The Influence of Aerosol Hygroscopicity on Precipitation Intensity During a Mesoscale Convective Event

We examine how aerosol composition affects precipitation intensity using the Weather and Research Forecasting Model with Chemistry (version 3.6). By changing the prescribed default hygroscopicity values to updated values from laboratory studies, we test model assumptions about individual component hygroscopicity values of ammonium, sulfate, nitrate, and organic species. We compare a baseline simulation (BASE, using default hygroscopicity values) with four sensitivity simulations (SULF, increasing the sulfate hygroscopicity; ORG, decreasing organic hygroscopicity; SWITCH, using a concentration‐dependent hygroscopicity value for ammonium; and ALL, including all three changes) to understand the role of aerosol composition on precipitation during a mesoscale convective system (MCS). Overall, the hygroscopicity changes influence the spatial patterns of precipitation and the intensity. Focusing on the maximum precipitation in the model domain downwind of an urban area, we find that changing the individual component hygroscopicities leads to bulk hygroscopicity changes, especially in the ORG simulation. Reducing bulk hygroscopicity (e.g., ORG simulation) initially causes fewer activated drops, weakened updrafts in the midtroposphere, and increased precipitation from larger hydrometeors. Increasing bulk hygroscopicity (e.g., SULF simulation) simulates more numerous and smaller cloud drops and increases precipitation. In the ALL simulation, a stronger cold pool and downdrafts lead to precipitation suppression later in the MCS evolution. In this downwind region, the combined changes in hygroscopicity (ALL) reduces the overprediction of intense events (>70 mm d−1) and better captures the range of moderate intensity (30–60 mm d−1) events. The results of this single MCS analysis suggest that aerosol composition can play an important role in simulating high‐intensity precipitation events.Key PointsAerosol composition can affect spatial patterns of precipitationHygroscopicity and hydrometeor vertical distributions are sensitive to aerosol composition and impact precipitation processesAltering speciated aerosol hygroscopicity can influence the simulation of precipitation intensityPeer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/141976/1/jgrd54341.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/141976/2/jgrd54341_am.pd

Development of a near-real-time global in situ daily precipitation dataset for 0000–0000 UTC

In this study, we have developed a global in situ daily precipitation dataset based on quasi-real-time sub-daily observations of precipitation totals for the 0000–0000 UTC (Co-ordinated Universal Time) day everywhere in the world. The sub-daily precipitation data from meteorological stations are obtained via the World Meteorological Organization's (WMO) Global Telecommunication System (GTS) and China Meteorological Administration Net (CMANet) archived by the National Meteorological Information Centre (NMIC) in China and the Integrated Surface Database (ISD) released by the National Centers for Environmental Information (NCEI) in the USA. We have combined these three sources into a global dataset, referred to as NMIC. Accumulated precipitation totals (depending on the country and the WMO region) are transmitted at a variety of times on the GTS. Of these, about 4,500 stations report daily for the 0000–0000 UTC day. Here, we significantly add to this, by developing two-way accumulation algorithms to decompose other reported sub-daily totals to shorter intervals, and then re-cumulate them where possible to the 0000–0000 UTC day. Using these algorithms, we increase by 51.1% of the number of stations during 2009–2016 to around 6,800 day−1. Additionally, date boundary adjustment (sliding between 1 and 6 hours either side of 0000 UTC) raises the data volume to between 7,800 and 8,700 day−1. We compare our NMIC product with the First Guess Daily (FGD) product from the Global Precipitation Climatology Centre (GPCC) and GHCN-Daily from NCEI (National Centers for Environmental Information). Root mean square differences between our NMIC and GPCC FGD products over the 2009–2016 period are around 3.4–3.7 mm·day−1 and the average consistency percentage is about 75.1–76.8%. Greater differences between NMIC and GHCN-daily are found which are probably due to the non-uniform date boundary in GHCN-Daily

Contemporary and future distributions of cobia, Rachycentron canadum

Climate change has influenced the distribution and phenology of marine species, globally. However, knowledge of the impacts of climate change is lacking for many species that support valuable recreational fisheries. Cobia (Rachycentron canadum) are the target of an important recreational fishery along the U.S. east coast that is currently the subject of a management controversy regarding allocation and stock structure. Further, the current and probable future distributions of this migratory species are unclear, further complicating decision-making. The objectives of this study are to better define the contemporary distribution of cobia along the U.S. east coast and to project potential shifts in distribution and phenology under future climate change scenarios

Meteotsunamis Accompanying Tropical Cyclone Rainbands During Hurricane Harvey

Meteotsunami waves can be triggered by atmospheric disturbances accompanying tropical cyclone rainbands (TCRs) before, during, and long after a tropical cyclone (TC) makes landfall. Due to a paucity of high-resolution field data along open coasts during TCs, relatively little is known about the atmospheric forcing that generate and resonantly amplify these ocean waves, nor their coastal impact. This study links high-resolution field measurements of sea level and air pressure from Hurricane Harvey (2017) with a numerical model to assess the potential for meteotsunami generation by sudden changes in air pressure accompanying TCRs. Previous studies, through the use of idealized models, have suggested that wind is the dominant forcing mechanism for TCR-induced meteotsunami with negligible contributions from air pressure. Our model simulations show that large air pressure perturbations (∼1–3 mbar) can generate meteotsunamis that are similar in period (∼20 min) and amplitude (∼0.2 m) to surf zone observations. The measured air pressure disturbances were often short in wavelength, which necessitates a numerical model with high temporal and spatial resolution to simulate meteotsunami triggered by this mechanism. Sensitivity analysis indicates that air pressure forcing can produce meteotsunami with amplitudes O(0.5 m) and large spatial extents, but model results are sensitive to atmospheric factors, including model uncertainties (length, forward translation speed, and trajectory of the air pressure disturbance), as well as oceanographic factors (storm surge). The present study provides observational and numerical evidence that suggest that air pressure perturbations likely play a larger role in meteotsunami generation by TCRs than previously identified.Environmental Fluid Mechanic

Spatial analysis of the effective coverage of land-based weather stations for traffic crashes

This paper investigates the effective spatial coverage of nationwide land-based weather stations of the National Oceanic Atmospheric Administration (NOAA) for traffic crash analysis. The weather data were collected from the Quality Controlled Local Climatological Data (QCLCD) and the fatal crashes were obtained from the Fatality Analysis Reporting Systems (FARS) during the year of 2007–2014. Both QCLCD and FARS contain geographic coordinates for locations and weather condition information as a categorical variable. The spatial coverage of weather stations for the analysis was made by geoprocessing, which uses multiple buffers (i.e. radii 5, 10, 15, and 20 miles), and then was evaluated via Cohen\u27s κ statistics, which is used to determine an agreement of weather between QCLCD and FARS within the buffer. The applicability of the weather station\u27s data by nine climate regions was assessed by developing a series of negative binomial models. According to the estimated Cohen\u27s κ statistics, the rain and snow weather conditions have a moderate agreement up to 20 miles. However, in the case of fog weather condition, it has a slight agreement. The statistical modeling results showed that weather stations data can be a good exposure measure for weather-related fatal crashes along with the vehicle-miles-traveled. Considering one geographical feature that approximately more than 75% of all fatal crashes are located within 20-miles radius of the weather stations in the USA, it is evident that the data from the existing weather stations can be cost-effective to develop geospatial crash risk analysis model