Short-term Averages Research Articles

Characterization of indoor and ambient air quality in modern commercial and recreational complex buildings in Hanoi

The paper provides new information on the correlation between indoor and ambient air quality in selected commercial and recreational buildings, representing a cross-section of metropolitan building complexes in Hanoi, Vietnam. Air sampling carried out simultaneously at fixed locations within the case study directed the varying behavior across different zones and thus highlighted the relationship between parking basements, commercial floors, and ambient air sites. The fractions of coarse and fine particles were analyzed to identify the associated emission sources on a short-term basis. The elevated 8-h average levels of CO in underground parking zones (11–22 mg/m3) were significantly higher than those measured on commercial floors (0.5–0.9 mg/m3) and relatively ambient air (0.8–1 mg/m3) and exceeded the recommended exposure limit of 10 mg/m3 in the corresponding guidelines. Short-term averages of SO2 mass concentration (112–225 μg/m3) remained below the limits of 500 μg/m3 concerning health exposure in existing standards. The correlation of daily NO2 patterns was analyzed accordingly in relation to the operation of the individual building, the central ventilation mechanism, and the penetration of outside air, which is usually conditioned by traffic emission. The mass concentrations of PM10 and PM2.5 measured outdoors (21–32 μg/m3 and 12–14 μg/m3) and in parking basements (28–67 μg/m3 and 15–27 μg/m3) were generally higher than those recorded indoors at ground levels (1–29 μg/m3 and 1–20 μg/m3), frequently reaching the reference exposure values (15 μg/m3 and 5 μg/m3) for public health concerns. The determined levels and profiles of benzene, toluene, ethylbenzene, (m + p)-xylene, and o-xylene corresponding to the relative ratios of T/B and X/E revealed the responsible emissions from gasoline-fueled vehicles in parking basements and the influence of associated local traffic.

Traffic-related air pollution is associated with glucose dysregulation, blood pressure, and oxidative stress in children

BackgroundMetabolic syndrome increases the risk of cardiovascular disease in adults. Antecedents likely begin in childhood and whether childhood exposure to air pollution plays a contributory role is not well understood. ObjectivesTo assess whether children's exposure to air pollution is associated with markers of risk for metabolic syndrome and oxidative stress, a hypothesized mediator of air pollution-related health effects. MethodsWe studied 299 children (ages 6–8) living in the Fresno, CA area. At a study center visit, questionnaire and biomarker data were collected. Outcomes included hemoglobin A1c (HbA1c), urinary 8-isoprostane, systolic blood pressure (SBP), and BMI. Individual-level exposure estimates for a set of four pollutants that are constituents of traffic-related air pollution (TRAP) – the sum of 4-, 5-, and 6-ring polycyclic aromatic hydrocarbon compounds (PAH456), NO2, elemental carbon, and fine particulate matter (PM2.5) – were modeled at the primary residential location for 1-day lag, and 1-week, 1-month, 3-month, 6-month, and 1-year averages prior to each participant's visit date. Generalized additive models were used to estimate associations between each air pollutant exposure and outcome. ResultsThe study population was 53% male, 80% Latinx, 11% Black and largely low-income (6% were White and 3% were Asian/Pacific Islander). HbA1c percentage was associated with longer-term increases in TRAP; for example a 4.42 ng/m3 increase in 6-month average PAH456 was associated with a 0.07% increase (95% CI: 0.01, 0.14) and a 3.62 μg/m3 increase in 6-month average PM2.5 was associated with a 0.06% increase (95% CI: 0.01, 0.10). The influence of air pollutants on blood pressure was strongest at 3 months; for example, a 6.2 ppb increase in 3-month average NO2 was associated with a 9.4 mmHg increase in SBP (95% CI: 2.8, 15.9). TRAP concentrations were not significantly associated with anthropometric or adipokine measures. Short-term TRAP exposure averages were significantly associated with creatinine-adjusted urinary 8-isoprostane. DiscussionOur results suggest that both short- and longer-term estimated individual-level outdoor residential exposures to several traffic-related air pollutants, including ambient PAHs, are associated with biomarkers of risk for metabolic syndrome and oxidative stress in children.

