Effects of atmospheric water and surface wind on passive microwave retrievals of sea ice concentration: a simulation study

The atmospheric effects on the retrieval of sea ice concentration from passive microwave sensors were examined using simulated data typical for the Arctic summer. The simulation includes atmospheric contributions of cloud liquid water (CLW), water vapour (WV) and surface wind on the microwave signatures. A plane parallel radiative transfer model was used to compute brightness temperatures at SSM/I frequencies over surfaces that contained open water, first‐year (FY) ice, multi‐year (MY) ice and their combinations. Synthetic retrievals were performed using the NASA Team (NT) algorithm for the estimation of sea ice concentrations. Our results show that if the satellite sensor's field of view is filled with only FY ice, the retrieval is hardly affected by the atmospheric conditions because of the high contrast between emission signals from the FY ice surface and the atmosphere. Pure MY ice concentration is generally underestimated because of the low MY ice surface emissivity, which results in the enhancement of emission signals from the atmosphere. In marginal ice areas, the atmospheric and surface effects tend to degrade the accuracy at low sea ice concentrations. FY ice concentration is overestimated and MY ice concentration is underestimated in the presence of atmospheric water and surface wind at low ice concentration. Moreover, strong surface wind appears to be more important than atmospheric water in contributing to the retrieval errors of total ice concentrations over marginal ice zones.     

Atmospheric effects on SMMR and SSM/1 37 GHz polarization difference over the Sahel

Abstract

The difference of vertically and horizontally polarized brightness temperatures (referred to here as the polarization difference, δT) observed at 37GHz frequency of the scanning multi-channel microwave radiometer (SMMR) on board the Nimbus-7 satellite and special sensor microwave imager (SSM/I) on board the DMSP-F8 satellite could provide useful information about land surface change within the span of these global observations, November 1978 to August 1987 for SMMR and July 1987 to present for SSM/I. The atmospheric effects on the δT are studied over two 2-5° by 2-5° regions within the Sahel and Sudan zones or Africa from January 1985 to December 1986 through radiative transfer analysis using surface temperature, atmospheric water vapour and cloud optical thickness developed under the International Satellite Cloud Climatology Project (ISCCP). The atmospheric effects are also studied using surface observations of air temperature and vapour pressure at Niamey (13-5° N, 2-2° E) for the period January 1979 to December 1990. It is found that atmospheric effects alone cannot explain the observed temporal variation of δT, although the atmosphere introduces important modulations on the observed seasonal variations of δT due to rather significant seasonal variation of precipitable water vapour. Therefore, these δT data should be corrected for atmospheric effects before any quantitative analysis of land surface change over the Sahel and Sudan zones. The entire global data set from December 1978 to December 1990 has been archived for unrestricted distribution and use.     

Improved retrieval of sea ice total concentration from spaceborne passive microwave observations using numerical weather prediction model fields: An intercomparison of nine algorithms

The accuracy of the sea ice concentration estimates in polar regions is reduced by the effects of atmospheric emission and absorption. A method is presented where a fast atmospheric radiative transfer model and Numerical Weather Prediction (NWP) model fields are used to correct brightness temperatures before they enter the sea ice concentration algorithm. The skill of the method is a function of the errors in the NWP model fields modulated by the sensitivities of the sea ice concentration algorithm used. The NWP model fields representing the most significant atmospheric parameters, i.e. water vapour, cloud liquid water, surface temperature and wind speed over open water are evaluated using remote sensing data. For wind speed and total water vapour, it is found that the standard deviation of the difference is less than the RMS error quoted for the remote sensing algorithms. The best consistency is found for water vapour followed by wind speed. The NWP model cloud liquid water displays standard deviations much higher than the RMS error of the remote sensing algorithm and close to the total average content. Nine sea ice concentration algorithms are further evaluated in a sensitivity study to the above-mentioned atmospheric constituents using a detailed atmospheric radiative transfer model. The result shows that the class of algorithms based solely on the 19 and 37 GHz vertically polarised channels display the smallest sensitivity to all three atmospheric parameters: total water vapour, wind speed and cloud liquid water. Finally, it is demonstrated that this method overcomes many problems associated with conventional weather filtering over mixed ice-water and new-ice pixels and allows the retrieval of sea ice concentrations below 10%.     

