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DOE OSTI2020

Air quality impact of the Northern California Camp Fire of November 2018

The Northern California Camp Fire that took place in November 2018 was one of the most damaging environmental events in California history. Here, we analyze ground-based station observations of airborne particulate matter that has a diameter <2.5 µm (PM 2.5 ) across Northern California and conduct numerical simulations of the Camp Fire using the Weather Research and Forecasting model online coupled with chemistry (WRF-Chem). Simulations are evaluated against ground-based observations of PM 2.5 , black carbon, and meteorology, as well as satellite measurements, such as Tropospheric Monitoring Instrument (TROPOMI) aerosol layer height and aerosol index. The Camp Fire led to an increase in Bay Area PM 2.5 to over 50 µg m –3 for nearly 2 weeks, with localized peaks exceeding 300 µg m –3 . Using the Visible Infrared Imaging Radiometer Suite (VIIRS) high-resolution fire detection products, the simulations reproduce the magnitude and evolution of surface PM 2.5 concentrations, especially downwind of the wildfire. The overall spatial patterns of simulated aerosol plumes and their heights are comparable with the latest satellite products from TROPOMI. WRF-Chem sensitivity simulations are carried out to analyze uncertainties that arise from fire emissions, meteorological conditions, feedback of aerosol radiative effects on meteorology, and various physical parameterizations, including the planetary boundary layer model and the plume rise model. Downwind PM 2.5 concentrations are sensitive to both flaming and smoldering emissions over the fire, so the uncertainty in the satellite-derived fire emission products can directly affect the air pollution simulations downwind. Our analysis also shows the importance of land surface and boundary layer parameterization in the fire simulation, which can result in large variations in magnitude and trend of surface PM 2.5 . Inclusion of aerosol radiative feedback moderately improves PM 2.5 simulations, especially over the most polluted days. Results of this study can assist in the development of data assimilation systems as well as air quality forecasting of health exposures and economic impact studies.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS1978

Instruments for measuring the amount of moisture in the air

A summarization and discussion of the many systems available for measuring moisture in the atmosphere is presented. Conventional methods used in the field of meteorology and methods used in the laboratory are discussed. Performance accuracies, and response of the instruments were reviewed as well as the advantages and disadvantages of each. Methods of measuring humidity aloft by instrumentation onboard aircraft and balloons are given, in addition to the methods used to measure moisture at the Earth's surface.

Johnson, D. L.↗

DOE OSTI2025

LANL Meteorology Program Self-Assessment 2025 Update

The LANL Meteorological (Met) Program has been subject to several external reviews over the past 19 years. The DOE Meteorological Coordinating Council (DMCC) conducted an initial Met Program Site Assist Visit (SAV) in August 2006 (DMCC 2006). A follow-up SAV in August 2015 assessed progress (DMCC 2015), and in June 2023, the DOE Meteorological Subcommittee (DMSC), successor to the DMCC, conducted a second follow-up SAV (DMSC 2023), in which the Met Program was evaluated relative to the following 8 high-level questions: • What is the state of the meteorological services provided to its customers? • What is the quality of meteorological data provided to its customers and is it adequate and available to meet all customer needs? • What is the quality of atmospheric transport and diffusion modeling provided to its customers and is it applicable to complex wind flow patterns at LANL? • Are the current and future meteorological service customers being serviced appropriately? • Are there adequate human resources to meet present and future program customer needs and are they being appropriately leveraged? • Are existing instrumentation, facilities, and systems adequate to meet present and future customer needs? • Are LANL meteorological services conducted in an efficient, cost-effective manner? • Is meteorological data used to ensure safety & health of LANL personnel? More specific evaluations were performed relative to 23 performance objectives extracted from the ANSI/ANS-3.11- 2024 national standard and 14 separate performance objectives associated with consequence assessment and atmospheric transport and diffusion modeling in the consequence assessment element of DOE G 151.1-1B. In 2023, the DMSC SAV Team also reviewed the status of each of the 18 remaining recommendations from its 2015 SAV. Based on this review, DMSC stated in its Exit Briefing that the LANL meteorological program has gotten much stronger and more robust since 2015 and now represents one of the better managed programs within the DOE complex.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS2010

Simulation of the Impact of New Aircraft-and Satellite-based Ocean Surface Wind Measurements on Wind Analyses and Numerical Forecasts