Recent growth trends of conifers across Western Europe are controlled by thermal and water constraints and favored by forest heterogeneity

Tree growing conditions are changing rapidly in the face of climate change. Capturing tree-growth response to such changes across environmental contexts and tree species calls for a continuous forest monitoring over space. Based on >10,000 tree-ring measurements sampled across the systematic grid of the continuous French national forest inventory (NFI) over the 2006–2016 period, we evaluated the radial growth trends of eight conifer tree species prevalent in European forests across their native and introduced ranges and various bioclimatic contexts (n = 16 forest systems). For each forest system, radial increments were filtered out from tree, plot, soil and climatic normal influences to isolate environment-driven growth signals and quantify residual time-series. Associated growth trends across forest systems were then confronted against environmental variables (e.g. short-term averages and trends in seasonal climate). Trends for a given species were systematically more positive in cooler contexts (higher elevations or northern distribution margins) than in warmer contexts (plains). Decreases and increases in precipitation regimes were found to be associated with negative and positive tree growth trends, respectively. Remarkably, positive growth trends were mainly observed for native forest systems (7/9) and negative trends for introduced systems (5/7). Native forests showed a more heterogeneous forest structure as compared to introduced forests that, in line with observed positive dependence of tree growth trends onto both water availability and forest heterogeneity, appears to modulate the competitive pressure on water resource with ongoing summer maximum temperature increase. Over a short annually-resolved study period, we were able to capture tree growth responses coherent with climate change across diverse forest ecosystems. With ongoing accumulation of data, the continuous French NFI hence arises as powerful support to monitoring climate change effects on forests.

A Novel Method of Seismic Signal Detection Using Waveform Features

The detection of seismic signals is vital in seismic data processing and analysis. Many algorithms have been proposed to resolve this issue, such as the ratio of short-term and long-term power averages (STA/LTA), F detector, Generalize F, and etc. However, the detection performance will be affected by the noise signals severely. In this paper, we propose a novel seismic signal detection method based on the historical waveform features to improve the seismic signals detection performance and reduce the affection from the noise signals. We use the historical events location information in a specific area and waveform features information to build the joint probability model. For the new signal from this area, we can determine whether it is the seismic signal according to the value of the joint probability. The waveform features used to construct the model include the average spectral energy on a specific frequency band, the energy of the component obtained by decomposing the signal through empirical mode decomposition (EMD), and the peak and the ratio of STA/LTA trace. We use the Gaussian process (GP) to build each feature model and finally get a multi-features joint probability model. The historical events location information is used as the kernel of the GP, and the historical waveform features are used to train the hyperparameters of GP. The beamforming data of the seismic array KSRS of International Monitoring System are used to train and test the model. The testing results show the effectiveness of the proposed method.

Efektywność i stabilność systemów transakcyjnych opartych na zwykłych, wykładniczo i liniowo ważonych średnich ruchomych

