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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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At least 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
NASA NTRS1998

Three-dimensional organization of otolith-ocular reflexes in rhesus monkeys. III. Responses To translation

The three-dimensional (3-D) properties of the translational vestibulo-ocular reflexes (translational VORs) during lateral and fore-aft oscillations in complete darkness were studied in rhesus monkeys at frequencies between 0.16 and 25 Hz. In addition, constant velocity off-vertical axis rotations extended the frequency range to 0.02 Hz. During lateral motion, horizontal responses were in phase with linear velocity in the frequency range of 2-10 Hz. At both lower and higher frequencies, phase lags were introduced. Torsional response phase changed more than 180 degrees in the tested frequency range such that torsional eye movements, which could be regarded as compensatory to "an apparent roll tilt" at the lowest frequencies, became anticompensatory at all frequencies above approximately 1 Hz. These results suggest two functionally different frequency bandwidths for the translational VORs. In the low-frequency spectrum (<<0.5 Hz), horizontal responses compensatory to translation are small and high-pass-filtered whereas torsional response sensitivity is relatively frequency independent. At higher frequencies however, both horizontal and torsional response sensitivity and phase exhibit a similar frequency dependence, suggesting a common role during head translation. During up-down motion, vertical responses were in phase with translational velocity at 3-5 Hz but phase leads progressively increased for lower frequencies (>90 degrees at frequencies <0.2 Hz). No consistent dependence on static head orientation was observed for the vertical response components during up-down motion and the horizontal and torsional response components during lateral translation. The frequency response characteristics of the translational VORs were fitted by "periphery/brain stem" functions that related the linear acceleration input, transduced by primary otolith afferents, to the velocity signals providing the input to the velocity-to-position neural integrator and the oculomotor plant. The lowest-order, best-fit periphery/brain stem model that approximated the frequency dependence of the data consisted of a second order transfer function with two alternating poles (at 0.4 and 7.2 Hz) and zeros (at 0.035 and 3.4 Hz). In addition to clearly differentiator dynamics at low frequencies (less than approximately 0.5 Hz), there was no frequency bandwidth where the periphery/brain stem function could be approximated by an integrator, as previously suggested. In this scheme, the oculomotor plant dynamics are assumed to perform the necessary high-frequency integration as required by the reflex. The detailed frequency dependence of the data could only be precisely described by higher order functions with nonminimum phase characteristics that preclude simple filtering of afferent inputs and might be suggestive of distributed spatiotemporal processing of otolith signals in the translational VORs.

Non-NASA Center↗
NASA NTRS2017

Improved Tree-Ring Archives Will Support Earth-System Science

A steep decline in archiving could make large tree-ring datasets irrelevant. But increased spatiotemporal coverage, the addition of novel parameters at sub-annual resolution, and integration with other in situ and remote Earth observations will elevate tree-ring data as an essential component of global-change research.

Babst, Flurin↗
NASA NTRSDate not supplied

How Well Satellite Remote Sensing Can Inform Surface Level Ozone Production Sensitivity to Precursor Trace Gas Emissions?

