Comparison of lower stratosphere wind observations from the USTC's Rayleigh Doppler lidar and the ESA's satellite mission Aeolus

Abstract. Measuring greenhouse gas (GHG) profiles with global coverage and high accuracy and vertical resolution in the upper troposphere and lower stratosphere (UTLS) is key for improved monitoring of GHG concentrations in the free atmosphere. In this respect a new satellite mission concept adding an infrared-laser part to the already well studied microwave occultation technique exploits the joint propagation of infrared-laser and microwave signals between Low Earth Orbit (LEO) satellites. This synergetic combination, referred to as LEO-LEO microwave and infrared-laser occultation (LMIO) method, enables to retrieve thermodynamic profiles (pressure, temperature, humidity) and accurate altitude levels from the microwave signals and GHG profiles from the simultaneously measured infrared-laser signals. However, due to the novelty of the LMIO method, a retrieval algorithm for GHG profiling did not yet exist. Here we introduce such an algorithm for retrieving GHGs from LEO-LEO infrared-laser occultation (LIO) data, applied as a second step after retrieving thermodynamic profiles from LEO-LEO microwave occultation (LMO) data as recently introduced in detail by Schweitzer et al. (2011b). We thoroughly describe the LIO retrieval algorithm and unveil the synergy with the LMO-retrieved pressure, temperature, and altitude information. We furthermore demonstrate the effective independence of the GHG retrieval results from background (a priori) information in discussing demonstration results from LMIO end-to-end simulations for a representative set of GHG profiles, including carbon dioxide (CO2), water vapor (H2O), methane (CH4), and ozone (O3). The GHGs except for ozone are well retrieved throughout the UTLS, while ozone is well retrieved from 10 km to 15 km upwards, since the ozone layer resides in the lower stratosphere. The GHG retrieval errors are generally smaller than 1% to 3% r.m.s., at a vertical resolution of about 1 km. The retrieved profiles also appear unbiased, which points to the climate benchmarking capability of the LMIO method. This performance, found here for clear-air atmospheric conditions, is unprecedented for vertical profiling of GHGs in the free atmosphere and encouraging for future LMIO implementation. Subsequent work will examine GHG retrievals in cloudy air, addressing retrieval performance when scanning through intermittent upper tropospheric cloudiness.

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Strong Kelvin wave activity observed during the westerly phase of QBO – a case study

Annales Geophysicae ◽

10.5194/angeo-31-581-2013 ◽

2013 ◽

Vol 31 (4) ◽

pp. 581-590 ◽

Cited By ~ 8

Author(s):

U. Das ◽

C. J. Pan

Keyword(s):

Lower Stratosphere ◽

Lower Atmosphere ◽

Wave Radiation ◽

Kelvin Wave ◽

Satellite Mission ◽

Large Wave ◽

Easterly Wind ◽

Global Positioning ◽

The Waves

Abstract. Temperature data from Global Positioning System based Radio Occultation (GPS RO) soundings of the Formosa Satellite mission 3/Constellation Observing System for Meteorology, Ionosphere and Climate (FORMOSAT-3/COSMIC or F-3/C) micro satellites have been investigated in detail to study the Kelvin wave (KW) properties during September 2008 to February 2009 using the two-dimensional Fourier transform. It is observed that there was strong KW activity during November and December 2008; large wave amplitudes are observed from above the tropopause to 40 km – the data limit of F-3/C. KW of wavenumbers E1 and E2 with time periods 7.5 and 13 days, dominated during this period and the vertical wavelengths of these waves varied from 12 to 18 km. This event is very interesting as the QBO during this period was westerly in the lower stratosphere (up to ~ 26 km) and easterly above, whereas, climatological studies show that KW get attenuated during westerlies and their amplitudes maximise during easterlies and westerly shears. In the present study, however, the eastward propagating KW crossed the westerly lower stratosphere as the vertical extent of the westerly wind regime was less than the vertical wavelengths of the KW. The waves might have deposited eastward momentum in the upper stratosphere at 26–40 km, thereby reducing the magnitude of the easterly wind by as much as 10 m s−1. The outgoing long wave radiation (OLR) is also investigated and it is found that these KW are produced due to deep convections in the lower atmosphere.

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Elliptical Structures of Gravity Waves Produced by Typhoon Soudelor in 2015 near Taiwan

Atmosphere ◽

10.3390/atmos10050260 ◽

2019 ◽

Vol 10 (5) ◽

pp. 260 ◽

Cited By ~ 6

Author(s):

Fabrice Chane Ming ◽

Samuel Jolivet ◽

Yuei-An Liou ◽

Fabrice Jégou ◽

Dominique Mekies ◽

...

