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At least 73 records · Page 4

Orbiting passive microwave sensor simulation applied to soil moisture estimation

A sensor/scene simulation program was developed and used to determine the effects of scene heterogeneity, resolution, frequency, look angle, and surface and temperature relations on the performance of a spaceborne passive microwave system designed to estimate soil water information. The ground scene is based on classified LANDSAT images which provide realistic ground classes, as well as geometries. It was determined that the average sensitivity of antenna temperature to soil moisture improves as the antenna footprint size increased. Also, the precision (or variability) of the sensitivity changes as a function of resolution.

Newton, R. W.↗

Active and Passive Hybrid Sensor

A hybrid ocean wind sensor (HOWS) can map ocean vector wind in low to hurricane-level winds, and non-precipitating and precipitating conditions. It can acquire active and passive measurements through a single aperture at two wavelengths, two polarizations, and multiple incidence angles. Its low profile, compact geometry, and low power consumption permits installation on air craft platforms, including high-altitude unmanned aerial vehicles (UAVs).

Carswell, James R.↗

How Do A-train Sensors Intercompare in the Retrieval of Above-cloud Aerosol Optical Depth? A Case Study-based Assessment

We intercompare the above-cloud aerosol optical depth (ACAOD) of biomass burning plumes retrieved from A-train sensors, i.e., Moderate Resolution Imaging Spectroradiometer (MODIS), Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP), Polarization and Directionality of Earth Reflectances (POLDER), and Ozone Monitoring Instrument (OMI). These sensors have shown independent capabilities to retrieve aerosol loading above marine boundary layer clouds-a kind of situation often found over the southeast Atlantic Ocean during dry burning season. A systematic comparison reveals that all passive sensors and CALIOP-based research methods derive comparable ACAOD with differences mostly within 0.2 over homogeneous cloud fields. The 532 nm ACAOD retrieved by CALIOP operational algorithm is underestimated. The retrieved 1064 nm AOD however shows closer agreement with passive sensors. Given the different types of measurements processed with different algorithms, the reported close agreement between them is encouraging. Due to unavailability of direct measurements above cloud, the validation of satellite-based ACAOD remains an open challenge. The intersatellite comparison however can be useful for the relative evaluation and consistency check

Aerosol↗

Pyrotechnic hazards classification and evaluation program test report. Heat flux study of deflagrating pyrotechnic munitions

A heat flux study of deflagrating pyrotechnic munitions is presented. Three tests were authorized to investigate whether heat flux measurements may be used as effective hazards evaluation criteria to determine safe quantity distances for pyrotechnics. A passive sensor study was conducted simultaneously to investigate their usefulness in recording events and conditions. It was concluded that heat flux measurements can effectively be used to evaluate hazards criteria and that passive sensors are an inexpensive tool to record certain events in the vicinity of deflagrating pyrotechnic stacks.

Fassnacht, P. O.↗

Detection of Multi-Layer and Vertically-Extended Clouds Using A-Train Sensors

The detection of mUltiple cloud layers using satellite observations is important for retrieval algorithms as well as climate applications. In this paper, we describe a relatively simple algorithm to detect multiple cloud layers and distinguish them from vertically-extended clouds. The algorithm can be applied to coincident passive sensors that derive both cloud-top pressure from the thermal infrared observations and an estimate of solar photon pathlength from UV, visible, or near-IR measurements. Here, we use data from the A-train afternoon constellation of satellites: cloud-top pressure, cloud optical thickness, the multi-layer flag from the Aqua MODerate-resolution Imaging Spectroradiometer (MODIS) and the optical centroid cloud pressure from the Aura Ozone Monitoring Instrument (OMI). For the first time, we use data from the CloudSat radar to evaluate the results of a multi-layer cloud detection scheme. The cloud classification algorithms applied with different passive sensor configurations compare well with each other as well as with data from CloudSat. We compute monthly mean fractions of pixels containing multi-layer and vertically-extended clouds for January and July 2007 at the OMI spatial resolution (l2kmx24km at nadir) and at the 5kmx5km MODIS resolution used for infrared cloud retrievals. There are seasonal variations in the spatial distribution of the different cloud types. The fraction of cloudy pixels containing distinct multi-layer cloud is a strong function of the pixel size. Globally averaged, these fractions are approximately 20% and 10% for OMI and MODIS, respectively. These fractions may be significantly higher or lower depending upon location. There is a much smaller resolution dependence for fractions of pixels containing vertically-extended clouds (approx.20% for OMI and slightly less for MODIS globally), suggesting larger spatial scales for these clouds. We also find higher fractions of vertically-extended clouds over land as compared with ocean, particularly in the tropics and summer hemisphere.

