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NASA NTRS2008

Generating Vegetation Leaf Area Index Earth System Data Record from Multiple Sensors: Implementation, Analysis and Validation - Part 2

The evaluation of a new global monthly leaf area index (LAI) data set for the period July 1981 to December 2006 derived from AVHRR Normalized Difference Vegetation Index (NDVI) data is described. The physically based algorithm is detailed in the first of the two part series. Here, the implementation, production and evaluation of the data set are described. The data set is evaluated both by direct comparisons to ground data and indirectly through inter-comparisons with similar data sets. This indirect validation showed satisfactory agreement with existing LAI products, importantly MODIS, at a range of spatial scales, and significant correlations with key climate variables in areas where temperature and precipitation limit plant growth. The data set successfully reproduced well-documented spatio-temporal trends and inter-annual variations in vegetation activity in the northern latitudes and semi-arid tropics. Comparison with plot scale field measurements over homogeneous vegetation patches indicated a 7% underestimation when all major vegetation types are taken into account. The error in mean values obtained from distributions of AVHRR LAI and high-resolution field LAI maps for different biomes is within 0.5 LAI for six out of the ten selected sites. These validation exercises though limited by the amount of field data, and thus less than comprehensive, indicated satisfactory agreement between the LAI product and field measurements. Overall, the intercomparison with short-term LAI data sets, evaluation of long term trends with known variations in climate variables, and validation with field measurements together build confidence in the utility of this new 26 year LAI record for long term vegetation monitoring and modeling studies.

Ganguly, Sangram↗

NASA NTRS2004

Evaluation and Application of Satellite-Based Latent Heating Profile Estimation Methods

In recent years, methods for estimating atmospheric latent heating vertical structure from both passive and active microwave remote sensing have matured to the point where quantitative evaluation of these methods is the next logical step. Two approaches for heating algorithm evaluation are proposed: First, application of heating algorithms to synthetic data, based upon cloud-resolving model simulations, can be used to test the internal consistency of heating estimates in the absence of systematic errors in physical assumptions. Second, comparisons of satellite-retrieved vertical heating structures to independent ground-based estimates, such as rawinsonde-derived analyses of heating, provide an additional test. The two approaches are complementary, since systematic errors in heating indicated by the second approach may be confirmed by the first. A passive microwave and combined passive/active microwave heating retrieval algorithm are evaluated using the described approaches. In general, the passive microwave algorithm heating profile estimates are subject to biases due to the limited vertical heating structure information contained in the passive microwave observations. These biases may be partly overcome by including more environment-specific a priori information into the algorithm s database of candidate solution profiles. The combined passive/active microwave algorithm utilizes the much higher-resolution vertical structure information provided by spaceborne radar data to produce less biased estimates; however, the global spatio-temporal sampling by spaceborne radar is limited. In the present study, the passive/active microwave algorithm is used to construct a more physically-consistent and environment-specific set of candidate solution profiles for the passive microwave algorithm and to help evaluate errors in the passive algorithm s heating estimates. Although satellite estimates of latent heating are based upon instantaneous, footprint- scale data, suppression of random errors requires averaging to at least half-degree resolution. Analysis of mesoscale and larger space-time scale phenomena based upon passive and passive/active microwave heating estimates from TRMM, SSMI, and AMSR data will be presented at the conference.

Olson, William S.↗

NASA NTRSDate not supplied

Analysis and Prediction of VFR Vs IFR Traffic Behavior to Support Uncrewed Aircraft Flight Operations at Regional Airports

Uncrewed Aircraft flight operations at a regional airport will be affected by the uncertainty in the Visual Flight Rules traffic around. This paper analyzes Visual Flight Rules traffic behavior, compares it with Instrument Flight Rules traffic and develops traffic prediction methods to support uncrewed aircraft flight operations. The spatio-temporal distribution of traffic operating under Visual Flight Rules and Instrument Flight Rules was analyzed from one month of historical track data around Fort Worth Alliance airport as a representative regional airport. The traffic behavior was visualized as occupancy maps generated at different altitudes. The Instrument Flight Rules traffic was concentrated in fewer regions of the airspace along structured routes where the risk of interacting with one or more flights reached close to fifty percent in some areas. Visual Flight Rules traffic was spread over more regions, mostly segregated from the Instrument Flight Rules regions, and the risk of interaction was lower reaching up to twenty-five percent on average over the month in some regions. The interaction risk was predicted using predictive occupancy maps over multiple time horizons and conditioned on time and partial observation of traffic in the vicinity. The month-to-month predictability of Visual Flight Rules risk was lower than that of the Instrument Flight Rules traffic, consistently over all the conditions analyzed. However, the prediction and its accuracy were demonstrated to be sensitive to the conditions used. The predictive models generated can be used to support both strategic planning and in-flight decision-making by uncrewed aircraft during flight operations while maintaining an acceptable risk of interacting with other traffic.