<p><strong>Uzasadnienie teoretyczne:</strong> Większość artykułów dotyczy optymalizacji systemów transakcyjnych jedynie w odniesieniu do pojedynczego aktywu lub indeksu. W literaturze przedmiotu zauważalny jest brak opracowań kompleksowych dotyczących całej grupy aktywów.</p><p><strong>Cel artykułu:</strong> Optymalizacja systemów transakcyjnych bazujących na przecięciu średniej ruchomej i ceny zamknięcia (sygnał kupna i sprzedaży) dla 404 akcji notowanych na Giełdzie Papierów Wartościowych w Warszawie. Dla każdego waloru badaniem objętych zostało 5000 sesji lub mniej, jeśli akcje były notowane w krótszym horyzoncie czasowym. Użytymi w badaniu rodzajami średnich były: zwykła średnia ruchoma (SMA), ważona liniowo (WMA) i ważona wykładniczo (EMA).W kolejnych częściach artykułu został przeprowadzony ranking średnich ruchomych oraz dokonano oceny stabilności systemów transakcyjnych.</p><p><strong>Metody badawcze:</strong> W opracowaniu zastosowano następujące metody: 1) średnie ruchome optymalizujące system transakcyjny – analiza korelacji stóp zwrotu i długości średnich ruchomych, regresja liniowa, 2) ranking skuteczności systemów transakcyjnych – rankingowanie proste i ważone stopą zwrotu, 3) analiza stabilności systemów transakcyjnych – korelacja długości pierwszej i drugiej średniej ruchomej, przynoszących najwyższe stopy zwrotu, współczynnik determinacji dla par średnich ruchomych i stóp zwrotu, a także współczynnik WF (średni spadek efektywności 16 najlepszych systemów transakcyjnych przypadający na jednostkową stopę zwrotu najlepszego systemu transakcyjnego).</p><p><strong>Główne wnioski:</strong> Otrzymane rezultaty jednoznacznie wskazują, że w przypadku wszystkich rodzajów średnich systemy transakcyjne były optymalizowane w przeważającej większości przez średnie krótkoterminowe, co potwierdza behawioralną skłonność inwestorów do zawierania transakcji raczej o zabarwieniu spekulacyjnym niż inwestycyjnym. Przeprowadzony ranking skuteczności trzech rodzajów średnich ruchomych (WMA, SMA, EMA) jednoznacznie wskazał, że w przeważającej części najwyższe stopy zwrotu były uzyskiwane przy zastosowaniu WMA, przed SMA i EMA. Różnice skuteczności systemów opartych o WMA i SMA były niewielkie, za to systemy bazujące na tych dwóch średnich ruchomych okazały się zdecydowanie bardziej efektywne od systemów wykorzystujących EMA.</p>

Comparison of Application of AERMOD and ISCST3 Models for Simulating the Dispersion of Emitted Pollutant from the Stack of an Industrial Plant in Different Time Scales

Background: One of the main parts of air quality management is known as modeling of atmospheric pollutants. In this regards, simultaneous application of several models in a project and comparing the results obtained from these models could have been a considerable contribution to air quality managers for taking a more efficient decision. Methods: In this study, the stack of an industrial plant in the southwest of Isfahan was selected as the emission source and the total suspended particles emitted from this stack was simulated by applying AERMOD and ISCST3 view models (version 8.2). In this vein, the modeling process was conducted using MM5 meteorological data in a 50 50 km extent with 2000 m network distance for each of the models in 1-h, 24-h term averages (short term averages) and monthly and annual periods (long term averages) at ground level concentrations (GLC). Results: Results indicated that the highest simulated concentration for both models occurred in a 2000 meters’ distance in the east of the stack. Moreover, the highest simulated concentration applying AERMOD was lower than that of applying ISCST3 in all term averages which is due to existing differences between applied algorithms in these two models. Conclusion: Consequently, applying AERMOD due to the use of more advanced and up-to-date algorithms have priority over ISCST3 model. Applying ISCST3 can also be useful for small projects that require less input data compared to the AERMOD.

Increased Impulsivity in Urban-Dwelling Teens: Role of Ambient Air Temperature and Lack of Neighborhood Greenspace

Ambient temperature and lack of residential greenspace have been associated with increased externalizing behavior, but the neuropsychological processes are unclear. We conducted a longitudinal analysis to examine the association between temperature, greenspace, and impulsivity in urban-dwelling adolescents. Participants (N=982) of the Risk Factors for Antisocial Behavior Study, a multi-ethnic cohort of Southern California twins/triplets examined in 2000-2014 (aged 9-21 years), each with repeat measures of impulsivity assessed by the Go/No Go test. Ambient temperature near the residence (6.8±3.0 km) were obtained from local Meteorological Information System and Normalized Difference Vegetation Index was used as a proxy for neighborhood greenspace (in 1000-m buffer). A monthly exposure time-series was created and aggregated in short-term (1-, 3-, and 6-month) and long-term (1-, 2-, and 3-year) averages prior to each behavioral assessment. Generalized Poisson mixed models were used to estimate the exposure effects on number of false alarms, adjusting for within-family/-individual correlations and confounders. In separate adjusted analyses, we found average temperature from 1-month to 2-years significantly (all p<0.05) increased impulsivity, while long-term greenspace decreased impulsivity. In models mutually adjusting for temperature and greenspace, the neurotrophic benefits of greenspace disappeared, while the adverse effects of 1- and 3-month temperature remained. These associations could not be explained by sociodemographics, neighborhood quality and socioeconomic characteristics, or season. This is the first epidemiologic evidence supporting the adverse temperature effects on impulsivity, which may contribute to the behavioral effect of diminished greenspace. Future studies need to examine whether increased impulsivity mediates the adverse behavioral effect and how neural networks underlying such neuropsychological processes are perturbed by ambient temperature.

