<p>Infrared thermal cameras offer reliable means of assessing atmospheric conditions by measuring the downward radiance from the sky, facilitating their usage in cloud monitoring endeavors. The precise identification and detection of clouds in images pose great challenges stemming from the indistinct boundaries inherent to cloud formations. Various methodologies for segmentation have been previously suggested. Most of them rely on color as the distinguishing criterion for cloud identification in the visible spectral domain and thus lack the ability to detect cloud structures in gray-scaled images with satisfying accuracy. In this work, we propose a new complete deep-learning framework to perform image classification and segmentation with convolutional neural networks. We demonstrate the effectiveness of this technique by conducting a series of tests and validations based on self-captured infrared sky images. Our findings reveal that the models can effectively differentiate between image types and accurately capture detailed cloud structure information at the pixel level, even when trained with a single binary ground-truth mask per input sample. The classifier model achieves an excellent accuracy of 99 % in image type distinction, while the segmentation model attains a mean pixel accuracy of 95 % in our dataset. We emphasize that our framework exhibits strong viability and can be used for infrared thermal ground-based cloud monitoring operations over extended durations. We expect to take advantage of this framework for astronomical applications by providing cloud cover selection criteria for ground-based photometric observations within the StarDICE experiment.</p>
<p>Commercial microwave links (CMLs) are opportunistic rainfall sensors that provide indirect rainfall estimates from attenuation data. This is achieved by separating raindrop path attenuation from observed total loss and converting it to rainfall intensity using the <span class="inline-formula"><i>k</i>−<i>R</i></span> formula. Various methods have been proposed for CML rainfall retrieval using either attenuation data alone or additional environmental variables. However, most studies evaluate CML rainfall estimates deterministically and do not reveal how individual processing steps and variables affect rainfall estimation uncertainty. This study proposes to evaluate CML processing using an information-theoretic framework and demonstrates this probabilistic concept on two particular problems. The first analysis reveals the reduction of uncertainty in CML rainfall estimates by measuring the information content of individual variables and their combinations. Both quantitative and qualitative predictors are used, including sensor variables such as CML signal attenuation, and environmental variables such as temperature, or synoptic types. The rainfall intensity derived from the <span class="inline-formula"><i>k</i>−<i>R</i></span> formula and combined with synoptic type forms an informative combination of sensor and environmental variables for reducing uncertainty regarding reference rainfall intensity. The second analysis demonstrates the application of information theory for classifying wet and dry periods using signal attenuation data and other environmental variables. In a limited single-link evaluation, a non-parametric model indicated better performance than the reference approaches suggesting the potential of information theory in CML processing. The proposed framework enables the identification of informative sensor and environmental variables, the evaluation of the effects of different processing steps on the estimated rainfall intensity, or the development of a wet-dry classification model calibrated in a probabilistic manner ultimately facilitating the improvement of CML rainfall estimates.</p>
<p>Non-linearity (NL) correction is a critical procedure to guarantee that the calibration accuracy of a spaceborne sensor approaches a reasonable level (i.e., better than 0.5 K). Unfortunately, such an NL correction is still not used in spectrum calibration from the Geostationary Interferometric InfraRed Sounder (GIIRS) onboard the Fengyun-4A (FY-4A) satellite. Different from the classical NL correction method where the NL coefficient is estimated from out-band spectral artifacts in an empirical low-frequency region, originally with prelaunch results and updated under in-orbit conditions, a new NL correction method for a spaceborne Fourier transform spectrometer (including GIIRS) is proposed. In particular, the NL parameter <span class="inline-formula"><i>μ</i></span>, independent of different working conditions (namely the thermal fields from environmental components), can be determined from laboratory results before launch and directly utilized during in-orbit calibration. Moreover, to overcome the inaccurate linear coefficient from the two-point calibration that influences the NL correction, an