Typhoon cloud image prediction based on enhanced multi-scale deep neural network

Typhoons threaten individuals’ lives and property. The accurate prediction of typhoon activity is crucial for reducing those threats and for risk assessment. Satellite images are widely used in typhoon research because of their wide coverage, timeliness, and relatively convenient acquisition. They are also important data sources for typhoon cloud image prediction. Studies on typhoon cloud image prediction have rarely used multi-scale features, which cause significant information loss and lead to fuzzy predictions with insufficient detail. Therefore, we developed an enhanced multi-scale deep neural network (EMSN) to predict a 3-hour-advance typhoon cloud image, which has two parts: a feature enhancement module and a feature encode-decode module. The inputs of the EMSN were eight consecutive images, and a feature enhancement module was applied to extract features from the historical inputs. To consider that the images of different time steps had different contributions to the output result, we used channel attention in this module to enhance important features. Because of the spatially correlated and spatially heterogeneous information at different scales, the feature encode-decode module used ConvLSTMs to capture spatiotemporal features at different scales. In addition, to reduce information loss during downsampling, skip connections were implemented to maintain more low-level information. To verify the effectiveness and applicability of our proposed EMSN, we compared various algorithms and explored the strengths and limitations of the model. The experimental results demonstrated that the EMSN efficiently and accurately predicted typhoon cloud images with higher quality than in the literature

Late Pleistocene stratigraphic sequence and geologic significance of the Kaolao Tableland in the Yuncheng Basin

The ancient Fen River diversion was a crucial earth's surface transformation in the Yuncheng Basin during the Cenozoic. The time frame for the diversion of the ancient Fen River is still characterized by two views: the middle Pleistocene and the late Pleistocene, which has yet to be finalized. This study investigated the late Pleistocene sedimentary sequence of the Kaolao Tableland in the Yuncheng Basin, and the critical time frame of the sedimentary sequence transition was determined based on optically stimulated luminescence (OSL) dating results. The causes of the late Pleistocene sedimentary sequence of the Kaolao Tableland and the geological factors that controlled the sequence were analyzed using detrital zircon U–Pb isotope dating. It is concluded that the late Pleistocene sedimentary sequence of the Kaolao Tableland in the Yuncheng Basin is characterized by a two-layer structure, with fluvial sands in the lower part and eolian loess in the upper part. Based on the OSL dating results, the formation time of the boundary between these two parts is between ~76–63 ka B.P. Comparative analysis of detrital zircon age sequences indicates that the early Pleistocene fluvial sands in the Kaolao Tableland and sediments in the ancient Fen River have similar age sequence characteristics. Therefore, it can be deduced that the regional tectonic uplift of the northeastern Emei Terrace in the middle of the late Pleistocene resulted in the diversion and exit of the ancient Fen River from the Yuncheng Basin and the sedimentary facies began to change from fluvial to eolian. The tectonic uplift in the middle of the late Pleistocene extensively developed around the Ordos Basin, and that indicates a significant tectonic uplift of the Tibet Plateau during this time, whose remote effect might be the major cause for the exit of the ancient Fen River from the Yuncheng Basin. This research provides new sedimentary evidence for the time frame of the ancient Fen River diversion in the Yuncheng Basin

Salvinorin A administration after global cerebral hypoxia/ischemia preserves cerebrovascular autoregulation via kappa opioid receptor in piglets.

Cerebral hypoxia/ischemia (HI) is not uncommon during the perinatal period. If occurring, it can result in severe neurologic disabilities that persist throughout life. Salvinorin A, a non-opioid Kappa opioid receptors (KOR) selective agonist, has the potential to address this devastating situation. We have demonstrated that salvinorin A administration before HI, preserves pial artery autoregulative function through both the KOR and extracellular signal-regulated kinases (ERK) pathways. In the present study, we tested the hypothesis that administration of salvinorin A after HI could preserve cerebral autoregulation via KOR and ERK pathway.The response of the pial artery to hypercapnia, hypotension and isoproterenol were monitored before and 1 hour after HI in piglets equipped with a cranial window. Four groups of drug administration were performed after HI. The control group had DMSO (1 µl/kg, i.v.) administrated immediately after HI. Two salvinorin A treated groups had salvinorin A (10 µg/kg, i.v.) administrated 0 and 30 min after HI, respectively. The 4(th) group had salvinorin A and the KOR antagonist norbinaltorphimine (Nor-BIN, 1 µM topical) co-administrated 0 min after HI (n = 5). The dilation responses of the pial artery to hypercapnia and hypotension were impaired after global HI and were preserved with salvinorin A administration immediately or 30 min after HI. The preservation of autoregulation was abolished when nor-BIN was administered. Levels of phosphor-ERK(pERK)/ERK in the cerebrospinal fluid (CSF) were measured before and 1 hour after HI. After HI, the pERK/ERK levels significantly increased in both DMSO control group and salvinorin A and nor-BIN co-administration group. The elevated levels of pERK/ERK were not observed with salvinorin A only groups.Salvinorin A administration 0 and 30 min after HI preserves autoregulation of pial artery to hypercapnia and hypotension via kappa opioid receptor and ERK pathway

Isoproterenol induced artery dilation was independent of KOR or ERK signaling.

