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Abstract Acute myeloid leukemia (AML) is initiated and sustained by a hierarchy of leukemia stem cells (LSCs), and elimination of this cell population is required for curative therapies. Here we show that transmembrane and immunoglobulin domain containing 2 (TMIGD2), a recently discovered co-stimulatory immune receptor, is aberrantly expressed by human AML cells, and can be used to identify and enrich functional LSCs. We demonstrate that TMIGD2 is required for the development and maintenance of AML and self-renewal of LSCs but is not essential for normal hematopoiesis. Mechanistically, TMIGD2 promotes proliferation, blocks myeloid differentiation and increases cell-cycle of AML cells via an ERK1/2-p90RSK-CREB signaling axis. Targeting TMIGD2 signaling with anti-TMIGD2 monoclonal antibodies attenuates LSC self-renewal and reduces leukemia burden in AML patient-derived xenograft models but has negligible effect on normal hematopoietic stem/progenitor cells. Thus, our studies reveal the function of TMIGD2 in LSCs and provide a promising therapeutic strategy for AML.

Over the past two decades, there have been significant advances in the treatment of multiple myeloma which has led to an improvement in overall survival.1,2 However, a notable proportion of patients continue to experience early mortality (EM), defined as 2 years from the time of diagnosis. This raises the possibility that improvements in myeloma survival have not extended equally to all groups. Using the latest data drawn from the Surveillance Epidemiology and End Results database of patients in the United States spanning 2000-2019, we study impact of important sociodemographic factors on EM. Through regression modeling, we demonstrate that patients diagnosed from 2000-2005, of older age, male sex, and of certain racial minority status (non-Hispanic Black and Hispanic) have higher odds of EM. Of these factors, minority status contributed to worse 2-year overall survival as well. We evaluate whether income, as a surrogate to access to care, could potentially explain this finding, but find that race has a distinct relationship with EM that is not modified by income. This is further reinforced by subgroup analysis. After characterizing groups vulnerable to EM, we examine reasons for these disparities and potential avenues to address them.

Abstract Anti-CD19 chimeric antigen receptor T-cell therapies have shown striking clinical activity in diffuse large B-cell lymphoma but robust biomarkers predictive of responsiveness are still needed. We treated a multi-ethnic cohort of 31 diffuse large B-cell lymphoma patients with axicabtagene ciloleucel with an overall response rate of 71%. Analysis of various biomarkers identified a significant decrease in overall survival with elevated lactate dehydrogenase, measured both at time of cell infusion and before lymphodepletion. Lactate dehydrogenase was prognostic in a multivariate analysis [HR = 1.47 (1.1–2.0)] and a value of 400 U/L at time of infusion and a value of 440 U/L before lymphodepletion provided the best prognostic cutoffs for overall survival in our cohort. These data demonstrate efficacy of anti-CD19 chimeric antigen receptor T-cell therapy in a diverse inner city population and demonstrate novel lactate dehydrogenase cutoffs as prognostic biomarkers.

While it is relatively straightforward to automate the processing of lidar signals, it is more difficult to choose periods of “good” measurements to process. Groups use various ad hoc procedures involving either very simple (e.g. signal-to-noise ratio) or more complex procedures (e.g. Wing et al., 2018) to perform a task that is easy to train humans to perform but is time-consuming. Here, we use machine learning techniques to train the machine to sort the measurements before processing. The presented method is generic and can be applied to most lidars. We test the techniques using measurements from the Purple Crow Lidar (PCL) system located in London, Canada. The PCL has over 200 000 raw profiles in Rayleigh and Raman channels available for classification. We classify raw (level-0) lidar measurements as “clear” sky profiles with strong lidar returns, “bad” profiles, and profiles which are significantly influenced by clouds or aerosol loads. We examined different supervised machine learning algorithms including the random forest, the support vector machine, and the gradient boosting trees, all of which can successfully classify profiles. The algorithms were trained using about 1500 profiles for each PCL channel, selected randomly from different nights of measurements in different years. The success rate of identification for all the channels is above 95 %. We also used the t-distributed stochastic embedding (t-SNE) method, which is an unsupervised algorithm, to cluster our lidar profiles. Because the t-SNE is a data-driven method in which no labelling of the training set is needed, it is an attractive algorithm to find anomalies in lidar profiles. The method has been tested on several nights of measurements from the PCL measurements. The t-SNE can successfully cluster the PCL data profiles into meaningful categories. To demonstrate the use of the technique, we have used the algorithm to identify stratospheric aerosol layers due to wildfires.

