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NASA NTRS2022

Understanding the Microphysical Control and Spatial-Temporal Variability of Warm Rain Probability Using CloudSat and MODIS Observations

By combining measurements from MODIS and the CloudSat radar, we develop a parameterization scheme to quantify the combined microphysical controls by liquid water path (LWP) and cloud droplet number concentration (CDNC) of the probability of precipitation (PoP) in marine low cloud over tropical oceans. We demonstrate that the spatial-temporal variation of grid-mean in-cloud can be largely explained by the variation of the joint probability density function of LWP and CDNC in the phase space specified by the bivariate PoP (LWP and CDNC) function. Through a series of sensitivity tests guided by this understanding, we find that in the Southeastern Pacific and Atlantic the stratocumulus to cumulus transition of the is mainly due to the variation of CDNC while the annual cycle is mainly due to the variation of LWP. The results of this study provide a viable way to diagnose the root cause of warm rain problems in global climate models.

Zhibo Zhang↗

NASA NTRS2016

Evaluation of the Sensitivity of the Amazonian Diurnal Cycle to Convective Intensity in Reanalyses

Model parameterizations of tropical deep convection are unable to reproduce the observed diurnal and spatial variability of convection in the Amazon, which contributes to climatological biases in the water cycle and energy budget. Convective intensity regimes are defined using percentiles of daily minimum 3-hourly averaged outgoing longwave radiation (OLR) from Clouds and the Earth's Radiant Energy System (CERES). This study compares the observed spatial variability of convective diurnal cycle statistics for each regime to MERRA-2 and ERA-Interim (ERA) reanalysis data sets. Composite diurnal cycle statistics are computed for daytime hours (06:00-21:00 local time) in the wet season (December-January-February). MERRA-2 matches observations more closely than ERA for domain averaged composite diurnal statistics-specifically precipitation. However, ERA reproduces mesoscale features of OLR and precipitation phase associated with topography and the propagation of the coastal squall line. Both reanalysis models are shown to underestimate extreme convection.

Itterly, Kyle F.↗

NASA NTRS1987

Time-marching solution of incompressible Navier-Stokes equations for internal flow

Primitive variables with central differencing on a staggered grid are used in the present, factored ADI finite-difference scheme for artificial compressibility method solution of the incompressible Navier-Stokes equations, leading to a close coupling between velocity and pressure that both enhances stability and eliminates the need for artificial damping. Computational efficiency is enhanced through the use of a spatially variable, fixed Courant number-based time-step. The numerical results obtained for a driven cavity at Re of 10,000, with local cell Re as high as 100, exhibits no flow variable spatial oscillations on a 40 x 40 stretched grid solution.

Soh, W. Y.↗

NASA NTRS2023

A Multi-Pixel Split-Window Approach to Sea Surface Temperature Retrieval from Thermal Imagers with Relatively High Radiometric Noise: Preliminary Studies

In the following decade(s), a set of satellite missions carrying thermal infrared (TIR) imagers with a relatively high noise equivalent differential temperature (NEdT) are expected, e.g., the high resolution TIR imagers flying on the future Thermal infraRed Imaging Satellite for High-resolution Natural resource Assessment (TRISHNA), Land Surface Temperature Monitoring (LSTM) and NASA-JPL/ASI Surface Biology and Geology Thermal (SBG) missions or the secondary payload on board the ESA Earth Explorer 10 Harmony. The instruments on board these missions are expected to be characterized by an NEdT of ⪆0.1 K. In order to reduce the impact of radiometric noise on the retrieved sea surface temperature (SST), this study investigates the possibility of applying a multi-pixel atmospheric correction based on the hypotheses that (i) the spatial variability scales of radiatively active atmospheric variables are, on average, larger than those of the SST and (ii) the effect of atmosphere is accounted for via the split window (SW) difference. Based on 32 Sentinel 3 SLSTR case studies selected in oceanic regions where SST features are mainly driven by meso to sub-mesoscale turbulence (e.g., corresponding to major western boundary currents), this study documents that the local spatial variability of the SW difference term on the scale of ≃3 × 3 km2 is comparable with the noise associated with the SW difference. Similarly, the power spectra of the SW term are shown to have, for small scales, the behavior of white noise spectra. On this basis, we suggest to average the SW term and to use it for the atmospheric correction procedure to reduce the impact of radiometric noise. In principle, this methodology can be applied on proper scales that can be dynamically defined for each pixel. The applicability of our findings to high-resolution TIR missions is discussed and an example of an application to ECOSTRESS data is reported.

