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DOE OSTI2022

Classification of Cloud Particle Imagery from Aircraft Platforms Using Convolutional Neural Networks

Abstract A vast amount of ice crystal imagery exists from a variety of field campaign initiatives that can be utilized for cloud microphysical research. Here, nine convolutional neural networks are used to classify particles into nine regimes on over 10 million images from the Cloud Particle Imager probe, including liquid and frozen states and particles with evidence of riming. A transfer learning approach proves that the Visual Geometry Group (VGG-16) network best classifies imagery with respect to multiple performance metrics. Classification accuracies on a validation dataset reach 97% and surpass traditional automated classification. Furthermore, after initial model training and preprocessing, 10 000 images can be classified in approximately 35 s using 20 central processing unit cores and two graphics processing units, which reaches real-time classification capabilities. Statistical analysis of the classified images indicates that a large portion (57%) of the dataset is unusable, meaning the images are too blurry or represent indistinguishable small fragments. In addition, 19% of the dataset is classified as liquid drops. After removal of fragments, blurry images, and cloud drops, 38% of the remaining ice particles are largely intersecting the image border (≥10% cutoff) and therefore are considered unusable because of the inability to properly classify and dimensionalize. After this filtering, an unprecedented database of 1 560 364 images across all campaigns is available for parameter extraction and bulk statistics on specific particle types in a wide variety of storm systems, which can act to improve the current state of microphysical parameterizations.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

Power System Waveform Classification Using Time-Frequency and CNN

Many modern reclosers and circuit breakers have microprocessor relays that record waveforms of system events. In some cases, utilities may record a half-a-dozen event captures for every event. This is thousands of events per year. The industry needs faster, more automated, more conclusive, and easy-to-use systems that can process massive amounts of event recordings without extensive input/support from power system engineers. To address the need for a commercially viable solution that can classify waveform data, energies were directed to develop a universal neural network (NN) structure (deep learning algorithm) that works for a wide variety of system event types. The structure that showed the most promise was one that included the use of spectrograms. The technique has shown positive results in audio engineering, particularly with respect to speech recognition. A waveform signature could be treated as a spoken word like audio waveforms for specific things such as “YES” or “UP”. No two people produce the exact same waveform when speaking each of these words, but audio processing algorithms based on spectrograms and convolutional neural networks (CNN) can still distinguish the word regardless of the speaker. No two circuits produce the exact same waveform for a given event, but the NN can be trained to classify the event type regardless of the circuit or location on the circuit. A Power System Neural Network (PSNN) has been developed to use a CNN to classify events within waveform data for power systems. The waveform is converted to an array of values by way of spectrograms and interpreted as an image. This image is passed into the CNN. The test results on independent simulated test and validation datasets show greater than 99% accuracy. While the results thus far are based on simulated data, the performance of the PSNN is very promising and should work for a wide variety of power system conditions of interest. Ultimately, much of the custom code and tools used today and much of the manual effort expended today may be automated using this PSNN.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2023

The 2021 Blind PVPMC Modeling Intercomparison

This document provides the instructions for participating in the 2021 blind photovoltaic (PV) modeling intercomparison organized by the PV Performance Modeling Collaborative (PVPMC). It describes the system configurations, metadata, and other information necessary for the modeling exercise. The practical details of the validation datasets are also described. The datasets were published online in open access in April 2023, after completing the analysis of the results.

14 SOLAR ENERGY↗

DOE OSTI2023

Seasonal forecasting skill for the High Mountain Asia region in the Goddard Earth Observing System