Surface-level ozone (O3) pollution mitigation strategies rely on a clear understanding of the atmospheric chemical processes involving O3, NOx (nitric oxide (NO) + nitrogen dioxide (NO2)) and volatile organic compounds (VOCs). A robust spatiotemporal classification of O3 photochemical regimes with relative chemical sensitivity of local O3 formation to emission reductions of NOx (NOx-limited regime) versus VOCs (radical-limited regime) is required to design anthropogenic emission reduction policies in order to improve surface air quality. Numerous studies found that the ratio of satellite-retrieved Vertical Column Densities (VCDs) of formaldehyde (HCHO) to NO2 can diagnose O3 production sensitivity. However, uncertainty still remains about the accuracy of such estimates using satellite-based retrievals. This study conducts an investigation to identify how well satellite data can inform surface-level O3 sensitivity by validating satellite-based estimates against aircraft-based retrievals taken during the Long Island Sound Tropospheric Ozone Study (LISTOS-2018) and Ozone Water-Land Environmental Transition Study (OWLETS-2) field campaigns. For this study, we use HCHO and NO2 retrievals from the Ozone Monitoring Instrument (OMI) onboard the Aura satellite, and high spatiotemporal resolution TROPOspheric Monitoring Instrument (TROPOMI) onboard the Sentinel-5 Precursor (S5P) satellite. The Community Multiscale Air Quality (CMAQ) modelling system is used to provide trace gas vertical profiles to recalculate air mass factor for deriving consistent satellite-based VCDs. To demonstrate the ability of satellites to diagnose O3 production regimes, we validate satellite-derived O3 sensitivity regimes, along with the OMI and TROPOMI products applied, using HCHO and NO2 retrievals from the aircraft-based instruments: Geostationary Trace gas and Aerosol Sensor Optimization (GeoTASO) spectrometer and Geostationary Coastal and Air Pollution Events Airborne Simulator (GCAS) instruments deployed during the LISTOS-2018 and OWLETS-2 campaigns. The results of this work will advance our fundamental knowledge of the spatiotemporal evolution of the complex O3-NOx-VOC chemistry, and will advance our understanding about the quality of remote-sensing, suborbital, and satellite data for accurately observing surface-level O3 production sensitivity.

Satellite↗
NASA NTRS2020

Eastern Washington Disasters: Integrating NASA Earth Observations to Analyze Spatiotemporal Distributions of Lightning-Caused Wildfires in Eastern Washington

According to the Washington Department of Natural Resources, roughly 36% of large fires in the statesince 2010 were caused by lightning. General trends also show a greater increase in the number of lightning-ignited fires over the last three decades. The NASA DEVELOP Eastern Washington Disasters team partnered with The Nature Conservancy’s Washington Chapter to analyze the relationship between lightning strikes and wildfire events in Eastern Washington, with an emphasis on Kittitas and Yakima Counties. Using the International Space Station Lightning Imaging Sensor, the Landsat 5 Thematic Mapper and Landsat 8 Operational Land Imager vegetation moisture index, and Washington Department of Natural Resources historical fire data, the team generated a lightning-caused fire vulnerability index for 2001-2019. Climatology maps of lightning, wildfire, and vegetation moisture of the study area, along with an Esri ArcGIS StoryMap, further communicated project findings. The project results demonstrated that spatiotemporal patterns of lightning-ignited wildfires in Eastern Washington can be useful to inform land management practices and better predict areas that may be more vulnerable to these events.

Disasters↗
NASA NTRS2020

Eastern Washington Disasters: Integrating NASA Earth Observations to Analyze Spatiotemporal Distributions of Lightning-Caused Wildfires in Eastern Washington

According to the Washington Department of Natural Resources, roughly 36% of large fires in the state since 2010 were caused by lightning. General trends also show a greater increase in the number of lightning-ignited fires over the last three decades. The NASA DEVELOP Eastern Washington Disasters team partnered with The Nature Conservancy’s Washington Chapter to analyze the relationship between lightning strikes and wildfire events in Eastern Washington, with an emphasis on Kittitas and Yakima Counties. Using the International Space Station Lightning Imaging Sensor, the Landsat 5 Thematic Mapper and Landsat 8 Operational Land Imager vegetation moisture index, and Washington Department of Natural Resources historical fire data, the team generated a lightning-caused fire vulnerability index for 2001-2019. Climatology maps of lightning, wildfire, and vegetation moisture of the study area, along with an Esri ArcGIS StoryMap, further communicated project findings. The project results demonstrated that spatiotemporal patterns of lightning-ignited wildfires in Eastern Washington can be useful to inform land management practices and better predict areas that may be more vulnerable to these events.

