Infrared Brightness Temperature Research Articles

Conjoint Inversion of Snow Temperature Profiles from Microwave and Infrared Brightness Temperature in Antarctica

The snow temperature above the ice sheet is one of the basic characteristic parameters of the ice sheet, which plays an important role in the study of the global climate. Because infrared and microwaves with different frequencies have different penetration depths in snow, it is possible to retrieve the snow temperature profiles by combining microwave and infrared brightness temperatures. This paper proposes a conjoint inversion algorithm to retrieve snow temperature profiles by combining multi-frequency microwave brightness temperature (BT) with infrared BT, in which different weight functions of microwave BT at different frequencies are adopted, and the atmosphere influence has also been corrected. The snow temperature profile data are retrieved based on AMSR2 microwave BT data and MODIS infrared BT data in 2017 and 2018, which are evaluated by comparing with the measured snow temperature at Dome-C station. The results confirm that the inverted snow temperature profiles are consistent with the field observation data from the Dome-C station. Multi-frequency microwave brightness temperature can be used to invert the snow temperature profiles; however, the inverted snow surface temperature is more accurate by combining the infrared BT with the microwave BT in the conjoint inversion algorithm.

Thermal environment of the Chang’E-5 landing site and its effect on the external temperature of the lander

<p indent="0mm">The thermal environment of the lunar surface is extremely important to the thermal design of the lander, the placement of the detection instruments, and the interpretation of scientific exploration data. Clarifying the source and intensity of radiation on the outer surface of the lander is the key to its thermal management. In this paper, we first reported continuous temperature records over a 47.2 h period from five thermal couple sensors mounted on the auxiliary pillars of the landing gear of Chang’E-5 (CE-5). The recorded temperatures varied from a relatively small range of <sc>245.68–272.52 K</sc> at the beginning to a considerably wider range of <sc>284.86–369.76 K</sc> at the end of the recording period. To analyze the obtained CE-5 temperature records, we first developed a lunar surface thermal environment model with a layered regolith and temperature-dependent thermal physical properties. The model calculated that the ambient temperature of the CE-5 landing site over the sensor recording period increased from 341.40 to <sc>356.15 K.</sc> The calculated temperature was comparable to the estimates based on the <italic>in</italic>-<italic>situ</italic> spectral measurement, whereas it was systematically <sc>6–12 K</sc> higher than Diviner thermal infrared brightness temperature. We further developed a heat conduction model of the sensor-mounted auxiliary pillar and considered the surface temperature and the pillar geometry with respect to the Sun’s and the Earth’s orbital parameters. By introducing an impact factor<italic> F</italic> and via trial and error, we fitted the five CE-5 lunar temperature records with the RMESs of 0.72, 2.14, 0.53, 1.35, and <sc>3.36 K,</sc> respectively. Our analysis showed that the lunar surface thermal environment has a significant effect on the temperatures of both the shaded and sunny sides of the lunar lander. The environmental impact on the shaded side even exceeds that on the sunny side during the CE-5 recording period. The results of this paper are of great significance to both the theoretical research on the mechanism of the lunar thermal environment and the engineering applications in the thermal management design of a lunar lander, site selection of a lunar exploration mission, and temperature-related data interpretations, such as <italic>in</italic>-<italic>situ</italic> spectral data analysis.

The Potential Benefits of Handling Mixture Statistics via a Bi-Gaussian EnKF: Tests With All-Sky Satellite Infrared Radiances.

The meteorological characteristics of cloudy atmospheric columns can be very different from their clear counterparts. Thus, when a forecast ensemble is uncertain about the presence/absence of clouds at a specific atmospheric column (i.e., some members are clear while others are cloudy), that column's ensemble statistics will contain a mixture of clear and cloudy statistics. Such mixtures are inconsistent with the ensemble data assimilation algorithms currently used in numerical weather prediction. Hence, ensemble data assimilation algorithms that can handle such mixtures can potentially outperform currently used algorithms. In this study, we demonstrate the potential benefits of addressing such mixtures through a bi-Gaussian extension of the ensemble Kalman filter (BGEnKF). The BGEnKF is compared against the commonly used ensemble Kalman filter (EnKF) using perfect model observing system simulated experiments (OSSEs) with a realistic weather model (the Weather Research and Forecast model). Synthetic all-sky infrared radiance observations are assimilated in this study. In these OSSEs, the BGEnKF outperforms the EnKF in terms of the horizontal wind components, temperature, specific humidity, and simulated upper tropospheric water vapor channel infrared brightness temperatures. This study is one of the first to demonstrate the potential of a Gaussian mixture model EnKF with a realistic weather model. Our results thus motivate future research toward improving numerical Earth system predictions though explicitly handling mixture statistics.

