Brightness Temperature Anomalies Research Articles

Pre-earthquake MBT anomalies in the Central and Eastern Qinghai-Tibet Plateau and their association to earthquakes

Thermal anomalies prior to large and hazardous earthquakes have been extensively detected by thermal infrared and microwave remote sensing techniques. However, few of the current research focuses on long-term tracking of the thermal anomalies for decades and their earthquake prediction potential in a seismically active area. Targeting at exploiting the association of thermal anomalies to earthquakes in terms of time, spatial patterns and magnitudes, comprehensive statistical analysis on the pre-earthquake microwave brightness temperature (MBT) anomalies of 103 destructive earthquakes with Ms. ≥ 5.0 in the Central and Eastern Qinghai-Tibet Plateau (90° E – 102° E, 28° N – 38° N) is conducted. To detect robust pre-earthquake thermal anomalies buried by the complex background and frequently variant meteorological noise, we propose a wavelet-based two-step difference (WTSD) approach which incorporates hierarchical clustering and wavelet decomposition, adopting it on the 17 years' microwave data collected by AMSR-E and AMSR-2 sensors. Interestingly, an NE trending MBT increase stripe has been repeatedly occurring prior to 60 of the 103 earthquakes (accounting for 58.25%). Then, we conduct comprehensive statistics on the occurring time, spatial patterns and intensities of the MBT anomalies, and find them closely associated to seismic events with Ms. ≥ 5.0 occurred in the Bayan Har and Qiangtang blocks. And, the MBT anomaly stripe is spatially consistent with the NE trending extensional faults and geothermal fields in this study area. Furthermore, the possible source mechanism of the MBT anomalies is explored with rock loading microwave observation experiments, and possible physical models suggested by predecessors are also discussed. On this basis, we discuss the relationship between the pre-earthquake MBT anomalies and extensional faults, and speculate the MBT anomalies are probably caused by intensified extrusion of the Indian plate to the Eurasian plate and the increased crustal stress, which indicates their significant potential as earthquake precursors in the Central and Eastern Qinghai-Tibet Plateau.

Application of Orthogonal Polynomial Fitting Method to Extract Gravity Wave Signals From AIRS Data Related to Typhoon Deep Convection

AbstractGravity waves can influence weather and climate patterns on various temporal and spatial scales in the atmosphere. Despite their recognized importance, there are clearly a lack of sufficient and accurate observations from currently available satellite observing systems to satisfy the requirements of many satellite users. Common method to detect gravity waves is to measure brightness temperature (BT) anomalies, which rely on an initial efficient background removal method. Before gravity waves can be extracted from Atmospheric Infrared Sounder raw radiances, Hoffmann and Alexander (2010, http://doi.org/10.1029/2010jd014401) used a fourth‐order polynomial fitting (4PF) method to remove the background variations. In this study, we propose a new strategy, an optimal orthogonal polynomial fitting (OPF) method using Chebyshev Polynomials as basis functions, to remove the background variations and estimate BT perturbations. By extending the classic 4PF method to the fifth‐order polynomial fitting (5PF) method and combining the Cressman interpolation method, some experiments are designed to validate the feasibility and superiority of the OPF method. The results show that OPF is the optimal method to remove the limb‐brightening effect in the extraction of gravity wave signals generated by typhoons. In addition, what we noticed is that an appropriate fitting orders have to be selected to get more accurate BT anomalies signals in the experiments to extract gravity wave signals.

