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At least 937 records · Page 52
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Soil Carbon Dynamics Following Land Use Changes and Conversion to Oil Palm Plantations in Tropical Lowlands Inferred From Radiocarbon

We measured the 14C and 13C isotopic values of soil organic carbon in mineral soil from lowland tropical forests to provide insight into how quickly carbon is turning over in the soil following conversion of primary forests to oil palm plantations. In addition to areas converted to oil palm plantations in Peru, Indonesia, and Cameroon, we examine pastures and secondary forests in Peru as a comparison to the carbon cycling processes operating in the oil palm plantations.This dataset includes radiocarbon (Δ14C) and stable carbon (δ13C) isotopes of soil organic carbon in mineral soils from natural lowland forests and oil palm plantations in Peru, Indonesia, and Cameroon. We additionally examine plots of secondary forests following agricultural use and pastures on cleared natural forest in Peru. In addition to isotopic data, this dataset includes soil carbon and nitrogen concentrations and stock, soil texture (percent sand, silt, and clay), pH, ECEC (effective cation exchange capacity), base saturation, and bulk density. Soils were sampled in 4 depth increments to 100 cm depth.This dataset supports the publication Finstad et al., 2020. Finstad, K., van Straaten, O., Veldkamp, E., & McFarlane, K. (2020). Soil carbon dynamics following land use changes and conversion to oil palm plantations in tropical lowlands inferred from radiocarbon. Global Biogeochemical Cycles, 34, e2019GB006461. https://doi.org/10.1029/2019GB006461

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Evaluation of simulated soil carbon dynamics in Arctic-Boreal ecosystems

Given the magnitude of soil carbon stocks in northern ecosystems, and the vulnerability of these stocks to climate warming, land surface models must accurately represent soil carbon dynamics in these regions. We evaluate soil carbon stocks and turnover rates, and the relationship between soil carbon loss with soil temperature and moisture, from an ensemble of eleven global land surface models. We focus on the region of NASA's Arctic-Boreal vulnerability experiment (ABoVE) in North America to inform data collection and model development efforts. Models exhibit an order of magnitude difference in estimates of current total soil carbon stocks, generally under- or overestimating the size of current soil carbon stocks by greater than 50 PgC. We find that a model's soil carbon stock at steady-state in 1901 is the prime driver of its soil carbon stock a hundred years later—overwhelming the effect of environmental forcing factors like climate. The greatest divergence between modeled and observed soil carbon stocks is in regions dominated by peat and permafrost soils, suggesting that models are failing to capture the frozen soil carbon dynamics of permafrost regions. Using a set of functional benchmarks to test the simulated relationship of soil respiration to both soil temperature and moisture, we find that although models capture the observed shape of the soil moisture response of respiration, almost half of the models examined show temperature sensitivities, or Q10 values, that are half of observed. Significantly, models that perform better against observational constraints of respiration or carbon stock size do not necessarily perform well in terms of their functional response to key climatic factors like changing temperature. This suggests that models may be arriving at the right result, but for the wrong reason. The results of this work can help to bridge the gap between data and models by both pointing to the need to constrain initial carbon pool sizes, as well as highlighting the importance of incorporating functional benchmarks into ongoing, mechanistic modeling activities such as those included in ABoVE.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1984

Biosphere/atmosphere Interactions

Vegetation influences the energy, mass and momentum exchange between land-surface and atmosphere and, in that way, may significantly affect weather and climate. The morphology and physiology of the vegetation determine the surface albedo, precipitation interception and interception loss, stomatal control of transpiration, moisture storage in the root zone and, through the vegetation roughness height, the aerodynamic resistance to the transfers of latent and sensible heat and momentum between land-surface and atmosphere. That the albedo and soil moisture have a large influence on the atmospheric circulation and rainfall was demonstrated in sensitivity experiments with general circulation models. Moreover, through its effect on the convergence of the horizontal water vapor transport in the planetary boundary layer, the surface roughness height has a large influence on rainfall.

