MODELING X-BAND MICROWAVE RADIATION OF THE ARCTIC SEAS BASED ON SATELLITE OBSERVATIONS: TAKING INTO ACCOUNT A MEASUREMENT ANGLE

2021 ◽  
pp. 69-77
Author(s):  
E. V. Zabolotskikh ◽  
◽  
B. Chapron ◽  
◽  
Keyword(s):  
Wind Speed ◽  
Microwave Radiation ◽  
Microwave Radiometer ◽  
Surface Wind ◽  
Microwave Emission ◽  
The Arctic ◽  
Observation Angle ◽  
Emission Model ◽  
Arctic Seas ◽  
X Band

The ocean X-band microwave emission model for modeling measurements of satellite radiometers over the cold Arctic seas at an observation angle of 65° is proposed. The model is based on the experimental geophysical model function (GMF) of microwave emission dependence on surface wind speed for an angle of 55°, that was developed from the AMSR2 (Advanced Microwave Scanning Radiometer 2) measurements and the two-scale theory of the ocean microwave radiation. The experimental GMF is derived from the comparison of AMSR2 measurements over the Arctic seas with surface wind speeds retrieved from these data. The model is limited by wind speed of 15 m/s and does not take into account the foam emission. The model allows discriminating between longwave and shortwave wind-induced microwave radiation and using the presented approach to proceed to the observation angle of the MTVZA-GYa (temperature and humidity atmospheric sounding unit) microwave radiometer on board the Meteor-M Russian polar orbiting satellites.

scholarly journals Variations in snowpack melt on the Greenland ice sheet based on passive-microwave measurements

1995 ◽  
Vol 41 (137) ◽  
pp. 51-60 ◽  
Author(s):  
Thomas L. Mote ◽  
Mark R. Anderson
Keyword(s):  
Climate Change ◽  
Ice Sheet ◽  
Greenland Ice Sheet ◽  
Ablation Rate ◽  
Passive Microwave ◽  
Microwave Emission ◽  
The Arctic ◽  
Emission Model ◽  
The Impact ◽  
Microwave Data

AbstractA simple microwave-emission model is used to simulate 37 GHz brightness temperatures associated with snowpack-melt conditions for locations across the Greenland ice sheet. The simulated values are utilized as threshold values and compared to daily, gridded SMMR and SSM/I passive-microwave data, in order to reveal regions experiencing melt. The spatial extent of the area classified as melting is examined on a daily, monthly and seasonal (May-August) basis for 1979–91. The typical seasonal cycle of melt coverage shows melt beginning in late April, a rapid increase in the melting area from mid-May to mid-July, a rapid decrease in melt extent from late July through mid-August, and cessation of melt in late September. Seasonal averages of the daily melt extents demonstrate an apparent increase in melt coverage over the 13 year period of approximately 3.8% annually (significant at the 95% confidence interval). This increase is dominated by statistically significant positive trends in melt coverage during July and August in the west and southwest of the ice sheet. We find that a linear correlation between microwave-derived melt extent and a surface measure of ablation rate is significant in June and July but not August, so caution must be exercised in using the microwave-derived melt extents in August. Nevertheless, knowledge of the variability of snowpack melt on the Greenland ice sheet as derived from microwave data should prove useful in detecting climate change in the Arctic and examining the impact of climate change on the ice sheet.

Comparing ERA-40-Based L-Band Brightness Temperatures with Skylab Observations: A Calibration/Validation Study Using the Community Microwave Emission Model

2009 ◽  
Vol 10 (1) ◽  
pp. 213-226 ◽  
Author(s):  
Matthias Drusch ◽  
Thomas Holmes ◽  
Patricia de Rosnay ◽  
Gianpaolo Balsamo
Keyword(s):  
Soil Moisture ◽  
Dynamic Range ◽  
Microwave Radiometer ◽  
Space Station ◽  
Microwave Emission ◽  
Emission Model ◽  
Soil Database ◽  
Brightness Temperatures ◽  
L Band ◽  
Rms Errors

Abstract The Community Microwave Emission Model (CMEM) has been used to compute global L-band brightness temperatures at the top of the atmosphere. The input data comprise surface fields from the 40-yr ECMWF Re-Analysis (ERA-40), vegetation data from the ECOCLIMAP dataset, and the Food and Agriculture Organization’s (FAO) soil database. Modeled brightness temperatures have been compared against (historic) observations from the S-194 passive microwave radiometer onboard the Skylab space station. Different parameterizations for surface roughness and the vegetation optical depth have been used to calibrate the model. The best results have been obtained for rather simple approaches proposed by Wigneron et al. and Kirdyashev et al. The rms errors after calibration are 10.7 and 9.8 K for North and South America, respectively. Comparing the ERA-40 soil moisture product against the corresponding in situ observations suggests that the uncertainty in the modeled soil moisture is the predominant contributor to these rms errors. Although the bias between model and observed brightness temperatures are reduced after the calibration, systematic differences in the dynamic range remain. For NWP analysis applications, bias correction schemes should be applied prior to data assimilation. The calibrated model has been used to compute a 10-yr brightness temperature climatology based on ERA-40 data.