Water vapour is the strongest greenhouse gas in our atmosphere, and its strength and its dependence on temperature lead to a strong feedback mechanism in both the troposphere and the stratosphere. Raman water vapour lidars can be used to make high-vertical-resolution measurements on the order of tens of metres, making height-resolved trend analyses possible. Raman water vapour lidars have not typically been used for trend analyses, primarily due to the lack of long-enough time series. However, the Raman Lidar for Meteorological Observations (RALMO), located in Payerne, Switzerland, is capable of making operational water vapour measurements and has one of the longest ground-based and well-characterized data sets available. We have calculated an 11.5-year water vapour climatology using RALMO measurements in the troposphere. Our study uses nighttime measurements during mostly clear conditions, which creates a natural selection bias. The climatology shows that the highest water vapour specific-humidity concentrations are in the summer months and the lowest in the winter months. We have also calculated the geophysical variability of water vapour. The percentage of variability of water vapour in the free troposphere is larger than in the boundary layer. We have also determined water vapour trends from 2009 to 2019. We first calculate precipitable water vapour (PWV) trends for comparison with the majority of water vapour trend studies. We detect a nighttime precipitable water vapour trend of 1.3 mm per decade using RALMO measurements, which is significant at the 90 % level. The trend is consistent with a 1.38 ∘C per decade surface temperature trend detected by coincident radiosonde measurements under the assumption that relative humidity remains constant; however, it is larger than previous water vapour trend values. We compare the nighttime RALMO PWV trend to daytime and nighttime PWV trends using operational radiosonde measurements and find them to agree with each other. We cannot detect a bias between the daytime and nighttime trends due to the large uncertainties in the trends. For the first time, we show height-resolved increases in water vapour through the troposphere. We detect positive tropospheric water vapour trends ranging from a 5 % change in specific humidity per decade to 15 % specific humidity per decade depending on the altitude. The water vapour trends at five layers are statistically significant at or above the 90 % level.

Abstract Axicabtagene ciloleucel (Axi-cel) is a CD-19 Chimeric Antigen Receptor T cell therapy approved for the treatment of relapsed/refractory diffuse large B cell lymphoma. We treated ten patients with DLBCL post-FDA approval in an inner-city tertiary center in the Bronx. Eight patients (80%) had received ≥ 3 lines of therapy, six patients had received prior radiation, and seven had recurrent disease after prior autologous hematopoietic stem cell transplant (AHCT). Our cohort included one patient with HIV, two patients with hepatitis B, and two patients with CNS involvement of lymphoma. Axi-cel treatment led to significant responses with 8/10 patients achieving a complete remission at 3 months, including both patients with prior CNS involvement. The treatment was generally well tolerated with 20% of patients experiencing grade ≥ 2 CRS. One patient each with HIV and hepatitis B responded without significant toxicities. In conclusion, Axi-cel led to significant efficacy with manageable toxicity in DLBCL in a real-world setting.

