Combining Differential Absorption Radar and Microwave Radiometer for Water Vapor Profiling in the Cloudy Trade-Wind Environment

2020 ◽  
Author(s):  
Sabrina Schnitt ◽  
Ulrich Löhnert ◽  
Rene Preusker
Keyword(s):  
Water Vapor ◽  
Information Content ◽  
Degrees Of Freedom ◽  
Microwave Radiometer ◽  
Cloud Layer ◽  
Dual Wavelength ◽  
Trade Wind ◽  
Differential Absorption ◽  
Humidity Profile ◽  
Brightness Temperatures

<p>Understanding atmospheric processes, such as e.g. cloud and precipitation formation, requires high-resolution water vapor and temperature profile observations particularly in the cloudy boundary-layer. As current observation techniques are limited by low spatial or temporal resolution, the potential of combining microwave radiometer (MWR) with differential absorption radar is investigated by analysing the retrieval information content and retrieval uncertainty. Two radar frequency combinations are analyzed: Ka- and W-band (KaW), available at e.g. Barbados Cloud Observatory, as well as a synthetic combination of G-band frequencies (167 and 175 GHz, G2), simulated using the Passive and Active Microwave TRAnsfer model PAMTRA.</p><p>The novel synergistic retrieval approach is based on an optimal estimation retrieval scheme. The absolute humidity profile is retrieved from the MWR K-band brightness temperatures, as well as the Dual-Wavelength Ratio (DWR) signal of the two radars. Evaluating a suite of radiosonde profiles measured at Barbados from 2018, adding the active KaW combination to K-band MWR brightness temperatures increases the information content for the retrieved profile from 3.2 to 3.4 degrees of freedom for signal (DoF). The usage of the higher G2 radar frequencies leads to higher Dual-Wavelength Ratios (DWRs), and, in combination with the MWR, to increased DoF (4.5), decreased retrieval errors, and a more realistic retrieved profile within the cloud layer. Information partitioning among MWR and the radars makes the synergy particularly beneficial: the profile below and within the cloud is restricted by the radar observations, whereas the water vapor above cloud top and the LWP are constrained by the MWR.</p><p>Based on selected case studies with single- as well as multi-layered clouds from the EUREC4A campaign, different retrieval configurations will be evaluated based on the resulting retrieval error, as well as the Degrees of Freedom. Tools for customizing the retrieval to the trade wind driven atmosphere will be analyzed by e.g. constraining the humidity profile to saturation within the cloud layer, or making use of a direct inversion approach of the differential attenuation signals.</p>

scholarly journals Potential of Dual-Frequency Radar and Microwave Radiometer Synergy for Water Vapor Profiling in the Cloudy Trade Wind Environment

2020 ◽  
Vol 37 (11) ◽  
pp. 1973-1986
Author(s):  
Sabrina Schnitt ◽  
Ulrich Löhnert ◽  
René Preusker
Keyword(s):  
Water Vapor ◽  
Information Content ◽  
Degrees Of Freedom ◽  
Microwave Radiometer ◽  
Optimal Estimation ◽  
Cloud Layer ◽  
Trade Wind ◽  
Dual Frequency ◽  
Shallow Convection ◽  
Brightness Temperatures

AbstractHigh-resolution boundary layer water vapor profile observations are essential for understanding the interplay between shallow convection, cloudiness, and climate in the trade wind atmosphere. As current observation techniques can be limited by low spatial or temporal resolution, the synergistic benefit of combining ground-based microwave radiometer (MWR) and dual-frequency radar is investigated by analyzing the retrieval information content and uncertainty. Synthetic MWR brightness temperatures, as well as simulated dual-wavelength ratios of two radar frequencies are generated for a combination of Ka and W band (KaW), as well as differential absorption radar (DAR) G-band frequencies (167 and 174.8 GHz, G2). The synergy analysis is based on an optimal estimation scheme by varying the configuration of the observation vector. Combining MWR and KaW only marginally increases the retrieval information content. The synergy of MWR with G2 radar is more beneficial due to increasing degrees of freedom (4.5), decreasing retrieval errors, and a more realistic retrieved profile within the cloud layer. The information and profile below and within the cloud is driven by the radar observations, whereas the synergistic benefit is largest above the cloud layer, where information content is enhanced compared to an MWR-only or DAR-only setup. For full synergistic benefits, however, G-band radar sensitivities need to allow full-cloud profiling; in this case, the results suggest that a combined retrieval of MWR and G-band DAR can help close the observational gap of current techniques.

