scholarly journals A Climate Hyperspectral Infrared Radiance Product (CHIRP) Combining the AIRS and CrIS Satellite Sounding Record

2021 ◽  
Vol 13 (3) ◽  
pp. 418
Author(s):  
L. Larrabee Strow ◽  
Chris Hepplewhite ◽  
Howard Motteler ◽  
Steven Buczkowski ◽  
Sergio DeSouza-Machado
Keyword(s):  
Response Function ◽  
Spectral Response ◽  
Calibration Standard ◽  
Atmospheric Infrared Sounder ◽  
Infrared Radiance ◽  
Combining Data ◽  
Instrument Bias ◽  
The Cross ◽  
Cross Track ◽  
Polar Satellite

A Climate Hyperspectral Infrared Radiance Product (CHIRP) is introduced combining data from the Atmospheric Infrared Sounder (AIRS) on NASA’s EOS-AQUA platform, the Cross-Track Infrared Sounder (CrIS) sounder on NASA’s SNPP platform, and continuing with CRIS sounders on the NOAA/NASA Joint Polar Satellite Series (JPSS) of polar satellites. The CHIRP product converts the parent instrument’s radiances to a common Spectral Response Function (SRF) and removes inter-satellite biases, providing a consistent inter-satellite radiance record. The CHIRP record starts in September 2002 with AIRS, followed by CrIS SNPP and the JPSS series of CrIS instruments. The CHIRP record should continue until the mid-2040’s as additional JPSS satellites are launched. These sensors, in CHIRP format, provide the climate community with a homogeneous sensor record covering much of the infrared. We give an overview of the conversion of AIRS and CrIS to CHIRP, and define the SRF for common CHIRP format. Considerable attention is paid to removing static bias offsets among these three sensors. The CrIS instrument on NASA’s SNPP satellite is used as the calibration standard. Simultaneous Nadir Overpasses (SNOs) as well as large statistical samplings of radiances from these three satellites are used to derive the instrument bias offsets and estimate the bias offset accuracy, which is ~0.03 K. In addition, possible scene-dependent calibration differences between CHIRP derived from AIRS and CHIRP derived from CrIS on the SNPP platform are presented.

Evolution of Peroxyacetyl Nitrate (PAN) in wildfire smoke plumes detected by the Cross-Track Infrared Sounder (CrIS) over the western U.S. during summer 2018.

2021 ◽  
Author(s):  
Julieta F. Juncosa Calahorrano ◽  
Vivienne H. Payne ◽  
Susan Kulawik ◽  
Bonne Ford ◽  
Frank Flocke ◽  
...  
Keyword(s):  
Peroxyacetyl Nitrate ◽  
Wildfire Smoke ◽  
Smoke Plumes ◽  
The Cross ◽  
Cross Track

Progress toward Cloud-Cleared Infrared radiance assimilation in a global modeling framework: Application to the 2017 Atlantic Tropical Cyclone Season.

2021 ◽  
Author(s):  
Niama Boukachaba ◽  
Oreste Reale ◽  
Erica L. McGrath-Spangler ◽  
Manisha Ganeshan ◽  
Will McCarty ◽  
...  
Keyword(s):  
Tropical Cyclone ◽  
Strong Impact ◽  
Modeling Framework ◽  
Atmospheric Infrared Sounder ◽  
Clear Sky ◽  
Earth Observing System ◽  
Infrared Radiance ◽  
Radiance Assimilation ◽  
The Impact ◽  
Tropical Cyclone Season

<p>Previous work by this team has demonstrated that assimilation of IR radiances in partially cloudy regions is beneficial to numerical weather predictions (NWPs), improving the representation of tropical cyclones (TCs) in global analyses and forecasts. The specific technique used by this team is based on the “cloud-clearing CC” methodology. Cloud-cleared hyperspectral IR radiances (CCRs), if thinned more aggressively than clear-sky radiances, have shown a strong impact on the analyzed representation and structure of TCs. However, the use of CCRs in an operational context is limited by 1) latency; and 2) external dependencies present in the original cloud-clearing algorithm. In this study, the Atmospheric InfraRed Sounder (AIRS) CC algorithm was (a) ported to NASA high end computing resources (HEC), (b) deprived of external dependencies, and (c) parallelized improving the processing by a factor of 70. The revised AIRS CC algorithm is now customizable, allowing user’s choice of channel selection, user’s model's fields as first guess, and could perform in real time. This study examines the benefits achieved when assimilating CCRs using the NASA’s Goddard Earth Observing System (GEOS) hybrid 4DEnVar system. The focus is on the 2017 Atlantic hurricane season with three infamous hurricanes (Harvey, Irma, and Maria) investigated in depth.  The impact of assimilating customized CCRs on the analyzed representation of tropical cyclone horizontal and vertical structure and on forecast skill is discussed.</p>

scholarly journals A two-dimensional Stockwell transform for gravity wave analysis of AIRS measurements

2016 ◽  
Vol 9 (6) ◽  
pp. 2545-2565 ◽  
Author(s):  
Neil P. Hindley ◽  
Nathan D. Smith ◽  
Corwin J. Wright ◽  
D. Andrew S. Rees ◽  
Nicholas J. Mitchell
Keyword(s):  
General Circulation ◽  
Drake Passage ◽  
General Circulation Models ◽  
Two Dimensional ◽  
Atmospheric Infrared Sounder ◽  
Spectral Window ◽  
Energy And Momentum ◽  
Stockwell Transform ◽  
Cross Track ◽  
Along Track

