decrease the noise by a factor of 2, providing greater sensitivity to anthropogenic emissions. The new algorithm is fast, eliminates the need for instrument-specific radiance correction schemes, and can be easily adapted to other sensors. These attributes make it a promising technique for producing longterm, consistent SO2 records for air quality and climate research.

Comparative Results of AIRS/AMSU and CrIS/ATMS Retrievals Using a Scientifically Equivalent Retrieval Algorithm

NASA Technical Reports Server (NTRS)

Susskind, Joel; Kouvaris, Louis; Iredell, Lena

2016-01-01

The AIRS Science Team Version-6 retrieval algorithm is currently producing high quality level-3 Climate Data Records (CDRs) from AIRS/AMSU which are critical for understanding climate processes. The AIRS Science Team is finalizing an improved Version-7 retrieval algorithm to reprocess all old and future AIRS data. AIRS CDRs should eventually cover the period September 2002 through at least 2020. CrIS/ATMS is the only scheduled follow on to AIRS/AMSU. The objective of this research is to prepare for generation of long term CrIS/ATMS CDRs using a retrieval algorithm that is scientifically equivalent to AIRS/AMSU Version-7.

Comparison of Ground- and Space-based Radar Observations with Disdrometer Measurements During the PECAN Field Campaign

NASA Astrophysics Data System (ADS)

Torres, A. D.; Rasmussen, K. L.; Bodine, D. J.; Dougherty, E.

2015-12-01

Plains Elevated Convection At Night (PECAN) was a large field campaign that studied nocturnal mesoscale convective systems (MCSs), convective initiation, bores, and low-level jets across the central plains in the United States. MCSs are responsible for over half of the warm-season precipitation across the central U.S. plains. The rainfall from deep convection of these systems over land have been observed to be underestimated by satellite radar rainfall-retrieval algorithms by as much as 40 percent. These algorithms have a strong dependence on the generally unmeasured rain drop-size distribution (DSD). During the campaign, our group measured rainfall DSDs, precipitation fall velocities, and total precipitation in the convective and stratiform regions of MCSs using Ott Parsivel optical laser disdrometers. The disdrometers were co-located with mobile pod units that measured temperature, wind, and relative humidity for quality control purposes. Data from the operational NEXRAD radar in LaCrosse, Wisconsin and space-based radar measurements from a Global Precipitation Measurement satellite overpass on July 13, 2015 were used for the analysis. The focus of this study is to compare DSD measurements from the disdrometers to radars in an effort to reduce errors in existing rainfall-retrieval algorithms. The error analysis consists of substituting measured DSDs into existing quantitative precipitation estimation techniques (e.g. Z-R relationships and dual-polarization rain estimates) and comparing these estimates to ground measurements of total precipitation. The results from this study will improve climatological estimates of total precipitation in continental convection that are used in hydrological studies, climate models, and other applications.

Retrieving Liquid Water Path and Precipitable Water Vapor from the Atmospheric Radiation Measurement (ARM) Microwave Radiometers

DOE Office of Scientific and Technical Information (OSTI.GOV)

Turner, David D.; Clough, Shepard A.; Liljegren, James C.

2007-11-01

Ground-based two-channel microwave radiometers have been used for over 15 years by the Atmospheric Radiation Measurement (ARM) program to provide observations of downwelling emitted radiance from which precipitable water vapor (PWV) and liquid water path (LWP) – twp geophysical parameters critical for many areas of atmospheric research – are retrieved. An algorithm that utilizes two advanced retrieval techniques, a computationally expensive physical-iterative approach and an efficient statistical method, has been developed to retrieve these parameters. An important component of this Microwave Retrieval (MWRRET) algorithm is the determination of small (< 1K) offsets that are subtracted from the observed brightness temperaturesmore » before the retrievals are performed. Accounting for these offsets removes systematic biases from the observations and/or the model spectroscopy necessary for the retrieval, significantly reducing the systematic biases in the retrieved LWP. The MWRRET algorithm provides significantly more accurate retrievals than the original ARM statistical retrieval which uses monthly retrieval coefficients. By combining the two retrieval methods with the application of brightness temperature offsets to reduce the spurious LWP bias in clear skies, the MWRRET algorithm provides significantly better retrievals of PWV and LWP from the ARM 2-channel microwave radiometers compared to the original ARM product.« less

Heading Toward Launch with the Integrated Multi-Satellite Retrievals for GPM (IMERG)

NASA Technical Reports Server (NTRS)

Huffman, George J.; Bolvin, David T.; Nelkin, Eric J.; Adler, Robert F.

2012-01-01

The Day-l algorithm for computing combined precipitation estimates in GPM is the Integrated Multi-satellitE Retrievals for GPM (IMERG). We plan for the period of record to encompass both the TRMM and GPM eras, and the coverage to extend to fully global as experience is gained in the difficult high-latitude environment. IMERG is being developed as a unified U.S. algorithm that takes advantage of strengths in the three groups that are contributing expertise: 1) the TRMM Multi-satellite Precipitation Analysis (TMPA), which addresses inter-satellite calibration of precipitation estimates and monthly scale combination of satellite and gauge analyses; 2) the CPC Morphing algorithm with Kalman Filtering (KF-CMORPH), which provides quality-weighted time interpolation of precipitation patterns following cloud motion; and 3) the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks using a Cloud Classification System (PERSIANN-CCS), which provides a neural-network-based scheme for generating microwave-calibrated precipitation estimates from geosynchronous infrared brightness temperatures. In this talk we summarize the major building blocks and important design issues driven by user needs and practical data issues. One concept being pioneered by the IMERG team is that the code system should produce estimates for the same time period but at different latencies to support the requirements of different groups of users. Another user requirement is that all these runs must be reprocessed as new IMERG versions are introduced. IMERG's status at meeting time will be summarized, and the processing scenario in the transition from TRMM to GPM will be laid out. Initially, IMERG will be run with TRMM-based calibration, and then a conversion to a GPM-based calibration will be employed after the GPM sensor products are validated. A complete reprocessing will be computed, which will complete the transition from TMPA.

Ground-Based Passive Microwave Remote Sensing Observations of Soil Moisture at S and L Band with Insight into Measurement Accuracy

NASA Technical Reports Server (NTRS)

Laymon, Charles A.; Crosson, William L.; Jackson, Thomas J.; Manu, Andrew; Tsegaye, Teferi D.; Soman, V.; Arnold, James E. (Technical Monitor)

2001-01-01

Accurate estimates of spatially heterogeneous algorithm variables and parameters are required in determining the spatial distribution of soil moisture using radiometer data from aircraft and satellites. A ground-based experiment in passive microwave remote sensing of soil moisture was conducted in Huntsville, Alabama from July 1-14, 1996 to study retrieval algorithms and their sensitivity to variable and parameter specification. With high temporal frequency observations at S and L band, we were able to observe large scale moisture changes following irrigation and rainfall events, as well as diurnal behavior of surface moisture among three plots, one bare, one covered with short grass and another covered with alfalfa. The L band emitting depth was determined to be on the order of 0-3 or 0-5 cm below 0.30 cubic centimeter/cubic centimeter with an indication of a shallower emitting depth at higher moisture values. Surface moisture behavior was less apparent on the vegetated plots than it was on the bare plot because there was less moisture gradient and because of difficulty in determining vegetation water content and estimating the vegetation b parameter. Discrepancies between remotely sensed and gravimetric, soil moisture estimates on the vegetated plots point to an incomplete understanding of the requirements needed to correct for the effects of vegetation attenuation. Quantifying the uncertainty in moisture estimates is vital if applications are to utilize remotely-sensed soil moisture data. Computations based only on the real part of the complex dielectric constant and/or an alternative dielectric mixing model contribute a relatively insignificant amount of uncertainty to estimates of soil moisture. Rather, the retrieval algorithm is much more sensitive to soil properties, surface roughness and biomass.

Combined neural network/Phillips-Tikhonov approach to aerosol retrievals over land from the NASA Research Scanning Polarimeter

NASA Astrophysics Data System (ADS)

Di Noia, Antonio; Hasekamp, Otto P.; Wu, Lianghai; van Diedenhoven, Bastiaan; Cairns, Brian; Yorks, John E.

2017-11-01

In this paper, an algorithm for the retrieval of aerosol and land surface properties from airborne spectropolarimetric measurements - combining neural networks and an iterative scheme based on Phillips-Tikhonov regularization - is described. The algorithm - which is an extension of a scheme previously designed for ground-based retrievals - is applied to measurements from the Research Scanning Polarimeter (RSP) on board the NASA ER-2 aircraft. A neural network, trained on a large data set of synthetic measurements, is applied to perform aerosol retrievals from real RSP data, and the neural network retrievals are subsequently used as a first guess for the Phillips-Tikhonov retrieval. The resulting algorithm appears capable of accurately retrieving aerosol optical thickness, fine-mode effective radius and aerosol layer height from RSP data. Among the advantages of using a neural network as initial guess for an iterative algorithm are a decrease in processing time and an increase in the number of converging retrievals.

The Complexity of Bit Retrieval

DOE PAGES

Elser, Veit

2018-09-20

Bit retrieval is the problem of reconstructing a periodic binary sequence from its periodic autocorrelation, with applications in cryptography and x-ray crystallography. After defining the problem, with and without noise, we describe and compare various algorithms for solving it. A geometrical constraint satisfaction algorithm, relaxed-reflect-reflect, is currently the best algorithm for noisy bit retrieval.

The Complexity of Bit Retrieval

DOE Office of Scientific and Technical Information (OSTI.GOV)

Elser, Veit

Bit retrieval is the problem of reconstructing a periodic binary sequence from its periodic autocorrelation, with applications in cryptography and x-ray crystallography. After defining the problem, with and without noise, we describe and compare various algorithms for solving it. A geometrical constraint satisfaction algorithm, relaxed-reflect-reflect, is currently the best algorithm for noisy bit retrieval.

Operational Retrievals of Evapotranspiration: Are we there yet?

NASA Astrophysics Data System (ADS)

Neale, C. M. U.; Anderson, M. C.; Hain, C.; Schull, M.; Isidro, C., Sr.; Goncalves, I. Z.

2017-12-01

Remote sensing based retrievals of evapotranspiration (ET) have progressed significantly over the last two decades with the improvement of methods and algorithms and the availability of multiple satellite sensors with shortwave and thermal infrared bands on polar orbiting platforms. The modeling approaches include simpler vegetation index (VI) based methods such as the reflectance-based crop coefficient approach coupled with surface reference evapotranspiration estimates to derive actual evapotranspiration of crops or, direct inputs to the Penman-Monteith equation through VI relationships with certain input variables. Methods that are more complex include one-layer or two-layer energy balance approaches that make use of both shortwave and longwave spectral band information to estimate different inputs to the energy balance equation. These models mostly differ in the estimation of sensible heat fluxes. For continental and global scale applications, other satellite-based products such as solar radiation, vegetation leaf area and cover are used as inputs, along with gridded re-analysis weather information. This presentation will review the state-of-the-art in satellite-based evapotranspiration estimation, giving examples of existing efforts to obtain operational ET retrievals over continental and global scales and discussing difficulties and challenges.

Radiance Assimilation Shows Promise for Snowpack Characterization: A 1-D Case Study

NASA Technical Reports Server (NTRS)

Durand, Michael; Kim, Edward; Margulis, Steve

2008-01-01

We demonstrate an ensemble-based radiometric data assimilation (DA) methodology for estimating snow depth and snow grain size using ground-based passive microwave (PM) observations at 18.7 and 36.5 GHz collected during the NASA CLPX-1, March 2003, Colorado, USA. A land surface model was used to develop a prior estimate of the snowpack states, and a radiative transfer model was used to relate the modeled states to the observations. Snow depth bias was -53.3 cm prior to the assimilation, and -7.3 cm after the assimilation. Snow depth estimated by a non-DA-based retrieval algorithm using the same PM data had a bias of -18.3 cm. The sensitivity of the assimilation scheme to the grain size uncertainty was evaluated; over the range of grain size uncertainty tested, the posterior snow depth estimate bias ranges from -2.99 cm to -9.85 cm, which is uniformly better than both the prior and retrieval estimates. This study demonstrates the potential applicability of radiometric DA at larger scales.

Optimization of the GSFC TROPOZ DIAL retrieval using synthetic lidar returns and ozonesondes - Part 1: Algorithm validation

NASA Astrophysics Data System (ADS)

Sullivan, J. T.; McGee, T. J.; Leblanc, T.; Sumnicht, G. K.; Twigg, L. W.

2015-10-01

The main purpose of the NASA Goddard Space Flight Center TROPospheric OZone DIfferential Absorption Lidar (GSFC TROPOZ DIAL) is to measure the vertical distribution of tropospheric ozone for science investigations. Because of the important health and climate impacts of tropospheric ozone, it is imperative to quantify background photochemical ozone concentrations and ozone layers aloft, especially during air quality episodes. For these reasons, this paper addresses the necessary procedures to validate the TROPOZ retrieval algorithm and confirm that it is properly representing ozone concentrations. This paper is focused on ensuring the TROPOZ algorithm is properly quantifying ozone concentrations, and a following paper will focus on a systematic uncertainty analysis. This methodology begins by simulating synthetic lidar returns from actual TROPOZ lidar return signals in combination with a known ozone profile. From these synthetic signals, it is possible to explicitly determine retrieval algorithm biases from the known profile. This was then systematically performed to identify any areas that need refinement for a new operational version of the TROPOZ retrieval algorithm. One immediate outcome of this exercise was that a bin registration error in the correction for detector saturation within the original retrieval was discovered and was subsequently corrected for. Another noticeable outcome was that the vertical smoothing in the retrieval algorithm was upgraded from a constant vertical resolution to a variable vertical resolution to yield a statistical uncertainty of <10 %. This new and optimized vertical-resolution scheme retains the ability to resolve fluctuations in the known ozone profile, but it now allows near-field signals to be more appropriately smoothed. With these revisions to the previous TROPOZ retrieval, the optimized TROPOZ retrieval algorithm (TROPOZopt) has been effective in retrieving nearly 200 m lower to the surface. Also, as compared to the previous version of the retrieval, the TROPOZopt had an overall mean improvement of 3.5 %, and large improvements (upwards of 10-15 % as compared to the previous algorithm) were apparent between 4.5 and 9 km. Finally, to ensure the TROPOZopt retrieval algorithm is robust enough to handle actual lidar return signals, a comparison is shown between four nearby ozonesonde measurements. The ozonesondes are mostly within the TROPOZopt retrieval uncertainty bars, which implies that this exercise was quite successful.

An enhanced VIIRS aerosol optical thickness (AOT) retrieval algorithm over land using a global surface reflectance ratio database

NASA Astrophysics Data System (ADS)

Zhang, Hai; Kondragunta, Shobha; Laszlo, Istvan; Liu, Hongqing; Remer, Lorraine A.; Huang, Jingfeng; Superczynski, Stephen; Ciren, Pubu

2016-09-01

The Visible/Infrared Imager Radiometer Suite (VIIRS) on board the Suomi National Polar-orbiting Partnership (S-NPP) satellite has been retrieving aerosol optical thickness (AOT), operationally and globally, over ocean and land since shortly after S-NPP launch in 2011. However, the current operational VIIRS AOT retrieval algorithm over land has two limitations in its assumptions for land surfaces: (1) it only retrieves AOT over the dark surfaces and (2) it assumes that the global surface reflectance ratios between VIIRS bands are constants. In this work, we develop a surface reflectance ratio database over land with a spatial resolution 0.1° × 0.1° using 2 years of VIIRS top of atmosphere reflectances. We enhance the current operational VIIRS AOT retrieval algorithm by applying the surface reflectance ratio database in the algorithm. The enhanced algorithm is able to retrieve AOT over both dark and bright surfaces. Over bright surfaces, the VIIRS AOT retrievals from the enhanced algorithm have a correlation of 0.79, mean bias of -0.008, and standard deviation (STD) of error of 0.139 when compared against the ground-based observations at the global AERONET (Aerosol Robotic Network) sites. Over dark surfaces, the VIIRS AOT retrievals using the surface reflectance ratio database improve the root-mean-square error from 0.150 to 0.123. The use of the surface reflectance ratio database also increases the data coverage of more than 20% over dark surfaces. The AOT retrievals over bright surfaces are comparable to MODIS Deep Blue AOT retrievals.

Global Soil Moisture from the Aquarius/SAC-D Satellite: Description and Initial Assessment

NASA Technical Reports Server (NTRS)

Bindlish, Rajat; Jackson, Thomas; Cosh, Michael; Zhao, Tianjie; O'Neil, Peggy

2015-01-01

Aquarius satellite observations over land offer a new resource for measuring soil moisture from space. Although Aquarius was designed for ocean salinity mapping, our objective in this investigation is to exploit the large amount of land observations that Aquarius acquires and extend the mission scope to include the retrieval of surface soil moisture. The soil moisture retrieval algorithm development focused on using only the radiometer data because of the extensive heritage of passive microwave retrieval of soil moisture. The single channel algorithm (SCA) was implemented using the Aquarius observations to estimate surface soil moisture. Aquarius radiometer observations from three beams (after bias/gain modification) along with the National Centers for Environmental Prediction model forecast surface temperatures were then used to retrieve soil moisture. Ancillary data inputs required for using the SCA are vegetation water content, land surface temperature, and several soil and vegetation parameters based on land cover classes. The resulting global spatial patterns of soil moisture were consistent with the precipitation climatology and with soil moisture from other satellite missions (Advanced Microwave Scanning Radiometer for the Earth Observing System and Soil Moisture Ocean Salinity). Initial assessments were performed using in situ observations from the U.S. Department of Agriculture Little Washita and Little River watershed soil moisture networks. Results showed good performance by the algorithm for these land surface conditions for the period of August 2011-June 2013 (rmse = 0.031 m(exp 3)/m(exp 3), Bias = -0.007 m(exp 3)/m(exp 3), and R = 0.855). This radiometer-only soil moisture product will serve as a baseline for continuing research on both active and combined passive-active soil moisture algorithms. The products are routinely available through the National Aeronautics and Space Administration data archive at the National Snow and Ice Data Center.

Sampling Errors in Monthly Rainfall Totals for TRMM and SSM/I, Based on Statistics of Retrieved Rain Rates and Simple Models

NASA Technical Reports Server (NTRS)

Bell, Thomas L.; Kundu, Prasun K.; Einaudi, Franco (Technical Monitor)

2000-01-01

Estimates from TRMM satellite data of monthly total rainfall over an area are subject to substantial sampling errors due to the limited number of visits to the area by the satellite during the month. Quantitative comparisons of TRMM averages with data collected by other satellites and by ground-based systems require some estimate of the size of this sampling error. A method of estimating this sampling error based on the actual statistics of the TRMM observations and on some modeling work has been developed. "Sampling error" in TRMM monthly averages is defined here relative to the monthly total a hypothetical satellite permanently stationed above the area would have reported. "Sampling error" therefore includes contributions from the random and systematic errors introduced by the satellite remote sensing system. As part of our long-term goal of providing error estimates for each grid point accessible to the TRMM instruments, sampling error estimates for TRMM based on rain retrievals from TRMM microwave (TMI) data are compared for different times of the year and different oceanic areas (to minimize changes in the statistics due to algorithmic differences over land and ocean). Changes in sampling error estimates due to changes in rain statistics due 1) to evolution of the official algorithms used to process the data, and 2) differences from other remote sensing systems such as the Defense Meteorological Satellite Program (DMSP) Special Sensor Microwave/Imager (SSM/I), are analyzed.

Direct Aerosol Radiative Forcing from Combined A-Train Observations - Preliminary Comparisons with AeroCom Models and Pathways to Observationally Based All-sky Estimates

NASA Astrophysics Data System (ADS)

Redemann, J.; Livingston, J. M.; Shinozuka, Y.; Kacenelenbogen, M. S.; Russell, P. B.; LeBlanc, S. E.; Vaughan, M.; Ferrare, R. A.; Hostetler, C. A.; Rogers, R. R.; Burton, S. P.; Torres, O.; Remer, L. A.; Stier, P.; Schutgens, N.

2014-12-01

We describe a technique for combining CALIOP aerosol backscatter, MODIS spectral AOD (aerosol optical depth), and OMI AAOD (absorption aerosol optical depth) retrievals for the purpose of estimating full spectral sets of aerosol radiative properties, and ultimately for calculating the 3-D distribution of direct aerosol radiative forcing. We present results using one year of data collected in 2007 and show comparisons of the aerosol radiative property estimates to collocated AERONET retrievals. Use of the recently released MODIS Collection 6 data for aerosol optical depths derived with the dark target and deep blue algorithms has extended the coverage of the multi-sensor estimates towards higher latitudes. Initial calculations of seasonal clear-sky aerosol radiative forcing based on our multi-sensor aerosol retrievals compare well with over-ocean and top of the atmosphere IPCC-2007 model-based results, and with more recent assessments in the "Climate Change Science Program Report: Atmospheric Aerosol Properties and Climate Impacts" (2009). For the first time, we present comparisons of our multi-sensor aerosol direct radiative forcing estimates to values derived from a subset of models that participated in the latest AeroCom initiative. We discuss the major challenges that exist in extending our clear-sky results to all-sky conditions. On the basis of comparisons to suborbital measurements, we present some of the limitations of the MODIS and CALIOP retrievals in the presence of adjacent or underlying clouds. Strategies for meeting these challenges are discussed.

Round Robin evaluation of soil moisture retrieval models for the MetOp-A ASCAT Instrument

NASA Astrophysics Data System (ADS)

Gruber, Alexander; Paloscia, Simonetta; Santi, Emanuele; Notarnicola, Claudia; Pasolli, Luca; Smolander, Tuomo; Pulliainen, Jouni; Mittelbach, Heidi; Dorigo, Wouter; Wagner, Wolfgang

2014-05-01

Global soil moisture observations are crucial to understand hydrologic processes, earth-atmosphere interactions and climate variability. ESA's Climate Change Initiative (CCI) project aims to create a global consistent long-term soil moisture data set based on the merging of the best available active and passive satellite-based microwave sensors and retrieval algorithms. Within the CCI, a Round Robin evaluation of existing retrieval algorithms for both active and passive instruments was carried out. In this study we present the comparison of five different retrieval algorithms covering three different modelling principles applied to active MetOp-A ASCAT L1 backscatter data. These models include statistical models (Bayesian Regression and Support Vector Regression, provided by the Institute for Applied Remote Sensing, Eurac Research Viale Druso, Italy, and an Artificial Neural Network, provided by the Institute of Applied Physics, CNR-IFAC, Italy), a semi-empirical model (provided by the Finnish Meteorological Institute), and a change detection model (provided by the Vienna University of Technology). The algorithms were applied on L1 backscatter data within the period of 2007-2011, resampled to a 12.5 km grid. The evaluation was performed over 75 globally distributed, quality controlled in situ stations drawn from the International Soil Moisture Network (ISMN) using surface soil moisture data from the Global Land Data Assimilation System (GLDAS-) Noah land surface model as second independent reference. The temporal correlation between the data sets was analyzed and random errors of the the different algorithms were estimated using the triple collocation method. Absolute soil moisture values as well as soil moisture anomalies were considered including both long-term anomalies from the mean seasonal cycle and short-term anomalies from a five weeks moving average window. Results show a very high agreement between all five algorithms for most stations. A slight vegetation dependency of the errors and a spatial decorrelation of the performance patterns of the different algorithms was found. We conclude that future research should focus on understanding, combining and exploiting the advantages of all available modelling approaches rather than trying to optimize one approach to fit every possible condition.

The operational cloud retrieval algorithms from TROPOMI on board Sentinel-5 Precursor

NASA Astrophysics Data System (ADS)

Loyola, Diego G.; Gimeno García, Sebastián; Lutz, Ronny; Argyrouli, Athina; Romahn, Fabian; Spurr, Robert J. D.; Pedergnana, Mattia; Doicu, Adrian; Molina García, Víctor; Schüssler, Olena

2018-01-01

This paper presents the operational cloud retrieval algorithms for the TROPOspheric Monitoring Instrument (TROPOMI) on board the European Space Agency Sentinel-5 Precursor (S5P) mission scheduled for launch in 2017. Two algorithms working in tandem are used for retrieving cloud properties: OCRA (Optical Cloud Recognition Algorithm) and ROCINN (Retrieval of Cloud Information using Neural Networks). OCRA retrieves the cloud fraction using TROPOMI measurements in the ultraviolet (UV) and visible (VIS) spectral regions, and ROCINN retrieves the cloud top height (pressure) and optical thickness (albedo) using TROPOMI measurements in and around the oxygen A-band in the near infrared (NIR). Cloud parameters from TROPOMI/S5P will be used not only for enhancing the accuracy of trace gas retrievals but also for extending the satellite data record of cloud information derived from oxygen A-band measurements, a record initiated with the Global Ozone Monitoring Experiment (GOME) on board the second European Remote-Sensing Satellite (ERS-2) over 20 years ago. The OCRA and ROCINN algorithms are integrated in the S5P operational processor UPAS (Universal Processor for UV/VIS/NIR Atmospheric Spectrometers), and we present here UPAS cloud results using the Ozone Monitoring Instrument (OMI) and GOME-2 measurements. In addition, we examine anticipated challenges for the TROPOMI/S5P cloud retrieval algorithms, and we discuss the future validation needs for OCRA and ROCINN.

Direct Radiative Forcing from Saharan Mineral Dust Layers from In-situ Measurements and Satellite Retrievals

NASA Astrophysics Data System (ADS)

Sauer, D. N.; Vázquez-Navarro, M.; Gasteiger, J.; Chouza, F.; Weinzierl, B.

2016-12-01

Mineral dust is the major species of airborne particulate matter by mass in the atmosphere. Each year an estimated 200-3000 Tg of dust are emitted from the North African desert and arid regions alone. A large fraction of the dust is lifted into the free troposphere and gets transported in extended dust layers westward over the Atlantic Ocean into the Caribbean Sea. Especially over the dark surface of the ocean, those dust layers exert a significant effect on the atmospheric radiative balance though aerosol-radiation interactions. During the Saharan Aerosol Long-range Transport and Aerosol-Cloud-Interaction Experiment (SALTRACE) in summer 2013 airborne in-situ aerosol measurements on both sides of the Atlantic Ocean, near the African coast and the Caribbean were performed. In this study we use data about aerosol microphysical properties acquired between Cabo Verde and Senegal to derive the aerosol optical properties and the resulting radiative forcing using the radiative transfer package libRadtran. We compare the results to values retrieved from MSG/SEVIRI data using the RRUMS algorithm. The RRUMS algorithm can derive shortwave and longwave top-of-atmosphere outgoing fluxes using only information issued from the narrow-band MSG/SEVIRI channels. A specific calibration based on collocated Terra/CERES measurements ensures a correct retrieval of the upwelling flux from the dust covered pixels. The comparison of radiative forcings based on in-situ data to satellite-retrieved values enables us to extend the radiative forcing estimates from small-scale in-situ measurements to large scale satellite coverage over the Atlantic Ocean.

Developments in the Aerosol Layer Height Retrieval Algorithm for the Copernicus Sentinel-4/UVN Instrument

NASA Astrophysics Data System (ADS)

Nanda, Swadhin; Sanders, Abram; Veefkind, Pepijn

2016-04-01

The Sentinel-4 mission is a part of the European Commission's Copernicus programme, the goal of which is to provide geo-information to manage environmental assets, and to observe, understand and mitigate the effects of the changing climate. The Sentinel-4/UVN instrument design is motivated by the need to monitor trace gas concentrations and aerosols in the atmosphere from a geostationary orbit. The on-board instrument is a high resolution UV-VIS-NIR (UVN) spectrometer system that provides hourly radiance measurements over Europe and northern Africa with a spatial sampling of 8 km. The main application area of Sentinel-4/UVN is air quality. One of the data products that is being developed for Sentinel-4/UVN is the Aerosol Layer Height (ALH). The goal is to determine the height of aerosol plumes with a resolution of better than 0.5 - 1 km. The ALH product thus targets aerosol layers in the free troposphere, such as desert dust, volcanic ash and biomass during plumes. KNMI is assigned with the development of the Aerosol Layer Height (ALH) algorithm. Its heritage is the ALH algorithm developed by Sanders and De Haan (ATBD, 2016) for the TROPOMI instrument on board the Sentinel-5 Precursor mission that is to be launched in June or July 2016 (tentative date). The retrieval algorithm designed so far for the aerosol height product is based on the absorption characteristics of the oxygen-A band (759-770 nm). The algorithm has heritage to the ALH algorithm developed for TROPOMI on the Sentinel 5 precursor satellite. New aspects for Sentinel-4/UVN include the higher resolution (0.116 nm compared to 0.4 for TROPOMI) and hourly observation from the geostationary orbit. The algorithm uses optimal estimation to obtain a spectral fit of the reflectance across absorption band, while assuming a single uniform layer with fixed width to represent the aerosol vertical distribution. The state vector includes amongst other elements the height of this layer and its aerosol optical thickness. We will present the development work around the ALH retrieval algorithm in the framework of the Sentinel-4/UVN instrument. The main challenges are highlighted and retrieval simulation results are provided. Also, an outlook towards application of the S4 bread board algorithm to Sentinel-5 Precursor data later this year will be discussed.

Evaluation of X-band polarimetric radar estimation of rainfall and rain drop size distribution parameters in West Africa

NASA Astrophysics Data System (ADS)

Koffi, A. K.; Gosset, M.; Zahiri, E.-P.; Ochou, A. D.; Kacou, M.; Cazenave, F.; Assamoi, P.

2014-06-01

As part of the African Monsoon Multidisciplinary Analysis (AMMA) field campaign an X-band dual-polarization Doppler radar was deployed in Benin, West-Africa, in 2006 and 2007, together with a reinforced rain gauge network and several optical disdrometers. Based on this data set, a comparative study of several rainfall estimators that use X-band polarimetric radar data is presented. In tropical convective systems as encountered in Benin, microwave attenuation by rain is significant and quantitative precipitation estimation (QPE) at X-band is a challenge. Here, several algorithms based on the combined use of reflectivity, differential reflectivity and differential phase shift are evaluated against rain gauges and disdrometers. Four rainfall estimators were tested on twelve rainy events: the use of attenuation corrected reflectivity only (estimator R(ZH)), the use of the specific phase shift only R(KDP), the combination of specific phase shift and differential reflectivity R(KDP,ZDR) and an estimator that uses three radar parameters R(ZH,ZDR,KDP). The coefficients of the power law relationships between rain rate and radar variables were adjusted either based on disdrometer data and simulation, or on radar-gauges observations. The three polarimetric based algorithms with coefficients predetermined on observations outperform the R(ZH) estimator for rain rates above 10 mm/h which explain most of the rainfall in the studied region. For the highest rain rates (above 30 mm/h) R(KDP) shows even better scores, and given its performances and its simplicity of implementation, is recommended. The radar based retrieval of two parameters of the rain drop size distribution, the normalized intercept parameter NW and the volumetric median diameter Dm was evaluated on four rainy days thanks to disdrometers. The frequency distributions of the two parameters retrieved by the radar are very close to those observed with the disdrometer. NW retrieval based on a combination of ZH-KDP-ZDR works well whatever the a priori assumption made on the drop shapes. Dm retrieval based on ZDR alone performs well, but if satisfactory ZDR measurements are not available, the combination ZH-KDP provides satisfactory results for both Dm and NW if an appropriate a priori assumption on drop shape is made.

Estimation of water quality parameters of inland and coastal waters with the use of a toolkit for processing of remote sensing data

DOE Office of Scientific and Technical Information (OSTI.GOV)

Dekker, A.G.; Hoogenboom, H.J.; Rijkeboer, M.

1997-06-01

Deriving thematic maps of water quality parameters from a remote sensing image requires a number of processing steps, such as calibration, atmospheric correction, air/water interface correction, and application of water quality algorithms. A prototype software environment has recently been developed that enables the user to perform and control these processing steps. Main parts of this environment are: (i) access to the MODTRAN 3 radiative transfer code for removing atmospheric and air-water interface influences, (ii) a tool for analyzing of algorithms for estimating water quality and (iii) a spectral database, containing apparent and inherent optical properties and associated water quality parameters.more » The use of the software is illustrated by applying implemented algorithms for estimating chlorophyll to data from a spectral library of Dutch inland waters with CHL ranging from 1 to 500 pg 1{sup -1}. The algorithms currently implemented in the Toolkit software are recommended for optically simple waters, but for optically complex waters development of more advanced retrieval methods is required.« less

Latent Heating from TRMM Satellite Measurements

NASA Technical Reports Server (NTRS)

Tao, W.-K.; Smith, E.; Olson, W.

2005-01-01

Rainfall production is a fundamental process within the Earth;s hydrological cycle because it represents both a principal forcing term in surface water budgets, and its energetics corollary, latent heating, is the principal source of atmospheric diabatic heating. Latent heat release itself is a consequence of phase changes between the vapor, liquid, and frozen states of water. The properties of the vertical distribution of latent heat release modulate large-scale meridional and zonal circulations with the Tropics - as well as modify the energetic efficiencies of mid-latitude weather systems. This paper highlights the retrieval of observatory, which was launched in November 1997 as a joint American-Japanese space endeavor. Since then, TRMM measurements have been providing an accurate four-dimensional amount of rainfall over the global Tropics and sub-tropics - information which can be used to estimate the spacetime structure of latent heating across the Earth's low latitudes. A set of algorithm methodologies has and continues to be developed to estimate latent heating based on rain rate profile retrievals obtained from TRMM measurements. These algorithms are briefly described followed by a discussion of the foremost latent heating products that can be generate from them. The investigation then provides an overview of how TRMM-derived latent heating information is currently being used in conjunction with global weather and climate models, concluding with remarks intended to stimulate further research on latent heating retrieval from satellites.

Consistent satellite XCO 2 retrievals from SCIAMACHY and GOSAT using the BESD algorithm

DOE PAGES

Heymann, J.; Reuter, M.; Hilker, M.; ...

2015-02-13

Consistent and accurate long-term data sets of global atmospheric concentrations of carbon dioxide (CO 2) are required for carbon cycle and climate related research. However, global data sets based on satellite observations may suffer from inconsistencies originating from the use of products derived from different satellites as needed to cover a long enough time period. One reason for inconsistencies can be the use of different retrieval algorithms. We address this potential issue by applying the same algorithm, the Bremen Optimal Estimation DOAS (BESD) algorithm, to different satellite instruments, SCIAMACHY on-board ENVISAT (March 2002–April 2012) and TANSO-FTS on-board GOSAT (launched inmore » January 2009), to retrieve XCO 2, the column-averaged dry-air mole fraction of CO 2. BESD has been initially developed for SCIAMACHY XCO 2 retrievals. Here, we present the first detailed assessment of the new GOSAT BESD XCO 2 product. GOSAT BESD XCO 2 is a product generated and delivered to the MACC project for assimilation into ECMWF's Integrated Forecasting System (IFS). We describe the modifications of the BESD algorithm needed in order to retrieve XCO 2 from GOSAT and present detailed comparisons with ground-based observations of XCO 2 from the Total Carbon Column Observing Network (TCCON). We discuss detailed comparison results between all three XCO 2 data sets (SCIAMACHY, GOSAT and TCCON). The comparison results demonstrate the good consistency between the SCIAMACHY and the GOSAT XCO 2. For example, we found a mean difference for daily averages of −0.60 ± 1.56 ppm (mean difference ± standard deviation) for GOSAT-SCIAMACHY (linear correlation coefficient r = 0.82), −0.34 ± 1.37 ppm ( r = 0.86) for GOSAT-TCCON and 0.10 ± 1.79 ppm ( r = 0.75) for SCIAMACHY-TCCON. The remaining differences between GOSAT and SCIAMACHY are likely due to non-perfect collocation (±2 h, 10° × 10° around TCCON sites), i.e., the observed air masses are not exactly identical, but likely also due to a still non-perfect BESD retrieval algorithm, which will be continuously improved in the future. Our overarching goal is to generate a satellite-derived XCO 2 data set appropriate for climate and carbon cycle research covering the longest possible time period. We therefore also plan to extend the existing SCIAMACHY and GOSAT data set discussed here by using also data from other missions (e.g., OCO-2, GOSAT-2, CarbonSat) in the future.« less

The Passive microwave Neural network Precipitation Retrieval (PNPR) algorithm for AMSU/MHS observations: description and application to European case studies

NASA Astrophysics Data System (ADS)

Sanò, P.; Panegrossi, G.; Casella, D.; Di Paola, F.; Milani, L.; Mugnai, A.; Petracca, M.; Dietrich, S.

2015-02-01

The purpose of this study is to describe a new algorithm based on a neural network approach (Passive microwave Neural network Precipitation Retrieval - PNPR) for precipitation rate estimation from AMSU/MHS observations, and to provide examples of its performance for specific case studies over the European/Mediterranean area. The algorithm optimally exploits the different characteristics of Advanced Microwave Sounding Unit-A (AMSU-A) and the Microwave Humidity Sounder (MHS) channels, and their combinations, including the brightness temperature (TB) differences of the 183.31 channels, with the goal of having a single neural network for different types of background surfaces (vegetated land, snow-covered surface, coast and ocean). The training of the neural network is based on the use of a cloud-radiation database, built from cloud-resolving model simulations coupled to a radiative transfer model, representative of the European and Mediterranean Basin precipitation climatology. The algorithm provides also the phase of the precipitation and a pixel-based confidence index for the evaluation of the reliability of the retrieval. Applied to different weather conditions in Europe, the algorithm shows good performance both in the identification of precipitation areas and in the retrieval of precipitation, which is particularly valuable over the extremely variable environmental and meteorological conditions of the region. The PNPR is particularly efficient in (1) screening and retrieval of precipitation over different background surfaces; (2) identification and retrieval of heavy rain for convective events; and (3) identification of precipitation over a cold/iced background, with increased uncertainties affecting light precipitation. In this paper, examples of good agreement of precipitation pattern and intensity with ground-based data (radar and rain gauges) are provided for four different case studies. The algorithm has been developed in order to be easily tailored to new radiometers as they become available (such as the cross-track scanning Suomi National Polar-orbiting Partnership (NPP) Advanced Technology Microwave Sounder (ATMS)), and it is suitable for operational use as it is computationally very efficient. PNPR has been recently extended for applications to the regions of Africa and the South Atlantic, and an extended validation over these regions (using 2 yr of data acquired by the Tropical Rainfall Measuring Mission precipitation radar for comparison) is the subject of a paper in preparation. The PNPR is currently used operationally within the EUMETSAT Hydrology Satellite Application Facility (H-SAF) to provide instantaneous precipitation from passive microwave cross-track scanning radiometers. It undergoes routinely thorough extensive validation over Europe carried out by the H-SAF Precipitation Products Validation Team.

Gradient descent algorithm applied to wavefront retrieval from through-focus images by an extreme ultraviolet microscope with partially coherent source

DOE PAGES

Yamazoe, Kenji; Mochi, Iacopo; Goldberg, Kenneth A.

2014-12-01

The wavefront retrieval by gradient descent algorithm that is typically applied to coherent or incoherent imaging is extended to retrieve a wavefront from a series of through-focus images by partially coherent illumination. For accurate retrieval, we modeled partial coherence as well as object transmittance into the gradient descent algorithm. However, this modeling increases the computation time due to the complexity of partially coherent imaging simulation that is repeatedly used in the optimization loop. To accelerate the computation, we incorporate not only the Fourier transform but also an eigenfunction decomposition of the image. As a demonstration, the extended algorithm is appliedmore » to retrieve a field-dependent wavefront of a microscope operated at extreme ultraviolet wavelength (13.4 nm). The retrieved wavefront qualitatively matches the expected characteristics of the lens design.« less

Gradient descent algorithm applied to wavefront retrieval from through-focus images by an extreme ultraviolet microscope with partially coherent source

DOE Office of Scientific and Technical Information (OSTI.GOV)

Yamazoe, Kenji; Mochi, Iacopo; Goldberg, Kenneth A.

The wavefront retrieval by gradient descent algorithm that is typically applied to coherent or incoherent imaging is extended to retrieve a wavefront from a series of through-focus images by partially coherent illumination. For accurate retrieval, we modeled partial coherence as well as object transmittance into the gradient descent algorithm. However, this modeling increases the computation time due to the complexity of partially coherent imaging simulation that is repeatedly used in the optimization loop. To accelerate the computation, we incorporate not only the Fourier transform but also an eigenfunction decomposition of the image. As a demonstration, the extended algorithm is appliedmore » to retrieve a field-dependent wavefront of a microscope operated at extreme ultraviolet wavelength (13.4 nm). The retrieved wavefront qualitatively matches the expected characteristics of the lens design.« less

Status on Iterative Transform Phase Retrieval Applied to the GBT Data

NASA Technical Reports Server (NTRS)

Dean, Bruce; Aronstein, David; Smith, Scott; Shiri, Ron; Hollis, Jan M.; Lyons, Richard; Prestage, Richard; Hunter, Todd; Ghigo, Frank; Nikolic, Bojan

2007-01-01

This slide presentation reviews the use of iterative transform phase retrieval in the analysis of the Green Bank Radio Telescope (GBT) Data. It reviews the NASA projects that have used phase retrieval, and the testbed for the algorithm to be used for the James Webb Space Telescope. It shows the comparison of phase retrieval with an interferometer, and reviews the two approaches used for phase retrieval, iterative transform (ITA) or parametric (non-linear least squares model fitting). The concept of ITA Phase Retrieval is reviewed, and the application to Radio Antennas is reviewed. The presentation also examines the National Radio Astronomy Observatory (NRAO) data from the GBT, and the Fourier model that NRAO uses to analyze the data. The challenge for ITA phase retrieval is reviewed, and the coherent approximation for incoherent data is shown. The validity of the approximation is good for a large tilt. There is a review of the proof of concept of the Phase Review simulation using the input wavefront, and the initial sampling parameters estimate from the focused GBT data.

Effects of vertical distribution of water vapor and temperature on total column water vapor retrieval error

NASA Technical Reports Server (NTRS)

Sun, Jielun

1993-01-01

Results are presented of a test of the physically based total column water vapor retrieval algorithm of Wentz (1992) for sensitivity to realistic vertical distributions of temperature and water vapor. The ECMWF monthly averaged temperature and humidity fields are used to simulate the spatial pattern of systematic retrieval error of total column water vapor due to this sensitivity. The estimated systematic error is within 0.1 g/sq cm over about 70 percent of the global ocean area; systematic errors greater than 0.3 g/sq cm are expected to exist only over a few well-defined regions, about 3 percent of the global oceans, assuming that the global mean value is unbiased.

