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NASA NTRS1986

Predicting the magnetospheric plasma of weather

The prediction of the plasma environment in time, the plasma weather, is discussed. It is important to be able to predict when large magnetic storms will produce auroras, which will affect the space station operating in low orbit, and what precautions to take both for personnel and sensitive control (computer) equipment onboard. It is also important to start to establish a set of plasma weather records and a record of the ability to predict this weather. A successful forecasting system requires a set of satellite weather stations to provide data from which predictions can be made and a set of plasma weather codes capable of accurately forecasting the status of the Earth's magnetosphere. A numerical magnetohydrodynamic fluid model which is used to model the flow in the magnetosphere, the currents flowing into and out of the auroral regions, the magnetopause, the bow shock location and the magnetotail of the Earth is discussed.

Dawson, John M.↗

NASA NTRS2019

Five Decades Observing Earth's Atmospheric Trace Gases Using Ultraviolet and Visible Backscatter Solar Radiation from Space

Over the last five decades, Earth’s atmosphere has been extensively monitored from space using different spectral ranges. Early efforts were directed at improving weather forecasts with the first meteorological satellites launched in the 1960s. Soon thereafter, the intersection between weather, climate and atmospheric chemistry led to the observation of atmospheric composition from space. During the 1970s the Nimbus satellite program started regular monitoring of ozone integrated columns and water vapor profiles using the Backscatter Ultraviolet Spectrometer, the Infrared Interferometer Spectrometer and the Satellite Infrared Spectrometer instruments. Five decades after these pioneer efforts, continuous progress in instrument design, and retrieval techniques allow researchers to monitor tropospheric concentrations of a wide range of species with implications for air quality, climate and weather. The time line of historic, present and future space-borne instruments measuring ultraviolet and visible backscattered solar radiation designed to quantify atmospheric trace gases is presented. We describe the instruments technological evolution and the basic concepts of retrieval theory. We include a review of algorithms developed for ozone, nitrogen dioxide, sulfur dioxide, formaldehyde, bromine monoxide, water vapor and glyoxal, a selection of studies using these algorithms, the challenges they face and how these challenges can be addressed. The paper ends by providing insights on the opportunities that new instruments will bring to the atmospheric chemistry, weather and air quality communities and how to address the pressing need for long-term, inter-calibrated data records necessary to monitor the response of the atmosphere to rapidly changing ecosystems.

Infrared Interferometer Spectrometer↗

NASA NTRS1999

The Design of a Remote Sensing Data Acquisition Campaign for Precision Agriculture and Some Early Results

In the 1970s NASA and the Department of Agriculture attempted to use the new Landsat MSS system for agricultural purposes. The program had relatively little success. With the advent of differential GPS, yield monitors on harvest equipment and higher spatial resolution remote sensing systems it seemed likely the situation should be reexamined. Therefore, a campaign of data acquisition involving remote sensing and other modalities with dependent research was assembled and funded by the Space Grant Consortia in Alabama and Georgia. The design of the remote sensing data acquisition was driven by the biology and physics of the crop system and limited by the available sensor platforms. Major parameters included crop stage, spatial resolution, seasonal and daily weather conditions, and which portion of the EM spectrum would actually capture the most discriminating information. Joint visible and Near IR with Thermal IR would permit use of existing indices, such as greenness, as well as phenomena driven by the plant' s evapotranspiration. Spatial resolution in the 2-5 meter range was chosen, avoiding many complexities caused by aliasing crop row spacing at, higher resolutions yet finer than the harvester's tightest recording rate. This dictates use of an airborne system. Use of an airborne system also makes scheduling around weather much simpler than use of satellite data. Active video calibration was recognized as essential if quantitative measures were ever to be obtained or reproduced. The system would also have to have onboard geoOF1 Based on these elements 3 data acquisitions have been flown. Seven flight lines were flown twice in 1998 and 16 lines flown in 1999. Total raw data is several GBytes. All of the data has now been geometrically corrected and some preliminary analysis accomplished. The thermal bands have an extremely high correlation with yield. For one@test case with corn, correlation in excess of 0.86 was obtained from a data acquisition two months prior to harvest! Soil images show significant within field variation in clay, soil brightness and emissivity. Light wind has been found to effect the reflectance and temperature of broad leaf crops, including soybeans, cotton and peanuts. Clearly, this work has already demonstrated some very important results. With continued development of the remote sensing technology there is good reason to believe this research will soon be able to help the individual farmer.

