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NASA NTRS2000

Use of Geostationary Satellite Data to Force Land Surface Schemes within Atmospheric Mesoscale Models

A technique has been developed for assimilating GOES-derived skin temperature tendencies and insolation into the surface energy budget equation of a mesoscale model so that the simulated rate of temperature change closely agrees with the satellite observations. A critical assumption of the technique is that the availability of moisture (either from the soil or vegetation) is the least known term in the model's surface energy budget. Therefore, the simulated latent heat flux, which is a function of surface moisture availability, is adjusted based upon differences between the modeled and satellite-observed skin temperature tendencies. An advantage of this technique is that satellite temperature tendencies are assimilated in an energetically consistent manner that avoids energy imbalances and surface stability problems that arise from direct assimilation of surface shelter temperatures. The fact that the rate of change of the satellite skin temperature is used rather than the absolute temperature means that sensor calibration is not as critical. The technique has been employed on a semi-operational basis at the GHCC within the PSU/NCAR MM5. Assimilation has been performed on a grid centered over the Southeastern US since November 1998. Results from the past year show that assimilation of the satellite data reduces both the bias and RMSE for simulations of surface air temperature and relative humidity. These findings are based on comparison of assimilation runs with a control using the simple 5-layer soil model available in MM5. A significant development in the past several months was the inclusion of the detailed Oregon State University land surface model (OSU/LSM) as an option within MM5. One of our working hypotheses has been that the assimilation technique, although simple, may provide better short-term forecasts than a detailed LSM that requires significant number initialized parameters. Preliminary results indicate that the assimilation out performs the OSU/LSM as it is incorporated in MM5. Details will be presented at the symposium.

Lapenta, William M.↗
NASA NTRS2020

Assessing the Impact of Soil Layer Depth Specification on the Observability of Modeled Soil Moisture and Brightness Temperature

The utility of hydrologic land surface models (LSMs) can be enhanced by using information from observational platforms, but mismatches between the two are common.This study assesses the degree to which model agreement with observations (observability) is affected by two mechanisms in particular: 1) physical incongruities between the support volumes being characterized and 2) inadequateor inconsistent parameterizations of physical processes. The Noah and Noah-MP LSMs by default characterize surface soil moisture (SSM) in the top 10 cm of the soil column. This depth may be reasonable when comparing against soil moisture from in situ probes centered at 5 cm,but it is notably different from the 5 cm (or less) sensing depth of NASA’s Soil Moisture Active Passive (SMAP) satellite mission. These depth inconsistencies are examined by using thinner model layers in the Noah and Noah-MP LSMs and comparing resultant simulations to in situ and SMAP soil moisture. In addition, a forward radiative transfer model to simulate microwave brightness temperatures (Tbs) is used to facilitate direct comparisons of LSM-based and SMAP-based L-band Tb retrievals. Observability is quantified using Kolmogorov-Smirnov distance values, calculated from empirical cumulative distribution functions of SSM and Tb time series. Experiment results depend on the particular subspace being analyzed (SSM or Tb). This study concludes that therole of increasedsoil layer discretizationon LSM observability is secondary to the influence of component parameterizations, the effects of which dominate systematic differences with observations

SMAP↗
NASA NTRS2002

Implementation of the NCAR Community Land Model (CLM) in the NASA/NCAR finite-volume Global Climate Model (fvGCM)