An Updated TRMM Composite Climatology of Tropical Rainfall and Its Validation

NASA Technical Reports Server (NTRS)

Wang, Jian-Jian; Adler, Robert F.; Huffman, George; Bolvin, David

2013-01-01

An updated 15-yr Tropical Rainfall Measuring Mission (TRMM) composite climatology (TCC) is presented and evaluated. This climatology is based on a combination of individual rainfall estimates made with data from the primaryTRMMinstruments: theTRMM Microwave Imager (TMI) and the precipitation radar (PR). This combination climatology of passive microwave retrievals, radar-based retrievals, and an algorithm using both instruments simultaneously provides a consensus TRMM-based estimate of mean precipitation. The dispersion of the three estimates, as indicated by the standard deviation sigma among the estimates, is presented as a measure of confidence in the final estimate and as an estimate of the uncertainty thereof. The procedures utilized by the compositing technique, including adjustments and quality-control measures, are described. The results give a mean value of the TCC of 4.3mm day(exp -1) for the deep tropical ocean beltbetween 10 deg N and 10 deg S, with lower values outside that band. In general, the TCC values confirm ocean estimates from the Global Precipitation Climatology Project (GPCP) analysis, which is based on passive microwave results adjusted for sampling by infrared-based estimates. The pattern of uncertainty estimates shown by sigma is seen to be useful to indicate variations in confidence. Examples include differences between the eastern and western portions of the Pacific Ocean and high values in coastal and mountainous areas. Comparison of the TCC values (and the input products) to gauge analyses over land indicates the value of the radar-based estimates (small biases) and the limitations of the passive microwave algorithm (relatively large biases). Comparison with surface gauge information from western Pacific Ocean atolls shows a negative bias (16%) for all the TRMM products, although the representativeness of the atoll gauges of open-ocean rainfall is still in question.

Optimization of Ocean Color Algorithms: Application to Satellite Data Merging

NASA Technical Reports Server (NTRS)

Ritorena, Stephane; Siegel, David A.; Morel, Andre

2004-01-01

The objective of the program is to develop and validate a procedure for ocean color data merging, which is one of the major goals of the SIMBIOS project. As part of the SIMBIOS Program, we have developed a merging method for ocean color data. Conversely to other methods our approach does not combine end-products like the subsurface chlorophyll concentration (chl) from different sensors to generate a unified product. Instead, our procedure uses the normalized water-leaving radiances L((sub wN)(lambda)) from single or multiple sensors and uses them in the inversion of a semi-analytical ocean color model that allows the retrieval of several ocean color variables simultaneously. Beside ensuring simultaneity and consistency of the retrievals (all products are derived from a single algorithm), this model-based approach has various benefits over techniques that blend end-products (e.g. chlorophyll): 1) It works with single or multiple data sources regardless of their specific bands; 2) It exploits band redundancies and band differences; 3) It accounts for uncertainties in the L((sub wN)(lambda)) data; 4) It provides uncertainty estimates for the retrieved variables.

Full-Physics Inverse Learning Machine for Satellite Remote Sensing of Ozone Profile Shapes and Tropospheric Columns

NASA Astrophysics Data System (ADS)

Xu, J.; Heue, K.-P.; Coldewey-Egbers, M.; Romahn, F.; Doicu, A.; Loyola, D.

2018-04-01

Characterizing vertical distributions of ozone from nadir-viewing satellite measurements is known to be challenging, particularly the ozone information in the troposphere. A novel retrieval algorithm called Full-Physics Inverse Learning Machine (FP-ILM), has been developed at DLR in order to estimate ozone profile shapes based on machine learning techniques. In contrast to traditional inversion methods, the FP-ILM algorithm formulates the profile shape retrieval as a classification problem. Its implementation comprises a training phase to derive an inverse function from synthetic measurements, and an operational phase in which the inverse function is applied to real measurements. This paper extends the ability of the FP-ILM retrieval to derive tropospheric ozone columns from GOME- 2 measurements. Results of total and tropical tropospheric ozone columns are compared with the ones using the official GOME Data Processing (GDP) product and the convective-cloud-differential (CCD) method, respectively. Furthermore, the FP-ILM framework will be used for the near-real-time processing of the new European Sentinel sensors with their unprecedented spectral and spatial resolution and corresponding large increases in the amount of data.

A multimedia retrieval framework based on semi-supervised ranking and relevance feedback.

PubMed

Yang, Yi; Nie, Feiping; Xu, Dong; Luo, Jiebo; Zhuang, Yueting; Pan, Yunhe

2012-04-01

We present a new framework for multimedia content analysis and retrieval which consists of two independent algorithms. First, we propose a new semi-supervised algorithm called ranking with Local Regression and Global Alignment (LRGA) to learn a robust Laplacian matrix for data ranking. In LRGA, for each data point, a local linear regression model is used to predict the ranking scores of its neighboring points. A unified objective function is then proposed to globally align the local models from all the data points so that an optimal ranking score can be assigned to each data point. Second, we propose a semi-supervised long-term Relevance Feedback (RF) algorithm to refine the multimedia data representation. The proposed long-term RF algorithm utilizes both the multimedia data distribution in multimedia feature space and the history RF information provided by users. A trace ratio optimization problem is then formulated and solved by an efficient algorithm. The algorithms have been applied to several content-based multimedia retrieval applications, including cross-media retrieval, image retrieval, and 3D motion/pose data retrieval. Comprehensive experiments on four data sets have demonstrated its advantages in precision, robustness, scalability, and computational efficiency.

OCRA radiometric cloud fractions for GOME-2 on MetOp-A/B

NASA Astrophysics Data System (ADS)

Lutz, R.; Loyola, D.; Gimeno García, S.; Romahn, F.

2015-12-01

This paper describes an approach for cloud parameter retrieval (radiometric cloud fraction estimation) using the polarization measurements of the Global Ozone Monitoring Experiment-2 (GOME-2) on-board the MetOp-A/B satellites. The core component of the Optical Cloud Recognition Algorithm (OCRA) is the calculation of monthly cloud-free reflectances for a global grid (resolution of 0.2° in longitude and 0.2° in latitude) and to derive radiometric cloud fractions. These cloud fractions will serve as a priori information for the retrieval of cloud top height (CTH), cloud top pressure (CTP), cloud top albedo (CTA) and cloud optical thickness (COT) with the Retrieval Of Cloud Information using Neural Networks (ROCINN) algorithm. This approach is already being implemented operationally for the GOME/ERS-2 and SCIAMACHY/ENVISAT sensors and here we present version 3.0 of the OCRA algorithm applied to the GOME-2 sensors. Based on more than six years of GOME-2A data (February 2007-June 2013), reflectances are calculated for ≈ 35 000 orbits. For each measurement a degradation correction as well as a viewing angle dependent and latitude dependent correction is applied. In addition, an empirical correction scheme is introduced in order to remove the effect of oceanic sun glint. A comparison of the GOME-2A/B OCRA cloud fractions with co-located AVHRR geometrical cloud fractions shows a general good agreement with a mean difference of -0.15±0.20. From operational point of view, an advantage of the OCRA algorithm is its extremely fast computational time and its straightforward transferability to similar sensors like OMI (Ozone Monitoring Instrument), TROPOMI (TROPOspheric Monitoring Instrument) on Sentinel 5 Precursor, as well as Sentinel 4 and Sentinel 5. In conclusion, it is shown that a robust, accurate and fast radiometric cloud fraction estimation for GOME-2 can be achieved with OCRA by using the polarization measurement devices (PMDs).

OCRA radiometric cloud fractions for GOME-2 on MetOp-A/B

NASA Astrophysics Data System (ADS)

Lutz, Ronny; Loyola, Diego; Gimeno García, Sebastián; Romahn, Fabian

2016-05-01

This paper describes an approach for cloud parameter retrieval (radiometric cloud-fraction estimation) using the polarization measurements of the Global Ozone Monitoring Experiment-2 (GOME-2) onboard the MetOp-A/B satellites. The core component of the Optical Cloud Recognition Algorithm (OCRA) is the calculation of monthly cloud-free reflectances for a global grid (resolution of 0.2° in longitude and 0.2° in latitude) to derive radiometric cloud fractions. These cloud fractions will serve as a priori information for the retrieval of cloud-top height (CTH), cloud-top pressure (CTP), cloud-top albedo (CTA) and cloud optical thickness (COT) with the Retrieval Of Cloud Information using Neural Networks (ROCINN) algorithm. This approach is already being implemented operationally for the GOME/ERS-2 and SCIAMACHY/ENVISAT sensors and here we present version 3.0 of the OCRA algorithm applied to the GOME-2 sensors. Based on more than five years of GOME-2A data (April 2008 to June 2013), reflectances are calculated for ≈ 35 000 orbits. For each measurement a degradation correction as well as a viewing-angle-dependent and latitude-dependent correction is applied. In addition, an empirical correction scheme is introduced in order to remove the effect of oceanic sun glint. A comparison of the GOME-2A/B OCRA cloud fractions with colocated AVHRR (Advanced Very High Resolution Radiometer) geometrical cloud fractions shows a general good agreement with a mean difference of -0.15 ± 0.20. From an operational point of view, an advantage of the OCRA algorithm is its very fast computational time and its straightforward transferability to similar sensors like OMI (Ozone Monitoring Instrument), TROPOMI (TROPOspheric Monitoring Instrument) on Sentinel 5 Precursor, as well as Sentinel 4 and Sentinel 5. In conclusion, it is shown that a robust, accurate and fast radiometric cloud-fraction estimation for GOME-2 can be achieved with OCRA using polarization measurement devices (PMDs).

The Greenhouse Gas Climate Change Initiative (GHG-CCI): comparative validation of GHG-CCI SCIAMACHY/ENVISAT and TANSO-FTS/GOSAT CO2 and CH4 retrieval algorithm products with measurements from the TCCON

NASA Astrophysics Data System (ADS)

Dils, B.; Buchwitz, M.; Reuter, M.; Schneising, O.; Boesch, H.; Parker, R.; Guerlet, S.; Aben, I.; Blumenstock, T.; Burrows, J. P.; Butz, A.; Deutscher, N. M.; Frankenberg, C.; Hase, F.; Hasekamp, O. P.; Heymann, J.; De Mazière, M.; Notholt, J.; Sussmann, R.; Warneke, T.; Griffith, D.; Sherlock, V.; Wunch, D.

2014-06-01

Column-averaged dry-air mole fractions of carbon dioxide and methane have been retrieved from spectra acquired by the TANSO-FTS (Thermal And Near-infrared Sensor for carbon Observations-Fourier Transform Spectrometer) and SCIAMACHY (Scanning Imaging Absorption Spectrometer for Atmospheric Cartography) instruments on board GOSAT (Greenhouse gases Observing SATellite) and ENVISAT (ENVIronmental SATellite), respectively, using a range of European retrieval algorithms. These retrievals have been compared with data from ground-based high-resolution Fourier transform spectrometers (FTSs) from the Total Carbon Column Observing Network (TCCON). The participating algorithms are the weighting function modified differential optical absorption spectroscopy (DOAS) algorithm (WFMD, University of Bremen), the Bremen optimal estimation DOAS algorithm (BESD, University of Bremen), the iterative maximum a posteriori DOAS (IMAP, Jet Propulsion Laboratory (JPL) and Netherlands Institute for Space Research algorithm (SRON)), the proxy and full-physics versions of SRON's RemoTeC algorithm (SRPR and SRFP, respectively) and the proxy and full-physics versions of the University of Leicester's adaptation of the OCO (Orbiting Carbon Observatory) algorithm (OCPR and OCFP, respectively). The goal of this algorithm inter-comparison was to identify strengths and weaknesses of the various so-called round- robin data sets generated with the various algorithms so as to determine which of the competing algorithms would proceed to the next round of the European Space Agency's (ESA) Greenhouse Gas Climate Change Initiative (GHG-CCI) project, which is the generation of the so-called Climate Research Data Package (CRDP), which is the first version of the Essential Climate Variable (ECV) "greenhouse gases" (GHGs). For XCO2, all algorithms reach the precision requirements for inverse modelling (< 8 ppm), with only WFMD having a lower precision (4.7 ppm) than the other algorithm products (2.4-2.5 ppm). When looking at the seasonal relative accuracy (SRA, variability of the bias in space and time), none of the algorithms have reached the demanding < 0.5 ppm threshold. For XCH4, the precision for both SCIAMACHY products (50.2 ppb for IMAP and 76.4 ppb for WFMD) fails to meet the < 34 ppb threshold for inverse modelling, but note that this work focusses on the period after the 2005 SCIAMACHY detector degradation. The GOSAT XCH4 precision ranges between 18.1 and 14.0 ppb. Looking at the SRA, all GOSAT algorithm products reach the < 10 ppm threshold (values ranging between 5.4 and 6.2 ppb). For SCIAMACHY, IMAP and WFMD have a SRA of 17.2 and 10.5 ppb, respectively.

Development of a remote sensing algorithm to retrieve atmospheric aerosol properties using multiwavelength and multipixel information

NASA Astrophysics Data System (ADS)

Hashimoto, Makiko; Nakajima, Teruyuki

2017-06-01

We developed a satellite remote sensing algorithm to retrieve the aerosol optical properties using satellite-received radiances for multiple wavelengths and pixels. Our algorithm utilizes spatial inhomogeneity of surface reflectance to retrieve aerosol properties, and the main target is urban aerosols. This algorithm can simultaneously retrieve aerosol optical thicknesses (AOT) for fine- and coarse-mode aerosols, soot volume fraction in fine-mode aerosols (SF), and surface reflectance over heterogeneous surfaces such as urban areas that are difficult to obtain by conventional pixel-by-pixel methods. We applied this algorithm to radiances measured by the Greenhouse Gases Observing Satellite/Thermal and Near Infrared Sensor for Carbon Observations-Cloud and Aerosol Image (GOSAT/TANSO-CAI) at four wavelengths and were able to retrieve the aerosol parameters in several urban regions and other surface types. A comparison of the retrieved AOTs with those from the Aerosol Robotic Network (AERONET) indicated retrieval accuracy within ±0.077 on average. It was also found that the column-averaged SF and the aerosol single scattering albedo (SSA) underwent seasonal changes as consistent with the ground surface measurements of SSA and black carbon at Beijing, China.

Abbreviation definition identification based on automatic precision estimates.

PubMed

Sohn, Sunghwan; Comeau, Donald C; Kim, Won; Wilbur, W John

2008-09-25

The rapid growth of biomedical literature presents challenges for automatic text processing, and one of the challenges is abbreviation identification. The presence of unrecognized abbreviations in text hinders indexing algorithms and adversely affects information retrieval and extraction. Automatic abbreviation definition identification can help resolve these issues. However, abbreviations and their definitions identified by an automatic process are of uncertain validity. Due to the size of databases such as MEDLINE only a small fraction of abbreviation-definition pairs can be examined manually. An automatic way to estimate the accuracy of abbreviation-definition pairs extracted from text is needed. In this paper we propose an abbreviation definition identification algorithm that employs a variety of strategies to identify the most probable abbreviation definition. In addition our algorithm produces an accuracy estimate, pseudo-precision, for each strategy without using a human-judged gold standard. The pseudo-precisions determine the order in which the algorithm applies the strategies in seeking to identify the definition of an abbreviation. On the Medstract corpus our algorithm produced 97% precision and 85% recall which is higher than previously reported results. We also annotated 1250 randomly selected MEDLINE records as a gold standard. On this set we achieved 96.5% precision and 83.2% recall. This compares favourably with the well known Schwartz and Hearst algorithm. We developed an algorithm for abbreviation identification that uses a variety of strategies to identify the most probable definition for an abbreviation and also produces an estimated accuracy of the result. This process is purely automatic.

Angular dependence of multiangle dynamic light scattering for particle size distribution inversion using a self-adapting regularization algorithm

NASA Astrophysics Data System (ADS)

Li, Lei; Yu, Long; Yang, Kecheng; Li, Wei; Li, Kai; Xia, Min

2018-04-01

The multiangle dynamic light scattering (MDLS) technique can better estimate particle size distributions (PSDs) than single-angle dynamic light scattering. However, determining the inversion range, angular weighting coefficients, and scattering angle combination is difficult but fundamental to the reconstruction for both unimodal and multimodal distributions. In this paper, we propose a self-adapting regularization method called the wavelet iterative recursion nonnegative Tikhonov-Phillips-Twomey (WIRNNT-PT) algorithm. This algorithm combines a wavelet multiscale strategy with an appropriate inversion method and could self-adaptively optimize several noteworthy issues containing the choices of the weighting coefficients, the inversion range and the optimal inversion method from two regularization algorithms for estimating the PSD from MDLS measurements. In addition, the angular dependence of the MDLS for estimating the PSDs of polymeric latexes is thoroughly analyzed. The dependence of the results on the number and range of measurement angles was analyzed in depth to identify the optimal scattering angle combination. Numerical simulations and experimental results for unimodal and multimodal distributions are presented to demonstrate both the validity of the WIRNNT-PT algorithm and the angular dependence of MDLS and show that the proposed algorithm with a six-angle analysis in the 30-130° range can be satisfactorily applied to retrieve PSDs from MDLS measurements.

A data-driven and physics-based single-pass retrieval of active-passive microwave covariation and vegetation parameters for the SMAP mission

NASA Astrophysics Data System (ADS)

Entekhabi, D.; Jagdhuber, T.; Das, N. N.; Baur, M.; Link, M.; Piles, M.; Akbar, R.; Konings, A. G.; Mccoll, K. A.; Alemohammad, S. H.; Montzka, C.; Kunstmann, H.

2016-12-01

The active-passive soil moisture retrieval algorithm of NASA's SMAP mission depends on robust statistical estimation of active-passive covariation (β) and vegetation structure (Γ) parameters in order to provide reliable global measurements of soil moisture on an intermediate level (9km) compared to the native resolution of the radiometer (36km) and radar (3km) instruments. These parameters apply to the SMAP radiometer-radar combination over the period of record that was cut short with the end of the SMAP radar transmission. They also apply to the current SMAP radiometer and Sentinel 1A/B radar combination for high-resolution surface soil moisture mapping. However, the performance of the statistically-based approach is directly dependent on the selection of a representative time frame in which these parameters can be estimated assuming dynamic soil moisture and stationary soil roughness and vegetation cover. Here, we propose a novel, data-driven and physics-based single-pass retrieval of active-passive microwave covariation and vegetation parameters for the SMAP mission. The algorithm does not depend on time series analyses and can be applied using minimum one pair of an active-passive acquisition. The algorithm stems from the physical link between microwave emission and scattering via conservation of energy. The formulation of the emission radiative transfer is combined with the Distorted Born Approximation of radar scattering for vegetated land surfaces. The two formulations are simultaneously solved for the covariation and vegetation structure parameters. Preliminary results from SMAP active-passive observations (April 13th to July 7th 2015) compare well with the time-series statistical approach and confirms the capability of this method to estimate these parameters. Moreover, the method is not restricted to a given frequency (applies to both L-band and C-band combinations for the radar) or incidence angle (all angles and not just the fixed 40° incidence). Therefore, the approach is applicable to the combination of SMAP and Sentinel-1A/B data for active-passive and high-resolution soil moisture estimation.

Estimation of the Total Atmospheric Water Vapor Content and Land Surface Temperature Based on AATSR Thermal Data

PubMed Central

Zhang, Tangtang; Wen, Jun; van der Velde, Rogier; Meng, Xianhong; Li, Zhenchao; Liu, Yuanyong; Liu, Rong

2008-01-01

The total atmospheric water vapor content (TAWV) and land surface temperature (LST) play important roles in meteorology, hydrology, ecology and some other disciplines. In this paper, the ENVISAT/AATSR (The Advanced Along-Track Scanning Radiometer) thermal data are used to estimate the TAWV and LST over the Loess Plateau in China by using a practical split window algorithm. The distribution of the TAWV is accord with that of the MODIS TAWV products, which indicates that the estimation of the total atmospheric water vapor content is reliable. Validations of the LST by comparing with the ground measurements indicate that the maximum absolute derivation, the maximum relative error and the average relative error is 4.0K, 11.8% and 5.0% respectively, which shows that the retrievals are believable; this algorithm can provide a new way to estimate the LST from AATSR data. PMID:27879795

A multi-band semi-analytical algorithm for estimating chlorophyll-a concentration in the Yellow River Estuary, China.

PubMed

Chen, Jun; Quan, Wenting; Cui, Tingwei

2015-01-01

In this study, two sample semi-analytical algorithms and one new unified multi-band semi-analytical algorithm (UMSA) for estimating chlorophyll-a (Chla) concentration were constructed by specifying optimal wavelengths. The three sample semi-analytical algorithms, including the three-band semi-analytical algorithm (TSA), four-band semi-analytical algorithm (FSA), and UMSA algorithm, were calibrated and validated by the dataset collected in the Yellow River Estuary between September 1 and 10, 2009. By comparing of the accuracy of assessment of TSA, FSA, and UMSA algorithms, it was found that the UMSA algorithm had a superior performance in comparison with the two other algorithms, TSA and FSA. Using the UMSA algorithm in retrieving Chla concentration in the Yellow River Estuary decreased by 25.54% NRMSE (normalized root mean square error) when compared with the FSA algorithm, and 29.66% NRMSE in comparison with the TSA algorithm. These are very significant improvements upon previous methods. Additionally, the study revealed that the TSA and FSA algorithms are merely more specific forms of the UMSA algorithm. Owing to the special form of the UMSA algorithm, if the same bands were used for both the TSA and UMSA algorithms or FSA and UMSA algorithms, the UMSA algorithm would theoretically produce superior results in comparison with the TSA and FSA algorithms. Thus, good results may also be produced if the UMSA algorithm were to be applied for predicting Chla concentration for datasets of Gitelson et al. (2008) and Le et al. (2009).

A New Inversion-Based Algorithm for Retrieval of Over-Water Rain Rate from SSM/I Multichannel Imagery

NASA Technical Reports Server (NTRS)

Petty, Grant W.; Stettner, David R.

1994-01-01

This paper discusses certain aspects of a new inversion based algorithm for the retrieval of rain rate over the open ocean from the special sensor microwave/imager (SSM/I) multichannel imagery. This algorithm takes a more detailed physical approach to the retrieval problem than previously discussed algorithms that perform explicit forward radiative transfer calculations based on detailed model hydrometer profiles and attempt to match the observations to the predicted brightness temperature.

Retrieval of Understory NDVI in Sparse Boreal Forests By MODIS Brdf Data

NASA Astrophysics Data System (ADS)

Yang, W.; Kobayashi, H.; Suzuki, R.; Nasahara, K. N.

2014-12-01

Global products of leaf area index (LAI) usually show large uncertainties in sparsely vegetated areas. The reason is that the understory contribution is not negligible in reflectance modeling for the case of low to intermediate canopy cover. Therefore many efforts have been carried out on inclusion of understory properties in the LAI estimation algorithms. Compared with conventional data bank method, estimation of forest understory property from satellite data is superior in the studies at global or continental scale during a long periods. However, the existing remote sensing method based on multi-angular observations is very complicated to implement. Alternatively, a simple method to retrieve understory NDVI (NDVIu) for sparse boreal forests was proposed in this study. The method is based on the property that the bi-directional variation of NDVIu is much smaller than that of the canopy-level NDVI. To retrieve NDVIu for a certain pixel, linear extrapolation was applied using the pixels within a 5×5 target-pixel-centered window. The NDVI values were reconstructed from the MODIS BRDF data corresponding to eight different solar-view angles. NDVIu was estimated as the average of the NDVI values corresponding to the position where the stand NDVI has the smallest angular variation. Validation by noise-free simulation dataset yielded high agreement between estimated and true NDVIu with R2 and RMSE of 0.99 and 0.03, respectively. By the MODIS BRDF data, we got the estimate of NDVIu close to the in situ measured value (0.61 vs. 0.66 for estimate and measurement, respectively), and also reasonable seasonal patterns of NDVIu in 2010-2013. The results imply a potential application of the retrieved NDVIu to improve the estimation of overstory LAI for sparse boreal forests.

Simultaneously inferring above-cloud absorbing aerosol optical thickness and underlying liquid phase cloud optical and microphysical properties using MODIS

NASA Astrophysics Data System (ADS)

Meyer, Kerry; Platnick, Steven; Zhang, Zhibo

2015-06-01

The regional haze over the southeast (SE) Atlantic Ocean induced by biomass burning in southern Africa can be problematic for passive imager-based retrievals of the underlying quasi-permanent marine boundary layer (MBL) clouds and for estimates of top-of-atmosphere (TOA) aerosol direct radiative effect (DRE). Here an algorithm is introduced to simultaneously retrieve above-cloud aerosol optical thickness (AOT), the cloud optical thickness (COT), and cloud effective particle radius (CER) of the underlying MBL clouds while also providing pixel-level estimates of retrieval uncertainty. This approach utilizes reflectance measurements at six Moderate Resolution Imaging Spectroradiometer (MODIS) channels from the visible to the shortwave infrared. Retrievals are run under two aerosol model assumptions on 8 years (2006-2013) of June-October Aqua MODIS data over the SE Atlantic, from which a regional cloud and above-cloud aerosol climatology is produced. The cloud retrieval methodology is shown to yield COT and CER consistent with those from the MODIS operational cloud product (MOD06) when forcing AOT to zero, while the full COT-CER-AOT retrievals that account for the above-cloud aerosol attenuation increase regional monthly mean COT and CER by up to 9% and 2%, respectively. Retrieved AOT is roughly 3 to 5 times larger than the collocated 532 nm Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) retrievals, though closer agreement is observed with the CALIOP 1064 nm retrievals, a result consistent with previous case study analyses. Regional cloudy-sky above-cloud aerosol DRE calculations are also performed that illustrate the importance of the aerosol model assumption and underlying cloud retrievals.

Estimation of wind speeds inside Super Typhoon Nepartak from AMSR2 low-frequency brightness temperatures

NASA Astrophysics Data System (ADS)

Zhang, Lei; Yin, Xiaobin; Shi, Hanqing; Wang, Zhenzhan; Xu, Qing

2018-04-01

Accurate estimations of typhoon-level winds are highly desired over the western Pacific Ocean. A wind speed retrieval algorithm is used to retrieve the wind speeds within Super Typhoon Nepartak (2016) using 6.9- and 10.7-GHz brightness temperatures from the Japanese Advanced Microwave Scanning Radiometer 2 (AMSR2) sensor on board the Global Change Observation Mission-Water 1 (GCOM-W1) satellite. The results show that the retrieved wind speeds clearly represent the intensification process of Super Typhoon Nepartak. A good agreement is found between the retrieved wind speeds and the Soil Moisture Active Passive wind speed product. The mean bias is 0.51 m/s, and the root-mean-square difference is 1.93 m/s between them. The retrieved maximum wind speeds are 59.6 m/s at 04:45 UTC on July 6 and 71.3 m/s at 16:58 UTC on July 6. The two results demonstrate good agreement with the results reported by the China Meteorological Administration and the Joint Typhoon Warning Center. In addition, Feng-Yun 2G (FY-2G) satellite infrared images, Feng-Yun 3C (FY-3C) microwave atmospheric sounder data, and AMSR2 brightness temperature images are also used to describe the development and structure of Super Typhoon Nepartak.

Estimation of wind stress using dual-frequency TOPEX data

NASA Astrophysics Data System (ADS)

Elfouhaily, Tanos; Vandemark, Douglas; Gourrion, Jéro‸me; Chapron, Bertrand

1998-10-01

The TOPEX/POSEIDON satellite carries the first dual-frequency radar altimeter. Monofrequency (Ku-band) algorithms are presently used to retrieve surface wind speed from the altimeter's radar cross-section measurement (σ0Ku). These algorithms work reasonably well, but it is also known that altimeter wind estimates can be contaminated by residual effects, such as sea state, embedded in the σ0Ku measurement. Investigating the potential benefit of using two frequencies for wind retrieval, it is shown that a simple evaluation of TOPEX data yields previously unavailable information, particularly for high and low wind speeds. As the wind speed increases, the dual-frequency data provides a measurement more directly linked to the short-scale surface roughness, which in turn is associated with the local surface wind stress. Using a global TOPEX σ0° data set and TOPEX's significant wave height (Hs) estimate as a surrogate for the sea state's degree of development, it is also shown that differences between the two TOPEX σ0 measurements strongly evidence nonlocal sea state signature. A composite scattering theory is used to show how the dual-frequency data can provide an improved friction velocity model, especially for winds above 7 m/s. A wind speed conversion is included using a sea state dependent drag coefficient fed with TOPEX Hs data. Two colocated TOPEX-buoy data sets (from the National Data Buoy Center (NDBC) and the Structure des Echanges Mer-Atmosphre, Proprietes des Heterogeneites Oceaniques: Recherche Expérimentale (SEMAPHORE) campaign) are employed to test the new wind speed algorithm. A measurable improvement in wind speed estimation is obtained when compared to the monofrequency Witter and Chelton [1991] model.

Estimation of improved resolution soil moisture in vegetated areas using passive AMSR-E data

NASA Astrophysics Data System (ADS)

Moradizadeh, Mina; Saradjian, Mohammad R.

2018-03-01

Microwave remote sensing provides a unique capability for soil parameter retrievals. Therefore, various soil parameters estimation models have been developed using brightness temperature (BT) measured by passive microwave sensors. Due to the low resolution of satellite microwave radiometer data, the main goal of this study is to develop a downscaling approach to improve the spatial resolution of soil moisture estimates with the use of higher resolution visible/infrared sensor data. Accordingly, after the soil parameters have been obtained using Simultaneous Land Parameters Retrieval Model algorithm, the downscaling method has been applied to the soil moisture estimations that have been validated against in situ soil moisture data. Advance Microwave Scanning Radiometer-EOS BT data in Soil Moisture Experiment 2003 region in the south and north of Oklahoma have been used to this end. Results illustrated that the soil moisture variability is effectively captured at 5 km spatial scales without a significant degradation of the accuracy.

Retrievals of atmospheric columnar carbon dioxide and methane from GOSAT observations with photon path-length probability density function (PPDF) method

NASA Astrophysics Data System (ADS)

Bril, A.; Oshchepkov, S.; Yokota, T.; Yoshida, Y.; Morino, I.; Uchino, O.; Belikov, D. A.; Maksyutov, S. S.

2014-12-01

We retrieved the column-averaged dry air mole fraction of atmospheric carbon dioxide (XCO2) and methane (XCH4) from the radiance spectra measured by Greenhouse gases Observing SATellite (GOSAT) for 48 months of the satellite operation from June 2009. Recent version of the Photon path-length Probability Density Function (PPDF)-based algorithm was used to estimate XCO2 and optical path modifications in terms of PPDF parameters. We also present results of numerical simulations for over-land observations and "sharp edge" tests for sun-glint mode to discuss the algorithm accuracy under conditions of strong optical path modification. For the methane abundance retrieved from 1.67-µm-absorption band we applied optical path correction based on PPDF parameters from 1.6-µm carbon dioxide (CO2) absorption band. Similarly to CO2-proxy technique, this correction assumes identical light path modifications in 1.67-µm and 1.6-µm bands. However, proxy approach needs pre-defined XCO2 values to compute XCH4, whilst the PPDF-based approach does not use prior assumptions on CO2 concentrations.Post-processing data correction for XCO2 and XCH4 over land observations was performed using regression matrix based on multivariate analysis of variance (MANOVA). The MANOVA statistics was applied to the GOSAT retrievals using reference collocated measurements of Total Carbon Column Observing Network (TCCON). The regression matrix was constructed using the parameters that were found to correlate with GOSAT-TCCON discrepancies: PPDF parameters α and ρ, that are mainly responsible for shortening and lengthening of the optical path due to atmospheric light scattering; solar and satellite zenith angles; surface pressure; surface albedo in three GOSAT short wave infrared (SWIR) bands. Application of the post-correction generally improves statistical characteristics of the GOSAT-TCCON correlation diagrams for individual stations as well as for aggregated data.In addition to the analysis of the observations over 12 TCCON stations we estimated temporal and spatial trends (interannual XCO2 and XCH4 variations, seasonal cycles, latitudinal gradients) and compared them with modeled results as well as with similar estimates from other GOSAT retrievals.

Fast perceptual image hash based on cascade algorithm

NASA Astrophysics Data System (ADS)

Ruchay, Alexey; Kober, Vitaly; Yavtushenko, Evgeniya

2017-09-01

In this paper, we propose a perceptual image hash algorithm based on cascade algorithm, which can be applied in image authentication, retrieval, and indexing. Image perceptual hash uses for image retrieval in sense of human perception against distortions caused by compression, noise, common signal processing and geometrical modifications. The main disadvantage of perceptual hash is high time expenses. In the proposed cascade algorithm of image retrieval initializes with short hashes, and then a full hash is applied to the processed results. Computer simulation results show that the proposed hash algorithm yields a good performance in terms of robustness, discriminability, and time expenses.

2007-2017: 10 years of IASI CO retrievals

NASA Astrophysics Data System (ADS)

George, M.; Clerbaux, C.; Hadji-Lazaro, J.; Pierre-Francois, C.; Hurtmans, D.; Edwards, D. P.; Worden, H. M.; Deeter, M. N.; Mao, D.; August, T.; Crapeau, M.

2017-12-01

Carbon monoxide (CO) is an important trace gas for understanding air quality and atmospheric composition. It is a good tracer of pollution plumes and atmospheric dynamics. IASI CO concentrations are retrieved from the radiance data using the Fast Operational Retrievals on Layers for IASI (FORLI) algorithm, based on the Optimal Estimation theory. The operational production is performed at EUMETSAT and the products are distributed in NRT via EUMETCast under the AC SAF auspices. We present here an analysis of 10 years of global distributions of CO. Improvements of the last FORLI-CO version (v20151001) will be shown. Updates in the auxiliary parameters (temperature, cloud information) have an impact on the retrieved product. Comparison with MOPITT CO data (v7T, record starting in 2000) was performed, both for partial and total columns. Harmonizing IASI and MOPITT CO products is challenging: a method using corrective factors (developed in the framework of the QA4ECV project) will be presented.

An Alternative Inter-Satellite Calibration of the UMD HIRS OLR Retrievals

NASA Technical Reports Server (NTRS)

Robertson, Franklin R.; Lee, Hai-Tien

2012-01-01

The observational record for determining Outgoing Longwave Radiation (OLR) from satellites remains largely fragmented with gaps over the past three decades among the Earth Radiation Budget Experiment (ERBE), Earth Radiation Budget Satellite (ERBS) and Clouds and the Earth s Radiant Energy System (CERES) measurements. Multi-band OLR retrievals are thus an important supplement to the broadband measurements. The most semi-continuous set of OLR retrievals comes from the University of Maryland (UMD) algorithm that uses four HIRS (High Resolution Infrared Sounder) channels on the NOAA polar orbiting satellites to estimate OLR. but residual biases due principally to diurnal drift of the polar orbiter platforms remain an issue. Here we show how an alternative recalibration of the UMD retrievals taking advantage of the relative diurnal drift rates between "morning" and "evening" satellite platforms removes much of the remaining uncertainty due to changes in equator crossing times.

Integration of SMAP and SMOS L-Band Observations

NASA Technical Reports Server (NTRS)

Bindlish, Rajat; Jackson, Thomas J.; Chan, Steven; Colliander, Andreas; Kerr, Yaan

2017-01-01

Soil Moisture Active Passive (SMAP) mission and the ESA Soil Moisture and Ocean Salinity (SMOS) missions provide brightness temperature and soil moisture estimates every 2-3 days. SMAP brightness temperature observations were compared with SMOS observations at 40 Degrees incidence angle. The brightness temperatures from the two missions are not consistent and have a bias of about 2.7K over land with respect to each other. SMAP and SMOS missions use different retrieval algorithms and ancillary datasets which result in further inconsistencies between the soil moisture products. The reprocessed constant-angle SMOS brightness temperatures were used in the SMAP soil moisture retrieval algorithm to develop a consistent multi-satellite product. The integrated product will have an increased global revisit frequency (1 day) and period of record that would be unattainable by either one of the satellites alone. Results from the development and validation of the integrated product will be presented.

Object recognition and pose estimation of planar objects from range data

NASA Technical Reports Server (NTRS)

Pendleton, Thomas W.; Chien, Chiun Hong; Littlefield, Mark L.; Magee, Michael

1994-01-01

The Extravehicular Activity Helper/Retriever (EVAHR) is a robotic device currently under development at the NASA Johnson Space Center that is designed to fetch objects or to assist in retrieving an astronaut who may have become inadvertently de-tethered. The EVAHR will be required to exhibit a high degree of intelligent autonomous operation and will base much of its reasoning upon information obtained from one or more three-dimensional sensors that it will carry and control. At the highest level of visual cognition and reasoning, the EVAHR will be required to detect objects, recognize them, and estimate their spatial orientation and location. The recognition phase and estimation of spatial pose will depend on the ability of the vision system to reliably extract geometric features of the objects such as whether the surface topologies observed are planar or curved and the spatial relationships between the component surfaces. In order to achieve these tasks, three-dimensional sensing of the operational environment and objects in the environment will therefore be essential. One of the sensors being considered to provide image data for object recognition and pose estimation is a phase-shift laser scanner. The characteristics of the data provided by this scanner have been studied and algorithms have been developed for segmenting range images into planar surfaces, extracting basic features such as surface area, and recognizing the object based on the characteristics of extracted features. Also, an approach has been developed for estimating the spatial orientation and location of the recognized object based on orientations of extracted planes and their intersection points. This paper presents some of the algorithms that have been developed for the purpose of recognizing and estimating the pose of objects as viewed by the laser scanner, and characterizes the desirability and utility of these algorithms within the context of the scanner itself, considering data quality and noise.

Effect of black carbon on dust property retrievals from satellite observations

NASA Astrophysics Data System (ADS)

Lin, Tang-Huang; Yang, Ping; Yi, Bingqi

2013-01-01

The effect of black carbon on the optical properties of polluted mineral dust is studied from a satellite remote-sensing perspective. By including the auxiliary data of surface reflectivity and aerosol mixing weight, the optical properties of mineral dust, or more specifically, the aerosol optical depth (AOD) and single-scattering albedo (SSA), can be retrieved with improved accuracy. Precomputed look-up tables based on the principle of the Deep Blue algorithm are utilized in the retrieval. The mean differences between the retrieved results and the corresponding ground-based measurements are smaller than 1% for both AOD and SSA in the case of pure dust. However, the retrievals can be underestimated by as much as 11.9% for AOD and overestimated by up to 4.1% for SSA in the case of polluted dust with an estimated 10% (in terms of the number-density mixing ratio) of soot aggregates if the black carbon effect on dust aerosols is neglected.

Retrieving Atmospheric Profiles Data in the Presence of Clouds from Hyperspectral Remote Sensing Data

NASA Technical Reports Server (NTRS)

Liu, Xu; Larar, Allen M.; Zhou, Daniel K.; Kizer, Susan H.; Wu, Wan; Barnet, Christopher; Divakarla, Murty; Guo, Guang; Blackwell, Bill; Smith, William L.;

Developing an A Priori Database for Passive Microwave Snow Water Retrievals Over Ocean

NASA Astrophysics Data System (ADS)

Yin, Mengtao; Liu, Guosheng

2017-12-01

A physically optimized a priori database is developed for Global Precipitation Measurement Microwave Imager (GMI) snow water retrievals over ocean. The initial snow water content profiles are derived from CloudSat Cloud Profiling Radar (CPR) measurements. A radiative transfer model in which the single-scattering properties of nonspherical snowflakes are based on the discrete dipole approximate results is employed to simulate brightness temperatures and their gradients. Snow water content profiles are then optimized through a one-dimensional variational (1D-Var) method. The standard deviations of the difference between observed and simulated brightness temperatures are in a similar magnitude to the observation errors defined for observation error covariance matrix after the 1D-Var optimization, indicating that this variational method is successful. This optimized database is applied in a Bayesian retrieval snow water algorithm. The retrieval results indicated that the 1D-Var approach has a positive impact on the GMI retrieved snow water content profiles by improving the physical consistency between snow water content profiles and observed brightness temperatures. Global distribution of snow water contents retrieved from the a priori database is compared with CloudSat CPR estimates. Results showed that the two estimates have a similar pattern of global distribution, and the difference of their global means is small. In addition, we investigate the impact of using physical parameters to subset the database on snow water retrievals. It is shown that using total precipitable water to subset the database with 1D-Var optimization is beneficial for snow water retrievals.

A novel method to improve MODIS AOD retrievals in cloudy pixels using an analog ensemble approach

NASA Astrophysics Data System (ADS)

Kumar, R.; Raman, A.; Delle Monache, L.; Alessandrini, S.; Cheng, W. Y. Y.; Gaubert, B.; Arellano, A. F.

2016-12-01

Particulate matter (PM) concentrations are one of the fundamental indicators of air quality. Earth orbiting satellite platforms acquire column aerosol abundance that can in turn provide information about the PM concentrations. One of the serious limitations of column aerosol retrievals from low earth orbiting satellites is that these algorithms are based on clear sky assumptions. They do not retrieve AOD in cloudy pixels. After filtering cloudy pixels, these algorithms also arbitrarily remove brightest and darkest 25% of remaining pixels over ocean and brightest and darkest 50% pixels over land to filter any residual contamination from clouds. This becomes a critical issue especially in regions that experience monsoon, like Asia and North America. In case of North America, monsoon season experiences wide variety of extreme air quality events such as fires in California and dust storms in Arizona. Assessment of these episodic events warrants frequent monitoring of aerosol observations from remote sensing retrievals. In this study, we demonstrate a method to fill in cloudy pixels in Moderate Imaging Resolution Spectroradiometer (MODIS) AOD retrievals based on ensembles generated using an analog-based approach (AnEn). It provides a probabilistic distribution of AOD in cloudy pixels using historical records of model simulations of meteorological predictors such as AOD, relative humidity, and wind speed, and past observational records of MODIS AOD at a given target site. We use simulations from a coupled community weather forecasting model with chemistry (WRF-Chem) run at a resolution comparable to MODIS AOD. Analogs selected from summer months (June, July) of 2011-2013 from model and corresponding observations are used as a training dataset. Then, missing AOD retrievals in cloudy pixels in the last 31 days of the selected period are estimated. Here, we use AERONET stations as target sites to facilitate comparison against in-situ measurements. We use two approaches to evaluate the estimated AOD: 1) by comparing against reanalysis AOD, 2) by inverting AOD to PM10 concentrations and then comparing those with measured PM10. AnEn is an efficient approach to generate an ensemble as it involves only one model run and provides an estimate of uncertainty that complies with the physical and chemical state of the atmosphere.

Cloud Retrieval Intercomparisons Between SEVIRI, MODIS and VIIRS with CHIMAERA PGE06 Data Collection 6 Products

NASA Technical Reports Server (NTRS)

Wind, Galina; Riedi, Jerome; Platnick, Steven; Heidinger, Andrew

2014-01-01

The Cross-platform HIgh resolution Multi-instrument AtmosphEric Retrieval Algorithms (CHIMAERA) system allows us to perform MODIS-like cloud top, optical and microphysical properties retrievals on any sensor that possesses a minimum set of common spectral channels. The CHIMAERA system uses a shared-core architecture that takes retrieval method out of the equation when intercomparisons are made. Here we show an example of such retrieval and a comparison of simultaneous retrievals done using SEVIRI, MODIS and VIIRS sensors. All sensor retrievals are performed using CLAVR-x (or CLAVR-x based) cloud top properties algorithm. SEVIRI uses the SAF_NWC cloud mask. MODIS and VIIRS use the IFF-based cloud mask that is a shared algorithm between MODIS and VIIRS. The MODIS and VIIRS retrievals are performed using a VIIRS branch of CHIMAERA that limits available MODIS channel set. Even though in that mode certain MODIS products such as multilayer cloud map are not available, the cloud retrieval remains fully equivalent to operational Data Collection 6.

