Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “distributed estimation”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 883 records · Page 49
All sourcesNASA NTRSDOE OSTI
NASA NTRS2020

Watershed Modeling with Remotely Sensed Big Data: MODIS Leaf Area Index Improves Hydrology and Water Quality Predictions

Traditional watershed modeling often overlooks the role of vegetation dynamics. There is also little quantitative evidence to suggest that increased physical realism of vegetation dynamics in process-based models improves hydrology and water quality predictions simultaneously. In this study, we applied a modified Soil and Water Assessment Tool (SWAT) to quantify the extent of improvements that the assimilation of remotely sensed Leaf Area Index (LAI) would convey to streamflow, soil moisture, and nitrate load simulations across a 16,860 km2 agricultural watershedin the midwestern United States. We modified the SWAT source code to automatically override the model’s built-in semiempirical LAI with spatially distributed and temporally continuous estimates from Moderate Resolution Imaging Spectroradiometer (MODIS). Compared to a “basic” traditional model with limited spatial information, our LAI assimilation model (i) significantly improved daily streamflow simulations during medium-to-low flow conditions, (ii) provided realistic spatial distributions of growing season soil moisture, and (iii) substantially reproduced the long-term observed variability of daily nitrate loads. Further analysis revealed that the overestimation or underestimation of LAI imparted a proportional cascading effect on how the model partitions hydrologic fluxes and nutrient pools. As such, assimilation of MODIS LAI data corrected the model’sLAI overestimation tendency, which led to a proportionally increased rootzone soil moisture and decreased plant nitrogen uptake. With these new findings, our study fills the existing knowledge gap regarding vegetation dynamics in watershed modeling and confirms that assimilation of MODIS LAI data in watershed models can effectively improve both hydrology and water quality predictions.

Adnan Rajib↗
DOE OSTI2025

Democratizing life cycle assessment by developing a streamlined model of greenhouse gas emissions from US natural gas supply chains

Natural gas (NG) supply chains contribute substantially to the global energy supply and anthropogenic methane emissions, making them frequent subjects of life cycle assessments (LCAs). To better characterize central tendencies and variability, we systematically reviewed and harmonized published estimates of life cycle greenhouse gas (GHG) emissions from United States NG supply chains. Results informed a streamlined LCA model (SLiNG-GHG: streamlined LCAs of NG-GHGs) that quantifies carbon dioxide and methane from three gates: transmission, distribution, and shipping. Median estimates employing harmonized emission inputs, are 10, 11, and 21 g CO2e/MJ gas (100-year global warming potentials [GWPs]), and 20, 22, and 33 g CO2e/MJ gas (20-year GWPs), delivered to each gate, respectively. Alternatively, inputting available, independent methane measurements, SLiNG-GHG estimates varied from -23% to +316% relative to baseline. Bottom-up inventories used in LCAs tend to underestimate methane compared with measurements. Results underscore the need for open-source, streamlined LCA models that can easily incorporate rapidly evolving measurements for non-experts like investors and regulators.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2020

