scholarly journals Dewpoint and Humidity Measurements and Trends at the Summit of Mount Washington, New Hampshire, 1935–2004

2007 ◽  
Vol 20 (22) ◽  
pp. 5629-5641 ◽  
Author(s):  
Thomas M. Seidel ◽  
Andrea N. Grant ◽  
Alexander A. P. Pszenny ◽  
Daniel J. Allman
Keyword(s):  
Relative Humidity ◽  
New Hampshire ◽  
Mixing Ratio ◽  
Dewpoint Temperature ◽  
Full Study ◽  
Humidity Measurements ◽  
Annual Frequency ◽  
Temperature And Pressure ◽  
Water Vapor Mixing Ratio ◽  
Mount Washington

Abstract Meteorological conditions have been recorded at the summit of Mount Washington, New Hampshire, (44°16′N, 71°18′W, 1914 m ASL) since November 1932. Use of consistent instrumentation allows analysis of humidity measurements as calculated from error-checked dry bulb temperature, wet bulb temperature, and pressure during the period 1935–2004. This paper presents seasonally and annually averaged dewpoint temperature, mixing ratio, and relative humidity means and trends, including clear-air and fog subsets and, beginning in 1939, day and night subsets. The majority of linear trends are negative over the full study period, although these decreases are not constant, with relatively large (small) values in the mid-1950s (late 1970s). Annual mean dewpoint (water vapor mixing ratio) over the 70-yr period has decreased by 0.06°C decade−1 (0.01 g kg−1 decade−1). During this period the annual frequency of fog increased by 0.5% decade−1. Dewpoint and mixing ratio trends, both generally decreasing, differ by season; they are smallest in spring and greatest in fall. Relative humidity has decreased most in winter. The clear-air subset shows significant decreases in both dewpoint and mixing ratio for all seasons except spring.

scholarly journals Factors controlling upper tropospheric relative humidity

2004 ◽  
Vol 22 (3) ◽  
pp. 705-715 ◽  
Author(s):  
B. Kärcher ◽  
W. Haag
Keyword(s):  
Relative Humidity ◽  
Ice Nucleation ◽  
Cirrus Clouds ◽  
Atmospheric Composition ◽  
Mixing Ratio ◽  
Separate Study ◽  
Air Masses ◽  
Composition And Structure ◽  
Ice Microphysics ◽  
Water Vapor Mixing Ratio

Abstract. Factors controlling the distribution of relative humidity in the absence of clouds are examined, with special emphasis on relative humidity over ice (RHI) under upper tropospheric and lower stratospheric conditions. Variations of temperature are the key determinant for the distribution of RHI, followed by variations of the water vapor mixing ratio. Multiple humidity modes, generated by mixing of different air masses, may contribute to the overall distribution of RHI, in particular below ice saturation. The fraction of air that is supersaturated with respect to ice is mainly determined by the distribution of temperature. The nucleation of ice in cirrus clouds determines the highest relative humdity that can be measured outside of cirrus clouds. While vertical air motion and ice microphysics determine the slope of the distributions of RHI, as shown in a separate study companion (Haag et al., 2003), clouds are not required to explain the main features of the distributions of RHI below the ice nucleation threshold. Key words. Atmospheric composition and structure (pressure, density and temperature; troposphere – composition and chemistry; general or miscellaneous)

Measurements of moisture in smoldering smoke and implications for fog

2006 ◽  
Vol 15 (4) ◽  
pp. 517 ◽  
Author(s):  
Gary L. Achtemeier
Keyword(s):  
Relative Humidity ◽  
Ground Level ◽  
Southern United States ◽  
Formation Temperature ◽  
Mixing Ratio ◽  
Prescribed Burns ◽  
Dry Air ◽  
Ambient Relative Humidity ◽  
Humidity Measurements ◽  
The Impact