Variation of Thermal Infrared Brightness Temperature Anomalies in the Madoi Earthquake and Associated Earthquakes in the Qinghai-Tibetan Plateau (China)

We processed MODIS data received from ground receiving stations into the spatial range of the Qinghai-Tibetan Plateau (China) and the eastern margin of the plateau, and then 283 K was set as the threshold value to remove the area covered by clouds. The monthly background field was calculated based on 17 years’ data, then we obtained the spatial Brightness Temperature anomaly of the current month by deducting the background field. Furthermore, the Brightness Temperature anomaly curves for secondary tectonic blocks in the plateau were calculated. The data indicated that since June 2020, the Brightness Temperature radiation within the Qinghai-Tibetan Plateau began to increase abnormally, starting from the western part of the study area and expanding eastward to cover the entire plateau. In January 2021, such an anomaly was seen again, extending to the Sichuan-Yunnan Block in the easternmost part of the study area in april. With the ongoing anomaly, a series of moderate and strong earthquakes occurred in the Qinghai-Tibetan Plateau, and finally, on 22 May 2021, the M7.4 earthquake struck the Madoi County. Moreover, according to the internal Brightness Temperature time series curves of the different secondary tectonic blocks, the Brightness Temperature has increased simultaneously since the beginning of 2020. A twofold standard deviation was found in the middle-east segment of the Bayanhar Block and the Qiangtang Block in October 2020, and an almost twofold standard deviation was found in March, while a twofold standard deviation was found in the Sichuan-Yunnan Block in april 2021. The occurrence of earthquakes in the plateau before the Madoi earthquake coincided with an upward trend of the time series curve. The spatial anomaly of Brightness Temperature over the Qinghai-Tibetan Plateau disappeared and the Brightness Temperature time series curve dropped drastically after the Madoi earthquake. The development of spatial anomaly of Brightness Temperature and the time series curve both coincide with the occurrence of earthquakes and are consistent with the generation of tectonic stress in the Qinghai-Tibetan Plateau. Our study showed that thermal infrared Brightness Temperature radiation reasonably reflects regional stress development and enables the detection of anomalies prior to moderate and strong earthquakes.

Extraction and Discrimination of MBT Anomalies Possibly Associated with the Mw 7.3 Maduo (Qinghai, China) Earthquake on 21 May 2021

Earthquakes are one of the most threatening natural disasters to human beings, and pre- and post-earthquake microwave brightness temperature (MBT) anomalies have attracted increasing attention from geosciences as well as remote sensing communities. However, there is still a lack of systematic description about how to extract and then discriminate the authenticity of seismic MBT anomalies. In this research, the first strong earthquake occurring near the northern edge of eastern Bayan Har block in nearly 20 years, the recent Mw 7.3 Maduo earthquake in Qinghai province, China on 21 May 2021, was selected as a case study. Based on the monthly mean background of MBT, the spatiotemporal features of MBT residuals with 10.65 GHz before and after the earthquake was firstly revealed. Referring to the spatial patterns and abnormal amplitudes of the results, four typical types of evident MBT positive residuals were obtained, and the time series of intensity features of each category was also quantitatively analyzed. Then, as the most influential factor on surface microwave radiation, air temperature, soil moisture and precipitation were analyzed to discriminate their contributions to these residuals. The fourth one, which occurred north to the epicenter after the earthquake, was finally confirmed to be caused by soil moisture reduction and thus ruled out as being related to seismicity. Therefore, the three retained typical MBT residuals with 10.65 GHz could be identified as possible anomalies associated with the Maduo earthquake, and were further analyzed collaboratively with some other reported abnormal phenomena related to the seismogenic process. Furthermore, through time series analysis, the MBT positive residuals inside the Bayan Har block were found to be more significant than that outside, and the abnormal behaviors of MBT residuals in the elevation range of 4000–5000 m reflected the shielding effect on microwave radiation from thawing permafrost on the plateau in March and April, 2021. This research provides a detailed technique to extract and discriminate the seismic MBT anomaly, and the revealed results reflect well the joint effect of seismic activity and regional coversphere environment on satellite-observed MBT.

