Brightness Temperature Difference Research Articles

High latitude sea surface temperatures derived from MODIS infrared measurements

Climate change is amplified in the Arctic region (north of 60°N) relative to elsewhere. By analyzing climate model simulations, it has been found that the largest factor in Arctic Amplification comes from enhanced temperature feedbacks, caused by the different vertical atmospheric warming profiles and a larger temperature change for longwave emission per unit of warming at high- compared to low-latitudes. Radiometers on polar-orbiting satellites offer the best mechanism to derive sea surface temperature (SST) in the Arctic, but given that the retrieval algorithms in the infrared (IR) are designed to compensate for the effects of the atmosphere, mainly water vapor (WV), IR satellite-derived SSTs have larger inaccuracies at high latitudes because the atmosphere can be very dry and cold. So, the motivation of this study is to improve the algorithms to obtain more accurate SSTs which can be used to research the feedback mechanisms. To undertake the study, the 5-year matchup database (MUDB) for MODIS (Moderate resolution Imaging Spectroradiometer) on Aqua has been analyzed to characterize the differences between collocated and simultaneous satellite retrieved skin SSTs (SSTskin) using the R2014 dataset and in situ buoy temperatures, and to identify the main causes of the discrepancies. According to the radiative transfer simulations, the sea surface emissivity is proven to be significant in satellite SSTskin retrievals at high latitudes due to the low atmospheric WV content, especially during winter. An Emissivity-introduced Brightness Temperature Difference (E∆BT) is developed to correct this emissivity effect in the algorithm. Furthermore, the reference temperature as a weighting factor for differences in brightness temperature (BT) measured in MODIS bands 31 and 32 has also been adjusted. The satellite SSTskin mean bias and standard deviation are reduced by 0.35 K and 0.085 K after the corrections. The approaches presented in this paper are capable to make more appropriate atmospheric corrections for MODIS SSTskin retrieval algorithm, leading to regional optimization of the SST retrievals. We report on the progress towards improving the satellite-derived SSTskin with the expectation that the near two-decadal time series of MODIS SSTskin fields will contribute to studying climate change in the Arctic.

Monitoring of aerosols and studying its effects on the environment and humans health in Iran.

Natural hazards affect different parts of living organisms. One of these hazards is aerosols. Addressing this issue in areas that suffer from this hazard is of critical importance. Scientific research over the past two decades has shown that aerosol particles are one of the main pollutants from the perspective of public health and health. The purpose of the present study is to monitor and model aerosol temporal and spatial variations in Iran. Aerosols or airborne particles with health effects such as heart, vascular and respiratory diseases are associated. For this purpose, MODIS (or Moderate Resolution Imaging Spectroradiometer) and NOAA (or National Oceanic and Atmospheric Administration) satellite data and BTD (or Brightness Temperature Difference) were used. Given the 20-year study period (2000-2019), the output of much satellite data was divided into four five-year periods to monitor aerosols with high accuracy. The results showed that aerosol values in terms of intensity and frequency of optical depth (AOD) increased over the 20-year time series, and its intensity was higher in the last 5years. According to the results obtained from the NODIS and NOAA satellite data and comparing their outputs, NOAA satellite data were associated with outliers, which was significantly different from the other cohort data. For this reason, the MODIS satellite image output was used to monitor aerosol images. The innovation of the present study is the use of remote sensing science to monitor the effects of aerosols on the environment and human's health. According to the results from MODIS satellite data, the maximum optical depth (AOD) of the aerosols is for July 2003 with a value of 0.63, but according to the NOAA satellite data output, the maximum optical depth (AOD) of the aerosols is for March 2013 with a value of 2.54. As the values of aerosols increase in frequency and intensity and the areas with the highest intensity of aerosols have been identified, it can be overcome by careful planning of their problems. Areas, where the amount and volume of aerosols were higher, should be observed in health protocols. By preventing and maintaining good hygiene, the negative effects of aerosols can be reduced.