Seasonal variability of the global hydrologic cycle directly impacts human activities, including hazard assessment and mitigation, agricultural decisions, and water resources management. This is particularly true across the High Mountain Asia (HMA) region, where availability of water resources can change depending on local seasonality of the hydrologic cycle. Forecasting the atmospheric states and surface conditions, including hydrometeorologically relevant variables, at subseasonal-to-seasonal (S2S) lead times of weeks to months is an area of active research and development. NASA's Goddard Earth Observing System (GEOS) S2S prediction system has been developed with this research goal in mind. Here, we benchmark the forecast skill of GEOS-S2S (version 2) hydrometeorological forecasts at 1–3-month lead times in the HMA region, including a portion of the Indian subcontinent, during the retrospective forecast period, 1981–2016. To assess forecast skill, we evaluate 2 m air temperature, total precipitation, fractional snow cover, snow water equivalent, surface soil moisture, and terrestrial water storage forecasts against the Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) and independent reanalysis data, satellite observations, and data fusion products. Anomaly correlation is highest when the forecasts are evaluated against MERRA-2 and particularly in variables with long memory in the climate system, likely due to the similar initial conditions and model architecture used in GEOS-S2S and MERRA-2. When compared to MERRA-2, results for the 1-month forecast skill range from an anomaly correlation of R anom =0.18 for precipitation to R anom =0.62 for soil moisture. Anomaly correlations are consistently lower when forecasts are evaluated against independent observations; results for the 1-month forecast skill range from R anom =0.13 for snow water equivalent to R anom =0.24 for fractional snow cover. We find that, generally, hydrometeorological forecast skill is dependent on the forecast lead time, the memory of the variable within the physical system, and the validation dataset used. Overall, these results benchmark the GEOS-S2S system's ability to forecast HMA hydrometeorology.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2026

Integration of GOES Data for Solar Resource Assessment of the Contiguous United States

The National Solar Radiation Database (NSRDB), produced by the National Laboratory of the Rockies (NLR), provides high-resolution solar resource data for the contiguous United States (CONUS) using Geostationary Operational Environmental Satellite (GOES) East and West observations. This study evaluates the integration of multi-satellite data within the GOES-East/West overlap regions, where conventional longitude-based selection methods often produce an artificial boundary seam. Our results demonstrate that an advanced blending algorithm, which incorporates sun-satellite scattering angles and satellite viewing zenith angles, improves NSRDB accuracy and creates a spatially continuous dataset. Validation against ground-based irradiance measurements reveals reductions in both percentage error (PE) and normalized Root Mean Square Error (nRMSE), particularly in the central United States. The dynamical integration of multi-satellite data provides a robust foundation for more precise modeling of solar resource and improved spatiotemporal analysis of solar ramp across the CONUS.

14 SOLAR ENERGY↗

NASA NTRS1995

Estimating the urban bias of surface shelter temperatures using upper-air and satellite data. Part 1: Development of models predicting surface shelter temperatures

Multiple regression techniques were used to predict surface shelter temperatures based on the time period 1986-89 using upper-air data from the European Centre for Medium-Range Weather Forecasts (ECMWF) to represent the background climate and site-specific data to represent the local landscape. Global monthly mean temperature models were developed using data from over 5000 stations available in the Global Historical Climate Network (GHCN). Monthly maximum, mean, and minimum temperature models for the United States were also developed using data from over 1000 stations available in the U.S. Cooperative (COOP) Network and comparative monthly mean temperature models were developed using over 1150 U.S. stations in the GHCN. Three-, six-, and full-variable models were developed for comparative purposes. Inferences about the variables selected for the various models were easier for the GHCN models, which displayed month-to-month consistency in which variables were selected, than for the COOP models, which were assigned a different list of variables for nearly every month. These and other results suggest that global calibration is preferred because data from the global spectrum of physical processes that control surface temperatures are incorporated in a global model. All of the models that were developed in this study validated relatively well, especially the global models. Recalibration of the models with validation data resulted in only slightly poorer regression statistics, indicating that the calibration list of variables was valid. Predictions using data from the validation dataset in the calibrated equation were better for the GHCN models, and the globally calibrated GHCN models generally provided better U.S. predictions than the U.S.-calibrated COOP models. Overall, the GHCN and COOP models explained approximately 64%-95% of the total variance of surface shelter temperatures, depending on the month and the number of model variables. In addition, root-mean-square errors (rmse's) were over 3 C for GHCN models and over 2 C for COOP models for winter months, and near 2 C for GHCN models and near 1.5 C for COOP models for summer months.

Epperson, David L.↗

NASA NTRS2000

A Catchment-Based Approach to Modeling Land Surface Processes in a GCM: Parameter Estimation and Model Demonstration - Part 2

The viability of a new catchment-based land surface model (LSM) developed for use with general circulation models is demonstrated. First, simple empirical functions -- tractable enough for operational use in the LSM -- are established that faithfully capture the control of topography on the subgrid variability of soil moisture and the surface water budget, as predicted by theory. Next, the full LSM is evaluated offline. Using forcing and validation datasets developed for PILPS Phase 2c, the minimally calibrated model is shown to reproduce observed evaporation and runoff fluxes successfully in the Red-Arkansas River Basin. A complementary idealized study that employs the range of topographic variability seen over North America demonstrates that the simulated surface water budget does vary strongly with topography, which can, by itself, induce variations in annual evaporation as high as 20%.

