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NASA NTRS2022

GPM DPR Retrievals: Algorithm, Evaluation, and Validation

The primary goal of the Dual-frequency Precipitation Radar (DPR) aboard the Global Precipitation Measurement (GPM) core satellite is to infer precipitationrate and raindrop/particle size distributions (DSD/PSD). The focus of this paper is threefold: 1)description of the DPR retrieval algorithm that uses an adjustable relationship between rainrate (R) and the mass-weighted diameter (Dm) or an R-Dm relationship in solving for R and Dmsimultaneously; 2) evaluation of the DPR algorithm based on the physical simulationsthat employ measured DSD/PSD to understand the mechanism and errorcharacteristics of the retrieval method; 3) review of ground validation studies for theDPR product as well as analysis of the strengths and weaknesses of the ground radarand rain gauge/disdrometer validations. Overall, the DPR Version-6 algorithmprovides reasonably accurate estimates of R and Dm in rain. Non-uniformity in therain profile, however, tends to degrade the accuracy of the R and Dm estimates tosome extent as the range-independent assumption of the adjustable parameter () ofthe R-Dm relation is not able to fully account for natural variation of DSD in the verticalprofile. Underestimation of the DPR snow rate is found when compared with theindependent dual-frequency ratio (DFR) technique. This is possibly the result of theconstraint associated with the path integral attenuation (PIA)/differential PIA (dPIA)used in the DPR algorithm to find the best  and range-independent  assumption. Arange-variable  model, proposed in the DPR Version-7 algorithm, is expected toimprove rain and snow retrieval.

Liang Liao↗

NASA NTRS2012

NASA's Water Solutions Using Remote Sensing

NASA Water Resources works within Earth sciences to leverage investments of space-based observation, model results, and development and deployment of enabling technologies, systems, and capabilities into water resources management decision support tools for the sustainable use of water. Earth science satellite observations and modelling products provide a huge volume of valuable data in both near-real-time and extended back nearly 50 years about the Earth's land surface conditions such as land cover type, vegetation type and health, precipitation, snow, soil moisture, and water levels and radiation. Observations of this type combined with models and analysis enable satellite-based assessment of the water cycle. With increasing population pressure and water usage coupled with climate variability and change, water issues are being reported by numerous groups as the most critical environmental problems facing us in the 21st century. Competitive uses and the prevalence of river basins and aquifers that extend across boundaries engender political tensions between communities, stakeholders and countries. The NASA Water Resources Program has the objective to provide NASA products to help deal with these issues with the goal for the sustainable use of water. The Water Resources program organizes its projects under five functional themes: 1) stream-flow and flood forecasting; 2) water consumptive use (includes evapotranspiration) and irrigation; 3) drought; 4) water quality; and 5) climate and water resources. NASA primarily works with national and international groups such as other US government agencies (NOAA, EPA, USGS, USAID) and various other groups to maximize the widest use of the water products. A summary of NASA's water activities linked to helping solve issues for developing countries will be highlighted.

Toll, David↗

NASA NTRS2012

Validation of Airborne FMCW Radar Measurements of Snow Thickness Over Sea Ice in Antarctica

Antarctic sea ice and its snow cover are integral components of the global climate system, yet many aspects of their vertical dimensions are poorly understood, making their representation in global climate models poor. Remote sensing is the key to monitoring the dynamic nature of sea ice and its snow cover. Reliable and accurate snow thickness data are currently a highly sought after data product. Remotely sensed snow thickness measurements can provide an indication of precipitation levels, predicted to increase with effects of climate change in the polar regions. Airborne techniques provide a means for regional-scale estimation of snow depth and distribution. Accurate regional-scale snow thickness data will also facilitate an increase in the accuracy of sea ice thickness retrieval from satellite altimeter freeboard estimates. The airborne data sets are easier to validate with in situ measurements and are better suited to validating satellite algorithms when compared with in situ techniques. This is primarily due to two factors: better chance of getting coincident in situ and airborne data sets and the tractability of comparison between an in situ data set and the airborne data set averaged over the footprint of the antennas. A 28-GHz frequency modulated continuous wave (FMCW) radar loaned by the Center for Remote Sensing of Ice Sheets to the Australian Antarctic Division is used to measure snow thickness over sea ice in East Antarctica. Provided with the radar design parameters, the expected performance parameters of the radar are summarized. The necessary conditions for unambiguous identification of the airsnow and snowice layers for the radar are presented. Roughnesses of the snow and ice surfaces are found to be dominant determinants in the effectiveness of layer identification for this radar. Finally, this paper presents the first in situ validated snow thickness estimates over sea ice in Antarctica derived from an FMCW radar on a helicopterborne platform.