In this study, the NCAR CLM version 2.0 land-surface model was integrated into the NASA/NCAR fvGCM. The CLM was developed collaboratively by an open interagency/university group of scientists and based on well-proven physical parameterizations and numerical schemes that combine the best features of BATS, NCAR-LSM, and IAP94. The CLM design is a one-dimensional point model with 1 vegetation layer, along with sub-grid scale tiles. The features of the CLM include 10-uneven soil layers with water, ice, and temperature states in each soil layer, and five snow layers, with water flow, refreezing, compaction, and aging allowed. In addition, the CLM utilizes two-stream canopy radiative transfer, the Bonan lake model and topographic enhanced streamflow based on TOPMODEL. The DAO fvGCM uses a genuinely conservative Flux-Form Semi-Lagrangian transport algorithm along with terrain- following Lagrangian control-volume vertical coordinates. The physical parameterizations are based on the NCAR Community Atmosphere Model (CAM-2). For our purposes, the fvGCM was run at 2 deg x 2.5 deg horizontal resolution with 55 vertical levels. The 10-year climate from the fvGCM with CLM2 was intercompared with the climate from fvGCM with LSM, ECMWF and NCEP. We concluded that the incorporation of CLM2 did not significantly impact the fvGCM climate from that of LSM. The most striking difference was the warm bias in the CLM2 surface skin temperature over desert regions. We determined that the warm bias can be partially attributed to the value of the drag coefficient for the soil under the canopy, which was too small resulting in a decoupling between the ground surface and the canopy. We also discovered that the canopy interception was high compared to observations in the Amazon region. A number of experiments were then performed focused on implementing model improvements. In order to correct the warm bias, the drag coefficient for the soil under the canopy was considered a function of LAI (Leaf Area Index). Analysis of the results revealed that there was a substantial impact, and the warm and dry bias in the CLM2 was significantly reduced. For the interception scheme, the canopy throughfall was increased to allow for more infiltration of precipitation into the soil, resulting in increased low-level moisture and a decrease in the interception loss ratio (canopy evaporation to precipitation).

Radakovich, Jon D.↗
NASA NTRS2020

The influence of assimilating leaf area index in a land surface model on global water fluxes and storages

Vegetation plays a fundamental role not only in theenergy and carbon cycles but also in the global water balanceby controlling surface evapotranspiration (ET). Thus, accu-rately estimating vegetation-related variables has the poten-tial to improve our understanding and estimation of the dy-namic interactions between the water, energy, and carbon cy-cles. This study aims to assess the extent to which a land sur-face model (LSM) can be optimized through the assimilationof leaf area index (LAI) observations at the global scale. Twoobserving system simulation experiments (OSSEs) are per-formed to evaluate the efficiency of assimilating LAI into anLSM through an ensemble Kalman filter (EnKF) to estimateLAI, ET, canopy-interception evaporation (CIE), canopy wa-ter storage (CWS), surface soil moisture (SSM), and terres-trial water storage (TWS). Results show that the LAI dataassimilation framework not only effectively reduces errorsin LAI model simulations but also improves all the modeledwater flux and storage variables considered in this study (ET,CIE, CWS, SSM, and TWS), even when the forcing pre-cipitation is strongly positively biased (extremely wet con-ditions). However, it tends to worsen some of the modeledwater-related variables (SSM and TWS) when the forcingprecipitation is affected by a dry bias. This is attributed tothe fact that the amount of water in the LSM is conservative,and the LAI assimilation introduces more vegetation, whichrequires more water than what is available within the soil

Terrestrial Vegetation↗
NASA NTRS2021

Bias Correction of Hydrologic Projections Strongly Impacts Inferred Climate Vulnerabilities in Institutionally Complex Water Systems

Water-resources planners use regional water management models (WMMs) to identify vulnerabilities to climate change. Frequently, dynamically downscaled climate inputs are used in conjunction with land-surface models (LSMs) to provide hydrologic streamflow projections, which serve as critical inputs for WMMs. Here, we show how even modest projection errors can strongly affect assessments of water availability and financial stability for irrigation districts in California. Specifically, our results highlight that LSM errors in projections of flood and drought extremes are highly interactive across timescales, path-dependent, and can be amplified when modeling infrastructure systems (e.g., misrepresenting banked groundwater). Common strategies for reducing errors in deterministic LSM hydrologic projections (e.g., bias correction) can themselves strongly distort projected climate vulnerabilities and misrepresent their inferred financial consequences. Overall, our results indicate a need to move beyond standard deterministic climate projection and error management frameworks that are dependent on single simulated climate change scenario outcomes.

