The atmosphere of Venus.
Venus atmosphere noting pressure induced absorption in carbon dioxide-nitrogen atmosphere as indicated in decline of microwave brightness temperature
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Examination of L-Band Brightness Temperature Forecasts in the SMAP Level-4 Soil Moisture Analysis
The NASA Soil Moisture Active Passive (SMAP) mission [4] has been providing L-band (1.4 GHz) passive microwave brightness temperature (Tb) observations since April 2015. By assimilating the Tb observations into the NASA Catchment land surface model [5] using a spatially distributed ensemble Kalman filter, the NASA Global Modeling and Assimilation Office generates the SMAP Level-4 Soil Moisture (L4_SM) product, which provides global, 3-hourly, 9-km resolution estimates of surface (0-5 cm) and root-zone (0-100 cm) soil moisture with ~2.5-day latency for use in research and applications [6]. The L4_SM product also includes estimates of soil temperature, land surface fluxes, and assimilation diagnostics such as the model forecast and observed Tb values [7]. The output from the L4_SM system is routinely monitored by the L4_SM team. Such monitoring provides valuable information; instances of unusually large Tb observation-minus-forecast (O-F) residuals can indicate events during which soil moisture conditions are poorly described in the land modeling system [7,8]. For example, repeated occurrences of very large Tb O-F values in central Australia were traced back to deficiencies in the gauge-based precipitation product used in the land modeling system through L4_SM Version 5; this discovery prompted the use, beginning in Version 6, of satellite- and gauge-based precipitation observations outside of North America [9]. Recently, a statistical analysis of the Tb O-F residuals revealed systematic errors in the “tau-omega” L-band radiative transfer model that converts the land-model simulated soil moisture and temperature into the forecast Tb prior to the L4_SM analysis. The L4_SM analysis is built on the ensemble Kalman filter and assumes unbiased forecast errors. The presence of systematic Tb forecast errors could thus adversely impact the quality of the analyzed soil moisture. In this paper, we examine the Tb O-F residuals of the latest Version 7 L4_SM data (Science Version ID Vv7030 and Vv7032) [10,11,12]. In the L4_SM Version 7 algorithm, key parameters of the L-band radiative transfer model, including soil roughness, scattering albedo, and a (seasonally varying) climatology of vegetation opacity, are obtained from the SMAP Level-2 Radiometer retrieval product (Version 5) [1]. Additionally, we also use ground measurements of surface soil moisture and soil temperature from the SMAP core validation sites as in situ reference of soil conditions [2,3]. As part of the L4_SM system calibration, the seasonally-varying bias between the model forecast Tb and the observed values is removed prior to the assimilation of the SMAP Tb observations [7]. That is, the L4_SM system is designed to only correct errors in synoptic-scale and interannual variations from the long-term mean seasonal cycle while maintaining the model’s (potentially erroneous) climatology. The Tb O-F residuals examined here are thus computed using SMAP Tb observations after they are rescaled to the mean seasonal cycle of Tb from the modeling system without SMAP data assimilation. Consequently, the long-term average of the Tb O-F residuals has a global mean of only 0.13 K and locally small values, ranging from -1 to 3 K. Despite the small time-average values of the Tb O-F residuals, the model forecast Tb was nevertheless found to exhibit undesirable systematic errors. At some locations, the time-average Tb O-F values strongly depend on surface soil moisture (SM). At the Yanco SMAP core validation site, for example, the Tb O-F residuals typically range from 5 to 15 K under dry soil moisture conditions (SM < 0.15 m3 m-3) yet are predominantly negative under wet soil moisture conditions (SM > 0.25 m3 m-3), with values ranging from 0 to -40 K. This results in soil moisture analysis increments that persistently make the soil drier under dry SM conditions and persistently make the soil wetter under wet SM conditions, suggesting an error in the dynamic range of the simulated Tb, soil moisture or soil temperature. In this paper, we describe the higher-order systematic Tb forecast errors in more detail, examine their impact on the L4_SM product quality, and explore potential avenues to further improve the L4_SM algorithm.
