On the quality of MIPAS kinetic temperature in the middle atmosphere

García‐Comas, M.; Funke, B.; López‐Puertas, M.; Bermejo-Pantaleón, Diego; Glatthor, N.; Clarmann, T. von; Stiller, G. P.; Grabowski, U.; Boone, C. D.; French, John; Leblanc, Thierry; López-González, M. J.; Schwartz, M.

doi:10.5194/acp-12-6009-2012

Cited by 38 publications

(39 citation statements)

References 63 publications

(88 reference statements)

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“…In the next data version of MIPAS IMK/IAA water vapor we will include non-LTE modelling in the radiative transfer calculations for the retrievals according to García-Comas et al (2012), which is expected to solve the problems at and above 45 km.…”

Section: Synopsis From All Instruments: Water Vapormentioning

confidence: 99%

Validation of MIPAS IMK/IAA temperature, water vapor, and ozone profiles with MOHAVE-2009 campaign measurements

et al. 2012

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Abstract. MIPAS observations of temperature, water vapor, and ozone in October 2009 as derived with the scientific level-2 processor run by Karlsruhe Institute of Technology (KIT), Institute for Meteorology and Climate Research (IMK) and CSIC, Instituto de Astrofísica de Andalucía (IAA) and retrieved from version 4.67 level-1b data have been compared to co-located field campaign observations obtained during the MOHAVE-2009 campaign at the Table Mountain Facility near Pasadena, California in October 2009. The MIPAS measurements were validated regarding any potential biases of the profiles, and with respect to their precision estimates. The MOHAVE-2009 measurement campaign provided measurements of atmospheric profiles of temperature, water vapor/relative humidity, and ozone from the ground to the mesosphere by a suite of instruments including radiosondes, ozonesondes, frost point hygrometers, lidars, microwave radiometers and Fourier transform infrared (FTIR) spectrometers. For MIPAS temperatures (version V4O T 204), no significant bias was detected in the middle stratosphere; between 22 km and the tropopause MIPAS temperatures were found to be biased low by up to 2 K, while below the tropopause, they were found to be too high by the same amount. These findings confirm earlier comparisons of MIPAS temperatures to ECMWF data which revealed similar differences. Above 12 km up to 45 km, MIPAS water vapor (version V4O H2O 203) is well within 10 % of the data of all correlative instruments. The well-known dry bias of MIPAS water vapor above 50 km due to neglect of non-LTE effects in the current retrievals has been confirmed. Some instruments indicate that MIPAS water vapor might be biased high by 20 to 40 % around 10 km (or 5 km below the tropopause), but a consistent picture from all comparisons could not be derived. MIPAS ozone (version V4O O3 202) has a high bias of up to +0.9 ppmv around 37 km which is due to a non-identified continuum like radiance contribution. No further significant biases have been detected. Cross-comparison to co-located observations of other satellite instruments (Aura/MLS, ACE-FTS, AIRS) is provided as well.

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Section: Synopsis From All Instruments: Water Vapormentioning

confidence: 99%

Validation of MIPAS IMK/IAA temperature, water vapor, and ozone profiles with MOHAVE-2009 campaign measurements

et al. 2012

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“…accuracy of the retrieved temperature and other atmospheric profiles (Healy, 2001;Rieder and Kirchengast, 2001;Gobiet and Kirchengast, 2004;Steiner and Kirchengast, 2005). Therefore, it is very important to have a best-possible initialization of the ionosphere-corrected bending angles at high altitudes for more accurate climate monitoring.…”

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confidence: 99%

Dynamic statistical optimization of GNSS radio occultation bending angles: advanced algorithm and performance analysis

Kirchengast

Scherllin‐Pirscher

et al. 2015

Atmos. Meas. Tech.

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Abstract. We introduce a new dynamic statistical optimization algorithm to initialize ionosphere-corrected bending angles of Global Navigation Satellite System (GNSS)-based radio occultation (RO) measurements. The new algorithm estimates background and observation error covariance matrices with geographically varying uncertainty profiles and realistic global-mean correlation matrices. The error covariance matrices estimated by the new approach are more accurate and realistic than in simplified existing approaches and can therefore be used in statistical optimization to provide optimal bending angle profiles for high-altitude initialization of the subsequent Abel transform retrieval of refractivity. The new algorithm is evaluated against the existing Wegener Center Occultation Processing System version 5.6 (OPSv5.6) algorithm, using simulated data on two test days from January and July 2008 and real observed CHAllenging Minisatellite Payload (CHAMP) and Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC) measurements from the complete months of January and July 2008. The following is achieved for the new method's performance compared to OPSv5.6: (1) significant reduction of random errors (standard deviations) of optimized bending angles down to about half of their size or more; (2) reduction of the systematic differences in optimized bending angles for simulated MetOp data; (3) improved retrieval of refractivity and temperature profiles; and (4) realistically estimated global-mean correlation matrices and realistic uncertainty fields for the background and observations. Overall the results indicate high suitability for employing the new dynamic approach in the processing of long-term RO data into a reference climate record, leading to well-characterized and high-quality atmospheric profiles over the entire stratosphere.