Keyvan Malek↗
NASA NTRSDate not supplied

GLDAS-2 Land Surface Model Data and Data Services at NASA GES DISC

The goal of the NASA Global Land Data Assimilation System (GLDAS, https://ldas.gsfc.nasa.gov/gldas(https://ldas.gsfc.nasa.gov/gldas)) is to generate optimal fields of land surface states and fluxes by ingesting satellite- and ground-based observational data products, using advanced land surface modeling and data assimilation techniques (Rodell et al., 2004).The GLDAS dataset currently archived at and distributed by the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC, https://disc.gsfc.nasa.gov/ (https://disc.gsfc.nasa.gov/)) is GLDAS Version 2 (GLDAS-2). It contains a series of output fields from the upgraded Noah-3.6, Catchment-F2.5, and VIC-4.1.2 Land Surface Models (LSMs) in the Land Information System (LIS-V7, https://lis.gsfc.nasa.gov/ (https://lis.gsfc.nasa.gov/)). GLDAS-2 has three components:GLDAS-2.0, GLDAS-2.1, and GLDAS-2.2. GLDAS-2.0 is forced entirely with the upgraded Princeton Meteorological ForcingV2.2 Dataset and provides a temporally consistent series from 1948 through 2014. GLDAS-2.1 is forced with a combination of model and observation data, with data spanning from 2000 to the present. The GLDAS-2.2 product suite uses data assimilation(DA), whereas the GLDAS-2.0 and GLDAS-2.1 products are "open-loop" (i.e., no data assimilation). The choice of forcing data, as well as DA observation source, variable, and scheme, varies for different GLDAS-2.2 products. The currently availableGLDAS-2.2 data contain a daily 0.25-degree output from the Catchment-F2.5 LSM in LIS-V7. The data are forced with the meteorological analysis fields from the operational European Centre for Medium-Range Weather Forecasts Integrated Forecasting System (ECMWF-IFS) and assimilated with GRACE and GRACE-FO data, ranging from February 1, 2003 to the present. The current GLDAS-2.0 and 2.1 Noah LSM data were reprocessed in November 2019 and January 2020 respectively and their data from Catchment and VIC LSMs are new to the GLDAS-2 collection. This presentation provides a summary of theGLDAS-2 data products, their land surface fields, and their related data services at the GES DISC; and a description of the majorGLDAS-2 climatological characteristics as well as the intercomparison with the data of the previous version.

Hydrology↗
NASA NTRS2022

Development of a “Nature Run” for Observing System Simulation Experiments (OSSEs) for Snow Mission Development

Snow is a fundamental component of global and regional water budgets, particularly in mountainous areas and regions downstream that rely on snowmelt for water resources. Land surface models (LSMs) are commonly used to develop spatially distributed estimates of snow water equivalent (SWE) and runoff. However, LSMs are limited by uncertainties in model physics and parameters, among other factors. In this study, we describe the use of model calibration tools to improve snow simulations within the Noah-MP LSM as the first step in an observing system simulation experiment (OSSE). Noah-MP is calibrated against the University of Arizona (UA) SWE product over a western Colorado domain. With spatially varying calibrated parameters, we run calibrated and default Noah-MP simulations for water years 2010–20. By evaluating both simulations against the UA dataset, we show that calibration decreases domain averaged temporal RMSE and bias for snow depth from 0.15 to 0.13 m and from −0.036 to −0.0023 m, respectively, and improves the timing of snow ablation. Increased snow simulation performance also improves estimates of model-simulated runoff in four of six study basins, though only one has statistically significant improvement. Spatially distributed Noah-MP snow parameters perform better than default uniform values. We demonstrate that calibrating variables related to snow albedo calculations and rain–snow partitioning, among other processes, is a necessary step for creating a nature run that reasonably approximates true snow conditions for the OSSEs. Additionally, the inclusion of a snowfall scaling term can address biases in precipitation from meteorological forcing datasets, further improving the utility of LSMs for generating reliable spatiotemporal estimates of snow.

Melissa L. Wrzesien↗
DOE OSTI2024

Beyond the visible: Accounting for ultraviolet and far‐red radiation in vegetation productivity and surface energy budgets

