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At least 217 records · Page 12
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NASA NTRS2021

Precipitation Retrievals from Passive Microwave Cross-Track Sensors: The Precipitation Retrieval and Profiling Scheme

The retrieval of precipitation (snowfall and rainfall) from satellite sensors on a global basis is essential in aiding our knowledge and understanding of the Earth System and to many societal applications. Measurements from surface-based instruments are essentially limited to populated regions, necessitating the use of satellite-based observations to provide estimates of precipitation across the whole of the Earth’s surface. The temporal and spatial variability of precipitation requires adequate sampling, especially at finer resolutions. It is therefore necessary to exploit all available data from precipitation-capable satellites to ensure the proper representation of precipitation. To date, the estimation of precipitation using passive microwave observations has been largely concentrated upon the conically-scanning imaging instruments, with relatively few techniques exploiting the observations made from the cross-track sounders. This paper describes the development of the Precipitation Retrieval and Profiling Scheme (PRPS) to retrieve precipitation from cross-track sensors, together with its performance against surface radar data and other satellite precipitation retrievals.

Chris Kidd↗
NASA NTRS2000

Sea Ice Variability in the Sea of Okhotsk from Passive Microwave Satellite Observations

The Sea of Okhotsk, located between 50 and 60 N, is bounded by the Kamchatka Peninsula, Siberia, Sakhalin Island, and the Kuril Island chain and is the largest midlatitude seasonal sea ice zone in the Northern Hemisphere. The winter sea ice cover begins to form in November and expands to cover most of the sea by March. Over the following three months, the ice retreats with only small ice-covered areas remaining by the beginning of June. The sea is ice free or nearly ice free on average for six months of the year, from June through November. The recent compilation of a consistent, long-term record of Northern Hemisphere sea ice extents based on passive microwave satellite observations from the Nimbus 7 Scanning Multichannel Microwave Radiometer and from four Defense Meteorological Satellite Program Special Sensor Microwave Imagers provides the basis for assessing long-term sea ice extent variability in the Sea of Okhotsk. Analysis of this 20-year data record (1979-1998) shows that based on yearly averages the overall extent of the Sea of Okhotsk ice cover is decreasing at the rate of -8.1+/-2.1x10(exp 3) sq km/yr (-17.2%/decade), in contrast to the rate of decrease of -33.3+/-0.7x10(exp 3) sq km/yr (-2.7%/decade) for the Northern Hemisphere as a whole. There is large regional sea ice extent variability of the Arctic ice cover. Two of the nine Arctic regions analyzed, the Bering Sea and the Gulf of St. Lawrence, show increases of 0.8+/-1.4xl0(exp 3) sq km/yr (2.7%/decade) and 1.2+/-0.5xl0(exp 3) sq km/yr (17.1%/decade), respectively. Interestingly, the Sea of Okhotsk and the Gulf of St. Lawrence show about equal percentage changes, but of opposite sign. The Sea of Okhotsk exhibits its greatest percent decrease (-24.3%/decade) during spring (April-June). The year of maximum winter sea ice extent for the Sea of Okhotsk was 1979, whereas the minimum winter sea ice extent occurred in 1984.