Long- and short-term air pollution exposure and measures of arterial stiffness in the Framingham Heart Study

BackgroundStudies of air pollution exposure and arterial stiffness have reported inconsistent results and large studies employing the reference standard of arterial stiffness, carotid-femoral pulse-wave velocity (CFPWV), have not been conducted. AimTo study long-term exposure to ambient fine particles (PM2.5), proximity to roadway, and short-term air pollution exposures in relation to multiple measures of arterial stiffness in the Framingham Heart Study. MethodsWe assessed central arterial stiffness using CFPWV, forward pressure wave amplitude, mean arterial pressure and augmentation index. We investigated long-and short-term air pollution exposure associations with arterial stiffness with linear regressions using long-term residential PM2.5 (2003 average from a spatiotemporal model using satellite data) and proximity to roadway in addition to short-term averages of PM2.5, black carbon, particle number, sulfate, nitrogen oxides, and ozone from stationary monitors. ResultsWe examined 5842 participants (mean age 51 ± 16, 54% women). Living closer to a major roadway was associated with higher arterial stiffness (0.11 m/s higher CFPWV [95% CI: 0.01, 0.22] living <50 m vs 400 ≤ 1000 m). We did not observe association between arterial stiffness measures and long-term PM2.5 or short-term levels of PM2.5, particle number, sulfate or ozone. Higher levels of black carbon and nitrogen oxides in the previous days were unexpectedly associated with lower arterial stiffness. ConclusionsLong-term exposure to PM2.5 was not associated with arterial stiffness but positive associations with living close to a major road may suggest that pollutant mixtures very nearby major roads, rather than PM2.5, may affect arterial stiffness. Furthermore, short-term air pollution exposures were not associated with higher arterial stiffness.

Estimating historical groundwater levels based on relations with hydrologic and meteorological variables in the U.S. glacial aquifer system

A number of previous studies have examined relations between groundwater levels and hydrologic and meteorological variables over parts of the glacial aquifer system, but systematic analyses across the entire U.S. glacial aquifer system are lacking. We tested correlations between monthly groundwater levels measured at 1043 wells in the U.S. glacial aquifer system considered to be minimally influenced by human disturbance and selected hydrologic and meteorological variables with the goal of extending historical groundwater records where there were strong correlations. Groundwater levels in the East region correlated most strongly with short-term (1 and 3 month) averages of hydrologic and meteorological variables, while those in the Central and West Central regions yielded stronger correlations with hydrologic and meteorological variables averaged over longer time intervals (6–12 months). Variables strongly correlated with high and low annual groundwater levels were identified as candidate records for use in statistical linear models as a means to fill in and extend historical high and low groundwater levels respectively. Overall, 37.4% of study wells meeting data criteria had successful models for high and (or) low groundwater levels; these wells shared characteristics of relatively higher local precipitation, higher local land-surface slope, lower amounts of clay within the surficial sediments, and higher base-flow index. Streamflow and base flow served as explanatory variables in about two thirds of both high- and low-groundwater-level models in all three regions, and generally yielded more and better models compared to precipitation and Palmer Drought Severity Index. The use of variables such as streamflow with substantially longer and more complete records than those of groundwater wells provide a means for placing contemporary groundwater levels in a longer historical context and can support site-specific analyses such as groundwater modeling.

Evaluation of air quality at the Birmingham New Street Railway Station.

