Radio Astronomy

Contains table of contents for Section 4 and reports on seven research projects.National Science Foundation Grant AST 92-24191MIT Class of 1948/Career Development ChairNational Science Foundation Presidential Young Investigator AwardDavid and Lucile Packard FellowshipMIT Lincoln Laboratory Agreement BX-4975National Aeronautics and Space Administration/Goddard Space Flight Center Grant NAS5-31276National Aeronautics and Space Administration/Goddard Space Flight Center Grant NAG5-10MIT Leaders for Manufacturing Progra

High-spatial-resolution passive microwave sounding systems

The principal contributions of this combined theoretical and experimental effort were to advance and demonstrate new and more accurate techniques for sounding atmospheric temperature, humidity, and precipitation profiles at millimeter wavelengths, and to improve the scientific basis for such soundings. Some of these techniques are being incorporated in both research and operational systems. Specific results include: (1) development of the MIT Microwave Temperature Sounder (MTS), a 118-GHz eight-channel imaging spectrometer plus a switched-frequency spectrometer near 53 GHz, for use on the NASA ER-2 high-altitude aircraft, (2) conduct of ER-2 MTS missions in multiple seasons and locations in combination with other instruments, mapping with unprecedented approximately 2-km lateral resolution atmospheric temperature and precipitation profiles, atmospheric transmittances (at both zenith and nadir), frontal systems, and hurricanes, (3) ground based 118-GHz 3-D spectral images of wavelike structure within clouds passing overhead, (4) development and analysis of approaches to ground- and space-based 5-mm wavelength sounding of the upper stratosphere and mesosphere, which supported the planning of improvements to operational weather satellites, (5) development of improved multidimensional and adaptive retrieval methods for atmospheric temperature and humidity profiles, (6) development of combined nonlinear and statistical retrieval techniques for 183-GHz humidity profile retrievals, (7) development of nonlinear statistical retrieval techniques for precipitation cell-top altitudes, and (8) numerical analyses of the impact of remote sensing data on the accuracy of numerical weather predictions; a 68-km gridded model was used to study the spectral properties of error growth

Spectrum Synergy for Investigating Cloud Microphysics

Observations from spaceborne microwave (MW) and infrared (IR) passive sensors are the backbone of current satellite meteorology, essential for data assimilation into modern numerical weather prediction and for climate benchmarking. While MW and IR observations from space offer complementary features with respect to cloud properties, their synergy for cloud investigation is currently underexplored, despite the presence of both MW and IR sensors on operational meteorological satellites such as the EUMETSAT Polar System (EPS) MetOp series. As such, several key cloud microphysical properties are not part of the operational products available from EPS MetOp sensors. In addition, the EPS Second Generation (EPS-SG) series, scheduled for launch starting from 2024 onward, will carry sensors such as the Microwave Sounder (MWS) and IASI Next Generation (IASI-NG), enhancing spatial and spectral resolutions and thus capacity to retrieve cloud properties. This article presents the Combined MWS and IASI-NG Soundings for Cloud Properties (ComboCloud) project, funded by EUMETSAT with the overall objective to specify, prototype, and validate algorithms for the retrieval of cloud microphysical properties (e.g., water content and drop effective radius) from the synergy of passive MW and IR observations. The article presents the synergy rationale, the algorithm design, and the results obtained exploiting simulated observations from EPS and EPS-SG sensors, quantifying the benefits to be expected from the MW-IR synergy and the new generation sensors

Radio Astronomy

Contains table of contents for Section 4 and reports on ten research projects.National Science Foundation Grant AST 90-22501Alfred P. Sloan FellowshipDavid and Lucile Packard Fellowship Award for Science and EngineeringNational Aeronautics and Space AdministrationNational Science Foundation Presidential Young Investigator AwardNational Aeronautics and Space Administration Grant NAGW-2310MIT Lincoln Laboratory Agreement BX-4975National Aeronautics and Space Administration/Goddard Space Flight Center Contract NAS 5-31276MIT Leaders for Manufacturing Progra

Estimation of atmospheric temperature and humidity profiles from MODIS data and radiosond data using artificial neural network

The aim of this study is to test the quality of the neural network for retrieving the temperature and humidity by comparison with the radiosond values and a linear regression method. Remote sensed images give useful information about the atmosphere. In this article, MODIS data is used to retrieve temperature and humidity profiles of the atmosphere. Two methods of linear regression and artificial neural network are used to retrieve the temperature and humidity profiles. A multilayer feed-forward neural network is tested to estimate the desired geophysical profiles. Retrievals are validated by comparison with coincident radiosond profiles