iteration algorithm is established to make both the linear and the NL coefficients converge to their stable values, with relative errors less than 0.5 % and 1 %, respectively, which is universally suitable for NL correction of both infrared and microwave sensors. Using the onboard internal blackbody (BB), which is identical to the in-orbit calibration, the final calibration accuracy for all the detectors and all the channels with the proposed NL correction method is validated to be around 0.2–0.3 K at an ordinary reference temperature of 305 K. Significantly, the relative error in the classical method NL parameter immediately transmitting to that of the linear one in theory, which inevitably introduces some additional errors around 0.1–0.2 K for the interfering radiance no longer exists. Moreover, the adopted internal BB with higher emissivity produces better NL correction performance in practice. The proposed NL correction method is scheduled for GIIRS implementation on board the FY-4A satellite and its successor after modifying their possible spectral response function variations.</p>
<p>Ocean surface wind speed (i.e., wind speed 10 m above sea level) is a critical parameter used by atmospheric models to estimate the state of the marine atmospheric boundary layer (MABL). Accurate surface wind speed measurements in diverse locations are required to improve characterization of MABL dynamics and assess how models simulate large-scale phenomena related to climate change and global weather patterns. To provide these measurements, this study introduces and evaluates a new surface wind speed data product from the NASA Langley Research Center nadir-viewing High Spectral Resolution Lidar – generation 2 (HSRL-2) using data collected as part of the NASA Aerosol Cloud meTeorology Interactions oVer the western ATlantic Experiment (ACTIVATE) mission. The HSRL-2 can directly measure vertically resolved aerosol backscatter and extinction profiles without additional constraints or assumptions, enabling the instrument to accurately derive atmospheric attenuation and directly determine surface reflectance (i.e., surface backscatter). Also, the high horizontal spatial resolution of the HSRL-2 retrievals (0.5 s or <span class="inline-formula">∼</span> 75 m along track) allows the instrument to probe the fine-scale spatial variability in surface wind speeds over time along the flight track and over breaks in broken cloud fields. A rigorous evaluation of these retrievals is performed by comparing coincident HSRL-2 and National Center for Atmospheric Research (NCAR) Airborne Vertical Atmosphere Profiling System (AVAPS) dropsonde data, owing to the joint deployment of these two instruments on the ACTIVATE King Air aircraft. These comparisons show correlations of 0.89, slopes of 1.04 and 1.17, and <span class="inline-formula"><i>y</i></span> intercepts of <span class="inline-formula">−</span>0.13 and <span class="inline-formula">−</span>1.05 <span class="inline-formula">m s<sup>−1</sup></span> for linear and bisector regressions, respectively, and the overall accuracy is calculated to be 0.15 <span class="inline-formula">±</span> 1.80 <span class="inline-formula">m s<sup>−1</sup></span>. It is also shown that the dropsonde surface wind speed data most closely follow the HSRL-2 distribution of wave slope variance using the distribution proposed by Hu et al. (2008) rather than the ones proposed by Cox and Munk (1954) and Wu (1990) for surface wind speeds below 7 <span class="inline-formula">m s<sup>−1</sup></span>, with this category comprising most of the ACTIVATE data set. The retrievals are then evaluated separately for surface wind speeds below 7 <span class="inline-formula">m s<sup>−1</sup></span> and between 7 and 13.3 <span class="inline-formula">m s<sup>−1</sup></span> and show that the HSRL-2 retrieves surface wind speeds with a bias of <span class="inline-formula">∼</span> 0.5 <span class="inline-formula">m s<sup>−1</sup></span> and an error of <span class="inline-formula">∼</span> 1.5 <span class="inline-formula">m s<sup>−1</sup></span>, a finding not apparent in the cumulative comparisons. Also, it is shown that the HSRL-2 retrievals are more accurate in the summer (<span class="inline-formula">−</span>0.18 <span class="inline-formula">±</span> 1.52 <span class="inline-formula">m s<sup>−1</sup></span>) than in the winter (0.63 <span class="inline-formula">±</span> 2.07 <span class="inline-formula">m s<sup>−1</sup></span>), but the HSRL-2 is still able to make numerous (<span class="inline-formula"><i>N</i>=236</span>) accurate retrievals in the winter. Overall, this study highlights the abilities and assesses the performance of the HSRL-2 surface wind speed retrievals,<span id="page3516"/> and it is hoped that further evaluation of these retrievals will be performed using other airborne and satellite data sets.</p>
M. López-Puertas, M. García-Comas, B. Funke
et al.
