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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 1,621 records · Page 90

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DOE OSTI2022

GOOML - Real World Applications of Machine Learning in Geothermal Operations

GOOML (Geothermal Operational Optimization with Machine Learning) is a machine-learning based framework that enables geothermal power plant operators to explore optimization opportunities for their assets in an efficient and robust digital environment. Backed by real-world data sources, thermodynamic constraints and steamfield intelligence, the GOOML environment provides new tools to explore how to best operate steamfields as well as test new scenarios and configurations prior to implementation in the field. To prove the effectiveness of GOOML, we have undertaken optimization experiments using reinforcement learning (RL) to generate operational suggestions using a balance of mass-take targets, sustainability considerations and net generation. Our experiments use the GOOML construct to explore different field parameters and perform multiple reinforcement learning experiments. Like a comprehensive laboratory workbench, we can change out components of a steamfield to perform testing under a variety of conditions (restrict mass, increase pressure, reroute steam, etc.). This flexibility allows us to explore conditions that would require significant infrastructure changes in a real-world setting at a fraction of the cost and time in a digital environment. The results highlight the benefits of using digital twins and advanced data analytics for the geothermal industry.

forecasting↗

NASA NTRS2004

The Stellar Imager (SI)"Vision Mission"

The Stellar Imager (SI) is a "Vision" mission in the Sun-Earth Connection (SEC) Roadmap, conceived for the purpose of understanding the effects of stellar magnetic fields, the dynamos that generate them, and the internal structure and dynamics of the stars in which they exist. The ultimate goal is to achieve the best possible forecasting of solar/stellar magnetic activity and its impact on life in the Universe. The science goals of SI require an ultra-high angular resolution, at ultraviolet wavelengths, on the order of 100 micro-arcsec and thus baselines on the order of 0.5 km. These requirements call for a large, multi-spacecraft (less than 20) imaging interferometer, utilizing precision formation flying in a stable environment, such as in a Lissajous orbit around the Sun-Earth L2 point. SI's resolution will make it an invaluable resource for many other areas of astrophysics, including studies of AGN s, supernovae, cataclysmic variables, young stellar objects, QSO's, and stellar black holes. ongoing mission concept and technology development studies for SI. These studies are designed to refine the mission requirements for the science goals, define a Design Reference Mission, perform trade studies of selected major technical and architectural issues, improve the existing technology roadmap, and explore the details of deployment and operations, as well as the possible roles of astronauts and/or robots in construction and servicing of the facility.

Carpenter, Ken↗

DOE OSTI2023

Predicting Kyasanur forest disease in resource-limited settings using event-based surveillance and transfer learning

In recent years, the reports of Kyasanur forest disease (KFD) breaking endemic barriers by spreading to new regions and crossing state boundaries is alarming. Effective disease surveillance and reporting systems are lacking for this emerging zoonosis, hence hindering control and prevention efforts. We compared time-series models using weather data with and without Event-Based Surveillance (EBS) information, i.e., news media reports and internet search trends, to predict monthly KFD cases in humans. We fitted Extreme Gradient Boosting (XGB) and Long Short-Term Memory models at the national and regional levels. We utilized the rich epidemiological data from endemic regions by applying Transfer Learning (TL) techniques to predict KFD cases in new outbreak regions where disease surveillance information was scarce. Overall, the inclusion of EBS data, in addition to the weather data, substantially increased the prediction performance across all models. The XGB method produced the best predictions at the national and regional levels. The TL techniques outperformed baseline models in predicting KFD in new outbreak regions. Novel sources of data and advanced machine-learning approaches, e.g., EBS and TL, show great potential towards increasing disease prediction capabilities in data-scarce scenarios and/or resource-limited settings, for better-informed decisions in the face of emerging zoonotic threats.

