SEARCH · Engineering Papers

Results for “event data models”

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.

SourceResearch connectionFrom yearThrough year

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

At least 451 records · Page 25

All sources NASA NTRS DOE OSTI

NASA NTRS1995

Evaluating the Type and State of Alaska Taiga Forests with Imaging Radar for Use in Ecosystem Models

Changes in the seasonal CO2 flux of the boreal forests may result from increased atmospheric CO2 concentrations and associated global warming patterns. To monitor this potential change, a combination of information derived from remote sensing data, including forest type and growing season length, and ecophysiological models which predict the CO2 flux and its seasonal amplitude based on meteorological data, are required. In this paper we address the use of synthetic aperture radar (SAR) to map fores type and monitor canopy and soil freeze/thaw, which define the growing season for conifers, and leaf on/off, which defines the growing season for deciduous species. Aircraft SAR (AIRSAR) data collected in March 1988 during a freeze/thaw event are used to generate species maps and to determine the sensitivity of SAR to canopy freeze/thaw transitions. These data are also used to validate a microwave scattering model which is then used to determine the sensitivity of SAR to leaf on/off transitions and soil freeze/thaw. Finally, a CO2 flux algorithm is presented which utilizes SAR data and an ecophysiological model to estimate CO2 flux. CO2 flux maps are generated, from which areal estimates of CO2 flux are derived.

Alaska↗

NASA NTRS2005

Spinning Spacecraft Attitude Estimation Using Markley Variables: Filter Implementation And Results

Attitude estimation is often more difficult for spinning spacecraft than for three-axis stabilized platforms due to the need to follow rapidly-varying state vector elements and the lack of three-axis rate measurements from gyros. The estimation problem simplifies when torques are negligible and nutation has damped out, but the general case requires a sequential filter with dynamics propagation. This paper describes the implementation and test results for an extended Kalman filter for spinning spacecraft attitude and rate estimation based on a novel set of variables suggested in a paper by Markley [AAS93-3301 (referred to hereafter as Markley variables). Markley has demonstrated that the new set of variables provides a superior parameterization for numerical integration of the attitude dynamics for spinning or momentum-biased spacecraft. The advantage is that the Markley variables have fewer rapidly-varying elements than other representations such as the attitude quaternion and rate vector. A filter based on these variables was expected to show improved performance due to the more accurate numerical state propagation. However, for a variety of test cases, it has been found that the new filter, as currently implemented, does not perform significantly better than a quaternion-based filter that was developed and tested in parallel. This paper reviews the mathematical background for a filter based on Markley variables. It also describes some features of the implementation and presents test results. The test cases are based on a mission using magnetometer and Sun sensor data and gyro measurements on two axes normal to the spin axis. The orbit and attitude scenarios and spacecraft parameters are modeled after one of the THEMIS (Time History of Events and Macroscale Interactions during Substorms) probes. Several tests are presented that demonstrate the filter accuracy and convergence properties. The tests include torque-free motion with various nutation angles, large constant-torque attitude slews, sensor misalignments, large initial attitude and rate errors, and cases with low data frequency. It is found that the convergence is rapid, the radius of convergence is large, and the results are reasonably accurate even in the presence of unmodeled perturbations.

Sedlak, Joseph E.↗

NASA NTRS1980

Cross-field transport of less than 1 MeV protons in energetic particle events

A systematic analysis of hourly averaged low-energy anisotropy data (0.3-0.5 MeV) has been carried out with the JHU/APL detectors onboard the IMP-7 and 8 spacecraft from 1972-1975. The energetic particle events were divided into two major groups: flare-associated or nonimpulsive. Resolving the anistropy vectors into components parallel and perpendicular to the measured magnetic field leads directly to the following model-independent conclusions: (1) the average perpendicular anisotropy component is entirely accounted for in terms of the ExB drift, and hence transverse diffusion is negligible at these energies; application of the diffusion model to the nonimpulsive data set implies a ratio of the diffusion mean free paths parallel and perpendicular to the magnetic field much less than 0.051; (2) the parallel anisotropy component averages nearly to zero in the nonimpulsive data set, indicating virtually no net streaming along the field at 1 AU, averaged over all events.

