Search

In the present paper, permittivity maps of equatorial lunar surface are generated using brightness temperature ( T B ) data obtained from Microwave Radiometer (MRM) of Chang'e-1 and physical temperature ( T P ) data obtained from Diviner of Lunar Reconnaissance Orbiter (LRO). Here, permittivity mapping is not carried out above 60° latitudes towards the lunar poles due to large anomaly in the physical temperature obtained from the Diviner. Microwave frequencies, which are used to generate these maps are 3 GHz, 7.8 GHz, 19.35 GHz and 37 GHz. Permittivity values are simulated using T B values at these four frequencies. Here, weighted average of physical temperature obtained from Diviner are used to compute permittivity at each microwave frequencies. Longer wavelengths of microwave signals give information of more deeper layers of the lunar surface as compared to smaller wavelength. Initially, microwave emissivity is estimated using T B values from MRM and physical temperature ( T P ) from Diviner. From estimated emissivity the real part of permittivity ( e ), is calculated using Fresnel equations. The permittivity maps of equatorial lunar surface is generated. The simulated permittivity values are normalized with respect to density for easy comparison of simulated permittivity values with the permittivity values of Apollo samples as well as with the permittivity values of Terrestrial Analogue of Lunar Soil (TALS) JSC-1A. Lower value of dielectric constant ( e ′) indicates that the corresponding lunar surface is smooth and doesn't have rough rocky terrain. Thus a future lunar astronaut can use these data to decide proper landing site for future lunar missions. The results of this paper will serve as input to future exploration of lunar surface.

In the present paper, an artificial neural network (ANN) based technique has been developed to estimate instantaneous rainfall by using brightness temperature from the IR sensors of SEVIRI radiometer, onboard Meteosat Second Generation (MSG) satellite. The study is carried out over north of Algeria. For estimation of rainfall, weight matrices of two ANNs namely MLP1 and MLP2 are developed. MLP1 is to identify raining or non-raining pixels. When rainy pixels are identified, then for those pixels, instantaneous rainfall is estimated by using MLP2. For identification of raining and non raining pixels, 7 input parameters from the IR sensors are utilized. Corresponding data of raining/non-raining pixels are taken from radar. For instantaneous rainfall estimation, 14 input parameters are utilized, where 7 parameters are information about raining pixels and 7 parameters are related with cloud features. The results obtained show the neural network performs reasonably well.

In this study, observations from microwave satellites, visible and infrared instruments have been analyzed to detect dust storm over north and north–west part of India during 18–23 March 2012. This study investigated the approach to utilize the multi satellite data of Moderate Resolution Imaging Spectroradiometer (MODIS) on-board the Terra and Aqua satellite and the Advanced Microwave Sounding Unit (AMSU) on-board NOAA satellite to study the characteristics of dust storms from real time direct broadcast (DB) receiving system installed at three places of India Meteorological Department (IMD). The dust storm detection is based on the infrared brightness temperature (BT) difference between channels at 11 and 12 μm and polarized BT difference between two channels of 89 and 23.8 GHz. It is found that the significant differences between the BT of channel 89 and 23.8 can be used as a discriminator of identifying dust storm. The Total Ozone Mapping Spectroradiometer (TOMS) Aerosol Index (AI) and AMSU-A 23 GHz channel BT from NOAA satellite over the north and north-west part of India have also been analyzed. The result indicated the characteristic behavior between BT and AI during the different phases of the dust storm. Finally, the occurrence of dust outbreaks has also been validated with sky radiometer of IMD, which confirms the presence of a dust storm over the Indian region. Further, the findings of the study and its approaches apply to the other dust storm cases which occurred during the months of April and June 2012. The integrated approach suggested the potential to use high resolution data of microwave as well as thermal–infrared using multi-satellite observations from real time direct broadcast system for the detection of severe, moderate or weak dust storms very well. The approach is found to be promising for operational application.

A multi-channel microwave radiometre (make: Radiometrics Corporation) is installed at Instituto Nacional de Pesquisas Espaciais–INPE, Brazil (22°S). The radiometric output of two channels of the radiometer in the form of brightness temperature at 23.834 GHz and 30 GHz, initially, were used to find out the ambient water vapor content and the non-precipitable cloud liquid water content. The necessary algorithm was developed for the purpose. The best results were obtained using the hinge frequency 23.834 GHz and 30 GHz pair having an r.m.s. error of only 2.64. The same methodology was then adopted exploiting 23.034 GHz and 30 GHz pair. In that case the r.m.s. error was 3.42. These results were then compared with those obtained over Kolkata (22°N), India, by using 22.234 GHz and 31.4 GHz radiometric data. This work conclusively suggests the use of a frequency should not be at the water vapor resonance line. Instead, while measuring the vapor content for separation of vapor and cloud liquid, one of them should be a few GHz left or right from the resonance line i.e., at 23.834 GHz and the other one should be around 30 GHz.

The pre-launch ground infrared calibration of the Geostationary Operational Environmental Satellite (GOES) N-Q Imager and Sounder is presented. Ground calibration provides information necessary to the accurate on-orbit calibration of these radiometers. Infrared channels are calibrated in a thermal vacuum environment, under minimum and maximum mission operation temperatures, using a variable-temperature warm target to simulate the Earth scene and a cold target to simulate the space scene. Brightness temperatures derived from observation of the instrument internal calibration target, used for on-orbit calibration, are compared to brightness temperatures of the external calibration target as a check of relative accuracy using these two sources. Changes to the GOES N-Q specification from GOES I-M are highlighted, as well as results of noise, relative calibration accuracy, and spectral response performance to date. For completeness, results of spatial and pointing performance will be presented. Enhancements to test methodology and data processing techniques are highlighted throughout.

The Stratospheric Sounding Unit (SSU) is part of the TOVS (TIROS Operational Vertical Sounder) on NOAA operational meteorological satellites. SSU measurements can be validated by comparison with temperature measurements from colocated rocket sondes. Systematic differences are found which vary with rocket station and sonde and are a function of height. However, these measurements are not adequate to define the performance of individual SSUs to a precision which would allow the observations from different SSUs to be combined in the study of diurnal and semidiurnal tides and of long term trends in stratospheric temperature. Instead this is achieved by detailed radiometric and spectroscopic investigation of each individual SSU, both prior to launch and during its operational life. Using the techniques descirbed, it is demonstrated that measurements from different SSUs can be combined with a relative error of less than 0.2K in equivalent brightness temperature.

With the objective of developing Microwave Remote Sensing technology in the country, India has launched a series of Satellites Bhaskara-I and II with the microwave radiometer capability. In this paper, an attempt is made to demonstrate the capability of the brightness temperature data acquired by these radiometers to discriminate various soil moisture conditions of Indian land mass. The analysis show that large areas assessment of soil moisture is possible to a limited extent.

We have continued our program to measure the long-wavelength spectrum of the cosmic background radiation. Our previous observations were at five wavelengths--0.33, 0.9, 3.0, 6.3, and 12.0 cm--and had a weighted average value of 2.73 {+-} 0.05 K and deviated from a Planckian spectrum by less than 6%. In August 1984, we repeated our observations at 3.0, 0.9, and 0.33 cm and made new observations with a radiometer tunable from 1.7 to 15 cm. Preliminary analysis indicate that the new data are consistent with our previous results.