VFR↗

NASA NTRSDate not supplied

Analysis and Prediction of VFR Vs IFR Traffic Behavior to Support Uncrewed Aircraft Flight Operations at Regional Airports

VFR↗

NASA NTRS2011

Variation of Accumulation Rates Over the Last Eight Centuries on the East Antarctic Plateau Derived from Volcanic Signals in Ice Cores

Volcanic signatures in ice-core records provide an excellent means to date the cores and obtain information about accumulation rates. From several ice cores it is thus possible to extract a spatio-temporal accumulation pattern. We show records of electrical conductivity and sulfur from firn cores from the Norwegian-USA scientific traverse during the International Polar Year 2007-2009 (IPY) through East Antarctica. Major volcanic eruptions are identified and used to assess century-scale accumulation changes. The largest changes seem to occur in the most recent decades with accumulation over the period 1963- 2007/08 being up to 25 % different from the long-term record. There is no clear overall trend, some sites show an increase in accumulation over the period 1963 to present while others show a decrease. Almost all of the sites above 3200 m above sea level (asl) suggest a decrease. These sites also show a significantly lower accumulation value than large-scale assessments both for the period 1963 to present and for the long-term mean at the respective drill sites. The spatial accumulation distribution is influenced mainly by elevation and distance to the ocean (continentality), as expected. Ground-penetrating radar data around the drill sites show a spatial variability within 10-20 % over several tens of kilometers, indicating that our drill sites are well representative for the area around them. Our results are important for large-scale assessments of Antarctic mass balance and model validation.

Anschuetz, H.↗

NASA NTRS1994

Directions of Moving Plaids is Biased by Asymmetric Viewing Windows

Directionally selective V1 neurons are tuned to particular spatio-temporal frequencies and respond to local 1-D edge motion. At least some MT neurons however appear to respond to the actual velocity of moving 2D patterns. To better understand how the 1D local motion information available from VI is integrated to derive a 2D velocity signal we investigated human perception of moving plaids, 2-D patterns composed of the sum of two 1-D sinusoidal gratings of different orientations. We measured the effect of the shape of the viewing window on the perceived direction of plaid motion. The plaids were spatially windowed by 2-D Gaussians with unequal standard deviations (sigma 1, sigma 2). Four observers indicated perceived direction by adjusting a pointer. Direction errors were measured as a function of the difference between window orientation and true plaid direction (DELTA THETA) for several grating spatial frequencies (SF = 0.3, 0.6, 1.2 c/d) and window aspect ratios (AR = sigma 1/sigma 2 = 1, 1.4, 2, 4). Observers showed systematic errors in perceived direction (approx. 15 for AR = 4 and SF = 0.6 c/d) that peaked at DELTA THETA approx. 40. The errors increased for increasing aspect ratio and decreased for increasing spatial frequencies (or number of cycles). These results show that despite the unambiguous motion of the plaids, under these conditions human misperceive the motion. These data constrain models of motion integration from V1 to MT and, in particular, are inconsistent with algorithms that use either the Intersection of Constraints rule or cross correlation to compute the perceived direction of motion.