More severe drought detected by the assimilation of brightness temperature and terrestrial water storage anomalies in Texas during 2010–2013

Texas experienced an extreme drought on record in 2011. Model simulations or satellite observations have been used to assess and analyze the drought. In this study, a method based on multi-source remote sensing data assimilation is proposed to evaluate the drought in Texas, which combines the advantages of model simulations and satellite observations. Brightness temperature from the Soil Moisture and Ocean Salinity (SMOS), the Advanced Microwave Scanning Radiometer for EOS/Advanced Microwave Scanning Radiometer 2 (AMSR-E/AMSR2) and terrestrial water storage anomalies from the Gravity Recovery and Climate Experiment (GRACE) are assimilated into the Noah-Multiparameterization model (Noah-MP) to improve the soil moisture and drought estimation. The Ensemble Kalman Filter (EnKF) was used as a data assimilation method with observation aggregation (dealing with the multi-resolution observations) and observation bias correction (dealing with the systematic bias between observations and simulations). Data from ground stations and satellites are used for the validation of soil moisture, terrestrial water storage anomalies, and evapotranspiration. With assimilation, slight improvement can be found for surface soil moisture over the open-loop configuration, but some notable degradations are also observed. More obvious improvement can be found at deep layers, which is mainly due to the assimilation of terrestrial water storage anomalies. In addition, the improvement for terrestrial water storage anomalies and evapotranspiration is more statistically significant in most regions of Texas. Overall, the assimilation experiment plays a positive role in the estimation of essential variables relate to drought. The drought is estimated using the soil moisture percentile and classified into five categories. The results from the assimilation experiment tend to predict a more severe drought in Texas for both magnitude and spatial patterns, which indicates not only the well-known drought in 2011 but also the continuous severe drought in 2012 and 2013. Moreover, the spatial distribution of drought reveals that drought occurred more frequently in the North, West, and Southwest.

Discriminating the Multi-Frequency Microwave Brightness Temperature Anomalies Relating to 2017 Mw 7.3 Sarpol Zahab (Iran-Iraq Border) Earthquake

A Mw 7.3 earthquake occurred near the Iran-Iraq border on November 12, 2017, as the result of oblique-thrust squeezing of the Eurasian plate and the Arabian plate. By employing the spatio-temporally weighted two-step method (STW-TSM) and microwave brightness temperature (MBT) data from AMSR-2 instrument on board Aqua satellite, this paper investigates carefully the spatiotemporal features of multi-frequency MBT anomalies relating to the earthquake. Soil moisture (SM), satellite cloud image, regional geological map and surface landcover data are utilized to discriminate the potential MBT anomalies revealed from STW-TSM. The low-frequency MBT residual images shows that positive anomalies mainly occurred in the mountainous Urmia lake and the plain region, which were 300 km north and 200 km southwest about to the epicenter, respectively. The north MBT anomaly firstly appeared 51 days before the mainshock and its magnitude increased over time with a maximum of about +40K. Then the anomaly disappeared 3 days before, reappeared 1d after and diminished completely 10 days after the mainshock. Meanwhile, the southwest MBT anomaly firstly occurred 18 days before and peaked 3 days before the mainshock with a maximum of about +20K, and then diminished gradually with aftershocks. It is speculated that the positive MBT anomaly in the Urmia lake was caused by microwave dielectric property change of water body due to gas bubbles leaking from the bottom of the lake disturbed by local crust stress alteration, while the southwest MBT positive anomaly was caused by microwave dielectric constant change of shallow surface due to accumulation of seismically-activated positive charges originated at deep crust. Besides, some accidental abnormal residual stripes existed in line with satellite orbit, which turned out to be periodic data errors of the satellite sensor. High-frequency MBT residual images exhibit some significant negative anomalies, including a narrow stripe pointing to the forthcoming epicenter, which were confirmed to be caused by synchronous altostratus clouds. This study is of guidance meaning for distinguishing non-seismic disturbances and identifying seismic MBT anomaly before, during and after some large earthquakes.