Simulating the synergy of microwave radiometer and differential absorption radar for advancing water vapor profiling in cloudy trade-wind conditions

2021 ◽  
Author(s):  
Sabrina Schnitt ◽  
Ulrich Löhnert ◽  
René Preusker
Keyword(s):  
Water Vapor ◽  
Information Content ◽  
Microwave Radiometer ◽  
Cloud Layer ◽  
Radar Signal ◽  
Trade Wind ◽  
Dual Frequency ◽  
Differential Absorption ◽  
Radar Measurements ◽  
Retrieval Errors

<p>Continuous, high vertical resolution water vapor profile measurements are key for advancing the understanding of how clouds interact with their environment through convection, precipitation and circulation processes.  Yet, current ground-based observation systems are limited by low temporal resolution in the case of soundings, signal saturation at cloud base in the case of optical sensors, or too coarse vertical resolution in the case of passive microwave measurements. Overcoming the limitations of each single sensor, we assess the synergistic benefits of combining ground-based microwave radiometer (MWR) and the novel Differential Absorption Radar technique, based on synthetic measurements generated for typical trade wind conditions as observed during the EUREC<sup>4</sup>A field study.</p><p>Based on the single and multiple cloud layer conditions observed at Barbados Cloud Observatory, we use the passive and active microwave transfer model PAMTRA to generate synthetic measurements of the K-band MWR channels, as well as for a G-band dual-frequency radar instrument operating at frequencies of 167 and 174.8 GHz.  The synthetic brightness temperatures and radar dual-frequency ratios are combined in an optimal estimation framework to retrieve the absolute humidity profile. Varying the observation vector setup, the synergy benefits are assessed by comparing the synergistic information content (Degrees of Freedom for Signal, DFS) and retrieval errors to the respective single-instrument configuration, and by evaluating the retrieved profile using the initial sounding profile.</p><p>In single-cloud conditions, the total synergistic retrieval information content increases by more than one DFS compared to a MWR-only retrieval. While the radar measurements dominate the retrieval below and throughout the cloud layer, the MWR drives the retrieval above the cloud layer. The synergy further enhances the information content above the cloud layer by up to 15% compared to the MWR-only retrieval, accompanied by decreased retrieval errors of up to 10%. Cases of a shallow cloud layer topped by a stratiform outflow confirm the identified patterns. The radar measurements further increase the information content between the cloud layers by up to 25%. In this case, the results suggest an improved partitioning of the water vapor amount below and above the trade inversion. </p><p>Current G-band radar signal attenuation in moist tropical conditions are expected to reduce the feasible synergy potential in a real application. Yet, increased radar signal sensitivities, adjusted frequency pairs, or drier atmospheric conditions motivate the application of this synergy concept to real measurements for advancing ground-based water vapor profiling in cloudy conditions.</p>

scholarly journals Spaceborne differential absorption radar water vapor retrieval capabilities in tropical and subtropical boundary layer cloud regimes

2021 ◽  
Vol 14 (10) ◽  
pp. 6443-6468
Author(s):  
Richard J. Roy ◽  
Matthew Lebsock ◽  
Marcin J. Kurowski
Keyword(s):  
Boundary Layer ◽  
Water Vapor ◽  
Trade Wind ◽  
Retrieval Algorithm ◽  
Line Center ◽  
Vertical Resolution ◽  
Horizontal Integration ◽  
Differential Absorption ◽  
Near Surface ◽  
Cloud Regimes

Abstract. Differential absorption radar (DAR) near the 183 GHz water vapor absorption line is an emerging measurement technique for humidity profiling inside of clouds and precipitation with high vertical resolution, as well as for measuring integrated water vapor (IWV) in clear-air regions. For radar transmit frequencies on the water line flank away from the highly attenuating line center, the DAR system becomes most sensitive to water vapor in the planetary boundary layer (PBL), which is a region of the atmosphere that is poorly resolved in the vertical by existing spaceborne humidity and temperature profiling instruments. In this work, we present a high-fidelity, end-to-end simulation framework for notional spaceborne DAR instruments that feature realistically achievable radar performance metrics and apply this simulator to assess DAR's PBL humidity observation capabilities. Both the assumed instrument parameters and radar retrieval algorithm leverage recent technology and algorithm development for an existing airborne DAR instrument. To showcase the capabilities of DAR for humidity observations in a variety of relevant PBL settings, we implement the instrument simulator in the context of large eddy simulations (LESs) of five different cloud regimes throughout the trade-wind subtropical-to-tropical cloud transition. Three distinct DAR humidity observations are investigated: IWV between the top of the atmosphere and the first detected cloud bin or Earth's surface; in-cloud water vapor profiles with 200 meter vertical resolution; and IWV between the last detected cloud bin and the Earth's surface, which can provide a precise measurement of the sub-cloud humidity. We provide a thorough assessment of the systematic and random errors for all three measurement products for each LES case and analyze the humidity precision scaling with along-track measurement integration. While retrieval performance depends greatly on the specific cloud regime, we find generally that for a radar with cross-track scanning capability, in-cloud profiles with 200 m vertical resolution and 10 %–20 % uncertainty can be retrieved for horizontal integration distances of 100–200 km. Furthermore, column IWV can be retrieved with 10 % uncertainty for 10–20 km of horizontal integration. Finally, we provide some example science applications of the simulated DAR observations, including estimating near-surface relative humidity using the cloud-to-surface column IWV and inferring in-cloud temperature profiles from the DAR water vapor profiles by assuming a fully saturated environment.

