Results for “Forecasting” — Page 18

NASA NTRS2010

Evaluation of Enhanced High Resolution MODIS/AMSR-E SSTs and the Impact on Regional Weather Forecast

Over the last few years, the NASA Short-term Prediction Research and Transition (SPoRT) Center has been generating a 1-km sea surface temperature (SST) composite derived from retrievals of the Moderate Resolution Imaging Spectroradiometer (MODIS) for use in operational diagnostics and regional model initialization. With the assumption that the day-to-day variation in the SST is nominal, individual MODIS passes aboard the Earth Observing System (EOS) Aqua and Terra satellites are used to create and update four composite SST products each day at 0400, 0700, 1600, and 1900 UTC, valid over the western Atlantic and Caribbean waters. A six month study from February to August 2007 over the marine areas surrounding southern Florida was conducted to compare the use of the MODIS SST composite versus the Real-Time Global SST analysis to initialize the Weather Research and Forecasting (WRF) model. Substantial changes in the forecast heat fluxes were seen at times in the marine boundary layer, but relatively little overall improvement was measured in the sensible weather elements. The limited improvement in the WRF model forecasts could be attributed to the diurnal changes in SST seen in the MODIS SST composites but not accounted for by the model. Furthermore, cloud contamination caused extended periods when individual passes of MODIS were unable to update the SSTs, leading to substantial SST latency and a cool bias during the early summer months. In order to alleviate the latency problems, the SPoRT Center recently enhanced its MODIS SST composite by incorporating information from the Advanced Microwave Scanning Radiometer-EOS (AMSR-E) instruments as well as the Operational Sea Surface Temperature and Sea Ice Analysis. These enhancements substantially decreased the latency due to cloud cover and improved the bias and correlation of the composites at available marine point observations. While these enhancements improved upon the modeled cold bias using the original MODIS SSTs, the discernable impacts on the WRF model were still somewhat limited. This paper explores several factors that may have contributed to this result. First, the original methodology to initialize the model used the most recent SST composite available in a hypothetical real ]time configuration, often matching the forecast initial time with an SST field that was 5-8 hours offset. To minimize the differences that result from the diurnal variations in SST, the previous day fs SST composite is incorporated at a time closest to the model initialization hour (e.g. 1600 UTC composite at 1500 UTC model initialization). Second, the diurnal change seen in the MODIS SST composites was not represented by the WRF model in previous simulations, since the SSTs were held constant throughout the model integration. To address this issue, we explore the use of a water skin-temperature diurnal cycle prediction capability within v3.1 of the WRF model to better represent fluctuations in marine surface forcing. Finally, the verification of the WRF model is limited to very few over-water sites, many of which are located near the coastlines. In order to measure the open ocean improvements from the AMSR-E, we could use an independent 2-dimensional, satellite-derived data set to validate the forecast model by applying an object-based verification method. Such a validation technique could aid in better understanding the benefits of the mesoscale SST spatial structure to regional models applications.

Schiferl, Luke D.↗

NASA NTRS2010

Impact of Atmospheric Infrared Sounder (AIRS) Thermodynamic Profiles on Regional Precipitation Forecasting

In data sparse regions, remotely-sensed observations can be used to improve analyses and lead to better forecasts. One such source comes from the Atmospheric Infrared Sounder (AIRS), which together with the Advanced Microwave Sounding Unit (AMSU), provides temperature and moisture profiles in clear and cloudy regions with accuracy which approaches that of radiosondes. The purpose of this paper is to describe an approach to assimilate AIRS thermodynamic profile data into a regional configuration of the Advanced Research WRF (ARW) model using WRF-Var. Quality indicators are used to select only the highest quality temperature and moisture profiles for assimilation in clear and partly cloudy regions, and uncontaminated portions of retrievals above clouds in overcast regions. Separate error characteristics for land and water profiles are also used in the assimilation process. Assimilation results indicate that AIRS profiles produce an analysis closer to in situ observations than the background field. Forecasts from a 37-day case study period in the winter of 2007 show that AIRS profile data can lead to improvements in 6-h cumulative precipitation forecasts resulting from improved thermodynamic fields. Additionally, in a convective heavy rainfall event from February 2007, assimilation of AIRS profiles produces a more unstable boundary layer resulting in enhanced updrafts in the model. These updrafts produce a squall line and precipitation totals that more closely reflect ground-based observations than a no AIRS control forecast. The location of available high-quality AIRS profiles ahead of approaching storm systems is found to be of paramount importance to the amount of impact the observations will have on the resulting forecasts.

