scholarly journals Aircraft Evaluation of Ground-Based Raman Lidar Water Vapor Turbulence Profiles in Convective Mixed Layers

2014 ◽  
Vol 31 (5) ◽  
pp. 1078-1088 ◽  
Author(s):  
D. D. Turner ◽  
R. A. Ferrare ◽  
V. Wulfmeyer ◽  
A. J. Scarino
Keyword(s):  
Water Vapor ◽  
Great Plains ◽  
Mixing Ratio ◽  
Raman Lidar ◽  
Southern Great Plains ◽  
Lidar Measurements ◽  
Atmospheric Radiation Measurement ◽  
Convective Mixed Layer ◽  
Water Vapor Mixing Ratio ◽  
Mixed Layers

AbstractHigh temporal and vertical resolution water vapor measurements by Raman and differential absorption lidar systems have been used to characterize the turbulent fluctuations in the water vapor mixing ratio field in convective mixed layers. Since daytime Raman lidar measurements are inherently noisy (due to solar background and weak signal strengths), the analysis approach needs to quantify and remove the contribution of the instrument noise in order to derive the desired atmospheric water vapor mixing ratio variance and skewness profiles. This is done using the approach outlined by Lenschow et al.; however, an intercomparison with in situ observations was not performed.Water vapor measurements were made by a diode laser hygrometer flown on a Twin Otter aircraft during the Routine Atmospheric Radiation Measurement (ARM) Program Aerial Facility Clouds with Low Optical Water Depths Optical Radiative Observations (RACORO) field campaign over the ARM Southern Great Plains (SGP) site in 2009. Two days with Twin Otter flights were identified where the convective mixed layer was quasi stationary, and hence the 10-s, 75-m data from the SGP Raman lidar could be analyzed to provide profiles of water vapor mixing ratio variance and skewness. Airborne water vapor observations measured during level flight legs were compared to the Raman lidar data, demonstrating good agreement in both variance and skewness. The results also illustrate the challenges of comparing a point sensor making measurements over time to a moving platform making similar measurements horizontally.

Airborne DIAL and ground-based Raman lidar measurements of water vapor over the Southern Great Plains

2003 ◽  
Author(s):  
Richard A. Ferrare ◽  
Edward V. Browell ◽  
Syed Ismail ◽  
Susan Kooi ◽  
Vince G. Brackett ◽  
...  
Keyword(s):  
Water Vapor ◽  
Great Plains ◽  
Raman Lidar ◽  
Southern Great Plains ◽  
Lidar Measurements

Study of the statistics of water vapor mixing ratio determined from Raman lidar measurements

2007 ◽  
Vol 46 (33) ◽  
pp. 8170 ◽  
Author(s):  
Pierre Bosser ◽  
Olivier Bock ◽  
Christian Thom ◽  
Jacques Pelon
Keyword(s):  
Water Vapor ◽  
Mixing Ratio ◽  
Raman Lidar ◽  
Lidar Measurements ◽  
Water Vapor Mixing Ratio

scholarly journals Ground-based temperature and humidity profiling: combining active and passive remote sensors

2021 ◽  
Vol 14 (4) ◽  
pp. 3033-3048
Author(s):  
David D. Turner ◽  
Ulrich Löhnert
Keyword(s):  
Water Vapor ◽  
Information Content ◽  
Great Plains ◽  
Optimal Estimation ◽  
Differential Absorption ◽  
Southern Great Plains ◽  
Atmospheric Research ◽  
Remote Sensors ◽  
Central Portugal ◽  
Atmospheric Radiation Measurement

Abstract. Thermodynamic profiles in the planetary boundary layer (PBL) are important observations for a range of atmospheric research and operational needs. These profiles can be retrieved from passively sensed spectral infrared (IR) or microwave (MW) radiance observations or can be more directly measured by active remote sensors such as water vapor differential absorption lidars (DIALs). This paper explores the synergy of combining ground-based IR, MW, and DIAL observations using an optimal-estimation retrieval framework, quantifying the reduction in the uncertainty in the retrieved profiles and the increase in information content as additional observations are added to IR-only and MW-only retrievals. This study uses ground-based observations collected during the Perdigão field campaign in central Portugal in 2017 and during the DIAL demonstration campaign at the Atmospheric Radiation Measurement Southern Great Plains site in 2017. The results show that the information content in both temperature and water vapor is higher for the IR instrument relative to the MW instrument (thereby resulting in smaller uncertainties) and that the combined IR + MW retrieval is very similar to the IR-only retrieval below 1.5 km. However, including the partial profile of water vapor observed by the DIAL increases the information content in the combined IR + DIAL and MW + DIAL water vapor retrievals substantially, with the exact impact vertically depending on the characteristics of the DIAL instrument itself. Furthermore, there is a slight increase in the information content in the retrieved temperature profile using the IR + DIAL relative to the IR-only; this was not observed in the MW + DIAL retrieval.