sea surface temperature↗

NASA NTRS2012

Satellite and Surface Perspectives of Snow Extent in the Southern Appalachian Mountains

Assessing snow cover patterns in mountain regions remains a challenge for a variety of reasons. Topography (e.g., elevation, exposure, aspect, and slope) strongly influences snowfall accumulation and subsequent ablation processes, leading to pronounced spatial variability of snow cover. In-situ observations are typically limited to open areas at lower elevations (<1000 m). In this paper, we use several products from the Moderate Resolution Imaging Spectroradiometer (MODIS) to assess snow cover extent in the Southern Appalachian Mountains (SAM). MODIS daily snow cover maps and true color imagery are analyzed after selected snow events (e.g., Gulf/Atlantic Lows, Alberta Clippers, and Northwest Upslope Flow) from 2006 to 2012 to assess the spatial patterns of snowfall across the SAM. For each event, we calculate snow cover area across the SAM using MODIS data and compare with the Interactive Multi-sensor Snow and ice mapping system (IMS) and available in-situ observations. Results indicate that Gulf/Atlantic Lows are typically responsible for greater snow extent across the entire SAM region due to intensified cyclogenesis associated with these events. Northwest Upslope Flow events result in snow cover extent that is limited to higher elevations (>1000 m) across the SAM, but also more pronounced along NW aspects. Despite some limitations related to the presence of ephemeral snow or cloud cover immediately after each event, we conclude that MODIS products are useful for assessing the spatial variability of snow cover in heavily forested mountain regions such as the SAM.

Sugg, Johnathan W.↗

NASA NTRS2002

Efficient Reformulation of the Thermoelastic Higher-order Theory for Fgms

Functionally graded materials (FGMs) are characterized by spatially variable microstructures which are introduced to satisfy given performance requirements. The microstructural gradation gives rise to continuously or discretely changing material properties which complicate FGM analysis. Various techniques have been developed during the past several decades for analyzing traditional composites and many of these have been adapted for the analysis of FGMs. Most of the available techniques use the so-called uncoupled approach in order to analyze graded structures. These techniques ignore the effect of microstructural gradation by employing specific spatial material property variations that are either assumed or obtained by local homogenization. The higher-order theory for functionally graded materials (HOTFGM) is a coupled approach developed by Aboudi et al. (1999) which takes the effect of microstructural gradation into consideration and does not ignore the local-global interaction of the spatially variable inclusion phase(s). Despite its demonstrated utility, however, the original formulation of the higher-order theory is computationally intensive. Herein, an efficient reformulation of the original higher-order theory for two-dimensional elastic problems is developed and validated. The use of the local-global conductivity and local-global stiffness matrix approach is made in order to reduce the number of equations involved. In this approach, surface-averaged quantities are the primary variables which replace volume-averaged quantities employed in the original formulation. The reformulation decreases the size of the global conductivity and stiffness matrices by approximately sixty percent. Various thermal, mechanical, and combined thermomechanical problems are analyzed in order to validate the accuracy of the reformulated theory through comparison with analytical and finite-element solutions. The presented results illustrate the efficiency of the reformulation and its advantages in analyzing functionally graded materials.