Rickmanl, D.↗

DOE OSTI2025

Identifying Vehicle Signals in Continuous Seismic Data Using Unsupervised Machine-Learning Techniques

Seismic sensors deployed near roadways effectively capture ground vibrations generated by passing vehicles. Although both traditional and machine‐learning algorithms have been utilized for analyzing such signals, independent validation of detected vehicle events remains limited. We applied two unsupervised machine‐learning algorithms, uniform manifold approximation and projection for dimension reduction, and hierarchical density‐based spatial clustering of applications with noise, to continuous seismic data collected along a road on the main campus of Oak Ridge National Laboratory. The algorithms identified seven distinct cluster labels across the entire dataset. By comparing these cluster labels with precipitation records from a nearby weather station and image‐derived labels from a local camera system, we identified one cluster associated with rainfall and another with vehicle activity. Our algorithms identified a greater number of vehicle‐related labels compared to the camera‐derived labels because seismic data are unaffected by poor lighting conditions. The arrival times of the newly detected vehicle signals corresponded well with the road’s speed limit, supporting our findings. Our algorithm outperformed the short‐term average/long‐term average method and k‐means clustering. Our results suggest that seismic data, when analyzed with machine‐learning algorithms, can complement existing vehicle monitoring systems, particularly under challenging environmental conditions.

Chai, Chengping [Oak Ridge National Laboratory (OR↗

NASA NTRSDate not supplied

Development of a Consistent GEOsat Cloud Property Dataset for the CERES Climate Data Record

Cloud properties are critical for understanding the Earth’s radiation budget and cloud feedbacks. At NASA Langley Research Center, the Satellite ClOud and Radiative Property retrieval System (SatCORPS) provides real-time and historical analyses of clouds derived from Geostationary satellite (GEOsat) data for weather and climate applications. For the Clouds and the Earth’s Radiant Energy System (CERES) program, the global constellation of GEOsats has been analyzed since 2000 to help characterize and account for the diurnal cycle of clouds and their radiative impacts in the CERES climate data record. Obtaining consistent cloud properties over the GEOsat data record during the CERES era is a major objective but a significant challenge considering the diversity of imaging capabilities deployed during that time. The GEOsat data analysis approach for the current CERES Edition-4 (Ed4) data products was focused on accuracy and consistency with MODIS by employing as much spectral information as possible from each satellite. However, the inconsistent use of spectral information across GEOsats led to marked discontinuities in the spatial and temporal record of cloud properties that had to be accounted for post facto in downstream CERES processing. This paper reports progress in developing a new GEOsat analysis system for the next CERES edition (Ed5) that has potential to improve cross-platform consistency and continuity. In this approach, the spectral channel complement is limited to just 3-channels during daytime, ~0.65 µm (VIS), ~3.9 µm (NIR), and ~10.8 µm (IR), common to nearly all of the satellites in the record. At night, a 2-channel approach is taken with the NIR and IR, and ~6.7 µm bands that includes a machine learning approach for optically thick cloud properties. A tradeoff is the potential for reduced accuracy particularly using data from the more advanced satellites that have more spectral channels (e.g. SEVIRI, AHI and ABI) that are known to help improve thin cirrus detection, cloud-aerosol discrimination and estimates in other difficult conditions that challenge cloud remote sensing. The new continuity approach is applied to one month of global GEOSat data for each year of the CERES record since 2000 and compared with the Ed4 GEO and MODIS cloud property time series in order to evaluate the level of improved consistency in the GEOsat record and to assess the accuracy impacts. Cloud fraction will also be assessed with CALIPSO data. Outstanding issues and challenges will be discussed. The results are expected to guide future work needed to develop a more robust GEOsat cloud data record for CERES.