Mintz, Y.↗
DOE OSTI2026

CMIP7 data request: land and land ice priorities and opportunities

The Land and Land Ice Theme in the Coupled Model Intercomparison Project Phase 7 (CMIP7) represents the current understanding of physical processes in land surface ecosystems, hydrology, cryosphere, and their physical interactions with other Earth system components. Simulations from Earth system models (ESMs) could provide crucial information for assessing planetary safety, such as critical tipping elements, and be used to inform climate risks for improving climate impact assessments and policy decisions. This paper presents a collaborative effort to identify scientific opportunities in the Land and Land Ice Theme of the CMIP7 Data Request. The proposed opportunities build upon advances in ESMs, including new freshwater system and land ice processes being included in CMIP7, as well as the scientific community's demand for high-frequency and sub-grid-scale land surface outputs. In total, 25 variable groups that contain 716 variables have been identified to be potentially available to the broad scientific audience for performing analysis in land–atmosphere coupling, hydrological processes and freshwater systems, glacier and ice sheet mass balance and their influence on the sea levels, land use, and plant phenology. Key reflections from this data request effort include advocacy for closer engagement between the user community and modeling groups, reduction in the technical barriers to tracking existing parameters and defining new variables, and more streamlined variable management. These will be essential to enhance the usability and reliability of CMIP7 outputs for climate and Earth system research and applications to a broad audience that relies on the CMIP7 endeavor.

Li, Yue [Univ. of California, Los Angeles, CA (Uni↗
DOE OSTI2024

Evaluating Large‐Storm Dominance in High‐Resolution GCMs and Observations Across the Western Contiguous United States

Abstract Extreme precipitation events are projected to increase in frequency across much of the land‐surface as the global climate warms, but such projections have typically relied on coarse‐resolution (100–250 km) general circulation models (GCMs). The ensemble of HighResMIP GCMs presents an opportunity to evaluate how a more finely resolved atmosphere and land‐surface might enhance the fidelity of the simulated contribution of large‐magnitude storms to total precipitation, particularly across topographically complex terrain. Here, the simulation of large‐storm dominance, that is, the number of wettest days to reach half of the total annual precipitation, is quantified across the western United States (WUS) using four GCMs within the HighResMIP ensemble and their coarse resolution counterparts. Historical GCM simulations (1950–2014) are evaluated against a baseline generated from station‐observed daily precipitation (4,803 GHCN‐D stations) and from three gridded, observationally based precipitation data sets that are coarsened to match the resolution of the GCMs. All coarse‐resolution simulations produce less large‐storm dominance than in observations across the WUS. For two of the four GCMs, bias in the median large‐storm dominance is reduced in the HighResMIP simulation, decreasing by as much as 62% in the intermountain west region. However, the other GCMs show little change or even an increase (+28%) in bias of median large‐storm dominance across multiple sub‐regions. The spread in differences with resolution amongst GCMs suggests that, in addition to resolution, model structure and parameterization of precipitation generating processes also contribute to bias in simulated large‐storm dominance.

Environmental Sciences & Ecology↗
DOE OSTI2026

Runoff evaluation in an Earth System Land Model for permafrost regions in Alaska

Modeling of hydrological runoff is essential for accurately capturing spatiotemporal feedbacks within the land–atmosphere system, particularly in sensitive regions such as permafrost landscapes. However, substantial uncertainties persist in the terrestrial runoff parameterization schemes used in Earth system and land surface models. This is particularly true in permafrost regions, where landscape heterogeneity is high and reliable observational data are scarce. In this study, we evaluate the performance of runoff parameterization schemes in the Energy Exascale Earth System Model (E3SM) land model (ELM). Our proposed framework leverages simulation results from the Advanced Terrestrial Simulator (ATS), which is a physics-based integrated surface/subsurface hydrologic model that has been successfully evaluated previously in Arctic tundra regions. We used ATS to simulate runoff from 22 representative hillslopes in the Sagavanirktok River basin, located on the North Slope of Alaska, then compared the output with ELM's parameterized representation of total runoff. Results show that (1) ELM's total runoff was the same order of magnitude as the ATS simulations, and both models were similarly variable over time; (2) minor adjustments to coefficients in ELM's runoff parameterization improved the match between the ATS simulation and ELM's parameterized representation of annual and seasonal total runoff; (3) overall, runoff responses in ATS and ELM are more similar in flat hillslope environments compared to steep hillslopes; and (4) shallower active layer thicknesses and higher precipitation simulations resulted in lower correlations between the two models due to greater total runoff. By incorporating the optimized runoff coefficients from the Sagavanirktok River basin into ELM, the simulated total runoff better matched the streamflow observations at a small watershed located on the Seward Peninsula of Alaska. Our findings revealed important insights into the effectiveness of runoff parameterizations in land surface models and pathways for improving runoff coefficients in typical Arctic regions.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2002