Bansal, Yogesh↗

NASA NTRS1994

A stochastic precipitation disaggregation scheme for GCM applications

In the surface hydrologic parameterization of general circulation models (GCMs), it is commonly assumed that the precipitation processes are homogeneous over a GCM grid square and that the precipitation intensity is uniformly distributed. Based on evidence that the spatial distribution of precipitation within a GCM grid square is crucial for the land surface hydrology parameterization, a few researchers have explored the impacts of assuming that the precipitation is exponentially distributed. This paper explores the suitability of the aforementioned assumptions. First, a statistical analysis is conducted of historical precipitation data for three GCM grids in different regions of the United States. The analysis suggests that neither the uniform nor the exponential distribution assumption may be suitable at the GCM grid scale and, that instead, the spatial variability in precipitation is characterized by statistical patterns that are inhomogeneous. These patterns vary from grid to grid and are induced by the interaction between atmospheric conditions and various land surface characteristics, such as topographical features, surface properties, etc. Within the same grid square, however, the statistical patterns are generally constant from year to year. Based on this analysis, a computationally viable (i.e., usable with GCMs) stochastic precipitation disaggregation scheme that utilizes these stable statistical patterns is proposed. The method was used to generate spatially distributed hourly rainfall for a summer season in the southwestern region of the continental United States. Analysis of the results shows that the methodology preserves the seasonal characteristics of spatial variability in precipitation that is observed in the long-term historical data.

Gao, Xiaogang↗

NASA NTRS1998

A Conceptual Approach to Assimilating Remote Sensing Data to Improve Soil Moisture Profile Estimates in a Surface Flux/Hydrology Model: Overview - Part 1

Knowledge of the amount of water in the soil is of great importance to many earth science disciplines. Soil moisture is a key variable in controlling the exchange of water and energy between the land surface and the atmosphere. Thus, soil moisture information is valuable in a wide range of applications including weather and climate, runoff potential and flood control, early warning of droughts, irrigation, crop yield forecasting, soil erosion, reservoir management, geotechnical engineering, and water quality. Despite the importance of soil moisture information, widespread and continuous measurements of soil moisture are not possible today. Although many earth surface conditions can be measured from satellites, we still cannot adequately measure soil moisture from space. Research in soil moisture remote sensing began in the mid 1970s shortly after the surge in satellite development. Recent advances in remote sensing have shown that soil moisture can be measured, at least qualitatively, by several methods. Quantitative measurements of moisture in the soil surface layer have been most successful using both passive and active microwave remote sensing, although complications arise from surface roughness and vegetation type and density. Early attempts to measure soil moisture from space-borne microwave instruments were hindered by what is now considered sub-optimal wavelengths (shorter than 5 cm) and the coarse spatial resolution of the measurements. L-band frequencies between 1 and 3 GHz (10-30 cm) have been deemed optimal for detection of soil moisture in the upper few centimeters of soil. The Electronically Steered Thinned Array Radiometer (ESTAR), an aircraft-based instrument operating a 1,4 GHz, has shown great promise for soil moisture determination. Initiatives are underway to develop a similar instrument for space. Existing space-borne synthetic aperture radars (SARS) operating at C- and L-band have also shown some potential to detect surface wetness. The advantage of radar is its much higher resolution than passive microwave systems, but it is currently hampered by surface roughness effects and the lack of a good algorithm based on a single frequency and single polarization. In addition, its repeat frequency is generally low (about 40 days). In the meantime, two new radiometers offer some hope for remote sensing of soil moisture from space. The Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI), launched in November 1997, possesses a 10.65 GHz channel and the Advanced Microwave Scanning Radiometer (AMSR) on both the ADEOS-11 and Earth Observing System AM-1 platforms to be launched in 1999 possesses a 6.9 GHz channel. Aside from issues about interference from vegetation, the coarse resolution of these data will provide considerable challenges pertaining to their application. The resolution of TMI is about 45 km and that of AMSR is about 70 km. These resolutions are grossly inconsistent with the scale of soil moisture processes and the spatial variability of factors that control soil moisture. Scale disparities such as these are forcing us to rethink how we assimilate data of various scales in hydrologic models. Of particular interest is how to assimilate soil moisture data by reconciling the scale disparity between what we can expect from present and future remote sensing measurements of soil moisture and modeling soil moisture processes. It is because of this disparity between the resolution of space-based sensors and the scale of data needed for capturing the spatial variability of soil moisture and related properties that remote sensing of soil moisture has not met with more widespread success. Within a single footprint of current sensors at the wavelengths optimal for this application, in most cases there is enormous heterogeneity in soil moisture created by differences in landcover, soils and topography, as well as variability in antecedent precipitation. It is difficult to interpret the meaning of 'mean' soil moisture under such conditions and even more difficult to apply such a value. Because of the non-linear relationships between near-surface soil moisture and other variables of interest, such as surface energy fluxes and runoff, mean soil moisture has little applicability at such large scales. It is for these reasons that the use of remote sensing in conjunction with a hydrologic model appears to be of benefit in capturing the complete spatial and temporal structure of soil moisture. This paper is Part I of a four-part series describing a method for intermittently assimilating remotely-sensed soil moisture information to improve performance of a distributed land surface hydrology model. The method, summarized in section II, involves the following components, each of which is detailed in the indicated section of the paper or subsequent papers in this series: Forward radiative transfer model methods (section II and Part IV); Use of a Kalman filter to assimilate remotely-sensed soil moisture estimates with the model profile (section II and Part IV); Application of a soil hydrology model to capture the continuous evolution of the soil moisture profile within and below the root zone (section III); Statistical aggregation techniques (section IV and Part II); Disaggregation techniques using a neural network approach (section IV and Part III); and Maximum likelihood and Bayesian algorithms for inversely solving for the soil moisture profile in the upper few cm (Part IV).