Chou, S.-H.↗

NASA NTRS2010

Advances in Landslide Hazard Forecasting: Evaluation of Global and Regional Modeling Approach

A prototype global satellite-based landslide hazard algorithm has been developed to identify areas that exhibit a high potential for landslide activity by combining a calculation of landslide susceptibility with satellite-derived rainfall estimates. A recent evaluation of this algorithm framework found that while this tool represents an important first step in larger-scale landslide forecasting efforts, it requires several modifications before it can be fully realized as an operational tool. The evaluation finds that the landslide forecasting may be more feasible at a regional scale. This study draws upon a prior work's recommendations to develop a new approach for considering landslide susceptibility and forecasting at the regional scale. This case study uses a database of landslides triggered by Hurricane Mitch in 1998 over four countries in Central America: Guatemala, Honduras, EI Salvador and Nicaragua. A regional susceptibility map is calculated from satellite and surface datasets using a statistical methodology. The susceptibility map is tested with a regional rainfall intensity-duration triggering relationship and results are compared to global algorithm framework for the Hurricane Mitch event. The statistical results suggest that this regional investigation provides one plausible way to approach some of the data and resolution issues identified in the global assessment, providing more realistic landslide forecasts for this case study. Evaluation of landslide hazards for this extreme event helps to identify several potential improvements of the algorithm framework, but also highlights several remaining challenges for the algorithm assessment, transferability and performance accuracy. Evaluation challenges include representation errors from comparing susceptibility maps of different spatial resolutions, biases in event-based landslide inventory data, and limited nonlandslide event data for more comprehensive evaluation. Additional factors that may improve algorithm performance accuracy include incorporating additional triggering factors such as tectonic activity, anthropogenic impacts and soil moisture into the algorithm calculation. Despite these limitations, the methodology presented in this regional evaluation is both straightforward to calculate and easy to interpret, making results transferable between regions and allowing findings to be placed within an inter-comparison framework. The regional algorithm scenario represents an important step in advancing regional and global-scale landslide hazard assessment and forecasting.

Kirschbaum, Dalia B.↗

NASA NTRS2010

The Impact of Ocean Data Assimilation on Seasonal-to-Interannual Forecasts: A Case Study of the 2006 El Nino Event

This study investigates the impact of four different ocean analyses on coupled forecasts of the 2006 El Nino event. Forecasts initialized in June 2006 using ocean analyses from an assimilation that uses flow-dependent background error covariances are compared with those using static error covariances that are not flow dependent. The flow-dependent error covariances reflect the error structures related to the background ENSO instability and are generated by the coupled breeding method. The ocean analyses used in this study result from the assimilation of temperature and salinity, with the salinity data available from Argo floats. Of the analyses, the one using information from the coupled bred vectors (BV) replicates the observed equatorial long wave propagation best and exhibits more warming features leading to the 2006 El Nino event. The forecasts initialized from the BV-based analysis agree best with the observations in terms of the growth of the warm anomaly through two warming phases. This better performance is related to the impact of the salinity analysis on the state evolution in the equatorial thermocline. The early warming is traced back to salinity differences in the upper ocean of the equatorial central Pacific, while the second warming, corresponding to the mature phase, is associated with the effect of the salinity assimilation on the depth of the thermocline in the western equatorial Pacific. The series of forecast experiments conducted here show that the structure of the salinity in the initial conditions is important to the forecasts of the extension of the warm pool and the evolution of the 2006 El Ni o event.