Variability and Predictability of Land-Atmosphere Interactions: Observational and Modeling Studies

The overall goal of this project is to increase our understanding of seasonal to interannual variability and predictability of atmosphere-land interactions. The project objectives are to: 1. Document the low frequency variability in land surface features and associated water and energy cycles from general circulation models (GCMs), observations and reanalysis products. 2. Determine what relatively wet and dry years have in common on a region-by-region basis and then examine the physical mechanisms that may account for a significant portion of the variability. 3. Develop GCM experiments to examine the hypothesis that better knowledge of the land surface enhances long range predictability. This investigation is aimed at evaluating and predicting seasonal to interannual variability for selected regions emphasizing the role of land-atmosphere interactions. Of particular interest are the relationships between large, regional and local scales and how they interact to account for seasonal and interannual variability, including extreme events such as droughts and floods. North and South America, including the Global Energy and Water Cycle Experiment Continental International Project (GEWEX GCIP), MacKenzie, and LBA basins, are currently being emphasized. We plan to ultimately generalize and synthesize to other land regions across the globe, especially those pertinent to other GEWEX projects.

Roads, John↗
DOE OSTI2024

Synchronization of the Recent Decline of East African Long Rains and Northwestern Eurasian Warming

Abstract The East African March–April–May (MAM, “long rains”) precipitation decline in recent decades remains a puzzle marked by various proposed large‐scale drivers. Here, the interannual variability of the long rains and their recent drying trend are examined using global model simulations and observations. Comparison of a control simulation and re‐initialized simulations in which land‐surface feedback is suppressed shows that much of the long rains deficit experienced between 1980 and 2014 is synchronized with the warming of the Northwestern Eurasian landmass. In agreement with the modeling results, multiple observational data sets reveal a strong negative correlation between MAM mean East African rainfall amount and the surface temperature over Northwestern Eurasia. Idealized simulations further indicate that warming in Northwestern Eurasia weakens the regional Hadley Cell and diverts the monsoonal transport of moisture away from Eastern Africa toward Europe and southern Africa, highlighting the role of remote land surface warming on the observed precipitation decline.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRSDate not supplied

Comparison of Multivariate Time Series Prediction Techniques for Emulating Noah-LSM Soil Moisture Outputs

Land surface models are crucial tools for many earth science applications including numerical weather prediction, water resource and crop monitoring, and climatological analysis. Given a set of atmospheric forcings, seasonal data, and static parameters, models like Noah-LSM solve for land surface quantities including skin temperature, sensible heat flux, and soil moisture. While these calculations are theoretically robust, they are often computationally expensive. Since artificial neural networks (ANNs) are universal function approximators, they can learn to emulate the output of a deterministic numerical model given a time series of input forcings, with the learned ANN having substantially shorter execution time. The ANN could efficiently parameterize other models, generate ensembles, and provide first-guess inputs for retrievals. As such, with the goal of developing a model that efficiently mimics the output of Noah-LSM given NLDAS2 forcings on a region covering much of the central US, we examine and compare several neural network architectures for the multi-horizon multivariate time series forecasting problem. Recent literature includes a diverse set of approaches including autoregressive architectures like LSTM and GRU, parametric and non-parametric statistical predictors (ForecastNet and MQRNN), self-attention (LSTM-attention-LSTM), and temporal convovlution (DeepTCN). We implement several of these models for the Noah-LSM prediction task, highlighting the features and challenges for each and providing practical insight on the training process.