Rolf Reichle↗
Recent Climate Variability in Antarctica from Satellite-derived Temperature Data
Recent Antarctic climate variability on month-to-month to interannual time scales is assessed through joint analysis of surface temperatures from satellite thermal infrared observations (T(sub IR)) and passive microwave brightness temperatures (T(sub B)). Although Tw data are limited to clear-sky conditions and T(sub B) data are a product of the temperature and emissivity of the upper approx. 1m of snow, the two data sets share significant covariance. This covariance is largely explained by three empirical modes, which illustrate the spatial and temporal variability of Antarctic surface temperatures. T(sub B) variations are damped compared to TIR variations, as determined by the period of the temperature forcing and the microwave emission depth; however, microwave emissivity does not vary significantly in time. Comparison of the temperature modes with Southern Hemisphere (SH) 500-hPa geopotential height anomalies demonstrates that Antarctic temperature anomalies are predominantly controlled by the principal patterns of SH atmospheric circulation. The leading surface temperature mode strongly correlates with the Southern Annular Mode (SAM) in geopotential height. The second temperature mode reflects the combined influences of the zonal wavenumber-3 and Pacific South American (PSA) patterns in 500-hPa height on month-to-month timescales. ENSO variability projects onto this mode on interannual timescales, but is not by itself a good predictor of Antarctic temperature anomalies. The third temperature mode explains winter warming trends, which may be caused by blocking events, over a large region of the East Antarctic plateau. These results help to place recent climate changes in the context of Antarctica's background climate variability and will aid in the interpretation of ice core paleoclimate records.
Schneider, David P.↗
Springtime microwave emissivity changes in the southern Kara Sea
Springtime microwave brightness temperatures over first-year ice are examined for the southern Kara Sea. Snow emissivity changes are revealed by episodic drops in the 37- to 18-GHz brightness temperature gradient ratio measured by the Nimbus 7 scanning multichannel microwave radiometer. We suggest that the negative gradient ratios in spring 1982 result from increased scatter at 37 GHz due to the formation of a near-surface hoar layer. This interpretation is supported by the results of a surface radiation balance model that shows the melt signature occurring at below freezing temperatures but under clear-sky conditions with increased solar input to the surface. Published observations from the Greenland ice cap show a surface hoar layer forming under similar atmospheric conditions owing to the increased penetration and absorption of solar radiation just below the surface layer. In spring/early summer 1984 similar gradient ratio signatures occur. They appear to be due to several days of freeze-thaw cycling following the movement of a low-pressure system through the region. These changes in surface emissivity represent the transition from winter to summer conditions (as defined by the microwave response) and are shown to be regional in extent and to vary with the synoptic circulations.
Crane, Robert G.↗
The Cold Land Processes Experiment (CLPX-1): Analysis and Modelling of LSOS Data (IOP3 Period)
Microwave brightness temperatures at 18.7,36.5, and 89 GHz collected at the Local-Scale Observation Site (LSOS) of the NASA Cold-Land Processes Field Experiment in February, 2003 (third Intensive Observation Period) were simulated using a Dense Media Radiative Transfer model (DMRT), based on the Quasi Crystalline Approximation with Coherent Potential (QCA-CP). Inputs to the model were averaged from LSOS snow pit measurements, although different averages were used for the lower frequencies vs. the highest one, due to the different penetration depths and to the stratigraphy of the snowpack. Mean snow particle radius was computed as a best-fit parameter. Results show that the model was able to reproduce satisfactorily brightness temperatures measured by the University of Tokyo s Ground Based Microwave Radiometer system (CBMR-7). The values of the best-fit snow particle radii were found to fall within the range of values obtained by averaging the field-measured mean particle sizes for the three classes of Small, Medium and Large grain sizes measured at the LSOS site.