Air pollution from diesel emissions is becoming an increased international concern, and whilst attention has been primarily focused on the automotive industry, concerns have also been raised about emissions from diesel rail vehicles. This paper reports an extensive series of measurements made at the Birmingham New Street station, a major rail interchange in the Midlands of England, with a mix of diesel and electric train movements, which is of particular concern because of the enclosed nature of the platforms. This study was undertaken in collaboration with Network Rail to better understand the environment in and around the station over a longer period to provide a more detailed analysis of the complex environment at the station. The station environment has been considered in terms of the European Union (EU) and Department of Environment, Food and Rural Affairs (DEFRA) limits as part of the monitoring methodology, but it should be noted that these limits do not apply in this environment as the Management of Health and Safety at Work Regulation 1999 and the Control of Substances Hazardous to Health Regulations 2002 are applicable. The monitoring campaign consisted of diffusion tube measurements to measure nitrogen dioxide at a large number of different locations throughout and around the station. These were followed by detailed measurements of oxides of nitrogen, particulate matter, carbon dioxide and black carbon (a diesel tracer) at a smaller number of sites at the platform level. The results are analysed to give concentrations over a wide variety of time scales, and long- and short-term averages. The effects of ambient wind conditions and individual train movements are also considered. Recommendations are made for possible remedial measures and for future work to more fully understand the physical mechanisms involved.

Trading volume, return variability and short-term momentum

ABSTRACTWe propose short-term averages of daily stock-level trading volume and return variability as proxies for latent corporate news flow. Conditioning momentum strategies on these two proxies give a significant boost to winner-minus-loser alphas. Regardless of the portfolio formation and holding periods, price drift is larger after elevated levels of volume and variability, supporting the view that prices underreact to news. This pattern is not driven by micro-cap stocks and it is robust to corrections for systematic risk factors and stock characteristics such as liquidity and credit quality.

Dependence of maximum concentration from chemical accidents on release duration

Chemical accidents often involve releases of a total mass, Q, of stored material in a tank over a time duration, td, of less than a few minutes. The value of td is usually uncertain because of lack of knowledge of key information, such as the size and location of the hole and the pressure and temperature of the chemical. In addition, it is rare that eyewitnesses or video cameras are present at the time of the accident. For inhalation hazards, serious health effects (such as damage to the respiratory system) are determined by short term averages (<1 min) of concentrations, C. It is intuitively obvious that, for a ground level source and with all conditions the same (e.g., the same mass Q released), the maximum C near the source will be larger for a shorter than a longer release duration, td. This paper investigates the variation with downwind distance, x, of the ratio of maximum C for two time durations of release. Some simplified formulas for dispersion from finite duration releases are presented based on dimensional analysis. A primary dimensionless number of importance is the ratio of the release duration, td, to the travel time tt = x/u, at distance, x, where u is wind speed. Examples of applications to pressurized liquefied chlorine releases from tanks are given, focusing on scenarios from the Jack Rabbit I (JR I) field experiment. The analytical calculations and the predictions of the SLAB dense gas dispersion model agree that the ratio of maximum C for two different td's is greatest (as much as a factor of ten) near the source. At large distances (beyond a few km for the JR I scenarios), where tt exceeds both td's, the ratio of maximum C approaches unity.

Integrated Optimization of Long-Range Underwater Signal Detection, Feature Extraction, and Classification for Nuclear Treaty Monitoring

We designed and jointly optimized an integrated signal processing chain for detection and classification of long-range passive-acoustic underwater signals recorded by the global geophysical monitoring network of the Comprehensive Nuclear-Test-Ban Treaty Organization. Starting at the level of raw waveform data, a processing chain of signal detection, feature extraction, and signal classification was designed and jointly optimized to the task. Relevant waveform segments were in a first step identified by a generic, flexibly parameterized detection algorithm on a long- to short-term averages' ratio of the spectral energy. For representation, general-purpose sound processing features, with an added focus on spectral and cepstral features, were extracted from the detected segments. As classifiers, support vector machines with different kernel functions were employed alongside other baseline learning algorithms. The free parameters of the overall toolchain (i.e., trigger algorithm parameters and classifier hyperparameters) were jointly optimized in a cross-validation setting, either according to the cross-validation classification error or the cross-validation area under the receiver operating characteristic curve. Experiments demonstrate that our method outperforms machine learning algorithms task-tailored to a previous, human-expert-designed preprocessing chain. The presented approach can be adapted to a wide range of problems that can benefit from jointly optimizing parameters of preprocessing and classification algorithm.

