Microwave Radiometer (MWR) Evaluation of Multi-Beam Satellite Antenna Boresight Pointing Using Land-Water Crossings, for the Aquarius/SAC-D Mission

This research concerns the CONAE Microwave Radiometer (MWR), on board the Aquarius/SAC-D platform. MWR\u27s main purpose is to provide measurements that are simultaneous and spatially collocated with those of NASA\u27s Aquarius radiometer/scatterometer. For this reason, knowledge of the MWR antenna beam footprint geolocation is crucial to mission success. In particular, this thesis addresses an on-orbit validation of the MWR antenna beam pointing, using calculated MWR instantaneous field of view (IFOV) centers, provided in the CONAE L-1B science data product. This procedure compares L-1B MWR IFOV centers at land/water crossings against high-resolution coastline maps. MWR IFOV locations versus time are computed from knowledge of the satellite\u27s instantaneous location relative to an earth-centric coordinate system (provided by on-board GPS receivers), and a priori measurements of antenna gain patterns and mounting geometry. Previous conical scanning microwave radiometer missions (e.g., SSM/I) have utilized observation of rapid change in brightness temperatures (T_B) to estimate the location of land/water boundaries, and subsequently to determine the antenna beam-pointing accuracy. In this thesis, results of an algorithm to quantify the geolocation error of MWR beam center are presented, based upon two-dimensional convolution between each beam\u27s gain pattern and land-water transition. The analysis procedures have been applied to on-orbit datasets that represent land-water boundaries bearing specific desirable criteria, which are also detailed herein. The goal of this research is to gain a better understanding of satellite radiometer beam-pointing error and thereby to improve the geolocation accuracy for MWR science data products

SATELLITE ATTITUDE ANALYSIS USING THE VICARIOUS COLD CALIBRATION METHOD FOR MICROWAVE RADIOMETERS

ABSTRACT A method for estimating the pitch and roll errors of a satellite with an onboard conical scanning microwave radiometer is described. The method makes use of the vicarious cold calibration algorithm which derives a stable cold brightness temperature (TB) over ocean. This cold TB is sensitive to the Earth Incidence Angle (EIA) of the radiometer. Given no pitch or roll errors, the EIA can be modeled as a function of the Earth radius and altitude of the satellite. Deviation from this EIA can then be used to estimate the pitch and roll errors. The pitch/roll algorithm is applied to the current spaceborne microwave radiometer WindSat to show its performance, and the results are compared to the derived pitch and roll of WindSat that are found using a different attitude analysis method

CONAE MicroWave Radiometer (MWR) Counts to Brightness Temperature Algorithm

This dissertation concerns the development of the MicroWave Radiometer (MWR) brightness temperature (Tb) algorithm and the associated algorithm validation using on-orbit MWR Tb measurements. This research is sponsored by the NASA Earth Sciences Aquarius Mission, a joint international science mission, between NASA and the Argentine Space Agency (Comision Nacional de Actividades Espaciales, CONAE). The MWR is a CONAE developed passive microwave instrument operating at 23.8 GHz (K-band) H-pol and 36.5 GHz (Ka-band) H- and V-pol designed to complement the Aquarius L-band radiometer/scatterometer, which is the prime sensor for measuring sea surface salinity (SSS). MWR measures the Earth\u27s brightness temperature and retrieves simultaneous, spatially collocated, environmental measurements (surface wind speed, rain rate, water vapor, and sea ice concentration) to assist in the measurement of SSS. This dissertation research addressed several areas including development of: 1) a signal processing procedure for determining and correcting radiometer system non-linearity; 2) an empirical method to retrieve switch matrix loss coefficients during thermal-vacuum (T/V) radiometric calibration test; and 3) an antenna pattern correction (APC) algorithm using Inter-satellite radiometric cross-calibration of MWR with the WindSat satellite radiometer. The validation of the MWR counts-to-Tb algorithm was performed using two years of on-orbit data, which included special deep space calibration measurements and routine clear sky ocean/land measurements

Spaceborne Microwave Radiometry: Calibration, Intercalibration, and Science Applications.

Spaceborne microwave radiometry is the backbone for assimilation into numerical weather forecasts and provides important information for Earth and environment science. The extensive radiometric data must go through the process of calibration and intercalibration prior to science application. This work deals with the entire process by providing systematic methods and addressing critical challenges. These methods have been applied to NASA and JAXA’s Global Precipitation Measurement (GPM) mission and many other radiometers to make important contributions and to solve long-standing issues with coastal science applications. Specifically, it addresses four important challenges: 1) improving cold calibration with scan dependent characterization; 2) reducing the uncertainty of warm calibration; 3) deriving calibration dependence across the full range of brightness temperatures with both cold and warm calibration; and 4) investigating calibration variability and dependence on geophysical parameters. One critical challenge in science applications of radiometer data is that coastal science products from radiometers have previously been largely unavailable due to land contamination. We therefore develop methods to correct for land contamination and derive coastal science products. This thesis addresses these challenges by developing their solutions and then applying them to the GPM mission and its radiometer constellation.PhDAtmospheric, Oceanic and Space SciencesUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/120728/1/johnxun_1.pd