<p>We present a new version of <span class="inline-formula">O<sub>3</sub></span> data retrieved from the three Michelson Interferometer for Passive Atmospheric Sounding (MIPAS) observation modes that we refer to for simplicity as the modes of the middle atmosphere (middle atmosphere, MA; upper atmosphere, UA; and noctilucent cloud, NLC). The <span class="inline-formula">O<sub>3</sub></span> profiles cover altitudes from 20 up to 100 <span class="inline-formula">km</span> for the daytime and up to 105 <span class="inline-formula">km</span> at nighttime, for all latitudes, and the period 2005 until 2012. The data have been obtained with the IMK–IAA (Institute of Meteorology and Climate Research and Instituto de Astrofísica de Andalucía) MIPAS level-2 data processor and are based on ESA version-8 re-calibrated radiance spectra with improved temporal stability. The processing included several improvements with respect to the previous version, such as the consistency of the microwindows and spectroscopic data with those used in the nominal-mode V8R data, the <span class="inline-formula">O<sub>3</sub></span> a priori profiles, and updates of the non-local thermodynamic equilibrium (non-LTE) parameters and the nighttime atomic oxygen. In particular, the collisional relaxation of <span class="inline-formula">O<sub>3</sub></span>(<span class="inline-formula"><i>v</i><sub>1</sub></span>,<span class="inline-formula"><i>v</i><sub>3</sub></span>) by the atomic oxygen was reduced by a factor of 2 in order to obtain a better agreement of nighttime mesospheric <span class="inline-formula">O<sub>3</sub></span> with “non-LTE-free” measurements. Random errors are dominated by the measurement noise with 1<span class="inline-formula"><i>σ</i></span> values for single profiles for the daytime of <span class="inline-formula"><</span> 5 % below <span class="inline-formula">∼</span> 60 <span class="inline-formula">km</span>, 5 %–10 % between 60 and 70 <span class="inline-formula">km</span>, 10 %–20 % at 70–90 <span class="inline-formula">km</span>, and about 30 % at 95 <span class="inline-formula">km</span>. For nighttime, they are very similar below 70 <span class="inline-formula">km</span> but smaller above (10 %–20 % at 75–95 <span class="inline-formula">km</span>, 20 %–30 % at 95–100 <span class="inline-formula">km</span> and larger than 30 % above 100 <span class="inline-formula">km</span>). The systematic error is <span class="inline-formula">∼</span> 6 % below <span class="inline-formula">∼</span> 60 <span class="inline-formula">km</span> (dominated by uncertainties in spectroscopic data) and 8 %–12 % above <span class="inline-formula">∼</span> 60 <span class="inline-formula">km</span>, mainly caused by non-LTE uncertainties. The systematic errors in the 80–100 <span class="inline-formula">km</span> range are significantly smaller than in the previous version. The major differences with respect to the previous version are as follows: (1) the new retrievals provide <span class="inline-formula">O<sub>3</sub></span> abundances in the 20–50 <span class="inline-formula">km</span> altitude range that are larger by about 2 %–5 % (0.2–0.5 <span class="inline-formula">ppmv</span>); (2) <span class="inline-formula">O<sub>3</sub></span> abundances were reduced by <span class="inline-formula">∼</span> 2 %–4 % between 50 and 60 <span class="inline-formula">km</span> in the tropics and mid-latitudes; (3) <span class="inline-formula">O<sub>3</sub></span> abundances in the nighttime <span class="inline-formula">O<sub>3</sub></span> minimum just below 80 <span class="inline-formula">km</span> were reduced, leading to a more realistic diurnal variation; (4) daytime <span class="inline-formula">O<sub>3</sub></span> concentrations in the secondary maximum at the tropical and middle latitudes (<span class="inline-formula">∼</span> 40 %, 0.2–0.3 <span class="inline-formula">ppmv</span>) were larger; and (5) nighttime <span class="inline-formula">O<sub>3</sub></span> abundances in the