Why Market Returns Favor Democrats in the White House

This study attempts to explain why the equity market earns greater returns for bearing risk when a Democrat is President in the USA versus a Republican. I look at data from 1929 through 2012. The data show that the value weighted return minus the corresponding period's risk free rate is 10.83% when a Democrat is President, versus a corresponding return of -1.20% under Republican Presidents. Two basic macroeconomic arguments exist that should affect market value between the two parties: differences in the risk free interest rates and differences in economic growth. On average the Democrats follow a policy of low interest rates. The rate of return on short-term T-bills averages 4.55% under the Republicans and a 2.48% under Democrats. Further, the Democrats overall economic policies create a higher average real growth rate with a 4.8% average versus only 1.8% under Republican administrations. Unfortunately, the growth and interest rate differences together do not explain the observed difference in equity market returns. A basic OLS approach with annual value weighted market returns minus the corresponding risk free rate as the dependent variable is run. Neither risk free interest rates nor real economic growth are significant in explaining the observed market returns though the party in power is significant predictor.

Adaptive STA–LTA with Outlier Statistics

The most common approach to seismic triggering is to compare short‐term averages (STA) with long‐term averages (LTA) of transformed amplitudes. In recording environments where this technique is of limited use, hidden Markov models (HMMs) are increasingly used for statistical event detection and classification, but these require training data and are often susceptible to false positive detection errors. In this work, we introduce an adaptive STA–LTA triggering algorithm that uses STA and LTA of state probabilities defined by restricting an HMM to a two population model of outliers in background noise. Monte Carlo simulations of noise and synthetic events are used to investigate detector sensitivity using statistical properties of latent states. We compare our method with traditional STA–LTA triggering on real data recorded by a 12‐station vertical borehole array near Hoadley gas field, Alberta, Canada. These tests suggest that our method is more accurate when dealing with closely spaced events and is less susceptible to false positive detection errors. When existing picking algorithms are adapted for HMM STA–LTA, the result is improvement in total picks, accuracy, and consistency. A narrow range of detection thresholds is optimal for a wide range of signal‐to‐noise ratios; this suggests HMM STA–LTA may be less sensitive to analyst parameter choices than even traditional STA–LTA.

Mode-2 hydraulic control of flow over a small ridge on a continental shelf

Some of the most intense turbulence in the ocean occurs in hydraulic jumps formed in the lee of sills where flows are hydraulically controlled, usually by the first internal mode. Observations on the outer Texas-Louisiana continental shelf reveal hydraulic control of internal mode-2 lasting more than 3 h over a 20 m high ridge on the 100 m deep continental shelf. When control began the base of the weakly stratified surface layer bulged upward and downward, a signature of mode-2. As the westward flow producing control was lost, large-amplitude disturbances, initially resembling a bore in the weakly stratified layer, began propagating eastward. Average dissipation rates inferred from density inversions over the ridge were 10−8 and 10−7W kg−1, one to two decades above local background. Corresponding diapycnal diffusivities, Kρ, were 10−4 to 10−3 m2 s−1. Short-term mixing averages did not evolve systematically with hydraulic control, possibly owing to our inability to observe small overturns in strongly stratified water directly over the ridge. To test the feasibility of our interpretation of the observations, hydrostatic runs with a three-dimensional MITgcm simulated mode-2 control and intense mixing over the ridge below the interface. Details differed from observations, principally because we lacked three-dimensional density fields to initialize the model which was forced with currents observed by a bottom-mounted ADCP several kilometers east of the ridge. Consequently, the model did not capture all flow features around the bank. The principal conclusion is that hydraulic responses to higher modes can dominate flows around even modest bathymetric irregularities.

