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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.

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All sourcesNASA NTRSDOE OSTI
NASA NTRS1990

The analysis of polar clouds from AVHRR satellite data using pattern recognition techniques

The cloud cover in a set of summertime and wintertime AVHRR data from the Arctic and Antarctic regions was analyzed using a pattern recognition algorithm. The data were collected by the NOAA-7 satellite on 6 to 13 Jan. and 1 to 7 Jul. 1984 between 60 deg and 90 deg north and south latitude in 5 spectral channels, at the Global Area Coverage (GAC) resolution of approximately 4 km. This data embodied a Polar Cloud Pilot Data Set which was analyzed by a number of research groups as part of a polar cloud algorithm intercomparison study. This study was intended to determine whether the additional information contained in the AVHRR channels (beyond the standard visible and infrared bands on geostationary satellites) could be effectively utilized in cloud algorithms to resolve some of the cloud detection problems caused by low visible and thermal contrasts in the polar regions. The analysis described makes use of a pattern recognition algorithm which estimates the surface and cloud classification, cloud fraction, and surface and cloudy visible (channel 1) albedo and infrared (channel 4) brightness temperatures on a 2.5 x 2.5 deg latitude-longitude grid. In each grid box several spectral and textural features were computed from the calibrated pixel values in the multispectral imagery, then used to classify the region into one of eighteen surface and/or cloud types using the maximum likelihood decision rule. A slightly different version of the algorithm was used for each season and hemisphere because of differences in categories and because of the lack of visible imagery during winter. The classification of the scene is used to specify the optimal AVHRR channel for separating clear and cloudy pixels using a hybrid histogram-spatial coherence method. This method estimates values for cloud fraction, clear and cloudy albedos and brightness temperatures in each grid box. The choice of a class-dependent AVHRR channel allows for better separation of clear and cloudy pixels than does a global choice of a visible and/or infrared threshold. The classification also prevents erroneous estimates of large fractional cloudiness in areas of cloudfree snow and sea ice. The hybrid histogram-spatial coherence technique and the advantages of first classifying a scene in the polar regions are detailed. The complete Polar Cloud Pilot Data Set was analyzed and the results are presented and discussed.

Smith, William L.↗
NASA NTRS1992

Global tracking of the SO2 clouds from the June, 1991 Mount Pinatubo eruptions

The explosive June 1991 eruptions of Mount Pinatubo produced the largest sulfur dioxide cloud detected by the Total Ozone Mapping Spectrometer (TOMS) during its 13 years of operation: approximately 20 million tons of SO2, predominantly from the cataclysmic June 15th eruption. The SO2 cloud observed by the TOMS encircled the earth in about 22 days (about 21 m/s); however, during the first three days the leading edge of the SO2 cloud moved with a speed that averaged about 35 m/s. Compared to the 1982 El Chichon eruptions, Pinatubo outgassed nearly three times the amount of SO2 during its explosive phases. The main cloud straddled the equator within the first two weeks of eruption, whereas the El Chichon cloud remained primarily in the Northern Hemisphere. The measurements indicate that Mount Pinatubo has produced a much larger and perhaps longer-lasting SO2 cloud; thus, climatic responses to the Pinatubo eruption can exceed those of El Chichon.

Bluth, Gregg J. S.↗
NASA NTRS1980

Polar cloud structure as derived from the Pioneer Venus Orbiter

Vertical absorption coefficient profiles of the Venus clouds in the north polar regions recorded by the Pioneer Venus Orbiter on orbits 9, 18, and 19 at the S band indicate dense cloud decks at the 1.5 to 4.7 bar levels in the Venus atmosphere. These cloud decks are at lower altitudes than the clouds detected by Mariner 10 and Pioneer Venus probes, and are uniform in absorption characteristics in the polar regions. The regions close to the polar hot spots have depressed the upper cloud heights and increased polar density; these areas are free of thermal inversions characteristic of the north polar regions away from the hot spots.

