Indoor Environment Dataset to Estimate Room Occupancy

The estimation of occupancy is a crucial contribution to achieve improvements in energy efficiency. The drawback of data or incomplete data related to occupancy in enclosed spaces makes it challenging to develop new models focused on estimating occupancy with high accuracy. Furthermore, considerable variation in the monitored spaces also makes it difficult to compare the results of different approaches. This dataset comprises the indoor environmental information (pressure, altitude, humidity, and temperature) and the corresponding occupancy level for two different rooms: (1) a fitness gym and (2) a living room. The fitness gym data were collected for six days between 18 September and 2 October 2019, obtaining 10,125 objects with a 1 s resolution according to the following occupancy levels: low (2442 objects), medium (5325 objects), and high (2358 objects). The living room data were collected for 11 days between 14 May and 4 June 2020, obtaining 295,823 objects with a 1 s resolution, according to the following occupancy levels: empty (50,978 objects), low (202,613 objects), medium (35,410 objects), and high (6822 objects). Additionally, the number of fans turned on is provided for the living room data. The data are publicly available in the Mendeley Data repository. This dataset can be used to train and compare different machine learning, deep learning, and physical models for estimating occupancy at enclosed spaces.

Climatology of Estimated Altimeter Error Due to Nonstandard Temperatures

General-aviation (GA) controlled flight into terrain accidents often occur when a pilot is unaware their aircraft’s true altitude is lower than the altitude indicated by the pressure altimeter due to colder than standard temperatures. However, little guidance is available that quantifies the magnitude of these altimeter errors and their variation with season. In this study, the fifth generation European Centre for Medium-Range Weather Forecasts (ECMWF) atmospheric reanalysis of the global climate (ERA5) data set is combined with the pressure-altitude equation to construct a 30-year monthly climatology covering much of the U.S. and Canada of D-value (i.e., true altitude minus pressure altitude) corrected for the standard atmosphere height separation between the altimeter setting and standard mean sea-level pressure. This “corrected” D-value therefore provides a useful estimate of the error between true and altimeter-indicated altitude. During winter, the mean corrected D-values reach values as low as −350 m (~ −1,200 feet) in northern, low-terrain regions for flights near a pressure altitude of 3,600 m, meaning the aircraft would be nearly 350 m lower than the altimeter indicates. Furthermore, the minimum (maximum negative) corrected D-values are nearly double their mean values for the same time period. In addition, the reanalysis-based corrected D-values are compared to estimated values calculated using a simple rule-of-thumb based solely on the air temperature at altitude and the surface elevation. The rule-of-thumb tends to under-predict the magnitude of the estimated error, in some cases by 70 m (~200 feet), and therefore gives a lower margin of safety.

Calibration of an airborne HO_<i>x</i> instrument using the All Pressure Altitude-based Calibrator for HO_<i>x</i> Experimentation (APACHE)

Laser-induced fluorescence (LIF) is a widely used technique for both laboratory-based and ambient atmospheric chemistry measurements. However, LIF instruments require calibrations in order to translate instrument response into concentrations of chemical species. Calibration of LIF instruments measuring OH and HO2 (HOx) typically involves the photolysis of water vapor by 184.9 nm light, thereby producing quantitative amounts of OH and HO2. For ground-based HOx instruments, this method of calibration is done at one pressure (typically ambient pressure) at the instrument inlet. However, airborne HOx instruments can experience varying cell pressures, internal residence times, temperatures, and humidity during flight. Therefore, replication of such variances when calibrating in the lab is essential to acquire the appropriate sensitivities. This requirement resulted in the development of the APACHE (All Pressure Altitude-based Calibrator for HOx Experimentation) chamber to characterize the sensitivity of the airborne LIF-FAGE (fluorescence assay by gas expansion) HOx instrument, HORUS, which took part in an intensive airborne campaign, OMO-Asia 2015. It utilizes photolysis of water vapor but has the additional ability to alter the pressure at the nozzle of the HORUS instrument. With APACHE, the HORUS instrument sensitivity towards OH (26.1–7.8 cts s−1 pptv−1 mW−1, ±22.6 % 1σ; cts stands for counts by the detector) and HO2 (21.2–8.1 cts s−1 pptv−1 mW−1, ±22.1 % 1σ) was characterized to the external pressure range at the instrument nozzle of 227–900 mbar. Measurements supported by a computational fluid dynamics model, COMSOL Multiphysics, revealed that, for all pressures explored in this study, APACHE is capable of initializing a homogenous flow and maintaining near-uniform flow speeds across the internal cross section of the chamber. This reduces the uncertainty regarding average exposure times across the mercury (Hg) UV ring lamp. Two different actinometrical approaches characterized the APACHE UV ring lamp flux as 6.37×1014(±1.3×1014) photons cm−2 s−1. One approach used the HORUS instrument as a transfer standard in conjunction with a calibrated on-ground calibration system traceable to NIST standards, which characterized the UV ring lamp flux to be 6.9(±1.1)×1014 photons cm−2 s−1. The second approach involved measuring ozone production by the UV ring lamp using an ANSYCO O3 41 M ozone monitor, which characterized the UV ring lamp flux to be 6.11(±0.8)×1014 photons cm−2 s−1. Data presented in this study are the first direct calibrations of an airborne HOx instrument, performed in a controlled environment in the lab using APACHE.