Cross-Comparison of Radiation Response Characteristics between the FY-4B/AGRI and GK-2A/AMI in China

In this study, we compare the data of the advanced geostationary radiation imager (AGRI) on board the FY-4B and the advanced meteorological imager (AMI) on board the GK-2A, in terms of overall data, different reflectivity/brightness temperature intervals, different regions, and different underlying surfaces. The results show that the AGRI and AMI data are generally consistent; the mean biases for reflectivity channels show a range of 0.50% to 1.69%, with channel VIR004 being exceptionally good, while brightness temperature (TB) differences in the IR channels ranging from 0.11 to 0.57 K, with channel IR120 being the most accurate. The reflectivity of the AGRI is higher than that of the AMI in terms of mean bias. The dispersion of the reflectivity difference between the AGRI and AMI is smaller at the short-wavelength channels than that at the longer-wavelength channels. The TB data observed by the AGRI are higher than those of AMI at conditions above 310 K. In the case of observing the same target, the difference in infrared brightness temperature due to the random noise signal is small. The differences between the two sensors can be considerably reduced by revising mean biases. In the following studies of quantitative product algorithms, the characteristics of sensor data need to be further analyzed in detail.

Changing of Rock Fragments Equivalent Emissivity and Its Impact on Localized Positive Infrared Brightness Temperature as Rock Fractured

Although thermal infrared imaging has been developed as an important tool of remote sensing rock mechanics (RSRM) since the 1990s, the relationship between infrared emissivity and rock fragments, which is essential for interpreting positive infrared abnormity, has not been investigated. In this letter, the infrared brightness temperature (IBT) ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$T_{b}$ </tex-math></inline-formula> ) of sandstone, marble, and granite, including an intact rock specimen and its fragments of six-level sizes, was experimentally detected with an infrared imaging system indoor. The relationship between the detected <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$T_{b}$ </tex-math></inline-formula> and the measured size of rock fragments is investigated, and the equivalent emissivity <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\varepsilon _{\!\!f}$ </tex-math></inline-formula> of rock fragments of different sizes is obtained. It was discovered that <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\varepsilon _{\!\!f}$ </tex-math></inline-formula> of rock fragments rose up to 8.43% compared to that of intact rock and behaved the maximum as the fragment sizes get close to the mineral particle scale (MPS) of the rock. <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$T_{b}$ </tex-math></inline-formula> is hence locally lifted by 6.16 K. This letter revealed that the rise of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\varepsilon _{\!\!f}$ </tex-math></inline-formula> is a third remote sensing mechanism, besides the known thermoelastic and friction thermal effects, of local <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$T_{b}$ </tex-math></inline-formula> enhancement in process of rock loaded to fracturing, which provides new experimental supports to infrared imaging detection on rock fracturing and IBT-based stability analysis in rock engineering and geostructures.

Analytical Study of the Changes in Brightness Temperature Based on the Tectonic Field Associated With Three Earthquakes in the Eastern Tibetan Plateau

The thermal infrared brightness temperature (BT) of the eastern Tibetan Plateau (TP) was retrieved from the Moderate Resolution Imaging Spectroradiometer (MODIS) level-1B data. The multiyear averaged BT background field was subtracted from the punctual BT data to yield monthly BT spatial anomaly, and calculated time series of BT for the secondary blocks. Then, the spatial and temporal changes in the BT of the study area before the Menyuan M6.4, Zaduo M6.2, and Jiuzhaigou M7.0 earthquakes were investigated and analyzed based on the tectonic setting. The results show the following. The spatial BT radiation enhancement frequency rose remarkably before strong earthquakes; each of the three earthquakes was preceded by marked spatiotemporal continuous BT anomalies. The tectonic setting significantly influences the BT anomaly feature. The spatial BT anomaly was not notable in the Qaidam and Qilian block before the Menyuan earthquake; the spatial BT anomaly mainly appeared in the Qiangtang and Bayan Har blocks before the Zaduo and Jiuzhaigou earthquakes. The Qiangtang and Bayan Har block’s BT time series curves have similar features. The Qaidam and Qilian block’s BT time series curves have analogous shapes. The three earthquakes may be regarded as one seismic event induced by a stage of tectonic stress enhancement rather than three independent occasions. The spatial BT anomalous behavior before earthquakes is, to a great extent, like the result of the rock stress loading experiment; the rock compression and the lithosphere-atmosphere-ionosphere coupling (LAIC) may be the main reasons for the intensification of the BT radiation.