Beutter, B. R.↗

NASA NTRS2019

Global analysis and forecasts of carbon monoxide on Mars

Spatial and temporal variations in the Martian carbon monoxide (CO) cycle have been investigated through combining Compact Reconnaissance Imaging Spectrometer for Mars (CRISM) retrievals of carbon monoxide mixing ratio and Mars Climate Sounder (MCS) temperature profiles with a Martian global circulation model (GCM) to produce the first global reanalysis of the Martian CO cycle. The reanalysis reduces the root mean square error between the forecast and CRISM CO retrievals by a factor of 2–4, dependent on the time of year. Forecasts initiated from the reanalysis show an improved match to standalone CRISM CO retrievals from northern winter solstice to northern summer solstice, indicating the benefit of data assimilation in simulating the CO abundance. The northern summer solstice CO minimum between 10°S – 50°S in the CRISM CO retrievals is found to be caused by a suppression of CO-enriched air leaking from the Hellas and Argyre basins, and is also strongly influenced by the amount of carbon dioxide condensing at the time. The GCM is the first reported to simulate the local CO minimum however it is weaker in strength, as CO-enriched air is also released northward in the area of Argyre basin in the GCM because of a shifted boundary between the southern polar vortex and mid-latitudes. The reanalysis, as a result of the assimilation of MCS temperature profiles, indicates that the polar vortex boundary is northward of Argyre basin and hence no northerly transport of CO-enriched air should be present over this location. Differences in local CO abundance between the reanalysis and GCM are also evident in spatio-temporal regions where no nearby CRISM CO retrievals are available as a result of alterations in local circulation and the sublimation/condensation of carbon dioxide through the combined assimilation of CRISM CO retrievals alongside MCS temperature profiles, illustrating how constraints can be imposed indirectly on the CO cycle through the powerful technique of data assimilation.

James A. Holmes↗

NASA NTRS2023

Wind Profiling With the Airborne Doppler Aerosol Wind Lidar During the 2022 Convective Processes Experiment

The 2017 Decadal Survey for Earth Science and Applications from Space (ESAS 2017) identifies a critical need for improving our understanding of Planetary Boundary Layer (PBL) processes and air-surface fluxes as well as why clouds, convection, and heavy precipitation occur when and where they do. Lidars are uniquely capable of collecting high precision and high spatio-temporal observations that have been used for atmospheric process studies from the ground, aircraft, and space. The wind lidar team at the NASA Langley Research Center (LaRC) started the development of Doppler wind lidar more than a decade ago to demonstrate technologies required for an Earth-orbiting system to globally measure wind profiles. Since then, an airborne Doppler Aerosol WiNd (DAWN) lidar system has been developed and participated in a series of field campaigns. The Doppler Aerosol WiNd (DAWN) lidar uses atmospheric aerosol motion to derive vertical profiles of horizontal wind speed and direction beneath the aircraft. In September 2022, DAWN, along with a suite of other instruments, was flown on a NASA DC-8 as part of The Convective Processes EXperiment – Cabo Verde (CPEX-CV) field campaign. A main objective of CPEX was to obtain a comprehensive set of temperature, humidity and, particularly, wind observations over tropical waters in undisturbed conditions, Saharan dust outbreaks, and in the vicinity of scattered through organized deep convection in all phases of the convective life cycle. DAWN collected data for approximately 90 hours across 13 CPEX-CV science flights. Airborne Vertical Atmospheric Profiling System (AVAPS) dropsondes were dropped throughout the flight for profiling the atmosphere and validating the DAWN instrument. DAWN had co-located data with 347 AVAPS NRD41 dropsondes, providing 32,117 vertical levels for a comprehensive validation of DAWN wind retrievals. DAWN showed very good agreement with dropsondes of ~0.2 m/s bias and ~1.8 m/s RMS. Given this agreement, DAWN is considered to be a worthy reference dataset, and its retrievals have been compared to winds derived from GOES Atmospheric Motion Vectors (AMVs), Advanced Scatterometer winds (ASCAT), and model data from GFS, GEOS, and MERRA-2 to better understand the quality of our current models and satellite wind observations. The proposed presentation will provide a brief description of the DAWN instrument, discuss the synergistic observations collected across a wide range of atmospheric conditions sampled during the CPEX-CV flights, and a summary of comparisons between DAWN, GOES AMV, ASCAT, and model analyses/predictions, with an emphasis on the PBL.