Disasters↗
NASA NTRS2006

Spatiotemporal Filtering Using Principal Component Analysis and Karhunen-Loeve Expansion Approaches for Regional GPS Network Analysis

Spatial filtering is an effective way to improve the precision of coordinate time series for regional GPS networks by reducing so-called common mode errors, thereby providing better resolution for detecting weak or transient deformation signals. The commonly used approach to regional filtering assumes that the common mode error is spatially uniform, which is a good approximation for networks of hundreds of kilometers extent, but breaks down as the spatial extent increases. A more rigorous approach should remove the assumption of spatially uniform distribution and let the data themselves reveal the spatial distribution of the common mode error. The principal component analysis (PCA) and the Karhunen-Loeve expansion (KLE) both decompose network time series into a set of temporally varying modes and their spatial responses. Therefore they provide a mathematical framework to perform spatiotemporal filtering.We apply the combination of PCA and KLE to daily station coordinate time series of the Southern California Integrated GPS Network (SCIGN) for the period 2000 to 2004. We demonstrate that spatially and temporally correlated common mode errors are the dominant error source in daily GPS solutions. The spatial characteristics of the common mode errors are close to uniform for all east, north, and vertical components, which implies a very long wavelength source for the common mode errors, compared to the spatial extent of the GPS network in southern California. Furthermore, the common mode errors exhibit temporally nonrandom patterns.

displacement↗
NASA NTRS2020

Building a landslide hazard indicator with machine learning and land surface models

The U.S. Pacific Northwest has a history of frequent and occasionally deadly landslides caused by various factors. Using a multivariate, machine-learning approach, we combined a Pacific Northwest Landslide Inventory with a 36-year gridded hydrologic dataset from the National Climate Assessment – Land Data Assimilation System to produce a landslide hazard indicator (LHI) on a daily 0.125-degree grid. The LHI identified where and when landslides were most probable over the years 1979–2016, addressing issues of bias and completeness that muddy the analysis of multi-decadal landslide inventories. The seasonal cycle was strong along the west coast, with a peak in the winter, but weaker east of the Cascade Range. This lagging indicator can fill gaps in the observational record to identify the seasonality of landslides over a large spatiotemporal domain and show how landslide hazard has responded to a changing climate.

XGBoost↗
NASA NTRS2010

Land Surface Phenology from MODIS: Characterization of the Collection 5 Global Land Cover Dynamics Product

Information related to land surface phenology is important for a variety of applications. For example, phenology is widely used as a diagnostic of ecosystem response to global change. In addition, phenology influences seasonal scale fluxes of water, energy, and carbon between the land surface and atmosphere. Increasingly, the importance of phenology for studies of habitat and biodiversity is also being recognized. While many data sets related to plant phenology have been collected at specific sites or in networks focused on individual plants or plant species, remote sensing provides the only way to observe and monitor phenology over large scales and at regular intervals. The MODIS Global Land Cover Dynamics Product was developed to support investigations that require regional to global scale information related to spatiotemporal dynamics in land surface phenology. Here we describe the Collection 5 version of this product, which represents a substantial refinement relative to the Collection 4 product. This new version provides information related to land surface phenology at higher spatial resolution than Collection 4 (500-m vs. 1-km), and is based on 8-day instead of 16-day input data. The paper presents a brief overview of the algorithm, followed by an assessment of the product. To this end, we present (1) a comparison of results from Collection 5 versus Collection 4 for selected MODIS tiles that span a range of climate and ecological conditions, (2) a characterization of interannual variation in Collections 4 and 5 data for North America from 2001 to 2006, and (3) a comparison of Collection 5 results against ground observations for two forest sites in the northeastern United States. Results show that the Collection 5 product is qualitatively similar to Collection 4. However, Collection 5 has fewer missing values outside of regions with persistent cloud cover and atmospheric aerosols. Interannual variability in Collection 5 is consistent with expected ranges of variance suggesting that the algorithm is reliable and robust, except in the tropics where some systematic differences are observed. Finally, comparisons with ground data suggest that the algorithm is performing well, but that end of season metrics associated with vegetation senescence and dormancy have higher uncertainties than start of season metrics.