Abstract: Hematopoietic cell transplantation (HCT) is potentially curative for patients with hematologic disorders, but carries significant risks of infection-related morbidity and mortality. Infectious diseases are the second most common cause of death in HCT recipients, surpassed only by progression of underlying disease. Many infectious diseases are difficult to diagnose and treat, and may only be first identified by autopsy. However, autopsy rates are decreasing despite their value. The clinical and autopsy records of adult HCT recipients at our center who underwent autopsy between 1 January 2000 and 31 December 2017 were reviewed. Discrepancies between premortem clinical diagnoses and postmortem autopsy diagnoses were evaluated. Of 185 patients who underwent autopsy, 35 patients (18.8%) had a total of 41 missed infections. Five patients (2.7%) had >1 missed infection. Of the 41 missed infections, 18 (43.9%) were viral, 16 (39.0%) were fungal, 5 (12.2%) were bacterial, and 2 (4.9%) were parasitic. According to the Goldman criteria, 31 discrepancies (75.6%) were class I, 5 (12.2%) were class II, 1 (2.4%) was class III, and 4 (9.8%) were class IV. Autopsies of HCT recipients frequently identify clinically significant infectious diseases that were not suspected premortem. Had these infections been suspected, a change in management might have improved patient survival in many of these cases. Autopsy is underutilized and should be performed regularly to help improve infection-related morbidity and mortality. Illustrative cases are presented and the lessons learned from them are also discussed.

Raman lidars have been designated as potential candidates for trend studies by the Network for the Detection of Atmospheric Composition Change (NDACC) and GCOS (Global Climate Observing System) Reference Upper Air Network (GRUAN); however, for such studies improved calibration techniques are needed as well as careful consideration of the calibration uncertainties. Trend determinations require frequent, accurate, and well-characterized measurements. However, water vapour Raman lidars produce a relative measurement and require calibration in order to transform the measurement into a mixing ratio, a conserved quantity when no sources or sinks for water vapour are present. Typically, the calibration is done using a reference instrument such as a radiosonde. We present an improved trajectory technique to calibrate water vapour Raman lidars based on the previous work of Whiteman et al. (2006), Leblanc and Mcdermid (2008), Adam et al. (2010), and Herold et al. (2011), who used radiosondes as an external calibration source and matched the lidar measurements to the corresponding radiosonde measurement. However, they did not consider the movement of the radiosonde relative to the air mass and fronts. Our trajectory method is a general technique which may be used for any lidar and only requires that the radiosonde report wind speed and direction. As calibrations can be affected by a lack of co-location with the reference instrument, we have attempted to improve their technique by tracking the air parcels measured by the radiosonde relative to the field of view of the lidar. This study uses GRUAN Vaisala RS92 radiosonde measurements and lidar measurements taken by the MeteoSwiss RAman Lidar for Meteorological Observation (RALMO), located in Payerne, Switzerland, from 2011 to 2016 to demonstrate this improved calibration technique. We compare this technique to the traditional radiosonde–lidar calibration technique which does not involve tracking the radiosonde and uses the same integration time for all altitudes. Both traditional and our trajectory methods produce similar profiles when the water vapour field is homogeneous over the 30 min calibration period. We show that the trajectory method reduces differences between the radiosonde and lidar by an average of 10 % when the water vapour field is not homogeneous over a 30 min calibration period. We also calculate a calibration uncertainty budget that can be performed on a nightly basis. The calibration uncertainty budget includes the uncertainties due to phototube paralysis, aerosol extinctions, the assumption of the Ångström exponent, and the radiosonde. The study showed that the radiosonde was the major source of uncertainty in the calibration at 4 % of the calibration value. This trajectory method showed small improvements for RALMO's calibration but would be more useful for stations in different climatological regions or when non-co-located radiosondes are the only available calibration source.

