Search NASA⌕ Search

SEARCH · Search NASA

Results for “Multi-angle Imaging SpectroRadiometer”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 181 records · Page 10
All sourcesNASA NTRSDOE OSTI
NASA NTRS2012

Remote Sensing of Tropical Ecosystems: Atmospheric Correction and Cloud Masking Matter

Tropical rainforests are significant contributors to the global cycles of energy, water and carbon. As a result, monitoring of the vegetation status over regions such as Amazonia has been a long standing interest of Earth scientists trying to determine the effect of climate change and anthropogenic disturbance on the tropical ecosystems and its feedback on the Earth's climate. Satellite-based remote sensing is the only practical approach for observing the vegetation dynamics of regions like the Amazon over useful spatial and temporal scales, but recent years have seen much controversy over satellite-derived vegetation states in Amazônia, with studies predicting opposite feedbacks depending on data processing technique and interpretation. Recent results suggest that some of this uncertainty could stem from a lack of quality in atmospheric correction and cloud screening. In this paper, we assess these uncertainties by comparing the current standard surface reflectance products (MYD09, MYD09GA) and derived composites (MYD09A1, MCD43A4 and MYD13A2 - Vegetation Index) from the Moderate Resolution Imaging Spectroradiometer (MODIS) onboard the Aqua satellite to results obtained from the Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm. MAIAC uses a new cloud screening technique, and novel aerosol retrieval and atmospheric correction procedures which are based on time-series and spatial analyses. Our results show considerable improvements of MAIAC processed surface reflectance compared to MYD09/MYD13 with noise levels reduced by a factor of up to 10. Uncertainties in the current MODIS surface reflectance product were mainly due to residual cloud and aerosol contamination which affected the Normalized Difference Vegetation Index (NDVI): During the wet season, with cloud cover ranging between 90 percent and 99 percent, conventionally processed NDVI was significantly depressed due to undetected clouds. A smaller reduction in NDVI due to increased aerosol levels was observed during the dry season, with an inverse dependence of NDVI on aerosol optical thickness (AOT). NDVI observations processed with MAIAC showed highly reproducible and stable inter-annual patterns with little or no dependence on cloud cover, and no significant dependence on AOT (p less than 0.05). In addition to a better detection of cloudy pixels, MAIAC obtained about 20-80 percent more cloud free pixels, depending on season, a considerable amount for land analysis given the very high cloud cover (75-99 percent) observed at any given time in the area. We conclude that a new generation of atmospheric correction algorithms, such as MAIAC, can help to dramatically improve vegetation estimates over tropical rain forest, ultimately leading to reduced uncertainties in satellite-derived vegetation products globally.

tropical ecosystems↗
NASA NTRS2018

Seasonal and Interannual Assessment of Cloud Cover and Atmospheric Constituents Across the Amazon (2000 - 2015): Insights for Remote Sensing and Climate Analysis

The quantitative assessment of cloud cover and atmospheric constituents improves our ability to exploit the climate feedback into the Amazon basin. In the 21st century, three droughts have already occurred in the Amazonia (e.g. 2005, 2010, 2015), inducing regional changes in the seasonal patterns of atmospheric constituents. In addition to climate, the atmospheric dynamic and attenuation properties are long-term challenges for satellite-based remote sensing of this ecosystem: high cloudiness, abundant water vapor content and biomass burning season. Therefore, while climatology analysis supports the understanding of atmospheric variability and trends, it also offers valuable insights for remote sensing applications. In this study, we evaluate the seasonal and interannual variability of cloud cover and atmospheric constituents (aerosol loading, water vapor and ozone content) over the Amazon basin, with focus on both climate analysis and remote sensing implications. We take the advantage of new atmosphere daily products at 1 km resolution derived from Multi-Angle Implementation for Atmospheric Correction (MAIAC) algorithm developed for Moderate Resolution Imaging Spectroradiometer (MODIS) data. An intercomparison of Aerosol Robotic Network (AERONET) and MAIAC aerosol optical depth (AOD) and columnar water vapor (CWV) showed quantitative information with a correlation coefficient higher than 0.81. Our results show distinct regional patterns of cloud cover across the Amazon basin: northwestern region presets a persistent cloud cover (> 80%) throughout the year, while low cloud cover (0-20%) occurs in the southern Amazon during the dry season. The cloud-free period in the southern Amazon is followed by an increase in the atmospheric burden due to fire emissions. Our results reveal that AOD records are changing in terms of area and intensity. During the 2005 and 2010 droughts, the positive AOD anomalies (δ > 0.1) occurred over 39.03% (240.3 million ha) and 27.14% (165.99 million ha) of total basin in the SON season, respectively. In contrast, the recent 2015 drought occurred towards the end of year (October through December) and these anomalies were observed over 23.72% (145 million ha) affecting areas in the central and eastern Amazon unlike previous droughts. The water vapor presents high concentration values (4.0-5.0 g/sq.cm) in the wet season (DJF), while we observed a strong spatial gradient from northwestern to southeastern of the basin during the dry season. In addition, we also found a positive trend of water vapor content ( ∼0.3 g/sq.cm) between 2000 and 2015. The total ozone typically varies between 220 and 270 DU, and it has a seasonal change of ∼25-35 DU from wet season to dry season caused by large emissions of ozone precursors and long-range transport. Finally, while this study contributes to climatological analysis of atmospheric constituents, the remote sensing users can also understand the regional constraints caused by atmospheric attenuation, such as high aerosol loading and cloud obstacles for surface observations.

