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NASA NTRSDate not supplied

Exploring Environmental and Aerosol Impacts on Maritime Tropical Convection using Airborne Radiometer, Radar, Lidar, and Dropsondes

The field deployment phase of NASA’s Cloud, Aerosol and Monsoon Processes Philippines Experiment (CAMP2Ex) took place around the Philippines during August–October 2019, with the primary goal of jointly investigating aerosols and tropical meteorology at the micro-β to meso-β scales. A suite of instruments was deployed on NASA’s P-3B Orion aircraft to accomplish this mission, including: the Advanced Microwave Precipitation Radiometer (AMPR), Airborne Precipitation and cloud Radar 3rd Generation (APR-3), High Spectral Resolution Lidar 2 (HSRL2), and Advanced Vertical Atmospheric Profiling System (AVAPS) dropsondes. To examine potential aerosol influences on maritime tropical convection, P-3 flight segments throughout CAMP2Ex were binned into similar environmental groups using “low,” “medium,” and “high” values of nine AVAPS-derived parameters with known physical connections to convective frequency and/or intensity. Aerosol concentrations in each flight segment were evaluated using three HSRL2 variables at 355 and 532 nm: aerosol backscatter, aerosol extinction, and aerosol optical thickness. A set of radiometer- and radar-derived variables directly related to convective frequency and/or intensity was used to characterize convection, which included: AMPR-derived integrated cloud liquid water; polarization-corrected temperatures at 10.7, 19.35, 37.1, and 85.5 GHz; peak equivalent radar reflectivity factor (ZH); peak height of 30-dBZ ZH; and the number of radar data columns with composite ZH > 30 dBZ. The ZH analyses were performed using both Ku- and Ka-band APR-3 data. For each flight segment, correlation coefficients were calculated between each convective parameter and aerosol concentrations within the “low,” “medium,” and “high” groups for each environmental variable. The environmental stratification thresholds were then varied in a series of sensitivity tests. Several noteworthy correlations were observed between the convective and aerosol parameters within the environmental groups, especially when stratifying the environments based on their 850–500-hPa temperature lapse rate, 700–500-hPa temperature lapse rate, and K-Index. The convective parameters were often correlated most strongly with 355-nm extinction, 532-nm extinction, and 532-nm backscatter, while the presence of precipitation-sized liquid and ice hydrometeors contributed to some unexpected negative correlations. In general, as environmental conditions became more favorable for convection, a stronger correlation was observed between the convective parameters and aerosol concentrations. However, a “Goldilocks” zone of medium aerosol concentration was correlated most strongly with the convective parameters in many cases. These results stress the importance of considering environmental and aerosol conditions together when evaluating their impacts on convection. This presentation will provide a detailed discussion of these results and their implications, a summary of the limitations associated with such an analysis, and suggestions for future work.

Corey G. Amiot↗

NASA NTRS2011

A Ground Validation Network for the Global Precipitation Measurement Mission

A prototype Validation Network (VN) is currently operating as part of the Ground Validation System for NASA's Global Precipitation Measurement (GPM) mission. The VN supports precipitation retrieval algorithm development in the GPM prelaunch era. Postlaunch, the VN will be used to validate GPM spacecraft instrument measurements and retrieved precipitation data products. The period of record for the VN prototype starts on 8 August 2006 and runs to the present day. The VN database includes spacecraft data from the Tropical Rainfall Measuring Mission (TRMM) precipitation radar (PR) and coincident ground radar (GR) data from operational meteorological networks in the United States, Australia, Korea, and the Kwajalein Atoll in the Marshall Islands. Satellite and ground radar data products are collected whenever the PR satellite track crosses within 200 km of a VN ground radar, and these data are stored permanently in the VN database. VN products are generated from coincident PR and GR observations when a significant rain event occurs. The VN algorithm matches PR and GR radar data (including retrieved precipitation data in the case of the PR) by calculating averages of PR reflectivity (both raw and attenuation corrected) and rain rate, and GR reflectivity at the geometric intersection of the PR rays with the individual GR elevation sweeps. The algorithm thus averages the minimum PR and GR sample volumes needed to "matchup" the spatially coincident PR and GR data types. The result of this technique is a set of vertical profiles for a given rainfall event, with coincident PR and GR samples matched at specified heights throughout the profile. VN data can be used to validate satellite measurements and to track ground radar calibration over time. A comparison of matched TRMM PR and GR radar reflectivity factor data found a remarkably small difference between the PR and GR radar reflectivity factor averaged over this period of record in stratiform and convective rain cases when samples were taken from high in the atmosphere. A significant difference in PR and GR reflectivity was found in convective cases, particularly in convective samples from the lower part of the atmosphere. In this case, the mean difference between PR and corrected GR reflectivity was −1.88 dBZ. The PR-GR bias was found to increase with the amount of PR attenuation correction applied, with the PR-GR bias reaching −3.07 dBZ in cases where the attenuation correction applied is greater than 6 dBZ. Additional analysis indicated that the version 6 TRMM PR retrieval algorithm underestimates rainfall in case of convective rain in the lower part of the atmosphere by 30%-40%.