Predictive factors and outcomes of cochlear implantation in patients with connexin 26 mutation: A comparative study

PurposeTo compare hearing outcomes in patients with connexin 26 (Cx 26) mutations undergoing cochlear implantation to age matched controls and to examine whether age at implantation, gender and type of mutation were correlated with hearing outcome. Materials and methodsRetrospective chart review of 21 patients with Cx 26 mutations that underwent cochlear implantation compared to 18 age-matched controls. Patients' characteristics, type of mutation and pre- and postoperative short and long-term hearing thresholds, word and sentence scores were analyzed. ResultsThere was no statistically significant difference between the Cx 26 and control group in the mean short term and mean long term post-operative pure tone averages (PTA), speech reception thresholds (SRT), word and sentence scores. Gender, age at implantation and type of connexin 26 mutation did not predict hearing outcomes. ConclusionsIn patients with connexin 26 mutation, cochlear implantation provides an effective mean of auditory habilitation. Mutational status, age and gender do not seem to predict hearing outcomes.

Spatially Adaptive Post-Processing of Ensemble Forecasts for Temperature

SummaryWe propose a statistical post-processing method that yields locally calibrated probabilistic forecasts of temperature, based on the output of an ensemble prediction system. It represents the mean of the predictive distributions as a sum of short-term averages of local temperatures and ensemble prediction system driven terms. For the spatial interpolation of temperature averages and local forecast uncertainty parameters we use an intrinsic Gaussian random-field model with a location-dependent nugget effect that accounts for small-scale variability. Applied to the COSMO-DE ensemble, our method yields locally calibrated and sharp probabilistic forecasts and compares favourably with other approaches.

Short-term variability and summer-2009 averages of the mean wind and tides in the mesosphere and lower thermosphere over Langfang, China (39.4°N, 116.7°E)

Data obtained from the newly installed MF radar at Langfang (39.4°N, 116.7°E), China, during the summer months of 2009 are used to study the short-term variability and summer averages of the mean wind and tidal oscillations in the mesosphere and lower thermosphere (MLT). Both the zonal and meridional daily winds between 80km and 98km show considerable day-to-day variability; their Lomb–Scargle spectra reveal active quasi-2-day and quasi-16-day planetary waves. The tidal large day-to-day variations have time scales of days or longer, which could be attributed to the contribution of planetary waves. The summer-averaged zonal wind is westward below 82km and eastward above 88km with the transition altitude at ∼85km. The meridional wind is dominated by the southward wind. The summer-averaged zonal and meridional semidiurnal tides exhibit amplitudes of 10–15m/s; they are larger than the diurnal tides, whose amplitudes are 5–10m/s. The phase tilt of the semidiurnal tide is downward, indicating this component is excited below the MLT. For the diurnal tide, the phase propagation is downward above 86km but it is upward below 86km, which indicates that the tide is evanescent or a mixture of evanescent and propagating modes. These wind and tide observations are compared with data from other mid-latitude stations at ∼40°N and with the HWM and GSWM models. Comparisons show that HWM-93 is better than HWM-07 in delineating the 2009 summer-averaged zonal wind over Langfang while both have systematic discrepancy in delineating the meridional mean winds. The prediction of the 2009 summer-averaged diurnal tide over Langfang by GSWM-09 is better than that from GSWM-02 but not the semidiurnal tide.

An Analysis of Sample Selection and the Reliability of Using Short-Term Earnings Averages in SIPP-SSA Matched Data

In this paper, we document the extent to which the sample of the Survey of Income and Program Participation that is matched to the Social Security Administration’s administrative earnings records is nationally representative. We conclude that the match bias is small, so selection is not a serious concern. The matched sample over‐represents individuals who are wealthy, who have financial assets or who have received a government‐transfer and under‐represents individuals who attributed from the SIPP. We use this matched sample to examine the relationship between short‐term averages of earnings from the SIPP earnings and average lifetime earnings from the administrative records. Our estimates suggest that using short averages of earnings may understate the effects of permanent income on particular outcomes of interest.