ozone↗
NASA NTRS2000

What we can Learn About Aerosols from EOS-MISR Multi-Angle Remote Sensing Observations

Multiangle, multispectral remote sensing observations, such as those anticipated from the Earth Observing System (EOS) Multiangle Imaging SpectroRadiometer (MISR), promise to significantly improve our ability to constrain aerosol properties from space. Recent advances in modeling the Earth's climate have brought us to a point where the contributions made by aerosols to the global radiation budget noticeably affect the results. Knowledge of both aerosol optical depth and the microphysical properties of particles is needed to adequately model aerosol effects. This talk explores the ability of multiangle, multi-spectral remote sensing observations anticipated from the EOS MISR instrument, to retrieve aerosol optical depth and information about mixes of particle types, globally, at 17.6 km spatial resolution. The instrument is scheduled for launch into a 10:30 AM, sun-synchronous polar orbit in 1999.

Kahn, Ralph↗
NASA NTRS2018

MODIS Collection 6 MAIAC Algorithm

This paper describes the latest version of the algorithm MAIAC (Multi-Angle Implementation of Atmospheric Correction) used for processing the MODIS (Moderate-resolution Imaging Spectroradiometer) Collection6 data record. Since initial publication in 2011-2012, MAIAC has changed considerably to adapt to global processing and improve cloud/snow detection, aerosol retrievals and atmospheric correction of MODIS data. The main changes include (1) transition from a 25 to 1 km scale for retrieval of the spectral regression coefficient (SRC) which helped to remove occasional blockiness at 25 km scale in the aerosol optical depth (AOD) and in the surface reflectance, (2) continuous improvements of cloud detection, (3) introduction of smoke and dust tests to discriminate absorbing fine- and coarse mode aerosols, (4) adding over-water processing, (5) general optimization of the LUT (LookUp-Table)-based radiative transfer for the global processing, and others. MAIAC provides an interdisciplinary suite of atmospheric and land products, including cloud mask (CM), column water vapor (CWV), AOD at 0.47 and 0.55 m, aerosol type (background, smoke or dust) and fine-mode fraction over water; spectral bidirectional reflectance factors (BRF), parameters of Ross-thick Lisparse (RTLS) bidirectional reflectance distribution function (BRDF) model and instantaneous albedo. For snow-covered surfaces, we provide subpixel snow fraction and snow grain size. All products come in standard HDF4 (software library) format at 1 km resolution, except for BRF, which is also provided at 500 m resolution on a sinusoidal grid adopted by the MODIS Land team. All products are provided on per-observation basis in daily files except for the BRDF/Albedo product, which is reported every 8 days. Because MAIAC uses a time series approach, BRDF/Albedo is naturally gap-filled over land where missing values are filled-in with results from the previous retrieval. While the BRDF model is reported for MODIS Land bands 1-7 and ocean band 8, BRF is reported for both land and ocean bands 1-12. This paper focuses on MAIAC cloud detection, aerosol retrievals and atmospheric correction and describes MCD19 data products and quality assurance (QA) flags.

