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NASA NTRS1990

SAM II and lidar aerosol profile comparisons during AASE

The NASA Langley Research Center aerosol lidar system was one of several instruments that flew aboard a DC-8 during the Airborne Arctic Stratospheric Expedition. Several of the Stratospheric Aerosol Measurement II (SAM II) altitude profiles of aerosol extinction were close enough in time and space to allow for intercomparison with the lidar measurements of aerosol backscatter. Comparisons between three SAM II 1.0 micron aerosol extinction profiles and analogous profiles inferred from nearly simultaneous airborne lidar measurements are discussed. The other two comparisons show the first near-simultaneous SAM II/lidar measurements of a polar stratospheric cloud (PSC). The comparison made well within the edge of a relatively homogeneous PSC layer was also good, especially using an extinction-to-backscatter model derived from in situ PSC particle size measurements.

Osborn, M. T.↗

NASA NTRS1987

A critical review of measurements of water vapor absorption in the 840 to 1100 cm(-1) spectral region

A set of eleven measurements of the water vapor continuum absorption in the 840 to 1100 sq cm spectral region is reviewed and compared with spectral models maintained by the Air Force Geophysics Laboratory. The measurements were made in four different ways: spectrometer with a White cell, CO2 laser with a White cell, CO2 laser with a spectrophone, and broadband radiation source over a long atmospheric path. Where possible, the data were selected at a water vapor partial pressure of ten torr buffered to 760 torr with N2 or synthetic air and a temperature of between 296 and 300 K. The intercomparison of the data leads to several observations and conclusions. First, there are four sets of laboratory data taken with nitrogen as the buffer gas which generally agree well mutually and with AFGL's HITRAN code. Second, there is one set of laboratory data that shows that using air as the buffer gas gives a few percent decrease in the water vapor continuum compared with using nitrogen as the buffer gas. Third, the atmospheric long-path measurements for water vapor partial pressure below about 12 torr are roughly grouped within 20 percent of the HITRAN values. Fourth, there are three sets of spectrophone data for water vapor in synthetic air which are significantly higher than any of the other measurements. This discrepancy is attributed to the effects of impurity gases in the cell.

Grant, William B.↗

NASA NTRS2008

Atmospheric Parameter Climatologies from AIRS: Monitoring Short-, and Longer-Term Climate Variabilities and 'Trends'

The AIRS instrument is currently the best space-based tool to simultaneously monitor the vertical distribution of key climatically important atmospheric parameters as well as surface properties, and has provided high quality data for more than 5 years. AIRS analysis results produced at the GODDARD/DAAC, based on Versions 4 & 5 of the AIRS retrieval algorithm, are currently available for public use. Here, first we present an assessment of interrelationships of anomalies (proxies of climate variability based on 5 full years, since Sept. 2002) of various climate parameters at different spatial scales. We also present AIRS-retrievals-based global, regional and 1x1 degree grid-scale "trend"-analyses of important atmospheric parameters for this 5-year period. Note that here "trend" simply means the linear fit to the anomaly (relative the mean seasonal cycle) time series of various parameters at the above-mentioned spatial scales, and we present these to illustrate the usefulness of continuing AIRS-based climate observations. Preliminary validation efforts, in terms of intercomparisons of interannual variabilities with other available satellite data analysis results, will also be addressed. For example, we show that the outgoing longwave radiation (OLR) interannual spatial variabilities from the available state-of-the-art CERES measurements and from the AIRS computations are in remarkably good agreement. Version 6 of the AIRS retrieval scheme (currently under development) promises to further improve bias agreements for the absolute values by implementing a more accurate radiative transfer model for the OLR computations and by improving surface emissivity retrievals.

