Linwood Jones

Inter-Satellite Radiometer Calibration of WindSat, TMI and SSMI

NASAs Global Precipitation Measurement (GPM) Mission will consist of a constellation of cooperative satellites with microwave radiometers to make global rainfall measurements. It is crucial that the brightness temperature (Tb) measurements from these satellite radiometers be consistent with each other. This may be achieved by the radiometric inter-comparison of near-simultaneous collocated oceanic measurements from pairs of satellites. In this paper, we describe two methods of performing such inter-comparisons. The first uses a radiative transfer model (RTM) to predict expected Tb differences between a pair of radiometer channels due to frequency and incidence angle differences, and then computes the unexplained relative radiometric bias between the channels over a wide variety of environmental conditions. The other approach uses a multi-variable linear regression built from RTM Tb simulations of all frequency channels for V and H polarizations. These two approaches are used to compare external measurements between the TRMM Microwave Imager (TMI), WindSat (on Coriolis), and SSMI (on DMSP F-13 and F-14).

Disdrometer calibration using an adaptive signal processing algorithm

Disdrometers are considered exotic instruments and provide valuable information. As such, their price tag is also high. Impact disdrometers are instruments that produce an electrical impulse output related to the mass of a rain drop colliding at terminal velocity with a sensor. The produced electrical impulse signal amplitude and energy are related to the drop diameters. This relation is in general nonlinear and depends heavily on the type of transducer used mechanical structure imperfections and electrical tolerances dictate the need for the individual calibration of each instrument in an attempt to create calibration curves that convert impulse amplitudes to equivalent drop diameters. Conventional calibration techniques using drop towers have been a tedious process to say the least. A proposed alternative calibration technique utilizing an adaptive signal processing algorithm eliminates the need of a single drop calibration. An accumulation rain gauge provides a reference signal to the disdrometer that is used for adaptive training and optimization of a model based calibration function. In this paper we describe a prototype low-cost disdrometer implementation at the University of Central Florida. A prototype impact sensor was built using an array of piezoelectric elements encapsulated in water resistant material. For the data acquisition and processing we use the soundboard of a general purpose computer. The signal processing algorithms and Matlab implementation will be described. Data have been collected and processed and results will be presented. Future plans on developing a low cost disdrometer will also be discussed. The availability of affordable disdrometers will benefit NASAs upcoming GPM program, as well as many other meteorological agencies.

Use of Monte Carlo simulation in remote sensing data analysis

In the summer of 2011, the Aquarius earth science satellite was launched to measure Sea Surface Salinity (SSS) using a L-band microwave radiometer/scatterometer. This is an important oceanic parameter for monitoring the earths water cycle over oceans and for modeling global climate change. The microwave remote sensing of SSS is a challenging objective. The SSS signal is weak and there are many interfering error sources that must be corrected to achieve an accurate SSS measurement. This paper deals with the use of random processes theory for assessing the effects of rainfall on the retrieved SSS. In this paper we use the Monte Carlo method that is one of the best methods for analysis of random processes, to investigate the multilayer effect caused by rainfall on the L-band brightness temperature and the resulting SSS retrieval.

MWR and WindSat inter-satellite radiometric calibration plan

In late 2010, the Aquarius/SAC-D joint international science mission, between the National Aeronautics and Space Administration (NASA) and the Argentine Space Agency (CONAE), will be launched on a polar-orbiting satellite. This mission of discovery will provide measurements of the global sea surface salinity, which contributes to understanding climatic changes in the global water cycle and how these variations influence the general ocean circulation [1]. The Microwave Radiometer (MWR) [2], a three channel Dicke radiometer operating at 23.8 GHz H-Pol and 36.5 GHz V-& H-Pol provided by CONAE, will complement Aquarius (NASAs L-band radiometer/scatterometer) by providing simultaneous spatially collocated environmental measurements such as water vapor, cloud liquid water, surface wind speed, rain rate and sea ice concentration. This paper presents a short description of the MWR system design with emphasis on the internal radiometric calibration approach and the plan for on-orbit radiometric calibration. A major part of the MWR on-orbit calibration plan involves the inter-satellite radiometric cross-calibration using the Naval Research Laboratorys multi-frequency polarimetric microwave radiometer, WindSat, on board the Coriolis satellite. Because Coriolis and Aquarius/SAC-D are both polar orbiting satellites with similar altitudes, inclinations, and ascending/descending nodes, these two satellites have high percentage overlapping swaths giving spatial/temporal collocations within a ±45 min window. Also, WindSat is an accepted well-calibrated radiometer, and MWR channels are a subset of WindSat, with only minor differences (incidence angles and frequencies), which simplifies the inter-comparison. Details of the inter-comparison are presented using orbital simulation for swath overlap, and a discussion of the radiative transfer modeling brightness temperature (Tb) normalization procedure to account for expected incidence angle and frequency differences is also discussed. Two examples are provided from our previous experience with WindSat and Tropical Rainfall Measurement Mission Microwave Imager (TMI).

Advantages of Calibration Attitude Maneuvers for spaceborne microwave radiometer missions

Earth observing satellite microwave radiometers have been in use since the 1960s providing geoscientists invaluable insight into the complex interaction of the atmosphere, ocean and land in the climate of our planet. Such key instruments must be vetted of any calibration issues so as to provide the utmost accurate and stabilized dataset for scientific analysis. There are several post-launch radiometric calibration methods currently in use, and most require multiple ancillary data sets and a lengthy duration (typically one year) of on-orbit brightness temperature observations to obtain conclusive results. However, one on-orbit calibration method that can provide accurate and early results is the Calibration Attitude Maneuver (CAM), which encompasses Deep Space Calibration (DSC) and a new use of the near-Nadir Second Stokes (SS) analysis. This paper provides examples of CAMs that have aided in the calibration of the Tropical Rainfall Measuring Mission Microwave Imager (TMI) and the Global Precipitation Measurement Microwave Imager (GMI). Excellent results obtained suggest the use of the CAM as a recommended tool for on-orbit calibration for microwave radiometers.