Characteristics of Thermal Infrared Anomalies during the Earthquakes in Wenchuan, Lushan in Ya’an and Jiuzhaigou

Satellite thermal infrared observation has advantages over other earthquake observation techniques. With MODIS remote-sensing images from the TERRA satellite, the thermal infrared brightness temperature difference variation rate was used to analyze the observation data prior to three Magnitude-7-and-above earthquakes along the fracture zone of Longmen Mountain, and the result showed that there were thermal infrared brightness temperature anomalies one to two months before these three Magnitude-7-and-above earthquakes, and the earthquakes took place along the boundaries of the area where the anomalies occurred. The observation data show that there have been five times of substantial anomalies in the study area, among which four times were correlated to earthquakes.

Microwave Brightness Temperature Anomalies Associated With the 2015 Mw 7.8 Gorkha and Mw 7.3 Dolakha Earthquakes in Nepal

Two catastrophic earthquakes (EQs) with magnitudes of Mw 7.8 and 7.3 occurred in Gorkha and Dolakha, Nepal on April 25 and May 12, 2015, respectively. By employing the spatio-temporally weighted two-step method (STW-TSM), significant positive and negative microwave brightness temperature (MBT) anomalies preceding the two EQs were uncovered with satellite microwave data from FY-3B. Two strip-shaped positive MBT anomalies appeared in the Higher Himalayan 1 day before the Gorkha shock and the Dolakha shock, respectively, which were parallel to the thrust belt and whose intensity distributions exhibited good topographic consistency with the elevation profiles of the central Himalayas. Referring to the P-hole theory and seismogenic mechanism of the Nepal EQs, seismically activated mobile positive charges are presumed to have caused the surface positive MBT anomalies by reducing the superficial dielectric constant and increasing the local temperature of the Himalayan cliffs. A significant negative MBT anomaly occurred plausibly as early as March 2015 in the southern Gangetic Plain and developed into a stripe parallel to the thrust belt on the days of the Gorkha and Dolakha events. The negative MBT anomalies are suggested to result from soil moisture increases due to local synchronous precipitation. This work presents the first case study to use data from the FY-3B microwave radiation imager (MWRI) to investigate seismic anomalies, and discriminate both positive and negative MBT anomalies preceding an EQ for the first time.

Establishment of AIRS climate-level radiometric stability using radiance anomaly retrievals of minor gases and sea surface temperature

Abstract. Temperature, H2O, and O3 profiles, as well as CO2, N2O, CH4, chlorofluorocarbon-12 (CFC-12), and sea surface temperature (SST) scalar anomalies are computed using a clear subset of AIRS observations over ocean for the first 16 years of NASA's Earth-Observing Satellite (EOS) Aqua Atmospheric Infrared Sounder (AIRS) operation. The AIRS Level-1c radiances are averaged over 16 d and 40 equal-area zonal bins and then converted to brightness temperature anomalies. Geophysical anomalies are retrieved from the brightness temperature anomalies using a relatively standard optimal estimation approach. The CO2, N2O, CH4, and CFC-12 anomalies are derived by applying a vertically uniform multiplicative shift to each gas in order to obtain an estimate for the gas mixing ratio. The minor-gas anomalies are compared to the National Oceanic and Atmospheric Administration (NOAA) Earth System Research Laboratory (ESRL) in situ values and used to estimate the radiometric stability of the AIRS radiances. Similarly, the retrieved SST anomalies are compared to the SST values used in the ERA-Interim reanalysis and to NOAA's Optimum Interpolation SST (OISST) product. These intercomparisons strongly suggest that many AIRS channels are stable to better than 0.02 to 0.03 K per decade, well below climate trend levels, indicating that the AIRS blackbody is not drifting. However, detailed examination of the anomaly retrieval residuals (observed – computed) shows various small unphysical shifts that correspond to AIRS hardware events (shutdowns, etc.). Some examples are given highlighting how the AIRS radiance stability could be improved, especially for channels sensitive to N2O and CH4. The AIRS shortwave channels exhibit larger drifts that make them unsuitable for climate trending, and they are avoided in this work. The AIRS Level 2 surface temperature retrievals only use shortwave channels. We summarize how these shortwave drifts impacts recently published comparisons of AIRS surface temperature trends to other surface climatologies.