Joiner, J.↗

The Feasibility of Detecting Supercooled Liquid with a Forward-Looking Radiometer

A three-dimensional radiative transfer model is utilized to determine the feasibility of a forward-viewing passive sensor for remotely detecting hazardous icing conditions. Wband ground-based radar simulations show no obvious ability to discriminate a cloud-top supercooled layer; however, the spectra for a forward-viewing passive sensor show a strong signal at two stand-off distances when compared with the clear sky spectrum. Such an instrument would be critical for manned and unmanned aircraft, particularly when size, weight, and power requirements restrict the installation of deicing equipment.

clouds↗

Tracking the Hunga Tonga-Hunga Ha’apai Eruption Stratospheric Aerosol and Trace Gas Plumes Using Machine Learning

The Hunga Tonga-Hunga Ha’apai (HTHH) submarine volcano had an explosive eruption phase on January 15, 2022, that thrusted ash, gases, and water vapor through the troposphere and into the stratosphere. The stratospheric volcanic plume included aerosol precursor gases such as SO2 and H2S as well as anomalously high water vapor concentrations due to the submarine oceanic origin. With these atmospheric constituents, the sulfuric gases and water vapor formed sulfate (SO4) particles via gas-to-particle reactions and these aerosols likely increased in size due to hygroscopic growth within anomalously humid regions. Strong easterlies and gravity waves propagated the volcanic impacts throughout the stratosphere. Orbital and suborbital passive sensor retrievals detected changes in the aerosol and trace gas characteristics within the atmospheric column for cloud-free regions over the southern hemisphere. While the CALIPSO lidar can detect aerosol layers in the stratosphere, passive sensors such as MODIS retrieved the total column aerosol abundance and characteristics. Previous studies used manual tracking methods to determine volcanic plume positions and compared them to ground observations. In this study, we examine the machine learning (ML) approaches including segmentation, object detection, and object tracking to identify and track aerosol and trace gas plumes using orbital and suborbital sensor data. This ML implementation strives to provide a more systematic approach to separate total column effects from those of the stratosphere. Similar ML tracking may be useful for stratospheric impact events observed historically by CALIPSO and in the future with EarthCare and the Atmosphere Observing System (AOS) lidar-capable missions.

Rhys Leahy↗

Absorption in Extended Inhomogeneous Clouds

The launch of several different sensors, including CloudSat, into the A-train constellation of satellites allows us for the first time to compute absorption that can occur in realistic vertically inhomogeneous clouds including multiple cloud decks. CloudSat data show that these situations are common. Therefore, understanding vertically inhomogeneous clouds is important from both climate and satellite atmospheric composition remote sensing perspectives. Satellite passive sensors that operate from the near IR to the UV often rely on radiative cloud pressures derived from absorption in oxygen bands (A, B, gamma, or O2-O2 bands) or from rotational-Raman scattering in order to retrieve information about atmospheric trace gases. The radiative cloud pressure is distinct from the physical cloud top derived from thermal infrared measurements. Therefore, the combination of information from different passive sensors yields some information about the cloud vertical profile. When either or both the clouds or atmospheric absorbers (trace gases and aerosols) are vertically inhomogeneous, the use of an effective cloud pressure derived from these approaches may lead to errors. Here, we focus on several scenarios (deep convective clouds and distinct two layer clouds) based on realistic cloud optical depth vertical profiles derived from the CloudSatfMODIS combination. We focus on implications for trace-gas column amount retrievals (specifically ozone and NO2) and derived surface UV irradiance from the Ozone Monitoring Instrument (OMI) on the Atrain Aura platform.

Joiner, Joanna↗

Active and Passive Radiative Transfer Simulations for GPM-Related Field Campaigns

Using a three-dimensional radiative transfer model combined with cloud-resolving model output, we simulate active and passive sensor observations of clouds and precipitaiton. This combination of tools allows us to diagnose the contributions of various hydrometeor types. Radar multiple scattering is most closely associated with the presence of graupel. At Wband, massive amounts multiple scattering in deep convection can decorrelate the reflectivity profile from the vertical structure, but for less intense events, multiple scattering could be a useful indicator of riming. For passive sensors, polarization differences at 166 GHz indicate the presence of horizontally aligned frozen particles with pronounced aspect ratios, while high concentrations of more isotropic aggregates and graupel dampen the polarization difference while also contributing to the lowest brightness temperature depressions. The insights into remote sensing measurements will facilitate the development of improved algorithms and advanced sensors.