Yang, Shu-Chih↗

NASA NTRS2008

Weather Research and Forecasting Model Wind Sensitivity Study at Edwards Air Force Base, CA

NASA prefers to land the space shuttle at Kennedy Space Center (KSC). When weather conditions violate Flight Rules at KSC, NASA will usually divert the shuttle landing to Edwards Air Force Base (EAFB) in Southern California. But forecasting surface winds at EAFB is a challenge for the Spaceflight Meteorology Group (SMG) forecasters due to the complex terrain that surrounds EAFB, One particular phenomena identified by SMG is that makes it difficult to forecast the EAFB surface winds is called "wind cycling". This occurs when wind speeds and directions oscillate among towers near the EAFB runway leading to a challenging deorbit bum forecast for shuttle landings. The large-scale numerical weather prediction models cannot properly resolve the wind field due to their coarse horizontal resolutions, so a properly tuned high-resolution mesoscale model is needed. The Weather Research and Forecasting (WRF) model meets this requirement. The AMU assessed the different WRF model options to determine which configuration best predicted surface wind speed and direction at EAFB, To do so, the AMU compared the WRF model performance using two hot start initializations with the Advanced Research WRF and Non-hydrostatic Mesoscale Model dynamical cores and compared model performance while varying the physics options.

Watson, Leela R.↗

NASA NTRS2009

Anvil Forecast Tool in the Advanced Weather Interactive Processing System (AWIPS)

Launch Weather Officers (LWOs) from the 45th Weather Squadron (45 WS) and forecasters from the National Weather Service (NWS) Spaceflight Meteorology Group (SMG) have identified anvil forecasting as one of their most challenging tasks when predicting the probability of violating the Lightning Launch Commit Criteria (LLCC) (Krider et al. 2006; Space Shuttle Flight Rules (FR), NASA/JSC 2004)). As a result, the Applied Meteorology Unit (AMU) developed a tool that creates an anvil threat corridor graphic that can be overlaid on satellite imagery using the Meteorological Interactive Data Display System (MIDDS, Short and Wheeler, 2002). The tool helps forecasters estimate the locations of thunderstorm anvils at one, two, and three hours into the future. It has been used extensively in launch and landing operations by both the 45 WS and SMG. The Advanced Weather Interactive Processing System (AWIPS) is now used along with MIDDS for weather analysis and display at SMG. In Phase I of this task, SMG tasked the AMU to transition the tool from MIDDS to AWIPS (Barrett et aI., 2007). For Phase II, SMG requested the AMU make the Anvil Forecast Tool in AWIPS more configurable by creating the capability to read model gridded data from user-defined model files instead of hard-coded files. An NWS local AWIPS application called AGRID was used to accomplish this. In addition, SMG needed to be able to define the pressure levels for the model data, instead of hard-coding the bottom level as 300 mb and the top level as 150 mb. This paper describes the initial development of the Anvil Forecast Tool for MIDDS, followed by the migration of the tool to AWIPS in Phase I. It then gives a detailed presentation of the Phase II improvements to the AWIPS tool.

Barrett, Joe H., III↗

NASA NTRS2011

Workshop Summary: International Cooperative for Aerosol Prediction Workshop On Aerosol Forecast Verification

The purpose of this workshop was to reinforce the working partnership between centers who are actively involved in global aerosol forecasting, and to discuss issues related to forecast verification. Participants included representatives from operational centers with global aerosol forecasting requirements, a panel of experts on Numerical Weather Prediction and Air Quality forecast verification, data providers, and several observers from the research community. The presentations centered on a review of current NWP and AQ practices with subsequent discussion focused on the challenges in defining appropriate verification measures for the next generation of aerosol forecast systems.