Characterization of Partially Observed Epidemics - Application to COVID-19

This report documents a statistical method for the "real-time" characterization of partially observed epidemics. Observations consist of daily counts of symptomatic patients, diagnosed with the disease. Characterization, in this context, refers to estimation of epidemiological parameters that can be used to provide short-term forecasts of the ongoing epidemic, as well as to provide gross information for the time-dependent infection rate. The characterization problem is formulated as a Bayesian inverse problem, and is predicated on a model for the distribution of the incubation period. The model parameters are estimated as distributions using a Markov Chain Monte Carlo (MCMC) method, thus quantifying the uncertainty in the estimates. The method is applied to the COVID-19 pandemic of 2020, using data at the country, provincial (e.g., states) and regional (e.g. county) levels. The epidemiological model includes a stochastic component due to uncertainties in the incubation period. This model-form uncertainty is accommodated by a pseudo-marginal Metropolis-Hastings MCMC sampler, which produces posterior distributions that reflect this uncertainty. We approximate the discrepancy between the data and the epidemiological model using Gaussian and negative binomial error models; the latter was motivated by the over-dispersed count data. For small daily counts we find the performance of the calibrated models to be similar for the two error models. For large daily counts the negative-binomial approximation is numerically unstable unlike the Gaussian error model. Application of the model at the country level (for the United States, Germany, Italy, etc.) generally provided accurate forecasts, as the data consisted of large counts which suppressed the day-to-day variations in the observations. Further, the bulk of the data is sourced over the duration before the relaxation of the curbs on population mixing, and is not confounded by any discernible country-wide second wave of infections. At the state-level, where reporting was poor or which evinced few infections (e.g., New Mexico), the variance in the data posed some, though not insurmountable, difficulties, and forecasts were able to capture the data with large uncertainty bounds. The method was found to be sufficiently sensitive to discern the flattening of the infection and epidemic curve due to shelter-in-place orders after around 90% quantile for the incubation distribution (about 10 days for COVID-19). The proposed model was also used at a regional level to compare the forecasts for the central and north-west regions of New Mexico. Modeling the data for these regions illustrated different disease spread dynamics captured by the model. While in the central region the daily counts peaked in the late April, in the north-west region the ramp-up continued for approximately three more weeks.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS2013

Technical Report Series on Global Modeling and Data Assimilation: Global Surface Ocean Carbon Estimates in a Model Forced by MERRA - Volume 31

MERRA products were used to force an established ocean biogeochemical model to estimate surface carbon inventories and fluxes in the global oceans. The results were compared to public archives of in situ carbon data and estimates. The model exhibited skill for ocean dissolved inorganic carbon (DIC), partial pressure of ocean CO2 (pCO2) and air-sea fluxes (FCO2). The MERRA-forced model produced global mean differences of 0.02% (approximately 0.3 microns) for DIC, -0.3% (about -1.2 (micro) atm; model lower) for pCO2, and -2.3% (-0.003 mol C/sq m/y) for FCO2 compared to in situ estimates. Basin-scale distributions were significantly correlated with observations for all three variables (r=0.97, 0.76, and 0.73, P<0.05, respectively for DIC, pCO2, and FCO2). All major oceanographic basins were represented as sources to the atmosphere or sinks in agreement with in situ estimates. However, there were substantial basin-scale and local departures.

Gregg, Watson W.↗
DOE OSTI2026

High Time Resolution Modelled and Measured New Particle Formation Rates During the 2023 Pittsburgh, PA Campaign

Particle size distributions are used to calculate nucleation rates during new particle formation (NPF). However, the low time resolution and poor sensitivity of instruments deployed to observe particle size distributions can lead to inaccurate estimates of nucleation rates. We calculated nucleation rates in Pittsburgh, PA from particle size distributions measured with the new high time resolution Condensation Particle Counters For Atmospheric Rapid Measurements (CPC FARM) and from gas precursor concentrations measured with a chemical ionization mass spectrometer. The CPC FARM nucleation rates were calculated by examining the appearance rate of particles in the smallest size bin at 1.6 nm. The nucleation rate of 1.6 nm determined every second resulted in positive and negative rates, indicating the influence of inhomogeneous mixing and advection of precursor gases and clusters. These effects are not currently considered when estimating nucleation rates from measurements. The nucleation rates from the CPC FARM were compared to the nucleation rates determined from a transient acid‐base reaction model using measured sulfuric acid concentration, strong base concentrations, and CPC detection efficiencies. The transient form of the model allowed the advection of heterogeneously mixed precursors to influence the nucleation rate. Good agreement between particle and gas‐based nucleation rates is observed during sulfuric acid‐driven NPF events. This study demonstrates that high time resolution measurements of short‐lived and rapidly formed particles are needed to accurately capture nucleation rates, which are likely influenced by advection of nucleation precursors and clusters in heterogeneous environments such as urban areas.

