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NASA NTRS1990

An experimental and analytical investigation of isolated rotor flap-lag stability in forward flight

The flap-lag stability of an isolated hingeless rotor is investigated, both experimentally and analytically, in hover and in forward flight. The effects of forward flight aerodynamics on regressing lead-lag mode stability are the focus of the investigation. The soft-inplane, three-bladed, isolated model rotor was operated untrimmed at advance ratios from hover to 0.55 and at shaft angles as high as 20 deg. The experimental data base includes forward flight damping data for two lead-lag natural frequencies, for three values of collective pitch, and for both zero and full-lag structural coupling. With the aid of computerized symbolic manipulation, a rigid-blade lag-flap model analysis was developed to calculate the Floquent eigenvalues and to identify the modes. Good correlation is shown for some cases, but other cases show large discrepancies between the theory and experiment.

Gaonkar, Gopal H.↗
NASA NTRS2011

Development of a Multivariable Parametric Cost Analysis for Space-Based Telescopes

Over the past 400 years, the telescope has proven to be a valuable tool in helping humankind understand the Universe around us. The images and data produced by telescopes have revolutionized planetary, solar, stellar, and galactic astronomy and have inspired a wide range of people, from the child who dreams about the images seen on NASA websites to the most highly trained scientist. Like all scientific endeavors, astronomical research must operate within the constraints imposed by budget limitations. Hence the importance of understanding cost: to find the balance between the dreams of scientists and the restrictions of the available budget. By logically analyzing the data we have collected for over thirty different telescopes from more than 200 different sources, statistical methods, such as plotting regressions and residuals, can be used to determine what drives the cost of telescopes to build and use a cost model for space-based telescopes. Previous cost models have focused their attention on ground-based telescopes due to limited data for space telescopes and the larger number and longer history of ground-based astronomy. Due to the increased availability of cost data from recent space-telescope construction, we have been able to produce and begin testing a comprehensive cost model for space telescopes, with guidance from the cost models for ground-based telescopes. By separating the variables that effect cost such as diameter, mass, wavelength, density, data rate, and number of instruments, we advance the goal to better understand the cost drivers of space telescopes.. The use of sophisticated mathematical techniques to improve the accuracy of cost models has the potential to help society make informed decisions about proposed scientific projects. An improved knowledge of cost will allow scientists to get the maximum value returned for the money given and create a harmony between the visions of scientists and the reality of a budget.

Dollinger, Courtnay↗
NASA NTRS2021

Probabilistic Machine Learning Estimation of Ocean Mixed Layer Depth from Dense Satellite and Sparse In-Situ Observations

The ocean mixed layer plays an important role in the coupling between the upper ocean and atmosphere across a wide range of time scales. Estimation of the variability of the ocean mixed layer is therefore important for atmosphere-ocean prediction and analysis. The increasing coverage of in situ Argo profile data allows for an increasingly accurate analysis of the mixed layer depth (MLD) variability associated with deviations from the seasonal climatology. However, sampling rates are not sufficient to fully resolve subseasonal (<90 day) MLD variability. Yet, many multivariate observations-based analyses include implicit modeled subseasonal MLD variability. One analysis method is optimal interpolation of in situ data, but the interior analysis can be improved by leveraging surface data with regression or variational approaches. Here, we demonstrate how machine learning methods and satellite sea surface temperature, salinity, and height facilitate MLD estimation in a pilot study of two regions: the mid-latitude southern Indian and the eastern equatorial Pacific Oceans. We construct multiple machine learning architectures to produce weekly 1/2° gridded MLD anomaly fields (relative to a monthly climatology) with uncertainty estimates. We test multiple traditional and probabilistic machine learning techniques to compare both accuracy and probabilistic calibration. We validate our methodology by applying it to ocean model simulations. We find that incorporating sea surface data through a machine learning model improves the performance of spatiotemporal MLD variability estimation compared to optimal interpolation of Argo observations alone. These preliminary results are a promising first step for the application of machine learning to MLD prediction.