High-resolution UV/Optical/IR Imaging of Jupiter in 2016–2019

Abstract Imaging observations of Jupiter with high spatial resolution were acquired beginning in 2016, with a cadence of 53 days to coincide with atmospheric observations of the Juno spacecraft during each perijove pass. The Wide Field Camera 3 (WFC3) aboard the Hubble Space Telescope (HST) collected Jupiter images from 236 to 925 nm in 14 filters. The Near-Infrared Imager (NIRI) at Gemini North imaged Jovian thermal emission using a lucky-imaging approach (co-adding the sharpest frames taken from a sequence of short exposures), using the M′ filter at 4.7 μm. We discuss the data acquisition and processing and an archive collection that contains the processed WFC3 and NIRI data (doi:10.17909/T94T1H). Zonal winds remain steady over time at most latitudes, but significant evolution of the wind profile near 24°N in 2016 and near 15°S in 2017 was linked with convective superstorm eruptions. Persistent mesoscale waves were seen throughout the 2016–2019 period. We link groups of lightning flashes observed by the Juno team with water clouds in a large convective plume near 15°S and in cyclones near 35°N–55°N. Thermal infrared maps at the 10.8 micron wavelength obtained at the Very Large Telescope show consistent high brightness temperature anomalies, despite a diversity of aerosol properties seen in the HST data. Both WFC3 and NIRI imaging reveal depleted aerosols consistent with downwelling around the periphery of the 15°S storm, which was also observed by the Atacama Large Millimeter/submillimeter Array. NIRI imaging of the Great Red Spot shows that locally reduced cloud opacity is responsible for dark features within the vortex. The HST data maps multiple concentric polar hoods of high-latitude hazes.

Microwave Brightness Temperature Characteristics of Three Strong Earthquakes in Sichuan Province, China

Passive microwave remote sensing technology is an effective means to identify the thermal anomalies associated with earthquakes due to its penetrating capability through clouds compared with infrared sensors. However, observed microwave brightness temperature is strongly influenced by soil moisture and other surface parameters. In the present article, the segmented threshold method has been proposed to detect anomalous microwave brightness temperature associated with the strong earthquakes occurred in Sichuan province, China, an earthquake-prone area with high soil moisture. The index of microwave radiation anomaly (IMRA) computed by the proposed method is found to enhance prior to the three strong earthquakes, 2008 Wenchuan ( M = 7.8), 2013 Lushan ( M = 6.6), and 2017 Jiuzhaigou ( M = 6.5), occurred during 2008–2018 using the Defense Meteorological Space Program Special Sensor Microwave Imager/Sounder F17 satellite data. Our results show that the microwave brightness temperature anomalies appeared about two months prior to the three strong earthquakes. For the Wenchuan and Lushan earthquakes, the enhanced IMRA distributed along the main fault, which is consistent with the variations of our earlier studies of the 1997 Manyi ( M = 7.5) and the 2001 Kokoxili ( M = 7.8) earthquakes in the region with low soil moisture. For the Jiuzhaigou earthquake, the anomalies distributed around the epicenter and do not indicate the seismogenic structure, which could be due to the presence of a blind fault. It should be noted that quantitative evaluation of IMRA is limited due to infrequent occurrence of earthquakes.

Relationship between the Brightness Temperature Anomalies of the Lower Troposphere and the Climate Indices in the Southern Urals

The average monthly temperature of the lower troposphere (TLT) retrieved from satellite sensing data for 1979–2017 in the Southern Urals is analyzed. The method of decomposition of the temperature series into empirical orthogonal components (EOCs) was used to study the spatiotemporal TLT structure. Correlation analysis of the identified EOCs for winter and summer seasons and the indices of large-scale modes of natural climatic variability in the Northern Hemisphere is carried out. The first leading EOC, which characterizes a negative temperature trend, produces the main contribution to the overall variability. In winter, the leading mode is associated with the North Atlantic oscillation. In summer, a significant contribution of the Atlantic multidecadal oscillation and the index of the Arctic sea ice concentration anomalies was revealed. This may be used to improve the reliability of forecasting the regional climate change in the coming decades. The results suggest that the natural climatic variability has a considerable effect on the temperature regime and that it might be difficult to isolate the anthropogenic component of climate change in the studied region.