The Hurricane Imaging Radiometer (HIRAD) is a new airborne microwave remote sensor for hurricane observations that is currently under development by NASA Marshall Space Flight Center, NOAA Hurricane Research Division, the University of Central Florida and the University of Michigan. HIRAD is being designed to enhance the realtime airborne ocean surface winds observation capabilities of NOAA and USAF Weather Squadron hurricane hunter aircraft currently using the operational airborne Stepped Frequency Microwave Radiometer (SFMR). Unlike SFMR, which measures wind speed and rain rate along the ground track directly beneath the aircraft, HIRAD will provide images of the surface wind and rain field over a wide swath (approx. 3 x the aircraft altitude). The present paper describes a set of Observing System Simulation Experiments (OSSEs) in which measurements from the new instrument as well as those from existing instruments (air, surface, and space-based) are simulated from the output of a detailed numerical model, and those results are used to construct H*Wind analyses, a product of the Hurricane Research Division of NOAA s Atlantic Oceanographic and Meteorological Laboratory. Evaluations will be presented on the impact of the HIRAD instrument on H*Wind analyses, both in terms of adding it to the full suite of current measurements, as well as using it to replace instrument(s) that may not be functioning at the future time the HIRAD instrument is implemented. Also shown will be preliminary results of numerical weather prediction OSSEs in which the impact of the addition of HIRAD observations to the initial state on numerical forecasts of the hurricane intensity and structure is assessed.

Miller, TImothy↗

NASA NTRS1988

Temporal and spatial distribution of stratospheric trace gases over Antarctica in August and September, 1987

There have been a large number of suggestions made concerning the origin of the Antarctic 'ozone hole' since its discovery; these changes include stratospheric chemistry, or changes in the solar input, or combinations of these effects. Supporting or refuting these theories requires a wide variety of data for comparison with the predictions. In Aug. and Sept., 1987, a field observation expedition was made over Antarctica from a base in Punta Arenas, Chile. Two aircraft, an ER-2 with in-situ instruments flew at altitudes up to 18 km measuring ozone, water, ClO, BrO, NO sub x, particles, and meteorological parameters in the ozone layer. A DC-8 flew at altitudes of 10 to 12 km, below the ozone layer, using remote sensing instruments for measuring composition and aerosol content of the ozone layer, as well as in-situ instruments for measuring composition at aircraft altitudes. The obsevation of a number of chemical species and their correlation with each other and with meteorological parameters gives a useful set of data for comparison with various theories.

Coffey, M. T.↗

DOE OSTI2021

Parameterization of arctic hydrometeor physics using new precipitation measurement technologies: Final Report

Predictions of precipitation are highly sensitive to the accuracy of parameterized growth and sedimentation processes, especially in remote regions such as the Arctic where observations are scarce. The goal of this study was to combine measurements of meteorological conditions and retrievals from passive and active remote sensors to provide refined parameterizations of precipitation properties and processes with a particular focus on the Arctic. To accomplish the project goals, new instrumentation was used from the Oliktok Point Mobile Facility and the Utqiagvik (Barrow) North Slope of Alaska ARM site focusing in particular on Multi-Angle Snowflake Camera (MASC) data. The MASC is the first device able to automatically photograph precipitation particles in free-fall from multiple angles while simultaneously measuring their fall speed. The MASC installed at the ARM Oliktok Point and Utqiagvik facilities was used to examine the nature of Arctic precipitation, taking particular advantage of the unique suite of precipitation, meteorological, and remote sensing instrumentation that is available at the high latitude sites. Combined with radiometer data we were able to explore the relationship between precipitation particle characteristics and the clouds where the snow is created. Ground-based wind measurements provided detailed data on turbulence. Further theoretical work explored the physical mechanisms controlling precipitation size distributions, the physics determining hydrometeor fallspeed, and the existence of general solutions to the Navier-Stokes equations for falling particles. Outreach work introduced hydrometeor classification to classrooms across the United States.