scholarly journals Raman lidar measurements of the aerosol extinction-to-backscatter ratio over the Southern Great Plains

2001 ◽  
Vol 106 (D17) ◽  
pp. 20333-20347 ◽  
Author(s):  
Richard A. Ferrare ◽  
David D. Turner ◽  
Lorraine Heilman Brasseur ◽  
Wayne F. Feltz ◽  
Oleg Dubovik ◽  
...  
Keyword(s):  
Great Plains ◽  
Aerosol Extinction ◽  
Raman Lidar ◽  
Southern Great Plains ◽  
Lidar Measurements

scholarly journals Temperature profiling of the atmospheric boundary layer with rotational Raman lidar during the HD(CP)<sup>2</sup> Observational Prototype Experiment

2015 ◽  
Vol 15 (5) ◽  
pp. 2867-2881 ◽  
Author(s):  
E. Hammann ◽  
A. Behrendt ◽  
F. Le Mounier ◽  
V. Wulfmeyer
Keyword(s):  
Boundary Layer ◽  
Water Vapor ◽  
Temperature Gradient ◽  
Atmospheric Boundary Layer ◽  
Average Power ◽  
Mixing Ratio ◽  
Raman Lidar ◽  
Backscatter Signal ◽  
Low Background ◽  
Water Vapor Mixing Ratio

Abstract. The temperature measurements of the rotational Raman lidar of the University of Hohenheim (UHOH RRL) during the High Definition of Clouds and Precipitation for advancing Climate Prediction (HD(CP)2) Observation Prototype Experiment (HOPE) in April and May 2013 are discussed. The lidar consists of a frequency-tripled Nd:YAG laser at 355 nm with 10 W average power at 50 Hz, a two-mirror scanner, a 40 cm receiving telescope, and a highly efficient polychromator with cascading interference filters for separating four signals: the elastic backscatter signal, two rotational Raman signals with different temperature dependence, and the vibrational Raman signal of water vapor. The main measurement variable of the UHOH RRL is temperature. For the HOPE campaign, the lidar receiver was optimized for high and low background levels, with a novel switch for the passband of the second rotational Raman channel. The instrument delivers atmospheric profiles of water vapor mixing ratio as well as particle backscatter coefficient and particle extinction coefficient as further products. As examples for the measurement performance, measurements of the temperature gradient and water vapor mixing ratio revealing the development of the atmospheric boundary layer within 25 h are presented. As expected from simulations, a reduction of the measurement uncertainty of 70% during nighttime was achieved with the new low-background setting. A two-mirror scanner allows for measurements in different directions. When pointing the scanner to low elevation, measurements close to the ground become possible which are otherwise impossible due to the non-total overlap of laser beam and receiving telescope field of view in the near range. An example of a low-level temperature measurement is presented which resolves the temperature gradient at the top of the stable nighttime boundary layer 100 m above the ground.

scholarly journals Ground-based Temperature and Humidity Profiling: Combining Active and Passive Remote Sensors

2020 ◽  
Author(s):  
David D. Turner ◽  
Ulrich Löhnert
Keyword(s):  
Water Vapor ◽  
Information Content ◽  
Great Plains ◽  
Optimal Estimation ◽  
Differential Absorption ◽  
Southern Great Plains ◽  
Atmospheric Research ◽  
Remote Sensors ◽  
Central Portugal ◽  
Atmospheric Radiation Measurement

Abstract. Thermodynamic profiles in the planetary boundary layer (PBL) are important observations for a range of atmospheric research and operational needs. These profiles can be retrieved from passively sensed spectral infrared (IR) or microwave (MW) radiance observations, or can be more directly measured by active remote sensors such as water vapor differential absorption lidars (DIALs). This paper explores the synergy of combining ground-based IR, MW, and DIAL observations using an optimal estimation retrieval framework, quantifying the reduction in the uncertainty in the retrieved profiles and the increase in information content as additional observations are added to IR-only and MW-only retrievals. This study uses ground-based observations collected during the Perdigao field campaign in central Portugal in 2017 and during the DIAL demonstration campaign at the Atmospheric Radiation Measurement Southern Great Plains site in 2017. The results show that the information content in both temperature and water vapor is higher for IR instrument relative to the MW instrument (thereby resulting in smaller uncertainties), and that the combined IR+MW retrieval is very similar to the IR-only retrieval below 1.5 km. However, including the partial profile of water vapor observed by the DIAL increases the information content in the combined IR+DIAL and MW+DIAL water vapor retrievals substantially, with the exact impact vertically depending on the characteristics of the DIAL instrument itself. Furthermore, there is slight increase in the information content in the retrieved temperature profile using the IR+DIAL relative to the IR-only; this was not observed in the MW+DIAL retrieval.