Cavalieri, Donald J.↗
NASA NTRS1999

Observations of Land Surface Variability Using Passive Microwave Sensing

Understanding the global variability of land surface wetness (soil moisture), skin temperature, and related surface fluxes of heat and moisture is key to assessing the importance of the land surface in influencing climate. The feasibility of producing model estimates of these quantities is being studied as part of the International Satellite Land Surface Climatology Project (ISLSCP) Global Soil Wetness Project (GSWP). In the GSWP approach, meteorological observations and analyses are used to drive global circulation models. Satellite measurements can provide independent estimates of key land surface parameters that are needed for initializing and validating the climate models and for monitoring long-term change. Satellite observations of the land surface can also be assimilated into soil models to estimate moisture in the root zone. In our research, passive microwave satellite data recorded during 1978-1987 from the Nimbus-7 Scanning Multichannel Microwave Radiometer (SMMR) are being used to examine spatial and temporal trends in surface soil moisture, vegetation, and temperature. These data include observations at C and X bands (6.6 and 10.7 GHz), which are not available on the current Special Sensor Microwave/Imager (SSM/I) and are precursors to data that will become available from the Advanced Microwave Scanning Radiometer (AMSR) on Advanced Earth Observing Satellite (ADEOS-II) and Earth Observing System (EOS) PM1 in the year 2000. A chart shows a time-series of SMMR-derived surface temperature, T-e and surface soil moisture M, retrieved on a 0.5 deg x 0.5 deg grid and further averaged over a 4 deg x 10 deg study region in the African Sahel. Also shown are National Center for Environmental Prediction (NCEP) model outputs of surface temperature, T-sfc, and soil wetness, Soil-w. The variables have been scaled to have similar dynamic ranges on the plots. The NCEP data from the NCEP Reanalysis Project are monthly averages on a 2.5 deg x 2.5 deg grid averaged over the 4 deg x 10 deg study area. Comparisons of SMMR retrievals with forecast model output show the potential of the satellite data for validating model output and monitoring long-term trends. Continuing work will extend these results to other regions to validate the retrievals more quantitatively. In preparation for the launch of AMSR, field experiments are planned in collaboration with the Global Energy and Water Cycle Experiment (GEWEX) Coordinated Enhanced Observing Period (CEOP) experiments to evaluate the satellite-derived soil moisture measurements and to demonstrate their usefulness for land surface hydrology and climate. Additional information is contained in the original.

Njoku, Eni G.↗
NASA NTRS1995

Ice surface temperature retrieval from AVHRR, ATSR, and passive microwave satellite data: Algorithm development and application

During the second phase project year we have made progress in the development and refinement of surface temperature retrieval algorithms and in product generation. More specifically, we have accomplished the following: (1) acquired a new advanced very high resolution radiometer (AVHRR) data set for the Beaufort Sea area spanning an entire year; (2) acquired additional along-track scanning radiometer(ATSR) data for the Arctic and Antarctic now totalling over eight months; (3) refined our AVHRR Arctic and Antarctic ice surface temperature (IST) retrieval algorithm, including work specific to Greenland; (4) developed ATSR retrieval algorithms for the Arctic and Antarctic, including work specific to Greenland; (5) developed cloud masking procedures for both AVHRR and ATSR; (6) generated a two-week bi-polar global area coverage (GAC) set of composite images from which IST is being estimated; (7) investigated the effects of clouds and the atmosphere on passive microwave 'surface' temperature retrieval algorithms; and (8) generated surface temperatures for the Beaufort Sea data set, both from AVHRR and special sensor microwave imager (SSM/I).

Key, Jeff↗
NASA NTRS2007

Using High Frequency Passive Microwave, A-train, and TRMM Data to Evaluate Hydrometer Structure in the NASA GEOS-5 Data Assimilation System

Validating water vapor and prognostic condensate in global models remains a challenging research task. Model parameterizations are still subject to a large number of tunable parameters; furthermore, accurate and representative in situ observations are very sparse, and satellite observations historically have significant quantitative uncertainties. Progress on improving cloud / hydrometeor fields in models stands to benefit greatly from the growing inventory ofA-Train data sets. ill the present study we are using a variety of complementary satellite retrievals of hydrometeors to examine condensate produced by the emerging NASA Modem Era Retrospective Analysis for Research and Applications, MERRA, and its associated atmospheric general circulation model GEOS5. Cloud and precipitation are generated by both grid-scale prognostic equations and by the Relaxed Arakawa-Schubert (RAS) diagnostic convective parameterization. The high frequency channels (89 to 183.3 GHz) from AMSU-B and MRS on NOAA polar orbiting satellites are being used to evaluate the climatology and variability of precipitating ice from tropical convective anvils. Vertical hydrometeor structure from the Tropical Rainfall Measuring Mission (TRMM) and CloudSat radars are used to develop statistics on vertical hydrometeor structure in order to better interpret the extensive high frequency passive microwave climatology. Cloud liquid and ice water path data retrieved from the Moderate Resolution Imaging Spectroradiometer, MODIS, are used to investigate relationships between upper level cloudiness and tropical deep convective anvils. Together these data are used to evaluate cloud / ice water path, gross aspects of vertical hydrometeor structure, and the relationship between cloud extent and surface precipitation that the MERRA reanalysis must capture.