MAIAC Algorithm↗
NASA NTRS2005

Combining MISR and MODIS data to automatically catalogue smoke plumes in North America

We are in the early stages of work on EPA task to investigate the effects of fires on air quality in North America, led by Jennifer Logan of Harvard University. JPL's contribution to this study is to find thousands of smoke plumes in satellite images of North America, and derive statistics about their geographic distribution, extent, orientation, and injection height.

smoke plume↗
NASA NTRS2013

10 Yr Spatial and Temporal Trends of PM2.5 Concentrations in the Southeastern US Estimated Using High-resolution Satellite Data

Long-term PM2.5 exposure has been reported to be associated with various adverse health outcomes. However, most ground monitors are located in urban areas, leading to a potentially biased representation of the true regional PM2.5 levels. To facilitate epidemiological studies, accurate estimates of spatiotemporally continuous distribution of PM2.5 concentrations are essential. Satellite-retrieved aerosol optical depth (AOD) has been widely used for PM2.5 concentration estimation due to its comprehensive spatial coverage. Nevertheless, an inherent disadvantage of current AOD products is their coarse spatial resolutions. For instance, the spatial resolutions of the Moderate Resolution Imaging Spectroradiometer (MODIS) and the Multiangle Imaging SpectroRadiometer (MISR) are 10 km and 17.6 km, respectively. In this paper, a new AOD product with 1 km spatial resolution retrieved by the multi-angle implementation of atmospheric correction (MAIAC) algorithm was used. A two-stage model was developed to account for both spatial and temporal variability in the PM2.5-AOD relationship by incorporating the MAIAC AOD, meteorological fields, and land use variables as predictors. Our study area is in the southeastern US, centered at the Atlanta Metro area, and data from 2001 to 2010 were collected from various sources. The model was fitted for each year individually, and we obtained model fitting R2 ranging from 0.71 to 0.85, MPE from 1.73 to 2.50 g m3, and RMSPE from 2.75 to 4.10 g m3. In addition, we found cross validation R2 ranging from 0.62 to 0.78, MPE from 2.00 to 3.01 g m3, and RMSPE from 3.12 to 5.00 g m3, indicating a good agreement between the estimated and observed values. Spatial trends show that high PM2.5 levels occurred in urban areas and along major highways, while low concentrations appeared in rural or mountainous areas. A time series analysis was conducted to examine temporal trends of PM2.5 concentrations in the study area from 2001 to 2010. The results showed that the PM2.5 levels in the study area followed a generally declining trend from 2001 to 2010 and decreased about 20 during the period. However, there was an exception of an increase in year 2005, which is attributed to elevated sulfate concentrations in the study area in warm months of 2005. An investigation of the impact of wild and prescribed fires on PM2.5 levels in 2007 suggests a positive relationship between them.

particulate matter (PM2.5)↗
NASA NTRS2013

High Resolution Aerosol Data from MODIS Satellite for Urban Air Quality Studies

The Moderate Resolution Imaging Spectroradiometer (MODIS) provides daily global coverage, but the 10 km resolution of its aerosol optical depth (AOD) product is not suitable for studying spatial variability of aerosols in urban areas. Recently, a new Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm was developed for MODIS which provides AOD at 1 km resolution. Using MAIAC data, the relationship between MAIAC AOD and PM(sub 2.5) as measured by the 27 EPA ground monitoring stations was investigated. These results were also compared to conventional MODIS 10 km AOD retrievals (MOD04) for the same days and locations. The coefficients of determination for MOD04 and for MAIAC are R(exp 2) =0.45 and 0.50 respectively, suggested that AOD is a reasonably good proxy for PM(sub 2.5) ground concentrations. Finally, we studied the relationship between PM(sub 2.5) and AOD at the intra-urban scale (10 km) in Boston. The fine resolution results indicated spatial variability in particle concentration at a sub-10 kilometer scale. A local analysis for the Boston area showed that the AOD-PM(sub 2.5) relationship does not depend on relative humidity and air temperatures below approximately 7 C. The correlation improves for temperatures above 7 - 16 C. We found no dependence on the boundary layer height except when the former was in the range 250-500 m. Finally, we apply a mixed effects model approach to MAIAC aerosol optical depth (AOD) retrievals from MODIS to predict PM(sub 2.5) concentrations within the greater Boston area. With this approach we can control for the inherent day-to-day variability in the AOD-PM(sub 2.5) relationship, which depends on time-varying parameters such as particle optical properties, vertical and diurnal concentration profiles and ground surface reflectance. Our results show that the model-predicted PM(sub 2.5) mass concentrations are highly correlated with the actual observations (out-of-sample R(exp 2) of 0.86). Therefore, adjustment for the daily variability in the AOD-PM(sub 2.5) relationship provides a means for obtaining spatially-resolved PM(sub 2.5) concentrations.

