Temporal Experiment for Storms and Tropical Systems Technology Demonstration (TEMPEST-D): Reducing risk for 6U-Class nanosatellite constellations

Reising, Steven C.; Gaier, T.; Kummerow, Christian D.; Padmanabhan, Sharmila; Lim, Boon; Brown, Shannon; Heneghan, Cate; Chandra, Chandrasekar V.; Olson, James D.; Berg, Wesley

doi:10.1109/igarss.2016.7730451

Cited by 18 publications

(12 citation statements)

References 4 publications

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“…Radiometer instrument technology has made rapid strides in the past several years to miniaturize and lower the cost of the sensors envisioned for this constellation, coupled with equal advancements in lowcost CubeSat and small satellites. The US Air Force plans to demonstrate a low-cost conical microwave imager (Brown et al, 2017) and NASA has two missions to demonstrate CubeSat microwave imager/sounders (Reising et al, 2016;Blackwell et al, 2018). In addition, launch costs have decreased through rideshare opportunities.…”

Section: Typementioning

confidence: 99%

Air-Sea Fluxes With a Focus on Heat and Momentum

et al. 2019

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Turbulent and radiative exchanges of heat between the ocean and atmosphere (hereafter heat fluxes), ocean surface wind stress, and state variables used to estimate them, are Essential Ocean Variables (EOVs) and Essential Climate Variables (ECVs) influencing weather and climate. This paper describes an observational strategy for producing 3-hourly, 25-km (and an aspirational goal of hourly at 10-km) heat flux and wind stress fields over the global, ice-free ocean with breakthrough 1-day random uncertainty of 15 W m −2 and a bias of less than 5 W m −2. At present this accuracy target is met only for OceanSITES reference station moorings and research vessels (RVs) that follow best practices. To meet these targets globally, in the next decade, satellite-based observations must be optimized for boundary layer measurements of air temperature, humidity, sea surface temperature, and ocean wind stress. In order to tune and validate these satellite measurements, a complementary global in situ flux array, built around an expanded OceanSITES network of time series reference station moorings, is also needed. The array would include 500-1000 measurement platforms, including autonomous surface vehicles, moored and drifting buoys, RVs, the existing OceanSITES network of 22 flux sites, and new OceanSITES expanded in 19 key regions. This array would be globally distributed, with 1-3 measurement platforms in each nominal 10 • by 10 • box. These improved moisture and temperature profiles and surface data, if assimilated into Numerical Weather Prediction (NWP) models, would lead to better representation of cloud formation processes, improving state variables and surface radiative and turbulent fluxes from these models. The in situ flux array provides globally distributed measurements and metrics for satellite algorithm development,

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Section: Typementioning

confidence: 99%

Air-Sea Fluxes With a Focus on Heat and Momentum

et al. 2019

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“…where J + = (J*J) −1 J* is the Moore-Penrose pseudoinverse of the matrix J = GF*Z, F is the Fourier transform operator andT = FT, and Z is the zero-padding operator beyond the experimental frequency coverage H. For the definitions of F and Z, please refer to Equation (2) and Equation (3) in a previous study [8].…”

Section: Band-limited Regularizationmentioning

confidence: 99%

“…Different from conventional real aperture radiometers, SAIRs improve the spatial resolution by use of the synthetic aperture technique, which avoids the difficulties of mechanical scanning such as the bulky volume and weight caused by a real large-aperture antenna. At present, SAIRs have been applied in many fields including remote sensing, atmospheric monitoring, and human security inspection [1][2][3]. The typical SAIR instruments that researchers have developed include electronically scanned thinned array radiometer (ESTAR) [4], microwave imaging radiometer with aperture synthesis (MIRAS) [5], geostationary synthetic thinned array radiometer (GeoSTAR) [6], and geostationary interferometric microwave sounder (GIMS) [7].…”

Section: Introductionmentioning

confidence: 99%

Multi-Parameter Regularization Method for Synthetic Aperture Imaging Radiometers

Yang

Yan

et al. 2021

Remote Sensing

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Synthetic aperture imaging radiometers (SAIRs) are powerful passive microwave systems for high-resolution imaging by use of synthetic aperture technique. However, the ill-posed inverse problem for SAIRs makes it difficult to reconstruct the high-precision brightness temperature map. The traditional regularization methods add a unique penalty to all the frequency bands of the solution, which may cause the reconstructed result to be too smooth to retain certain features of the original brightness temperature map such as the edge information. In this paper, a multi-parameter regularization method is proposed to reconstruct SAIR brightness temperature distribution. Different from classical single-parameter regularization, the multi-parameter regularization adds multiple different penalties which can exhibit multi-scale characteristics of the original distribution. Multiple regularization parameters are selected by use of the simplified multi-dimensional generalized cross-validation method. The experimental results show that, compared with the conventional total variation, Tikhonov, and band-limited regularization methods, the multi-parameter regularization method can retain more detailed information and better improve the accuracy of the reconstructed brightness temperature distribution, and exhibit superior noise suppression, demonstrating the effectiveness and the robustness of the proposed method.

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“…Millimeter-wave interferometric synthetic aperture radiometer (InSAR) is a powerful observation system with high-resolution for many applications across the geographical and life sciences, including remote sensing, atmosphere monitoring, weather and climate forecast, anti-terrorist and security check [1][2][3][4]. It outperforms millimeter-wave real aperture radiometry mainly due to the advantages of high-resolution without very large and massive real aperture antennas and a large field of view without mechanical scanning.…”

Section: Introductionmentioning

confidence: 99%

Millimeter-Wave InSAR Image Reconstruction Approach by Total Variation Regularized Matrix Completion

et al. 2018

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Millimeter-wave interferometric synthetic aperture radiometer (InSAR) can provide high-resolution observations for many applications by using small antennas to achieve very large synthetic aperture. However, reconstruction of a millimeter-wave InSAR image has been proven to be an ill-posed inverse problem that degrades the performance of InSAR imaging. In this paper, a novel millimeter-wave InSAR image reconstruction approach, referred to as InSAR-TVMC, by total variation (TV) regularized matrix completion (MC) in two-dimensional data space, is proposed. Based on the a priori knowledge that natural millimeter-wave images statistically hold the low-rank property, the proposed approach represents the object images as low-rank matrices and formulates the data acquisition of InSAR in two-dimensional data space directly to undersample visibility function samples. Subsequently, using the undersampled visibility function samples, the optimal solution of the InSAR image reconstruction problem is obtained by simultaneously adopting MC techniques and TV regularization. Experimental results on simulated and real millimeter-wave InSAR image data demonstrate the effectiveness and the significant improvement of the reconstruction performance of the proposed InSAR-TVMC approach over conventional and one-dimensional sparse InSAR image reconstruction approaches.

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Temporal Experiment for Storms and Tropical Systems Technology Demonstration (TEMPEST-D): Reducing risk for 6U-Class nanosatellite constellations

Cited by 18 publications

References 4 publications

Air-Sea Fluxes With a Focus on Heat and Momentum

Air-Sea Fluxes With a Focus on Heat and Momentum

Multi-Parameter Regularization Method for Synthetic Aperture Imaging Radiometers

Millimeter-Wave InSAR Image Reconstruction Approach by Total Variation Regularized Matrix Completion

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