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At least 685 records · Page 38
All sourcesNASA NTRSDOE OSTI
NASA NTRS1979

A large-scale numerical model of sea ice

The described large-scale sea ice model, which is capable of coupling with atmospheric and oceanic models of comparable resolution, simulates the yearly cycle of ice in both the Northern and Southern Hemispheres. Model results for the yearly cycle of sea ice thickness and extent in both the Arctic and the Antarctic are presented. Horizontally the model resolution is approximately 200 km, while vertically four layers - ice, snow, ocean, and atmosphere - are considered. Thermodynamic processes based on energy balances at the various interfaces and dynamic processes based on wind stress, water stress, Coriolis force, internal ice resistance, and the stress from the tilt of the sea surface are incorporated. It is assumed that the ice within a given grid square is of uniform thickness, although each square has a variable percentage of its area ice free.

Parkinson, C. L.↗
NASA NTRS1984

Scattering from randomly oriented scatterers of arbitrary shape in the low-frequency limit with application to vegetation

A general theory of intensity scattering from small particles of arbitrary shape was developed based on the radiative transfer theory. Upon permitting the particles to orient in accordance with any prescribed distribution, scattering models can be derived. By making an appropriate choice of the particle size, the scattering model may be used to estimate scattering from media such as snow, vegetation and sea ice. For the purpose of illustration only comparisons with measurements from a vegetated medium are shown. The difference in scattering between elliptic and circular shaped leaves is demonstrated. In the low frequency limit, the major factors on backscattering from vegetation are found to be the depth of the vegetation layer and the orientation distribution of the leaves. The shape of the leaf is of secondary importance.

Karam, M. A.↗
NASA NTRS1983

Scattering from randomly oriented scatterers of arbitrary shape in the low-frequency limit with application to vegetation

A general theory of intensity scattering from small particles of arbitrary shape has been developed based on the radiative transfer theory. Upon permitting the particles to orient in accordance with any prescribed distribution, scattering models can be derived. By making an appropriate choice of the particle size, the scattering model may be used to estimate scattering from media such as snow, vegetation and sea ice. For the purpose of illustration only comparisons with measurements from a vegetated medium are shown. The difference in scattering between elliptic- and circular-shaped leaves is demonstrated. In the low-frequency limit, the major factors on backscattering from vegetation are found to be the depth of the vegetation layer and the orientation distribution of the leaves. The shape of the leaf is of secondary importance.

Karam, M. A.↗
NASA NTRS1988

Global surface-water-induced seasonal variations in the earth's rotation and gravitational field

The effects of seasonal changes in continental surface-water storage on the low-degree gravitational-field coefficients (J), the annual wobble excitation (Psi), and the seasonal length-of-day (LOD) variations are investigated by means of numerical simulations based on compiled meteorological data (Willmott et al., 1985) and satellite snow-load estimates (Chao et al., 1987). The formulation of the model equations and the overall characteristics of the data sets are discussed in detail, and the computation results are presented in tables and graphs. The effect on Psi is found to be relatively small due to longitudinal cancellation, but those on LOD and J are considered significant.

Chao, B. F.↗
NASA NTRS1997

Multiangle Observations of Directional Reflectances of Snow Fields

Accurate measurements of snow areas and surface albedo are crucial to advancing our understanding of the global climate system. This is because of the highly reflective nature of snow combined with its large surface coverage (snow can cover up to 40 % of the Earth's land surface during the Northern Hemisphere winter). The reflectance of snow varies with both solar incidence angle and the viewing angle. Visible sensors with different spatial resolutions have been used to infer the snow parameters. Currently, only nadir-viewing directional reflectance data are available from satellite observations. Observations at multiple angles are needed to infer the hemispheric reflectance albedo of snow fields. We propose to study the directional reflectance of snow fields using POLDER data, which contains information from different viewing angles and polarization. POLDER was successfully launched an the ADEOS-1 satellite in August, 1996, however, because POLDER data are not yet available, data from ASAS, a pointable, airborne spectroradiometer, were used in this study. Data collected over Glacier National Park of Montana show strong angular dependence. Preliminary results confirm the anisotropic nature of the snow reflectance. Knowledge of the bi-directional reflectance function(BDRF) of snow -covered surfaces is the key to developing a true albedo model in the future.