Lidar retrievals of atmospheric temperature and water vapor mixing ratio profiles using the optimal estimation method (OEM) typically use a retrieval grid with a number of points larger than the number of pieces of independent information obtainable from the measurements. Consequently, retrieved geophysical quantities contain some information from their respective a priori values or profiles, which can affect the results in the higher altitudes of the temperature and water vapor profiles due to decreasing signal-to-noise ratios. The extent of this influence can be estimated using the retrieval's averaging kernels. The removal of formal a priori information from the retrieved profiles in the regions of prevailing a priori effects is desirable, particularly when these greatest heights are of interest for scientific studies. We demonstrate here that removal of a priori information from OEM retrievals is possible by repeating the retrieval on a coarser grid where the retrieval is stable even without the use of formal prior information. The averaging kernels of the fine-grid OEM retrieval are used to optimize the coarse retrieval grid. We demonstrate the adequacy of this method for the case of a large power-aperture Rayleigh scatter lidar nighttime temperature retrieval and for a Raman scatter lidar water vapor mixing ratio retrieval during both day and night.

This paper provides a detailed description of a first-principle optimal estimation method (OEM) applied to ozone retrieval analysis using differential absorption lidar (DIAL) measurements. The air density, detector dead times, background coefficients, and lidar constants are simultaneously retrieved along with ozone density profiles. Using an averaging kernel, the OEM provides the vertical resolution of the retrieval as a function of altitude. A maximum acceptable height at which the a priori has a small contribution to the retrieval is calculated for each profile as well. Moreover, a complete uncertainty budget including both systematic and statistical uncertainties is given for each individual retrieved profile. Long-term stratospheric DIAL ozone measurements have been carried out at the Observatoire de Haute-Provence (OHP) since 1985. The OEM is applied to three nights of measurements at OHP during an intensive ozone campaign in July 2017 for which coincident lidar–ozonesonde measurements are available. The retrieved ozone density profiles are in good agreement with both traditional analysis and the ozonesonde measurements. For the three nights of measurements, below 15 km the difference between the OEM and the sonde profiles is less than 25 %, and at altitudes between 15 and 25 km the difference is less than 10 %; the OEM can successfully catch many variations in ozone, which are detected in the sonde profiles due to its ability to adjust its vertical resolution as the signal varies. Above 25 km the difference between the OEM and the sonde profiles does not exceed 20 %.

Hauchecorne and Chanin (1980) developed a robust method to calculate middle-atmosphere temperature profiles using measurements from Rayleigh-scatter lidars. This traditional method has been successfully used to greatly improve our understanding of middle-atmospheric dynamics, but the method has some shortcomings regarding the calculation of systematic uncertainties and the vertical resolution of the retrieval. Sica and Haefele (2015) have shown that the optimal estimation method (OEM) addresses these shortcomings and allows temperatures to be retrieved with confidence over a greater range of heights than the traditional method. We have calculated a temperature climatology from 519 nights of Purple Crow Lidar Rayleigh-scatter measurements using an OEM. Our OEM retrieval is a first-principle retrieval in which the forward model is the lidar equation and the measurements are the level-0 count returns. It includes a quantitative determination of the top altitude of the retrieved temperature profiles, the evaluation of nine systematic plus random uncertainties, and the vertical resolution of the retrieval on a profile-by-profile basis. Our OEM retrieval allows for the vertical resolution to vary with height, extending the retrieval in altitude 5 to 10 km higher than the traditional method. It also allows the comparison of the traditional method's sensitivity to two in-principle equivalent methods of specifying the seed pressure: using a model pressure seed versus using a model temperature combined with the lidar's density measurement to calculate the seed pressure. We found that the seed pressure method is superior to using a model temperature combined with the lidar-derived density. The increased altitude capability of our OEM retrievals allows for a comparison of the Rayleigh-scatter lidar temperatures throughout the entire altitude range of the sodium lidar temperature measurements. Our OEM-derived Rayleigh temperatures are shown to have improved agreement relative to our previous comparisons using the traditional method, and the agreement of the OEM-derived temperatures is the same as the agreement between existing sodium lidar temperature climatologies. This detailed study of the calculation of the new Purple Crow Lidar temperature climatology using the OEM establishes that it is both highly advantageous and practical to reprocess existing Rayleigh-scatter lidar measurements that cover long time periods, during which time the lidar may have undergone several significant equipment upgrades, while gaining an upper limit to useful temperature retrievals equivalent to an order of magnitude increase in power-aperture product due to the use of an OEM.