scholarly journals Characteristics and Applications of the Ground-Based X Band Low Elevation Angle Brightness Temperatures under Low Sea State Based on Measured Data

2020 ◽  
Vol 12 (11) ◽  
pp. 1736
Author(s):  
Zhongqing Cao ◽  
Lixin Guo ◽  
Shifeng Kang ◽  
Xianhai Cheng ◽  
Qingliang Li ◽  
...  
Keyword(s):  
Wind Speed ◽  
Meteorological Data ◽  
Surface Wind ◽  
Elevation Angle ◽  
Surface Wind Speed ◽  
Sea Surface ◽  
Sea State ◽  
Sea Surface Wind Speed ◽  
Sea Surface Wind ◽  
X Band

In ground-based microwave radiometer remote sensing, low-elevation-angle (−3°~3°) radiation data are often discarded because they are considered to be of little value and are often difficult to model due to the complicated mechanism. Based on the observed X-band horizontal polarization low elevation angle microwave radiation data and the meteorological data at the same time, this study investigated the generation mechanism of low elevation angle brightness temperature (LEATB) and its relationship with meteorological data, i.e., temperature, humidity, and wind speed, under low sea state. As a result, one could find that the LEATB was sensitive to the atmosphere at the elevation angle between 1° to 3°, and a diurnal variation of the LEATB reached up to 10 K. This study also found a linear relationship between the LEATB and sea surface wind speed under low sea state at an elevation range from −3° to 0°, i.e., the brightness temperature decreased as the wind speed increased, which was inconsistent with the observations at the elevation angle from −10° to −5°. The variation of the LEATB difference according to the change in the over-the-horizon detection capability (OTHDC) of the shipborne microwave radar was examined to identify the reason for this phenomenon theoretically. The results showed that the LEATB difference was significantly influenced by a change in the OTHDC. Further, this study examined a remote sensing method to extract the sea surface wind speed data from experimental LEATB data under low sea state. The results demonstrated that the X-band horizontal polarization LEATBs were useful to retrieve the sea surface wind speed data at a reasonable accuracy—the root mean square error of 0.02408 m/s. Overall, this study proved the promising potential of the LEATB data for retrieving temperature profiles, humidity profiles, sea surface winds, and the OTHDC.

Characterization of Errors in a Coupled Snow Hydrology–Microwave Emission Model

2008 ◽  
Vol 9 (1) ◽  
pp. 149-164 ◽  
Author(s):  
Konstantinos M. Andreadis ◽  
Ding Liang ◽  
Leung Tsang ◽  
Dennis P. Lettenmaier ◽  
Edward G. Josberger
Keyword(s):  
Forest Cover ◽  
Microwave Radiometer ◽  
Coupled Model ◽  
Microwave Remote Sensing ◽  
Quantitative Information ◽  
Microwave Emission ◽  
Prediction Errors ◽  
Emission Model ◽  
Snow Hydrology ◽  
Infiltration Capacity

Abstract Traditional approaches to the direct estimation of snow properties from passive microwave remote sensing have been plagued by limitations such as the tendency of estimates to saturate for moderately deep snowpacks and the effects of mixed land cover within remotely sensed pixels. An alternative approach is to assimilate satellite microwave emission observations directly, which requires embedding an accurate microwave emissions model into a hydrologic prediction scheme, as well as quantitative information of model and observation errors. In this study a coupled snow hydrology [Variable Infiltration Capacity (VIC)] and microwave emission [Dense Media Radiative Transfer (DMRT)] model are evaluated using multiscale brightness temperature (TB) measurements from the Cold Land Processes Experiment (CLPX). The ability of VIC to reproduce snowpack properties is shown with the use of snow pit measurements, while TB model predictions are evaluated through comparison with Ground-Based Microwave Radiometer (GBMR), aircraft [Polarimetric Scanning Radiometer (PSR)], and satellite [Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E)] TB measurements. Limitations of the model at the point scale were not as evident when comparing areal estimates. The coupled model was able to reproduce the TB spatial patterns observed by PSR in two of three sites. However, this was mostly due to the presence of relatively dense forest cover. An interesting result occurs when examining the spatial scaling behavior of the higher-resolution errors; the satellite-scale error is well approximated by the mode of the (spatial) histogram of errors at the smaller scale. In addition, TB prediction errors were almost invariant when aggregated to the satellite scale, while forest-cover fractions greater than 30% had a significant effect on TB predictions.