Schwaller, Mathew R.↗

NASA NTRS2011

A Ground Validation Network for the Global Precipitation Measurement Mission

A prototype Validation Network (VN) is currently operating as part of the Ground Validation System for NASA's Global Precipitation Measurement (GPM) mission. The VN supports precipitation retrieval algorithm development in the GPM prelaunch era. Postlaunch, the VN will be used to validate GPM spacecraft instrument measurements and retrieved precipitation data products. The period of record for the VN prototype starts on 8 August 2006 and runs to the present day. The VN database includes spacecraft data from the Tropical Rainfall Measuring Mission (TRMM) precipitation radar (PR) and coincident ground radar (GR) data from operational meteorological networks in the United States, Australia, Korea, and the Kwajalein Atoll in the Marshall Islands. Satellite and ground radar data products are collected whenever the PR satellite track crosses within 200 km of a VN ground radar, and these data are stored permanently in the VN database. VN products are generated from coincident PR and GR observations when a significant rain event occurs. The VN algorithm matches PR and GR radar data (including retrieved precipitation data in the case of the PR) by calculating averages of PR reflectivity (both raw and attenuation corrected) and rain rate, and GR reflectivity at the geometric intersection of the PR rays with the individual GR elevation sweeps. The algorithm thus averages the minimum PR and GR sample volumes needed to "matchup" the spatially coincident PR and GR data types. The result of this technique is a set of vertical profiles for a given rainfall event, with coincident PR and GR samples matched at specified heights throughout the profile. VN data can be used to validate satellite measurements and to track ground radar calibration over time. A comparison of matched TRMM PR and GR radar reflectivity factor data found a remarkably small difference between the PR and GR radar reflectivity factor averaged over this period of record in stratiform and convective rain cases when samples were taken from high in the atmosphere. A significant difference in PR and GR reflectivity was found in convective cases, particularly in convective samples from the lower part of the atmosphere. In this case, the mean difference between PR and corrected GR reflectivity was -1.88 dBZ. The PR-GR bias was found to increase with the amount of PR attenuation correction applied, with the PR-GR bias reaching -3.07 dBZ in cases where the attenuation correction applied is greater than 6 dBZ. Additional analysis indicated that the version 6 TRMM PR retrieval algorithm underestimates rainfall in case of convective rain in the lower part of the atmosphere by 30%-40%.

Schwaller, Mathew R.↗

NASA NTRS1991

Magellan - Radar performance and data products

The Magellan Venus orbiter carries only one scientific instrument: a 12.6-centimeter-wavelength radar system shared among three data-taking modes. The synthetic-aperture mode images radar echoes from the Venus surface at a resolution of between 120 and 300 meters, depending on spacecraft altitude. In the altimetric mode, relative height measurement accuracies may approach 5 meters, depending on the terrain's roughness, although orbital uncertainties place a floor of about 50 meters on the absolute uncertainty. In areas of extremely rough topography, accuracy is limited by the inherent line-of-sight radar resolution of about 88 meters. The maximum elevation observed to date, corresponding to a planetary radius of 6062 kilometers, lies within Maxwell Mons. When used as a thermal emission radiometer, the system can determine surface emissivities to an absolute accuracy of about 0.02. Mosaicked and archival digital data products will be released in compact disk (CDROM) format.