TCCL-DenseFuse: Infrared and Water Vapor Satellite Image Fusion Model Using Deep Learning

This article proposes an infrared and water vapor channel satellite image fusion model (TCCL-DenseFuse) based on DenseNet. The infrared channel satellite image reflects the ground and cloud top infrared radiation or the distribution of temperature, and the water vapor channel satellite image reflects the spatial distribution of water vapor in the upper atmosphere. Studies have shown that infrared brightness temperature gradient and water vapor transport are closely related to tropical cyclone (TC) generation and evolution. In order to facilitate the fusion image obtained by the proposed fusion model to have a positive effect on TC monitoring and warning, the brightness temperature gradient and multiscale structural similarity in the satellite image are used to construct loss function of the proposed TCCL-DenseFuse model. The quality of the fused images is evaluated by seven objective quantitative indicators. In order to further verify the real application value of the proposed TCCL-DenseFuse model, fused images are also used to TC center location. Experimental results show that the proposed TCCL-DenseFuse fused satellite image not only contains rich information from both infrared and water vapor channels but also improves the accuracy of TC center positioning. The comprehensive performance of the proposed fusion model has certain advantages compared with similar fusion methods and can provide a reference for typhoon prevention and disaster mitigation.

An ensemble Kalman filter-based ocean data assimilation system improved by adaptive observation error inflation (AOEI)

Abstract. A previous study proposed an adaptive observation error inflation (AOEI) method for an ensemble Kalman filter (EnKF)-based atmospheric data assimilation system to assimilate all-sky infrared brightness temperatures. Brightness temperature differences between clear- and cloudy-sky radiances are large, and observation-minus-forecast differences (or innovations) are therefore likely to be large around boundaries between clear- and cloudy-sky regions. The AOEI method mitigates these discrepancies by adaptively inflating observation errors. Ocean frontal regions have similar characteristics to the borders between clear- and cloudy-sky regions with large innovations. Consequently, we have implemented the AOEI with an EnKF-based regional ocean data assimilation system, in which the assimilation interval is set to 1 d to utilize frequent satellite observations. We conducted sensitivity experiments to investigate the impacts of the AOEI on salinity structure, geostrophic balance, and accuracy. A control run, in which the AOEI is not applied, shows the degradation of low-salinity North Pacific Intermediate Water around the Kuroshio Extension region, where the innovation amplitude and forecast ensemble spread are large in association with the fronts and eddies. The resulting large temperature and salinity increments weaken the density stratification, leading to large vertical diffusivity. As a result, the low-salinity water in the intermediate layer is lost through strong vertical diffusion. When the AOEI is used, the salinity structure in the ocean interior is preserved because the AOEI suppresses the salinity degradation by reducing the temperature and salinity increments. We also demonstrate that the AOEI provides significant improvement of the geostrophic balance and the analysis accuracy of temperature, salinity, and surface-flow fields.

Quality Assessment of FY-3D/MERSI-II Thermal Infrared Brightness Temperature Data from the Arctic Region: Application to Ice Surface Temperature Inversion

