Meteorological Satellite Research Articles

FINER-SCALE MONITORING AND ANALYSIS OF 2016–2021 DUST STORMS DISASTERS IN CHINA BASED ON HIMAWARI-8 SATELLITE IMAGERIES

Abstract. Today, remote sensing is one of the important techniques for natural disasters monitoring. Using the Himawari-8 new generation of geostationary meteorological satellite image data, we identified and monitored 2016–2021 dust storm disasters in China at the finer scale, and compared the object-oriented classification, supervised and unsupervised classification methods. Taking the dust storm on May 5, 2016 as example, the average accuracy and kappa coefficient of the supervised classification were 0.78 and 0.68, while the object-oriented classification were 0.81, 0.73. We used the object-oriented classification method to classify the 2016-2021 dust storm incidents across China. In 2021, the largest area affected by sandstorms was about 68.33km2, the smallest area was about 2.08km2, and the average area was about 22.25km2. During 2016–2021, the most severe and wide-spreading sandstorm occurred on March 14–18 2021. The dust storm first appeared in northwestern Mongolia on March 14, and then spread southeast to China. The dust area covered Beijing on the 15th, and then spread from eastern China to the western regions of North China and Northeast China, including most of the Hexi Corridor. On the 16th and 17th, the influence of the dust storms narrowed to central and western Inner Mongolia, northern Gansu and southern Mongolia. The dust storm gradually disappeared on the 18th.

Assessment of Geo-Kompsat-2A Atmospheric Motion Vector Data and Its Assimilation Impact in the GEOS Atmospheric Data Assimilation System

Korea’s second geostationary meteorological satellite, Geo-Kompsat-2A (Geostationary-Korean Multi-Purpose Satellite-2A, GK2A), was successfully launched on 4 December 2018. GK2A generates Atmospheric Motion Vectors (AMVs) every 10 min in the full disk area. This data has been disseminated via Global Telecommunication System (GTS) since 25 October 2019. This article evaluates the quality of GK2A AMVs in the Goddard Earth Observing System (GEOS) atmospheric data assimilation system (ADAS). The data show slow wind speed biases at 200–300 hPa and 600–800 hPa in the northern and southern hemispheres. These biases are caused by observation height assignment errors near jet streams. The Equivalent Blackbody Temperature (EBBT) method of GK2A tends to assign clouds at higher altitude, which mainly causes slow wind speed biases, especially in the lower atmosphere. The IR/WV intercept method of GK2A assigns clouds slightly lower in the atmospheric layers below the altitude of 400 hPa, which causes positive biases. Quality control (QC) criteria to select the most suitable GK2A AMV data for assimilation are presented based on these quality assessments. A new QC criterion utilizing height errors within the GEOS ADAS is introduced to exclude data with slow wind speed biases and large errors. GEOS forecast accuracy is slightly improved after assimilating GK2A AMVs along with other conventional, radiance, and satellite winds which include AMVs made by the Himawari-8 satellite in nearly the same observational area of GK2A. Additionally, the present work shows that GEOS forecasts can be significantly improved, especially in the tropics and southern hemisphere after assimilating GK2A data in the absence of Himawari-8 AMVs. This study demonstrates that GK2A AMV data is a valuable data source to enhance the robustness of GEOS ADAS.

Potential role of high-altitude wetlands in preservation of plant biodiversity under changing climatic conditions in the arid Eastern Pamir

