https://data.fs.usda.gov/research/pubs/iitf/ja_iitf_2018_Roman.pdf

NASA's Black Marble Nighttime Lights Product Suite

NASA's Black Marble nighttime lights product suite (VNP46) is available at 500m resolution since January 2012 
with data from the Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB) onboard the Suomi 
National Polar-orbiting Platform (SNPP). The retrieval algorithm, developed and implemented for routine global 
processing at NASA's Land Science Investigator-led Processing System (SIPS), utilizes all high-quality, cloud-free, 
atmospheric-, terrain-, vegetation-, snow-, lunar-, and stray light-corrected radiances to estimate daily nighttime 
lights (NTL) and other intrinsic surface optical properties. Key algorithm enhancements include: (1) lunar irradiance 
modeling to resolve non-linear changes in phase and libration; (2) vector radiative transfer and lunar 
bidirectional surface anisotropic reflectance modeling to correct for atmospheric and BRDF effects; (3) geometric- 
optical and canopy radiative transfer modeling to account for seasonal variations in NTL; and (4) temporal 
gap-filling to reduce persistent data gaps. Extensive benchmark tests at representative spatial and temporal 
scales were conducted on the VNP46 time series record to characterize the uncertainties stemming from upstream 
data sources. Initial validation results are presented together with example case studies illustrating the 
scientific utility of the products. This includes an evaluation of temporal patterns of NTL dynamics associated 
with urbanization, socioeconomic variability, cultural characteristics, and displaced populations affected by 
conflict. Current and planned activities under the Group on Earth Observations (GEO) Human Planet Initiative are aimed at evaluating the products at different geographic locations and time periods representing the full 
range of retrieval conditions.

Refinements to NASA's Black Marble Nighttime Lights Product Suite

With the economic growth and increasing urbanization in the last three decades, the air quality over China has continuously degraded, which poses a great threat to human health. The concentration of fine particulate matter (PM2.5) directly affects the mortality of people living in the polluted areas where air quality is poor. The Beijing-Tianjin-Hebei (BTH) region, one of the well organized urban regions in northern China, has suffered with poor air quality and atmospheric pollution due to recent growth of the industrial sector and vehicle emissions. In the present study, we used the back propagation neural network model approach to estimate the spatial distribution of PM2.5 concentration in the BTH region for the period January 2014–December 2016, combining the satellite-derived aerosol optical depth (S-DAOD) and meteorological data. The results were validated using the ground PM2.5 data. The general method including all PM2.5 training data and 10-fold cross-method have been used for validation for PM2.5 estimation (R2 = 0.68, RMSE = 20.99 for general validation; R2 = 0.54, RMSE = 24.13 for cross-method validation). The study provides a new approach to monitoring the distribution of PM2.5 concentration. The results discussed in the present paper will be of great help to government agencies in developing and implementing environmental conservation policy.

/pdf/spatio-temporal-pattern-estimation-of-pm2-5-in-beijing-3yeb39ykym.pdf

Spatio-Temporal Pattern Estimation of PM2.5 in Beijing-Tianjin-Hebei Region Based on MODIS AOD and Meteorological Data Using the Back Propagation Neural Network

This work reports the day-night differences of a suite of chemical species including elemental carbon (EC), organic carbon (OC), water-soluble organic carbon (WSOC), water-soluble organic nitrogen (WSON), selected polycyclic aromatic hydrocarbons (PAHs), and secondary inorganic ions (NO3−, SO42−, NH4+) in ambient fine particles (PM25) collected from 23 July to 23 August 2016 in Changzhou, China Mass concentrations of PM25 and SO42− show a 10–20% increase during daytime, while NO3− concentration decreases by a factor of three from nighttime to daytime due to its semi-volatile nature PAHs, EC, and WSON show higher mass concentration in the night too Mass ratios of WSOC to OC are high in both day and night, indicating that secondary organic aerosol (SOA) formation could occur throughout the day, while the slightly higher ratio during daytime suggests a more significant contribution from daytime photochemical oxidation Strong positive correlations between HULIS-C and WSOC, and HULIS-C with O3 both in day and night, imply that HULIS-C, similar to WSOC, is mainly composed of secondary species HULIS-C accounted for a large fraction of WSOC, with an average of ~60% Moreover, the average WSON concentrations are 108 and 146 µg/m3, constituting ~16% and ~18% of water-soluble total nitrogen in day and night, respectively Correlation analyses suggest that WSON is also predominantly produced from secondary processes PAHs concentrations are found to be very low in summer aerosols Overall, our findings highlight the dominant contribution of secondary processes to the major aerosol components in Changzhou, suggesting proper measures to effectively reduce gaseous precursors are also important to improve air quality

https://www.mdpi.com/2073-4433/8/10/189/pdf

Summertime Day-Night Differences of PM2.5 Components (Inorganic Ions, OC, EC, WSOC, WSON, HULIS, and PAHs) in Changzhou, China