Zwickl, R. D.↗

NASA NTRS2005

Lightning Mapping and the Nowcasting of Severe Storms

This paper describes a successful research and operational collaboration between NASA scientists and NWS forecasters to improve severe stor m warnings using real-time data from a regional VHF total lightning mapping array (LMA). Key objectives of our research using LMA data ar e: a) Identification of intensifying and weakening storms using the time rate-of-change of total flash rate; b) Improved severe storm poten tial situational awareness; c) Evaluation of the potential of total f lash rate trend to improve severe storm probability of detection (POD ) and lead time; and d) Validation of mesoscale model forecasts of th understorm initiation. The LMA data are distributed for ingest and di splay in the WFO AWIPS decision support system, and archived at each WFO for case studies, event playbacks, and assessments using the NWS Warning Event Simulator. The Huntsville WFO has upgraded severe thund erstorm warnings to verified tornado warnings and avoided a false ala rm on a severe storm through the added information on storm growth, intensification, and decay that can be deduced from the magnitude and temporal trend of total flash rates. We present detailed case studies of the observed relationships between lightning activity and tornadi c storm development as determined by radar reflectivity and velocity fields, and thunderstorms forecast by the Weather Research and Foreca st (WRF) model. From these collaborative studies, forecasters can eva luate the added value of total lightning data within the forecast and warning decision-making process (http://weather.msfc.nasa.gov/sport) .

Goodman, S.↗

NASA NTRS2008

Processing LiDAR Data to Predict Natural Hazards

ELF-Base and ELF-Hazards (wherein 'ELF' signifies 'Extract LiDAR Features' and 'LiDAR' signifies 'light detection and ranging') are developmental software modules for processing remote-sensing LiDAR data to identify past natural hazards (principally, landslides) and predict future ones. ELF-Base processes raw LiDAR data, including LiDAR intensity data that are often ignored in other software, to create digital terrain models (DTMs) and digital feature models (DFMs) with sub-meter accuracy. ELF-Hazards fuses raw LiDAR data, data from multispectral and hyperspectral optical images, and DTMs and DFMs generated by ELF-Base to generate hazard risk maps. Advanced algorithms in these software modules include line-enhancement and edge-detection algorithms, surface-characterization algorithms, and algorithms that implement innovative data-fusion techniques. The line-extraction and edge-detection algorithms enable users to locate such features as faults and landslide headwall scarps. Also implemented in this software are improved methodologies for identification and mapping of past landslide events by use of (1) accurate, ELF-derived surface characterizations and (2) three LiDAR/optical-data-fusion techniques: post-classification data fusion, maximum-likelihood estimation modeling, and hierarchical within-class discrimination. This software is expected to enable faster, more accurate forecasting of natural hazards than has previously been possible.

Fairweather, Ian↗

NASA NTRSDate not supplied

Building a Real-Time Predictive Flood Model for Improving Early Warning Systems in Ellicott City, Maryland

As flood events in the United States grow in frequency and intensity, the use of applied remote sensing analyses is increasingly necessary for effective flood monitoring and warning systems. The NASA DEVELOP National Program partnered with the Howard County government in Maryland to investigate the use of machine learning for advanced flood risk detection, and to test the feasibility of integrating this approach into the county’s flood early warning system. To strengthen the efforts of the Howard County Office of Emergency Management (OEM), the project developed a prediction model capable of hindcasting the two severe flash flood events that devastated Ellicott City, and transitioned to an Long Short-Term Memory (LSTM) based sequence-to-sequence deep learning model with 8-hour forecast capability. The team combined data inputs from public sources including river and precipitation gauges, NASA and NOAA Earth observations, and numerical weather model products using scripts written in the Google Colaboratory Python scripting environment. In addition to designing the deep learning architecture, the team trained and tested the model, and evaluated its performance using the Nash-Sutcliffe Efficiency (NSE). The final product, called the Sequentially Trained Real-time EstimAted Model (STREAM), predicts stage height for the Hudson Branch gauge in Ellicott City using data products available in near real-time, including the High-Resolution Rapid Refresh (HRRR) model’s accumulated precipitation forecasts supplemented by stream gauge data from the OEM and the U.S. Geological Survey. STREAM was incorporated into an online dashboard in a user-friendly interface capable of triggering the alarms that initiate the OEM’s emergency response protocols up to 8 hours in advance of a predicted severe flood event. The project demonstrated the potential for the integration of open data and Earth observations into a flood risk forecasting tool capable of informing near real-time decision making.