Robertson, Franklin↗
NASA NTRS1994

Ice surface temperature retrieval from AVHRR, ATSR, and passive microwave satellite data: Algorithm development and application

One essential parameter used in the estimation of radiative and turbulent heat fluxes from satellite data is surface temperature. Sea and land surface temperature (SST and LST) retrieval algorithms that utilize the thermal infrared portion of the spectrum have been developed, with the degree of success dependent primarily upon the variability of the surface and atmospheric characteristics. However, little effort has been directed to the retrieval of the sea ice surface temperature (IST) in the Arctic and Antarctic pack ice or the ice sheet surface temperature over Antarctica and Greenland. The reason is not one of methodology, but rather our limited knowledge of atmospheric temperature, humidity, and aerosol vertical, spatial and temporal distributions, the microphysical properties of polar clouds, and the spectral characteristics of snow, ice, and water surfaces. Over the open ocean the surface is warm, dark, and relatively homogeneous. This makes SST retrieval, including cloud clearing, a fairly straightforward task. Over the ice, however, the surface within a single satellite pixel is likely to be highly heterogeneous, a mixture of ice of various thicknesses, open water, and snow cover in the case of sea ice. Additionally, the Arctic is cloudy - very cloudy - with typical cloud cover amounts ranging from 60-90 percent. There are few observations of cloud cover amounts over Antarctica. The goal of this research is to increase our knowledge of surface temperature patterns and magnitudes in both polar regions, by examining existing data and improving our ability to use satellite data as a monitoring tool. Four instruments are of interest in this study: the AVHRR, ATSR, SMMR, and SSM/I. Our objectives are as follows. Refine the existing AVHRR retrieval algorithm defined in Key and Haefliger (1992; hereafter KH92) and applied elsewhere. Develop a method for IST retrieval from ATSR data similar to the one used for SST. Further investigate the possibility of estimating surface temperature from passive microwave data (in conjunction with AVHRR clear sky samples) through the use of 'effective emissivities' and physical relationships between skin temperature and subsurface temperature. Use the general method outlined in MK93 to calculate a 12-year record of clear sky equivalent surface temperatures, or possibly all-sky snow-ice interface physical temperatures, from SMMR and SSM/I, compare these temperatures to climatologies, ECMWF modeled surface temperatures, and surface temperatures predicted by a 2-D ice model. And intercompare several ice surface retrieval methods and validate them against ground measurements from the Swiss Camp on the Greenland ice sheet. Additionally, we intend to develop a surface temperature product based on AVHRR data and possibly blended with drifting buoy and meteorological station temperatures.

Key, Jeff↗
NASA NTRS1989

Science requirements for passive microwave sensors on earth science geostationary platforms

It is suggested that the science requirements for passive geostationary microwave observations be met by near- and far-term sensors for each of two overlapping bands, with each band covering no more than a decade in frequency. The low-frequency band includes channels near 6, 10, 18, 22, 31 to 37, and possibly 50 to 60 GHz. The high-frequency band includes channels near 220 to 230, 183, 166, 118, 90 to 110, and possibly 50 to 60 and 31 to 37 GHz. The precise channel specifications will have to comply with international frequency allocations. The near-term goal is a high-frequency sensor based on a filled-aperture solid reflector antenna, which should rely on currently existing technology. The most critical issues for the near-term sensor are momentum compensation and the design of the feed assembly; these issues are coupled through the desired scan rate. The successful demonstration of the near-term (high-frequency) sensor will be essential for the continued development of far-term sensors satisfying the ideal science requirements. The far-term goal includes both a high-frequency sensor which meets the ideal science requirements, and a low-frequency sensor whose design will depend on advances in large antenna technology. The low-frequency (far-term) sensor might be based on one of several concepts: a deployable mesh reflector antenna of diameter at least 20 m, which shows promise for use at frequencies up to 30-GHz, a synthetic aperture interferometer of maximum baseline from 100 to 300 m, or a deployable phased-array bootlace lens, of diameter from 100 to 300 m. The first of these, a deployable mesh reflector antenna, will satisfy only the adequate spatial resolution requirements. The last two concepts meet the ideal spatial resolution science requirements, although they present significant structural and meteorological challenges.