Benedetti, Angela↗

NASA NTRS2011

International Cooperative for Aerosol Prediction Workshop on Aerosol Forecast Verification

The purpose of this workshop was to reinforce the working partnership between centers who are actively involved in global aerosol forecasting, and to discuss issues related to forecast verification. Participants included representatives from operational centers with global aerosol forecasting requirements, a panel of experts on Numerical Weather Prediction and Air Quality forecast verification, data providers, and several observers from the research community. The presentations centered on a review of current NWP and AQ practices with subsequent discussion focused on the challenges in defining appropriate verification measures for the next generation of aerosol forecast systems.

Benedetti, Angela↗

NASA NTRS2011

Lightning Initiation Forecasting: An Operational Dual-Polarimetric Radar Technique

The objective of this NASA MSFC and NOAA CSTAR funded study is to develop and test operational forecast algorithms for the prediction of lightning initiation utilizing the C-band dual-polarimetric radar, UAHuntsville's Advanced Radar for Meteorological and Operational Research (ARMOR). Although there is a rich research history of radar signatures associated with lightning initiation, few studies have utilized dual-polarimetric radar signatures (e.g., Z(sub dr) columns) and capabilities (e.g., fuzzy-logic particle identification [PID] of precipitation ice) in an operational algorithm for first flash forecasting. The specific goal of this study is to develop and test polarimetric techniques that enhance the performance of current operational radar reflectivity based first flash algorithms. Improving lightning watch and warning performance will positively impact personnel safety in both work and leisure environments. Advanced warnings can provide space shuttle launch managers time to respond appropriately to secure equipment and personnel, while they can also provide appropriate warnings for spectators and players of leisure sporting events to seek safe shelter. Through the analysis of eight case dates, consisting of 35 pulse-type thunderstorms and 20 non-thunderstorm case studies, lightning initiation forecast techniques were developed and tested. The hypothesis is that the additional dual-polarimetric information could potentially reduce false alarms while maintaining high probability of detection and increasing lead-time for the prediction of the first lightning flash relative to reflectivity-only based techniques. To test the hypothesis, various physically-based techniques using polarimetric variables and/or PID categories, which are strongly correlated to initial storm electrification (e.g., large precipitation ice production via drop freezing), were benchmarked against the operational reflectivity-only based approaches to find the best compromise between forecast skill and lead-time. Forecast skill is determined by statistical analysis of probability of detection (POD), false alarm ratio (FAR), Operational Utility Index (OUI), and critical success index (CSI).

Woodard, Crystal J.↗

NASA NTRS2012

Using Science Data and Models for Space Weather Forecasting - Challenges and Opportunities

Space research, and, consequently, space weather forecasting are immature disciplines. Scientific knowledge is accumulated frequently, which changes our understanding or how solar eruptions occur, and of how they impact targets near or on the Earth, or targets throughout the heliosphere. Along with continuous progress in understanding, space research and forecasting models are advancing rapidly in capability, often providing substantially increases in space weather value over time scales of less than a year. Furthermore, the majority of space environment information available today is, particularly in the solar and heliospheric domains, derived from research missions. An optimal forecasting environment needs to be flexible enough to benefit from this rapid development, and flexible enough to adapt to evolving data sources, many of which may also stem from non-US entities. This presentation will analyze the experiences obtained by developing and operating both a forecasting service for NASA, and an experimental forecasting system for Geomagnetically Induced Currents.