High-altitude wetlands in the Eastern Pamir are habitat islands located in an arid landscape. They play several important roles, including that of water sources and forage grounds for people and their livestock. Considering high variation in micro-relief and complexity of water supplies observed in these wetlands, we assessed their potential to play a role of refugia for species whose present ranges may be limited by climate changes projected for Central Asia. Such species currently occur typically in less moist habitats, often located at lower elevations (e.g. steppe and meadow species). To perform this assessment, we analyzed current spatial structure and species diversity of vegetation accompanying selected water bodies in the studied catchments, identified present changes in temperature and precipitation in these catchments and analyzed alterations in area of lakes and small water bodies located in the vegetation mosaic. Presently, the biodiversity of the studied wetlands comprises 110 species of vascular plants from 26 families, forming ten distinct vegetation types, over 40 recorded species are typical for steppes and various types of alpine meadows. Both meteorological data and satellite data on open water area indicated that with growing temperatures, areas of the studied lakes and small water bodies in the vegetation mosaic increase. This effect is probably caused by thawing of ground ice and permafrost, whose occurrence helps mitigate the effects of the ongoing warming and lessening precipitation. Thus, the wetlands may temporarily maintain their cold, wet and mesic characteristics under the changing climatic conditions and, as such, potentially become refugia for steppe and meadow species. However, after the period of intense thawing, lowering of groundwater level and huge surface water fluctuations may occur, potentially resulting in a decrease in the total vegetated area on lacustrine and riverine terraces in the Eastern Pamir.

Comparative Analysis of Binhu and Cosmic-2 Radio Occultation Data

Global Navigation Satellite System (GNSS) radio occultation (RO) technology has been widely used in Earth atmospheric detection and has a significant impact on numerical weather prediction (NWP), climate detection, and other fields. Cosmic-2 LEO-1 (C2E1) is a well-known RO data provider; however, its observations are confined to 45°S and 45°N. Recently, the Binhu meteorological observation test satellite (BH) has provided global coverage of RO data, including refractivity, specific humidity, and temperature data. In this study, RO data from BH and C2E1 are analyzed and compared from 8 February 2022 to 17 February 2022. Employing the European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis (ERA-5) data as a reference, both BH and C2E1 RO data agree with the ERA data, with the refractivity, temperature, and specific humidity profiles reflecting the real conditions of the natural atmosphere. In addition, BH data are comparable to C2E1 data at low and middle latitudes (0–45°), and BH data at middle and high latitudes (45–90°) are of better quality than those at low and middle latitudes (0–45°). For example, without considering the errors introduced by the interpolation of the ERA-5 data for comparative analysis, the BH refractivity profiles show a mean absolute bias of 0.73 N at low and middle latitudes and only 0.23 N at middle and high latitudes, and that for BH specific humidity profiles at middle and high latitudes is 0.015 g/kg, which is only half of that at low and middle latitudes. The BH temperature and specific humidity data show promising data accuracy. Therefore, BH RO data may provide important supplementary data at higher latitudes and may improve future NWPs through assimilation.

Thermospheric Conditions Associated With the Loss of 40 Starlink Satellites

AbstractWe analyzed far ultraviolet data from Defense Meteorological Satellite Program (DMSP)/Special Sensor Ultraviolet Spectrographic Imager (SSUSI) and Thermosphere Ionosphere Mesosphere Energetics and Dynamics (TIMED)/Global Ultraviolet Imager (GUVI) and found significant changes in the thermospheric density and composition during the 3–5 February 2022 storm when 40 Starlink satellites started to re‐enter the atmosphere associated with increased neutral drag at an altitude around 210 km. The standard NRLMSISE‐00 model predicts only ∼5% increase in neutral density at 210 km. TIMED/GUVI observations showed a clear increase in the thermospheric N2/O column density ratio and an increase in the nitric oxide (NO) column density, indicating high thermospheric density, and temperature. DMSP SSUSI 130.4 nm limb data showed a clear increase in neutral density in the low latitude region (−30°–30°). On the duskside (17:32 LT), the density increase (compared to the pre‐storm density) is around 11%–18% at 210 km and around 18%–26% at 520 km. On the dawn‐side (05:19 LT), the density increase is more significant, 40%–59% at 210 km and ∼300% at 520 km. There are a few reasons for the dawn‐dusk asymmetry in the relative density increase: (a) the pre‐storm background density or heat capacity is lower on the dawn‐side than that on the duskside, (b) stronger auroral heating was observed on the dawnside based on analysis of auroral images, (c) the thermosphere density enhancement partially recovered when the disturbance co‐rotated with the Earth from dawn to dusk side due to enhanced infrared cooling by NO. Note that the neutral density increase is expected to be much higher at high latitudes where the neutral disturbances are initiated.