60 APPLIED LIFE SCIENCES↗

NASA NTRS2010

Forecasting Lightning Threat Using WRF Proxy Fields

Objectives: Given that high-resolution WRF forecasts can capture the character of convective outbreaks, we seek to: 1. Create WRF forecasts of LTG threat (1-24 h), based on 2 proxy fields from explicitly simulated convection: - graupel flux near -15 C (captures LTG time variability) - vertically integrated ice (captures LTG threat area). 2. Calibrate each threat to yield accurate quantitative peak flash rate densities. 3. Also evaluate threats for areal coverage, time variability. 4. Blend threats to optimize results. 5. Examine sensitivity to model mesh, microphysics. Methods: 1. Use high-resolution 2-km WRF simulations to prognose convection for a diverse series of selected case studies. 2. Evaluate graupel fluxes; vertically integrated ice (VII). 3. Calibrate WRF LTG proxies using peak total LTG flash rate densities from NALMA; relationships look linear, with regression line passing through origin. 4. Truncate low threat values to make threat areal coverage match NALMA flash extent density obs. 5. Blend proxies to achieve optimal performance 6. Study CAPS 4-km ensembles to evaluate sensitivities.

McCaul, E. W., Jr.↗

NASA NTRS2014

Automating the SMAP Ground Data System to Support Lights-Out Operations

The Soil Moisture Active Passive (SMAP) Mission is a first tier mission in NASA's Earth Science Decadal Survey. SMAP will provide a global mapping of soil moisture and its freeze/thaw states. This mapping will be used to enhance the understanding of processes that link the terrestrial water, energy, and carbon cycles, and to enhance weather and forecast capabilities. NASA's Jet Propulsion Laboratory has been selected as the lead center for the development and operation of SMAP. The Jet Propulsion Laboratory (JPL) has an extensive history of successful deep space exploration. JPL missions have typically been large scale Class A missions with significant budget and staffing. SMAP represents a new area of JPL focus towards low cost Earth science missions. Success in this new area requires changes to the way that JPL has traditionally provided the Mission Operations System (MOS)/Ground Data System (GDS) functions. The operation of SMAP requires more routine operations activities and support for higher data rates and data volumes than have been achieved in the past. These activities must be addressed by a reduced operations team and support staff. To meet this challenge, the SMAP ground data system provides automation that will perform unattended operations, including automated commanding of the SMAP spacecraft.

GDS↗

NASA NTRS2015

Monitoring Changes of Tropical Extreme Rainfall Events Using Differential Absorption Barometric Radar (DiBAR)

This work studies the potential of monitoring changes in tropical extreme rainfall events such as tropical storms from space using a Differential-absorption BArometric Radar (DiBAR) operating at 50-55 gigahertz O2 absorption band to remotely measure sea surface air pressure. Air pressure is among the most important variables that affect atmospheric dynamics, and currently can only be measured by limited in-situ observations over oceans. Analyses show that with the proposed radar the errors in instantaneous (averaged) pressure estimates can be as low as approximately 5 millibars (approximately 1 millibar) under all weather conditions. With these sea level pressure measurements, the forecasts, analyses and understanding of these extreme events in both short and long time scales can be improved. Severe weathers, especially hurricanes, are listed as one of core areas that need improved observations and predictions in WCRP (World Climate Research Program) and NASA Decadal Survey (DS) and have major impacts on public safety and national security through disaster mitigation. Since the development of the DiBAR concept about a decade ago, our team has made substantial progress in advancing the concept. Our feasibility assessment clearly shows the potential of sea surface barometry using existing radar technologies. We have developed a DiBAR system design, fabricated a Prototype-DiBAR (P-DiBAR) for proof-of-concept, conducted lab, ground and airborne P-DiBAR tests. The flight test results are consistent with our instrumentation goals. Observational system simulation experiments for space DiBAR performance show substantial improvements in tropical storm predictions, not only for the hurricane track and position but also for the hurricane intensity. DiBAR measurements will lead us to an unprecedented level of the prediction and knowledge on tropical extreme rainfall weather and climate conditions.