remote sensing↗
NASA NTRS2016

Seasonal Monitoring and Estimation of Regional Aerosol Distribution over Po Valley, Northern Italy, Using a High-Resolution MAIAC Product

In this work, the new 1-km-resolved Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm is employed to characterize seasonal AOD-PM10 correlations over northern Italy. The accuracy of the new dataset is assessed versus the widely used Moderate Resolution Imaging Spectroradiometer (MODIS) Collection 5.1 Aerosol Optical Depth (AOD) data, retrieved at 0.55 microns with spatial resolution of 10 km (MYD04). We focused on evaluating the ability of these two products to characterize both temporal and spatial distributions of aerosols within urban and suburban areas. Ground PM10 measurements were obtained from 73 of the Italian Regional Agency for Environmental Protection (ARPA) monitoring stations, spread across northern Italy, for a three-year period from 2010 to 2012. The Po Valley area (northern Italy) was chosen as the study domain because of severe urban air pollution, resulting from the highest population and industrial manufacturing density in the country, being located in a valley where two surrounding mountain chains favor the stagnation of pollutants. We found that the global correlations between PM10 and AOD are R(sup 2) = 0.83 and R(sup 2) = 0.44 for MYD04_L2 and for MAIAC, respectively, suggesting for a greater sensitiveness of the high-resolution product to small-scale deviations. However, the introduction of Relative Humidity (RH) and Planetary Boundary Layer (PBL) depth corrections gave a significant improvement to the PM AOD correlation, which led to similar performance: R(sup 2) = 0.96 for MODIS and R(sup 2) = 0.95 for MAIAC. Furthermore, the introduction of the PBL information in the corrected AOD values was found to be crucial in order to capture the clear seasonal cycle shown by measured PM10 values. The study allowed us to define four seasonal linear correlations that estimate PM10 concentrations satisfactorily from the remotely sensed MAIAC AOD retrieval. Overall, the results show that the high resolution provided by MAIAC retrieval data is much more relevant than 10km MODIS data to characterize PM10 in this region of Italy which has a pretty limited geographical domain, but a broad variety of land usages and consequent particulate concentrations.

MODIS↗
NASA NTRS2012

Aerosol and Surface Parameter Retrievals for a Multi-Angle, Multiband Spectrometer

This software retrieves the surface and atmosphere parameters of multi-angle, multiband spectra. The synthetic spectra are generated by applying the modified Rahman-Pinty-Verstraete Bidirectional Reflectance Distribution Function (BRDF) model, and a single-scattering dominated atmosphere model to surface reflectance data from Multiangle Imaging SpectroRadiometer (MISR). The aerosol physical model uses a single scattering approximation using Rayleigh scattering molecules, and Henyey-Greenstein aerosols. The surface and atmosphere parameters of the models are retrieved using the Lavenberg-Marquardt algorithm. The software can retrieve the surface and atmosphere parameters with two different scales. The surface parameters are retrieved pixel-by-pixel while the atmosphere parameters are retrieved for a group of pixels where the same atmosphere model parameters are applied. This two-scale approach allows one to select the natural scale of the atmosphere properties relative to surface properties. The software also takes advantage of an intelligent initial condition given by the solution of the neighbor pixels.

Broderick, Daniel↗
NASA NTRS2016

Scaling Estimates of Vegetation Structure in Amazonian Tropical Forests Using Multi-Angle MODIS Observations

Detailed knowledge of vegetation structure is required for accurate modelling of terrestrial ecosystems, but direct measurements of the three dimensional distribution of canopy elements, for instance from LiDAR, are not widely available. We investigate the potential for modelling vegetation roughness, a key parameter for climatological models, from directional scattering of visible and near-infrared (NIR) reflectance acquired from NASA's Moderate Resolution Imaging Spectroradiometer (MODIS). We compare our estimates across different tropical forest types to independent measures obtained from: (1) airborne laser scanning (ALS), (2) spaceborne Geoscience Laser Altimeter System (GLAS)/ICESat, and (3) the spaceborne SeaWinds/QSCAT. Our results showed linear correlation between MODIS-derived anisotropy to ALS-derived entropy (r(exp 2)= 0.54, RMSE= 0.11), even in high biomass regions. Significant relationships were also obtained between MODIS-derived anisotropy and GLAS-derived entropy(0.52 less than or equal to r(exp 2) less than or equal to 0.61; p less than 0.05), with similar slopes and offsets found throughout the season, and RMSE between 0.26 and 0.30 (units of entropy). The relationships between the MODIS-derived anisotropy and backscattering measurements (sigma(sup 0)) from SeaWinds/QuikSCAT presented an r(exp 2) of 0.59 and a RMSE of 0.11. We conclude that multi-angular MODIS observations are suitable to extrapolate measures of canopy entropy across different forest types, providing additional estimates of vegetation structure in the Amazon.