Cimino, J. B.↗
NASA NTRS2016

Using High-Resolution Airborne Remote Sensing to Study Aerosol Near Clouds

The horizontal space in between clear and cloudy air is very complex. This so-called twilight zone includes activated aerosols that are not quite clouds, thin cloud fragments that are not easily observable, and dying clouds that have not quite disappeared. This is a huge challenge for satellite remote sensing, specifically for retrieval of aerosol properties. Identifying what is cloud versus what is not cloud is critically important for attributing radiative effects and forcings to aerosols. At the same time, the radiative interactions between clouds and the surrounding media (molecules, surface and aerosols themselves) will contaminate retrieval of aerosol properties, even in clear skies. Most studies on aerosol cloud interactions are relevant to moderate resolution imagery (e.g. 500 m) from sensors such as MODIS. Since standard aerosol retrieval algorithms tend to keep a distance (e.g. 1 km) from the nearest detected cloud, it is impossible to evaluate what happens closer to the cloud. During Studies of Emissions, Atmospheric Composition, Clouds and Climate Coupling by Regional Surveys (SEAC4RS), the NASA ER-2 flew with the enhanced MODIS Airborne Simulator (eMAS), providing MODIS-like spectral observations at high (50 m) spatial resolution. We have applied MODIS-like aerosol retrieval for the eMAS data, providing new detail to characterization of aerosol near clouds. Interpretation and evaluation of these eMAS aerosol retrievals is aided by independent MODIS-like cloud retrievals, as well as profiles from the co-flying Cloud Physics Lidar (CPL). Understanding aerosolcloud retrieval at high resolution will lead to better characterization and interpretation of long-term, global products from lower resolution (e.g.MODIS) satellite retrievals.

aerosols near clouds↗
NASA NTRS1971

The Nimbus image dissector camera system - An evaluation of its meteorological applications.

Brief description of the electronics and operation of the Nimbus image dissector camera system (IDCS). The geometry and distortions of the IDCS are compared to the conventional AVCS camera on board the operational ITOS and ESSA satellites. The unique scanning of the IDCS provides for little distortion of the image, making it feasible to use a strip grid for the IDCS received in real time by local APT stations. The dynamic range of the camera favors the white end (high reflectance) of the gray scale. Thus, the camera is good for detecting cloud structure and ice features through brightness changes. Examples of cloud features, ice, and snow-covered land are presented. Land features, on the other hand, show little contrast. The 2600 x 2600 km coverage by the IDCS is adequate for the early detection of weather systems which may affect the local area. An example of IDCS coverage obtained by an APT station in midlatitudes is presented.

Sabatini, R. R.↗
NASA NTRS2003

The 94 GHz Cloud Radar System on a NASA ER-2 Aircraft

The 94-GHz (W-band) Cloud Radar System (CRS) has been developed and flown on a NASA ER-2 high-altitude (20 km) aircraft. The CRS is a fully coherent, polarimeteric Doppler radar that is capable of detecting clouds and precipitation from the surface up to the aircraft altitude in the lower stratosphere. The radar is especially well suited for cirrus cloud studies because of its high sensitivity and fine spatial resolution. This paper describes the CRS motivation, instrument design, specifications, calibration, and preliminary data &om NASA s Cirrus Regional Study of Tropical Anvils and Cirrus Layers - Florida Area Cirrus Experiment (CRYSTAL-FACE) field campaign. The unique combination of CRS with other sensors on the ER-2 provides an unprecedented opportunity to study cloud radiative effects on the global energy budget. CRS observations are being used to improve our knowledge of atmospheric scattering and attenuation characteristics at 94 GHz, and to provide datasets for algorithm implementation and validation for the upcoming NASA CloudSat mission that will use a 94-GHz spaceborne cloud radar to provide the first direct global survey of the vertical structure of cloud systems.

Li, Lihua↗
NASA NTRS1994

Observations of cloud liquid water path over oceans: Optical and microwave remote sensing methods