Lin, Bing↗

NASA NTRS2018

The Influence of Prescribed Boundary Conditions on Near-Surface Temperature over the Arctic in the MERRA-2 Atmospheric Model

An accurate historical record of evolving Arctic conditions is integral to furthering our understanding of climate processes and to providing a foundation for predicting future climate scenarios in northern high latitudes. Atmospheric reanalyses are seen as an important source of information on the recent past for the data-sparse Arctic region. An assessment of near-surface Arctic air temperatures finds significant discrepancies among the various modern reanalyses. An important point is the treatment of surface boundary conditions: specifically, the sea ice cover and sea surface temperatures (SSTs) over the Arctic Ocean. Reanalyses use different methodologies and data sources for SSTs and sea ice concentration boundary forcing. Notably, the Modern Era Retrospective analysis for Research and Applications, version 2 (MERRA-2) and the European Centre for Medium-Range Weather Forecasts Interim Re-Analysis (ERA-Interim) both use boundary forcing derived from the Operational Sea Surface Temperature and Sea Ice Analysis (OSTIA) over an extended, overlapping period of time. This allows for an examination of differences between the two systems while both concurrently employ the same fractional sea ice coverage. To further understand these differences, an ensemble of AMIP-style simulations using the MERRA-2 atmospheric model - but without data assimilation - shows considerable differences in Arctic temperatures as compared to reanalyses, particularly in autumn and winter months. Results from the AMIP simulations suggest that the surface representation over sea ice used in the MERRA-2 model provides an intrinsic warm bias and obfuscates Arctic Amplification, an established feature present in observations and reanalyses. An additional ensemble of AMIP-style simulations using the MERRA-2 atmospheric model was performed using boundary conditions derived from the ERA-Interim reanalysis. An in-depth comparison of surface temperatures over the Arctic from the two reanalyses and two AMIP-style ensembles will be presented, along with an assessment of the effects of the varying Arctic temperature time series on the atmospheric general circulation and energy budget.

Collow, Allison B. Marquardt↗

DOE OSTI2023

Using Satellite‐Derived Fire Arrival Times for Coupled Wildfire‐Air Quality Simulations at Regional Scales of the 2020 California Wildfire Season

Abstract Wildfire frequency has increased in the Western US over recent decades, driven by climate change and a legacy of forest management practices. Consequently, human structures, health, and life are increasingly at risk due to wildfires. Furthermore, wildfire smoke presents a growing hazard for regional and national air quality. In response, many scientific tools have been developed to study and forecast wildfire behavior, or test interventions that may mitigate risk. In this study, we present a retrospective analysis of 1 month of the 2020 Northern California wildfire season, when many wildfires with varying environments and behavior impacted regional air quality. We simulated this period using a coupled numerical weather prediction model with online atmospheric chemistry, and compare two approaches to representing smoke emissions: an online fire spread model driven by remotely sensed fire arrival times and a biomass burning emissions inventory. First, we quantify the differences in smoke emissions and timing of fire activity, and characterize the subsequent impact on estimates of smoke emissions. Next, we compare the simulated smoke to surface observations and remotely sensed smoke; we find that despite differences in the simulated smoke surface concentrations, the two models achieve similar levels of accuracy. We present a detailed comparison between the performance and relative strengths of both approaches, and discuss potential refinements that could further improve future simulations of wildfire smoke. Finally, we characterize the interactions between smoke and meteorology during this event, and discuss the implications that increases in regional smoke may have on future meteorological conditions.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2020

Mesoscale to Microscale Simulations over Complex Terrain with the Immersed Boundary Method in the Weather Research and Forecasting Model