Machine Learning↗
NASA NTRS1994

Canopy reflectance modelling of semiarid vegetation

Three different types of remote sensing algorithms for estimating vegetation amount and other land surface biophysical parameters were tested for semiarid environments. These included statistical linear models, the Li-Strahler geometric-optical canopy model, and linear spectral mixture analysis. The two study areas were the National Science Foundation's Jornada Long Term Ecological Research site near Las Cruces, NM, in the northern Chihuahuan desert, and the HAPEX-Sahel site near Niamey, Niger, in West Africa, comprising semiarid rangeland and subtropical crop land. The statistical approach (simple and multiple regression) resulted in high correlations between SPOT satellite spectral reflectance and shrub and grass cover, although these correlations varied with the spatial scale of aggregation of the measurements. The Li-Strahler model produced estimated of shrub size and density for both study sites with large standard errors. In the Jornada, the estimates were accurate enough to be useful for characterizing structural differences among three shrub strata. In Niger, the range of shrub cover and size in short-fallow shrublands is so low that the necessity of spatially distributed estimation of shrub size and density is questionable. Spectral mixture analysis of multiscale, multitemporal, multispectral radiometer data and imagery for Niger showed a positive relationship between fractions of spectral endmembers and surface parameters of interest including soil cover, vegetation cover, and leaf area index.

Franklin, Janet↗
NASA NTRSDate not supplied

Using Dual-Regression to Produce 16-Day Average AIRS Soundings

Temperature and humidity profiles are needed to estimate surface radiation budget. The Clouds and the Earth’s Radiant Energy System (CERES) team uses temperature and humidity profiles from a reanalysis product produced by NASA’s Global Modeling and Assimilation Office for surface irradiance computations. Biases and drifts in temperature and humidity profiles in the reanalysis product result in biases and drifts in surface irradiances computed with them. One approach to correct biases and drifts in temperature and humidity profile is to use satellite observations, similar to assimilating instantaneous spectral radiances to correct modeled temperature and humidity profiles. In this work, we use mean spectral radiances to test the possibility of understanding biases in reanalysis mean temperature and humidity profiles. Specifically, we use 16-day mean Atmospheric Infrared Sounder (AIRS) radiances and only use clear-sky spectral radiances with a viewing zenith angle nadir to near-nadir. We use the dual-regression method (Smith et al. 2012) to test whether temperature and humidity profiles retrieved from the 16-day mean spectral radiances agree with the average of temperature and humidity profiles derived from instantaneous spectral radiances. When daytime and nighttime spectral radiances are averaged separately and daytime and nighttime retrievals are performed separately, temperature and humidity profiles derived from the mean spectral radiances agree well with mean temperature and humidity profiles derived from instantaneous spectral radiances. The agreement improves when clouds are further screened to compute 16-day mean clear-sky spectral radiances.

Anthony DiNorscia↗
NASA NTRS1999

Noninvasive assessment of left atrial maximum dP/dt by a combination of transmitral and pulmonary venous flow