Published estimates of cloud liquid water path (LWP) from satellite-measured microwave radiation show little agreement, even about the relative magnitudes of LWP in the tropics and midlatitudes. To understand these differences and to obtain more reliable estimate, optical and microwave LWP retrieval methods are compared using the International Satellite Cloud Climatology Project (ISCCP) and special sensor microwave/imager (SSM/I) data. Errors in microwave LWP retrieval associated with uncertainties in surface, atmosphere, and cloud properties are assessed. Sea surface temperature may not produce great LWP errors, if accurate contemporaneous measurements are used in the retrieval. An uncertainty of estimated near-surface wind speed as high as 2 m/s produces uncertainty in LWP of about 5 mg/sq cm. Cloud liquid water temperature has only a small effect on LWP retrievals (rms errors less than 2 mg/sq cm), if errors in the temperature are less than 5 C; however, such errors can produce spurious variations of LWP with latitude and season. Errors in atmospheric column water vapor (CWV) are strongly coupled with errors in LWP (for some retrieval methods) causing errors as large as 30 mg/sq cm. Because microwave radiation is much less sensitive to clouds with small LWP (less than 7 mg/sq cm) than visible wavelength radiation, the microwave results are very sensitive to the process used to separate clear and cloudy conditions. Different cloud detection sensitivities in different microwave retrieval methods bias estimated LWP values. Comparing ISCCP and SSM/I LWPs, we find that the two estimated values are consistent in global, zonal, and regional means for warm, nonprecipitating clouds, which have average LWP values of about 5 mg/sq cm and occur much more frequently than precipitating clouds. Ice water path (IWP) can be roughly estimated from the differences between ISCCP total water path and SSM/I LWP for cold, nonprecipitating clouds. IWP in the winter hemisphere is about 3 times the LWP but only half the LWP in the summer hemisphere. Precipitating clouds contribute significantly to monthly, zonal mean LWP values determined from microwave, especially in the intertropical convergence zone (ITCZ), because they have almost 10 times the liquid water (cloud plus precipitation) of nonprecipitating clouds on average. There are significant differences among microwave LWP estimates associated with the treatment of precipitating clouds.

Lin, Bing↗
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 NTRSDate not supplied

Toward Consistent Long-term Records of Cloud Fraction from MODIS and VIIRS for CERES

In order to produce a long-term and stable climate record of Earth’s energy budget for NASA’s Clouds and Earth’s Radiant Energy System (CERES) project, a consistent cloud fraction record across different measurement platforms­­ is a crucial first step. As Aqua satellite is approaching the end of its operational lifetime, NOAA-20 VIIRS observations on the JPSS-2 spacecraft will be used for cloud detection to continue the long-term Earth energy budget record. Two steps are designed to achieve this goal. The first step is a quick approach that revises the CERES NOAA-20 VIIRS Edition 1 A (CV Ed1A) cloud mask to incorporate Cross-track Infrared Sounder (CrIS) water vapor and CO_2 channels into the VIIRS cloud mask (CV Ed1B) to produce a consistent cloud fraction with CERES MODIS Edition 4 (CM Ed4) to avoid potential discontinuity when Aqua orbit drifts beyond tolerance before the next Edition is completed. The second step is to develop, as part of CERES next edition (Ed5), a unified MODIS and VIIRS cloud mask using common channels to produce consistent cloud products with improved radiative transfer models, refined clear sky predictions, new GMAO reanalysis products, and the latest radiance collections of MODIS and VIIRS data. This poster will present cloud fraction comparison between CV Ed1B and CM Ed4, as well as the preliminary results of ongoing Ed5 including pixel level cloud mask results, monthly global cloud fraction differences between MODIS and VIIRS (consistency), and initial validation using CALIPSO data (accuracy).

CERES↗
NASA NTRS1978

Utilizing the dust streak-dryline intersection phenomenon in severe local storm forecasting

Several characteristics of a major dust storm on 20 February 1976 are presented. Four primary areas of discussion are considered: dust storm characteristics; comparisons between the cell detection capabilities of the geosynchronous satellite and radar; the use of the NASA Atmospheric and Oceanographic Information Processing System (an interactive image processing system) for interactive display of visual and IR digital data to obtain qualitative and quantitative results on specific dust cloud and cloud top characteristics; and evidence that the intersection downwind of dust clouds (streaks) with the dryline can be used to forecast the initial storm development on the dryline and that the dust clouds detected on 20 February 1976 are a reflection of upper tropospheric speed maxima, which according to McNulty (1978) and others, are associated with rapid severe convective local storm development.

Anthony, R. W.↗
NASA NTRS2016

Developing Atmospheric Retrieval Methods for Direct Imaging Spectroscopy of Gas Giants in Reflected Light I: Methane Abundances and Basic Cloud Properties