Molnar, Gyula↗

DOE OSTI2021

LANL Critical Benchmark Comparison Study and Subsequent Revision for Cases Involving LEU and MIX

As part of an international collaboration within the DOE Nuclear Criticality Safety Program (NCSP), LANL is involved in a comparison study to quantify differences in k-effective results from neutron transport simulations of critical benchmark experiments. The DOE NCSP Mission and Vision details the activity in which the French Institut de Radioprotection et de Sûreté Nucléaire (IRSN) leads the study with LANL, ORNL, and LLNL to compare results of various neutron transport codes and nuclear data libraries for ICSBEP benchmarks held in common by the entities. The task statement from the DOE NCSP Five-Year Execution Plan is given as: "The proposal is for IRSN to lead a new intercomparison based on the MORET code with the latest JEFF-3.3 data and ENDF/B-VIII.0 data, when available, using their existing comprehensive selection of 2,714 benchmarks and collate their results together with those from LLNL (COG), LANL (MCNP) and ORNL (SCALE). Due to the large number of benchmarks involved, this effort is envisioned to take three years with an additional year for IRSN to complete a summary report. The benchmark development will be performed independently to minimize modeling errors through discovery and resolution of discrepant results. A summary report will be generated (led by IRSN) to document the results of this study." This report documents results obtained for revisions made to cases involving Intermediate Enriched Uranium (IEU) and a mixture of Pu and Uranium (MIX), with a focus on the changes made to LANL benchmarks modeled with MCNP6 using ENDF/B-VII.1 nuclear data that appeared to have discrepant results when compared with results of other codes. Feedback was used to pinpoint review of particular benchmark input files and to revise them when necessary. This report documents the results of review and revision of specific benchmarks highlighted as possibly discrepant in the comparison study. In addition, there is an effort tied to this work involving collaboration between LANL XCP and NCS Divisions in the development of a shared review/revision procedure and use of a new benchmark repository.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

NASA NTRS1997

Technical Report Series on Global Modeling and Data Assimilation: Interannual Variability and Potential Predictability in Reanalysis Products - Volume 13

The Data Assimilation Office (DAO) at Goddard Space Flight Center and the National Center for Environmental Prediction and National Center for Atmospheric Research (NCEP/NCAR) have produced multi-year global assimilations of historical data employing fixed analysis systems. These "reanalysis" products are ideally suited for studying short-term climatic variations. The availability of multiple reanalysis products also provides the opportunity to examine the uncertainty in the reanalysis data. The purpose of this document is to provide an updated estimate of seasonal and interannual variability based on the DAO and NCEP/NCAR reanalyses for the 15-year period 1980-1995. Intercomparisons of the seasonal means and their interannual variations are presented for a variety of prognostic and diagnostic fields. In addition, atmospheric potential predictability is re-examined employing selected DAO reanalysis variables.

Min, Wei↗

NASA NTRSDate not supplied

Evaluating Long-Term Changes in Atmospheric Ozone

Ozonesondes have made inexpensive, accurate measurements of ozone from the ground to 30km for more than 50 years. The data are used extensively for trend analyses and for evaluation of satellite and model data products, and are also part of climatologies that are used as a priori data for satellite retrievals. They are essential as a transfer standard when merging shorter satellite-derived time series, and are the most important source of trend-quality long-term records below about 18 km. The importance of long-term ozonesonde records as a stable reference has led to increased attention to quantifying uncertainties and changes in ozonesonde data. Based on past intercomparison data, ECC sondes show a modest (~1-5%) high bias with respect to UV-absorption measurements (including MOZAIC-IAGOS) in the troposphere, with an uncertainty of 5%, but no evidence of a change with time. Other sonde types show an increase of 5-20% in sensitivity to tropospheric ozone from 1970-1995. Agreement in the stratosphere is much better. In the past 20 years ozonesonde precision has improved by a factor of 2, primarily through the adoption of strict standard operating procedures. In addition, many of the most important ozonesonde records have been re-evaluated and corrected, and detailed estimates made of their biases and uncertainties. Corrections to historical data for known issues may reduce biases but simultaneously introduce additional uncertainties. The uncertainty budget for the ozone partial pressure reading has contributions from stoichiometry, cell background current, pump efficiency and temperature, sensing solution type and volume. The much-discussed “background current” has recently been shown to be primarily related to reaction stoichiometry. Several quality assurance issues remain, but are tractable problems that can be addressed with further research. This will be required if the present goal of better than 5% overall uncertainty throughout the global ozonesonde network is to be achieved. Finally, the importance of regular sonde intercomparisons, employing UV photometers traceable to the modern UV-absorption standard, and of regular comparison of operational station records with multiple satellite sensors, is emphasized.

