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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 775 records · Page 43
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Spatiotemporal Variability in Wind Turbine Blade Leading Edge Erosion

Wind turbine blade leading edge erosion (LEE) reduces energy production and increases wind energy operation and maintenance costs. Degradation of the blade coating and ultimately damage to the underlying blade structure are caused by collisions of falling hydrometeors with rotating blades. The selection of optimal methods to mitigate/reduce LEE are critically dependent on the rates of coating fatigue accumulation at a given location and the time variance in the accumulation of material stresses. However, no such assessment currently exists for the United States of America (USA). To address this research gap, blade coating lifetimes at 883 sites across the USA are generated based on high-frequency (5-min) estimates of material fatigue derived using a mechanistic model and robust meteorological measurements. Results indicate blade coating failure at some sites in as few as 4 years, and that the frequency and intensity of material stresses are both highly episodic and spatially varying. Time series analyses indicate that up to one-third of blade coating lifetime is exhausted in just 360 5-min periods in the Southern Great Plains (SGP). Conversely, sites in the Pacific Northwest (PNW) exhibit the same level of coating lifetime depletion in over three times as many time periods. Thus, it may be more cost-effective to use wind turbine deregulation (erosion-safe mode) for damage reduction and blade lifetime extension in the SGP, while the application of blade leading edge protective measures may be more appropriate in the PNW. Annual total precipitation and mean wind speed are shown to be poor predictors of blade coating lifetime, re-emphasizing the need for detailed modeling studies such as that presented herein.

Pryor, Sara C. (ORCID:0000000348473440)↗
NASA NTRS1981

The photometric center of the Gegenschein

Model calculations are employed to assess two factors that determine the position of the photometric center of the Gegenschein: (1) the increased scattering efficiency of the interplanetary dust near backscattering (at scattering angles of 165 to 180 deg); and (2) the spatial density distribution of the dust. Computer-generated brightness contours are used to examine which of these two factors dominates. It is found that the effect of the enhanced scattering of light by dust in the backscattering region is more important than the effect of the spatial density distribution of the dust. This means that the photometric center should be observed at the antisolar point at all times, except possibly when the antisolar point is at its maximum displacement from the symmetry plane.

Misconi, N. Y.↗
NASA NTRS1991

A tentative identification of methanol as the progenitor of the 3.52-micron emission feature in several comets

Continuum emission has been detected during 3.44-3.64 micron spectral region observations of Comets P/Brorsen-Metcalf, Okazaki-Levy-Rudenko, and Levy. The spatial brightness profile of the 3.52-micron emission feature was measured and found to be consistent with uniform outflow from a central, unresolved source; the molecule generating the emission is either directly sublimated from the cometary surface or is a short-lived parent specie's dissociation product. The modeled IR spectrum of CH3OH implies the consistency of the observed spectra with a methanol origin for the 3.52 emission.

Hoban, Susan↗
NASA NTRS2003

Modeling Charge Collection in Detector Arrays

A detector array charge collection model has been developed for use as an engineering tool to aid in the design of optical sensor missions for operation in the space radiation environment. This model is an enhancement of the prototype array charge collection model that was developed for the Next Generation Space Telescope (NGST) program. The primary enhancements were accounting for drift-assisted diffusion by Monte Carlo modeling techniques and implementing the modeling approaches in a windows-based code. The modeling is concerned with integrated charge collection within discrete pixels in the focal plane array (FPA), with high fidelity spatial resolution. It is applicable to all detector geometries including monolithc charge coupled devices (CCDs), Active Pixel Sensors (APS) and hybrid FPA geometries based on a detector array bump-bonded to a readout integrated circuit (ROIC).