<p>Effects of isoproterenol (10 nM, 1 µM) on pial artery diameter before (baseline) and after HI did not change significantly in the presence and absence of various interventions, compared to baseline p>0.05. N = 5 in each group. Percentage change =  (diameter after isoproterenol−diameter before isoproterenol)/diameter before isoproterenol)*100; SA: Salvinorin A; Norbin: norbinaltorphimine.</p

Effects of post HI salvinorin A administration on pial artery dilation to hypotension.

<p>HI with DMSO damaged dilation of pial artery to hypotension. SA administrated at onset and 30 min after HI preserved the dilations of pial artery to moderate and severe hypotension, which were blunted by co-administration of norbinaltorphimine (Norbin). Percentage change =  (diameter after hypotension–diameter before hypotension)/diameter before hypotension)*100. N = 5 in each group. SA: Salvinorin A. Moderate: 25% decrease of mean blood pressure. Severe: 45% decrease of mean blood pressure.</p

Salvinorin A administration blocked the elevated CSF ERK activity observed 1 h after HI.

<p>The ration of pERK/ERK at 1 hour after HI in the control groups (n = 10, DMSO and nor-BIN groups) increased significantly compared with the baseline. The baseline for all the groups are pulled together (n = 20) and the data from DMSO and nor-BIN groups were pulled together and presented as DMSO+Norbin (n = 10) to increase the power of the statistical analysis because of some large variances were observed. The elevated ERK activities were abolished in the groups with salvinorin A administrated immediately (n = 5) or 30 min (n = 5) after HI. Norbin: norbinaltorphimine; SA: Salvinorin A.</p

Effects of post HI salvinorin A administration on pial artery dilation to hypercapnia.

<p>HI with DMSO impaired dilation of pial artery to hypercapnia. SA administrated at onset and 30 min after HI preserved the dilation of pial artery to moderate and severe hypercapnia, which were blunted by norbinaltorphimine (Norbin). N = 5 in each group. Percentage change =  (diameter after hypercapnia−diameter before hypercapnia)/diameter before hypercapnia)*100. SA: Salvinorin A; Moderate: hypercapnia with PaCO₂ of 50 to 60 mmHg; Severe: hypercapnia with PaCO₂ of 70 to 80 mmHg.</p

Using cygnss data to map flood inundation during the 2021 extreme precipitation in henan province, China

On 20 July 2021, parts of China’s Henan Province received the highest precipitation levels ever recorded in the region. Floods caused by heavy rainfall resulted in hundreds of casualties and tens of billions of dollars’ worth of property loss. Due to the highly dynamic nature of flood disasters, rapid and timely spatial monitoring is conducive for early disaster prevention, mid-term disaster relief, and post-disaster reconstruction. However, existing remote sensing satellites cannot provide high-resolution flood monitoring results. Seeing as spaceborne global navigation satellite system-reflectometry (GNSS-R) can observe the Earth’s surface with high temporal and spatial resolutions, it is expected to provide a new solution to the problem of flood hazards. Here, using the Cyclone Global Navigation Satellite System (CYGNSS) L1 data, we first counted various signal-to-noise ratios and the corresponding reflectivity to surface features in Henan Province. Subsequently, we analyzed changes in the delay-Doppler map of CYGNSS when the observed area was submerged and not submerged. Finally, we determined the submerged area affected by extreme precipitation using the threshold detection method. The results demonstrated that the flood range retrieved by CYGNSS agreed with that retrieved by the Soil Moisture Active Passive (SMAP) mission and the precipitation data retrieved and measured by the Global Precipitation Measurement mission and meteorological stations. Compared with the SMAP results, those obtained by CYGNSS have a higher spatial resolution and can monitor changes in the areas affected by the floods over a shorter period.This research was funded by the National Natural Science Foundation of China Projects (42074041,41731066); The National Key Research and Development Program of China (2020YFC1512000, 2019YFC1509802); State Key Laboratory of Geo-Information Engineering (SKLGIE2019-Z-2-1); Shaanxi Natural Science Research Program (2020JM-227)