Ozonesondes↗

NASA NTRS1998

Intercomparisons of Total Precipitable Water from Satellite and Other Long Term Data Sets

Global water vapor data sets from satellite (NVAP) and reanalysis (NCEP and DAO) are intercompared for a 5 year period (1988-1992). Global average indicates that the NCEP and DAO reanalyses are dryer than NVAP over much of the period. Spatial patterns of the NCEP/NVAP and DAO/NVAP differences show regional variations in the 60 month climatogical fields. For example, in the Eastern Pacific just south of the equator NVAP is much dryer than the NCEP and more moist than DAO. Rather large discrepancies exist in other regions as well. North Africa, Saudi Arabia, India, and Australia show that NVAP TPW values are more moist than the NCEP and DAO model analyses by 4-8 mm. In general, the NCEP and DAO exhibits a tendency to be dryer than NVAP over the tropical ocean region. Over the Americas little differences exist except over the west coast. The shapes of the differences fields over the Eastern Pacific region are significantly different between NCEP/NVAP and DAO/NVAP differences. Over South America and Central Africa, DAO TPW values are significantly higher than those of NVAP. These difference fields show monthly and seasonal variability as well. These results will be highlighted in the paper and on the poster.

Chang, Fong-Chiau↗

NASA NTRS2003

Intercomparison of Latent Heat Fluxes Over Global Oceans

Turbulent fluxes of momentum, moisture, and heat at the air-sea interface are essential for climate studies. Version 2 Goddard Satellite-based Surface Turbulent Fluxes (GSSTF2) has been derived from the Special Sensor Microwave/Imager (SSM/I) radiance measurements. This dataset, covering the period July 1987-December 2000 over global oceans, has a spatial resolution of 1 deg x 1 deg lat-long and a temporal resolution of 1 day. Turbulent fluxes are derived from the SSM/I surface winds and surface air humidity, as well as the 2-m air and sea surface temperatures (SST) of the NCEP/NCAR reanalysis, using a bulk aerodynamic algorithm based on the surface layer similarity theory. The GSSTF2 bulk flux model, and retrieved daily wind stress, latent heat flux, wind speed, and surface air humidity validate well with ship observations of ten field experiments over the tropical and midlatitude oceans during 1991-99. The global distributions of 1988-2000 annual- and seasonal-mean turbulent fluxes show reasonable patterns related to the atmospheric general circulation and seasonal variations. Latent heat fluxes and related input parameters over global oceans during 1992-93 have been compared among GSSTF1 (version 1), GSSTF2, HOAPS (Hamburg Ocean Atmosphere Parameters and Fluxes from Satellite Data), NCEP/NCAR reanalysis, and one based on COADS (Comprehensive Ocean-Atmosphere Data Set). Our analyses suggest that the GSSTF2 latent heat flux, surface air humidity, surface wind, and SST are quite realistic compared to the other four flux datasets examined. However, significant differences are found among these five flux datasets. The GSSTF2, available at http://daac.gsfc.nasa.gov/CAMPAIGN_DOCS/hydrology/hd_gsstf2.O.html, is useful for climate studies.

Chou, Shu-Hsien↗

NASA NTRS2024

Low-Cost Sensor Performance Intercomparison, Correction Factor Development, and 2+ Years of Ambient PM2.5 Monitoring in Accra, Ghana