Hesse, Michael↗

NASA NTRS2012

Forecasting Space Weather from Magnetograms

Large flares and fast CMEs are the drivers of the most severe space weather including Solar Energetic Particle Events (SEP Events). Large flares and their co-produced CMEs are powered by the explosive release of free magnetic energy stored in non-potential magnetic fields of sunspot active regions. The free energy is stored in and released from the low-beta regime of the active region s magnetic field above the photosphere, in the chromosphere and low corona. From our work over the past decade and from similar work of several other groups, it is now well established that (1) a proxy of the free magnetic energy stored above the photosphere can be measured from photospheric magnetograms, maps of the measured field in the photosphere, and (2) an active region s rate of production of major CME/flare eruptions in the coming day or so is strongly correlated with its present measured value of the free-energy proxy. These results have led us to use the large database of SOHO/MDI full-disk magnetograms spanning Solar Cycle 23 to obtain empirical forecasting curves that from an active region s present measured value of the free-energy proxy give the active region s expected rates of production of major flares, CMEs, fast CMEs, and SEP Events in the coming day or so (Falconer et al 2011, Space Weather, 9, S04003). For each type of event, the expected rate is readily converted to the chance that the active region will produce such an event in any given forward time window of a day or so. If the chance is small enough (e.g. <5%), the forecast is All Clear for that type of event. We will present these forecasting curves and demonstrate the accuracy of their forecasts. In addition, we will show that the forecasts for major flares and fast CMEs can be made significantly more accurate by taking into account not only the value of the free energy proxy but also the active region s recent productivity of major flares; specifically, whether the active region has produced a major flare (GOES class M or X) during the past 24 hours before the time of the measured magnetogram.

Falconer, David A.↗

NASA NTRS2013

Evaluating Downscaling Methods for Seasonal Climate Forecasts over East Africa

The U.S. National Multi-Model Ensemble seasonal forecasting system is providing hindcast and real-time data streams to be used in assessing and improving seasonal predictive capacity. The NASA / USAID SERVIR project, which leverages satellite and modeling-based resources for environmental decision making in developing nations, is focusing on the evaluation of NMME forecasts specifically for use in impact modeling within hub regions including East Africa, the Hindu Kush-Himalayan (HKH) region and Mesoamerica. One of the participating models in NMME is the NASA Goddard Earth Observing System (GEOS5). This work will present an intercomparison of downscaling methods using the GEOS5 seasonal forecasts of temperature and precipitation over East Africa. The current seasonal forecasting system provides monthly averaged forecast anomalies. These anomalies must be spatially downscaled and temporally disaggregated for use in application modeling (e.g. hydrology, agriculture). There are several available downscaling methodologies that can be implemented to accomplish this goal. Selected methods include both a non-homogenous hidden Markov model and an analogue based approach. A particular emphasis will be placed on quantifying the ability of different methods to capture the intermittency of precipitation within both the short and long rain seasons. Further, the ability to capture spatial covariances will be assessed. Both probabilistic and deterministic skill measures will be evaluated over the hindcast period.

Robertson, Franklin R.↗

NASA NTRS2013

Evaluating Downscaling Methods for Seasonal Climate Forecasts over East Africa

The U.S. National Multi-Model Ensemble seasonal forecasting system is providing hindcast and real-time data streams to be used in assessing and improving seasonal predictive capacity. The NASA / USAID SERVIR project, which leverages satellite and modeling-based resources for environmental decision making in developing nations, is focusing on the evaluation of NMME forecasts specifically for use in impact modeling within hub regions including East Africa, the Hindu Kush-Himalayan (HKH) region and Mesoamerica. One of the participating models in NMME is the NASA Goddard Earth Observing System (GEOS5). This work will present an intercomparison of downscaling methods using the GEOS5 seasonal forecasts of temperature and precipitation over East Africa. The current seasonal forecasting system provides monthly averaged forecast anomalies. These anomalies must be spatially downscaled and temporally disaggregated for use in application modeling (e.g. hydrology, agriculture). There are several available downscaling methodologies that can be implemented to accomplish this goal. Selected methods include both a non-homogenous hidden Markov model and an analogue based approach. A particular emphasis will be placed on quantifying the ability of different methods to capture the intermittency of precipitation within both the short and long rain seasons. Further, the ability to capture spatial covariances will be assessed. Both probabilistic and deterministic skill measures will be evaluated over the hindcast period

Roberts, J. Brent↗

NASA NTRS2014

Improved Satellite Techniques for Monitoring and Forecasting the Transition of Hurricanes to Extratropical Storms