sea ice↗

NASA NTRS1992

On the estimation of snow depth from microwave radiometric measurements

Multiple-channel microwave radiometric measurements made over Alaska at aircraft (near 90 and 183 GHz) and satellite (at 37 and 85 GHz) altitudes are used to study the effect of atmospheric absorption on the estimation of snow depth. The estimation is based on the radiative transfer calculations using an early theoretical model of Mie scattering of single-size particles. It is shown that the radiometric correction for the effect of atmospheric absorption is important even at 37 GHz for a reliable estimation of snow depth. Under a dry atmosphere and based on single-frequency radiometric measurements, the underestimation of snow depth could amount to 50 percent at 85 GHz and 20-30 percent at 37 GHz if the effect of atmospheric absorption is not taken into account. The snow depths estimated from the 90-GHz aircraft and 85-GHz satellite measurements are found to be in reasonable agreement. However, there is a discrepancy in the snow depths estimated from the 37-GHz (at both vertical and horizontal polarizations) and 85-GHz satellite measurements.

Wang, James R.↗

NASA NTRSDate not supplied

Development of the GeoNEX Level 2G Products: Exploiting the Diurnal Variability of TOA Reflectance in Atmospheric Correction

This study develops a new atmospheric correction algorithm to generate the Level 2G products, in particular the gap-filled Surface Reflectance at 10-minute time steps, for the Geostationary-NASA Earth Exchange (GeoNEX) project. The algorithm is based on the MODIS MAIAC (Multi-Angle Implementation of Atmospheric Correction) framework but with significant modifications to exploit angular/temporal information from the diurnal variability of the GeoNEX L1G TOA (Top-of-Atmosphere) reflectance. The algorithm starts by evaluating the roughness/smoothness of the diurnal time series of the TOA reflectance. Because rapid changes in TOA reflectance are generally caused by passing clouds or shadows, rough segments of the time series are automatically filtered out while the smooth segments are further tested for brightness and temperature to identify clear-sky and snow-free observations. Next the algorithm runs the MAIAC RTM (Radiative Transfer Model) to retrieve the Ross-Thick-Li-Sparse (RTLS) BRDF model parameters and the daily-mean atmospheric optical depth (AOD) that allow the RTM to optimally simulate the observed diurnal variability of clear-sky TOA reflectance. Once the initial RTLS parameters are retrieved after the algorithm’s burn-in period, they are used as the prior information to predict the AOD level for the next days, while the subsequent clear-sky observations are used to make necessary adjustments to the RTLS parameters in an continuous fashion. This “prediction-analysis” cycle is then iterated to process the full time series of the L1G data, skipping only total-cloudy days or when surface snow is detected. We tested the algorithm over a list of selected AERONET sites. The retrieved results (the daily mean AOD and the RTLS parameters) reasonably agree with the ground-based measurements. Importantly, the results indicate that the diurnal cycles of surface reflectance are continuous functions of the illumination-view geometry. Thus we can use the retrieved RTLS model to accurately fill in data gaps on partial cloudy days. Also, our algorithm is totally independent from the traditional approaches based on the use of spectral band ratios between the shortwave infrared (e.g., 2.2µm) and the visible (e.g., 0.47µm and 0.64µm) bands. Our results thus demonstrate that the high-frequent diurnal geostationary observations contain unique information that helps us improve atmospheric correction of remote sensing data.