Ryan Hammock↗

NASA NTRS1994

Hybird state of the tail mangetic configuration during steady convection events

Previous observations have shown that during periods of steady magnetospheric convection (SMC) a large amount of magnetic flux crosses the plasma sheet (corresponding to approximately 10 deg wide auroral oval at the nightside) and that the magnetic configuration in the midtail is relaxed (the curent sheet is thick and contains enhanced B(sub Z). These signatures are typical for the substorm recovery phase. Using near-geostationary magnetic field data, magnetic field modeling and a noval diagostic technique (isotropic boundary algorithm), we show that in the near-Earth tail the magnetic confirguration is very stretched during the SMC events. This stretching is caused by an intense, thin westward current. Because of the srongly depressed B(sub Z), there is a large radial gradient in the near-tail magetic field. These signatures have been peviously associated only with the substorm growth phase. Our results indicate that during the SMC periods the magnetic configuration is very peculiar, with co-existing thin near-Earth current sheet and thick midtail plasma sheet. The deep local minimum of the equatorial B(sub Z) that devleops at R approximately 12 R(sub E) is consistent with steady, adiabatic, Earthward convection in the midtail. These results impose contraints on the existing substorm theories, and call for an explanation of how such a stressed configuration can persist for such a long time without tail current disruptions that occur at the end of a substorm growth phase.

Sergeev, V. A.↗

NASA NTRSDate not supplied

Detecting And Characterizing Archetypes of Unintended Consequences in Engineered Systems

When designing engineered systems, the potential for unintended consequences of design policies or design decisions exists despite best intentions. Conditions that might cause the formation of unintended consequences are often known only in hindsight. However, since these conditions are associated with a single event, it is difficult to uncover the general patterns of conditions leading to unintended consequences. In this research, patterns of conditions associated with unintended consequences are learned from historical data and represented in the form of archetypes. While previous work using systems theoretic modeling has identified high-level archetypes, this work leverages a self-organizing map to learn archetypes of unintended consequences from human-tagged risk factors in a large data set of lessons learned from adverse events at NASA. The sixty-six identified archetypes contain patterns of conditions such as complexity and human-machine interaction associated with the formation of unintended consequences. To validate the archetypes, a sample of the archetypes is represented using system dynamics in order to illustrate that the identified archetypes are specialized versions of known high-level archetypes of unintended consequences. While the research is based upon a specific dataset, the archetypes apply to any engineered system and the pattern of leading indicators open a new path to manage unintended consequences and mitigate the magnitude of potentially adverse outcomes.

Hannah S Walsh↗

NASA NTRS1993

Rain-rate data base development and rain-rate climate analysis

The single-year rain-rate distribution data available within the archives of Consultative Committee for International Radio (CCIR) Study Group 5 were compiled into a data base for use in rain-rate climate modeling and for the preparation of predictions of attenuation statistics. The four year set of tip-time sequences provided by J. Goldhirsh for locations near Wallops Island were processed to compile monthly and annual distributions of rain rate and of event durations for intervals above and below preset thresholds. A four-year data set of tropical rain-rate tip-time sequences were acquired from the NASA TRMM program for 30 gauges near Darwin, Australia. They were also processed for inclusion in the CCIR data base and the expanded data base for monthly observations at the University of Oklahoma. The empirical rain-rate distributions (edfs) accepted for inclusion in the CCIR data base were used to estimate parameters for several rain-rate distribution models: the lognormal model, the Crane two-component model, and the three parameter model proposed by Moupfuma. The intent of this segment of the study is to obtain a limited set of parameters that can be mapped globally for use in rain attenuation predictions. If the form of the distribution can be established, then perhaps available climatological data can be used to estimate the parameters rather than requiring years of rain-rate observations to set the parameters. The two-component model provided the best fit to the Wallops Island data but the Moupfuma model provided the best fit to the Darwin data.

Crane, Robert K.↗

NASA NTRS2014

The Situational Awareness Sensor Suite for the ISS (SASSI): A Mission Concept to Investigate ISS Charging and Wake Effects