Photosynthetically active radiation (PAR) is typically defined as light with a wavelength within 400–700 nm. However, ultra-violet (UV) radiation within 280–400 nm and far-red (FR) radiation within 700–750 nm can also excite photosystems, though not as efficiently as PAR. Vegetation and land surface models (LSMs) typically do not explicitly account for UV's contribution to energy budgets or photosynthesis, nor FR's contribution to photosynthesis. However, whether neglecting UV and FR has significant impacts remains unknown. Here, we explored how canopy radiative transfer (RT) and photosynthesis are impacted when explicitly implementing UV in the canopy RT model and accounting for UV and FR in the photosynthesis models within a next-generation LSM that can simulate hyperspectral canopy RT. We validated our improvements using photosynthesis measurements from plants under different light sources and intensities and surface reflection from an eddy-covariance tower. Our model simulations suggested that at the whole plant level, after accounting for UV and FR explicitly, chlorophyll content, leaf area index (LAI), clumping index, and solar radiation all impact the modeling of gross primary productivity (GPP). At the global scale, mean annual GPP within a grid would increase by up to 7.3% and the increase is proportional to LAI; globally integrated GPP increases by 4.6 PgC year −1 (3.8% of the GPP without accounting for UV + FR). Further, using PAR to proxy UV could overestimate surface albedo by more than 0.1, particularly in the boreal forests. Our results highlight the importance of improving UV and FR in canopy RT and photosynthesis modeling and the necessity to implement hyperspectral or multispectral canopy RT schemes in future vegetation and LSMs.

energy budget↗
NASA NTRS2014

How Accurate is Land/Ocean Moisture Transport Variability in Reanalyses?

Quantifying the global hydrological cycle and its variability across various time scales remains a challenge to the climate community. Direct measurements of evaporation (E), evapotranspiration (ET), and precipitation (P) are not feasible on a global scale, nor is the transport of water vapor over the global oceans and sparsely populated land areas. Expanding satellite data streams have enabled development of various water (and energy) flux products, complementing reanalyses and facilitating observationally constrained modeling. But the evolution of the global observing system has produced additional complications--improvements in satellite sensor resolution and accuracy have resulted in "epochs" of observational quasi-uniformity that can adversely affect reanalysis trends. In this work we focus on vertically integrated moisture flux convergence (VMFC) variations within the period 1979 - present integrated over global land. We show that VMFC in recent reanalyses (e.g. ERA-I, NASA MERRA, NOAA CFSR and JRA55) suffers from observing system changes, though differently in each product. Land Surface Models (LSMs) forced with observations-based precipitation, radiation and near-surface meteorology share closely the interannual P-ET variations of the reanalyses associated with ENSO events. (VMFC over land and P-ET estimates are equivalent quantities since atmospheric storage changes are small on these scales.) But the long-term LSM trend over the period since 1979 is approximately one-fourth that of the reanalyses. Additional reduced observation reanalyses assimilating only surface pressure and /or specifying seasurface temperature also have a much smaller trend in P-ET like the LSMs. We explore the regional manifestation of the reanalysis P-ET / VMFC problems, particularly over land. Both principal component analysis and a simple time series changepoint analysis highlight problems associated with data poor regions such as Equatorial Africa and, for one reanalysis, the Equatorial Andes region. Onset of the availability of passive microwave Special Sensor Microwave Imager (SSMI) moisture data in July 1987 and the transition from the Microwave Sounder Unit (MSU) to an advanced version (AMSU) have significant impacts on VMFC variability. Simple accounting for these errors of leading importance results in modified reanalysis VMFC estimates that agree much better with the LSM results. Regional details of the modified reanalysis VMFC and LSM P-ET are related to changes in Pacific Decadal Variability as manifest in SST changes after the late 1990s.

Robertson, F. R.↗
NASA NTRS2022

Assimilation of NASA’s Airborne Snow Observatory Snow Measurements for Improved Hydrological Modeling: A Case Study Enabled by the Coupled LIS/WRF-Hydro System

he NASA LIS/WRF-Hydro system is a coupled modeling framework that combines the modeling and data assimilation (DA) capabilities of the NASA Land Information System (LIS) with the multi-scale surface hydrological modeling capabilities of the WRF-Hydro model, both of which are widely used in both operations and research. This coupled modeling framework builds on the linkage between land surface models (LSMs), which simulate surface boundary conditions in atmospheric models, and distributed hydrologic models, which simulate horizontal surface and sub-surface flow, adding new land DA capabilities. In the present study, we employ this modeling framework in the Tuolumne River basin in central California. We demonstrate the added value of the assimilation of NASA Airborne Snow Observatory (ASO) snow water equivalent (SWE) estimates in the Tuolumne basin. This analysis is performed in both LIS as an LSM column model and LIS/WRF-Hydro, with hydrologic routing. Results demonstrate that ASO DA in the basin reduced snow bias by as much as 30% from an open-loop (OL) simulation compared to three independent datasets. It also reduces downstream streamflow runoff biases by as much as 40%, and improves streamflow skill scores in both wet and dry years. Analysis of soil moisture and evapotranspiration (ET) also reveals the impacts of hydrologic routing from WRF-Hydro in the simulations, which would otherwise not be resolved in an LSM column model. By demonstrating the beneficial impact of SWE DA on the improving streamflow forecasts, the article outlines the importance of such observational inputs for reservoir operations and related water management applications.