DAWN↗

NASA NTRSDate not supplied

An Overview of the NASA ADM-Aeolus Cal/Val Test Flight Campaign

Lidars are uniquely capable of collecting high precision and high spatio-temporal observations that have been used for atmospheric process studies from the ground, aircraft, and space for over 30 years. The Atmospheric Dynamics Mission Aeolus (ADM-Aeolus), the first-ever spaceborne Doppler wind lidar, was developed by the European Space Agency and launched in August 2018. The ADM-Aeolus Atmospheric Laser Doppler Instrument (ALADIN) observes profiles of the component of the wind vector and aerosol optical properties along the instrument’s line-of-sight direction on a global scale. Two airborne lidar systems have been developed at NASA Langley Research Center in recent years that collect measurements in support of several NASA Earth Science Division focus areas. The Doppler Aerosol WiNd (DAWN) lidar measures vertical profiles of horizontal wind speed and direction. The High Altitude Lidar Observatory (HALO) measure high resolution profiles of atmospheric water vapor (WV) and aerosol and cloud optical properties. Though new space-based missions with advanced capabilities such as Aeolus are continuously being conceived and developed, there are limitations in terms of spatial and vertical detail and measurement precision that can be accomplished from space, thus airborne remote sensing observations like those from DAWN and HALO are required to fill these observational gaps as well as to calibrate and validate space-based measurements. Over a two-week period in April 2019 during their Aeolus Cal/Val Test Flight campaign, NASA conducted five research flights over the Eastern Pacific Ocean with the DC-8 aircraft to 1) demonstrate the DAWN Doppler wind lidar, HALO high spectral resolution lidar aerosol/cloud measurement and differential absorption lidar WV measurement capabilities across a range of atmospheric conditions, 2) demonstrate Aeolus Cal/Val flight strategies and compare DAWN and HALO measurements with Aeolus to gain an initial perspective of Aeolus performance, and 3) demonstrate how atmospheric dynamic processes can be resolved and better understood through simultaneous observations of wind, WV, and aerosol profile observations, coupled with numerical model and other remote sensing observations. This paper provides a brief description of the DAWN and HALO instruments, discusses the synergistic observations collected across a wide range of atmospheric conditions sampled during the DC-8 flights, and a summary of the validation of DAWN, HALO, and Aeolus observations and comparisons.

Kristopher M. Bedka↗

DOE OSTI2024

A regularized-interface method as a unified formulation for simulations of high-pressure multiphase flows

The injection of multi-species fluids into high-pressure and high-temperature environments beyond the species' critical points is commonly found in engineering applications. At these conditions, for immiscible species, both subcritical interfacial dynamics and supercritical mixing can coexist due to variations in temperature around the mixture critical point. The modeling of these complex transcritical phenomena for large-scale configurations is so far not possible. To address this issue, we propose the Regularized-Interface Method (RIM) as a unified formulation that can describe both sub- and supercritical processes as well as the transition between them. The proposed method is derived via filtering of the nanoscale interface-resolving formulation based on van der Waals' linear gradient theory. Thus, this approach allows for the consistent modeling of interfacial dynamics that vanishes at supercritical conditions, while significantly reducing the temporal and spatial resolution constraints of the original nanoscale formulation. The resulting RIM formulation is examined in interface-capturing simulations of sub-, trans-, and supercritical fuel injection processes, involving droplets and jets. Furthermore, these results highlight the importance of resolving spatio-temporal transitions from subcritical interfacial dynamics to supercritical mixing in high-pressure multiphase simulations, in contrast to commonly employed diffused-interface methods, where interfacial dynamics are often neglected.

Interface capturing↗

DOE OSTI2025

Unsupervised Clustering of Microseismic Events and Focal Mechanism Analysis at the CO 2 Injection Site in Decatur, Illinois

Characterization of induced microseismicity at a carbon dioxide (CO 2 ) storage site is critical for preserving reservoir integrity and mitigating seismic hazards. We apply a multilevel machine learning (ML) approach that combines the nonnegative matrix factorization and hidden Markov model to extract spectral representations of microseismic events and cluster them to identify seismic patterns at the Illinois Basin-Decatur Project. Unlike traditional waveform correlation methods, this approach leverages spectral characteristics of first arrivals to improve event classification and detect previously undetected planes of weakness. By integrating ML-based clustering with focal mechanism analysis, we resolve small-scale fault structures that are below the detection limits of conventional seismic imaging. Our findings reveal temporal bursts of microseismicity associated with brittle failure, providing insights into the spatio-temporal evolution of fault reactivation during CO 2 injection. This approach enhances seismic monitoring capabilities at CO 2 injection sites by improving fault characterization beyond the resolution of standard geophysical surveys.