MODIS↗
NASA NTRS2012

Multi-Angle Implementation of Atmospheric Correction for MODIS (MAIAC). Part 3: Atmospheric Correction

This paper describes the atmospheric correction (AC) component of the Multi-Angle Implementation of Atmospheric Correction algorithm (MAIAC) which introduces a new way to compute parameters of the Ross-Thick Li-Sparse (RTLS) Bi-directional reflectance distribution function (BRDF), spectral surface albedo and bidirectional reflectance factors (BRF) from satellite measurements obtained by the Moderate Resolution Imaging Spectroradiometer (MODIS). MAIAC uses a time series and spatial analysis for cloud detection, aerosol retrievals and atmospheric correction. It implements a moving window of up to 16 days of MODIS data gridded to 1 km resolution in a selected projection. The RTLS parameters are computed directly by fitting the cloud-free MODIS top of atmosphere (TOA) reflectance data stored in the processing queue. The RTLS retrieval is applied when the land surface is stable or changes slowly. In case of rapid or large magnitude change (as for instance caused by disturbance), MAIAC follows the MODIS operational BRDF/albedo algorithm and uses a scaling approach where the BRDF shape is assumed stable but its magnitude is adjusted based on the latest single measurement. To assess the stability of the surface, MAIAC features a change detection algorithm which analyzes relative change of reflectance in the Red and NIR bands during the accumulation period. To adjust for the reflectance variability with the sun-observer geometry and allow comparison among different days (view geometries), the BRFs are normalized to the fixed view geometry using the RTLS model. An empirical analysis of MODIS data suggests that the RTLS inversion remains robust when the relative change of geometry-normalized reflectance stays below 15%. This first of two papers introduces the algorithm, a second, companion paper illustrates its potential by analyzing MODIS data over a tropical rainforest and assessing errors and uncertainties of MAIAC compared to conventional MODIS products.

Lyapustin, A.↗
NASA NTRS2008

Mapping Snow Grain Size over Greenland from MODIS

This paper presents a new automatic algorithm to derive optical snow grain size (SGS) at 1 km resolution using Moderate Resolution Imaging Spectroradiometer (MODIS) measurements. Differently from previous approaches, snow grains are not assumed to be spherical but a fractal approach is used to account for their irregular shape. The retrieval is conceptually based on an analytical asymptotic radiative transfer model which predicts spectral bidirectional snow reflectance as a function of the grain size and ice absorption. The analytical form of solution leads to an explicit and fast retrieval algorithm. The time series analysis of derived SGS shows a good sensitivity to snow metamorphism, including melting and snow precipitation events. Preprocessing is performed by a Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm, which includes gridding MODIS data to 1 km resolution, water vapor retrieval, cloud masking and an atmospheric correction. MAIAC cloud mask (CM) is a new algorithm based on a time series of gridded MODIS measurements and an image-based rather than pixel-based processing. Extensive processing of MODIS TERRA data over Greenland shows a robust performance of CM algorithm in discrimination of clouds over bright snow and ice. As part of the validation analysis, SGS derived from MODIS over selected sites in 2004 was compared to the microwave brightness temperature measurements of SSM\I radiometer, which is sensitive to the amount of liquid water in the snowpack. The comparison showed a good qualitative agreement, with both datasets detecting two main periods of snowmelt. Additionally, MODIS SGS was compared with predictions of the snow model CROCUS driven by measurements of the automatic whether stations of the Greenland Climate Network. We found that CROCUS grain size is on average a factor of two larger than MODIS-derived SGS. Overall, the agreement between CROCUS and MODIS results was satisfactory, in particular before and during the first melting period in mid-June. Following detailed time series analysis of SGS for four permanent sites, the paper presents SGS maps over the Greenland ice sheet for the March-September period of 2004.