Pettengill, Gordon H.↗

NASA NTRS2019

Comprehensive Severe Weather Impact Assessment and Monitoring using Synthetic Aperture Radar and Auxiliary Data

Remote sensing datasets, particularly acquired by Synthetic Aperture Radar (SAR) sensors, have become increasingly important in severe weather disaster impact studies given their ability to observe the Earth largely irrespective of weather and sunlight conditions. The reliability of existing SAR change detection products applied on a pair of SAR images is constrained by the limitation of current methods to differentiate and classify disaster specific changes from anthropogenic surface alterations. Moreover, the inherent properties of the sensors, variations in SAR backscatter due to changes in surface conditions, and other factors exacerbate these limitations. We proposed a novel procedure expanding on earlier SAR-based change detection methods to exclude anthropogenic alterations and other sources of ambiguity that might lead to inaccurate mapping of the impacts of severe weather disasters. We applied the proposed procedure that is based on long term interferometric and amplitude-based change detection analyses of Sentinel- 1 SAR imagery to two study sites recently impacted by severe weather disasters (Flooding post severe weather events in urban centers; Hailstorm damage on crops). For the first case study, Sentinel-1 SLC scenes from two flood events in the Houston area (April 2016 flooding event and Hurricane Harvey of August-September 2017) were used to construct a flood map depicting areas repeatedly affected by the flood. Pixels with consistent coherence values in the pre-disaster coherence stack were retained for comparison with the pre- and post-disaster coherence stack and pixels with significant decline (greater than 60%) in coherence values were retained in the final flood map. The findings of the applied technique were calibrated and validated through datasets from NOAA/NWS Service storm reports, aerial imaging (NOAA and Civil Air Patrol), Federal Emergency Management Agency (FEMA) reporting, and targeted collections of NASA’s L-band UAVSAR data. Findings and products derived from the adopted methodology can be useful in disaster response and mitigation activities.

Gebremichael, Esayas↗

NASA NTRS1989

Nonlinear estimation of monsoon rainfall from radar and raingage data

Estimates of climatological rainfall are made as part of the validation program for the Tropical Rainfall Measuring Mission. Radar and raingage data collected during AMEX in Darwin, Australia during 1987 and 1988 are analyzed using a nonlinear merging methodology known as disjunctive cokriging (Azimi-Zonooz et al., 1989). The methodology is outlined and the results are used to determine the most advantageous time scale for estimation.

Krajewski, Witold F.↗

NASA NTRS1972

Determination of sounding rocket position and attitude from radar and magnetometer data

The techniques are described that were used to determine the trajectories and orientations of three sounding rockets instrumented to study the aurora. The radar plot board data were fitted to a near earth expansion of the force of gravity to determine the trajectory. Only onboard magnetometer data were used to determine the attitude of the payload with respect to the earth's magnetic field. Computer programs in the FORTRAN language are available which generate the trajectory and attitude parameters.

Pongratz, M.↗

NASA NTRS1974

Multi-frequency fine resolution imaging radar instrumentation and data acquisition

Development of a dual polarized L-band radar imaging system to be used in conjunction with the present dual polarized X-band radar is described. The technique used called for heterodyning the transmitted frequency from X-band to L-band and again heterodyning the received L-band signals back to X-band for amplification, detection, and recording.

Rendleman, R. A.↗

NASA NTRS1987

Data volume reduction for imaging radar polarimetry

Two data reduction algorithms developed using the scattering and phase matrix approaches are described. In the scattering matrix approach, the scattering matrices of four consecutive along-track pixels are averaged and in the phase matrix approach, the phase matrices of four consecutive along-track pixels are averaged. The basic procedures necessary to generate a synthetic polarization image from original data sets are discussed. The two algorithms are evaluated in terms of data volume reduction and the number of errors introduced in the synthesized images. It is observed that the reduced data set produced by the scattering matrix algorithm is smaller than that generated by the phase matrix algorithm; however, greater errors are introduced into the data set by the scattering matrix algorithm than the phase algorithm. Flowcharts for the scattering and phase matrix approaches and for synthesis of uncompressible data are presented.