Cheng, D. [Carnegie Mellon Univ., Pittsburgh, PA (↗
DOE OSTI2022

Aggregation bias and its drivers in large‐scale flood loss estimation: A Massachusetts case study

Abstract Large‐scale estimations of flood losses are often based on spatially aggregated inputs. This makes risk assessments vulnerable to aggregation bias, a well‐studied, sometimes substantial outcome in analyses that model fine‐grained spatial phenomena at coarse spatial units. To evaluate this potential in the context of large‐scale flood risk assessments, we use data from a high‐resolution flood hazard model and structure inventory for over 1.3 million properties in Massachusetts and examine how prominent data aggregation approaches affect the magnitude and spatial distribution of flood loss estimates. All considered aggregation approaches rely on aggregate structure inventories but differ in whether flood hazard is also aggregated. We find that aggregating only structure inventories slightly underestimates overall losses (−10% bias), and when flood hazard data is spatially aggregated to even relatively small spatial units (census block), statewide aggregation bias can reach +366%. All aggregation‐based procedures fail to capture the spatial covariation of inputs distributions in the upper tails that disproportionately generate total expected losses. Our findings are robust to several key assumptions, add important context to published risk assessments and highlight opportunities to improve flood loss estimation uncertainty quantification.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

A Distributionally Robust Resilience Enhancement Strategy for Distribution Networks Considering Decision-Dependent Contingencies

When performing the resilience enhancement for distribution networks, there are two obstacles to reliably model the uncertain contingencies: 1) decision-dependent uncertainty (DDU) due to various line hardening decisions, and 2) distributional ambiguity due to limited outage information during extreme weather events (EWEs). Here, to address these two challenges, this paper develops scenario-wise decision-dependent ambiguity sets (SWDD-ASs), where the DDU and distributional ambiguity inherent in EWE-induced contingencies are simultaneously captured for each possible EWE scenario. Then, a two-stage tri-level decision-dependent distributionally robust resilient enhancement (DD-DRRE) model is formulated, whose outputs include the optimal line hardening, distributed generation (DG) allocation, and proactive network reconfiguration strategy under the worst-case distributions in SWDD-ASs. Subsequently, the DD-DRRE model is equivalently recast to a mixed-integer linear programming (MILP)-based master problem and multiple scenario-wise subproblems, facilitating the adoption of a customized column-and-constraint generation (C&CG) algorithm. Finally, case studies demonstrate a remarkable improvement in the out-of-sample performance of our model, compared to its prevailing stochastic and robust counterparts. Moreover, the potential values of incorporating the ambiguity and distributional information are quantitatively estimated, providing a useful reference for planners with different budgets and risk-aversion levels.

decision-dependent uncertainty↗
NASA NTRS2006

Improving Lightning NO(x) Parameterizations for Global Chemical Transport Models

Parameterization of lightning NO(x) in global chemical transport models requires a method to specify the temporal and geographic distribution of flash rates, an estimate of NO production per flash, and a method to specify the effective vertical distribution of NO production. Flash rate parameterizations using the upward convective cloud mass flux have been developed and tested for several GCM and assimilated meteorological data sets. This parameterization and others involving the cloud-top height and convective precipitation provide a generally acceptable climatological distribution of flashes after normalization with satellite data. However, considerable improvement in terms of geographic and temporal variability may be achieved in the future using cloud microphysics data available from next-generation GCMs. Cloud-resolving case-study simulations of convective transport and lightning NO production have yielded results which are directly applicable to the NO production per flash and the vertical NO distribution portions of the global model lightning parameterizations. In this work we have used cloud-resolving models (the Goddard Cumulus Ensemble Model (GCE) and MM5) to drive an off-line cloud-scale chemical transport model (CSCTM). The CSCTM, in conjunction with aircraft measurements of NO(x) in thunderstorms and ground-based lightning observations, has been used to constrain the amount of NO produced per flash. Observed lightning flash rates have been incorporated into the CSCTM, and several scenarios of NO production per intracloud (IC) and per cloud-to-ground (CG) flash have been tested in each of several case study storms. The resulting NO(x) mixing ratios are compared with aircraft measurements taken within the storm (typically the anvil region) to determine the most likely NO production scenario. The range of values of NO production per flash (or per meter of lightning channel length) that have been deduced from the model will be shown and compared with values of production in the literature that have been deduced from observed NO spikes and from anvil flux calculations. Results show that on a per flash basis, IC flashes are nearly as productive of NO as CG flashes. Estimates of mean NO production per flash vary by a factor of three from one simulated storm to another. Vertical profiles of lightning NOx mass at the end of the 3-D storm simulations have been summarized to yield suggested profiles for use in global models. These profiles show a larger percentage of the total lightning NO(x) mass in the free troposphere than those suggested by Pickering et al. (1998).