Crosson, William L.↗

NASA NTRS2012

Calculating Soil Wetness, Evapotranspiration and Carbon Cycle Processes Over Large Grid Areas Using a New Scaling Technique

Soil wetness typically shows great spatial variability over the length scales of general circulation model (GCM) grid areas (approx 100 km ), and the functions relating evapotranspiration and photosynthetic rate to local-scale (approx 1 m) soil wetness are highly non-linear. Soil respiration is also highly dependent on very small-scale variations in soil wetness. We therefore expect significant inaccuracies whenever we insert a single grid area-average soil wetness value into a function to calculate any of these rates for the grid area. For the particular case of evapotranspiration., this method - use of a grid-averaged soil wetness value - can also provoke severe oscillations in the evapotranspiration rate and soil wetness under some conditions. A method is presented whereby the probability distribution timction(pdf) for soil wetness within a grid area is represented by binning. and numerical integration of the binned pdf is performed to provide a spatially-integrated wetness stress term for the whole grid area, which then permits calculation of grid area fluxes in a single operation. The method is very accurate when 10 or more bins are used, can deal realistically with spatially variable precipitation, conserves moisture exactly and allows for precise modification of the soil wetness pdf after every time step. The method could also be applied to other ecological problems where small-scale processes must be area-integrated, or upscaled, to estimate fluxes over large areas, for example in treatments of the terrestrial carbon budget or trace gas generation.

Sellers, Piers↗

NASA NTRS1989

Three dimensional simulation of spatial and temporal variability of stratospheric hydrogen chloride

Spatial and temporal variability of atmospheric HCl columns are calculated for January 1979 using a three-dimensional chemistry-transport model designed to provide the best possible representation of stratospheric transport. Large spatial and temporal variability of the HCl columns is shown to be correlated with lower stratospheric potential vorticity and thus to be of dynamical origin. Systematic longitudinal structure is correlated with planetary wave structure. These results can help place spatially and temporally isolated column and profile measurements in a regional and/or global perspective.

Kaye, Jack A.↗

NASA NTRSDate not supplied

Spatial-temporal variability of in situ cyanobacteria vertical structure in Western Lake Erie: Implications for remote sensing observations

Explore the source record for details and available documents.

Karl R. Bosse↗

NASA NTRS2006

The EPIC-MOS Particle-Induced Background Spectrum

We have developed a method for constructing a spectrum of the particle-induced instrumental background of the XMM-Newton EPIC MOS detectors that can be used for observations of the diffuse background and extended sources that fill a significant fraction of the instrument field of view. The strength and spectrum of the particle-induced background, that is, the background due to the interaction of particles with the detector and the detector surroundings, is temporally variable as well as spatially variable over individual chips. Our method uses a combination of the filter-wheel-closed data and a database of unexposed-region data to construct a spectrum of the "quiescent" background. We show that, using this method of background subtraction, the differences between independent observations of the same region of "blank sky" are consistent with the statistical uncertainties except when there is clear evidence of solar wind charge exchange emission. We use the blank sky observations to show that contamination by SWCX emission is a strong function of the solar wind proton flux, and that observations through the flanks of the magnetosheath appear to be contaminated only at much higher solar wind fluxes. We have also developed a spectral model of the residual soft proton flares, which allows their effects to be removed to a substantial degree during spectral fitting.

