Adam B. Milstein

Radiometer Calibration Using Colocated GPS Radio Occultation Measurements

We present a new high-fidelity method of calibrating a cross-track scanning microwave radiometer using Global Positioning System (GPS) radio occultation (GPSRO) measurements. The radiometer and GPSRO receiver periodically observe the same volume of atmosphere near the Earths limb, and these overlapping measurements are used to calibrate the radiometer. Performance analyses show that absolute calibration accuracy better than 0.25 K is achievable for temperature sounding channels in the 50-60-GHz band for a total-power radiometer using a weakly coupled noise diode for frequent calibration and proximal GPSRO measurements for infrequent (approximately daily) calibration. The method requires GPSRO penetration depth only down to the stratosphere, thus permitting the use of a relatively small GPS antenna. Furthermore, only coarse spacecraft angular knowledge (approximately one degree rms) is required for the technique, as more precise angular knowledge can be retrieved directly from the combined radiometer and GPSRO data, assuming that the radiometer angular sampling is uniform. These features make the technique particularly well suited for implementation on a low-cost CubeSat hosting both radiometer and GPSRO receiver systems on the same spacecraft. We describe a validation platform for this calibration method, the Microwave Radiometer Technology Acceleration (MiRaTA) CubeSat, currently in development for the National Aeronautics and Space Administration (NASA) Earth Science Technology Office. MiRaTA will fly a multiband radiometer and the Compact TEC/Atmosphere GPS Sensor in 2015.

Acquisition algorithm for direct-detection ladars with Geiger-mode avalanche photodiodes

An optimal algorithm for detecting a target using a ladar system employing Geiger-mode avalanche photodiodes (GAPDs) is presented. The algorithm applies to any scenario where a ranging direct detection ladar is used to determine the presence of a target against a sky background within a specified range window. A complete statistical model of the detection process for GAPDs is presented, including GAPDs that are inactive for a fixed period of time each time they fire. The model is used to develop a constant false alarm rate detection algorithm that minimizes acquisition time. Numerical performance predictions, simulation results, and experimental results are presented.

Polarimetric lidar signatures for remote detection of biological warfare agents

Polarimetric Lidar has been recently proposed as a method for remote detection of aerosolized biological warfare agents. Accurate characterization of the optical signatures for both biological agents and environmental interferents is a critical first step toward successful sensor deployment. MIT Lincoln Laboratory has developed the Standoff Aerosol Active Signature Testbed (SAAST) as a tool for characterizing aerosol elastic scattering cross sections.1 The spectral coverage of the SAAST includes both the nearinfrared (1-1.6 μm) and mid-infrared (3-4 μm) spectral regions. The SAAST source optics are capable of generating all six classic optical polarization states, while the polarization-sensitive receiver is able to reconstruct the full Stokes vector of the scattered wave. All scattering angles, including those near direct backscatter, can be investigated. The SAAST also includes an aerosol generation system capable of producing biological and inert samples with various size distributions. This paper discusses the underlying scattering phenomenology, SAAST design details, and presents some representative data.

A Neural Network Retrieval Technique for High-Resolution Profiling of Cloudy Atmospheres

The synergistic use of microwave and hyperspectral infrared sounding observations gives rise to a rich array of signal processing challenges. Of particular interest are the following elements which are combined for the first time in the retrieval technique presented here: (1) radiance noise filtering and redundancy removal (compression) using principal components transforms and canonical correlations, (2) data fusion (infrared plus microwave at possibly different spatial and spectral resolutions) and stochastic cloud clearing (SCC), and (3) geophysical product retrieval from spectral radiance measurements using neural networks. In this paper, we describe the algorithm and demonstrate performance using the Atmospheric Infrared Sounder (AIRS) and the Advanced Microwave Sounding Unit (AMSU). We show that performance is improved by approximately 25%-50% using the neural network method relative to other common techniques. Furthermore, we quantify the improvement in the vertical resolution of the retrieved products.

Neural Network Estimation of Atmospheric Thermodynamic State for Weather Forecasting Applications

We present recent work using neural network estimation techniques to process satellite observation of the Earth’s atmosphere to improve weather forecasting performance. A novel statistical method for the retrieval of atmospheric temperature and moisture (relative humidity) profiles has been developed and evaluated with sounding data from the Atmospheric InfraRed Sounder (AIRS) and the Advanced Microwave Sounding Unit (AMSU) on the NASA Aqua satellite and the Infrared Atmospheric Sounding Interferometer (IASI) and AMSU on the EUMETAT MetOp-A satellite. The present work focuses on the cloud impact on the AIRS and IASI radiances and explores the use of stochastic cloud clearing mechanisms together with neural network estimation. The algorithm outputs are ingested into a numerical model, and forecast information and decision support tools are then presented to a meteorologist. We discuss the underlying physical problem, the algorithmic framework, and the interaction with forecaster.

On the Use of Gaussian Random Processes for Probabilistic Interpolation of CubeSat Data in the Presence of Geolocation Error

With their greatly reduced sizes, low development cost, and rapid construction time, CubeSats have merged as a platform of considerable interest for a wide range of applications, including remote sensing. Many applications require the interpolation of sensor data into a regularly spaced grid for the development of downstream scientific products. This problem is complicated for CubeSat platforms due to potentially significant uncertainties associated with the spatial position of the satellite. In this paper, we present a probabilistic approach to the data interpolation problem in which we estimate both the platform location and data samples on a regular grid given observations corrupted by noise and location error. Our approach is based on a Gaussian process model to connect the measured data to the values on the grid. Two statistical models for positional uncertainties are considered, one based on an assumption of independent errors and another motivated by positional errors associated with a specific platform of interest, the MicroMAS radiometer. In each case, the maximum a posteriori estimate of the positions and the data is generated using an optimized Gaussian process regression (OGPR) method resulting in two algorithms: OGPR-IID and OGPR-PCA. The performance of this approach is tested on both simulated data and advanced technology microwave sounder data where significant improvements both qualitatively and quantitatively relative to traditional interpolation methods are observed.

