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

scholarly journals Evolution of Acyl Peroxynitrates (PANs) 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):  
Wildfire Smoke ◽  
Smoke Plumes ◽  
The Cross ◽  
Cross Track

scholarly journals Satellite measurements of peroxyacetyl nitrate from the Cross-Track Infrared Sounder: Comparison with ATom aircraft measurements

2021 ◽  
Author(s):  
Vivienne H. Payne ◽  
Susan S. Kulawik ◽  
Emily V. Fischer ◽  
Jared F. Brewer ◽  
L. Gregory Huey ◽  
...  
Keyword(s):  
Standard Deviation ◽  
Degrees Of Freedom ◽  
Large Scale ◽  
Estimation Algorithm ◽  
Field Of View ◽  
Ionization Mass ◽  
Aircraft Measurements ◽  
Peroxyacetyl Nitrate ◽  
The Cross ◽  
Cross Track

Abstract. We present an overview of an optimal estimation algorithm to retrieve peroxyacetyl nitrate (PAN) from single field of view Level 1B radiances measured by the Cross-Track Infrared Sounder (CrIS). CrIS PAN retrievals show peak sensitivity in the mid-troposphere, with degrees of freedom for signal less than or equal to 1.0. We show comparisons with two sets of aircraft measurements from the Atmospheric Tomography Mission (ATom), the PAN and Trace Hydrohalocarbon ExpeRiment (PANTHER) and the Georgia Tech Chemical Ionization Mass Spectrometer (GT-CIMS). We find a systematic difference between the two aircraft datasets, with vertically averaged mid-tropospheric values from the GT-CIMS around 14 % lower than equivalent values from the PANTHER. However, the two sets of aircraft measurements are strongly correlated (R2 value of 0.92) and do provide a consistent view of the large-scale variation of PAN. We demonstrate that the retrievals of PAN from CrIS show skill in measurement of these large-scale PAN distributions in the remote mid-troposphere compared to the retrieval prior. The standard deviation of individual CrIS-aircraft differences is 0.08 ppbv, which we take as an estimate of the uncertainty of the CrIS mid-tropospheric PAN for a single satellite field of view. The standard deviation of the CrIS-aircraft comparisons for averaged CrIS retrievals (median of 20 satellite co-incidences with each aircraft profile) is lower, at 0.05 ppbv. This would suggest that the retrieval error reduces with averaging, although not with the square root of the number of observations. We find a negative bias of order 0.1 ppbv in the CrIS PAN results with respect to the aircraft measurements. This bias does not appear to show a dependence on latitude or season.

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>

Impacts of wildfire smoke plumes on regional air quality

2016 ◽  
Vol 2016 (1) ◽  
Author(s):  
Ana Rappold* ◽  
Alexandra Larsen ◽  
Brian Reich
Keyword(s):  
Air Quality ◽  
Wildfire Smoke ◽  
Smoke Plumes ◽  
Regional Air Quality

Wildfire smoke plumes transport under a subsidence inversion: Climate and health implications in a distant urban area

2018 ◽  
Vol 619-620 ◽  
pp. 988-1002 ◽  
Author(s):  
Elisabeth Alonso-Blanco ◽  
Amaya Castro ◽  
Ana I. Calvo ◽  
Veronique Pont ◽  
Marc Mallet ◽  
...  
Keyword(s):  
Urban Area ◽  
Health Implications ◽  
Wildfire Smoke ◽  
Smoke Plumes ◽  
Climate And Health

scholarly journals The new Passive microwave Neural network Precipitation Retrieval (PNPR) algorithm for the cross-track scanning ATMS radiometer: description and verification study over Europe and Africa using GPM and TRMM spaceborne radars

2016 ◽  
Author(s):  
Paolo Sanò ◽  
Giulia Panegrossi ◽  
Daniele Casella ◽  
Anna Cinzia Marra ◽  
Francesco Di Paola ◽  
...  
Keyword(s):  
Neural Network ◽  
Mean Squared Error ◽  
Passive Microwave ◽  
Precipitation Radar ◽  
Water Vapor Absorption ◽  
The Neural Network ◽  
Trmm Pr ◽  
The Cross ◽  
Cross Track ◽  
Good Agreement