Improvements to the Weather Research and Forecasting (WRF) Model are made to enable multiscale simulations over highly complex terrain with dynamically downscaled boundary conditions from the mesoscale to the microscale. Over steep terrain, the WRF Model develops numerical errors that are due to grid deformation of the terrain-following coordinates. An alternative coordinate system, the immersed boundary method (IBM), has been implemented into WRF, allowing for simulations over highly complex terrain; however, the new coordinate system precluded nesting within mesoscale simulations using WRF’s native terrain-following coordinates. Here, the immersed boundary method and WRF’s grid-nesting framework are modified to seamlessly work together. This improved framework for the first time allows for large-eddy simulation over complex (urban) terrain with IBM to be nested within a typical mesoscale WRF simulation. Simulations of the Joint Urban 2003 field campaign in Oklahoma City, Oklahoma, are performed using a multiscale five-domain nested configuration, spanning horizontal grid resolutions from 6 km to 2 m. These are compared with microscale-only simulations with idealized lateral boundary conditions and with observations of wind speed/direction and SF 6 concentrations from a controlled release from intensive observation period 3. The multiscale simulation, which is configured independent of local observations, shows similar model skill predicting wind speed/direction and improved skill predicting SF 6 concentrations when compared with the idealized simulations, which require use of observations to set mean flow conditions. Use of this improved multiscale framework shows promise for enabling large-eddy simulation over highly complex terrain with dynamically downscaled boundary conditions from mesoscale models.

42 ENGINEERING↗

DOE OSTI2022

Hierarchical Model-Free Transactive Control of Building Loads to Support Grid Services

Residential buildings consume 4.4 quads of electricity annually, approximately 37% of the total electricity consumption in the United States. This represents a vast resource that can be used for demand management and other ancillary services. This project aims to develop a robust, scalable hierarchical transactional control mechanism incorporating elements of model-free control (MFC) and game theory to harness buildings to provide ancillary services to the grid. This approach is being taken to address the challenges of incorporating traditional transactional control schemes into existing buildings. The challenges include small individual building sizes requiring aggregation of many buildings, unpredictable energy usage that makes model identification difficult, and satisfying the sensitive occupant comfort constraints. In the proposed approach, by separating the control mechanism into two layers above and below the load aggregator, MFC can be used below the aggregator to modulate flexible building loads in response to pricing signals with guaranteed performance. This allows the burden of identifying an accurate model of the system to be shifted to the above-aggregator layer, where fluctuations in individual building usage have less impact on predicted building system behavior. Game theory concepts can then be used to determine pricing curves and control signals among regional aggregators. Managing this control in a game-theoretic approach will allow us to build in financial incentives that increase customer engagement. Additionally, the usage of MFC necessitates less burdensome computational and communication requirements, thus, it is easily deployable on small, embedded devices. In a broader sense, developing a strategy capable of effectively incorporating residential and small commercial buildings will allow greater throughput of existing and emerging grid services in addition to future transactive energy grid management methods. Using MFC within a hierarchical control architecture will allow the shifting of existing forecasting challenges to an aggregate level, where dynamics are slower and more predictable. This will enable a smooth interface between the grid services requests of utilities and the reliable control required by participating buildings. MFC, which supports distributed control architecture, permits a scalable solution that can be deployed to neighborhood-size systems as well as individual buildings. This project focuses on three objectives: (1) developing the mathematical framework, algorithm toolkit, and software toolset of the two-layer transactive control testbed; (2) developing a scalable solution for application over many residential and small-size commercial buildings with sparse distributed communication; and (3) field testing and implementation on hardware of the control strategies developed in the previous two objectives. The research and development activities are focused and designed to be impactful within the relevant 2025 targets timeframe. An open-source control framework for exploiting variability and dispatchability of building loads will be delivered as the outcome of the project. This capability enables greater participation of loads in electricity markets and ancillary services that are both useful for the utility and financially beneficial for building owners.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

NASA NTRS1985

Some Recent Developments in Numerical Modelling at ECMWF

A new atmospheric model was introduced into operational forecasting at the European Center for Medium Range Weather Forecasts, on 21 April 1983. The principal differences between this model and the Center's first operational model were in the adiabatic formulation, which, in the new model, includes use of a spectral representation in the horizontal, a more general vertical coordinate, and a modified, more-efficient, time-stepping scheme. In addition, new programming techniques and standards were adopted to facilitate both the model's use as a research tool and its adaptation to make full use of the features of the recently-acquired CRAY X-MP computer. A number of revisions were also made in detailed aspects of the formulation of the parameterization schemes. The operational change to this new model was accompanied by a second important change, namely the use of a higher envelope orography in the lower boundary conditions of the model. Problems in the operational performance of the model and development preparatory to the future operational implementation of a high resolution version of the model on the CRAY X-MP are discussed.