Kuntz, K. D.↗

NASA NTRS2016

Evaluation of VIIRS, GOCI, and MODIS Collection 6 AOD Retrievals Against Ground Sunphotometer Observations Over East Asia

Persistent high aerosol loadings together with extremely high population densities have raised serious air quality and public health concerns in many urban centers in East Asia. However, ground-based air quality monitoring is relatively limited in this area. Recently, satellite-retrieved Aerosol Optical Depth (AOD) at high resolution has become a powerful tool to characterize aerosol patterns in space and time. Using ground AOD observations from the Aerosol Robotic Network (AERONET) and the Distributed Regional Aerosol Gridded Observation Networks (DRAGON)-Asia Campaign, as well as from handheld sunphotometers, we evaluated emerging aerosol products from the Visible Infrared Imaging Radiometer Suite (VIIRS) aboard the Suomi National Polar-orbiting Partnership (S-NPP), the Geostationary Ocean Color Imager (GOCI) aboard the Communication, Ocean, and Meteorology Satellite (COMS), and Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) (Collection 6) in East Asia in 2012 and 2013. In the case study in Beijing, when compared with AOD observations from handheld sunphotometers, 51% of VIIRS Environmental Data Record (EDR) AOD, 37% of GOCI AOD, 33% of VIIRS Intermediate Product (IP) AOD, 26% of Terra MODIS C6 3km AOD, and 16% of Aqua MODIS C6 3km AOD fell within the reference expected error (EE) envelope (+/-0.05/+/- 0.15 AOD). Comparing against AERONET AOD over the JapanSouth Korea region, 64% of EDR, 37% of IP, 61% of GOCI, 39% of Terra MODIS, and 56% of Aqua MODIS C6 3km AOD fell within the EE. In general, satellite aerosol products performed better in tracking the day-to-day variability than tracking the spatial variability at high resolutions. The VIIRS EDR and GOCI products provided the most accurate AOD retrievals, while VIIRS IP and MODIS C6 3km products had positive biases.

Xiao, Q.↗

NASA NTRS2011

Quantifying Spatial and Seasonal Variability in Atmospheric Ammonia with In Situ and Space-Based Observations

Ammonia plays an important role in many biogeochemical processes, yet atmospheric mixing ratios are not well known. Recently, methods have been developed for retrieving NH3 from space-based observations, but they have not been compared to in situ measurements. We have conducted a field campaign combining co-located surface measurements and satellite special observations from the Tropospheric Emission Spectrometer (TES). Our study includes 25 surface monitoring sites spanning 350 km across eastern North Carolina, a region with large seasonal and spatial variability in NH3. From the TES spectra, we retrieve a NH3 representative volume mixing ratio (RVMR), and we restrict our analysis to times when the region of the atmosphere observed by TES is representative of the surface measurement. We find that the TES NH3 RVMR qualitatively captures the seasonal and spatial variability found in eastern North Carolina. Both surface measurements and TES NH3 show a strong correspondence with the number of livestock facilities within 10 km of the observation. Furthermore, we find that TES H3 RVMR captures the month-to-month variability present in the surface observations. The high correspondence with in situ measurements and vast spatial coverage make TES NH3 RVMR a valuable tool for understanding regional and global NH3 fluxes.

Pinder, Robert W.↗

NASA NTRS1977

A computer software system for the generation of global ocean tides including self-gravitation and crustal loading effects

A computer software system is described which computes global numerical solutions of the integro-differential Laplace tidal equations, including dissipation terms and ocean loading and self-gravitation effects, for arbitrary diurnal and semidiurnal tidal constituents. The integration algorithm features a successive approximation scheme for the integro-differential system, with time stepping forward differences in the time variable and central differences in spatial variables. Solutions for M2, S2, N2, K2, K1, O1, P1 tidal constituents neglecting the effects of ocean loading and self-gravitation and a converged M2, solution including ocean loading and self-gravitation effects are presented in the form of cotidal and corange maps.