遥感技术监测海冰密集度

概要评述可见近红外、主/ 被动微波遥感技术监测海冰密集度的基本原理、算法及其优缺点。着重介绍和讨论被动微波传感器SMMR 和SMM/ I 遥感图像混合像元内海冰总密集度, 一年海冰及多年海冰密集度的NASA 算法及其天气滤波器。     

Remotely-sensed and simulated variability of Arctic sea-ice concentrations in response to atmospheric synoptic systems

Responses of the Beaufort Sea and Canada Basin icc pack to the passage of synoptic-scale weather systems are studied using Synthetic Aperture Radar (SAR), Advanced Very High Resolution Radiometer (AVHRR) and Special Sensor Microwave/Imager (SSM/I) data combined with ice model simulations. Changes within the consolidated ice pack are examined in detail for October 1991. The analysis is then extended to consider general conditions from October 1991 to June 1992. The SAR, SSM/I, and modelled concentrations concur generally, showing a 1–5 per cent decrease in ice fraction during the passage of low-pressure systems through the study area. The AVHRR imagery indicates a greater proportion of thin ice within the pack, but comparable decreases in concentration. While changes in SSM/I-derived open-water fractions are similar to changes in the other data sets, the SSM/I data suggest substantial increases in first-year ice concentration, indicative of the formation of refreezing open water areas. Sensible heat fluxes calculated using open-water and ice-type fractions from the SAR and SSM/I imagery point out the sensitivity of heat transfer estimates to, the data types and classification method used to derive ice information.     

Study of geophysical parameters associated with the Orissa super cyclone using active and passive microwave remote sensing measurements

A detailed study of the Orissa super cyclone over the Bay of Bengal from 25 to 29 October 1999 has been carried out using various spaceborne sensors, namely VHRR, SSM/I, TMI, MSMR, TOPEX‐RA and TMR. The raining areas are delineated using dual frequency TOPEX altimeter and coaligned three‐frequency TOPEX microwave radiometer (TMR) in addition to DMSP SSM/I, TRMM TMI and IRS‐P4 MSMR. Various oceanic parameters like rain rate (RR), cloud liquid water (CLW), integrated water vapour (IWV), ocean surface wind speed (OWS), and wave conditions on the ocean surface near and within the cyclonic vicinity were studied. This paper has two innovative aspects: (1) it indirectly validates the empirically developed MSMR algorithms for water vapour and wind speed under moderate cloud water conditions, and (2) it makes non‐conventional use of TOPEX altimeter and TMR, especially for rain rate and cloud liquid water over the cyclone. Results and significance of the synergistic measurements from various active and passive microwave and infrared observations from satellites have been discussed. The combined capabilities of these measurements portray the several important features associated with cyclones in a more informative way than any individual satellite component.     

SAR observations of Arctic freeze-up compared to SSM/I during ARCTIC'91

Ice concentration in the Arctic derived from ERS-1 Synthetic Aperture Radar (SAR) and Special Scanning Microwave/Imager (SSM/I) images are compared. The satellite data are compared to video images and in situ measurements. The data were acquired during the freeze-up period of the ARCTIC'91 expedition. The studied areas were characterized by melting conditions and new ice formation with frost flowers. The ERS-1 SAR images are classified by a local averaging method and a segmentation method. Parameters for the methods are derived from the backscattering distributions. Temporal sequences and meteorological information are used for consistent results. Ice concentration derived from SAR are compared with the SSM/I ice concentration (NASA team algorithm) and ship observations. SSM/I may underestimate the ice concentration by 20 per cent due to thin ice formation and melting conditions while SAR may overestimate. However, by using the SAR estimate of the different ice classes we believe it is possible to increase the accuracy of the NASA team algorithm. We conclude that it is important to compare results from different sensors and methods.     