Hydrologic modeling↗
NASA NTRS2022

Assimilation of NASA's Airborne Snow Observatory Snow Measurements for Improved Hydrological Modeling: A Case Study Enabled by the Coupled LIS/WRF-Hydro System

The NASA LIS/WRF-Hydro system is a coupled modeling framework that combines the modeling and data assimilation (DA) capabilities of the NASA Land Information System (LIS) with the multi-scale surface hydrological modeling capabilities of the WRF-Hydro model, both of which are widely used in both operations and research. This coupled modeling framework builds on the linkage between land surface models (LSMs), which simulate surface boundary conditions in atmospheric models, and distributed hydrologic models, which simulate horizontal surface and sub-surface flow, adding new land DA capabilities. In the present study, we employ this modeling framework in the Tuolumne River basin in central California. We demonstrate the added value of the assimilation of NASA Airborne Snow Observatory (ASO) snow water equivalent (SWE) estimates in the Tuolumne basin. This analysis is performed in both LIS as an LSM column model and LIS/WRF-Hydro, with hydrologic routing. Results demonstrate that ASO DA in the basin reduced snow bias by as much as 30% from an open-loop (OL) simulation compared to three independent datasets. It also reduces downstream streamflow runoff biases by as much as 40%, and improves streamflow skill scores in both wet and dry years. Analysis of soil moisture and evapotranspiration (ET) also reveals the impacts of hydrologic routing from WRF-Hydro in the simulations, which would otherwise not be resolved in an LSM column model. By demonstrating the beneficial impact of SWE DA on the improving streamflow forecasts, the article outlines the importance of such observational inputs for reservoir operations and related water management applications.

Timothy M. Lahmers↗
DOE OSTI2025

Incorporating the Acclimation of Photosynthesis and Leaf Respiration in the Noah‐MP Land Surface Model: Model Development and Evaluation

Abstract Realistic simulation of leaf photosynthetic and respiratory processes is needed for accurate prediction of the global carbon cycle. These two processes systematically acclimate to long‐term environmental changes by adjusting photosynthetic and respiratory traits (e.g., the maximum photosynthetic capacity at 25°C ( V cmax,25 ) and the leaf respiration rate at 25°C ( R 25 )) following increasingly well‐understood principles. While some land surface models (LSMs) now account for thermal acclimation, they do so by assigning empirical parameterizations for individual plant functional types (PFTs). Here, we have implemented an Eco‐Evolutionary Optimality (EEO)‐based scheme to represent the universal acclimation of photosynthesis and leaf respiration to multiple environmental effects, and that therefore requires no PFT‐specific parameterizations, in a standard version of the widely used LSM, Noah MP. We evaluated model performance with plant trait data from a 5‐year experiment and extensive global field measurements, and carbon flux measurements from FLUXNET2015. We show that observed R 25 and V cmax,25 vary substantially both temporally and spatially within the same PFT ( C.V. >20%). Our EEO‐based scheme captures 62% of the temporal and 70% of the spatial variations in V cmax,25 (73% and 54% of the variations in R 25 ). The standard scheme underestimates gross primary production by 10% versus 2% for the EEO‐based scheme and generates a larger spread in r (correlation coefficient) across flux sites (0.79 ± 0.16 vs. 0.84 ± 0.1, mean ± S.D.). The standard scheme greatly overestimates canopy respiration (bias: ∼200% vs. 8% for the EEO scheme), resulting in less CO 2 uptake by terrestrial ecosystems. Our approach thus simulates climate‐carbon coupling more realistically, with fewer parameters.