secondary maximum were reduced by 10 %–30 %. The <span class="inline-formula">O<sub>3</sub></span> profiles retrieved from the nominal mode (NOM) and the middle-atmosphere modes are fully consistent in their common altitude range (20–70 <span class="inline-formula">km</span>). Only at 60–70 <span class="inline-formula">km</span> does daytime <span class="inline-formula">O<sub>3</sub></span> of NOM seem to be larger than that of MA/UA by 2 %–10 %. Compared to other satellite instruments, MIPAS seems to have a positive bias of 5 %–8 % below 70 <span class="inline-formula">km</span>. Noticeably, the new version of MIPAS data agrees much better than before with all instruments in the upper mesosphere–lower thermosphere, reducing the differences from <span class="inline-formula"><math xmlns="http://www.w3.org/1998/Math/MathML" id="M46" display="inline" overflow="scroll" dspmath="mathml"><mrow><mo>∼</mo><mo>±</mo></mrow></math><span><svg:svg xmlns:svg="http://www.w3.org/2000/svg" width="22pt" height="8pt" class="svg-formula" dspmath="mathimg" md5hash="98e83d48da1eccbd58399d2b2d880fb0"><svg:image xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="amt-16-5609-2023-ie00001.svg" width="22pt" height="8pt" src="amt-16-5609-2023-ie00001.png"/></svg:svg></span></span> 20 % to <span class="inline-formula"><math xmlns="http://www.w3.org/1998/Math/MathML" id="M47" display="inline" overflow="scroll" dspmath="mathml"><mrow><mo>∼</mo><mo>±</mo></mrow></math><span><svg:svg xmlns:svg="http://www.w3.org/2000/svg" width="22pt" height="8pt" class="svg-formula" dspmath="mathimg" md5hash="339d4643f35a23e68b14d5655a4fc756"><svg:image xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="amt-16-5609-2023-ie00002.svg" width="22pt" height="8pt" src="amt-16-5609-2023-ie00002.png"/></svg:svg></span></span> 10 %. Further, the diurnal variation in <span class="inline-formula">O<sub>3</sub></span> in the upper mesosphere (near 80 <span class="inline-formula">km</span>) has been significantly improved.</p>
<p>Ground-based observations of horizontal winds have been performed at Leipzig (51.35<span class="inline-formula"><sup>∘</sup></span> N, 12.43<span class="inline-formula"><sup>∘</sup></span> E), Germany, and at Punta Arenas (53.15<span class="inline-formula"><sup>∘</sup></span> S, 70.91<span class="inline-formula"><sup>∘</sup></span> W), Chile, in the framework of the German initiative EVAA (Experimental Validation and Assimilation of Aeolus observations) with respect to the validation of the Mie and Rayleigh wind products of Aeolus (L2B data).
In Leipzig, at the Leibniz Institute for Tropospheric Research (TROPOS), radiosondes have been launched for the Aeolus overpasses on each Friday (ascending orbit) since the middle of May 2019.
In Punta Arenas, scanning Doppler cloud radar observations have been performed in the framework of the DACAPO-PESO campaign (dacapo.tropos.de) for more than 3 years from the end of 2018 until the end of 2021 and could be used to validate Aeolus measurements on its ascending and descending orbits. We present two case studies and long‐term statistics of the horizontal winds derived with the ground-based reference instruments compared to Aeolus horizontal line-of-sight (HLOS) winds. The wind products of Aeolus considered are the Mie cloudy and Rayleigh clear products. It was found that the deviation of the Aeolus HLOS winds from the ground reference is usually of Gaussian shape, which allowed the use of the median bias and the scaled median absolute deviation (MAD) for the determination of the systematic and random errors of Aeolus wind products, respectively. The case study from August 2020 with impressive atmospheric conditions at Punta Arenas shows that Aeolus is able to capture strong wind speeds of up to more than 100 m s<span class="inline-formula"><sup>−1</sup></span>.</p>
<p>The long-term validation was performed in Punta Arenas covering the period from December 2018 to November 2021 and in Leipzig from May 2019 until September 2022.