Pickering, Kenneth E.↗
DOE OSTI2026

Evaluating the Collision‐Coalescence Process in Idealized Cloud Convection Using Large‐Eddy Simulations With Lagrangian Microphysics

Drizzle initiation through the collision and coalescence of cloud droplets plays a crucial role in warm cloud precipitation. Recent theoretical studies suggest that the influence of collisional growth on the droplet size distribution can be quantified by a non-dimensional drizzle number (Dz). Here, large-eddy simulations with Lagrangian microphysics are employed to evaluate the theory by simulating a tall convection-cloud chamber under various conditions. Results show that the smaller the Dz, the larger the impact of collisions on the right tail of the droplet size distribution, consistent with the theory. The simulations confirm that the collision rate can be estimated from the droplet size distribution interacting only with cloud droplets of the same size at the mode radius. This suggests that the idealized theory can be a useful tool to design a cloud chamber for drizzle investigation, as well as to represent drizzle formation in models of real atmospheric clouds.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1996

Measurements of Ocean Features With AIRSAR

Here we discuss an indirect approach for measuring ocean features from synthetic aperture radar (SAR) imagery. The approach is based on the refraction of swell that can be seen in the SAR imagery. Locally, swell refraction is due only to changes in bathymetry and surface current shear and it is easy to separate these two effects. It has been shown that linear energetic shears can be detected and measured from high resolution images of the ocean. A simple approach to measuring surface velocity and shear is to divide the imagery into smaller segments where the shear in each segment is approximately linear. The known linear algorithm would then be applied to each segment and the velocity/shear distribution can then be determined by assembling the segments with linear shear into an estimate of the actual shear/velocity distribution.

Sheres, David↗
NASA NTRS1995

The fate of atmospheric phosgene and the stratospheric chlorine loadings of its parent compounds: CCl4, C2Cl4, C2HCL3, CH3CCl3, and CHCl3

A study of the tropospheric and stratospheric cycles of phosgene is carried out to determine its fate and ultimate role in controlling the ozone depletion potentials of its parent compounds. Tropospheric phosgene is produced from the OH-initiated oxidation of C2Cl4, CH3CCl3, CHCl3, and C2HCl3. Simulations using a two-dimensional model indicate that these processes produce about 90 pptv/yr of tropospheric phosgene with an average concentration of about 18 pptv, in reasonable agreement with observations. We estimate a residence time of about 70 days for tropospheric phosgene, with the vast majority being removed by hydrolysis in cloudwater. Only about 0.4% of the phosgene produced in the troposphere avoids wet removal and is transported to the stratosphere, where its chlorine can be released to participate in the catalytic destruction of ozone. Stratospheric phosgene is produced from the photochemical degradation of CCl4, C2Cl4, CHCl3, and CH3CCl3 and is removed by photolysis and downward transport to the troposphere. Model calculations, in good agreement with observations, indicate that these processes produce a peak stratospheric concentration of about 25-30 pptv at an altitude of about 25 km. In contrast to tropospheric phosgene, stratospheric phosgene is found to have a lifetime against photochemical removal of the order of years. As a result, a significant portion of the phosgene that is produced in the stratosphere is ultimately returned to the troposphere, where it is rapidly removed by clouds. This phenomenon effectively decreases the amount of reactive chlorine injected into the stratosphere and available for ozone depletion from phosgene's parent compounds. A similar phenomenon due to the downward transport of stratospheric COFCl produced from CFC-11 is estimated to cause a 7% decrease in the amount of reactive chlorine injected into the stratosphere from this compound. Our results are potentially sensitive to a variety of parameters, most notably the rate of reaction of phosgene with sulfate aerosols. However, on the basis of the observed vertical distribution of COCl2, we estimate that the reaction of COCl2 with sulfate aerosol most likely has a gamma less than 5 x 10(exp -5) and, as a result, has a negligible impact on the stratospheric chlorine loadings of the phosgene parent compounds.