47 OTHER INSTRUMENTATION↗

NASA NTRS2004

Assimilation of Stratospheric Meteorological and Constituent Observations: A Review

This talk reviews the assimilation of meteorological and constituent observations of the stratosphere. The first efforts to assimilate observations into stratospheric models were during the early 1980s, and a number of research studies followed during the next decade. Since the launch of the Upper Atmospheric Research Satellite (UARS) in 1991, model-assimilated data sets of the stratospheric meteorological state have been routinely available. These assimilated data sets were critical in bringing together observations from the different instruments on UARS as well as linking UARS observations to measurements from other platforms. Using trajectory-mapping techniques, meteorological assimilation analyses are, now, widely used in the analysis of constituent observations and have increased the level of quantitative study of stratospheric chemistry and transport. During the 1990s the use of winds and temperatures from assimilated data sets became standard for offline chemistry and transport modeling. variability in middle latitudes. The transport experiments, however, reveal a set of shortcomings that become obvious as systematic errors are integrated over time. Generally, the tropics are not well represented, mixing between the tropics and middle latitudes is overestimated, and the residual circulation is not accurate. These shortcomings reveal underlying fundamental challenges related to bias and noise. Current studies using model simulation and data assimilation in controlled experimentation are highlighting the issues that must be addressed if assimilated data sets are to be convincingly used to study interannual variability and decadal change. observations. The primary focus has been on stratospheric ozone, but there are efforts that investigate a suite of reactive chemical constituents. Recent progress in ozone assimilation shows the potential of assimilation to contribute to the validation of ozone observations and, ultimately, the retrieval of ozone profiles from space-based radiance measurements. Assimilated data sets provide accurate analyses of synoptic and planetary Scale At the same time, stratospheric assimilation is evolving to include constituent