Mitchell Dodson↗
NASA NTRS1996

Thermal signatures of urban land cover types: High-resolution thermal infrared remote sensing of urban heat island in Huntsville, AL

The main objective of this research is to apply airborne high-resolution thermal infrared imagery for urban heat island studies, using Huntsville, AL, a medium-sized American city, as the study area. The occurrence of urban heat islands represents human-induced urban/rural contrast, which is caused by deforestation and the replacement of the land surface by non-evaporating and non-porous materials such as asphalt and concrete. The result is reduced evapotranspiration and more rapid runoff of rain water. The urban landscape forms a canopy acting as a transitional zone between the atmosphere and the land surface. The composition and structure of this canopy have a significant impact on the thermal behavior of the urban environment. Research on the trends of surface temperature at rapidly growing urban sites in the United States during the last 30 to 50 years suggests that significant urban heat island effects have caused the temperatures at these sites to rise by 1 to 2 C. Urban heat islands have caused changes in urban precipitation and temperature that are at least similar to, if not greater than, those predicted to develop over the next 100 years by global change models. Satellite remote sensing, particularly NOAA AVHRR thermal data, has been used in the study of urban heat islands. Because of the low spatial resolution (1.1 km at nadir) of the AVHRR data, these studies can only examine and map the phenomenon at the macro-level. The present research provides the rare opportunity to utilize 5-meter thermal infrared data acquired from an airplane to characterize more accurately the thermal responses of different land cover types in the urban landscape as input to urban heat island studies.

Lo, Chor Pang↗
NASA NTRS2007

An Integrated High Resolution Hydrometeorological Modeling Testbed using LIS and WRF

Scientists have made great strides in modeling physical processes that represent various weather and climate phenomena. Many modeling systems that represent the major earth system components (the atmosphere, land surface, and ocean) have been developed over the years. However, developing advanced Earth system applications that integrates these independently developed modeling systems have remained a daunting task due to limitations in computer hardware and software. Recently, efforts such as the Earth System Modeling Ramework (ESMF) and Assistance for Land Modeling Activities (ALMA) have focused on developing standards, guidelines, and computational support for coupling earth system model components. In this article, the development of a coupled land-atmosphere hydrometeorological modeling system that adopts these community interoperability standards, is described. The land component is represented by the Land Information System (LIS), developed by scientists at the NASA Goddard Space Flight Center. The Weather Research and Forecasting (WRF) model, a mesoscale numerical weather prediction system, is used as the atmospheric component. LIS includes several community land surface models that can be executed at spatial scales as fine as 1km. The data management capabilities in LIS enable the direct use of high resolution satellite and observation data for modeling. Similarly, WRF includes several parameterizations and schemes for modeling radiation, microphysics, PBL and other processes. Thus the integrated LIS-WRF system facilitates several multi-model studies of land-atmosphere coupling that can be used to advance earth system studies.

Kumar, Sujay V.↗
NASA NTRS2014

A Super-Resolution Laser Altimetry Concept

A super-resolution laser altimetry technique has been proposed to provide improved lidar altimetry from Cloud Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) lidar data, and it is applicable to other similar atmospheric profiling lidar with low-pass filters. To achieve high altimetry resolution, the new technique relies on an empirical relationship between the peak signal ratio and the distance between land surface and the peak signal range bin center, which is directly derived from the CALIPSO lidar measurements and does not require the CALIPSO's transient response. The CALIPSO surface elevation results in Northern America retrieved by the new technique agree with the National Elevation Database high resolution elevation maps, and the comparisons suggest that the precision of the technique is much better than 1.4 m. The preliminary data product of land surface elevation retrieved by the new technique from CALIPSO lidar measurements is available to the altimetry community for evaluation.

Lu, Xiaomei↗
DOE OSTI2020

The Role of Subsurface Flow on Evapotranspiration: A Global Sensitivity Analysis

Abstract Water resources are impacted by water‐energy balance fluxes at the land surface, most notably evapotranspiration ( ET ), the largest component of surface energy balance. While integrated hydrologic models show promise in quantifying the nonlinear dynamics at this interface, the model results are plagued by parametric uncertainties. Given the high computational demand of running multiple parameter spaces of these models, little is known about how these uncertainties propagate into land surface processes. In this work, we perform a global sensitivity analysis by computing Sobol and AMAE indices using a surrogate model constructed with a polynomial chaos expansion to assess the impacts of the subsurface physical properties on ET . We do so by modeling a semisynthetic test case. Our results show that the effects of vertical hydraulic conductivity, porosity, and the water retention curve parameter Van Genuchten α mainly control ET . However, we note that while evaporation ( E ) shows behavior similar to the ET with high sensitivity to the parameters controlling the flow in the unsaturated zone, transpiration ( T ) is very sensitive to the saturated zone parameters and groundwater flow, especially during periods without rain. Our results also show that heterogeneities in land cover impact the flow in the saturated zone. Furthermore, our work demonstrates that reduced‐order models can be developed to make integrated models more accessible for rigorous sensitivity analyses and calibration purposes.