Abstract. The objective of this paper is to describe the development and evaluate the performance of a totally new version of the Passive microwave Neural network Precipitation Retrieval (PNPR v2), an algorithm based on a neural network approach, designed to retrieve the instantaneous surface precipitation rate using the cross-track ATMS radiometer measurements. This algorithm, developed within the EUMETSAT H-SAF program, represents an evolution of the previous version (PNPR v1), developed for AMSU/MHS radiometers (and used and distributed operationally within H-SAF), with improvements aimed at exploiting the new precipitation sensing capabilities of ATMS with respect to AMSU/MHS. In the design of the neural network the new ATMS channels compared to AMSU/MHS, and their combinations, including the brightness temperature differences in the water vapor absorption band, around 183 GHz, are considered . The algorithm is based on a single neural network, for all types of surface background, trained using a large database based on 94 cloud-resolving model simulations over the European and the African areas. The performance of PNPR v2 has been evaluated through an intercomparison of the instantaneous precipitation estimates with co-located estimates from the TRMM Precipitation Radar (TRMM-PR) and from the GPM Core Observatory Ku-band Precipitation Radar (GPM-KuPR). In the comparison with TRMM-PR, over the African area, the statistical analysis was carried out for a two-year (2013-2014) dataset of coincident observations, over a regular grid at 0.5° × 0.5° resolution. The results have shown a good agreement between PNPR v2 and TRMM-PR for the different surface types. The correlation coefficient (CC) was equal to 0.69 over ocean and 0.71 over vegetated land (lower values were obtained over arid land and coast), and the root mean squared error (RMSE) was equal to 1.30 mm h−1 over ocean and 1.11 mm h−1 over vegetated land. The results showed a slight tendency to underestimate moderate to high precipitation, mostly over land, and overestimate moderate to light precipitation over ocean. Similar results were obtained for the comparison with GPM-KuPR over the European area (15 months, from March 2014 to May 2015 of coincident overpasses) with slightly lower CC (0.59 over vegetated land and 0.57 over ocean) and RMSE (0.82 mm h−1 over vegetated land and 0.71 mm h−1 over ocean), confirming a good agreement also between PNPR v2 and GPM-KuPR. The performance of PNPR v2 over the African area was also compared to that of PNPR v1. PNPR v2 has higher R over the different surfaces, with general better estimate of low precipitation, mostly over ocean, thanks to improvements in the design of the neural network and also to the improved capabilities of ATMS compared to AMSU/MHS. Both versions of PNPR algorithm have shown a general consistency with the TRMM-PR.

scholarly journals GLACIER VOLUME CHANGE ESTIMATION USING TIME SERIES OF IMPROVED ASTER DEMS

2016 ◽  
Vol XLI-B8 ◽  
pp. 489-494 ◽  
Author(s):  
Luc Girod ◽  
Christopher Nuth ◽  
Andreas Kääb
Keyword(s):  
Climate Change ◽  
Time Series ◽  
Volume Change ◽  
Thermal Emission ◽  
Aster Data ◽  
Rational Polynomial ◽  
The Cross ◽  
Cross Track ◽  
Along Track

Volume change data is critical to the understanding of glacier response to climate change. The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) system embarked on the Terra (EOS AM-1) satellite has been a unique source of systematic stereoscopic images covering the whole globe at 15m resolution and at a consistent quality for over 15 years. While satellite stereo sensors with significantly improved radiometric and spatial resolution are available to date, the potential of ASTER data lies in its long consistent time series that is unrivaled, though not fully exploited for change analysis due to lack of data accuracy and precision. Here, we developed an improved method for ASTER DEM generation and implemented it in the open source photogrammetric library and software suite MicMac. The method relies on the computation of a rational polynomial coefficients (RPC) model and the detection and correction of cross-track sensor jitter in order to compute DEMs. ASTER data are strongly affected by attitude jitter, mainly of approximately 4 km and 30 km wavelength, and improving the generation of ASTER DEMs requires removal of this effect. Our sensor modeling does not require ground control points and allows thus potentially for the automatic processing of large data volumes. <br><br> As a proof of concept, we chose a set of glaciers with reference DEMs available to assess the quality of our measurements. We use time series of ASTER scenes from which we extracted DEMs with a ground sampling distance of 15m. Our method directly measures and accounts for the cross-track component of jitter so that the resulting DEMs are not contaminated by this process. Since the along-track component of jitter has the same direction as the stereo parallaxes, the two cannot be separated and the elevations extracted are thus contaminated by along-track jitter. Initial tests reveal no clear relation between the cross-track and along-track components so that the latter seems not to be easily modeled analytically from the first one. We thus remove the remaining along-track jitter effects in the DEMs statistically through temporal DEM stacks to finally compute the glacier volume changes over time. Our method yields cleaner and spatially more complete elevation data, which also proved to be more in accordance to reference DEMs, compared to NASA’s AST14DMO DEM standard products. <br><br> The quality of the demonstrated measurements promises to further unlock the underused potential of ASTER DEMs for glacier volume change time series on a global scale. The data produced by our method will help to better understand the response of glaciers to climate change and their influence on runoff and sea level.

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