Kindler, T. P.↗
NASA NTRS2014

An Update on Oceanic Precipitation Rate and its Zonal Distribution in Light of Advanced Observations from Space

This study contributes to the estimation of the global mean and zonal distribution of oceanic precipitation rate using complementary information from advanced precipitation measuring sensors and provides an independent reference to assess current precipitation products. Precipitation estimates from the Tropical Rainfall Measuring Mission (TRMM) precipitation radar (PR) and CloudSat cloud profiling radar (CPR) were merged, as the two complementary sensors yield an unprecedented range of sensitivity to quantify rainfall from drizzle through the most intense rates. At higher latitudes, where TRMM PR does not exist, precipitation estimates from Aqua's Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E) complemented CloudSat CPR to capture intense precipitation rates. The high sensitivity of CPR allows estimation of snow rate, an important type of precipitation at high latitudes, not directly observed in current merged precipitation products. Using the merged precipitation estimate from the CloudSat, TRMM, and Aqua platforms (this estimate is abbreviated to MCTA), the authors' estimate for 3-yr (2007-09) nearglobal (80degS-80degN) oceanic mean precipitation rate is approx. 2.94mm/day. This new estimate of mean global ocean precipitation is about 9% higher than that of the corresponding Climate Prediction Center (CPC) Merged Analysis of Precipitation (CMAP) value (2.68mm/day) and about 4% higher than that of the Global Precipitation Climatology Project (GPCP; 2.82mm/day). Furthermore, MCTA suggests distinct differences in the zonal distribution of precipitation rate from that depicted in GPCPand CMAP, especially in the Southern Hemisphere.

profiling radar↗
DOE OSTI2023

Comparative Analysis of Imaging and Measurements of Micrometer-Scale Fracture Aperture Fields Within a Heterogeneous Rock Using PET and X-ray CT

Knowledge of the spatial distribution of fracture apertures is essential for reliable characterization of flow and transport processes in fractured systems and for better understanding of physicochemical matrix–fracture interactions. Here, we propose and test two image-based methods, thereby extending the current experimental capabilities to characterize aperture size distribution in structurally heterogeneous geologic porous media noninvasively. The first approach utilizes an inversion method based on the dataset acquired from positron emission tomography (PET) and the second approach considers an extension of the classic missing attenuation technique that relies on clinical X-ray computed tomography (X-ray CT). Independent sets of imaging experiments are conducted on a fractured basalt core with heterogeneous matrix properties and aperture distributions to compare the two methodologies. A repeat of each experiment is conducted to verify the proposed workflows. The performance of these two imaging techniques is systematically evaluated through the analysis of signal-to-noise ratio, minimum fracture size detectability, and measurement errors. While both approaches provide a reliable estimation of fracture aperture distributions, PET yields a signal-to-noise ratio that is substantially higher than the corresponding X-ray CT measurements. Furthermore, uncertainties of the aperture values for PET are considerably lower ($\bar {\sigma}_{\text {d}} = 15\%$) compared to those obtained from X-ray CT ($\bar {\sigma}_{\text {d}} = 29\%$), allowing for the detection of minimum aperture sizes of 20 $\mu$m with 70% confidence level. Finally, these approaches provide key experimental tools for better understanding dynamic hydromechanical fracture properties in geologic systems.