Land cover dynamics↗
NASA NTRSDate not supplied

Remote Sensing of Atmospheric Temperature, Water Vapor, Trace Gases, Cloud, and Surface Properties on Daily and Decadal Time Scales

Hyperspectral IR sounders such as AIRS on Aqua, CrIS on S-NPP, NOAA20 and JPSS-2, IASI on Metop A, B, and C provide high-quality atmospheric temperature, water, vapor, and greenhouse gas vertical profiles. Additionally, they provide atmospheric cloud properties, surface emissivity, and surface skin temperatures. We have developed two algorithms which can consistently derive these products from multiple IR sounders. The first one is a Single Field-of-view Sounder Atmospheric Product (SIFSAP) algorithm and the second one is a Climate Fingerprinting Sounder Product (ClimFiSP) algorithm. The SiFSAP algorithm performs one retrieval for each FOV using an all-sky optimal estimation approach. The core of the SiFSAP algorithm is an accurate and fast Principal Component-based Radiative Transfer Model (PCRTM), which can calculate hyperspectral radiance spectra under both clear and cloudy conditions. The PCRTM was developed in the past decade using consistent reference line-by-line radiative transfer model and spectroscopy for hyperspectral sounders such as AIRS, CrIS, IASI, NAST-I, and S-HIS. The ClimFiSP algorithm, which performs retrievals from spatiotemporally averaged L1 hyperspectral radiances directly, will be orders of magnitude faster than traditional method. he ClimFiSP algorithm uses consistent radiative kernels and a robust spectral fingerprinting method. It provides accurate data climate data fusion products from multiple satellite sensors. Both SiFSAP and ClimFiSP will be available at NASA GES DISC data center for public access.

Xu Liu↗
NASA NTRSDate not supplied

Development of Radiative Transfer Models and Retrieval Algorithms for Satellite Remote Sensors

Hyperspectral IR sounders such as AIRS on Aqua, CrIS on S-NPP, NOAA20 and JPSS-2, IASI on Metop A, B, and C provide high-quality atmospheric temperature, water, vapor, and greenhouse gas vertical profiles. Additionally, they provide atmospheric cloud properties, surface emissivity, and surface skin temperatures. We have developed two algorithms which can consistently derive these products from multiple IR sounders. The first one is a Single Field-of-view Sounder Atmospheric Product (SIFSAP) algorithm and the second one is a Climate Fingerprinting Sounder Product (ClimFiSP) algorithm. The SiFSAP algorithm performs one retrieval for each FOV using an all-sky optimal estimation approach. The core of the SiFSAP algorithm is an accurate and fast Principal Component-based Radiative Transfer Model (PCRTM), which can calculate hyperspectral radiance spectra under both clear and cloudy conditions. The PCRTM was developed in the past decade using consistent reference line-by-line radiative transfer model and spectroscopy for hyperspectral sounders such as AIRS, CrIS, IASI, NAST-I, and S-HIS. The ClimFiSP algorithm, which performs retrievals from spatiotemporally averaged L1 hyperspectral radiances directly, will be orders of magnitude faster than traditional method. he ClimFiSP algorithm uses consistent radiative kernels and a robust spectral fingerprinting method. It provides accurate data climate data fusion products from multiple satellite sensors. Both SiFSAP and ClimFiSP will be available at NASA GES DISC data center for public access.

Xu Liu↗
NASA NTRS2014

Assessing the Ability of Instantaneous Aircraft and Sonde Measurements to Characterize Climatological Means and Long-Term Trends in Tropospheric Composition

Over four decades of measurements exist that sample the 3-D composition of reactive trace gases in the troposphere from approximately weekly ozone sondes, instrumentation on civil aircraft, and individual comprehensive aircraft field campaigns. An obstacle to using these data to evaluate coupled chemistry-climate models (CCMs)the models used to project future changes in atmospheric composition and climateis that exact space-time matching between model fields and observations cannot be done, as CCMs generate their own meteorology. Evaluation typically involves averaging over large spatiotemporal regions, which may not reflect a true average due to limited or biased sampling. This averaging approach generally loses information regarding specific processes. Here we aim to identify where discrete sampling may be indicative of long-term mean conditions, using the GEOS-Chem global chemical-transport model (CTM) driven by the MERRA reanalysis to reflect historical meteorology from 2003 to 2012 at 2o by 2.5o resolution. The model has been sampled at the time and location of every ozone sonde profile available from the Would Ozone and Ultraviolet Radiation Data Centre (WOUDC), along the flight tracks of the IAGOSMOZAICCARABIC civil aircraft campaigns, as well as those from over 20 individual field campaigns performed by NASA, NOAA, DOE, NSF, NERC (UK), and DLR (Germany) during the simulation period. Focusing on ozone, carbon monoxide and reactive nitrogen species, we assess where aggregates of the in situ data are representative of the decadal mean vertical, spatial and temporal distributions that would be appropriate for evaluating CCMs. Next, we identically sample a series of parallel sensitivity simulations in which individual emission sources (e.g., lightning, biogenic VOCs, wildfires, US anthropogenic) have been removed one by one, to assess where and when the aggregated observations may offer constraints on these processes within CCMs. Lastly, we show results of an additional 31-year simulation from 1980-2010 of GEOS-Chem driven by the MACCity emissions inventory and MERRA reanalysis at 4o by 5o. We sample the model at every WOUDC sonde and flight track from MOZAIC and NASA field campaigns to evaluate which aggregate observations are statistically reflective of long-term trends over the period.