Particulate matter air pollution is a leading cause of global mortality, particularly in Asia and Africa. Addressing the high and wide-ranging air pollution levels requires ambient monitoring, but many low- and middle-income countries (LMICs) remain scarcely monitored. To address these data gaps, recent studies have utilized low-cost sensors. These sensors have varied performance, and little literature exists about sensor intercomparison in Africa. By colocating 2 QuantAQ Modulair-PM, 2 PurpleAir PA-II SD, and 16 Clarity Node-S Generation II monitors with a reference-grade Teledyne monitor in Accra, Ghana, we present the first intercomparisons of different brands of low-cost sensors in Africa, demonstrating that each type of low-cost sensor PM2.5 is strongly correlated with reference PM2.5, but biased high for ambient mixture of sources found in Accra. When compared to a reference monitor, the QuantAQ Modulair-PM has the lowest mean absolute error at 3.04 μg/m3, followed by PurpleAir PA-II (4.54 μg/m3) and Clarity Node-S (13.68 μg/m3). We also compare the usage of 4 statistical or machine learning models (Multiple Linear Regression, Random Forest, Gaussian Mixture Regression, and XGBoost) to correct low-cost sensors data, and find that XGBoost performs the best in testing (R2: 0.97, 0.94, 0.96; mean absolute error: 0.56, 0.80, and 0.68 μg/m3 for PurpleAir PA-II, Clarity Node-S, and Modulair-PM, respectively), but tree-based models do not perform well when correcting data outside the range of the colocation training. Therefore, we used Gaussian Mixture Regression to correct data from the network of 17 Clarity Node-S monitors deployed around Accra, Ghana, from 2018 to 2021. We find that the network daily average PM2.5 concentration in Accra is 23.4 μg/m3, which is 1.6 times the World Health Organization Daily PM2.5 guideline of 15 μg/m3. While this level is lower than those seen in some larger African cities (such as Kinshasa, Democratic Republic of the Congo), mitigation strategies should be developed soon to prevent further impairment to air quality as Accra, and Ghana as a whole, rapidly grow.

Humidity↗

NASA NTRS1990

Angular dependence models for radiance to flux conversion

Angular dependence models (ADM) used for converting the measured radiance to flux at the top of the atmosphere are reviewed, and emphasis is placed on the measure of their effectiveness and the implications of requiring the ADMs to satisfy reciprocity. The overall significance of the ADMs is figured out by analyzing the same satellite data with a single Lambertian model, single mean model, and the 12 Earth Radiation Budget Experiment (ERBE) ADMs. It is shown that the Lambertian ADM is inadequate, while the mean ADM results in nearly unbiased fluxes but creates substantial differences for individual pixel fluxes. The standard ERBE ADM works well except for a 10-pct to 15-pct albedo growth across the scan; a modified ADM based on the standard ERBE ADM but forced to satisfy the principle of reciprocity increases the limb brightening and reduces the albedo growth but does not improve the scanner and nonscanner intercomparison.

Green, Richard N.↗

DOE OSTI2024

Results from a multi-laboratory ocean metaproteomic intercomparison: effects of LC-MS acquisition and data analysis procedures

Metaproteomics is an increasingly popular methodology that provides information regarding the metabolic functions of specific microbial taxa and has potential for contributing to ocean ecology and biogeochemical studies. A blinded multi-laboratory intercomparison was conducted to assess comparability and reproducibility of taxonomic and functional results and their sensitivity to methodological variables. Euphotic zone samples from the Bermuda Atlantic Time-series Study (BATS) in the North Atlantic Ocean collected by in situ pumps and the autonomous underwater vehicle (AUV) Clio were distributed with a paired metagenome, and one-dimensional (1D) liquid chromatographic data-dependent acquisition mass spectrometry analysis was stipulated. Analysis of mass spectra from seven laboratories through a common bioinformatic pipeline identified a shared set of 1056 proteins from 1395 shared peptide constituents. Quantitative analyses showed good reproducibility: pairwise regressions of spectral counts between laboratories yielded R 2 values averaged 0.62±0.11, and a Sørensen similarity analysis of the top 1000 proteins revealed 70 %–80 % similarity between laboratory groups. Taxonomic and functional assignments showed good coherence between technical replicates and different laboratories. A bioinformatic intercomparison study, involving 10 laboratories using eight software packages, successfully identified thousands of peptides within the complex metaproteomic datasets, demonstrating the utility of these software tools for ocean metaproteomic research. Lessons learned and potential improvements in methods were described. Future efforts could examine reproducibility in deeper metaproteomes, examine accuracy in targeted absolute quantitation analyses, and develop standards for data output formats to improve data interoperability. Together, these results demonstrate the reproducibility of metaproteomic analyses and their suitability for microbial oceanography research, including integration into global-scale ocean surveys and ocean biogeochemical models.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2023