The complex interaction between the International Space Station (ISS) and the surrounding plasma environment often generates unpredictable environmental situations that affect operations. Examples of affected systems include extravehicular activity (EVA) safety, solar panel efficiency, and scientific instrument integrity. Models and heuristically‐derived best practices are well‐suited for routine operations, but when it comes to unusual or anomalous events or situations, especially those driven by space weather, there is no substitute for real‐time monitoring. Space environment data collected in real‐time (or near‐real time) can be used operationally for both real‐time alarms and data sources in assimilative models to predict environmental conditions important for operational planning. Fixed space weather instruments mounted to the ISS can be used for monitoring the ambient space environment, but knowing whether or not (or to what extent) the ISS affects the measurements themselves requires adequate space situational awareness (SSA) local to the ISS. This paper presents a mission concept to use a suite of plasma instruments mounted at the end of the ISS robotic arm to systematically explore the interaction between the Space Station structure and its surrounding environment. The Situational Awareness Sensor Suite for the ISS (SASSI) would be deployed and operated on the ISS Express Logistics Carrier (ELC) for long‐term "survey mode" observations and the Space Station Remote Manipulator System (SSRMS) for short‐term "campaign mode" observations. Specific areas of investigation include: 1) ISS frame and surface charging during perturbations of the local ISS space environment, 2) calibration of the ISS Floating Point Measurement Unit (FPMU), 3) long baseline measurements of ambient ionospheric electric potential structures, 4) electromotive force-induced currents within large structures moving through a magnetized plasma, and 5) wake‐induced ion waves in both electrostatic (i.e. particles) and electromagnetic modes. SASSI will advance the understanding of plasma‐boundary interaction phenomena, demonstrate a suite a sensors acting in concert to provide effective SSA, and validate and/or calibrate existing ISS space environment instruments and models.

Krause, L. Habash↗

NASA NTRS2010

Improving the Representation of Snow Crystal Properties with a Single-Moment Mircophysics Scheme

Single-moment microphysics schemes are utilized in an increasing number of applications and are widely available within numerical modeling packages, often executed in near real-time to aid in the issuance of weather forecasts and advisories. In order to simulate cloud microphysical and precipitation processes, a number of assumptions are made within these schemes. Snow crystals are often assumed to be spherical and of uniform density, and their size distribution intercept may be fixed to simplify calculation of the remaining parameters. Recently, the Canadian CloudSat/CALIPSO Validation Project (C3VP) provided aircraft observations of snow crystal size distributions and environmental state variables, sampling widespread snowfall associated with a passing extratropical cyclone on 22 January 2007. Aircraft instrumentation was supplemented by comparable surface estimations and sampling by two radars: the C-band, dual-polarimetric radar in King City, Ontario and the NASA CloudSat 94 GHz Cloud Profiling Radar. As radar systems respond to both hydrometeor mass and size distribution, they provide value when assessing the accuracy of cloud characteristics as simulated by a forecast model. However, simulation of the 94 GHz radar signal requires special attention, as radar backscatter is sensitive to the assumed crystal shape. Observations obtained during the 22 January 2007 event are used to validate assumptions of density and size distribution within the NASA Goddard six-class single-moment microphysics scheme. Two high resolution forecasts are performed on a 9-3-1 km grid, with C3VP-based alternative parameterizations incorporated and examined for improvement. In order to apply the CloudSat 94 GHz radar to model validation, the single scattering characteristics of various crystal types are used and demonstrate that the assumption of Mie spheres is insufficient for representing CloudSat reflectivity derived from winter precipitation. Furthermore, snow density and size distribution characteristics are allowed to vary with height, based upon direct aircraft estimates obtained from C3VP data. These combinations improve the representation of modeled clouds versus their radar-observed counterparts, based on profiles and vertical distributions of reflectivity. These meteorological events are commonplace within the mid-latitude cold season and present a challenge to operational forecasters. This study focuses on one event, likely representative of others during the winter season, and aims to improve the representation of snow for use in future operational forecasts.

Molthan, Andrew L.↗

NASA NTRS2012

The Effects of Climate Variability on Phytoplankton Composition in the Equatorial Pacific Ocean using a Model and a Satellite-Derived Approach

Compared the interannual variation in diatoms, cyanobacteria, coccolithophores and chlorophytes from the NASA Ocean Biogeochemical Model with those derived from satellite data (Hirata et al. 2011) between 1998 and 2006 in the Equatorial Pacific. Using NOBM, La Ni a events were characterized by an increase in diatoms (correlation with MEI, r=-0.81, P<0.05), while cyanobacteria concentrations decreased significantly (r=0.61; P<0.05). El Nino produced the reverse pattern, with cyanobacteria populations increasing while diatoms plummeted. This represented a radical shift in the phytoplankton community in response to climate variability. However, satellite-derived phytoplankton groups were all negatively correlated with climate variability (r ranged from -0.39 for diatoms to -0.64 for coccolithophores, P<0.05). Spatially, the satellite-derived approach was closer to an independent in situ dataset for all phytoplankton groups except diatoms than NOBM. However, the different responses of phytoplankton to intense interannual events in the Equatorial Pacific raises questions about the representation of phytoplankton dynamics in models and algorithms: is a phytoplankton community shift as in the model or an across-the-board change in abundances of all phytoplankton as in the satellite-derived approach.