Analysing the Epoch of Reionization with three-point correlation functions and machine learning techniques

ABSTRACT Three-point and high-order clustering statistics of the high-redshift 21 cm signal contain valuable information about the Epoch of Reionization (EoR). We present 3PCF-fast, an optimized code for estimating the three-point correlation function (3PCF) of 3D pixelized data such as the outputs from numerical and seminumerical simulations. After testing 3PCF-fast on data with known analytical 3PCF, we use machine learning techniques to recover the mean bubble size and global ionization fraction from correlations in the outputs of the publicly available 21cmfast code. We assume that foregrounds have been perfectly removed and negligible instrumental noise. Using ionization fraction data, our best multilayer perceptron (MLP) model recovers the mean bubble size with a median prediction error of around $10 {{\ \rm per\ cent}}$, or from the 21 cm differential brightness temperature with median prediction error of around $14 {{\ \rm per\ cent}}$. A further two MLP models recover the global ionization fraction with median prediction errors of around $4 {{\ \rm per\ cent}}$ (using ionization fraction data) or around $16 {{\ \rm per\ cent}}$ (using brightness temperature). Our results indicate that clustering in both the ionization fraction field and the brightness temperature field encode useful information about the progress of the EoR in a complementary way to other summary statistics. Using clustering would be particularly useful in regimes where high signal-to-noise ratio prevents direct measurement of bubble size statistics. We compare the quality of MLP models using the power spectrum, and find that using the 3PCF outperforms the power spectrum at predicting both global ionization fraction and mean bubble size.

A Fundamental Climate Data Record Derived from AMSR-E, MWRI, and AMSR2

Fundamental climate data records (FCDRs) play a vital role in monitoring climate change. In this article, we develop a spaceborne passive microwave-based FCDR by recalibrating the Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E) on the Aqua satellite, the microwave radiometer imager (MWRI) onboard the Feng-Yun-3B (FY3B) satellite, and the Advanced Microwave Scanning Radiometer-2 (AMSR2) onboard the JAXA’s Global Change Observation Mission first-Water (GCOM-W1) satellite. Before recalibration, it is found that AMSR-E and AMSR2 observations are stable over time, but MWRI drifted colder before May 2015 and had nonnegligible errors in geolocation for most channels. In addition, intersensor differences of brightness temperatures (TBs) are as large as 5–10 K. To improve data consistency and continuity, several intersensor calibration methods are applied by using AMSR2 as a reference while using MWRI to bridge the data gap between AMSR2 and AMSR-E. The double difference method is used to provide intersensor difference time series for correcting calibration biases, such as scene temperature-dependent bias, solar-heating-induced bias, and systematic constant bias. Hardware differences between sensors are corrected using principal component analysis. After recalibration, the mean biases of both MWRI and AMSR-E are less than 0.3 K compared to the AMSR2 reference and their standard deviations are less than 1 K for all channels. Under oceanic rain-free conditions, the TB biases are less than 0.2 K for all channels and no significant relative bias drifts were found between sensors for overlapping observations. These statistics suggest that the consistency between these instruments was significantly improved and the derived FCDR could be useful to obtain long-term water cycle-related variables for climate research.

Development of Geo-KOMPSAT-2A Algorithm for Sea-Ice Detection Using Himawari-8/AHI Data

Sea ice is an important meteorological factor affecting the global climate system, but it is difficult to observe in sea ice ground truth data because of its location mainly at high latitudes and in polar regions. Accordingly, sea-ice detection research has been actively conducted using satellites, since the 1970s. Polar-orbiting and geostationary satellites are used for this purpose; notably, geostationary satellites are capable of real-time monitoring of specific regions. In this paper, we introduce the Geo-KOMPSAT-2A (GK-2A)/Advanced Meteorological Imager (AMI) sea-ice detection algorithm using Japan Meteorological Agency (JMA) Himawari-8/Advanced Himawari Imager (AHI) data as proxy data. The GK-2A/AMI, which is Korea Meteorological Administration (KMA)’s next-generation geostationary satellite launched in December 2018 and Himawari-8/AHI have optically similar channel data, and the observation area includes East Asia and the Western Pacific. The GK-2A/AMI sea-ice detection algorithm produces sea-ice data with a 10-min temporal resolution, a 2-km spatial resolution and sets the Okhotsk Sea and Bohai Sea, where the sea ice is distributed during the winter in the northern hemisphere. It used National Meteorological Satellite Center (NMSC) cloud mask as the preceding data and a dynamic threshold method instead of the static threshold method that is commonly performed in existing sea-ice detection studies. The dynamic threshold methods for sea-ice detection are dynamic wavelength warping (DWW) and IST0 method. The DWW is a method for determining the similarity by comparing the pattern of reflectance change according to the wavelength of two satellite data. The IST0 method detects sea ice by using the correlation between 11.2-μm brightness temperature (BT11.2) and brightness temperature difference (BTD) [BT11.2–BT12.3] according to ice surface temperature (IST). In addition, the GK-2A/AMI sea-ice detection algorithm reclassified the cloud area into sea ice using a simple test. A comparison of the sea-ice data derived the GK-2A/AMI sea-ice detection algorithm with the S-NPP/visible infrared imaging radiometer suite (VIIRS) sea ice characterization product indicates consistency of 99.0% and inconsistency of 0.9%. The overall accuracy (OA) of GK-2A/AMI sea-ice data with the sea ice region of interest (ROI) data, which is constructed by photo-interpretation method from RGB images, is 97.2%.