Relationship of the brightness temperature anomalies of the lower troposphere with the climate indices on the Southern Urals

This paper presents an analysis of the average monthly temperature of the lower troposphere (TLT) according to satellite sensing data for the period 1979–2017 in the Southern Urals. In order to study the space-time structure of TLT, the method of decomposition of the temperature series into empirical orthogonal components (EOC) was used. A correlation analysis of the link between the identified EOC for winter and summer seasons and indices of large-scale modes of natural climate variability in the Northern hemisphere was carried out. The first leading EOC, which describes a negative temperature trend, makes the major contribution to the overall variability. For winter, the leading mode is associated with the North Atlantic oscillation. For summer, a significant contribution of the Atlantic multi-decadal oscillation and the index of the Arctic sea ice concentration anomalies is revealed, which can be used to improve the reliability of the future scenarios of the regional climate change. The results suggest a significant impact of natural climatic variability on the temperature regime and a possible difficulty in identifying the anthropogenic component of climate change in the studied region.

Thermal Imaging and Biometrical Thermography of Humpback Whales

Determining species distributions through time and space remains a primary challenge in cetacean science and conservation. For example, many whales migrate thousands of kilometers every year between remote seasonal habitats along migratory corridors that cross major shipping lanes and intensively harvested fisheries creating a dynamic spatial and temporal context that conservation decisions must take into account. Technological advances enabling automated whale detection have the potential to dramatically improve our knowledge of when and where whales are located, presenting opportunities to help minimize adverse human-whale interactions. Using thermographic data we show that near-horizontal (i.e. high zenith angle) infrared images of humpback whale (Megaptera novaeangliae) blows, dorsal fins, flukes and rostrums record similar magnitude brightness temperature anomalies relative to the adjacent ocean surface. Our results demonstrate that these anomalies are similar in both low latitude and high latitude environments despite a ~16°C difference in ocean surface temperature between study areas. We show that these similarities occur in both environments due to emissivity effects associated with oblique target imaging, rather than differences in cetacean thermoregulation. The consistent and reproducible brightness temperature anomalies we report provide important quantitative constraints that will help facilitate the development of transient temperature anomaly detection algorithms in diverse marine environments. Thermographic videography coupled with laser range finding further enables calculation of whale blow velocity, demonstrating that biometrical measurements are possible for near-horizontal datasets that otherwise suffer from emissivity effects. The thermographic research we present creates a platform for the delivery of 3 important contributions to cetacean conservation: 1) non-invasive species-level identifications based on whale blow shapes and velocities recorded by infrared videography; 2) reduced ship-strike rates through automated thermographic cetacean detection systems deployed in high traffic areas; 3) monitoring the spatial and temporal distributions of endangered animals in remote habitats.

SMOS brightness temperature assimilation into the Community Land Model

Abstract. SMOS (Soil Moisture and Ocean Salinity mission) brightness temperatures at a single incident angle are assimilated into the Community Land Model (CLM) across Australia to improve soil moisture simulations. Therefore, the data assimilation system DasPy is coupled to the local ensemble transform Kalman filter (LETKF) as well as to the Community Microwave Emission Model (CMEM). Brightness temperature climatologies are precomputed to enable the assimilation of brightness temperature anomalies, making use of 6 years of SMOS data (2010–2015). Mean correlation R with in situ measurements increases moderately from 0.61 to 0.68 (11 %) for upper soil layers if the root zone is included in the updates. A reduced improvement of 5 % is achieved if the assimilation is restricted to the upper soil layers. Root-zone simulations improve by 7 % when updating both the top layers and root zone, and by 4 % when only updating the top layers. Mean increments and increment standard deviations are compared for the experiments. The long-term assimilation impact is analysed by looking at a set of quantiles computed for soil moisture at each grid cell. Within hydrological monitoring systems, extreme dry or wet conditions are often defined via their relative occurrence, adding great importance to assimilation-induced quantile changes. Although still being limited now, longer L-band radiometer time series will become available and make model output improved by assimilating such data that are more usable for extreme event statistics.