Rood, Richard B.↗

DOE OSTI2021

Combined Mesonet and Tracker

Title: Combined Mesonet and Trackers (UNL Mobile Mesonets) Authors University of Nebraska PI: Adam Houston, UNL Professor (ahouston2@unl.edu) Mailing Address: 126 Bessey Hall P.O. Box 880340 Lincoln, NE 68588-0340 CoMeT Overview The University of Nebraska-Lincoln operates three Combined Mesonet and Tracker (CoMeTs). CoMeTs are Ford Explorers (model years 2015, 2017, and 2019) with forward-mounted suites of meteorological sensors and dual moonroofs, combining the capability of a mobile mesonet to collect near-surface observations with the capability of an unmanned aircraft systems (UAS) tracker vehicle, which enables an observer in the second row of seats to see the aircraft and maintain compliance with Federal Aviation Administration policies on UAS operation. The CoMeTs collect observations of slow temperature and humidity at ~2 m above ground level (AGL) using a Vaisala HMP155A, fast temperature at ~2 m AGL using a Campbell Scientific 109SS-L thermistor, pressure at ~2.5 m AGL using a Vaisala PTB210, wind speed and direction at ~3.25 m AGL using an R.M. Young 05103 propeller anemometer, and vehicle heading using a KVH Industries C-100 fluxgate compass (Barbieri et al. 2019). The HMP155A and 109SS-L thermistor are shielded and aspirated within a U-tube (Waugh and Frederickson 2010; Houston et al. 2016). This list of sensors is also included in the CoMeT data file metadata. Manufacturer specifications for these instruments are given in Table 1 of Hanft and Houston (2018). The reported measured quantities are summarized below.CoMeT-3 was funded through an equipment allocation included in the NSF TORUS award (AGS-1824649). Instrument Description The specific sensors included on each CoMeT are summarized in the table at the end of this section. In general each CoMeT collects observations of slow temperature and humidity at ~2 m above ground level (AGL) using a Vaisala HMP155, fast temperature at ~2 m AGL using a Campbell Scientific 109SS-L thermistor, pressure at ~2.5 m AGL using a Vaisala PTB210 barometer with a Gill pressure port, wind speed and direction at ~3.25 m AGL using an R.M. Young 05103 propeller anemometer, position using a Garmin 19x HVS receiver, and vehicle heading using a KVH Industries C-100 fluxgate compass. The HMP155 and 109SS are shielded and aspirated within a U-tube (Waugh and Frederickson 2010; Houston et al. 2016). Fast temperature and corrected RH measurements (using sensors housed within the U-tube) have a time constant of 10-12 s based on data collected across a temperature and RH shock during the CLOUD-MAP 2017 calibration/validation tests on June 26, 2017. Vehicle speed was < 10 kts for this test. CoMeT-1 CoMeT-2 CoMeT-3 Slow Temperature Slow RH Vaisala HMP155A-L20-PT Part #: 22280-7 Vaisala HMP155E Part #: E1AA11A0B1A1A0A Vaisala HMP155E Part #: E1AA11A0B1A1A0A Fast temperature Campbell Scientific 109SS-L20-PT Part #: 21448-3 Campbell Scientific 109SS-L12-PW Part #: 21448-109 Campbell Scientific 109SS-L12-PW Part #: 21448-150 Pressure Vaisala PTB-210 Part #: A1A1B Gill Pressure Port Part #: 61002 Vaisala PTB-210 Part #: A1A1B Gill Pressure Port Part #: 61002 Vaisala PTB-210 Part #: A1A1B Gill Pressure Port Part #: 61002 Wind RM Young 05103-L20-PT Part #: 18435-310 RM Young 05103-L20-PW Part #: 18435-244 RM Young 05103-L20-PW Part #: 18435-244 GPS Garmin GPS 19x HVS (NMEA 0183) Part #: 010-01010-00 Garmin GPS 19x HVS (NMEA 0183) Part #: 010-01010-00 Garmin GPS 19x HVS (NMEA 0183) Part #: 010-01010-00 Compass KVH C-100 Part #: 01-0177-15 KVH C-100 Part #: 01-0177-15 KVH C-100 Part #: 01-0177-15 Logger Campbell Scientific CR6-NA-XT-SW Part #: 28385-9 Campbell Scientific CR6-WIFI-XT-SW Part #: 28385-6 Campbell Scientific CR6-WIFI-XT-SW Part #: 28385-6 Data Collection and Real-Time Processing The reported measured quantities are summarized in the table below. Quantity Units Source Epoch time Seconds GPS Latitude and longitude Degrees GPS Altitude m GPS Pressure hPa PTB210 Temperature (fast) deg C 109SS-L Temperature (slow) deg C HMP155 RH (slow) % HMP155 Vehicle speed m/s GPS Vehicle heading deg C-100 and GPS In addition to the measured variables, several derived variables are calculated. Corrected/fast relative humidity (%) Relative humidity is adjusted to the fast temperature following Richardson et al. (1998) and Houston et al. (2016). Water vapor mixing ratio (g/kg) Dew point temperature (°C) Potential temperature (Kelvin) Virtual potential temperature (Kelvin) Equivalent potential temperature (Kelvin) Regular intercomparisons between all three CoMeTs were performed during TORUS 2019. Comparisons were also conducted between CoMeT-1 and CoMeT-2 during LAPSE-RATE (2018) on 14 July. In these intercomparisons, the vehicles were parked adjacent to each other aligned perpendicular to (and facing into) the wind. To minimize engine heating effects, intercomparisons were only conducted when the wind speed was >10 kts. Data Format Original data files for each deployment are saved as text files and then converted to NetCDF. NetCDF versions have units that are CF compliant and may not match the original units in the txt files. The naming convention for the NetCDF files is as follows: UNL.CoMeT3.{deployment date YYYYMMDD}.{start time of observation collection in UTC HHMM}.L2.{post-processing codes}.cdf example: UNL.CoMeT3.20190627.1931.L2.g1.f1.cdf Post-processing codes are included to track modifications to the raw data. These codes are closely connected to error flags associated with each record. Each letter corresponds to a particular instrument: g: GPS p: Barometer tf: Fast temperature ts: Slow temperature rh: Relative humidity f: Compass w: Wind monitor a: All instruments Each number corresponds to a particular post-processing action described more below. Measured and derived variables are included in the following table. Variable Heading Standard Name Units time Time seconds since 00:00:00, 01-01-1970 Alt Altitude meters lat Latitude degrees north lon Longitude degrees east fast_temp Air Temperature kelvin slow_temp Air Temperature kelvin pressure Air Pressure pascals logger_RH Relative Humidity percent calc_corr_RH Relative Humidity percent wind_speed Wind Speed meters per second wind_dir Wind From Direction degrees vehicle_dir Vehicle Direction degrees dewpoint Dew Point Temperature kelvin mixing_ratio Humidity Mixing Ratio g/g theta Air Potential Temperature kelvin theta_v Virtual Potential Temperature kelvin theta_e Equivalent Potential Temperature Kelvin error_flag The error_flag variable is a string that matches the post-processing codes listed above. All instruments will have an associated code, but will have a “0” if the datum is unchanged from the initial processed value. Error Codes The following table summarizes the error codes for data collected before 2020: Error Code Relevant CoMeT Description g1 1,2,3 Exact correction. GPS position and time reprocessed from raw data g2 1 As far as we can tell this is an exact correction to an error in the GPS time. During the correct time periods the time suddenly went backwards ~250s and stayed at this offset for 750s when it corrected itself. The offset was applied to the “time warp” period. p1 2 Approximate correction. Hole in the pressure tube connecting the pressure port to the barometer. Resulted in erroneously low air pressure measurements when the vehicle was in motion. Derived variables recalculated (dew point temperature [e depends on qv and p], water vapor mixing ratio, potential temperature, virtual potential temperature, equivalent potential temperature) a1 3 Exact correction. Missing data reprocessed from raw data a2 1 Bug fix to bias correction for ts1, ts2, and rh1: water vapor mixing ratio was off by a factor of 10 and virtual potential temperature was wrong because of this. f1 3 No correction, missing data. Fluxgate compass inoperable. Wind speed and direction calculated using GPS-derived vehicle heading instead. rh1 1 Approximate correction. Constant bias of +1.7% removed from relative humidity. Derived variables recalculated (corrected/fast relative humidity, dew point temperature, water vapor mixing ratio, virtual potential temperature, equivalent potential temperature) ts1 1 Approximate correction. Constant bias of +0.6 K removed from slow temperature. Derived variables recalculated (corrected/fast relative humidity, dew point temperature, water vapor mixing ratio, virtual potential temperature, equivalent potential temperature) ts2 1 Approximate correction. Constant bias of +1.0 K removed. Derived variables recalculated (corrected/fast relative humidity, dew point temperature, water vapor mixing ratio, virtual potential temperature, equivalent potential temperature) References Bolton, D., 1980: The Computation of Equivalent Potential Temperature. Mon. Wea. Rev., 108, 1046–1053, https://doi.org/10.1175/1520-0493(1980)108<1046:TCOEPT>2.0.CO;2. Hanft, W., and A. L. Houston, 2018: An Observational and Modeling Study of Mesoscale Air Masses with High Theta-E. Mon. Wea. Rev., 146, 2503–2524, https://doi.org/10.1175/MWR-D-17-0389.1.Wexler Houston, A. L., R. J. Laurence III, T. W. Nichols, S. Waugh, B. Argrow, and C. L. Ziegler, 2016: Intercomparison of unmanned aircraft-borne and mobile mesonet atmospheric sensors. Journal of Atmospheric and Oceanic Technology. 33, 1569-1582, doi: 10.1175/JTECH-D-15-0178.1. Lowe, P. R., 1977: An Approximating Polynomial for the Computation of Saturation Vapor Pressure. J. Applied Meteorology, 16, 100–103. Richardson, S. J., S. E. Frederickson, F. V. Brock, and J. A. Brotzge, 1998: Combination temperature and relative humidity probes: Avoiding large air temperature errors and associated relative humidity errors. Preprints, 10th Symp. On Meteorological Observations and Instrumentation, Phoenix, AZ, Amer. Meteor. Soc., 278–283. Waugh, S., and S. E. Frederickson, 2010: An improved aspirated temperature system for mobile meteorological observations, especially in severe weather. 25th Conf. on Severe Local Storms, Denver, CO, Amer. Meteor. Soc., P5.2. [Available online at https://ams.confex.com/ams/25SLS/techprogram/paper_176205.htm.]

