Comparison of Advanced Technology Microwave Sounder Biases Estimated Using Radio Occultation and Hurricane Florence(2018) Captured by NOAA-20 and S-NPP

The second Advanced Technology Microwave Sounder(ATMS)was onboard the National Oceanic and Atmospheric Administration(NOAA)-20 satellite when launched on 18 November 2017.Using nearly six months of the earliest NOAA-20 observations,the biases of the ATMS instrument were compared between NOAA-20 and the Suomi National Polar-Orbiting Partnership(S-NPP)satellite.The biases of ATMS channels 8 to 13 were estimated from the differences between antenna temperature observations and model simulations generated from Meteorological Operational(MetOp)-A and MetOp-B satellites’Global Positioning System(GPS)radio occultation(RO)temperature and water vapor profiles.It was found that the ATMS onboard the NOAA-20 satellite has generally larger cold biases in the brightness temperature measurements at channels 8 to 13 and small standard deviations.The observations from ATMS on both S-NPP and NOAA-20 are shown to demonstrate an ability to capture a less than 1-h temporal evolution of Hurricane Florence(2018)due to the fact that the S-NPP orbits closely follow those of NOAA-20.

Dynamic Spatio-Temporal Tweet Mining for Event Detection:A Case Study of Hurricane Florence

Extracting information about emerging events in large study areas through spatiotemporal and textual analysis of geotagged tweets provides the possibility of monitoring the current state of a disaster.This study proposes dynamic spatio-temporal tweet mining as a method for dynamic event extraction from geotagged tweets in large study areas.It introduces the use of a modified version of ordering points to identify the clustering structure to address the intrinsic heterogeneity of Twitter data.To precisely calculate the textual similarity,three state-of-theart text embedding methods of Word2vec,GloVe,and Fast Text were used to capture both syntactic and semantic similarities.The impact of selected embedding algorithms on the quality of the outputs was studied.Different combinations of spatial and temporal distances with the textual similarity measure were investigated to improve the event detection outcomes.The proposed method was applied to a case study related to 2018 Hurricane Florence.The method was able to precisely identify events of varied sizes and densities before,during,and after the hurricane.The feasibility of the proposed method was qualitatively evaluated using the Silhouette coefficient and qualitatively discussed.The proposed method was also compared to an implementation based on the standard density-based spatial clustering of applications with noise algorithm,where it showed more promising results.

Social Vulnerability Factors and Reported Post-Disaster Needs in the Aftermath of Hurricane Florence

This research examines the relationship between social vulnerability factors and reported needs following Hurricane Florence.Weighted least squares regression models were used to identify predictor variables for valid registrations that reported needs pertaining to emergencies,food,and shelter.Data consisted of zip codes in North Carolina and South Carolina that received individual assistance for Hurricane Florence(N=406).The results suggest that when controlling for event-specific factors and flood mitigation factors,the proportions of the population that is female,the population over 65,the population aged5 and under,the population older than 5 years not speaking English,and the minority population were all predictors of the per capita reported emergency needs.When controlling for the same variables,the proportions of the population over the age of 25 with a Bachelor’s degree,the female population,the population aged 5 and under,the population above 5 years old that does not speak English,and the minority population were all predictors of the per capita reported food needs.With the same variables controlled for,three variables—the proportions of the population over65,the population aged 5 and under,and the non-Englishspeaking population above 5 years of age—were all predictors of the per capita reported shelter needs.The results suggest that more attention should be given to these vulnerable populations in the pre-disaster planning process.