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Profiling the Molecular Destruction Rates of Temperature and Humidity as well as the Turbulent Kinetic Energy Dissipation in the Convective Boundary Layer
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Pd Nanoparticles-Enabled Optical Fiber Hydrogen Sensor with a Hydrophobic Protective Layer for Humid Conditions
SPIE Defense and Commercial Sensing, Orlando, FL, April 30-May 4, 2023
Multi-parameter Optical Fiber Sensor for Simultaneous Monitoring of Humidity, Pressure, CO2, and Corrosion
For presentation at the University of Pittsburgh Infrastructure Sensing Collaboration Workshop, Pittsburgh, PA, November 7-8, 2023.
Aging Processes of Silver Mordenite and Silver Functionalized Aerogel in Dry Air, Humid Air and NO/N{sub 2}
One major problem in the treatment of nuclear-fuel reprocessing off-gas streams is the loss of capacity of the adsorbents exposed to off-gas streams containing air, I{sub 2} vapor, H{sub 2}O vapor, NO and NO{sub 2} over extended periods of time. These aging processes decrease the adsorption capacity of the reduced silver exchanged mordenite (Ag{sup 0}Z) and silver functionalized silica aerogel (Ag{sup 0}-aerogel) columns during the long processing time of iodine adsorption. Significant work has been performed over the past few years at ORNL on Ag{sup 0}Z and Ag{sup 0}-aerogel aging. The Ag{sup 0}Z and Ag{sup 0}-aerogel were aged in 4 different gas streams at 150 deg. C for periods of time up to 6 months. The aging samples were then loaded with I{sub 2} to determine the capacity loss after the aging. The results are summaries in Tab. 1. It was found that both Ag{sup 0}Z and Ag{sup 0}-aerogel had different amounts of capacity loss in different gases. For example, 40% and 22% capacity loss were observed for Ag{sup 0}Z and Ag{sup 0}-aerogel when aged in dry air for 6 months, while 78% and 43% capacity loss were observed when aged in 1% NO{sub 1} for 2 months. In addition, it was found that Ag{sup 0}Z and Ag{sup 0}-aerogel had a different capacity loss when aged at the same conditions. For example, after aged in 2% NO{sub 2} for 2 months, Ag{sup 0}Z lost 30% iodine capacity, but there was no capacity loss for Ag{sup 0}- aerogel. The differences in capacity loss should be due to different aging processes occurred when Ag{sup 0}Z and Ag{sup 0}- aerogel were exposed to different gases. The mechanisms of the aging processes have not been determined and need to be studied with proper chemical and physical characterization methods. Towards this objective, experimental work is proposed and on-going to obtain data to determine the mechanisms of the aging processes of Ag{sup 0}Z and Ag{sup 0}-aerogel in different aging gas streams. In addition, models will be developed to describe the deactivation process of the adsorbents when exposed to off gases. The deactivation model will be included in the transport models for predicting the adsorption processes over these long-time periods.
Temperature and Humidity Susceptibility of Instrumentation Components
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Durability Assessment of Retrofitted VIP Insulated Cladding System in a Hot-Humid Climate
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On the Choice of Variable for Atmospheric Moisture Analysis
The implications of using different control variables for the analysis of moisture observations in a global atmospheric data assimilation system are investigated. A moisture analysis based on either mixing ratio or specific humidity is prone to large extrapolation errors, due to the high variability in space and time of these parameters and to the difficulties in modeling their error covariances. Using the logarithm of specific humidity does not alleviate these problems, and has the further disadvantage that very dry background estimates cannot be effectively corrected by observations. Relative humidity is a better choice from a statistical point of view, because this field is spatially and temporally more coherent and error statistics are therefore easier to obtain. If, however, the analysis is designed to preserve relative humidity in the absence of moisture observations, then the analyzed specific humidity field depends entirely on analyzed temperature changes. If the model has a cool bias in the stratosphere this will lead to an unstable accumulation of excess moisture there. A pseudo-relative humidity can be defined by scaling the mixing ratio by the background saturation mixing ratio. A univariate pseudo-relative humidity analysis will preserve the specific humidity field in the absence of moisture observations. A pseudorelative humidity analysis is shown to be equivalent to a mixing ratio analysis with flow-dependent covariances. In the presence of multivariate (temperature-moisture) observations it produces analyzed relative humidity values that are nearly identical to those produced by a relative humidity analysis. Based on a time series analysis of radiosonde observed-minus-background differences it appears to be more justifiable to neglect specific humidity-temperature correlations (in a univariate pseudo-relative humidity analysis) than to neglect relative humidity-temperature correlations (in a univariate relative humidity analysis). A pseudo-relative humidity analysis is easily implemented in an existing moisture analysis system, by simply scaling observed-minus background moisture residuals prior to solving the analysis equation, and rescaling the analyzed increments afterward.