Validation of LOCA2 and STAR-ESDM Statistically Downscaled Products

The National Climate Assessment (NCA) is the preeminent national report examining current and future risks posed by climate change. Countless agencies, policymakers, stakeholders and other end-users rely upon guidance from the NCA to plan for an uncertain future. These groups all depend on modern curated data, provided alongside the NCA, to quantify the impact of climate change on metrics of relevance for their decision processes. In its fifth iteration (NCA5), two statistically downscaled ensemble products, each providing data at grid spacing of approximately 5km over the contiguous United States, were selected to accompany the report. These include LOCalized Analogs version 2 (LOCA2) and Seasonal Trends and Analysis of Residuals Empirical-Statistical Downscaling Model (STAR-ESDM). Both data products are produced through a process known as statistical downscaling, where relatively coarse Global Climate Model (GCM) data is refined to locally relevant scales through the application of scientifically-supported empirical and algorithmic relationships. In support of the NCA effort, this report provides an independent validation of these two products against historical observations, with a focus on precipitation and near-surface temperature variables. Based on the results of this validation, several recommendations are provided related to the use of these data products. The structure of this report is as follows: In section 2, we review three gridded observational products that are used as part of our intercomparison. In section 3, we describe the two statistical downscaling techniques and their corresponding datasets that are the focus of this study. In section 4, the methodology we employ for validation is described. Section 5 provides results of the validation, which in turn motivate our recommendations on the use of these data products. A brief summary is provided in section 6.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS1996

Secondary particle contribution to LET spectra on LDEF

Four experiments utilizing passive detectors (P0006, P0004, A0015, M0004) were flown on LDEF to study the radiation environment. These experiments have been summarized in a companion paper (Benton et al., 1996). One of the experimental goals was to measure LET spectra at different locations and shielding depths with plastic nuclear track detectors (PNTD). It was found that the LET spectra extended well above the LET cutoff imposed by the geomagnetic field on GCR particle penetration into LEO. The high LET particles detected were mostly short-range (range < 2000 m), indicating that they were secondaries produced locally within the PNTD. The presence of these high LET particle fluences is important for the determination of dose equivalent because of the high Quality Factors (Q) involved. A relatively small fraction of particle fluence can contribute a large fraction of dose equivalent. Short-range, inelastic secondary particles produced by trapped protons in the South Atlantic Anomaly (SAA) were found to be a major contributor to the LET spectra above 100 keV/micrometer. The LET spectra were found to extend beyond the approximately 137 keV/micrometer relativistic GCR Fe peak to over 1000 keV/micrometer. The high LET tail of the LET spectra was measured in CR-39 and polycarbonate PNTDs using different techniques. GCR made a relatively modest contribution to the LET spectra as compared to the contributions from short-range secondary particles and stopping protons. LET spectra intercomparisons were made between LDEF measurements and exposures to 154 MeV accelerated proton beams. The similarities support the role of nuclear interactions by trapped protons as the major source of secondary particles in the PNTDs. Also techniques were employed to reduce the range cutoff for detection of the short-range secondaries to approximately 1 micrometer, so that essentially all secondary particles were included in the LET spectra. This has allowed a more realistic assessment of secondary contribution to dose equivalent. Comparisons of measured and calculated LET spectra have been made that demonstrate the need for more accurate modeling of secondary particles in radiation transport codes. Comparisons include preliminary calculations in which attempts have been made to include secondary particles.

long duration↗

NASA NTRS1992

An intercomparison of surface energy flux measurement systems used during FIFE 1987

During FIFE 1987, surface energy fluxes were measured at 22 flux sites by nine groups of scientists using different measuring systems. A rover Bowen ratio station was taken to 20 of the flux stations to serve as a reference for estimating the instrument-related differences. The rover system was installed within a few meters from the host instrument of a site. Using linear regression analysis, net radiation, Bowen ratio, and latent heat fluxes were compared between the rover measurements and the host measurements. The average differences in net radiation, Bowen ratio, and latent heat flux from different types of instruments can be up to 10, 30, and 20 percent, respectively. The Didcot net radiometer gave higher net radiation while the Swissteco type showed lower values, as compared to the corrected radiation energy balance system (REBS) model. The four-way components method and the Thornthwaite type give similar values to the REBS. The surface energy radiation balance systems type Bowen ratio systems exhibit slightly lower Bowen ratios and thus higher latent heat fluxes, compared to the arid zone evapotranspiration systems. Eddy correlation systems showed slightly lower latent heat flux in comparison to the Bowen ratio systems. It is recommended that users of the flux data take these differences into account.