Tedesco, Marco↗
35 GHz Measurements of CO2 Crystals for Simulating Observations of the Martian Polar Caps
In order to learn more about the Martian polar caps, it is important to compare and contrast the behavior of both frozen H2O and CO2 in different parts of the electromagnetic spectrum. Relatively little attention has been given, thus far, to observing the thermal microwave part of the spectrum. In this experiment, passive microwave radiation emanating from within a 33 cm snowpack was measured with a 35 GHz hand-held radiometer, and in addition to the natural snow measurements, the radiometer was used to measure the microwave emission and scattering from layers of manufactured CO2 (dry ice). A 1 m x 2 m plate of aluminum sheet metal was positioned beneath the natural snow so that microwave emissions from the underlying soil layers would be minimized. Compared to the natural snow crystals, results for the dry ice layers exhibit lower' microwave brightness temperatures for similar thicknesses, regardless of the incidence angle of the radiometer. For example, at 50 degree H (horizontal polarization) and with a covering of 21 cm of snow and 18 cm of dry ice, the brightness temperatures were 150 K and 76 K, respectively. When the snow depth was 33 cm, the brightness temperature was 144 K, and when the total thickness of the dry ice was 27 cm, the brightness temperature was 86 K. The lower brightness temperatures are due to a combination of the lower physical temperature and the larger crystal sizes of the commercial CO2 Crystals compared to the snow crystals. As the crystal size approaches the size of the microwave wavelength, it scatters microwave radiation more effectively, thus lowering the brightness temperature. The dry ice crystals in this experiment were about an order of magnitude larger than the snow crystals and three orders of magnitude larger than the CO2 Crystals produced in the cold stage of a scanning electron microscope. Spreading soil, approximately 2 mm in thickness, on the dry ice appeared to have no effect on the brightness temperatures.
Foster, J. L.↗
L-Band Brightness Temperature Variations at Dome C and Snow Metamorphism at the Surface
The Antarctic Plateau is a promising site to monitor microwave radiometers' drift, and to inter-calibrate microwave radiometers, especially 1.4 GigaHertz (L-band) radiometers on board the Soil Moisture and Ocean Salinity (SMOS), and AquariusSAC-D missions. The Plateau is a thick ice cover, thermally stable in depth, with large dimensions, and relatively low heterogeneities. In addition, its high latitude location in the Southern Hemisphere enables frequent observations by polar-orbiting satellites, and no contaminations by radio frequency interference. At Dome C (75S, 123E), on the Antarctic Plateau, the substantial amount of in-situ snow measurements available allows us to interpret variations in space-borne microwave brightness temperature (TB) (e.g. Macelloni et al., 2007, 2013, Brucker et al., 2011, Champollion et al., 2013). However, to analyze the observations from the Aquarius radiometers, whose sensitivity is 0.15 K, the stability of the snow layers near the surface that are most susceptible to rapidly change needs to be precisely assessed. This study focuses on the spatial and temporal variations of the Aquarius TB over the Antarctic Plateau, and at Dome C in particular, to highlight the impact of snow surface metamorphism on the TB observations at L-band.
l-band↗
Aquarius Brightness Temperature Variations at Dome C and Snow Metamorphism at the Surface
The Antarctic Plateau is a promising site to monitor microwave radiometers' drift, and to inter-calibrate microwave radiometers, especially 1.4 GHz (L-band) radiometers on board the Soil Moisture and Ocean Salinity (SMOS), and AquariusSAC-D missions. The Plateau is a thick ice cover, thermally stable in depth, with large dimensions, and relatively low heterogeneities. In addition, its high latitude location in the Southern Hemisphere enables frequent observations by polar-orbiting satellites, and no contaminations by radio frequency interference. At Dome C (75S, 123E), on the Antarctic Plateau, the substantial amount of in-situ snow measurements available allows us to interpret variations in space-borne microwave brightness temperature (TB) (e.g. Macelloni et al., 2007, 2013, Brucker et al., 2011, Champollion et al., 2013). However, to analyze the observations from the Aquarius radiometers, whose sensitivity is 0.15 K, the stability of the snow layers near the surface that are most susceptible to rapidly change needs to be precisely assessed. This study focuses on the spatial and temporal variations of the Aquarius TB over the Antarctic Plateau, and at Dome C in particular, to highlight the impact of snow surface metamorphism on the TB observations at L-band.