Snowfall Rate Retrieval Using Passive Microwave Measurements and Its Applications in Weather Forecast and Hydrology

NASA Technical Reports Server (NTRS)

Meng, Huan; Ferraro, Ralph; Kongoli, Cezar; Yan, Banghua; Zavodsky, Bradley; Zhao, Limin; Dong, Jun; Wang, Nai-Yu

2015-01-01

(AMSU), Microwave Humidity Sounder (MHS) and Advance Technology Microwave Sounder (ATMS). ATMS is the follow-on sensor to AMSU and MHS. Currently, an AMSU and MHS based land snowfall rate (SFR) product is running operationally at NOAA/NESDIS. Based on the AMSU/MHS SFR, an ATMS SFR algorithm has also been developed. The algorithm performs retrieval in three steps: snowfall detection, retrieval of cloud properties, and estimation of snow particle terminal velocity and snowfall rate. The snowfall detection component utilizes principal component analysis and a logistic regression model. It employs a combination of temperature and water vapor sounding channels to detect the scattering signal from falling snow and derives the probability of snowfall. Cloud properties are retrieved using an inversion method with an iteration algorithm and a two-stream radiative transfer model. A method adopted to calculate snow particle terminal velocity. Finally, snowfall rate is computed by numerically solving a complex integral. The SFR products are being used mainly in two communities: hydrology and weather forecast. Global blended precipitation products traditionally do not include snowfall derived from satellites because such products were not available operationally in the past. The ATMS and AMSU/MHS SFR now provide the winter precipitation information for these blended precipitation products. Weather forecasters mainly rely on radar and station observations for snowfall forecast. The SFR products can fill in gaps where no conventional snowfall data are available to forecasters. The products can also be used to confirm radar and gauge snowfall data and increase forecasters' confidence in their prediction.

Added value of far-infrared radiometry for remote sensing of ice clouds

NASA Astrophysics Data System (ADS)

Libois, Quentin; Blanchet, Jean-Pierre

2017-06-01

Several cloud retrieval algorithms based on satellite observations in the infrared have been developed in the last decades. However, these observations only cover the midinfrared (MIR, λ < 15 μm) part of the spectrum, and none are available in the far-infrared (FIR, λ≥ 15 μm). Using the optimal estimation method, we show that adding a few FIR channels to existing spaceborne radiometers would significantly improve their ability to retrieve ice cloud radiative properties. For clouds encountered in the polar regions and the upper troposphere, where the atmosphere is sufficiently transparent in the FIR, using FIR channels would reduce by more than 50% the uncertainties on retrieved values of optical thickness, effective particle diameter, and cloud top altitude. Notably, this would extend the range of applicability of current retrieval methods to the polar regions and to clouds with large optical thickness, where MIR algorithms perform poorly. The high performance of solar reflection-based algorithms would thus be reached in nighttime conditions. Since the sensitivity of ice cloud thermal emission to effective particle diameter is approximately 5 times larger in the FIR than in the MIR, using FIR observations is a promising venue for studying ice cloud microphysics and precipitation processes. This is highly relevant for cirrus clouds and convective towers. This is also essential to study precipitation in the driest regions of the atmosphere, where strong feedbacks are at play between clouds and water vapor. The deployment in the near future of a FIR spaceborne radiometer is technologically feasible and should be strongly supported.

NPP ATMS Snowfall Rate Product

NASA Technical Reports Server (NTRS)

Meng, Huan; Ferraro, Ralph; Kongoli, Cezar; Wang, Nai-Yu; Dong, Jun; Zavodsky, Bradley; Yan, Banghua

2015-01-01

Passive microwave measurements at certain high frequencies are sensitive to the scattering effect of snow particles and can be utilized to retrieve snowfall properties. Some of the microwave sensors with snowfall sensitive channels are Advanced Microwave Sounding Unit (AMSU), Microwave Humidity Sounder (MHS) and Advance Technology Microwave Sounder (ATMS). ATMS is the follow-on sensor to AMSU and MHS. Currently, an AMSU and MHS based land snowfall rate (SFR) product is running operationally at NOAA/NESDIS. Based on the AMSU/MHS SFR, an ATMS SFR algorithm has been developed recently. The algorithm performs retrieval in three steps: snowfall detection, retrieval of cloud properties, and estimation of snow particle terminal velocity and snowfall rate. The snowfall detection component utilizes principal component analysis and a logistic regression model. The model employs a combination of temperature and water vapor sounding channels to detect the scattering signal from falling snow and derive the probability of snowfall (Kongoli et al., 2015). In addition, a set of NWP model based filters is also employed to improve the accuracy of snowfall detection. Cloud properties are retrieved using an inversion method with an iteration algorithm and a two-stream radiative transfer model (Yan et al., 2008). A method developed by Heymsfield and Westbrook (2010) is adopted to calculate snow particle terminal velocity. Finally, snowfall rate is computed by numerically solving a complex integral. NCEP CMORPH analysis has shown that integration of ATMS SFR has improved the performance of CMORPH-Snow. The ATMS SFR product is also being assessed at several NWS Weather Forecast Offices for its usefulness in weather forecast.

High Resolution Monthly Oceanic Rainfall Based on Microwave Brightness Temperature Histograms

NASA Astrophysics Data System (ADS)

Shin, D.; Chiu, L. S.

2005-12-01

A statistical emission-based passive microwave retrieval algorithm has been developed by Wilheit, Chang and Chiu (1991) to estimate space/time oceanic rainfall. The algorithm has been applied to Special Sensor Microwave Imager (SSM/I) data taken on board the Defense Meteorological Satellite Program (DMSP) satellites to provide monthly oceanic rainfall over 2.5ox2.5o and 5ox5o latitude-longitude boxes by the Global Precipitation Climatology Project-Polar Satellite Precipitation Data Center (GPCP-PSPDC, URL: http://gpcp-pspdc.gmu.edu/) as part of NASA's contribution to the GPCP. The algorithm has been modified and applied to the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) data to produce a TRMM Level 3 standard product (3A11) over 5ox5o latitude/longitude boxes. In this study, the algorithm code is modified to retrieve rain rates at 2.5ox2.5o and 1ox1o resolutions for TMI. Two months of TMI data have been tested and the results compared with the monthly mean rain rates derived from TRMM Level 2 TMI rain profile algorithm (2A12) and the original 5ox5o data from 3A11. The rainfall pattern is very similar to the monthly average of 2A12, although the intensity is slightly higher. Details in the rain pattern, such as rain shadow due to island blocking, which were not discernible from the low resolution products, are now easily discernible. The spatial average of the higher resolution rain rates are in general slightly higher than lower resolution rain rates, although a Student-t test shows no significant difference. This high resolution product will be useful for the calibration of IR rain estimates for the production of the GPCP merge rain product.

Latent Heating Structures Derived from TRMM

NASA Technical Reports Server (NTRS)

Tao, W.-K.; Smith, E. A.; Adler, R.; Hou, A.; Kakar, R.; Krishnamurti, T.; Kummerow, C.; Lang, S.; Olson, W.; Satoh, S.

2004-01-01

Rainfall is the fundamental variable within the Earth's hydrological cycle because it is both the main forcing term leading to variations in continental and oceanic surface water budgets. The vertical distribution of latent heat release, which is accompanied with rain, modulates large-scale meridional and zonal circulations within the tropics as well as modifying the energetic efficiency of mid-latitude weather systems. Latent heat release itself is a consequence of phase changes between the vapor, liquid, and frozen states of water.This paper focuses on the retrieval of latent heat release from satellite measurements generated by the Tropical Rainfall Measuring Mission 0. The TRMM observatory, whose development was a joint US-Japan space endeavor, was launched in November 1997. TRMM measurements provide an accurate account of rainfall over the global tropics, information which can be .used to estimate the four-dimensional structure of latent heating across the entire tropical and sub-tropical regions. Various algorithm methodologies for estimating latent heating based on rain rate measurements from TRMM observations are described. The strengths and weaknesses of these algorithms, as well as the latent heating products generated by these algorithms, are also discussed along with validation analyses of the products. The investigation paper provides an overview of how TRMM-derived latent heating information is currently being used in conjunction with global weather and climate models, and concludes with remarks designed to stimulate further research on latent heating retrieval

Assessment of Chlorophyll-a Algorithms Considering Different Trophic Statuses and Optimal Bands.

PubMed

Salem, Salem Ibrahim; Higa, Hiroto; Kim, Hyungjun; Kobayashi, Hiroshi; Oki, Kazuo; Oki, Taikan

2017-07-31

Numerous algorithms have been proposed to retrieve chlorophyll- a concentrations in Case 2 waters; however, the retrieval accuracy is far from satisfactory. In this research, seven algorithms are assessed with different band combinations of multispectral and hyperspectral bands using linear (LN), quadratic polynomial (QP) and power (PW) regression approaches, resulting in altogether 43 algorithmic combinations. These algorithms are evaluated by using simulated and measured datasets to understand the strengths and limitations of these algorithms. Two simulated datasets comprising 500,000 reflectance spectra each, both based on wide ranges of inherent optical properties (IOPs), are generated for the calibration and validation stages. Results reveal that the regression approach (i.e., LN, QP, and PW) has more influence on the simulated dataset than on the measured one. The algorithms that incorporated linear regression provide the highest retrieval accuracy for the simulated dataset. Results from simulated datasets reveal that the 3-band (3b) algorithm that incorporate 665-nm and 680-nm bands and band tuning selection approach outperformed other algorithms with root mean square error (RMSE) of 15.87 mg·m -3 , 16.25 mg·m -3 , and 19.05 mg·m -3 , respectively. The spatial distribution of the best performing algorithms, for various combinations of chlorophyll- a (Chla) and non-algal particles (NAP) concentrations, show that the 3b_tuning_QP and 3b_680_QP outperform other algorithms in terms of minimum RMSE frequency of 33.19% and 60.52%, respectively. However, the two algorithms failed to accurately retrieve Chla for many combinations of Chla and NAP, particularly for low Chla and NAP concentrations. In addition, the spatial distribution emphasizes that no single algorithm can provide outstanding accuracy for Chla retrieval and that multi-algorithms should be included to reduce the error. Comparing the results of the measured and simulated datasets reveal that the algorithms that incorporate the 665-nm band outperform other algorithms for measured dataset (RMSE = 36.84 mg·m -3 ), while algorithms that incorporate the band tuning approach provide the highest retrieval accuracy for the simulated dataset (RMSE = 25.05 mg·m -3 ).

Assessment of Chlorophyll-a Algorithms Considering Different Trophic Statuses and Optimal Bands

PubMed Central

Higa, Hiroto; Kobayashi, Hiroshi; Oki, Kazuo

2017-01-01

Numerous algorithms have been proposed to retrieve chlorophyll-a concentrations in Case 2 waters; however, the retrieval accuracy is far from satisfactory. In this research, seven algorithms are assessed with different band combinations of multispectral and hyperspectral bands using linear (LN), quadratic polynomial (QP) and power (PW) regression approaches, resulting in altogether 43 algorithmic combinations. These algorithms are evaluated by using simulated and measured datasets to understand the strengths and limitations of these algorithms. Two simulated datasets comprising 500,000 reflectance spectra each, both based on wide ranges of inherent optical properties (IOPs), are generated for the calibration and validation stages. Results reveal that the regression approach (i.e., LN, QP, and PW) has more influence on the simulated dataset than on the measured one. The algorithms that incorporated linear regression provide the highest retrieval accuracy for the simulated dataset. Results from simulated datasets reveal that the 3-band (3b) algorithm that incorporate 665-nm and 680-nm bands and band tuning selection approach outperformed other algorithms with root mean square error (RMSE) of 15.87 mg·m−3, 16.25 mg·m−3, and 19.05 mg·m−3, respectively. The spatial distribution of the best performing algorithms, for various combinations of chlorophyll-a (Chla) and non-algal particles (NAP) concentrations, show that the 3b_tuning_QP and 3b_680_QP outperform other algorithms in terms of minimum RMSE frequency of 33.19% and 60.52%, respectively. However, the two algorithms failed to accurately retrieve Chla for many combinations of Chla and NAP, particularly for low Chla and NAP concentrations. In addition, the spatial distribution emphasizes that no single algorithm can provide outstanding accuracy for Chla retrieval and that multi-algorithms should be included to reduce the error. Comparing the results of the measured and simulated datasets reveal that the algorithms that incorporate the 665-nm band outperform other algorithms for measured dataset (RMSE = 36.84 mg·m−3), while algorithms that incorporate the band tuning approach provide the highest retrieval accuracy for the simulated dataset (RMSE = 25.05 mg·m−3). PMID:28758984

Validation of YCAR algorithm over East Asia TCCON sites

NASA Astrophysics Data System (ADS)

Kim, W.; Kim, J.; Jung, Y.; Lee, H.; Goo, T. Y.; Cho, C. H.; Lee, S.

2016-12-01

In order to reduce the retrieval error of TANSO-FTS column averaged CO2 concentration (XCO2) induced by aerosol, we develop the Yonsei university CArbon Retrieval (YCAR) algorithm using aerosol information from TANSO-Cloud and Aerosol Imager (TANSO-CAI), providing simultaneous aerosol optical depth properties for the same geometry and optical path along with the FTS. Also we validate the retrieved results using ground-based TCCON measurement. Particularly this study first utilized the measurements at Anmyeondo, the only TCCON site located in South Korea, which can improve the quality of validation in East Asia. After the post screening process, YCAR algorithms have higher data availability by 33 - 85 % than other operational algorithms (NIES, ACOS, UoL). Although the YCAR algorithm has higher data availability, regression analysis with TCCON measurements are better or similar to other algorithms; Regression line of YCAR algorithm is close to linear identity function with RMSE of 2.05, bias of - 0.86 ppm. According to error analysis, retrieval error of YCAR algorithm is 1.394 - 1.478 ppm at East Asia. In addition, spatio-temporal sampling error of 0.324 - 0.358 ppm for each single sounding retrieval is also analyzed with Carbon Tracker - Asia data. These results of error analysis reveal the reliability and accuracy of latest version of our YCAR algorithm. Both XCO2 values retrieved using YCAR algorithm on TANSO-FTS and TCCON measurements show the consistent increasing trend about 2.3 - 2.6 ppm per year. Comparing to the increasing rate of global background CO2 amount measured in Mauna Loa, Hawaii (2 ppm per year), the increasing trend in East Asia shows about 30% higher trend due to the rapid increase of CO2 emission from the source region.

On the synergy of SMAP, AMSR2 AND SENTINEL-1 for retrieving soil moisture

NASA Astrophysics Data System (ADS)

Santi, E.; Paloscia, S.; Pettinato, S.; Brocca, L.; Ciabatta, L.; Entekhabi, D.

2018-03-01

An algorithm for retrieving soil moisture content (SMC) from synergic use of both active and passive microwave acquisitions is presented. The algorithm takes advantage of the integration of microwave data from SMAP, Sentinel-1 and AMSR2 for overcoming the SMAP radar failure and obtaining a SMC product at enhanced resolution (0.1° × 0.1°) and improved accuracy with respect to the original SMAP radiometric SMC product. A disaggregation technique based on the Smoothing filter based intensity modulation (SFIM) allows combining the radiometric and SAR data. Disaggregated microwave data are used as inputs of an Artificial Neural Networks (ANN) based algorithm, which is able to exploit the synergy between active and passive acquisitions. The algorithm is defined, trained and tested using the SMEX02 experimental dataset and data simulated by forward electromagnetic models based on the Radiative Transfer Theory. Then the algorithm is adapted to satellite data and tested using one year of SMAP, AMSR2 and Sentinel-1 co-located data on a flat agricultural area located in the Po Valley, in northern Italy. Spatially distributed SMC values at 0.1° × 0.1° resolution generated by the Soil Water Balance Model (SWBM) are considered as reference for this purpose. The synergy of SMAP, Sentinel-1 and AMSR2 allowed increasing the correlation between estimated and reference SMC from R ≅ 0.68 of the SMAP based retrieval up to R ≅ 0.86 of the combination SMAP + Sentinel-1 + AMSR2. The corresponding Root Mean Square Error (RMSE) decreased from RMSE ≅ 0.04 m3/m3 to RMSE ≅ 0.024 m3/m3.

A Preliminary ZEUS Lightning Location Error Analysis Using a Modified Retrieval Theory

NASA Technical Reports Server (NTRS)

Elander, Valjean; Koshak, William; Phanord, Dieudonne

2004-01-01

The ZEUS long-range VLF arrival time difference lightning detection network now covers both Europe and Africa, and there are plans for further expansion into the western hemisphere. In order to fully optimize and assess ZEUS lightning location retrieval errors and to determine the best placement of future receivers expected to be added to the network, a software package is being developed jointly between the NASA Marshall Space Flight Center (MSFC) and the University of Nevada Las Vegas (UNLV). The software package, called the ZEUS Error Analysis for Lightning (ZEAL), will be used to obtain global scale lightning location retrieval error maps using both a Monte Carlo approach and chi-squared curvature matrix theory. At the core of ZEAL will be an implementation of an Iterative Oblate (IO) lightning location retrieval method recently developed at MSFC. The IO method will be appropriately modified to account for variable wave propagation speed, and the new retrieval results will be compared with the current ZEUS retrieval algorithm to assess potential improvements. In this preliminary ZEAL work effort, we defined 5000 source locations evenly distributed across the Earth. We then used the existing (as well as potential future ZEUS sites) to simulate arrival time data between source and ZEUS site. A total of 100 sources were considered at each of the 5000 locations, and timing errors were selected from a normal distribution having a mean of 0 seconds and a standard deviation of 20 microseconds. This simulated "noisy" dataset was analyzed using the IO algorithm to estimate source locations. The exact locations were compared with the retrieved locations, and the results are summarized via several color-coded "error maps."

Estimating Contrail Climate Effects from Satellite Data

NASA Technical Reports Server (NTRS)

Minnis, Patrick; Duda, David P.; Palikonda, Rabindra; Bedka, Sarah T.; Boeke, Robyn; Khlopenkov, Konstantin; Chee, Thad; Bedka, Kristopher T.

2011-01-01

An automated contrail detection algorithm (CDA) is developed to exploit six of the infrared channels on the 1-km MODerate-resolution Imaging Spectroradiometer (MODIS) on the Terra and Aqua satellites. The CDA is refined and balanced using visual error analysis. It is applied to MODIS data taken by Terra and Aqua over the United States during 2006 and 2008. The results are consistent with flight track data, but differ markedly from earlier analyses. Contrail coverage is a factor of 4 less than other retrievals and the retrieved contrail optical depths and radiative forcing are smaller by approx.30%. The discrepancies appear to be due to the inability to detect wider, older contrails that comprise a significant amount of the contrail coverage. An example of applying the algorithm to MODIS data over the entire Northern Hemisphere is also presented. Overestimates of contrail coverage are apparent in some tropical regions. Methods for improving the algorithm are discussed and are to be implemented before analyzing large amounts of Northern Hemisphere data. The results should be valuable for guiding and validating climate models seeking to account for aviation effects on climate.

Validation of new satellite aerosol optical depth retrieval algorithm using Raman lidar observations at radiative transfer laboratory in Warsaw

NASA Astrophysics Data System (ADS)

Zawadzka, Olga; Stachlewska, Iwona S.; Markowicz, Krzysztof M.; Nemuc, Anca; Stebel, Kerstin

2018-04-01

During an exceptionally warm September of 2016, the unique, stable weather conditions over Poland allowed for an extensive testing of the new algorithm developed to improve the Meteosat Second Generation (MSG) Spinning Enhanced Visible and Infrared Imager (SEVIRI) aerosol optical depth (AOD) retrieval. The development was conducted in the frame of the ESA-ESRIN SAMIRA project. The new AOD algorithm aims at providing the aerosol optical depth maps over the territory of Poland with a high temporal resolution of 15 minutes. It was tested on the data set obtained between 11-16 September 2016, during which a day of relatively clean atmospheric background related to an Arctic airmass inflow was surrounded by a few days with well increased aerosol load of different origin. On the clean reference day, for estimating surface reflectance the AOD forecast available on-line via the Copernicus Atmosphere Monitoring Service (CAMS) was used. The obtained AOD maps were validated against AODs available within the Poland-AOD and AERONET networks, and with AOD values obtained from the PollyXT-UW lidar. of the University of Warsaw (UW).

An advanced retrieval algorithm for greenhouse gases using polarization information measured by GOSAT TANSO-FTS SWIR I: Simulation study

NASA Astrophysics Data System (ADS)

Kikuchi, N.; Yoshida, Y.; Uchino, O.; Morino, I.; Yokota, T.

2016-11-01

We present an algorithm for retrieving column-averaged dry air mole fraction of carbon dioxide (XCO2) and methane (XCH4) from reflected spectra in the shortwave infrared (SWIR) measured by the TANSO-FTS (Thermal And Near infrared Sensor for carbon Observation Fourier Transform Spectrometer) sensor on board the Greenhouse gases Observing SATellite (GOSAT). The algorithm uses the two linear polarizations observed by TANSO-FTS to improve corrections to the interference effects of atmospheric aerosols, which degrade the accuracy in the retrieved greenhouse gas concentrations. To account for polarization by the land surface reflection in the forward model, we introduced a bidirectional reflection matrix model that has two parameters to be retrieved simultaneously with other state parameters. The accuracy in XCO2 and XCH4 values retrieved with the algorithm was evaluated by using simulated retrievals over both land and ocean, focusing on the capability of the algorithm to correct imperfect prior knowledge of aerosols. To do this, we first generated simulated TANSO-FTS spectra using a global distribution of aerosols computed by the aerosol transport model SPRINTARS. Then the simulated spectra were submitted to the algorithms as measurements both with and without polarization information, adopting a priori profiles of aerosols that differ from the true profiles. We found that the accuracy of XCO2 and XCH4, as well as profiles of aerosols, retrieved with polarization information was considerably improved over values retrieved without polarization information, for simulated observations over land with aerosol optical thickness greater than 0.1 at 1.6 μm.

Simultaneous inversion of multiple land surface parameters from MODIS optical-thermal observations

NASA Astrophysics Data System (ADS)

Ma, Han; Liang, Shunlin; Xiao, Zhiqiang; Shi, Hanyu

2017-06-01

Land surface parameters from remote sensing observations are critical in monitoring and modeling of global climate change and biogeochemical cycles. Current methods for estimating land surface variables usually focus on individual parameters separately even from the same satellite observations, resulting in inconsistent products. Moreover, no efforts have been made to generate global products from integrated observations from the optical to Thermal InfraRed (TIR) spectrum. Particularly, Middle InfraRed (MIR) observations have received little attention due to the complexity of the radiometric signal, which contains both reflected and emitted radiation. In this paper, we propose a unified algorithm for simultaneously retrieving six land surface parameters - Leaf Area Index (LAI), Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), land surface albedo, Land Surface Emissivity (LSE), Land Surface Temperature (LST), and Upwelling Longwave radiation (LWUP) by exploiting MODIS visible-to-TIR observations. We incorporate a unified physical radiative transfer model into a data assimilation framework. The MODIS visible-to-TIR time series datasets include the daily surface reflectance product and MIR-to-TIR surface radiance, which are atmospherically corrected from the MODIS data using the Moderate Resolution Transmittance program (MODTRAN, ver. 5.0). LAI was first estimated using a data assimilation method that combines MODIS daily reflectance data and a LAI phenology model, and then the LAI was input to the unified radiative transfer model to simulate spectral surface reflectance and surface emissivity for calculating surface broadband albedo and emissivity, and FAPAR. LST was estimated from the MIR-TIR surface radiance data and the simulated emissivity, using an iterative optimization procedure. Lastly, LWUP was estimated using the LST and surface emissivity. The retrieved six parameters were extensively validated across six representative sites with different biome types, and compared with MODIS, GLASS, and GlobAlbedo land surface products. The results demonstrate that the unified inversion algorithm can retrieve temporally complete and physically consistent land surface parameters, and provides more accurate estimates of surface albedo, LST, and LWUP than existing products, with R2 values of 0.93 and 0.62, RMSE of 0.029 and 0.037, and BIAS values of 0.016 and 0.012 for the retrieved and MODIS albedo products, respectively, compared with field albedo measurements; R2 values of 0.95 and 0.93, RMSE of 2.7 and 4.2 K, and BIAS values of -0.6 and -2.7 K for the retrieved and MODIS LST products, respectively, compared with field LST measurements; and R2 values of 0.93 and 0.94, RMSE of 18.2 and 22.8 W/m2, and BIAS values of -2.7 and -14.6 W/m2 for the retrieved and MODIS LWUP products, respectively, compared with field LWUP measurements.

Extending "Deep Blue" aerosol retrieval coverage to cases of absorbing aerosols above clouds: Sensitivity analysis and first case studies

NASA Astrophysics Data System (ADS)

Sayer, A. M.; Hsu, N. C.; Bettenhausen, C.; Lee, J.; Redemann, J.; Schmid, B.; Shinozuka, Y.

2016-05-01

Cases of absorbing aerosols above clouds (AACs), such as smoke or mineral dust, are omitted from most routinely processed space-based aerosol optical depth (AOD) data products, including those from the Moderate Resolution Imaging Spectroradiometer (MODIS). This study presents a sensitivity analysis and preliminary algorithm to retrieve above-cloud AOD and liquid cloud optical depth (COD) for AAC cases from MODIS or similar sensors, for incorporation into a future version of the "Deep Blue" AOD data product. Detailed retrieval simulations suggest that these sensors should be able to determine AAC AOD with a typical level of uncertainty ˜25-50% (with lower uncertainties for more strongly absorbing aerosol types) and COD with an uncertainty ˜10-20%, if an appropriate aerosol optical model is known beforehand. Errors are larger, particularly if the aerosols are only weakly absorbing, if the aerosol optical properties are not known, and the appropriate model to use must also be retrieved. Actual retrieval errors are also compared to uncertainty envelopes obtained through the optimal estimation (OE) technique; OE-based uncertainties are found to be generally reasonable for COD but larger than actual retrieval errors for AOD, due in part to difficulties in quantifying the degree of spectral correlation of forward model error. The algorithm is also applied to two MODIS scenes (one smoke and one dust) for which near-coincident NASA Ames Airborne Tracking Sun photometer (AATS) data were available to use as a ground truth AOD data source, and found to be in good agreement, demonstrating the validity of the technique with real observations.

Development of a generalized algorithm of satellite remote sensing using multi-wavelength and multi-pixel information (MWP method) for aerosol properties by satellite-borne imager

NASA Astrophysics Data System (ADS)

Hashimoto, M.; Nakajima, T.; Morimoto, S.; Takenaka, H.

2014-12-01

We have developed a new satellite remote sensing algorithm to retrieve the aerosol optical characteristics using multi-wavelength and multi-pixel information of satellite imagers (MWP method). In this algorithm, the inversion method is a combination of maximum a posteriori (MAP) method (Rodgers, 2000) and the Phillips-Twomey method (Phillips, 1962; Twomey, 1963) as a smoothing constraint for the state vector. Furthermore, with the progress of computing technique, this method has being combined with the direct radiation transfer calculation numerically solved by each iteration step of the non-linear inverse problem, without using LUT (Look Up Table) with several constraints.Retrieved parameters in our algorithm are aerosol optical properties, such as aerosol optical thickness (AOT) of fine and coarse mode particles, a volume soot fraction in fine mode particles, and ground surface albedo of each observed wavelength. We simultaneously retrieve all the parameters that characterize pixels in each of horizontal sub-domains consisting the target area. Then we successively apply the retrieval method to all the sub-domains in the target area.We conducted numerical tests for the retrieval of aerosol properties and ground surface albedo for GOSAT/CAI imager data to test the algorithm for the land area. The result of the experiment showed that AOTs of fine mode and coarse mode, soot fraction and ground surface albedo are successfully retrieved within expected accuracy. We discuss the accuracy of the algorithm for various land surface types. Then, we applied this algorithm to GOSAT/CAI imager data, and we compared retrieved and surface-observed AOTs at the CAI pixel closest to an AERONET (Aerosol Robotic Network) or SKYNET site in each region. Comparison at several sites in urban area indicated that AOTs retrieved by our method are in agreement with surface-observed AOT within ±0.066.Our future work is to extend the algorithm for analysis of AGEOS-II/GLI and GCOM/C-SGLI data.

A Consistent Treatment of Microwave Emissivity and Radar Backscatter for Retrieval of Precipitation over Water Surfaces

NASA Technical Reports Server (NTRS)

Munchak, S. Joseph; Meneghini, Robert; Grecu, Mircea; Olson, William S.

2016-01-01

The Global Precipitation Measurement satellite's Microwave Imager (GMI) and Dual-frequency Precipitation Radar (DPR) are designed to provide the most accurate instantaneous precipitation estimates currently available from space. The GPM Combined Algorithm (CORRA) plays a key role in this process by retrieving precipitation profiles that are consistent with GMI and DPR measurements; therefore, it is desirable that the forward models in CORRA use the same geophysical input parameters. This study explores the feasibility of using internally consistent emissivity and surface backscatter cross-sectional (sigma(sub 0)) models for water surfaces in CORRA. An empirical model for DPR Ku and Ka sigma(sub 0) as a function of 10m wind speed and incidence angle is derived from GMI-only wind retrievals under clear-sky conditions. This allows for the sigma(sub 0) measurements, which are also influenced by path-integrated attenuation (PIA) from precipitation, to be used as input to CORRA and for wind speed to be retrieved as output. Comparisons to buoy data give a wind rmse of 3.7 m/s for Ku+GMI and 3.2 m/s for Ku+Ka+GMI retrievals under precipitation (compared to 1.3 m/s for clear-sky GMI-only), and there is a reduction in bias from GANAL background data (-10%) to the Ku+GMI (-3%) and Ku+Ka+GMI (-5%) retrievals. Ku+GMI retrievals of precipitation increase slightly in light (less than 1 mm/h) and decrease in moderate to heavy precipitation (greater than 1 mm/h). The Ku+Ka+GMI retrievals, being additionally constrained by the Ka reflectivity, increase only slightly in moderate and heavy precipitation at low wind speeds (less than 5 m/s) relative to retrievals using the surface reference estimate of PIA as input.

Characterization of snowfall properties at high-latitude sites through use of a combined Multi-Angle Snow Camera (MASC) and radar approach

NASA Astrophysics Data System (ADS)

Cooper, S.; Wood, N.; Garrett, T. J.; L'Ecuyer, T. S.; Pettersen, C.

2016-12-01

Estimates of snowfall rate derived from radar reflectivities alone are non-unique, as different combinations of snowfall rates and snowflake microphysical properties can conspire to produce nearly identical radar reflectivity signatures. Such ambiguities can result in retrieval uncertainties on the order of 100-200% for individual events. Here, we use observations of snowflake particle size distribution, fallspeed, and habit from the Multi-Angle Snow Camera (MASC) to constrain estimates of snowfall derived from radar reflectivities. MASC measurements of microphysical properties and uncertainties are introduced into a modified form of the optimal-estimation CloudSat snowfall algorithm (2C-SNOW-PROFILE) via the a priori guess and variance terms. Initial results focus on the MASC and Ka-band Zenith Radar (KaZR) measurements at the ARM NSA Barrow Climate Facility site. Use of MASC fallspeed, MASC PSD, and a CloudSat particle model as base assumptions resulted in retrieved total accumulations with a -17% difference relative to nearby National Weather Service observations averaged over five snow events. Use of different but reasonable combinations of retrieval assumptions resulted in estimated snowfall accumulations with differences ranging from -63% to + 86% for the same storm events. Retrieved snowfall rates were particularly sensitive to assumed fallspeed and habit, suggesting that MASC measurements may provide a path forward in reducing the non-uniqueness of the snowfall retrieval problem. Preliminary results also will be presented for the deployment of the MASC, MicroRain Radar (MRR), and Precipitation Imaging Package (PIP) to Haukeliseter, Norway during winter season 2016-17. These instruments will then be deployed to northern Sweden for winter 2017-18. It is hoped more accurate knowledge of snowfall properties dependent upon location and meteorological conditions will be useful for both weather and climate applications.

A New Operational Snow Retrieval Algorithm Applied to Historical AMSR-E Brightness Temperatures

NASA Technical Reports Server (NTRS)

Tedesco, Marco; Jeyaratnam, Jeyavinoth

2016-01-01

Snow is a key element of the water and energy cycles and the knowledge of spatio-temporal distribution of snow depth and snow water equivalent (SWE) is fundamental for hydrological and climatological applications. SWE and snow depth estimates can be obtained from spaceborne microwave brightness temperatures at global scale and high temporal resolution (daily). In this regard, the data recorded by the Advanced Microwave Scanning Radiometer-Earth Orbiting System (EOS) (AMSR-E) onboard the National Aeronautics and Space Administration's (NASA) AQUA spacecraft have been used to generate operational estimates of SWE and snow depth, complementing estimates generated with other microwave sensors flying on other platforms. In this study, we report the results concerning the development and assessment of a new operational algorithm applied to historical AMSR-E data. The new algorithm here proposed makes use of climatological data, electromagnetic modeling and artificial neural networks for estimating snow depth as well as a spatio-temporal dynamic density scheme to convert snow depth to SWE. The outputs of the new algorithm are compared with those of the current AMSR-E operational algorithm as well as in-situ measurements and other operational snow products, specifically the Canadian Meteorological Center (CMC) and GlobSnow datasets. Our results show that the AMSR-E algorithm here proposed generally performs better than the operational one and addresses some major issues identified in the spatial distribution of snow depth fields associated with the evolution of effective grain size.

TRMM Microwave Radiometer Rain Rate Estimation Method with Convective and Stratiform Discrimination

NASA Technical Reports Server (NTRS)

Prabhakara, Cuddapah; Iacovazzi, R.; Weinman, J. A.; Dalu, G.; Einaudi, Franco (Technical Monitor)

2000-01-01

Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) radiometer brightness temperature data in the 85 GHz channel (T85) reveal distinct local minima (T85min) in a regional map containing a Mesoscale Convective System (MCS). A map of surface rain rate for that region, deduced from simultaneous measurements made by the Precipitation Radar (PR) on board the TRMM satellite, reveals that these T85min, produced by scattering, correspond to local PR rain maxima. Utilizing the PR rain rate map as a guide, we have developed a TMI algorithm to retrieve convective and stratiform rain. In this algorithm, two parameters are used to classify three kinds of thunderstorms (Cbs) based on the T85 data: a) the magnitude of scattering depression deduced from local T85mi, and b) the mean horizontal gradient of T85 around such minima. Initially, the algorithm is optimized or tuned utilizing the PR and TMI data of a few MCS events. The areal distribution of light (1-10 mm/hr), moderate (10-20 mm/hr), and intense (greater than or equal to 20 mm/hr) rain rates are retrieved on the average with an accuracy of about 15%. Taking advantage of this ability of our retrieval method, one could derive the latent heat input into the atmosphere over the 760 km wide swath of the TMI radiometer in the tropics.

Differences in liquid cloud droplet effective radius and number concentration estimates between MODIS Collections 5.1 and 6 over global oceans.

PubMed

Rausch, John; Meyer, Kerry; Bennartz, Ralf; Platnick, Steven

2017-01-01

Differences in cloud droplet effective radius and cloud droplet number concentration (CDNC) estimates inferred from the Aqua MODIS Collections 5.1 and 6 cloud products (MYD06) are examined for warm clouds over global oceans for the year 2008. Individual pixel level retrievals for both collections are aggregated to 1° × 1° and compared globally and regionally for the three main spectral channel pairs used for MODIS cloud optical property retrievals. Comparisons between both collections are performed for cases in which all three effective radii retrievals are classified by the MODIS Cloud Product as valid. The contribution to the observed differences of several key MYD06 Collection 6 algorithm updates are also explored, with a focus on changes to the surface reflectance model, assumed solar irradiance, above cloud emission, cloud top pressure, and pixel registration. Global results show a neutral to positive (> 50 cm -3 ) change for C6-derived CDNC relative to C5.1 for the 1.6 µm and 2.1 µm channel retrievals, corresponding to a neutral to -2 µm difference in droplet effective radius. For 3.7 µm retrievals, CDNC results show a negative change in the tropics, with differences transitioning toward positive values with increasing latitude spanning -25 to +50 cm -3 related to a +2.5 to -1 µm transition in effective radius. Cloud optical thickness differences were small relative to effective radius, and found to not significantly impact CDNC estimates. Regionally, the magnitude and behavior of the annual CDNC cycle are compared for each effective radius retrieval. Results from this study indicate significant intercollection differences in aggregated values of effective radius due to changes to the pre-computed retrieval lookup tables for ocean scenes, changes to retrieved cloud top pressure, solar irradiance, or above cloud thermal emission, depending upon spectral channel. The observed differences between collections may have implications for existing MODIS derived climatologies and validation studies of effective radius and CDNC.

A TRMM/GPM retrieval of the total mean generator current for the global electric circuit

NASA Astrophysics Data System (ADS)

Peterson, Michael; Deierling, Wiebke; Liu, Chuntao; Mach, Douglas; Kalb, Christina

2017-09-01

A specialized satellite version of the passive microwave electric field retrieval algorithm (Peterson et al., 2015) is applied to observations from the Tropical Rainfall Measuring Mission (TRMM) and Global Precipitation Measurement (GPM) satellites to estimate the generator current for the Global Electric Circuit (GEC) and compute its temporal variability. By integrating retrieved Wilson currents from electrified clouds across the globe, we estimate a total mean current of between 1.4 kA (assuming the 7% fraction of electrified clouds producing downward currents measured by the ER-2 is representative) to 1.6 kA (assuming all electrified clouds contribute to the GEC). These current estimates come from all types of convective weather without preference, including Electrified Shower Clouds (ESCs). The diurnal distribution of the retrieved generator current is in excellent agreement with the Carnegie curve (RMS difference: 1.7%). The temporal variability of the total mean generator current ranges from 110% on semi-annual timescales (29% on an annual timescale) to 7.5% on decadal timescales with notable responses to the Madden-Julian Oscillation and El Nino Southern Oscillation. The geographical distribution of current includes significant contributions from oceanic regions in addition to the land-based tropical chimneys. The relative importance of the Americas and Asia chimneys compared to Africa is consistent with the best modern ground-based observations and further highlights the importance of ESCs for the GEC.

Advantages of measuring the Q Stokes parameter in addition to the total radiance I in the detection of absorbing aerosols

NASA Astrophysics Data System (ADS)

Stamnes, Snorre; Fan, Yongzhen; Chen, Nan; Li, Wei; Tanikawa, Tomonori; Lin, Zhenyi; Liu, Xu; Burton, Sharon; Omar, Ali; Stamnes, Jakob J.; Cairns, Brian; Stamnes, Knut

2018-05-01

A simple but novel study was conducted to investigate whether an imager-type spectroradiometer instrument like MODIS, currently flying on board the Aqua and Terra satellites, or MERIS, which flew on board Envisat, could detect absorbing aerosols if they could measure the Q Stokes parameter in addition to the total radiance I, that is if they could also measure the linear polarization of the light. Accurate radiative transfer calculations were used to train a fast neural network forward model, which together with a simple statistical optimal estimation scheme was used to retrieve three aerosol parameters: aerosol optical depth at 869 nm, optical depth fraction of fine mode (absorbing) aerosols at 869 nm, and aerosol vertical location. The aerosols were assumed to be bimodal, each with a lognormal size distribution, located either between 0 and 2 km or between 2 and 4 km in the Earth's atmosphere. From simulated data with 3% random Gaussian measurement noise added for each Stokes parameter, it was found that by itself the total radiance I at the nine MODIS VIS channels was generally insufficient to accurately retrieve all three aerosol parameters (˜ 15% to 37% successful), but that together with the Q Stokes component it was possible to retrieve values of aerosol optical depth at 869 nm to ± 0.03, single-scattering albedo at 869 nm to ± 0.04, and vertical location in ˜ 65% of the cases. This proof-of-concept retrieval algorithm uses neural networks to overcome the computational burdens of using vector radiative transfer to accurately simulate top-of-atmosphere (TOA) total and polarized radiances, enabling optimal estimation techniques to exploit information from multiple channels. Therefore such an algorithm could, in concept, be readily implemented for operational retrieval of aerosol and ocean products from moderate or hyperspectral spectroradiometers.

FPGA implementation of sparse matrix algorithm for information retrieval

NASA Astrophysics Data System (ADS)

Bojanic, Slobodan; Jevtic, Ruzica; Nieto-Taladriz, Octavio

2005-06-01

Information text data retrieval requires a tremendous amount of processing time because of the size of the data and the complexity of information retrieval algorithms. In this paper the solution to this problem is proposed via hardware supported information retrieval algorithms. Reconfigurable computing may adopt frequent hardware modifications through its tailorable hardware and exploits parallelism for a given application through reconfigurable and flexible hardware units. The degree of the parallelism can be tuned for data. In this work we implemented standard BLAS (basic linear algebra subprogram) sparse matrix algorithm named Compressed Sparse Row (CSR) that is showed to be more efficient in terms of storage space requirement and query-processing timing over the other sparse matrix algorithms for information retrieval application. Although inverted index algorithm is treated as the de facto standard for information retrieval for years, an alternative approach to store the index of text collection in a sparse matrix structure gains more attention. This approach performs query processing using sparse matrix-vector multiplication and due to parallelization achieves a substantial efficiency over the sequential inverted index. The parallel implementations of information retrieval kernel are presented in this work targeting the Virtex II Field Programmable Gate Arrays (FPGAs) board from Xilinx. A recent development in scientific applications is the use of FPGA to achieve high performance results. Computational results are compared to implementations on other platforms. The design achieves a high level of parallelism for the overall function while retaining highly optimised hardware within processing unit.

Potential of the Sentinel-2 Red Edge Spectral Bands for Estimation of Eco-Physiological Plant Parameters

NASA Astrophysics Data System (ADS)

Malenovsky, Zbynek; Homolova, Lucie; Janoutova, Ruzena; Landier, Lucas; Gastellu-Etchegorry, Jean-Philippe; Berthelot, Beatrice; Huck, Alexis

2016-08-01

In this study we investigated importance of the space- borne instrument Sentinel-2 red edge spectral bands and reconstructed red edge position (REP) for retrieval of the three eco-physiological plant parameters, leaf and canopy chlorophyll content and leaf area index (LAI), in case of maize agricultural fields and beech and spruce forest stands. Sentinel-2 spectral bands and REP of the investigated vegetation canopies were simulated in the Discrete Anisotropic Radiative Transfer (DART) model. Their potential for estimation of the plant parameters was assessed through training support vector regressions (SVR) and examining their P-vector matrices indicating significance of each input. The trained SVR were then applied on Sentinel-2 simulated images and the acquired estimates were cross-compared with results from high spatial resolution airborne retrievals. Results showed that contribution of REP was significant for canopy chlorophyll content, but less significant for leaf chlorophyll content and insignificant for leaf area index estimations. However, the red edge spectral bands contributed strongly to the retrievals of all parameters, especially canopy and leaf chlorophyll content. Application of SVR on Sentinel-2 simulated images demonstrated, in general, an overestimation of leaf chlorophyll content and an underestimation of LAI when compared to the reciprocal airborne estimates. In the follow-up investigation, we will apply the trained SVR algorithms on real Sentinel-2 multispectral images acquired during vegetation seasons 2015 and 2016.