scholarly journals Satellite Microwave Surface Observations in Tropical Cyclones

2010 ◽  
Vol 138 (2) ◽  
pp. 421-437 ◽  
Author(s):  
Yves Quilfen ◽  
Bertrand Chapron ◽  
Jean Tournadre
Keyword(s):  
Wind Speed ◽  
Tropical Cyclones ◽  
Rain Rate ◽  
Microwave Radiometer ◽  
Surface Wind ◽  
Tropical Rainfall Measuring Mission ◽  
Heavy Rain ◽  
Surface Wind Speed ◽  
Microwave Measurements ◽  
Dual Frequency

Abstract Sea surface estimates of local winds, waves, and rain-rate conditions are crucial to complement infrared/visible satellite images in estimating the strength of tropical cyclones (TCs). Satellite measurements at microwave frequencies are thus key elements of present and future observing systems. Available for more than 20 years, passive microwave measurements are very valuable but still suffer from insufficient resolution and poor wind vector retrievals in the rainy conditions encountered in and around tropical cyclones. Scatterometer and synthetic aperture radar active microwave measurements performed at the C and Ku band on board the European Remote Sensing (ERS), the Meteorological Operational (MetOp), the Quick Scatterometer (QuikSCAT), the Environmental Satellite (Envisat), and RadarSat satellites can also be used to map the surface wind field in storms. Their accuracy is limited in the case of heavy rain and possible saturation of the microwave signals is reported. Altimeter dual-frequency measurements have also been shown to provide along-track information related to surface wind speed, wave height, and vertically integrated rain rate at about 6-km resolution. Although limited for operational use by their dimensional sampling, the dual-frequency capability makes altimeters a unique satellite-borne sensor to perform measurements of key surface parameters in a consistent way. To illustrate this capability two Jason-1 altimeter passes over Hurricanes Isabel and Wilma are examined. The area of maximum TC intensity, as described by the National Hurricane Center and by the altimeter, is compared for these two cases. Altimeter surface wind speed and rainfall-rate observations are further compared with measurements performed by other remote sensors, namely, the Tropical Rainfall Measuring Mission instruments and the airborne Stepped Frequency Microwave Radiometer.

scholarly journals X-band COSMO-SkyMed wind field retrieval, with application to coastal circulation modeling

Ocean Science ◽  
2013 ◽  
Vol 9 (1) ◽  
pp. 121-132 ◽  
Author(s):  
A. Montuori ◽  
P. de Ruggiero ◽  
M. Migliaccio ◽  
S. Pierini ◽  
G. Spezie
Keyword(s):  
Wind Speed ◽  
Wind Direction ◽  
Surface Wind ◽  
Circulation Model ◽  
Coastal Circulation ◽  
Wind Fields ◽  
Sea Surface Wind ◽  
X Band ◽  
Sar Data ◽  
Ecmwf Model

Abstract. In this paper, X-band COSMO-SkyMed© synthetic aperture radar (SAR) wind field retrieval is investigated, and the obtained data are used to force a coastal ocean circulation model. The SAR data set consists of 60 X-band Level 1B Multi-Look Ground Detected ScanSAR Huge Region COSMO-SkyMed© SAR data, gathered in the southern Tyrrhenian Sea during the summer and winter seasons of 2010. The SAR-based wind vector field estimation is accomplished by resolving both the SAR-based wind speed and wind direction retrieval problems independently. The sea surface wind speed is retrieved by means of a SAR wind speed algorithm based on the azimuth cut-off procedure, while the sea surface wind direction is provided by means of a SAR wind direction algorithm based on the discrete wavelet transform multi-resolution analysis. The obtained wind fields are compared with ground truth data provided by both ASCAT scatterometer and ECMWF model wind fields. SAR-derived wind vector fields and ECMWF model wind data are used to construct a blended wind product regularly sampled in both space and time, which is then used to force a coastal circulation model of a southern Tyrrhenian coastal area to simulate wind-driven circulation processes. The modeling results show that X-band COSMO-SkyMed© SAR data can be valuable in providing effective wind fields for coastal circulation modeling.