58 GEOSCIENCES↗
DOE OSTI2022

AutoEnRichness: A hybrid empirical and analytical approach for estimating the richness of galaxy clusters

ABSTRACT We introduce AutoEnRichness, a hybrid approach that combines empirical and analytical strategies to determine the richness of galaxy clusters (in the redshift range of 0.1 ≤ z ≤ 0.35) using photometry data from the Sloan Digital Sky Survey Data Release 16, where cluster richness can be used as a proxy for cluster mass. In order to reliably estimate cluster richness, it is vital that the background subtraction is as accurate as possible when distinguishing cluster and field galaxies to mitigate severe contamination. AutoEnRichness is comprised of a multistage machine learning algorithm that performs background subtraction of interloping field galaxies along the cluster line of sight and a conventional luminosity distribution fitting approach that estimates cluster richness based only on the number of galaxies within a magnitude range and search area. In this proof-of-concept study, we obtain a balanced accuracy of 83.20 per cent when distinguishing between cluster and field galaxies as well as a median absolute percentage error of 33.50 per cent between our estimated cluster richnesses and known cluster richnesses within r200. In the future, we aim for AutoEnRichness to be applied on upcoming large-scale optical surveys, such as the Legacy Survey of Space and Time and Euclid, to estimate the richness of a large sample of galaxy groups and clusters from across the halo mass function. This would advance our overall understanding of galaxy evolution within overdense environments as well as enable cosmological parameters to be further constrained.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS2018

Intercomparison of Precipitation Estimates over the Arctic Ocean and Its Peripheral Seas from Reanalyses

Precipitation over the Arctic Ocean has a significant impact on the basin-scale freshwater and energy budgets but is one of the most poorly constrained variables in atmospheric reanalyses. Precipitation controls the snow cover on sea ice, which impedes the exchange of energy between the ocean and atmosphere, inhibiting sea ice growth. Thus, accurate precipitation amounts are needed to inform sea ice modeling, especially for the production of thickness estimates from satellite altimetry freeboard data. However, obtaining a quantitative estimate of the precipitation distribution in the Arctic is notoriously difficult because of a number of factors, including a lack of reliable, long-term in situ observations; difficulties in remote sensing over sea ice; and model biases in temperature and moisture fields and associated uncertainty of modeled cloud microphysical processes in the polar regions. Here, we compare precipitation estimates over the Arctic Ocean from eight widely used atmospheric reanalyses over the period 2000–16 (nominally the ‘‘new Arctic’’). We find that the magnitude, frequency, and phase of precipitation vary drastically, although interannual variability is similar. Reanalysis-derived precipitation does not increase with time as expected; however, an increasing trend of higher fractions of liquid precipitation (rainfall) is found. When compared with drifting ice mass balance buoys, three reanalyses (ERA-Interim, MERRA, and NCEP R2) produce realistic magnitudes and temporal agreement with observed precipitation events, while two products [MERRA, version 2 (MERRA-2), and CFSR] show large, implausible magnitudes in precipitation events. All the reanalyses tend to produce overly frequent Arctic precipitation. Future work needs to be undertaken to determine the specific factors in reanalyses that contribute to these discrepancies in the new Arctic.

Mcpartland, Linette N.↗
DOE OSTI2023

Revealing the Statistics of Extreme Events Hidden in Short Weather Forecast Data

Extreme weather events have significant consequences, dominating the impact of climate on society. While high-resolution weather models can forecast many types of extreme events on synoptic timescales, long-term climatological risk assessment is an altogether different problem. A once-in-a-century event takes, on average, 100 years of simulation time to appear just once, far beyond the typical integration length of a weather forecast model. Therefore, this task is left to cheaper, but less accurate, low-resolution or statistical models. But there is untapped potential in weather model output: despite being short in duration, weather forecast ensembles are produced multiple times a week. Integrations are launched with independent perturbations, causing them to spread apart over time and broadly sample phase space. Collectively, these integrations add up to thousands of years of data. We establish methods to extract climatological information from these short weather simulations. Using ensemble hindcasts by the European Center for Medium-range Weather Forecasting archived in the subseasonal-to-seasonal (S2S) database, we characterize sudden stratospheric warming (SSW) events with multi-centennial return times. Consistent results are found between alternative methods, including basic counting strategies and Markov state modeling. By carefully combining trajectories together, we obtain estimates of SSW frequencies and their seasonal distributions that are consistent with reanalysis-derived estimates for moderately rare events, but with much tighter uncertainty bounds, and which can be extended to events of unprecedented severity that have not yet been observed historically. These methods hold potential for assessing extreme events throughout the climate system, beyond this example of stratospheric extremes.