Ducharne, Agnes↗

NASA NTRS2010

Evolving the Technical Infrastructure of the Planetary Data System for the 21st Century

The Planetary Data System (PDS) was established in 1989 as a distributed system to assure scientific oversight. Initially the PDS followed guidelines recommended by the National Academies Committee on Data Management and Computation (CODMAC, 1982) and placed emphasis on archiving validated datasets. But overtime user demands, supported by increased computing capabilities and communication methods, have placed increasing demands on the PDS. The PDS must add additional services to better enable scientific analysis within distributed environments and to ensure that those services integrate with existing systems and data. To face these challenges the Planetary Data System (PDS) must modernize its architecture and technical implementation. The PDS 2010 project addresses these challenges. As part of this project, the PDS has three fundamental project goals that include: (1) Providing more efficient client delivery of data by data providers to the PDS (2) Enabling a stable, long-term usable planetary science data archive (3) Enabling services for the data consumer to find, access and use the data they require in contemporary data formats. In order to achieve these goals, the PDS 2010 project is upgrading both the technical infrastructure and the data standards to support increased efficiency in data delivery as well as usability of the PDS. Efforts are underway to interface with missions as early as possible and to streamline the preparation and delivery of data to the PDS. Likewise, the PDS is working to define and plan for data services that will help researchers to perform analysis in cost-constrained environments. This presentation will cover the PDS 2010 project including the goals, data standards and technical implementation plans that are underway within the Planetary Data System. It will discuss the plans for moving from the current system, version PDS 3, to version PDS 4.

Beebe, Reta F.↗

NASA NTRS2011

Fluid Structure Interaction of Parachutes in Supersonic Planetary Entry

A research program to provide physical insight into disk-gap-band parachute operation in the supersonic regime on Mars was conducted. The program included supersonic wind tunnel tests, computational fluid dynamics and fluid structure interaction simulations. Specifically, the nature and cause of the "area oscillation" phenomenon were investigated to determine the scale, aerodynamic, and aero-elastic dependence of the supersonic parachute collapse and re-inflation event. A variety of non-intrusive, temporally resolved, and high resolution diagnostic techniques were used to interrogate the flow and generate validation datasets. The results of flow visualization, particle image velocimetry, load measurements, and photogrammetric reconstruction will be presented. Implications to parachute design, use, and verification will also be discussed.

parachute↗

NASA NTRS2019

Antarctic Ice Shelf Thickness Change from Multimission Lidar Mapping

We calculate rates of ice thickness change and bottom melt for ice shelves in West Antarctica and the Antarctic Peninsula from a combination of elevation measurements from NASA–CECS Antarctic ice mapping campaigns and NASA Operation IceBridge corrected for oceanic processes from measurements and models, surface velocity measurements from synthetic aperture radar, and high-resolution outputs from regional climate models. The ice thickness change rates are calculated in a Lagrangian reference frame to reduce the effects from advection of sharp vertical features, such as cracks and crevasses, that can saturate Eulerian-derived estimates. We use our method over different ice shelves in Antarctica, which vary in terms of size, repeat coverage from airborne altimetry, and dominant processes governing their recent changes. We find that the Larsen-C Ice Shelf is close to steady state over our observation period with spatial variations in ice thickness largely due to the flux divergence of the shelf. Firn and surface processes are responsible for some short-term variability in ice thickness of the Larsen-C Ice Shelf over the time period. The Wilkins Ice Shelf is sensitive to short-timescale coastal and upper-ocean processes, and basal melt is the dominant contributor to the ice thickness change over the period. At the Pine Island Ice Shelf in the critical region near the grounding zone, we find that ice shelf thickness change rates exceed 40m/yr, with the change dominated by strong submarine melting. Regions near the grounding zones of the Dotson and Crosson ice shelves are decreasing in thickness at rates greater than 40m/yr, also due to intense basal melt. NASA–CECS Antarctic ice mapping and NASA Operation IceBridge campaigns provide validation datasets for floating ice shelves at moderately high resolution when coregistered using Lagrangian methods.