Reflected light spectroscopy and photometry of cool, directly imaged extrasolar giant planets are expected to be performed in the next decade by space-based telescopes equipped with optical wavelength coronagraphs and integral field spectrographs, such as the Wide-Field Infrared Survey Telescope (WFIRST). We are developing a new atmospheric retrieval methodology to help assess the science return and inform the instrument design for such future missions, and ultimately interpret the resulting observations. Our retrieval technique employs an albedo model coupled with both a Markov chain Monte Carlo Ensemble Sampler (emcee) and a multimodal nested sampling algorithm (MultiNest) to map the posterior distribution. This combination makes the global evidence calculation more robust for any given model, and highlights possible discrepancies in the likelihood maps. Here we apply this methodology to simulated spectra of cool giant planets. As a proof-of-concept, our current atmospheric model contains 1 or 2 cloud layers, methane as a major absorber, and a H2-He background gas. This 6-to-9 parameter model is appropriate for Jupiter-like planets and can be easily expanded in the future. In addition to deriving the marginal likelihood distribution and confidence intervals for the model parameters, we perform model selection to determine the significance of methane and cloud detection as a function of expected signal-to-noise, in the presence of spectral noise correlations. After internal validation, the method is applied to realistic reflected-light spectra of Jupiter, Saturn, and HD 99492 c, a likely observing target. We find that the presence or absence of clouds and methane can be determined with high accuracy, while parameters uncertainties are model-dependent.

spectroscopy↗
NASA NTRS2021

Joint cloud water path and rainwater path retrievals from airborne ORACLES observations

This study presents a new algorithm that combines W-band reflectivity measurements from the Airborne Precipitation Radar – third generation (APR-3) passive radiometric cloud optical depth and effective radius retrievals from the Research Scanning Polarimeter (RSP) to estimate total liquid water path in warm clouds and identify the contributions from cloud water path (CWP) and rainwater path (RWP). The resulting CWP estimates are primarily determined by the optical depth input, although reflectivity measurements contribute ∼10 %–50 % of the uncertainty due to attenuation through the profile. Uncertainties in CWP estimates across all conditions are 25 % to 35 %, while RWP uncertainty estimates frequently exceed 100 %. Two-thirds of all radar-detected clouds observed during the ObseRvations of Aerosols above CLouds and their intEractionS (ORACLES) campaign that took place from 2016–2018 over the southeast Atlantic Ocean have CWP between 41 and 168 g/sq. m and almost all CWPs (99 %) between 6 to 445 g/sq. m. RWP, by contrast, typically makes up a much smaller fraction of total liquid water path (LWP), with more than 70 % of raining clouds having less than 10 g/sq. m of rainwater. In heavier warm rain (i.e., rain rate exceeding 40 mm/h or 1000 mm/d), however, RWP is observed to exceed 2500 g/sq. m. CWP (RWP) is found to be approximately 30 g/sq. m (7 g/sq. m) larger in unstable environments compared to stable environments. Surface precipitation is also more than twice as likely in unstable environments. Comparisons against in situ cloud microphysical probe data spanning the range of thermodynamic stability and meteorological conditions encountered across the southeast Atlantic basin demonstrate that the combined APR-3 and RSP dataset enable a robust joint cloud–precipitation retrieval algorithm to support future ORACLES precipitation susceptibility and cloud–aerosol–precipitation interaction studies.

algorithm↗
NASA NTRS1998

Aerosol analysis techniques and results from micro pulse lidar

The effect of clouds and aerosol on the atmospheric energy balance is a key global change problem. Full knowledge of aerosol distributions is difficult to obtain by passive sensing alone. Aerosol and cloud retrievals in several important areas can be significantly improved with active remote sensing by lidar. Micro Pulse Lidar (MPL) is an aerosol and cloud profilometer that provides a detailed picture of the vertical structure of boundary layer and elevated dust or smoke plume aerosols. MPL is a compact, fully eyesafe, ground-based, zenith pointing instrument capable of full-time, long-term unattended operation at 523 nm. In October of 1993, MPL began taking full-time measurements for the Atmospheric Radiation Measurement (ARM) program at its Southern Great Plains (SGP) site and has since expanded to ARM sites in the Tropical West Pacific (TWP) and the North Slope of Alaska (NSA). Other MPL's are moving out to some of the 60 world-wide Aerosol Robotic Network (AERONET) sites which are already equipped with automatic sun-sky scanning spectral radiometers providing total column optical depth measurements. Twelve additional MPL's have been purchased by NASA to add to the aerosol and cloud database of the EOS ground validation network. The original MPL vertical resolution was 300 meters but the newer versions have a vertical resolution of 30 meters. These expanding data sets offer a significant new resource for atmospheric radiation analysis. Under the direction of Jim Spinhirne, the MPL analysis team at NASA/GSFC has developed instrument correction and backscatter analysis techniques for ARM to detect cloud boundaries and analyze vertical aerosol structures. A summary of MPL applications is found in Hlavka (1997). With the aid of independent total column optical depth instruments such as the Multifilter Rotating Shadowband Radiometer (MFRSR) at the ARM sites or sun photometers at the AERONET sites, the MPL data can be calibrated, and time-resolved vertical profiles of aerosol optical depth as well as aerosol extinction can be calculated. The techniques used to calibrate the lidar, calculate the aerosol extinction-to-backscatter ratio, and produce profiles of aerosol extinction and aerosol optical depths, will be described. Results using these techniques will be presented for case studies at the ARM site in the Tropical West Pacific and later in the Southern Great Plains.