The Arctic region plays an important role in the global climate system. To promote the application of Medium Resolution Spectral Imager-II (MERSI-II) data in the ice surface temperature (IST) inversion, we used the thermal infrared channels (channels 24 and 25) of the MERSI-II onboard Chinese FY-3D satellite and the thermal infrared channels (channels 31 and 32) of the Earth Observing System (EOS) Moderate-Resolution Imaging Spectroradiometer (MODIS) onboard the National Aeronautical and Space Administration (NASA) Aqua satellite for data analysis. Using the Observation–Observation cross-calibration algorithm to cross-calibrate the MERSI and MODIS thermal infrared brightness temperature (Tb) data in the Arctic, channel 24 and 25 data from the FY-3D/MERSI-II on Arctic ice were evaluated. The thermal infrared Tb data of the MERSI-II were used to retrieve the IST via the split-window algorithm. In this study, the correlation coefficients of the thermal infrared channel Tb data between the MERSI and MODIS were &gt;0.95, the mean bias was −0.5501–0.1262 K, and the standard deviation (Std) was &lt;1.3582 K. After linear fitting, the MERSI-II thermal infrared Tb data were closer to the MODIS data, and the bias range of the 11 μm and 12 μm channels was −0.0214–0.0119 K and the Std was &lt;1.2987 K. These results indicate that the quality of the MERSI-II data is comparable to that of the MODIS data, so that can be used for application to IST inversion. When using the MERSI thermal infrared Tb data after calibration to retrieve the IST, the results of the MERSI and MODIS IST were more consistent. By comparing the IST retrieved from the MERSI thermal infrared calibrated Tb data with MODIS MYD29 product, the mean bias was −0.0612–0.0423 °C and the Std was &lt;1.3988 °C. Using the MERSI thermal infrared Tb data after calibration is better than that before calibration for retrieving the IST. When comparing the Arctic ocean sea and ice surface temperature reprocessed data (L4 SST/IST) with the IST data retrieved from MERSI, the bias was 0.9891–2.7510 °C, and the Std was &lt;3.5774 °C.

PRSOT: Precipitation Retrieval from Satellite Observations Based on Transformer

Precipitation with high spatial and temporal resolution can improve the defense capability of meteorological disasters and provide indispensable instruction and early warning for social public services, such as agriculture, forestry, and transportation. Therefore, a deep learning-based algorithm entitled precipitation retrieval from satellite observations based on Transformer (PRSOT) is proposed to fill the observation gap of ground rain gauges and weather radars in deserts, oceans, and other regions. In this algorithm, the multispectral infrared brightness temperatures from Himawari-8, the new-generation geostationary satellite, have been used as predictor variables and the Global Precipitation Measurement (GPM) precipitation product has been employed to train the retrieval model. We utilized two data normalization schemes, area-based and pixel-based normalization, and conducted comparative experiments. Comparing the estimated results with the GPM product on the test set, PRSOT_Pixel_based model achieved a Probability Of Detection (POD) of 0.74, a False Alarm Ratio (FAR) of 0.44 and a Critical Success Index (CSI) of 0.47 for two-class metrics, and an Accuracy (ACC) of 0.75 for multi-class metrics. Pixel-based normalization is more suitable for meteorological data, highlighting the precipitation characteristics and obtaining better comprehensive retrieval performance in visualization and evaluation metrics. In conclusion, the proposed PRSOT model has made a remarkable and essential contribution to precipitation retrieval and outperforms the benchmark machine learning model Random Forests.

A New Objective Typhoon Location Algorithm Considering a Perturbation Factor Based on FY-4A Brightness Temperature Data

Abstract In this paper, a new objective typhoon positioning algorithm was proposed. The algorithm uses L1 12-channel far-infrared data of the FY-4A geostationary meteorological satellite for objective positioning, verified against best path data provided by the Tropical Cyclone Data Center of the China Meteorological Administration (CMA). By calculating the tangential and radial perturbation values of infrared brightness temperature images, the perturbation factor can be obtained. By adopting the position of the maximum perturbation factor as the center of a circle and considering a radius of no more than 20 km, the position of the minimum perturbation factor was determined; this value represents the central position of the typhoon. Tropical cyclones in 2019 and 2020 were selected for objective positioning, and the objective positioning results were verified against the corresponding time in the best path dataset. The results included centralized verification results for 29 typhoons and optimal path data in 2019. The maximum average error reached 54.67 km, with an annual average typhoon positioning error of 16.15 km. The centralization verification results for 23 typhoons and optimal path data in 2020 indicated a minimum average error of 12.71 km, a maximum average error of 18.56 km, and an annual average typhoon positioning error of 14.82 km. The positioning results for these two years suggest that the longitude obtained with the perturbation factor positioning method is satisfactory, exhibiting an error of less than 20 km. Significance Statement The purpose of this study is to help researchers make scientific discoveries and help the development of typhoon center location technology in the future. This is important because accurate positioning of typhoon center can provide effective help for typhoon path prediction and typhoon intensity determination.