CERES CDR↗

NASA NTRSDate not supplied

Flight Mechanics Analysis of Low-Earth Orbit Flight Test of an Inflatable Decelerator

The Low-Earth Orbit Flight Test of an Inflatable Decelerator (LOFTID) successfully demonstrated the capability of an inflatable aeroshell. Prior to launch, flight mechanics analysis was conducted to better understand the predicted splashdown point of the re-entry vehicle and ejectable data recorder. Best estimated trajectory separation states were utilized to understand the LOFTID trajectory over the entire 36 minute launch window. Weather forecasting model were integrated into the simulation to improve trajectory prediction accuracy. Automation tools were developed to facilitate the rapid generation of trajectory predictions during operations. The resulting improvements to the flight mechanics modeling and simulation allowed for pre-launch splashdown point to be within 3 nautical miles of the actual splashdown point.

Rohan G Deshmukh↗

NASA NTRSDate not supplied

Flight Mechanics Analysis of Low-Earth Orbit Flight Test of an Inflatable Decelerator (LOFTID)

Rohan Deshmukh↗

NASA NTRS2014

Worldwide Weather Radar Imagery May Allow Substantial Increase in Meteorite Fall Recovery

Weather radar imagery is a valuable new technique for the rapid recovery of meteorite falls, to include falls which would not otherwise be recovered (e.g. Battle Mountain). Weather radar imagery reveals about one new meteorite fall per year (18 falls since 1998), using weather radars in the United States alone. However, an additional ~75 other nations operate weather radar networks according to the UN World Meteorological Organization (WMO). If the imagery of those radars were analyzed, the current rate of meteorite falls could be improved considerably, to as much as ~3.6 times the current recovery rate based on comparison of total radar areal coverage. Recently, the addition of weather radar imagery, seismometry and internet-based aggregation of eyewitness reports has improved the speed and accuracy of fresh meteorite fall recovery [e.g. 1,2]. This was demonstrated recently with the radar-enabled recovery of the Sutter's Mill fall [3]. Arguably, the meteorites recovered via these methods are of special scientific value as they are relatively unweathered, fresh falls. To illustrate this, a recent SAO/NASA ADS search using the keyword "meteorite" shows that all 50 of the top search results included at least one named meteorite recovered from a meteorite fall. This is true even though only ~1260 named meteorite falls are recorded among the >49,000 individual falls recorded in the Meteoritical Society online database. The US NEXRAD system used thus far to locate meteorite falls covers most of the United States' surface area. Using a WMO map of the world's weather radars, we estimate that the total coverage of the other ~75 national weather radar networks equals about 3.6x NEXRAD's coverage area. There are two findings to draw from this calculation: 1) For the past 16 years during which 18 falls are seen in US radar data, there should be an additional ~65 meteorite falls recorded in worldwide radar imagery. Also: 2) if all of the world's radar data could be analyzed, the rate of recovery of fresh meteorite falls can increase by as much as ~3.6x the current rate. The authors' experience to date indicates that the most effective course of action would be to have local meteorite research groups (outside of the US) form research consortia and develop a working relationship with their nation's weather bureau for access to data. These research consortia could utilize the same, proven methods used for US NEXRAD imagery, internet eyewitness report aggregation, seismometry analysis, etc. to locate meteorite falls. The consortia could then recover and analyze meteorite falls and enrich their own research efforts. It would be beneficial to conduct a global program to coordinate the development of methods and data tools, as well as to coordinate meteorite sample sharing and research. Perhaps an institution such as the Meteoritical Society could lead such an effort.