Hlavka, Dennis L.↗
NASA NTRS1991

First results from Goddard High-Resolution Spectrograph - C I, S I, and CO toward Xi Persei and the physical conditions in diffuse clouds

Observations made by the Goddard High Resolution Spectrograph of the cool, neutral interstellar gas in the line of sight to Xi Per are reported. Heliocentric velocities and equivalent widths were measured for absorption lines of C I and S I. Synthetic spectra were computed and fitted to the observed CO(2-0) and (3-0) bands in the A IPi - X 1Sigma(+) system. Derived populations of the C I ground-state fine-structure levels and the CO ground-state rotational levels were used to derive densities of two of the three, and possibly four, detected cloud components. The velocity component displaying the strongest C I absorption reveals extraordinarily high pressure.

Smith, Andrew M.↗
NASA NTRS1995

Calculation of surface and top of atmosphere radiative fluxes from physical quantities based on ISCCP data sets. 1: Method and sensitivity to input data uncertainties

The largest uncertainty in upwelling shortwave (SW) fluxes (approximately equal 10-15 W/m(exp 2), regional daily mean) is caused by uncertainties in land surface albedo, whereas the largest uncertainty in downwelling SW at the surface (approximately equal 5-10 W/m(exp 2), regional daily mean) is related to cloud detection errors. The uncertainty of upwelling longwave (LW) fluxes (approximately 10-20 W/m(exp 2), regional daily mean) depends on the accuracy of the surface temperature for the surface LW fluxes and the atmospheric temperature for the top of atmosphere LW fluxes. The dominant source of uncertainty is downwelling LW fluxes at the surface (approximately equal 10-15 W/m(exp 2)) is uncertainty in atmospheric temperature and, secondarily, atmospheric humidity; clouds play little role except in the polar regions. The uncertainties of the individual flux components and the total net fluxes are largest over land (15-20 W/m(exp 2)) because of uncertainties in surface albedo (especially its spectral dependence) and surface temperature and emissivity (including its spectral dependence). Clouds are the most important modulator of the SW fluxes, but over land areas, uncertainties in net SW at the surface depend almost as much on uncertainties in surface albedo. Although atmospheric and surface temperature variations cause larger LW flux variations, the most notable feature of the net LW fluxes is the changing relative importance of clouds and water vapor with latitude. Uncertainty in individual flux values is dominated by sampling effects because of large natrual variations, but uncertainty in monthly mean fluxes is dominated by bias errors in the input quantities.

Zhang, Y.-C.↗
NASA NTRS2009

Potential for Monitoring Snow Cover in Boreal Forests by Combining MODIS Snow Cover and AMSR-E SWE Maps