On-orbit signal processing procedure for determining Microwave Radiometer non-linearity

This paper presents a novel signal processing approach used to diagnose non-linear (gain compression) operation of the MicroWave Radiometer (MWR) flown on the Aquarius/SAC-D satellite. This instrument was commissioned in September 2011, and on-orbit observations of blackbody earth emissions (brightness temperatures) have been evaluated over the past year. Results are presented, which suggest that the radiometer system is slightly non-linear, and the resulting receiver gain is a function of the observed radiometric brightness temperature.

Intersatellite radiometric calibration for a satellite radar scatterometer

After the launch of NASAs SeaWinds radar scatterometer on the QuikSCAT satellite in 1999, a radiometer function, known as the QuikSCAT Radiometer - QRad, was implemented in the Science Ground Data Processing Systems to allow the measurement of the earths microwave brightness temperature (Tb) using the radar system noise temperature [1, 2]. This paper will describe an inter-satellite radiometric calibration technique to validate the QRad brightness temperature algorithm and the QuikSCAT L2A Tb product. This approach allows the inter-comparison of two satellite sensors (radiometers) that have significant differences in their designs. To assess the quality of the QRad instrument, we compare its Tb measurements with the near simultaneous and collocated ocean brightness temperature observations from WindSat on the Coriolis Satellite, which serves as the brightness temperature calibration standard. Since the QRad and WindSat instruments were of different designs, brightness temperature normalizations were made for WindSat before comparison to account for expected differences in Tb because of incidence angle and channel frequency differences. Brightness temperatures for nine months during 2005 and 2006 were spatially collocated for rainfree homogeneous ocean scenes (match-ups) within 1° latitude x longitude boxes and within a ± 60 minute window. To ensure high quality comparison, these collocations were quality controlled and edited to remove non-homogenous ocean scenes and/or transient environmental conditions, including rain contamination. WindSat and QRad Tbs were averaged within 1° boxes and were used for the radiometric inter-calibration analysis on a monthly basis. Results show that QRad radiometric calibration is stable in the mean over the yearly seasonal cycle.

Vertical air motion estimates from the disdrometer flux conservation model and related experimental observations

The use of meteorological radar reflectivity Z to estimate rainfall rate R is approached using a different perspective from the classical Z-R relation. Simultaneous rain measurements from different sensors are combined to construct a model that estimates the vertical air velocity by minimizing the error in reflectivity between the different sensors. This model is based on the fact that rain rate and reflectivity are both dependent on the integrals of rain drop size distribution (DSD) but only R depends on vertical air velocity. This study attempts to validate the vertical air velocity estimates and quantify their affects on the rainfall rate estimation. Disdrometer Flux Conservation Model (DFC) uses measurements from disdrometers and other sensors such as vertically pointing radar profilers and scanning radars. Disdrometers measure a drop size flux (Phi) (D), defined as the number of drops passing a horizontal surface per unit time, per unit area, per drop size. The flux is equal to the product of the drop size distribution near the ground NG(D) and drop velocity near the ground vG(D). The drop velocity is the difference between the droplet terminal velocity and the vertical component of the wind velocity, which varies with altitude. The estimates derived from the DFC model using two pair wise selected sensors are used to study the change of reflectivity and vertical air velocity with altitude. Sensitivity tests for the DFC model are also discussed and these outcomes are validated by comparison with independent profiler vertical velocity observations.

Corruption of the TRMM microwave imager cold sky mirror due to RFI

By its end of mission in 2015, the Tropical Rainfall Measuring Mission (TRMM) far exceeded its three-year design life, providing over 17 years of invaluable Earth observations. This paper details how a previously undiscovered source of calibration error for the TRMM Microwave Imager (TMI) has been recently detected and how a simple mitigation technique may be applied for correcting bogus data. The source of error is believed to be Radio Frequency Interference (RFI) from geostationary satellites transmitters that are collected by the TMI Cold Sky Reflector antenna beams as they sweep through the equatorial plane. This paper details how this source of error was discovered, its characteristics, and how to flag and correct for the upcoming TMI 1B11 Archive/Legacy Data.

Aquarius/SAC-D Microwave Radiometer brightness temperature validation

The Microwave Radiometer (MWR) on-board Aquarius/SAC-D is part of a joint international science mission between the National Aeronautics and Space Administration (NASA) and the Argentine Space Agency (Comision Nacional de Actividades Espaciales, CONAE). MWR, developed by CONAE, is a three channel Dicke radiometer operating at 23.8 GHz H-Pol and 36.5 GHz V-& H-Pol. This instrument complements the prime sensor, Aquarius L-band radiometer/scatterometer, by providing simultaneous spatially collocated environmental measurements such as integrated atmospheric water vapor, ocean surface wind speed, oceanic rain rate, and sea ice concentration, which aid in retrieving accurate Sea Surface Salinity. This paper presents the post-launch brightness temperature (Tb) validation, which was conducted using the CFRSL XCAL approach for inter-satellite radiometric comparison with the US Navys WindSat radiometer during the first 10 months of MWR on-orbit measurements.