OBJECTIVES: The study assessed whether hemodynamic parameters of left atrial (LA) systolic function could be estimated noninvasively using Doppler echocardiography. BACKGROUND: Left atrial systolic function is an important aspect of cardiac function. Doppler echocardiography can measure changes in LA volume, but has not been shown to relate to hemodynamic parameters such as the maximal value of the first derivative of the pressure (LA dP/dt(max)). METHODS: Eighteen patients in sinus rhythm were studied immediately before and after open heart surgery using simultaneous LA pressure measurements and intraoperative transesophageal echocardiography. Left atrial pressure was measured with a micromanometer catheter, and LA dP/dt(max) during atrial contraction was obtained. Transmitral and pulmonary venous flow were recorded by pulsed Doppler echocardiography. Peak velocity, and mean acceleration and deceleration, and the time-velocity integral of each flow during atrial contraction was measured. The initial eight patients served as the study group to derive a multilinear regression equation to estimate LA dP/dt(max) from Doppler parameters, and the latter 10 patients served as the test group to validate the equation. A previously validated numeric model was used to confirm these results. RESULTS: In the study group, LA dP/dt(max) showed a linear relation with LA pressure before atrial contraction (r = 0.80, p < 0.005), confirming the presence of the Frank-Starling mechanism in the LA. Among transmitral flow parameters, mean acceleration showed the strongest correlation with LA dP/dt(max) (r = 0.78, p < 0.001). Among pulmonary venous flow parameters, no single parameter was sufficient to estimate LA dP/dt(max) with an r2 > 0.30. By stepwise and multiple linear regression analysis, LA dP/dt(max) was best described as follows: LA dP/dt(max) = 0.1 M-AC +/- 1.8 P-V - 4.1; r = 0.88, p < 0.0001, where M-AC is the mean acceleration of transmitral flow and P-V is the peak velocity of pulmonary venous flow during atrial contraction. This equation was tested in the latter 10 patients of the test group. Predicted and measured LA dP/dt(max) correlated well (r = 0.90, p < 0.0001). Numerical simulation verified that this relationship held across a wide range of atrial elastance, ventricular relaxation and systolic function, with LA dP/dt(max) predicted by the above equation with r = 0.94. CONCLUSIONS: A combination of transmitral and pulmonary venous flow parameters can provide a hemodynamic assessment of LA systolic function.

NASA Discipline Cardiopulmonary↗
NASA NTRS1997

Friction Velocity Estimation Using Dual-Frequency Altimeter Data

Global estimation of wind speeds near the sea-surface can be obtained using normalized radar cross sections sigma(o) from satellite-borne single-frequency altimeters. Indeed recent validation studies of Geosat altimeter-derived wind speeds show that several often cited semiempirical algorithms yield linear trends between wind-speeds from altimeter data and wind speeds from (a) ships of opportunity (Etcheto 1992), (b) buoys (Carter 1992), and (c) atmospheric models (Guillaume 1992). Although the linear trends are striking, there is considerable scatter since the range of standard deviations is from 1.5 to 3 m/s. In fact, the scatter of some particular measurements about the regressions is striking because they depart from the 'standard' by as much as +/- 5 m/s, which is large - even for full gale winds of 20 m/s. These anomalies can cause serious problems for gas exchange, heat, and aerosol estimates. They can also induce considerable errors in wind-driven oceanic circulation models and sea-surface wave models. This paper explores the possibility of improving the accuracy of wind speed estimation using dual-frequency altimeters.

Bliven, Larry F.↗
NASA NTRS2021

Contribution of Meteorological Downscaling to Skill and Precision of Seasonal Drought Forecasts

Research in meteorological prediction on sub-seasonal to seasonal (S2S) timescales has seen growth in recent years. Concurrent with this, demand for seasonal drought forecasting has risen. While there is obvious synergy between these fields, S2S meteorological forecasting has typically focused on low resolution global models, while the development of drought can be sensitive to the local expression of weather anomalies and their interaction with local surface properties and processes. This suggests that downscaling might play an important role in the application of meteorological S2S forecasts to skillful forecasting of drought. Here, we apply the Generalized Analog Regression Downscaling (GARD) algorithm to downscale meteorological hindcasts from the NASA Goddard Earth Observing System (GEOS) global S2S forecast system. Downscaled meteorological fields are then applied to drive offline simulations with the Catchment Land Surface Model (CLSM) to forecast United States Drought Monitor (USDM) style drought indicators derived from simulated surface hydrology variables. We compare the representation of drought in these downscaled hindcasts to hindcasts that are not downscaled, using the North American Land Data Assimilation System Phase 2 (NLDAS-2) dataset as an observational reference. We find that downscaling using GARD improves hindcasts of temperature and temperature anomalies, but the results for precipitation are mixed and generally small. Overall, GARD downscaling led to improved hindcast skill for total drought across the Contiguous United States (CONUS), and improvements were greatest for extreme (D3) and exceptional (D4) drought categories.