Soil Moisture ActivePassive (SMAP) L-Band Microwave Radiometer Post-Launch Calibration

The SMAP microwave radiometer is a fully-polarimetric L-band radiometer flown on the SMAP satellite in a 6 AM/ 6 PM sun-synchronous orbit at 685 km altitude. Since April, 2015, the radiometer is under calibration and validation to assess the quality of the radiometer L1B data product. Calibration methods including the SMAP L1B TA2TB (from Antenna Temperature (TA) to the Earths surface Brightness Temperature (TB)) algorithm and TA forward models are outlined, and validation approaches to calibration stability/quality are described in this paper including future work. Results show that the current radiometer L1B data satisfies its requirements

Creating a Consistent Oceanic Multi-decadal Intercalibrated TMI-GMI Constellation Data Record

The Tropical Rainfall Measuring Mission (TRMM), launched in late November 1997 into a low earth orbit, produced the longest microwave radiometric data time series of 17-plus years from the TRMM Microwave Imager (TMI). The Global Precipitation Measuring (GPM) mission is the follow-on to TRMM, designed to provide data continuity and advance precipitation measurement capabilities. The GPM Microwave Imager (GMI) performs as a brightness temperature (Tb) calibration standard for the intersatellite radiometric calibration (XCAL) for the other constellation members; and before GPM was launched, TMI was the XCAL standard. This dissertation aims at creating a consistent oceanic multi-decadal Tb data record that ensures an undeviating long-term precipitation record covering TRMM-GPM eras. As TMI and GMI share only a 13-month common operational period, the U.S. Naval Research Laboratory\u27s WindSat radiometer, launched in 2003 and continuing today provides the calibration bridge between the two. TMI/WindSat XCAL for their \u3e 9 years\u27 period, and WindSat/GMI XCAL for one year are performed using a robust technique developed by the Central Florida Remote Sensing Lab, named CFRSL XCAL Algorithm, to estimate the Tb bias of one relative to the other. The 3-way XCAL of GMI/TMI/WindSat for their joint overlap period is performed using an extended CFRSL XCAL algorithm. Thus, a multi-decadal oceanic Tb dataset is created. Moreover, an important feature of this dataset is a quantitative estimate of the Tb uncertainty derived from a generic Uncertainty Quantification Model (UQM). In the UQM, various sources contributing to the Tb bias are identified systematically. Next, methods for quantifying uncertainties from these sources are developed and applied individually. Finally, the resulting independent uncertainties are combined into a single overall uncertainty to be associated with the Tb bias on a channel basis. This dissertation work is remarkably important because it provides the science community with a consistent oceanic multi-decadal Tb data record, and also allows the science community to better understand the uncertainty in precipitation products based upon the Tb uncertainties provided

Geolocating Low-Earth-Orbit satellite data from next-generation millimeter-wave radiometers using natural targets

The main goal of this work is to perform the geolocation error assessment of the channel imagery at 183.31 GHz of the Special Sensor Microwave Imager/Sounder (SSMIS). The frequency around 183.31 GHz still represents the highest channel frequency of current spaceborne microwave and millimeter-wave radiometers. The latter will be extended to frequencies up to 664 GHz, as in the case of EUMETSAT Ice Cloud Imager (ICI). This use of submillimeter observations unfortunately prevents a straightforward geolocation error assessment using landmark-based techniques. This work uses SSMIS data at 183.31 GHz as a submillimeter proxy to identify the most suitable targets for geolocation error validation in very dry atmospheric conditions, as suggested by radiative transfer modeling. Using a yearly SSMIS dataset, 3 candidates landmark targets are selected: i) high-altitude lakes and high-latitude bays using a coastline reference database; ii) Antarctic ice shelves and Arctic shorelines using coastlines derived from Sentinel-1 Synthetic Aperture Radar (SAR) imagery; iii) high altitude mountains using digital elevation model as reference. Data processing is carried out by using spatial cross-correlation methods in the spatial frequency domain and performing a numerical sensitivity analysis to contour displacement. Cloud masking, based on a fuzzy-logic approach, is applied to automatically selected clear-air days. Results show that the average geolocation error is about 6.2 km for mountainous lakes and sea bays and 5.4 km for ice shelves, respectively, with a standard deviation of about 2.7 and 2.0 km. Results are in line with SSMIS previous estimates, whereas annual clear-air days are about 10% for mountainous lakes and sea bays and 18% for ice shelves. The second goal of this work is to investigate ICI channels, focusing on 243 GHz at horizontal polarization (ICI-4). The results of the simulations using radiative transfer model and artificial neural network (ANN) confirm that ICI-4 will be the best candidate to validate the geolocation of the future ICI radiometer. At 243 GHz the atmosphere is less opaque and the surface could be more visible with respect to other frequencies. This work proposes an artificial neural network to reconstruct the 243 GHz starting from real data at 150 GHz and 183 GHz. ANN provides an average value of about 5.8 km with a standard deviation of about 2.7 km. These numbers are in line with those obtained for 183 GHz, but at 243 GHz the number of images that contains visible surface targets are much more with respect to 183 GHz

