Passive microwave sensors
Passive microwave sensors based on brightness temperature vs frequency model for various atmospheric conditions, presenting vegetation pictures
Hovis, W.↗
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Soil moisture detection by Skylab's microwave sensors
Terrain microwave backscatter and emission response to soil moisture variations are investigated using Skylab's 13.9 GHz RADSCAT (Radiometer-Scatterometer) system. Data acquired on June 5, 1973 over a test site in west-central Texas indicates a fair degree of correlation with composite rainfall. The scan mode was cross-track contiguous (CTC) with a pitch of 29.4 deg and no roll offset. Vertical polarization was employed with both radiometer and scatterometer. The composite rainfall was computed according to the flood prediction technique using rainfall data supplied by weather reporting stations.
Ulaby, F. T.↗
The Effects of Rainfall Inhomogeneity on Climate Variability of Rainfall Estimated from Passive Microwave Sensors
Passive microwave rainfall estimates that exploit the emission signal of raindrops in the atmosphere are sensitive to the inhomogeneity of rainfall within the satellite field of view (FOV). In particular, the concave nature of the brightness temperature (T(sub b)) versus rainfall relations at frequencies capable of detecting the blackbody emission of raindrops cause retrieval algorithms to systematically underestimate precipitation unless the rainfall is homogeneous within a radiometer FOV, or the inhomogeneity is accounted for explicitly. This problem has a long history in the passive microwave community and has been termed the beam-filling error. While not a true error, correcting for it requires a priori knowledge about the actual distribution of the rainfall within the satellite FOV, or at least a statistical representation of this inhomogeneity. This study first examines the magnitude of this beam-filling correction when slant-path radiative transfer calculations are used to account for the oblique incidence of current radiometers. Because of the horizontal averaging that occurs away from the nadir direction, the beam-filling error is found to be only a fraction of what has been reported previously in the literature based upon plane-parallel calculations. For a FOV representative of the 19-GHz radiometer channel (18 km X 28 km) aboard the Tropical Rainfall Measuring Mission (TRMM), the mean beam-filling correction computed in this study for tropical atmospheres is 1.26 instead of 1.52 computed from plane-parallel techniques. The slant-path solution is also less sensitive to finescale rainfall inhomogeneity and is, thus, able to make use of 4-km radar data from the TRMM Precipitation Radar (PR) in order to map regional and seasonal distributions of observed rainfall inhomogeneity in the Tropics. The data are examined to assess the expected errors introduced into climate rainfall records by unresolved changes in rainfall inhomogeneity. Results show that global mean monthly errors introduced by not explicitly accounting for rainfall inhomogeneity do not exceed 0.5% if the beam-filling error is allowed to be a function of rainfall rate and freezing level and does not exceed 2% if a universal beam-filling correction is applied that depends only upon the freezing level. Monthly regional errors can be significantly larger. Over the Indian Ocean, errors as large as 8% were found if the beam-filling correction is allowed to vary with rainfall rate and freezing level while errors of 15% were found if a universal correction is used.
Kummerow, Christian↗
Preliminary observations of polar sea ice with the Special Sensor Microwave Imager
The Special Sensor Microwave Imager (SSM/I) was launched as part of the Defense Meteorological Satellite Program. The SSM/I operates at 19.4, 22.2, 37.0, and 85.5 GHz with horizontal and vertical polarizations measured at each frequency except 22 GHz which has only a vertical polarization channel. Examination of polarization and spectral gradient ratios derived from the SSM/I radiances for open ocean, first-year, and multiyear Arctic sea ice reveals striking differences between the ratios calculated using lower frequencies and those ratios using the 85 GHz channels. These differences may serve as the basis for deriving sea ice information.
Cavalieri, D. J.↗
The mutual interpretation of active and passive microwave sensor outputs.