Maina, Fadji Zaouna↗
NASA NTRSDate not supplied

Version 8 of the SMAP Level-4 Soil Moisture Data Assimilation Product

The NASA Soil Moisture Active Passive (SMAP) mission Level-4 Soil Moisture (L4_SM) product provides global, 9-km resolution, 3-hourly surface (0-5 cm) and root-zone (0-100 cm) soil moisture from April 2015 to present with a mean latency of 2.5 days from the time of observation. The product is based on the assimilation of SMAP L-band (1.4 GHz) brightness temperature (Tb) observations into the NASA Catchment land surface model as the model is driven with observations-based precipitation forcing. This presentation discusses the improvements in the forthcoming Version 8 of L4_SM, including updates in the precipitation forcing, the Catchment model parameters, and the L-band microwave radiative transfer model (mwRTM). The precipitation observations used in L4_SM Version 8 outside of North America and the high latitudes are from the latest (Version 7) NASA Integrated Multi-satellitE Retrievals for the Global Precipitation Measurement mission (IMERG) products. Moreover, occasionally excessive precipitation rates in earlier versions of L4_SM along certain longitudes in North America were eliminated by a bug fix in the precipitation corrections algorithm. The Catchment model in L4_SM Version 8 uses climatological snow albedo values based on observations from the Moderate Resolution Imaging Spectroradiometer, replacing the look-up table parameterization of earlier versions. Additionally, corrected soil parameters were implemented for a small region in Argentina that had erroneously been classified as peat because of an error in the ancillary soil data. Finally, the mwRTM in L4_SM Version 8 uses the Mironov soil mixing approach and updated values of the L-band scattering albedo, soil roughness, and vegetation opacity climatology obtained from the latest (Release 19) SMAP Level-2 dual-channel soil moisture retrieval product. During the development of L4_SM Version 8, the change in the mwRTM parameterization resulted in a reduced unbiased RMSE of surface soil moisture when verified against in situ measurements. It also reduced the standard deviation of the Tb observation-minus-forecast residuals by ~0.15 K, highlighting the importance of the mwRTM for successful data assimilation. The bug fixes in the precipitation corrections algorithm and the Catchment model soil parameters in the Argentina region result in locally large improvements of the simulated land surface states. In summary, the ongoing refinements of the L4_SM product continue to improve its science quality and performance for global soil moisture monitoring

Rolf Reichle↗
NASA NTRS2014

The Goddard Cumulus Ensemble Model (GCE): Improvements and Applications for Studying Precipitation Processes

Convection is the primary transport process in the Earth's atmosphere. About two-thirds of the Earth's rainfall and severe floods derive from convection. In addition, two-thirds of the global rain falls in the tropics, while the associated latent heat release accounts for three-fourths of the total heat energy for the Earth's atmosphere. Cloud-resolving models (CRMs) have been used to improve our understanding of cloud and precipitation processes and phenomena from micro-scale to cloud-scale and mesoscale as well as their interactions with radiation and surface processes. CRMs use sophisticated and realistic representations of cloud microphysical processes and can reasonably well resolve the time evolution, structure, and life cycles of clouds and cloud systems. CRMs also allow for explicit interaction between clouds, outgoing longwave (cooling) and incoming solar (heating) radiation, and ocean and land surface processes. Observations are required to initialize CRMs and to validate their results. The Goddard Cumulus Ensemble model (GCE) has been developed and improved at NASA/Goddard Space Flight Center over the past three decades. It is amulti-dimensional non-hydrostatic CRM that can simulate clouds and cloud systems in different environments. Early improvements and testing were presented in Tao and Simpson (1993) and Tao et al. (2003a). A review on the application of the GCE to the understanding of precipitation processes can be found in Simpson and Tao (1993) and Tao (2003). In this paper, recent model improvements (microphysics, radiation and land surface processes) are described along with their impact and performance on cloud and precipitation events in different geographic locations via comparisons with observations. In addition, recent advanced applications of the GCE are presented that include understanding the physical processes responsible for diurnal variation, examining the impact of aerosols (cloud condensation nuclei or CCN and ice nuclei or IN) on precipitation processes, utilizing a satellite simulator to improve the microphysics, providing better simulations for satellite-derived latent heating retrieval, and coupling with a general circulation model to improve the representation of precipitation processes.