Chang, A.T.C.↗
NASA NTRS2000

Scaling Issues Between Plot and Satellite Radiobrightness Observations of Arctic Tundra

Data from generation of satellite microwave radiometer will allow the detection of seasonal to decadal changes in the arctic hydrology cycle as expressed in temporal and spatial patterns of moisture stored in soil and snow This nw capability will require calibrated Land Surface Process/Radiobrightness (LSP/R) model for the principal terrains found in the circumpolar Arctic. These LSP/R models can than be used in weak constraint. Dimensional Data Assimilation (DDA)of the daily satellite observation to estimate temperature and moisture profiles within the permafrost in active layer.

Kim, Edward J.↗
NASA NTRS2001

Modeled Seasonal Variations of Firn Density Induced by Steady State Surface Air Temperature Cycle

Seasonal variations of firn density in ice-sheet firn layers have been attributed to variations in deposition processes or other processes within the upper firn. A recent high-resolution (mm scale) density profile, measured along a 181 m core from Antarctica, showed small-scale density variations with a clear seasonal cycle that apparently was not-related to seasonal variations in deposition or known near-surface processes (Gerland and others 1999). A recent model of surface elevation changes (Zwally and Li, submitted) produced a seasonal variation in firn densification, and explained the seasonal surface elevation changes observed by satellite radar altimeters. In this study, we apply our 1-D time-dependent numerical model of firn densification that includes a temperature-dependent formulation of firn densification based on laboratory measurements of grain growth. The model is driven by a steady-state seasonal surface temperature and a constant accumulation rate appropriate for the measured Antarctic ice core. The modeled seasonal variations in firn density show that the layers of snow deposited during spring to mid-summer with the highest temperature history compress to the highest density, and the layers deposited during later summer to autumn with the lowest temperature history compress to the lowest density. The initial amplitude of the seasonal difference of about 0.13 reduces to about 0.09 in five years and asymptotically to 0.92 at depth, which is consistent with the core measurements.

Jun, Li↗
NASA NTRS2011

Foreword to the Special Issue on the 11th Specialist Meeting on Microwave Radiometry and Remote Sensing Applications (MicroRad 2010)

The Specialist Meeting on Microwave Radiometry and Remote Sensing of the Environment (MicroRad 2010) was held in Washington, DC from March 1 to 4, 2010. The objective of MicroRad 2010 was to provide an open forum to report and discuss recent advances in the field of microwave radiometry, particularly with application to remote sensing of the environment. The meeting was highly successful, with more than 200 registrations representing 48 countries. There were 80 oral presentations and more than 100 posters. MicroRad has become a venue for the microwave radiometry community to present new research results, instrument designs, and applications to an audience that is conversant in these issues. The meeting was divided into 16 sessions (listed in order of presentation): 1) SMOS Mission; 2) Future Passive Microwave Remote Sensing Missions; 3) Theory and Physical Principles of Electromagnetic Models; 4) Field Experiment Results; 5) Soil Moisture and Vegetation; 6) Snow and Cryosphere; 7) Passive/Active Microwave Remote Sensing Synergy; 8) Oceans; 9) Atmospheric Sounding and Assimilation; 10) Clouds and Precipitation; 11) Instruments and Advanced Techniques I; 12) Instruments and Advanced Techniques II; 13) Cross Calibration of Satellite Radiometers; 14) Calibration Theory and Methodology; 15) New Technologies for Microwave Radiometry; 16) Radio Frequency Interference.

Le Vine, David M↗
NASA NTRS2021

Detection and Height Measurement of Tenuous Clouds and Blowing Snow in ICESat-2 ATLAS Data

Tenuous atmospheric layers, such as high, thin cirrus clouds and blowing snow over Antarctica, play an important role in the climate system, affecting energy fluxes between the Earth and the atmosphere. Our knowledge of the structure of the atmosphere is largely derived from atmospheric satellite measurements. Yet tenuous layers can be hard to detect in satellite lidar data, especially in daylight data characterized by high solar background. In this study, we introduce an approach to detect tenuous atmospheric layers in the atmospheric lidar measurements of NASA's ICESat-2. The density-dimension algorithm for ICESat-2 atmospheric data (DDA-atmos) identifies atmospheric layers while automatically adapting to different background conditions of night, twilight, and daylight data. This capability, demonstrated for tenuous clouds and blowing snow, offers a data-based solution to an important climate modeling problem. Atmospheric layer boundaries, detection confidence, and density fields resultant from the DDA-atmos are reported in ICESat-2 atmospheric data product ATL09.