Smoke from wildland burning in association with fog has been implicated as a visibility hazard over roadways in the southern United States. A project began in 2002 to determine whether moisture released during the smoldering phases of southern prescribed burns could contribute to fog formation. Temperature and relative humidity measurements were taken from 27 smoldering ‘smokes’ during 2002 and 2003. These data were converted to a measure of the mass of water vapor present to the mass of dry air containing the vapor (smoke mixing ratio). Some smokes were dry with almost no moisture beyond ambient. Other smokes were moist with moisture excesses as large as 39 g kg–1. Calculations show that ground-level smoke moisture excesses have no impact on ambient relative humidity during the day. However, the impact at night can be large enough to increase the ambient relative humidity to 100%. Therefore smoke moisture may be a contributing factor to the location and timing of fog formation.

scholarly journals Water Vapor Measurements by Howard University Raman Lidar during the WAVES 2006 Campaign

2010 ◽  
Vol 27 (1) ◽  
pp. 42-60 ◽  
Author(s):  
M. Adam ◽  
B. B. Demoz ◽  
D. D. Venable ◽  
E. Joseph ◽  
R. Connell ◽  
...  
Keyword(s):  
Water Vapor ◽  
Relative Humidity ◽  
Relative Difference ◽  
Mixing Ratio ◽  
State Variables ◽  
Raman Lidar ◽  
Ensemble Averaging ◽  
Howard University ◽  
Validation Experiment ◽  
Water Vapor Mixing Ratio

Abstract Water vapor mixing ratio retrieval using the Howard University Raman lidar is presented with emphasis on three aspects: (i) comparison of the lidar with collocated radiosondes and Raman lidar, (ii) investigation of the relationship between atmospheric state variables and the relative performance of the lidar and sonde (in particular, their poor agreement), and (iii) comparison with satellite-based measurements. The measurements were acquired during the Water Vapor Validation Experiment Sondes/Satellites 2006 campaign. Ensemble averaging of water vapor mixing ratio data from 10 nighttime comparisons with Vaisala RS92 radiosondes shows, on average, an agreement within ±10%, up to ∼8 km. A similar analysis of lidar-to-lidar data of over 700 profiles revealed an agreement to within 20% over the first 7 km (10% below 4 km). A grid analysis, defined in the temperature–relative humidity space, was developed to characterize the lidar–radiosonde agreement and quantitatively localizes regions of strong and weak correlations as a function of altitude, temperature, or relative humidity. Three main regions of weak correlation emerge: (i) regions of low relative humidity and low temperature, (ii) regions of moderate relative humidity at low temperatures, and (iii) regions of low relative humidity at moderate temperatures. Comparison of Atmospheric Infrared Sounder and Tropospheric Emission Sounder satellite retrievals of moisture with those of Howard University Raman lidar showed a general agreement in the trend, but the satellites miss details in atmospheric structure because of their low resolution. A relative difference of about ±20% is usually found between lidar and satellite measurements for the coincidences available.

scholarly journals Comparisons of temperature, pressure and humidity measurements by balloon-borne radiosondes and frost point hygrometers during MOHAVE 2009

2011 ◽  
Vol 4 (4) ◽  
pp. 4357-4401 ◽  
Author(s):  
D. F. Hurst ◽  
E. G. Hall ◽  
A. F. Jordan ◽  
L. M. Miloshevich ◽  
D. N. Whiteman ◽  
...  
Keyword(s):  
Water Vapor ◽  
Relative Humidity ◽  
Mixing Ratios ◽  
Frost Point ◽  
Humidity Measurements ◽  
Large Measurement ◽  
Measurement Uncertainties ◽  
Temperature And Pressure ◽  
Measurement Biases