Estimation of thin ice thickness from AMSR-E data in the Chukchi Sea

In this study, we have developed an algorithm for estimating thin ice thickness in the Chukchi Sea of the Arctic Ocean using Advanced Microwave Scanning Radiometer Earth Observing System (AMSR-E) data. The algorithm is based on comparisons between the polarization ratio (PR) of AMSR-E brightness temperatures from the 89 and 36 GHz channels (PR₈₉ and PR₃₆) and the thermal ice thickness. The thermal ice thickness is estimated from a heat budget calculation using the ice surface temperature from clear-sky Moderate-Resolution Imaging Spectroradiometer (MODIS) infrared data. Whereas coastal polynyas have been the main target of previous algorithms, this algorithm is also applicable for marginal ice zones. AMSR-E has twice the spatial resolution of Special Sensor Microwave/Imager (SSM/I) data and can therefore resolve polynyas at a smaller scale. Although the spatial resolution of the 89 GHz data (6.25 km) is twice that of the 36 GHz data (12.5 km), the 89 GHz data can be contaminated by atmospheric water vapour. We propose an exclusion method of data affected by water vapour to resolve this issue. A combined algorithm of thin ice and ice concentration is also discussed, in which the ice thickness can be estimated independently from the open water fraction in grid cells with less than 100% ice concentration. The PR–thickness relationship in this study is somewhat different from previous studies, which is likely due to the difference in prevailing ice types caused by background environmental conditions.     

Multi-year sea ice mapping by thermal infrared radiometry

Marine operations in polar and subpolar regions rely on accurate sea ice information for operational planning purposes. Before venturing into operations, however, mapping of the prevailing sea ice conditions are important to feasibility analyses and planning of the operation. Multi-year sea ice information is often derived from passive microwave radiometers such as the Special Sensor Microwave Imager (SSM/I) on board the U.S. Defense Meteorological Satellite Program (DMSP) satellites, or the Scanning Multichannel Microwave Radiometer (SMMR) on board the Nimbus-7 satellite. These sources provide wide aerial coverage and all weather capability, but offer only low spatial resolution, 30 km. In contrast, the thermal infrared channel of the Advanced Very High Resolution Radiometer (AVHRR) on board the NOAA satellites, provides a 1 1km spatial resolution at nadir with a reasonable cost. A technique for extraction of multi-year sea ice information from thermal infrared AVHRR data was thus created. It relies on surface temperature differences between first-year and multi-year sea ice. Adjustments of the absolute concentration levels were made based on a regression relation between AVHRR and SMMR based multi-year sea ice concentrations. The technique is inapplicable during periods of dark, cloud cover, or melting conditions.     

A model for retrieving total sea ice concentration from a spaceborne dual-polarized passive microwave instrument operating near 90 GHz

Abstract

An algorithm has been developed for estimating total ice concentration from spaceborne high-frequency passive microwave instrumentation. The algorithm is intended for use with the coming Special Sensor Microwave/Imager (SSM/I) data giving a spatial resolution of 12 km. It is based on radiation physics and detailed millimetre wave surface signature measurements and can therefore be applied to other similar data. However, due to large effects on the signals caused by time varying atmospheric conditions and radiation properties of the ice, the algorithm is made self-adjusting. The atmospheric effects are implicitly treated as a smooth function of the ice concentration with tie points over open ocean and 100 per cent ice for each orbit. This means that the main errors are due to patches of heavy clouds and ice floes with atypical radiation properties. An error analysis indicates possible errors of the order of 5 percent for concentrations representative for the Arctic Basin, increasing with decreasing concentration.     

Simultaneous MSMR and SSM/I observations and analysis of sea‐ice characteristics over the Antarctic region

Polar sea ice has been monitored quasi‐continuously over the last 30 years using passive microwave radiometers onboard three satellites in polar orbit, namely Nimbus‐5, Nimbus‐7 and Defense Meteorological Satellite Program (DMSP) series. A short overlap between Scanning Multichannel Microwave Radiometer (SMMR) on Nimbus‐7 and Special Sensor Microwave Imager (SSM/I) onboard DMSP allowed inter‐calibration of the two sensors leading to a consistent series of long‐term sea‐ice measurements since 1978. With the launch of Multifrequency Scanning Microwave Radiometer (MSMR) onboard OCEANSAT‐1 in the polar sun‐synchronous orbit during 1999, India developed the capability to monitor the polar sea ice on a regular basis. The concurrent availability of SSM/I and MSMR over the last few years presents a valuable opportunity to attempt an inter‐comparison of MSMR with SSM/I measurements and derived sea‐ice parameters.