Hardage, Donna↗
NASA NTRS2009

Evaluating the Impact of Land Use Change on Submerged Aquatic Vegetation Stressors in Mobile Bay

Alabama coastal systems have been subjected to increasing pressure from a variety of activities including urban and rural development, shoreline modifications, industrial activities, and dredging of shipping and navigation channels. The impacts on coastal ecosystems are often observed through the use of indicator species. One such indicator species for aquatic ecosystem health is submerged aquatic vegetation (SAV). Watershed and hydrodynamic modeling has been performed to evaluate the impact of land use change in Mobile and Baldwin counties on SAV stressors and controlling factors (temperature, salinity, and sediment) in Mobile Bay. Watershed modeling using the Loading Simulation Package in C++ (LSPC) was performed for all watersheds contiguous to Mobile Bay for land use scenarios in 1948, 1992, 2001, and 2030. Landsat-derived National Land Cover Data (NLCD) were used in the 1992 and 2001 simulations after having been reclassified to a common classification scheme. The Prescott Spatial Growth Model was used to project the 2030 land use scenario based on current trends. The LSPC model simulations provided output on changes in flow, temperature, and sediment for 22 discharge points into the Bay. Theses results were inputted in the Environmental Fluid Dynamics Computer Code (EFDC) hydrodynamic model to generate data on changes in temperature, salinity, and sediment on a grid with four vertical profiles throughout Mobile Bay. The changes in the aquatic ecosystem were used to perform an ecological analysis to evaluate the impact on SAV habitat suitability. This is the key product benefiting the Mobile Bay coastal environmental managers that integrates the influences of temperature, salinity, and sediment due to land use driven flow changes with the restoration potential of SAVs.

Al-Hamdan, Mohammad↗
DOE OSTI2022

Modeling Spatial Distribution of Snow Water Equivalent by Combining Meteorological and Satellite Data with Lidar Maps

Abstract An accurate characterization of the water content of snowpack, or snow water equivalent (SWE), is necessary to quantify water availability and constrain hydrologic and land surface models. Recently, airborne observations (e.g., lidar) have emerged as a promising method to accurately quantify SWE at high resolutions (scales of ∼100 m and finer). However, the frequency of these observations is very low, typically once or twice per season in the Rocky Mountains of Colorado. Here, we present a machine learning framework that is based on random forests to model temporally sparse lidar-derived SWE, enabling estimation of SWE at unmapped time points. We approximated the physical processes governing snow accumulation and melt as well as snow characteristics by obtaining 15 different variables from gridded estimates of precipitation, temperature, surface reflectance, elevation, and canopy. Results showed that, in the Rocky Mountains of Colorado, our framework is capable of modeling SWE with a higher accuracy when compared with estimates generated by the Snow Data Assimilation System (SNODAS). The mean value of the coefficient of determination R 2 using our approach was 0.57, and the root-mean-square error (RMSE) was 13 cm, which was a significant improvement over SNODAS (mean R 2 = 0.13; RMSE = 20 cm). We explored the relative importance of the input variables and observed that, at the spatial resolution of 800 m, meteorological variables are more important drivers of predictive accuracy than surface variables that characterize the properties of snow on the ground. This research provides a framework to expand the applicability of lidar-derived SWE to unmapped time points. Significance Statement Snowpack is the main source of freshwater for close to 2 billion people globally and needs to be estimated accurately. Mountainous snowpack is highly variable and is challenging to quantify. Recently, lidar technology has been employed to observe snow in great detail, but it is costly and can only be used sparingly. To counter that, we use machine learning to estimate snowpack when lidar data are not available. We approximate the processes that govern snowpack by incorporating meteorological and satellite data. We found that variables associated with precipitation and temperature have more predictive power than variables that characterize snowpack properties. Our work helps to improve snowpack estimation, which is critical for sustainable management of water resources.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Myco-CORPSE simulations assessing mycorrhizal carbon allocation across U.S. forests and global change scenarios

Plants allocate a substantial portion of their fixed carbon belowground to mycorrhizal fungi in exchange for nutrients and other benefits. However, most current ecosystem models omit mycorrhizal processes, limiting our ability to predict plant–soil carbon dynamics under environmental change. To address this gap, we used a mycorrhiza-explicit soil biogeochemical model, Myco-CORPSE (Mycorrhizal Carbon, Organisms, Rhizosphere, and Protection in the Soil Environment), to simulate tree carbon allocation to arbuscular mycorrhizal (AM) and ectomycorrhizal (ECM) fungi in temperate forests.The dataset includes outputs from two sets of model simulations:1. Perturbation experiments: Simulations across gradients of ECM dominance (0–100%), nitrogen deposition, soil temperature, and net primary productivity (NPP) to test how these factors affect mycorrhizal C allocation and nutrient cycling.2. FIA-based simulations: Model applications to over 1,800 U.S. forest sites using site-specific data from the U.S. Forest Inventory and Analysis (FIA) program, including vegetation composition, mycorrhizal type, climate, litter traits, soil properties, and N deposition.Model outputs include simulated mycorrhizal carbon allocation and related biogeochemical variables, such as soil and microbial carbon and nitrogen stocks. Data are provided in CSV format and organized by experiment type (in separate ZIP files). Python scripts for running simulations, plotting, and spatial mapping are also included and organized similarly. No proprietary software is required. These outputs support a peer-reviewed study and were used to generate figures and tables in the associated publication.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2016