The Geostationary Operational Environmental Satellites R-Series (GOES-R) and Joint Polar Satellite System (JPSS) Satellite Proving Grounds have introduced multiple proxy and operational products into operations over the last few years. Some of these products have proven to be useful in current operations at various National Weather Service (NWS) offices and national centers as a first look at future satellite capabilities. Forecasters at the National Hurricane Center (NHC), Ocean Prediction Center (OPC), NESDIS Satellite Analysis Branch (SAB) and the NASA Hurricane and Severe Storms Sentinel (HS3) field campaign have had access to a few of these products to assist in monitoring extratropical transitions of hurricanes. The red, green, blue (RGB) Air Mass product provides forecasters with an enhanced view of various air masses in one complete image to help differentiate between possible stratospheric/tropospheric interactions, moist tropical air masses, and cool, continental/maritime air masses. As a compliment to this product, a new Atmospheric Infrared Sounder (AIRS) and Cross-track Infrared Sounder (CrIS) Ozone product was introduced in the past year to assist in diagnosing the dry air intrusions seen in the RGB Air Mass product. Finally, a lightning density product was introduced to forecasters as a precursor to the new Geostationary Lightning Mapper (GLM) that will be housed on GOES-R, to monitor the most active regions of convection, which might indicate a disruption in the tropical environment and even signal the onset of extratropical transition. This presentation will focus on a few case studies that exhibit extratropical transition and point out the usefulness of these new satellite techniques in aiding forecasters forecast these challenging events.

Folmer, Michael↗

NASA NTRS2015

Snowfall Rate Retrieval Using Passive Microwave Measurements and Its Applications in Weather Forecast and Hydrology

(AMSU), Microwave Humidity Sounder (MHS) and Advance Technology Microwave Sounder (ATMS). ATMS is the follow‐on sensor to AMSU and MHS. Currently, an AMSU and MHS based land snowfall rate (SFR) product is running operationally at NOAA/NESDIS. Based on the AMSU/MHS SFR, an ATMS SFR algorithm has also been developed. The algorithm performs retrieval in three steps: snowfall detection, retrieval of cloud properties, and estimation of snow particle terminal velocity and snowfall rate. The snowfall detection component utilizes principal component analysis and a logistic regression model. It employs a combination of temperature and water vapor sounding channels to detect the scattering signal from falling snow and derives the probability of snowfall. Cloud properties are retrieved using an inversion method with an iteration algorithm and a two‐stream radiative transfer model. A method adopted to calculate snow particle terminal velocity. Finally, snowfall rate is computed by numerically solving a complex integral. The SFR products are being used mainly in two communities: hydrology and weather forecast. Global blended precipitation products traditionally do not include snowfall derived from satellites because such products were not available operationally in the past. The ATMS and AMSU/MHS SFR now provide the winter precipitation information for these blended precipitation products. Weather forecasters mainly rely on radar and station observations for snowfall forecast. The SFR products can fill in gaps where no conventional snowfall data are available to forecasters. The products can also be used to confirm radar and gauge snowfall data and increase forecasters' confidence in their prediction.

Meng, Huan↗

NASA NTRS2014

Weather Forecasting Systems and Methods

A weather forecasting system has weather forecasting logic that receives raw image data from a satellite. The raw image data has values indicative of light and radiance data from the Earth as measured by the satellite, and the weather forecasting logic processes such data to identify cumulus clouds within the satellite images. For each identified cumulus cloud, the weather forecasting logic applies interest field tests to determine a score indicating the likelihood of the cumulus cloud forming precipitation and/or lightning in the future within a certain time period. Based on such scores, the weather forecasting logic predicts in which geographic regions the identified cumulus clouds will produce precipitation and/or lighting within during the time period. Such predictions may then be used to provide a weather map thereby providing users with a graphical illustration of the areas predicted to be affected by precipitation within the time period.