A Demonstration of Three-Satellite Stereo Winds

Stereo tracking of clouds from multiple satellites permits the simultaneous retrieval of an atmospheric motion vector (“wind”) and its height in the atmosphere. The direct measurement of height is a major advantage of stereo methods over observations made from a single satellite where the height must be inferred from infrared brightness temperatures. A pair of operational geostationary satellites over the Americas provides stereo coverage where their two fields of view intersect. Stereo coverage can be extended to nearly a full hemisphere with a third satellite. We demonstrate this configuration with the operational GOES-R constellation of GOES-16 (east) and GOES-17 (west) augmented by GOES-18 in its central test slot and use the 500-m resolution Advanced Baseline Imager Band 2. We examine the consistency of the pairwise products created from GOES-18 and -16 versus GOES-18 and -17 and create a fused triple-GOES product that spans nearly the full hemisphere seen from GOES-18. We also examine the retrieval of ground points observed under clear skies and compare their retrievals to zero speed and known terrain heights. The results are compatible with a wind accuracy about 0.1 m/s with height assignment uncertainty of 175 m.

Application of Empirical Orthogonal Function Analysis to 1 km Ensemble Simulations and Himawari–8 Observation in the Intensification Phase of Typhoon Hagibis (2019)

An empirical orthogonal function (EOF) analysis was performed for the inner core of Typhoon Hagibis (2019) in the intensification phase. The Himawari–8 geostationary infrared (IR) brightness temperature (BT) collocated at the Hagibis’s center was combined with the IR BT simulated by a radiative transfer model, with 1 km ensemble simulations conducted by an atmosphere model and the coupled atmosphere–wave–ocean model. The ensemble simulations were conducted under one control atmospheric initial condition and the 26 perturbed ones with two different oceanic initial conditions. The first four EOF modes showed symmetric and asymmetric patterns such as a curved band, cloud dense overcast, and eye pattern used in the classification of the Dvorak technique. The influence of ocean coupling on the modes appeared only in the early intensification phase but was relatively small compared to the difference from the Himawari–8 observations. While ocean coupling and different oceanic initial condition quantitatively affected the IR BT, the normalized amplitude for the first EOF mode did not become close to that of the Himawari–8 observation in the late intensification phase. The intensification rate in the late intensification phase was inconsistent between the simulation results and the estimate from the Himawari–8 normalized amplitude by multiple linear regression analysis.

Investigation of the Convection‐Allowing Prediction Error of an Extreme Precipitation Event of China Using CRTM‐Simulated Brightness Temperature

AbstractAn extreme rainfall case occurred on 20 July 2016 in northern China, producing over 600 mm of maximum 24 hr accumulated rainfall. The case is characterized by low‐echo centroid (LEC) based on radar observations. It is simulated by Weather Research and Forecasting model at 4 km grid spacing using the Morrison two‐moment microphysics scheme. Infrared brightness temperature (BT) simulated using a radiative transfer model together with simulated reflectivity are used to evaluate the simulated cloud structures. In general, the model predicts the rainfall amount, location, and propagation well. However, it fails to accurately predict the three‐dimensional cloud structures. The predicted convective cores (&gt;35 dBZ) are higher than the freezing level, suggesting the presence of active cold‐cloud processes while the observed LEC suggests that warm‐cloud processes dominate. The simulation also produces too many upper level clouds and over‐predicts the fractions of overshooting clouds, resulting great over‐prediction of cloud top height (CTH). Additionally, the sensitivity of simulated BT to predicted cloud properties including CTH, cloud species and hydrometeor effective radii are examined to better understand the sources of error in simulated BT. Among those factors, CTH is found to be most critical to BT simulation. For every kilometer of CTH over‐prediction, there is about 6.431 K of negative BT bias owing to the lapse rate and the absorption and scattering effects of cloud particles. For most cloudy regions, cloud ice dominates the effect on simulated BT. Using diagnosed effective radii of simulated hydrometeors within the radiative transfer model results in small improvement to the BT simulation.