Ryan A Zamora↗
NASA NTRS1993

Improvement and further development of SSM/I overland parameter algorithms using the WetNet workstation

Since the launch of the DMSP Special Sensor Microwave/Imager (SSM/I), several algorithms have been developed to retrieve overland parameters. These include the present operational algorithms resulting from the Navy calibration/validation effort such as land surface type (Neale et al. 1990), land surface temperature (McFarland et al. 1990), surface moisture (McFarland and Neale, 1991) and snow parameters (McFarland and Neale, 1991). In addition, other work has been done including the classification of snow cover and precipitation using the SSM/I (Grody, 1991). Due to the empirical nature of most of the above mentioned algorithms, further research is warranted and improvements can probably be obtained through a combination of radiative transfer modelling to study the physical processes governing the microwave emissions at the SSM/I frequencies, and the incorporation of additional ground truth data and special cases into the regression data sets. We have proposed specifically to improve the retrieval of surface moisture and snow parameters using the WetNet SSM/I data sets along with ground truth information namely climatic variables from the NOAA cooperative network of weather stations as well as imagery from other satellite sensors such as the AVHRR and Thematic Mapper. In the case of surface moisture retrievals the characterization of vegetation density is of primary concern. The higher spatial resolution satellite imagery collected at concurrent periods will be used to characterize vegetation types and amounts which, along with radiative transfer modelling should lead to more physically based retrievals. Snow parameter retrieval algorithm improvement will initially concentrate on the classification of snowpacks (dry snow, wet snow, refrozen snow) and later on specific products such as snow water equivalent. Significant accomplishments in the past year are presented.

Neale, Christopher M. U.↗
NASA NTRS1979

Reduction of particulate carryover from a pressurized fluidized bed

A bench scale fluidized bed combustor was constructed with a conical shape so that the enlarged upper part of the combustor would also serve as a granular bed filter. The combustor was fed coal and limestone. Ninety-nine tests of about four hours each were conducted over a range of conditions. Coal-to-air ratio varied from 0.033 to 0.098 (all lean). Limestone-to-coal ratio varied from 0.06 to 0.36. Bed depth varied from 3.66 to 8.07 feet. Temperature varied from 1447 to 1905 F. Pressure varied from 40 to 82 psia. Heat transfer area had the range zero to 2.72 ft squared. Two cone angles were used. The average particulate carry over of 2.5 grains/SCF was appreciably less than cylindrical fluidized bed combustors. The carry over was correlated by multiple regression analysis to yield the dependence on bed depth and hence the collection efficiency, which was 20%. A comparison with a model indicated that the exhaust port may be below the transport disengaging height for most of the tests, indicating that further reduction in carry over and increase in collection efficiency could be affected by increasing the freeboard and height of the exhaust port above the bed.

Patch, R. W.↗
NASA NTRS1988

An expert system for reliability modeling

A method is presented for computing the probability of failure of a unit during the next usage or mission based on sound statistical techniques. With the use of logit regression embedded in Lotus 1-2-3 macros, this package computes the probability of unit failure based upon a historical data base. Using this computed probability of failure, the package then makes a replacement recommendation based upon the experts acceptable risk parameters.

Goss, Ernst↗
NASA NTRS1994

Modeling ozone episodes in the Baltimore-Washington region

Surface ozone (O3) concentrations in excess of the National Ambient Air Quality Standard (NAAQS) continue to occur in metropolitan areas in the United States despite efforts to control emissions of O3 precursors. Future O3 control strategies will be based on results from modeling efforts that have just begun in many areas. Two initial questions that arise are model sensitivity to domain-specific conditions and the selection of episodes for model evaluation and control strategy development. For the Baltimore-Washington region (B-W), the presence of the Chesapeake Bay introduces a number of issues relevant to model sensitivity. In this paper, the specific questions of the determination of model volume (mixing height) for the Urban Airshed Model (UAM) is discussed and various alternative methods compared. For the latter question, several analytic approaches, Cluster Analysis and classification and Regression Tree (CART) analysis are undertaken to determine meteorological conditions associated with severe O3 events in the B-W domain.