Development, Comparisons and Evaluation of Aerosol Retrieval Algorithms

NASA Astrophysics Data System (ADS)

de Leeuw, G.; Holzer-Popp, T.; Aerosol-cci Team

2011-12-01

The Climate Change Initiative (cci) of the European Space Agency (ESA) has brought together a team of European Aerosol retrieval groups working on the development and improvement of aerosol retrieval algorithms. The goal of this cooperation is the development of methods to provide the best possible information on climate and climate change based on satellite observations. To achieve this, algorithms are characterized in detail as regards the retrieval approaches, the aerosol models used in each algorithm, cloud detection and surface treatment. A round-robin intercomparison of results from the various participating algorithms serves to identify the best modules or combinations of modules for each sensor. Annual global datasets including their uncertainties will then be produced and validated. The project builds on 9 existing algorithms to produce spectral aerosol optical depth (AOD and Ångström exponent) as well as other aerosol information; two instruments are included to provide the absorbing aerosol index (AAI) and stratospheric aerosol information. The algorithms included are: - 3 for ATSR (ORAC developed by RAL / Oxford university, ADV developed by FMI and the SU algorithm developed by Swansea University ) - 2 for MERIS (BAER by Bremen university and the ESA standard handled by HYGEOS) - 1 for POLDER over ocean (LOA) - 1 for synergetic retrieval (SYNAER by DLR ) - 1 for OMI retreival of the absorbing aerosol index with averaging kernel information (KNMI) - 1 for GOMOS stratospheric extinction profile retrieval (BIRA) The first seven algorithms aim at the retrieval of the AOD. However, each of the algorithms used differ in their approach, even for algorithms working with the same instrument such as ATSR or MERIS. To analyse the strengths and weaknesses of each algorithm several tests are made. The starting point for comparison and measurement of improvements is a retrieval run for 1 month, September 2008. The data from the same month are subsequently used for several runs with a prescribed set of aerosol models and an a priori data set derived from the median of AEROCOM model runs. The aerosol models and a priori data can be used in several ways, i.e. fully prescribed or with some freedom to choose a combination of aerosol models, based on the a priori or not. Another test gives insight in the effect of the cloud masks used, i.e. retrievals using the same cloud mask (the AATSR APOLLO cloud mask for collocated instruments) are compared with runs using the standard cloud masks. Tests to determine the influence of surface treatment are planned as well. The results of all these tests are evaluated by an independent team which compares the retrieval results with ground-based remote sensing (in particular AERONET) and in-situ data, and by a scoring method. Results are compared with other satellites such as MODIS and MISR. Blind tests using synthetic data are part of the algorithm characterization. The presentation will summarize results of the ongoing phase 1 inter-comparison and evaluation work within the Aerosol_cci project.

On the retrieval of significant wave heights from spaceborne Synthetic Aperture Radar using the Max-Planck Institut algorithm.

PubMed

Violante-Carvalho, Nelson

2005-12-01

Synthetic Aperture Radar (SAR) onboard satellites is the only source of directional wave spectra with continuous and global coverage. Millions of SAR Wave Mode (SWM) imagettes have been acquired since the launch in the early 1990's of the first European Remote Sensing Satellite ERS-1 and its successors ERS-2 and ENVISAT, which has opened up many possibilities specially for wave data assimilation purposes. The main aim of data assimilation is to improve the forecasting introducing available observations into the modeling procedures in order to minimize the differences between model estimates and measurements. However there are limitations in the retrieval of the directional spectrum from SAR images due to nonlinearities in the mapping mechanism. The Max-Planck Institut (MPI) scheme, the first proposed and most widely used algorithm to retrieve directional wave spectra from SAR images, is employed to compare significant wave heights retrieved from ERS-1 SAR against buoy measurements and against the WAM wave model. It is shown that for periods shorter than 12 seconds the WAM model performs better than the MPI, despite the fact that the model is used as first guess to the MPI method, that is the retrieval is deteriorating the first guess. For periods longer than 12 seconds, the part of the spectrum that is directly measured by SAR, the performance of the MPI scheme is at least as good as the WAM model.

Fast Simulators for Satellite Cloud Optical Centroid Pressure Retrievals, 1. Evaluation of OMI Cloud Retrievals

NASA Technical Reports Server (NTRS)

Joiner, J.; Vasilkov, A.; Gupta, P.; Bhartia, P. K.; Veefkind, P.; Sneep, M.; de Haan, J.; Polonsky, I.; Spurr, R.

2012-01-01

The cloud Optical Centroid Pressure (OCP), also known as the effective cloud pressure, is a satellite-derived parameter that is commonly used in trace-gas retrievals to account for the effects of clouds on near-infrared through ultraviolet radiance measurements. Fast simulators are desirable to further expand the use of cloud OCP retrievals into the operational and climate communities for applications such as data assimilation and evaluation of cloud vertical structure in general circulation models. In this paper, we develop and validate fast simulators that provide estimates of the cloud OCP given a vertical profile of optical extinction. We use a pressure-weighting scheme where the weights depend upon optical parameters of clouds and/or aerosol. A cloud weighting function is easily extracted using this formulation. We then use fast simulators to compare two different satellite cloud OCP retrievals from the Ozone Monitoring Instrument (OMI) with estimates based on collocated cloud extinction profiles from a combination of CloudS at radar and MODIS visible radiance data. These comparisons are made over a wide range of conditions to provide a comprehensive validation of the OMI cloud OCP retrievals. We find generally good agreement between OMI cloud OCPs and those predicted by CloudSat. However, the OMI cloud OCPs from the two independent algorithms agree better with each other than either does with the estimates from CloudSat/MODIS. Differences between OMI cloud OCPs and those based on CloudSat/MODIS may result from undetected snow/ice at the surface, cloud 3-D effects, low altitude clouds missed by CloudSat, and the fact that CloudSat only observes a relatively small fraction of an OMI field-of-view.

Global Precipitation Estimates from Cross-Track Passive Microwave Observations Using a Physically-Based Retrieval Scheme

NASA Technical Reports Server (NTRS)

Kidd, Chris; Matsui, Toshi; Chern, Jiundar; Mohr, Karen; Kummerow, Christian; Randel, Dave

2015-01-01

The estimation of precipitation across the globe from satellite sensors provides a key resource in the observation and understanding of our climate system. Estimates from all pertinent satellite observations are critical in providing the necessary temporal sampling. However, consistency in these estimates from instruments with different frequencies and resolutions is critical. This paper details the physically based retrieval scheme to estimate precipitation from cross-track (XT) passive microwave (PM) sensors on board the constellation satellites of the Global Precipitation Measurement (GPM) mission. Here the Goddard profiling algorithm (GPROF), a physically based Bayesian scheme developed for conically scanning (CS) sensors, is adapted for use with XT PM sensors. The present XT GPROF scheme utilizes a model-generated database to overcome issues encountered with an observational database as used by the CS scheme. The model database ensures greater consistency across meteorological regimes and surface types by providing a more comprehensive set of precipitation profiles. The database is corrected for bias against the CS database to ensure consistency in the final product. Statistical comparisons over western Europe and the United States show that the XT GPROF estimates are comparable with those from the CS scheme. Indeed, the XT estimates have higher correlations against surface radar data, while maintaining similar root-mean-square errors. Latitudinal profiles of precipitation show the XT estimates are generally comparable with the CS estimates, although in the southern midlatitudes the peak precipitation is shifted equatorward while over the Arctic large differences are seen between the XT and the CS retrievals.

V2.1.4 L2AS Detailed Release Description September 27, 2001

Atmospheric Science Data Center

2013-03-14

... 27, 2001 Algorithm Changes Change method of selecting radiance pixels to use in aerosol retrieval over ... het. surface retrieval algorithm over areas of 100% dark water. Modify algorithm for selecting a default aerosol model to use in ...

Uncertainty Assessment of Space-Borne Passive Soil Moisture Retrievals

NASA Technical Reports Server (NTRS)

Quets, Jan; De Lannoy, Gabrielle; Reichle, Rolf; Cosh, Michael; van der Schalie, Robin; Wigneron, Jean-Pierre

2017-01-01

The uncertainty associated with passive soil moisture retrieval is hard to quantify, and known to be underlain by various, diverse, and complex causes. Factors affecting space-borne retrieved soil moisture estimation include: (i) the optimization or inversion method applied to the radiative transfer model (RTM), such as e.g. the Single Channel Algorithm (SCA), or the Land Parameter Retrieval Model (LPRM), (ii) the selection of the observed brightness temperatures (Tbs), e.g. polarization and incidence angle, (iii) the definition of the cost function and the impact of prior information in it, and (iv) the RTM parameterization (e.g. parameterizations officially used by the SMOS L2 and SMAP L2 retrieval products, ECMWF-based SMOS assimilation product, SMAP L4 assimilation product, and perturbations from those configurations). This study aims at disentangling the relative importance of the above-mentioned sources of uncertainty, by carrying out soil moisture retrieval experiments, using SMOS Tb observations in different settings, of which some are mentioned above. The ensemble uncertainties are evaluated at 11 reference CalVal sites, over a time period of more than 5 years. These experimental retrievals were inter-compared, and further confronted with in situ soil moisture measurements and operational SMOS L2 retrievals, using commonly used skill metrics to quantify the temporal uncertainty in the retrievals.

Bias Correction of MODIS AOD using DragonNET to obtain improved estimation of PM2.5

NASA Astrophysics Data System (ADS)

Gross, B.; Malakar, N. K.; Atia, A.; Moshary, F.; Ahmed, S. A.; Oo, M. M.

2014-12-01

MODIS AOD retreivals using the Dark Target algorithm is strongly affected by the underlying surface reflection properties. In particular, the operational algorithms make use of surface parameterizations trained on global datasets and therefore do not account properly for urban surface differences. This parameterization continues to show an underestimation of the surface reflection which results in a general over-biasing in AOD retrievals. Recent results using the Dragon-Network datasets as well as high resolution retrievals in the NYC area illustrate that this is even more significant at the newest C006 3 km retrievals. In the past, we used AERONET observation in the City College to obtain bias-corrected AOD, but the homogeneity assumptions using only one site for the region is clearly an issue. On the other hand, DragonNET observations provide ample opportunities to obtain better tuning the surface corrections while also providing better statistical validation. In this study we present a neural network method to obtain bias correction of the MODIS AOD using multiple factors including surface reflectivity at 2130nm, sun-view geometrical factors and land-class information. These corrected AOD's are then used together with additional WRF meteorological factors to improve estimates of PM2.5. Efforts to explore the portability to other urban areas will be discussed. In addition, annual surface ratio maps will be developed illustrating that among the land classes, the urban pixels constitute the largest deviations from the operational model.

A Parallel Relational Database Management System Approach to Relevance Feedback in Information Retrieval.

ERIC Educational Resources Information Center

Lundquist, Carol; Frieder, Ophir; Holmes, David O.; Grossman, David

1999-01-01

Describes a scalable, parallel, relational database-drive information retrieval engine. To support portability across a wide range of execution environments, all algorithms adhere to the SQL-92 standard. By incorporating relevance feedback algorithms, accuracy is enhanced over prior database-driven information retrieval efforts. Presents…

AATSR land surface temperature product algorithm verification over a WATERMED site

NASA Astrophysics Data System (ADS)

Noyes, E. J.; Sòria, G.; Sobrino, J. A.; Remedios, J. J.; Llewellyn-Jones, D. T.; Corlett, G. K.

A new operational Land Surface Temperature (LST) product generated from data acquired by the Advanced Along-Track Scanning Radiometer (AATSR) provides the opportunity to measure LST on a global scale with a spatial resolution of 1 km2. The target accuracy of the product, which utilises nadir data from the AATSR thermal channels at 11 and 12 μm, is 2.5 K for daytime retrievals and 1.0 K at night. We present the results of an experiment where the performance of the algorithm has been assessed for one daytime and one night time overpass occurring over the WATERMED field site near Marrakech, Morocco, on 05 March 2003. Top of atmosphere (TOA) brightness temperatures (BTs) are simulated for 12 pixels from each overpass using a radiative transfer model, with the LST product and independent emissivity values and atmospheric data as inputs. We have estimated the error in the LST product over this biome for this set of conditions by applying the operational AATSR LST retrieval algorithm to the modelled BTs and comparing the results with the original AATSR LSTs input into the model. An average bias of -1.00 K (standard deviation 0.07 K) for the daytime data, and -1.74 K (standard deviation 0.02 K) for the night time data is obtained, which indicates that the algorithm is yielding an LST that is too cold under these conditions. While these results are within specification for daytime retrievals, this suggests that the target accuracy of 1.0 K at night is not being met within this biome.

Spectral Retrieval of Latent Heating Profiles from TRMM PR data. Part 3; Moistening Estimates over Tropical Ocean Regions

NASA Technical Reports Server (NTRS)

Shige, S.; Takayabu, Y.; Tao, W.-K.

2007-01-01

The global hydrological cycle is central to the Earth's climate system, with rainfall and the physics of precipitation formation acting as the key links in the cycle. Two-thirds of global rainfall occurs in the tropics with the associated latent heating (LH) accounting for threefourths of the total heat energy available to the Earth's atmosphere. In the last decade, it has been established that standard products of LH from satellite measurements, particularly TRMM measurements, would be a valuable resource for scientific research and applications. Such products would enable new insights and investigations concerning the complexities of convection system life cycles, the diabatic heating controls and feedbacks related to rne-sosynoptic circulations and their forecasting, the relationship of tropical patterns of LH to the global circulation and climate, and strategies for improving cloud parameterizations In environmental prediction models. However, the LH and water vapor profile or budget (called the apparent moisture sink, or Q2) is closely related. This paper presented the development of an algorithm for retrieving Q2 using 'TRMM precipitation radar. Since there is no direct measurement of LH and Q2, the validation of algorithm usually applies a method called consistency check. Consistency checking involving Cloud Resolving Model (CRM)-generated LH and 42 profiles and algorithm-reconstructed is a useful step in evaluating the performance of a given algorithm. In this process, the CRM simulation of a time-dependent precipitation process (multiple-day time series) is used to obtain the required input parameters for a given algorithm. The algorithm is then used to "econsti-LKth"e heating and moisture profiles that the CRM simulation originally produced, and finally both sets of conformal estimates (model and algorithm) are compared each other. The results indicate that discrepancies between the reconstructed and CM-simulated profiles for Q2, especially at low levels, are larger than those for latent heat. Larger discrepancies in Q2 at low levels are due to moistening for non-precipitating region that algorithm cannot reconstruct. Nevertheless, the algorithm-reconstructed total Q2 profiles are in good agreement with the CRM-simulated ones.

Direct Retrieval of Sulfur Dioxide Amount and Altitude from Spaceborne Hyperspectral UV Measurements: Theory and Application

NASA Technical Reports Server (NTRS)

Yang, Kau; Liu, Xiong; Bhartia, Pawan K.; Krotkov, Nickolay A.; Carn, Simon A.; Hughes, Eric J.; Krueger, Arlin J.; Spurr, Robert D.; Trahan, Samuel G.

2010-01-01

We describe the physical processes by which a vertically localized absorber perturbs the top-of-atmosphere solar backscattered ultraviolet (UV) radiance. The distinct spectral responses to perturbations of an absorber in its column amount and layer altitude provide the basis for a practical satellite retrieval technique, the Extended Iterative Spectral Fitting (EISF) algorithm, for the simultaneous retrieval of these quantities of a SO2 plume. In addition, the EISF retrieval provides an improved UV aerosol index for quantifying the spectral contrast of apparent scene reflectance at the bottom of atmosphere bounded by the surface and/or cloud; hence it can be used for detection of the presence or absence of UV absorbing aerosols. We study the performance and characterize the uncertainties of the EISF algorithm using synthetic backscattered UV radiances, retrievals from which can be compared with those used in the simulation. Our findings indicate that the presence of aerosols (both absorbing and nonabsorbing) does not cause large errors in EISF retrievals under most observing conditions when they are located below the SO2 plume. The EISF retrievals assuming a homogeneous field of view can provide accurate column amounts for inhomogeneous scenes, but they always underestimate the plume altitudes. The EISF algorithm reduces systematic errors present in existing linear retrieval algorithms that use prescribed SO2 plume heights. Applying the EISF algorithm to Ozone Monitoring Instrument satellite observations of the recent Kasatochi volcanic eruption, we demonstrate the successful retrieval of effective plume altitude of volcanic SO2, and we also show the improvement in accuracy in the corresponding SO2 columns.

Soil Moisture Estimate under Forest using a Semi-empirical Model at P-Band

NASA Astrophysics Data System (ADS)

Truong-Loi, M.; Saatchi, S.; Jaruwatanadilok, S.

2013-12-01

In this paper we show the potential of a semi-empirical algorithm to retrieve soil moisture under forests using P-band polarimetric SAR data. In past decades, several remote sensing techniques have been developed to estimate the surface soil moisture. In most studies associated with radar sensing of soil moisture, the proposed algorithms are focused on bare or sparsely vegetated surfaces where the effect of vegetation can be ignored. At long wavelengths such as L-band, empirical or physical models such as the Small Perturbation Model (SPM) provide reasonable estimates of surface soil moisture at depths of 0-5cm. However for densely covered vegetated surfaces such as forests, the problem becomes more challenging because the vegetation canopy is a complex scattering environment. For this reason there have been only few studies focusing on retrieving soil moisture under vegetation canopy in the literature. Moghaddam et al. developed an algorithm to estimate soil moisture under a boreal forest using L- and P-band SAR data. For their studied area, double-bounce between trunks and ground appear to be the most important scattering mechanism. Thereby, they implemented parametric models of radar backscatter for double-bounce using simulations of a numerical forest scattering model. Hajnsek et al. showed the potential of estimating the soil moisture under agricultural vegetation using L-band polarimetric SAR data and using polarimetric-decomposition techniques to remove the vegetation layer. Here we use an approach based on physical formulation of dominant scattering mechanisms and three parameters that integrates the vegetation and soil effects at long wavelengths. The algorithm is a simplification of a 3-D coherent model of forest canopy based on the Distorted Born Approximation (DBA). The simplified model has three equations and three unknowns, preserving the three dominant scattering mechanisms of volume, double-bounce and surface for three polarized backscattering coefficients: σHH, σVV and σHV. The inversion process, which is not an ill-posed problem, uses the non-linear optimization method of Levenberg-Marquardt and estimates the three model parameters: vegetation aboveground biomass, average soil moisture and surface roughness. The model analytical formulation will be first recalled and sensitivity analyses will be shown. Then some results obtained with real SAR data will be presented and compared to ground estimates.

1km Soil Moisture from Downsampled Sentinel-1 SAR Data: Harnessing Assets and Overcoming Obstacles.

NASA Astrophysics Data System (ADS)

Bauer-Marschallinger, Bernhard; Cao, Senmao; Schaufler, Stefan; Paulik, Christoph; Naeimi, Vahid; Wagner, Wolfgang

2017-04-01

Radars onboard Earth observing satellites allow estimating Surface Soil Moisture (SSM) regularly and globally. The use of coarse-scale measurements from active or passive radars for SSM retrieval is well established and in operational use. Thanks to the Sentinel-1 mission, launched in 2014 and deploying Synthetic Aperture Radars (SAR), high-resolution radar imagery is routinely available at the scale of 20 meters, with a high revisit frequency of 3-6 days and with unprecedented radiometric accuracy. However, the direct exploitation of high-resolution SAR data for SSM retrieval is complicated by several problems: Small-scaled contributions to the radar backscatter from individual ground features often obscure the soil moisture signal, rendering common algorithms insensitive to SSM. Furthermore, the influence of vegetation dynamics on the radar signal is less understood than in the coarse-scale case, leading to biases during the vegetation period. Finally, the large data volumes of high-resolution remote sensing data present a great load on hardware systems. Consequently, a spatial resampling of the high-resolution SAR data to a 500 meters sampling is done, allowing the exploitation of information at 10 meter sampling, but reducing effectively the inherent uncertainties. The thereof retrieved 1km SSM product aims to describe the soil moisture dynamics at medium scale with high quality. We adopted the TU-Wien Change Detection algorithm to the Sentinel-1 data, which was already successfully used for retrieving SSM from ERS-1/2 and Envisat-ASAR observations. The adoption entails a new method for SAR image resampling, including a masking for pixels that do not carry soil moisture signals, preventing them to spread during downsampling. Furthermore, the observation angle between the radar sensors and the ground is treated in a different way, as Sentinel-1 sensors observe from fixed orbit paths (in contrast to other radar sensors). Here, a regression model is developed that successfully estimates the dependency of radar backscatter to observation angle with statistical parameters from the Sentinel-1 SAR time series archive. We present the Sentinel-1 1km-SSM product generated by the adopted change detection algorithm. The dataset covers the European continent and holds data from October 2014 ongoing. In addition to a validation of the SSM product, the statistical SAR parameters used during SSM retrieval are examined.

Comparative Analysis of Aerosol Retrievals from MODIS, OMI and MISR Over Sahara Region

NASA Technical Reports Server (NTRS)

Lyapustin, A.; Wang, Y.; Hsu, C.; Terres, O.; Leptoukh, G.; Kalashnikova, O.; Korkin, S.

2011-01-01

MODIS is a wide field-of-view sensor providing daily global observations of the Earth. Currently, global MODIS aerosol retrievals over land are performed with the main Dark Target algorithm complimented with the Deep Blue (DB) Algorithm over bright deserts. The Dark Target algorithm relies on surface parameterization which relates reflectance in MODIS visible bands with the 2.1 micrometer region, whereas the Deep Blue algorithm uses an ancillary angular distribution model of surface reflectance developed from the time series of clear-sky MODIS observations. Recently, a new Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm has been developed for MODIS. MAIAC uses a time series and an image based processing to perform simultaneous retrievals of aerosol properties and surface bidirectional reflectance. It is a generic algorithm which works over both dark vegetative surfaces and bright deserts and performs retrievals at 1 km resolution. In this work, we will provide a comparative analysis of DB, MAIAC, MISR and OMI aerosol products over bright deserts of northern Africa.

A Comparison of Soil Moisture Retrieval Models Using SIR-C Measurements over the Little Washita River Watershed

NASA Technical Reports Server (NTRS)

Wang, J. R.; Hsu, A.; Shi, J. C.; ONeill, P. E.; Engman, E. T.

1997-01-01

Six SIR-C L-band measurements over the Little Washita River watershed in Chickasha, Oklahoma during 11-17 April 1994 have been analyzed for studying the change of soil moisture in the region. Two algorithms developed recently for estimation of moisture content in bare soil were applied to these measurements and the results were compared with those sampled on the ground. There is a good agreement between the values of soil moisture estimated by either one of the algorithms and those measured from ground sampling for bare or sparsely vegetated fields. The standard error from this comparison is on the order of 0.05-0.06 cu cm/cu cm, which is comparable to that expected from a regression between backscattering coefficients and measured soil moisture. Both algorithms provide a poor estimation of soil moisture or fail to give solutions to areas covered with moderate or dense vegetation. Even for bare soils the number of pixels that bear no numerical solution from the application of either one of the two algorithms to the data is not negligible. Results from using one of these algorithms indicate that the fraction of these pixels becomes larger as the bare soils become drier. The other algorithm generally gives a larger fraction of these pixels when the fields are vegetation-covered. The implication and impact of these features are discussed in this article.

Satellite aerosol retrieval using dark target algorithm by coupling BRDF effect over AERONET site

NASA Astrophysics Data System (ADS)

Yang, Leiku; Xue, Yong; Guang, Jie; Li, Chi

2012-11-01

For most satellite aerosol retrieval algorithms even for multi-angle instrument, the simple forward model (FM) based on Lambertian surface assumption is employed to simulate top of the atmosphere (TOA) spectral reflectance, which does not fully consider the surface bi-directional reflectance functions (BRDF) effect. The approximating forward model largely simplifies the radiative transfer model, reduces the size of the look-up tables, and creates faster algorithm. At the same time, it creates systematic biases in the aerosol optical depth (AOD) retrieval. AOD product from the Moderate Resolution Imaging Spectro-radiometer (MODIS) data based on the dark target algorithm is considered as one of accurate satellite aerosol products at present. Though it performs well at a global scale, uncertainties are still found on regional in a lot of studies. The Lambertian surface assumpiton employed in the retrieving algorithm may be one of the uncertain factors. In this study, we first use radiative transfer simulations over dark target to assess the uncertainty to what extent is introduced from the Lambertian surface assumption. The result shows that the uncertainties of AOD retrieval could reach up to ±0.3. Then the Lambertian FM (L_FM) and the BRDF FM (BRDF_FM) are respectively employed in AOD retrieval using dark target algorithm from MODARNSS (MODIS/Terra and MODIS/Aqua Atmosphere Aeronet Subsetting Product) data over Beijing AERONET site. The validation shows that accuracy in AOD retrieval has been improved by employing the BRDF_FM accounting for the surface BRDF effect, the regression slope of scatter plots with retrieved AOD against AEROENET AOD increases from 0.7163 (for L_FM) to 0.7776 (for BRDF_FM) and the intercept decreases from 0.0778 (for L_FM) to 0.0627 (for BRDF_FM).

A New Understanding for the Rain Rate retrieval of Attenuating Radars Measurement

NASA Astrophysics Data System (ADS)

Koner, P.; Battaglia, A.; Simmer, C.

2009-04-01

The retrieval of rain rate from the attenuated radar (e.g. Cloud Profiling Radar on board of CloudSAT in orbit since June 2006) is a challenging problem. ĹEcuyer and Stephens [1] underlined this difficulty (for rain rates larger than 1.5 mm/h) and suggested the need of additional information (like path-integrated attenuations (PIA) derived from surface reference techniques or precipitation water path estimated from co-located passive microwave radiometer) to constrain the retrieval. It is generally discussed based on the optimal estimation theory that there are no solutions without constraining the problem in a case of visible attenuation because there is no enough information content to solve the problem. However, when the problem is constrained by the additional measurement of PIA, there is a reasonable solution. This raises the spontaneous question: Is all information enclosed in this additional measurement? This also contradicts with the information theory because one measurement can introduce only one degree of freedom in the retrieval. Why is one degree of freedom so important in the above problem? This question cannot be explained using the estimation and information theories of OEM. On the other hand, Koner and Drummond [2] argued that the OEM is basically a regularization method, where a-priori covariance is used as a stabilizer and the regularization strength is determined by the choices of the a-priori and error covariance matrices. The regularization is required for the reduction of the condition number of Jacobian, which drives the noise injection from the measurement and inversion spaces to the state space in an ill-posed inversion. In this work, the above mentioned question will be discussed based on the regularization theory, error mitigation and eigenvalue mathematics. References 1. L'Ecuyer TS and Stephens G. An estimation based precipitation retrieval algorithm for attenuating radar. J. Appl. Met., 2002, 41, 272-85. 2. Koner PK, Drummond JR. A comparison of regularization techniques for atmospheric trace gases retrievals. JQSRT 2008; 109:514-26.

Use of reflected GNSS SNR data to retrieve either soil moisture or vegetation height from a wheat crop

NASA Astrophysics Data System (ADS)

Zhang, Sibo; Roussel, Nicolas; Boniface, Karen; Ha, Minh Cuong; Frappart, Frédéric; Darrozes, José; Baup, Frédéric; Calvet, Jean-Christophe

2017-09-01

This work aims to estimate soil moisture and vegetation height from Global Navigation Satellite System (GNSS) Signal to Noise Ratio (SNR) data using direct and reflected signals by the land surface surrounding a ground-based antenna. Observations are collected from a rainfed wheat field in southwestern France. Surface soil moisture is retrieved based on SNR phases estimated by the Least Square Estimation method, assuming the relative antenna height is constant. It is found that vegetation growth breaks up the constant relative antenna height assumption. A vegetation-height retrieval algorithm is proposed using the SNR-dominant period (the peak period in the average power spectrum derived from a wavelet analysis of SNR). Soil moisture and vegetation height are retrieved at different time periods (before and after vegetation's significant growth in March). The retrievals are compared with two independent reference data sets: in situ observations of soil moisture and vegetation height, and numerical simulations of soil moisture, vegetation height and above-ground dry biomass from the ISBA (interactions between soil, biosphere and atmosphere) land surface model. Results show that changes in soil moisture mainly affect the multipath phase of the SNR data (assuming the relative antenna height is constant) with little change in the dominant period of the SNR data, whereas changes in vegetation height are more likely to modulate the SNR-dominant period. Surface volumetric soil moisture can be estimated (R2 = 0.74, RMSE = 0.009 m3 m-3) when the wheat is smaller than one wavelength (˜ 19 cm). The quality of the estimates markedly decreases when the vegetation height increases. This is because the reflected GNSS signal is less affected by the soil. When vegetation replaces soil as the dominant reflecting surface, a wavelet analysis provides an accurate estimation of the wheat crop height (R2 = 0.98, RMSE = 6.2 cm). The latter correlates with modeled above-ground dry biomass of the wheat from stem elongation to ripening. It is found that the vegetation height retrievals are sensitive to changes in plant height of at least one wavelength. A simple smoothing of the retrieved plant height allows an excellent matching to in situ observations, and to modeled above-ground dry biomass.

Improvement of OMI Ozone Profile Retrievals in the Troposphere and Lower Troposphere by the Use of the Tropopause-Based Ozone Profile Climatology

NASA Technical Reports Server (NTRS)

Bak, Juseon; Liu, X.; Wei, J.; Kim, J. H.; Chance, K.; Barnet, C.

2011-01-01

An advance algorithm based on the optimal estimation technique has beeen developed to derive ozone profile from GOME UV radiances and have adapted it to OMI UV radiances. OMI vertical resolution : 7-11 km in the troposphere and 10-14 km in the stratosphere. Satellite ultraviolet measurements (GOME, OMI) contain little vertical information for the small scale of ozone, especially in the upper troposphere (UT) and lower stratosphere (LS) where the sharp O3 gradient across the tropopause and large ozone variability are observed. Therefore, retrievals depend greatly on the a-priori knowledge in the UTLS

Ensembles of satellite aerosol retrievals based on three AATSR algorithms within aerosol_cci

NASA Astrophysics Data System (ADS)

Kosmale, Miriam; Popp, Thomas

2016-04-01

Ensemble techniques are widely used in the modelling community, combining different modelling results in order to reduce uncertainties. This approach could be also adapted to satellite measurements. Aerosol_cci is an ESA funded project, where most of the European aerosol retrieval groups work together. The different algorithms are homogenized as far as it makes sense, but remain essentially different. Datasets are compared with ground based measurements and between each other. Three AATSR algorithms (Swansea university aerosol retrieval, ADV aerosol retrieval by FMI and Oxford aerosol retrieval ORAC) provide within this project 17 year global aerosol records. Each of these algorithms provides also uncertainty information on pixel level. Within the presented work, an ensembles of the three AATSR algorithms is performed. The advantage over each single algorithm is the higher spatial coverage due to more measurement pixels per gridbox. A validation to ground based AERONET measurements shows still a good correlation of the ensemble, compared to the single algorithms. Annual mean maps show the global aerosol distribution, based on a combination of the three aerosol algorithms. In addition, pixel level uncertainties of each algorithm are used for weighting the contributions, in order to reduce the uncertainty of the ensemble. Results of different versions of the ensembles for aerosol optical depth will be presented and discussed. The results are validated against ground based AERONET measurements. A higher spatial coverage on daily basis allows better results in annual mean maps. The benefit of using pixel level uncertainties is analysed.

A Physical Model to Estimate Snowfall over Land using AMSU-B Observations

NASA Technical Reports Server (NTRS)

Kim, Min-Jeong; Weinman, J. A.; Olson, W. S.; Chang, D.-E.; Skofronick-Jackson, G.; Wang, J. R.

2008-01-01

In this study, we present an improved physical model to retrieve snowfall rate over land using brightness temperature observations from the National Oceanic and Atmospheric Administration's (NOAA) Advanced Microwave Sounder Unit-B (AMSU-B) at 89 GHz, 150 GHz, 183.3 +/- 1 GHz, 183.3 +/- 3 GHz, and 183.3 +/- 7 GHz. The retrieval model is applied to the New England blizzard of March 5, 2001 which deposited about 75 cm of snow over much of Vermont, New Hampshire, and northern New York. In this improved physical model, prior retrieval assumptions about snowflake shape, particle size distributions, environmental conditions, and optimization methodology have been updated. Here, single scattering parameters for snow particles are calculated with the Discrete-Dipole Approximation (DDA) method instead of assuming spherical shapes. Five different snow particle models (hexagonal columns, hexagonal plates, and three different kinds of aggregates) are considered. Snow particle size distributions are assumed to vary with air temperature and to follow aircraft measurements described by previous studies. Brightness temperatures at AMSU-B frequencies for the New England blizzard are calculated using these DDA calculated single scattering parameters and particle size distributions. The vertical profiles of pressure, temperature, relative humidity and hydrometeors are provided by MM5 model simulations. These profiles are treated as the a priori data base in the Bayesian retrieval algorithm. In algorithm applications to the blizzard data, calculated brightness temperatures associated with selected database profiles agree with AMSU-B observations to within about +/- 5 K at all five frequencies. Retrieved snowfall rates compare favorably with the near-concurrent National Weather Service (NWS) radar reflectivity measurements. The relationships between the NWS radar measured reflectivities Z(sub e) and retrieved snowfall rate R for a given snow particle model are derived by a histogram matching technique. All of these Z(sub e)-R relationships fall in the range of previously established Z(sub e)-R relationships for snowfall. This suggests that the current physical model developed in this study can reliably estimate the snowfall rate over land using the AMSU-B measured brightness temperatures.

The Community Cloud retrieval for CLimate (CC4CL) - Part 1: A framework applied to multiple satellite imaging sensors

NASA Astrophysics Data System (ADS)

Sus, Oliver; Stengel, Martin; Stapelberg, Stefan; McGarragh, Gregory; Poulsen, Caroline; Povey, Adam C.; Schlundt, Cornelia; Thomas, Gareth; Christensen, Matthew; Proud, Simon; Jerg, Matthias; Grainger, Roy; Hollmann, Rainer

2018-06-01

We present here the key features of the Community Cloud retrieval for CLimate (CC4CL) processing algorithm. We focus on the novel features of the framework: the optimal estimation approach in general, explicit uncertainty quantification through rigorous propagation of all known error sources into the final product, and the consistency of our long-term, multi-platform time series provided at various resolutions, from 0.5 to 0.02°. By describing all key input data and processing steps, we aim to inform the user about important features of this new retrieval framework and its potential applicability to climate studies. We provide an overview of the retrieved and derived output variables. These are analysed for four, partly very challenging, scenes collocated with CALIOP (Cloud-Aerosol lidar with Orthogonal Polarization) observations in the high latitudes and over the Gulf of Guinea-West Africa. The results show that CC4CL provides very realistic estimates of cloud top height and cover for optically thick clouds but, where optically thin clouds overlap, returns a height between the two layers. CC4CL is a unique, coherent, multi-instrument cloud property retrieval framework applicable to passive sensor data of several EO missions. Through its flexibility, CC4CL offers the opportunity for combining a variety of historic and current EO missions into one dataset, which, compared to single sensor retrievals, is improved in terms of accuracy and temporal sampling.

Synergy of stereo cloud top height and ORAC optimal estimation cloud retrieval: evaluation and application to AATSR

NASA Astrophysics Data System (ADS)

Fisher, Daniel; Poulsen, Caroline A.; Thomas, Gareth E.; Muller, Jan-Peter

2016-03-01

In this paper we evaluate the impact on the cloud parameter retrievals of the ORAC (Optimal Retrieval of Aerosol and Cloud) algorithm following the inclusion of stereo-derived cloud top heights as a priori information. This is performed in a mathematically rigorous way using the ORAC optimal estimation retrieval framework, which includes the facility to use such independent a priori information. Key to the use of a priori information is a characterisation of their associated uncertainty. This paper demonstrates the improvements that are possible using this approach and also considers their impact on the microphysical cloud parameters retrieved. The Along-Track Scanning Radiometer (AATSR) instrument has two views and three thermal channels, so it is well placed to demonstrate the synergy of the two techniques. The stereo retrieval is able to improve the accuracy of the retrieved cloud top height when compared to collocated Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO), particularly in the presence of boundary layer inversions and high clouds. The impact of the stereo a priori information on the microphysical cloud properties of cloud optical thickness (COT) and effective radius (RE) was evaluated and generally found to be very small for single-layer clouds conditions over open water (mean RE differences of 2.2 (±5.9) microns and mean COD differences of 0.5 (±1.8) for single-layer ice clouds over open water at elevations of above 9 km, which are most strongly affected by the inclusion of the a priori).

Satellite Remote Sensing of Tropical Precipitation and Ice Clouds for GCM Verification

NASA Technical Reports Server (NTRS)

Evans, K. Franklin

2001-01-01

This project, supported by the NASA New Investigator Program, has primarily been funding a graduate student, Darren McKague. Since August 1999 Darren has been working part time at Raytheon, while continuing his PhD research. Darren is planning to finish his thesis work in May 2001, thus some of the work described here is ongoing. The proposed research was to use GOES visible and infrared imager data and SSM/I microwave data to obtain joint distributions of cirrus cloud ice mass and precipitation for a study region in the Eastern Tropical Pacific. These joint distributions of cirrus cloud and rainfall were to be compared to those from the CSU general circulation model to evaluate the cloud microphysical amd cumulus parameterizations in the GCM. Existing algorithms were to be used for the retrieval of cloud ice water path from GOES (Minnis) and rainfall from SSM/I (Wilheit). A theoretical study using radiative transfer models and realistic variations in cloud and precipitation profiles was to be used to estimate the retrieval errors. Due to the unavailability of the GOES satellite cloud retrieval algorithm from Dr. Minnis (a co-PI), there was a change in the approach and emphasis of the project. The new approach was to develop a completely new type of remote sensing algorithm - one to directly retrieve joint probability density functions (pdf's) of cloud properties from multi-dimensional histograms of satellite radiances. The usual approach is to retrieve individual pixels of variables (i.e. cloud optical depth), and then aggregate the information. Only statistical information is actually needed, however, and so a more direct method is desirable. We developed forward radiative transfer models for the SSM/I and GOES channels, originally for testing the retrieval algorithms. The visible and near infrared ice scattering information is obtained from geometric ray tracing of fractal ice crystals (Andreas Macke), while the mid-infrared and microwave scattering is computed with Mie scattering. The radiative transfer is performed with the Spherical Harmonic Discrete Ordinate Method (developed by the PI), and infrared molecular absorption is included with the correlated k-distribution method. The SHDOM radiances have been validated by comparison to version 2 of DISORT (the community "standard" discrete-ordinates radiative transfer model), however we use SHDOM since it is computationally more efficient.

Polarimetric Signatures of Initiating Convection During MC3E

NASA Technical Reports Server (NTRS)

Emory, Amber

2012-01-01

One of the goals of the Mid-latitude Continental Convective Clouds Experiment (MC3E) field campaign was to provide constraints for space-based rainfall retrieval algorithms over land. This study used datasets collected during the 2011 field campaign to combine radiometer and ground-based radar polarimetric retrievals in order to better understand hydrometeor type, habit and distribution for initiating continental convection. Cross-track and conically scanning nadir views from the Conical Scanning Millimeter-wave Imaging Radiometer (CoSMIR) were compared with ground-based polarimetric radar retrievals along the ER-2 flight track. Polarimetric signatures for both airborne radiometers and ground-based radars were well co-located with deep convection to relate radiometric signatures with low-level polarimetric radar data for hydrometeor identification and diameter estimation. For the time period of study, Z(sub DR) values indicated no presence of hail at the surface. However, the Z(sub DR) column extended well above the melting level into the mixed phase region, suggesting a possible source of frozen drop embryos for the future formation of hail. The results shown from this study contribute ground truth datasets for GPM PR algorithm development for convective events, which is an improvement upon previous stratiform precipitation centered framework.

Phase retrieval using regularization method in intensity correlation imaging

NASA Astrophysics Data System (ADS)

Li, Xiyu; Gao, Xin; Tang, Jia; Lu, Changming; Wang, Jianli; Wang, Bin

2014-11-01

Intensity correlation imaging(ICI) method can obtain high resolution image with ground-based low precision mirrors, in the imaging process, phase retrieval algorithm should be used to reconstituted the object's image. But the algorithm now used(such as hybrid input-output algorithm) is sensitive to noise and easy to stagnate. However the signal-to-noise ratio of intensity interferometry is low especially in imaging astronomical objects. In this paper, we build the mathematical model of phase retrieval and simplified it into a constrained optimization problem of a multi-dimensional function. New error function was designed by noise distribution and prior information using regularization method. The simulation results show that the regularization method can improve the performance of phase retrieval algorithm and get better image especially in low SNR condition

A New Satellite Aerosol Retrieval Using High Spectral Resolution Oxygen A-Band Measurements

NASA Astrophysics Data System (ADS)

Winker, D. M.; Zhai, P.

2014-12-01

Efforts to advance current satellite aerosol retrieval capabilities have mostly focused on polarimetric techniques. While there has been much interest in recent decades in the use of the oxygen A-band for retrievals of cloud height or surface pressure, these techniques are mostly based on A-band measurements with relatively low spectral resolution. We report here on a new aerosol retrieval technique based on high-resolution A-band spectra. 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 measurements which resolve the A-band line structure, and 2) the use of co-located lidar profile measurements to constrain the vertical distribution of scatterers. The OCO-2 satellite, launched in July this year and now flying in formation with the CALIPSO satellite, carries an oxygen A-band spectrometer with a spectral resolution of 21,000:1. This is sufficient to resolve the A-band line structure, which contains information on atmospheric photon path lengths. Combining channels with oxygen absorption ranging from weak to strong allows the separation of atmospheric and surface scattering. An optimal estimation algorithm for simultaneous retrieval of aerosol optical depth, aerosol absorption, and surface albedo has been developed. Lidar profile data is used for scene identification and to provide constraints on the vertical distribution of scatterers. As calibrated OCO-2 data is not expected until the end of this year, the algorithm has been developed and tested using simulated OCO-2 spectra. The simulations show that AOD and surface albedo can be retrieved with high accuracy. Retrievals of aerosol single scatter albedo are encouraging, showing good performance when AOD is larger than about 0.15. Retrieval performance improves as the albedo of the underlying surface increases. Thus, the technique shows great promise for retrieving the absorption optical depth of aerosols located above clouds. This presentation will discuss the basis of the approach and results of the A-band/lidar retrievals based on simulated data.