Mathematical modeling of distribution of the chemicals and the solid phase of tailings deposited on the mountain gorge slopes near the Fiagdonskoye tailings, North Ossetia-Alania

Introduction. Tailings are a source of chemicals and finely dispersed solid tailings carried by air currents over considerable distances. Purpose of research. Creation of a predictive map of the concentration of chemicals and the solid phase of tailings carried by air currents in mountain landscapes, and a description of the pattern of their distribution along the slopes of mountain gorges near the Fiagdon tailing dump. With the help of a simplified aerodynamic model, the processes of aerosol dispersion of the solid phase of tailings and chemical elements in mountain landscapes from the Fiagdon tailing dump, located in the Kurtat gorge, North OssetiaAlania, Russia, are considered. These tailings contain lead, zinc, arsenic and other chemical elements. The mathematical model takes into account the complex mountainous landscape of the area, the characteristic wind roses, as well as the transport and sedimentation of the solid phase of the tailings. A forecast of the distribution of chemicals along the slopes of the Kurtat gorge, adjacent gorges and in the floodplain of the Khanikom-Don River (the right side tributary of the Fiagdon River) is presented, local wind roses are calculated, which are compared with field measurements and with measurements taken by meteorological satellites. The data of meteorological satellites of the American aerospace agency NASA are used: satellites Terra, Aqua, Geos, as well as the Copernicus global monitoring system for the environment and security of the European Space Agency. These data are averaged over an area of the order of 10 × 10 km or more, and the wind roses obtained on their basis characterize the study area on an average. The comparison of the simulations and these wind roses shows a qualitative agreement. Comparison of the wind rose measured by the weather station and the wind rose obtained from the computational model for this area shows good agreement. The results of the simulations show that the important factors determining the spatial distribution of the tailings and chemical elements deposited on the slopes of the solid phase in mountain landscapes are the features of the terrain, the characteristic direction of the wind speed and the atmospheric turbulence. The forecast of concentrations of the solid phase of tailings and chemical elements from the Fiagdon tailing dump in the mountain villages of Kakadur, Fazikau, Lamardon, Dargavs, Staraya Saniba, Karmadon, Verkhny Fiagdon is presented. Based on the results of simulations of aerodynamic processes in the atmosphere of the Kurtat Gorge, North OssetiaAlania, predictive map of the distribution of chemicals and the solid phase of tailings from source in the area of the Fiagdon is built. Chemicals and the solid phase of tailings are carried out in the Kurtat Gorge mainly in the eastern and northern directions, while in the settlement of Verkhny Fiagdon, the concentrations remain insignificant and lower than the MPC. The model can be used to estimate the concentration of chemicals and the solid phase of tailings carried by air currents in mountain landscapes and to describe their distribution patterns along the slopes of mountain gorges.

Климато-географический анализ влияния температурного режима на деградацию ледников бассейна реки Зерафшан

The problems of global climate warming in general and their impact on the melting of glaciers in Tajikistan are analyzed. Based on long-term observations of weather stations located in different zones of the Zerafshan River basin and comparison of changes in average air temperature over two decades (1960-1990 and 1990-2020), the process of climatic warming in the area is considered. Using long-term meteorological data (air temperature) and satellite images that recorded the degradation of the linguistic part of the Zerafshan, Rama, Rossinj, Touro, Dihadang and GGP glaciers, the linear regression dependence of the retreat of these glaciers on temperature growth is investigated.

A Simple Band Ratio Library (BRL) Algorithm for Retrieval of Hourly Aerosol Optical Depth Using FY-4A AGRI Geostationary Satellite Data