Ren, Yanghang [Department of Earth System Science ↗
NASA NTRS2024

Enabling Advanced Snow Physics within Land Surface Models Through an Interoperable Model-Physics Coupling Framework

Accurate estimation of snow accumulation and melt is a critical part of decision-making in snow-dominated watersheds. In this study, we demonstrate a flexible methodology to couple a detailed snow model, Crocus, separately to two different land surface models (LSMs), Noah-MP and Noah. The original LSMs and the coupled models (Noah-MP-Crocus and Noah-Crocus) are used to simulate snow depth, snow water equivalent, and other water and energy states and fluxes. The results of simulations are compared against a wide range of independent gridded and point scale reference datasets. Our results show that coupling the detailed snow model, Crocus, with the LSMs improves the snow depth and snow water equivalent relative to independent observations. Overall, larger improvements are obtained with coupling Crocus to the Noah LSM, with the coupled Noah-Crocus configuration reducing the RMSE and bias of snow depth from 2-12% and 57-75%, respectively, relative to Snow Data Assimilation System (SNODAS) and snow product from the University of Arizona. On the other hand, smaller improvements are obtained by coupling Crocus with Noah-MP. The Coupled Noah-MP-Crocus reduces the snow depth bias but slightly degrades the RMSE of snow depth and snow water equivalent. The corresponding impacts in other water budget terms such as evapotranspiration, soil moisture, and streamflow, however, are mixed, pointing to the significant need to improve the coupling assumptions of these processes within land models. Overall, the interoperable coupling framework demonstrated here offers the opportunity to include more detailed snow physics and processes, and to advance data assimilation systems through improved exploitation of information from snow remote sensing instruments.

Snow model↗
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.↗
NASA NTRS2011

Incorporating JULES into NASA's Land Information System (LIS) and Investigations of Land-Atmosphere Coupling

NASA's Land Information System (LIS; lis.gsfc.nasa.gov) is a flexible land surface modeling and data assimilation framework developed over the past decade with the goal of integrating satellite- and ground-based observational data products and advanced land surface modeling techniques to produce optimal fields of land surface states and fluxes. LIS features a high performance and flexible design, and operates on an ensemble of land surface models for extension over user-specified regional or global domains. The extensible interfaces of LIS allow the incorporation of new domains, land surface models (LSMs), land surface parameters, meteorological inputs, data assimilation and optimization algorithms. In addition, LIS has also been demonstrated for parameter estimation and uncertainty estimation, and has been coupled to the Weather Research and Forecasting (WRF) mesoscale model. A visiting fellowship is currently underway to implement JULES into LIS and to undertake some fundamental science on the feedbacks between the land surface and the atmosphere. An overview of the LIS system, features, and sample results will be presented in an effort to engage the community in the potential advantages of LIS-JULES for a range of applications. Ongoing efforts to develop a framework for diagnosing land-atmosphere coupling will also be presented using the suite of LSM and PBL schemes available in LIS and WRF along with observations from the U. S .. Southern Great Plains. This methodology provides a potential pathway to study factors controlling local land-atmosphere coupling (LoCo) using the LIS-WRF system, which will serve as a testbed for future experiments to evaluate coupling diagnostics within the community.

Santanello, Joseph↗
NASA NTRS2012

P69 Using the NASA-Unified WRF to Assess the Impacts of Real-Time Vegetation on Simulations of Severe Weather

Since June 2010, the NASA Short-term Prediction Research and Transition (SPoRT; Goodman et al. 2004; Darden et al. 2010; Stano et al. 2012; Fuell et al. 2012) Center has been generating a real-time Normalized Difference Vegetation Index (NDVI) and corresponding Green Vegetation Fraction (GVF) composite based on reflectances from NASA s Moderate Resolution Imaging Spectroradiometer (MODIS) instrument. This dataset is generated at 0.01 resolution across the Continental United States (CONUS), and updated daily. The goal of producing such a vegetation dataset is to improve over the default climatological GVF dataset in land surface and numerical weather prediction models, in order to have better simulations of heat and moisture exchange between the land surface and the planetary boundary layer. Details on the SPoRT/MODIS vegetation composite algorithm are presented in Case et al. (2011). Vegetation indices such as GVF and Leaf Area Index (LAI) are used by land surface models (LSMs) to represent the horizontal and vertical density of plant vegetation (Gutman and Ignatov 1998), in order to calculate transpiration, interception and radiative shading. Both of these indices are related to the NDVI; however, there is an inherent ambiguity in determining GVF and LAI simultaneously from NDVI, as described in Gutman and Ignatov (1998). One practice is to specify the LAI while allowing the GVF to vary both spatially and temporally, as is done in the Noah LSM (Chen and Dudhia 2001; Ek et al. 2003). Operational versions of Noah within several of the National Centers for Environmental Prediction (NCEP) global and regional modeling systems hold the LAI fixed, while the GVF varies according to a global monthly climatology. This GVF climatology was derived from NDVI data on the NOAA Advanced Very High Resolution Radiometer (AVHRR) polar orbiting satellite, using information from 1985 to 1991 (Gutman and Ignatov 1998; Jiang et al. 2010). Representing data at the mid-point of every month, the climatological dataset is on a grid with 0.144 (~16 km) spatial resolution and is distributed with the community WRF model (Ek et al. 2003; Jiang et al. 2010; Skamarock et al. 2008).

