Historically, the Defense Meteorological Satellite Program's Operational Linescan System (DMSP-OLS) was the unique satellite sensor used to collect the nighttime light, which is an efficient means to map the global economic activities. Since it was launched in October 2011, the Visible Infrared Imaging Radiometer Suite (VIIRS) sensor on the Suomi National Polar-orbiting Partnership (NPP) Satellite has become a new satellite used to monitor nighttime light. This study performed the first evaluation on the NPP-VIIRS nighttime light imagery in modeling economy, analyzing 31 provincial regions and 393 county regions in China. For each region, the total nighttime light (TNL) and gross regional product (GRP) around the year of 2010 were derived, and a linear regression model was applied on the data. Through the regression, the TNL from NPP-VIIRS were found to exhibit R 2 values of 0.8699 and 0.8544 with the provincial GRP and county GRP, respectively, which are significantly stronger than the relationship between the TNL from DMSP-OLS (F16 and F18 satellites) and GRP. Using the regression models, the GRP was predicted from the TNL for each region, and we found that the NPP-VIIRS data is more predictable for the GRP than those of the DMSP-OLS data. This study demonstrates that the recently released NPP-VIIRS nighttime light imagery has a stronger capacity in modeling regional economy than those of the DMSP-OLS data. These findings provide a foundation OPEN ACCESSRemote Sens. 2013, 5 3058 to model the global and regional economy with the recently availability of the NPP-VIIRS data, especially in the regions where economic census data is difficult to access.

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Luojia 1-01 satellite, launched on 2 June 2018, provides a new data source of nighttime light at 130 m resolution and shows potential for mapping urban extent. In this paper, using Luojia 1-01 and VIIRS nighttime light imagery, we compared several methods for extracting urban areas, including Human Settlement Index (HSI), Simple Thresholding Segmentation (STS) and SVM supervised classification. According to the accuracy assessment, the HSI method using LJ1-01 data had the best performance in urban extent extraction, which presented the largest Kappa Coefficient value, 0.834, among all the results. For the urban areas extracted by VIIRS based HSI method, the largest Kappa Coefficient value was 0.772. In contrast, the largest Kappa Coefficient values obtained by STS method were 0.79 and 0.7512 respectively when using LJ1-01 and VIIRS data, while for SVM method the values were 0.7829 and 0.7486 when using Landsat-LJ and Landsat-VIIRS composite data respectively. The experimented results demonstrated that the utilization of nighttime light imagery can largely improve the accuracy of urban extent extraction and LJ1-01 data, with a higher resolution and more abundant spatial information, can lead to better identification results than its predecessors.

The extreme rainfall from 19 to 21 July 2021, which caused massive flooding and loss of life, is the second heaviest rainfall that has occurred in Henan province, central China. To identify the key factors controlling this rainfall event, we conducted an ensemble‐based analysis using ECMWF operational global ensemble forecasts. The forecasts of extreme rainfall had a relatively large spread and there was a large bias in the ensemble mean precipitation, indicating uncertainties in the forecast. The extreme rainfall was closely related to the Huang‐Huai cyclone and the southerly and southeasterly flows. Although they had little influence on the southeasterly flow, the uncertainty and predictability of typhoons In‐fa and Cempaka probably caused the variation in the southerly flow and the maintenance of the Huang‐Huai cyclone, which determined the amount of precipitation for this extreme event in the ensemble forecast model. When typhoon In‐fa was located further northwest, the northeasterly airflow generated by the binary interaction between typhoons In‐fa and Cempaka weakened the intensity of the southerly flow, reduced the transport of water vapor to the rainstorm area and weakened the Huang‐Huai cyclone, thus reducing precipitation in the control area. The results of this study indicate that ensemble‐based analysis can improve our understanding and forecasting of extreme precipitation events under the influence of multiple remote tropical cyclones.

This study used the Night Light Development Index (NLDI) to measure the regional inequality of public services in Mainland China at multiple scales. The NLDI was extracted based on a Gini Coefficient approach to measure the spatial differences of population distribution and night light distribution. Population data were derived from the dataset of China's population density grid, and night light data were acquired from satellite imagery. In the multi-scale analysis, we calculated the NLDI for China as a whole, eight 13470The same pattern was observed from the provincial and prefectural analysis, suggesting that public services in Mainland China became more equal within the five-year period. A regression analysis indicated that provincial and prefectural regions with more public services per capita and higher population density had more equal public services.

During 6-9 August 2009, Typhoon Morakot hit Taiwan with exceptional rainfall and caused severe flooding and landslides, which resulted in a large number of human loss of life and significant property damage. The rainfall intensity broke the 50-year record and was largely under-forecasted. In this study, we have identified a possible mechanism responsible for this unusually heavy rainfall. It is found that the presence of the tropical storm Goni upstream of Morakot may be an important factor for the maintenance and intensification of Morakot. Goni transported a large amount of moisture and energy to Morakot. Numerical simulations indicate that the interaction of Goni and Morakot accounts for about 30% more rainfall than if Goni was not presented. This study may explain the unusual amount of rainfall for Morakot and thus provide additional considerations for future forecasts of similar situations.

Super Typhoon Saomai (2006, 08W), which caused historical disaster in the landfall region, is the most powerful typhoon ever making landfall in Mainland China since 1949. The impact of Tropical Storm Bopha (2006, 10W) on Saomai is regarded as a binary tropical cyclone (TC) interaction. In order to quantify the influence of Bopha on the intensity of Saomai, a set of numerical experiments are performed by artificially modifying the intensity of Bopha in its initial conditions. It is shown that changing the intensity of Bopha has significant effects on simulating Saomai's intensities, structures, and tracks. We find that moisture transport is a pivotal process of binary TC interaction. It is interesting that there are opposite effects by Bopha at different development stages of Saomai. The existence of Bopha and increasing its intensity would weaken Saomai at its intensifying stage while intensifying Saomai at its weakening stage. A possible explanation of these effects is the direction change of moisture transport from/to Saomai at its intensifying/weakening stages through the channel. It may suggest a significant relevance for operational intensity forecasts under active binary TC interaction.

An extreme precipitation event over Henan province, China from 19 to 21 July 2021 led to flood disasters in this region and widespread concern about the subsequent loss of life and livelihoods. We conducted numerical simulations to examine the impacts of typhoons In‐fa (2021) and Cempaka (2021) on this extreme rainfall event. The control simulation reasonably reproduced the motion of typhoons In‐fa and Cempaka and the associated distribution and amount of extreme rainfall. Sensitivity experiments were conducted in which typhoon In‐fa was artificially moved in both northerly and westerly directions in the initial conditions. The results indicated that the southerly flow between typhoon Cempaka and Henan, which determined the structure and distribution of the extreme rainfall event, was sensitive to the motion of typhoon In‐fa. Numerical experiments that removed typhoon Cempaka confirmed that both the movement of In‐fa and its interaction with Cempaka were closely associated with southerly flows and had a significant effect on the extreme precipitation event. In the absence of typhoon Cempaka, although typhoon In‐fa still had a remote effect on precipitation, the effect was much smaller than in the presence of Cempaka. Our simulation of this extreme rainfall event in Henan and the associated sensitivity experiments are consistent with the results of previous studies of multiple tropical cyclones, which showed that interactions among multiple tropical cyclones can lead to changes in their outer circulation that affect extreme precipitation events.