Applying Advances in GPM Radiometer Intercalibration and Algorithm Development to a Long-Term TRMM/GPM Global Precipitation Dataset

NASA Astrophysics Data System (ADS)

Berg, W. K.

2016-12-01

The Global Precipitation Mission (GPM) Core Observatory, which was launched in February of 2014, provides a number of advances for satellite monitoring of precipitation including a dual-frequency radar, high frequency channels on the GPM Microwave Imager (GMI), and coverage over middle and high latitudes. The GPM concept, however, is about producing unified precipitation retrievals from a constellation of microwave radiometers to provide approximately 3-hourly global sampling. This involves intercalibration of the input brightness temperatures from the constellation radiometers, development of an apriori precipitation database using observations from the state-of-the-art GPM radiometer and radars, and accounting for sensor differences in the retrieval algorithm in a physically-consistent way. Efforts by the GPM inter-satellite calibration working group, or XCAL team, and the radiometer algorithm team to create unified precipitation retrievals from the GPM radiometer constellation were fully implemented into the current version 4 GPM precipitation products. These include precipitation estimates from a total of seven conical-scanning and six cross-track scanning radiometers as well as high spatial and temporal resolution global level 3 gridded products. Work is now underway to extend this unified constellation-based approach to the combined TRMM/GPM data record starting in late 1997. The goal is to create a long-term global precipitation dataset employing these state-of-the-art calibration and retrieval algorithm approaches. This new long-term global precipitation dataset will incorporate the physics provided by the combined GPM GMI and DPR sensors into the apriori database, extend prior TRMM constellation observations to high latitudes, and expand the available TRMM precipitation data to the full constellation of available conical and cross-track scanning radiometers. This combined TRMM/GPM precipitation data record will thus provide a high-quality high-temporal resolution global dataset for use in a wide variety of weather and climate research applications.

An Alternative Retrieval Algorithm for the Ozone Mapping and Profiler Suite Limb Profiler

DTIC Science & Technology

2012-05-01

behavior of aerosol extinction from the upper troposphere through the stratosphere is critical for retrieving ozone in this region. Aerosol scattering is......include area code) b. ABSTRACT c. THIS PAGE 18. NUMBER OF PAGES 17. LIMITATION OF ABSTRACT An Alternative Retrieval Algorithm for the Ozone Mapping and

Estimation of phytoplankton production from space: current status and future potential of satellite remote sensing.

PubMed

Joint; Groom

2000-07-30

A new generation of ocean colour satellites is now operational, with frequent observation of the global ocean. This paper reviews the potential to estimate marine primary production from satellite images. The procedures involved in retrieving estimates of phytoplankton biomass, as pigment concentrations, are discussed. Algorithms are applied to SeaWiFS ocean colour data to indicate seasonal variations in phytoplankton biomass in the Celtic Sea, on the continental shelf to the south west of the UK. Algorithms to estimate primary production rates from chlorophyll concentration are compared and the advantages and disadvantage discussed. The simplest algorithms utilise correlations between chlorophyll concentration and production rate and one equation is used to estimate daily primary production rates for the western English Channel and Celtic Sea; these estimates compare favourably with published values. Primary production for the central Celtic Sea in the period April to September inclusive is estimated from SeaWiFS data to be 102 gC m(-2) in 1998 and 93 gC m(-2) in 1999; published estimates, based on in situ incubations, are ca. 80 gC m(-2). The satellite data demonstrate large variations in primary production between 1998 and 1999, with a significant increase in late summer in 1998 which did not occur in 1999. Errors are quantified for the estimation of primary production from simple algorithms based on satellite-derived chlorophyll concentration. These data show the potential to obtain better estimates of marine primary production than are possible with ship-based methods, with the ability to detect short-lived phytoplankton blooms. In addition, the potential to estimate new production from satellite data is discussed.

Orbiting Carbon Observatory-2 (OCO-2) Cloud Screening; Validation Against Collocated MODIS and Initial Comparison to CALIOP Data

NASA Technical Reports Server (NTRS)

Taylor, Thomas E.; O'Dell, Christopher W.; Frankenberg, Christian; Partain, Philip; Cronk, Heather W.; Savtchenko, Andrey; Nelson, Robert R.; Rosenthal, Emily J.; Chang, Albert; Crisp, David;

A fuzzy measure approach to motion frame analysis for scene detection. M.S. Thesis - Houston Univ.

NASA Technical Reports Server (NTRS)

Leigh, Albert B.; Pal, Sankar K.

1992-01-01

This paper addresses a solution to the problem of scene estimation of motion video data in the fuzzy set theoretic framework. Using fuzzy image feature extractors, a new algorithm is developed to compute the change of information in each of two successive frames to classify scenes. This classification process of raw input visual data can be used to establish structure for correlation. The algorithm attempts to fulfill the need for nonlinear, frame-accurate access to video data for applications such as video editing and visual document archival/retrieval systems in multimedia environments.

Linear Estimation of Particle Bulk Parameters from Multi-Wavelength Lidar Measurements

NASA Technical Reports Server (NTRS)

Veselovskii, Igor; Dubovik, Oleg; Kolgotin, A.; Korenskiy, M.; Whiteman, D. N.; Allakhverdiev, K.; Huseyinoglu, F.

2012-01-01

An algorithm for linear estimation of aerosol bulk properties such as particle volume, effective radius and complex refractive index from multiwavelength lidar measurements is presented. The approach uses the fact that the total aerosol concentration can well be approximated as a linear combination of aerosol characteristics measured by multiwavelength lidar. Therefore, the aerosol concentration can be estimated from lidar measurements without the need to derive the size distribution, which entails more sophisticated procedures. The definition of the coefficients required for the linear estimates is based on an expansion of the particle size distribution in terms of the measurement kernels. Once the coefficients are established, the approach permits fast retrieval of aerosol bulk properties when compared with the full regularization technique. In addition, the straightforward estimation of bulk properties stabilizes the inversion making it more resistant to noise in the optical data. Numerical tests demonstrate that for data sets containing three aerosol backscattering and two extinction coefficients (so called 3 + 2 ) the uncertainties in the retrieval of particle volume and surface area are below 45% when input data random uncertainties are below 20 %. Moreover, using linear estimates allows reliable retrievals even when the number of input data is reduced. To evaluate the approach, the results obtained using this technique are compared with those based on the previously developed full inversion scheme that relies on the regularization procedure. Both techniques were applied to the data measured by multiwavelength lidar at NASA/GSFC. The results obtained with both methods using the same observations are in good agreement. At the same time, the high speed of the retrieval using linear estimates makes the method preferable for generating aerosol information from extended lidar observations. To demonstrate the efficiency of the method, an extended time series of observations acquired in Turkey in May 2010 was processed using the linear estimates technique permitting, for what we believe to be the first time, temporal-height distributions of particle parameters.

Verification of Satellite Rainfall Estimates from the Tropical Rainfall Measuring Mission over Ground Validation Sites

NASA Astrophysics Data System (ADS)

Fisher, B. L.; Wolff, D. B.; Silberstein, D. S.; Marks, D. M.; Pippitt, J. L.

2007-12-01

The Tropical Rainfall Measuring Mission's (TRMM) Ground Validation (GV) Program was originally established with the principal long-term goal of determining the random errors and systematic biases stemming from the application of the TRMM rainfall algorithms. The GV Program has been structured around two validation strategies: 1) determining the quantitative accuracy of the integrated monthly rainfall products at GV regional sites over large areas of about 500 km2 using integrated ground measurements and 2) evaluating the instantaneous satellite and GV rain rate statistics at spatio-temporal scales compatible with the satellite sensor resolution (Simpson et al. 1988, Thiele 1988). The GV Program has continued to evolve since the launch of the TRMM satellite on November 27, 1997. This presentation will discuss current GV methods of validating TRMM operational rain products in conjunction with ongoing research. The challenge facing TRMM GV has been how to best utilize rain information from the GV system to infer the random and systematic error characteristics of the satellite rain estimates. A fundamental problem of validating space-borne rain estimates is that the true mean areal rainfall is an ideal, scale-dependent parameter that cannot be directly measured. Empirical validation uses ground-based rain estimates to determine the error characteristics of the satellite-inferred rain estimates, but ground estimates also incur measurement errors and contribute to the error covariance. Furthermore, sampling errors, associated with the discrete, discontinuous temporal sampling by the rain sensors aboard the TRMM satellite, become statistically entangled in the monthly estimates. Sampling errors complicate the task of linking biases in the rain retrievals to the physics of the satellite algorithms. The TRMM Satellite Validation Office (TSVO) has made key progress towards effective satellite validation. For disentangling the sampling and retrieval errors, TSVO has developed and applied a methodology that statistically separates the two error sources. Using TRMM monthly estimates and high-resolution radar and gauge data, this method has been used to estimate sampling and retrieval error budgets over GV sites. More recently, a multi- year data set of instantaneous rain rates from the TRMM microwave imager (TMI), the precipitation radar (PR), and the combined algorithm was spatio-temporally matched and inter-compared to GV radar rain rates collected during satellite overpasses of select GV sites at the scale of the TMI footprint. The analysis provided a more direct probe of the satellite rain algorithms using ground data as an empirical reference. TSVO has also made significant advances in radar quality control through the development of the Relative Calibration Adjustment (RCA) technique. The RCA is currently being used to provide a long-term record of radar calibration for the radar at Kwajalein, a strategically important GV site in the tropical Pacific. The RCA technique has revealed previously undetected alterations in the radar sensitivity due to engineering changes (e.g., system modifications, antenna offsets, alterations of the receiver, or the data processor), making possible the correction of the radar rainfall measurements and ensuring the integrity of nearly a decade of TRMM GV observations and resources.

Visualizing and improving the robustness of phase retrieval algorithms

DOE PAGES

Tripathi, Ashish; Leyffer, Sven; Munson, Todd; ...

2015-06-01

Coherent x-ray diffractive imaging is a novel imaging technique that utilizes phase retrieval and nonlinear optimization methods to image matter at nanometer scales. We explore how the convergence properties of a popular phase retrieval algorithm, Fienup's HIO, behave by introducing a reduced dimensionality problem allowing us to visualize and quantify convergence to local minima and the globally optimal solution. We then introduce generalizations of HIO that improve upon the original algorithm's ability to converge to the globally optimal solution.

Visualizing and improving the robustness of phase retrieval algorithms

DOE Office of Scientific and Technical Information (OSTI.GOV)

Tripathi, Ashish; Leyffer, Sven; Munson, Todd

Coherent x-ray diffractive imaging is a novel imaging technique that utilizes phase retrieval and nonlinear optimization methods to image matter at nanometer scales. We explore how the convergence properties of a popular phase retrieval algorithm, Fienup's HIO, behave by introducing a reduced dimensionality problem allowing us to visualize and quantify convergence to local minima and the globally optimal solution. We then introduce generalizations of HIO that improve upon the original algorithm's ability to converge to the globally optimal solution.

Error and Uncertainty Quantification in Precipitation Retrievals from GPM/DPR Using Ground-based Dual-Polarization Radar Observations

NASA Astrophysics Data System (ADS)

Chandra, Chandrasekar V.; Chen*, Haonan; Petersen, Walter

2017-04-01

The active Dual-frequency Precipitation Radar (DPR) and passive radiometer onboard Global Precipitation Measurement (GPM) mission's Core Observatory extend the observation range attained by Tropical Rainfall Measuring Mission (TRMM) from tropical to most of the globe [1]. Through improved measurements of precipitation, the GPM mission is helping to advance our understanding of Earth's water and energy cycle, as well as climate changes. Ground Validation (GV) is an indispensable part of the GPM satellite mission. In the pre-launch era, several international validation experiments had already generated a substantial dataset that could be used to develop and test the pre-launch GPM algorithms. After launch, more ground validation field campaigns were conducted to further evaluate GPM precipitation data products as well as the sensitivities of retrieval algorithms. Among various validation equipment, ground based dual-polarization radar has shown great advantages to conduct precipitation estimation over a wide area in a relatively short time span. Therefore, radar is always a key component in all the validation field experiments. In addition, the radar polarization diversity has great potential to characterize precipitation microphysics through the identification of raindrop size distribution and different hydrometeor types [2]. Currently, all the radar sites comprising the U.S. National Weather Service (NWS) Weather Surveillance Radar-1988 Doppler (WSR-88DP) network are operating in dual-polarization mode. However, most of the operational radar based precipitation products are produced at coarse resolution typically on 1 km by 1 km spatial grids, focusing on precipitation accumulations at temporal scales of 1-h, 3-h, 6-h, 12-h, and/or 24-h (daily). Their capability for instantaneous GPM product validation is severely limited due to the spatial and temporal mismatching between observations from the ground and space. This paper first presents the rationale and opportunities of using dual-polarization radar in validation of precipitation retrievals from GPM/DPR. A new dual-polarization radar rainfall algorithm is proposed on this ground and implemented for WSR-88DP radar observations, especially when there are GPM satellite overpasses. In addition, an interpolation scheme is developed in order to map the WSR-88DP radar rainfall estimates that are updated every five-six minutes into instantaneous scale ( 1 minute). Detailed comparisons between instantaneous precipitation retrievals from GPM/DPR and WSR-88DP estimates before and after interpolation are investigated from a statistical perspective. [1] Hou, A., R. Kakar, S. Neeck, and Coauthors, 2014: The Global Precipitation Measurement Mission. Bull. Amer. Meteor. Soc., 95, 701-722. [2] Chen, Haonan, V. Chandrasekar, and R. Bechini, 2017: An Improved Dual-Polarization Radar Rainfall Algorithm (DROPS2.0): Application in NASA IFloodS Field Campaign. Journal of Hydrometeorology. doi:10.1175/JHM-D-16-0124.1

Improving Simulated Soil Moisture Fields Through Assimilation of AMSR-E Soil Moisture Retrievals with an Ensemble Kalman Filter and a Mass Conservation Constraint

NASA Technical Reports Server (NTRS)

Li, Bailing; Toll, David; Zhan, Xiwu; Cosgrove, Brian

2011-01-01

Model simulated soil moisture fields are often biased due to errors in input parameters and deficiencies in model physics. Satellite derived soil moisture estimates, if retrieved appropriately, represent the spatial mean of soil moisture in a footprint area, and can be used to reduce model bias (at locations near the surface) through data assimilation techniques. While assimilating the retrievals can reduce model bias, it can also destroy the mass balance enforced by the model governing equation because water is removed from or added to the soil by the assimilation algorithm. In addition, studies have shown that assimilation of surface observations can adversely impact soil moisture estimates in the lower soil layers due to imperfect model physics, even though the bias near the surface is decreased. In this study, an ensemble Kalman filter (EnKF) with a mass conservation updating scheme was developed to assimilate the actual value of Advanced Microwave Scanning Radiometer (AMSR-E) soil moisture retrievals to improve the mean of simulated soil moisture fields by the Noah land surface model. Assimilation results using the conventional and the mass conservation updating scheme in the Little Washita watershed of Oklahoma showed that, while both updating schemes reduced the bias in the shallow root zone, the mass conservation scheme provided better estimates in the deeper profile. The mass conservation scheme also yielded physically consistent estimates of fluxes and maintained the water budget. Impacts of model physics on the assimilation results are discussed.

Overlapped Fourier coding for optical aberration removal

PubMed Central

Horstmeyer, Roarke; Ou, Xiaoze; Chung, Jaebum; Zheng, Guoan; Yang, Changhuei

2014-01-01

We present an imaging procedure that simultaneously optimizes a camera’s resolution and retrieves a sample’s phase over a sequence of snapshots. The technique, termed overlapped Fourier coding (OFC), first digitally pans a small aperture across a camera’s pupil plane with a spatial light modulator. At each aperture location, a unique image is acquired. The OFC algorithm then fuses these low-resolution images into a full-resolution estimate of the complex optical field incident upon the detector. Simultaneously, the algorithm utilizes redundancies within the acquired dataset to computationally estimate and remove unknown optical aberrations and system misalignments via simulated annealing. The result is an imaging system that can computationally overcome its optical imperfections to offer enhanced resolution, at the expense of taking multiple snapshots over time. PMID:25321982

Nowcast and Short-Term Forecast of Solar Radiation for Photovoltaic power and Solar thermal using 3rd generation geostationary satellite.

NASA Astrophysics Data System (ADS)

Takenaka, H.; Teruyuki, N.; Nakajima, T. Y.; Higurashi, A.; Hashimoto, M.; Suzuki, K.; Uchida, J.; Nagao, T. M.; Shi, C.; Inoue, T.

2017-12-01

It is important to estimate the earth's radiation budget accurately for understanding of climate. Clouds can cool the Earth by reflecting solar radiation but also maintain warmth by absorbing and emitting terrestrial radiation. similarly aerosols also have an effect on radiation budget by absorption and scattering of Solar radiation. In this study, we developed the high speed and accurate algorithm for shortwave (SW) radiation budget and it's applied to geostationary satellite for rapid analysis. It enabled highly accurate monitoring of solar radiation and photo voltaic (PV) power generation. Next step, we try to update the algorithm for retrieval of Aerosols and Clouds. It indicates the accurate atmospheric parameters for estimation of solar radiation. (This research was supported in part by CREST/EMS).

Improvement of Aerosol Optical Depth Retrieval over Hong Kong from a Geostationary Meteorological Satellite Using Critical Reflectance with Background Optical Depth Correction

NASA Technical Reports Server (NTRS)

Kim, Mijin; Kim, Jhoon; Wong, Man Sing; Yoon, Jongmin; Lee, Jaehwa; Wu, Dong L.; Chan, P.W.; Nichol, Janet E.; Chung, Chu-Yong; Ou, Mi-Lim

2014-01-01

Despite continuous efforts to retrieve aerosol optical depth (AOD) using a conventional 5-channelmeteorological imager in geostationary orbit, the accuracy in urban areas has been poorer than other areas primarily due to complex urban surface properties and mixed aerosol types from different emission sources. The two largest error sources in aerosol retrieval have been aerosol type selection and surface reflectance. In selecting the aerosol type from a single visible channel, the season-dependent aerosol optical properties were adopted from longterm measurements of Aerosol Robotic Network (AERONET) sun-photometers. With the aerosol optical properties obtained fromthe AERONET inversion data, look-up tableswere calculated by using a radiative transfer code: the Second Simulation of the Satellite Signal in the Solar Spectrum (6S). Surface reflectance was estimated using the clear sky composite method, awidely used technique for geostationary retrievals. Over East Asia, the AOD retrieved from the Meteorological Imager showed good agreement, although the values were affected by cloud contamination errors. However, the conventional retrieval of the AOD over Hong Kong was largely underestimated due to the lack of information on the aerosol type and surface properties. To detect spatial and temporal variation of aerosol type over the area, the critical reflectance method, a technique to retrieve single scattering albedo (SSA), was applied. Additionally, the background aerosol effect was corrected to improve the accuracy of the surface reflectance over Hong Kong. The AOD retrieved froma modified algorithmwas compared to the collocated data measured by AERONET in Hong Kong. The comparison showed that the new aerosol type selection using the critical reflectance and the corrected surface reflectance significantly improved the accuracy of AODs in Hong Kong areas,with a correlation coefficient increase from0.65 to 0.76 and a regression line change from tMI [basic algorithm] = 0.41tAERONET + 0.16 to tMI [new algorithm] = 0.70tAERONET + 0.01.

Information Retrieval and Graph Analysis Approaches for Book Recommendation.

PubMed

Benkoussas, Chahinez; Bellot, Patrice

2015-01-01

A combination of multiple information retrieval approaches is proposed for the purpose of book recommendation. In this paper, book recommendation is based on complex user's query. We used different theoretical retrieval models: probabilistic as InL2 (Divergence from Randomness model) and language model and tested their interpolated combination. Graph analysis algorithms such as PageRank have been successful in Web environments. We consider the application of this algorithm in a new retrieval approach to related document network comprised of social links. We called Directed Graph of Documents (DGD) a network constructed with documents and social information provided from each one of them. Specifically, this work tackles the problem of book recommendation in the context of INEX (Initiative for the Evaluation of XML retrieval) Social Book Search track. A series of reranking experiments demonstrate that combining retrieval models yields significant improvements in terms of standard ranked retrieval metrics. These results extend the applicability of link analysis algorithms to different environments.

Information Retrieval and Graph Analysis Approaches for Book Recommendation

PubMed Central

Benkoussas, Chahinez; Bellot, Patrice

2015-01-01

A combination of multiple information retrieval approaches is proposed for the purpose of book recommendation. In this paper, book recommendation is based on complex user's query. We used different theoretical retrieval models: probabilistic as InL2 (Divergence from Randomness model) and language model and tested their interpolated combination. Graph analysis algorithms such as PageRank have been successful in Web environments. We consider the application of this algorithm in a new retrieval approach to related document network comprised of social links. We called Directed Graph of Documents (DGD) a network constructed with documents and social information provided from each one of them. Specifically, this work tackles the problem of book recommendation in the context of INEX (Initiative for the Evaluation of XML retrieval) Social Book Search track. A series of reranking experiments demonstrate that combining retrieval models yields significant improvements in terms of standard ranked retrieval metrics. These results extend the applicability of link analysis algorithms to different environments. PMID:26504899

Recent Progress on the Second Generation CMORPH: LEO-IR Based Precipitation Estimates and Cloud Motion Vector

NASA Astrophysics Data System (ADS)

Xie, Pingping; Joyce, Robert; Wu, Shaorong

2015-04-01

As reported at the EGU General Assembly of 2014, a prototype system was developed for the second generation CMORPH to produce global analyses of 30-min precipitation on a 0.05olat/lon grid over the entire globe from pole to pole through integration of information from satellite observations as well as numerical model simulations. The second generation CMORPH is built upon the Kalman Filter based CMORPH algorithm of Joyce and Xie (2011). Inputs to the system include rainfall and snowfall rate retrievals from passive microwave (PMW) measurements aboard all available low earth orbit (LEO) satellites, precipitation estimates derived from infrared (IR) observations of geostationary (GEO) as well as LEO platforms, and precipitation simulations from numerical global models. Key to the success of the 2nd generation CMORPH, among a couple of other elements, are the development of a LEO-IR based precipitation estimation to fill in the polar gaps and objectively analyzed cloud motion vectors to capture the cloud movements of various spatial scales over the entire globe. In this presentation, we report our recent work on the refinement for these two important algorithm components. The prototype algorithm for the LEO IR precipitation estimation is refined to achieve improved quantitative accuracy and consistency with PMW retrievals. AVHRR IR TBB data from all LEO satellites are first remapped to a 0.05olat/lon grid over the entire globe and in a 30-min interval. Temporally and spatially co-located data pairs of the LEO TBB and inter-calibrated combined satellite PMW retrievals (MWCOMB) are then collected to construct tables. Precipitation at a grid box is derived from the TBB through matching the PDF tables for the TBB and the MWCOMB. This procedure is implemented for different season, latitude band and underlying surface types to account for the variations in the cloud - precipitation relationship. At the meantime, a sub-system is developed to construct analyzed fields of cloud motion vectors from the GEO/LEO IR based precipitation estimates and the CFS Reanalysis (CFSR) precipitation fields. Motion vectors are first derived separately from the satellite IR based precipitation estimates and the CFSR precipitation fields. These individually derived motion vectors are then combined through a 2D-VAR technique to form an analyzed field of cloud motion vectors over the entire globe. Error function is experimented to best reflect the performance of the satellite IR based estimates and the CFSR in capturing the movements of precipitating cloud systems over different regions and for different seasons. Quantitative experiments are conducted to optimize the LEO IR based precipitation estimation technique and the 2D-VAR based motion vector analysis system. Detailed results will be reported at the EGU.

Passive Microwave Precipitation Retrieval Uncertainty Characterized based on Field Campaign Data over Complex Terrain

NASA Astrophysics Data System (ADS)

Derin, Y.; Anagnostou, E. N.; Anagnostou, M.; Kalogiros, J. A.; Casella, D.; Marra, A. C.; Panegrossi, G.; Sanò, P.

2017-12-01

Difficulties in representation of high rainfall variability over mountainous areas using ground based sensors make satellite remote sensing techniques attractive for hydrologic studies over these regions. Even though satellite-based rainfall measurements are quasi global and available at high spatial resolution, these products have uncertainties that necessitate use of error characterization and correction procedures based upon more accurate in situ rainfall measurements. Such measurements can be obtained from field campaigns facilitated by research quality sensors such as locally deployed weather radar and in situ weather stations. This study uses such high quality and resolution rainfall estimates derived from dual-polarization X-band radar (XPOL) observations from three field experiments in Mid-Atlantic US East Coast (NASA IPHEX experiment), the Olympic Peninsula of Washington State (NASA OLYMPEX experiment), and the Mediterranean to characterize the error characteristics of multiple passive microwave (PMW) sensor retrievals. The study first conducts an independent error analysis of the XPOL radar reference rainfall fields against in situ rain gauges and disdrometer observations available by the field experiments. Then the study evaluates different PMW precipitation products using the XPOL datasets (GR) over the three aforementioned complex terrain study areas. We extracted matchups of PMW/GR rainfall based on a matching methodology that identifies GR volume scans coincident with PMW field-of-view sampling volumes, and scaled GR parameters to the satellite products' nominal spatial resolution. The following PMW precipitation retrieval algorithms are evaluated: the NASA Goddard PROFiling algorithm (GPROF), standard and climatology-based products (V 3, 4 and 5) from four PMW sensors (SSMIS, MHS, GMI, and AMSR2), and the precipitation products based on the algorithms Cloud Dynamics and Radiation Database (CDRD) for SSMIS and Passive microwave Neural network Precipitation Retrieval (PNPR) for AMSU/MHS, developed at ISAC-CNR within the EUMETSAT H-SAF. We will present error analysis results for the different PMW rainfall retrievals and discuss dependences on precipitation type, elevation and precipitation microphysics (derived from XPOL).

Information content of ozone retrieval algorithms

NASA Technical Reports Server (NTRS)

Rodgers, C.; Bhartia, P. K.; Chu, W. P.; Curran, R.; Deluisi, J.; Gille, J. C.; Hudson, R.; Mateer, C.; Rusch, D.; Thomas, R. J.

1989-01-01

The algorithms are characterized that were used for production processing by the major suppliers of ozone data to show quantitatively: how the retrieved profile is related to the actual profile (This characterizes the altitude range and vertical resolution of the data); the nature of systematic errors in the retrieved profiles, including their vertical structure and relation to uncertain instrumental parameters; how trends in the real ozone are reflected in trends in the retrieved ozone profile; and how trends in other quantities (both instrumental and atmospheric) might appear as trends in the ozone profile. No serious deficiencies were found in the algorithms used in generating the major available ozone data sets. As the measurements are all indirect in someway, and the retrieved profiles have different characteristics, data from different instruments are not directly comparable.

Extending "Deep Blue" Aerosol Retrieval Coverage to Cases of Absorbing Aerosols Above Clouds: Sensitivity Analysis and First Case Studies

NASA Technical Reports Server (NTRS)

Sayer, A. M.; Hsu, N. C.; Bettenhausen, C.; Lee, J.; Redemann, J.; Schmid, B.; Shinozuka, Y.

2016-01-01

Cases of absorbing aerosols above clouds (AACs), such as smoke or mineral dust, are omitted from most routinely processed space-based aerosol optical depth (AOD) data products, including those from the Moderate Resolution Imaging Spectroradiometer (MODIS). This study presents a sensitivity analysis and preliminary algorithm to retrieve above-cloud AOD and liquid cloud optical depth (COD) for AAC cases from MODIS or similar sensors, for incorporation into a future version of the "Deep Blue" AOD data product. Detailed retrieval simulations suggest that these sensors should be able to determine AAC AOD with a typical level of uncertainty approximately 25-50 percent (with lower uncertainties for more strongly absorbing aerosol types) and COD with an uncertainty approximately10-20 percent, if an appropriate aerosol optical model is known beforehand. Errors are larger, particularly if the aerosols are only weakly absorbing, if the aerosol optical properties are not known, and the appropriate model to use must also be retrieved. Actual retrieval errors are also compared to uncertainty envelopes obtained through the optimal estimation (OE) technique; OE-based uncertainties are found to be generally reasonable for COD but larger than actual retrieval errors for AOD, due in part to difficulties in quantifying the degree of spectral correlation of forward model error. The algorithm is also applied to two MODIS scenes (one smoke and one dust) for which near-coincident NASA Ames Airborne Tracking Sun photometer (AATS) data were available to use as a ground truth AOD data source, and found to be in good agreement, demonstrating the validity of the technique with real observations.

Sensitivity Study of IROE Cloud Retrievals Using VIIRS M-Bands and Combined VIIRS/CrIS IR Observations

NASA Astrophysics Data System (ADS)

Wang, C.; Platnick, S. E.; Meyer, K.; Ackerman, S. A.; Holz, R.; Heidinger, A.

2017-12-01

The Visible Infrared Imaging Radiometer Suite (VIIRS) on board the Suomi-NPP spacecraft is considered as the next generation of instrument providing operational moderate resolution imaging capabilities after the Moderate Resolution Imaging Spectroradiometer (MODIS) on Terra and Aqua. However, cloud-top property (CTP) retrieval algorithms designed for the two instruments cannot be identical because of the absence of CO2 bands on VIIRS. In this study, we conduct a comprehensive sensitivity study of cloud retrievals utilizing a IR-Optimal Estimation (IROE) based algorithm. With a fast IR radiative transfer model, the IROE simultaneously retrieves cloud-top height (CTH), cloud optical thickness (COT), cloud effective radius (CER) and corresponding uncertainties using a set of IR bands. Three retrieval runs are implemented for this sensitivity study: retrievals using 1) three native VIIRS M-Bands at 750m resolution (8.5-, 11-, and 12-μm), 2) three native VIIRS M-Bands with spectrally integrated CO2 bands from the Cross-Track Infrared Sounder (CrIS), and 3) six MODIS IR bands (8.5-, 11-, 12-, 13.3-, 13.6-, and 13.9-μm). We select a few collocated MODIS and VIIRS granules for pixel-level comparison. Furthermore, aggregated daily and monthly cloud properties from the three runs are also compared. It shows that, the combined VIIRS/CrIS run agrees well with the MODIS-only run except for pixels near cloud edges. The VIIRS-only run is close to its counterparts when clouds are optically thick. However, for optically thin clouds, the VIIRS-only run can be readily influenced by the initial guess. Large discrepancies and uncertainties can be found for optically thin clouds from the VIIRS-only run.

Polarimetric Radar Retrievals in Southeast Texas During Hurricane Harvey

NASA Astrophysics Data System (ADS)

Wolff, D. B.; Petersen, W. A.; Tokay, A.; Marks, D. A.; Pippitt, J. L.; Kirstetter, P. E.

2017-12-01

Hurricane Harvey hit the Texas Gulf Coast as a major hurricane on August 25, 2017 before exiting the state as a tropical storm on September 1, 2017. In its wake, it left a flood of historic proportions, with some areas measuring 60 inches of rain over a five-day period. Although the storm center stayed west of the immediate Houston area training bands of precipitation impacted the Houston area for five full days. The National Weather Service (NWS) WSR88D dual-polarimetric radar (KHGX), located southeast of Houston, maintained operations for the entirety of the event. The Harris County Flood Warning System (HCFWS) had 150 rain gauges deployed in its network and seven NWS Automated Surface Observing Systems (ASOS) rain gauges are also located in the area. In this study, we used the full radar data set to retrieve daily and event-total precipitation estimates within 120 km of the KHGX radar for the period August 25-29, 2017. These estimates were then compared to the HCFWS and ASOS gauges. Three different polarimetric hybrid rainfall retrievals were used: Ciffeli et al. 2011; Bringi et al. 2004; and, Chen et al. 2017. Each of these hybrid retrievals have demonstrated robust performance in the past. However, both daily and event-total comparisons from each of these retrievals compared to those of HCFWS and ASOS rain gauge networks resulted in significant underestimates by the radar retrievals. These radar underestimates are concerning. Sources of error and variance will be investigated to understand the source of radar-gauge disagreement. One current hypothesis is that due to the large number of small drops often found in hurricanes, the differential reflectivity and specific differential phase are relatively small so that the hybrid algorithms use only the reflectivity/rain rate procedure (so called Z-R relationships), and hence rarely invoke the ZDR or KDP procedures. Thus, an alternative Z-R relationship must be invoked to retrieve accurate rain rate estimates.

Estimating optically-thin cirrus cloud induced cold bias on infrared radiometric satellite sea surface temperature retrieval in the tropics

NASA Astrophysics Data System (ADS)

Marquis, Jared Wayne

Passive longwave infrared radiometric satellite-based retrievals of sea surface temperature (SST) at instrument nadir are investigated for cold bias caused by unscreened optically-thin cirrus (OTC) clouds (cloud optical depth ≤ 0.3; COD). Level 2 split-window SST retrievals over tropical oceans (30° S - 30° N) from Moderate Resolution Imaging Spectroradiometer (MODIS) radiances collected aboard the NASA Aqua satellite (Aqua-MODIS) are collocated with cloud profiles from the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) instrument, mounted on the independent NASA CALIPSO satellite. OTC are present in approximately 25% of tropical quality-assured (QA) Aqua-MODIS Level-2 data, representing over 99% of all contaminating cirrus found. This results in cold-biased SST retrievals using either split- (MODIS, AVHRR and VIIRS) or triple-window (AVHRR and VIIRS only) retrieval methods. SST retrievals are modeled based on operational algorithms using radiative transfer model simulations conducted with a hypothetical 1.5 km thick OTC cloud placed incrementally from 10.0 - 18.0 km above mean sea level for cloud optical depths (COD) between 0.0 - 0.3. Corresponding cold bias estimates for each sensor are estimated using relative Aqua-MODIS cloud contamination frequencies as a function of cloud top height and COD (assuming them consistent across each platform) integrated within each corresponding modeled cold bias matrix. Split-window relative OTC cold biases, for any single observation, range from 0.40° - 0.49° C for the three sensors, with an absolute (bulk mean) bias between 0.10° - 0.13° C. Triple-window retrievals are more resilient, ranging from 0.03° - 0.04° C relative and 0.11° - 0.16° C absolute. Cold biases are constant across the Pacific and Indian Ocean domains. Absolute bias is smaller over the Atlantic, but relative bias is larger due to different cloud properties indicating that this issue persists globally.

Multispectrum retrieval techniques applied to Venus deep atmosphere and surface problems

NASA Astrophysics Data System (ADS)

Kappel, David; Arnold, Gabriele; Haus, Rainer

The Visible and Infrared Thermal Imaging Spectrometer (VIRTIS) aboard ESA's Venus Express is continuously collecting nightside emission data (among others) from Venus. A radiative transfer model of Venus' atmosphere in conjunction with a suitable retrieval algorithm can be used to estimate atmospheric and surface parameters by fitting simulated spectra to the measured data. Because of the limited spectral resolution of VIRTIS-M-IR-spectra, that have been used so far, many different parameter sets can explain the same measurement equally well. As a common regulative measure, reasonable a priori knowledge of some parameters is applied to suppress solutions implausibly far from the expected range. It is beneficial to introduce a parallel coupled retrieval of several measurements. Since spa-tially and temporally contiguous measurements are not expected to originate from completely unrelated parameters, an assumed a priori correlation of the parameters during the retrieval can help to reduce arbitrary fluctuations of the solutions, to avoid subsidiary solutions, and to attenuate the interference of measurement noise by keeping the parameters close to a gen-eral trend. As an illustration, the resulting improvements for some swaths on the Northern hemisphere are presented. Some atmospheric features are still not very well constrained, for instance CO2 absorption under the extreme environmental conditions close to the surface. A broad band continuum due to far wing and collisional induced absorptions is commonly used to correct individual line absorption. Since the spectrally dependent continuum is constant for all measurements, the retrieval of parameters common to all spectra may be used to give some estimates of the continuum absorption. These estimates are necessary, for example, for the coupled parallel retrieval of a consistent local cloud modal composition, which in turn enables a refined surface emissivity retrieval. We gratefully acknowledge the support from the VIRTIS/Venus Express Team, from ASI, CNES, CNRS, and from the DFG funding the ongoing work.

Empirical Soil Moisture Estimation with Spaceborne L-band Polarimetric Radars: Aquarius, SMAP, and PALSAR-2

NASA Astrophysics Data System (ADS)

Burgin, M. S.; van Zyl, J. J.

2017-12-01

Traditionally, substantial ancillary data is needed to parametrize complex electromagnetic models to estimate soil moisture from polarimetric radar data. The Soil Moisture Active Passive (SMAP) baseline radar soil moisture retrieval algorithm uses a data cube approach, where a cube of radar backscatter values is calculated using sophisticated models. In this work, we utilize the empirical approach by Kim and van Zyl (2009) which is an optional SMAP radar soil moisture retrieval algorithm; it expresses radar backscatter of a vegetated scene as a linear function of soil moisture, hence eliminating the need for ancillary data. We use 2.5 years of L-band Aquarius radar and radiometer derived soil moisture data to determine two coefficients of a linear model function on a global scale. These coefficients are used to estimate soil moisture with 2.5 months of L-band SMAP and L-band PALSAR-2 data. The estimated soil moisture is compared with the SMAP Level 2 radiometer-only soil moisture product; the global unbiased RMSE of the SMAP derived soil moisture corresponds to 0.06-0.07 cm3/cm3. In this study, we leverage the three diverse L-band radar data sets to investigate the impact of pixel size and pixel heterogeneity on soil moisture estimation performance. Pixel sizes range from 100 km for Aquarius, over 3, 9, 36 km for SMAP, to 10m for PALSAR-2. Furthermore, we observe seasonal variation in the radar sensitivity to soil moisture which allows the identification and quantification of seasonally changing vegetation. Utilizing this information, we further improve the estimation performance. The research described in this paper is supported by the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics and Space Administration. Copyright 2017. All rights reserved.

Optimal Aerosol Parameterization for Remote Sensing Retrievals

NASA Technical Reports Server (NTRS)

Newchurch, Michael J.

2004-01-01

We have developed a new algorithm for the retrieval of aerosol and gases from SAGE It1 solar transmission measurements. This algorithm improves upon the NASA operational algorithm in several key aspects, including solving the problem non-linearly and incorporating a new methodology for separating the contribution of aerosols and gases. In order to extract aerosol information we have built a huge database of aerosol models for both stratospheric and tropospheric aerosols, and polar stratospheric cloud particles. This set of models allows us to calculate a vast range of possible extinction spectra for aerosols. and from these, derive a set of eigenvectors which then provide the basis set used in our inversion algorithm. Our aerosol algorithm and retrievals are described in several articles (listed in References Section) published under this grant. In particular they allow us to analyze the spectral properties of aerosols and PSCs and ultimately derive their microphysical properties. We have found some considerable differences between our spectra and the ones derived from the SAGE III operational algorithm. These are interesting as they provide an independent check on the validity of published aerosol data and, in particular, on their associated uncertainties. In order to understand these differences, we are assembling independent aerosol data from other sources with which to make comparisons. We have carried out extensive comparisons of our ozone retrievals with both SAGE III and independent lidar, ozonesonde, and satellite measurements (Polyakov et al., 2004). These show very good agreement throughout the stratosphere and help to quantify differences which can be attributed to natural variation in ozone versus that produced by algorithmic differences. In the mid - upper stratosphere, agreement with independent data was generally within 5 - 20%. but in the lower stratosphere the differences were considerably larger. We believe that a large proportion of this discrepancy in the lower stratosphere is attributable to natural variation, and is also seen in comparisons between lidar and ozonesonde measurements. NO2 profiles obtained with our algorithm were compared to those obtained through the SAGE III operational algorithm and exhibited differences of 20 - 40%. Our retrieved profiles agree with the HALOE NO2 measurements significantly better than those of the operational retrieval. In other work (described below), we are extending our aerosol retrievals into the infrared regime and plan to perform retrievals from combined uv-visible-infrared spectra. This work will allow us to use the spectra to derive the size and composition of aerosols, and we plan to employ our algorithms in the analysis of PSC spectra. We are presently also developing a limb-scattering algorithm to retrieve aerosol data from limb measurements of solar scattered radiation.

Dreaming of Atmospheres

NASA Astrophysics Data System (ADS)

Waldmann, I. P.

2016-04-01

Here, we introduce the RobERt (Robotic Exoplanet Recognition) algorithm for the classification of exoplanetary emission spectra. Spectral retrieval of exoplanetary atmospheres frequently requires the preselection of molecular/atomic opacities to be defined by the user. In the era of open-source, automated, and self-sufficient retrieval algorithms, manual input should be avoided. User dependent input could, in worst-case scenarios, lead to incomplete models and biases in the retrieval. The RobERt algorithm is based on deep-belief neural (DBN) networks trained to accurately recognize molecular signatures for a wide range of planets, atmospheric thermal profiles, and compositions. Reconstructions of the learned features, also referred to as the “dreams” of the network, indicate good convergence and an accurate representation of molecular features in the DBN. Using these deep neural networks, we work toward retrieval algorithms that themselves understand the nature of the observed spectra, are able to learn from current and past data, and make sensible qualitative preselections of atmospheric opacities to be used for the quantitative stage of the retrieval process.

Remote Sensing of Cloud Properties using Ground-based Measurements of Zenith Radiance

NASA Technical Reports Server (NTRS)

Chiu, J. Christine; Marshak, Alexander; Knyazikhin, Yuri; Wiscombe, Warren J.; Barker, Howard W.; Barnard, James C.; Luo, Yi

2006-01-01

An extensive verification of cloud property retrievals has been conducted for two algorithms using zenith radiances measured by the Atmospheric Radiation Measurement (ARM) Program ground-based passive two-channel (673 and 870 nm) Narrow Field-Of-View Radiometer. The underlying principle of these algorithms is that clouds have nearly identical optical properties at these wavelengths, but corresponding spectral surface reflectances (for vegetated surfaces) differ significantly. The first algorithm, the RED vs. NIR, works for a fully three-dimensional cloud situation. It retrieves not only cloud optical depth, but also an effective radiative cloud fraction. Importantly, due to one-second time resolution of radiance measurements, we are able, for the first time, to capture detailed changes in cloud structure at the natural time scale of cloud evolution. The cloud optical depths tau retrieved by this algorithm are comparable to those inferred from both downward fluxes in overcast situations and microwave brightness temperatures for broken clouds. Moreover, it can retrieve tau for thin patchy clouds, where flux and microwave observations fail to detect them. The second algorithm, referred to as COUPLED, couples zenith radiances with simultaneous fluxes to infer 2. In general, the COUPLED and RED vs. NIR algorithms retrieve consistent values of tau. However, the COUPLED algorithm is more sensitive to the accuracies of measured radiance, flux, and surface reflectance than the RED vs. NIR algorithm. This is especially true for thick overcast clouds where it may substantially overestimate z.