GeoNEX↗

NASA NTRS2022

Predicting Antarctic Net Snow Accumulation at the Kilometer Scale and Its Impact on Observed Height Changes

Sub-grid-scale processes occurring at or near the surface of an ice sheet have a potentially large impact on local and integrated net accumulation of snow via redistribution and sublimation. Given observational complexity, they are either ignored or parameterized over large-length scales. Here, we train random forest (RF) models to predict variability in net accumulation over the Antarctic Ice Sheet using atmospheric variables and topographic characteristics as predictors at 1 km resolution. Observations of net snow accumulation from both in situ and airborne radar data provide the input observable targets needed to train the RF models. We find that local net accumulation deviates by as much as 172% of the atmospheric model mean. The correlation in space between the predicted net accumulation variability and satellite-derived surface-height change indicates that surface processes operate differently through time, driven largely by the seasonal anomalies in snow accumulation.

Antarctic↗

NASA NTRS1984

Remote Sensing of Earth Terrain

Theoretical models that are useful and practical in relating remote sensing data to the important physical parameters characterizing Earth terrain are developed. The development of models that are useful in data analysis and interpretation, scene simulation, and developing new remote sensing approaches and techniques is discussed. Numerous theoretical models that are applicable to the active and passive remote sensing of plowed fields, atmospheric precipitation, vegetation, and snow fields were developed. The radiative transfer theory is used to interpret the active and passive data as a function of rain rate. Both the random medium model and the discrete scatterer model is used to study the remote sensing of vegetation fields. Due to the non-spherical geometry of the scatterers there is strong azimuthal dependence in the observed data. Thus, the anisotropic random medium model and the discrete scatterer model with nonspherical particles was developed. In order to relate the remote sensing data to the actual physical parameters, the scattering of electromagnetic waves from randomly distributed dielectric scatterers was studied. Both the rigorous random discrete scatterer theory and the strong fluctuation theory are used to derive the backscattering cross section in terms of the actual physical parameters and the results agree well with the data obtained from the snow fields.

Kong, J. A.↗

NASA NTRS1999

Predicting Decade-to-Century Climate Change: Prospects for Improving Models

Recent research has led to a greatly increased understanding of the uncertainties in today's climate models. In attempting to predict the climate of the 21st century, we must confront not only computer limitations on the affordable resolution of global models, but also a lack of physical realism in attempting to model key processes. Until we are able to incorporate adequate treatments of critical elements of the entire biogeophysical climate system, our models will remain subject to these uncertainties, and our scenarios of future climate change, both anthropogenic and natural, will not fully meet the requirements of either policymakers or the public. The areas of most-needed model improvements are thought to include air-sea exchanges, land surface processes, ice and snow physics, hydrologic cycle elements, and especially the role of aerosols and cloud-radiation interactions. Of these areas, cloud-radiation interactions are known to be responsible for much of the inter-model differences in sensitivity to greenhouse gases. Recently, we have diagnostically evaluated several current and proposed model cloud-radiation treatments against extensive field observations. Satellite remote sensing provides an indispensable component of the observational resources. Cloud-radiation parameterizations display a strong sensitivity to vertical resolution, and we find that vertical resolutions typically used in global models are far from convergence. We also find that newly developed advanced parameterization schemes with explicit cloud water budgets and interactive cloud radiative properties are potentially capable of matching observational data closely. However, it is difficult to evaluate the realism of model-produced fields of cloud extinction, cloud emittance, cloud liquid water content and effective cloud droplet radius until high-quality measurements of these quantities become more widely available. Thus, further progress will require a combination of theoretical and modeling research, together with intensified emphasis on both in situ and space-based remote sensing observations.