Case, Jonathan L.↗
NASA NTRS2021

Land Surface Modeling Over the Dry Chaco: The Impact of Model Structures, and Soil, Vegetation and Land Cover Parameters

In this study, we tested the impact of a revised set of soil, vegetation and land cover parameters on the performance of three different state-of-the-art land surface models (LSMs) within the NASA Land Information System (LIS). The impact of this revision was tested over the South American Dry Chaco, an ecoregion characterized by deforestation and forest degradation since the 1980s. Most large-scale LSMs may lack the ability to correctly represent the ongoing deforestation processes in this region, because most LSMs use climatological vegetation indices and static land cover information. The default LIS parameters were revised with improved soil parameters, satellite-based interannually varying vegetation indices (leaf area index and green vegetation fraction) instead of climatological vegetation indices, and yearly land cover information instead of static land cover. A relative comparison in terms of water budget components and “efficiency space” for various baseline and revised experiments showed that large regional and long-term differences in the simulated water budget partitioning relate to different LSM structures, whereas smaller local differences resulted from updated soil, vegetation and land cover parameters. Furthermore, the different LSM structures redistributed water differently in response to these parameter updates. A time-series comparison of the simulations to independent satellite-based estimates of evapotranspiration and brightness temperature (Tb) showed that no LSM setup significantly outperformed another for the entire region and that not all LSM simulations improved with updated parameter values. However, the revised soil parameters generally reduced the bias between simulated surface soil moisture and pixel-scale in situ observations and the bias between simulated Tb and regional Soil Moisture Ocean Salinity (SMOS) observations. Our results suggest that the different hydrological responses of various LSMs to vegetation changes may need further attention to gain benefits from vegetation data assimilation.

Michiel Maertens↗
DOE OSTI2026

A multi‐variable framework for selecting WRF physics configurations at convection‐permitting scales: An Amazon wet‐season case study

Tropical convection over rainforests modulates atmospheric circulation and the energy and hydrological cycles across multiple scales. However, the scarcity of observations still limits our understanding of these processes. Although numerical models are utilized to investigate atmospheric physical processes, their performance depends on the choice of parameterizations and grid resolution. Here, in this work, we introduce and apply a multi‐variable, multi‐physics framework to evaluate and rank the Advanced Research Weather Research and Forecasting (WRF–ARW) configurations at 1‐km resolution over the central Amazon during the wet season. A 48‐member ensemble combines three land‐surface models (LSMs), four planetary boundary layer (PBL), and four microphysics (MP) schemes. Model performance for seven convection‐related near‐surface and boundary‐layer variables is assessed using Taylor diagrams and the Taylor skill score (TSS), analysis of variance (ANOVA)‐based sensitivity metrics, and non‐parametric rank tests. We then construct a combined, weighted TSS to identify configurations that are comparatively robust across variables. Results showed that most configurations reproduce near‐surface temperature, sensible and latent heat fluxes, and boundary‐layer height reasonably well, whereas humidity and rainfall remain challenging. LSM choice has the strongest impact on the surface fluxes and a secondary influence on near‐surface temperature and humidity, PBL schemes dominate boundary‐layer height, and MP schemes exert the largest control on rainfall. No single configuration is optimal for all variables, but the combination of Noah (LSM), Yonsei University (PBL), and Morrison (MP) emerges as the most robust configuration for this case, with the WRF single‐moment six‐class scheme (WSM6) providing a competitive, computationally cheaper MP alternative. The framework is general and can be applied to other regions, seasons, and convective regimes.

Amazon rainforest↗