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NASA NTRS2013

Hydrology Research with the North American Land Data Assimilation System (NLDAS) Datasets at the NASA GES DISC Using Giovanni

The North American Land Data Assimilation System (NLDAS) is a collaboration project between NASA/GSFC, NOAA, Princeton Univ., and the Univ. of Washington. NLDAS has created a surface meteorology dataset using the best-available observations and reanalyses the backbone of this dataset is a gridded precipitation analysis from rain gauges. This dataset is used to drive four separate land-surface models (LSMs) to produce datasets of soil moisture, snow, runoff, and surface fluxes. NLDAS datasets are available hourly and extend from Jan 1979 to near real-time with a typical 4-day lag. The datasets are available at 1/8th-degree over CONUS and portions of Canada and Mexico from 25-53 North. The datasets have been extensively evaluated against observations, and are also used as part of a drought monitor. NLDAS datasets are available from the NASA GES DISC and can be accessed via ftp, GDS, Mirador, and Giovanni. GES DISC news articles were published showing figures from the heat wave of 2011, Hurricane Irene, Tropical Storm Lee, and the low-snow winter of 2011-2012. For this presentation, Giovanni-generated figures using NLDAS data from the derecho across the U.S. Midwest and Mid-Atlantic will be presented. Also, similar figures will be presented from the landfall of Hurricane Isaac and the before-and-after drought conditions of the path of the tropical moisture into the central states of the U.S. Updates on future products and datasets from the NLDAS project will also be introduced.

Mocko, David M.↗

NASA NTRS2014

Aerosol Remote Sensing in Polar Regions

Multi-year sets of ground-based sun-photometer measurements conducted at 12 Arctic sites and 9 Antarctic sites were examined to determine daily mean values of aerosol optical thickness tau(lambda) at visible and near-infrared wavelengths, from which best-fit values of Ångström's exponent alpha were calculated. Analyzing these data, the monthly mean values of tau(0.50 micrometers) and alpha and the relative frequency histograms of the daily mean values of both parameters were determined for winter-spring and summer-autumn in the Arctic and for austral summer in Antarctica. The Arctic and Antarctic covariance plots of the seasonal median values of alpha versus tau(0.50 micrometers) showed: (i) a considerable increase in tau(0.50 micrometers) for the Arctic aerosol from summer to winter-spring, without marked changes in alpha; and (ii) a marked increase in tau(0.50 micrometer) passing from the Antarctic Plateau to coastal sites, whereas alpha decreased considerably due to the larger fraction of sea-salt aerosol. Good agreement was found when comparing ground-based sun-photometer measurements of tau(lambda) and alpha at Arctic and Antarctic coastal sites with Microtops measurements conducted during numerous AERONET/MAN cruises from 2006 to 2013 in three Arctic Ocean sectors and in coastal and off-shore regions of the Southern Atlantic, Pacific, and Indian Oceans, and the Antarctic Peninsula. Lidar measurements were also examined to characterize vertical profiles of the aerosol backscattering coefficient measured throughout the year at Ny-Ålesund. Satellite-based MODIS, MISR, and AATSR retrievals of tau(lambda) over large parts of the oceanic polar regions during spring and summer were in close agreement with ship-borne and coastal ground-based sun-photometer measurements. An overview of the chemical composition of mode particles is also presented, based on in-situ measurements at Arctic and Antarctic sites. Fourteen log-normal aerosol number size-distributions were defined to represent the average features of nuclei, accumulation and coarse mode particles for Arctic haze, summer background aerosol, Asian dust and boreal forest fire smoke, and for various background austral summer aerosol types at coastal and high-altitude Antarctic sites. The main columnar aerosol optical characteristics were determined for all 14 particle modes, based on in-situ measurements of the scattering and absorption coefficients. Diurnally averaged direct aerosol-induced radiative forcing and efficiency were calculated for a set of multimodal aerosol extinction models, using various Bidirectional Reflectance Distribution Function models over vegetation-covered, oceanic and snow-covered surfaces. These gave a reliable measure of the pronounced effects of aerosols on the radiation balance of the surface-atmosphere system over polar regions.