Abstract. Gravity waves (GWs) play a crucial role in the dynamics of the earth's atmosphere. These waves couple lower, middle and upper atmospheric layers by transporting and depositing energy and momentum from their sources to great heights. The accurate parameterisation of GW momentum flux is of key importance to general circulation models but requires accurate measurement of GW properties, which has proved challenging. For more than a decade, the nadir-viewing Atmospheric Infrared Sounder (AIRS) aboard NASA's Aqua satellite has made global, two-dimensional (2-D) measurements of stratospheric radiances in which GWs can be detected. However, one problem with current one-dimensional methods for GW analysis of these data is that they can introduce significant unwanted biases. Here, we present a new analysis method that resolves this problem. Our method uses a 2-D Stockwell transform (2DST) to measure GW amplitudes, horizontal wavelengths and directions of propagation using both the along-track and cross-track dimensions simultaneously. We first test our new method and demonstrate that it can accurately measure GW properties in a specified wave field. We then show that by using a new elliptical spectral window in the 2DST, in place of the traditional Gaussian, we can dramatically improve the recovery of wave amplitude over the standard approach. We then use our improved method to measure GW properties and momentum fluxes in AIRS measurements over two regions known to be intense hotspots of GW activity: (i) the Drake Passage/Antarctic Peninsula and (ii) the isolated mountainous island of South Georgia. The significance of our new 2DST method is that it provides more accurate, unbiased and better localised measurements of key GW properties compared to most current methods. The added flexibility offered by the scaling parameter and our new spectral window presented here extend the usefulness of our 2DST method to other areas of geophysical data analysis and beyond.

An initial assessment of observations from the Suomi-NPP satellite: data from the Cross-track Infrared Sounder (CrIS)

2015 ◽  
Vol 16 (3) ◽  
pp. 260-266 ◽  
Author(s):  
Andrew Smith ◽  
Nigel Atkinson ◽  
William Bell ◽  
Amy Doherty
Keyword(s):  
Satellite Data ◽  
Initial Assessment ◽  
The Cross ◽  
Cross Track

Statistical estimates of auroral Pedersen conductance using electric and magnetic measurements by the Swarm spacecraft

2021 ◽  
Author(s):  
Heikki Vanhamäki ◽  
Anita Aikio ◽  
Kirsti Kauristie ◽  
Sebastian Käki ◽  
David Knudsen
Keyword(s):  
Electric Field ◽  
Magnetic Measurements ◽  
Statistical Estimates ◽  
Ionospheric Electrodynamics ◽  
Swarm Satellites ◽  
Statistical Sense ◽  
The Cross ◽  
Cross Track ◽  
Initial Results ◽  
Crucial Parameter

<p>Height-integrated ionospheric Pedersen and Hall conductances play a major role in ionospheric electrodynamics and Magnetosphere-Ionosphere coupling. Especially the Pedersen conductance is a crucial parameter in estimating ionospheric energy dissipation via Joule heating. Unfortunately, the conductances are rather difficult to measure directly in extended regions, so statistical models and various proxies are often used.</p><p>We discuss a method for estimating the Pedersen Conductance from magnetic and electric field data provided by the Swarm satellites. We need to assume that the height-integrated Pedersen current is identical to the curl-free part of the height integrated ionospheric horizontal current density, which is strictly valid only if the conductance gradients are parallel to the electric field. This may not be a valid assumption in individual cases but could be a good approximation in a statistical sense. Further assuming that the cross-track magnetic disturbance measured by Swarm is mostly produced by field-aligned currents and not affected by ionospheric electrojets, we can use the cross-track ion velocity and the magnetic perturbation to directly estimate the height-integrated Pedersen conductance.</p><p>We present initial results of a statistical study utilizing 5 years of data from the Swarm-A and Swarm-B spacecraft, and discuss possible applications of the results and limitations of the method.</p>

scholarly journals Uncertainty Characterization and Propagation in the Community Long-Term Infrared Microwave Combined Atmospheric Product System (CLIMCAPS)

2019 ◽  
Vol 11 (10) ◽  
pp. 1227 ◽  
Author(s):  
Nadia Smith ◽  
Christopher D. Barnet
Keyword(s):  
Uncertainty Propagation ◽  
State Variables ◽  
Product System ◽  
Atmospheric Infrared Sounder ◽  
Cloud Properties ◽  
Infrared Measurements ◽  
Highly Correlated ◽  
Cross Track ◽  
Correlated Signals

The Community Long-term Infrared Microwave Combined Atmospheric Product System (CLIMCAPS) retrieves multiple Essential Climate Variables (ECV) about the vertical atmosphere from hyperspectral infrared measurements made by the Atmospheric InfraRed Sounder (AIRS, 2002–present) and its successor, the Cross-track Infrared Sounder (CrIS, 2011–present). CLIMCAPS ECVs are profiles of temperature and water vapor, column amounts of greenhouse gases (CO2, CH4), ozone (O3) and precursor gases (CO, SO2) as well as cloud properties. AIRS (and CrIS) spectral measurements are highly correlated signals of many atmospheric state variables. CLIMCAPS inverts an AIRS (and CrIS) measurement into a set of discrete ECVs by employing a sequential Bayesian approach in which scene-dependent uncertainty is rigorously propagated. This not only linearizes the inversion problem but explicitly accounts for spectral interference from other state variables so that the correlation among ECVs (and their uncertainty) may be minimized. Here, we outline the CLIMCAPS retrieval methodology with specific focus given to its sequential scene-dependent uncertainty propagation system. We conclude by demonstrating continuity in two CLIMCAPS ECVs across AIRS and CrIS so that a long-term data record may be generated to study the feedback cycles characterizing our climate system.

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