Monitoring of snow cover extent and snow water equivalent (SWE) in boreal forests is important for determining the amount of potential runoff and beginning date of snowmelt. The great expanse of the boreal forest necessitates the use of satellite measurements to monitor snow cover. Snow cover in the boreal forest can be mapped with either the Moderate Resolution Imaging Spectroradiometer (MODIS) or the Advanced Microwave Scanning Radiometer for EOS (AMSR-E) microwave instrument. The extent of snow cover is estimated from the MODIS data and SWE is estimated from the AMSR-E. Environmental limitations affect both sensors in different ways to limit their ability to detect snow in some situations. Forest density, snow wetness, and snow depth are factors that limit the effectiveness of both sensors for snow detection. Cloud cover is a significant hindrance to monitoring snow cover extent Using MODIS but is not a hindrance to the use of the AMSR-E. These limitations could be mitigated by combining MODIS and AMSR-E data to allow for improved interpretation of snow cover extent and SWE on a daily basis and provide temporal continuity of snow mapping across the boreal forest regions in Canada. The purpose of this study is to investigate if temporal monitoring of snow cover using a combination of MODIS and AMSR-E data could yield a better interpretation of changing snow cover conditions. The MODIS snow mapping algorithm is based on snow detection using the Normalized Difference Snow Index (NDSI) and the Normalized Difference Vegetation Index (NDVI) to enhance snow detection in dense vegetation. (Other spectral threshold tests are also used to map snow using MODIS.) Snow cover under a forest canopy may have an effect on the NDVI thus we use the NDVI in snow detection. A MODIS snow fraction product is also generated but not used in this study. In this study the NDSI and NDVI components of the snow mapping algorithm were calculated and analyzed to determine how they changed through the seasons. A blended snow product, the Air Force Weather Agency and NASA (ANSA) snow algorithm and product has recently been developed. The ANSA algorithm blends the MODIS snow cover and AMSR-E SWE products into a single snow product that has been shown to improve the performance of snow cover mapping. In this study components of the ANSA snow algorithm are used along with additional MODIS data to monitor daily changes in snow cover over the period of 1 February to 30 June 2008.

Riggs, George A.↗
NASA NTRS2019

Assimilating microwave cloudy observations into NASA GEOS model using a novel Bayesian Monte Carlo technique

Despite the importance of clouds and their influence on atmospheric water and energy balance, Numerical Weather Prediction (NWP) centers systematically exclude cloud information from the assimilation process and only assimilate clear-sky radiances (Janiskov´a et al. 2012). In order to ensure that only clear sky radiances are assimilated, strict cloud detection thresholds are applied before radiances are fed into data assimilation (DA) systems. This process not only excludes a large portion of satellite radiances, but causes loss of information in the regions that are of high interest to meteorologists and are most challenging for weather forecasts (Errico et al. 2007; Haddad et al. 2015). Although, in recent years there has been great advances in the operational weather forecasting, the prediction of tropical cyclones (TC), especially the intensity of TCs, remains challenging. According to Aksoy et al. (2013), in addition to the model deficiencies, another important factor that contributes to this challenge includes lack of observations in the peripheral environment (rain- bands) of TCs mainly because of the selective assimilation of existing observations. Satellite observations provide more than 90 % of the input data for the initialization of NWP models but more than 75 % of satellite observations are discarded due to the cloud contamination as well as land, snow, and ice emissivity issues (Bauer et al. 2010).

Rainband↗
NASA NTRS2013

Spatially Resolving Ocean Color and Sediment Dispersion in River Plumes, Coastal Systems, and Continental Shelf Waters

Satellite remote sensing of ocean color in dynamic coastal, inland, and nearshorewaters is impeded by high variability in optical constituents, demands specialized atmospheric correction, and is limited by instrument sensitivity. To accurately detect dispersion of bio-optical properties, remote sensors require ample signal-to-noise ratio (SNR) to sense small variations in ocean color without saturating over bright pixels, an atmospheric correction that can accommodate significantwater-leaving radiance in the near infrared (NIR), and spatial and temporal resolution that coincides with the scales of variability in the environment. Several current and historic space-borne sensors have met these requirements with success in the open ocean, but are not optimized for highly red-reflective and heterogeneous waters such as those found near river outflows or in the presence of sediment resuspension. Here we apply analytical approaches for determining optimal spatial resolution, dominant spatial scales of variability ("patches"), and proportions of patch variability that can be resolved from four river plumes around the world between 2008 and 2011. An offshore region in the Sargasso Sea is analyzed for comparison. A method is presented for processing Moderate Resolution Imaging Spectroradiometer (MODIS) Aqua and Terra imagery including cloud detection, stray lightmasking, faulty detector avoidance, and dynamic aerosol correction using short-wave- and near-infrared wavebands in extremely turbid regions which pose distinct optical and technical challenges. Results showthat a pixel size of approx. 520 mor smaller is generally required to resolve spatial heterogeneity in ocean color and total suspended materials in river plumes. Optimal pixel size increases with distance from shore to approx. 630 m in nearshore regions, approx 750 m on the continental shelf, and approx. 1350 m in the open ocean. Greater than 90% of the optical variability within plume regions is resolvable with 500 m resolution, and small, but significant, differences were found between peak and nadir river flow periods in terms of optimal resolution and resolvable proportion of variability.

MODIS↗