Atmospheric composition↗
NASA NTRS2016

Satellite-Based Spatiotemporal Trends in PM2.5 Concentrations: China 2004-2013

Three decades of rapid economic development is causing severe and widespread PM2.5(particulate matter (is) less than 2.5 ) pollution in China. However, research on the health impacts of PM2.5 exposure has been hindered by limited historical PM2.5 concentration data. We estimated ambient PM2.5 concentrations from 2004 to 2013 in China at 0.1 deg resolution using the most recent satellite data and evaluated model performance with available ground observations. We developed a two-stage spatial statistical model using the Moderate Resolution Imaging Spectroradiometer (MODIS) Collection 6 aerosol optical depth (AOD) and assimilated meteorology, land use data, and PM2.5 concentrations from China's recently established ground monitoring network. An inverse variance weighting (IVW) approach was developed to combine MODIS Dark Target and Deep Blue AOD to optimize data coverage. We evaluated model predicted PM2.5 concentrations from 2004 to early 2014 using ground observations. The overall model cross-validation R(sup 2) and relative prediction error were 0.79 and 35.6%, respectively. Validation beyond the model year (2013) indicated that it accurately predicted PM(sub 2.5) concentrations with little bias at the monthly (R(sup 2) = 0.73), regression slope = 0.91) and seasonal (R(sup 2) = 0.79), regression slope = 0.92) levels. Seasonal variations revealed that winter was the most polluted season and that summer was the cleanest season. Analysis of predicted PM2.5 levels showed a mean annual increase of 1.97 micro-g/cu cm between 2004 and 2007 and a decrease of 0.46 micro-g/cu cm between 2008 and 2013. Our satellite-driven model can provide reliable historical PM2.5 estimates in China at a resolution comparable to those used in epidemiologic studies on the health effects of long-term PM2.5 exposure in North America. This data source can potentially advance research on PM2.5 health effects in China.

satellite remote sensing↗
NASA NTRSDate not supplied

Unveiling Urban Pollution Patterns from a Bird’s-Eye View

Low-earth orbiting satellites have been observing the global distribution of nitrogen dioxide (NO2) since the 1990s and these observations have been used to identify pollution source regions, temporal trends, and links to health effects and community disparities. However, historically, space-based NO2 observations have been coarse in spatial and temporal resolution resulting in challenges in data interpretation and product validation; therefore, NASA developed airborne capabilities and measurements strategies to observe ultraviolet-visible (UV-VIS) trace gases at high temporal and spatial resolutions to unveiling never before seen spatiotemporal patterns over major urban hubs during recent air quality field studies. In this talk, Dr. Judd will share the story of these field studies with datasets collected from NASA’s airborne spectrometers, GeoTASO and GCAS, and how they integrate in with other space- and ground-based instrumentation to illustrate the conceptual model of air quality episodes in coastal urban environments, contribute to satellite validation and regional chemical transport model evaluation. These imagers have the capability to observe UV-VIS absorbing species, typically NO2 and HCHO, at spatial resolutions as fine as 250 m when observing from the upper-troposphere. Flight strategies allowed for the collection of repeated raster samples over urban areas multiple times per flight day in cities like Chicago, Los Angeles, and New York City, capturing never before seen pictures of the diurnal evolution of emissions, chemistry, and meteorology associated with NO2 heterogeneity. These airborne efforts help local air quality management better understand their air quality challenges as well as benefit the larger air quality community in preparation for geostationary air quality observations and the development of the global air quality observing system.