scholarly journals Profiling water vapor mixing ratios in Finland by means of a Raman lidar, a satellite and a model

2017 ◽  
Vol 10 (11) ◽  
pp. 4303-4316 ◽  
Author(s):  
Maria Filioglou ◽  
Anna Nikandrova ◽  
Sami Niemelä ◽  
Holger Baars ◽  
Tero Mielonen ◽  
...  
Keyword(s):  
Water Vapor ◽  
Weather Prediction ◽  
Limited Area ◽  
Mixing Ratio ◽  
Atmospheric Conditions ◽  
Raman Lidar ◽  
Mixing Ratios ◽  
Seasonal Analysis ◽  
Relative Errors ◽  
Water Vapor Mixing Ratio

Abstract. We present tropospheric water vapor profiles measured with a Raman lidar during three field campaigns held in Finland. Co-located radio soundings are available throughout the period for the calibration of the lidar signals. We investigate the possibility of calibrating the lidar water vapor profiles in the absence of co-existing on-site soundings using water vapor profiles from the combined Advanced InfraRed Sounder (AIRS) and the Advanced Microwave Sounding Unit (AMSU) satellite product; the Aire Limitée Adaptation dynamique Développement INternational and High Resolution Limited Area Model (ALADIN/HIRLAM) numerical weather prediction (NWP) system, and the nearest radio sounding station located 100 km away from the lidar site (only for the permanent location of the lidar). The uncertainties of the calibration factor derived from the soundings, the satellite and the model data are  < 2.8, 7.4 and 3.9 %, respectively. We also include water vapor mixing ratio intercomparisons between the radio soundings and the various instruments/model for the period of the campaigns. A good agreement is observed for all comparisons with relative errors that do not exceed 50 % up to 8 km altitude in most cases. A 4-year seasonal analysis of vertical water vapor is also presented for the Kuopio site in Finland. During winter months, the air in Kuopio is dry (1.15±0.40 g kg−1); during summer it is wet (5.54±1.02 g kg−1); and at other times, the air is in an intermediate state. These are averaged values over the lowest 2 km in the atmosphere. Above that height a quick decrease in water vapor mixing ratios is observed, except during summer months where favorable atmospheric conditions enable higher mixing ratio values at higher altitudes. Lastly, the seasonal change in disagreement between the lidar and the model has been studied. The analysis showed that, on average, the model underestimates water vapor mixing ratios at high altitudes during spring and summer.

scholarly journals An Automated Detection Methodology for Dry Well-Mixed Layers

2019 ◽  
Vol 36 (5) ◽  
pp. 761-779
Author(s):  
Stephen D. Nicholls ◽  
Karen I. Mohr
Keyword(s):  
Water Vapor ◽  
Vertical Profile ◽  
Input Data ◽  
Profile Analysis ◽  
Potential Temperature ◽  
Data File ◽  
Mixing Ratio ◽  
Land Surfaces ◽  
Water Vapor Mixing Ratio ◽  
Mixed Layers

AbstractThe intense surface heating over arid land surfaces produces dry well-mixed layers (WML) via dry convection. These layers are characterized by nearly constant potential temperature and low, nearly constant water vapor mixing ratio. To further the study of dry WMLs, we created a detection methodology and supporting software to automate the identification and characterization of dry WMLs from multiple data sources including rawinsondes, remote sensing platforms, and model products. The software is a modular code written in Python, an open-source language. Radiosondes from a network of synoptic stations in North Africa were used to develop and test the WML detection process. The detection involves an iterative decision tree that ingests a vertical profile from an input data file, performs a quality check for sufficient data density, and then searches upward through the column for successive points where the simultaneous changes in water vapor mixing ratio and potential temperature are less than the specified maxima. If points in the vertical profile meet the dry WML identification criteria, statistics are generated detailing the characteristics of each layer in the profile. At the end of the vertical profile analysis, there is an option to plot analyzed profiles in a variety of file formats. Initial results show that the detection methodology can be successfully applied across a wide variety of input data and North African environments and for all seasons. It is sensitive enough to identify dry WMLs from other types of isentropic phenomena such as subsidence layers and distinguish the current day’s dry WML from previous days.

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