Adams, Ian S.↗

Different Features of Cloud Macrophysical Changes Observed by MODIS, CALIPSO, and CloudSat for the 11-Year Period

From the A-train satellite mission, more than 11 years of Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO), and CloudSat satellite measurements are available from 2007 to 2017. In this study, we examine cloud macrophysical changes from a passive sensor, Moderate Resolution Imaging Spectroradiometer (MODIS), and two active sensors, CALIPSO and CloudSat (CALCS). MODIS and CALCS capture common features of the cloud changes related to El Niño–Southern Oscillation (ENSO) events, i.e., increase of low clouds during La Niña and increase of mid and high clouds during El Niño over the eastern Pacific. However, optically thin cirrus clouds are well detected by CALCS while these are often missed by MODIS. As a result, MODIS shows much flatter distributions of cloud top heights. In addition, compared to MODIS, CALCS cloud volume anomalies are better correlated with relative humidity anomalies. The differences between MODIS and CALCS appear in low cloud variations. Particularly, fluctuations in MODIS low cloud anomalies are larger than CALCS, and MODIS low cloud anomalies are anticorrelated with mid/high cloud anomalies. This is because of the limitation in detecting underlying clouds by MODIS passive sensor. Also, the layer thickness of MODIS mid/high clouds is thinner than that from CALCS, less affecting cloud amounts at 0-3 km altitude.

Cloud↗

Multi-Sensor Mud Detection

Robust mud detection is a critical perception requirement for Unmanned Ground Vehicle (UGV) autonomous offroad navigation. A military UGV stuck in a mud body during a mission may have to be sacrificed or rescued, both of which are unattractive options. There are several characteristics of mud that may be detectable with appropriate UGV-mounted sensors. For example, mud only occurs on the ground surface, is cooler than surrounding dry soil during the daytime under nominal weather conditions, is generally darker than surrounding dry soil in visible imagery, and is highly polarized. However, none of these cues are definitive on their own. Dry soil also occurs on the ground surface, shadows, snow, ice, and water can also be cooler than surrounding dry soil, shadows are also darker than surrounding dry soil in visible imagery, and cars, water, and some vegetation are also highly polarized. Shadows, snow, ice, water, cars, and vegetation can all be disambiguated from mud by using a suite of sensors that span multiple bands in the electromagnetic spectrum. Because there are military operations when it is imperative for UGV's to operate without emitting strong, detectable electromagnetic signals, passive sensors are desirable. JPL has developed a daytime mud detection capability using multiple passive imaging sensors. Cues for mud from multiple passive imaging sensors are fused into a single mud detection image using a rule base, and the resultant mud detection is localized in a terrain map using range data generated from a stereo pair of color cameras.

Rankin, Arturo L.↗

Monitoring snowpack properties by passive microwave sensors on board of aircraft and satellites

Snowpack properties such as water equivalent and snow wetness may be inferred from variations in measured microwave brightness temperatures. This is because the emerged microwave radiation interacts directly with snow crystals within the snowpack. Using vertically and horizontally polarized brightness temperatures obtained from the multifrequency microwave radiometer (MFMR) on board a NASA research aircraft and the electrical scanning microwave radiometer (ESMR) and scanning multichannel microwave radiometer (SMMR) on board the Nimbus 5, 6, and 7 satellites, linear relationships between snow depth or water equivalent and microwave brightness temperature were developed. The presence of melt water in the snowpack generally increases the brightness temperatures, which can be used to predict snowpack priming and timing of runoff.

Chang, A. T. C.↗

Freshwater ice thickness observations using passive microwave sensors

Walden Reservoir, a freshwater lake in north-central Colorado, was overflown six times by a NASA C-130 aircraft between January 1977 and April 1980. The aircraft was equipped with four microwave radiometers operating between 0.81 and 6.0 cm in wavelength (37.0 to 5.0 GHz). The 6.0-cm radiometer data showed a good relationship with ice thickness based on a sample of four ice thickness values. The 1.67- and 1.35-cm radiometer data showed weaker relationships with ice thickness. The 0.81-cm sensor data showed no positive relationship with ice thickness. None of the relationships was statistically significant because of the small sample size. The 6.0-cm sensor data in the nadir-viewing mode was found to have the most potential of all the wavelengths studied, for use in remotely determining ice thickness. The 6.0-cm radiometer probably sensed the entire thickness of the ice on the reservoir (ranging from 25.4 to 67.3 cm in thickness) and was apparently not significantly affected by the snow overlying the ice. The shorter wavelengths are scattered by the snow overlying the ice and are more suitable for snow studies than for ice thickness studies.

Hall, D. K.↗

Integration of motion and stereo sensors in passive ranging systems

A recursive approach is described for processing a sequence of stereo images. It will be the basis for an integrated stereo and motion method to provide more accurate range information using a passive ranging system. Results based on motion sequences of stereo images are presented. The approach is also applicable to other autonomous systems and in robotics.

Sridhar, Banavar↗

Bias correction for rainrate retrievals from satellite passive microwave sensors

Rainrates retrieved from past and present satellite-borne microwave sensors are affected by a fundamental remote sensing problem. Sensor fields-of-view are typically large enough to encompass substantial rainrate variability, whereas the retrieval algorithms, based on radiative transfer calculations, show a non-linear relationship between rainrate and microwave brightness temperature. Retrieved rainrates are systematically too low. A statistical model of the bias problem shows that bias correction factors depend on the probability distribution of instantaneous rainrate and on the average thickness of the rain layer.

Short, David A.↗