Gasiewski, A. J.↗
NASA NTRS1980

Passive microwave observations of snow, ice, and rain from satellites

Observations of precipitation performed with the five-channel passive Scanning Microwave Spectrometer of Nimbus 6 are discussed. It is shown that, at centimeter wavelengths, the rain emission is generally dominated by absorption processes except in the cores of intense precipitation cells where the wavelength dependence can shift markedly. The observations of rainfall over the ocean exhibit considerable long-range order in both instantaneous and two-week average maps. The observations of snow and ice also reveal interesting systematic behavior associated with the inhomogeneities and loss tangent of the bulk subsurface material. In general, ice is more isotropically organized internally than the accumulated layers of snow in Antarctica and Greenland.

Staelin, D. H.↗
NASA NTRS1992

The use of expert systems in combination with active and passive microwave data to classify sea ice

The authors report on a system for sea ice classification which combines passive and active microwave data with algorithmic and heuristic classification techniques in order to classify sea ice unambiguously. They describe initial algorithmic classification, extraction of features and symbolic information, the application of data-dependent expert systems, and the establishment of feature correspondence between data sets.

Haverkamp, Donna↗
NASA NTRS1979

Theory and experiment for passive microwave remote sensing of snowpacks

Both the theory and experiment for the passive remote sensing of snow with microwave radiometers have been studied. The volume scattering effects of snow are accounted for by incorporating Mie scattering theory into a radiative transfer model. The theory is applied to the interpretation of experimental data obtained from various snow measurements. The spectral and angular dependences of the brightness temperatures are illustrated and show good agreement between the theory and experiment. Brightness temperatures as a function of snow depths are also interpreted and discussed. It is observed that as the snow depth increases, the brightness temperature increases when the subsurface is an aluminum plate due to the fact that the plate is cold and snow absorption induces a brightening effect and the brightness temperature decreases when the subsurface is soil due to the fact that snow scattering induces darkening effects.

Kong, J. A.↗
NASA NTRS1977

Technology advances in active and passive microwave sensing through 1985

As a result of a growing awareness by the remote sensing community of the unique capabilities of passive and active microwave sensors, these instruments are expected to grow in the next decade in numbers, versatility and complexity. The Nimbus-G and Seasat-A Scanning Multichannel Microwave Spectrometer (SMMR), 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.↗
NASA NTRS2023

A Comparison of Passive Microwave Emission Models for Estimating Brightness Temperature at L- and P-band Under Bare and Vegetated Soil Conditions

P-band radiometry has been demonstrated to have a deeper sensing depth than at L-band, making the consideration of multi-layer microwave interactions necessary. Additionally, the scattering and phase interference effects are different at P-band, requiring a re-consideration of the need for coherent models. However, the impact remains to be clarified, and understanding the validity and limitations of these models at both L-band and P-band is crucial for their refinement and application. Therefore, two general categories of microwave emission models, including two stratified coherent models (Njoku and Wilhite) and four incoherent models (conventional tau-omega model and three multi-layer models being zero-order, first-order, and incoherent solution), were intercompared for the first time on the same dataset. This evaluation utilized observations of L-band and P-band radiometry under different land cover conditions from a tower-based experiment in Victoria, Australia. Model estimations of brightness temperature (TB) were consistent with measurements, with the lowest root mean square error (RMSE) at P-band V-polarization under corn (2 K) and the highest RMSE at L-band H-polarization under bare soil (13 K). Coherent models performed slightly better than incoherent models under bare soil (3 K less RMSE), while the opposite was true under vegetated soil conditions (1 K less RMSE). Coherent and incoherent models showed maximum differences (3 K at P-band, 2 K at L-band), correlating strongly with soil moisture variations at 0-10 cm. Findings suggest that coherent and incoherent models perform similarly; thus, incoherent models may be preferable for estimating TB at L- and P-band due to reduced computational complexity.

Soil moisture profile↗