Exploration of machine learning methods for the classification of infrared limb spectra of polar stratospheric clouds

Abstract. Polar stratospheric clouds (PSCs) play a key role in polar ozone depletion in the stratosphere. Improved observations and continuous monitoring of PSCs can help to validate and improve chemistry–climate models that are used to predict the evolution of the polar ozone hole. In this paper, we explore the potential of applying machine learning (ML) methods to classify PSC observations of infrared limb sounders. Two datasets were considered in this study. The first dataset is a collection of infrared spectra captured in Northern Hemisphere winter 2006/2007 and Southern Hemisphere winter 2009 by the Michelson Interferometer for Passive Atmospheric Sounding (MIPAS) instrument on board the European Space Agency's (ESA) Envisat satellite. The second dataset is the cloud scenario database (CSDB) of simulated MIPAS spectra. We first performed an initial analysis to assess the basic characteristics of the CSDB and to decide which features to extract from it. Here, we focused on an approach using brightness temperature differences (BTDs). From both the measured and the simulated infrared spectra, more than 10 000 BTD features were generated. Next, we assessed the use of ML methods for the reduction of the dimensionality of this large feature space using principal component analysis (PCA) and kernel principal component analysis (KPCA) followed by a classification with the support vector machine (SVM). The random forest (RF) technique, which embeds the feature selection step, has also been used as a classifier. All methods were found to be suitable to retrieve information on the composition of PSCs. Of these, RF seems to be the most promising method, being less prone to overfitting and producing results that agree well with established results based on conventional classification methods.

Multitechnique Rain Classification From Ground-Based Measurements Over a Tropical Location

This article aims to classify precipitation into two categories, namely stratiform and convective. Multiple techniques, such as utilizing the micro rain radar (MRR), electric field monitor (EFM), radiometer, and disdrometer measurements, have been deployed for this purpose, at a tropical location Kolkata, India. A new rain classification technique, using logistic regression modeling of the sixth to third moment ratio ${(M6/M3)}$ , has been proposed. Classification of rain types based on the new technique shows high consistency with that based on radar reflectivity ( ${Z}$ ) values obtained from disdrometer measurements. This article also distinguishes mixed rain from stratiform and convective rain. The observations on the bright band structure by MRR and on differential brightness temperature at 31.4 and 22.23 GHz by a radiometer are utilized to classify mixed rain types. Although the EFM measurements do not classify rain types directly, they give a distinct signature of the impending stratiform/convective rain events. A comparative analysis between the percentage of stratiform and convective rain durations shows significant dominance of stratiform rain over convective rain. At the present location, the convective phenomenon shows higher occurrences during the pre-monsoon period compared to the monsoon period.

Deep-Learning Study of the 21-cm Differential Brightness Temperature During the Epoch of Reionization

We propose a deep learning analyzing technique with convolutional neural network (CNN) to predict the evolutionary track of the Epoch of Reionization (EoR) from the 21-cm differential brightness temperature tomography images. We use 21cmFAST, a fast semi-numerical cosmological 21-cm signal simulator, to produce mock 21-cm maps between $z=6 \sim 13$. We then apply two observational effects into those 21-cm maps, such as instrumental noise and limit of (spatial and depth) resolution somewhat suitable for realistic choices of the Square Kilometre Array (SKA). We design our deep learning model with CNN to predict the sliced-averaged neutral hydrogen fraction from the given 21-cm map. The estimated neutral fraction from our CNN model has a great agreement with its true value even after coarsely smoothing with broad beamsize and frequency bandwidth, and also heavily covered by noise with narrow. Our results have shown that deep learning analyzing method has a large potential to efficiently reconstruct the EoR history from the 21-cm tomography surveys in future.