Rousseaux, C. S.↗

NASA NTRS2018

CFD Model Of The Transonic Dynamics Tunnel With Applications

This paper presents the Computational Fluid Dynamics (CFD) model of the flow in the NASA Langley Research Center Transonic Dynamics Tunnel (TDT) with some recent applications. The TDT is a continuous-flow, closed circuit, slotted-test-section wind tunnel with a 16- by 16-foot test section with cropped corners. The tunnel was originally built as the 19-ft Pressure Tunnel in 1938, but it was converted to the current transonic tunnel in the 1950s, with capabilities to use either air or heavy gas at pressures from atmosphere down to near vacuum. In this study, experimental data acquired in the empty tunnel using R-134a as the test medium was used to calibrate the computational data. Experimental data from a recent TDT test of a full-span fighter configuration in air was then selected for comparison with the numerical data. During this test, the configuration experienced a flutter event in the transonic flow regime. Numerically, the flutter event is simulated both inside the CFD model of the TDT and in a classical free-air model. The preliminary results show that the wind-tunnel walls do not affect flutter prediction.

Chwalowski, Pawel↗

NASA NTRS2022

Preferential Acceleration of Heavy Ions in a Spontaneously Fragmenting Flare Current Sheet

We study the ion acceleration in a mesoscale, spontaneously fragmenting flare current sheet (SFCS) characterized by the presence of a plasmoid cascade. The main subject of our investigation is to determine whether and how plasmoid cascades at intermediate scales in a fragmented current sheet of a solar flare can impact the (preferential) acceleration of specific ions. The time evolution of the SFCS is obtained from high-resolution 2.5D MHD simulations. The ion trajectories (in the background fields resulting from the MHD model), energies, and pitch angles are calculated using a relativistic test-particle code based on the half-acceleration–rotation–half-acceleration method. For light ions, the main acceleration effects of electromagnetic fields within the SFCS are analyzed using the guiding center approximation. We identify regions with the most-efficient ion acceleration within the SFCS, the accelerator efficiency, and spectra of the accelerated ions. The influence of the charge-to-mass ratio on ion behavior is also studied and resulting ion abundances are compared with observational data. The main ion acceleration takes place in the regions with a strong polarization term, which is part of the first-order Fermi acceleration. Because the term is mass dependent, heavier ions undergo preferential acceleration. The ion energy spectra, abundance-enhancement factors, and differential fluxes, obtained from the model, exhibit power-law profiles, in agreement with observed solar energetic particle events. Nonetheless, the obtained slopes for the abundance-enhancement factor do not exactly match the observed data. The computed slopes and profiles are not sensitive to changes in the initial plasma temperature.

David Kramoliš↗

NASA NTRS1976

Analysis of information systems for hydropower operations

The operations of hydropower systems were analyzed with emphasis on water resource management, to determine how aerospace derived information system technologies can increase energy output. Better utilization of water resources was sought through improved reservoir inflow forecasting based on use of hydrometeorologic information systems with new or improved sensors, satellite data relay systems, and use of advanced scheduling techniques for water release. Specific mechanisms for increased energy output were determined, principally the use of more timely and accurate short term (0-7 days) inflow information to reduce spillage caused by unanticipated dynamic high inflow events. The hydrometeorologic models used in predicting inflows were examined to determine the sensitivity of inflow prediction accuracy to the many variables employed in the models, and the results used to establish information system requirements. Sensor and data handling system capabilities were reviewed and compared to the requirements, and an improved information system concept outlined.

