Antonia Gambacorta

This study presents a first-of-its-kind comprehensive data fusion approach combining hyperspectral microwave (HMW) with backscatter lidar (BSL) measurements for improved atmospheric thermodynamic sounding, with particular emphasis on the Earth’s Planetary Boundary Layer (PBL). This is a simulation-based trade study to demonstrate the enhancement of HMW over traditional microwave (MW) only measurements and the additional benefits of incorporating BSL with both approaches. This pioneering HMW+BSL fusion methodology represents a major advancement, achieving superior performance compared to traditional thermodynamic remote sensing approaches. Specifically, this configuration demonstrates significant enhancement in PBL temperature bias vertical stability and reduces standard deviation error (SDV) by 30% compared to traditional MW-only performance. Water vapor retrievals show similar improvements, with SDV reductions of 50% in the PBL and bias values consistently maintained below the 10% requirement threshold of the PBL DSI program, compared to PoR errors exceeding 30% bias in challenging cloudy regimes. Case studies across diverse oceanic regions reveal particular advantages of this data fusion approach in complex atmospheric conditions, especially in regions dominated by marine stratocumulus clouds and strong temperature inversions where conventional passive-only retrievals are challenging. Beyond thermodynamic profile improvements, our analysis demonstrates remarkable advances in the detection of PBL height (PBLH), with the HMW+BSL configuration achieving mean absolute errors within the 100 meter requirement threshold of the PBL DSI program, representing a step-change improvement over passive-only approaches. This work directly addresses observational gaps identified in the 2017 Earth Science Decadal Survey, positioning our integrated sensing approach as both a near-term enhancement to existing Earth observation capabilities and a pathfinder for future PBL mission architectures.

This work demonstrates the noise performance of the photonic back end of a hyperspectral microwave-photonic spectrometer aimed at augmenting the remote sensing capability from space, with a focus on the Earth’s planetary boundary layer (PBL). Using a distributed-feedback (DFB) laser and an electro-optical phase modulator, a 40 GHz spectrum is upconverted into the optical domain and sent to a 10-channel photonic integrated serial array waveguide grating (SAWG) where the spectra are separated into 4 GHz channels. Spectral content of each channel is downconverted to baseband using a second DFB laser and measured using an electrical spectrum analyzer (ESA) at a resolution of 8 MHz. Measured results of the end-to-end system Noise Figure (NF) for all channels agree with the theoretical analysis and demonstrate the suitability of microwave photonics for atmospheric studies.

An ultra-compact, narrow-bandwidth, and high-density photonic integrated channelizer has been developed on a silicon nitride platform and demonstrated for parallel processing of wide band hyperspectral microwave spectra. This device is based on an updated generation of arrayed waveguide gratings (AWG) named serial-AWG (SAWG). The design consists of 33 tunable optical delay lines and 10 output channels. Fabrication inaccuracies are compensated by the use of metal heaters to obtain narrow bandwidth and channel separation, and high side lobe suppression. Our experimental results demonstrate ten 2.6 GHz-bandwidth channels, separated by 3.9 GHz and with 20 dB of side-lobes suppression.

We present a comprehensive sensitivity analysis and geophysical retrieval product demonstration to assess the enhanced information content in atmospheric temperature and water vapor, harnessed in hyperspectral microwave measurements. A particular focus of this study is devoted to quantifying and comparing the impact on retrieval performance resulting from novel spectral bands of the microwave thermal spectrum, by means of data addition and data denial trade studies. Various spectral configurations are assessed, each reflecting specific technology solutions intended to maximize geophysical product performance within feasible size, weight, power and cost constraints. Our results indicate that the use of a hyperspectral sampling in the oxygen and water vapor sounding lines alone provides significant improvements in the lower and free tropospheric thermodynamic fields (up to ∼40%), when compared against the program of record (i.e., the Advanced Technology Microwave Sounder, ATMS). Our experiments also demonstrate the essential role played by extending the coverage in the window regions, leading to an overall improvement of up to ∼50% in the Earth's Planetary Boundary Layer thermodynamic fields. This work concludes with an overview on the state of the art in hyperspectral microwave technology and a discussion on future applications of interest to numerical weather prediction (NWP) and climate science. The work presented in this study focuses on ocean, clear-sky demonstrations. All-sky, all-surface investigations will be the focus of a follow-up study, as we advance our capability to simulate more complex scenarios and improve scene variability.