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS2003

Validation and Error Characterization for the Global Precipitation Measurement

The Global Precipitation Measurement (GPM) is an international effort to increase scientific knowledge on the global water cycle with specific goals of improving the understanding and the predictions of climate, weather, and hydrology. These goals will be achieved through several satellites specifically dedicated to GPM along with the integration of numerous meteorological satellite data streams from international and domestic partners. The GPM effort is led by the National Aeronautics and Space Administration (NASA) of the United States and the National Space Development Agency (NASDA) of Japan. In addition to the spaceborne assets, international and domestic partners will provide ground-based resources for validating the satellite observations and retrievals. This paper describes the validation effort of Global Precipitation Measurement to provide quantitative estimates on the errors of the GPM satellite retrievals. The GPM validation approach will build upon the research experience of the Tropical Rainfall Measuring Mission (TRMM) retrieval comparisons and its validation program. The GPM ground validation program will employ instrumentation, physical infrastructure, and research capabilities at Supersites located in important meteorological regimes of the globe. NASA will provide two Supersites, one in a tropical oceanic and the other in a mid-latitude continental regime. GPM international partners will provide Supersites for other important regimes. Those objectives or regimes not addressed by Supersites will be covered through focused field experiments. This paper describes the specific errors that GPM ground validation will address, quantify, and relate to the GPM satellite physical retrievals. GPM will attempt to identify the source of errors within retrievals including those of instrument calibration, retrieval physical assumptions, and algorithm applicability. With the identification of error sources, improvements will be made to the respective calibration, assumption, or algorithm. The instrumentation and techniques of the Supersites will be discussed. The GPM core satellite, with its dual-frequency radar and conically scanning radiometer, will provide insight into precipitation drop-size distributions and potentially increased measurement capabilities of light rain and snowfall. The ground validation program will include instrumentation and techniques commensurate with these new measurement capabilities.