The Canadian Network for the Detection of Atmospheric Change (CANDAC) Rayleigh–Mie–Raman lidar (CRL) at Eureka, Nunavut, has measured tropospheric clouds, aerosols, and water vapour since 2007. In remote and meteorologically significant locations, such as the Canadian High Arctic, the ability to add new measurement capability to an existing well-tested facility is extremely valuable. In 2010, linear depolarization 532 nm measurement hardware was installed in the lidar's receiver. To minimize disruption in the existing lidar channels and to preserve their existing characterization so far as is possible, the depolarization hardware was placed near the end of the receiver cascade. The upstream optics already in place were not optimized for preserving the polarization of received light. Calibrations and Mueller matrix calculations are used to determine and mitigate the contribution of these upstream optics on the depolarization measurements. The results show that with appropriate calibration, indications of cloud particle phase (ice vs. water) through the use of the depolarization parameter are now possible to a precision of ±0.05 absolute uncertainty ( ≤ 10 % relative uncertainty) within clouds at time and altitude resolutions of 5 min and 37.5 m respectively, with higher precision and higher resolution possible in select cases. The uncertainty is somewhat larger outside of clouds at the same altitude, typically with absolute uncertainty ≤ 0.1. Monitoring changes in Arctic cloud composition, including particle phase, is essential for an improved understanding of the changing climate locally and globally.

A standardized approach for the definition and reporting of vertical resolution of the ozone and temperature lidar profiles contributing to the Network for the Detection for Atmospheric Composition Change (NDACC) database is proposed. Two standardized definitions homogeneously and unequivocally describing the impact of vertical filtering are recommended. The first proposed definition is based on the width of the response to a finite-impulse-type perturbation. The response is computed by convolving the filter coefficients with an impulse function, namely, a Kronecker delta function for smoothing filters, and a Heaviside step function for derivative filters. Once the response has been computed, the proposed standardized definition of vertical resolution is given by Δz = δz × HFWHM, where δz is the lidar's sampling resolution and HFWHM is the full width at half maximum (FWHM) of the response, measured in sampling intervals. The second proposed definition relates to digital filtering theory. After applying a Laplace transform to a set of filter coefficients, the filter's gain characterizing the effect of the filter on the signal in the frequency domain is computed, from which the cut-off frequency fC, defined as the frequency at which the gain equals 0.5, is computed. Vertical resolution is then defined by Δz = δz∕(2fC). Unlike common practice in the field of spectral analysis, a factor 2fC instead of fC is used here to yield vertical resolution values nearly equal to the values obtained with the impulse response definition using the same filter coefficients. When using either of the proposed definitions, unsmoothed signals yield the best possible vertical resolution Δz = δz (one sampling bin). Numerical tools were developed to support the implementation of these definitions across all NDACC lidar groups. The tools consist of ready-to-use “plug-in” routines written in several programming languages that can be inserted into any lidar data processing software and called each time a filtering operation occurs in the data processing chain. When data processing implies multiple smoothing operations, the filtering information is analytically propagated through the multiple calls to the routines in order for the standardized values of vertical resolution to remain theoretically and numerically exact at the very end of data processing.