Keyword(s):

Tropical Cyclones ◽

Gravity Waves ◽

Lower Stratosphere ◽

Wave Number ◽

Weather Research And Forecasting ◽

Forecasting Model ◽

Satellite Mission ◽

Upper Troposphere ◽

Atmospheric Gravity ◽

Wavelet Analyses

Tropical cyclones (TCs) are complex sources of atmospheric gravity waves (GWs). In this study, the Weather Research and Forecasting Model was used to model TC Soudelor (2015) and the induced elliptical structures of GWs in the upper troposphere (UT) and lower stratosphere (LS) prior to its landfall over Taiwan. Conventional, spectral and wavelet analyses exhibit dominant GWs with horizontal and vertical wavelengths, and periods of 16–700 km, 1.5–5 km, and 1–20 h, respectively. The wave number one (WN1) wind asymmetry generated mesoscale inertia GWs with dominant horizontal wavelengths of 100–300 km, vertical wavelengths of 1.5–2.5 km (3.5 km) and westward (eastward) propagation at the rear of the TC in the UT (LS). It was also revealed to be an active source of GWs. The two warm anomalies of the TC core induced two quasi-diurnal GWs and an intermediate GW mode with a 10-h period. The time evolution of dominant periods could be indicative of changes in TC dynamics. The FormoSat-3/COSMIC (Formosa Satellite Mission-3/Constellation Observing System for Meteorology, Ionosphere, and Climate) dataset confirmed the presence of GWs with dominant vertical wavelengths of about 3.5 km in the UT and LS.

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Improved multi-spectral polarimetric observations of UTLS aerosol and cloud from stratospheric balloon with the Aerosol Limb Imager

10.5194/egusphere-egu21-6714 ◽

2021 ◽

Author(s):

Daniel Letros ◽

Adam Bourassa ◽

Paul Loewen ◽

Liam Graham ◽

Nick Lloyd ◽

...

Keyword(s):

Lower Stratosphere ◽

Polarization State ◽

Tunable Filter ◽

Orthogonal Polarization ◽

Tangent Point ◽

Satellite Mission ◽

Upper Troposphere ◽

Satellite Platform ◽

Stratospheric Balloon ◽

Spectral Imager

<p>The Aerosol Limb Imager (ALI) is a multi-spectral imager capable designed to observe aerosol extinction and particle size profiles in the upper-troposphere lower-stratosphere. ALI uses a system of linear polarizers, a liquid crystal rotator, and an acoustic-optic tunable filter to select the linear polarization state and wavelength of limb scattered sunlight radiance between 600 nm and 1500 nm. From stratospheric balloon, spectral images have spatial resolution of <100 meters at the tangent point, and can produce useful aerosol observations between 5 km and 30 km in altitude. Of novelty is the polarimetric capability of ALI, which uses the orthogonal polarization states to detect cloud in the spectral data and facilitate its distinction from aerosol. Two previous iterations of the ALI instrument concept have already been successfully demonstrated, once in 2014 and again in 2018. Currently, a third iteration is being developed which improves upon the thermal, structural, and optical performance of the previous iterations. This improved iteration is scheduled for demonstration as part of the HEMERA program out of Kiruna, Sweden in the summer of 2021. This demonstration serves the larger objective of further proving the engineering and scientific readiness of the ALI instrument concept for eventual high-altitude aircraft and satellite platform deployments.&#160; ALI is a proposed Canadian contribution to the NASA A-CCP satellite mission study.</p>

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Greenhouse gas profiling by infrared-laser and microwave occultation: retrieval algorithm and demonstration results from end-to-end simulations

Atmospheric Measurement Techniques ◽

10.5194/amt-4-2035-2011 ◽

2011 ◽

Vol 4 (10) ◽

pp. 2035-2058 ◽

Cited By ~ 12

Author(s):

V. Proschek ◽

G. Kirchengast ◽

S. Schweitzer

Keyword(s):