Possible thermal brightness temperature anomalies associated with the Lushan (China) M S7.0 earthquake on 20 April 2013

Lushan MS7.0 earthquake occurred in Lushan county, Ya’an city, Sichuan province of China, on 20 April 2013, and caused 196 deaths, 23 people of missing and more than 12 thousand of people injured. In order to analyze the possible seismic brightness temperature anomalies which may be associated with Lushan earthquake, daily brightness temperature data for the period from 1 June 2011 to 31 May 2013 and the geographical extent of 25°E–35°N latitude and 98°E–108°E longitude are collected from Chinese geostationary meteorological satellite FY-2E. Continuous wavelet transform method which has good resolution both in time and frequency domains is used to analyze power spectrum of brightness temperature data. The results show that the relative wavelet power spectrum (RWPS) anomalies appeared since 24 January 2013 and still lasted on 19 April. Anomalies firstly appeared at the middle part of Longmenshan fault zone and gradually spread toward the southwestern part of Longmenshan fault. Anomalies also appeared along the Xianshuihe fault since about 1 March. Eventually, anomalies gathered at the intersection zone of Longmenshan and Xianshuihe faults. The anomalous areas and RWPS amplitude increased since the appearance of anomalies and reached maximum in late March. Anomalies attenuated with the earthquake approaching. And eventually the earthquake occurred at the southeastern edge of anomalous areas. Lushan earthquake was the only obvious geological event within the anomalous area during the time period, so the anomalous changes of RWPS are possibly associated with the earthquake.

Thermal infrared brightness temperature anomalies associated with the Yushu (China) <i>M</i><sub>s</sub> = 7.1 earthquake on 14 April 2010

Abstract. The paper presents a wavelet transform method to identify possible seismic brightness temperature anomalies that might be associated with the Yushu (Ms = 7.1) earthquake that occurred in Qinghai province of China on 14 April 2010. Daily infrared data from the Chinese geostationary meteorological satellite FY-2E were used for the period from 1 January 2010 to 31 December 2011 and the spatial region 28.1–38.1° N by 91.7–101.7° E. We find that the wavelet transform is an effective method to analyse time series which contain nonstationary power, and identify here anomalous power both in the time and frequency domain. The results show that, over the two years, the relative wavelet power spectrum (RWPS) showed anomalous RWPS variations in nine cases: two of these were followed by earthquakes; seven were not. One of the two RWPS anomalies that were followed by an earthquake was in the southern area of the epicentre of the Yushu earthquake, with the RWPS anomaly starting to appear around 29 March 2010, decreasing in the period before the earthquake, and completely gone by 14 April 2010. The Yushu earthquake was the only strong earthquake within the region and two-year time period chosen, so the abnormal change of RWPS during this time period was possibly associated with the Yushu earthquake.

Intercomparison of SSU temperature data records with Lidar, GPS RO, and MLS observations