Fries, Marc↗

NASA NTRS1975

The Large Area Crop Inventory Experiment (LACIE). Part 2: Yield estimates from meteorological information

Crop-weather models developed for wheat from an assembled base of approximately 45 years of climatic records and historic wheat yield and production data at the stratum level are described. Utilization of meteorological data acquired by the NOAA satellites is discussed.

Waite, P. J.↗

NASA NTRS1983

JAWS data collection, analysis highlights, and microburst statistics

Organization, equipment, and the current status of the Joint Airport Weather Studies project initiated in relation to the microburst phenomenon are summarized. Some data collection techniques and preliminary statistics on microburst events recorded by Doppler radar are discussed as well. Radar studies show that microbursts occur much more often than expected, with majority of the events being potentially dangerous to landing or departing aircraft. Seventy events were registered, with the differential velocities ranging from 10 to 48 m/s; headwind/tailwind velocity differentials over 20 m/s are considered seriously hazardous. It is noted that a correlation is yet to be established between the velocity differential and incoherent radar reflectivity.

Mccarthy, J.↗

DOE OSTI2024

Assessing Historical Extreme Weather Event Impacts

Resilience planning, particularly energy and water resilience planning, has been a key priority for the federal government for many years, leading federal agencies to develop processes for identifying and addressing critical resilience gaps at their facilities and sites. Furthermore, recent federal policy is driving agencies to prioritize climate change impacts as a more central component of their resilience planning efforts. To achieve this, federal sites must understand their vulnerability to climate change, which involves identifying climate hazards projected to impact the site (known as exposure), as well as understanding the sensitivity (the degree to which a site, including its people and the things they value, could be harmed by that exposure), and adaptive capacity of the site (the degree to which the site could lessen bathe potential for harm by taking action to reduce exposure and sensitivity). To assess and understand sensitivity and adaptive capacity, it is important to first obtain a baseline and understand how a site has been impacted by past events, in addition to considering the potential for unprecedented impacts based on climate projections. This information paper highlights current limitations for developing event history assessments and suggests a framework for more consistently capturing key data points. The purpose of this paper is to help inform how organizations could begin structuring a comprehensive process for recording the impacts of extreme weather events in order to facilitate climate vulnerability assessments, and thus, resilience planning.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

Temporal Subsampling Diminishes Small Spatial Scales in Recurrent Neural Network Emulators of Geophysical Turbulence

The immense computational cost of traditional numerical weather and climate models has sparked the development of machine learning (ML) based emulators. Because ML methods benefit from long records of training data, it is common to use data sets that are temporally subsampled relative to the time steps required for the numerical integration of differential equations. Here, we investigate how this often overlooked processing step affects the quality of an emulator's predictions. We implement two ML architectures from a class of methods called reservoir computing: (a) a form of Nonlinear Vector Autoregression (NVAR), and (b) an Echo State Network (ESN). Despite their simplicity, it is well documented that these architectures excel at predicting low dimensional chaotic dynamics. We are therefore motivated to test these architectures in an idealized setting of predicting high dimensional geophysical turbulence as represented by Surface Quasi-Geostrophic dynamics. In all cases, subsampling the training data consistently leads to an increased bias at small spatial scales that resembles numerical diffusion. Interestingly, the NVAR architecture becomes unstable when the temporal resolution is increased, indicating that the polynomial based interactions are insufficient at capturing the detailed nonlinearities of the turbulent flow. The ESN architecture is found to be more robust, suggesting a benefit to the more expensive but more general structure. Spectral errors are reduced by including a penalty on the kinetic energy density spectrum during training, although the subsampling related errors persist. Future work is warranted to understand how the temporal resolution of training data affects other ML architectures.