Sohn, R. L.↗

NASA NTRS2020

Space Weather Architecture Options to Support Human and Robotic Deep Space Exploration

Understanding solar energetic particle (SEP) event characteristics is critical to the proper design of human and robotic space missions. This report details a study of operational response time for monitoring space weather for crewed flights, specifically targeting lunar and Mars missions. The NESC assessment team evaluated the required minimum latency for data streams and forecasts that will directly affect mission operations using a 41-year database (i.e., European Space Agency (ESA) Solar Energetic Particle Environment Modeling (SEPEM) RSDv2.0) of SEP events. The database contained 192 SEP events that resulted in a dose increase above background levels. Of those, 10% were “multiple events,” or events that occurred in quick succession. The analysis provides probabilistic values for time to peak flux and dose rate for the duration of each event. This NESC assessment also evaluated the SEP threshold levels for exploration missions to determine the relevant energy range of required proton measurements.

Solar energetic particle↗

NASA NTRS1995

Comment on 'The Global Resurfacing of Venus' by R. G. Strom, G.G. Schaber, and D.D. Dawson

The distribution of impact craters on Venus has been the subject of a great deal of analysis since the return of Magellan data. Phillips el al. (1992) performed Monte Carlo two-dimensional (2-D) modeling of the areal distribution of craters, and the results of that exercise allowed a restricted, but still quite large, range of possible planetary resurfacing histories, including the possibility that the crater, were emplaced on a geologically inactive planet. However, the nonrandom distribution of embayed and deformed craters (Phillips el al., 1992), the hypsometric distribution of craters (Herrick and Phillips, 1994), the varied degradation states of craters (Izenberg et al., 1994), their nonrandom distribution with different geologic terrain types (Namiki and Solomon, 1994; Price et al, 1994), and three-dimensional resurfacing modeling (Bullock el al., 1993) all seem to argue against that particular possibility. In contrast, Strom el al. (1994) have collected a refined and more comprehensive data set of impact features, and they input these data into more sophisticated 2-D Monte Carlo modeling and statistical analyses of the areal distribution of craters, the hypsometric distribution of craters, and the number of embayed craters. They concluded that 'Venus experienced a global resurfacing event about 300 m.y. ago followed by a dramatic reduction of volcanism and tectonism. This global resurfacing event ended abruptly (less than 10 m.y.). The present crater population has accumulated since then and remains largely intact . . . only about 4%-6% of the planet has been volcanically resurfaced since the global event . . .' If these conclusions are well-founded, this work certainly represents a significant advancement in restricting tile number of plausible resurfacing histories for the planet. If Strom et al. (1994) are correct, it would also mean that all of the other aforementioned works are in error to various degrees, or at least represent overzealous interpretation of the data. However, we have identified apparent flaws in the observations, modeling, and interpretations presented by Strom el al. (1994) that lead us to question whether their conclusions are warranted. We limit our comments to three areas of their analysis: (1) observations pertaining to the number and area of disrupted and pristine craters and crater-related features, (2) modeling of the areal and elevation distribution of craters, and (3) interpretations of resurfacing models.

Herrick, Robert R.↗

NASA NTRS2011

Evaluating the Performance of Single and Double Moment Microphysics Schemes During a Synoptic-Scale Snowfall Event

Increases in computing resources have allowed for the utilization of high-resolution weather forecast models capable of resolving cloud microphysical and precipitation processes among varying numbers of hydrometeor categories. Several microphysics schemes are currently available within the Weather Research and Forecasting (WRF) model, ranging from single-moment predictions of precipitation content to double-moment predictions that include a prediction of particle number concentrations. Each scheme incorporates several assumptions related to the size distribution, shape, and fall speed relationships of ice crystals in order to simulate cold-cloud processes and resulting precipitation. Field campaign data offer a means of evaluating the assumptions present within each scheme. The Canadian CloudSat/CALIPSO Validation Project (C3VP) represented collaboration among the CloudSat, CALIPSO, and NASA Global Precipitation Measurement mission communities, to observe cold season precipitation processes relevant to forecast model evaluation and the eventual development of satellite retrievals of cloud properties and precipitation rates. During the C3VP campaign, widespread snowfall occurred on 22 January 2007, sampled by aircraft and surface instrumentation that provided particle size distributions, ice water content, and fall speed estimations along with traditional surface measurements of temperature and precipitation. In this study, four single-moment and two double-moment microphysics schemes were utilized to generate hypothetical WRF forecasts of the event, with C3VP data used in evaluation of their varying assumptions. Schemes that incorporate flexibility in size distribution parameters and density assumptions are shown to be preferable to fixed constants, and that a double-moment representation of the snow category may be beneficial when representing the effects of aggregation. These results may guide forecast centers in optimal configurations of their forecast models for winter weather and identify best practices present within these various schemes.

Molthan, Andrew L.↗