High‐Cadence Lidar Observations of Middle Atmospheric Temperature and Gravity Waves at the Southern Andes Hot Spot

Reichert, Robert; Kaifler, Bernd; Kaifler, Natalie; Dörnbrack, Andreas; Rapp, Markus; Hormaechea, José Luis

doi:10.1029/2021jd034683

Cited by 17 publications

(34 citation statements)

References 97 publications

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“…(2015) with T ′ 2 averaged in the vertical (Tsuda et al., 2004), our E p values are qualitatively similar to the E p values in Reichert et al. (2021) (see their Figure 6). Moreover, E p uncertainties due to lidar temperature uncertainties are insignificant at altitudes between 30 and 60 km (Reichert et al., 2021).…”

Section: Resultssupporting

confidence: 88%

“…E p of all individual profiles, vertically averaged for the same altitude range, are also shown in Figure 8. This shows that even though E p is calculated following Ehard et al (2015) with T′ 2 averaged in the vertical (Tsuda et al, 2004), our E p values are qualitatively similar to the E p values in Reichert et al (2021) (see their Figure 6). Moreover, E p uncertainties due to lidar temperature uncertainties are insignificant at altitudes between 30 and 60 km (Reichert et al, 2021).…”

Section: Gravity Wave Activity Intermittency and Effect Of The Model ...supporting

confidence: 77%

“…This shows that even though E p is calculated following Ehard et al (2015) with T′ 2 averaged in the vertical (Tsuda et al, 2004), our E p values are qualitatively similar to the E p values in Reichert et al (2021) (see their Figure 6). Moreover, E p uncertainties due to lidar temperature uncertainties are insignificant at altitudes between 30 and 60 km (Reichert et al, 2021). E p for the individual profiles also reveals that IFS indeed captures high E p values of some strong GW events like the one in June 2018 (crosses in Figure 8), which was analyzed in detail by N. Kaifler et al (2020).…”

Section: Gravity Wave Activity Intermittency and Effect Of The Model ...supporting

confidence: 77%

“…The nightly lidar temperature measurements have high temporal (15 min) and vertical (900 m) resolutions between 15 and 95 km altitude. Comprehensive analyses of the whole 3-year data set including GW characteristics are presented by Reichert et al (2021).…”

Section: 1029/2021jd036270mentioning

confidence: 99%

“…Comprehensive analyses of the whole 3‐year data set including GW characteristics are presented by Reichert et al. (2021).…”

Section: Introductionmentioning

confidence: 99%

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Gravity‐Wave‐Driven Seasonal Variability of Temperature Differences Between ECMWF IFS and Rayleigh Lidar Measurements in the Lee of the Southern Andes

et al. 2022

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Long‐term high‐resolution temperature data of the Compact Rayleigh Autonomous Lidar (CORAL) is used to evaluate temperature and gravity wave (GW) activity in ECMWF Integrated Forecasting System (IFS) over Río Grande (53.79°S, 67.75°W), which is a hot spot of stratospheric GWs in winter. Seasonal and altitudinal variations of the temperature differences between the IFS and lidar are studied for 2018 with a uniform IFS version. Moreover, interannual variations are considered taking into account updated IFS versions. We find monthly mean temperature differences <2 K at 20–40 km altitude. At 45–55 km, the differences are smaller than 4 K during summer. The largest differences are found during winter (4 K in May 2018 and −10 K in August 2018, July 2019 and 2020). The width of the difference distribution (15th/85th percentiles), the root mean square error, and maximum differences between instantaneous individual profiles are also larger during winter (>±10 K) and increase with altitude. We relate this seasonal variability to middle atmosphere GW activity. In the upper stratosphere and lower mesosphere, the observed temperature differences result from both GW amplitude and phase differences. The IFS captures the seasonal cycle of GW potential energy (Ep) well, but underestimates Ep in the middle atmosphere. Experimental IFS simulations without damping by the model sponge for May and August 2018 show an increase in the monthly mean Ep above 45 km from only ≈10% of the Ep derived from the lidar measurements to 26% and 42%, respectively. GWs not resolved in the IFS are likely explaining the remaining underestimation of the Ep.