Ryan, William F.↗
NASA NTRS2018

Generating Exploration Mission-3 Trajectories to a 9:2 NRHO using Machine Learning

The purpose of this thesis is to design a machine learning algorithm platform that provides expanded knowledge of mission availability through a launch season by improving trajectory resolution and introducing launch mission forecasting. The specific scenario addressed in this paper is one in which data is provided for four deterministic translational maneuvers through a mission to a Near Rectilinear Halo Orbit (NRHO) with a 9:2 synodic frequency. Current launch availability knowledge under NASA's Orion Orbit Performance Team is established by altering optimization variables associated to given reference launch epochs. This current method can bean abstract task and relies on an orbit analyst to structure a mission based on an established mission design methodology associated to the performance of Orion and NASA's Space Launch System. Introducing a machine learning algorithm trained to construct mission scenarios within the feasible range of known trajectories reduces the required interaction of the orbit analyst by removing the needed step of optimizing the orbit to fit an expected translational response required of the spacecraft. In this study, k-Nearest Neighbor and Bayesian Linear Regression successfully predicted classical orbital elements for the launch windows observed. However both algorithms had limitations due to their approaches to model fitting. Training machine learning algorithms off of classical orbital elements introduced a repetitive approach to reconstructing mission segments for different arrival opportunities through the launch window and can prove to be a viable method of launch window scan generation for future missions.

Guzman, Esteban↗
NASA NTRS2019

Chlorophyll Algorithms for Ocean Color Sensors - OC4, OC5 and OC6

A high degree of consistency and comparability among chlorophyll algorithms is necessary to meet the goals of merging data from concurrent overlapping ocean color missions for increased coverage of the global ocean and to extend existing time series to encompass data from recently launched missions and those planned for the near future, such as PACE, OLCI, HawkEye, EnMAP and SABIA-MAR. To accomplish these goals, we developed 65 empirical ocean color (OC) chlorophyll algorithms for 25 satellite instruments using the largest available and most globally representative database of coincident in situ chlorophyll a and remote sensing reflectances. Excellent internal consistency was achieved across these OC ‘Version -7’ algorithms, as demonstrated by a median regression slope and coefficient of determination (R (sup 2)) of 0.985 and 0.859, respectively, among 903 pairwise comparisons of OC-modeled chlorophyll. SeaWiFS and MODIS-Aqua satellite-to-in situ match-up results indicated equivalent, and sometimes superior, performance to current heritage chlorophyll algorithms.

ocean color↗
NASA NTRS2021

Developing a Hydrological Monitoring and Sub-Seasonal to Seasonal Forecasting System for South and Southeast Asian River Basins

South and Southeast Asia is subject to significant hydrometeorological extremes, including drought. Under rising temperatures, growing populations, and an apparent weakening of the South Asian monsoon in recent decades, concerns regarding drought and its potential impacts on water and food security are on the rise. Reliable sub-seasonal to seasonal (S2S) hydrological forecasts could, in principle, help governments and international organizations to better assess risk and act in the face of an oncoming drought. Here, we leverage recent improvements in S2S meteorological forecasts and the growing power of Earth Observations to provide more accurate monitoring of hydrological states for forecast initialization. Information from both sources is merged in a South and Southeast Asia sub-seasonal to seasonal hydrological forecasting system (SAHFS-S2S), developed collaboratively with the NASA SERVIR program and end-users across the region. This system applies the Noah-MultiParameterization (NoahMP) Land Surface Model (LSM) in the NASA Land Information System (LIS), driven by downscaled meteorological fields from the Global Data Assimilation System (GDAS) and Climate Hazards InfraRed Precipitation products (CHIRP and CHIRPS) to optimize initial conditions. The NASA Goddard Earth Observing System Model - sub-seasonal to seasonal (GEOS-S2S) forecasts, downscaled using the National Center for Atmospheric Research (NCAR) General Analog Regression Downscaling (GARD) tool and quantile mapping, are then applied to drive 5-km resolution hydrological forecasts to a 9-month forecast time horizon. Results show that the skillful predictions of root zone soil moisture can be made one to two months in advance for forecasts initialized in rainy seasons and up to 8 months when initialized in dry seasons. The memory of accurate initial conditions can positively contribute to forecast skills throughout the entire 9-month prediction period in areas with limited precipitation. This SAHFS-S2S has been operationalized at the International Centre for Integrated Mountain Development (ICIMOD) to support drought monitoring and warning needs in the region.