Evaluating GOES-16 ABI surface brightness temperature observation biases over the central Sierra Nevada of California

Thermal infrared imagery from NOAA's Geostationary Operational Environmental Satellites (GOES) R-series presents an opportunity to observe mountain surface temperatures at high temporal resolution. These observations are needed to better understand the transient surface energy balance, influenced by near-surface air temperatures and water fluxes ranging from evapotranspiration to snowmelt. From geostationary orbit, the GOES Advanced Baseline Imager (ABI) instrument views the Earth from a fixed longitude above the equator. ABI images therefore have increasingly off-nadir view angles and increasingly larger pixel dimensions the further a point on the Earth's surface is from the sub-satellite point. In thermal infrared imagery from ABI, these 2+ km pixels blur together the different surface temperatures of heterogeneous mountain landscapes. We compared GOES-16 ABI band 14 (11.2 μm) brightness temperatures to 90 m spatial resolution ASTER band 14 (11.3 μm) and 1 km spatial resolution MODIS band 31 (11.03 μm) brightness temperatures and investigated how differences change over space and time for a study region in the central Sierra Nevada of California for the 2017–2020 snow seasons. We demonstrated the necessity of orthorectifying ABI imagery of mountain terrain to correct for the parallax effect in off-nadir imagery. This reduced the mean difference between ABI and ASTER ~11 μm brightness temperatures from 1.6 °C to 1.0 °C and increased the r-squared value between ABI and ASTER thermal infrared brightness temperatures from 0.43 to 0.93. The ABI brightness temperatures were found to more closely match those of forest canopy temperatures than snow surface (with RMS differences of 1.2 °C and 3.0 °C respectively), and those of sunlit slopes as opposed to slopes facing away from the sun (with RMS differences of 1.6 °C and 3.4 °C respectively), in coincident ASTER imagery. This work demonstrates the use of GOES ABI, and associated challenges, for observing surface temperatures of forested mountain environments with seasonal snow.

Examining the Role of the Land Surface on Convection Using High‐Resolution Model Forecasts Over the Southeastern United States

AbstractThe influence of the Unified Noah and Noah‐MP land surface models (LSMs) on the evolution of cumulus clouds reaching convective initiation (CI) is assessed using infrared brightness temperatures (BT) from GOES‐16. Cloud properties from individual cloud objects are examined using output from high‐resolution (500 m horizontal grid spacing) model simulations. Cloud objects are tracked over time and related to observed clouds reaching CI to examine differences in cloud extent, longevity, and growth rate. The results demonstrate that differences in assumed surface properties can lead to large discrepancies in the net surface radiative budget, particularly in the sensible and latent heating components where differences exceed 40 W m−2. These differences lead to changes in the local mesoscale circulation patterns that are more pronounced near the edges of forested and grassland boundaries where lower‐level convergence is stronger. Higher sensible heating from the Noah‐MP LSM produced growth of CI clouds earlier and with increased longevity, which was closer to the timing and growth observed from GOES‐16. The increased cloud growth in the Noah‐MP experiment results from stronger and deeper updrafts, which lofts more cloud water into the upper levels of the troposphere. The weaker updrafts from the Noah LSM experiment results in shallower convection after CI is detected due to slower growth rates. The differences in cloud properties and growth are directly related to the land surfaces they develop above and point to the importance of accurately representing land properties and radiative characteristics when simulating convection in numerical weather prediction models.

Improving the Efficiency of Oncological Diagnosis of the Breast Based on the Combined Use of Simulation Modeling and Artificial Intelligence Algorithms

This work includes a brief overview of the applications of the powerful and easy-to-perform method of microwave radiometry (MWR) for the diagnosis of various diseases. The main goal of this paper is to develop a method for diagnosing breast oncology based on machine learning algorithms using thermometric data, both real medical measurements and simulation results of MWR examinations. The dataset includes distributions of deep and skin temperatures calculated in numerical models of the dynamics of thermal and radiation fields inside biological tissue. The constructed combined dataset allows us to explore the limits of applicability of the MWR method for detecting weak tumors. We use convolutional neural networks and classic machine learning algorithms (k-nearest neighbors, naive Bayes classifier, support vector machine) to classify data. The construction of Kohonen self-organizing maps to explore the structure of our combined dataset demonstrated differences between the temperatures of patients with positive and negative diagnoses. Our analysis shows that the MWR can detect tumors with a radius of up to 0.5 cm if they are at the stage of rapid growth, when the tumor volume doubling occurs in approximately 100 days or less. The use of convolutional neural networks for MWR provides both high sensitivity (sens=0.86) and specificity (spec=0.82), which is an advantage over other methods for diagnosing breast cancer. A new modified scheme for medical measurements of IR temperature and brightness temperature is proposed for a larger number of points in the breast compared to the classical scheme. This approach can increase the effectiveness and sensitivity of diagnostics by several percent.

