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Xu Liu

Publications and source records attributed to Xu Liu.

At least 55 records · Page 3
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Deriving Climate Change Signal from Hyperspectral Sounders Using Spectral Fingerprinting Method

Hyperspectral observations from satellite-based sensors provide high information content for the Earth’s atmospheric temperature, water vapor and trace gas vertical profiles. We have developed a radiometrically consistent spectral fingerprinting method to derive climate change signals from Aqua AIRS/AMSU and S-NPP CrIS/ATMS data. The climate variables include temperature and water vapor profiles, cloud, trace gases, and surface skin temperature. The radiative kernels obtained via a single field of view physical retrieval algorithm under all-sky conditions. A key component to this work is a Principal Component-based Radiative Transfer Model (PCRTM). It is 4 orders of magnitude faster than a line-by-line radiative transfer model while keeping a similar accuracy (0.03 K RMS errors with close to zero bias). The PCRTM includes multiple scattering of clouds and non-thermodynamics equilibrium of CO2 in the RT calculations. Instead of quantifying the radiometric differences between AIRS/AMSU and CrIS/ATMS measurements directly using Simultaneous Nadir Overpass (SNO) or Double Difference Technique (DDT), we use the radiometric consistent fingerprinting scheme to derive two sets of space-time averaged anomalies from the Level 1 data of AIRS/AMSU and CrIS/ATMS. The derived anomalies in geophysical space will form a long-term, stable, and continuous climate data record. We can further infer the causes of any offset or drift by studying the differences between two overlapping data sets. For example, the offset in surface skin temperature anomaly time series will most likely caused by the Blackbody temperature calibration errors of the sounder instruments.

climate↗
NASA NTRSDate not supplied

Observtion of Carbon Monoxide and Ozone from 2019-2020 Australia Fires Using Thermal Infrred and Near-Infrared Satellite Sensors

The Single Field-of-View (SFOV) retrieval products from CrIS on SNPP have a spatial resolution of 14 km. These products include temperature, water vapor, clouds and trace gases, such as ozone (O3) and carbon monoxide (CO). Such high-resolution sounder products enable us to make process-oriented analysis of CO emission and transport from Australia’s unprecedented wildfires as well as the O3 production along the transport of fire plumes in the end of 2019 to early 2020. Comparisons of CO with other satellite products, such as AIRS, TROPOMI and MOPPIT, show a good agreement of CO from these space-borne observations. The mean difference SFOV total CO with TROPOMI CO is 0.22%14.0% (R=0.92) and 1.1%17.6% (R=0.87) using data on December, 30, 2019 and January 1, 2020, respectively. This study also demonstrates the advantage of the SFOV CO in capturing the feature of CO transport for air quality study.

Xiaozhen Xiong↗
NASA NTRSDate not supplied

Single Field-of-View Sounding Atmospheric Products

The Single Field-of-view Sounder Atmospheric Products (SiFSAP) answer the need for a novel high spatial resolution atmospheric data product for major hyper-spectral infrared (IR) sounder missions. SiFSAP include a complete set of atmospheric vertical profiles, cloud, and surface properties, which are physically retrieved from top-of-atmosphere (TOA) spectral radiances under all-sky conditions via a rigorously defined radiative transfer relationship. By using a state-of-art fast radiative transfer model and a carefully designed optimal estimation based physical retrieval scheme, the SiFSAP algorithm ensures both an ultra-fast data processing speed needed for operational weather applications and the radiometric consistency desired by long-term climate studies. SiFSAP supplement existing operational products by providing data at the native resolution of the sounder instruments, the direct and accurate retrieval of cloud scattering properties, and the establishment of ‘radiance closure.’