Sutterley, Tyler C.↗

NASA NTRS2011

TPSAS-NF1676L-12839-DND

Hyper-spectral remote sensors, such as Atmospheric Infrared Sounder (AIRS) and Infrared Atmospheric Sounding Interferometer (IASI), provides top of atmospheric radiances with high information content on atmospheric and surface properties. In order to analyze these data in real time, fast, and accurate forward and inverse models are needed. We will describe a method of simultaneously retrieving atmospheric temperature, moisture, cloud, and surface properties using all available spectral channels without sacrificing computational speed. The method has been successfully applied to AIRS, IASI, and NAST-I data. By applying the same method to the current and future hyperspectral sounders, the derived products will have less errors due to biases introduced by different retrieval methods. We have compared the retrieved products to radiosondes, aircraft measurements and other validation dataset. Simulations have done to quantify retrieval errors associated with the retrievals. The essence of the method is to convert channel radiance spectra into super-channels by an Empirical Orthogonal Function (EOF) transformation. A Principal Component-based Radiative Transfer Model (PCRTM) developed at NASA Langley Research Center is used to calculate both the super-channel magnitudes and derivatives with respect to atmospheric profiles and other properties. The inversion algorithm is based on a non-linear Levenberg-Marquardt method with climatology covariance matrices and a priori information as constraints. One advantage of this approach is that it uses all information content from the hyper-spectral data so that the retrieval is less sensitive to instrument noise and eliminates the need for selecting a subset of the channels. The PCRTM forward model has also been used to performance end-to-end sensor performance simulations of the Climate Absolute Radiance and Refractivity Observatory (CLARREO).

Xu Liu↗

NASA NTRSDate not supplied

Optical Characterization of DebriSat Fragments in Support of Orbital Debris Environmental Models

The NASA Orbital Debris Program Office (ODPO) develops, maintains, and updates orbital debris environmental models, such as the NASA Orbital Debris Engineering Model (ORDEM), to support satellite designers and operators by estimating the risk from orbital debris impacts on their vehicles in orbit. Updates to ORDEM utilize the most recent validated datasets from radar, optical, and in situ sources to provide estimates of the debris flux as a function of size, material density, impact speed, and direction along a mission orbit. On-going efforts within the NASA ODPO to update the next version of ORDEM include a new parameter that highly affects the damage risk – shape. Shape can be binned by material density and size to better understand the damage assessments on spacecraft. The in situ and laboratory research activities at the NASA ODPO are focused on cataloging and characterizing fragments from a laboratory hypervelocity-impact test using a high-fidelity, mock-up satellite, DebriSat, in controlled and instrumented laboratory conditions. DebriSat is representative of present-day, low Earth orbit satellites, having been constructed with modern spacecraft materials and techniques. The DebriSat fragment ensemble provides a variety of shapes, bulk densities, and dimensions. Fragments down to 2 mm in size are being characterized by their physical and derived properties. A subset of fragments is being analyzed further in NASA’s Optical Measurement Center (OMC) using broadband, bidirectional reflectance measurements to provide insight into the optical-based NASA Size Estimation Model. Additionally, pre-impact spectral measurements on a subset of DebriSat materials were acquired for baseline material characterization. This paper provides an overview of DebriSat, the status of the project, and ongoing fragment characterization efforts within the OMC.

Heather M Cowardin↗

NASA NTRS2021

Optical Characterization of DebriSat Fragments in Support of Orbital Debris Environmental Models

