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.

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>

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>

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.

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 Why Scanning Instruments Are a Necessity for Constraining Temperature and Humidity Fields in the Lower Atmosphere

2014 ◽  
Vol 31 (11) ◽  
pp. 2462-2481 ◽  
Author(s):  
David Themens ◽  
Frédéric Fabry
Keyword(s):  
Water Vapor ◽  
Microwave Radiometer ◽  
Three Dimensional ◽  
Optimal Estimation ◽  
Lower Atmosphere ◽  
Temperature Fields ◽  
Vertical Profiling ◽  
Thermodynamic Variables ◽  
Free Environment ◽  
Measurement Strategies

AbstractThe ability of different ground-based measurement strategies for constraining thermodynamic variables in the troposphere, particularly at the mesoscale, is investigated. First, a preliminary assessment of the capability of pure-vertical sounders for constraining temperature and water vapor fields in clear-sky conditions to current accuracy requirements is presented. Using analyses over one month from the Rapid Refresh model as input to an optimal estimation technique, it is shown that the horizontal density of a network of nonexisting, ideal vertical profiling instruments must be greater than 30 km in order to achieve accuracies of 0.5 g kg−1 for water vapor and 0.5 K for temperature. Then, an assessment of a scanning microwave radiometer’s capability for retrieving water vapor and temperature fields in a cloud-free environment over two- and three-dimensional mesoscale domains is also presented. The information content of an elevation and azimuthal scanning microwave radiometer is assessed using the same optimal estimation framework. Even though, in any specific pointing direction, the scanning radiometer does not provide much information, it is capable of providing considerably more constraints on thermodynamic fields, particularly water vapor, than a near-perfect vertical sounder. These constraints on water vapor are largely located within 80 km of the radiometer and between 1000- and 7000-m altitude, while temperature constraints are limited to within 35 km of the instrument at altitudes between the ground and 1500 m. The findings suggest that measurements from scanning radiometers will be needed to properly constrain the temperature and especially moisture fields to accuracies needed for mesoscale forecasting.

scholarly journals Optimal Estimation of Water Vapour Profiles using a Combination of Raman Lidar and Microwave Radiometer

2017 ◽  
Author(s):  
Andreas Foth ◽  
Bernhard Pospichal
Keyword(s):  
Kalman Filter ◽  
Water Vapour ◽  
Microwave Radiometer ◽  
Optimal Estimation ◽  
Data Availability ◽  
Lidar Data ◽  
Raman Lidar ◽  
Brightness Temperatures ◽  
Step Algorithm ◽  
Full Height

Abstract. In this work, a two-step algorithm to obtain water vapour profiles from a combination of Raman lidar and microwave radiometer is presented. Both instruments were applied during an intensive two-month measurement campaign (HOPE) close to Jülich, western Germany, during spring 2013. To retrieve reliable water vapour information from inside or above the cloud a two-step algorithm is applied. The first step is a Kalman filter that extends the profiles, truncated at cloud base, to the full height range (up to 10 km) by combining previous information and current measurement. Then the complete water vapour profile serves as input to the one-dimensional variational (1D-VAR) method, also known as optimal estimation. A forward model simulates the brightness temperatures which would be observed by the microwave radiometer for the given atmospheric state. The profile is iteratively modified according to its error bars until the modelled and the actually measured brightness temperatures sufficiently agree. The functionality of the retrieval is presented in detail by means of case studies under different conditions. A statistical analysis shows that the availability of Raman lidar data (night) improves the accuracy of the profiles even under cloudy conditions. During the day, the absence of lidar data results in larger differences in comparison to reference radiosondes. The data availability of the full height water vapour lidar profiles of 17 % during the two-month campaign is significantly enhanced to 60 % by applying the retrieval. The bias with respect to radiosonde and the retrieved a posteriori uncertainty of the retrieved profiles clearly show that the application of the Kalman filter considerably improves the accuracy and quality of the retrieved mixing ratio profiles.