Using NASA's GeneLab for VESGEN Systems Analysis of Vascular Phenotypes from Stress and Other Signaling Pathways

One fundamental requirement shared by humans with all higher terrestrial life forms, including other vertebrates, insects, and higher land plants, is a complex, fractally branching vascular system. NASA's VESsel GENeration Analysis (VESGEN) software maps and quantifies vascular trees, networks, and tree-network composites according to weighted physiological rules such as vessel connectivity, tapering and bifurcational branching. According to fluid dynamics, successful vascular transport requires a complex distributed system of highly regulated laminar flow. Microvascular branching rules within vertebrates, dicot leaves and the other organisms therefore display many similarities. A unifying perspective is that vascular patterning offers a useful readout of molecular signaling that necessarily integrates these complex pathways. VESGEN has elucidated changes in vascular pattern resulting from inflammatory, developmental and other signaling within numerous tissues and major model organisms studied for Space Biology. For a new VESGEN systems approach, we analyzed differential gene expression in leaves of Arabidopsis thaliana reported by GeneLab (GLDS-7) for spaceflight. Vascularrelated changes in leaf gene expression were identified that can potentially be phenocopied by mutants in ground-based experiments. To link transcriptional, protein and other molecular change with phenotype, alterations in the spatial and dynamic dimensions of vascular patterns for Arabidopsis leaves and other model species are being co-localized with signaling patterns of single molecular expression analyzed as information dimensions. Previously, Drosophila microarray data returned from space suggested significant changes in genes related to wing venation development that include EGF, Notch, Hedghog, Wingless and Dpp signaling. Phenotypes of increasingly abnormal ectopic wing venation in the (non-spaceflight) Drosophila wing generated by overexpression of a Notch antagonist were analyzed by VESGEN. Other VESGEN research applications include the mouse retina, GI and coronary vessels, avian placental analogs and translational studies in the astronaut retina related to health challenges for long-duration missions.

Venation↗
NASA NTRS2014

Wake Vortex Detection: Phased Microphone vs. Linear Infrasonic Array

Sensor technologies can make a significant impact on the detection of aircraft-generated vortices in an air space of interest, typically in the approach or departure corridor. Current state-of-the art sensor technologies do not provide three-dimensional measurements needed for an operational system or even for wake vortex modeling to advance the understanding of vortex behavior. Most wake vortex sensor systems used today have been developed only for research applications and lack the reliability needed for continuous operation. The main challenges for the development of an operational sensor system are reliability, all-weather operation, and spatial coverage. Such a sensor has been sought for a period of last forty years. Acoustic sensors were first proposed and tested by National Oceanic and Atmospheric Administration (NOAA) early in 1970s for tracking wake vortices but these acoustic sensors suffered from high levels of ambient noise. Over a period of the last fifteen years, there has been renewed interest in studying noise generated by aircraft wake vortices, both numerically and experimentally. The German Aerospace Center (DLR) was the first to propose the application of a phased microphone array for the investigation of the noise sources of wake vortices. The concept was first demonstrated at Berlins Airport Schoenefeld in 2000. A second test was conducted in Tarbes, France, in 2002, where phased microphone arrays were applied to study the wake vortex noise of an Airbus 340. Similarly, microphone phased arrays and other opto-acoustic microphones were evaluated in a field test at the Denver International Airport in 2003. For the Tarbes and Denver tests, the wake trajectories of phased microphone arrays and lidar were compared as these were installed side by side. Due to a built-in pressure equalization vent these microphones were not suitable for capturing acoustic noise below 20 Hz. Our group at NASA Langley Research Center developed and installed an infrasonic array at the Newport News-Williamsburg International Airport early in the year 2013. A pattern of pressure burst, high-coherence intervals, and diminishing-coherence intervals was observed for all takeoff and landing events without exception. The results of a phased microphone vs. linear infrasonic array comparison will be presented.