Yifan Zhou↗
NASA NTRS2003

X-Ray Spectra of Young Pulsars and Their Wind Nebulae: Dependence on Spin-Down Energy Loss Rate

An observational model is presented for the spectra of young rotation-powered pulsars and their nebulae based on a study of nine bright Crab-like pulsar systems observed with the Chandra X-ray observatory. A significant correlation is discovered between the X-ray spectra of these pulsars and that of their associated pulsar wind nebulae, both of which are observed to be a function of the spin-down energy loss rate, E. The 2-10 keV spectra of these objects are well characterized by an absorbed power-law model with photon indices, Gamma, in the range of 0.6 < Gamma (sub PSR) < 2.1 and 1.3 < Gamma(sub PWN) < 2.3, for the pulsars and their nebulae, respectively. A linear regression fit relating these two sets of indexes yields Gamma(sub PWN) = 0.91 +/- 0.18 + (0.66 +/- 0.11) Gamma (sub PSR), with a correlation coefficient of r = 0.97. The spectra of these pulsars are found to steepen as Gamma = Gamma(sub max) + alpha E (exp -1/2), with Gamma(sub max) providing an observational limit on the spectral slopes of young rotation-powered pulsars. These results reveal basic properties of young pulsar systems, allow new observational constraints on models of pulsar wind emission, and provide a means of predicting the energetics of pulsars lacking detected pulsations.