Ute Herzfeld↗
NASA NTRSDate not supplied

Development of a Consistent MODIS and VIIRS Cloud Detection Approach for CERES

A consistent cloud fraction record across various satellite platforms is essential for maintaining a long-term and stable climate data record of Earth's energy budget. With the Aqua satellite nearing the end of its operational lifetime, the continuation of this record relies on utilizing VIIRS observations from NOAA20 for cloud detection in NASA’s Clouds and Earth’s Radiant Energy System (CERES) project. However, integrating data from VIIRS and MODIS instruments poses challenges due to their distinct characteristics, such as varying spatial resolutions and different spectral channels. As a result, deriving consistent cloud properties from these two sensors without introducing artificial discontinuities in the time series remains a complex and challenging task. This paper will present progress toward developing a unified MODIS and VIIRS cloud mask using common channels to produce consistent cloud properties for CERES next edition (Ed5) Earth radiation budget data products. The fundamental approach taken in the CERES cloud mask is to compare the observed radiances to the expected background clear sky radiances. Therefore, one vital step is to compute clear sky radiances with a radiative transfer model that accurately accounts for satellite-specific, spectrally dependent surface reflectance, surface emission, and atmospheric absorption. Refined radiative transfer models and updated ancillary data inputs including surface emissivity maps, IGBP, snow and ice maps are incorporated into the processing framework to improve cloud detection consistency and accuracy. Pixel level cloud mask results and monthly global cloud fraction comparisons between MODIS and VIIRS will be presented to evaluate their consistency. Remaining challenges will be discussed. It is expected that this work will contribute consistent cloud properties for CERES that adequately bridges the MODIS and VIIRS imager data records.

CERES↗
NASA NTRS2014

Improved Satellite Retrievals of NO2 and SO2 over the Canadian Oil Sands and Comparisons with Surface Measurements

Satellite remote sensing is increasingly being used to monitor air quality over localized sources such as the Canadian oil sands. Following an initial study, significantly low biases have been identified in current NO2 and SO2 retrieval products from the Ozone Monitoring Instrument (OMI) satellite sensor over this location resulting from a combination of its rapid development and small spatial scale. Air mass factors (AMFs) used to convert line-of-sight "slant" columns to vertical columns were re-calculated for this region based on updated and higher resolution input information including absorber profiles from a regional-scale (15 km × 15 km resolution) air quality model, higher spatial and temporal resolution surface reflectivity, and an improved treatment of snow. The overall impact of these new Environment Canada (EC) AMFs led to substantial increases in the peak NO2 and SO2 average vertical column density (VCD), occurring over an area of intensive surface mining, by factors of 2 and 1.4, respectively, relative to estimates made with previous AMFs. Comparisons are made with long-term averages of NO2 and SO2 (2005-2011) from in situ surface monitors by using the air quality model to map the OMI VCDs to surface concentrations. This new OMI-EC product is able to capture the spatial distribution of the in situ instruments (slopes of 0.65 to 1.0, correlation coefficients of greater than 0.9). The concentration absolute values from surface network observations were in reasonable agreement, with OMI-EC NO2 and SO2 biased low by roughly 30%. Several complications were addressed including correction for the interference effect in the surface NO2 instruments and smoothing and clear-sky biases in the OMI measurements. Overall these results highlight the importance of using input information that accounts for the spatial and temporal variability of the location of interest when performing retrievals.

surface concentration↗
DOE OSTI2023

A Surface Radiation Balance Dataset from Siple Dome in West Antarctica for Atmospheric and Climate Model Evaluation