Shams, Qamar A.↗
DOE OSTI2022

Joint physics-based and data-driven time-lapse seismic inversion: Mitigating data scarcity

In carbon capture and sequestration (CCS), developing rapid and effective imaging techniques is crucial for real-time monitoring of the spatial and temporal dynamics of CO 2 propagation during/after injection. With continuing improvements in computational power and data storage, data-driven techniques based on machine learning (ML) have been effectively applied to seismic inverse problems. In particular, ML helps alleviate the ill-posedness and high computational cost of full-waveform inversion (FWI). However, such data-driven inversion techniques require massive high-quality training data sets to ensure prediction accuracy, which hinders their application to time-lapse monitoring of CO 2 sequestration. We propose an efficient “hybrid” time-lapse workflow that combines physics-based FWI and data-driven ML inversion. The scarcity of the available training data is addressed by developing a new data-generation technique with physics constraints. The method is vali dated on a synthetic CO 2 -sequestration model based on the Kimberlina storage reservoir in California. The proposed approach is shown to synthesize a large volume of high-quality, physically realistic training data, which is critically important in accurately characterizing the CO 2 movement in the reservoir. In conclusion, the developed hybrid methodology can also simultaneously predict the variations in velocity and saturation and achieve high spatial resolution in the presence of realistic noise in the data.

58 GEOSCIENCES↗
NASA NTRS1994

Physical processes in eclipsing pulsars: Eclipse mechanisms and diagnostics

We investigate how the radio emission of a pulsar interacts with plasma derived from a stellar companion. Various physical mechanisms that can cause radio pulse eclipse are discussed, and predictions are made for the polarization properties of the emergent radio wave. We consider eclipses by a wind from the stellar companion, by a stellar magnetosphere, or by material entrained in the pulsar wind. Eclipses due to refraction require either a relatively high plasma density or a sharp edge to the plasma distribution. The conditions that must prevail for free-free absorption to be effective in eclipsing a radio beam are also outlined. Pulse smearing may be important at higher frequencies; related eclipse mechanisms include pulse spreading due to a rapidly changing electron column, and scattering by Langmuir turbulence. The high brightness temperature radio beam can generate its own plasma turbulence via a number of nonlinear parametric instabilities, such as the instability associated with stimulated Raman scattering. When the plasma turbulence is heavily damped, the radio bean can still undergo induced Compton scattering. Stimulated scattering effects such as these are very sensitive to the presence of narrow-band substructure in the pulsar radio emission. Finally, we consider the possibility that plasma derived from a stellar companion may mix with the relativistic pulsar wind and cause cyclotron absorption at low radio frequencies. Even if the cyclotron optical depth is small, fluctuations in the emergent polarization of the radio beam on the timescale of a few seconds are a very sensitive probe of the spatial structure of the magnetic field in the pulsar wind. The current observational properties of two known eclipsing pulsar systems, PSR 1957+20 and PSR 1744-24A, are used to construct tentative eclipse models. The favored model for PSR 1957+20 is cyclotron or synchrotron absorption by plasma embedded in the pulsar wind combined with pulse smearing at high frequency, and the favored model for PSR 1744-24A is backscattering off plasma turbulence generated by the stimulated Raman scattering parametric instability. Pulsar eclipses promise to provide a good diagnostic of pulsar winds and possible of the pulse emission mechanism.

Thompson, C.↗
DOE OSTI2023

Two-dimensional patterning of mesoscale fibers using acoustophoresis

The performance of functional composites can rely critically on the arrangement of secondary phases; for example, patterned networks of conductive particles can impart anisotropic thermal, electric or ionic conductivity while preserving flexibility in the matrix. We demonstrate the use of standing acoustic waves to generate periodic patterns of short fibers. We extend the range of possible patterns with the first demonstration of both rectangular grids and arrays of octagons interspersed with rectangles. These newly demonstrated patterns are rationalized using theoretical models of acoustic forces and torques on fibers that account for two-dimensional spatial variations arising from applied acoustic fields. The models enable simulations of fiber motion, which are used to (i) map out final fiber positions as a function of initial position and orientation, and (ii) corroborate experiments visualizing fiber motion and final patterns. This approach provides a fast and accurate way to predict emergent fiber patterns as a function of excitation modality and fiber length. The theory and experiments clearly indicate strong coupling between the length of the fibers and the spacing of the acoustic nodes. Furthermore, this coupling is used to estimate reductions in percolation thresholds associated with the ratio of fiber length and acoustic wavelength.