In this paper, we present an indirect validation of the brightness temperatures (T _b) observed by MSMR with near‐simultaneous measurements from SSM/I over the Antarctic and Southern Polar Ocean regions. Simultaneous MSMR and SSM/I data from two contrasting seasons—summer and winter—for the 1999–2000 period have been used. Analysis includes a comparison of T _b scatterograms to achieve confidence in the quantitative use of the T _b data to derive various geophysical parameters, e.g. sea‐ice concentration and extent. Additionally, the T _b images produced by the two sensors are compared to establish the capability of MSMR in reliable two‐dimensional portrayal of all the sea and continental ice features over the Antarctic Region. Based on a regression analysis between MSMR observed T _b in different frequency channels and polarizations, and SSM/I‐derived sea‐ice concentration (SIC) values, we have developed algorithms to estimate SIC over the Southern Polar Ocean from MSMR data. The MSMR algorithms allow estimation of SIC with better than 10% rms error. MSMR SIC images faithfully capture the observed distribution of sea ice in all the sectors of the Southern Ocean both during summer and winter periods. Using the MSMR‐derived SIC, we have also derived monthly sea‐ice extent (SIE) estimates for a period extending for about 20 months from the beginning of the launch of MSMR. These estimates show excellent agreement with values derived from SSM/I. These analyses bring out the very high level of compatibility in the measurements and derived sea‐ice parameters produced by the two sensors.     

Sea ice small-scale surface roughness estimation based on AMSR-E observations

In this article, the retrieval of a sea ice small-scale surface roughness parameter using a proposed model is investigated at several Advanced Microwave Scanning Radiometer Earth Observing System (AMSR-E) channels (6.9, 10.7, and 89 GHz) over the Arctic oceans. The AMSR-E 89 GHz observations with a spatial resolution of approximately 6 km?× 4 km, nearly three times the resolution of the currently operational radiometer SSM/I 85 GHz (15 km?× 13 km), are fully exploited to retrieve the total and multiyear (MY) ice concentrations through the utilization of the ARTIST sea ice (ASI) and polarization corrected temperature (PCT) algorithms, respectively. To improve the accuracy of the retrieved ice concentration, a tie-point adaption scheme was used to obtain daily adaptable tie-points for the two ice concentration algorithms. A sea ice small-scale roughness parameter was then calculated with the model proposed by Hong for the above-mentioned three frequencies. At lower frequencies, such as 6.9 and 10.7 GHz, roughness estimates are available for all ice types. However, estimates at 89 GHz are physically illegitimate over the wintertime MY ice cover. The model estimates at the two low frequencies were further studied over a protracted period (2003–2010). The annual time series of the averaged estimate over the Arctic sea ice were found to exhibit a slightly decreasing trend (?2.1 × 10^?3 and??1.9 × 10^?3 cm year^?1 for 6.9 and 10.7 GHz, respectively). Meanwhile, the winter time series showed an increasing trend whereas the summer time series showed a remarkably decreasing trend, which indicates more serious melting activity occurring over the Arctic ice.     

Water vapour profile retrievals from SSM/T-2 data constrained by infrared-based cloud parameters

An algorithm has been developed for the retrieval of water vapour profiles from passive microwave observations near 183GHz. The algorithm treats clouds explicitly and can incorporate cloud information from other sources. As a test, the algorithm has been configured to use data from the SSM /T-2 on the DMSP polar orbiting satellite series. Cloud top temperature and phase information have been derived from the AVHRR on the NOAA polar orbiting satellite series. Obtaining suitable coincidences between the SSM /T-2 and the AVHRR is challenging but two reasonable cases have been found. In one case, a liquid cloud injected into the retrieval results in significant improvement in the accuracy and vertical resolution in the neighbourhood of the cloud top. A cloud base is a by-product of the algorithm, but its validity has yet to be established. In the other case, a cirrus cloud injected into the algorithm resulted in little change in the retrieval which indicates that the algorithm was performing well in the cloud top vicinity (400mb).     