GCE↗
NASA NTRS2021

Emerging satellite observations for diurnal cycling of ecosystem processes

Diurnal cycling of plant carbon uptake and water use, and their responses to water and heat stresses, provide direct insight into assessing ecosystem productivity, agricultural production and management practices, carbon and water cycles, and feedbacks to the climate. Temperature, light, atmospheric water demand, soil moisture and leaf water potential vary over the course of the day, leading to diurnal variations in stomatal conductance, photosynthesis and transpiration. Earth observations from polar-orbiting satellites are incapable of studying these diurnal variations. Here, we review the emerging satellite observations that have the potential for studying how plant functioning and ecosystem processes vary over the course of the diurnal cycle. The recently launched ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) and Orbiting Carbon Observatory-3 (OCO-3) provide land surface temperature, evapotranspiration (ET), gross primary production (GPP) and solar-induced chlorophyll fluorescence data at different times of day. New generation operational geostationary satellites such as Himawari-8 and the GOES-R series can provide continuous, high-frequency data of land surface temperature, solar radiation, GPP and ET. Future satellite missions such as GeoCarb, TEMPO and Sentinel-4 are also planned to have diurnal sampling capability of solar-induced chlorophyll fluorescence. We explore the unprecedented opportunities for characterizing and understanding how GPP, ET and water use efficiency vary over the course of the day in response to temperature and water stresses, and management practices. We also envision that these emerging observations will revolutionize studies of plant functioning and ecosystem processes in the context of climate change and that these observations and findings can inform agricultural and forest management and lead to improvements in Earth system models and climate projections.

Plant functioning↗
NASA NTRS1998

Technical Report Series on Global Modeling and Data Assimilation: A Comparison of GEOS Assimilated Data with FIFE Observations - Volume 14

First ISLSCP Field Experiment (FIFE) observations have been used to validate the near-surface proper- ties of various versions of the Goddard Earth Observing System (GEOS) Data Assimilation System. The site- averaged FIFE data set extends from May 1987 through November 1989, allowing the investigation of several time scales, including the annual cycle, daily means and diurnal cycles. Furthermore, the development of the daytime convective planetary boundary layer is presented for several days. Monthly variations of the surface energy budget during the summer of 1988 demonstrate the affect of the prescribed surface soil wetness boundary conditions. GEOS data comes from the first frozen version of the assimilation system (GEOS-1 DAS) and two experimental versions of GEOS (v. 2.0 and 2.1) with substantially greater vertical resolution and other changes that influence the boundary layer. This report provides a baseline for future versions of the GEOS data assimilation system that will incorporate a state-of-the-art land surface parameterization. Several suggestions are proposed to improve the generality of future comparisons. These include the use of more diverse field experiment observations and an estimate of gridpoint heterogeneity from the new land surface parameterization.

Bosilovich, Michael G.↗
NASA NTRS2018

Accounting for Static and Dynamic Open Water in the Modeling of SMAP Brightness Temperatures over Peatlands

Hydrological change in peatlands due to anthropogenic disturbance and global warming can release enormous amounts of greenhouse gas emissions. Passive microwave satellite observations are an opportunity to globally monitor these changes. Abundant static and dynamic open water surfaces in peatlands strongly affect observed brightness temperatures (Tb). Here, we account for these contributions in radiative transfer modeling using NASA's Goddard Earth Observing System Model version 5 (GEOS-5) static open water mask and, for the dynamic open water fraction, the simulated inundated area using a version of the GEOS-5 Catchment land surface model that has been modified for peatland areas (PEAT-CLSM). Modeled Tb is compared against two years of SMAP L-band Tb. Preliminary results indicate: (i) a bias reduction when including the static open water fraction in a simple RTM mixing model, and ii) significantly improved correlation between modeled and observed Tb when using land surface output from PEAT-CLSM instead of the operational CLSM.

passive microwave↗