A consistent long‐term stratospheric temperature observation is a crucial part for global change studies. In this study, we compare newly developed Stratospheric Sounding Unit (SSU) layer‐averaged stratospheric temperatures with lidar, GPS Radio Occultation (RO), and Microwave Limb Sounder (MLS) stratospheric temperature profiles, each with unique error characteristics, spatial and temporal coverage, and observational principles. The vertical temperature profiles are converted into SSU‐equivalent layer temperatures, and diurnal correction is applied to adjust the observations into an identical observational time. The comparison is carried out on pentad grids with a 2.5° latitude × 2.5° longitude resolution. Grid‐by‐grid comparison of SSU and MLS gives the mean differences between them from August 2004 to May 2006 of ‐0.041, 0.169, and ‐0.447 K with standard deviation of 1.180, 1.485, and 1.715 K for SSU channels 1–3, respectively. The correlation of GPS RO and SSU brightness temperature anomalies are 0.943, 0.877, and 0.699 from channels 1–3, respectively, and the correlation decreases with altitude (channels). SSU channel 3 brightness temperature anomalies are correlated with lidar observations with correlation coefficients of 0.839 at the Hohenpeissenberg Observatory in Germany and 0.725 at the Observatoire de Haute‐Provence in France. Overall, the comparison results do not show that the newly developed SSU data set is significantly different from any of the three independent data sets based on known limitations and advantages of these data sets.

Geochemical Indications of Possible Gas Hydrates in the Northeastern South China Sea

Abstract Gas hydrate, mainly composed of hydrocarbon gas and water, is considered to be a clean energy in the 21st century. Many indicators such as BSRs (Bottom-Simulating Reflections), which are thought to be related to gas hydrate, are found in the South China Sea (SCS) in recent years. The northeastern part of the SCS is taken as one of the most potentials in the area by many scientists. It is situated in the conjunction of the northern divergent continental margin and the eastern convergent island margin, whose geological settings are much preferable for gas hydrate to occur. Through this study, brightness temperature anomalies recorded by satellite-based thermal infrared remotely sensed images before or within the imminent earthquake, the high content of hydrocarbon gas acid-degassed from subsurface sediment and the high radioactive thermoluminescence value of subsurface sediment were found in the region. Sometimes brightness temperature anomalies alone exist in the surrounding of the Dongsha Islands. The highest content of hydrocarbon gas amounts to 393 μL methane per kilogram sediment and the highest radioactive thermoluminescence value is 31752 unit; their geometric averages are 60,5 μL/kg and 2688.9 unit respectively. What is more inspiring is that there are three sites where the methane contents are up to 243, 268 and 359 μL/kg and their radioactive thermoluminescence values are 8430, 9537 and 20826 unit respectively. These three locations are just in the vicinity of one of the highest confident BSRs identified by predecessors. Meanwhile, the anomalies are generally coincident with other results such as headspace gas anomaly in the sediment and chloride anomaly in the interstitial water in the site 1146 of Leg 184. The above-mentioned anomalies are most possibly to indicate the occurrence of gas hydrate in the northeastern SCS.

Assessments of F16 Special Sensor Microwave Imager and Sounder Antenna Temperatures at Lower Atmospheric Sounding Channels

The main reflector of the Special Sensor Microwave Imager/Sounder (SSMIS) aboard the Defense Meteorological Satellite Program (DMSP) F-16 satellite emits variable radiation, and the SSMIS warm calibration load is intruded by direct and indirect solar radiation. These contamination sources produce antenna brightness temperature anomalies of around 2 K at SSMIS sounding channels which are obviously inappropriate for assimilation into numerical weather prediction models and remote sensing retrievals of atmospheric and surface parameters. In this study, antenna brightness temperature anomalies at several lower atmospheric sounding (LAS) channels are assessed, and the algorithm is developed for corrections of these antenna temperature anomalies. When compared against radiative transfer model simulations and simultaneous observations from AMSU-A aboard NOAA-16, the SSMIS antenna temperatures at 52.8, 53.6, 54.4, 55.5, 57.3, and 59.4 GHz after the anomaly correction exhibit small residual errors (<0.5 K). After such SSMIS antenna temperatures are applied to the National Center for Environmental Prediction Numerical Weather Prediction (NWP) model, more satellite data is used and the analysis field of the geopotential height is significantly improved throughout troposphere and lower stratosphere. Therefore, the SSMIS antenna temperatures after the anomaly correction have demonstrated their potentials in NWP models.