Somerville, Richard C. J.↗

NASA NTRS2000

A Melting Layer Model for Passive/Active Microwave Remote Sensing Applications: Simulation of TRMM Observations - Part 2

The one-dimensional, steady-state melting layer model developed in Part I of this study is used to calculate both the microphysical and radiative properties of melting precipitation, based upon the computed concentrations of snow and graupel just above the freezing level at applicable horizontal gridpoints of 3-dimensional cloud resolving model simulations. The modified 3-dimensional distributions of precipitation properties serve as input to radiative transfer calculations of upwelling radiances and radar extinction/reflectivities at the TRMM Microwave Imager (TMI) and Precipitation Radar (PR) frequencies, respectively. At the resolution of the cloud resolving model grids (approx. 1 km), upwelling radiances generally increase if mixed-phase precipitation is included in the model atmosphere. The magnitude of the increase depends upon the optical thickness of the cloud and precipitation, as well as the scattering characteristics of ice-phase precipitation aloft. Over the set of cloud resolving model simulations utilized in this study, maximum radiance increases of 43, 28, 18, and 10 K are simulated at 10.65, 19.35 GHz, 37.0, and 85.5 GHz, respectively. The impact of melting on TMI-measured radiances is determined not only by the physics of the melting particles but also by the horizontal extent of the melting precipitation, since the lower-frequency channels have footprints that extend over 10''s of kilometers. At TMI resolution, the maximum radiance increases are 16, 15, 12, and 9 K at the same frequencies. Simulated PR extinction and reflectivities in the melting layer can increase dramatically if mixed-phase precipitation is included, a result consistent with previous studies. Maximum increases of 0.46 (-2 dB) in extinction optical depth and 5 dBZ in reflectivity are simulated based upon the set of cloud resolving model simulations.

Olson, William S.↗

NASA NTRS2007

Improving Land-Surface Model Hydrology: Is an Explicit Aquifer Model Better than a Deeper Soil Profile?

Land surface models (LSMs) are computer programs, similar to weather and climate prediction models, which simulate the storage and movement of water (including soil moisture, snow, evaporation, and runoff) after it falls to the ground as precipitation. It is not currently possible to measure all of the variables of interest everywhere on Earth with sufficient accuracy. Hence LSMs have been developed to integrate the available information, including satellite observations, using powerful computers, in order to track water storage and redistribution. The maps are used to improve weather forecasts, support water resources and agricultural applications, and study the Earth's water cycle and climate variability. Recently, the models have begun to simulate groundwater storage. In this paper, we compare several possible approaches, and examine the pitfalls associated with trying to estimate aquifer parameters (such as porosity) that are required by the models. We find that explicit representation of groundwater, as opposed to the addition of deeper soil layers, considerably decreases the sensitivity of modeled terrestrial water storage to aquifer parameter choices. We also show that approximate knowledge of parameter values is not sufficient to guarantee realistic model performance: because interaction among parameters is significant, they must be prescribed as a harmonious set.

Gulden, L. E.↗

NASA NTRS1975

Interstellar molecular abundances toward omicron Persei

Extensive scanning of the B1 III star omicron Per has been done with the Copernicus telescope-spectrometer in an effort to determine column densities or meaningful upper limits for several molecules expected to be relatively abundant in H I regions of moderate density. The combination of radio H II, OH, and CO emission-line data with the optical absorption measurements indicates that omicron Per is embedded in the near edge of a dense molecular cloud and that the material seen in front of the star must be close to it. Molecules detected in ultraviolet absorption are H2, HD, CO, and possibly OH. The CO rotational temperature is between 2.2 and 3.8 K. The observed column densities and upper limits from Copernicus and ground-based data are compared with recent models for molecular formation in diffuse clouds.

Snow, T. P., Jr.↗

NASA NTRS1982

Seeing through flows in Langley's 0.3-meter Transonic Cryogenic Tunnel

Viewing problems associated with the measurement of model deformation in cryogenic wind tunnels are discussed. Tests were conducted in the Langley 0.3-Meter Transonic Cryogenic Tunnel to assess viewing capabilities thru the flow field. The effects of condensation and turbulent boundary layers are discussed and a modelling procedure for image degradation is described.