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Section: Resultssupporting

confidence: 88%

Section: Gravity Wave Activity Intermittency and Effect Of The Model ...supporting

confidence: 77%

Section: Gravity Wave Activity Intermittency and Effect Of The Model ...supporting

confidence: 77%

Section: 1029/2021jd036270mentioning

confidence: 99%

“…Comprehensive analyses of the whole 3‐year data set including GW characteristics are presented by Reichert et al. (2021).…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Gravity‐Wave‐Driven Seasonal Variability of Temperature Differences Between ECMWF IFS and Rayleigh Lidar Measurements in the Lee of the Southern Andes

et al. 2022

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Gravity-Wave-Driven Seasonal Variability of Temperature Differences between ECMWF IFS and Rayleigh Lidar Measurements in the Lee of the Southern Andes

Gisinger

Polichtchouk

Dörnbrack³

et al. 2021

Preprint

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Even now with a growing understanding of stratospheric processes, highly developed numerical models, and increasing computational resources, middle atmosphere temperature (re)analyses have a larger uncertainty than their tropospheric counterparts. Improving the representation of the past (reanalysis), current (analysis), and future (forecast) state of the middle atmosphere in general circulation models (GCMs) is important for the validation and forecasting of tropospheric weather and future climate. It is known that the circulation in the middle and upper atmosphere is strongly influenced by internal gravity waves (GWs) triggered for example, by flow over mountains (Fritts & Alexander, 2003). At the same time, processes in the stratosphere such as anomalies in the winter-and spring-time stratospheric polar vortex impact the tropospheric circulation (Baldwin & Dunkerton, 2001;Byrne & Shepherd, 2018;Garfinkel & Hartmann, 2011).One issue when modeling the middle atmosphere is that there is a limited amount of observations to constrain the current model state (e.g., Eckermann et al., 2018). Above 10 hPa, most of the observations assimilated into the Integrated Forecasting System (IFS) of the European Center for Medium-Range Weather Forecasts (ECMWF) are from satellites and have limited spatial and temporal resolutions. They mainly provide temperature-related data (e.g., Global Navigation Satellite System Radio Occultation (GNSS-RO), Atmospheric Infrared Sounder (AIRS), Advanced Microwave Sounding Unit (AMSU-A)) and the topmost radiances assimilated peak at approximately 1-2 hPa. The range of sensitivity of the satellite observations to certain horizontal and vertical scales of GWs depends on the instrument and viewing geometry (observational filter, see Alexander, 1998) as can be seen in for example, Figure 9 of Preusse et al. (2008). To produce the most accurate representation of the atmospheric state, all the observations irregularly distributed in time and space and each having their limitations and uncertainties are combined with the numerical weather prediction model on a global grid. For the (re)analyses at ECMWF, this is achieved by 4-dimensional variational data assimilation (4D-Var).

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Gravity-Wave-Driven Seasonal Variability of Temperature Differences between ECMWF IFS and Rayleigh Lidar Measurements in the Lee of the Southern Andes

Gisinger

Polichtchouk

Dörnbrack³

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

High‐Cadence Lidar Observations of Middle Atmospheric Temperature and Gravity Waves at the Southern Andes Hot Spot

Cited by 17 publications

References 97 publications

Gravity‐Wave‐Driven Seasonal Variability of Temperature Differences Between ECMWF IFS and Rayleigh Lidar Measurements in the Lee of the Southern Andes

Gravity‐Wave‐Driven Seasonal Variability of Temperature Differences Between ECMWF IFS and Rayleigh Lidar Measurements in the Lee of the Southern Andes

Gravity-Wave-Driven Seasonal Variability of Temperature Differences between ECMWF IFS and Rayleigh Lidar Measurements in the Lee of the Southern Andes

Gravity-Wave-Driven Seasonal Variability of Temperature Differences between ECMWF IFS and Rayleigh Lidar Measurements in the Lee of the Southern Andes

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