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At least 199 records · Page 11

Remote Sensing for Agriculture, Ecosystems and Hydrology III

The science need for remotely sensed soil moisture has been well established in the hydrologic, climate change and weather forecasting communities. In spite of this well documented science need there are currently no satellite missions flying or funded to make this very important geophysical measurement. There have been a number of experimental aircraft programs that have demonstrated the feasibility of using long wave microwave sensors for estimating soil moisture. Unfortunately, this science driver, soil moisture, imposes very difficult technical requirements for a satellite sensor system. Global soil moisture is driven by a spatial resolution on the order of 20 to 30 km and measurements need to be taken every two to three days to be useful to the science community. The principal sensor to accomplish the soil moisture measurements is an L- band passive microwave radiometer and achieving the spatial and temporal requirements requires a very large antenna. This paper describes the several alternatives to solve the very large antenna challenge and still meet the radiometer sensitivity requirement. The paper also discusses the alternatives considered to obtain the necessary ancillary data for characterizing the surface roughness, the surface temperature and the attenuation affects of vegetation needed to derive the geophysical parameter. Finally, the paper discusses proposed missions and how well they will meet the science requirements.

Engman, Edwin T.↗

Monthly Oceanic Rainfall Derived from Space-Borne Microwave Data

A technique for estimating monthly oceanic rainfall rate using multi-channel microwave measurements has been developed. This technique is applied to the Special Sensor Microwave Imager (SSM/I) data since 1987 to infer monthly rainfall for the Global Precipitation Climatology Project (GPCP). A modified version of this algorithm is now being applied to the TRMM Microwave Imager (TMI) data. There are three prominent features of this algorithm. First, the knowledge of the form of the rainfall intensity probability density function used to augment the measurements. Second, utilizing a linear combination of the 19.35 and 22.235 GHz channels to deemphasize the effect of water vapor. Third, an objective technique has been developed to estimate the rain layer thickness from the 19.35 and 22.23 5 GHz brightness temperature histograms, TMI data with better spatial resolution and 24 hour sampling (vs. sun-synchronized sampling, which is limited to two narrow intervals of local solar time for DMSP satellites) prompt us to study the similarity and difference between these two rainfall estimates. One year of rainfall data (January to December 1998) are used in this study. The mean differences of these two rain estimates are typically less than 15%. Statistical tests are administrated to evaluate the differences between rainfall estimates from SSM/I and TMI data. Differences between TMI and SSM/I rain estimates are discussed in the context of radiative transfer calculations and rainfall estimate algorithm.

Chang, Alfred T. C.↗

Seasonal Snow Extent and Snow Mass in South America using SMMR and SSM/I Passive Microwave Data (1979-2006)

Seasonal snow cover in South America was examined in this study using passive microwave satellite data from the Scanning Multichannel Microwave Radiometer (SMMR) on board the Nimbus-7 satellite and the Special Sensor Microwave Imagers (SSM/I) onboard Defense Meteorological Satellite Program (DMSP) satellites. For the period from 1979-2006, both snow cover extent and snow water equivalent (snow mass) were investigated during the coldest months (May-September), primarily in the Patagonia area of Argentina and in the Andes of Chile, Argentina and Bolivia, where most of the seasonal snow is found. Since winter temperatures in this region are often above freezing, the coldest winter month was found to be the month having the most extensive snow cover and usually the month having the deepest snow cover as well. Sharp year-to-year differences were recorded using the passive microwave observations. The average snow cover extent for July, the month with the greatest average extent during the 28-year period of record, is 321,674 km(exp 2). In July of 1984, the average monthly snow cover extent was 701,250 km(exp 2) the most extensive coverage observed between 1979 and 2006. However, in July of 1989, snow cover extent was only 120,000 km(exp 2). The 28-year period of record shows a sinusoidal like pattern for both snow cover and snow mass, though neither trend is significant at the 95% level.