Bidwell, Steven W.↗

NASA NTRS1987

Meteorological variations in the middle clouds of Venus from Vega balloon in situ measurements

Instruments aboard the gondolas of the two Vega balloons obtained in situ measurements of pressure, temperature, vertical velocity relative to the balloon, cloud particle backscatter, lightning and the ambient light level. Atmospheric motions at the balloon float altitudes were also determined from earth-based tracking results. To illustrate the history of the balloon flights and to facilitate comparisons between some of the different observed quantities, measurements of pressure, temperature and backscatter are presented as time series for the entire lifetime of each balloon. Both long and short period variations have been detected. In addition, the environmental entropy encountered by each balloon will be discussed.

Elson, Lee S.↗

NASA NTRS2008

Simulation of the Impact of New Air-Based Ocean Surface Wind Measurements on H*Wind Analyses

The H'Wind analysis, a product of the Hurricane Research Division of NOAA's Atlantic Oceanographic and Meteorological Laboratory, brings together wind measurements from a variety of observation platforms into an objective analysis of the distribution of wind speeds in a tropical cyclone. This product is designed to improve understanding of the extent and strength of the wind field, and to improve the assessment of hurricane intensity. See http://www.aoml.noaa.gov/hrd/data sub/wind.html. The Hurricane Imaging Radiometer (HIRad) is a new airborne microwave remote sensor for hurricane observations that is currently under development by NASA Marshall Space Flight Center, NOAA Hurricane Research Division, the University of Central Florida and the University of Michigan. HIRad is being designed to enhance the real-time airborne ocean surface winds observation capabilities of NOAA and USAF Weather Squadron hurricane hunter aircraft using the operational airborne Stepped Frequency Microwave Radiometer (SFMR). Unlike SFMR, which measures wind speed and rain rate along the ground track directly beneath the aircraft, HIRad will provide images of the surface wind and rain field over a wide swath (approx. 3 x the aircraft altitude). The instrument is described in a paper presented to the Hurricanes and Tropical Meteorology Symposium. The present paper describes a set of Observing System Simulation Experiments (OSSEs) in which measurements from the new instrument as well as those from existing instruments (air, surface, and space-based) are simulated from the output of a numerical model from the University of Miami and those results are used to construct H*Wind analyses. Evaluations will be presented on the impact of the HIRad instrument on H'Wind analyses, both in terms of adding it to the full suite of current measurements, as well as using it to replace instrument(s) that may not be functioning at the future tame the HIRad instrument is implemented.

Miller, Timothy↗

NASA NTRS2020

Synergistic Use of Hyperspectral UV-Visible OMI and Broadband Meteorological Imager MODIS Data for a Merged Aerosol Product

The retrieval of optimal aerosol datasets by the synergistic use of hyperspectral ultraviolet(UV)–visible and broadband meteorological imager (MI) techniques was investigated. The Aura Ozone Monitoring Instrument (OMI) Level 1B (L1B) was used as a proxy for hyperspectral UV–visible instrument data to which the Geostationary Environment Monitoring Spectrometer (GEMS) aerosol algorithm was applied. Moderate-Resolution Imaging Spectroradiometer (MODIS) L1B and dark target aerosol Level 2 (L2) data were used with a broadband MI to take advantage of the consistent time gap between the MODIS and the OMI. First, the use of cloud mask information from the MI infrared (IR) channel was tested for synergy. High-spatial-resolution and IR channels of the MI helped mask cirrus and sub-pixel cloud contamination of GEMS aerosol, as clearly seen in aerosol optical depth (AOD) validation with Aerosol Robotic Network (AERONET) data. Second, dust aerosols were distinguished in the GEMS aerosol-type classification algorithm by calculating the total dust confidence index (TDCI) from MODIS L1B IR channels. Statistical analysis indicates that the Probability of Correct Detection (POCD) between the forward and inversion aerosol dust models (DS) was increased from 72% to 94% by use of the TDCI for GEMS aerosol-type classification, and updated aerosol types were then applied to the GEMS algorithm. Use of the TDCI for DS type classification in the GEMS retrieval procedure gave improved single-scattering albedo (SSA) values for absorbing fine pollution particles (BC) and DS aerosols. Aerosol layer height (ALH) retrieved from GEMS was compared with Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) data, which provides high-resolution vertical aerosol profile information. The CALIOP ALH was calculated from total attenuated backscatter data at 1064 nm, which is identical to the definition of GEMS ALH. Application of the TDCI value reduced the median bias of GEMS ALH data slightly. The GEMS ALH bias approximates zero, especially for GEMS AOD values of>~0.4 and GEMS SSA values of<~0.95.Finally, the AOD products from the GEMS algorithm and MI were used in aerosol merging with the maximum-likelihood estimation method, based on a weighting factor derived from the standard deviation of the original AOD products. With the advantage of the UV–visible channel in retrieving aerosol properties over bright surfaces, the combined AOD products demonstrated better spatial data availability than the original AOD products, with comparable accuracy. Furthermore, pixel-level error analysis of GEMS AOD data indicates improvement through MI synergy.