Abstract. We compare coincident, balloon-borne, in situ measurements of temperature and pressure by two radiosondes (Vaisala RS92, Intermet iMet-1-RSB) and measurements of relative humidity (RH) by Vaisala RS92 sondes and frost point hygrometers. Data from a total of 28 balloon flights with mixed payloads are analyzed in 1-km altitude bins to quantify measurement biases between sensors and how they vary with altitude. The disparities between sensors determined here are compared to measurement uncertainties quoted by the two radiosonde manufacturers. Our comparisons expose several flight profiles with anomalously large measurement differences. Excluding these anomalous profiles, 33 % of RS92-iMet median temperature differences exceed the uncertainty limits calculated from manufacturer-quoted uncertainties. A statistically significant, altitude-independent bias of about 0.5 ± 0.2 °C is revealed for the RS92-iMet temperature differences. Similarly, 23 % of RS92-iMet median pressure differences exceed the quoted uncertainty limits, with 83 % of these excessive differences above 16 km altitude. The pressure differences are altitude dependent, increasing from −0.6 ± 0.9 hPa at the surface to 0.7 ± 0.1 hPa above 15 km. Temperature and pressure differences between redundant RS92 sondes on the same balloon exceed manufacturer-quoted reproducibility limits 20 % and 2 % of the time, respectively, with most of the excessive differences belonging to anomalous difference profiles. Relative humidity measurements by RS92 sondes are compared to other RS92 sondes and to RH values calculated using frost point hygrometer measurements and coincident radiosonde temperature measurements. For some flights the RH differences are anomalously large, but in general are within the ±5 % RH measurement uncertainty limits quoted for the RS92. The quantitative effects of RS92 and iMet pressure and temperature differences on frost point-based water vapor mixing ratios and RH values, respectively, are also presented.

scholarly journals Calibration of Raman lidar water vapor profiles by means of AERONET photometer observations and GDAS meteorological data

2017 ◽  
Author(s):  
Guangyao Dai ◽  
Dietrich Althausen ◽  
Julian Hofer ◽  
Ronny Engelmann ◽  
Patric Seifert ◽  
...  
Keyword(s):  
Water Vapor ◽  
Precipitable Water ◽  
Precipitable Water Vapor ◽  
Mixing Ratio ◽  
Raman Lidar ◽  
Pressure Profiles ◽  
Temperature And Pressure ◽  
Water Vapor Mixing Ratio ◽  
Lidar Observations ◽  
Humidity Profiles

Abstract. We present a practical method to continuously calibrate Raman lidar observations of the water vapor mixing ratio profile. The water vapor profile measured with the multiwavelength polarization Raman lidar PollyXT is calibrated by means of co-located AErosol RObotic NETwork (AERONET) sun photometer observations and Global Data Assimilation System (GDAS) temperature and pressure profiles. This method is applied to lidar observations conducted during the Cyprus Cloud Aerosol and Rain Experiment (CyCARE) in Limassol, Cyprus. We use the GDAS temperature and pressure profiles to retrieve the water vapor density. In the next step, the precipitable water vapor is obtained from the lidar observation. During CyCARE, 9 measurement cases with cloud-free and stable meteorological conditions are selected to calculate the precipitable water vapor from the lidar and the sun photometer observations. The ratio of these two precipitable water vapor values yields the water vapor calibration constant. The calibration constant for the PollyXT Raman lidar is 6.56 g kg−1 ± 0.72 g kg−1 (with a statistical uncertainty of 0.08 g kg−1 and an instrumental uncertainty of 0.72 g kg−1). To check the quality of the water vapor calibration, the water vapor mixing ratio profiles from the simultaneous nighttime observations with Raman lidar and Vaisala radiosonde sounding are compared. The correlation of the water vapor mixing ratios from these two instruments is determined by using all of the 19 simultaneous nighttime measurements during CyCARE. Excellent agreement with the slope of 1.01 and the R2 of 0.99 is found. One example is presented to demonstrate the full potential of a well calibrated Raman lidar. The relative humidity profiles from lidar, GDAS (simulation) and radiosonde are compared. It is found that the combination of water vapor mixing ratio and GDAS temperature profiles allow us to derive relative humidity profiles with good accuracy.

Influence of Non-Equilibrium Fluid Properties During Fogging on Intake Duct and Compressor Characteristics

2017 ◽  
Author(s):  
Christoph Günther ◽  
Franz Joos
Keyword(s):  
Relative Humidity ◽  
Gas Turbine ◽  
Thermophysical Properties ◽  
Ambient Air ◽  
Compressor Stage ◽  
Compression Process ◽  
Fluid Properties ◽  
Temperature And Pressure ◽  
Gas Turbine Compressor ◽  
Non Equilibrium

