Modification of the Schrödinger equation from Bohr’s theory

The mass-energy equation ε = mc2 is obtained from Bohr’s theory. The Schrödinger equation (SE) is modified using this equation. In the hydrogenlike atom, the modified SE is solved for consistency with the eigenvalues of electron’s energy derived from the Dirac equation. Based on the consistency of their eigenvalues, the microwave emission (e.g., 21 cm radio wave) by the hydrogen atom is expressed.

Monitoring Protein Denaturation of Egg White Using Passive Microwave Radiometry (MWR)

Passive microwave radiometry (MWR) is a measurement technique based on the detection of passive radiation in the microwave spectrum from different objects. This radiation in equilibrium is known to be proportional to the thermodynamic temperature of an emitting body. We hypothesize that living systems feature other mechanisms of emission that are based on protein unfolding and water rotational transitions. To understand the nature of these emissions, microwave radiometry has been used in several in vitro experiments. In our study, we performed pilot measurements of microwave emissions from egg whites during denaturation induced by ethanol. Egg whites are 10% proteins such as albumins, mucoproteins, and globulins. We found a novel phenomenon that microwave emissions changed without a corresponding change of the water thermodynamic temperature. increase 100 times faster than thermodynamic temperature. We have also found striking differences between microwave emission and thermodynamic temperature kinetics. Therefore, we hypothesize that these two processes are unrelated, contrary to what was thought before. It is known that some pathologies like stroke or brain trauma feature increased microwave emissions. We hypothesize that this phenomenon originates from protein denaturation and is not related to the thermodynamic temperature. So, our finding could explain first time the reason for microwave emissions increase after trauma and postmortem. It could be used for the development of novel diagnostics methods. The MWR method is inexpensive, and it does not require fluorescent or radioactive labels. It can be used in different areas of basic and applied pharmaceutical research, including kinetics studies in biomedicine.

Integrated Water Vapor during Rain and Rain-Free Conditions above the Swiss Plateau

Water vapor column density, or vertically-integrated water vapor (IWV), is monitored by ground-based microwave radiometers (MWR) and ground-based receivers of the Global Navigation Satellite System (GNSS). For rain periods, the retrieval of IWV from GNSS Zenith Wet Delay (ZWD) neglects the atmospheric propagation delay of the GNSS signal by rain droplets. Similarly, it is difficult for ground-based dual-frequency single-polarisation microwave radiometers to separate the microwave emission of water vapor and cloud droplets from the rather strong microwave emission of rain. For ground-based microwave radiometry at Bern (Switzerland), we take the approach that IWV during rain is derived from linearly interpolated opacities before and after the rain period. The intermittent rain periods often appear as spikes in the time series of integrated liquid water (ILW) and are indicated by ILW ≥ 0.4 mm. In the present study, we assume that IWV measurements from radiosondes are not affected by rain. We intercompare the climatologies of IWV(rain), IWV(no rain), and IWV(all) obtained by radiosonde, ground-based GNSS atmosphere sounding, ground-based MWR, and ECMWF reanalysis (ERA5) at Payerne and Bern in Switzerland. In all seasons, IWV(rain) is 3.75 to 5.94 mm greater than IWV(no rain). The mean IWV differences between GNSS and radiosonde at Payerne are less than 0.26 mm. The datasets at Payerne show a better agreement than the datasets at Bern. However, the MWR at Bern agrees with the radiosonde at Payerne within 0.41 mm for IWV(rain) and 0.02 mm for IWV(no rain). Using the GNSS and rain gauge measurements at Payerne, we find that IWV(rain) increases with increase of the precipitation rate during summer as well as during winter. IWV(rain) above the Swiss Plateau is quite well estimated by GNSS and MWR though the standard retrievals are limited or hampered during rain periods.