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 Validation of a Water Vapor Micropulse Differential Absorption Lidar (DIAL)

2016 ◽  
Vol 33 (11) ◽  
pp. 2353-2372 ◽  
Author(s):  
Tammy M. Weckwerth ◽  
Kristy J. Weber ◽  
David D. Turner ◽  
Scott M. Spuler
Keyword(s):  
Water Vapor ◽  
Correlation Coefficient ◽  
Microwave Radiometer ◽  
Temporal Trends ◽  
Differential Absorption ◽  
Differential Absorption Lidar ◽  
Pearson’S Correlation ◽  
Pearson’S Correlation Coefficient ◽  
Pearson's Correlation ◽  
Pearson's Correlation Coefficient

AbstractA water vapor micropulse differential absorption lidar (DIAL) instrument was developed collaboratively by the National Center for Atmospheric Research (NCAR) and Montana State University (MSU). This innovative, eye-safe, low-power, diode-laser-based system has demonstrated the ability to obtain unattended continuous observations in both day and night. Data comparisons with well-established water vapor observing systems, including radiosondes, Atmospheric Emitted Radiance Interferometers (AERIs), microwave radiometer profilers (MWRPs), and ground-based global positioning system (GPS) receivers, show excellent agreement. The Pearson’s correlation coefficient for the DIAL and radiosondes is consistently greater than 0.6 from 300 m up to 4.5 km AGL at night and up to 3.5 km AGL during the day. The Pearson’s correlation coefficient for the DIAL and AERI is greater than 0.6 from 300 m up to 2.25 km at night and from 300 m up to 2.0 km during the day. Further comparison with the continuously operating GPS instrumentation illustrates consistent temporal trends when integrating the DIAL measurements up to 6 km AGL.

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 A Quasi-Simultaneous Dual Wavelength Water Vapor Differential Absorption Lidar Using PAS Cells.

1994 ◽  
Vol 22 (12) ◽  
pp. 1000-1006
Author(s):  
Chikao NAGASAWA ◽  
Makoto ABO ◽  
Kenji KIMIYAMA ◽  
Osamu UCHINO
Keyword(s):  
Water Vapor ◽  
Dual Wavelength ◽  
Differential Absorption ◽  
Differential Absorption Lidar

Information Content and Uncertainties in Thermodynamic Profiles and Liquid Cloud Properties Retrieved from the Ground-Based Atmospheric Emitted Radiance Interferometer (AERI)

2014 ◽  
Vol 53 (3) ◽  
pp. 752-771 ◽  
Author(s):  
D. D. Turner ◽  
U. Löhnert
Keyword(s):  
Water Vapor ◽  
Information Content ◽  
Degrees Of Freedom ◽  
Optimal Estimation ◽  
Retrieval Algorithm ◽  
Electromagnetic Spectrum ◽  
Slight Reduction ◽  
Liquid Water Path ◽  
Cloud Properties ◽  
Mean Square Errors

AbstractThe Atmospheric Emitted Radiance Interferometer (AERI) observes spectrally resolved downwelling radiance emitted by the atmosphere in the infrared portion of the electromagnetic spectrum. Profiles of temperature and water vapor, and cloud liquid water path and effective radius for a single liquid cloud layer, are retrieved using an optimal estimation–based physical retrieval algorithm from AERI-observed radiance data. This algorithm provides a full error covariance matrix for the solution, and both the degrees of freedom for signal and the Shannon information content. The algorithm is evaluated with both synthetic and real AERI observations. The AERI is shown to have approximately 85% and 70% of its information in the lowest 2 km of the atmosphere for temperature and water vapor profiles, respectively. In clear-sky situations, the mean bias errors with respect to the radiosonde profiles are less than 0.2 K and 0.3 g kg−1 for heights below 2 km for temperature and water vapor mixing ratio, respectively; the maximum root-mean-square errors are less than 1 K and 0.8 g kg−1. The errors in the retrieved profiles in cloudy situations are larger, due in part to the scattering contribution to the downwelling radiance that was not accounted for in the forward model. Scattering is largest in one of the spectral regions used in the retrieval, however, and removing this spectral region results in a slight reduction of the information content but a considerable improvement in the accuracy of the retrieved thermodynamic profiles.