Estes, R. H.↗

NASA NTRS1977

A computer software system for the generation of global ocean tides including self-gravitation and crustal loading effects

Estes, R. H.↗

NASA NTRSDate not supplied

Influence of sediment resuspension on deep-ocean Pa-231 and Th-230 cycling: A PROGRESS REPORT

The naturally-occurring particle-reactive radionuclides protactinium-231 (231Pa) and thorium-230 (230Th) are used as tracers of a variety of oceanic processes, both at present and in the past. Most notably, the sediment 231Pa/230Th ratio has been used to infer changes in the Atlantic Meridional Overturning Circulation over the last (de)glaciation. However, recent measurements along the U.S. GEOTRACES North Atlantic transect (GA03) revealed two features which are at odds with current understanding about 231Pa and 230Th behaviour in the ocean: (i) a sharp decrease in dissolved 231Pa and 230Th activities with depth below 2000-4000 m and (ii) very large particulate 231Pa and 230Th activities near the bottom, at a number of stations between the New England continental shelf and Bermuda. Concomitant measurements of particulate matter concentration and potential temperature showed that both features are associated with the benthic nepheloid layer (BNL) and the bottom mixed layer (BML) that are present at these stations. Here we develop and apply a simplified model of the exchange of particles, 231Pa, and 230Th between the BNL and the upper sediment, to explore the extent to which the radionuclide anomalies observed near the bottom at a number of GA03 stations can be explained by local sediment resuspension. We find that the model can broadly reproduce the observed anomalies at two stations where samples for radionuclide analyses were collected near the seafloor. Sensitivity tests with the model show that the 231Pa/230Th ratio of particles in the BML and the sediment varies by a factor of 3 as the sediment resuspension rate fluctuates within a range consistent with observational estimates. The modelled variability is comparable to the spatial variability of 231Pa/230Th of suspended particles in the modern North Atlantic and to the variability of Atlantic sediment 231Pa/230Th records across the last (de)glacial period. Two factors are found to contribute to the modelled sensitivity of the sediment 231Pa/230Th to sediment resuspension rate: the vertical turbulent mixing in the BML and the differential scavenging intensity of Pa and Th due to variation in particle concentration. Overall, our study indicates that the exchange of material between the BNL and the upper sediment can affect the particulate 231Pa/230Th ratio in the bottom water and the sediment, which may complicate the use of sediment 231Pa/230Th as a palaeoceanographic tracer.

particle-reactive radionuclides↗

NASA NTRS2021

Maximum entropy modeling to identify physical drivers of shallow snowpack heterogeneity using unpiloted aerial system (UAS) lidar

Understanding the spatial variability of the snowpack is valuable for hydrologists and ecologists seeking to predict hydrological processes in a cold region. Snow distribution is a function of interactions among static variables, such as terrain, vegetation, and soil properties, and dynamic meteorological variables, such as solar radiation, wind speed and direction, and soil moisture. However, identifying the dominant physical drivers responsible for spatial patterns of the snowpack, particularly for ephemeral, shallow snowpacks, has been challenging due to the lack of the high-resolution snowpack and physical variables with high vertical accuracy as well as inherent limitations in traditional approaches. This study uses an Unpiloted Aerial System (UAS) lidar-based snow depth and static variables (1-m spatial resolution) to analyze field-scale spatial structures of snow depth and apply the Maximum Entropy (MaxEnt) model to identify primary controls over open terrain and forests at the University of New Hampshire Thompson Farm Research Observatory, New Hampshire, United States. We found that, among nine topographic and soil variables, plant functional type and terrain roughness contribute up to 80% and 76% of relative importance in the MaxEnt framework to predict locations of deeper or shallower snowpacks, respectively, across a mixed temperate forested and field landscape. Soil variables, such as organic matter and saturated hydraulic conductivity, were also important controls (up to 70% and 81%) on snow depth spatial variations for both open and forested landscapes suggesting spatial variations in soil variables under snow can control thermal transfer among soil, snowpack, and surface-atmosphere. This work contributes to improving land surface and snow models by informing parameterization of the sub-grid scale snow depths, down-scaling remotely sensed snow products, and understanding field scale snow states.

Eunsang Cho↗