Sun-photometer measurements↗

NASA NTRSDate not supplied

North American Land Data Assimilation (NLDAS) Data and Services at NASA GES DISC

The North American Land Data Assimilation System (NLDAS, https://ldas.gsfc.nasa.gov/nldas) integrates a large quantity of observation-based and model reanalysis data to drive offline (not coupled to the atmosphere) land surface models (LSMs) to produce fields of soil moisture, snow, and surface fluxes. The NLDAS-2 data sets currently archived at, and distributed by, the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC, https://disc.gsfc.nasa.gov) have been updated to NLDAS Version 2.0 (NLDAS-2.0). This presentation provides an overview of the updated NLDAS-2.0 data at NASA GES DISC and discusses the methods for the public to access the data as well as the data services available.

Ashley Heath↗

NASA NTRS1983

LANDSAT-D investigations in snow hydrology

Progress on the registration of TM data to digital topographic data; on comparison of TM, MSS and NOAA meteorological satellite data for snowcover mapping; and on radiative transfer models for atmospheric correction is reported. Some methods for analyzing spatial contiguity of snow within the snow covered area were selected. The methods are based on a two-channel version of the grey level co-occurence matrix, combined with edge detection derived from an algorithm for computing slopes and exposures from digital terrain data.

Dozier, J.↗

NASA NTRS1981

Applications systems verification and transfer project. Volume 1: Operational applications of satellite snow cover observations: Executive summary

Both LANDSAT and NOAA satellite data were used in improving snowmelt runoff forecasts. When the satellite snow cover data were tested in both empirical seasonal runoff estimation and short term modeling approaches, a definite potential for reducing forecast error was evident. A cost benefit analysis run in conjunction with the snow mapping indicated a $36.5 million annual benefit accruing from a one percent improvement in forecast accuracy using the snow cover data for the western United States. The annual cost of employing the system would be $505,000. The snow mapping has proven that satellite snow cover data can be used to reduce snowmelt runoff forecast error in a cost effective manner once all operational satellite data are available within 72 hours after acquisition. Executive summaries of the individual snow mapping projects are presented.

Rango, A.↗

NASA NTRS1985

The error associated with use of various forms of the divergence equation to diagnose geopotential and temperature

An experimental study was made of errors arising from diagnosing the geopotential and temperature for numerical weather prediction models by means of an inviscid divergence equation (DE). The experiment consisted of a simulation of a snow storm which occurred in 1979, with observational data being input for every point of the model grid and for the geopotential boundary condition, thereby eliminating errors in the horizontal and vertical analysis. The DE terms were recorded at each grid point, and were found to include synoptic-scale geopotential height errors up to 23 m and rms temperature errors exceeding 26 C in the PBL. The errors were minimal above 800 mb. The various unsuccessful attempts to reduce or eliminate the errors are described.

Modica, G. M.↗

NASA NTRS2001

Parameterizations of Depositional Growth of Cloud Ice in a Bulk Microphysical Scheme

The conversion of cloud ice to snow by depositional growth, designated P(sub SFI), in the Goddard Cumulus Ensemble Model cloud physics parameterization is examined. The original formulation of P(sub SFI) is shown to produce excessive conversion of cloud ice to snow because of an implicit assumption that the relative humidity is 100% with respect to water even though the air may actually be quite less humid. Two possible corrections to this problem are proposed, the first involving application of a relative humidity dependent correction factor to the original formulation of P(sub SFI), and the second involving a new formulation of P(sub SFI) based on the equation for depositional growth of cloud ice.

Braun, Scott A.↗

DOE OSTI2025

Divergent responses of historic rain-on-snow flood extremes to a warmer climate

Global warming is altering flood risks induced by rain-on-snow events. However, decision-makers lack guidance on how rain-on-snow induced extreme floods could be altered with warming. Here, storyline analyses using a kilometer-scale land surface model reveal diverse responses of four historically-impactful, decision-relevant rain-on-snow induced extreme flood events over the contiguous U.S. to warming, due to alterations in their water budgets. For the 2017-Feb California floods, runoff first increases and then decreases with warming, peaking under the +3 K scenario, while runoff of the 2017-Jan California floods increases monotonically by ~53%/K. Contrastingly, runoff of the 1996-Jan Mid-Atlantic floods decreases gradually with warming. Despite these differences, warming generally shifts flood-generating regimes along elevation profiles. High elevations could experience notably increased runoff, while low elevations encounter a shift from rain-on-snow-driven to rainfall-dominated runoff. These findings underscore the need for flood control planning to quantify region- and elevation-specific changes in rain-on-snow events in a warmer climate.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS1981

Global and local fluctuations of winter and summer simulations with the GLAS climate model

Winter and summer simulations were carried out with an improved version of the GLAS general circulation model. An improved method of computing the boundary layer fluxes, and a more realistic specification of the albedo of snow and ice covered surfaces were used. Each particular diagnostic quantity was computed from the model data and each of the 15 years of observations in precisely the same way, wherever possible. The reported observational results are averaged over the 15 winters or summers, as appropriate.