Eigenvalues of energy in j (l + 1/2, l ‐ 1/2) electron state in the hydrogen atom derived from the relativistically modified Schrödinger equation

In the hydrogen atom, the eigenvalues of energy in j (l + 1/2, l ‐ 1/2) electron state cannot be correctly evaluated from the nonrelativistic Schrödinger equation. In order to express the relativistic properties of the wave equation for a particle with 1/2 spin, the Schrödinger equation is relativistically modified. The modified Schrödinger equation is solved for consistency with the eigenvalues of electron's energy derived from the Dirac equation. Based on the consistency of their eigenvalues, the different electron state is expressed. The microwave emission (e.g., 21 cm radio wave) by the hydrogen atom was thus predicted from this state.

Surface melting over the Greenland ice sheet derived from enhanced resolution passive microwave brightness temperatures (1979–2019)

Surface melting is a major component of the Greenland ice sheet surface mass balance, and it affects sea level rise through direct runoff and the modulation of ice dynamics and hydrological processes, supraglacially, englacially and subglacially. Passive microwave (PMW) brightness temperature observations are of paramount importance in studying the spatial and temporal evolution of surface melting due to their long temporal coverage (1979–present) and high temporal resolution (daily). However, a major limitation of PMW datasets has been the relatively coarse spatial resolution, which has historically been of the order of tens of kilometers. Here, we use a newly released PMW dataset (37 GHz, horizontal polarization) made available through a NASA “Making Earth System Data Records for Use in Research Environments” (MeASUREs) program to study the spatiotemporal evolution of surface melting over the Greenland ice sheet at an enhanced spatial resolution of 3.125 km. We assess the outputs of different detection algorithms using data collected by automatic weather stations (AWSs) and the outputs of the Modèle Atmosphérique Régional (MAR) regional climate model. We found that sporadic melting is well captured using a dynamic algorithm based on the outputs of the Microwave Emission Model of Layered Snowpack (MEMLS), whereas a fixed threshold of 245 K is capable of detecting persistent melt. Our results indicate that, during the reference period from 1979 to 2019 (from 1988 to 2019), surface melting over the ice sheet increased in terms of both duration, up to 4.5 (2.9) d per decade, and extension, up to 6.9 % (3.6 %) of the entire ice sheet surface extent per decade, according to the MEMLS algorithm. Furthermore, the melting season started up to 4.0 (2.5) d earlier and ended 7.0 (3.9) d later per decade. We also explored the information content of the enhanced-resolution dataset with respect to the one at 25 km and MAR outputs using a semi-variogram approach. We found that the enhanced product is more sensitive to local-scale processes, thereby confirming the potential of this new enhanced product for monitoring surface melting over Greenland at a higher spatial resolution than the historical products and for monitoring its impact on sea level rise. This offers the opportunity to improve our understanding of the processes driving melting, to validate modeled melt extent at high resolution and, potentially, to assimilate these data in climate models.

Real-Time Visualization and Monitoring of Physiological Dynamics by Aggregation-Induced Emission Luminogens (AIEgens)

Physiological dynamics in living cells and tissues are crucial for maintenance and regulation of their normal activities and functionalities. Tiny fluctuations in physiological microenvironments can leverage significant influences on cell growth, metabolism, differentiation, and apoptosis as well as disease evolution. Fluorescence imaging based on aggregation-induced emission luminogens (AIEgens) exhibits superior advantages in real-time sensing and monitoring of the physiological dynamics in living systems, including its unique properties such as high sensitivity and rapid response, flexible molecular design, and versatile nano- to mesostructural fabrication. The introduction of canonic AIEgens with long-wavelength, near-infrared, or microwave emission, persistent luminescence, and diversified excitation source (e.g., chemo- or bioluminescence) offers researchers a tool to evaluate the resulting molecules with excellent performance in response to subtle fluctuations in bioactivities with broader dimensionalities and deeper hierarchies.