This analysis showed that the systematic error of the Aeolus wind products could be significantly lowered during the mission lifetime with the changes introduced into the processing chain (different versions are called baselines). While in the early mission phase, systematic errors of more than 2 m s<span class="inline-formula"><sup>−1</sup></span> (absolute values) were observed for both wind types (Mie and Rayleigh), these biases could be reduced with the algorithm improvements, such as the introduction of the correction for temperature fluctuations at the main telescope of Aeolus (M1 temperature correction) with Baseline 09.
Hence, since Baseline 10, a significant improvement in the Aeolus data was found, leading to a low systematic error (close to 0 m s<span class="inline-formula"><sup>−1</sup></span>) and similar values for the midlatitudinal sites in both hemispheres. The random errors for both wind products were first decreasing with an increasing baseline but later increasing again due to performance losses of the Aeolus lidar instrument. Nevertheless, no significant increase in the systematic error in the Aeolus wind products was found. Thus, one can conclude that the uncertainty introduced by the reduced atmospheric return signal received by Aeolus mostly affects the random error.</p>
<p>Even when considering all the challenges during the mission, we can confirm the general validity of Aeolus observations during its lifetime. Therefore, this space explorer mission could demonstrate that it is possible to perform active wind observations from space with the applied technique.</p>
<p>Total column ozone (TCO) is commonly measured by Brewer and Dobson spectroradiometers. Both types of instruments use solar irradiance
measurements at four wavelengths in the ultraviolet radiation range to
derive TCO. For the calibration and quality assurance of the measured TCO
both instrument types require periodic field comparisons with a reference
instrument.</p>
<p>This study presents traceable TCO retrievals from direct solar spectral
irradiance measurements with the portable UV reference instrument QASUME.
TCO is retrieved by a spectral fitting technique derived by a minimal least
square fit algorithm using spectral measurements in the wavelength range
between 305 and 345 nm. The retrieval is based on an atmospheric model
accounting for different atmospheric parameters such as effective ozone
temperature, aerosol optical depth, Rayleigh scattering, SO<span class="inline-formula"><sub>2</sub></span>, ground
air pressure, ozone absorption cross sections and top-of-the-atmosphere solar
spectrum. Traceability is achieved by fully characterizing and calibrating
the QASUME spectroradiometer in the laboratory to SI standards
(International System of Units). The TCO retrieval method from this
instrument is independent from any reference instrument and does not require periodic in situ field calibration.</p>
<p>The results show that TCO from QASUME can be retrieved with a relative
standard uncertainty of less than 0.8 % when accounting for uncertainties from the measurements and the retrieval model, such as different ozone absorption cross sections, different reference top-of-the-atmosphere solar spectra, uncertainties from effective ozone temperature or other atmospheric
parameters. The long-term comparison of QASUME TCO with TCO derived from a
Brewer and a Dobson in Davos, Switzerland, reveals that all three
instruments are consistent within 1 % when using the ozone absorption
cross section from the University of Bremen. From the results and method
presented here, other absolute SI calibrated cost-effective solar
spectroradiometers, such as array spectroradiometers, may be applied for
traceable TCO monitoring.</p>
<p>In the recent decade it became evident that we need to revise our picture of how gravity waves (GWs) reach the mesosphere and lower thermosphere (MLT). This has consequences for our understanding not just of the properties of the GWs themselves, but in particular of the global circulation in the MLT. Information on spectral distribution, direction, and zonal mean GW momentum flux is required to test the theoretical and modeling findings.