58 GEOSCIENCES↗
NASA NTRS1997

Estimation of Snow Parameters from Dual-Wavelength Airborne Radar

Estimation of snow characteristics from airborne radar measurements would complement In-situ measurements. While In-situ data provide more detailed information than radar, they are limited in their space-time sampling. In the absence of significant cloud water contents, dual-wavelength radar data can be used to estimate 2 parameters of a drop size distribution if the snow density is assumed. To estimate, rather than assume, a snow density is difficult, however, and represents a major limitation in the radar retrieval. There are a number of ways that this problem can be investigated: direct comparisons with in-situ measurements, examination of the large scale characteristics of the retrievals and their comparison to cloud model outputs, use of LDR measurements, and comparisons to the theoretical results of Passarelli(1978) and others. In this paper we address the first approach and, in part, the second.

Liao, Liang↗
NASA NTRS2012

Understanding Climate Policy Data Needs. NASA Carbon Monitoring System Briefing: Characterizing Flux Uncertainty, Washington D.C., 11 January 2012

Climate policy in the United States is currently guided by public-private partnerships and actions at the local and state levels. This mitigation strategy is made up of programs that focus on energy efficiency, renewable energy, agricultural practices and implementation of technologies to reduce greenhouse gases. How will policy makers know if these strategies are working, particularly at the scales at which they are being implemented? The NASA Carbon Monitoring System (CMS) will provide information on carbon dioxide fluxes derived from observations of earth's land, ocean and atmosphere used in state of the art models describing their interactions. This new modeling system could be used to assess the impact of specific policy interventions on CO2 reductions, enabling an iterative, results-oriented policy process. In January of 2012, the CMS team held a meeting with carbon policy and decision makers in Washington DC to describe the developing modeling system to policy makers. The NASA CMS will develop pilot studies to provide information across a range of spatial scales, consider carbon storage in biomass, and improve measures of the atmospheric distribution of carbon dioxide. The pilot involves multiple institutions (four NASA centers as well as several universities) and over 20 scientists in its work. This pilot study will generate CO2 flux maps for two years using observational constraints in NASA's state-of -the-art models. Bottom-up surface flux estimates will be computed using data-constrained land and ocean models; comparison of the different techniques will provide some knowledge of uncertainty in these estimates. Ensembles of atmospheric carbon distributions will be computed using an atmospheric general circulation model (GEOS-5), with perturbations to the surface fluxes and to transport. Top-down flux estimates will be computed from observed atmospheric CO2 distributions (ACOS/GOSAT retrievals) alongside the forward-model fields, in conjunction with an inverse approach based on the CO2 model of GEOS ]Chem. The forward model ensembles will be used to build understanding of relationships among surface flux perturbations, transport uncertainty and atmospheric carbon concentration. This will help construct uncertainty estimates and information on the true spatial resolution of the top-down flux calculations. The relationship between the top-down and bottom-up flux distributions will be documented. Because the goal of NASA CMS is to be policy relevant, the scientists involved in the flux modeling pilot need to understand and be focused on the needs of the climate policy and decision making community. If policy makers are to use CMS products, they must be aware of the modeling effort and begin to design policies that can be evaluated with information. Improving estimates of carbon sequestered in forests, for example, will require information on the spatial variability of forest biomass that is far more explicit than is presently possible using only ground observations. Carbon mitigation policies being implemented by cities around the United States could be designed with the CMS data in mind, enabling sequential evaluation and subsequent improvements in incentives, structures and programs. The success of climate mitigation programs being implemented in the United States today will hang on the depth of the relationship between scientists and their policy and decision making counterparts. Ensuring that there is two-way communication between data providers and users is important for the success both of the policies and the scientific products meant to support them..

Brown, Molly E.↗