25 ENERGY STORAGE↗
DOE OSTI2019

PreMevE: New Predictive Model for Megaelectron-volt Electrons inside Earth's Outer Radiation Belt

Here, this work designs a new model called PreMevE to predict storm–time distributions of relativistic electrons within Earth's outer radiation belt. This model takes advantage of the cross–energy, –L–shell, and –pitch–angle coherence associated with wave–electron resonant interactions, ingests observations from belt boundaries—mainly by NOAA POES in low–Earth–orbits (LEOs), and provides high–fidelity nowcast (multiple–hour prediction) and forecast (> ~1 day) of MeV electron fluxes over L–shells between 2.8–7 through linear prediction filters. PreMevE can not only reliably anticipate incoming enhancements of MeV electrons during storms with at least 1–day forewarning time, but also accurately specify the evolving event–specific electron spatial distributions afterwards. The performance of PreMevE is assessed against long–term in situ data from one Van Allen Probe and a LANL geosynchronous satellite. Furthermore, this new model enhances our preparedness for severe MeV electron events in the future, and further adds new science utility to existing and next–generation LEO space infrastructure.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Improving E3SM Land Model Photosynthesis Parameterization via Satellite SIF, Machine Learning, and Surrogate Modeling

The parameterization of key photosynthesis parameters is one of the key uncertain sources in modeling ecosystem gross primary productivity (GPP). Solar-induced chlorophyll fluorescence (SIF) offers a good proxy for GPP since it marks the actual process of photosynthesis; while machine learning (ML) provides a robust approach to model the GPP-SIF relationship. Here, we trained the boosted regressing tree (BRT) and the Random Forest ML models with Greenhouse Gases Observing Satellite SIF data and in situ GPP observations from 49 eddy covariance towers. These trained ML GPP-SIF models were fed into the Energy Exascale Earth System Model (E3SM) Land Model (ELM) to generate ELM-simulated global SIF estimates, which were then benchmarked against satellite SIF observations with a surrogate modeling approach. Our results indicated good modeling performance of the ML-based GPP-SIF relationship. The ELM model when fed with the ML GPP-SIF models also can well predict the spatial-temporal variations in SIF. We also found high model accuracy for the surrogate modeling. Model parameter sensitivity analysis suggested that the fraction of leaf nitrogen in RuBisCO (flnr) is the most sensitive parameter to the SIF; other sensitive parameters include the Ball-Berry stomatal conductance slope (mbbopt) and the vcmax entropy (vcmaxse). The posterior uncertainty in simulated GPP was greatly reduced after benchmarking, and the model produced improved spatial patterns of mean GPP relative to FLUXCOM GPP. Our integrated approach provides a new avenue for improving land models and using remote-sensing SIF, which can be further improved in the future with more ground- and satellite-based observations.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Chemically tuned intermediate band states in atomically thin Cu x GeSe/SnS quantum material for photovoltaic applications

A new generation of quantum material derived from intercalating zerovalent atoms such as Cu into the intrinsic van der Waals gap at the interface of atomically thin two-dimensional GeSe/SnS heterostructure is designed, and their optoelectronic features are explored for next-generation photovoltaic applications. Advanced ab initio modeling reveals that many-body effects induce intermediate band (IB) states, with subband gaps (~0.78 and 1.26 electron volts) ideal for next-generation solar devices, which promise efficiency greater than the Shockley-Queisser limit of ~32%. The charge carriers across the heterojunction are both energetically and spontaneously spatially confined, reducing nonradiative recombination and boosting quantum efficiency. Using this IB material in a solar cell prototype enhances absorption and carrier generation in the near-infrared to visible light range. Tuning the active layer’s thickness increases optical activity at wavelengths greater than 600 nm, achieving ~190% external quantum efficiency over a broad solar wavelength range, underscoring its potential in advanced photovoltaic technology.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Deep Learning-based Non-Stationary Bias Correction (NSBC)