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 Optimal estimation of water vapour profiles using a combination of Raman lidar and microwave radiometer

2017 ◽  
Vol 10 (9) ◽  
pp. 3325-3344 ◽  
Author(s):  
Andreas Foth ◽  
Bernhard Pospichal
Keyword(s):  
Kalman Filter ◽  
Water Vapour ◽  
Microwave Radiometer ◽  
Optimal Estimation ◽  
Data Availability ◽  
Lidar Data ◽  
Raman Lidar ◽  
Brightness Temperatures ◽  
Step Algorithm ◽  
Full Height

Abstract. In this work, a two-step algorithm to obtain water vapour profiles from a combination of Raman lidar and microwave radiometer is presented. Both instruments were applied during an intensive 2-month measurement campaign (HOPE) close to Jülich, western Germany, during spring 2013. To retrieve reliable water vapour information from inside or above the cloud a two-step algorithm is applied. The first step is a Kalman filter that extends the profiles, truncated at cloud base, to the full height range (up to 10 km) by combining previous information and current measurement. Then the complete water vapour profile serves as input to the one-dimensional variational (1D-VAR) method, also known as optimal estimation. A forward model simulates the brightness temperatures which would be observed by the microwave radiometer for the given atmospheric state. The profile is iteratively modified according to its error bars until the modelled and the actually measured brightness temperatures sufficiently agree. The functionality of the retrieval is presented in detail by means of case studies under different conditions. A statistical analysis shows that the availability of Raman lidar data (night) improves the accuracy of the profiles even under cloudy conditions. During the day, the absence of lidar data results in larger differences in comparison to reference radiosondes. The data availability of the full-height water vapour lidar profiles of 17 % during the 2-month campaign is significantly enhanced to 60 % by applying the retrieval. The bias with respect to radiosonde and the retrieved a posteriori uncertainty of the retrieved profiles clearly show that the application of the Kalman filter considerably improves the accuracy and quality of the retrieved mixing ratio profiles.

The GPM Operational Radiometer Algorithm - Changes for 2021

2020 ◽  
Author(s):  
Christian Kummerow ◽  
Paula Brown
Keyword(s):  
A Priori ◽  
Microwave Radiometer ◽  
The United States ◽  
Passive Microwave ◽  
Dual Frequency ◽  
Brightness Temperatures ◽  
Precipitation Measurement ◽  
Global Precipitation Measurement ◽  
Global Precipitation ◽  
Combined Algorithm

<p>The Global Precipitation Measurement (GPM) mission was launched in February 2014 as a joint mission between JAXA from Japan and NASA from the United States.  GPM carries a state of the art dual-frequency precipitation radar and a multi-channel passive microwave radiometer that acts not only to enhance the radar’s retrieval capability, but also as a reference for a constellation of existing satellites carrying passive microwave sensors.  In May of 2017, GPM released Version 5 of its precipitation products starting with GMI and continuing with the constellation of radiometers.  The precipitation products from these sensors are consistent by design and show relatively minor differences in the mean global sense.  Since this release, the Combined Algorithm hydrometeor profiles have shown good consistency with surface observations and computed brightness temperatures agree reasonably well with GMI observations in precipitating regions.  The same is true for MIRS profiles in non-precipitating regions.  Version 7 of the GPROF code will therefore make use of these operational products to construct it's a-priori databases.  This will allow continuous improvements in the a-priori database as these operational products are reprocessed with newer versions, while allowing the user community to better focus on the algorithm’s error covariance matrix and its validation.  Results from early versions of this algorithm will be presented.  In addition to creating an a-priori database that can be more directly updated with improvement to the raining and non-raining scenes, GPROF is also undertaking steps to improve the orographic representation of snow and a Neural Network based Convective/Stratiform classification of precipitation that will both help improve instantaneous correlations with in-situ observations.</p>

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