Foster, J. L.↗

Seasonal Snow Extent and Snow Mass in South America Using SMMR and SSM/I Passive Microwave Data (1979-2003)

Seasonal snow cover in South America was examined in this study using passive microwave satellite data from the Scanning Multichannel Microwave Radiometer (SMMR) on board the Nimbus-satellite and the Special Sensor Microwave Imagers (SSM/I) on board Defense Meteorological Satellite Program (DMSP) satellites. For the period from 1979-2003, both snow cover extent and snow depth (snow mass) were investigated during coldest months (May-September), primarily in the Patagonia area of Argentina and in Chile. Most of the seasonal snow in South America is in the Patagonia region of Argentina. Since winter temperatures in this region are often above freezing, the coldest winter month was found to be the month having the most extensive snow cover and also usually the month having the deepest snow cover as well. Sharp year-to-year differences were recorded using the passive microwave observations. The average snow cover extent for July, the month with the greatest average snow extent during the 25-year period of record, is 320,700 km(exp 2). In July of 1984, the average monthly snow cover was 701,250 km(exp 2) - the most extensive coverage observed between 1979 and 2003. However, in July of 1989, snow cover extent was only 120 km(exp 2). The 25-year period of record shows a sinusoidal like pattern, though there appears to be no obvious trend in either increasing or decreasing snow extent or snow mass between 1979 and 2003.

Foster, J. L.↗

Testing of a Microwave Blade Tip Clearance Sensor at the NASA Glenn Research Center

The development of new active tip clearance control and structural health monitoring schemes in turbine engines and other types of rotating machinery requires sensors that are highly accurate and can operate in a high-temperature environment. The use of a microwave sensor to acquire blade tip clearance and tip timing measurements is being explored at the NASA Glenn Research Center. The microwave blade tip clearance sensor works on principles that are very similar to a short-range radar system. The sensor sends a continuous microwave signal towards a target and measures the reflected signal. The phase difference of the reflected signal is directly proportional to the distance between the sensor and the target 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 turbine engines. The use of microwave sensors for this application is a new concept. Techniques on calibrating the sensors along with installation effects are not well quantified as they are for other sensor technologies. Developing calibration techniques and evaluating installation effects are essential in using these sensors to make tip clearance and tip timing measurements. As a means of better understanding these issues, the microwave sensors were used on a benchtop calibration rig, a large axial vane fan, and a turbofan. Background on the microwave tip clearance sensor, an overview of their calibration, and the results from their use on the axial vane fan and the turbofan will be presented in this paper.

Woike, Mark R.↗

Passive microwave data for snow and ice research - Planned products from the DMSP SSM/I system

Recommendations which have been made for processing and distributing passive microwave data for snow and ice research obtained with the Defense Meteorological Satellite Program (DMSP) Special Sensor Microwave Imager (SSM/I) are discussed. The general objectives for SSM/I data are reviewed, and the sensor and data flow are described. The SSM/I sea ice products are discussed, and algorithm/product validation is addressed. Proposed services and implementation after SSM/I launch are summarized.

Weawer, Ronald↗

Applications of remote sensing for water quality and biological measurements in coastal waters

Potential applications of remote sensing technology to the study of coastal marine environments are reviewed, emphasizing water quality and biological measurements. Parameters measurable by airborne or spaceborne remote sensors include particulates, measured by visual or multispectral photography, chlorophyll a, measured by the Ocean Color Scanner or Coastal Zone Color Scanner, temperature distributions, by IR or microwave sensors, and salinity, by means of microwave radiometers. Research projects in which wide area synoptic or repetitive remote sensing can make a major contribution include the study of estuarine and continental shelf sediment transport dynamics, marine pollutant transport, marine phytoplankton dynamics and ocean fronts.

Johnson, R. W.↗

Technology advances in active and passive microwave sensing through 1985

The capabilities of passive and active microwave sensors are discussed. The Nimbus-G and Seasat-A scanning multichannel microwave spectrometer, the Seasat-A radar altimeter, scatterometer and synthetic aperture radar represent the first systematic attempt at exploring a wide variety of applications utilizing microwave sensing techniques and are indicators of the directions in which the pertinent technology is likely to evolve. The trend is toward high resolution multi-frequency imagers spanning wide frequency ranges and wide swaths requiring sophisticated receivers, real-time data processors and most importantly, complex antennas.