Willis, Rachel Marie [Sandia National Laboratories↗

DOE OSTI2026

Bayesian Physics Informed Spatio-Temporal Network for Streamflow Data Imputation

Reliable reconstruction of incomplete streamflow records is critical for improving hydrological forecasting, flood preparedness, and water resource management. However, large observational gaps and uncertainties in governing physical parameters limit the accuracy of traditional statistical and machinelearning imputation frameworks. To address these challenges, we develop a Bayesian Physics-Informed Spatio-Temporal Network (BPI-STNet) that jointly captures spatial and temporal dependencies while enforcing hydrologic consistency through embedded physical constraints. The framework integrates a GraphSAGE-LSTM architecture to model spatial connectivity across gauges and temporal flow dynamics, coupled with a Bayesian update mechanism to estimate uncertain parameters in a simplified water-balance framework. Unlike conventional physics-informed networks that rely on sampling-based posterior estimation, BPI-STNet derives an analytic solution to the inverse problem, allowing closed-form Bayesian updates of uncertain parameters Λ={α,β,k} using Gaussian priors and likelihoods. Applied to daily observations from the Susquehanna River Basin (1980-2022), BPI-STNet achieves substantial improvements over a purely data-driven RGNN baseline, which reduced RMSE by 23 % and MAE by 9 %, and achieving an average NSE values up to 0.96. The results demonstrate that coupling Bayesian inference with physics-informed learning yields physically consistent, uncertainty-aware reconstructions that preserve the temporal persistence and statistical distribution of observed flows. The proposed framework establishes a generalizable paradigm for data-sparse hydrologic systems where both data fidelity and physical interpretability are essential.

Krishnan Kutty Ambika, Anukesh [ORNL] (ORCID:00000↗

DOE OSTI2025

Self-Supervised T-GCN for Detection of Disturbance and Propagation in Power Grid

Urban power systems increasingly rely on dense sensing to monitor grid reliability, yet disturbance labels are scarce and events are rare. We present a self-supervised spatio-temporal method that detects, localizes, and characterizes grid frequency disturbances across urban areas using only unlabeled data. Our approach trains a tiny Temporal Graph Convolutional Network (T-GCN) to forecast per-site frequency residuals (deviation from 60 Hz). The sensor graph is constructed directly from signals using pre-event Pearson correlation with a cross-correlation lag penalty without geocoding. At inference, node-level anomalies are the model's forecast errors; region-level alarms arise from connected components of high-score nodes. We estimate disturbance propagation by computing per-node arrival times (first persistent exceedance), then fit a planar or time-of-arrival model to obtain direction, speed, and an epicenter proxy. With only three real events collected at decisecond resolution across U.S. cities, we evaluate the T-GCN and report time-to-detect, footprint size, and propagation consistency. We further show that short-window embeddings from the T-GCN's hidden states enable few-shot event-vs-background recognition via a simple prototypical classifier. Despite minimal data and no labels, our system yields fast, spatially coherent detection and interpretable propagation maps, offering a practical, lightweight pathway to city-scale grid resilience analytics.

Niu, Haoran [ORNL] (ORCID:0000000155228297)↗

NASA NTRS1992

Classification with spatio-temporal interpixel class dependency contexts

A contextual classifier which can utilize both spatial and temporal interpixel dependency contexts is investigated. After spatial and temporal neighbors are defined, a general form of maximum a posterior spatiotemporal contextual classifier is derived. This contextual classifier is simplified under several assumptions. Joint prior probabilities of the classes of each pixel and its spatial neighbors are modeled by the Gibbs random field. The classification is performed in a recursive manner to allow a computationally efficient contextual classification. Experimental results with bitemporal TM data show significant improvement of classification accuracy over noncontextual pixelwise classifiers. This spatiotemporal contextual classifier should find use in many applications of remote sensing, especially when the classification accuracy is important.

Jeon, Byeungwoo↗

NASA NTRS1994

Optimizing tertiary storage organization and access for spatio-temporal datasets

We address in this paper data management techniques for efficiently retrieving requested subsets of large datasets stored on mass storage devices. This problem represents a major bottleneck that can negate the benefits of fast networks, because the time to access a subset from a large dataset stored on a mass storage system is much greater that the time to transmit that subset over a network. This paper focuses on very large spatial and temporal datasets generated by simulation programs in the area of climate modeling, but the techniques developed can be applied to other applications that deal with large multidimensional datasets. The main requirement we have addressed is the efficient access of subsets of information contained within much larger datasets, for the purpose of analysis and interactive visualization. We have developed data partitioning techniques that partition datasets into 'clusters' based on analysis of data access patterns and storage device characteristics. The goal is to minimize the number of clusters read from mass storage systems when subsets are requested. We emphasize in this paper proposed enhancements to current storage server protocols to permit control over physical placement of data on storage devices. We also discuss in some detail the aspects of the interface between the application programs and the mass storage system, as well as a workbench to help scientists to design the best reorganization of a dataset for anticipated access patterns.

