This paper analyzes and compares multiple techniques for estimating the mean square slope (MSS) of surface waves during Hurricane Ike in the Gulf of Mexico and studies the correlation of the MSS estimates with wind speed measurements along the same tracks. Three separate instruments collected measurements in parallel, including a GPS reflectometry (GPS-R) receiver, a stepped frequency microwave radiometer (SFMR), and a wide swath radar altimeter (WSRA). These datasets were used to study the correlation between the flight level and near-surface wind and MSS during Hurricane Ike in 2008. The GPS-R, SFMR, and WSRA instruments recorded temporally and spatially coincident data during two passes over the hurricane eye. This paper estimates the ocean surface MSS using GPS-R for two eye transects using: a least squares model fitting technique, the reflected signal waveform width, and an integration of the reflected signals in an area around the peak. Subsequently, the correlations between the GPS-R and WSRA MSS estimates and the SFMR wind speed estimates are compared to reveal regions of high and low surface MSS to wind speed correlation. Finally, a relationship between wind and MSS was derived from the GPS-R and SFMR data and is compared to existing MSS/wind models, including the results obtained by Katzberg et al. for hurricane conditions.
11 August 2020 in IEEE Transactions on Geoscience and Remote Sensing
The Microsized Microwave Atmospheric Satellite (MicroMAS-2A) is a 3U CubeSat that was launched in January 2018 as a technology demonstration for future microwave sounding constellation missions, such as the NASA Time-Resolved Observations of Precipitation structure and storm Intensity with a Constellation of Smallsats (TROPICS) mission now in development. MicroMAS-2A has a miniaturized 1U ten-channel passive microwave radiometer with channels near 90, 118, 183, and 206 GHz for moisture and temperature profiling and precipitation imaging [4]. MicroMAS-2A provided the first CubeSat atmospheric vertical sounding data from orbit, and to date it is the only CubeSat to provide temperature and moisture sounding and surface imaging. In this article, we analyze six segments of data collected from MicroMAS-2A in April 2018 and compare them to ERA5 reanalysis fields coupled with the Community Radiative Transfer Model (CRTM). This initial assessment of CubeSat radiometric accuracy shows biases relative to ERA5 with magnitudes ranging from 0.4 to 2.2 K (with standard deviations ranging from 0.7 to 1.2 K) for the four mid-tropospheric temperature channels and biases of 2.2 and 2.8 K (standard deviations 1.8 and 2.6 K) for the two lower tropospheric water vapor channels.
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.
26 February 2021 in IEEE Transactions on Geoscience and Remote Sensing
CubeSats with miniaturized microwave radiometers are now demonstrating the potential to provide science-quality weather measurements. For example, the Micro-Sized Microwave Atmospheric Satellite-2A (MicroMAS-2A) and Temporal Experiment for Storms and Tropical Systems-Demonstration (TEMPEST-D) CubeSats are both launched in 2018 and have demonstrated microwave atmospheric sounder data from orbit. The NASA Time-Resolved Observations of Precipitation structure and storm Intensity with a Constellation of Smallsats (TROPICS) mission is a follow-on constellation of six 3U CubeSats based on the MicroMAS-2 design that is scheduled for launch no sooner than 2021. The TROPICS sensors use internal noise diodes (NDs) for calibration. Although the NDs on TROPICS are similar to technology flown on GMI, they have not been tested on-orbit at TROPICS frequencies. In order to track and correct ND drift, we develop a novel method of calibration for CubeSat constellations, such as TROPICS, by incorporating periodic solar and lunar intrusions as an additional source of information to counter ND drift. These lunar intrusions also occur for existing satellites hosting microwave radiometers in sun-synchronous polar orbits but are much more infrequent than for the TROPICS constellation's scanning payload. In this work, we develop a solar/lunar calibration correction algorithm and test it using advanced technology microwave sounder (ATMS) lunar intrusion data. The mean bias and standard deviation between the solar/lunar calibration correction algorithm and actual ATMS data fall within the expected ATMS error budget of 0.6-3.9 K, validating our model.