EDGES signal in the presence of magnetic fields

ABSTRACT We study the 21-cm differential brightness temperature in the presence of primordial helical magnetic fields for redshift z = 10–30. We argue that the α-effect that sets in at earlier time can be helpful in lowering the gas temperature to 3.2 K at z = 17. This effect can arise in the early Universe due to some parity-violating high-energy processes. Using the EDGES (Experiment to Detect the Global Epoch of Reionization Signature) results, we find the upper and lower limits on the primordial magnetic field to be 6 × 10−3 and 5 × 10−4 nG, respectively. We also discuss the effect of Ly α background on the bounds. Our results do not require any new physics in terms of dark matter.

An accurate and efficient radiative transfer model for simulating all-sky images from Fengyun satellite radiometers

Forward radiative transfer models (RTM) are an indispensable tool for quantitative applications of satellite radiometers, e.g., for data calibration, instrument development, retrieval, and so on. In this study, we develop an accurate and efficient RTM for radiometers onboard Fengyun satellites, namely FYRTM (RTM for Fengyun Radiometers). Correlated k-distribution models are developed to improve the computational efficiency for gas absorption, and the effects of cloud and aerosol multiple scattering and emission are accelerated with pre-computed look-up tables. FYRTM is evaluated with a rigorous simulation based on discrete ordinate radiative transfer model (DISORT) as well as a popular fast forward model, i.e., the Community Radiative Transfer Model (CRTM). Results indicate that FYRTM-based simulations are two to three orders of magnitudes faster than the DISORT-based simulations. Compared to the rigorous model, FYRTM relative errors are within 2% at solar channels, and brightness temperatures (BT) differences are within 1 K at infrared channels. Compared with CRTM, FYRTM is computationally similar at solar channels, but three times faster at infrared channels. Furthermore, simulated reflectances/BTs using FYRTM are in a good agreement with the satellite observations. Overall, FYRTM is capable to simulate satellite observations under different atmospheric conditions, and can be extended to other radiometers onboard the Fengyun satellites (both geostationary and polar-orbiting satellites). It is expected to play important roles in future applications with Fengyun observations.

Which dual-band infrared indices are optimum for identifying aerosol compositional change using Himawari-8 data?

Abstract Aerosol optical depth algorithms predominantly use the visible portion of the electromagnetic spectrum. However, quantifying sporadic dust events throughout the full 24-h period requires using continuous wavelengths such as infrared (IR). Identifying aerosols, using IR from geostationary data, has relied on subtraction indices rather than normalised differences. Limited attention has been given to determining which IR indices could be suitable for identifying aerosol compositional change. Suitable IR indices could potentially result in multi-spectral data from geostationary satellites, such as Himawari, being used to separate dust from other types of aerosols. This study evaluated three index types: subtraction (brightness temperature difference (BTD)), normalised differences, and division (i.e. quotient). The effectiveness of these three indices were assessed against three sample matrix types: (i) pure soil spectra from the USGS Spectral Library Version 7 database; (ii) potential aerosol plumes, estimated from data relating to the formation and dissipation of a dust storm; and (iii) annual variance, at a surface monitoring site. Absorbance values from the USGS spectral database were aggregated into the spectrally broad Himawari-8 infrared bands. Potential plumes (i.e. transient over a small area) were identified from daily variances in the Himawari-8 satellite data. Principal component analysis was used to determine the variance explained by the first principal component for each of the sample and index types and to evaluate the effectiveness of the indices for detecting dust events. Simple subtraction indices explained more of the variance than normalised differences or division for all sample types. Of the 45 BTD indices analysed, only seven resolved the cloud and aerosol plumes into separate groups. Of these seven indices, BTD3.9–6.2 μm and BTD11–12 μm had the least correlation with the other indices and were chosen as the best indices to identify aerosol compositional change. A further three indices BTD9.6–13 μm, BTD8.6–10 μm, and BTD6.9–7.3 μm were selected based on low correlations between other indices and ensuring that all ten IR wavelengths were utilised. This study indicates that the combination of these five indices, rather than a single index, may optimise the identification of aerosol compositional change.