Simmons, A. J.↗

DOE OSTI2021

Predicting concrete compressive strength using hybrid ensembling of surrogate machine learning models

This study aims to implement a hybrid ensemble surrogate machine learning technique in predicting the compressive strength (CS) of concrete, an important parameter used for durability design and service life prediction of concrete structures in civil engineering projects. For this purpose, an experimental database consisting of 1030 records has been compiled from the machine learning repository of the University of California, Irvine. The database was used to train and validate four conventional machine learning (CML) models, namely Artificial Neural Network (ANN), Linear and Non-Linear Multivariate Adaptive Regression Splines (MARS-L and MARS-C), Gaussian Process Regression (GPR), and Minimax Probability Machine Regression (MPMR). Subsequently, the predicted outputs of CML models were combined and trained using ANN to construct the Hybrid Ensemble Model (HENSM). It is observed that the proposed HENSM produces higher predictive accuracy compared to the CML models used in the present study. The predictive performance of all models for CS prediction was compared using the testing dataset and it is found that the HENSM model attained the highest predictive accuracy in both phases. Based on the experimental results, the newly constructed HENSM model is very potential to be a new alternative in handling the overfitting issues of CML models and hence, can be used to predict the concrete CS, including the design of less polluting and more sustainable concrete constructions.

36 MATERIALS SCIENCE↗

DOE OSTI2022

Validation of wind resource and energy production simulations for small wind turbines in the United States

Abstract. Due to financial and temporal limitations, the small wind community relies upon simplified wind speed models and energy production simulation tools to assess site suitability and produce energy generation expectations. While efficient and user-friendly, these models and tools are subject to errors that have been insufficiently quantified at small wind turbine heights. This study leverages observations from meteorological towers and sodars across the United States to validate wind speed estimates from the Wind Integration National Dataset (WIND) Toolkit, the European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis v5 (ERA5), and the Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2), revealing average biases within ±0.5 m s−1 at small wind hub heights. Observations from small wind turbines across the United States provide references for validating energy production estimates from the System Advisor Model (SAM), Wind Report, MyWindTurbine.com, and Global Wind Atlas 3 (GWA3), which are seen to overestimate actual annual capacity factors by 2.5, 4.2, 11.5, and 7.3 percentage points, respectively. In addition to quantifying the error metrics, this paper identifies sources of model and tool discrepancies, noting that interannual fluctuation in the wind resource, wind speed class, and loss assumptions produces more variability in estimates than different horizontal and vertical interpolation techniques. The results of this study provide small wind installers and owners with information about these challenges to consider when making performance estimates and thus possible adjustments accordingly. Looking to the future, recognizing these error metrics and sources of discrepancies provides model and tool researchers and developers with opportunities for product improvement that could positively impact small wind customer confidence and the ability to finance small wind projects.

17 WIND ENERGY↗

NASA NTRS2019

Using Wind Observations from Nearby Aircraft to Update the Optimal Descent Trajectory in Real-Time

The ability to meet a controlled time of arrival during a continuous descent operation will enable environmentally friendly and fuel efficient descent operations while simultaneously maintaining airport throughput. However, if the wind forecast used to compute the initial trajectory plan is not accurate enough, the guidance system will need to correct time deviations from the plan during the execution of the descent. Previous work proposed an on-board guidance strategy based on model predictive control, which repeatedly updates the trajectory plan in real-time from the current aircraft state and for the remainder of the descent. However, the wind conditions downstream, at altitudes not explored yet, were difficult to predict due to the lack of data. This paper shows the potential benefits of using wind observations, broadcast by nearby aircraft, to reconstruct the wind profile downstream. The wind profile in the trajectory optimization problem is modeled as a spline, which control points are updated to fit the observations before re-planning the trajectory. Results from simulations using realistic wind data show that the performance of model predictive control significantly improves when including up-to-date wind observations, in terms of time and energy errors at the metering fix and fuel consumption.