Earth limb calibration of scanning spaceborne microwave radiometers

We introduce a new technique for absolute “through-theantenna” calibration of cross-track-scanning passive microwave radiometers viewing earth from a low-earth orbit. This method offers significant advantages, in that neither internal calibration targets nor noise diodes are needed to calibrate the radiometer. The algorithm does require periodic updates of the atmospheric state, which can be readily provided by GPS radio occultation observations, for example. An iterative algorithm retrieves the radiometer gain given a sequence of observations of the earths limb. The algorithm uses a parameterized radiative transfer model of a spherically-stratified atmosphere. The algorithm works best for opaque temperature sounding channels. This method, when used on idealized radiometer measurements (impulse response functions in frequency and space), yields calibration accuracies similar to those that could be obtained with ideal internal calibration targets. This analysis is based on global Monte Carlo simulations using the NOAA88b profile set. An analysis will also be presented showing how calibration performance degrades as the radiometer characteristics deviate from the ideal case. Among the factors considered are: 1) antenna pattern, 2) spectral passband, 3) pointing errors, 4) atmospheric state variability, 5) the number of limb observations required, and 6) sensitivity to sensor noise.

Calibration and validation of small satellite passive microwave radiometers: MicroMAS-2A and TROPICS

Miniaturized microwave radiometers deployed on nanosatellites in Low Earth Orbit are now demonstrating cost-effective weather monitoring capability, with increased temporal and spatial resolution compared to larger weather satellites. MicroMAS-2A is a 3U CubeSat that launched on January 11, 2018 with a 1U 10-channel passive microwave radiometer with channels near 90, 118, 183, and 206 GHz for moisture and temperature profiling and precipitation imaging. The Time-Resolved Observations of Precipitation structure and storm Intensity with a Constellation of Smallsats (TROPICS) mission is projected to launch in 2020, and its 1U 12-channel passive microwave radiometer is based on the current CubeSat mission MicroMAS-2A. TROPICS will provide rapid-refresh measurements over the tropics and measure environmental and inner-core conditions for tropical cyclones. In order to effectively use small satellites such as MicroMAS-2A and TROPICS as a weather monitoring platform, calibration must ensure consistency with state of the art measurements, such as the Advanced Technology Microwave Sounder (ATMS), which has a noise equivalent delta temperature (NEDT) at 300 K of 0.5 - 3.0 K. In this work, we present initial analysis from the MicroMAS-2A radiometric bias validation, which compares MicroMAS-2A measured brightness temperatures to simulated brightness temperatures calculated by the Community Radiative Transfer Model (CRTM) using input from GPS radio occultation (GPSRO), radiosonde, and numerical weather prediction (NWP) atmospheric profiles. We also model solar and lunar intrusions for TROPICS, and show that the frequency of intrusions with a scanning payload allows for the novel opportunity of using the solar and lunar intrusions as a calibration source.

A Probabilistic Analysis of Positional Errors on Satellite Remote Sensing Data Using Scattered Interpolation

With the recent development of CubeSats, several ultracompact, low cost, and rapidly deployable satellites have been developed for earth observation missions. Because of the geometry of the acquisition process, measurements are irregularly sampled, whereas in meteorological applications, data are preferred on a regular grid. This problem is further complicated by the fact that, due to CubeSats’ compact sizes and constraints, such as limited power, errors occur in geolocation calibration, resulting in positional errors. In this letter, we analyze how the commonly used triangulation-based linear data interpolation scheme behaves under probabilistic models for the positional errors. The derived distribution of interpolation error caused by positional error is intractable even under a Gaussian distribution for positional errors. To address this problem, we developed an analytical closed-form solution to the first two moments of the interpolation error. Using models for positional errors motivated by our prior work, experimental results show that, compared with the first-order linear model, the second-order one provides a better approximation in terms of the mean and variance, which is very close to that is obtained using more computationally intensive Monte Carlo simulations. This model also allows for the closed-form calculation of mean squared interpolation error, which can be of use in the context of system design where the impact of positional errors on remote sensing products must be considered.

Estimation theoretic methods for cubesat data interpolation in the presence of geolocation errors

With their greatly reduced sizes, low development cost and rapid construction times, CubeSats have emerged as a platform of intense interest for a wide range of applications, including remote sensing. However, due to their compact form factor, performance tradeoffs relative to larger existing platforms have been encountered. Of specific interest in this paper are data processing challenges associated with the Micro-MAS platform. In meteorological applications, the radiometer samples are preferred on a regularly spaced grid for generating subsequent scientific products such as vertical temperature and water vapor profiles, or fusing with other gridded datasets. However, in reality, MicroMAS radiometer samples are not regularly spaced, and are expected to have geolocation errors comparable in magnitude to the beam-width [10]. In this work, we present a joint maximum a posteriori (MAP) estimation approach to determine both sample locations as well as brightness temperature on a regular spatial grid given irregularly sampled data corrupted by noise and uncertainty in sample locations. The performance of this approach is tested on Advanced Technology Microwave Sounder (ATMS) data which demonstrates significant improvement both qualitatively and quantitatively compared with traditional estimation methods.