Estimating the time of melt onset and freeze onset over Arctic sea-ice area using active and passive microwave data

Accurate calculation of the time of melt onset, freeze onset, and melt duration over Arctic sea-ice area is crucial for climate and global change studies because it affects accuracy of surface energy balance estimates. This comparative study evaluates several methods used to estimate sea-ice melt and freeze onset dates: (1) the melt onset database derived from SSM/I passive microwave brightness temperatures (T_bs) using Drobot and Anderson's [J. Geophys. Res. 106 (2001) 24033] Advanced Horizontal Range Algorithm (AHRA) and distributed by the National Snow and Ice Data Center (NSIDC); (2) the International Arctic Buoy Program/Polar Exchange at the Sea (IABP/POLES) surface air temperatures (SATs); (3) an elaborated version of the AHRA that uses IABP/POLES to avoid anomalous results (Passive Microwave and Surface Temperature Analysis [PMSTA]); (4) another elaborated version of the AHRA that uses T_b variance to avoid anomalous results (Mean Differences and Standard Deviation Analysis [MDSDA]); (5) Smith's [J. Geophys. Res. 103 (1998) 27753] vertically polarized T_b algorithm for estimating melt onset in multiyear (MY) ice (SSM/I 19V-37V); and (6) analyses of concurrent backscattering cross section (σ°) and brightness temperature (T_b) from OKEAN-01 satellite series. Melt onset and freeze onset maps were created and compared to understand how the estimates vary between different satellite instruments and methods over different Arctic sea-ice regions. Comparisons were made to evaluate relative sensitivities among the methods to slight adjustments of the T_b calibration coefficients and algorithm threshold values. Compared to the PMSTA method, the AHRA method tended to estimate significantly earlier melt dates, likely caused by the AHRA's susceptibility to prematurely identify melt onset conditions. In contrast, the IABP/POLES surface air temperature data tended to estimate later melt and earlier freeze in all but perennial ice. The MDSDA method was least sensitive to small adjustments of the SMMR-SSM/I inter-satellite calibration coefficients. Differences among methods varied by latitude. Freeze onset dates among methods were most disparate in southern latitudes, and tended to converge northward. Surface air temperatures (IABP/POLES) indicated freeze onset well before the MDSDA method, especially in southern peripheral seas, while PMSTA freeze estimates were generally intermediate. Surface air temperature data estimated latest melt onset dates in southern latitudes, but earliest melt onset in northern latitudes. The PMSTA estimated earliest melt onset dates in southern regions, and converged with the MDSDA northward. Because sea-ice melt and freeze are dynamical transitional processes, differences among these methods are associated with differing sensitivities to changing stages of environmental and physical development. These studies contribute to the growing body of documentation about the levels of disparity obtained when Arctic seasonal transition parameters are estimated using various types of microwave data and algorithms.     

Retrieval of atmospheric water vapour using a ground-based single-channel microwave radiometer

Estimates of the amount of atmospheric water vapour derived from algorithms for a ground-based single-channel (21.0 GHz) microwave radiometer have been investigated. Ten datasets covering 44 days were used to derive the methods and two other sets (in total 32 days) were used to assess the quality of these. It is shown how the rms estimation error can be reduced by recognizing the rapid variations in sky brightness temperatures during periods when cloud liquid is present. Data was either discarded, guided by the variability, or an adaptive Kalman filter was applied with different parameter values for different degrees of variability. The resulting estimates were compared to the estimates obtained from a dual-channel algorithm (21.0 and 31.4 GHz), which were used as reference. The amount of water vapour was represented as the ‘wet delay’, the excess radio path length due to the atmospheric water vapour. Applying the Kalman filter to the single-frequency estimates reduced the wet delay rms error from 20 mm to 9 and 14 mm for the two datasets. Further reduction of the rms error was achieved by the removal of data in periods with high variability; discarding about 40% of the data led to rms errors of 5 and 7 mm for the two datasets.     