The Advanced Geostationary Radiation Imager (AGRI) is one of the primary payloads aboard the FY-4A geostationary meteorological satellite, which can provide high-frequency, wide coverage, and multiple spectral channel observations for China and surrounding areas. There are currently few studies on aerosol optical depth (AOD) inversion from FY-4A AGRI data. Based on AGRI data, a new land AOD retrieval algorithm called the band ratio library (BRL) algorithm was proposed in this study. The monthly average surface reflectance band ratio library was established after obtaining the relationship of band surface reflectance ratio from the MODIS combined AOD dataset. In order to calculate the hourly AOD, look-up tables (LUT) for the various aerosol models were constructed using the 6SV model. We quantitatively compared AOD produced from AGRI data with AERONET ground observations to validate the BRL algorithm. AGRI-retrieved AOD is in good agreement with AOD measured by AERONET, which has a correlation coefficient of R is 0.84, the linear regression function is AODAGRI = 0.80 ∗ AODAERONET − 0.004, the root-mean-square error (RMSE) is 0.16, and approximately 60% of the AGRI AOD results fall within the uncertain range of AOD = ±(0.2 × AODAERONET + 0.05). A cross-comparison was made with the MODIS AOD product provided by NASA. The comparison and verification show the proposed algorithm has a good accuracy of land AOD estimation from AGRI data.

Performance Evaluation and Noise Mitigation of the FY-3E Microwave Humidity Sounder

Fengyun-3E, which is equipped with MicroWave Humidity Sounder 2 (MWHS-2) for detecting both temperature and humidity, is China’s latest polar orbiting meteorological satellite and China’s first satellite in early-morning orbit. The observation bias and observation error characteristics of MWHS-2 are evaluated by using ERA-5 as the background field. The results show that the biases range from −4 to 2 K, and the observation errors are within 1.5 K except for the window channels. A further analysis of the dependence on the scanning angles indicates that observation errors for the window channels and the 118-GHz channels increase with decreasing scanning angles. Observation errors of the window channels and water vapor channels are also latitudinally dependent, and the maximum errors in the high latitudes reach 2.0 K, while those in the tropical regions are approximately 0.8 K. In addition, the observed brightness temperature of FY-3E MWHS-2 is accompanied by striping noises along the track and high-frequency oscillation noises along the scanlines. The noise mitigation results show that both noises are approximately 0.2 K.

Spatio-Temporal Dynamics and Driving Forces of Multi-Scale CO2 Emissions by Integrating DMSP-OLS and NPP-VIIRS Data: A Case Study in Beijing-Tianjin-Hebei, China

The emission of greenhouse gases, especially CO2, is the main factor causing global warming. Due to incomplete statistical data on energy consumption at and below the urban scale of Beijing-Tianjin-Hebei (BTH), in this study, Defense Meteorological Satellite Program Operational Linescan System (DMSP-OLS) and Suomi National Polar-orbiting Partnership Visible Infrared Imaging Radiometer Suite (NPP-VIIRS) data were combined, and a neural network model and weighted average method based on DN (Digital Number) value were used to obtain CO2 emissions at the municipal and county scales with a resolution of 1 km × 1 km from 2000–2019. Next, a spatial-temporal analysis model and spatial econometric model were used to study the CO2 emissions at different scales of BTH. This study also solved the problem that STIRPAT analysis cannot be carried out due to insufficient urban statistical CO2 emissions data. The results show that the energy CO2 emissions in BTH present a distribution pattern of “East greater than West”, with a trend of first rising and then slowing down. Moreover, the rapid growth areas are mainly located in Chengde and Tianjin. The degree of regional spatial aggregation decreased year by year from 2000–2019. Population, affluence and technology factors were positively correlated with CO2 emissions in Tianjin and Hebei. For Beijing, in addition to foreign investment, factors such as urbanization rate, energy intensity, construction and transportation factors all contributed to the increase in CO2 emissions. Among them, the growth of population is the main reason for the increase of CO2 at the urban scale in BTH. Finally, based on the research results and the specific situation of the cities, corresponding policies and measures are proposed for the future low-carbon development of the cities.

PECA-FY4A: Precipitation Estimation using Chromatographic Analysis methodology for full-disc multispectral observations from FengYun-4A/AGRI