Abstract A field campaign at Siple Dome in West Antarctica during the austral summer 2019/20 offers an opportunity to evaluate climate model performance, particularly cloud microphysical simulation. Over Antarctic ice sheets and ice shelves, clouds are a major regulator of the surface energy balance, and in the warm season their presence occasionally induces surface melt that can gradually weaken an ice shelf structure. This dataset from Siple Dome, obtained using transportable and solar-powered equipment, includes surface energy balance measurements, meteorology, and cloud remote sensing. To demonstrate how these data can be used to evaluate model performance, comparisons are made with meteorological reanalysis known to give generally good performance over Antarctica (ERA5). Surface albedo measurements show expected variability with observed cloud amount, and can be used to evaluate a model’s snowpack parameterization. One case study discussed involves a squall with northerly winds, during which ERA5 fails to produce cloud cover throughout one of the days. A second case study illustrates how shortwave spectroradiometer measurements that encompass the 1.6- μ m atmospheric window reveal cloud phase transitions associated with cloud life cycle. Here, continuously precipitating mixed-phase clouds become mainly liquid water clouds from local morning through the afternoon, not reproduced by ERA5. We challenge researchers to run their various regional or global models in a manner that has the large-scale meteorology follow the conditions of this field campaign, compare cloud and radiation simulations with this Siple Dome dataset, and potentially investigate why cloud microphysical simulations or other model components might produce discrepancies with these observations. significance statement Antarctica is a critical region for understanding climate change and sea level rise, as the great ice sheets and the ice shelves are subject to increasing risk as global climate warms. Climate models have difficulties over Antarctica, particularly with simulation of cloud properties that regulate snow surface melting or refreezing. Atmospheric and climate-related field work has significant challenges in the Antarctic, due to the small number of research stations that can support state-of-the-art equipment. Here we present new data from a suite of transportable and solar-powered instruments that can be deployed to remote Antarctic sites, including regions where ice shelves are most at risk, and we demonstrate how key components of climate model simulations can be evaluated against these data.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1997

The Interplay Between Transpiration and Runoff Formulations in Land Surface Schemes Used with Atmospheric Models

The Project for Intercomparison of Land-surface Parameterization Schemes (PILPS) has shown that different land surface models (LSMS) driven by the same meteorological forcing can produce markedly different surface energy and water budgets, even when certain critical aspects of the LSMs (vegetation cover, albedo, turbulent drag coefficient, and snow cover) are carefully controlled. To help explain these differences, the authors devised a monthly water balance model that successfully reproduces the annual and seasonal water balances of the different PILPS schemes. Analysis of this model leads to the identification of two quantities that characterize an LSM's formulation of soil water balance dynamics: (1) the efficiency of the soil's evaporation sink integrated over the active soil moisture range, and (2) the fraction of this range over which runoff is generated. Regardless of the LSM's complexity, the combination of these two derived parameters with rates of interception loss, potential evaporation, and precipitation provides a reasonable estimate for the LSM's simulated annual water balance. The two derived parameters shed light on how evaporation and runoff formulations interact in an LSM, and the analysis as a whole underscores the need for compatibility in these formulations.

Koster, Rindal D.↗
DOE OSTI2022

Physical Controls on the Hydrology of Perennially Ice-Covered Lakes, Taylor Valley, Antarctica (1996–2013)

The McMurdo Dry Valleys, Antarctica, are a polar desert populated with numerous closed-watershed, perennially ice-covered lakes primarily fed by glacial melt. Lake levels have varied by as much as 8 m since 1972 and are currently rising after a decade of decreasing. Precipitation falls as snow, so lake hydrology is dominated by energy available to melt glacier ice and to sublimate lake ice. To understand the energy and hydrologic controls on lake level changes and to explain the variability between neighboring lakes, only a few kilometers apart, we model the hydrology for the three largest lakes in Taylor Valley. Here, we apply a physically based hydrological model that includes a surface energy balance model to estimate glacial melt and lake sublimation to constrain mass fluxes to and from the lakes. Results show that lake levels are very sensitive to small changes in glacier albedo, air temperature, and wind speed. We were able to balance the hydrologic budget in two watersheds using meltwater inflow and sublimation loss from the ice-covered lake alone. A third watershed, closest to the coast, required additional inflow beyond model uncertainties. We hypothesize a shallow groundwater system within the active layer, fed by dispersed snow patches, contributes 23% of the inflow to this watershed. The lakes are out of equilibrium with the current climate. If the climate of our study period (1996–2013) persists into the future, the lakes will reach equilibrium starting in 2300, with levels 2–17 m higher, depending on the lake, relative to the 2020 level.

58 GEOSCIENCES↗
NASA NTRS2005

Analysis of Multiple Precipitation Products and Preliminary Assessment of Their Impact on Global Land Data Assimilation System (GLDAS) Land Surface States