This paper provides an overview of the validation of National Oceanic and Atmospheric Administration (NOAA) operational retrievals of atmospheric carbon trace gas profiles, specifically carbon monoxide (CO), methane (CH₄) and carbon dioxide (CO₂), from the NOAA-Unique Combined Atmospheric Processing System (NUCAPS), a NOAA enterprise algorithm that retrieves atmospheric profile environmental data records (EDRs) under global non-precipitating (clear to partly cloudy) conditions. Vertical information about atmospheric trace gases is obtained from the Cross-track Infrared Sounder (CrIS), an infrared Fourier transform spectrometer that measures high resolution Earth radiance spectra from NOAA operational low earth orbit (LEO) satellites, including the Suomi National Polar-orbiting Partnership (SNPP) and follow-on Joint Polar Satellite System (JPSS) series beginning with NOAA-20. The NUCAPS CO, CH₄, and CO₂ profile EDRs are rigorously validated in this paper using well-established independent truth datasets, namely total column data from ground-based Total Carbon Column Observing Network (TCCON) sites, and in situ vertical profile data obtained from aircraft and balloon platforms via the NASA Atmospheric Tomography (ATom) mission and NOAA AirCore sampler, respectively. Statistical analyses using these datasets demonstrate that the NUCAPS carbon gas profile EDRs generally meet JPSS Level 1 global performance requirements, with the absolute accuracy and precision of CO 5% and 15%, respectively, in layers where CrIS has vertical sensitivity; CH₄ and CO₂ product accuracies are both found to be within ±1%, with precisions of ≈1.5% and ⪅0.5%, respectively, throughout the tropospheric column.

This work presents an analysis of the vertical resolution of the temperature and water vapor retrieved by the National Oceanic and Atmospheric Administration (NOAA) Unique Combined Atmospheric Processing System (NUCAPS) using averaging kernels as a diagnostic tool. One of the goals of an atmospheric profile retrieval system is to estimate the state of the atmosphere using an optimal set of observations containing independent information content in order to simultaneously minimize the retrieval error and maximize the vertical resolution. The averaging kernels are also used to compute the number of degrees of freedom of retrieved temperature and water vapor in order to estimate the number of useful independent observations contained in the infrared and microwave observations. Our work uses data obtained from the Suomi National Polar-orbiting Partnership (S-NPP) Cross-track Infrared Sounder (CrIS) and the Advanced Technology Microwave Sounder (ATMS) to diagnose the vertical resolution of NUCAPS retrieved sounding products over different latitudes and seasons, and to support comparisons of retrievals against correlative radiosonde measurements. This work is expected to be extended to other NUCAPS products, since the generation of the averaging kernels is a functionality embedded as part of NUCAPS.

Several recent studies have observed systematic differences between measurements in the 183.31 GHz water vapor line by space-borne sounders and calculations using radiative transfer models, with inputs from either radiosondes (radiosonde observations, RAOBs) or short-range forecasts by numerical weather prediction (NWP) models. This paper discusses all the relevant categories of observation-based or model-based data, quantifies their uncertainties and separates biases that could be common to all causes from those attributable to a particular cause. Reference observations from radiosondes, Global Navigation Satellite System (GNSS) receivers, differential absorption lidar (DIAL) and Raman lidar are thus overviewed. Biases arising from their calibration procedures, NWP models and data assimilation, instrument biases and radiative transfer models (both the models themselves and the underlying spectroscopy) are presented and discussed. Although presently no single process in the comparisons seems capable of explaining the observed structure of bias, recommendations are made in order to better understand the causes.

Several recent studies have observed biases between measurements in the 183.31GHz water vapour line by space-borne sounders and calculations using radiative transfer models with inputs from either radiosondes (RAOBS) or short range forecasts by Numerical Weather Prediction (NWP) models. This paper discusses all the relevant categories of observation-based or model-based data, quantification of their uncertainties, and separation of biases that could be common to all causes from those attributable to a particular cause. Reference observations from radiosondes, Global Navigation Satellite Systems (GNSS), Differential Absorption Lidar (DIAL) and Raman lidar are thus overviewed. Biases arising from their procedures of calibration, NWP models and data assimilation, instrument biases and radiative transfer models (both the models themselves and the underlying spectroscopy) are presented and discussed. Although no single process in the comparisons seems capable of explaining the observed structure of bias, recommendations are made in order to better understand the causes.