Straus, D. M.↗

NASA NTRS2020

Snow Property Inversion from Remote Sensing (SPIReS): A Generalized Multispectral Unmixing Approach with Examples from MODIS and Landsat 8 OLI

Spectral mixture analysis has a history in mappingsnow, especially where mixed pixels prevail. Using multiplespectral bands rather than band ratios or band indices, retrievalsof snow properties that affect its albedo lead to more accu-rate estimates than widely used age-based models of albedoevolution. Nevertheless, there is substantial room for improve-ment. We present the Snow Property Inversion from RemoteSensing (SPIReS) approach, offering the following improve-ments: 1) Solutions for grain size and concentrations of lightabsorbing particles are computed simultaneously; 2) Only snowand snow-free endmembers are employed; 3) Cloud-maskingand smoothing are integrated; 4) Similar spectra are groupedtogether and interpolants are used to reduce computation time.The source codes are available in an open repository. Com-putation is fast enough that users can process imagery ondemand. Validation of retrievals from Landsat 8 operational landimager (OLI) and moderate-resolution imaging spectroradiome-ter (MODIS) against WorldView-2/3 and the Airborne SnowObservatory shows accurate detection of snow and estimatesof fractional snow cover. Validation of albedo shows low errorsusing terrain-correctedin situmeasurements. We conclude bydiscussing the applicability of this approach to any airborne orspaceborne multispectral sensor and options to further improve retrievals.

Edward H Blair↗

NASA NTRSDate not supplied

A Case Study of AI-assisted Creation of a Thermodynamics Model of Precipitation Formation During Rapid Depressurization of a Vented Container

Precipitation may form in humid containers undergoing rapid depressurization. This precipitation may be liquid, i.e. fog, if the dewpoint is crossed above the freezing point of water, or direct snow crystallization if the dewpoint is crossed below the freezing point. Accurate modeling of this effect is potentially important for rapidly ascending vented containers in aircraft, spacecraft, and launch vehicles, as well as rapidly depressurizing vacuum chambers. A transient thermodynamics model of precipitation formation during the rapid depressurization of a container was developed in python. The model is written for a generic container and includes an optional water pool and water vapor source. Details of the model and results from several example cases spanning the full capabilities of the model, including a validation case, will be presented. Although the model is not novel, in contrast to prior works, this one was treated as a case study of the assistance of AI Large Language Models (LLMs) to create physical models. Impressions, performance, time, and cost of using AI for this task will be discussed.

precipitation↗

NASA NTRS2012

A Robust Multi-Scale Modeling System for the Study of Cloud and Precipitation Processes

During the past decade, numerical weather and global non-hydrostatic models have started using more complex microphysical schemes originally developed for high resolution cloud resolving models (CRMs) with 1-2 km or less horizontal resolutions. These microphysical schemes affect the dynamic through the release of latent heat (buoyancy loading and pressure gradient) the radiation through the cloud coverage (vertical distribution of cloud species), and surface processes through rainfall (both amount and intensity). Recently, several major improvements of ice microphysical processes (or schemes) have been developed for cloud-resolving model (Goddard Cumulus Ensemble, GCE, model) and regional scale (Weather Research and Forecast, WRF) model. These improvements include an improved 3-ICE (cloud ice, snow and graupel) scheme (Lang et al. 2010); a 4-ICE (cloud ice, snow, graupel and hail) scheme and a spectral bin microphysics scheme and two different two-moment microphysics schemes. The performance of these schemes has been evaluated by using observational data from TRMM and other major field campaigns. In this talk, we will present the high-resolution (1 km) GeE and WRF model simulations and compared the simulated model results with observation from recent field campaigns [i.e., midlatitude continental spring season (MC3E; 2010), high latitude cold-season (C3VP, 2007; GCPEx, 2012), and tropical oceanic (TWP-ICE, 2006)].