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“…These studies have focused on varied topics: analysis of the impact of atmospheric waves on regional and global scales, the detection of geo-hazards, the effect of sudden stratospheric warmings (SSWs), seasonal and interannual variations, external forcing response, longterm trends, cross-validation for satellite measurements, the detection of satellite drifts and the determination of OH radiative properties (e.g., López-González et al, 2009;Cho et al, 2010;Bittner et al, 2010;French and Mulligan, 2010; M. García-Comas et al: OH layer altitude over the OSN Offermann et al, 2010;Shepherd et al, 2010a, b;French and Klekociuk, 2011;García-Comas et al, 2012;Ammosov et al, 2014;Reisin et al, 2014;Liu et al, 2015;Wüst et al, 2017, to mention just a few. ).…”

Section: Introductionmentioning

confidence: 99%

Mesospheric OH layer altitude at midlatitudes: variability over the Sierra Nevada Observatory in Granada, Spain (37° N, 3° W)

García‐Comas¹,

López-González²,

González‐Galindo³

et al. 2017

Ann. Geophys.

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Abstract. The mesospheric OH layer varies on several timescales, primarily driven by variations in atomic oxygen, temperature, density and transport (advection). Vibrationally excited OH airglow intensity, rotational temperature and altitude are closely interrelated and thus accompany each other through these changes. A correct interpretation of the OH layer variability from airglow measurements requires the study of the three variables simultaneously. Ground-based instruments measure excited OH intensities and temperatures with high temporal resolution, but they do not generally observe altitude directly. Information on the layer height is crucial in order to identify the sources of its variability and the causes of discrepancies in measurements and models. We have used SABER space-based 2002-2015 data to infer an empirical function for predicting the altitude of the layer at midlatitudes from ground-based measurements of OH intensity and rotational temperature. In the course of the analysis, we found that the SABER altitude (weighted by the OH volume emission rate) at midlatitudes decreases at a rate of 40 m decade −1 , accompanying an increase of 0.7 % decade −1 in OH intensity and a decrease of 0.6 K decade −1 in OH equivalent temperature. SABER OH altitude barely changes with the solar cycle, whereas OH intensity and temperature vary by 7.8 % per 100 s.f.u. and 3.9 K per 100 s.f.u., respectively. For application of the empirical function to Sierra Nevada Observatory SATI data, we have calculated OH intensity and temperature SATI-to-SABER transfer functions, which point to relative instrumental drifts of −1.3 % yr −1 and 0.8 K yr −1 , respectively, and a temperature bias of 5.6 K. The SATI predicted altitude using the empirical function shows significant short-term variability caused by overlapping waves, which often produce changes of more than 3-4 km in a few hours, going along with 100 % and 40 K changes in intensity and temperature, respectively. SATI OH layer wave effects are smallest in summer and largest around New Year's Day. Moreover, those waves vary significantly from day to day. Our estimations suggest that peak-to-peak OH nocturnal variability, mainly due to wave variability, changes within 60 days at least 0.8 km for altitude in autumn, 45 % for intensity in early winter and 6 K for temperature in midwinter. Plausible upper limit ranges of those variabilities are 0.3-0.9 km, 40-55 % and 4-7 K, with the exact values depending on the season.

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On the quality of MIPAS kinetic temperature in the middle atmosphere

Cited by 38 publications

References 63 publications

Validation of MIPAS IMK/IAA temperature, water vapor, and ozone profiles with MOHAVE-2009 campaign measurements

Validation of MIPAS IMK/IAA temperature, water vapor, and ozone profiles with MOHAVE-2009 campaign measurements

Dynamic statistical optimization of GNSS radio occultation bending angles: advanced algorithm and performance analysis

Mesospheric OH layer altitude at midlatitudes: variability over the Sierra Nevada Observatory in Granada, Spain (37° N, 3° W)

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On the quality of MIPAS kinetic temperature in the middle atmosphere

Cited by 38 publications

References 63 publications

Validation of MIPAS IMK/IAA temperature, water vapor, and ozone profiles with MOHAVE-2009 campaign measurements

Validation of MIPAS IMK/IAA temperature, water vapor, and ozone profiles with MOHAVE-2009 campaign measurements

Dynamic statistical optimization of GNSS radio occultation bending angles: advanced algorithm and performance analysis

Mesospheric OH layer altitude at midlatitudes: variability over the Sierra Nevada Observatory in Granada, Spain (37° N, 3° W)

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Mesospheric OH layer altitude at midlatitudes: variability over the Sierra Nevada Observatory in Granada, Spain (37° N, 3° W)