In this study, we propose a constellation of two CubeSats for observing mesoscale GWs in the MLT region by means of temperature limb sounding in order to derive such constraints. Each CubeSat deploys a highly miniaturized spatial heterodyne interferometer (SHI) for the measurement of global oxygen atmospheric band emissions. From these emissions, the 3-D temperature structure can be inferred. We propose obtaining four independent observation tracks by splitting the interferograms in the center and thus gaining two observation tracks for each satellite. We present a feasibility study of this concept based on self-consistent, high-resolution global model data. This yields a full chain of end-to-end (E2E) simulations incorporating (1) orbit simulation, (2) airglow forward modeling, (3) tomographic temperature retrieval, (4) 3-D wave analysis, and (5) GW momentum flux (GWMF) calculation. The simulation performance is evaluated by comparing the retrieved zonal mean GWMF with that computed directly from the model wind data. A major question to be considered in our assessment is the minimum number of tracks required for the derivation of 3-D GW parameters. The main result from our simulations is that the GW polarization relations are still valid in the MLT region and can thus be employed for inferring GWMF from the 3-D temperature distributions. Based on the E2E simulations for gaining zonal mean climatologies of GW momentum flux, we demonstrate that our approach is robust and stable, given a four-track observation geometry and the expected instrument noise under nominal operation conditions. Using phase speed and direction spectra we show also that the properties of individual wave events are recovered when employing four tracks. Finally, we discuss the potential of the proposed observations to address current topics in the GW research. We outline for which investigations ancillary data are required to answer science questions.</p>
The earliest formulation of the Higgs naturalness argument has been criticized on the grounds that it relies on a particular cutoff-based regularization scheme. One response to this criticism has been to circumvent the worry by reformulating the naturalness argument in terms of a renormalized, regulator-independent parametrization. An alternative response is to deny that regulator dependence poses a problem for the naturalness argument, because nature itself furnishes a particular, physically correct regulator for any effective field theory (EFT) in the form of that EFT’s physical cutoff, together with an associated set of bare parameters that constitute the unique physically preferred “fundamental parameters” of the EFT. Here, I argue that both lines of defense against the initial worry about regulator dependence are flawed. I argue that reformulation of the naturalness argument in terms of renormalized parameters simply trades dependence on a particular regularization scheme for dependence on a particular renormalization scheme, and that one or another form of scheme dependence afflicts all formulations of the Higgs naturalness argument. Concerning the second response, I argue that the grounds for suspending the principle of regularization or renormalization scheme independence in favor of a physically preferred parametrization are thin; the assumption of a physically preferred parametrization, whether in the form of bare “fundamental parameters” or renormalized “physical parameters,” constitutes a theoretical idle wheel in generating the confirmed predictions of established EFTs, which are invariably scheme-independent. I highlight certain features of the alternative understanding of EFTs, and the EFT-based approach to understanding the foundations of QFT, that emerges when one abandons the assumption of a physically preferred parametrization. I explain how this understanding departs from several dogmas concerning the mathematical formulation and physical interpretation of EFTs in high-energy physics.
L. Zeng, A. P. Sullivan, R. A. Washenfelder
et al.