Mecikalski, John↗

NASA NTRS2017

Meteoroid Environment Modeling: the Meteoroid Engineering Model and Shower Forecasting

The meteoroid environment is often divided conceptually into meteor showers plus a sporadic background component. The sporadic complex poses the bulk of the risk to spacecraft, but showers can produce significant short-term enhancements of the meteoroid flux. The Meteoroid Environment Office (MEO) has produced two environment models to handle these cases: the Meteoroid Engineering Model (MEM) and an annual meteor shower forecast. Both MEM and the forecast are used by multiple manned spaceflight projects in their meteoroid risk evaluation, and both tools are being revised to incorporate recent meteor velocity, density, and timing measurements. MEM describes the sporadic meteoroid complex and calculates the flux, speed, and directionality of the meteoroid environment relative to a user-supplied spacecraft trajectory, taking the spacecraft's motion into account. MEM is valid in the inner solar system and offers near-Earth and cis-lunar environments. While the current version of MEM offers a nominal meteoroid environment corresponding to a single meteoroid bulk density, the next version of MEMR3 will offer both flux uncertainties and a density distribution in addition to a revised near-Earth environment. We have updated the near-Earth meteor speed distribution and have made the first determination of uncertainty in this distribution. We have also derived a meteor density distribution from the work of Kikwaya et al. (2011). The annual meteor shower forecast takes the form of a report and data tables that can be used in conjunction with an existing MEM assessment. Fluxes are typically quoted to a constant limiting kinetic energy in order to comport with commonly used ballistic limit equations. For the 2017 annual forecast, the MEO substantially revised the list of showers and their characteristics using 14 years of meteor flux measurements from the Canadian Meteor Orbit Radar (CMOR). Defunct or insignificant showers were removed and the temporal profiles of many showers were improved. In 2016 the MEO also adapted the forecast to the cislunar environment for the first time. We plan to make additional improvements to the model in the next two years using optical meteor flux measurements and mass indices.

Moorhead, Althea V.↗

NASA NTRS2017

Meteoroid Environment Modeling: the Meteoroid Engineering Model and Shower Forecasting

The meteoroid environment is often divided conceptually into meteor showers plus a sporadic background component. The sporadic complex poses the bulk of the risk to spacecraft, but showers can produce significant short-term enhancements of the meteoroid flux. The Meteoroid Environment Office (MEO) has produced two environment models to handle these cases: the Meteoroid Engineering Model (MEM) and an annual meteor shower forecast. Both MEM and the forecast are used by multiple manned spaceflight projects in their meteoroid risk evaluation, and both tools are being revised to incorporate recent meteor velocity, density, and timing measurements. MEM describes the sporadic meteoroid complex and calculates the flux, speed, and directionality of the meteoroid environment relative to a user-supplied spacecraft trajectory, taking the spacecraft's motion into account. MEM is valid in the inner solar system and offers near-Earth and cis-lunar environments. While the current version of MEM offers a nominal meteoroid environment corresponding to a single meteoroid bulk density, the next version of MEMR3 will offer both flux uncertainties and a density distribution in addition to a revised near-Earth environment. We have updated the near-Earth meteor speed distribution and have made the first determination of uncertainty in this distribution. We have also derived a meteor density distribution from the work of Kikwaya et al. (2011). The annual meteor shower forecast takes the form of a report and data tables that can be used in conjunction with an existing MEM assessment. Fluxes are typically quoted to a constant limiting kinetic energy in order to comport with commonly used ballistic limit equations. For the 2017 annual forecast, the MEO substantially revised the list of showers and their characteristics using 14 years of meteor flux measurements from the Canadian Meteor Orbit Radar (CMOR). Defunct or insignificant showers were removed and the temporal profiles of many showers were improved. In 2016 the MEO also adapted the forecast to the cislunar environment for the first time. We plan to make additional improvements to the model in the next two years using optical meteor flux measurements and mass indices.

Moorhead, Althea V.↗