l-band↗
User Guide for TROPICS Data Products
This document provides information for using the data products available from the “Time-Resolved Observations of Precipitation structure and storm Intensity with a Constellation of Smallsats” (TROPICS) mission. The TROPICS mission will produce a range of data products that will be available at the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC). The data products will be produced at the TROPICS Data Processing Center (UW-M SSEC), and consist of Level-1 radiances (antenna and brightness temperatures), Level-2a unified resolution radiance, Level-2b Atmospheric Vertical Temperature Profiles (AVTP), Level-2b Atmospheric Vertical Moisture Profiles (AVMP), Level-2b Instantaneous Surface Rain Rate (ISRR), and Level-2b Tropical Cyclone (TC) intensity algorithms to estimate two primary variables: Minimum Sea Level Pressure (MSLP) and Maximum Sustained Winds (MSW). Two independent intensity estimation methods are included: 1) the Tropical Cyclone Intensity Estimate algorithm (TCIE) developed at the University of Wisconsin/CIMSS using native microwave brightness temperatures and 2) the Hurricane Intensity and Structure Algorithm (HISA) developed at Colorado State University/CIRA using microwave retrievals of temperature, moisture, and integrated quantities. In addition to MSW and MSLP, HISA also provides estimates of surface wind radii and 2D winds at standard pressure levels. TROPICS is adapting the NOAA STAR Microwave Integrated Retrieval System (MIRS) to retrieve the AVTP and AVMP data products. The ISRR algorithm uses the NASA Goddard Precipitation Retrieval and Profiling Scheme (PRPS).
TROPICS↗
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.↗
Ground-Based Microwave Radiometer Measurements over Various Surface Covers
Microwave brightness temperatures were measured over a young wheat crop of varying canopy density during the First NOPEX Concentrated Field Effort near Uppsala, Sweden. Measurements were made with a portable ground-based microwave radiometer system at a frequency of 1.4 GHz. The brightness temperature at each wavelength was measured at horizontal and vertical polarization, and at incidence angles ranging from nadir to 60 degrees. Three measuring sequences were made; at full canopy, after the partial harvest, and of the bare soil. Supporting data such as soil and canopy temperature, soil moisture, other soil physical properties, and canopy biomass were also collected. Preliminary results of some of the field measurements and comparisons with theoretical calculations are presented.
Owe, Manfred↗
A New Operational Snow Retrieval Algorithm Applied to Historical AMSR-E Brightness Temperatures
Snow is a key element of the water and energy cycles and the knowledge of spatio-temporal distribution of snow depth and snow water equivalent (SWE) is fundamental for hydrological and climatological applications. SWE and snow depth estimates can be obtained from spaceborne microwave brightness temperatures at global scale and high temporal resolution (daily). In this regard, the data recorded by the Advanced Microwave Scanning Radiometer-Earth Orbiting System (EOS) (AMSR-E) onboard the National Aeronautics and Space Administration's (NASA) AQUA spacecraft have been used to generate operational estimates of SWE and snow depth, complementing estimates generated with other microwave sensors flying on other platforms. In this study, we report the results concerning the development and assessment of a new operational algorithm applied to historical AMSR-E data. The new algorithm here proposed makes use of climatological data, electromagnetic modeling and artificial neural networks for estimating snow depth as well as a spatio-temporal dynamic density scheme to convert snow depth to SWE. The outputs of the new algorithm are compared with those of the current AMSR-E operational algorithm as well as in-situ measurements and other operational snow products, specifically the Canadian Meteorological Center (CMC) and GlobSnow datasets. Our results show that the AMSR-E algorithm here proposed generally performs better than the operational one and addresses some major issues identified in the spatial distribution of snow depth fields associated with the evolution of effective grain size.