A standardized approach for the definition, propagation, and reporting of uncertainty in the temperature lidar data products contributing to the Network for the Detection for Atmospheric Composition Change (NDACC) database is proposed. One important aspect of the proposed approach is the ability to propagate all independent uncertainty components in parallel through the data processing chain. The individual uncertainty components are then combined together at the very last stage of processing to form the temperature combined standard uncertainty. The identified uncertainty sources comprise major components such as signal detection, saturation correction, background noise extraction, temperature tie-on at the top of the profile, and absorption by ozone if working in the visible spectrum, as well as other components such as molecular extinction, the acceleration of gravity, and the molecular mass of air, whose magnitudes depend on the instrument, data processing algorithm, and altitude range of interest. The expression of the individual uncertainty components and their step-by-step propagation through the temperature data processing chain are thoroughly estimated, taking into account the effect of vertical filtering and the merging of multiple channels. All sources of uncertainty except detection noise imply correlated terms in the vertical dimension, which means that covariance terms must be taken into account when vertical filtering is applied and when temperature is integrated from the top of the profile. Quantitatively, the uncertainty budget is presented in a generic form (i.e., as a function of instrument performance and wavelength), so that any NDACC temperature lidar investigator can easily estimate the expected impact of individual uncertainty components in the case of their own instrument. Using this standardized approach, an example of uncertainty budget is provided for the Jet Propulsion Laboratory (JPL) lidar at Mauna Loa Observatory, Hawai'i, which is typical of the NDACC temperature lidars transmitting at 355 nm. The combined temperature uncertainty ranges between 0.1 and 1 K below 60 km, with detection noise, saturation correction, and molecular extinction correction being the three dominant sources of uncertainty. Above 60 km and up to 10 km below the top of the profile, the total uncertainty increases exponentially from 1 to 10 K due to the combined effect of random noise and temperature tie-on. In the top 10 km of the profile, the accuracy of the profile mainly depends on that of the tie-on temperature. All other uncertainty components remain below 0.1 K throughout the entire profile (15–90 km), except the background noise correction uncertainty, which peaks around 0.3–0.5 K. It should be kept in mind that these quantitative estimates may be very different for other lidar instruments, depending on their altitude range and the wavelengths used.

The Canadian Network for the Detection of Atmospheric Change and Environment Canada DIAL lidar located at the Polar Environment Atmospheric Research Laboratory (PEARL) in Eureka, Nunavut, has been upgraded to measure water vapour mixing ratio profiles. The lidar is capable of measuring water vapour in the dry Arctic atmosphere up to the tropopause region. Measurements were obtained in the February to March polar sunrise during 2007, 2008 and 2009 as part of the Canadian Arctic ACE (Atmospheric Chemistry Experiment) Validation Campaign. Before such measurements can be used to address important questions in understanding dynamics and chemistry, the lidar measurements must be calibrated against an independent determination of water vapour. Here, radiosonde measurements of relative humidity have been used to empirically calibrate the lidar measurements. It was found that the calibration varied significantly between each year's campaign. However, the calibration of the lidar during an individual polar sunrise campaign agrees on average with the local radiosonde measurements to better than 12%. To independently validate the calibration of the lidar derived from the radiosondes, comparisons are made between the calibrated lidar measurements and water vapour measurements from the ACE satellite-borne Fourier Transform Spectrometer (ACE-FTS). The comparisons between the lidar and satellite-borne spectrometer for both a campaign average and single overpasses show favourable agreement between the two instruments and help validate the lidar's calibration. The 39 nights of high-Arctic water vapour measurements obtained offer the most detailed high spatial-temporal resolution measurement set available for understanding this time of transition from the long polar night to polar day.

The Optical Spectrograph and InfraRed Imaging System (OSIRIS) on the Odin satellite is currently in its 12th year of observing the Earth's limb. For the first time, continuous temperature profiles extending from the stratopause to the upper mesosphere have been derived from OSIRIS measurements of Rayleigh-scattered sunlight. Through most of the mesosphere, OSIRIS temperatures are in good agreement with coincident temperature profiles derived from other satellite and ground-based measurements. In the altitude region of 55–80 km, OSIRIS temperatures are typically within 4–5 K of those from the SABER, ACE-FTS, and SOFIE instruments on the TIMED, SciSat-I, and AIM satellites, respectively. The mean differences between individual OSIRIS profiles and those of the other satellite instruments are typically within the combined uncertainties and previously reported biases. OSIRIS temperatures are typically within 2 K of those from the University of Western Ontario's Purple Crow Lidar in the altitude region of 52–79 km, where the mean differences are within combined uncertainties. Near 84 km, OSIRIS temperatures exhibit a cold bias of 10–15 K, which is due to a cold bias in OSIRIS O2 A-band temperatures at 85 km, the upper boundary of the Rayleigh-scatter derived temperatures; and near 48 km OSIRIS temperatures exhibit a cold bias of 5–15 K, which is likely due to multiple-scatter effects that are not taken into account in the retrieval.

