Christian Melsheimer

Investigation of multifrequency/multipolarization radar signatures of rain cells over the ocean using SIR‐C/X‐SAR data

Radar signatures of rain cells are investigated using multifrequency/multipolarization synthetic aperture radar (SAR) images acquired from the space shuttle Endeavour during the spaceborne imaging radar-C/X-band SAR (SIR-C/X-SAR) missions in April and October 1994. In SIR-C/X-SAR images, radar signatures of rain cells over the ocean usually consist of irregularly shaped bright and dark patches that strongly depend on radar frequency and polarization. The radar signatures of rain cells observed in SIR-C/X-SAR imagery of the ocean originate from (1) the scattering and attenuation of the microwaves by raindrops and ice particles in the atmosphere and (2) the modification of the sea surface roughness induced by the impact of raindrops and by wind gusts associated with rain cells. Raindrops impinging on the sea surface generate ring waves, which enhance the sea surface roughness, but they also generate turbulence in the upper water layer, which reduces the sea surface roughness. Depending on the radar wavelength, ocean areas struck by rain can have higher or lower normalized radar cross section (NRCS) than the surrounding rain-free area; in ocean areas where heavy rain is impinging on the sea surface, the X- and C-band NRCS is usually enhanced, and the L-band NRCS is reduced. From the phase difference between the horizontally and vertically copolarized signals, estimates of the rain rate are obtained. The present analysis shows further that the presently used wind speed retrieval algorithms for the scatterometers aboard the ERS and ADEOS satellites may yield biased wind fields if several rain cells lie within a scatterometer resolution cell.

Intercomparison of general purpose clear sky atmospheric radiative transfer models for the millimeter/submillimeter spectral range

[1] We compare a number of radiative transfer models for atmospheric sounding in the millimeter and submillimeter wavelength range, check their consistency, and investigate their deviations from each other. This intercomparison deals with three different aspects of radiative transfer models: (1) the inherent physics of gaseous absorption lines and how they are modeled, (2) the calculation of absorption coefficients, and (3) the full calculation of radiative transfer for different geometries, i.e., up-looking, down-looking, and limblooking. The correctness and consistency of the implementations are tested by comparing calculations with predefined input such as spectroscopic data, line shape, continuum absorption model, and frequency grid. The absorption coefficients and brightness temperatures calculated by the different models are generally within about 1% of each other. Furthermore, the variability or uncertainty of the model results is estimated if (except for the atmospheric scenario) the input such as spectroscopic data, line shape, and continuum absorption model could be chosen freely. Here the models deviate from each other by about 10% around the center of major absorption lines. The main cause of such discrepancies is the variability of reported spectroscopic data for line absorption and of the continuum absorption model. Further possible causes of discrepancies are different frequency and pressure grids and differences in the corresponding interpolation routines, as well as differences in the line shape functions used, namely a prefactor of (n/n0 )o r (n/n0) 2 of the Van-Vleck-Weisskopf line shape function. Whether or not the discrepancies affect retrieval results remains to be investigated for each application individually.

Simultaneous observations of rain cells over the ocean by the synthetic aperture radar aboard the ERS satellites and by surface-based weather radars

Radar images acquired over the ocean by the C band synthetic aperture radar (SAR) aboard the European Remote Sensing satellites ERS 1 and ERS 2 often show sea surface manifestations of rain cells. We have searched the archives of several weather stations for weather radar data acquired concurrently with ERS SAR data and have found four concurrent data pairs: one in the South China Sea, two in the Baltic Sea and one in the North Sea. The comparison of ERS 1/2 SAR images showing radar signatures of rain cells with weather radar images reveals that the radar signatures of rain cells on ERS SAR images vary considerably, which makes it often difficult to distinguish them from radar signatures of other mesoscale or submesoscale atmospheric and oceanic phenomena. The present analysis, together with results obtained from previous analyses of spaceborne multifrequency SAR data and laboratory data as well as results obtained from theoretical models on radar backscattering at the sea surface suggest the following: C band radar signatures of rain cells with rain rates below 50 mm/h are mainly caused by a modification of the sea surface roughness induced by (1) the raindrops impinging on the sea surface and thus modifying the sea surface roughness and by (2) local wind field variations associated with rain cells (spreading downdrafts). Raindrops impinging on the sea surface generate ring waves as well as turbulence in the upper water layer. Depending on rain rate, drop size distribution, wind speed, and temporal evolution of the rain event, the net effect can be an increase or a reduction of the amplitude of the C band Bragg waves and thus of the backscattered radar power. Thus ocean areas struck by rain can show up on ERS SAR images as areas with higher or lower image brightness than the surroundings.

Surface Emissivity of Arctic Sea Ice at AMSU Window Frequencies

A method to retrieve the surface emissivity of sea ice at the window channels of the Advanced Microwave Sounding Unit (AMSU) radiometers in the polar region is presented. The instruments are on the new-generation satellites of the U.S. National Oceanic and Atmospheric Administration (NOAA-15, NOAA-16, and NOAA-17). The method assumes hypothetical surfaces with emissivities zero and one and simulates brightness temperatures at the top of the atmosphere using profiles of atmospheric parameters, e.g., from the European Centre for Medium-Range Weather Forecasts (ECMWF) model runs, as input for a radiative transfer model. The retrieval of surface emissivity is done by combining simulated brightness temperatures with the satellite-measured brightness temperature. The AMSU window channels differ in surface penetration depths and, thus, in the surface microphysical parameters that they depend on. Lowest layer air temperatures from ECMWF are used to infer temperatures of emitting layers at different frequencies of sea ice. A complete yearly cycle of monthly average emissivities in two selected regions (first- and multiyear ice) is giving insight into the variation of emissivities in various development stages of sea ice.