Wan Wu↗
NASA NTRSDate not supplied

Calibrating CERES and VIIRS using CLARREO Pathfinder: Adjustment for the Viewing Geometry Mismatch

The Climate Absolute Radiance and Refractivity Observatory Pathfinder (CPF) mission on the International Space Station (ISS) will provide, for the first-time, an SI-traceable on-orbit calibration reference with a reflectance uncertainty of 0.3% (1 sigma). One major mission objective is to demonstrate the capability of transferring CPF’s radiometric accuracy to other satellite-based instruments, such as Clouds and Earth's Radiant Energy System (CERES) and Visible Infrared Imaging Radiometer Suite (VIIRS) on NOAA-20. CPF inter-calibration measurements will be planned to obtain inter-calibration samples that closely match satellite-based target sensor observations in time, space, angle, and wavelength. The inter-calibration event observations cover a wide range of viewing geometry angles with potential angular mismatch between CPF and the target sensors which can introduce non-negligible errors against the mission’s inter-calibration uncertainty budget. We will introduce the CPF approach to adjust for angular mismatch differences, thereby reducing the incurred uncertainties to within the inter-calibration uncertainty budget. The angular adjustment algorithm has been developed and validated based on the high-fidelity simulations of the CPF spectra using the Principal Component based Radiative Transfer Model (PCRTM). The implementation of the algorithm on event simulation data and the characterization for the angular adjustment uncertainty will be presented.

Wan Wu↗
NASA NTRSDate not supplied

Observation of Stratospheric Intrusions and Ozone Transport using CrIS SiFSAP Products

The Single Field of View (SFOV) ozone (O3) profiles derived from the Cross-track Infrared Sounder (CrIS) onboard Suomi National Polar-Orbiting Partnership (SNPP), with a high horizontal resolution of approximately 14 km at nadir and good sensitivity to O3 in the upper troposphere and lower stratosphere (UTLS), provide a prominent opportunity to examine stratosphere to troposphere (STT) transport. A process-oriented analysis was performed to examine the fine-scale features of a stratospheric intrusion (SI) event on June 11-13, 2017 in the southwestern US using the CrIS SFOV products together with wind and potential velocity (PV) from models. It was found that the location and strength of O3 enhancement correlate well with the PV contours, and the intrusion depth can be characterized using the vertical cross-sections of O3 and relative humidity (RH). In addition, the capability to use total column ozone (TCO) to identify SI events was confirmed through analysis of TCO from CrIS SFOV and other satellite and reanalysis products. The ozone/PV ratio was derived using SFOV O3 and model PV, and the values, ranging from 23.2 to 35.8 ppbv PVU-1 (1 PV unit (PVU) = 10-6 km2 kg-1 s-1), are in the lower end of previous estimations. These results demonstrate the advantages of the SFOV products in monitoring the fine-scale ozone transport and thermodynamic structure of SI events, as well as their potential value for weather and climate study.

Xiaozhen Xiong↗
NASA NTRSDate not supplied

Simultaneous Aerosol and Ocean Polarimeter Products Using Coupled Atmosphere-Ocean Vector Radiative Transfer and Neural Networks: The PACE-MAPP Algorithm

We describe the PACE-MAPP algorithm that simultaneously retrieves aerosol and ocean optical parameters using multiangle and multi-channel polarimeter measurements from the SPEXone, Hyper-Angular Rainbow Polarimeter 2 (HARP2), and Ocean Color Instrument (OCI) instruments onboard the NASA Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) observing system PACE-MAPP is adapted from the Research Scanning Polarimeter (RSP) Microphysical Aerosol Properties from Polarimetry (RSP-MAPP) algorithm. A key feature of the MAPP family of algorithms is the use of a coupled vector radiative transfer model such that the atmosphere and ocean are always considered together as one system. Consequently, conservation of energy ensures that negative water-leaving radiances do not occur. PACE-MAPP uses optimal estimation to simultaneously characterize the optical and microphysical properties of aerosol and ocean constituents, find the optimal solution, and reliably account for the uncertainties of each parameter. This coupled approach, together with multiangle, multi-channel polarimeter measurements, will enable retrievals of aerosol and water properties across the Earth’s oceans. The PACE-MAPP algorithm provides aerosol and ocean products for both the open ocean and coastal areas and is designed to be accurate, modular, and efficient by using fast neural networks that replace the time-consuming vector radiative transfer calculations. We provide an overview of the PACE-MAPP framework and also describe its modular components including its aerosol and hydrosol models, ocean bio-optical models, and thin cirrus model.

Snorre Stamnes↗