. We introduce tobac (Tracking and Object-Based Analysis of Clouds), a newly developed framework for tracking and analysing individual clouds in different types of datasets, such as cloud-resolving model simulations and geostationary satellite retrievals. The software has been designed to be used flexibly with any two- or three-dimensional time-varying input. The application of high-level data formats, such as Iris cubes or xarray arrays, for input and output allows for convenient use of metadata in the tracking analysis and visualisation. Comprehensive analysis routines are provided to derive properties like cloud lifetimes or statistics of cloud properties along with tools to visualise the results in a convenient way. The application of tobac is presented in two examples. We first track and analyse scattered deep convective cells based on maximum vertical velocity and the three-dimensional condensate mixing ratio field in cloud-resolving model simulations. We also investigate the performance of the tracking algorithm for different choices of time resolution of the model output. In the second application, we show how the framework can be used to effectively combine information from two different types of datasets by simultaneously tracking convective clouds in model simulations and in geostationary satellite images based on outgoing longwave radiation. The tobac framework provides a flexible new way to include the evolution of the characteristics of individual clouds in a range of important analyses like model intercomparison studies or model assessment based on observational data.

/pdf/tobac-1-2-towards-a-flexible-framework-for-tracking-and-4abqbrbr4r.pdf

tobac 1.2: towards a flexible framework for tracking and analysis of clouds in diverse datasets

In order to monitor nighttime particular matter (PM) air quality in urban area, a back propagation neural network (BP neural network) inversion model is established, using low-light radiation data from the day/night band (DNB) of the Visible Infrared Imaging Radiometer Suite (VIIRS) aboard the Suomi National Polar-orbiting Partnership (S-NPP) satellite. The study focuses on the moonless and cloudless nights in Beijing during March–May 2015. A test is carried out by selecting surface PM2.5 data from 12 PM2.5 automatic monitoring stations and the corresponding night city light intensity from DNB. As indicated by the results, the linear correlation coefficient (R) between the results and the corresponding measured surface PM2.5 concentration is 0.91, and the root-mean-square error (RMSE) is 14.02 μg/m3 with the average of 59.39 μg/m3. Furthermore, the BP neural network model shows better accuracy when air relative humility ranges from 40% to 80% and surface PM2.5 concentration exceeds 40 μg/m3. The study provides a superiority approach for monitoring PM2.5 air quality from space with visible light remote sensing data at night.

https://www.mdpi.com/2073-4433/7/10/136/pdf

Inversion of Nighttime PM2.5 Mass Concentration in Beijing Based on the VIIRS Day-Night Band

The characteristics of convective clouds on infrared brightness temperature (BTIR) and brightness temperature difference (BTD) image were analyzed using successive Infrared and Visible Spin-Scan Radiometer (VISSR) data of FY-2, and an integrated detection method of convective clouds using infrared multi-thresholds in combination with tracking techniques was implemented. In this method, BT and BTD thresholds are used to detect severe convection and uncertain clouds, then the tracking technique including overlap ratio, minimum BT change and cross-correlation coefficient is used to detect convection activities in uncertain clouds. The Application test results show that our integrated detection method can effectively detect convective clouds in different life periods, which show a better performance than any single step in it. The statistical results show that the α-type clouds are mostly large-scale systems, and the β- and γ-type clouds have the highest proportion of general type. However, the proportion of weak convective cloud is higher than that of severe ones in γ-type cloud, and an opposite result is found in the β-type.

/pdf/an-integrated-convective-cloud-detection-method-using-fy-2-2fml0ym0vp.pdf

Xiaoran Zhao

Papers

Inversion of Nighttime PM2.5 Mass Concentration in Beijing Based on the VIIRS Day-Night Band

An Integrated Convective Cloud Detection Method Using FY-2 VISSR Data