Near-real-time precipitation estimates based on geostationary meteorological satellites are of great importance for monitoring hydrology-related disasters. Currently, the contributions of multispectral infrared observations to precipitation estimation have not been fully understood thus far. Motivated by estimating precipitation based on real-time data from geostationary satellites, this study proposes the Precipitation Estimation using Chromatographic Analysis (PECA) methodology based on full-disc observations from Advanced Geosynchronous Radiation Imager (AGRI) of FengYun-4A (FY-4A). Compared with PERSIANN-CCS and FY4AQPE-Official, the results of PECA (PECA-FY4A) are comprehensively evaluated against the GSOD (global gauge data), CMS (Chinese gauge data) and IMERG-LR (micro-wave precipitation products) for the period from January, 2019 to November, 2021. The findings and conclusions mainly include but are not limited to the following: (1) PECA clearly reveals that T12, ∆T10−14, ∆T7−14, and ∆T7−8 are effective for estimating precipitation volumes, with strong correlation to precipitation volumes but significantly less redundant information. (2) IR band differences are still sensitive to heavy precipitation although the abilities of single IR bands have become insufficient. For example, ∆T7−8 are sensitive to heavy precipitation in the eye walls of the typhoons. (3) The evaluation results show that PERSIANN-CCS generally outperforms FY4AQPE-Official, while PECA-FY4A features significant improvements over PERSIANN-CCS (the international benchmark of IR precipitation products) in terms of CC (∼10%, ∼100%, and ∼ 21%), RMSE (∼20%, ∼36%, and ∼ 11%), and CSI (∼25%, ∼100%, and ∼ 12%) against IMERG-LR, CMS and GSOD, respectively. The findings are expected to considerably contribute towards extending the current three-geostationary-satellite-series network (GOES, Meteosat, and Himawari) in PERSIANN-CCS to a four-geostationary-satellite-series network (by adding FY-4A and FY-4B) using PECA with higher quality, especially over the Asia-Pacific areas.

Remote Sensing-Based Prediction of Temporal Changes in Land Surface Temperature and Land Use-Land Cover (LULC) in Urban Environments

Pakistan has the highest rate of urbanization in South Asia. The climate change effects felt all over the world have become a priority for regulation agencies and governments at global and regional scales with respect assessing and mitigating the rising temperatures in urban areas. This study investigated the temporal variability in urban microclimate in terms of land surface temperature (LST) and its correlation with land use-land cover (LULC) change in Lahore city for prediction of future impact patterns of LST and LULC. The LST variability was determined using the Landsat Thermal Infrared Sensor (TIRS) and the land surface emissivity factor. The influence of LULC, using the normalized difference vegetation index (NDVI), the normalized difference building index (NDBI), and the normalized difference bareness index (NDBaI) on the variability LST was investigated applying Landsat Satellite data from 1992 to 2020. The pixel-level multivariate linear regression analysis was employed to compute urban LST and influence of LULC classes. Results revealed that an overall increase of 41.8% in built-up areas at the expense of 24%, 17.4%, and 0.4% decreases in vegetation, bare land, and water from 1992–2020, respectively. Comparison of LST obtained from the meteorological station and satellite images showed a significant coherence. An increase of 4.3 °C in temperature of built-up areas from 1992–2020 was observed. Based on LULC and LST trends, the same were predicted for 2025 and 2030, which revealed that LST may further increase up to 1.3 °C by 2030. These changes in LULC and LST in turn have detrimental effects on local as well as global climate, emphasizing the need to address the issue especially in developing countries like Pakistan.

Urban-Rural Fringe Long-Term Sequence Monitoring Based on a Comparative Study on DMSP-OLS and NPP-VIIRS Nighttime Light Data: A Case Study of Shenyang, China.

Urban–rural fringes, as special zones where urban and rural areas meet, are the most sensitive areas in the urbanization process. The quantitative identification of urban–rural fringes is the basis for studying the social structure, landscape pattern, and development gradient of fringes, and is also a prerequisite for quantitative analyses of the ecological effects of urbanization. However, few studies have been conducted to compare the identification accuracy of The US Air Force Defence Meteorological Satellite Program’s (DMSP) and the Visible Infrared Imaging Radiometer Suite (VIIRS) nighttime light data from the same year, subsequently enabling long time series monitoring of the urban–rural fringe. Therefore, in this study, taking Shenyang as an example, a K-means algorithm was used to delineate and compare the urban–rural fringe identification results of DMSP and VIIRS nighttime light data for 2013 and analyzed the changes between 2013 and 2020. The results of the study showed a high degree of overlap between the two types of data in 2013, with the overlap accounting for 75% of the VIIRS data identification results. Furthermore, the VIIRS identified more urban and rural details than the DMSP data. The area of the urban–rural fringe in Shenyang increased from 1872 km2 to 2537 km2, with the growth direction mainly concentrated in the southwest. This study helps to promote the study of urban–rural fringe identification from static identification to dynamic tracking, and from spatial identification to temporal identification. The research results can be applied to the comparative analysis of urban–rural differences and the study of the ecological and environmental effects of urbanization.