Dalmau, Ramon↗

DOE OSTI2024

Catalytic Resonance Theory: Forecasting the Flow of Programmable Catalytic Loops

Chemical transformations on catalyst surfaces occur through series and parallel reaction pathways. These complex networks and their behavior can be most simply evaluated through a three-species surface reaction loop (A* to B* to C* to A*) that is internal to the overall chemical reaction. Application of an oscillating dynamic catalyst to this reactive loop has been shown to exhibit one of three types of behavior: (1) a positive net flux of molecules about the loop in the clockwise direction, (2) a negative net flux of molecules about the loop in the counterclockwise direction, or (3) negligible flux of molecules about the loop at the limit cycle of reaction. Three-species surface loops were simulated with microkinetic modeling to assess the reaction loop behavior resulting from a catalytic surface oscillating between two or more catalyst surface energy states. Selected input parameters for the simulations spanned an 11-dimensional parameter space using 127 688 different parameter combinations. Their converged limit cycle solutions were analyzed for their loop turnover frequencies, the majority of which were found to be approximately zero. Classification and regression machine learning models were trained to predict the sign and magnitude of the loop turnover frequency and successfully performed above accessible baselines. Notably, the classification models exhibited a baseline weighted F1 score of 0.49, whereas trained models achieved weighted F1 scores of 0.94 and 0.96 when trained on the parameters used to define the simulations and derived rate constants, respectively. The trained models successfully predicted catalytic loop behavior, and interpretation of these models revealed all input parameters to be important for the prediction and performance of each model.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2023

Updating and Evaluating Anthropogenic Emissions for NOAA’s Global Ensemble Forecast Systems for Aerosols (GEFS-Aerosols): Application of an SO 2 Bias-Scaling Method

We updated the anthropogenic emissions inventory in NOAA’s operational Global Ensemble Forecast for Aerosols (GEFS-Aerosols) to improve the model’s prediction of aerosol optical depth (AOD). We used a methodology to quickly update the pivotal global anthropogenic sulfur dioxide (SO 2 ) emissions using a speciated AOD bias-scaling method. The AOD bias-scaling method is based on the latest model predictions compared to NASA’s Modern-Era Retrospective analysis for Research and Applications, version 2 (MERRA2). The model bias was subsequently applied to the CEDS 2019 SO 2 emissions for adjustment. The monthly mean GEFS-Aerosols AOD predictions were evaluated against a suite of satellite observations (e.g., MISR, VIIRS, and MODIS), ground-based AERONET observations, and the International Cooperative for Aerosol Prediction (ICAP) ensemble results. The results show that transitioning from CEDS 2014 to CEDS 2019 emissions data led to a significant improvement in the operational GEFS-Aerosols model performance, and applying the bias-scaled SO 2 emissions could further improve global AOD distributions. The biases of the simulated AODs against the observed AODs varied with observation type and seasons by a factor of 3~13 and 2~10, respectively. The global AOD distributions showed that the differences in the simulations against ICAP, MISR, VIIRS, and MODIS were the largest in March–May (MAM) and the smallest in December–February (DJF). When evaluating against the ground-truth AERONET data, the bias-scaling methods improved the global seasonal correlation (r), Index of Agreement (IOA), and mean biases, except for the MAM season, when the negative regional biases were exacerbated compared to the positive regional biases. The effect of bias-scaling had the most beneficial impact on model performance in the regions dominated by anthropogenic emissions, such as East Asia. However, it showed less improvement in other areas impacted by the greater relative transport of natural emissions sources, such as India. The accuracies of the reference observation or assimilation data for the adjusted inputs and the model physics for outputs, and the selection of regions with less seasonal emissions of natural aerosols determine the success of the bias-scaling methods. A companion study on emission scaling of anthropogenic absorbing aerosols needs further improved aerosol prediction.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS2007

Evaluating Model Performance of an Ensemble-based Chemical Data Assimilation System During INTEX-B Field Mission