Dubois, Pascale C.↗

NASA NTRSDate not supplied

Investigating the Use of Machine Learning (ML) to Assess Tropospheric Doppler Radar Wind Profiler (TDRWP) Data Quality

Manual Quality Control (MQC) of Tropospheric Doppler Radar Wind Profiler (TDRWP) data is essential for defining an accurate climatology for downstream aerospace vehicle assessments. MQC traditionally takes around 30.5 hours per year of radar data. The Marshall Space Flight Center Natural Environments Branch (MSFC NE) used machine learning (ML) to test the feasibility of automating the MQC process, showing a potential to reduce labor by 300%. However, analysis of the model showed some false positives. We compared a neural network to the model to validate it and develop a process for assessing comparable solutions in the future.

Corey Walker↗

NASA NTRS1991

Comparison of lightning location data and polarisation radar observations of clouds

Simultaneous observations of both the precipitation and the lightning associated with thunderstorms show that the lightning is within 3 km of the maximum precipitation echo. The intensity and type of the precipitation is observed with 500 m spatial accuracy using an S-band polarization radar and the position of the lightning is inferred from a low frequency magnetic direction finding location system. Empirical adjustment to the angles using the redundancy of the lightning data reduce this error. Radar echoes above 45dBZ may be caused by soft hail or hailstones, but similarly intense echoes may result from melting snow. The data show that a new polarization radar parameter, the linear depolarization ratio, can distinguish between soft hail and melting snow, and that the intense radar echoes associated with melting snow pose no threat of lightning. A lightning risk only exists when the radar indicates that the clouds contain soft hail or hailstones.

Illingworth, A. J.↗

DOE OSTI2021

Radar - ANL Wind Profiler, Walla Walla - Raw Data

**Winds** A radar wind profiler measures the Doppler shift of electromagnetic energy scattered back from atmospheric turbulence and hydrometeors along 3-5 vertical and off-vertical point beam directions. Back-scattered signal strength and radial-component velocities are remotely sensed along all beam directions and combined to derive the horizontal wind field over the radar. These data typically are sampled and averaged hourly and usually have 6-m and/or 100-m vertical resolutions up to 4 km for the 915 MHz and 8 km for the 449 MHz systems. **Temperature** To measure atmospheric temperature, a radio acoustic sound system (RASS) is used in conjunction with the wind profile. These data typically are sampled and averaged for five minutes each hour and have a 60-m vertical resolution up to 1.5 km for the 915 MHz and 60-m up to 3.5k m for the 449 MHz.

17 WIND ENERGY↗

DOE OSTI2021

Radar - ARL Wind Profilerwith RASS, Boardman - Raw Data

17 WIND ENERGY↗

NASA NTRS1998

Integrating Radar, Multispectral, and Landing Site Data for Analysis of the Lunar Surface

Radar maps of the Moon have been produced since the late 1960s, and have been used by a number of authors to study the surface roughness, subsurface rock abundance, and dielectric properties of the lunar surface. These studies focused on a range of topics, including the depth and rock population of the regolith, crater ejecta blankets, pyroclastic mantling layers, and cryptomare deposits. Limited radar sounding data from the Apollo missions identified layering in some regions of the maria. As radar datasets have improved in resolution and calibration, it has become more possible to make quantitative comparisons between the backscatter properties of the Moon, other remote-sensing observations, and the ground truth provided by Surveyor photos and Apollo traverses. This presentation will focus on the results of recent studies of the lunar regolith that make use of these diverse sources of information, and discuss research directions that will be possible with radar data to be collected in the near future.

Campbell, B. A.↗

NASA NTRS2005

Comparing Goldstone Solar System Radar Earth-based Observations of Mars with Orbital Datasets

The Goldstone Solar System Radar (GSSR) has collected a self-consistent set of delay-Doppler near-nadir radar echo data from Mars since 1988. Prior to the Mars Global Surveyor (MGS) Mars Orbiter Laser Altimeter (MOLA) global topography for Mars, these radar data provided local elevation information, along with radar scattering information with global coverage. Two kinds of GSSR Mars delay-Doppler data exist: low 5 km x 150 km resolution and, more recently, high (5 to 10 km) spatial resolution. Radar data, and non-imaging delay-Doppler data in particular, requires significant data processing to extract elevation, reflectivity and roughness of the reflecting surface. Interpretation of these parameters, while limited by the complexities of electromagnetic scattering, provide information directly relevant to geophysical and geomorphic analyses of Mars. In this presentation we want to demonstrate how to compare GSSR delay-Doppler data to other Mars datasets, including some idiosyncracies of the radar data. Additional information is included in the original extended abstract.