This study reports on numerically calculated thermophysical properties of air passing through a gas turbine compressor after passage through an intake duct affected by wet compression. Case of reference is unaffected ambient air (referenced to as dry scenario) passing through intake duct and compressor. Furthermore, ambient air cooled down by (overspray) fogging (referenced to as wet scenarios) was considered. Acceleration at the end of intake duct causing reduction of static temperature and pressure results in supersaturated fluid properties at inlet to gas turbine compressor. These supersaturated fluid properties are non-equilibrium with saturation level above relative humidity of φ = 1. Entrance of supersaturated fluid into gas turbine compressor can result in condensation within first compressor stage. At the same time delayed impact of evaporative cooling influences compression process.

scholarly journals Tropospheric water vapour and relative humidity profiles from lidar and microwave radiometry

2014 ◽  
Vol 7 (5) ◽  
pp. 1201-1211 ◽  
Author(s):  
F. Navas-Guzmán ◽  
J. Fernández-Gálvez ◽  
M. J. Granados-Muñoz ◽  
J. L. Guerrero-Rascado ◽  
J. A. Bravo-Aranda ◽  
...  
Keyword(s):  
Relative Humidity ◽  
Water Vapour ◽  
Microwave Radiometer ◽  
Radiosonde Data ◽  
Mixing Ratio ◽  
Raman Lidar ◽  
Night Time ◽  
New Approach ◽  
Lidar Measurements ◽  
Humidity Profiles

Abstract. In this paper, we outline an iterative method to calibrate the water vapour mixing ratio profiles retrieved from Raman lidar measurements. Simultaneous and co-located radiosonde data are used for this purpose and the calibration results obtained during a radiosonde campaign in summer and autumn 2011 are presented. The water vapour profiles measured during night-time by the Raman lidar and radiosondes are compared and the differences between the methodologies are discussed. Then, a new approach to obtain relative humidity profiles by combination of simultaneous profiles of temperature (retrieved from a microwave radiometer) and water vapour mixing ratio (from a Raman lidar) is addressed. In the last part of this work, a statistical analysis of water vapour mixing ratio and relative humidity profiles obtained during 1 year of simultaneous measurements is presented.

Subcloud and Cloud-Base Latent Heat Fluxes during Shallow Cumulus Convection

2019 ◽  
Vol 77 (3) ◽  
pp. 1081-1100 ◽  
Author(s):  
Neil P. Lareau
Keyword(s):  
Boundary Layer ◽  
Water Vapor ◽  
Latent Heat ◽  
Surface Fluxes ◽  
Heat Fluxes ◽  
Cloud Base ◽  
Cumulus Convection ◽  
Mixing Ratio ◽  
Convective Boundary ◽  
Water Vapor Mixing Ratio

Abstract Doppler and Raman lidar observations of vertical velocity and water vapor mixing ratio are used to probe the physics and statistics of subcloud and cloud-base latent heat fluxes during cumulus convection at the ARM Southern Great Plains (SGP) site in Oklahoma, United States. The statistical results show that latent heat fluxes increase with height from the surface up to ~0.8Zi (where Zi is the convective boundary layer depth) and then decrease to ~0 at Zi. Peak fluxes aloft exceeding 500 W m−2 are associated with periods of increased cumulus cloud cover and stronger jumps in the mean humidity profile. These entrainment fluxes are much larger than the surface fluxes, indicating substantial drying over the 0–0.8Zi layer accompanied by moistening aloft as the CBL deepens over the diurnal cycle. We also show that the boundary layer humidity budget is approximately closed by computing the flux divergence across the 0–0.8Zi layer. Composite subcloud velocity and water vapor anomalies show that clouds are linked to coherent updraft and moisture plumes. The moisture anomaly is Gaussian, most pronounced above 0.8Zi and systematically wider than the velocity anomaly, which has a narrow central updraft flanked by downdrafts. This size and shape disparity results in downdrafts characterized by a high water vapor mixing ratio and thus a broad joint probability density function (JPDF) of velocity and mixing ratio in the upper CBL. We also show that cloud-base latent heat fluxes can be both positive and negative and that the instantaneous positive fluxes can be very large (~10 000 W m−2). However, since cloud fraction tends to be small, the net impact of these fluxes remains modest.

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