Laura Judd↗
NASA NTRS2021

The Analysis of Using Satellite Soil Moisture Observations for Flood Detection, Evaluating Over the Thailand’s Great Flood of 2011

A flood monitoring and warning system provides critical information that can protect property and save lives. A basin-scale flood monitoring system requires an effective observation platform that offers extensive ground coverage of flood conditions, low latency, and high spatiotemporal resolution. While satellite imagery offers substantial spatial flood extent in detail due to its high spatial resolution, the coarse temporal resolution and cloud obstruction limit its near real-time application. Daily soil moisture data derived from satellite sensors at a scale of a few km can be used to monitor extreme wet surface conditions arising in flood occurrences. This study analyses the flood detection capabilities of several sources of soil moisture information, including the Soil Moisture and Ocean Salinity mission (SMOS), the Advanced Microwave Scanning Radiometer on EOS (AMSR-E), the Advanced SCATterometer (ASCAT) on MetOp, the Global Land Data Assimilation System (GLDAS), and the WaterGAP Global Hydrology Model (WGHM). In addition to soil moisture, the analysis includes measurements of surface reflectance from the Moderate Resolution Imaging Spectroradiometer (MODIS), precipitation measurements from the Tropical Rainfall Measuring Mission (TRMM), and terrestrial water storage estimates from the Gravity Recovery And Climate Experiment (GRACE) as proxies for flood inundations. The analysis was conducted over the Chao Phraya River Basin (CPB) in Thailand, where the Great Flood of 2011 led to one of the most significant economic losses in the country's history. Satellite-derived soil moisture exhibits a stronger correlation with the flood inundations than the precipitation, model-derived soil moisture, and terrestrial water storage data. SMOS soil moisture observation agrees best with the MODIS-derived flood extent/occurrence, both in terms of spatial distribution and timing, and providing approximated flood lead-time of one week or longer. A neural network constructed from SMOS and MODIS data is used to predict flood intensity/occurrence (given soil moisture input) with a predicted time window from eight days to thirty-two days. The short-term prediction (e.g., eight days) achieves the highest accuracy with an averaged recovery rate of approximately 60% (correlation coefficient). This study's results suggest a potential application of satellite soil moisture data in assisting flood monitoring and warning systems.

Satellite soil moisture↗
NASA NTRS2021

Spatiotemporal Methane Emissions from Global Reservoirs

Methane (CH4) is a greenhouse gas which contributes significantly to global warming and has atmospheric concentrations which have increased considerably in the last few decades, primarily due to human-induced emissions. Natural sources such as wetlands and inland aquatic systems (i.e., reservoirs, lakes, rivers) contribute substantially to global emissions but these natural systems comprise the most uncertain components of the CH4 budget. This study addresses multiple gaps and uncertainties associated with global CH4 emissions from reservoirs and undertakes a spatial and temporal assessment of global reservoir emissions. The results from this study suggest that reservoirs occupy a global area about 300,000 km2 (comparable to the size of the country of Italy) and emit 10.1 Tg CH4 yr-1. We identify data and methodological elements of previous estimates that indicate they may overestimate these emissions. This work provides a suite of global data sets, gridded at 0.25° × 0.25°, considering reservoir surface area, spatial distribution, eco-climatic system type, and the full annual cycle of daily CH4 emissions.