A Spectral Data Compression (SDCOMP) Radiative Transfer Model for High-Spectral-Resolution Radiation Simulations

Abstract With the increasing use of satellite and ground-based high-spectral-resolution (HSR) measurements for weather and climate applications, accurate and efficient radiative transfer (RT) models have become essential for accurate atmospheric retrievals, for instrument calibration, and to provide benchmark RT solutions. This study develops a spectral data compression (SDCOMP) RT model to simulate HSR radiances in both solar and infrared spectral regions. The SDCOMP approach “compresses” the spectral data in the optical property and radiance domains, utilizing principal component analysis (PCA) twice to alleviate the computational burden. First, an optical-property-based PCA is performed for a given atmospheric scenario (atmospheric, trace gas, and aerosol profiles) to simulate relatively low-spectral-resolution radiances at a small number of representative wavelengths. Second, by using precalculated principal components from an accurate radiance dataset computed for a large number of atmospheric scenarios, a radiance-based PCA is carried out to extend the low-spectral-resolution results to desired HSR results at all wavelengths. This procedure ensures both that individual monochromatic RT calculations are efficiently performed and that the number of such computations is optimized. SDCOMP is approximately three orders of magnitude faster than numerically exact RT calculations. The resulting monochromatic radiance has relative errors less than 0.2% in the solar region and brightness temperature differences less than 0.1 K for over 95% of the cases in the infrared region. The efficiency and accuracy of SDCOMP not only make it useful for analysis of HSR measurements, but also hint at the potential for utilizing this model to perform RT simulations in mesoscale numerical weather and general circulation models.

Sea Fog Detection Based on Normalized Difference Snow Index Using Advanced Himawari Imager Observations

Many previous studies have attempted to distinguish fog from clouds using low-orbit and geostationary satellite observations from visible (VIS) to longwave infrared (LWIR) bands. However, clouds and fog have often been misidentified because of their similar spectral features. Recently, advanced meteorological geostationary satellites with improved spectral, spatial, and temporal resolutions, including Himawari-8/9, GOES-16/17, and GeoKompsat-2A, have become operational. Accordingly, this study presents an improved algorithm for detecting daytime sea fog using one VIS and one near-infrared (NIR) band of the Advanced Himawari Imager (AHI) of the Himawari-8 satellite. We propose a regression-based relationship for sea fog detection using a combination of the Normalized Difference Snow Index (NDSI) and reflectance at the green band of the AHI. Several case studies, including various foggy and cloudy weather conditions in the Yellow Sea for three years (2017–2019), have been performed. The results of our algorithm showed a successful detection of sea fog without any cloud mask information. The pixel-level comparison results with the sea fog detection based on the shortwave infrared (SWIR) band (3.9 μm) and the brightness temperature difference between SWIR and LWIR bands of the AHI showed high statistical scores for probability of detection (POD), post agreement (PAG), critical success index (CSI), and Heidke skill score (HSS). Consequently, the proposed algorithms for daytime sea fog detection can be effective in daytime, particularly twilight, conditions, for many satellites equipped with VIS and NIR bands.

Analysis of properties of the 19 February 2018 volcanic eruption of Mount Sinabung in S5P/TROPOMI and Himawari-8 satellite data

Abstract. This study presents an analysis of TROPOMI cloud heights as a proxy for volcanic plume heights in the presence of absorbing aerosols and sulfur dioxide for the 19 February 2018 eruption plume of the Sinabung volcano on Sumatra, Indonesia. Comparison with CALIPSO satellite data shows that all three TROPOMI cloud height data products based on oxygen absorption which are considered here (FRESCO, ROCINN, O22CLD) provide volcanic ash cloud heights comparable to heights measured by CALIPSO for optically thick volcanic ash clouds. FRESCO and ROCINN heights are very similar, with the only differences for FRESCO cloud top heights above 14 km altitude. O22CLD cloud top heights unsurprisingly fall below those of FRESCO and ROCINN, as the O22CLD retrieval is less sensitive to cloud top heights above 10 km altitude. For optically thin volcanic ash clouds, i.e., when Earth's surface or clouds at lower altitudes shine through the volcanic ash cloud, retrieved heights fall below the volcanic ash cloud heights derived from CALIPSO data. Evaluation of corresponding Himawari-8 geostationary infrared (IR) brightness temperature differences (ΔBTs) – a signature for detection of volcanic ash clouds in geostationary satellite data and widely used as input for quantitative volcanic ash cloud retrievals – reveals that for this particular eruption the ΔBT volcanic ash signature changes to a ΔBT ice crystal signature for the part of the ash plume reaching the upper troposphere beyond 10 km altitude several hours after the start of the eruption and which TROPOMI clearly characterizes as volcanic (SO2 > 1 DU – Dobson units – and AAI > 4 – absorbing aerosol index – or, more conservatively, SO2 > 10). The presence of ice in volcanic ash clouds is known to prevent the detection of volcanic ash clouds based on broadband geostationary satellite data. TROPOMI does not suffer from this effect and can provide valuable and accurate information about volcanic ash clouds and ash top heights in cases where commonly used geostationary IR measurements of volcanic ash clouds fail.

