scholarly journals The Influence of Wildfire Smoke on Cloud Microphysics during the September 2020 Pacific Northwest Wildfires

2021 ◽  
Author(s):  
Robert Conrick ◽  
Clifford F. Mass ◽  
Joseph P. Boomgard-Zagrodnik ◽  
David Ovens
Keyword(s):  
Pacific Northwest ◽  
Prediction Models ◽  
Weather Prediction ◽  
Lower Troposphere ◽  
Cloud Microphysics ◽  
Low Level ◽  
Wildfire Smoke ◽  
Cloud Coverage ◽  
The Pacific ◽  
The Impact

AbstractDuring late summer 2020, large wildfires over the Pacific Northwest produced dense smoke that impacted the region for an extended period. During this period of poor air quality, persistent low-level cloud coverage was poorly forecast by operational numerical weather prediction models, which dissipated clouds too quickly or produced insufficient cloud coverage extent. This deficiency raises questions about the influence of wildfire smoke on low-level clouds in the marine environment of the Pacific Northwest.This paper investigates the effects of wildfire smoke on the properties of low-level clouds, including their formation, microphysical properties, and dissipation. A case study from 12-14 September 2020 is used as a testbed to evaluate the impact of wildfire smoke on such clouds. Observations from satellites and surface observing sites, coupled with mesoscale model simulations, are applied to understand the influence of wildfire smoke during this event. Results indicate that the presence of thick smoke over Washington led to decreased temperatures in the lower troposphere which enhanced low-level cloud coverage, with smoke particles altering the microphysical structure of clouds to favor high concentrations of small droplets. Thermodynamic changes due to smoke are found to be the primary driver of enhanced cloud lifetime during these events, with microphysical changes to clouds as a secondary contributing factor. However, both the thermodynamic and microphysical effects are necessary to produce a realistic simulation.

The impact of London on a low-level jet

2021 ◽  
Author(s):  
Aristofanis Tsiringakis ◽  
Natalie Theeuwes ◽  
Janet Barlow ◽  
Gert-Jan Steeneveld
Keyword(s):  
Urban Area ◽  
Prediction Models ◽  
Weather Prediction ◽  
Vertical Mixing ◽  
Forecasting Model ◽  
Low Level ◽  
Low Level Jet ◽  
The Difference ◽  
The Impact ◽  
Numerical Weather Prediction Models

<p>The low-level jet (LLJ) is an important phenomenon that can affect (and is affected by) the turbulence in the nocturnal urban boundary layer (UBL). We investigate the interaction of a regional LLJ with the UBL during a 2-day period over London. Observations from two Doppler Lidars and two numerical weather prediction models (Weather Research & Forecasting model and UKV Met Office Unified Model) are used to compared the LLJ characteristics (height, speed and fall-off) between a urban (London) and a rural (Chilbolton) site. We find that LLJs are elevated (70m) over London, due to the deeper UBL, an effect of the increased vertical mixing over the urban area and the difference in the topography between the two sites. Wind speed and fall-off are slightly reduced with respect to the rural LLJ. The effects of the urban area and the surrounding topography on the LLJ characteristics over London are isolated through idealized sensitivity experiments. We find that topography strongly affects the LLJ characteristics (height, falloff, and speed), but there is still a substantial urban influence.</p>

scholarly journals Atmospheric Scale Interaction on Wintertime Intermountain West Low-Level Inversions

2010 ◽  
Vol 25 (4) ◽  
pp. 1196-1210 ◽  
Author(s):  
Robert R. Gillies ◽  
Shih-Yu Wang ◽  
Marty R. Booth
Keyword(s):  
Pacific Northwest ◽  
Prediction Models ◽  
Salt Lake ◽  
Weather Prediction ◽  
Salt Lake City ◽  
The United States ◽  
Intermountain West ◽  
The Pacific ◽  
Medium Range ◽  
Lake City