Snow, W. L.↗

NASA NTRS2016

Performance of the Goddard Multiscale Modeling Framework with Goddard Ice Microphysical Schemes

The multiscale modeling framework (MMF), which replaces traditional cloud parameterizations with cloud-resolving models (CRMs) within a host atmospheric general circulation model (GCM), has become a new approach for climate modeling. The embedded CRMs make it possible to apply CRM-based cloud microphysics directly within a GCM. However, most such schemes have never been tested in a global environment for long-term climate simulation. The benefits of using an MMF to evaluate rigorously and improve microphysics schemes are here demonstrated. Four one-moment microphysical schemes are implemented into the Goddard MMF and their results validated against three CloudSat/CALIPSO cloud ice products and other satellite data. The new four-class (cloud ice, snow, graupel, and frozen drops/hail) ice scheme produces a better overall spatial distribution of cloud ice amount, total cloud fractions, net radiation, and total cloud radiative forcing than earlier three-class ice schemes, with biases within the observational uncertainties. Sensitivity experiments are conducted to examine the impact of recently upgraded microphysical processes on global hydrometeor distributions. Five processes dominate the global distributions of cloud ice and snow amount in long-term simulations: (1) allowing for ice supersaturation in the saturation adjustment, (2) three additional correction terms in the depositional growth of cloud ice to snow, (3) accounting for cloud ice fall speeds, (4) limiting cloud ice particle size, and (5) new size-mapping schemes for snow and graupel. Despite the cloud microphysics improvements, systematic errors associated with subgrid processes, cyclic lateral boundaries in the embedded CRMs, and momentum transport remain and will require future improvement.

snow↗

NASA NTRS2016

The Darkening of the Greenland Ice Sheet: Trends, Drivers and Projections (1981-2100)

The surface energy balance and meltwater production of the Greenland ice sheet (GrIS) are modulated by snow and ice albedo through the amount of absorbed solar radiation. Here we show, using space-borne multispectral data collected during the 3 decades from 1981 to 2012, that summertime surface albedo over the GrIS decreased at a statistically significant (99 %) rate of 0.02 decade(sup -1) between 1996 and 2012. Over the same period, albedo modelled by the Modele Atmospherique Regionale (MAR) also shows a decrease, though at a lower rate (approximately -0.01 decade(sup -1)) than that obtained from space-borne data. We suggest that the discrepancy between modelled and measured albedo trends can be explained by the absence in the model of processes associated with the presence of light-absorbing impurities. The negative trend in observed albedo is confined to the regions of the GrIS that undergo melting in summer, with the dry snow zone showing no trend. The period 1981-1996 also showed no statistically significant trend over the whole GrIS. Analysis of MAR outputs indicates that the observed albedo decrease is attributable to the combined effects of increased near-surface air temperatures, which enhanced melt and promoted growth in snow grain size and the expansion of bare ice areas, and to trends in light-absorbing impurities (LAI) on the snow and ice surfaces. Neither aerosol models nor in situ and remote sensing observations indicate increasing trends in LAI in the atmosphere over Greenland. Similarly, an analysis of the number of fires and BC emissions from fires points to the absence of trends for such quantities. This suggests that the apparent increase of LAI in snow and ice might be related to the exposure of a "dark band" of dirty ice and to increased consolidation of LAI at the surface with melt, not to increased aerosol deposition. Albedo projections through to the end of the century under different warming scenarios consistently point to continued darkening, with albedo anomalies averaged over the whole ice sheet lower by 0.08 in 2100 than in 2000, driven solely by a warming climate. Future darkening is likely underestimated because of known underestimates in modelled melting (as seen in hindcasts) and because the model albedo scheme does not currently include the effects of LAI, which have a positive feedback on albedo decline through increased melting, grain growth, and darkening.