Development of an infrared pollution index to identify ground-level compositional, particle size, and humidity changes using Himawari-8

Abstract Speciated air quality data informs health studies and quantitates impacts. However, monitoring is concentrated around populated regions whilst, large remote and rural regions remain unmonitored despite risks of dust-storms or wild-fires. Sub-hourly, infrared, geostationary data, such as the 10-min data from Himawari 8, could potentially be used to quantify regional air quality continually. Monitoring of Aerosol Optical Depth (AOD) is restricted to visible spectra (i.e. daytime only), while newer quantification methods using geostationary infrared (IR) data have focused on detecting the presence, or absence, of an event. Limited attention has been given to the determination of particle size and aerosol composition (such as sulfates, black carbon, sea-salt, and mineral dust), using IR exclusively, and more appropriate methods are required to improve the understanding of source impacts. Hourly data were collected for a three-year study period (July 2015 to July 2018) across the greater Sydney region in Eastern Australia from seventeen ground-based sites that measured meteorological data and quantified ambient concentrations of NO, NO2, SO2, PM2.5, PM10, and O3. This data was combined with source-apportioned categories (soil, sea-spray, smoke, secondary sulfates, and vehicles) from positive matrix factorization (PMF) of elemental aerosol collected on daily filters at five monitoring sites across the region. Regression analysis of five brightness temperature difference (BTD) infrared indices were used to determine a pollution index. The pollution index was shown to be related to humidity, particle size, and compositional changes. Unlike fixed thresholds, the continual index function can be aggregated spatially and temporarily. Good resolution is obtained between PM2.5 and O3. BTD appears insensitive to concentration, and the pollution index was used to detect and identify composition prior to determining concentration.

Examining Asian dust refractive indices for brightness temperature simulations in the 650–1135 cm−1 spectral range

In this paper, we modeled dust impacts on atmospheric radiative transfer in the thermal infrared region of 650–1135 cm−1 by using the radiative transfer for TOVs model (RTTOV). We assessed three sets of Asian dust refractive indices (the Volz refractive index implemented in the RTTOV as default, and the Gobi and Taklamakan refractive indices) for brightness temperature simulations. The US 76 standard profile was employed to represent the atmospheric status. Three scenarios of high (532.6~575.2 hPa), middle (661~703.6 hPa) and low (786.3~825.8 hPa) dust layers and four cases of aerosol optical thickness (AOT) (0.1, 0.5, 1.0, and 1.5 at 1000 cm−1) were considered in the simulations. It was found that the refractive indices have a clear impact on simulating the values and the spectral slope of brightness temperature in the mid-thermal infrared band. Brightness temperature difference (BTD) for different refractive indices (Gobi/Taklamakan–Volz) increases with the height of dust layer and the AOT. The BTD reaches ~4 K for the high dust layer (532.6~575.2 hPa) and the AOT of 1.5. Most BTDs (0.5–4 K) are much larger than the noise-equivalent temperature difference of high-spectral resolution infrared sounders. Even for the AOT of 0.1, BTDs for different refractive indices could be close to or larger than 0.2 K. This study emphasizes the requirement of accurate refractive indices according to dust origins for improving brightness temperature simulations and provides valuable references for potential improvement of dust retrievals.

Detectability of the τes−21cm cross-correlation: a tomographic probe of patchy reionization

The cross-correlation between fluctuations in the electron scattering optical depth τes as probed by future Cosmic Microwave Background (CMB) experiments, and fluctuations in the 21cm differential brightness temperature Δ T 21cm as probed by ground-based radio interferometers, will trace the reionization history of the Universe. In particular, the τes−21cm cross-correlation should yield a determination of the characteristic bubble size distribution and ionization fraction as a function of redshift. When assuming that the cross-correlation signal is limited by instrumental noise rather than by foregrounds, we estimate its potential detectability by upcoming experiments. Specifically, the combination of HERA and Simons Observatory, CMB-S4 and PICO should yield a signal-to-noise ratio around 3–6, while and the exploitation of the SKA should increase it to 10-20. Finally, we have discussed how such levels of detectability can be affected when (simply modeled) 21cm foregrounds are present. For the most promising PICO×SKA configuration, an efficiency of foreground removal to a level of 7× 10−4 is needed to achieve a 5σ detection of the cross-correlation signal; in addition, safe avoidance of foreground contamination in the line-of-sight Fourier modes above 0.03 h Mpc−1 would guarantee a detection significance around 3σ.