Interdependent microwave radar- and radiometer- sensor measurements of backscatter and albedo characteristics of earth surface
MICROWAVE RADIOMETER↗
Correlation of microwave sensor returns with soil moisture
Microwave sensor soil data were collected by aircraft over agricultural croplands. Multiple incident angle scatterometer data (13.3, 4.75, 1.6 and 0.4 GHz), passive radiometer data (L and C-band), and soil moisture ground truth measurements were collected coincidentally. Each sensor and angle of incidence was linearly analyzed against the measured soil moisture. For bare agricultural soils, the optimal single sensor for soil moisture preduction is the L-band passive radiometer. The effects of vegetation and differing surface roughness prove significant. When both bare and vegetated surfaces were studied, the masking due to the vegetation renders the single sensor approach ineffective in soil moisture prediction. Multisensor techniques are necessary to remotely measure soil moisture when a priori knowledge of vegetation is not available.
Taube, D. W.↗
Aircraft active and passive microwave validation of sea ice concentration from the Defense Meteorological Satellite Program special sensor microwave imager
Results are presented of a series of coordinate special sensor microwave imager (SSM/I) underflights that were carried out during March 1988 with NASA and Navy aircraft over portions of the Bering, Beaufort, and Chukchi seas. NASA DC-8 AMMR data from Bering Sea ice edge crossings were used to verify that the ice edge location, defined as the position of the initial ice bands encountered by the aircraft, corresponds to an SSM/I ice concentration of 15 percent. Direct comparison of SSM/I and aircraft ice concentrations for regions having at least 80 percent aircraft coverage reveals that the SSM/I total ice concentration is lower on average by 2.4 +/-2.4 percent. For multiyear ice, NASA and Navy flights across the Beaufort and Chukchi seas show that the SSM/I algorithm correctly maps the large-scale distribution of multiyear ice: the zone of first-year ice off the Alaskan coast, the large areas of mixed first-year and multiyear ice, and the region of predominantly multiyear ice north of the Canadian archipelago.
Cavalieri, D. J.↗
The Mutual Interpretation of Active and Passive Microwave Sensor Outputs
Mutual interpretation of active and passive microwave sensor outputs
MICROWAVE APPARATUS↗
Special Sensor Microwave Imager/Sounder Updates for the Global Precipitation Measurement V07 Data Suite
Observations from the Special Sensor Microwave Imager/Sounder (SSMIS) onboard the Defense Meteorological Satellite Program F16, F17, F18, and F19 spacecrafts provide a significant portion of the microwave radiometer data within the Global Precipitation Measurement (GPM) mission constellation. In preparation for the GPM Version 7 (V07) data release, SSMIS corrections developed over a decade ago and incorporated in GPM Version 5 (V05) are reexamined and updated. The calibration updates presented here include pointing parameters affecting geolocation and viewing geometry, along-scan bias adjustments to account for scan edge falloffs, and sun angle corrections to account for heating anomalies including an emissive reflector. To address errors in the V05 geolocation, the sensor roll, pitch, yaw, half cone angle, and timing offsets are reanalyzed and updated. The along-scan bias adjustment is updated in a manner consistent with recent Special Sensor Microwave Imager updates to account for variations in scene temperature. Finally, significant improvements to the SSMIS sun angle correction are made by using data from the entire mission, extending the corrections to include the higher frequency channels, and deriving a more consistent channel-to-channel correction. From V05 to V07, the geolocation adjustment is approximately 5-10 km, the earth incidence angle difference is 0-0.4°, and the average brightness temperature change is 0-2 K, but individual pixels may be up to several Kelvin difference depending on sensor and channel. The result of these updates is a significant improvement in the quality and long-term consistency of the SSMIS data that are included in the GPM V07 dataset.
Rachael A Kroodsma↗
A model for atmospheric brightness temperatures observed by the special sensor microwave imager (SSM/I)
A closed-form mathematical model for the atmospheric contribution to microwave the absorption and emission at the SSM/I frequencies is developed in order to improve quantitative interpretation of microwave imagery from the Special Sensor Microwave Imager (SSM/I). The model is intended to accurately predict upwelling and downwelling atmospheric brightness temperatures at SSM/I frequencies, as functions of eight input parameters: the zenith (nadir) angle, the integrated water vapor and vapor scale height, the integrated cloud water and cloud height, the effective surface temperature, atmospheric lapse rate, and surface pressure. It is shown that the model accurately reproduces clear-sky brightness temperatures computed by explicit integration of a large number of radiosonde soundings representing all maritime climate zones and seasons.