<p>Brown carbon (BrC) consists of particulate organic species that preferentially absorb light at visible and ultraviolet wavelengths. Ambient studies show that as a component of aerosol particles, BrC affects photochemical reaction rates and regional to global climate. Some organic chromophores are especially toxic, linking BrC to adverse health effects. The lack of direct measurements of BrC has limited our understanding of its prevalence, sources, evolution, and impacts. We describe the first direct, online measurements of water-soluble BrC on research aircraft by three separate instruments. Each instrument measured light absorption over a broad wavelength range using a liquid waveguide capillary cell (LWCC) and grating spectrometer, with particles collected into water by a particle-into-liquid sampler (CSU PILS-LWCC and NOAA PILS-LWCC) or a mist chamber (MC-LWCC). The instruments were deployed on the NSF C-130 aircraft during WE-CAN 2018 as well as the NASA DC-8 and the NOAA Twin Otter aircraft during FIREX-AQ 2019, where they sampled fresh and moderately aged wildfire plumes. Here, we describe the instruments, calibrations, data analysis and corrections for baseline drift and hysteresis. Detection limits (<span class="inline-formula">3<i>σ</i></span>) at 365 nm were 1.53 <span class="inline-formula">Mm<sup>−1</sup></span> (MC-LWCC; 2.5 min sampling time), 0.89 <span class="inline-formula">Mm<sup>−1</sup></span> (CSU PILS-LWCC; 30 s sampling time), and 0.03 <span class="inline-formula">Mm<sup>−1</sup></span> (NOAA PILS-LWCC; 30 s sampling time). Measurement uncertainties were 28 % (MC-LWCC), 12 % (CSU PILS-LWCC), and 11 % (NOAA PILS-LWCC). The MC-LWCC system agreed well with offline measurements from filter samples, with a slope of 0.91 and <span class="inline-formula"><i>R</i><sup>2</sup>=0.89</span>. Overall, these instruments provide soluble BrC measurements with specificity and geographical coverage that is unavailable by other methods, but their sensitivity and time resolution can be challenging for aircraft studies where large and rapid changes in BrC concentrations may be encountered.</p>
<p>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.</p>
<p>We provide an initial report on polar mesospheric cloud (PMC) observations by
the Japanese Geostationary Earth Orbit (GEO) meteorological satellite
Himawari-8. Heights of the observed PMCs were estimated to be 80–82 km.
Observed PMCs were active only during summertime in both the northern and
southern polar regions. These observations are consistent with known PMC
behavior. From its almost fixed location relative to the Earth, Himawari-8 is
capable of continuously monitoring PMC every 10 min with three visible bands:
blue (0.47 µm), green (0.51 µm), and red (0.64 µm). Thus, Himawari-8
can contribute to PMC research in the near future.</p>
The determination of the distribution of water vapor in the atmosphere plays
an important role in the atmospheric monitoring. Global Navigation Satellite
Systems (GNSS) tomography can be used to construct 3-D distribution of water
vapor over the field covered by a GNSS network with high temporal and spatial
resolutions. In current tomographic approaches, a pre-set fixed rectangular
field that roughly covers the area of the distribution of the GNSS signals on
the top plane of the tomographic field is commonly used for all tomographic
epochs. Due to too many unknown parameters needing to be estimated, the
accuracy of the tomographic solution degrades. Another issue of these
approaches is their unsuitability for GNSS networks with a low number of stations, as
the shape of the field covered by the GNSS signals is, in fact, roughly that of an
upside-down cone rather than the rectangular cube as the pre-set. In this
study, a new approach for determination of tomographic fields fitting the
real distribution of GNSS signals on different tomographic planes at
different tomographic epochs and also for discretization of the tomographic
fields based on the perimeter of the tomographic boundary on the plane and
meshing techniques is proposed. The new approach was tested using three
stations from the Hong Kong GNSS network and validated by comparing the
tomographic results against radiosonde data from King's Park Meteorological
Station (HKKP) during the one month period of May 2015. Results indicated
that the new approach is feasible for a three-station GNSS network
tomography. This is significant due to the fact that the conventional
approaches cannot even solve a network tomography from a few stations.
We review the famous no-hidden-variables theorem in von Neumann’s 1932 book on the mathematical foundations of quantum mechanics (Mathematische Grundlagen der Quantenmechanik, Springer, Berlin, 1932). We describe the notorious gap in von Neumann’s argument, pointed out by Hermann (Abhandlungen der Fries’schen Schule 6:75–152, 1935) and, more famously, by Bell (Rev Modern Phys 38:447–452, 1966). We disagree with recent papers claiming that Hermann and Bell failed to understand what von Neumann was actually doing.