Sea ice surface temperatures from helicopter-borne thermal infrared imaging during the MOSAiC expedition

The sea ice surface temperature is important to understand the Arctic winter heat budget. We conducted 35 helicopter flights with an infrared camera in winter 2019/2020 during the Multidisciplinary Drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition. The flights were performed from a local, 5 to 10 km scale up to a regional, 20 to 40 km scale. The infrared camera recorded thermal infrared brightness temperatures, which we converted to surface temperatures. More than 150000 images from all flights can be investigated individually. As an advanced data product, we created surface temperature maps for every flight with a 1 m resolution. We corrected image gradients, applied an ice drift correction, georeferenced all pixels, and corrected the surface temperature by its natural temporal drift, which results in time-fixed surface temperature maps for a consistent analysis of one flight. The temporal and spatial variability of sea ice characteristics is an important contribution to an increased understanding of the Arctic heat budget and, in particular, for the validation of satellite products.

Atmospheric Effects and Precursors of Rainfall over the Swiss Plateau

In this study, we investigate the characteristics of atmospheric parameters before, during, and after rain events in Bern, Switzerland. Ground-based microwave radiometer data of the TROpospheric WAter RAdiometer (TROWARA) with a time resolution of 7 s, observations of a weather station, and the composite analysis method are used to derive the temporal evolution of rain events and to identify possible rainfall precursors during a 10-year period (1199 available rain events). A rainfall climatology is developed using parameters integrated water vapor (IWV), integrated liquid water (ILW), rain rate, infrared brightness temperature (TIR), temperature, pressure, relative humidity, wind speed, and air density. It was found that the IWV is reduced by about 2.2 mm at the end of rain compared to the beginning. IWV and TIR rapidly increase to a peak at the onset of the rainfall. Precursors of rainfall are that the temperature reaches its maximum around 30 to 60 min before rain, while the pressure and relative humidity are minimal. IWV fluctuates the most before rain (obtained with a 10 min bandpass). In 60% of rain events, the air density decreases 2 to 6 h before the onset of rain. The seasonality and the duration of rain events as well as the diurnal cycle of atmospheric parameters are also considered. Thus, a prediction of rainfall is possible with a true detection rate of 60% by using the air density as a precursor. Further improvements in the nowcasting of rainfall are possible by using a combination of various atmospheric parameters which are monitored by a weather station and a ground-based microwave radiometer.

Characteristics of the Quasi‐Periodic Outbreaks of Deep Convection During Tropical Cyclone Genesis

AbstractNineteen years of high resolution infrared brightness temperature data are adopted to investigate the characteristics of deep convective activities during the genesis and early evolution of tropical cyclones (TC) in the Northwest Pacific. It is found that quasi‐periodic outbreaks of deep convection are common during TC genesis regardless of the large‐scale flow pattern or the magnitude of wind shear. Spatially, deep convection concentrates within 3° of the disturbance center at peak time (when convection is active), and will shrink in coverage and become scattered at trough time (when convection is inactive). At the decay stage deep convection in the central area tends to weaken first. Among the quasi‐periodic cycles of deep convection, quasi‐diurnal cycles are the most distinct feature, followed by cycles with periods of 10–18 hr. The quasi‐diurnal cycles with an early morning peak only account for less than 50%. Statistically the amplitude of deep convection cycles will decrease a bit under shear greater than 9 m⋅s−1, and period length in the range of 12–28 hr decreases slowly with the increase in wind shear and will rise slightly when shear is greater than 9 m⋅s−1. Besides, genesis does not necessarily occur at the peak time of coverage in deep convection, and the oscillatory feature is not an effective indicator of genesis acceleration.