Heather M. Cowardin↗

NASA NTRSDate not supplied

Machine Learning Methods for Estimating Propeller Source Noise Spheres

In this work, several neural network function approximations are compared for inter- polating, storing, and sampling acoustic source spheres with applications to propeller noise estimation. These methods are compared using an acoustic model of the three bladed GL-10 propeller at different flight conditions, with training data generated using NASA’s ANOPP-PAS module. The source spheres used to train the networks capture the tonal propeller noise due to both the blade thickness and loading. This tonal noise prediction method allows the vehicle noise to be estimated for auralization and acoustic control. Three radial basis function neural network architectures are compared in this work. The first two networks directly estimate the parameters of the source sphere at different flight conditions but differ in the number of layers used. The third network estimates the parameters of the source sphere using a weighted combination of spherical basis functions. These networks are trained on numerically generated source spheres, with operating points given in terms of the propeller rotation rate, freestream speed, and propeller angle of attack. The performance of the neural network is determined using a validation dataset of withheld data points. This performance is quantified in terms of the approximation error, training time, and sample time. The third network, which estimates the weights of the spherical basis functions, performs the best in both average and maximum approximation errors in all cases. This network’s worst case performance is 5.6 % relative dif- ference of a model parameter associated with acoustic pressure. The direct estimation network with a single layer has the worst approximation error in all cases. Additionally, the spherically defined network has the slowest sample time at 0.05 seconds per thousand points. Both direct estimation methods produce a thousand sample points in approximately 0.001 seconds.

Acoustics↗

NASA NTRS2020

Evaluating China's fossil-fuel CO2 emissions from a comprehensive dataset of nine inventories

China's fossil-fuel CO2 (FFCO2) emissions accounted for approximately 28 % of the global total FFCO2 in 2016. An accurate estimate of China's FFCO2 emissions is a prerequisite for global and regional carbon budget analyses and the monitoring of carbon emission reduction efforts. However, significant uncertainties and discrepancies exist in estimations of China's FFCO2 emissions due to a lack of detailed traceable emission factors (EFs) and multiple statistical data sources. Here, we evaluated China's FFCO2 emissions from nine published global and regional emission datasets. These datasets show that the total emissions increased from 3.4 (3.0–3.7) in 2000 to 9.8 (9.2–10.4) Gt CO2 per yr in 2016. The variations in these estimates were largely due to the different EF (0.491–0.746 t C per t of coal) and activity data. The large-scale patterns of gridded emissions showed a reasonable agreement, with high emissions being concentrated in major city clusters, and the standard deviation mostly ranged from 10 % to 40 % at the provincial level. However, patterns beyond the provincial scale varied significantly, with the top 5 % of the grid level accounting for 50 %–90 % of total emissions in these datasets. Our findings highlight the significance of using locally measured EF for Chinese coal. To reduce uncertainty, we recommend using physical CO2 measurements and use these values for dataset validation, key input data sharing (e.g., point sources), and finer-resolution validations at various levels.

Pengfei Han↗

NASA NTRSDate not supplied

Mass Inferencing Model Creation and Deployment to the RASSOR Lunar Excavation Robot

The Regolith Advanced Surface Systems Operations Robot (RASSOR) Excavator is a teleoperated mobile robotic platform with a unique space regolith excavation capability. The Intelligent Capabilities Enhanced RASSOR research project developed functionality for inferencing regolith mass ingested during RASSOR operation, enhancing RASSOR’s ability to successfully complete ISRU missions. To teleoperate or run autonomously, it is crucial for the quantity of regolith mass ingested by RASSOR to be available as a system state for efficient operation. For example, during autonomous operation, RASSOR should navigate and move to a processing plant to offload the collected regolith when the drums are full; without knowledge of how much mass is in the drums, this type of high-level planning is not possible. Four distinct modeling approaches were employed in developing a mass inferencing approach that could work on RASSOR. All take in system states, such as arm/drum positions, velocities, currents, voltages, and robot pose, and output a mass prediction for each set of the robot’s bucket drums.1) A neural network model that takes a vector of normalized system states; 2) A model that uses the integrated power consumption of an arm-raise (normalized by velocity); 3) A model that uses average drum current over a variable length interval of the drum disengaged from the surface; and 4) A real-time estimation model that aggregates excavation drum current. The developed models run in real time, outputting predictions for the front and rear drums, timestamp of the last prediction, and total mass in RASSOR’s drums. Further testing is required to validate the arm-raise model (2), though initial tests indicate reasonable performance (<10% mean error) on the hardware. The linear fit of average drum-current model (3) had a front value of r^2=0.99 and a rear value of r^2=0.98 on the validation dataset. This model currently has the best performance on unseen data. The real time model (4) is still in development, though initial results on a small subset of the training data show that it has high accuracy in predicting the increase in mass during excavation. Though work remains to be done with deploying a high-fidelity model to the physical system that makes predictions with error below the desired threshold, the modular architecture for model development allows quick adjustment of parameters to increase model fidelity. This architecture can also be adapted to use lunar excavation data to create models that are reflective of RASSOR’s dynamics when operating on the lunar surface. The results are promising as it has been shown that models can be developed that accurately estimate excavated regolith mass.