We present a global chemical data assimilation system using a global atmosphere model, the Community Atmosphere Model (CAM3) with simplified chemistry and the Data Assimilation Research Testbed (DART) assimilation package. DART is a community software facility for assimilation studies using the ensemble Kalman filter approach. Here, we apply the assimilation system to constrain global tropospheric carbon monoxide (CO) by assimilating meteorological observations of temperature and horizontal wind velocity and satellite CO retrievals from the Measurement of Pollution in the Troposphere (MOPITT) satellite instrument. We verify the system performance using independent CO observations taken on board the NSFINCAR C-130 and NASA DC-8 aircrafts during the April 2006 part of the Intercontinental Chemical Transport Experiment (INTEX-B). Our evaluations show that MOPITT data assimilation provides significant improvements in terms of capturing the observed CO variability relative to no MOPITT assimilation (i.e. the correlation improves from 0.62 to 0.71, significant at 99% confidence). The assimilation provides evidence of median CO loading of about 150 ppbv at 700 hPa over the NE Pacific during April 2006. This is marginally higher than the modeled CO with no MOPITT assimilation (-140 ppbv). Our ensemble-based estimates of model uncertainty also show model overprediction over the source region (i.e. China) and underprediction over the NE Pacific, suggesting model errors that cannot be readily explained by emissions alone. These results have important implications for improving regional chemical forecasts and for inverse modeling of CO sources and further demonstrate the utility of the assimilation system in comparing non-coincident measurements, e.g. comparing satellite retrievals of CO with in-situ aircraft measurements. The work described above also brought to light several short-comings of the data assimilation approach for CO profiles. Because of the limited vertical resolution of the measurement, the retrievals at different altitudes are correlated which can lead to problems with numerical error and overall efficiency. This has resulted in a manuscript that is about to be submitted to JGR:

Arellano, A. F., Jr.↗

NASA NTRS2015

Impact of Asian Aerosols on Precipitation Over California: An Observational and Model Based Approach

Dust and pollution emissions from Asia are often transported across the Pacific Ocean to over the western United States. Therefore, it is essential to fully understand the impact of these aerosols on clouds and precipitation forming over the eastern Pacific and western United States, especially during atmospheric river events that account for up to half of California's annual precipitation and can lead to widespread flooding. In order for numerical modeling simulations to accurately represent the present and future regional climate of the western United States, we must account for the aerosol-cloud-precipitation interactions associated with Asian dust and pollution aerosols. Therefore, we have constructed a detailed study utilizing multi-sensor satellite observations, NOAA-led field campaign measurements, and targeted numerical modeling studies where Asian aerosols interacted with cloud and precipitation processes over the western United States. In particular, we utilize aerosol optical depth retrievals from the NASA Moderate Resolution Imaging Spectroradiometer (MODIS), NOAA Geostationary Operational Environmental Satellite (GOES-11), and Japan Meteorological Agency (JMA) Multi-functional Transport Satellite (MTSAT) to effectively detect and monitor the trans-Pacific transport of Asian dust and pollution. The aerosol optical depth (AOD) retrievals are used in assimilating the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) in order to provide the model with an accurate representation of the aerosol spatial distribution across the Pacific. We conduct WRF-Chem model simulations of several cold-season atmospheric river events that interacted with Asian aerosols and brought significant precipitation over California during February-March 2011 when the NOAA CalWater field campaign was ongoing. The CalWater field campaign consisted of aircraft and surface measurements of aerosol and precipitation processes that help extensively validate our WRF-Chem model simulations. After validating the capability of the WRF-Chem in realistically simulating the aerosol-cloud precipitation interactions, we conduct sensitivity studies where the AOD is doubled to diagnose whether an increasing concentration of Asian aerosols over the western United States will lead to further impacts on the cloud and precipitation processes over California. We also perform sensitivity studies where the aerosols will be partitioned into dust-only and pollution-only in order to separate the impacts of the differing Asian aerosol species. The results of our WRF-Chem model simulations aim to show that the trans-Pacific transport of Asian aerosols influence the precipitation associated with atmospheric river events that can ultimately impact the regional climate of the western United States. 1 University

Naeger, Aaron R.↗