This work develops the NSBC (non-stationary bias correction) methodology to correct temperature projection bias from E3SM. The NSBC deep learning framework consists of a three-part architecture: an auto-encoder for compressing the spatial information, an LSTM for predicting annual temperature mean, and a U-Net for capturing the residual bias in temperature. The non-stationary bias correction (NSBC) framework can correct the non-stationarity of the biases of the climate models, which significantly improves the accuracy of future temperature prediction and improves the overestimation of extreme high temperatures that many existing bias correction methods suffer from. Getting started 1. Obtain the historical climate simulation and observation data. The E3SM simulation data are available through https://aims2.llnl.gov/search/cmip6/. The pseudo observations, the Geophysical Fluid Dynamics Laboratory (GFDL)-ESM4 model (Krasting et al., 2018) are available through https://aims2.llnl.gov/search/cmip6/. The spatial resolution of E3SM and pseudo observation datasets are both regridded to a common 1° resolution grid using conservative interpolation. The regridded E3SM and pseudo observation with 1° resolution can be found throught ./data/. 2. Train the Auto-encoder model. Python 0-autoencoder.py 3. Train the LSTM Python 1-LSTM.py 4. Generate the annual mean temperature based on trained LSTM Python 2-generate_annual_mean_LSTM.py 5. Train the U-Net. Python 3-unet.py 6. Evaluation and compared with the baseline Python 4_evaluation.py Is there a deadline approaching that requires the release of yo

Lucas, Donald↗
NASA NTRS1981

Computing aerodynamic sound using advanced statistical turbulence theories

It is noted that the calculation of turbulence-generated aerodynamic sound requires knowledge of the spatial and temporal variation of Q sub ij (xi sub k, tau), the two-point, two-time turbulent velocity correlations. A technique is presented to obtain an approximate form of these correlations based on closure of the Reynolds stress equations by modeling of higher order terms. The governing equations for Q sub ij are first developed for a general flow. The case of homogeneous, stationary turbulence in a unidirectional constant shear mean flow is then assumed. The required closure form for Q sub ij is selected which is capable of qualitatively reproducing experimentally observed behavior. This form contains separation time dependent scale factors as parameters and depends explicitly on spatial separation. The approximate forms of Q sub ij are used in the differential equations and integral moments are taken over the spatial domain. The velocity correlations are used in the Lighthill theory of aerodynamic sound by assuming normal joint probability.

Hecht, A. M.↗
NASA NTRS2010

Regional Model Nesting Within GFS Daily Forecasts Over West Africa

The study uses the RM3, the regional climate model at the Center for Climate Systems Research of Columbia University and the NASA/Goddard Institute for Space Studies (CCSR/GISS). The paper evaluates 30 48-hour RM3 weather forecasts over West Africa during September 2006 made on a 0.5 grid nested within 1 Global Forecast System (GFS) global forecasts. September 2006 was the Special Observing Period #3 of the African Monsoon Multidisciplinary Analysis (AMMA). Archived GFS initial conditions and lateral boundary conditions for the simulations from the US National Weather Service, National Oceanographic and Atmospheric Administration were interpolated four times daily. Results for precipitation forecasts are validated against Tropical Rainfall Measurement Mission (TRMM) satellite estimates and data from the Famine Early Warning System (FEWS), which includes rain gauge measurements, and forecasts of circulation are compared to reanalysis 2. Performance statistics for the precipitation forecasts include bias, root-mean-square errors and spatial correlation coefficients. The nested regional model forecasts are compared to GFS forecasts to gauge whether nesting provides additional realistic information. They are also compared to RM3 simulations driven by reanalysis 2, representing high potential skill forecasts, to gauge the sensitivity of results to lateral boundary conditions. Nested RM3/GFS forecasts generate excessive moisture advection toward West Africa, which in turn causes prodigious amounts of model precipitation. This problem is corrected by empirical adjustments in the preparation of lateral boundary conditions and initial conditions. The resulting modified simulations improve on the GFS precipitation forecasts, achieving time-space correlations with TRMM of 0.77 on the first day and 0.63 on the second day. One realtime RM3/GFS precipitation forecast made at and posted by the African Centre of Meteorological Application for Development (ACMAD) in Niamey, Niger is shown.

Druyan, Leonard M.↗