The first three Arctic winters of the ACE mission represented two extremes of winter variability: Stratospheric sudden warmings (SSWs) in 2004 and 2006 were among the strongest, most prolonged on record; 2005 was a record cold winter. Canadian Arctic Atmospheric Chemistry Experiment (ACE) Validation Campaigns were conducted at Eureka (80° N, 86° W) during each of these winters. New satellite measurements from ACE-Fourier Transform Spectrometer (ACE-FTS), Sounding of the Atmosphere using Broadband Emission Radiometry (SABER), and Aura Microwave Limb Sounder (MLS), along with meteorological analyses and Eureka lidar temperatures, are used to detail the meteorology in these winters, to demonstrate its influence on transport, and to provide a context for interpretation of ACE-FTS and validation campaign observations. During the 2004 and 2006 SSWs, the vortex broke down throughout the stratosphere, reformed quickly in the upper stratosphere, and remained weak in the middle and lower stratosphere. The stratopause reformed at very high altitude, near 75 km. ACE measurements covered both vortex and extra-vortex conditions in each winter, except in late-February through mid-March 2004 and 2006, when the strong, pole-centered vortex that reformed after the SSWs resulted in ACE sampling only inside the vortex in the middle through upper stratosphere. The 2004 and 2006 Eureka campaigns were during the recovery from the SSWs, with the redeveloping vortex over Eureka. 2005 was the coldest winter on record in the lower stratosphere, but with an early final warming in mid-March. The vortex was over Eureka at the start of the 2005 campaign, but moved away as it broke up. Disparate temperature profile structure and vortex evolution resulted in much lower (higher) temperatures in the upper (lower) stratosphere in 2004 and 2006 than in 2005. Satellite temperatures agree well with lidar data up to 50–60 km, and ACE-FTS, MLS and SABER show good agreement in high-latitude temperatures throughout the winters. Consistent with a strong, cold upper stratospheric vortex and enhanced radiative cooling after the SSWs, MLS and ACE-FTS trace gas measurements show strongly enhanced descent in the upper stratospheric vortex in late January through March 2006 compared to that in 2005.

The Purple Crow Lidar (PCL) is a large power-aperture product monostatic Rayleigh-Raman-Sodium-resonance-fluorescence lidar, which has been in operation at the Delaware Observatory (42.9° N, 81.4° W, 237 m elevation) near the campus of The University of Western Ontario since 1992. Kinetic-energy density has been calculated from the Rayleigh-scatter system measurements of density fluctuations at temporal-spatial scales relevant for gravity waves, e.g. soundings at 288 m height resolution and 9 min temporal resolution in the upper stratosphere and mesosphere. The seasonal averages from 10 years of measurements show in all seasons some loss of gravity-wave energy in the upper stratosphere. During the equinox periods and summer the measurements are consistent with gravity waves growing in height with little saturation, in agreement with the classic picture of the variations in the height at which gravity waves break given by Lindzen (1981). The mean values compare favourably to previous measurements when computed as nightly averages, but the high temporal-spatial resolution measurements show considerable day-to-day variability. The variability over a night is often extremely large, with typical RMS fluctuations of 50 to 100% at all heights and seasons common. These measurements imply that using a daily or nightly-averaged gravity-wave energy density in numerical models may be highly unrealistic.