aerosol↗

NASA NTRS1993

The MESUR Mission

The MESUR mission will place a network of small, robust landers on the Martian surface, making a coordinated set of observations for at least one Martian year. MESUR presents some major challenges for development of instruments, instrument deployment systems, and on board data processing techniques. The instrument payload has not yet been selected, but the straw man payload is (1) a three-axis seismometer; (2) a meteorology package that senses pressure, temperature, wind speed and direction, humidity, and sky brightness; (3) an alphaproton-X-ray spectrometer (APXS); (4) a thermal analysis/evolved gas analysis (TA/EGA) instrument; (5) a descent imager, (6) a panoramic surface imager; (7) an atmospheric structure instrument (ASI) that senses pressure, temperature, and acceleration during descent to the surface; and (8) radio science. Because of the large number of landers to be sent (about 16), all these instruments must be very lightweight. All but the descent imager and the ASI must survive landing loads that may approach 100 g. The meteorology package, seismometer, and surface imager must be able to survive on the surface for at least one Martian year. The seismometer requires deployment off the lander body. The panoramic imager and some components of the meteorology package require deployment above the lander body. The APXS must be placed directly against one or more rocks near the lander, prompting consideration of a micro rover for deployment of this instrument. The TA/EGA requires a system to acquire, contain, and heat a soil sample. Both the imagers and, especially, the seismometer will be capable of producing large volumes of data, and will require use of sophisticated data compression techniques.

Squyres, S. W.↗

NASA NTRS2018

Development of a Mars Lidar (MARLI) for Measuring Wind and Aerosol Profiles from Orbit

Our understanding of the Mars atmosphere and the coupled atmospheric processes that drive its seasonal cycles is limited by a lack of observation data, particularly measurements that capture diurnal and seasonal variations on a global scale. As outlined in the 2011 Planetary Science Decadal Survey and the recent Mars Exploration Program Analysis Group(MEPAG) Goals Document, near-polar-orbital measurements of height-resolved aerosol backscatter and wind profiles area high-priority for the scientific community and would be valuable science products as part of a next-generation orbital science package. To address these needs, we have designed and tested a breadboard version of a direct detection atmospheric wind lidar for Mars orbit. It uses a single-frequency, seeded Nd:YAG laser ring oscillator operating at 1064nm (4 kHz repetition rate), with a 30-ns pulse duration amplified to 4 mJ pulse energy. The receiver uses a Fabry-Perotetalon as part of a dual-edge optical discrimination technique to isolate the Doppler-induced frequency shift of the back scattered photons. To detect weak aerosol backscatter profiles, the instrument uses a 4x4 photon-counting HgCdTeAPD detector with a 7 MHz bandwidth and < 0.4 fW/Hz(exp 1/2) noise equivalent power. With the MARLI lidar breadboard instrument, we were able to measure Doppler shifts continuously between 1 and 30 m/s by using a rotating chopper wheel to impart a Doppler shift to incident laser pulses. We then coupled the transmitter and receiver systems to a laser ranging telescope at the Goddard Geophysical and Astronomical Observatory (GGAO) to measure backscatter and Doppler wind profiles in the atmosphere from the ground. We measured a 5.3 ± 0.8 m/s wind speed from clouds in the planetary boundary layer at a range of 4 to 6 km. This measurement was confirmed with a range-over-time measurement to the same clouds as well as compared to EMC meteorological models. Here we describe the lidar approach and the breadboard instrument, and report some early results from ongoing field experiments.