58 GEOSCIENCES↗

NASA NTRS2014

Space Weathering of Olivine in Lunar Soils: A Comparison to Itokawa Regolith Samples

Regolith particles from airless bodies preserve a record of the space weathering processes that occurred during their surface exposure history. These processes have major implications for interpreting remote-sensing data from airless bodies. Solar wind irradiation effects occur in the rims of exposed grains, and impact processes result in the accumulation of vapordeposited elements and other surface-adhering materials. The grains returned from the surface of Itokawa by the Hayabusa mission allow the space weathering "style" of a chondritic, asteroidal "soil" to be compared to the lunar case. Here, we present new studies of space-weathered olivine grains from lunar soils, and compare these results to olivine grains from Itokawa. Samples and Methods: We analyzed microtome thin sections of olivine grains from the 20-45 micron fractions of three lunar soils: 71061, 71501 and 10084 (immature, submature and mature, respectively). Imaging and analytical data were obtained using a JEOL 2500SE 200kV field-emission scanning-transmission electron microscope equipped with a thin-window energy-dispersive x-ray spectrometer. Similar analyses were obtained from three Hayabusa olivine grains. Results and Discussion: We observed lunar grains showing a range of solar flare track densities (from <10(exp 9) to approx.10(exp 12)/sq cm). The lunar olivines all show disordered, highly strained, nanocrystalline rims up to 150-nm thick. The disordered rim thickness is positively correlated with solar flare track density. All of the disordered rims are overlain by a Si-rich amorphous layer, ranging up to 50-nm thick, enriched in elements that are not derived from the host olivine (e.g., Ca, Al, and Ti). The outmost layer represents impact-generated vapor deposits typically observed on other lunar soil grains. The Hayabusa olivine grains show track densities <10(exp 10)/sq cm and display disordered rims 50- to 100-nm thick. The track densities are intermediate to those observed in olivines in immature and submature lunar soils and indicate surface exposures of approx. 10(exp 5) years. The outermost few nanometers of the disordered rims on Hayabusa olivines are more Si-rich and Mg- and Fe-depleted relative to the cores of the grains and likely represent a minor accumulation of impact-generated vapors or sputter deposits. Nanophase Fe metal particles are less abundant in the Hayabusa rims compared to the rims on lunar grains. Conclusions: The Hayabusa and lunar olivine grain rims have widths and microstructures consistent with formation from atomic displacement damage from solar wind ions. The space weathering features in the Hayabusa grains are similar to those observed in olivines from immature to submature lunar soils. A major difference, however, is that the Hayabusa grains appear to lack the hypervelocity impact products (melt spherules, thick vapor deposits, and abundant nanophase Fe metal particles) that are common in lunar soil grains with a similar exposure history.

Keller, L. P.↗

NASA NTRS2019

Improvements in the Uncertainty Model in the Goddard Institute for Space Studies Surface Temperature (GISTEMP) Analysis

We outline a new and improved uncertainty analysis for the Goddard Institute of Space Studies (GISS) Surface Temperature product version 4 (GISTEMP v4). Historical spatial variations in surface temperature anomalies are derived from historical weather station data and ocean data from ships, buoys and other sensors. Uncertainties arise from measurement uncertainty, changes in spatial coverage of the station record, and systematic biases due to technology shifts and land cover changes. Previously published uncertainty estimates for GISTEMP included only the effect of incomplete station coverage. Here, we update this term using currently available spatial distributions of source data, state‐of‐the‐art reanalyses and incorporate independently derived estimates for ocean data processing, station homogenization and other structural biases. The resulting 95% uncertainties are near 0.05° C in the global annual mean for the last 50 years, and increase going back further in time reaching 0.15° C in 1880. In addition, we quantify the benefits and inherent uncertainty due to the GISTEMP interpolation and averaging method. We use the total uncertainties to estimate the probability for each record year in the GISTEMP to actually be the true record year (to that date), and conclude with 86% likelihood that 2016 was indeed the hottest year of the instrumental period (so far).