Spatiotemporal↗
NASA NTRS1996

Smooth Pursuit of a Partially Occluded Object

There has long been qualitative evidence that humans can pursue an object defined only by the motion of its parts. We explored this quantitatively using an occluded diamond stimulus. Four subjects (one naive) tracked a line-figure diamond moving along an elliptical path (0.9 Hz) either clockwise (CW) or counterclockwise (CCW) behind either an X-shaped aperture (CROSS) or two vertical rectangular apertures (BARS), which obscured the corners. Although the stimulus consisted of only four line segments (108 cd/square m) moving within a visible aperture (0.2 cd/square m) behind a foreground (38 cd/square m), it is largely perceived as a coherently moving diamond. The inter-saccadic portions of eye-position traces were fit with sinusoids. All subjects tracked object motion with considerable temporal accuracy. The mean phase lag was 5 deg/6 deg (CROSS/BARS) and the mean relative phase between the horizontal and vertical components was +95 deg/+92 deg (CW) and -85 deg/-75 deg (CCW), which is close to perfect. Furthermore, a chi-square analysis showed that 56% of BARS trials were consistent with tracking the correct elliptical shape (p is less than 0.05), although segment motion was purely vertical. These data disprove the main tenet of most models of pursuit: that it is a system that seeks to minimize retinal image motion through negative feedback. Rather, the main drive must be a visual signal which has already integrated spatiotemporal retinal information into an object-motion signal.

Stone, L. S.↗
NASA NTRS2007

Persistent Nature of Secondary Diurnal Modes of Precipitation over Oceanic and Continental Regimes

This investigation seeks a better understanding of the assorted mechanisms controlling the global distribution of precipitation diurnal variability based on the use of Tropical Rainfall Measuring Mission (TRMM) microwave radiometer and radar data. The horizontal distributions of precipitation's diurnal cycle are derived from eight years of TRMM Microwave Imager (TMI) and Precipitation Radar (PR) measurements involving three TRMM standard rain rate retrieval algorithms -- the resultant distributions analyzed at various spatiotemporal scales. The results reveal the prominent and expected late-evening to early-morning (LE-EM) precipitation maxima over oceans and the counterpart prominent and expected mid- to late-afternoon (MLA) maxima over continents. Moreover, and not generally recognized, the results reveal a widespread distribution of secondary maxima occurring over both oceans and continents -- maxima which generally mirror their counterpart regime's behavior. That is, many ocean regions exhibit clearcut secondary MLA precipitation maxima while many continental regions exhibit just as evident secondary LE-EM maxima. This investigation is the first comprehensive study of these globally prevalent secondary maxima and their widespread nature, a type of study only made possible when the analysis procedure is applied to a high-quality global-scale precipitation dataset. The characteristics of the secondary maxima are mapped and described on global grids using an innovative clock-face format, while a current study to be published at a later date provides physically-based explanations of the seasonal-regional distributions of the secondary maxima. In addition to an "explicit" maxima identification scheme, a "Fourier decomposition" maxima identification scheme is used to examine the amplitude and phase properties of the primary and secondary maxima -- as well as tertiary and quaternary maxima. Accordingly, the advantages, ambiguities, and pitfalls resulting from use of Fourier harmonic analysis are explained.

Yang, S.↗
NASA NTRS2020

Radiometric approach for the detection of picophytoplankton assemblages across oceanic fronts

Cell abundances of Prochlorococcus, Synechococcus, and autotrophic picoeukaryotes were estimated in surface waters using principal component analysis (PCA) of hyperspectral and multispectral remote-sensing reflectance data. This involved the development of models that employed multilinear correlations between cell abundances across the Atlantic Ocean and a combination of PCA scores and sea surface temperatures. The models retrieve high Prochlorococcus abundances in the Equatorial Convergence Zone and show their numerical dominance in oceanic gyres, with decreases in Prochlorococcus abundances towards temperate waters where Synechococcus flourishes, and an emergence of picoeukaryotes in temperate waters. Fine-scale in-situ sampling across ocean fronts provided a large dynamic range of measurements for the training dataset, which resulted in the successful detection of fine-scale Synechococcus patches. Satellite implementation of the models showed good performance (R(exp 2) > 0.50) when validated against in-situ data from six Atlantic Meridional Transect cruises. The improved relative performance of the hyperspectral models highlights the importance of future high spectral resolution satellite instruments, such as the NASA PACE mission’s Ocean Color Instrument, to extend our spatiotemporal knowledge about ecologically relevant phytoplankton assemblages.

Priscila Kienteca Lange↗