Gotthelf, E. V.↗
NASA NTRS2010

Peak Wind Tool for General Forecasting

The expected peak wind speed of the day is an important forecast element in the 45th Weather Squadron's (45 WS) daily 24-Hour and Weekly Planning Forecasts. The forecasts are used for ground and space launch operations at the Kennedy Space Center (KSC) and Cape Canaveral Air Force Station (CCAFS). The 45 WS also issues wind advisories for KSC/CCAFS when they expect wind gusts to meet or exceed 25 kt, 35 kt and 50 kt thresholds at any level from the surface to 300 ft. The 45 WS forecasters have indicated peak wind speeds are challenging to forecast, particularly in the cool season months of October - April. In Phase I of this task, the Applied Meteorology Unit (AMU) developed a tool to help the 45 WS forecast non-convective winds at KSC/CCAFS for the 24-hour period of 0800 to 0800 local time. The tool was delivered as a Microsoft Excel graphical user interface (GUI). The GUI displayed the forecast of peak wind speed, 5-minute average wind speed at the time of the peak wind, timing of the peak wind and probability the peak speed would meet or exceed 25 kt, 35 kt and 50 kt. For the current task (Phase II ), the 45 WS requested additional observations be used for the creation of the forecast equations by expanding the period of record (POR). Additional parameters were evaluated as predictors, including wind speeds between 500 ft and 3000 ft, static stability classification, Bulk Richardson Number, mixing depth, vertical wind shear, temperature inversion strength and depth and wind direction. Using a verification data set, the AMU compared the performance of the Phase I and II prediction methods. Just as in Phase I, the tool was delivered as a Microsoft Excel GUI. The 45 WS requested the tool also be available in the Meteorological Interactive Data Display System (MIDDS). The AMU first expanded the POR by two years by adding tower observations, surface observations and CCAFS (XMR) soundings for the cool season months of March 2007 to April 2009. The POR was expanded again by six years, from October 1996 to April 2002, by interpolating 1000-ft sounding data to 100-ft increments. The Phase II developmental data set included observations for the cool season months of October 1996 to February 2007. The AMU calculated 68 candidate predictors from the XMR soundings, to include 19 stability parameters, 48 wind speed parameters and one wind shear parameter. Each day in the data set was stratified by synoptic weather pattern, low-level wind direction, precipitation and Richardson Number, for a total of 60 stratification methods. Linear regression equations, using the 68 predictors and 60 stratification methods, were created for the tool's three forecast parameters: the highest peak wind speed of the day (PWSD), 5-minute average speed at the same time (A WSD), and timing of the PWSD. For PWSD and A WSD, 30 Phase II methods were selected for evaluation in the verification data set. For timing of the PWSD, 12 Phase\I methods were selected for evaluation. The verification data set contained observations for the cool season months of March 2007 to April 2009. The data set was used to compare the Phase I and II forecast methods to climatology, model forecast winds and wind advisories issued by the 45 WS. The model forecast winds were derived from the 0000 and 1200 UTC runs of the 12-km North American Mesoscale (MesoNAM) model. The forecast methods that performed the best in the verification data set were selected for the Phase II version of the tool. For PWSD and A WSD, linear regression equations based on MesoNAM forecasts performed significantly better than the Phase I and II methods. For timing of the PWSD, none of the methods performed significantly bener than climatology. The AMU then developed the Microsoft Excel and MIDDS GUls. The GUIs display the forecasts for PWSD, AWSD and the probability the PWSD will meet or exceed 25 kt, 35 kt and 50 kt. Since none of the prediction methods for timing of the PWSD performed significantly better thanlimatology, the tool no longer displays this predictand. The Excel and MIDDS GUIs display forecasts for Day-I to Day-3 and Day-I to Day-5, respectively. The Excel GUI uses MesoNAM forecasts as input, while the MIDDS GUI uses input from the MesoNAM and Global Forecast System model. Based on feedback from the 45 WS, the AMU added the daily average wind speed from 30 ft to 60 ft to the tool, which is one of the parameters in the 24-Hour and Weekly Planning Forecasts issued by the 45 WS. In addition, the AMU expanded the MIDDS GUI to include forecasts out to Day-7.

Barrett, Joe H., III↗
NASA NTRS2019

Atmospheric Chemistry Modeling Using Machine Learning

Atmospheric chemistry models are a central tool to study the impact of chemical constituents on the environment, vegetation and human health. These models split the atmosphere in a large number of grid-boxes and consider the emission of compounds into these boxes and their subsequent transport, deposition, and chemical processing. The chemistry is represented through a series of simultaneous ordinary differential equations, one for each compound. Given the difference in life-times between the chemical compounds (milli-seconds for O1D to years for CH4) these equations are numerically stiff and solving them consists of a significant fraction of the computational burden of a chemistry model. We have investigated a machine learning approach to emulate the chemistry instead of solving the differential equations numerically. From a one-month simulation of the GEOS-Chem model we have produced a training dataset consisting of the concentration of compounds before and after the differential equations are solved, together with some key physical parameters for every grid-box and time-step. From this dataset we have trained a machine learning algorithm (regression forest) to be able to predict the concentration of the compounds after the integration step based on the concentrations and physical state at the beginning of the time step. We have then included this algorithm back into the GEOS-Chem model, bypassing the need to integrate the chemistry. This machine learning approach shows many of the characteristics of the full simulation and has the potential to be substantially faster. There are a wide range of application for such an approach - generating boundary conditions, for use in air quality forecasts, chemical data assimilation systems, etc. We discuss speed and accuracy of our approach, and highlight some potential future directions for improving it.

Keller, Christoph A.↗