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.]
Preservation of H 2 production activity in nanoporous latex coatings of Rhodopseudomonas palustris CGA009 during dry storage at ambient temperatures: Preservation of R.palustris latex coatings
To assess the applicability of latex cell coatings as an ‘off-the-shelf’ biocatalyst, the effect of osmoprotectants, temperature, humidity and O 2 on preservation of H 2 production in Rhodopseudomonas palustris coatings was evaluated. Immediately following latex coating coalescence (24 h) and for up to 2 weeks of dry storage, rehydrated coatings containing different osmoprotectants displayed similar rates of H 2 production. Beyond 2 weeks of storage, sorbitol-treated coatings lost all H 2 production activity, whereas considerable H 2 production was still detected in sucrose- and trehalose-stabilized coatings. We stored the coatings at a relative humidity level which significantly impacts the recovery and subsequent rates of H 2 production. After 4 weeks storage under air at 60% humidity, coatings produced only trace amounts of H 2 (0–0.1% headspace accumulation), whereas those stored at < 5% humidity retained 27–53% of their H 2 production activity after 8 weeks of storage. Furthermore, when stored in argon at < 5% humidity and room temperature, R. palustris coatings retained full H 2 production activity for 3 months, implicating oxidative damage as a key factor limiting coating storage. Ultimately, the results demonstrate that biocatalytic latex coatings are an attractive cell immobilization platform for preservation of bioactivity in the dry state.
Using Dual-Regression to Produce 16-Day Average AIRS Soundings
Temperature and humidity profiles are needed to estimate surface radiation budget. The Clouds and the Earth’s Radiant Energy System (CERES) team uses temperature and humidity profiles from a reanalysis product produced by NASA’s Global Modeling and Assimilation Office for surface irradiance computations. Biases and drifts in temperature and humidity profiles in the reanalysis product result in biases and drifts in surface irradiances computed with them. One approach to correct biases and drifts in temperature and humidity profile is to use satellite observations, similar to assimilating instantaneous spectral radiances to correct modeled temperature and humidity profiles. In this work, we use mean spectral radiances to test the possibility of understanding biases in reanalysis mean temperature and humidity profiles. Specifically, we use 16-day mean Atmospheric Infrared Sounder (AIRS) radiances and only use clear-sky spectral radiances with a viewing zenith angle nadir to near-nadir. We use the dual-regression method (Smith et al. 2012) to test whether temperature and humidity profiles retrieved from the 16-day mean spectral radiances agree with the average of temperature and humidity profiles derived from instantaneous spectral radiances. When daytime and nighttime spectral radiances are averaged separately and daytime and nighttime retrievals are performed separately, temperature and humidity profiles derived from the mean spectral radiances agree well with mean temperature and humidity profiles derived from instantaneous spectral radiances. The agreement improves when clouds are further screened to compute 16-day mean clear-sky spectral radiances.
Influence of Lower Tropospheric Moisture on Local Soil Moisture-Precipitation Feedback Over the U.S. Southern Great Plains
Land-atmosphere coupling (LAC) has long been studied focusing on land surface and atmospheric boundary layer processes. However, the influence of lower tropospheric (LT) humidity on LAC remains largely unexplored. In this study, we use radiosonde observations from the U.S. Southern Great Plains (SGP) site and an entrained parcel buoyancy model to investigate the impact of LT humidity on LAC there during the warm season (May–September). We quantify the effect of LT humidity on convective buoyancy by measuring the difference between the 2–4 km vertically integrated buoyancy with and without the influence of background LT humidity. Our results show that, under dry soil conditions, anomalously high LT humidity is necessary to produce the buoyancy profiles required for afternoon precipitation events (APEs). These APEs under dry soil moisture cannot be explained by commonly used local land-atmosphere coupling indices such as the convective triggering potential/low-level humidity index (CTP/HI Low ), which do not account for the influence of the LT humidity. On the other hand, consideration of LT humidity is unnecessary to explain APEs under wet soil moisture conditions, suggesting the boundary layer moisture alone could be sufficient to generate the required buoyancy profiles. These findings highlight the need to consider the impact of LT humidity, which is often decoupled from the humidity near the surface and largely controlled by moisture transport, in understanding land-atmospheric feedbacks over dry soil conditions, especially during droughts or dry spells over the SGP.