Haldemann, A. F. C.↗

NASA NTRS2013

Integrating a Microwave Radiometer into Radar Hardware for Simultaneous Data Collection Between the Instruments

The conventional method for integrating a radiometer into radar hardware is to share the RF front end between the instruments, and to have separate IF receivers that take data at separate times. Alternatively, the radar and radiometer could share the antenna through the use of a diplexer, but have completely independent receivers. This novel method shares the radar's RF electronics and digital receiver with the radiometer, while allowing for simultaneous operation of the radar and radiometer. Radars and radiometers, while often having near-identical RF receivers, generally have substantially different IF and baseband receivers. Operation of the two instruments simultaneously is difficult, since airborne radars will pulse at a rate of hundreds of microseconds. Radiometer integration time is typically 10s or 100s of milliseconds. The bandwidth of radar may be 1 to 25 MHz, while a radiometer will have an RF bandwidth of up to a GHz. As such, the conventional method of integrating radar and radiometer hardware is to share the highfrequency RF receiver, but to have separate IF subsystems and digitizers. To avoid corruption of the radiometer data, the radar is turned off during the radiometer dwell time. This method utilizes a modern radar digital receiver to allow simultaneous operation of a radiometer and radar with a shared RF front end and digital receiver. The radiometer signal is coupled out after the first down-conversion stage. From there, the radar transmit frequencies are heavily filtered, and the bands outside the transmit filter are amplified and passed to a detector diode. This diode produces a DC output proportional to the input power. For a conventional radiometer, this level would be digitized. By taking this DC output and mixing it with a system oscillator at 10 MHz, the signal can instead be digitized by a second channel on the radar digital receiver (which typically do not accept DC inputs), and can be down-converted to a DC level again digitally. This unintuitive step allows the digital receiver to sample both the radiometer and radar data at a rapid, synchronized data rate (greater than 1 MHz bandwidth). Once both signals are sampled by the same digital receiver, high-speed quality control can be performed on the radiometer data to allow it to take data simultaneously with the radar. The radiometer data can be blanked during radar transmit, or when the radar return is of a power level high enough to corrupt the radiometer data. Additionally, the receiver protection switches in the RF front end can double as radiometer calibration sources, the short (four-microsecond level) switching periods integrated over many seconds to estimate the radiometer offset. The major benefit of this innovation is that there is minimal impact on the radar performance due to the integration of the radiometer, and the radiometer performance is similarly minimally affected by the radar. As the radar and radiometer are able to operate simultaneously, there is no extended period of integration time loss for the radiometer (maximizing sensitivity), and the radar is able to maintain its full number of pulses (increasing sensitivity and decreasing measurement uncertainty).

McLinden, Matthew↗

NASA NTRS1983

Low-level microburst wind structure using Doppler radar and PAM data

The Joint Airport Weather Studies (JAWS) observation network used at an airport in Denver, CO consisted of one S-band and two C-band radars, together with 27 Portable Automated Mesonet (PAM) stations. Each of the three microburst case studies observed during the JAWS experiment occurred within the surface PAM network, and these surface data were compared with the radar analyses. It emerges that if microburst vertical motions are to be accurately resolved, the spacing of the surface stations must be considerably less than 4 km.

Kessinger, C.↗

NASA NTRS2018

NASA D3R: 2.0, Enhanced Radar with New Data and Control Features

The NASA dual-frequency, dual-polarization, Doppler radar (D3R) was developed to support development of algorithms and validation for the global precipitation measurement (GPM) missions dual-frequency precipitation radar (DPR). The D3R has participated extensively in various field campaigns in North America with geographic features covering both summer and winter climatic regimes. During the year 2017, D3R went through a major upgrade, specially with the digital receiver and waveform generation subsystems. In this work, the D3R systems upgrade will be discussed with a focus on key features of the new system. The new flexible architecture will enable new research capabilities that will be described.

Kumar, Mohit↗