Comparison of the MODIS and VIIRS Thermal Emissive Band Radiometric Calibration

Moderate Resolution Imaging Spectroradiometer (MODIS) and Visible Infrared Imaging Radiometer Suite (VIIRS) are major instruments for Earth science observations. Nearly 40 MODIS scientific products and a wide range of VIIRS environmental data records are produced using their global observations. The consistency of the MODIS and VIIRS calibrated data is important for the study of Earth science. This article assesses the calibration consistency of the Aqua MODIS and VIIRS thermal emissive band (TEB) data. To remove the impact of the mismatched relative spectral response (RSR) on the comparisons, the simultaneous nadir observation data from the cross-track infrared sounder (CrIS) and the infrared atmospheric sounding interferometer (IASI) are used as references in two different methods to independently verify the consistency. The comparisons of the MODIS and VIIRS TEB calibrated data show that the brightness temperature (BT) differences for comparable bands between MODIS and VIIRS are in general within 0.2 K for BT larger than 230 K. The differences of all comparable MODIS and VIIRS TEBs are consistent over time. MODIS measurements agree better with N20 VIIRS than with Suomi National Polar-orbiting Partnership (S-NPP) VIIRS. The S-NPP VIIRS measurements are higher than N20 VIIRS, within 0.1 K for long-wave infrared (LWIR) bands and 0.3 K for band M13.

Identifying and characterizing dust point sources in the southwestern United States using remote sensing and GIS

Documenting geomorphology and land use/land cover associated with the aeolian dust sources is needed to build more robust models simulating wind erosion and dust emission. It is also important to set the base for future studies aiming to quantify the effect of anthropogenic land-use change on dust emission. In this research, dust point sources were detected in the Southern Great Plains and Chihuahuan Desert regions of the United States (U.S.) for 2001–2016, encompassing a period of extreme drought. Dust points were detected using overlay analysis of brightness temperature difference images of MODIS and MODIS true color images for the days where dust plumes were visible. This study aims to identify low emission surfaces as well as high emission surfaces in terms of geomorphology and land cover. A total of 1508 dust points were detected, where 1258 points are located in the Southern Great Plains, and 187 points are located in the Chihuahuan Desert. Point pattern analysis showed a significant cluster of these points in West Texas (Nearest Neighbor Ratio = 0.33, р < 0.001) where cultivated lands and grasslands are the dominant land cover and aeolian sand sheet is the dominant geomorphic class. Ephemeral lakes (i.e., playas) produce the most dust sources per unit area. Cultivated croplands enclose 43% of the dust points, while shrublands and grasslands, combined, enclose 45% of the points. Results from this study confirms the importance of playas as a dynamic source of dust in southwestern U.S. Moreover, this study suggests that anthropogenic factors play a major role in dust emission within southwestern U.S, although bare lands in particular that are not subjected to anthropogenic factors (e.g., the White Sands dune field) are also emitting dust. Future research is needed to statistically analyze the contribution of different land-cover types on dust emission in the region.

Complex Mare Deposits Revealed by CE-2 CELMS Data in Mare Nubium

The study of mare basalts in microwave range will be of great significance to better understand the basaltic units with complex surface contaminations, particularly in Mare Nubium. In this article, the China Chang'E-2 Lunar Microwave Sounder data and the generated normalized brightness temperature (nTB) and brightness temperature difference (dTB) maps are employed to evaluate the basaltic units in Mare Nubium. The results are as follows. First, two regions with hot anomaly are found, which is likely brought by the high substrate temperature. The anomaly in central Nubium is extensive in range and high in intensity. Second, the nTB and dTB performances can pursue the mare deposits contaminated by the impact ejecta, which reveals special mare basalt with the thin layer in the southeastern part of Mare Nubium. Third, the nTB and dTB maps indicate the complexity of the mare deposits in Mare Nubium. Fourth, the cold dTB anomaly in the western part is proposed as the floor deposits produced during the formation of the Nubium basin. These unusual findings will be of fundamental significance to better understand the basaltic volcanism and the thermal evolution of the Moon.