A Coupled-Pixel Model (CPM) Atmospheric Retrieval Algorithm for High-Resolution Imagers

2015 ◽  
Vol 32 (10) ◽  
pp. 1866-1879 ◽  
Author(s):  
Mary Morris ◽  
Christopher S. Ruf
Keyword(s):  
Wind Speed ◽  
Rain Rate ◽  
Microwave Radiometer ◽  
Surface Wind ◽  
Low Frequency ◽  
Surface Wind Speed ◽  
Retrieval Algorithm ◽  
Single Column ◽  
Simplifying Assumptions ◽  
Cross Track

AbstractLow-frequency passive microwave observations allow for oceanic remote sensing of surface wind speed and rain rate from spaceborne and airborne platforms. For most instruments, the modeling of contributions of rain absorption and reemission in a particular field of view is simplified by the observing geometry. However, the simplifying assumptions that can be applied in most applications are not always valid for the scenes that the airborne Hurricane Imaging Radiometer (HIRAD) regularly observes. Collocated Stepped Frequency Microwave Radiometer (SFMR) and HIRAD observations of Hurricane Earl (2010) indicate that retrieval algorithms based on the usual simplified model, referred to here as the decoupled-pixel model (DPM), are not able to resolve two neighboring rainbands at the edge of HIRAD’s swath. The DPM does not allow for the possibility that a single column of atmosphere can affect the observations at multiple cross-track positions. This motivates the development of a coupled-pixel model (CPM) that is developed and tested in this paper. Simulated observations as well as HIRAD’s observations of Hurricane Earl (2010) are used to test the CPM algorithm. Key to the performance of the CPM algorithm is its ability to deconvolve the cross-track scene, as well as unscramble the signatures of surface wind speed and rain rate in HIRAD’s observations. While the CPM approach was developed specifically for HIRAD, other sensors could employ this method in similar complicated observing scenarios.

scholarly journals Research on Wind Speed Inversion Method for X-Band Networked SAR Satellite

2020 ◽  
Vol 8 (9) ◽  
pp. 626
Author(s):  
Yong Wan ◽  
Xiaolei Shi ◽  
Yongshou Dai ◽  
Ligang Li ◽  
Xiaojun Qu ◽  
...  
Keyword(s):  
Wind Speed ◽  
Cost Function ◽  
Wind Direction ◽  
Incident Angle ◽  
Surface Wind ◽  
Sea Surface ◽  
Inversion Method ◽  
X Band ◽  
Direction Information ◽  
The Cost

Synthetic aperture radar (SAR) can extract sea surface wind speed information. To extract wind speed information through the geophysical model function (GMF), the corresponding wind direction information must be input. This article introduces some concepts about networked SAR satellites. The networked satellites enable multiple SARs to observe the same sea surface at different incidence angles at the same time. Aiming at the X-band networked SAR data with different incident angles, the cost function is established by using the GMF. By minimizing the cost function, accurate wind speed information can be extracted without inputting wind direction information. When the noise is small, the wind direction information is introduced, and the accuracy of the extracted wind speed will be improved. When the noise is less than 1 dB and the incident angle is greater than 30°, the root-mean-square error (RMSE) of the wind speed extracted by this method is basically less than 2 m/s.

scholarly journals Retrieval of Sea Surface Wind Speed from Spaceborne SAR over the Arctic Marginal Ice Zone with a Neural Network

2020 ◽  
Vol 12 (20) ◽  
pp. 3291 ◽  
Author(s):  
Xiao-Ming Li ◽  
Tingting Qin ◽  
Ke Wu
Keyword(s):  
Neural Network ◽  
Wind Speed ◽  
Surface Wind ◽  
Surface Wind Speed ◽  
Sea Surface ◽  
The Arctic ◽  
Sea Surface Wind Speed ◽  
Sea Surface Wind ◽  
The Neural Network ◽  
Marginal Ice Zone

In this paper, we presented a method for retrieving sea surface wind speed (SSWS) from Sentinel-1 synthetic aperture radar (SAR) horizontal-horizontal (HH) polarization data in extra-wide (EW) swath mode, which have been extensively acquired over the Arctic for polar monitoring. In contrast to the conventional algorithm, i.e., using a geophysical model function (GMF) to retrieve SSWS by spaceborne SAR, we introduced an alternative retrieval method based on a GMF-guided neural network. The SAR normalized radar cross section, incidence angle, and wind direction are used as the inputs of a back propagation (BP) neural network, and the output is the SSWS. The network is developed based on 11,431 HH-polarized EW images acquired in the marginal ice zone (MIZ) of the Arctic from 2015 to 2018 and their collocated scatterometer wind measurements. Verification of the neural network based on the testing dataset yields a bias of 0.23 m/s and a root mean square error (RMSE) of 1.25 m/s compared to the scatterometer wind data for wind speeds less than approximately 30 m/s. Further comparison of the SAR retrieved SSWS with independent buoy measurements shows a bias and RMSE of 0.12 m/s and 1.42 m/s, respectively. We also analyzed the uncertainty of the retrieval when reanalysis model wind direction data are used as inputs to the neural network. By combining the detected sea ice cover information based on SAR data, sea ice and marine-meteorological parameters can be derived simultaneously by spaceborne SAR at a high spatial resolution in the Arctic.

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