rassor↗

NASA NTRSDate not supplied

Updates on the Debrisat Hypervelocity Experiment and Characterization of Fragments in Support of Environmental Models

To develop, maintain, and update orbital debris environmental and break-up models, such as the NASA Orbital Debris Engineering Model (ORDEM) and the NASA Standard Satellite Breakup Model (SSBM), the NASA Orbital Debris Program Office (ODPO) relies on the most recent validated datasets from various measurement sources, both laboratory and environmental. One key project that will provide insight for break-up events using modern-day spacecraft materials and construction techniques is the DebriSat laboratory hypervelocity-impact test. Based on the mass of the target, the projectile, and the impact velocity, the expected number of fragments greater than 2 mm, generated using the NASA SSBM, was estimated to be close to 85,000. To date, the DebriSat fragment database continues to grow, with over 200,000 fragments collected that will help inform updates to the SSBM. Additionally, the growing fragment ensemble will support key parameters for the next release of NASA’s environmental models, employing fragment shapes, densities, and size distributions. To further support these environmental models, specifically the size parameter used in ground-based optical measurements, optical characterization on a subset of DebriSat fragments is being conducted in ODPO’s Optical Measurement Center (OMC). Broadband bidirectional reflectance distribution (BRDF) measurements will provide insight into the optical-based NASA Size Estimation Model (OSEM). The OSEM equates an object’s brightness to size (e.g., diameter of a disk or sphere) given several assumed parameters, including a defined phase function, albedo, and range. To address the first defined parameter, the ODPO has been using ray-tracing software to simulate light conditions in the OMC and to generate phase functions (i.e., specular, Lambertian, and experimentally-derived) of known shapes, materials, and sizes. This simulated data, in addition to the experimentally derived measurements collected in the OMC, will aid in determining whether a new phase function would be suitable for an updated OSEM. The OSEM also assumes a single-value albedo, thus pre-impact spectral measurements on a subset of DebriSat materials were acquired for baseline material characterization and to provide insight into spacecraft material taxonomies. This DebriSat spectral data, along with spectral measurements from other known spacecraft material samples, will allow for better analyses of albedo variations and the effect on size calculations of defined laboratory targets, thus further supporting OSEM updates. This paper provides an overview of DebriSat, the status of the project, updates on the parameter distributions, an overview of the NASA SSBM, and ongoing fragment characterization efforts within the OMC.

Heather Cowardin↗

NASA NTRS2022

The Joint Assimilation of Remotely Sensed Leaf Area Index and Surface Soil Moisture into a Land Surface Model

This work tests the hypothesis that jointly assimilating satellite observations of leaf area index and surface soil moisture into a land surface model improves the estimation of land vegetation and water variables. An Ensemble Kalman Filter is used to test such hypothesis across the Contiguous United States during April 2015 – December 2018. The performance of the proposed methodology is assessed for several modeled vegetation and water variables (evapotranspiration, net ecosystem exchange, and soil moisture) in terms of random errors and anomaly correlation coefficients against a set of independent validation datasets (i.e., Global Land Evaporation Amsterdam Model, FLUXCOM, and International Soil Moisture Network). Results show that the assimilation of leaf area index mostly improves the estimation of evapotranspiration and net ecosystem exchange, whereas the assimilation of surface soil moisture alone improves surface soil moisture content, especially in the western US, in terms of both root mean squared error and anomaly correlation coefficient. The joint assimilation of vegetation and soil moisture information combines the results of individual vegetation and soil moisture assimilations and reduces errors (and increases correlations with the reference datasets) in evapotranspiration, net ecosystem exchange, and surface soil moisture simulated by the land surface model. However, because soil moisture satellite observations only provide information on the water content in the top 5 cm of the soil column, the impact of the proposed data assimilation technique on root zone soil moisture is limited. This work moves one step forward in the direction of improving our estimation and understanding of land surface interactions using a multi-variate data assimilation approach, which can be particularly useful in regions of the world where ground observations are sparse or missing altogether.

Data assimilation↗