Estimation of physical variables from multichannel remotely sensed imagery using a neural network: Application to rainfall estimation

Satellite-based remotely sensed data have the potential to provide hydrologically relevant information about spatially and temporally varying physical variables. A methodology for estimating such variables from multichannel remotely sensed data is presented; the approach is based on a modified counterpropagation neural network (MCPN) and is both effective and efficient at building complex nonlinear input-output function mappings from large amounts of data. An application to high-resolution estimation of the spatial and temporal variation of surface rainfall using geostationary satellite infrared and visible imagery is presented. Test results also indicate that spatially and temporally sparse ground-based observations can be assimilated via an adaptive implementation of the MCPN method, thereby allowing on-line improvement of the estimates

Global estimation of precipitation using opaque microwave bands

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2004.Includes bibliographical references (p. 115-125).This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.This thesis describes the use of opaque microwave bands for global estimation of precipitation rate. An algorithm was developed for estimating instantaneous precipitation rate for the Advanced Microwave Sounding Unit (AMSU) on the NOAA-15, NOAA-16, and NOAA-17 satellites, and the Advanced Microwave Sounding Unit and Humidity Sounder for Brazil (AMSU/HSB) aboard the NASA Aqua satellite. The algorithm relies primarily on channels in the opaque 54-GHz oxygen and 183-GHz water vapor resonance bands. Many methods for estimating precipitation rate using surface-sensitive microwave window channels have been developed by others. The algorithm involves a set of signal processing components whose outputs are fed into a neural net to produce a rain rate estimate for each 15-km spot. The signal processing components utilize techniques such as principal component analysis for characterizing groups of channels, spatial filtering for cloud-clearing brightness temperature images, and data fusion for sharpening images in order to optimize sensing of small precipitation cells. An effort has been made to make the algorithm as blind to surface variations as possible. The algorithm was trained using data over the eastern U.S. from the NEXRAD ground-based radar network, and was validated through numerical comparisons with NEXRAD data and visual examination of the morphology of precipitation from over the eastern U.S. and around the world. It performed reasonably well over the eastern U.S. and showed potential for detecting and estimating falling snow. However, it tended to overestimate rain rate in summer Arctic climates. Adjustments to the algorithm were made by developing a neural-net-based estimator for estimating a multiplicative correction factor based on data from(cont.) the Advanced Microwave Sounding Radiometer for the Earth Observing System (AMSR-E) on the Aqua satellite. The correction improved estimates in the Arctic to more reasonable levels. The final estimator was a hybrid of the NEXRAD-trained estimator and the AMSR-E-corrected estimator. Climatological metrics were computed over one year during which all AMSU-A/B instruments on NOAA-15, NOAA-16, and NOAA-17 were working. Annual mean rain rates appear to agree morphologically with those from the Global Precipitation Climatology Project. Maps of precipitation frequencies and the diurnal variations of precipitation rate were produced.by Frederick Wey-Min Chen.Ph.D

Atmospheric Infrared Sounder

The microwave 'first-guess' algorithm was run on the cloudy test simulations. Eight datasets were considered in the cloudy test, comprising approximately 360 retrievals, of which one failed to converge. Retrievals were done on the AMSU-A grid. Examination of the true profiles (provided for the A, C and D tracks) showed numerous cases of very pronounced temperature inversion layers in the troposphere which the retrieval does not have enough vertical resolution to reproduce. A typical example with an inversion layer near 700 mbar is shown in Figure 1. The inversion layers also exhibit strong vertical gradients of water vapor which are not resolved in the retrieval. (Water vapor volume density is given in g/sq cm per layer. Layer thickness is 20 mb from 200 to 400 mb, and 25 mb from 400 to 1,000 mb.) The retrievals do reproduce the overall smoothed shape of the profiles, and therefore as a first guess should be within the range of linear methods for IR retrievals using AIRS

Radio Astronomy

Contains table of contents for Section 4 and reports on five research projects.National Science Foundation Grant AST 92-24191MIT Lincoln Laboratory Agreement BX-4975National Aeronautics and Space Administration/Goddard Space Flight Center Grant NAS 5-31376National Aeronautics and Space Administration/Goddard Space Flight Center Grant NAG5-10MIT Leaders for Manufacturing Progra