Tao, Wei-Kuo↗

NASA NTRS2021

Groundwater Recharge Estimated by Land Surface Models: An Evaluation in the Conterminous U.S.

Estimating diffuse recharge of precipitation is fundamental to assessing groundwater sustainability. Diffuse recharge is also the process through which climate and climate change directly affect groundwater. In this study, we evaluated diffuse recharge over the conterminous U.S. simulated by a suite of land surface models (LSMs) that were forced using a common set of meteorological input data. Simulated annual recharge exhibited spatial patterns that were similar among the LSMs, with the highest values in the eastern U.S. and Pacific Northwest. However, the magnitudes of annual recharge varied significantly among the models and were associated with differences in simulated ET, runoff and snow. Evaluation against two independent datasets did not answer the question of whether the ensemble mean performs the best, due to inconsistency between those datasets. The amplitude and timing of seasonal maximum recharge differed among the models, influenced strongly by model physics governing deep soil moisture drainage rates and, in cold regions, snowmelt. Evaluation using in situ soil moisture observations suggested that true recharge peaks 1-3 months later than simulated recharge, indicating systematic biases in simulating deep soil moisture. However, recharge from lateral flows and through preferential flows cannot be inferred from soil moisture data, and the seasonal cycle of simulated groundwater storage actually compared well with in situ groundwater observations. Long-term trends in recharge were not consistently correlated with either precipitation trends or temperature trends. This study highlights the need to employ dynamic flow models in LSMs, among other improvements, to enable more accurate simulation of recharge.

snowmelt↗

NASA NTRS2006

March 2003 EOS Aqua AMSR-E Arctic Sea Ice Field Campaign

An overview of the March 2003 coordinated sea ice field campaign in the Alaskan Arctic is presented with reference to the papers in this special section. This campaign is part of the program to validate the Aqua Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E) sea ice products. Standard AMSR-E sea ice products include sea ice concentration, sea ice temperature, and snow depth on sea ice. The validation program consists of three elements, namely: 1) satellite data comparisons; 2) coordinated satellite/aircraft surface measurements; and 3) modeling and sensitivity analyses. Landsat-7 and RADARSAT observations were used in comparative studies with the retrieved AMSR-E sea ice concentrations. The aircraft sensors provided high-resolution microwave imagery of the surface, atmospheric profiles of temperature and humidity, and digital records of sea ice conditions. When combined with in situ measurements, aircraft data were used to validate the AMSR-E sea ice temperature and snow-depth products. The modeling studies helped interpret the field-data comparisons, provided insight on the limitations of the AMSR-E sea ice algorithms, and suggested potential improvements to the AMSR-E retrieval algorithms.

Cavalieri, Donald J.↗

NASA NTRS1999

Land-Atmosphere Interactions: Successes, Problems and Prospects

After two decades of active research, a much better understanding of the broader role of biospheric processes on the local climate has emerged. A surface-albedo increase, particularly in desert border regions of the subtropics (as well as the deforested tropical regions), leads to a net surface energy deficit, which in turn leads to a relative sinking and reduced rainfall. On the other hand, studies of the influence of altered ratios of evapotranspiration and sensible fluxes, in situations where the net solar income is unchanged, show that evapotranspiration is a more desirable flux for increased precipitation and vitality of the biosphere. Besides providing water vapor and convective available potential energy (CAPE) to the lower troposphere, evapotranspiration helps in building larger CAPE before "turning on" the moist-convection. Larger CAPE in the lower troposphere enables convection to reach into the deeper atmosphere thereby heating the upper troposphere; indeed, moist-convection is also accompanied by the evaporation of falling precipitation that cools and moistens the lower atmosphere. While convective, as opposed to stratiform, precipitation reduces the fractional cloud cover; it also allows more solar radiation to reach the surface thereby invigorating surface fluxes. These, together with moist convection and associated downdrafts help to maintain the characteristic upper temperature limit(s) of the moist-land as well as oceanic regions. Regardless of the above understanding, several important problems continue to hinder the accurate simulation of a realistic land atmosphere interaction in a numerical model (both GCM and/or Meso-scale models). Among the unsolved problems are parameterization of sub-grid scale land processes that include small-scale variability of soil moisture, snow-cover and snow-physics, the biodiversity of the biosphere, orography, local drainage characteristics under natural conditions, and surface flow over the natural terrain. A well-known non-linear response of surface fluxes to these variations makes the problem of parameterizing land-atmosphere interaction processes hard-to-address and simulate, particularly in a GCM. In our presentation, we will discuss how orographic, snow-cover, and water table interactions can be included into a Simple Biosphere Model such as SiB/SSiB. Figure I shows how, in the Russian region, spring snowmelt affects the soil moisture profile. Corresponding figure 2 shows how interaction with the water table decreases the natural evapotranspiration in the Sahel region simulation. While these simulations need better validation with data, the simulations reveal that surface processes are sensitive to these parameterizations. With these developments, we continue to advance our understanding of the interaction of land with the atmosphere aloft, but the intrinsic variability of the newer parameters, e. g., hydraulic properties of the soil, diminish the positive influences of these advances on the improved climate simulation with GCMs.