passive microwave↗
Thermal models and microwave temperatures of the planet Mercury
Physical properties of epilith of Mercury from comparison of infrared and microwave brightness temperatures with theoretical thermal models
Morrison, D.↗
Estimates of Long Term Surface Soil Moisture in the Midwestern U.S. Derived from Satellite Microwave Observations
Soil moisture is a key component of the water and energy balances of the Earth's surface, and has been identified as a parameter of significant potential for improving the accuracy of large-scale land surface-atmosphere interaction models. However, soil moisture is often somewhat difficult to measure accurately in both space and time, especially at large spatial scales. Soil moisture is highly variable, and while point measurements are typically quite accurate, subsequent areal averaging of these measurements often leads to large errors. Since remotely sensed land surface observations are already a spatially averaged or areally integrated value, they are a logical input parameter to regional or larger scale land process models. A database of long-term soil moisture was compared to satellite microwave observations over test sites in the Midwestern United States. Ground measurements of average volumetric surface soil moisture in the top ten cm were made bimonthly at 19 locations throughout the state of Illinois. Nighttime microwave brightness temperatures were observed at a frequency of 6.6 GHz, by the Scanning Multichannel Microwave Radiometer (SMMR), onboard the Nimbus 7 satellite. The life of the SMMR instrument spanned from Nov. 1978 to Aug. 1987. At 6.6 GHz, the instrument provided a spatial resolution of approximately 150 km, and a temporal frequency over the test area of about 3 nighttime orbits per week. Vegetation radiative transfer characteristics, such as the canopy transmissivity, were estimated from vegetation indices such as the Normalized Difference Vegetation Index (NDVI) and the 37 GHz Microwave Polarization Difference Index (MPDI). Because the time of satellite coverage does not always coincide with the ground measurements of soil moisture, the existing ground data were used to calibrate a water balance for the top IO cm surface layer in order to interpolate daily surface moisture values. Such a climate-based approach is often more appropriate for estimating large-area average soil moisture because meteorological data are generally more spatially representative than isolated point measurements of soil moisture, Passive microwave remote sensing presents the greatest potential for providing regular spatially representative estimates of surface soil moisture at global scales. Real time estimates should improve weather and climate modelling efforts, while the development of historical data sets will provide necessary information for simulation and validation of long-term climate and global change studies.
Owe, M.↗
Estimating Long Term Surface Soil Moisture in the GCIP Area From Satellite Microwave Observations
Soil moisture is an important component of the water and energy balances of the Earth's surface. Furthermore, it has been identified as a parameter of significant potential for improving the accuracy of large-scale land surface-atmosphere interaction models. However, accurate estimates of surface soil moisture are often difficult to make, especially at large spatial scales. Soil moisture is a highly variable land surface parameter, and while point measurements are usually accurate, they are representative only of the immediate site which was sampled. Simple averaging of point values to obtain spatial means often leads to substantial errors. Since remotely sensed observations are already a spatially averaged or areally integrated value, they are ideally suited for measuring land surface parameters, and as such, are a logical input to regional or larger scale land process models. A nine-year database of surface soil moisture is being developed for the Central United States from satellite microwave observations. This region forms much of the GCIP study area, and contains most of the Mississippi, Rio Grande, and Red River drainages. Daytime and nighttime microwave brightness temperatures were observed at a frequency of 6.6 GHz, by the Scanning Multichannel Microwave Radiometer (SMMR), onboard the Nimbus 7 satellite. The life of the SMMR instrument spanned from Nov. 1978 to Aug. 1987. At 6.6 GHz, the instrument provided a spatial resolution of approximately 150 km, and an orbital frequency over any pixel-sized area of about 2 daytime and 2 nighttime passes per week. Ground measurements of surface soil moisture from various locations throughout the study area are used to calibrate the microwave observations. Because ground measurements are usually only single point values, and since the time of satellite coverage does not always coincide with the ground measurements, the soil moisture data were used to calibrate a regional water balance for the top 1, 5, and 10 cm surface layers in order to interpolate daily surface moisture values. Such a climate-based approach is often more appropriate for estimating large-area spatially averaged soil moisture because meteorological data are generally more spatially representative than isolated point measurements of soil moisture. Vegetation radiative transfer characteristics, such as the canopy transmissivity, were estimated from vegetation indices such as the Normalized Difference Vegetation Index (NDVI) and the 37 GHz Microwave Polarization Difference Index (MPDI). Passive microwave remote sensing presents the greatest potential for providing regular spatially representative estimates of surface soil moisture at global scales. Real time estimates should improve weather and climate modelling efforts, while the development of historical data sets will provide necessary information for simulation and validation of long-term climate and global change studies.