Barath, F. T.↗

Data processing for the DMSP microwave radiometer system

A software program was developed and tested to process microwave radiometry data to be acquired by the microwave sensor (SSM/T) on the Defense Meteorological Satellite Program spacecraft. The SSM/T 7-channel microwave radiometer and systems data will be data-linked to Air Force Global Weather Central (AFGWC) where they will be merged with ephemeris data prior to product processing for use in the AFGWC upper air data base (UADB). The overall system utilizes an integrated design to provide atmospheric temperature soundings for global applications. The fully automated processing at AFGWC was accomplished by four related computer processor programs to produce compatible UADB soundings, evaluate system performance, and update the a priori developed inversion matrices. Tests with simulated data produced results significantly better than climatology.

Rigone, J. L.↗

Simulated appearance of an evolving supercell hail storm at SSM/I microwave frequencies

The contribution of distinct classes of hydrometeors generated from a cloud model to top-of-atmosphere microwave brightness temperatures is studied at the four separate frequencies of the Special Sensor Microwave Imager (SSM/I). The relative radiative role of individual modes of hydrometeors during the evolution of a severe thunderstorm is examined using a microwave radiative transfer model coupled with a three-dimensional cloud model capable of simulating the essential dynamical and microphysical features of an intense hail store. Consideration is given to plans to use the simulation results with actual multichannel measurements from SSM/I to provide a framework for multispectral precipitation retrieval algorithms.

Smith, Eric A.↗

Antarctic Ultraviolet Radiation Climatology from Total Ozone Mapping Spectrometer Data

This project has successfully produced a climatology of local noon spectral surface irradiance covering the Antarctic continent and the Southern Ocean, the spectral interval 290-700 nm (UV-A, UV-B, and photosynthetically active radiation, PAR), and the entire sunlit part of the year for November 1979-December 1999. Total Ozone Mapping Spectrometer (TOMS) data were used to specify column ozone abundance and UV-A (360- or 380-nm) reflectivity, and passive microwave (MW) sea ice concentrations were used to specify the surface albedo over the Southern Ocean. For this latter task, sea ice concentration retrievals from the Nimbus-7 Scanning Multichannel Microwave Radiometer (SMMR) and its successor, the Defense Meteorological Satellite Program (DMSP) Special Sensor Microwave Imager (SSM/I) were identified with ultraviolet/visible-wavelength albedos based on an empirical TOMS/MW parameterization developed for this purpose (Lubin and Morrow, 2001). The satellite retrievals of surface albedo and UV-A reflectivity were used in a delta-Eddington radiative transfer model to estimate cloud effective optical depth. These optical depth estimates were then used along with the total ozone and surface albedo to calculate the downwelling spectral UV and PAR irradiance at the surface. These spectral irradiance maps were produced for every usable day of TOMS data between 1979-1999 (every other day early in the TOMS program, daily later on).

Lubin, Dan↗

A Look at Seasonal Snow Cover and Snow Mass in the Southern Hemisphere from 1979-2006 Using SMMR and SSM/I Passive Microwave Data