Association between outdoor light at night, sleep duration, and sleep quality in the US based-Health Professionals Follow-Up Study

Background and aim Artificial illumination on the Earth’s surface has increased over time. Light plays a central role in regulating circadian rhythm, the body’s internal clock, mainly by inhibiting the production of the sleep promoting hormone melatonin. The increase in artificial light at night (LAN) has negative consequences for human health, as circadian disruption is a risk factor for weight gain, cardiovascular disease, and cancer. We evaluated whether exposure to outdoor LAN is associated with sleep duration and sleep quality in the US based-Health Professionals Follow-up Study (HPFS). Methods HPFS consists of 51,529 men enrolled in 1986 aged 40-75, our study is limited to men who reported a home address at baseline. We estimated participants’ LAN with 1 km2 resolution using data from the US Defense Meteorological Satellite Program’s Operational Linescan System to geocoded address histories. Participants self-reported their sleep duration in 1987, 2000, and 2008 and answered 5 questions regarding their sleep quality in 2004. We used multinomial logistic regression to estimate odds ratios and 95% confidence intervals adjusting for participants’ age, population density, and neighborhood SES (nSES). Results The mean level of LAN at baseline was 30.8 nW/cm2/sr. The distribution of age was similar, but population density and nSES differed across levels of LAN. An interquartile increase in LAN was associated with increased odds of sleeping ≤6 hours compared to 7-8 hours (OR: 1.20, 95%CI: 1.12,1.29), never feeling rested compared to most of the time (OR: 1.18, 95%CI: 1.05,1.34), and difficulty falling asleep (OR: 1.18, 95%CI: 1.02,1.36). Conclusion We found positive associations between LAN and measures of sleep duration and sleep quality in a large cohort of older adult men. This aligns with findings from other studies that living in areas of greater LAN has a negative impact on sleep health.

Physical Environmental Hazards and Major Depression in Older U.S. Adults: The Health and Retirement Study

Background and Aim. Major depression is a leading cause of disability. Although much research focuses on individual risk factors and interventions, environmental risk factors may also play an important role in major depression. We quantified associations of road noise and light at night with prevalent major depression. Methods. We used data from the 2008-2016 biennial interviews of the Health and Retirement Study, a nationally-representative cohort of older adults in the United States. Major depression status was determined using the Composite International Diagnostic Interview Short Form, with a cutoff of ≥5. We estimated average road noise and light at night for the year prior to each interview at participant residences using the Department of Transportation Road Noise Model and satellite imagery from the Defense Meteorological Satellite Program, respectively. To estimate prevalence ratios, we used a modified Poisson regression model that accounted for sampling weights and design effects, adjusted for demographic characteristics, individual and neighborhood SES, time, co-exposures, and geographic area. Results. There were 23,874 participants with complete data. The mean baseline age was 63 years, 53% were female, 74% were Non-Hispanic White, and 15% ever reported major depression. Compared to the lowest exposure quartiles, those in the highest quartiles of noise and light exposure were more likely to be Black, have less than a high school education and reside in an urban area. In fully adjusted models, interquartile differences in both noise and light were suggestively associated with an approximately 10% greater prevalence of major depression (PR per IQR of noise: 1.08, 95% CI: 0.98, 1.20; and light: 1.12, 95%CI: 0.96, 1.31). Conclusion. Exposure to noise and light at night may increase depression in older adults, possibly by disrupting health sleep. Modifying physical hazards in the environment may be a way to reduce the burden of depression. Keywords. Noise, Light, Depression