Greenhouse Gas ◽

Lower Stratosphere ◽

Infrared Laser ◽

Low Earth Orbit ◽

Retrieval Algorithm ◽

Vertical Resolution ◽

Satellite Mission ◽

Free Atmosphere ◽

Microwave Signals ◽

End To End

Abstract. Measuring greenhouse gas (GHG) profiles with global coverage and high accuracy and vertical resolution in the upper troposphere and lower stratosphere (UTLS) is key for improved monitoring of GHG concentrations in the free atmosphere. In this respect a new satellite mission concept adding an infrared-laser part to the already well studied microwave occultation technique exploits the joint propagation of infrared-laser and microwave signals between Low Earth Orbit (LEO) satellites. This synergetic combination, referred to as LEO-LEO microwave and infrared-laser occultation (LMIO) method, enables to retrieve thermodynamic profiles (pressure, temperature, humidity) and accurate altitude levels from the microwave signals and GHG profiles from the simultaneously measured infrared-laser signals. However, due to the novelty of the LMIO method, a retrieval algorithm for GHG profiling is not yet available. Here we introduce such an algorithm for retrieving GHGs from LEO-LEO infrared-laser occultation (LIO) data, applied as a second step after retrieving thermodynamic profiles from LEO-LEO microwave occultation (LMO) data. We thoroughly describe the LIO retrieval algorithm and unveil the synergy with the LMO-retrieved pressure, temperature, and altitude information. We furthermore demonstrate the effective independence of the GHG retrieval results from background (a priori) information in discussing demonstration results from LMIO end-to-end simulations for a representative set of GHG profiles, including carbon dioxide (CO2), water vapor (H2O), methane (CH4), and ozone (O3). The GHGs except for ozone are well retrieved throughout the UTLS, while ozone is well retrieved from about 10 km to 15 km upwards, since the ozone layer resides in the lower stratosphere. The GHG retrieval errors are generally smaller than 1% to 3% r.m.s., at a vertical resolution of about 1 km. The retrieved profiles also appear unbiased, which points to the climate benchmarking capability of the LMIO method. This performance, found here for clear-air atmospheric conditions, is unprecedented for vertical profiling of GHGs in the free atmosphere and encouraging for future LMIO implementation. Subsequent work will examine GHG retrievals in cloudy air, addressing retrieval performance when scanning through intermittent upper tropospheric cloudiness.

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Radiosonde Temperature Anomalies in the Troposphere and Lower Stratosphere for the Globe, Hemispheres, and Latitude Zones

Carbon Dioxide Information Analysis Center (CDIAC) Datasets ◽

10.3334/cdiac/cli.004 ◽

2007 ◽

Cited By ~ 1

Author(s):

A.M. Sterin,

Keyword(s):

Lower Stratosphere ◽

Temperature Anomalies

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Theater control of satellite mission payloads

10.2514/6.1999-4572 ◽

1999 ◽

Author(s):

Tom Riebe ◽

John Haaren

Keyword(s):

Satellite Mission

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`X-red': A satellite mission concept for detecting...

10.2514/6.iac-05-a3.1.08 ◽

2005 ◽

Keyword(s):

Satellite Mission

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Using Machine Learning Methods to Identify Particle Types from Doppler Lidar Measurements in Iceland

Remote Sensing ◽

10.3390/rs13132433 ◽

2021 ◽

Vol 13 (13) ◽

pp. 2433

Author(s):

Shu Yang ◽

Fengchao Peng ◽

Sibylle von Löwis ◽

Guðrún Nína Petersen ◽

David Christian Finger

Keyword(s):

Machine Learning ◽

Weather Conditions ◽

Dust Storms ◽

Machine Learning Algorithms ◽

Lidar Data ◽

Data Sets ◽

Doppler Lidar ◽

Lidar Measurements ◽

Using Data ◽

Filter Noise

Doppler lidars are used worldwide for wind monitoring and recently also for the detection of aerosols. Automatic algorithms that classify the lidar signals retrieved from lidar measurements are very useful for the users. In this study, we explore the value of machine learning to classify backscattered signals from Doppler lidars using data from Iceland. We combined supervised and unsupervised machine learning algorithms with conventional lidar data processing methods and trained two models to filter noise signals and classify Doppler lidar observations into different classes, including clouds, aerosols and rain. The results reveal a high accuracy for noise identification and aerosols and clouds classification. However, precipitation detection is underestimated. The method was tested on data sets from two instruments during different weather conditions, including three dust storms during the summer of 2019. Our results reveal that this method can provide an efficient, accurate and real-time classification of lidar measurements. Accordingly, we conclude that machine learning can open new opportunities for lidar data end-users, such as aviation safety operators, to monitor dust in the vicinity of airports.

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