Nie, D.↗

DOE OSTI2021

Radon Kinetics in a Basement Space Measured with Different Devices

Although indoor monitoring of radon and benchmarking of radon measurement devices remain important research topics, few intercomparisons of active radon measurement devices have been performed under realistic conditions, let alone dynamic ones enabling comparison of their transient behavior. Five different radon monitors were therefore placed in a poorly ventilated basement space under three different conditions: 24 h under a steady, elevated radon level, 24 h with fans turned on to produce a radon washout transient, and 9 d with fans turned off for a radon buildup transient. Resulting radon concentrations varied between ~200 and ~2,000 Bq m -3 . Accuracy of the devices were evaluated using root-mean-square error, and ventilation data were fit to first order linear compartmental models. To more accurately model behaviors such as cyclic diurnal variations, the source term corresponding to entry of radon from soil into the basement was considered to be non-constant, as it is likely to vary drastically with both the indoor-outdoor pressure differential and soil concentration variations. Here, the improved radon washout model fit very well with the measurements. Despite a wide variety in list prices, all devices performed similarly during transients and at different radon concentrations.

60 APPLIED LIFE SCIENCES↗

NASA NTRS2001

Vertical Profiles of Latent Heating in TRMM: Validation and Assimilation in the GEOS Data Assimilation System

I have derived the vertical profiles of apparent heating Q and Q2 by using 3-hourly balloon sounding data from the ARM SGP sounding array for three IOPs in 2000. These IOPs cover the periods from 3/1 to 3/22, from 9/25 to 10/8, and from 11/27 to 12/22. These heating profiles will be collocated with the TRMM heating profiles for validation studies once the TRMM profiles become available to the investigators. I have also produced the objective analyses of Q1, Q2 and forcing fields for the international GCSS Case 3 Intercomparison project. The GSFC CRM participated in study, based on which TRMM heating profiles are derived. I have also studied to assimilate the vertical heating profile by constraining the cloud-base mass flux in the cumulus convection scheme using the CCM3. I first derive the vertical heating profile for each convective plume of unit cloud-base mass flux. The observed heating profile form ARM is then de-convoluted to derive the cloud base mass fluxes of all individual plumes. These fluxes are compared with those derived from the quasi-equilibrium hypothesis. They are shown to improve many other aspects of the model behavior.

Zhang, Ming-Hua↗

NASA NTRS2010

Analysis of Atmosphere-Ocean Surface Flux Feedbacks in Recent Satellite and Model Reanalysis Products

Recent investigations have examined observations in an attempt to determine when and how the ocean forces the atmosphere, and vice versa. These studies focus primarily on relationships between sea surface temperature anomalies and the turbulent and radiative surface heat fluxes. It has been found that both positive and negative feedbacks, which enhance or reduce sea surface temperature anomaly amplitudes, can be generated through changes in the surface boundary layer. Consequent changes in sea surface temperature act to change boundary layer characteristics through changes in static stability or turbulent fluxes. Previous studies over the global oceans have used coarse-resolution observational and model products such as ICOADS and the NCEP Reanalysis. This study focuses on documenting the atmosphere ocean feedbacks that exist in recently produced higher resolution products, namely the SeaFlux v1.0 product and the NASA Modern Era Retrospective-Analysis for Research and Applications (MERRA). It has been noted in recent studies that evidence of oceanic forcing of the atmosphere exists on smaller scales than the usually more dominant atmospheric forcing of the ocean, particularly in higher latitudes. It is expected that use of these higher resolution products will allow for a more comprehensive description of these small-scale ocean-atmosphere feedbacks. The SeaFlux intercomparisons have revealed large scatter between various surface flux climatologies. This study also investigates the uncertainty in surface flux feedbacks based on several of these recent satellite based climatologies

Roberts, J. Brent↗

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.]

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