Development of the Microwave Radiometer Technology Acceleration (MiRaTA) CubeSat for all-weather atmospheric sounding

Cahoy, Kerri; Marinan, Anne; Marlow, Weston; Cordeiro, Timothy; Blackwell, William J.; Bishop, Rebecca; Erickson, N. R.

doi:10.1109/igarss.2015.7327032

Cited by 13 publications

(6 citation statements)

References 2 publications

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“…Operational TC intensity analysis remains largely dependent on quasi-subjective techniques such as the Dvorak technique (Dvorak 1984;Velden et al 2006), requires intensive analyst training, and lacks a formal incorporation of imagery beyond visible and infrared window frequencies. Furthermore, a new generation of infrared imagers (Schmit et al 2005(Schmit et al , 2017 and emerging cubesat sensors (Blackwell et al 2012;Cahoy et al 2015;Reising et al 2016) require new, robust techniques for TC nowcasting and forecasting that can assimilate a spectrally diverse variety of images all at once.…”

Section: Introductionmentioning

confidence: 99%

Using Deep Learning to Estimate Tropical Cyclone Intensity from Satellite Passive Microwave Imagery

Wimmers

Velden

Cossuth

2019

Monthly Weather Review

125

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A deep learning convolutional neural network model is used to explore the possibilities of estimating tropical cyclone (TC) intensity from satellite images in the 37- and 85–92-GHz bands. The model, called “DeepMicroNet,” has unique properties such as a probabilistic output, the ability to operate from partial scans, and resiliency to imprecise TC center fixes. The 85–92-GHz band is the more influential data source in the model, with 37 GHz adding a marginal benefit. Training the model on global best track intensities produces model estimates precise enough to replicate known best track intensity biases when compared to aircraft reconnaissance observations. Model root-mean-square error (RMSE) is 14.3 kt (1 kt ≈ 0.5144 m s−1) compared to two years of independent best track records, but this improves to an RMSE of 10.6 kt when compared to the higher-standard aircraft reconnaissance-aided best track dataset, and to 9.6 kt compared to the reconnaissance-aided best track when using the higher-resolution TRMM TMI and Aqua AMSR-E microwave observations only. A shortage of training and independent testing data for category 5 TCs leaves the results at this intensity range inconclusive. Based on this initial study, the application of deep learning to TC intensity analysis holds tremendous promise for further development with more advanced methodologies and expanded training datasets.

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Section: Introductionmentioning

confidence: 99%

Using Deep Learning to Estimate Tropical Cyclone Intensity from Satellite Passive Microwave Imagery

Wimmers

Velden

Cossuth

2019

Monthly Weather Review

125

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“…VoutRMS = 6.71x10 -5 *Tcal + 0.289 (8) The radiometer was characterized and calibrated at different physical temperatures ranging from 287.15 K to 308.15 K (see Fig. 7).…”

Section: The Calibration Processmentioning

confidence: 99%

A Power and Performance Study of CompactL-Band Total Power Radiometers for UAV Remote Sensing Based in the Processing on ZYNQ and ARM Architectures

Romo¹,

Rodríguez-Solís²,

Reyes³

et al. 2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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This paper presents a study on the power consumption and performance analysis of a small, portable, and ultra-low power total power L-band radiometer. The study explores two processing architectures: the ZYNQ 7010 and the ARM A8 embedded microprocessor. The processing algorithm based in C++ was tested for different clock frequencies, ADC sampling speeds, and sizes of the ADC buffer. To reduce the power consumption and the algorithm execution time, high-level and system-level optimizations, along with fixed-point Q(16,16) data representation, were applied to the main code running on LINUX Debian V8. In the case with the ZYNQ 7010, the optimizations had no notable impact on reducing power or execution time in comparison with the ARM A8, where significant variations were measured, showing a tradeoff between power consumption and algorithm performance that limits the processing capability and the system flight time. The ZYNQ 7010 runs the algorithm faster, but the power consumption is higher than the ARM A8. Using the fixed-point Q(16,16) implementation reduced the power consumption and the execution time in both architectures.Based on these results, we developed a heuristic methodology to minimize power consumption and increase the performance. Energy consumption savings for the radiometer during 20 minutes of flight was 48%. The size of the radiometer was reduced to 30cm x 30cm x 10cm, with a weight of 1.36 kg, (3 lb) allowing the system be carried by small drones. The results were validated measuring salinity at two locations in Western Puerto Rico.

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“…18 The MiRaTA CubeSat mission had a secondary payload on-board to pull GPSRO near the same time as radiometer measurements, but MiRaTA had a power or communications anomaly. 19 For our MicroMAS-2 analysis, we pull GPSRO wet retrievals from Constellation Observing System for Meteorology, Ionosphere and Climate (COSMIC), a constellation that is composed of four fully functional satellites in a 500 km, 72 degree inclination orbit. GPSRO measurements are very accurate, with global mean differences of approximately ~0.65 K between COSMIC and high quality analysis at altitudes between 8 and 30 km.…”

Section: Crtm For Radiance Validationmentioning

confidence: 99%

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

Crews

Blackwell

Leslie

et al. 2018

Active and Passive Microwave Remote Sensing for Environmental Monitoring II

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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. 1 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. 2 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. 3 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.

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Development of the Microwave Radiometer Technology Acceleration (MiRaTA) CubeSat for all-weather atmospheric sounding

Cited by 13 publications

References 2 publications

Using Deep Learning to Estimate Tropical Cyclone Intensity from Satellite Passive Microwave Imagery

Using Deep Learning to Estimate Tropical Cyclone Intensity from Satellite Passive Microwave Imagery

A Power and Performance Study of CompactL-Band Total Power Radiometers for UAV Remote Sensing Based in the Processing on ZYNQ and ARM Architectures

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

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