Lyapustin, Alexei↗
NASA NTRS1992

Moderate resolution imaging spectroradiometer (MODIS) and observations of the land surface

The moderate resolution imaging spectroradiometer (MODIS) is a NASA facility instrument that is being designed for flight on the Earth Observing System (EOS) series of missions. It is designed to measure biophysical states and dynamics of the land, atmosphere, and ocean. Plans are required for use of other instruments that will be accompanying MODIS on the EOS missions, such as the High-Resolution Imaging Spectrometer (HIRIS) and the Multi-angle Imaging Spectro-Radiometer (MISR). The HIRIS instrument, a spectrometer operating in the visible to shortwave infrared parts of the spectrum, would be employed in combination with the MODIS to understand the impact of sampling the spectrum and the effects of land cover mixtures within the MODIS pixel. The MISR will help in understanding the effects of anisotropy in reflected solar radiation. Both instruments will work in combination with MODIS to better quantify the effects of the atmosphere on observations of surface properties.

Salomonson, V. V.↗
NASA NTRS2005

The application of support vector machines to analysis of global satellite data sets from MlSR

The Multi-angle Imaging Spectro Radiometer (MISR) is one of a suite of five instruments onboard NASA's Terra EOS satellite, launched in December 1999. Typical satellite imagers view the earth from a single direction, but MISR's cameras image the earth simultaneously from nine different directions in four spectral bands. In this way, MISR provides unique multiangle information about solar radiation scattered from clouds, aerosols and other terrestrial surfaces. One of the primary goals of the MISR mission is to improve our understanding of how clouds and aerosols affect the earth's global energy balance.

support vector machines↗
NASA NTRS2004

Dust aerosol retrieval results from MISR (multi-angle imaging spectro-radiometer)

Satellite measurements provide important tools for understanding the effect of mineral dust aerosols on past and present climate and climate predictions. Multi-angle instruments such as Multi-angle Imaging Spectro- Radiometer (MISR) provide independent constraints on aerosol properties based on their sensitivity to the shape of aerosol scattering phase functions. The current MISR operational retrieval algorithm (version 16 and higher) was modified by incorporating new non-spherical dust models that account for naturally occurring dust shapes and compositions. We present selected examples of MISR version 16 retrievals over AERONET sunphotometer land and ocean sites during the passage of dust fronts. Our analysis shows that during such events MISR retrieves Angstrom exponents characteristic of large particles, having little spectral variation in extinction over the MISR wavelength range (442, 550, 672 and 866 nm channels), as expected. The retrieved fraction of non-spherical particles is also very high. This quantity is not retrieved by satellite instruments having only nadir-viewing cameras. Our comparison of current (version 16) MISR-retrieved aerosol optical thickness (AOT) with AERONET instantaneous AOT shows better coverage and stronger correlations than when making identical comparisons with previous AOT retrievals (version 15). The MISR algorithm successful mixtures include a non-spherical dust component with high frequency in retrievals over dark water and slightly lower frequency over land. Selection frequencies of non-spherical dust models also decrease in dusty regions affected by pollution.

Multiangle Imaging Spectroradiometer (MISR)↗
NASA NTRS2010

MISR CMVs and Multiangular Views of Tropical Cyclone Inner-Core Dynamics

Multi-camera stereo imaging of cloud features from the MISR (Multiangle Imaging SpectroRadiometer) instrument on NASA's Terra satellite provides accurate and precise measurements of cloud top heights (CTH) and cloud motion vector (CMV) winds. MISR observes each cloudy scene from nine viewing angles (Nadir, +/-26(sup o), +/-46(sup o), +/-60(sup o), +/-70(sup o)) with approximatel 275-m pixel resolution. This paper provides an update on MISR CMV and CTH algorithm improvements, and explores a high-resolution retrieval of tangential winds inside the eyewall of tropical cyclones (TC). The MISR CMV and CTH retrievals from the updated algorithm are significantly improved in terms of spatial coverage and systematic errors. A new product, the 1.1-km cross-track wind, provides high accuracy and precision in measuring convective outflows. Preliminary results obtained from the 1.1-km tangential wind retrieval inside the TC eyewall show that the inner-core rotation is often faster near the eyewall, and this faster rotation appears to be related linearly to cyclone intensity.

hurricanes↗