Chen, Ling Tony↗

NASA NTRS2006

Imaging Spectroscopy of Sunspots using IBIS

We use the Interferometric BIdimensional Spectrometer (IBIS) of the INAF/Arcetri Astrophysical Observatory and installed at the National Solar Observatory (NSO) Dunn Solar Telescope, to understand the structure of sunspots. These high resolution observations were acquired on 2004 July 30-31, of active region NOAA 10654, using the high order NSO adaptive optics system. We map the spatio-temporal variation of the penumbral Doppler signatures in three spectral lines, FeI 6301.5\AA, FeII 7224.4\AA, and CaII 8542.6\AA, from the photosphere to the chromosphere. From a 70-minute temporal average of individual 32-second cadence Doppler observations we find that the averaged velocities decrease with height, about 3.5 times larger in the deeper photosphere (FeII 7224.4\AA; height-of-formation $\approx$\50 km) than in the upper photosphere FeI 6301.5\AA; height-of-formation $\approx$\350 km), There is a remarkable coherence of Doppler signals over the height difference of 300 km. From a highspeed animation of the Doppler sequence we find evidence for what appears to be ejection of high speed gas concentrations from edges of penumbral filaments into the surrounding granular photosphere. The Evershed flow persists a few arcseconds beyond the traditionally demarcated penumbra-granulation boundary. We present these and other results and discuss the implications of these measurements for sunspot models

Balasubramaniam, K. S.↗

NASA NTRS2006

3-D Structure of Sunspots using Imaging Spectroscopy

We use the Interferometric BIdimensional Spectrometer (IBIS) of the INAF/Arcetri Astrophysical Observatory and installed at the National Solar Observatory (NSO) Dunn Solar Telescope, to understand the structure of sunspots. Using the spectral lines FeI 6301.5 A, FeII 7224.4 A, and CaII 8542.6 A, we examine the spectroscopic variation of sunspot penumbral and umbral structures at the heights of formation of these lines. These high resolution observations were acquired on 2004 July 30-31, of active region NOAA 10654, using the high order NSO adaptive optics system. We map the spatio-temporal variation of Doppler signatures in these spectral lines, from the photosphere to the chromosphere. From a 70-minute temporal average of individual 32-second cadence Doppler observations we find that the averaged velocities decrease with height, about 3.5 times larger in the deeper photosphere (FeII 7224.4 A; height-of-formation approx. 50 km) than in the upper photosphere FeI 6301.5 A; height-of-formation approx. 350 km), There is a remarkable coherence of Doppler signals over the height difference of 300 km. From a high-speed animation of the Doppler sequence we find evidence for what appears to be ejection of high speed gas concentrations from edges of penumbral filaments into the surrounding granular photosphere. The Evershed flow persists a few arcseconds beyond the traditionally demarcated penumbra-granulation boundary. We present these and other results and discuss the implications of these measurements for sunspot models.

Balasubramaniam, K. S.↗

NASA NTRS2010

Integrating Enhanced Grace Terrestrial Water Storage Data Into the U.S. and North American Drought Monitors

NASA's Gravity Recovery and Climate Experiment (GRACE) satellites measure time variations nf the Earth's gravity field enabling reliable detection of spatio-temporal variations in total terrestrial water storage (TWS), including ground water. The U.S. and North American Drought Monitors are two of the premier drought monitoring products available to decision-makers for assessing and minimizing drought impacts, but they rely heavily on precipitation indices and do not currently incorporate systematic observations of deep soil moisture and groundwater storage conditions. Thus GRACE has great potential to improve the Drought Monitors hy filling this observational gap. Horizontal, vertical and temporal disaggregation of the coarse-resolution GRACE TWS data has been accomplished by assimilating GRACE TWS anomalies into the Catchment Land Surface Model using ensemble Kalman smoother. The Drought Monitors combine several short-term and long-term drought indices and indicators expressed in percentiles as a reference to their historical frequency of occurrence for the location and time of year in question. To be consistent, we are in the process of generating a climatology of estimated soil moisture and ground water based on m 60-year Catchment model simulation which will subsequently be used to convert seven years of GRACE assimilated fields into soil moisture and groundwater percentiles. for systematic incorporation into the objective blends that constitute Drought Monitor baselines. At this stage we provide a preliminary evaluation of GRACE assimilated Catchment model output against independent datasets including soil moisture observations from Aqua AMSR-E and groundwater level observations from the U.S. Geological Survey's Groundwater Climate Response Network.

Housborg, Rasmus↗