Soil Moisture Active Passive (SMAP) Project Algorithm Theoretical Basis Document SMAP L1B Radiometer Data Product: L1B_TB

The purpose of the Soil Moisture Active Passive (SMAP) radiometer calibration algorithm is to convert Level 0 (L0) radiometer digital counts data into calibrated estimates of brightness temperatures referenced to the Earth's surface within the main beam. The algorithm theory in most respects is similar to what has been developed and implemented for decades for other satellite radiometers; however, SMAP includes two key features heretofore absent from most satellite borne radiometers: radio frequency interference (RFI) detection and mitigation, and measurement of the third and fourth Stokes parameters using digital correlation. The purpose of this document is to describe the SMAP radiometer and forward model, explain the SMAP calibration algorithm, including approximations, errors, and biases, provide all necessary equations for implementing the calibration algorithm and detail the RFI detection and mitigation process. Section 2 provides a summary of algorithm objectives and driving requirements. Section 3 is a description of the instrument and Section 4 covers the forward models, upon which the algorithm is based. Section 5 gives the retrieval algorithm and theory. Section 6 describes the orbit simulator, which implements the forward model and is the key for deriving antenna pattern correction coefficients and testing the overall algorithm

Inter-satellite Microwave Radiometer Calibration

The removal of systematic brightness temperature (Tb) biases is necessary when producing decadal passive microwave data sets for weather and climate research. It is crucial to achieve Tb measurement consistency among all satellites in a constellation as well as to maintain sustained calibration accuracy over the lifetime of each satellite sensor. In-orbit inter-satellite radiometric calibration techniques provide a long term, group-wise solution; however, since radiometers operate at different frequencies and viewing angles, Tb normalizations are made before making intermediate comparisons of their near-simultaneous measurements. In this dissertation, a new approach is investigated to perform these normalizations from one satellite\u27s measurements to another. It uses Taylor\u27s series expansion around a source frequency to predict Tb of a desired frequency. The relationship between Tb\u27s and frequencies are derived from simulations using an oceanic Radiative Transfer Model (RTM) over a wide variety of environmental conditions. The original RTM is built on oceanic radiative transfer theory. Refinements are made to the model by modifying and tuning algorithms for calculating sea surface emission, atmospheric emission and attenuations. Validations were performed with collocated WindSat measurements. This radiometric calibration approach is applied to establish an absolute brightness temperature reference using near-simultaneous pair-wise comparisons between a non-sun synchronous radiometer and two sun-synchronous polar-orbiting radiometers: the Tropical Rain Measurement Mission (TRMM) Microwave Imager (TMI), WindSat (on Coriolis) and Advanced Microwave Scanning Radiometer (AMSR) on Advanced Earth Observing System -II (ADEOSII), respectively. Collocated measurements between WindSat and TMI as well as between AMSR and TMI, within selected 10 weeks in 2003 for each pair, are collected, filtered and applied in the cross calibration. AMSR is calibrated to WindSat using TMI as a transfer standard. Accuracy prediction and error source analysis are discussed along with calibration results. This inter-satellite radiometric calibration approach provides technical support for NASA\u27s Global Precipitation Mission which relies on a constellation of cooperative satellites with a variety of microwave radiometers to make global rainfall measurements

Microwave Radiometer Inter-Calibration: Algorithm Development and Application.

Microwave radiometer inter-calibration is an essential component of any effort to combine measurements from two or more radiometers into one dataset for scientific studies. One spaceborne instrument in low Earth orbit is not sufficient to perform long-term climate studies or to provide measurements more than twice per day at any given location on Earth. Measurements from several radiometers are necessary for analyses over extended temporal and spatial ranges. In order to combine the measurements, the radiometers need to be inter-calibrated due to the instruments having unique instrument designs and calibrations. Inter-calibration ensures that consistent scientific parameters are retrieved from the radiometers. The development of a cold end inter-calibration algorithm is presented. The algorithm makes use of vicarious cold calibration, along with the double difference method, to calculate calibration differences between radiometers. The performance of the algorithm is characterized using data from current conical scanning microwave radiometers. The vicarious cold calibration double difference is able to sufficiently account for design differences between two radiometers including frequency, earth incidence angle, and orbital characteristics. An estimate of the uncertainty in the inter-calibration algorithm is given as a result of potential errors in the geophysical inputs and improper accounting of seasonal and diurnal variability. The vicarious cold calibration double difference method is shown to be a valid and accurate inter-calibration algorithm. Results are compared with calibration differences calculated using alternate algorithms and sufficient agreement is attained. Inter-calibration is shown to be necessary for achieving consistency in retrieved scientific parameters by using the vicarious cold calibration double difference method to inter-calibrate two radiometers that are then used to derive rain accumulations. Inter-calibration results in a significant improvement in the rain accumulation agreement between the radiometers. This validates inter-calibration algorithm development and shows that it has a positive impact on achieving consistency in scientific parameter retrievals.PhDAtmospheric, Oceanic and Space SciencesUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/107078/1/rakro_1.pd