Owe, Manfred↗
Top/bottom multisensor remote sensing of Arctic sea ice
Results are presented on the Aircraft/Submarine Sea Ice Project experiment carried out in May 1987 to investigate concurrently the top and the bottom features of the Arctic sea-ice cover. Data were collected nearly simultaneously by instruments aboard two aircraft and a submarine, which included passive and active (SAR) microwave sensors, upward looking and sidescan sonars, a lidar profilometer, and an IR sensor. The results described fall into two classes of correlations: (1) quantitative correlations between profiles, such as ice draft (sonar), ice elevation (laser), SAR backscatter along the track line, and passive microwave brightness temperatures; and (2) qualitative and semiquantitative correlations between corresponding areas of imagery (i.e., passive microwave, AR, and sidescan sonar).
Comiso, J. C.↗
2-D Lunar Microwave Radiance Observations From the NOAA-20 ATMS
Reported here are disk-integrated Moon surface microwave brightness temperature (Tb) retrievals covering the frequency range of 23–183 GHz. FullMoon observations obtained from the advanced technology microwave sounder (ATMS) onboard the NOAA-20 satellite during a special spacecraft pitch–maneuver operation forms the basis of the retrievals. Instrument nonlinearity, Earth sidelobe contamination, cosmic background radiation, and reflector thermal emission corrections are applied to the observations to obtain accurate values of the Moon’s Tb at all frequencies. The measured full Moon Tb ranges from ~240 to 293 K with frequency increases from 23 to 183 GHz. A clear frequency trend is detected when the brightness temperature increases.
Hu Yang↗
Comparison of Passive Microwave-Derived Early Melt Onset Records on Arctic Sea Ice
Two long records of melt onset (MO) on Arctic sea ice from passive microwave brightness temperatures (Tbs) obtained by a series of satellite-borne instruments are compared. The Passive Microwave (PMW) method and Advanced Horizontal Range Algorithm (AHRA) detect the increase in emissivity that occurs when liquid water develops around snow grains at the onset of early melting on sea ice. The timing of MO on Arctic sea ice influences the amount of solar radiation absorbed by the ice-ocean system throughout the melt season by reducing surface albedos in the early spring. This work presents a thorough comparison of these two methods for the time series of MO dates from 1979through 2012. The methods are first compared using the published data as a baseline comparison of the publically available data products. A second comparison is performed on adjusted MO dates we produced to remove known differences in inter-sensor calibration of Tbs and masking techniques used to develop the original MO date products. These adjustments result in a more consistent set of input Tbs for the algorithms. Tests of significance indicate that the trends in the time series of annual mean MO dates for the PMW and AHRA are statistically different for the majority of the Arctic Ocean including the Laptev, E. Siberian, Chukchi, Beaufort, and central Arctic regions with mean differences as large as 38.3 days in the Barents Sea. Trend agreement improves for our more consistent MO dates for nearly all regions. Mean differences remain large, primarily due to differing sensitivity of in-algorithm thresholds and larger uncertainties in thin-ice regions.
sea ice↗