surface energy↗

NASA NTRS1995

Polarimetric scattering from layered media with multiple species of scatterers

Geophysical media are usually heterogeneous and contain multiple species of scatterers. In this paper a model is presented to calculate effective permittivities and polarimetric backscattering coefficients of multispecies-layered media. The same physical description is consistently used in the derivation of both permittivities and scattering coefficients. The strong permittivity fluctuation theory is extended to account for the multiple species of scatterers with a general ellipsoidal shape whose orientations are randomly distributed. Under the distorted Born approximation, polarimetric scattering coefficients are obtained. These calculations are applicable to the special cases of spheroidal and spherical scatterers. The model is used to study effects of scatterer shapes and multispecies mixtures on polarimetric signatures of heterogeneous media. The multispecies model accounts for moisture content in scattering media such as snowpack in an ice sheet. The results indicate a high sensitivity of backscatter to moisture with a stronger dependence for drier snow and ice grain size is important to the backscatter. For frost-covered saline ice, model results for bare ice are compared with measured data at C band and then the frost flower formation is simulated with a layer of fanlike ice crystals including brine infiltration over a rough interface. The results with the frost cover suggest a significant increase in scattering coefficients and a polarimetric signature closer to isotropic characteristics compared to the thin saline ice case.

Nghiem, S. V.↗

NASA NTRS1981

Spectral albedos of midlatitude snowpacks

Spectral albedos of impure-nonhomogeneous snowpacks, typical of midlatitudes, from 400 to 2200 nm were modeled through a numerical solution of the radiative transfer equation in the two-stream approximation. Discrete depth-dependent values of density, grain size and impurity concentration were used to characterize the snowpacks. The model is for diffuse incident radiation, and the numerical method is based on doubling and invariant imbedding. The effect of soot impurities on snowpack albedos is illustrated when a snowpack is several centimeters deep and soot reduces the albedos at visible wavelengths, however, when a snowpack is only a few centimeters deep, soot may increase the albedos at visible wavelengths. By adjusting soot content and snow grain size, good quantitative agreement with some observations at the Cascade Mountains (Washington) and at Point Barrow (Alaska) are obtained; however, the model grain sizes are found to be fifty to four hundred percent larger than the measured values. For satellite snowcover observations, a model for effective albedo of partially snow-covered areas was developed and compared with some NOAA-2 observations of the southeastern United States.

Choudhury, B.↗

NASA NTRS2004

Graupel and Snow Determination in Convection Using Dual-Wavelength Radar

Convective storms contain significant amounts of snow and graupel that each have different scattering properties for radar and radiometer remote sensing. TRMM TMI algorithms have relied heavily on radiative transfer calculations based on cloud model predicted microphysics information. In general, without in situ aircraft measurements, it is very difficult to separate snow and graupel using remote measurements. At the 94 GHZ frequency, snow is weakly attenuating while graupel is strongly attenuating. With a non-attenuating frequency such as X-band, one can distinguish Mie scattering from larger hydrometeors as well as attenuation from graupel. The presence of supercooled water will also cause attenuation of the 94 GHz radar signal, but this is mainly in convective updrafts. We will present preliminary results aimed at distinguishing snow from graupel using dual-wavelength (9.6 GHz, 94 GHz) measurements from aircraft overflights of convective storms in Florida. Measurements were made with the 9.6 GHz ER-2 Doppler Radar (EDOP) and the 94 GHz Cloud Radar System (CRS) over a number of different precipitation systems. Analysis of the vertical profiles at the two wavelengths provides information on snow and graupel regions, as well as supercooled water. The presentation will include examples from different cases and discussion of the results.

Heymsfield, Gerald↗

NASA NTRS2005

[Multi-Scale Convergence of Cold-Land Process Representation in Land-Surface Models, Microwave Remote Sensing, and Field Observations]

The cryosphere is a major component of the hydrosphere and interacts significantly with the global climate system, the geosphere, and the biosphere. Measurement of the amount of water stored in the snow pack and forecasting the rate of melt are thus essential for managing water supply and flood control systems. Snow hydrologists are confronted with the dual problems of estimating both the quantity of water held by seasonal snow packs and time of snow melt. Monitoring these snow parameters is essential for one of the objectives of the Earth Science Enterprise-understanding of the global hydrologic cycle. Measuring spatially distributed snow properties, such as snow water equivalence (SWE) and wetness, from space is a key component for improvement of our understanding of coupled atmosphere-surface processes. Through the GWEC project, we have significantly advanced our understandings and improved modeling capabilities of the microwave signatures in response to snow and underground properties.

Shi, Jiancheng↗