Petty, Grant W.↗
Regional rainfall climatologies derived from Special Sensor Microwave Imager (SSM/I) data
Climatologies of convective precipitation were derived from passive microwave observations from the Special Sensor Microwave Imager using a scattering-based algorithm of Adler et al. Data were aggregated over periods of 3-5 months using data from 4 to 5 years. Data were also stratified by satellite overpass times (primarily 06 00 and 18 00 local time). Four regions (Mexico, Amazonia, western Africa, and the western equatorial Pacific Ocean (TOGA COARE area) were chosen for their meteorological interest and relative paucity of conventional observations. The strong diurnal variation over Mexico and the southern United States was the most striking aspect of the climatologies. Pronounced morning maxima occured offshore, often in concativities in the coastline, the result of the increased convergence caused by the coastline shape. The major feature of the evening rain field was a linear-shaped maximum along the western slope of the Sierra Madre Occidental. Topography exerted a strong control on the rainfall in other areas, particularly near the Nicaragua/Honduras border and in Guatemala, where maxima in excess of 700 mm/month were located adjacent to local maxima in terrain. The correlation between the estimates and monthly gage data over the southern United States was low (0.45), due mainly to poor temporal sampling in any month and an inadequate sampling of the diurnal cycle. Over the Amazon Basin the differences in morning versus evening rainfall were complex, with an alternating series of morning/evening maxima aligned southwest to northeast from the Andes to the northeast Brazilian coast. A real extent of rainfall in Amazonia was slightly higher in the evening, but a maximum in morning precipitation was found on the Amazon River just east of Manaus. Precipitation over the water in the intertropical convergence zone (ITCZ) north of Brazil was more pronounced in the morning, and a pronounced land-/sea-breeze circulation was found along the northeast coast of Brazil. Inter-comparison of four years revealed 1992 to be the driest over Amazonia, with about a 23% decrease in mean rate compared to the 4-year mean estimated rain rate.
Negri, Andrew J.↗
Assimilation of Precipitation Information Retrieved from Passive Microwave Sensors
Estimates of surface precipitation and the associated vertical latent heating structure can be retrieved from space-borne passive microwave sensors such as the TRMM Microwave Imager (TMI) and Special Sensor Microwave/Imager (SSM/I). Studies have shown that assimilation of microwave rain rates can improve the quality of global analysis and forecast. Numerical weather prediction (NWP) centers are beginning to make operational use of these data. In the next few years, there will be a gradual increase in microwave rain products available from operational and research satellites, culminating to a target constellation of 9 satellites to provide global rain measurements every 3 hours with the proposed Global Precipitation Measurement (GPM) mission to be launched around 2007. Realizing the full potential of these observations in data assimilation will require continued advances in retrieval algorithms, assimilation techniques, and model physics. I present results from two research efforts at NASA GSFC. The first is on the assimilation of tropical precipitation using temperature/moisture tendency corrections within a continuous variational assimilation framework to improve analyses and forecasts produced by the Goddard Earth Observing System (GEOS) global data assimilation system. Results show that assimilation of TMI and SSM/I rain rates improves not only precipitation and moisture, but also the related clouds, radiation energy fluxes, and large-scale circulations in GEOS analyses. The improved analyses also yield better short-range forecasts, quantitative precipitation forecast (QPF) threat scores, and storm track predictions. The second part consists of results from exploratory experiments on variational assimilation of convective and stratiform latent heating profiles within the general framework of model parameter estimation as a way to identify systematic model errors and optimize physical paremeterization schemes in global forecast and climate models.