Seasonal snow cover in extra-tropical areas of South America was examined in this study using passive microwave satellite data from the Scanning Multichannel Microwave Radiometer (SMMR) on board the Nimbus-7 satellite and from the Special Sensor Microwave Imagers (SSM/I) on board the Defense Meteorological Satellite Program (DMSP) satellites. For the period from 1979-2006, both snow cover extent and snow mass were estimated for the months of May-September. Most of the seasonal snow in South America occurs in the Patagonia region of Argentina. The average snow cover extent for July, the month with the greatest average extent during the 28-year period of record, is 321,674 sq km. The seasonal (May-September) 2 average snow cover extent was greatest in 1984 (464,250 sq km) and least in 1990 (69,875 sq km). In terms of snow mass, 1984 was also the biggest year (1.19 x 10(exp 13) kg) and 1990 was the smallest year (0.12 X 10(exp 13) kg). A strong relationship exists between the snow cover area and snow mass, correlated at 0.95, though no significant trend was found over the 28 year record for either snow cover extent or snow mass. For this long term climatology, snow mass and snow cover extent are shown to vary considerably from month to month and season to season. This analysis presents a consistent approach to mapping and measuring snow in South America utilizing an appropriate and readily available long term snow satellite dataset. This is the optimal dataset available, thus far, for deriving seasonal snow cover and snow mass in this region. Nonetheless, shallow snow, wet snow, snow beneath forests, as well as snow along coastal areas all may confound interpretation using passive microwave approaches. More work needs to be done to reduce the uncertainties in the data and hence, increase the confidence of the interpretation

Foster, James↗

Chapter 13: Errors and Uncertainties Associated with Quasi-Global Satellite Precipitation Products

Measuring precipitation on a global scale is only possible from satellite platforms. Satellite precipitation estimates are based on geosynchronous infrared sensors on geostationary satellites, characterized by high sampling frequency, and polar-orbiting microwave sensors on low-Earth-orbiting satellites with less-frequent sampling. Assessing satellite product performance is fundamental to infer the reliability of such estimates and effectively use them in water resources management, extreme event characterization, disease control, or weather forecasting. Nevertheless, errors and uncertainties associated with satellite precipitation products are often masked due to temporal and spatial sampling, as well as bias-corrections against a reference dataset. Moreover, the verification of satellite precipitation products is easier over land areas, where rain gauges and ground radars are available as benchmark, but extremely limited over the oceans. Substantial work still remains to better quantify relative and absolute errors and uncertainties within satellite-based precipitation products over land and oceans.

Viviana Maggioni↗

Passive microwave studies of frozen lakes

Lakes of various sizes, depths and ice thicknesses in Alaska, Utah and Colorado were overflown with passive microwave sensors providing observations at several wavelengths. A layer model is used to calculate the microwave brightness temperature, T sub B (a function of the emissivity and physical temperatures of the object), of snowcovered ice underlain with water. Calculated T sub B's are comparable to measured T sub B's. At short wavelengths, e.g., 0.8 cm, T sub B data provide information on the near surface properties of ice covered lakes where the long wavelength, 21.0 cm, observations sense the entire thickness of ice including underlying water. Additionally, T sub B is found to increase with ice thickness. 1.55 cm observations on Chandalar Lake in Alaska show a T sub B increase of 38 K with an approximate 124 cm increase in ice thickness.

Hall, D. K.↗

Assessment of Superflux relative to remote sensing

The state-of-the-art advancements in remote sensor technology due to the Superflux program are examined. Three major individual sensor technologies benefitted from the program: laser fluorosensors, optical-range scanners, and passive microwave sensors. Under Superflux, convincing evidence was obtained that the airborne oceanographic lidar fluorosensor can map chlorophyll, i.e., is linear, over a wide range from less than 0.5 to 5.0 mg/cu m. The lidar oceanographic probe dual-excitation concept for addressing phytoplankton color group composition was also demonstrated convincingly. Algorithm development, real time capabilities, and multisensor integration are also addressed.

Campbell, J. W.↗

Detection of rainfall rates utilizing spaceborne microwave radiometers

To demonstrate the success of utilizing passive microwave sensors in monitoring synoptic scale rainfall, two studies are described in which electrically scanning microwave radiometers (ESMR-5 and 6) on board Nimbus 5 and 6 were employed using a Langrangian frame of reference. The first study suggests a method of utilizing ESMR-5 measurements to quantize rainfall over water within tropical and extratropical storms and to use these measurements to monitor and possibly predict storm intensity. The second study suggests a method of monitoring the coverage and movement of synoptic rain over land by employing ESMR-6.