High Level Waste in Salt Repositories: Experiments and Simulations of Evaporation in the Underground - 17167
Advances in simulation of coupled heat and mass transfer in granular salt using the Finite Element Heat and Mass Transfer Code (FEHM) is presented in this paper. The modeling focuses on reproducing the results of an in-situ subsurface evaporation experiment beginning during the summer of 2015 carried out in the underground at the Waste Isolation Pilot Plant (WIPP). Run-of-mine (granular) salt was placed in a metal pan, and sample mass, as well as ambient temperature and humidity, were measured continuously on a data logger through May 2016. Changes in moisture content of the salt are interpreted from the change in mass, and these data provide insight into evaporation and water retention characteristics from salt in the WIPP underground. To allow simulation of the experimental data, FEHM was modified to include a variable humidity boundary condition. The new humidity boundary condition can adjust streams of inflowing air to reflect fractions of air and water at prescribed temperature and pressure conditions that in turn represent relative humidity. Attributes for the flow such as temperature, air mass fraction, and humidity can be specified for inflowing air. The in-situ experiment provided data for validation of FEHM code development to accurately compute water-vapor pressure lowering due to dissolved salt and capillary pressure for pore water stored granular WIPP salt. Using this new boundary condition capabilities, a numerical representation of the pan experiment was created. The numerical domain is a 2-dimensional square grid of 30 by 30 cm axes. Salt and air properties are assigned based on measured values. A low flow rate of air is specified across the pile of salt to represent drift ventilation airflow. Humidity and temperature of the flowing air are fixed to measured values, and the simulated pile of salt is allowed to take up or release water based only on gradients in humidity and water-vapor pressure. Mass changes of the modeled salt pan were compared to measured mass changes in the bench-scale experiment. Directionality of the modeled changes tracks well with measured values, with good correlation between peak and valley inflection points. This shows that in situations with airflow where the relative humidity boundary can apply, FEHM can produce accurate values if humidity, air flow, and initial conditions are well established. Finally, this work supports phased-field-thermal testing in salt planned for the underground at WIPP that is expected to begin in early 2017. (authors)
Rupturing of Biological Spores As a Source of Secondary Particles in Amazonia
Airborne biological particles, such as fungal spores and pollen, are ubiquitous in the Earth’s atmosphere and play an important role in the atmospheric environment and climate, impacting air quality, cloud formation, and the Earth’s radiation budget. The atmospheric transformations of airborne biological spores at elevated relative humidity remain poorly understood and their climatic role is uncertain. Using an environmental scanning electron microscope (ESEM), we observed rupturing of Amazonian fungal spores and subsequent release of nanometer to submicron size fragments after exposure to high humidity. We find that fungal fragments contain elements of inorganic salts (e.g., Na and Cl). They are hygroscopic in nature with a growth factor up to 2.3 at 96% relative humidity, thus they may potentially influence cloud formation. Due to their hygroscopic growth, light scattering cross sections of the fragments are enhanced by up to a factor of 10. Furthermore, rupturing of fungal spores at high humidity may explain the bursting events of nanoparticles and may provide insight into new particle formation in Amazonia.
The controlling role of atmosphere in dawsonite versus gibbsite precipitation from tetrahedral aluminate species
In highly alkaline solution, aluminum speciates as the tetrahedrally coordinated aluminate monomer, Al(OH)4- and/or dimer Al2O(OH)62-, yet precipitates as octahedrally coordinated gibbsite (Al(OH)3). This tetrahedral to octahedral transformation governs Al precipitation, which is crucial to worldwide Al production, and to the processing of caustic high-level radioactive wastes. Despite its significance, the transformation pathway remains unknown. Here we explore the roles of atmospheric water and carbon dioxide in mediating the transformation of the tetrahedrally coordinated potassium aluminate dimer salt (K2Al2O(OH)6) to gibbsite versus potassium dawsonite (KAl(CO3)(OH)2). A combination of in-situ attenuated total reflection infrared spectroscopy, ex-situ micro X-ray diffraction, and multivariate curve resolution-alternating least squares chemometrics analysis reveals that humidity plays a key role in the transformation by limiting the amount of alkalinity neutralization by dissolved CO2. Lower humidity favors higher alkalinity and incorporation of carbonate species in the final Al product to form KAl(CO3)(OH)2. Higher humidity enables more acid generation that destabilizes dawsonite and favors gibbsite as the solubility limiting phase. Because the transformation was restricted to occur in thin water films, the results suggest that transition from tetra- to octahedrally coordinated Al does not have to occur in bulk solution, as has often been hypothesized, but may instead appear on the source mineral surface.