Land surface models (LSMs) are computer programs, similar to weather and climate prediction models, which simulate the stocks and fluxes of water (including soil moisture, snow, evaporation, and runoff) and energy (including the temperature of and sensible heat released from the soil) after they arrive on the land surface as precipitation and sunlight. It is not currently possible to measure all of the variables of interest everywhere on Earth with sufficient accuracy and space-time resolution. Hence LSMs have been developed to integrate the available observations with our understanding of the physical processes involved, using powerful computers, in order to map these stocks and fluxes as they change in time. The maps are used to improve weather forecasts, support water resources and agricultural applications, and study the Earth's water cycle and climate variability. NASA's Global Land Data Assimilation System (GLDAS) project facilitates testing of several different LSMs with a variety of input datasets (e.g., precipitation, plant type). Precipitation is arguably the most important input to LSMs. Many precipitation datasets have been produced using satellite and rain gauge observations and weather forecast models. In this study, seven different global precipitation datasets were evaluated over the United States, where dense rain gauge networks contribute to reliable precipitation maps. We then used the seven datasets as inputs to GLDAS simulations, so that we could diagnose their impacts on output stocks and fluxes of water. In terms of totals, the Climate Prediction Center (CPC) Merged Analysis of Precipitation (CMAP) had the closest agreement with the US rain gauge dataset for all seasons except winter. The CMAP precipitation was also the most closely correlated in time with the rain gauge data during spring, fall, and winter, while the satellitebased estimates performed best in summer. The GLDAS simulations revealed that modeled soil moisture is highly sensitive to precipitation, with differences in spring and summer as large as 45% depending on the choice of precipitation input.

Gottschalck, Jon↗
NASA NTRS2003

Radar Reflectivity Simulated by a 2-D Spectra Bin Model: Sensitivity of Cloud-aerosol Interaction

The Goddard Cumulus Ensemble (GCE) model with bin spectra microphysics is used to simulate mesoscale convective systems.The model uses explicit bins to represent size spectra of cloud nuclei, water drops, ice crystals, snow and graupel. Each hydrometeorite category is described by 33 mass bins. The simulations provide a unique data set of simulated raindrop size distribution in a realistic dynamic frame. Calculations of radar parameters using simulated drop size distribution serve as an evaluation of numerical model performance. In addition, the GCE bin spectra modes is a very useful tool to study uncertainties related to radar observations; all the environmental parameters are precisely known. In this presentation, we concentrate on the discussion of Z-R (ZDR-R) relation in the simulated systems. Due to computational limitations, the spectra bin model has been run in two dimensions with 31 stretched vertical layers and 1026 horizontal grid points (1 km resolution). Two different cases, one in midlatitude continent, the other in tropical ocean, have been simulated. The continental case is a strong convection which lasted for two hours. The oceanic case is a persistent system with more than 10 hours' life span. It is shown that the simulated Z-R (ZDR-R) relations generally agree with observations using radar and rain gauge data. The spatial and temporal variations of Z-R relation in different locations are also analyzed. Impact of aerosols on cloud formation and raindrop size distribution was studied. Both clean (low CCN) and dirty (high CCN) cases are simulated. The Z-R relation is shown to vary considerable in the initial CCN concentrations.

Li, Kiaowen↗
NASA NTRS1981

Remote sensing of stored precipitation, i.e., soil moisture and snow

The characteristics and performance of spaceborne precipitation radar systems are discussed. The development of a model is discussed. Examples of simulation results are given. It was found that the accuracy of rain rate estimates is improved by using higher resolution radar.

Schmugge, T. J.↗
NASA NTRS2023

Changing Nature of High-Impact Snowfall Events in Eastern North America

Snowstorms cause substantial disruption in the eastern United States and Canada each winter. While reductions in annual snowfall are projected over most of this region due to anthropogenic global warming, daily snowfall extremes that have the greatest impact may not decrease in the same manner. We examine changes to two extreme snowfall metrics: the 95th percentile of daily snowfall (SF95, cm) and the number of events during which 10% of the mean annual snowfall is exceeded during a single day (TC10, events yr−1). We explore changes to these metrics in two ensembles of the fifth-generation Canadian Regional Climate Model (CRCM5), including four 0.22° ( ≈ 25 km) simulations driven by different coupled general circulation models as well as the higher-resolution (0.11°, ≈ 12 km) ClimEx ensemble, driven by 50 members of a large initial-condition ensemble of one global model. We find that while mean annual snowfall is projected to decrease over our domain, SF95 is projected to remain relatively constant, suggesting that the most extreme daily snowfalls currently observed are likely to occur even in a warmer future climate. The region of the largest TC10 values exhibits a northward shift, with a larger percentage of annual snowfall occurring during a few large events along the U.S.-Canada border. These projected changes to the nature of snowfall events may have important socioeconomic consequences in this densely populated region of North America.

heavy snow↗