Burke, H. H. K.↗

A Combined Infrared and Microwave Technique for Studying the Diurnal Variation of Rainfall over Amazonia

Over 10 years of continuous data from the Special Sensor microwave Imager (SSM/I) aboard a series of Defense Department satellites has made it possible to construct regional rainfall climatologies at high spatial resolution. Using the Goddard Profiling Algorithm (GPROF), monthly estimates of precipitation were made over the region of northern Brazil, including the Amazon Basin, for 1987 to 1998. GPROF is a physical approach to passive microwave precipitation retrieval, which uses the Goddard Cumulus Ensemble (cloud) model to establish prior probability densities of precipitation structures. Precipitation fields from GPROF were stratified into morning and evening satellite overpasses, and accumulated at monthly intervals at 0.5 degree spatial resolution. Important diurnal effects were noted in the analysis, the most pronounced being a land/sea breeze circulation along the northern coast of Brazil and a mountain/valley circulation along the Andes. There were also indications of morning rainfall maxima along the major rivers, and evening maxima between the rivers. The addition of simultaneous geosynchronous infrared (IR) data leads to the current technique, which takes advantage of the 30 minute sampling and 4 km spatial resolution of the infrared channel and the better physics of the microwave retrieval. The resultant IR method is subsequently used to derive the diurnal variability of rainfall over the Amazon basin, and further, to investigate the relative contribution from its convective and stratiform components.

Negri, Andrew J.↗

Precipitation from Space: Advancing Earth System Science

Of the three primary sources of spatially contiguous precipitation observations (surface networks, ground-based radar, and satellite-based radar/radiometers), only the last is a viable source over ocean and much of the Earth's land. As recently as 15 years ago, users needing quantitative detail of precipitation on anything under a monthly time scale relied upon products derived from geostationary satellite thermal infrared (IR) indices. The Special Sensor Microwave Imager (SSMI) passive microwave (PMW) imagers originated in 1987 and continue today with the SSMI sounder (SSMIS) sensor. The fortunate longevity of the joint National Aeronautics and Space Administration (NASA) and Japan Aerospace Exploration Agency (JAXA) Tropical Rainfall Measuring Mission (TRMM) is providing the environmental science community a nearly unbroken data record (as of April 2012, over 14 years) of tropical and sub-tropical precipitation processes. TRMM was originally conceived in the mid-1980s as a climate mission with relatively modest goals, including monthly averaged precipitation. TRMM data were quickly exploited for model data assimilation and, beginning in 1999 with the availability of near real time data, for tropical cyclone warnings. To overcome the intermittently spaced revisit from these and other low Earth-orbiting satellites, many methods to merge PMW-based precipitation data and geostationary satellite observations have been developed, such as the TRMM Multisatellite Precipitation Product and the Climate Prediction Center (CPC) morphing method (CMORPH. The purpose of this article is not to provide a survey or assessment of these and other satellite-based precipitation datasets, which are well summarized in several recent articles. Rather, the intent is to demonstrate how the availability and continuity of satellite-based precipitation data records is transforming the ways that scientific and societal issues related to precipitation are addressed, in ways that would not be otherwise possible. These developments have taken place in parallel with the growth of an increasingly interconnected scientific environment. Scientists from different disciplines can easily interact with each other via information and materials they encounter online, and collaborate remotely without ever meeting each other in person. Likewise, these precipitation datasets are quickly and easily available via various data portals and are widely used. Within the framework of the NASA/JAXA Global Precipitation Measurement (GPM mission, these applications will become increasingly interconnected. We emphasize that precipitation observations by themselves provide an incomplete picture of the state of the atmosphere. For example, it is unlikely that a richer understanding of the global water cycle will be possible by standalone missions and algorithms, but must also involve some component of data, where model analyses of the physical state are constrained alongside multiple observations (e.g., precipitation, evaporation, radiation). The next section provides examples extracted from the many applications that use various high-resolution precipitation products. The final section summarizes the future system for global precipitation processing.

Kucera, Paul A.↗