Sud, Y. C.↗

NASA NTRS2020

Over-Winter Persistence of Supraglacial Lakes on the Greenland Ice Sheet: Results and Insights From a New Model

We present a newly developed 1-D numerical energy-balance and phase transition supraglacial lake model: Glacier Lake. Glacier Lake incorporates snowfall, in situ snow and ice melt, incoming water from the surrounding catchment, ice lid formation, basal freeze-up and thermal stratification. Snow cover and temperature are varied to test lake development through winter and the maximum lid thickness is recorded. Average wintertime temperatures of −2 to −30◦C and total snowfall of 0 to 3.45 m lead to a range of the maximum lid thickness from 1.2 to 2.8 m after ~250 days, with snow cover exerting the dominant control. An initial ice temperature of −15◦C with simulated advection of cold ice from upstream results in 0.6 m of basal freeze-up. This suggests that lakes with water depths above 1.3 to 3.4 m (dependent on winter snowfall and temperature) upon lid formation will persist through winter. These buried lakes can provide a sizeable water store at the start of the melt season, expedite future lake formation and warm underlying ice even in winter.

glacier hydrology↗

DOE OSTI2021

Intercomparison of Thermal Regime Algorithms in 1-D Lake Models

Lakes are an important component of the global weather and climate system, but the modeling of their thermal regimes has shown large uncertainties due to the highly diverse lake properties and model configurations. Here we evaluate the algorithms of four key lake thermal processes including turbulent heat fluxes, wind-driven mixing, light extinction, and snow density, using a highly diverse lake dataset provided by the Inter-sectoral Impact Model Intercomparison Project (ISIMIP) 2a lake sector. Algorithm codes are configured and run separately within the same parent model to rule out any interference from factors apart from the algorithms examined. Evaluations are based on both simulation accuracy and recalibration complexity for application to global lakes. For turbulent heat fluxes, the non-Monin-Obukhov similarity (MOS) based, more simplified algorithms perform better in predicting lake epilimnion temperatures and achieve high convergence in the values of the calibrated parameters. For wind-driven mixing, a two-algorithm strategy considering lake shape and season is suggested with the regular mixing algorithm used for spring and earlier summer and the mixing-enhanced algorithm for summer steady stratification and fall overturn periods. There are no evident differences in the simulated thermocline depths using different light extinction algorithms or the observation. Finally, for lake ice phenology, a constant snow density at around 110 kg m-3 is found to be sufficient for most northern lakes while the Arctic lakes require a higher value. Our study provides highly practical guides for improving 1-D lake models and feasible parameterization strategies to better simulate global lake thermal regimes.

58 GEOSCIENCES↗

NASA NTRS1992

A snow wetness retrieval algorithm for SAR

The objectives of this study are: (1) to evaluate the backscattering signals response to snow wetness; and (2) to develop an algorithm for snow wetness measurement using C-band polarimetric synthetic aperture radar (SAR). In hydrological investigations, modeling and forecasting of snowmelt runoff requires information about snowpack properties and their spatial variability. In particular, timely measurement of snow parameters is needed for operational hydrology. The liquid water content of snowpack is one of the important parameters. Active microwave sensors are highly sensitive to liquid water in the snowpack because of the large dielectric contrast between ice and water in the microwave spectrum. They are not affected by weather and have a spatial resolution compatible with the topographic variation in alpine regions. However, a quantitative algorithm for retrieval snow wetness has not yet been developed.

Shi, Jian-Cheng↗