Evaluation of the Effective Microstructure Parameter of the Microwave Emission Model of Layered Snowpack for Multiple-Layer Snow

Natural snow, one of the most important components of the cryosphere, is fundamentally a layered medium. In forward simulation and retrieval, a single-layer effective microstructure parameter is widely used to represent the emission of multiple-layer snowpacks. However, in most cases, this parameter is fitted instead of calculated based on a physical theory. The uncertainty under different frequencies, polarizations, and snow conditions is uncertain. In this study, we explored different methods to reduce the layered snow properties to a set of single-layer values that can reproduce the same brightness temperature (TB) signal. A validated microwave emission model of layered snowpack (MEMLS) was used as the modelling tool. Multiple-layer snow TB from the snow’s surface was compared with the bulk TB of single-layer snow. The methods were tested using snow profile samples from the locally validated and global snow process model simulations, which follow the natural snow’s characteristics. The results showed that there are two factors that play critical roles in the stability of the bulk TB error, the single-layer effective microstructure parameter, and the reflectivity at the air–snow and snow–soil boundaries. It is important to use the same boundary reflectivity as the multiple-layer snow case calculated using the snow density at the topmost and bottommost layers instead of the average density. Afterwards, a mass-weighted average snow microstructure parameter can be used to calculate the volume scattering coefficient at 10.65 to 23.8 GHz. At 36.5 and 89 GHz, the effective microstructure parameter needs to be retrieved based on the product of the snow layer transmissivity. For thick snow, a cut-off threshold of 1/e is suggested to be used to include only the surface layers within the microwave penetration depth. The optimal method provides a root mean squared error of bulk TB of less than 5 K at 10.65 to 36.5 GHz and less than 10 K at 89 GHz for snow depths up to 130 cm.

Does Processing or Formation of Water Ice Mantles Affect the Capacity of Nanosilicates to Be the Source of Anomalous Microwave Emission?

Anomalous microwave emission (AME) is detected in many astrophysical environments as a foreground feature typically peaking between 20–30 GHz and extending over a 10–60 GHz range. One of the leading candidates for the source of AME is small spinning dust grains. Such grains should be very small (approx. ≤1 nm diameter) in order for the rotational emission to fall within the observed frequency range. In addition, these nanosized grains should possess a significant dipole moment to account for the observed emissivities. These constraints have been shown to be compatible with spinning bare nanosilicate clusters, assuming that ∼1% of the total Si mass budget is held in these ultrasmall grains. Silicate dust can be hydroxylated by processing in the interstellar medium and is generally known to provide seeds for molecular water ice nucleation in denser regions. Herein, we use quantum chemical calculations to investigate how the dipole moment of Mg-rich pyroxenic (MgSiO3) nanoclusters is affected by both accretion of molecular water and dissociative hydration. Our work thus provides an indication of how the formation of water ice mantles is likely to affect the capacity of nanosilicates to generate AME.

Retrieval of Snow Depth on Arctic Sea Ice from the FY3B/MWRI

Given their high albedo and low thermal conductivity, snow and sea ice are considered key reasons for amplified warming in the Arctic. Snow-covered sea ice is a more effective insulator, which greatly limits the energy and momentum exchange between the atmosphere and surface, and further controls the thermal dynamic processes of snow and ice. In this study, using the Microwave Emission Model of Layered Snowpacks (MEMLS), the sensitivities of the brightness temperatures (TBs) from the FengYun-3B/MicroWave Radiometer Imager (FY3B/MWRI) to changes in snow depth were simulated, on both first-year and multiyear ice in the Arctic. Further, the correlation coefficients between the TBs and snow depths in different atmospheric and sea ice environments were investigated. Based on the simulation results, the most sensitive factors to snow depth, including channels of MWRI and their combination form, were determined for snow depth retrieval. Finally, using the 2012–2013 Operational IceBridge (OIB) snow depth data, retrieval algorithms of snow depth were developed for the Arctic on first-year and multiyear ice, separately. Validation using the 2011 OIB data indicates that the bias and standard deviation (Std) of the algorithm are 2.89 cm and 2.6 cm on first-year ice (FYI), respectively, and 1.44 cm and 4.53 cm on multiyear ice (MYI), respectively.