Cremons, Daniel R.↗

DOE OSTI2026

Experimental Report: Multi-Instrument Comparison of AAF Size Distribution Instruments

Aerosols are particles suspended in the atmosphere, ranging in size from nanometers to micrometers. Their size distribution affects key atmospheric processes, including nucleation, coagulation, scavenging, activation, and radiative properties (Seinfeld and Pandis 2016). Aerosol size distribution is a critical parameter in atmospheric science, influencing processes such as cloud formation and radiative forcing. Accurate representation of aerosol size distributions is essential for understanding their impact on climate, air quality, and human health. However, aerosol size and composition vary significantly across time and space due to meteorological conditions and natural or anthropogenic sources. (Wu and Boor 2021). Various instruments are used to measure aerosol size distributions, each with distinct principles, advantages, and limitations. This report begins with an in-depth overview of aerosol size-distribution comparison studies, focusing on the passive cavity aerosol spectrometer probe (PCASP), portable optical particle spectrometer (POPS), ultra-high-sensitivity aerosol spectrometer (UHSAS), aerodynamic particle sizer (APS), and scanning mobility particle sizer (SMPS). All of these instruments are used by the U.S. Department of Energy Atmospheric Radiation Measurement (ARM) Aerial Facility (AAF), which commissioned this comparison and report. The report evaluates the strengths and weaknesses of these instruments, highlights their applications, and discusses efforts to merge data from multiple instruments for comprehensive analysis.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS1991

The 1991 International Aerospace and Ground Conference on Lightning and Static Electricity, volume 1

The proceedings of the 1991 International Aerospace and Ground Conference on Lightning and Static Electricity are reported. Some of the topics covered include: lightning, lightning suppression, aerospace vehicles, aircraft safety, flight safety, aviation meteorology, thunderstorms, atmospheric electricity, warning systems, weather forecasting, electromagnetic coupling, electrical measurement, electrostatics, aircraft hazards, flight hazards, meteorological parameters, cloud (meteorology), ground effect, electric currents, lightning equipment, electric fields, measuring instruments, electrical grounding, and aircraft instruments.

William Jafferis↗

NASA NTRS2011

Determining the Completeness of the Nimbus Meteorological Data Archive

NASA launched the Nimbus series of meteorological satellites in the 1960s and 70s. These satellites carried instruments for making observations of the Earth in the visible, infrared, ultraviolet, and microwave wavelengths. The original data archive consisted of a combination of digital data written to 7-track computer tapes and on various film media. Many of these data sets are now being migrated from the old media to the GES DISC modern online archive. The process involves recovering the digital data files from tape as well as scanning images of the data from film strips. Some of the challenges of archiving the Nimbus data include the lack of any metadata from these old data sets. Metadata standards and self-describing data files did not exist at that time, and files were written on now obsolete hardware systems and outdated file formats. This requires creating metadata by reading the contents of the old data files. Some digital data files were corrupted over time, or were possibly improperly copied at the time of creation. Thus there are data gaps in the collections. The film strips were stored in boxes and are now being scanned as JPEG-2000 images. The only information describing these images is what was written on them when they were originally created, and sometimes this information is incomplete or missing. We have the ability to cross-reference the scanned images against the digital data files to determine which of these best represents the data set from the various missions, or to see how complete the data sets are. In this presentation we compared data files and scanned images from the Nimbus-2 High-Resolution Infrared Radiometer (HRIR) for September 1966 to determine whether the data and images are properly archived with correct metadata.

Johnson, James↗

NASA NTRS2017

Data Recovery Effort of Nimbus Era Observations by the NASA GES DISC

NASA launched seven Nimbus meteorological satellites in the 1960s and 70s. These satellites carried instruments for making observations of the Earth in the visible, infrared, ultraviolet, and microwave wavelengths. The original data archive consisted of a combination of magnetic tapes and various film media. As these media are well past their expected end of life, the valuable data they contain are now being migrated to the GES DISC modern online archive. The process involves recovering the digital data files from the tapes as well as scanning images of the data from film strips. This presentation will address the status and challenges of recovering the Nimbus data. The old data products were written on now obsolete hardware systems and outdated file formats. They lack any metadata standards and each product is often written in its own proprietary file structure. This requires creating metadata by reading the contents of the old data files. The job is tedious and laborious, as documentation may be incomplete, data files and tapes are sometimes corrupted, or were improperly copied at the time they were created.

Nimbus↗