External exposome and sleep outcomes in the Nurses’ Health Study

BACKGROUND AND AIM: Multiple environmental exposures, including greenness, light-at-night, and air pollution, have been associated with sleep. However, these associations have not been investigated using large longitudinal cohorts with repeated measures. We examined the association between multiple environmental exposures and sleep duration and quality in the Nurses’ Health Study (NHS). METHODS: Between 2000 and 2014, 81,202 NHS participants self-reported sleep duration on biennial questionnaires, and in 2000, 81,294 NHS participants self-reported sleep quality. We estimated annual average greenness using Landsat Normalized Difference Vegetation Index (NDVI) in 270m buffers, annual average light-at-night (LAN) from the U.S. Defense Meteorological Satellite Program’s Operational Linescan System, and predicted PM2.5 from validated nationwide spatiotemporal models at each residential address. Due to evidence of non-linearity, we modeled exposures in quintiles. Sleep duration was dichotomized (≥7 hours; <7 hours) and modeled with generalized estimating equations. Sleep quality was dichotomized (difficulty sleeping none/a little/some of the time; difficulty sleeping a good bit of/all the time) and modeled using logistic regression. We estimated odds ratios (OR) of adverse sleep outcomes comparing the highest and lowest quintile and 95% confidence intervals (CI) for each exposure. We adjusted for individual-level risk factors and included all exposures simultaneously for mutual adjustment. RESULTS: In mutually-adjusted models, higher annual average residential greenness exposure was associated with suggestive lower odds of short sleep duration (OR: 0.97 95% CI: 0.94, 1.00) with similar associations for poor sleep quality (OR: 0.97; 95% CI: 0.90, 1.00). Higher exposure to LAN was associated higher odds of with short sleep duration (OR: 1.18 95% CI: 1.14, 1.23) and poorer sleep quality (OR: 1.12; 95% CI: 1.03, 1.22). Higher exposure to PM2.5 was not associated with sleep outcomes. CONCLUSION: In the NHS cohort, lower residential greenness and higher LAN exposures were associated with lower self-reported sleep duration and quality.

A New Post-hoc Flat Field Measurement Method for the Solar X-Ray and Extreme Ultraviolet Imager Onboard the FengYun-3E Satellite

Extreme ultraviolet (EUV) observations are widely used in solar activity research and space weather forecasting since they can observe both the solar eruptions and the source regions of the solar wind. Flat field processing is indispensable to remove the instrumental non-uniformity of a solar EUV imager in producing high-quality scientific data from original observed data. FengYun-3E (FY-3E) is a meteorological satellite operated in a Sun-synchronous orbit, and the routine EUV imaging data from the Solar X-ray and Extreme Ultraviolet Imager (X-EUVI) onboard FY-3E has the characteristic of concentric rotation. Taking advantage of the concentric rotation, we propose a post-hoc flat field measurement method for its EUV 195 Å channel in this paper. This method removes the small-scale and time-varying component of coronal activities by taking the median value for each pixel along the time axis of a concentric rotation data cube, and then derives the large-scale and invariable component of the quiet coronal radiation, and finally generates a flat field image. The flat field can be generated with cadences from hundreds of minutes (one orbit) to several days. Higher flat field accuracy can be achieved by employing more data. Further analysis shows that our method is able to measure the instrumental spot-like non-uniformity possibly caused by contamination on the detector, which mostly disappears after the in-orbit self-cleaning process. It can also measure the quasi-periodic grid-like non-uniformity, possibly from the obscuration of the support mesh on the rear filter. After flat field correction, these instrumental non-uniformities from the original data are effectively removed. Moreover, the X-EUVI 195 Å data after dark and flat field corrections are consistent with the 193 Å imaging data from the Atmospheric Imaging Assembly onboard the Solar Dynamics Observatory, verifying the suitability of the method. The post-hoc method does not occupy observation time, which is advantageous for space weather operations. Our method is not only suitable for FY-3E/X-EUVI but also a candidate method for the flat field measurement of future solar EUV telescopes.