The modelling of sea ice melt-water ponds for the High Arctic using an Airborne line scan camera,and applied to the Satellite Special Sensor Microwave/Imager (SSM/I)

Melt-water ponds on sea ice in the Northeast Water Polynya (77-82 N, 1-18 W) were mapped using a line scan camera (LSC) mounted on a helicopter. Passive microwave satellite data from the Special Sensor Microwave/ Imager (SSM/I) were employed to analyse the temporal trend of radiances of shorefast ice for 1993 and sea ice during sixteen flights of the LSC (June-July). A simple, linear algorithm tailored to accommodate the summer ice regime, was developed. The LSC measurements of ice (50.9 12.5%), water and melt-water pond fractions compared very well with the SSM/I derived mean ice concentrations (50.9 12.8%). The comparison resulted in a correlation coefficient of 0.953. Combining the LSC melt-water pond fraction data with other data available from the literature provided the basis to construct a second degree polynomial function of a melt-water empirical model to correct the under estimation of SSM/I derived sea ice concentration due to the effect of melt-water ponds.     

AMSR被动微波数据介绍及主要应用研究领域分析

由于微波具有全天候、穿透性以及不受云的影响，使其在遥感研究全球变化中具有越来越大的优势。本主要是对当前星上主要的被动微波数据SMMR、SSM、AMSR做了介绍并做了对比。其中主要是介绍对地观测卫星上的AMSR-E数据。然后分析了被动微波主要的应用研究领域。     

Simulations of microwave brightness temperatures at AMSU-B frequencies over a 3D convective cloud system

Numerical simulations have been carried out to understand the effects of clouds associated with a tropical deep convective cloud system on the Advanced Microwave Sensor Unit-B (AMSU-B) channels at 89, 150, 183.3 ± 7, 183.3 ± 3, and 183.3 ± 1 GHz. The hydrometeor profiles including cloud liquid water, cloud ice, snow, graupel, and rain water for a deep convective cloud system simulated by a realistic dynamical cloud model, the Goddard Cumulus Ensemble model, have been input to a Vector Discrete Ordinate Radiative Transfer model to simulate the nadir down-looking microwave brightness temperatures at the top of the atmosphere. It is found that the AMSU-B channels have large brightness temperature depressions occurring over the clouds with large ice water paths. Moreover, for the three water vapour sounding frequencies around 183.3 GHz, the frequencies broader and further away from the centre of the water vapour absorption line show stronger depressions. The three water vapour channels, particularly the channels closer to the absorption line centre, essentially have negligible influence from liquid water. However, the window frequencies at 89 and 150 GHz have distinct influence from liquid water, particularly the 150 GHz, although they are also strongly influenced by frozen hydrometeors. The AMSU-B frequencies at 150 GHz and water vapour channels of 183.3 ± 7 and 183.3 ± 3 GHz are sensitive to cirrus clouds with total ice water paths above 0.1–0.2 kg m^?2. The influence of deep convective clouds and thick cirrus clouds on the AMSU-B water vapour channels demonstrates that they have a potential to estimate ice water paths in thick cirrus clouds and in the upper parts of deep convective clouds, which can complement the retrievals from the 89 and 150 GHz channels.     

A comparison between SSM/I passive microwave melt onset dates and satellite-derived albedo melt onset dates in the Arctic

Arctic sea ice undergoes a very strong annual cycle. This study sets out to look at the transition when the Arctic sea ice starts to melt using satellite-obtained passive microwave brightness temperatures and satellite-derived albedo data for 13 points within the Arctic, including both first-year and multiyear ice locations, for 1995–2000. Special sensor microwave imager (SSM/I) brightness temperature differences are used to determine melt onset dates once surface temperatures approach freezing. Independently, satellite-derived albedo data are obtained and a melt onset date is derived. Generally, the two methods produce the same date for melt onset with optimum conditions. However, in most cases there are clouds present, which for the albedo data restrict observations and generate melt dates that are several days later than the passive microwave melt onset which is not affected by cloud cover. Melt onset dates, determined from the passive microwave brightness temperatures, are compared to those from the albedo observation to determine differences between the two methods. For first-year ice (FYI) locations, the average differences in melt onset dates for the study locations between the passive microwave and albedo-derived methods are +/?3 days. The average difference for multiyear ice (MYI) locations melt onset dates is around 8 days, slightly longer than the (FYI) locations, however, this is due to more cloudy conditions. The results indicate that the passive microwave-derived melt onset dates and albedo-derived dates are very close and either method could be used to determine melt. The advantage of using microwave data would be the independence of having to have cloud free conditions.