Improved Retrieval of Total Water Vapor Over Polar Regions From AMSU-B Microwave Radiometer Data

The polar regions are among those where the least information is available about the current and predicted states of surface and atmosphere. We present advances in a method to retrieve the total water vapor (TWV) of the polar atmosphere from data from spaceborne microwave radiometers such as the Advanced Microwave Sounding Unit B (AMSU-B) on the polar-orbiting satellites of the National Oceanic and Atmospheric Administration (NOAA), NOAA-15, -16, and -17. The starting point of the retrieval is a recently proposed algorithm that uses the three AMSU-B channels centered around the 183-GHz water vapor line and the window channel at 150 GHz, and that can retrieve the TWV with little dependence on the surface emissivity. This works up to TWV values of about 7 kg/m2. We extend the retrievable range toward higher TWV values by including the window channel at 89 GHz. However, now, the algorithm needs information on the surface emissivity, which we have extracted from emissivity measurements over sea ice and open water during the Surface Emissivities in Polar Regions-Polar Experiment campaign. The resulting algorithm can retrieve TWV up to about 15 kg/m2, with reduced accuracy as compared to the original algorithm. It now allows the monitoring of the TWV over the central Arctic sea ice and over Antarctica, and the surrounding sea ice during most of the year with a spatial resolution of about 50 km. Such TWV fields can show details which might be missed out by standard weather model analysis data.

Efficient forward modelling by matrix representation of sensor responses

The polarization, frequency and spatial responses of the sensor can be considered by calculating the Stokes vector of monochromatic pencil beam radiances for a set of frequencies and viewing directions, and weight these radiances with the instrument responses. This paper presents a highly efficient solution for this calculation procedure. The basic idea is to pre‐calculate a matrix that represents the mapping from polarisation, frequency and spatial values to measured data. Sensor impacts can then be included by a simple matrix multiplication. The full sensor matrix can be obtained by determining the response matrix for the sensor parts individually. Data reduction methods can also be incorporated. A simple method for optimizing the calculation grids is further presented. The described approach for sensor modeling has been implemented in two public available softwares for atmospheric radiative transfer simulations.

Arctic Total Water Vapor: Comparison of Regional Climate Simulations with Observations, and Simulated Decadal Trends

Abstract Satellite-retrieved data of total water vapor (TWV) over the Arctic are patchy, with large areas of missing data because of various limitations of the retrieval algorithms. To overcome these observational difficulties, a new retrieval algorithm has been developed that allows for monitoring the TWV over the Arctic during most of the year. This method retrieves TWV from satellite microwave radiometer data [the Advanced Microwave Sounding Unit B (AMSU-B)]. These new data have been made available for 4 yr (2000–03) and have been used to evaluate high-resolution simulations with the Arctic regional atmospheric climate model HIRHAM at daily, monthly, and seasonal time scales. The strong dynamic TWV variability on the daily time scale, linked with moisture transport by weather systems, is discussed for selected case studies. Both the simulated climatological seasonal mean patterns and the variability on interannual and decadal time scales are in agreement with those of the 40-yr European Centre for Medi...

Surface Emissivity of the Arctic Sea Ice at AMSR-E Frequencies

Surface emissivity is an essential quantity to retrieve surface and atmospheric parameters from satellite measurements. The surface emissivity of the Arctic sea ice is calculated using Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) radiance. The method accounts for the variation of the penetration depth in the snow-covered ice with frequency, air temperature, and sea-ice temperature. The variation of emissivity for different frequencies at different seasons is noticed, together with their correlations.

Automatic Estimation of Oil Seep Locations in Synthetic Aperture Radar Images

A framework for the automatic detection of natural oil slicks and estimation of their associated oil seeps using synthetic aperture radar (SAR) images is presented, and the methodology used has been explained in detail. The designed detection system is the first automatic oil seep estimation system known to exist. The system detects oil slicks in individual SAR images and estimates their origins on the sea surface. Spatial clustering of temporally recurrent slick origins is conducted in order to estimate the locations of the associated oil seeps on the sea floor. The system is implemented in the programming language Python and a direct rule-based approach is employed for the classification unit. A data set of 178 images of the Black Sea acquired by ENVISATs Advanced Synthetic Aperture Radar was used to test the algorithm. In this paper, the methodology used to design the algorithm and the automatically estimated oil seep locations are reported. The efficiency of the system with respect to manual detection is discussed.

An automatic detection system for natural oil seep origin estimation in SAR images

A framework for the automatic detection of natural oil seeps using Synthetic Aperture Radar (SAR) images, implemented in Python, is presented. Dark objects are detected using morphological thresholding. For each object, features are computed, which are used to classify the object as either a natural oil slick or a look-alike. The classification scheme has been implemented using a rule-based approach. The slick origins are detected and clustered together spatially, in order to detect the seep origin. A dataset of 122 images from ENVISATs Advanced Synthetic Aperture Radar (ASAR) were used to test the algorithm. In this paper, only preliminary results are reported.