A Study of a Retrieval Method for Temperature and Humidity Profiles from Microwave Radiometer Observations Based on Principal Component Analysis and Stepwise Regression

2011 ◽  
Vol 28 (3) ◽  
pp. 378-389 ◽  
Author(s):  
Haobo Tan ◽  
Jietai Mao ◽  
Huanhuan Chen ◽  
P. W. Chan ◽  
Dui Wu ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Water Vapor ◽  
Stepwise Regression ◽  
Microwave Radiometer ◽  
Principal Component ◽  
Component Analysis ◽  
Mean Square ◽  
Vapor Density ◽  
Retrieval Method ◽  
Brightness Temperatures

Abstract This paper discusses the application of principal component analysis and stepwise regression in the retrieval of vertical profiles of temperature and humidity based on the measurements of a 35-channel microwave radiometer. It uses the radiosonde data of 6 yr from Hong Kong, China, and the monochromatic radiative transfer model (MonoRTM) to calculate the brightness temperatures of the 35 channels of the radiometer. The retrieval of the atmospheric profile is then established based on principal component analysis and stepwise regression. The accuracy of the retrieval method is also analyzed. Using an independent sample, the root-mean-square error of the retrieved temperature is less than 1.5 K, on average, with better retrieval results in summer than in winter. Likewise, the root-mean-square error of the retrieved water vapor density reaches a maximum value of 1.4 g m−3 between 0.5 and 2 km, and is less than 1 g m−3 for all other heights. The retrieval method is then applied to the actual measured brightness temperatures by the 35-channel microwave radiometer at a station in Nansha, China. It is shown that the statistical model as developed in this paper has better retrieval results than the profiles obtained from the neural network as supplied with the radiometer. From numerical analysis, the error with the water vapor density retrieval is found to arise from the treatment of cloud liquid water. Finally, the retrieved profiles from the radiometer are studied for two typical weather phenomena during the observation period, and the retrieved profiles using the method discussed in the present paper is found to capture the evolution of the atmospheric condition very well.

scholarly journals Ground based lidar and microwave radiometry synergy for high vertically resolved thermodynamic profiling

2015 ◽  
Vol 8 (5) ◽  
pp. 5467-5509
Author(s):  
M. Barrera-Verdejo ◽  
S. Crewell ◽  
U. Löhnert ◽  
E. Orlandi ◽  
P. Di Girolamo
Keyword(s):  
Water Vapor ◽  
Relative Humidity ◽  
Degrees Of Freedom ◽  
Cloud Formation ◽  
Retrieval Algorithm ◽  
Vertical Resolution ◽  
Raman Lidar ◽  
Significant Information ◽  
Brightness Temperatures ◽  
Overlap Region

Abstract. Continuous monitoring of atmospheric humidity and temperature profiles is important for many applications, e.g. assessment of atmospheric stability and cloud formation. While lidar measurements can provide high vertical resolution albeit with limited coverage, microwave radiometers receive information throughout the troposphere though their vertical resolution is poor. In order to overcome these specific limitations the synergy of a Microwave Radiometer (MWR) and a Raman Lidar (RL) system is presented in this work. The retrieval algorithm that combines these two instruments is an Optimal Estimation Method (OEM) that allows for a uncertainty analysis of the retrieved profiles. The OEM combines measurements and a priori information taking the uncertainty of both into account. The measurement vector consists of a set of MWR brightness temperatures and RL water vapor profiles. The method is applied for a two month field campaign around Jülich, Germany for clear sky periods. Different experiments are performed to analyse the improvements achieved via the synergy compared to the individual retrievals. When applying the combined retrieval, on average the theoretically determined absolute humidity error can be reduced by 59.8% (37.9%) with respect to the retrieval using only-MWR (only-RL) data. The analysis in terms of degrees of freedom for signal reveals that most information is gained above the usable lidar range. The retrieved profiles are further evaluated using radiosounding and GPS water vapor measurements. Within a single case study we also explore the potential of the OEM for deriving the relative humidity profile, which is especially interesting to study cloud formation in the vicinity of cloud edges. To do so temperature information is added both from RL and MWR. For temperature, it is shown that the error is reduced by 47.1% (24.6%) with respect to the only-MWR (only-RL) profile. Due to the use of MWR brightness temperatures at multiple elevation angles, the MWR provides significant information below the lidar overlap region as shown by the degrees of freedom for signal. Therefore it might be sufficient to combine RL water vapor with multi-angle, multi-wavelength MWR for the retrieval of relative humidity, however, long-term studies are necessary in the future. In general, the benefit of the sensor combination is especially strong in regions where Raman Lidar data is not available (i.e. overlap region, poor signal to noise ratio), whereas if both instruments are available, RL dominates the retrieval.

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