Evaluating the Public Value of Improving Early Detection Accuracy of Cumulonimbus Using a Geostationary Satellite in South Korea

The Cheollian Satellite 2A called Geo-Kompsat-2A is a geostationary meteorological satellite operated by Korea Meteorological Administration for weather and space-weather observation purposes. This article seeks to evaluate the public value arising from improving the accuracy of early detection of cumulonimbus using Geo-Kompsat-2A. To this end, an economic technique called contingent valuation (CV) is adopted taking a three step approach. In step 1, a CV questionnaire was prepared for deriving the willingness to pay (WTP) for the improvement. In step 2, a nationwide survey of 1000 households was conducted using the CV questionnaire and employing person-to-person interviews with household visits. In step 3, the average WTP was estimated. The yearly household average WTP was KRW 2143 (USD 1.91), securing statistical significance. Adjusting this value to suit the entire population of South Korea produces a yearly public value of KRW 44.1 billion (USD 39.3 million). Although it is not easy to accurately evaluate the costs involved in the improvement, the results of conducting a pilot cost-benefit analysis suggest that the improvement secures social desirability.

A historical re-analysis of the calamitous midget typhoon passing through Hong Kong on 18 September 1906 and its storm surge impact to Hong Kong

A typhoon passed through Hong Kong suddenly and unexpectedly on 18 September 1906 (the “Typhoon 1906”) and had a disastrous impact on Victoria Harbour and its surroundings in just a couple of hours. Since the year 1906 was the “Bingwu” year in the Chinese calendar, the typhoon is also known historically as the “Bingwu Typhoon”. Tremendous loss of lives and property resulted, and the shipping and fishing communities were devastated. Two mysteries arising from this calamitous typhoon have existed to date: 1) Why the Hong Kong Observatory was not able to provide any forewarning? 2) whether the storm surge reported in some contemporary records is entirely credible? This paper will focus on both of these.In this paper, we re-analyse historical weather observations recorded in various historical documents and estimate the possible storm size, intensity and track of Typhoon 1906 using tropical cyclone models. Based on the re-analyses, the storm surges, storm tides and wave heights in Hong Kong are also estimated using storm surge and wave models. The results reveal that Typhoon 1906 was a midget typhoon, with a radius of maximum winds of 11 km or smaller, during its passage through Hong Kong. This explains why it was technically impossible for a forewarning to be given at that time when real-time weather observations from ships, meteorological satellites and radars were non-existent. We also estimate that the maximum storm surges (storm tides) in Hong Kong were not higher than 0.82 m (2.43 mCD) and 1.98 m (4.15 mCD) in Victoria Harbour and Tolo Harbour respectively. These figures are found to be limited by the intensity and the storm size of the typhoon. Therefore, we conclude that the previously documented storm surge figures are not supported by the present study.

Application of artificial intelligence technology in typhoon monitoring and forecasting

In recent years, with the emergence of new artificial intelligence (AI) technology and more observational data from automatic meteorological stations, radars and satellites, the deep learning has very broad application scenarios in the context of meteorological big data. The deep learning has powerful data learning ability and feature capturing ability of complex structures, which has now occupied an important position in the meteorological field and also become a hot topic in meteorological research. Especially, AI has shown great potential advantages in image recognition, which can provide new ideas and new directions for typhoon monitoring and forecasting. In this study, the data used include the typhoon best track data set provided by the China Meteorological Administration and the Himawari-8 and FY4 satellite image data from 2005 to 2020. We use the deep learning model to conduct the typhoon vortex identification, the determination of typhoon location and intensity, and the detection of typhoon intensity mutation with AI techniques. The main research content includes a typhoon vortex identification model based on deep image target detection, an intelligent typhoon intensity determination model based on image classification and retrieval, and a typhoon rapid intensification identification model. Then, a typhoon intelligent monitoring and forecasting system is constructed. The results show that the system can correctly identify typhoon vortices above the strong tropical storm grade in a percentage of 88.6%. The mean absolute error (MAE) and Root mean square deviation (RMSE) of typhoon intensity estimation are 3.8 m/s and 5.05 m/s, respectively, and the comprehensive accuracy of rapid intensification estimation of annual independent samples reaches 92.0%. The system is capable of performing the automatic identification, location and intensity determination, and intelligent tracking of tropical cyclones in real time by using high spatial and temporal resolution satellite images. This study may help further improve the operational techniques for typhoon monitoring and forecasting.