Hou, Arthur Y.↗
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.↗
Microwave sensors for earth resource observations in the 1980's
Future trends in microwave sensing are identified with reference to the workshops organized by the Active Microwave Remote Sensing Research Program. The workshops demonstrated that (1) microwave techniques have great potential for earth observations of renewable and nonrenewable resources and (2) existing research does not adequately assess microwave sensor measurement capabilities. The need for synoptic information includes such areas as cloud-free, surface-roughness and electrical-properties data. Attention is given to applications including all-weather imaging, sensitivity to vegetation and soil-moisture conditions. Research tasks to be accomplished during the next five years are discussed.
Rouse, J. W., Jr.↗
Microwave Sensor for Blade Tip Clearance and Structural Health Measurements
The use of microwave based sensors for the health monitoring of rotating machinery is being explored at the NASA Glenn Research Center. The microwave sensor works on the principle of sending a continuous signal towards a rotating component and measuring the reflected signal. The phase shift of the reflected signal is proportional to the distance between the sensor and the component that is being measured. This type of sensor is beneficial in that it has the ability to operate at extremely high temperatures and is unaffected by contaminants that may be present in the rotating machinery. It is intended to use these probes in the hot sections of turbine engines for closed loop turbine clearance control and structural health measurements. Background on the sensors, an overview of their calibration and preliminary results from using them to make blade tip clearance and health measurements on a large axial vane fan will be presented.
Woike, Mark R.↗
The use of active microwave sensors in search and rescue operations
The possibility of utilizing low-orbit active microwave sensors in Search and Rescue (SAR) operations to detect and positionally locate scenes of distress is studied. The use of a scanning type system, i.e., rotating a highly directional antenna as opposed to more complex synthetic aperture type system is addressed. Targets for this system will be passive reflectors, not active transmitters like EPIRBs; reflectors which could be easily stowed, require little to no maintenance, could be quickly erected and can be more affordable than battery powered alternatives. Presented are parametric analyses to determine the required antenna gains and reflector areas for various frequencies and orbit altitudes. Also, major issues are raised as well as indicating areas requiring further study.
Tillotson, Tom↗
A multifrequency evaluation of active and passive microwave sensors for oil spill detection and assessment
An evaluation is given of how active and passive microwave sensors can best be used in oil spill detection and assessment. Radar backscatter curves taken over oil spills are presented and their effect on synthetic aperture radar (SAR) imagery are discussed. Plots of microwave radiometric brightness variations over oil spills are presented and discussed. Recommendations as to how to select the best combination of frequency, viewing angle, and sensor type for evaluation of various aspects of oil spills are also discussed.
Fenner, R. G.↗
Arctic sea ice concentrations from special sensor microwave imager and advanced very high resolution radiometer satellite data
Nearly coincident data from the special sensor microwave imager (SSM/I) and the advanced very high resolution radiometer (AVHRR) are used to compute and compare Arctic sea ice concentrations for different regions and times of the year. To help determine overall accuracies and to highlight sources of differences between passive microwave, optical wavelength, and thermal wavelength data, ice concentrations are estimated using two operational SSM/I ice concentration algorithms and with visible- and thermal-infrared wavelength AVHRR data. All algorithms capture the seasonal patterns of ice growth and melt. The ranges of differences fall within the general levels of uncertainty expected for each method and are similar to previous accuracy estimates. The estimated ice concentrations are all highly correlated, with uniform biases, although differences between individual pairs of observations can be large. On average, the NASA Team algorithm yielded 5% higher ice concentrations than the Bootstrap algorithm, while during nonmelt periods the two SSM/I algorithms agree to within 0.5%. These seasonal differences are consistent with the ways that the 19-GHz and 37-GHz microwave channels are used in the algorithms. When compared to the AVHRR-derived ice concentrations, the Team-algorithm results are more similar on average in terms of correlation and mean differences. However, the Team algorithm underestimates concentrations relative to the AVHRR output by 6% during cold months and overestimates by 3% during summer. Little seasonal difference exists between the Bootstrap and AVHRR results, with a mean difference of about 5%. Although the mean differences are less between the SSM/I-derived concentrations and concentrations estimated using AVHRR channel 1, the correlations appear substantially better between the SSM/I data and concentrations derived from AVHRR channel 4, particularly for the Team algorithm output.
Emery, W. J.↗