Improving Satellite Quantitative Precipitation Estimation Using GOES-Retrieved Cloud Optical Depth

DOE Office of Scientific and Technical Information (OSTI.GOV)

Stenz, Ronald; Dong, Xiquan; Xi, Baike

To address significant gaps in ground-based radar coverage and rain gauge networks in the U.S., geostationary satellite quantitative precipitation estimates (QPEs) such as the Self-Calibrating Multivariate Precipitation Retrievals (SCaMPR) can be used to fill in both the spatial and temporal gaps of ground-based measurements. Additionally, with the launch of GOES-R, the temporal resolution of satellite QPEs may be comparable to that of Weather Service Radar-1988 Doppler (WSR-88D) volume scans as GOES images will be available every five minutes. However, while satellite QPEs have strengths in spatial coverage and temporal resolution, they face limitations particularly during convective events. Deep Convective Systemsmore » (DCSs) have large cloud shields with similar brightness temperatures (BTs) over nearly the entire system, but widely varying precipitation rates beneath these clouds. Geostationary satellite QPEs relying on the indirect relationship between BTs and precipitation rates often suffer from large errors because anvil regions (little/no precipitation) cannot be distinguished from rain-cores (heavy precipitation) using only BTs. However, a combination of BTs and optical depth (τ) has been found to reduce overestimates of precipitation in anvil regions (Stenz et al. 2014). A new rain mask algorithm incorporating both τ and BTs has been developed, and its application to the existing SCaMPR algorithm was evaluated. The performance of the modified SCaMPR was evaluated using traditional skill scores and a more detailed analysis of performance in individual DCS components by utilizing the Feng et al. (2012) classification algorithm. SCaMPR estimates with the new rain mask applied benefited from significantly reduced overestimates of precipitation in anvil regions and overall improvements in skill scores.« less

Does the Acquisition of Spatial Skill Involve a Shift from Algorithm to Memory Retrieval?

ERIC Educational Resources Information Center

Frank, David J.; Macnamara, Brooke N.

2017-01-01

Performance on verbal and mathematical tasks is enhanced when participants shift from using algorithms to retrieving information directly from memory (Siegler, 1988a). However, it is unknown whether a shift to retrieval is involved in dynamic spatial skill acquisition. For example, do athletes mentally extrapolate the trajectory of the ball, or do…

Assessment of Uncertainty in Cloud Radiative Effects and Heating Rates through Retrieval Algorithm Differences: Analysis using 3-years of ARM data at Darwin, Australia

DOE Office of Scientific and Technical Information (OSTI.GOV)

Comstock, Jennifer M.; Protat, Alain; McFarlane, Sally A.

2013-05-22

Ground-based radar and lidar observations obtained at the Department of Energy’s Atmospheric Radiation Measurement Program’s Tropical Western Pacific site located in Darwin, Australia are used to retrieve ice cloud properties in anvil and cirrus clouds. Cloud microphysical properties derived from four different retrieval algorithms (two radar-lidar and two radar only algorithms) are compared by examining mean profiles and probability density functions of effective radius (Re), ice water content (IWC), extinction, ice number concentration, ice crystal fall speed, and vertical air velocity. Retrieval algorithm uncertainty is quantified using radiative flux closure exercises. The effect of uncertainty in retrieved quantities on themore » cloud radiative effect and radiative heating rates are presented. Our analysis shows that IWC compares well among algorithms, but Re shows significant discrepancies, which is attributed primarily to assumptions of particle shape. Uncertainty in Re and IWC translates into sometimes-large differences in cloud radiative effect (CRE) though the majority of cases have a CRE difference of roughly 10 W m-2 on average. These differences, which we believe are primarily driven by the uncertainty in Re, can cause up to 2 K/day difference in the radiative heating rates between algorithms.« less

Attitude angle effects on Nimbus-7 Scanning Multichannel Microwave Radiometer radiances and geophysical parameter retrievals

NASA Technical Reports Server (NTRS)

Macmillan, Daniel S.; Han, Daesoo

1989-01-01

The attitude of the Nimbus-7 spacecraft has varied significantly over its lifetime. A summary of the orbital and long-term behavior of the attitude angles and the effects of attitude variations on Scanning Multichannel Microwave Radiometer (SMMR) brightness temperatures is presented. One of the principal effects of these variations is to change the incident angle at which the SMMR views the Earth's surface. The brightness temperatures depend upon the incident angle sensitivities of both the ocean surface emissivity and the atmospheric path length. Ocean surface emissivity is quite sensitive to incident angle variation near the SMMR incident angle, which is about 50 degrees. This sensitivity was estimated theoretically for a smooth ocean surface and no atmosphere. A 1-degree increase in the angle of incidence produces a 2.9 C increase in the retrieved sea surface temperature and a 5.7 m/sec decrease in retrieved sea surface wind speed. An incident angle correction is applied to the SMMR radiances before using them in the geophysical parameter retrieval algorithms. The corrected retrieval data is compared with data obtained without applying the correction.

Active learning methods for interactive image retrieval.

PubMed

Gosselin, Philippe Henri; Cord, Matthieu

2008-07-01

Active learning methods have been considered with increased interest in the statistical learning community. Initially developed within a classification framework, a lot of extensions are now being proposed to handle multimedia applications. This paper provides algorithms within a statistical framework to extend active learning for online content-based image retrieval (CBIR). The classification framework is presented with experiments to compare several powerful classification techniques in this information retrieval context. Focusing on interactive methods, active learning strategy is then described. The limitations of this approach for CBIR are emphasized before presenting our new active selection process RETIN. First, as any active method is sensitive to the boundary estimation between classes, the RETIN strategy carries out a boundary correction to make the retrieval process more robust. Second, the criterion of generalization error to optimize the active learning selection is modified to better represent the CBIR objective of database ranking. Third, a batch processing of images is proposed. Our strategy leads to a fast and efficient active learning scheme to retrieve sets of online images (query concept). Experiments on large databases show that the RETIN method performs well in comparison to several other active strategies.

Nadir Ozone Profile Retrieval from SCIAMACHY: application to the Antarctic Ozone Hole

NASA Astrophysics Data System (ADS)

Shah, Sweta; Piet, Stammes; Tuinder, Olaf N. E.; de Laat, Jos

2017-04-01

We present new nadir ozone profile retrievals using SCIAMACHY UV reflectance spectra for the mission period of the Envisat satellite. We have used the most recent Level-1 data version (v8 with degradation correction included) in the UV range (265-330 nm) and have used the OPERA optimal estimation algorithm (van Peet et al., AMT, 2014) developed in KNMI. We first show the comparison of the retrieved satellite profiles to co-located ozone sonde profiles in order to evaluate the accuracy of the retrieved ozone profile dataset. Based on these results, we have further processed the SCIAMCHY nadir dataset, specifically all the southern hemisphere pixels south of 45 degrees latitude for the months of August-November for the complete years 2003-2011. We show the monthly mean profiles, time-series of daily averages and minima of the retrieved stratospheric columns, and finally the ozone profile trend over the years 2003-2011. We also show the comparison of our results with the literature and hence the consistency of this new SCIAMACHY dataset.

Optimization of the GSFC TROPOZ DIAL retrieval using synthetic lidar returns and ozonesondes - Part 1: Algorithm validation

NASA Astrophysics Data System (ADS)

Sullivan, J. T.; McGee, T. J.; Leblanc, T.; Sumnicht, G. K.; Twigg, L. W.

2015-04-01

The main purpose of the NASA Goddard Space Flight Center TROPospheric OZone DIfferential Absorption Lidar (GSFC TROPOZ DIAL) is to measure the vertical distribution of tropospheric ozone for science investigations. Because of the important health and climate impacts of tropospheric ozone, it is imperative to quantify background photochemical and aloft ozone concentrations, especially during air quality episodes. To better characterize tropospheric ozone, the Tropospheric Ozone Lidar Network (TOLNet) has recently been developed, which currently consists of five different ozone DIAL instruments, including the TROPOZ. This paper addresses the necessary procedures to validate the TROPOZ retrieval algorithm and develops a primary standard for retrieval consistency and optimization within TOLNet. This paper is focused on ensuring the TROPOZ and future TOLNet algorithms are properly quantifying ozone concentrations and the following paper will focus on defining a systematic uncertainty analysis standard for all TOLNet instruments. Although this paper is used to optimize the TROPOZ retrieval, the methodology presented may be extended and applied to most other DIAL instruments, even if the atmospheric product of interest is not tropospheric ozone (e.g. temperature or water vapor). The analysis begins by computing synthetic lidar returns from actual TROPOZ lidar return signals in combination with a known ozone profile. From these synthetic signals, it is possible to explicitly determine retrieval algorithm biases from the known profile, thereby identifying any areas that may need refinement for a new operational version of the TROPOZ retrieval algorithm. A new vertical resolution scheme is presented, which was upgraded from a constant vertical resolution to a variable vertical resolution, in order to yield a statistical uncertainty of <10%. The optimized vertical resolution scheme retains the ability to resolve fluctuations in the known ozone profile and now allows near field signals to be more appropriately smoothed. With these revisions, the optimized TROPOZ retrieval algorithm (TROPOZopt) has been effective in retrieving nearly 200 m lower to the surface. Also, as compared to the previous version of the retrieval, the TROPOZopt has reduced the mean profile bias by 3.5% and large reductions in bias (near 15 %) were apparent above 4.5 km. Finally, to ensure the TROPOZopt retrieval algorithm is robust enough to handle actual lidar return signals, a comparison is shown between four nearby ozonesonde measurements. The ozonesondes agree well with the retrieval and are mostly within the TROPOZopt retrieval uncertainty bars (which implies that this exercise was quite successful). A final mean percent difference plot is shown between the TROPOZopt and ozonesondes, which indicates that the new operational retrieval is mostly within 10% of the ozonesonde measurement and no systematic biases are present. The authors believe that this analysis has significantly added to the confidence in the TROPOZ instrument and provides a standard for current and future TOLNet algorithms.

Shoreline to Height (S2H): an algorithm to monitor reservoirs' water height from satellite images. A flood risk management application

NASA Astrophysics Data System (ADS)

Cenci, Luca; Boni, Giorgio; Pulvirenti, Luca; Gabellani, Simone; Gardella, Fabio; Squicciarino, Giuseppe; Pierdicca, Nazzareno; Benedetto, Catia

2016-04-01

In a reservoir, water level monitoring is important for emergency management purposes. This information can be used to estimate the degree of filling of the water body, thus helping decision makers in flood control operations. Furthermore, if assimilated in hydrological models and coupled with rainfall forecasts, this information can be used for flood forecast and early warning. In many cases, water level is not known (e.g. data-scarce environments), or not shared by operators. Remote sensing may allow overcoming these limitations, enabling its estimation. The objective of this work is to present the Shoreline to Height (S2H) algorithm, developed to retrieve the height of the water stored in reservoirs from satellite images. To this aim, some auxiliary data are needed: a DEM and the maximum/minimum height that can be reached by the water. In data-scarce environments, these information can be easily obtained on the Internet (e.g. free, worldwide DEM and design data for artificial reservoirs). S2H was tested with different satellite data, both optical and SAR (Landsat and Cosmo SkyMed®-CSK®) in order to assess the impact of different sensors on the final estimates. The study area was the Place-Moulin Lake (Valle d'Aosta-VdA, Italy), where it is present a monitoring network that can provide reliable ground-truths for validating the algorithm and assessing its accuracy. When the algorithm was developed, it was assumed to be in absence of any "official"-auxiliary data. Therefore, two DEMs (SRTM 1 arc-second and ASTER GDEM) were used to evaluate their performances. The maximum/minimum water height values were found on the website of VdA Region. The S2H is based on three steps: i) satellite data preprocessing (Landsat: atmospheric correction; CSK®: geocoding and speckle filtering); ii) water mask generation (using a thresholding and region growing algorithm) and shoreline extraction; iii) retrieval of the shoreline height according to the reference DEMs (adopting a statistical approach). The algorithm was tested for different water heights and results were compared against ground-truths. Findings showed that the combination CSK®-SRTM provided more reliable results. It was also found that the overall quality of the estimates increases as the water height increases, reaching an accuracy up to some centimetres. This result is particularly interesting for flood control applications, where it is important to be accurate when the reservoir's degree of filling is high. The potentialities of S2H for operational hydrology purposes were tested in a real-case simulation, in which the river discharge's prediction downstream of the dam was needed for flood risk management purposes. The water height value retrieved with S2H was assimilated within a semi-distributed, event-based, hydrological model (DRiFt) by using a simple direct insertion algorithm. DRiFt is usually run in operative way on the reservoir by using ground-truths as input data. The result of the data assimilation experiment was compared with the "real", operative run of the model. Findings showed a high agreement between the two simulations, proving the utility/quality of the S2H algorithm. "Project carried out using CSK® Products, © of the Italian Space Agency (ASI), delivered under a license to use by ASI."

Application of OMI, SCIAMACHY and GOME-2 Satellite SO2 Retrievals for Detection of Large Emission Sources

NASA Technical Reports Server (NTRS)

Fioletov, V.E.; McLinden, C. A.; Krotkov, N.; Yang, K.; Loyola, D. G.; Valks, P.; Theys, N.; Van Roozendael, M.; Nowlan, C. R.; Chance, K.;

Surface soil moisture retrieval over a Mediterranean semi-arid region using X-band TerraSAR-X SAR data

NASA Astrophysics Data System (ADS)

Azza, Gorrab; Zribi, Mehrez; Baghdadi, Nicolas; Mougenot, Bernard; Boulet, Gilles; Lili-Chabaane, Zohra

2015-04-01

Mapping surface soil moisture with meter-scale spatial resolution is appropriate for multi- domains particularly hydrology and agronomy. It allows water resources and irrigation management decisions, drought monitoring and validation of multi-hydrological water balance models. In the last years, various studies have demonstrated the large potential of radar remote sensing data, mainly from C frequency band, to retrieve soil moisture. However, the accuracy of the soil moisture estimation, by inversing backscattering radar coefficients (σ°), is affected by the influence of surface roughness and vegetation biomass contributions. In recent years, different empirical, semi empirical and physical approaches are developed for bare soil conditions, to estimate accurately spatial soil moisture variability. In this study, we propose an approach based on the change detection method for the retrieval of surface soil moisture at a higher spatial resolution. The proposal algorithm combines multi-temporal X-band SAR images (TerraSAR-X) with different continuous thetaprobe measurements. Seven thetaprobe stations are installed at different depths over the central semi arid region of Tunisia (9°23' - 10°17' E, 35° 1'-35°55' N). They cover approximately the entire of our study site and provide regional scale information. Ground data were collected over agricultural bare soil fields simultaneously to various TerraSAR-X data acquired during 2013-2014 and 2014-2015. More than fourteen test fields were selected for each spatial acquisition campaign, with variations in soil texture and in surface soil roughness. For each date, we considered the volumetric water content with thetaprobe instrument and gravimetric sampling; we measured also the roughness parameters with pin profilor. To retrieve soil moisture from X-band SAR data, we analyzed statistically the sensitivity between radar measurements and ground soil moisture derived from permanent thetaprobe stations. Our analyses are applied over bare soil class identified from an optical image SPOT / HRV acquired in the same period of the measurements. Results have shown linear relationship for the radar signals as a function of volumetric soil moisture with high sensitivity about 0.21 dB/vol%. For estimation of change in soil moisture, we considered two options: On the first one, we applied the change detection approach between successive radar images (∆σ°) assuming unchanged soil roughness effects. Our soil moisture retrieval algorithm was validated on the basis of comparisons between estimated and in situ soil moisture measurements over test fields. Using this option, results have shown an accuracy (RMSE) of about 4.8 %. Secondly, we corrected the sensitivity of the radar backscatter images to the surface roughness variability. Results have shown a reduction of the difference between the retrieved soil moisture and ground measurements with an RMSE about 3.7%.

Dreaming of Atmospheres

NASA Astrophysics Data System (ADS)

Waldmann, Ingo

2016-10-01

Radiative transfer retrievals have become the standard in modelling of exoplanetary transmission and emission spectra. Analysing currently available observations of exoplanetary atmospheres often invoke large and correlated parameter spaces that can be difficult to map or constrain.To address these issues, we have developed the Tau-REx (tau-retrieval of exoplanets) retrieval and the RobERt spectral recognition algorithms. Tau-REx is a bayesian atmospheric retrieval framework using Nested Sampling and cluster computing to fully map these large correlated parameter spaces. Nonetheless, data volumes can become prohibitively large and we must often select a subset of potential molecular/atomic absorbers in an atmosphere.In the era of open-source, automated and self-sufficient retrieval algorithms, such manual input should be avoided. User dependent input could, in worst case scenarios, lead to incomplete models and biases in the retrieval. The RobERt algorithm is build to address these issues. RobERt is a deep belief neural (DBN) networks trained to accurately recognise molecular signatures for a wide range of planets, atmospheric thermal profiles and compositions. Using these deep neural networks, we work towards retrieval algorithms that themselves understand the nature of the observed spectra, are able to learn from current and past data and make sensible qualitative preselections of atmospheric opacities to be used for the quantitative stage of the retrieval process.In this talk I will discuss how neural networks and Bayesian Nested Sampling can be used to solve highly degenerate spectral retrieval problems and what 'dreaming' neural networks can tell us about atmospheric characteristics.

Comparison of soil moisture retrieval algorithms based on the synergy between SMAP and SMOS-IC

NASA Astrophysics Data System (ADS)

Ebrahimi-Khusfi, Mohsen; Alavipanah, Seyed Kazem; Hamzeh, Saeid; Amiraslani, Farshad; Neysani Samany, Najmeh; Wigneron, Jean-Pierre

2018-05-01

This study was carried out to evaluate possible improvements of the soil moisture (SM) retrievals from the SMAP observations, based on the synergy between SMAP and SMOS. We assessed the impacts of the vegetation and soil roughness parameters on SM retrievals from SMAP observations. To do so, the effects of three key input parameters including the vegetation optical depth (VOD), effective scattering albedo (ω) and soil roughness (HR) parameters were assessed with the emphasis on the synergy with the VOD product derived from SMOS-IC, a new and simpler version of the SMOS algorithm, over two years of data (April 2015 to April 2017). First, a comprehensive comparison of seven SM retrieval algorithms was made to find the best one for SM retrievals from the SMAP observations. All results were evaluated against in situ measurements over 548 stations from the International Soil Moisture Network (ISMN) in terms of four statistical metrics: correlation coefficient (R), root mean square error (RMSE), bias and unbiased RMSE (UbRMSE). The comparison of seven SM retrieval algorithms showed that the dual channel algorithm based on the additional use of the SMOS-IC VOD product (selected algorithm) led to the best results of SM retrievals over 378, 399, 330 and 271 stations (out of a total of 548 stations) in terms of R, RMSE, UbRMSE and both R & UbRMSE, respectively. Moreover, comparing the measured and retrieved SM values showed that this synergy approach led to an increase in median R value from 0.6 to 0.65 and a decrease in median UbRMSE from 0.09 m3/m3 to 0.06 m3/m3. Second, using the algorithm selected in a first step and defined above, the ω and HR parameters were calibrated over 218 rather homogenous ISMN stations. 72 combinations of various values of ω and HR were used for the calibration over different land cover classes. In this calibration process, the optimal values of ω and HR were found for the different land cover classes. The obtained results indicated that the impact of the VOD parameter on SM retrievals is more considerable than the effects of HR and ω. Overall, the inclusion of the VOD parameter in the SMAP SM retrieval algorithm was found to be a very interesting approach and showed the large potential benefit of the synergy between SMAP and SMOS.

CloudSat 2C-ICE product update with a new Ze parameterization in lidar-only region.

PubMed

Deng, Min; Mace, Gerald G; Wang, Zhien; Berry, Elizabeth

2015-12-16

The CloudSat 2C-ICE data product is derived from a synergetic ice cloud retrieval algorithm that takes as input a combination of CloudSat radar reflectivity ( Z e ) and Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation lidar attenuated backscatter profiles. The algorithm uses a variational method for retrieving profiles of visible extinction coefficient, ice water content, and ice particle effective radius in ice or mixed-phase clouds. Because of the nature of the measurements and to maintain consistency in the algorithm numerics, we choose to parameterize (with appropriately large specification of uncertainty) Z e and lidar attenuated backscatter in the regions of a cirrus layer where only the lidar provides data and where only the radar provides data, respectively. To improve the Z e parameterization in the lidar-only region, the relations among Z e , extinction, and temperature have been more thoroughly investigated using Atmospheric Radiation Measurement long-term millimeter cloud radar and Raman lidar measurements. This Z e parameterization provides a first-order estimation of Z e as a function extinction and temperature in the lidar-only regions of cirrus layers. The effects of this new parameterization have been evaluated for consistency using radiation closure methods where the radiative fluxes derived from retrieved cirrus profiles compare favorably with Clouds and the Earth's Radiant Energy System measurements. Results will be made publicly available for the entire CloudSat record (since 2006) in the most recent product release known as R05.

A passive microwave technique for estimating rainfall and vertical structure information from space. Part 2: Applications to SSM/I data

NASA Technical Reports Server (NTRS)

Kummerow, Christian; Giglio, Louis

1994-01-01

A multi channel physical approach for retrieving rainfall and its vertical structure from Special Sensor Microwave/Imager (SSM/I) observations is examined. While a companion paper was devoted exclusively to the description of the algorithm, its strengths, and its limitations, the main focus of this paper is to report on the results, applicability, and expected accuraciesfrom this algorithm. Some examples are given that compare retrieved results with ground-based radar data from different geographical regions to illustrate the performance and utility of the algorithm under distinct rainfall conditions. More quantitative validation is accomplished using two months of radar data from Darwin, Australia, and the radar network over Japan. Instantaneous comparisons at Darwin indicate that root-mean-square errors for 1.25 deg areas over water are 0.09 mm/h compared to the mean rainfall value of 0.224 mm/h while the correlation exceeds 0.9. Similar results are obtained over the Japanese validation site with rms errors of 0.615 mm/h compared to the mean of 0.0880 mm/h and a correlation of 0.9. Results are less encouraging over land with root-mean-square errors somewhat larger than the mean rain rates and correlations of only 0.71 and 0.62 for Darwin and Japan, respectively. These validation studies are further used in combination with the theoretical treatment of expected accuracies developed in the companion paper to define error estimates on a broader scale than individual radar sites from which the errors may be analyzed. Comparisons with simpler techniques that are based on either emission or scattering measurements are used to illustrate the fact that the current algorithm, while better correlated with the emission methods over water, cannot be reduced to either of these simpler methods.

An Integrated Retrieval Framework for AMSR2: Implications for Light Precipitation and Sea Ice Edge Detectability

NASA Astrophysics Data System (ADS)

Duncan, D.; Kummerow, C. D.; Meier, W.

2016-12-01

Over the lifetime of AMSR-E, operational retrieval algorithms were developed and run for precipitation, ocean suite (SST, wind speed, cloud liquid water path, and column water vapor over ocean), sea ice, snow water equivalent, and soil moisture. With a separate algorithm for each group, the retrievals were never interactive or integrated in any way despite many co-sensitivities. AMSR2, the follow-on mission to AMSR-E, retrieves the same parameters at a slightly higher spatial resolution. We have combined the operational algorithms for AMSR2 in a way that facilitates sharing information between the retrievals. Difficulties that arose were mainly related to calibration, spatial resolution, coastlines, and order of processing. The integration of all algorithms for AMSR2 has numerous benefits, including better detection of light precipitation and sea ice, fewer screened out pixels, and better quality flags. Integrating the algorithms opens up avenues for investigating the limits of detectability for precipitation from a passive microwave radiometer and the impact of spatial resolution on sea ice edge detection; these are investigated using CloudSat and MODIS coincident observations from the A-Train constellation.

Efficient Retrieval of Massive Ocean Remote Sensing Images via a Cloud-Based Mean-Shift Algorithm.

PubMed

Yang, Mengzhao; Song, Wei; Mei, Haibin

2017-07-23

The rapid development of remote sensing (RS) technology has resulted in the proliferation of high-resolution images. There are challenges involved in not only storing large volumes of RS images but also in rapidly retrieving the images for ocean disaster analysis such as for storm surges and typhoon warnings. In this paper, we present an efficient retrieval of massive ocean RS images via a Cloud-based mean-shift algorithm. Distributed construction method via the pyramid model is proposed based on the maximum hierarchical layer algorithm and used to realize efficient storage structure of RS images on the Cloud platform. We achieve high-performance processing of massive RS images in the Hadoop system. Based on the pyramid Hadoop distributed file system (HDFS) storage method, an improved mean-shift algorithm for RS image retrieval is presented by fusion with the canopy algorithm via Hadoop MapReduce programming. The results show that the new method can achieve better performance for data storage than HDFS alone and WebGIS-based HDFS. Speedup and scaleup are very close to linear changes with an increase of RS images, which proves that image retrieval using our method is efficient.

Efficient Retrieval of Massive Ocean Remote Sensing Images via a Cloud-Based Mean-Shift Algorithm

PubMed Central

Song, Wei; Mei, Haibin

2017-01-01

The rapid development of remote sensing (RS) technology has resulted in the proliferation of high-resolution images. There are challenges involved in not only storing large volumes of RS images but also in rapidly retrieving the images for ocean disaster analysis such as for storm surges and typhoon warnings. In this paper, we present an efficient retrieval of massive ocean RS images via a Cloud-based mean-shift algorithm. Distributed construction method via the pyramid model is proposed based on the maximum hierarchical layer algorithm and used to realize efficient storage structure of RS images on the Cloud platform. We achieve high-performance processing of massive RS images in the Hadoop system. Based on the pyramid Hadoop distributed file system (HDFS) storage method, an improved mean-shift algorithm for RS image retrieval is presented by fusion with the canopy algorithm via Hadoop MapReduce programming. The results show that the new method can achieve better performance for data storage than HDFS alone and WebGIS-based HDFS. Speedup and scaleup are very close to linear changes with an increase of RS images, which proves that image retrieval using our method is efficient. PMID:28737699

Estimating snow depth of alpine snowpack via airborne multifrequency passive microwave radiance observations: Colorado, USA

NASA Astrophysics Data System (ADS)

Kim, R. S.; Durand, M. T.; Li, D.; Baldo, E.; Margulis, S. A.; Dumont, M.; Morin, S.

2017-12-01

This paper presents a newly-proposed snow depth retrieval approach for mountainous deep snow using airborne multifrequency passive microwave (PM) radiance observation. In contrast to previous snow depth estimations using satellite PM radiance assimilation, the newly-proposed method utilized single flight observation and deployed the snow hydrologic models. This method is promising since the satellite-based retrieval methods have difficulties to estimate snow depth due to their coarse resolution and computational effort. Indeed, this approach consists of particle filter using combinations of multiple PM frequencies and multi-layer snow physical model (i.e., Crocus) to resolve melt-refreeze crusts. The method was performed over NASA Cold Land Processes Experiment (CLPX) area in Colorado during 2002 and 2003. Results showed that there was a significant improvement over the prior snow depth estimates and the capability to reduce the prior snow depth biases. When applying our snow depth retrieval algorithm using a combination of four PM frequencies (10.7,18.7, 37.0 and 89.0 GHz), the RMSE values were reduced by 48 % at the snow depth transects sites where forest density was less than 5% despite deep snow conditions. This method displayed a sensitivity to different combinations of frequencies, model stratigraphy (i.e. different number of layering scheme for snow physical model) and estimation methods (particle filter and Kalman filter). The prior RMSE values at the forest-covered areas were reduced by 37 - 42 % even in the presence of forest cover.

Evaluation of the operational SAR based Baltic sea ice concentration products

NASA Astrophysics Data System (ADS)

Karvonen, Juha

Sea ice concentration is an important ice parameter both for weather and climate modeling and sea ice navigation. We have developed an fully automated algorithm for sea ice concentration retrieval using dual-polarized ScanSAR wide mode RADARSAT-2 data. RADARSAT-2 is a C-band SAR instrument enabling dual-polarized acquisition in ScanSAR mode. The swath width for the RADARSAT-2 ScanSAR mode is about 500 km, making it very suitable for operational sea ice monitoring. The polarization combination used in our concentration estimation is HH/HV. The SAR data is first preprocessed, the preprocessing consists of geo-rectification to Mercator projection, incidence angle correction fro both the polarization channels. and SAR mosaicking. After preprocessing a segmentation is performed for the SAR mosaics, and some single-channel and dual-channel features are computed for each SAR segment. Finally the SAR concentration is estimated based on these segment-wise features. The algorithm is similar as introduced in Karvonen 2014. The ice concentration is computed daily using a daily RADARSAT-2 SAR mosaic as its input, and it thus gives the concentration estimated at each Baltic Sea location based on the most recent SAR data at the location. The algorithm has been run in an operational test mode since January 2014. We present evaluation of the SAR-based concentration estimates for the Baltic ice season 2014 by comparing the SAR results with gridded the Finnish Ice Service ice charts and ice concentration estimates from a radiometer algorithm (AMSR-2 Bootstrap algorithm results). References: J. Karvonen, Baltic Sea Ice Concentration Estimation Based on C-Band Dual-Polarized SAR Data, IEEE Transactions on Geoscience and Remote Sensing, in press, DOI: 10.1109/TGRS.2013.2290331, 2014.

A linear method for the retrieval of sun-induced chlorophyll fluorescence from GOME-2 and SCIAMACHY data

NASA Astrophysics Data System (ADS)

Köhler, P.; Guanter, L.; Joiner, J.

2014-12-01

Global retrievals of near-infrared sun-induced chlorophyll fluorescence (SIF) have been achieved in the last years by means of a number of space-borne atmospheric spectrometers. Here, we present a new retrieval method for medium spectral resolution instruments such as the Global Ozone Monitoring Experiment (GOME-2) and the SCanning Imaging Absorption SpectroMeter for Atmospheric ChartographY (SCIAMACHY). Building upon the previous work by Joiner et al. (2013), our approach solves existing issues in the retrieval such as the non-linearity of the forward model and the arbitrary selection of the number of free parameters. In particular, we use a backward elimination algorithm to optimize the number of coefficients to fit, which reduces also the retrieval noise and selects the number of state vector elements automatically. A sensitivity analysis with simulated spectra has been utilized to evaluate the performance of our retrieval approach. The method has also been applied to estimate SIF from real spectra from GOME-2 and for the first time, from SCIAMACHY. We find a good correspondence of the absolute SIF values and the spatial patterns from the two sensors, which suggests the robustness of the proposed retrieval method. In addition, we examine uncertainties and use our GOME-2 retrievals to show empirically the low sensitivity of the SIF retrieval to cloud contamination.

Multi-Mission Remote Sensing of Suspended Particulate Matter and Diffuse Attenuation Coefficient in the Yangtze Estuarine and Coastal Waters

NASA Astrophysics Data System (ADS)

Yu, X.; Salama, S.; Shen, F.

2016-08-01

During the Dragon-3 project (ID: 10555) period, we developed and improved the atmospheric correction algorithms (AC) and retrieval models of suspended sediment concentration ( ) and diffuse attenuation coefficient ( ) for the Yangtze estuarine and coastal waters. The developed models were validated by measurements with consistently stable and fairly accurate estimations, reproducing reasonable distribution maps of and over the study area. Spatial-temporal variations of were presented and the mechanisms of the sediment transport were discussed. We further examined the compatibility of the developed AC algorithms and retrieval model and the consistency of satellite products for multi-sensor such as MODIS/Terra/Aqua, MERIS/Envisat, MERSI/ FY-3 and GOCI. The inter-comparison of multi- sensor suggested that different satellite products can be combined to increase revisit frequency and complement a temporal gap of time series satellites that may exist between on-orbit and off- orbit, facilitating a better monitor on the spatial- temporal dynamics of .

Optical biopsy using fluorescence spectroscopy for prostate cancer diagnosis

NASA Astrophysics Data System (ADS)

Wu, Binlin; Gao, Xin; Smith, Jason; Bailin, Jacob

2017-02-01

Native fluorescence spectra are acquired from fresh normal and cancerous human prostate tissues. The fluorescence data are analyzed using a multivariate analysis algorithm such as non-negative matrix factorization. The nonnegative spectral components are retrieved and attributed to the native fluorophores such as collagen, reduced nicotinamide adenine dinucleotide (NADH), and flavin adenine dinucleotide (FAD) in tissue. The retrieved weights of the components, e.g. NADH and FAD are used to estimate the relative concentrations of the native fluorophores and the redox ratio. A machine learning algorithm such as support vector machine (SVM) is used for classification to distinguish normal and cancerous tissue samples based on either the relative concentrations of NADH and FAD or the redox ratio alone. The classification performance is shown based on statistical measures such as sensitivity, specificity, and accuracy, along with the area under receiver operating characteristic (ROC) curve. A cross validation method such as leave-one-out is used to evaluate the predictive performance of the SVM classifier to avoid bias due to overfitting.

Improvement of Forest Height Retrieval By Integration of Dual-Baseline PolInSAR Data And External DEM Data

NASA Astrophysics Data System (ADS)

Xie, Q.; Wang, C.; Zhu, J.; Fu, H.; Wang, C.

2015-06-01

In recent years, a lot of studies have shown that polarimetric synthetic aperture radar interferometry (PolInSAR) is a powerful technique for forest height mapping and monitoring. However, few researches address the problem of terrain slope effect, which will be one of the major limitations for forest height inversion in mountain forest area. In this paper, we present a novel forest height retrieval algorithm by integration of dual-baseline PolInSAR data and external DEM data. For the first time, we successfully expand the S-RVoG (Sloped-Random Volume over Ground) model for forest parameters inversion into the case of dual-baseline PolInSAR configuration. In this case, the proposed method not only corrects terrain slope variation effect efficiently, but also involves more observations to improve the accuracy of parameters inversion. In order to demonstrate the performance of the inversion algorithm, a set of quad-pol images acquired at the P-band in interferometric repeat-pass mode by the German Aerospace Center (DLR) with the Experimental SAR (E-SAR) system, in the frame of the BioSAR2008 campaign, has been used for the retrieval of forest height over Krycklan boreal forest in northern Sweden. At the same time, a high accuracy external DEM in the experimental area has been collected for computing terrain slope information, which subsequently is used as an inputting parameter in the S-RVoG model. Finally, in-situ ground truth heights in stand-level have been collected to validate the inversion result. The preliminary results show that the proposed inversion algorithm promises to provide much more accurate estimation of forest height than traditional dualbaseline inversion algorithms.

Transfer and distortion of atmospheric information in the satellite temperature retrieval problem

NASA Technical Reports Server (NTRS)

Thompson, O. E.

1981-01-01

A systematic approach to investigating the transfer of basic ambient temperature information and its distortion by satellite systems and subsequent analysis algorithms is discussed. The retrieval analysis cycle is derived, the variance spectrum of information is examined as it takes different forms in that process, and the quality and quantity of information existing at each stop is compared with the initial ambient temperature information. Temperature retrieval algorithms can smooth, add, or further distort information, depending on how stable the algorithm is, and how heavily influenced by a priori data.

Experiments at SRT Using the NOAA CrIS/ATMS Proxy Data Set

NASA Technical Reports Server (NTRS)

Susskind, Joel; Kouvaris, Louis; Iredell, Lena

2011-01-01

The objectives of the talk are: (1) Assess the performance of NGAS Version-1.5.03.00 CrIS/ATMS retrieval algorithm as delivered by LaRC, modified to include the MW and IR tuning coefficients and new CrIS noise model (a) Percent acceptance (b) RMS and mean differences of T(p) vs. ECMWF truth as a function of % yield (2) Compare performance of NGAS retrieval algorithm with an AIRS Science Team Version-6 like retrieval algorithm modified at Sounder Research Team (SRT) for CrIS/ATMS

The Validation of Cloud Retrieval Algorithms Using Synthetic Datasets

NASA Astrophysics Data System (ADS)

Kokhanovsky, Alexander; Fischer, Jurgen; Linstrot, Rasmus; Meirink, Jan Fokke; Poulsen, Caroline; Preusker, Rene; Siddans, Richard; Thomas, Gareth; Arnold, Chris; Grainger, Roy; Lilli, Luca; Rozanov, Vladimir

2012-11-01

We have performed the inter-comparison study of cloud property retrievals using algorithms initially developed for AATSR (ORAC, RAL-Oxford University), AVHRR and SEVIRI (CPP, KNMI), SCIAMACHY/GOME (SACURA, University of Bremen), and MERIS (ANNA, Free University of Berlin). The accuracy of retrievals of cloud optical thickness (COT), effective radius (ER) of droplets, and cloud top height (CTH) is discussed.

Fuzzy Reasoning to More Accurately Determine Void Areas on Optical Micrographs of Composite Structures

NASA Technical Reports Server (NTRS)

Dominquez, Jesus A.; Tate, Lanetra C.; Wright, M. Clara; Caraccio, Anne

2013-01-01

Accomplishing the best-performing composite matrix (resin) requires that not only the processing method but also the cure cycle generate low-void-content structures. If voids are present, the performance of the composite matrix will be significantly reduced. This is usually noticed by significant reductions in matrix-dominated properties, such as compression and shear strength. Voids in composite materials are areas that are absent of the composite components: matrix and fibers. The characteristics of the voids and their accurate estimation are critical to determine for high performance composite structures. One widely used method of performing void analysis on a composite structure sample is acquiring optical micrographs or Scanning Electron Microscope (SEM) images of lateral sides of the sample and retrieving the void areas within the micrographs/images using an image analysis technique. Segmentation for the retrieval and subsequent computation of void areas within the micrographs/images is challenging as the gray-scaled values of the void areas are close to the gray-scaled values of the matrix leading to the need of manually performing the segmentation based on the histogram of the micrographs/images to retrieve the void areas. The use of an algorithm developed by NASA and based on Fuzzy Reasoning (FR) proved to overcome the difficulty of suitably differentiate void and matrix image areas with similar gray-scaled values leading not only to a more accurate estimation of void areas on composite matrix micrographs but also to a faster void analysis process as the algorithm is fully autonomous.

Artificial neural network for the determination of Hubble Space Telescope aberration from stellar images

NASA Technical Reports Server (NTRS)

Barrett, Todd K.; Sandler, David G.

1993-01-01

An artificial-neural-network method, first developed for the measurement and control of atmospheric phase distortion, using stellar images, was used to estimate the optical aberration of the Hubble Space Telescope. A total of 26 estimates of distortion was obtained from 23 stellar images acquired at several secondary-mirror axial positions. The results were expressed as coefficients of eight orthogonal Zernike polynomials: focus through third-order spherical. For all modes other than spherical the measured aberration was small. The average spherical aberration of the estimates was -0.299 micron rms, which is in good agreement with predictions obtained when iterative phase-retrieval algorithms were used.

Estimation of cloud optical thickness by processing SEVIRI images and implementing a semi analytical cloud property retrieval algorithm

NASA Astrophysics Data System (ADS)

Pandey, P.; De Ridder, K.; van Lipzig, N.

2009-04-01

Clouds play a very important role in the Earth's climate system, as they form an intermediate layer between Sun and the Earth. Satellite remote sensing systems are the only means to provide information about clouds on large scales. The geostationary satellite, Meteosat Second Generation (MSG) has onboard an imaging radiometer, the Spinning Enhanced Visible and Infrared Imager (SEVIRI). SEVIRI is a 12 channel imager, with 11 channels observing the earth's full disk with a temporal resolution of 15 min and spatial resolution of 3 km at nadir, and a high resolution visible (HRV) channel. The visible channels (0.6 µm and 0.81 µm) and near infrared channel (1.6µm) of SEVIRI are being used to retrieve the cloud optical thickness (COT). The study domain is over Europe covering the region between 35°N - 70°N and 10°W - 30°E. SEVIRI level 1.5 images over this domain are being acquired from the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) archive. The processing of this imagery, involves a number of steps before estimating the COT. The steps involved in pre-processing are as follows. First, the digital count number is acquired from the imagery. Image geo-coding is performed in order to relate the pixel positions to the corresponding longitude and latitude. Solar zenith angle is determined as a function of latitude and time. The radiometric conversion is done using the values of offsets and slopes of each band. The values of radiance obtained are then used to calculate the reflectance for channels in the visible spectrum using the information of solar zenith angle. An attempt is made to estimate the COT from the observed radiances. A semi analytical algorithm [Kokhanovsky et al., 2003] is implemented for the estimation of cloud optical thickness from the visible spectrum of light intensity reflected from clouds. The asymptotical solution of the radiative transfer equation, for clouds with large optical thickness, is the basis of this algorithm. The two visible channels of SEVIRI are used to find the COT and the near infra red channel to estimate the effective radius of droplets. Estimation of COT using a semi analytical scheme, which doesn't involve the conventional look-up table approach, is the aim of this work and henceforth, vertically integrated liquid water (w) or ice water content will be retrieved. The COT estimated and w obtained, will be compared with the values obtained from other approaches and will be validated with in situ measurements. Corresponding author address: Praveen Pandey, VITO - Flemish Institute for Technological Research, Boeretang 200, B 2400, Mol, Belgium. E-mail: praveen.pandey@vito.be

Solar Occultation Retrieval Algorithm Development

NASA Technical Reports Server (NTRS)

Lumpe, Jerry D.

2004-01-01

This effort addresses the comparison and validation of currently operational solar occultation retrieval algorithms, and the development of generalized algorithms for future application to multiple platforms. initial development of generalized forward model algorithms capable of simulating transmission data from of the POAM II/III and SAGE II/III instruments. Work in the 2" quarter will focus on: completion of forward model algorithms, including accurate spectral characteristics for all instruments, and comparison of simulated transmission data with actual level 1 instrument data for specific occultation events.

Precipitation and Latent Heating Distributions from Satellite Passive Microwave Radiometry. Part 1; Improved Method and Uncertainties

NASA Technical Reports Server (NTRS)

Olson, William S.; Kummerow, Christian D.; Yang, Song; Petty, Grant W.; Tao, Wei-Kuo; Bell, Thomas L.; Braun, Scott A.; Wang, Yansen; Lang, Stephen E.; Johnson, Daniel E.;

Radiative transfer codes for atmospheric correction and aerosol retrieval: intercomparison study.

PubMed

Kotchenova, Svetlana Y; Vermote, Eric F; Levy, Robert; Lyapustin, Alexei

2008-05-01

Results are summarized for a scientific project devoted to the comparison of four atmospheric radiative transfer codes incorporated into different satellite data processing algorithms, namely, 6SV1.1 (second simulation of a satellite signal in the solar spectrum, vector, version 1.1), RT3 (radiative transfer), MODTRAN (moderate resolution atmospheric transmittance and radiance code), and SHARM (spherical harmonics). The performance of the codes is tested against well-known benchmarks, such as Coulson's tabulated values and a Monte Carlo code. The influence of revealed differences on aerosol optical thickness and surface reflectance retrieval is estimated theoretically by using a simple mathematical approach. All information about the project can be found at http://rtcodes.ltdri.org.