The Midlatitude Continental Convective Clouds Experiment (MC3E) sounding network: operations, processing and analysis
The Midlatitude Continental Convective Clouds Experiment (MC3E) took place during the spring of 2011 centered in north-central Oklahoma, USA. The main goal of this field campaign was to capture the dynamical and microphysical characteristics of precipitating convective systems in the US Central Plains. A major component of the campaign was a six-site radiosonde array designed to capture the large-scale variability of the atmospheric state with the intent of deriving model forcing data sets. Over the course of the 46-day MC3E campaign, a total of 1362 radiosondes were launched from the enhanced sonde network. This manuscript provides details on the instrumentation used as part of the sounding array, the data processing activities including quality checks and humidity bias corrections and an analysis of the impacts of bias correction and algorithm assumptions on the determination of convective levels and indices. It is found that corrections for known radiosonde humidity biases and assumptions regarding the characteristics of the surface convective parcel result in significant differences in the derived values of convective levels and indices in many soundings. In addition, the impact of including the humidity corrections and quality controls on the thermodynamic profiles that are used in the derivation of a large-scale model forcing data set are investigated. The results show a significant impact on the derived large-scale vertical velocity field illustrating the importance of addressing these humidity biases.
Hygroscopic Properties of Magnetic Recording Tape
Relative humidity has been recognized as an important environmental factor in many head-tape interface phenomena such as headwear, friction, staining, and tape shed. Accordingly, the relative humidity is usually specified in many applications of tape use, especially when tape recorders are enclosed in hermetically sealed cases. Normally, the relative humidity is believed regulated by humidification of the fill gas to the specification relative humidity. This study demonstrates that the internal relative humidity in a sealed case is completely controlled by the time-dpendence of the hygroscopic properties of the pack of magnetic recording tape. Differences are found in the hygroscopic properties of the same brand of tape, which apparently result from aging, and which may have an effect on the long-term humidity-regulating behavior in a sealed case, and on the occurrence of head-tape interface phenomena from the long-term use of the tape. Results are presented on the basic hygroscopic properties of magnetic tape, its humidity-regulating behavior in a sealed case, and a theoretical commentary on the relative humidity dependence of head-wear by tape, is included.
Comparison of upper tropospheric water vapor from GOES, Raman lidar, and Cross-chain Loran Atmospheric Sounding System measurements
Observations of upper tropospheric relative humidity obtained from Raman lidar and Cross-chain Loran Atmospheric Sounding System (CLASS) sonde instruments obtained during the First ISCCP Regional Experiment (FIRE) Cirrus-II field program are compared with satellite measurements from the GOES 6.7-micron channel. The 6.7-micron channel is sensitive to water vapor integrated over a broad layer in the upper troposphere (roughly 500-200 mbar). Instantaneous measurements of the upper tropospheric relative humidity from GOES are shown to agree to within roughly 6% of the nearest lidar observations and 9% of the nearest CLASS observations. The CLASS data exhibit a slight yet systematic dry bias in upper tropospheric humidity, a result which is consistent with previous radiosonde intercomparisons. Temporal stratification of the CLASS data indicates that the magnitude of the bias is dependent upon the time of day, suggesting a solar heating effect in the radiosonde sensor. Using CLASS profiles, the impact of vertical variability in relative humidity upon the GOES upper tropospheric humidity measurements is also examined. The upper tropospheric humidity inferred from the GOES 6.7-micron channel is demonstrated to agree to within roughly 5% of the relative humidity vertically averaged over the depth of atmosphere to which the 6.7-micron channel is sensitive. The results of this study encourage the use of satellite measurements in the 6.7-micron channel to quantitatively describe the distribution and temporal evolution of the upper tropospheric humidity field.