Calibration of an airborne HO_X instrument using the All Pressure Altitude based Calibrator for HO_X Experimentation (APACHE)

Laser induced fluorescence (LIF) is a widely used technique for both laboratory-based and ambient atmospheric chemistry measurements. However, LIF instruments require calibrations in order to translate instrument response into concentrations of chemical species. Calibration of LIF instruments measuring OH and HO2 (HOX), typically involves the photolysis of water vapor by 184.9 nm light thereby producing quantitative amounts of OH and HO2. For ground-based systems HOX instruments, this method of calibration is done at one pressure (typically ambient pressure) at the instrument inlet. However, airborne HOX instruments can experience varying cell pressures, internal residence times, temperatures, and humidity during flight. Therefore, replication of such variances when calibrating are essential to acquire the appropriate sensitivities. This requirement resulted in the development of the APACHE (All Pressure Altitude-based Calibrator for HOX Experimentation) chamber. It utilizes photolysis of water vapor, but has the additional ability to alter the pressure at the inlet of the HOX instrument thus relating instrument sensitivity to the external pressure ranges experienced during flight (275 to 1000 mbar). Measurements supported by COMSOL multiphysics and its computational fluid dynamics calculations revealed that, for all pressures explored in this study, APACHE is capable of initializing homogenous flow and maintain near uniform flow speeds across the internal cross-section of the chamber. This reduces the uncertainty regarding average exposure times across the mercury (Hg) UV ring lamp. Two different actinometrical approaches characterized the APACHE UV ring lamp flux as 6.3 x 1014 (± 0.9 x 1014) s-1 depending on pressure. Data presented in this study are the first direct calibrations, performed in a controlled environment using APACHE of an airborne HOX system instrument.

IVAN: An Interactive Herlofson’s Nomogram Visualizer for Local Weather Forecast

In 1947, N. Herlofson proposed a modification to the 1884 Heinrich Hertz’s Emagram with the goal of getting more precise hand-made weather forecasts providing larger angles between isotherms and adiabats. Since then, the Herlofson’s nomogram has been used every day to visualize the results of about 800 radiosonde balloons that, twice a day, are globally released, sounding the atmosphere and reading pressure, altitude, temperature, dew point, and wind velocity. Relevant weather forecasts use such pieces of information to predict fog, cloud height, rain, thunderstorms, etc. However, despite its diffusion, non-technical people (e.g., private gliding pilots) do not use the Herlofson’s nomogram because they often consider it hard to interpret and confusing. This paper copes with this problem presenting a visualization based environment that presents the Herlofson’s nomogram in an easier to interpret way, allowing the selection of the right level of detail and at the same time inspection of the sounding row data and the plotted diagram. Our visual environment was compared with the classic way of representing the Herlofson’s nomogram in a formal user study, demonstrating the higher efficacy and better comprehensibility of the proposed solution.

Introducing an Approach for Extracting Temperature from Aircraft GNSS and Pressure Altitude Reports in ADS-B Messages

Recently work has been conducted in using routine air traffic management (ATM) data from aircraft to derive meteorological observations (de Haan; de Haan and Stoffelen). The paper at hand introduces and provides an initial analysis for a method of finding layer temperatures from aircraft broadcast messages. The method is analyzed using error analysis and is shown capable of producing mean layer temperatures with below ±1-K error with a layer thickness of 2000 m. Observed aircraft data have been compared to the expected errors from the analysis and have shown to be consistent to within 0.01 K. An initial comparison using four Aircraft Meteorological Data Relay (AMDAR) flights is also provided. The new layer temperature, existing Mode-S enhanced surveillance (EHS)-derived temperature, and an average Mode-S EHS-derived temperature are all compared to the AMDAR temperatures. The averaged Mode-S EHS-derived layer temperature is shown to have the lowest spread (mean standard deviation K), followed by the layer temperature introduced by this paper (mean standard deviation K), and then the unaveraged Mode-S EHS-derived temperature (mean standard deviation K). The layer temperature method has the advantage that no requested data are required from the aircraft, as all of the required parameters are part of the routine broadcast messages, making the method ideal in areas with a limited air traffic management infrastructure where the existing methods would not work.