Radiative transfer codes for atmospheric correction and aerosol retrieval: intercomparison study

NASA Astrophysics Data System (ADS)

Kotchenova, Svetlana Y.; Vermote, Eric F.; Levy, Robert; Lyapustin, Alexei

2008-05-01

Results are summarized for a scientific project devoted to the comparison of four atmospheric radiative transfer codes incorporated into different satellite data processing algorithms, namely, 6SV1.1 (second simulation of a satellite signal in the solar spectrum, vector, version 1.1), RT3 (radiative transfer), MODTRAN (moderate resolution atmospheric transmittance and radiance code), and SHARM (spherical harmonics). The performance of the codes is tested against well-known benchmarks, such as Coulson's tabulated values and a Monte Carlo code. The influence of revealed differences on aerosol optical thickness and surface reflectance retrieval is estimated theoretically by using a simple mathematical approach. All information about the project can be found at http://rtcodes.ltdri.org.

Integrated Active Fire Retrievals and Biomass Burning Emissions Using Complementary Near-Coincident Ground, Airborne and Spaceborne Sensor Data

NASA Technical Reports Server (NTRS)

Schroeder, Wilfrid; Ellicott, Evan; Ichoku, Charles; Ellison, Luke; Dickinson, Matthew B.; Ottmar, Roger D.; Clements, Craig; Hall, Dianne; Ambrosia, Vincent; Kremens, Robert

2013-01-01

Ground, airborne and spaceborne data were collected for a 450 ha prescribed fire implemented on 18 October 2011 at the Henry W. Coe State Park in California. The integration of various data elements allowed near coincident active fire retrievals to be estimated. The Autonomous Modular Sensor-Wildfire (AMS) airborne multispectral imaging system was used as a bridge between ground and spaceborne data sets providing high quality reference information to support satellite fire retrieval error analyses and fire emissions estimates. We found excellent agreement between peak fire radiant heat flux data (less than 1% error) derived from near-coincident ground radiometers and AMS. Both MODIS and GOES imager active fire products were negatively influenced by the presence of thick smoke, which was misclassified as cloud by their algorithms, leading to the omission of fire pixels beneath the smoke, and resulting in the underestimation of their retrieved fire radiative power (FRP) values for the burn plot, compared to the reference airborne data. Agreement between airborne and spaceborne FRP data improved significantly after correction for omission errors and atmospheric attenuation, resulting in as low as 5 difference between AquaMODIS and AMS. Use of in situ fuel and fire energy estimates in combination with a collection of AMS, MODIS, and GOES FRP retrievals provided a fuel consumption factor of 0.261 kg per MJ, total energy release of 14.5 x 10(exp 6) MJ, and total fuel consumption of 3.8 x 10(exp 6) kg. Fire emissions were calculated using two separate techniques, resulting in as low as 15 difference for various species

Constraining the Structure of Hot Jupiter Atmospheres Using a Hybrid Version of the NEMESIS Retrieval Algorithm

NASA Astrophysics Data System (ADS)

Badhan, Mahmuda A.; Mandell, Avi M.; Hesman, Brigette; Nixon, Conor; Deming, Drake; Irwin, Patrick; Barstow, Joanna; Garland, Ryan

2015-11-01

Understanding the formation environments and evolution scenarios of planets in nearby planetary systems requires robust measures for constraining their atmospheric physical properties. Here we have utilized a combination of two different parameter retrieval approaches, Optimal Estimation and Markov Chain Monte Carlo, as part of the well-validated NEMESIS atmospheric retrieval code, to infer a range of temperature profiles and molecular abundances of H2O, CO2, CH4 and CO from available dayside thermal emission observations of several hot-Jupiter candidates. In order to keep the number of parameters low and henceforth retrieve more plausible profile shapes, we have used a parametrized form of the temperature profile based upon an analytic radiative equilibrium derivation in Guillot et al. 2010 (Line et al. 2012, 2014). We show retrieval results on published spectroscopic and photometric data from both the Hubble Space Telescope and Spitzer missions, and compare them with simulations from the upcoming JWST mission. In addition, since NEMESIS utilizes correlated distribution of absorption coefficients (k-distribution) amongst atmospheric layers to compute these models, updates to spectroscopic databases can impact retrievals quite significantly for such high-temperature atmospheres. As high-temperature line databases are continually being improved, we also compare retrievals between old and newer databases.

Methods for coherent lensless imaging and X-ray wavefront measurements

NASA Astrophysics Data System (ADS)

Guizar Sicairos, Manuel

X-ray diffractive imaging is set apart from other high-resolution imaging techniques (e.g. scanning electron or atomic force microscopy) for its high penetration depth, which enables tomographic 3D imaging of thick samples and buried structures. Furthermore, using short x-ray pulses, it enables the capability to take ultrafast snapshots, giving a unique opportunity to probe nanoscale dynamics at femtosecond time scales. In this thesis we present improvements to phase retrieval algorithms, assess their performance through numerical simulations, and develop new methods for both imaging and wavefront measurement. Building on the original work by Faulkner and Rodenburg, we developed an improved reconstruction algorithm for phase retrieval with transverse translations of the object relative to the illumination beam. Based on gradient-based nonlinear optimization, this algorithm is capable of estimating the object, and at the same time refining the initial knowledge of the incident illumination and the object translations. The advantages of this algorithm over the original iterative transform approach are shown through numerical simulations. Phase retrieval has already shown substantial success in wavefront sensing at optical wavelengths. Although in principle the algorithms can be used at any wavelength, in practice the focus-diversity mechanism that makes optical phase retrieval robust is not practical to implement for x-rays. In this thesis we also describe the novel application of phase retrieval with transverse translations to the problem of x-ray wavefront sensing. This approach allows the characterization of the complex-valued x-ray field in-situ and at-wavelength and has several practical and algorithmic advantages over conventional focused beam measurement techniques. A few of these advantages include improved robustness through diverse measurements, reconstruction from far-field intensity measurements only, and significant relaxation of experimental requirements over other beam characterization approaches. Furthermore, we show that a one-dimensional version of this technique can be used to characterize an x-ray line focus produced by a cylindrical focusing element. We provide experimental demonstrations of the latter at hard x-ray wavelengths, where we have characterized the beams focused by a kinoform lens and an elliptical mirror. In both experiments the reconstructions exhibited good agreement with independent measurements, and in the latter a small mirror misalignment was inferred from the phase retrieval reconstruction. These experiments pave the way for the application of robust phase retrieval algorithms for in-situ alignment and performance characterization of x-ray optics for nanofocusing. We also present a study on how transverse translations help with the well-known uniqueness problem of one-dimensional phase retrieval. We also present a novel method for x-ray holography that is capable of reconstructing an image using an off-axis extended reference in a non-iterative computation, greatly generalizing an earlier approach by Podorov et al. The approach, based on the numerical application of derivatives on the field autocorrelation, was developed from first mathematical principles. We conducted a thorough theoretical study to develop technical and intuitive understanding of this technique and derived sufficient separation conditions required for an artifact-free reconstruction. We studied the effects of missing information in the Fourier domain, and of an imperfect reference, and we provide a signal-to-noise ratio comparison with the more traditional approach of Fourier transform holography. We demonstrated this new holographic approach through proof-of-principle optical experiments and later experimentally at soft x-ray wavelengths, where we compared its performance to Fourier transform holography, iterative phase retrieval and state-of-the-art zone-plate x-ray imaging techniques (scanning and full-field). Finally, we present a demonstration of the technique using a single 20 fs pulse from a high-harmonic table-top source. Holography with an extended reference is shown to provide fast, good quality images that are robust to noise and artifacts that arise from missing information due to a beam stop. (Abstract shortened by UMI.)

A circular median filter approach for resolving directional ambiguities in wind fields retrieved from spaceborne scatterometer data

NASA Technical Reports Server (NTRS)

Schultz, Howard

1990-01-01

The retrieval algorithm for spaceborne scatterometry proposed by Schultz (1985) is extended. A circular median filter (CMF) method is presented, which operates on wind directions independently of wind speed, removing any implicit wind speed dependence. A cell weighting scheme is included in the algorithm, permitting greater weights to be assigned to more reliable data. The mathematical properties of the ambiguous solutions to the wind retrieval problem are reviewed. The CMF algorithm is tested on twelve simulated data sets. The effects of spatially correlated likelihood assignment errors on the performance of the CMF algorithm are examined. Also, consideration is given to a wind field smoothing technique that uses a CMF.

TOPEX/POSEIDON microwave radiometer performance and in-flight calibration

NASA Technical Reports Server (NTRS)

Ruf, C. S.; Keihm, Stephen J.; Subramanya, B.; Janssen, Michael A.

1994-01-01

Results of the in-flight calibration and performance evaluation campaign for the TOPEX/POSEIDON microwave radiometer (TMR) are presented. Intercomparisons are made between TMR and various sources of ground truth, including ground-based microwave water vapor radiometers, radiosondes, global climatological models, special sensor microwave imager data over the Amazon rain forest, and models of clear, calm, subpolar ocean regions. After correction for preflight errors in the processing of thermal/vacuum data, relative channel offsets in the open ocean TMR brightness temperatures were noted at the approximately = 1 K level for the three TMR frequencies. Larger absolute offsets of 6-9 K over the rain forest indicated a approximately = 5% gain error in the three channel calibrations. This was corrected by adjusting the antenna pattern correction (APC) algorithm. AS 10% scale error in the TMR path delay estimates, relative to coincident radiosondes, was corrected in part by the APC adjustment and in part by a 5% modification to the value assumed for the 22.235 FGHz water vapor line strength in the path delay retrieval algorithm. After all in-flight corrections to the calibration, TMR global retrieval accuracy for the wet tropospheric range correction is estimated at 1.1 cm root mean square (RMS) with consistent peformance under clear, cloudy, and windy conditions.

Retrieval of ozone profiles from OMPS limb scattering observations

NASA Astrophysics Data System (ADS)

Arosio, Carlo; Rozanov, Alexei; Malinina, Elizaveta; Eichmann, Kai-Uwe; von Clarmann, Thomas; Burrows, John P.

2018-04-01

This study describes a retrieval algorithm developed at the University of Bremen to obtain vertical profiles of ozone from limb observations performed by the Ozone Mapper and Profiler Suite (OMPS). This algorithm is based on the technique originally developed for use with data from the SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY (SCIAMACHY) instrument. As both instruments make limb measurements of the scattered solar radiation in the ultraviolet (UV) and visible (Vis) spectral ranges, an underlying objective of the study is to obtain consolidated and consistent ozone profiles from the two satellites and to produce a combined data set. The retrieval algorithm uses radiances in the UV and Vis wavelength ranges normalized to the radiance at an upper tangent height to obtain ozone concentrations in the altitude range of 12-60 km. Measurements at altitudes contaminated by clouds in the instrument field of view are identified and filtered out. An independent aerosol retrieval is performed beforehand and its results are used to account for the stratospheric aerosol load in the ozone inversion. The typical vertical resolution of the retrieved profiles varies from ˜ 2.5 km at lower altitudes ( < 30 km) to ˜ 1.5 km (about 45 km) and becomes coarser at upper altitudes. The retrieval errors resulting from the measurement noise are estimated to be 1-4 % above 25 km, increasing to 10-30 % in the upper troposphere. OMPS data are processed for the whole of 2016. The results are compared with the NASA product and validated against profiles derived from passive satellite observations or measured in situ by balloon-borne sondes. Between 20 and 60 km, OMPS ozone profiles typically agree with data from the Microwave Limb Sounder (MLS) v4.2 within 5-10 %, whereas in the lower altitude range the bias becomes larger, especially in the tropics. The comparison of OMPS profiles with ozonesonde measurements shows differences within ±5 % between 13 and 30 km at northern middle and high latitudes. At southern middle and high latitudes, an agreement within 5-7 % is also achieved in the same altitude range. An unexpected bias of approximately 10-20 % is detected in the lower tropical stratosphere. The processing of the 2013 data set using the same retrieval settings and its validation against ozonesondes reveals a much smaller bias; a possible reason for this behaviour is discussed.

Aerosol Retrievals over the Ocean using Channel 1 and 2 AVHRR Data: A Sensitivity Analysis and Preliminary Results

NASA Technical Reports Server (NTRS)

Mishchenko, Michael I.; Geogdzhayev, Igor V.; Cairns, Brian; Rossow, William B.; Lacis, Andrew A.

1999-01-01

This paper outlines the methodology of interpreting channel 1 and 2 AVHRR radiance data over the oceans and describes a detailed analysis of the sensitivity of monthly averages of retrieved aerosol parameters to the assumptions made in different retrieval algorithms. The analysis is based on using real AVHRR data and exploiting accurate numerical techniques for computing single and multiple scattering and spectral absorption of light in the vertically inhomogeneous atmosphere-ocean system. We show that two-channel algorithms can be expected to provide significantly more accurate and less biased retrievals of the aerosol optical thickness than one-channel algorithms and that imperfect cloud screening and calibration uncertainties are by far the largest sources of errors in the retrieved aerosol parameters. Both underestimating and overestimating aerosol absorption as well as the potentially strong variability of the real part of the aerosol refractive index may lead to regional and/or seasonal biases in optical thickness retrievals. The Angstrom exponent appears to be the most invariant aerosol size characteristic and should be retrieved along with optical thickness as the second aerosol parameter.

Ground based measurements on reflectance towards validating atmospheric correction algorithms on IRS-P6 AWiFS data

NASA Astrophysics Data System (ADS)

Rani Sharma, Anu; Kharol, Shailesh Kumar; Kvs, Badarinath; Roy, P. S.

In Earth observation, the atmosphere has a non-negligible influence on the visible and infrared radiation which is strong enough to modify the reflected electromagnetic signal and at-target reflectance. Scattering of solar irradiance by atmospheric molecules and aerosol generates path radiance, which increases the apparent surface reflectance over dark surfaces while absorption by aerosols and other molecules in the atmosphere causes loss of brightness to the scene, as recorded by the satellite sensor. In order to derive precise surface reflectance from satellite image data, it is indispensable to apply the atmospheric correction which serves to remove the effects of molecular and aerosol scattering. In the present study, we have implemented a fast atmospheric correction algorithm to IRS-P6 AWiFS satellite data which can effectively retrieve surface reflectance under different atmospheric and surface conditions. The algorithm is based on MODIS climatology products and simplified use of Second Simulation of Satellite Signal in Solar Spectrum (6S) radiative transfer code, which is used to generate look-up-tables (LUTs). The algorithm requires information on aerosol optical depth for correcting the satellite dataset. The proposed method is simple and easy to implement for estimating surface reflectance from the at sensor recorded signal, on a per pixel basis. The atmospheric correction algorithm has been tested for different IRS-P6 AWiFS False color composites (FCC) covering the ICRISAT Farm, Patancheru, Hyderabad, India under varying atmospheric conditions. Ground measurements of surface reflectance representing different land use/land cover, i.e., Red soil, Chick Pea crop, Groundnut crop and Pigeon Pea crop were conducted to validate the algorithm and found a very good match between surface reflectance and atmospherically corrected reflectance for all spectral bands. Further, we aggregated all datasets together and compared the retrieved AWiFS reflectance with aggregated ground measurements which showed a very good correlation of 0.96 in all four spectral bands (i.e. green, red, NIR and SWIR). In order to quantify the accuracy of the proposed method in the estimation of the surface reflectance, the root mean square error (RMSE) associated to the proposed method was evaluated. The analysis of the ground measured versus retrieved AWiFS reflectance yielded smaller RMSE values in case of all four spectral bands. EOS TERRA/AQUA MODIS derived AOD exhibited very good correlation of 0.92 and the data sets provides an effective means for carrying out atmospheric corrections in an operational way. Keywords: Atmospheric correction, 6S code, MODIS, Spectroradiometer, Sun-Photometer

Millimeter-wave Imaging Radiometer (MIR) data processing and development of water vapor retrieval algorithms

NASA Technical Reports Server (NTRS)

Chang, L. Aron

1995-01-01

This document describes the progress of the task of the Millimeter-wave Imaging Radiometer (MIR) data processing and the development of water vapor retrieval algorithms, for the second six-month performing period. Aircraft MIR data from two 1995 field experiments were collected and processed with a revised data processing software. Two revised versions of water vapor retrieval algorithm were developed, one for the execution of retrieval on a supercomputer platform, and one for using pressure as the vertical coordinate. Two implementations of incorporating products from other sensors into the water vapor retrieval system, one from the Special Sensor Microwave Imager (SSM/I), the other from the High-resolution Interferometer Sounder (HIS). Water vapor retrievals were performed for both airborne MIR data and spaceborne SSM/T-2 data, during field experiments of TOGA/COARE, CAMEX-1, and CAMEX-2. The climatology of water vapor during TOGA/COARE was examined by SSM/T-2 soundings and conventional rawinsonde.

Differences in liquid cloud droplet effective radius and number concentration estimates between MODIS Collections 5.1 and 6 over global oceans

PubMed Central

Rausch, John; Meyer, Kerry; Bennartz, Ralf; Platnick, Steven

2017-01-01

Differences in cloud droplet effective radius and cloud droplet number concentration (CDNC) estimates inferred from the Aqua MODIS Collections 5.1 and 6 cloud products (MYD06) are examined for warm clouds over global oceans for the year 2008. Individual pixel level retrievals for both collections are aggregated to 1° × 1° and compared globally and regionally for the three main spectral channel pairs used for MODIS cloud optical property retrievals. Comparisons between both collections are performed for cases in which all three effective radii retrievals are classified by the MODIS Cloud Product as valid. The contribution to the observed differences of several key MYD06 Collection 6 algorithm updates are also explored, with a focus on changes to the surface reflectance model, assumed solar irradiance, above cloud emission, cloud top pressure, and pixel registration. Global results show a neutral to positive (> 50 cm−3) change for C6-derived CDNC relative to C5.1 for the 1.6 µm and 2.1 µm channel retrievals, corresponding to a neutral to −2 µm difference in droplet effective radius. For 3.7 µm retrievals, CDNC results show a negative change in the tropics, with differences transitioning toward positive values with increasing latitude spanning −25 to +50 cm−3 related to a +2.5 to −1 µm transition in effective radius. Cloud optical thickness differences were small relative to effective radius, and found to not significantly impact CDNC estimates. Regionally, the magnitude and behavior of the annual CDNC cycle are compared for each effective radius retrieval. Results from this study indicate significant intercollection differences in aggregated values of effective radius due to changes to the pre-computed retrieval lookup tables for ocean scenes, changes to retrieved cloud top pressure, solar irradiance, or above cloud thermal emission, depending upon spectral channel. The observed differences between collections may have implications for existing MODIS derived climatologies and validation studies of effective radius and CDNC. PMID:29098040

Differences in Liquid Cloud Droplet Effective Radius and Number Concentration Estimates Between MODIS Collections 5.1 and 6 Over Global Oceans

NASA Technical Reports Server (NTRS)

Rausch, John; Meyer, Kerry; Bennartz, Ralf; Platnick, Steven

2017-01-01

Differences in cloud droplet effective radius and cloud droplet number concentration (CDNC) estimates inferred from the Aqua MODIS Collections 5.1 and 6 cloud products (MYD06) are examined for warm clouds over global oceans for the year 2008. Individual pixel level retrievals for both collections are aggregated to 1 degree x 1 degree and compared globally and regionally for the three main spectral channel pairs used for MODIS cloud optical property retrievals. Comparisons between both collections are performed for cases in which all three effective radii retrievals are classified by the MODIS Cloud Product as valid. The contribution to the observed differences of several key MYD06 Collection 6 algorithm updates are also explored, with a focus on changes to the surface reflectance model, assumed solar irradiance, above cloud emission, cloud top pressure, and pixel registration. Global results show a neutral to positive ( greater than 50cm(exp. -3) change for C6-derived CDNC relative to C5.1 for the 1.6 micrometers and 2.1 micrometers channel retrievals, corresponding to a neutral to -2 micrometers difference in droplet effective radius. For 3.7 micrometer retrievals, CDNC results show a negative change in the tropics, with differences transitioning toward positive values with increasing latitude spanning -25 to +50 cm(exp. -3) related to a +2.5 to -1 micrometers transition in effective radius. Cloud optical thickness differences were small relative to effective radius, and found to not significantly impact CDNC estimates. Regionally, the magnitude and behavior of the annual CDNC cycle are compared for each effective radius retrieval. Results from this study indicate significant intercollection differences in aggregated values of effective radius due to changes to the pre-computed retrieval lookup tables for ocean scenes, changes to retrieved cloud top pressure, solar irradiance, or above cloud thermal emission, depending upon spectral channel. The observed differences between collections may have implications for existing MODIS derived climatologies and validation studies of effective radius and CDNC.

Rainfall Estimation over the Nile Basin using an Adapted Version of the SCaMPR Algorithm

NASA Astrophysics Data System (ADS)

Habib, E. H.; Kuligowski, R. J.; Elshamy, M. E.; Ali, M. A.; Haile, A.; Amin, D.; Eldin, A.

2011-12-01

Management of Egypt's Aswan High Dam is critical not only for flood control on the Nile but also for ensuring adequate water supplies for most of Egypt since rainfall is scarce over the vast majority of its land area. However, reservoir inflow is driven by rainfall over Sudan, Ethiopia, Uganda, and several other countries from which routine rain gauge data are sparse. Satellite-derived estimates of rainfall offer a much more detailed and timely set of data to form a basis for decisions on the operation of the dam. A single-channel infrared algorithm is currently in operational use at the Egyptian Nile Forecast Center (NFC). This study reports on the adaptation of a multi-spectral, multi-instrument satellite rainfall estimation algorithm (Self-Calibrating Multivariate Precipitation Retrieval, SCaMPR) for operational application over the Nile Basin. The algorithm uses a set of rainfall predictors from multi-spectral Infrared cloud top observations and self-calibrates them to a set of predictands from Microwave (MW) rain rate estimates. For application over the Nile Basin, the SCaMPR algorithm uses multiple satellite IR channels recently available to NFC from the Spinning Enhanced Visible and Infrared Imager (SEVIRI). Microwave rain rates are acquired from multiple sources such as SSM/I, SSMIS, AMSU, AMSR-E, and TMI. The algorithm has two main steps: rain/no-rain separation using discriminant analysis, and rain rate estimation using stepwise linear regression. We test two modes of algorithm calibration: real-time calibration with continuous updates of coefficients with newly coming MW rain rates, and calibration using static coefficients that are derived from IR-MW data from past observations. We also compare the SCaMPR algorithm to other global-scale satellite rainfall algorithms (e.g., 'Tropical Rainfall Measuring Mission (TRMM) and other sources' (TRMM-3B42) product, and the National Oceanographic and Atmospheric Administration Climate Prediction Center (NOAA-CPC) CMORPH product. The algorithm has several potential future applications such as: improving the performance accuracy of hydrologic forecasting models over the Nile Basin, and utilizing the enhanced rainfall datasets and better-calibrated hydrologic models to assess the impacts of climate change on the region's water availability.

Modified retrieval algorithm for three types of precipitation distribution using x-band synthetic aperture radar

NASA Astrophysics Data System (ADS)

Xie, Yanan; Zhou, Mingliang; Pan, Dengke

2017-10-01

The forward-scattering model is introduced to describe the response of normalized radar cross section (NRCS) of precipitation with synthetic aperture radar (SAR). Since the distribution of near-surface rainfall is related to the rate of near-surface rainfall and horizontal distribution factor, a retrieval algorithm called modified regression empirical and model-oriented statistical (M-M) based on the volterra integration theory is proposed. Compared with the model-oriented statistical and volterra integration (MOSVI) algorithm, the biggest difference is that the M-M algorithm is based on the modified regression empirical algorithm rather than the linear regression formula to retrieve the value of near-surface rainfall rate. Half of the empirical parameters are reduced in the weighted integral work and a smaller average relative error is received while the rainfall rate is less than 100 mm/h. Therefore, the algorithm proposed in this paper can obtain high-precision rainfall information.

Passive microwave algorithm development and evaluation

NASA Technical Reports Server (NTRS)

Petty, Grant W.

1995-01-01

The scientific objectives of this grant are: (1) thoroughly evaluate, both theoretically and empirically, all available Special Sensor Microwave Imager (SSM/I) retrieval algorithms for column water vapor, column liquid water, and surface wind speed; (2) where both appropriate and feasible, develop, validate, and document satellite passive microwave retrieval algorithms that offer significantly improved performance compared with currently available algorithms; and (3) refine and validate a novel physical inversion scheme for retrieving rain rate over the ocean. This report summarizes work accomplished or in progress during the first year of a three year grant. The emphasis during the first year has been on the validation and refinement of the rain rate algorithm published by Petty and on the analysis of independent data sets that can be used to help evaluate the performance of rain rate algorithms over remote areas of the ocean. Two articles in the area of global oceanic precipitation are attached.

Retrieval of volcanic ash properties from the Infrared Atmospheric Sounding Interferometer (IASI)

NASA Astrophysics Data System (ADS)

Ventress, Lucy; Carboni, Elisa; Smith, Andrew; Grainger, Don; Dudhia, Anu; Hayer, Catherine

2014-05-01

The Infrared Atmospheric Sounding Interferometer (IASI), on board both the MetOp-A and MetOp-B platforms, is a Fourier transform spectrometer covering the mid-infrared (IR) from 645-2760cm-1 (3.62-15.5 μm) with a spectral resolution of 0.5cm-1 (apodised) and a pixel diameter at nadir of 12km. These characteristics allow global coverage to be achieved twice daily for each instrument and make IASI a very useful tool for the observation of larger aerosol particles (such as desert dust and volcanic ash) and the tracking of volcanic plumes. In recent years, following the eruption of Eyjafjallajökull, interest in the the ability to detect and characterise volcanic ash plumes has peaked due to the hazards to aviation. The thermal infrared spectra shows a rapid variation with wavelength due to absorption lines from atmospheric and volcanic gases as well as broad scale features principally due to particulate absorption. The ash signature depends upon both the composition and size distribution of ash particles as well as the altitude of the volcanic plume. To retrieve ash properties, IASI brightness temperature spectra are analysed using an optimal estimation retrieval scheme and a forward model based on RTTOV. Initially, IASI pixels are flagged for the presence of volcanic ash using a linear retrieval detection method based on departures from a background state. Given a positive ash signal, the RTTOV output for a clean atmosphere (containing atmospheric gases but no cloud or aerosol/ash) is combined with an ash/cloud layer using the same scheme as for the Oxford-RAL Retrieval of Aerosol and Cloud (ORAC) algorithm. The retrieved parameters are ash optical depth (at a reference wavelength of 550nm), ash effective radius, layer altitude and surface temperature. The potential for distinguishing between different ash types is explored and a sensitivity study of the retrieval algorithm is presented. Results are shown from studies of the evolution and composition of ash plumes for recent volcanic eruptions.

Retrieval of the Nitrous Oxide Profiles using the AIRS Data in China

NASA Astrophysics Data System (ADS)

Chen, L.; Ma, P.; Tao, J.; Li, X.; Zhang, Y.; Wang, Z.; Li, S.; Xiong, X.

2014-12-01

As an important greenhouse gas and ozone-depleting substance, the 100-year global warming potential of Nitrous Oxide (N2O) is almost 300 times higher than that of carbon dioxide. However, there are still large uncertainties about the quantitative N2O emission and its feedback to climate change due to the coarse ground-based network. This approach attempts to retrieve the N2O profiles from the Atmospheric InfraRed Sounder (AIRS) data. First, the sensitivity of atmospheric temperature and humidity profiles and surface parameters between two spectral absorption bands were simulated by using the radiative transfer model. Second, the eigenvector regression algorithm is used to construct a priori state. Third, an optimal estimate method was developed based on the band selection of N2O. Finally, we compared our retrieved AIRS profiles with HIPPO data, and analyzed the seasonal and annual N2O distribution in China from 2004 to 2013.

Mapping the low salinity Changjiang Diluted Water using satellite-retrieved colored dissolved organic matter (CDOM) in the East China Sea during high river flow season

NASA Astrophysics Data System (ADS)

Sasaki, Hiroaki; Siswanto, Eko; Nishiuchi, Kou; Tanaka, Katsuhisa; Hasegawa, Toru; Ishizaka, Joji

2008-02-01

Absorption coefficients of colored dissolved organic matter (CDOM) [a g(λ)] were measured and relationship with salinity was derived in the East China Sea (ECS) during summer when amount of the Changjiang River discharge is large. Low salinity Changjiang Diluted Water (CDW) was observed widely in the shelf region and was considered to be the main origin of CDOM, resulting in a strong relationship between salinity and a g(λ). Error of satellite a g(λ) estimated by the present ocean color algorithm could be corrected by satellite-retrieved chlorophyll data. Satellite-retrieved salinity could be predicted with about +/-1.0 accuracy from satellite a g(λ) and the relation between salinity and a g(λ). Our study suggests that satellite-derived a g(λ) can be an indicator of the low salinity CDW during summer.

Information retrieval algorithms: A survey

DOE Office of Scientific and Technical Information (OSTI.GOV)

Raghavan, P.

We give an overview of some algorithmic problems arising in the representation of text/image/multimedia objects in a form amenable to automated searching, and in conducting these searches efficiently. These operations are central to information retrieval and digital library systems.

A New, More Physically Based Algorithm, for Retrieving Aerosol Properties over Land from MODIS

NASA Technical Reports Server (NTRS)

Levy, Robert C.; Kaufman, Yoram J.; Remer, Lorraine A.; Mattoo, Shana

2004-01-01

The MOD Imaging Spectrometer (MODIS) has been successfully retrieving aerosol properties, beginning in early 2000 from Terra and from mid 2002 from Aqua. Over land, the retrieval algorithm makes use of three MODIS channels, in the blue, red and infrared wavelengths. As part of the validation exercises, retrieved spectral aerosol optical thickness (AOT) has been compared via scatterplots against spectral AOT measured by the global Aerosol Robotic NETwork (AERONET). On one hand, global and long term validation looks promising, with two-thirds (average plus and minus one standard deviation) of all points falling between published expected error bars. On the other hand, regression of these points shows a positive y-offset and a slope less than 1.0. For individual regions, such as along the U.S. East Coast, the offset and slope are even worse. Here, we introduce an overhaul of the algorithm for retrieving aerosol properties over land. Some well-known weaknesses in the current aerosol retrieval from MODIS include: a) rigid assumptions about the underlying surface reflectance, b) limited aerosol models to choose from, c) simplified (scalar) radiative transfer (RT) calculations used to simulate satellite observations, and d) assumption that aerosol is transparent in the infrared channel. The new algorithm attempts to address all four problems: a) The new algorithm will include surface type information, instead of fixed ratios of the reflectance in the visible channels to the mid-IR reflectance. b) It will include updated aerosol optical properties to reflect the growing aerosol retrieved from eight-plus years of AERONE". operation. c) The effects of polarization will be including using vector RT calculations. d) Most importantly, the new algorithm does not assume that aerosol is transparent in the infrared channel. It will be an inversion of reflectance observed in the three channels (blue, red, and infrared), rather than iterative single channel retrievals. Thus, this new formulation of the MODIS aerosol retrieval over land includes more physically based surface, aerosol and radiative transfer with fewer potentially erroneous assumptions.

A differential optical absorption spectroscopy method for retrieval from ground-based Fourier transform spectrometers measurements of the direct solar beam

NASA Astrophysics Data System (ADS)

Huo, Yanfeng; Duan, Minzheng; Tian, Wenshou; Min, Qilong

2015-08-01

A differential optical absorption spectroscopy (DOAS)-like algorithm is developed to retrieve the column-averaged dryair mole fraction of carbon dioxide from ground-based hyper-spectral measurements of the direct solar beam. Different to the spectral fitting method, which minimizes the difference between the observed and simulated spectra, the ratios of multiple channel-pairs—one weak and one strong absorption channel—are used to retrieve from measurements of the shortwave infrared (SWIR) band. Based on sensitivity tests, a super channel-pair is carefully selected to reduce the effects of solar lines, water vapor, air temperature, pressure, instrument noise, and frequency shift on retrieval errors. The new algorithm reduces computational cost and the retrievals are less sensitive to temperature and H2O uncertainty than the spectral fitting method. Multi-day Total Carbon Column Observing Network (TCCON) measurements under clear-sky conditions at two sites (Tsukuba and Bremen) are used to derive xxxx for the algorithm evaluation and validation. The DOAS-like results agree very well with those of the TCCON algorithm after correction of an airmass-dependent bias.

Significant Advances in the AIRS Science Team Version-6 Retrieval Algorithm

NASA Technical Reports Server (NTRS)

Susskind, Joel; Blaisdell, John; Iredell, Lena; Molnar, Gyula

2012-01-01

AIRS/AMSU is the state of the art infrared and microwave atmospheric sounding system flying aboard EOS Aqua. The Goddard DISC has analyzed AIRS/AMSU observations, covering the period September 2002 until the present, using the AIRS Science Team Version-S retrieval algorithm. These products have been used by many researchers to make significant advances in both climate and weather applications. The AIRS Science Team Version-6 Retrieval, which will become operation in mid-20l2, contains many significant theoretical and practical improvements compared to Version-5 which should further enhance the utility of AIRS products for both climate and weather applications. In particular, major changes have been made with regard to the algOrithms used to 1) derive surface skin temperature and surface spectral emissivity; 2) generate the initial state used to start the retrieval procedure; 3) compute Outgoing Longwave Radiation; and 4) determine Quality Control. This paper will describe these advances found in the AIRS Version-6 retrieval algorithm and demonstrate the improvement of AIRS Version-6 products compared to those obtained using Version-5,

DREAMING OF ATMOSPHERES

DOE Office of Scientific and Technical Information (OSTI.GOV)

Waldmann, I. P., E-mail: ingo@star.ucl.ac.uk

Here, we introduce the RobERt (Robotic Exoplanet Recognition) algorithm for the classification of exoplanetary emission spectra. Spectral retrieval of exoplanetary atmospheres frequently requires the preselection of molecular/atomic opacities to be defined by the user. In the era of open-source, automated, and self-sufficient retrieval algorithms, manual input should be avoided. User dependent input could, in worst-case scenarios, lead to incomplete models and biases in the retrieval. The RobERt algorithm is based on deep-belief neural (DBN) networks trained to accurately recognize molecular signatures for a wide range of planets, atmospheric thermal profiles, and compositions. Reconstructions of the learned features, also referred to as themore » “dreams” of the network, indicate good convergence and an accurate representation of molecular features in the DBN. Using these deep neural networks, we work toward retrieval algorithms that themselves understand the nature of the observed spectra, are able to learn from current and past data, and make sensible qualitative preselections of atmospheric opacities to be used for the quantitative stage of the retrieval process.« less

Minimizing the semantic gap in biomedical content-based image retrieval

NASA Astrophysics Data System (ADS)

Guan, Haiying; Antani, Sameer; Long, L. Rodney; Thoma, George R.

2010-03-01

A major challenge in biomedical Content-Based Image Retrieval (CBIR) is to achieve meaningful mappings that minimize the semantic gap between the high-level biomedical semantic concepts and the low-level visual features in images. This paper presents a comprehensive learning-based scheme toward meeting this challenge and improving retrieval quality. The article presents two algorithms: a learning-based feature selection and fusion algorithm and the Ranking Support Vector Machine (Ranking SVM) algorithm. The feature selection algorithm aims to select 'good' features and fuse them using different similarity measurements to provide a better representation of the high-level concepts with the low-level image features. Ranking SVM is applied to learn the retrieval rank function and associate the selected low-level features with query concepts, given the ground-truth ranking of the training samples. The proposed scheme addresses four major issues in CBIR to improve the retrieval accuracy: image feature extraction, selection and fusion, similarity measurements, the association of the low-level features with high-level concepts, and the generation of the rank function to support high-level semantic image retrieval. It models the relationship between semantic concepts and image features, and enables retrieval at the semantic level. We apply it to the problem of vertebra shape retrieval from a digitized spine x-ray image set collected by the second National Health and Nutrition Examination Survey (NHANES II). The experimental results show an improvement of up to 41.92% in the mean average precision (MAP) over conventional image similarity computation methods.

Continental-Scale Mapping of Adelie Penguin Colonies from Landsat Imagery

NASA Technical Reports Server (NTRS)

Schwaller, Mathew R.; Southwell, Colin; Emmerson, Louise

2013-01-01

Breeding distribution of the Adlie penguin, Pygoscelis adeliae, was surveyed with Landsat-7 Enhanced Thematic Mapper Plus (ETM+) data in an area covering approximately 330 of longitude along the coastline of Antarctica.An algorithm was designed to minimize radiometric noise and to retrieve Adlie penguin colony location and spatial extent from the ETM+data. In all, 9143 individual pixels were classified as belonging to an Adlie penguin colony class out of the entire dataset of 195 ETM+ scenes, where the dimension of each pixel is 30 m by 30 m,and each scene is approximately 180 km by 180 km. Pixel clustering identified a total of 187 individual Adlie penguin colonies, ranging in size from a single pixel (900 sq m) to a maximum of 875 pixels (0.788 sq km). Colony retrievals have a very low error of commission, on the order of 1% or less, and the error of omission was estimated to be 3% to 4% by population based on comparisons with direct observations from surveys across east Antarctica. Thus, the Landsat retrievals successfully located Adlie penguin colonies that accounted for 96 to 97% of the regional population used as ground truth. Geographic coordinates and the spatial extent of each colony retrieved from the Landsat data are available publically. Regional analysis found several areas where the Landsat retrievals suggest populations that are significantly larger than published estimates. Six Adlie penguin colonies were found that are believed to be previously unreported in the literature.

Lessons learned and way forward from 6 years of Aerosol_cci

NASA Astrophysics Data System (ADS)

Popp, Thomas; de Leeuw, Gerrit; Pinnock, Simon

2017-04-01

Within the ESA Climate Change Initiative (CCI) Aerosol_cci (2010 - 2017) conducts intensive work to improve and qualify algorithms for the retrieval of aerosol information from European sensors. Meanwhile, several validated (multi-) decadal time series of different aerosol parameters from complementary sensors are available: Aerosol Optical Depth (AOD), stratospheric extinction profiles, a qualitative Absorbing Aerosol Index (AAI), fine mode AOD, mineral dust AOD; absorption information and aerosol layer height are in an evaluation phase and the multi-pixel GRASP algorithm for the POLDER instrument is used for selected regions. Validation (vs. AERONET, MAN) and inter-comparison to other satellite datasets (MODIS, MISR, SeaWIFS) proved the high quality of the available datasets comparable to other satellite retrievals and revealed needs for algorithm improvement (for example for higher AOD values) which were taken into account in an iterative evolution cycle. The datasets contain pixel level uncertainty estimates which were also validated and improved in the reprocessing. The use of an ensemble method was tested, where several algorithms are applied to the same sensor. The presentation will summarize and discuss the lessons learned from the 6 years of intensive collaboration and highlight major achievements (significantly improved AOD quality, fine mode AOD, dust AOD, pixel level uncertainties, ensemble approach); also limitations and remaining deficits shall be discussed. An outlook will discuss the way forward for the continuous algorithm improvement and re-processing together with opportunities for time series extension with successor instruments of the Sentinel family and the complementarity of the different satellite aerosol products.

An integrated content and metadata based retrieval system for art.

PubMed

Lewis, Paul H; Martinez, Kirk; Abas, Fazly Salleh; Fauzi, Mohammad Faizal Ahmad; Chan, Stephen C Y; Addis, Matthew J; Boniface, Mike J; Grimwood, Paul; Stevenson, Alison; Lahanier, Christian; Stevenson, James

2004-03-01

A new approach to image retrieval is presented in the domain of museum and gallery image collections. Specialist algorithms, developed to address specific retrieval tasks, are combined with more conventional content and metadata retrieval approaches, and implemented within a distributed architecture to provide cross-collection searching and navigation in a seamless way. External systems can access the different collections using interoperability protocols and open standards, which were extended to accommodate content based as well as text based retrieval paradigms. After a brief overview of the complete system, we describe the novel design and evaluation of some of the specialist image analysis algorithms including a method for image retrieval based on sub-image queries, retrievals based on very low quality images and retrieval using canvas crack patterns. We show how effective retrieval results can be achieved by real end-users consisting of major museums and galleries, accessing the distributed but integrated digital collections.

Optically secured information retrieval using two authenticated phase-only masks.

PubMed

Wang, Xiaogang; Chen, Wen; Mei, Shengtao; Chen, Xudong

2015-10-23

We propose an algorithm for jointly designing two phase-only masks (POMs) that allow for the encryption and noise-free retrieval of triple images. The images required for optical retrieval are first stored in quick-response (QR) codes for noise-free retrieval and flexible readout. Two sparse POMs are respectively calculated from two different images used as references for authentication based on modified Gerchberg-Saxton algorithm (GSA) and pixel extraction, and are then used as support constraints in a modified double-phase retrieval algorithm (MPRA), together with the above-mentioned QR codes. No visible information about the target images or the reference images can be obtained from each of these authenticated POMs. This approach allows users to authenticate the two POMs used for image reconstruction without visual observation of the reference images. It also allows user to friendly access and readout with mobile devices.

Optically secured information retrieval using two authenticated phase-only masks

PubMed Central

Wang, Xiaogang; Chen, Wen; Mei, Shengtao; Chen, Xudong

2015-01-01

We propose an algorithm for jointly designing two phase-only masks (POMs) that allow for the encryption and noise-free retrieval of triple images. The images required for optical retrieval are first stored in quick-response (QR) codes for noise-free retrieval and flexible readout. Two sparse POMs are respectively calculated from two different images used as references for authentication based on modified Gerchberg-Saxton algorithm (GSA) and pixel extraction, and are then used as support constraints in a modified double-phase retrieval algorithm (MPRA), together with the above-mentioned QR codes. No visible information about the target images or the reference images can be obtained from each of these authenticated POMs. This approach allows users to authenticate the two POMs used for image reconstruction without visual observation of the reference images. It also allows user to friendly access and readout with mobile devices. PMID:26494213

Optically secured information retrieval using two authenticated phase-only masks

NASA Astrophysics Data System (ADS)

Wang, Xiaogang; Chen, Wen; Mei, Shengtao; Chen, Xudong

2015-10-01

We propose an algorithm for jointly designing two phase-only masks (POMs) that allow for the encryption and noise-free retrieval of triple images. The images required for optical retrieval are first stored in quick-response (QR) codes for noise-free retrieval and flexible readout. Two sparse POMs are respectively calculated from two different images used as references for authentication based on modified Gerchberg-Saxton algorithm (GSA) and pixel extraction, and are then used as support constraints in a modified double-phase retrieval algorithm (MPRA), together with the above-mentioned QR codes. No visible information about the target images or the reference images can be obtained from each of these authenticated POMs. This approach allows users to authenticate the two POMs used for image reconstruction without visual observation of the reference images. It also allows user to friendly access and readout with mobile devices.

New Approach to Monitor Transboundary Particulate Pollution over Northeast Asia

NASA Technical Reports Server (NTRS)

Park, M. E.; Song, C. H.; Park, R. S.; Lee, Jaehwa; Kim, J.; Lee, S.; Woo, J. H.; Carmichael, G. R.; Eck, Thomas F.; Holben, Brent N.;

New approach to monitor transboundary particulate pollution over Northeast Asia

NASA Astrophysics Data System (ADS)

Park, M. E.; Song, C. H.; Park, R. S.; Lee, J.; Kim, J.; Lee, S.; Woo, J.-H.; Carmichael, G. R.; Eck, T. F.; Holben, B. N.; Lee, S.-S.; Song, C. K.; Hong, Y. D.