Nathan J L Lenssen↗

NASA NTRSDate not supplied

Cloud Climate Data Records from Geostationary Satellites to Support CERES: Challenges and Recent Progress

clouds↗

NASA NTRS2009

Situational Lightning Climatologies for Central Florida: Phase IV

The threat of lightning is a daily concern during the warm season in Florida. Research has revealed distinct spatial and temporal distributions of lightning occurrence that are strongly influenced by large-scale atmospheric flow regimes. Previously, the Applied Meteorology Unit (AMU) calculated the gridded lightning climatologies based on seven flow regimes over Florida for 1-, 3- and 6-hr intervals in 5-, 10-,20-, and 30-NM diameter range rings around the Shuttle Landing Facility (SLF) and eight other airfields in the National Weather Service in Melbourne (NWS MLB) county warning area (CWA). In this update to the work, the AMU recalculated the lightning climatologies for using individual lightning strike data to improve the accuracy of the climatologies. The AMU included all data regardless of flow regime as one of the stratifications, added monthly stratifications, added three years of data to the period of record and used modified flow regimes based work from the AMU's Objective Lightning Probability Forecast Tool, Phase II. The AMU made changes so the 5- and 10-NM radius range rings are consistent with the aviation forecast requirements at NWS MLB, while the 20- and 30-NM radius range rings at the SLF assist the Spaceflight Meteorology Group in making forecasts for weather Flight Rule violations during Shuttle landings. The AMU also updated the graphical user interface with the new data.

Bauman, William H., III↗

NASA NTRS2009

Situational Lightning Climatologies for Central Florida: Phase IV: Central Florida Flow Regime Based Climatologies of Lightning Probabilities

The threat of lightning is a daily concern during the warm season in Florida. Research has revealed distinct spatial and temporal distributions of lightning occurrence that are strongly influenced by large-scale atmospheric flow regimes. Previously, the Applied Meteorology Unit (AMU) calculated the gridded lightning climatologies based on seven flow regimes over Florida for 1-, 3- and 6-hr intervals in 5-, 10-, 20-, and 30-NM diameter range rings around the Shuttle Landing Facility (SLF) and eight other airfields in the National Weather Service in Melbourne (NWS MLB) county warning area (CWA). In this update to the work, the AMU recalculated the lightning climatologies for using individual lightning strike data to improve the accuracy of the climatologies. The AMU included all data regardless of flow regime as one of the stratifications, added monthly stratifications, added three years of data to the period of record and used modified flow regimes based work from the AMU's Objective Lightning Probability Forecast Tool, Phase II. The AMU made changes so the 5- and 10-NM radius range rings are consistent with the aviation forecast requirements at NWS MLB, while the 20- and 30-NM radius range rings at the SLF assist the Spaceflight Meteorology Group in making forecasts for weather Flight Rule violations during Shuttle landings. The AMU also updated the graphical user interface with the new data.

Bauman, William H., III↗

NASA NTRS2005

Severe Weather Forecast Decision Aid

This report presents a 15-year climatological study of severe weather events and related severe weather atmospheric parameters. Data sources included local forecast rules, archived sounding data, Cloud-to-Ground Lightning Surveillance System (CGLSS) data, surface and upper air maps, and two severe weather event databases covering east-central Florida. The local forecast rules were used to set threat assessment thresholds for stability parameters that were derived from the sounding data. The severe weather events databases were used to identify days with reported severe weather and the CGLSS data was used to differentiate between lightning and non-lightning days. These data sets provided the foundation for analyzing the stability parameters and synoptic patterns that were used to develop an objective tool to aid in forecasting severe weather events. The period of record for the analysis was May - September, 1989 - 2003. The results indicate that there are certain synoptic patterns more prevalent on days with severe weather and some of the stability parameters are better predictors of severe weather days based on locally tuned threat values. The results also revealed the stability parameters that did not display any skill related to severe weather days. An interactive web-based Severe Weather Decision Aid was developed to assist the duty forecaster by providing a level of objective guidance based on the analysis of the stability parameters, CGLSS data, and synoptic-scale dynamics. The tool will be tested and evaluated during the 2005 warm season.

Bauman, William H., III↗