2014-01-01

A new approach to more accurately monitor and evaluate transboundary particulate matter (PM) pollution is introduced based on aerosol optical products from Korea's Geostationary Ocean Color Imager (GOCI). The area studied is Northeast Asia (including eastern parts of China, the Korean peninsula and Japan), where GOCI has been monitoring since June 2010. The hourly multi-spectral aerosol optical data that were retrieved from GOCI sensor onboard geostationary satellite COMS (Communication, Ocean, and Meteorology Satellite) through the Yonsei aerosol retrieval algorithm were first presented and used in this study. The GOCI-retrieved aerosol optical data are integrated with estimated aerosol distributions from US EPA Models-3/CMAQ (Community Multi-scale Air Quality) v4.5.1 model simulations via data assimilation technique, thereby making the aerosol data spatially continuous and available even for cloud contamination cells. The assimilated aerosol optical data are utilized to provide quantitative estimates of transboundary PM pollution from China to the Korean peninsula and Japan. For the period of 1 April to 31 May, 2011 this analysis yields estimates that AOD as a proxy for PM2.5 or PM10 during long-range transport events increased by 117-265% compared to background average AOD (aerosol optical depth) at the four AERONET sites in Korea, and average AOD increases of 121% were found when averaged over the entire Korean peninsula. This paper demonstrates that the use of multi-spectral AOD retrievals from geostationary satellites can improve estimates of transboundary PM pollution. Such data will become more widely available later this decade when new sensors such as the GEMS (Geostationary Environment Monitoring Spectrometer) and GOCI-2 are scheduled to be launched.

High-Resolution Soil Moisture Retrieval using SMAP-L Band Radiometer and RISAT-C band Radar Data for the Indian Subcontinent

NASA Astrophysics Data System (ADS)

Singh, G.; Das, N. N.; Panda, R. K.; Mohanty, B.; Entekhabi, D.; Bhattacharya, B. K.

2016-12-01

Soil moisture status at high resolution (1-10 km) is vital for hydrological, agricultural and hydro-metrological applications. The NASA Soil Moisture Active Passive (SMAP) mission had potential to provide reliable soil moisture estimate at finer spatial resolutions (3 km and 9 km) at the global extent, but suffered a malfunction of its radar, consequently making the SMAP mission observations only from radiometer that are of coarse spatial resolution. At present, the availability of high-resolution soil moisture product is limited, especially in developing countries like India, which greatly depends on agriculture for sustaining a huge population. Therefore, an attempt has been made in the reported study to combine the C-band synthetic aperture radar (SAR) data from Radar Imaging Satellite (RISAT) of the Indian Space Research Organization (ISRO) with the SMAP mission L-band radiometer data to obtain high-resolution (1 km and 3 km) soil moisture estimates. In this study, a downscaling approach (Active-Passive Algorithm) implemented for the SMAP mission was used to disaggregate the SMAP radiometer brightness temperature (Tb) using the fine resolution SAR backscatter (σ0) from RISAT. The downscaled high-resolution Tb was then subjected to tau-omega model in conjunction with high-resolution ancillary data to retrieve soil moisture at 1 and 3 km scale. The retrieved high-resolution soil moisture estimates were then validated with ground based soil moisture measurement under different hydro-climatic regions of India. Initial results show tremendous potential and reasonable accuracy for the retrieved soil moisture at 1 km and 3 km. It is expected that ISRO will implement this approach to produce high-resolution soil moisture estimates for the Indian subcontinent.

Liquid water path retrieval using the lowest frequency channels of Fengyun-3C Microwave Radiation Imager (MWRI)

NASA Astrophysics Data System (ADS)

Tang, Fei; Zou, Xiaolei

2017-12-01

The Microwave Radiation Imager (MWRI) on board Chinese Fengyun-3 (FY-3) satellites provides measurements at 10.65, 18.7, 23.8, 36.5, and 89.0 GHz with both horizontal and vertical polarization channels. Brightness temperature measurements of those channels with their central frequencies higher than 19 GHz from satellite-based microwave imager radiometers had traditionally been used to retrieve cloud liquid water path (LWP) over ocean. The results show that the lowest frequency channels are the most appropriate for retrieving LWP when its values are large. Therefore, a modified LWP retrieval algorithm is developed for retrieving LWP of different magnitudes involving not only the high frequency channels but also the lowest frequency channels of FY-3 MWRI. The theoretical estimates of the LWP retrieval errors are between 0.11 and 0.06 mm for 10.65- and 18.7-GHz channels and between 0.02 and 0.04 mm for 36.5- and 89.0-GHz channels. It is also shown that the brightness temperature observations at 10.65 GHz can be utilized to better retrieve the LWP greater than 3 mm in the eyewall region of Super Typhoon Neoguri (2014). The spiral structure of clouds within and around Typhoon Neoguri can be well captured by combining the LWP retrievals from different frequency channels.

A linear method for the retrieval of sun-induced chlorophyll fluorescence from GOME-2 and SCIAMACHY data

NASA Astrophysics Data System (ADS)

Köhler, P.; Guanter, L.; Joiner, J.

2015-06-01

Global retrievals of near-infrared sun-induced chlorophyll fluorescence (SIF) have been achieved in the last few years by means of a number of space-borne atmospheric spectrometers. Here, we present a new retrieval method for medium spectral resolution instruments such as the Global Ozone Monitoring Experiment-2 (GOME-2) and the SCanning Imaging Absorption SpectroMeter for Atmospheric CHartographY (SCIAMACHY). Building upon the previous work by Guanter et al. (2013) and Joiner et al. (2013), our approach provides a solution for the selection of the number of free parameters. In particular, a backward elimination algorithm is applied to optimize the number of coefficients to fit, which reduces also the retrieval noise and selects the number of state vector elements automatically. A sensitivity analysis with simulated spectra has been utilized to evaluate the performance of our retrieval approach. The method has also been applied to estimate SIF at 740 nm from real spectra from GOME-2 and for the first time, from SCIAMACHY. We find a good correspondence of the absolute SIF values and the spatial patterns from the two sensors, which suggests the robustness of the proposed retrieval method. In addition, we compare our results to existing SIF data sets, examine uncertainties and use our GOME-2 retrievals to show empirically the relatively low sensitivity of the SIF retrieval to cloud contamination.

Development of ocean color algorithms for estimating chlorophyll-a concentrations and inherent optical properties using gene expression programming (GEP).

PubMed

Chang, Chih-Hua

2015-03-09

This paper proposes new inversion algorithms for the estimation of Chlorophyll-a concentration (Chla) and the ocean's inherent optical properties (IOPs) from the measurement of remote sensing reflectance (Rrs). With in situ data from the NASA bio-optical marine algorithm data set (NOMAD), inversion algorithms were developed by the novel gene expression programming (GEP) approach, which creates, manipulates and selects the most appropriate tree-structured functions based on evolutionary computing. The limitations and validity of the proposed algorithms are evaluated by simulated Rrs spectra with respect to NOMAD, and a closure test for IOPs obtained at a single reference wavelength. The application of GEP-derived algorithms is validated against in situ, synthetic and satellite match-up data sets compiled by NASA and the International Ocean Color Coordinate Group (IOCCG). The new algorithms are able to provide Chla and IOPs retrievals to those derived by other state-of-the-art regression approaches and obtained with the semi- and quasi-analytical algorithms, respectively. In practice, there are no significant differences between GEP, support vector regression, and multilayer perceptron model in terms of the overall performance. The GEP-derived algorithms are successfully applied in processing the images taken by the Sea Wide Field-of-view Sensor (SeaWiFS), generate Chla and IOPs maps which show better details of developing algal blooms, and give more information on the distribution of water constituents between different water bodies.

Classification of case-II waters using hyperspectral (HICO) data over North Indian Ocean

NASA Astrophysics Data System (ADS)

Srinivasa Rao, N.; Ramarao, E. P.; Srinivas, K.; Deka, P. C.

2016-05-01

State of the art Ocean color algorithms are proven for retrieving the ocean constituents (chlorophyll-a, CDOM and Suspended Sediments) in case-I waters. However, these algorithms could not perform well at case-II waters because of the optical complexity. Hyperspectral data is found to be promising to classify the case-II waters. The aim of this study is to propose the spectral bands for future Ocean color sensors to classify the case-II waters. Study has been performed with Rrs's of HICO at estuaries of the river Indus and GBM of North Indian Ocean. Appropriate field samples are not available to validate and propose empirical models to retrieve concentrations. The sensor HICO is not currently operational to plan validation exercise. Aqua MODIS data at case-I and Case-II waters are used as complementary to in- situ. Analysis of Spectral reflectance curves suggests the band ratios of Rrs 484 nm and Rrs 581 nm, Rrs 490 nm and Rrs 426 nm to classify the Chlorophyll -a and CDOM respectively. Rrs 610 nm gives the best scope for suspended sediment retrieval. The work suggests the need for ocean color sensors with central wavelength's of 426, 484, 490, 581 and 610 nm to estimate the concentrations of Chl-a, Suspended Sediments and CDOM in case-II waters.

Aerosol layer height from synergistic use of VIIRS and OMPS

NASA Astrophysics Data System (ADS)

Lee, J.; Hsu, N. Y. C.; Sayer, A. M.; Kim, W.; Seftor, C. J.

2017-12-01

This study presents an Aerosol Single-scattering albedo and Height Estimation (ASHE) algorithm, which retrieves the height of UV-absorbing aerosols by synergistically using the Visible Infrared Imaging Radiometer Suite (VIIRS) and the Ozone Mapping and Profiler Suite (OMPS). ASHE provides height information over a much broader area than ground-based or spaceborne lidar measurements by benefitting from the wide swaths of the two instruments used. As determination of single-scattering albedo (SSA) of the aerosol layer is the most critical part for the performance and coverage of ASHE, here we demonstrate three different strategies to constrain the SSA. First, ASHE is able to retrieve the SSA of UV-absorbing aerosols when Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) provides vertical profiles of the aerosol layer of interest. Second, Aerosol Robotic Network (AERONET) inversions can directly constrain the SSA of the aerosol layer when collocated with VIIRS or OMPS. Last, a SSA climatology from ASHE, AERONET, or other data sources can be used for large-scale, aged aerosol events, for which climatological SSA is well-known, at the cost of a slight decrease in retrieval accuracy. The same algorithm can be applied to measurements of similar type, such as those made by the Moderate Resolution Imaging Spectroradiometer (MODIS) and Ozone Monitoring Instrument (OMI), for a long-term, consistent data record.

Assessment of the Broadleaf Crops Leaf Area Index Product from the Terra MODIS Instrument

NASA Technical Reports Server (NTRS)

Tan, Bin; Hu, Jiannan; Huang, Dong; Yang, Wenze; Zhang, Ping; Shabanov, Nikolay V.; Knyazikhin, Yuri; Nemani, Ramakrishna R.; Myneni, Ranga B.

2005-01-01

The first significant processing of Terra MODIS data, called Collection 3, covered the period from November 2000 to December 2002. The Collection 3 leaf area index (LAI) and fraction vegetation absorbed photosynthetically active radiation (FPAR) products for broadleaf crops exhibited three anomalies (a) high LAI values during the peak growing season, (b) differences in LAI seasonality between the radiative transfer-based main algorithm and the vegetation index based back-up algorithm, and (c) too few retrievals from the main algorithm during the summer period when the crops are at full flush. The cause of these anomalies is a mismatch between reflectances modeled by the algorithm and MODIS measurements. Therefore, the Look-Up-Tables accompanying the algorithm were revised and implemented in Collection 4 processing. The main algorithm with the revised Look-Up-Tables generated retrievals for over 80% of the pixels with valid data. Retrievals from the back-up algorithm, although few, should be used with caution as they are generated from surface reflectances with high uncertainties.

Atmospheric parameterization schemes for satellite cloud property retrieval during FIRE IFO 2

NASA Technical Reports Server (NTRS)

Titlow, James; Baum, Bryan A.

1993-01-01

Satellite cloud retrieval algorithms generally require atmospheric temperature and humidity profiles to determine such cloud properties as pressure and height. For instance, the CO2 slicing technique called the ratio method requires the calculation of theoretical upwelling radiances both at the surface and a prescribed number (40) of atmospheric levels. This technique has been applied to data from, for example, the High Resolution Infrared Radiometer Sounder (HIRS/2, henceforth HIRS) flown aboard the NOAA series of polar orbiting satellites and the High Resolution Interferometer Sounder (HIS). In this particular study, four NOAA-11 HIRS channels in the 15-micron region are used. The ratio method may be applied to various channel combinations to estimate cloud top heights using channels in the 15-mu m region. Presently, the multispectral, multiresolution (MSMR) scheme uses 4 HIRS channel combination estimates for mid- to high-level cloud pressure retrieval and Advanced Very High Resolution Radiometer (AVHRR) data for low-level (is greater than 700 mb) cloud level retrieval. In order to determine theoretical upwelling radiances, atmospheric temperature and water vapor profiles must be provided as well as profiles of other radiatively important gas absorber constituents such as CO2, O3, and CH4. The assumed temperature and humidity profiles have a large effect on transmittance and radiance profiles, which in turn are used with HIRS data to calculate cloud pressure, and thus cloud height and temperature. For large spatial scale satellite data analysis, atmospheric parameterization schemes for cloud retrieval algorithms are usually based on a gridded product such as that provided by the European Center for Medium Range Weather Forecasting (ECMWF) or the National Meteorological Center (NMC). These global, gridded products prescribe temperature and humidity profiles for a limited number of pressure levels (up to 14) in a vertical atmospheric column. The FIRE IFO 2 experiment provides an opportunity to investigate current atmospheric profile parameterization schemes, compare satellite cloud height results using both gridded products (ECMWF) and high vertical resolution sonde data from the National Weather Service (NWS) and Cross Chain Loran Atmospheric Sounding System (CLASS), and suggest modifications in atmospheric parameterization schemes based on these results.

Classification and assessment of retrieved electron density maps in coherent X-ray diffraction imaging using multivariate analysis.

PubMed

Sekiguchi, Yuki; Oroguchi, Tomotaka; Nakasako, Masayoshi

2016-01-01

Coherent X-ray diffraction imaging (CXDI) is one of the techniques used to visualize structures of non-crystalline particles of micrometer to submicrometer size from materials and biological science. In the structural analysis of CXDI, the electron density map of a sample particle can theoretically be reconstructed from a diffraction pattern by using phase-retrieval (PR) algorithms. However, in practice, the reconstruction is difficult because diffraction patterns are affected by Poisson noise and miss data in small-angle regions due to the beam stop and the saturation of detector pixels. In contrast to X-ray protein crystallography, in which the phases of diffracted waves are experimentally estimated, phase retrieval in CXDI relies entirely on the computational procedure driven by the PR algorithms. Thus, objective criteria and methods to assess the accuracy of retrieved electron density maps are necessary in addition to conventional parameters monitoring the convergence of PR calculations. Here, a data analysis scheme, named ASURA, is proposed which selects the most probable electron density maps from a set of maps retrieved from 1000 different random seeds for a diffraction pattern. Each electron density map composed of J pixels is expressed as a point in a J-dimensional space. Principal component analysis is applied to describe characteristics in the distribution of the maps in the J-dimensional space. When the distribution is characterized by a small number of principal components, the distribution is classified using the k-means clustering method. The classified maps are evaluated by several parameters to assess the quality of the maps. Using the proposed scheme, structure analysis of a diffraction pattern from a non-crystalline particle is conducted in two stages: estimation of the overall shape and determination of the fine structure inside the support shape. In each stage, the most accurate and probable density maps are objectively selected. The validity of the proposed scheme is examined by application to diffraction data that were obtained from an aggregate of metal particles and a biological specimen at the XFEL facility SACLA using custom-made diffraction apparatus.

Retrieving handwriting by combining word spotting and manifold ranking

NASA Astrophysics Data System (ADS)

Peña Saldarriaga, Sebastián; Morin, Emmanuel; Viard-Gaudin, Christian

2012-01-01

Online handwritten data, produced with Tablet PCs or digital pens, consists in a sequence of points (x, y). As the amount of data available in this form increases, algorithms for retrieval of online data are needed. Word spotting is a common approach used for the retrieval of handwriting. However, from an information retrieval (IR) perspective, word spotting is a primitive keyword based matching and retrieval strategy. We propose a framework for handwriting retrieval where an arbitrary word spotting method is used, and then a manifold ranking algorithm is applied on the initial retrieval scores. Experimental results on a database of more than 2,000 handwritten newswires show that our method can improve the performances of a state-of-the-art word spotting system by more than 10%.

Empirical wind retrieval model based on SAR spectrum measurements

NASA Astrophysics Data System (ADS)

Panfilova, Maria; Karaev, Vladimir; Balandina, Galina; Kanevsky, Mikhail; Portabella, Marcos; Stoffelen, Ad

The present paper considers polarimetric SAR wind vector applications. Remote-sensing measurements of the near-surface wind over the ocean are of great importance for the understanding of atmosphere-ocean interaction. In recent years investigations for wind vector retrieval using Synthetic Aperture Radar (SAR) data have been performed. In contrast with scatterometers, a SAR has a finer spatial resolution that makes it a more suitable microwave instrument to explore wind conditions in the marginal ice zones, coastal regions and lakes. The wind speed retrieval procedure from scatterometer data matches the measured radar backscattering signal with the geophysical model function (GMF). The GMF determines the radar cross section dependence on the wind speed and direction with respect to the azimuthal angle of the radar beam. Scatterometers provide information on wind speed and direction simultaneously due to the fact that each wind vector cell (WVC) is observed at several azimuth angles. However, SAR is not designed to be used as a high resolution scatterometer. In this case, each WVC is observed at only one single azimuth angle. That is why for wind vector determination additional information such as wind streak orientation over the sea surface is required. It is shown that the wind vector can be obtained using polarimetric SAR without additional information. The main idea is to analyze the spectrum of a homogeneous SAR image area instead of the backscattering normalized radar cross section. Preliminary numerical simulations revealed that SAR image spectral maxima positions depend on the wind vector. Thus the following method for wind speed retrieval is proposed. In the first stage of the algorithm, the SAR spectrum maxima are determined. This procedure is carried out to estimate the wind speed and direction with ambiguities separated by 180 degrees due to the SAR spectrum symmetry. The second stage of the algorithm allows us to select the correct wind direction ambiguity from polarimetric SAR. A criterion based on the complex correlation coefficient between the VV and VH signals sign is applied to select the wind direction. An additional quality control on the wind speed value retrieved with the spectral method is applied. Here, we use the direction obtained with the spectral method and the backscattered signal for CMOD wind speed estimate. The algorithm described above may be refined by the use of numerous SAR data and wind measurements. In the present preliminary work the first results of SAR images combined with in situ data processing are presented. Our results are compared to the results obtained using previously developed models CMOD, C-2PO for VH polarization and statistical wind retrieval approaches [1]. Acknowledgments. This work is supported by the Russian Foundation of Basic Research (grants 13-05-00852-a). [1] M. Portabella, A. Stoffelen, J. A. Johannessen, Toward an optimal inversion method for synthetic aperture radar wind retrieval, Journal of geophysical research, V. 107, N C8, 2002

The Error Structure of the SMAP Single and Dual Channel Soil Moisture Retrievals

NASA Astrophysics Data System (ADS)

Dong, Jianzhi; Crow, Wade T.; Bindlish, Rajat

2018-01-01

Knowledge of the temporal error structure for remotely sensed surface soil moisture retrievals can improve our ability to exploit them for hydrologic and climate studies. This study employs a triple collocation analysis to investigate both the total variance and temporal autocorrelation of errors in Soil Moisture Active and Passive (SMAP) products generated from two separate soil moisture retrieval algorithms, the vertically polarized brightness temperature-based single-channel algorithm (SCA-V, the current baseline SMAP algorithm) and the dual-channel algorithm (DCA). A key assumption made in SCA-V is that real-time vegetation opacity can be accurately captured using only a climatology for vegetation opacity. Results demonstrate that while SCA-V generally outperforms DCA, SCA-V can produce larger total errors when this assumption is significantly violated by interannual variability in vegetation health and biomass. Furthermore, larger autocorrelated errors in SCA-V retrievals are found in areas with relatively large vegetation opacity deviations from climatological expectations. This implies that a significant portion of the autocorrelated error in SCA-V is attributable to the violation of its vegetation opacity climatology assumption and suggests that utilizing a real (as opposed to climatological) vegetation opacity time series in the SCA-V algorithm would reduce the magnitude of autocorrelated soil moisture retrieval errors.

A Unified Approach for Reporting ARM Measurement Uncertainties Technical Report

DOE Office of Scientific and Technical Information (OSTI.GOV)

Campos, E; Sisterson, Douglas

The U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) Climate Research Facility is observationally based, and quantifying the uncertainty of its measurements is critically important. With over 300 widely differing instruments providing over 2,500 datastreams, concise expression of measurement uncertainty is quite challenging. The ARM Facility currently provides data and supporting metadata (information about the data or data quality) to its users through a number of sources. Because the continued success of the ARM Facility depends on the known quality of its measurements, the Facility relies on instrument mentors and the ARM Data Quality Office (DQO) to ensure, assess,more » and report measurement quality. Therefore, an easily accessible, well-articulated estimate of ARM measurement uncertainty is needed. Note that some of the instrument observations require mathematical algorithms (retrievals) to convert a measured engineering variable into a useful geophysical measurement. While those types of retrieval measurements are identified, this study does not address particular methods for retrieval uncertainty. As well, the ARM Facility also provides engineered data products, or value-added products (VAPs), based on multiple instrument measurements. This study does not include uncertainty estimates for those data products. We propose here that a total measurement uncertainty should be calculated as a function of the instrument uncertainty (calibration factors), the field uncertainty (environmental factors), and the retrieval uncertainty (algorithm factors). The study will not expand on methods for computing these uncertainties. Instead, it will focus on the practical identification, characterization, and inventory of the measurement uncertainties already available in the ARM community through the ARM instrument mentors and their ARM instrument handbooks. As a result, this study will address the first steps towards reporting ARM measurement uncertainty: 1) identifying how the uncertainty of individual ARM measurements is currently expressed, 2) identifying a consistent approach to measurement uncertainty, and then 3) reclassifying ARM instrument measurement uncertainties in a common framework.« less

Assessment of the improvements in accuracy of aerosol characterization resulted from additions of polarimetric measurements to intensity-only observations using GRASP algorithm (Invited)

NASA Astrophysics Data System (ADS)

Dubovik, O.; Litvinov, P.; Lapyonok, T.; Herman, M.; Fedorenko, A.; Lopatin, A.; Goloub, P.; Ducos, F.; Aspetsberger, M.; Planer, W.; Federspiel, C.

2013-12-01

During last few years we were developing GRASP (Generalized Retrieval of Aerosol and Surface Properties) algorithm designed for the enhanced characterization of aerosol properties from spectral, multi-angular polarimetric remote sensing observations. The concept of GRASP essentially relies on the accumulated positive research heritage from previous remote sensing aerosol retrieval developments, in particular those from the AERONET and POLDER retrieval activities. The details of the algorithm are described by Dubovik et al. (Atmos. Meas. Tech., 4, 975-1018, 2011). The GRASP retrieves properties of both aerosol and land surface reflectance in cloud-free environments. It is based on highly advanced statistically optimized fitting and deduces nearly 50 unknowns for each observed site. The algorithm derives a similar set of aerosol parameters as AERONET including detailed particle size distribution, the spectrally dependent the complex index of refraction and the fraction of non-spherical particles. The algorithm uses detailed aerosol and surface models and fully accounts for all multiple interactions of scattered solar light with aerosol, gases and the underlying surface. All calculations are done on-line without using traditional look-up tables. In addition, the algorithm uses the new multi-pixel retrieval concept - a simultaneous fitting of a large group of pixels with additional constraints limiting the time variability of surface properties and spatial variability of aerosol properties. This principle is expected to result in higher consistency and accuracy of aerosol products compare to conventional approaches especially over bright surfaces where information content of satellite observations in respect to aerosol properties is limited. The GRASP is a highly versatile algorithm that allows input from both satellite and ground-based measurements. It also has essential flexibility in measurement processing. For example, if observation data set includes spectral measurements of both total intensity and polarization, the algorithm can be easily set to use either total intensity or polarization, as well as both of them in the same retrieval. Using this feature of the algorithm design we have studied the relative importance of total intensity and polarization measurements for retrieving different parameters of aerosol. In this presentation, we present the quantitative assessment of the improvements in aerosol retrievals associated with additions of polarimetric measurements to the intensity-only observations. The study has been performed using satellite measurements by POLDER/PARASOL polarimeter and ground-based measurements by new generation AERONET sun/sky-radiometers implementing measurements of polarization at each spectral channel.

Open-source sea ice drift algorithm for Sentinel-1 SAR imagery using a combination of feature-tracking and pattern-matching

NASA Astrophysics Data System (ADS)

Muckenhuber, Stefan; Sandven, Stein

2017-04-01

An open-source sea ice drift algorithm for Sentinel-1 SAR imagery is introduced based on the combination of feature-tracking and pattern-matching. A computational efficient feature-tracking algorithm produces an initial drift estimate and limits the search area for the pattern-matching, that provides small to medium scale drift adjustments and normalised cross correlation values as quality measure. The algorithm is designed to utilise the respective advantages of the two approaches and allows drift calculation at user defined locations. The pre-processing of the Sentinel-1 data has been optimised to retrieve a feature distribution that depends less on SAR backscatter peak values. A recommended parameter set for the algorithm has been found using a representative image pair over Fram Strait and 350 manually derived drift vectors as validation. Applying the algorithm with this parameter setting, sea ice drift retrieval with a vector spacing of 8 km on Sentinel-1 images covering 400 km x 400 km, takes less than 3.5 minutes on a standard 2.7 GHz processor with 8 GB memory. For validation, buoy GPS data, collected in 2015 between 15th January and 22nd April and covering an area from 81° N to 83.5° N and 12° E to 27° E, have been compared to calculated drift results from 261 corresponding Sentinel-1 image pairs. We found a logarithmic distribution of the error with a peak at 300 m. All software requirements necessary for applying the presented sea ice drift algorithm are open-source to ensure free implementation and easy distribution.

Arctic Sea Ice Parameters from AMSR-E Data using Two Techniques, and Comparisons with Sea Ice from SSM

NASA Technical Reports Server (NTRS)

Comiso, Josefino C.; Parkinson, Claire L.

2007-01-01

We use two algorithms to process AMSR-E data in order to determine algorithm dependence, if any, on the estimates of sea ice concentration, ice extent and area, and trends and to evaluate how AMSR-E data compare with historical SSM/I data. The monthly ice concentrations derived from the two algorithms from AMSR-E data (the AMSR-E Bootstrap Algorithm, or ABA, and the enhanced NASA Team algorithm, or NT2) differ on average by about 1 to 3%, with data from the consolidated ice region being generally comparable for ABA and NT2 retrievals while data in the marginal ice zones and thin ice regions show higher values when the NT2 algorithm is used. The ice extents and areas derived separately from AMSR-E using these two algorithms are, however, in good agreement, with the differences (ABA-NT2) being about 6.6 x 10(exp 4) square kilometers on average for ice extents and -6.6 x 10(exp 4) square kilometers for ice area which are small compared to mean seasonal values of 10.5 x 10(exp 6) and 9.8 x 10(exp 6) for ice extent and area: respectively. Likewise, extents and areas derived from the same algorithm but from AMSR-E and SSM/I data are consistent but differ by about -24.4 x 10(exp 4) square kilometers and -13.9 x 10(exp 4) square kilometers, respectively. The discrepancies are larger with the estimates of extents than area mainly because of differences in channel selection and sensor resolutions. Trends in extent during the AMSR-E era were also estimated and results from all three data sets are shown to be in good agreement (within errors).

Validation of aerosol optical depth uncertainties within the ESA Climate Change Initiative

NASA Astrophysics Data System (ADS)

Stebel, Kerstin; Povey, Adam; Popp, Thomas; Capelle, Virginie; Clarisse, Lieven; Heckel, Andreas; Kinne, Stefan; Klueser, Lars; Kolmonen, Pekka; de Leeuw, Gerrit; North, Peter R. J.; Pinnock, Simon; Sogacheva, Larisa; Thomas, Gareth; Vandenbussche, Sophie

2017-04-01

Uncertainty is a vital component of any climate data record as it provides the context with which to understand the quality of the data and compare it to other measurements. Therefore, pixel-level uncertainties are provided for all aerosol products that have been developed in the framework of the Aerosol_cci project within ESA's Climate Change Initiative (CCI). Validation of these estimated uncertainties is necessary to demonstrate that they provide a useful representation of the distribution of error. We propose a technique for the statistical validation of AOD (aerosol optical depth) uncertainty by comparison to high-quality ground-based observations and present results for ATSR (Along Track Scanning Radiometer) and IASI (Infrared Atmospheric Sounding Interferometer) data records. AOD at 0.55 µm and its uncertainty was calculated with three AOD retrieval algorithms using data from the ATSR instruments (ATSR-2 (1995-2002) and AATSR (2002-2012)). Pixel-level uncertainties were calculated through error propagation (ADV/ASV, ORAC algorithms) or parameterization of the error's dependence on the geophysical retrieval conditions (SU algorithm). Level 2 data are given as super-pixels of 10 km x 10 km. As validation data, we use direct-sun observations of AOD from the AERONET (AErosol RObotic NETwork) and MAN (Maritime Aerosol Network) sun-photometer networks, which are substantially more accurate than satellite retrievals. Neglecting the uncertainty in AERONET observations and possible issues with their ability to represent a satellite pixel area, the error in the retrieval can be approximated by the difference between the satellite and AERONET retrievals (herein referred to as "error"). To evaluate how well the pixel-level uncertainty represents the observed distribution of error, we look at the distribution of the ratio D between the "error" and the ATSR uncertainty. If uncertainties are well represented, D should be normally distributed and 68.3% of values should fall within the range [-1, +1]. A non-zero mean of D indicates the presence of residual systematic errors. If the fraction is smaller than 68%, uncertainties are underestimated; if it is larger, uncertainties are overestimated. For the three ATSR algorithms, we provide statistics and an evaluation at a global scale (separately for land and ocean/coastal regions), for high/low AOD regimes, and seasonal and regional statistics (e.g. Europe, N-Africa, East-Asia, N-America). We assess the long-term stability of the uncertainty estimates over the 17-year time series, and the consistency between ATSR-2 and AATSR results (during their period of overlap). Furthermore, we exploit the possibility to adapt the uncertainty validation concept to the IASI datasets. Ten-year data records (2007-2016) of dust AOD have been generated with four algorithms using IASI observations over the greater Sahara region [80°W - 120°E, 0°N - 40°N]. For validation, the coarse mode AOD at 0.55 μm from the AERONET direct-sun spectral deconvolution algorithm (SDA) product may be used as a proxy for desert dust. The uncertainty validation results for IASI are still tentative, as larger IASI pixel sizes and the conversion of the IASI AOD values from infrared to visible wavelengths for comparison to ground-based observations introduces large uncertainties.

Use of Multiangle Satellite Observations to Retrieve Aerosol Properties and Ocean Color

NASA Technical Reports Server (NTRS)

Martonchik, John V.; Diner, David; Khan, Ralph

2005-01-01

A new technique is described for retrieving aerosol over ocean water and the associated ocean color using multiangle satellite observations. Unlike current satellite aerosol retrieval algorithms which only utilize observations at red wavelengths and longer, with the assumption that these wavelengths have a negligible ocean (water-leaving radiance), this new algorithm uses all available spectral bands and simultaneously retrieves both aerosol properties and the spectral ocean color. We show some results of case studies using MISR data, performed over different water conditions (coastal water, blooms, and open water).

Improved OSIRIS NO2 retrieval algorithm: description and validation

NASA Astrophysics Data System (ADS)

Sioris, Christopher E.; Rieger, Landon A.; Lloyd, Nicholas D.; Bourassa, Adam E.; Roth, Chris Z.; Degenstein, Douglas A.; Camy-Peyret, Claude; Pfeilsticker, Klaus; Berthet, Gwenaël; Catoire, Valéry; Goutail, Florence; Pommereau, Jean-Pierre; McLinden, Chris A.

2017-03-01

A new retrieval algorithm for OSIRIS (Optical Spectrograph and Infrared Imager System) nitrogen dioxide (NO2) profiles is described and validated. The algorithm relies on spectral fitting to obtain slant column densities of NO2, followed by inversion using an algebraic reconstruction technique and the SaskTran spherical radiative transfer model (RTM) to obtain vertical profiles of local number density. The validation covers different latitudes (tropical to polar), years (2002-2012), all seasons (winter, spring, summer, and autumn), different concentrations of nitrogen dioxide (from denoxified polar vortex to polar summer), a range of solar zenith angles (68.6-90.5°), and altitudes between 10.5 and 39 km, thereby covering the full retrieval range of a typical OSIRIS NO2 profile. The use of a larger spectral fitting window than used in previous retrievals reduces retrieval uncertainties and the scatter in the retrieved profiles due to noisy radiances. Improvements are also demonstrated through the validation in terms of bias reduction at 15-17 km relative to the OSIRIS operational v3.0 algorithm. The diurnal variation of NO2 along the line of sight is included in a fully spherical multiple scattering RTM for the first time. Using this forward model with built-in photochemistry, the scatter of the differences relative to the correlative balloon NO2 profile data is reduced.

Tropospheric nitrogen dioxide column retrieval from ground-based zenith-sky DOAS observations

NASA Astrophysics Data System (ADS)

Tack, F.; Hendrick, F.; Goutail, F.; Fayt, C.; Merlaud, A.; Pinardi, G.; Hermans, C.; Pommereau, J.-P.; Van Roozendael, M.

2015-01-01

We present an algorithm for retrieving tropospheric nitrogen dioxide (NO2) vertical column densities (VCDs) from ground-based zenith-sky (ZS) measurements of scattered sunlight. The method is based on a four-step approach consisting of (1) the Differential Optical Absorption Spectroscopy (DOAS) analysis of ZS radiance spectra using a fixed reference spectrum corresponding to low NO2 absorption, (2) the determination of the residual amount in the reference spectrum using a Langley-plot-type method, (3) the removal of the stratospheric content from the daytime total measured slant column based on stratospheric VCDs measured at sunrise and sunset, and simulation of the rapid NO2 diurnal variation, (4) the retrieval of tropospheric VCDs by dividing the resulting tropospheric slant columns by appropriate air mass factors (AMFs). These steps are fully characterized and recommendations are given for each of them. The retrieval algorithm is applied on a ZS dataset acquired with a Multi-AXis (MAX-) DOAS instrument during the Cabauw (51.97° N, 4.93° E, sea level) Intercomparison campaign for Nitrogen Dioxide measuring Instruments (CINDI) held from the 10 June to the 21 July 2009 in the Netherlands. A median value of 7.9 × 1015 molec cm-2 is found for the retrieved tropospheric NO2 VCDs, with maxima up to 6.0 × 1016 molec cm-2. The error budget assessment indicates that the overall error σTVCD on the column values is less than 28%. In case of low tropospheric contribution, σTVCD is estimated to be around 39% and is dominated by uncertainties in the determination of the residual amount in the reference spectrum. For strong tropospheric pollution events, σTVCD drops to approximately 22% with the largest uncertainties on the determination of the stratospheric NO2 abundance and tropospheric AMFs. The tropospheric VCD amounts derived from ZS observations are compared to VCDs retrieved from off-axis and direct-sun measurements of the same MAX-DOAS instrument as well as to data from a co-located Système d'Analyse par Observations Zénithales (SAOZ) spectrometer. The retrieved tropospheric VCDs are in good agreement with the different datasets with correlation coefficients and slopes close to or larger than 0.9. The potential of the presented ZS retrieval algorithm is further demonstrated by its successful application on a 2 year dataset, acquired at the NDACC (Network for the Detection of Atmospheric Composition Change) station Observatoire de Haute Provence (OHP; Southern France).

Tropospheric nitrogen dioxide column retrieval from ground-based zenith-sky DOAS observations

NASA Astrophysics Data System (ADS)

Tack, F.; Hendrick, F.; Goutail, F.; Fayt, C.; Merlaud, A.; Pinardi, G.; Hermans, C.; Pommereau, J.-P.; Van Roozendael, M.

2015-06-01

We present an algorithm for retrieving tropospheric nitrogen dioxide (NO2) vertical column densities (VCDs) from ground-based zenith-sky (ZS) measurements of scattered sunlight. The method is based on a four-step approach consisting of (1) the differential optical absorption spectroscopy (DOAS) analysis of ZS radiance spectra using a fixed reference spectrum corresponding to low NO2 absorption, (2) the determination of the residual amount in the reference spectrum using a Langley-plot-type method, (3) the removal of the stratospheric content from the daytime total measured slant column based on stratospheric VCDs measured at sunrise and sunset, and simulation of the rapid NO2 diurnal variation, (4) the retrieval of tropospheric VCDs by dividing the resulting tropospheric slant columns by appropriate air mass factors (AMFs). These steps are fully characterized and recommendations are given for each of them. The retrieval algorithm is applied on a ZS data set acquired with a multi-axis (MAX-) DOAS instrument during the Cabauw (51.97° N, 4.93° E, sea level) Intercomparison campaign for Nitrogen Dioxide measuring Instruments (CINDI) held from 10 June to 21 July 2009 in the Netherlands. A median value of 7.9 × 1015 molec cm-2 is found for the retrieved tropospheric NO2 VCDs, with maxima up to 6.0 × 1016 molec cm-2. The error budget assessment indicates that the overall error σTVCD on the column values is less than 28%. In the case of low tropospheric contribution, σTVCD is estimated to be around 39% and is dominated by uncertainties in the determination of the residual amount in the reference spectrum. For strong tropospheric pollution events, σTVCD drops to approximately 22% with the largest uncertainties on the determination of the stratospheric NO2 abundance and tropospheric AMFs. The tropospheric VCD amounts derived from ZS observations are compared to VCDs retrieved from off-axis and direct-sun measurements of the same MAX-DOAS instrument as well as to data from a co-located Système d'Analyse par Observations Zénithales (SAOZ) spectrometer. The retrieved tropospheric VCDs are in good agreement with the different data sets with correlation coefficients and slopes close to or larger than 0.9. The potential of the presented ZS retrieval algorithm is further demonstrated by its successful application on a 2-year data set, acquired at the NDACC (Network for the Detection of Atmospheric Composition Change) station Observatoire de Haute Provence (OHP; Southern France).

Iterative algorithms for a non-linear inverse problem in atmospheric lidar

NASA Astrophysics Data System (ADS)

Denevi, Giulia; Garbarino, Sara; Sorrentino, Alberto

2017-08-01

We consider the inverse problem of retrieving aerosol extinction coefficients from Raman lidar measurements. In this problem the unknown and the data are related through the exponential of a linear operator, the unknown is non-negative and the data follow the Poisson distribution. Standard methods work on the log-transformed data and solve the resulting linear inverse problem, but neglect to take into account the noise statistics. In this study we show that proper modelling of the noise distribution can improve substantially the quality of the reconstructed extinction profiles. To achieve this goal, we consider the non-linear inverse problem with non-negativity constraint, and propose two iterative algorithms derived using the Karush-Kuhn-Tucker conditions. We validate the algorithms with synthetic and experimental data. As expected, the proposed algorithms out-perform standard methods in terms of sensitivity to noise and reliability of the estimated profile.

[Simulation of vegetation indices optimizing under retrieval of vegetation biochemical parameters based on PROSPECT + SAIL model].

PubMed

Wu, Ling; Liu, Xiang-Nan; Zhou, Bo-Tian; Liu, Chuan-Hao; Li, Lu-Feng

2012-12-01

This study analyzed the sensitivities of three vegetation biochemical parameters [chlorophyll content (Cab), leaf water content (Cw), and leaf area index (LAI)] to the changes of canopy reflectance, with the effects of each parameter on the wavelength regions of canopy reflectance considered, and selected three vegetation indices as the optimization comparison targets of cost function. Then, the Cab, Cw, and LAI were estimated, based on the particle swarm optimization algorithm and PROSPECT + SAIL model. The results showed that retrieval efficiency with vegetation indices as the optimization comparison targets of cost function was better than that with all spectral reflectance. The correlation coefficients (R2) between the measured and estimated values of Cab, Cw, and LAI were 90.8%, 95.7%, and 99.7%, and the root mean square errors of Cab, Cw, and LAI were 4.73 microg x cm(-2), 0.001 g x cm(-2), and 0.08, respectively. It was suggested that to adopt vegetation indices as the optimization comparison targets of cost function could effectively improve the efficiency and precision of the retrieval of biochemical parameters based on PROSPECT + SAIL model.

Combining approaches to on-line handwriting information retrieval

NASA Astrophysics Data System (ADS)

Peña Saldarriaga, Sebastián; Viard-Gaudin, Christian; Morin, Emmanuel

2010-01-01

In this work, we propose to combine two quite different approaches for retrieving handwritten documents. Our hypothesis is that different retrieval algorithms should retrieve different sets of documents for the same query. Therefore, significant improvements in retrieval performances can be expected. The first approach is based on information retrieval techniques carried out on the noisy texts obtained through handwriting recognition, while the second approach is recognition-free using a word spotting algorithm. Results shows that for texts having a word error rate (WER) lower than 23%, the performances obtained with the combined system are close to the performances obtained on clean digital texts. In addition, for poorly recognized texts (WER > 52%), an improvement of nearly 17% can be observed with respect to the best available baseline method.

Temperature Crosstalk Sensitivity of the Kummerow Rainfall Algorithm

NASA Technical Reports Server (NTRS)

Spencer, Roy W.; Petrenko, Boris

1999-01-01

Even though the signal source for passive microwave retrievals is thermal emission, retrievals of non-temperature geophysical parameters typically do not explicitly take into account the effects of temperature change on the retrievals. For global change research, changes in geophysical parameters (e.g. water vapor, rainfall, etc.) are referenced to the accompanying changes in temperature. If the retrieval of a certain parameter has a cross-talk response from temperature change alone, the retrievals might not be very useful for climate research. We investigated the sensitivity of the Kummerow rainfall retrieval algorithm to changes in air temperature. It was found that there was little net change in total rainfall with air temperature change. However, there were non-negligible changes within individual rain rate categories.

Flash-Type Discrimination

NASA Technical Reports Server (NTRS)

Koshak, William J.

2010-01-01

This viewgraph presentation describes the significant progress made in the flash-type discrimination algorithm development. The contents include: 1) Highlights of Progress for GLM-R3 Flash-Type discrimination Algorithm Development; 2) Maximum Group Area (MGA) Data; 3) Retrieval Errors from Simulations; and 4) Preliminary Global-scale Retrieval.