Abstract Persistent winter inversions result in poor air quality in the Intermountain West of the United States. Although the onset of an inversion is relatively easy to predict, the duration and the subsequent breakup of a persistent inversion event remains a forecasting challenge. For this reason and for this region, historic soundings were analyzed for Salt Lake City, Utah, with reanalysis and station data to investigate how persistent inversion events are modulated by synoptic and intraseasonal variabilities. The results point to a close linkage between persistent inversions and the dominant intraseasonal (30 day) mode that characterizes the winter circulation regime over the Pacific Northwest. Meteorological variables and pollution (e.g., particulate matter of ≤2.5-μm diameter, PM2.5) revealed coherent variations with this intraseasonal mode. The intraseasonal mode also modulates the characteristics of the synoptic (6 day) variability and further influences the duration of persistent inversions in the Intermountain West. The interaction between modes suggests that a complete forecast of persistent inversions is more involved and technically beyond numerical weather prediction models intended for the medium range (∼10 day). Therefore, to predict persistent inversions, the results point to the adoption of standard medium-range forecasts with a longer-term climate diagnostic approach.

scholarly journals An OSSE Framework Based on the Ensemble Square Root Kalman Filter for Evaluating the Impact of Data from Radar Networks on Thunderstorm Analysis and Forecasting

2006 ◽  
Vol 23 (1) ◽  
pp. 46-66 ◽  
Author(s):  
Ming Xue ◽  
Mingjing Tong ◽  
Kelvin K. Droegemeier
Keyword(s):  
Kalman Filter ◽  
Prediction Models ◽  
Weather Prediction ◽  
Propagation Speed ◽  
Square Root ◽  
Test Bed ◽  
Low Level ◽  
Radar Network ◽  
The Impact

Abstract A framework for Observing System Simulation Experiments (OSSEs) based on the ensemble square root Kalman filter (EnSRF) technique for assimilating data from more than one radar network is described. The system is tested by assimilating simulated radial velocity and reflectivity data from a Weather Surveillance Radar-1988 Doppler (WSR-88D) radar and a network of four low-cost radars planned for the Oklahoma test bed by the new National Science Foundation (NSF) Engineering Research Center for Collaborative Adaptive Sensing of the Atmosphere (CASA). Such networks are meant to adaptively probe the lower atmosphere that is often missed by the existing WSR-88D radar network, so as to improve the detection of low-level hazardous weather events and to provide more complete data for the initialization of numerical weather prediction models. Different from earlier OSSE work with ensemble Kalman filters, the radar data are sampled on the radar elevation levels and a more realistic forward operator based on the Gaussian power-gain function is used. A stretched vertical grid with high vertical resolution near the ground allows for a better examination of the impact of low-level data. Furthermore, the impacts of storm propagation and higher-volume scan frequencies up to one volume scan per minute on the quality of analysis are examined, using a domain of a sufficient size. The generally good analysis compared to earlier work indicates that the filter can effectively handle the non-uniform-resolution data on the radar elevation levels. The assimilation of additional data from a well-positioned (relative to the storm) CASA radar improves the analysis of a supercell storm system that uses data from one WSR-88D radar alone; and the improvement is most significant at the low levels. When data from a single CASA radar are assimilated and when the radar does not provide full coverage of the storm system, significant errors develop in the analysis that cannot be effectively corrected. The combination of three CASA radars produces analyses of similar quality as the combination of one WSR-88D radar and one well-positioned CASA radar. The most significant effect of storm propagation speed appears to be on the data coverage, which in turn affects the analysis quality. It is generally true that the more observations, the better the analysis. The results of the EnSRF assimilation are not very sensitive to the propagation speed. The quality of analysis can be improved by employing faster volume scans. The sensitivity of the EnSRF analysis to the volume scan interval is however much less than that of traditional velocity and thermodynamic retrieval schemes, suggesting the superiority of the EnSRF method compared to traditional methods. The very frequent update of the model state by the filter, even at 1-min intervals, does not show any negative effect, indicating that the analyzed fields are well balanced.

Real-Time Simulation of the GOES-R ABI for User Readiness and Product Evaluation

2016 ◽  
Vol 97 (2) ◽  
pp. 245-261 ◽  
Author(s):  
Thomas J. Greenwald ◽  
R. Bradley Pierce ◽  
Todd Schaack ◽  
Jason Otkin ◽  
Marek Rogal ◽  
...  
Keyword(s):  
Real Time ◽  
Pacific Northwest ◽  
Prediction Models ◽  
Weather Prediction ◽  
Atmospheric Stability ◽  
Product Evaluation ◽  
Hurricane Sandy ◽  
Front Range ◽  
Test Bed ◽  
The Pacific

Abstract In support of the Geostationary Operational Environmental Satellite R series (GOES-R) program, the Cooperative Institute for Meteorological Satellite Studies (CIMSS) at the University of Wisconsin–Madison is generating high quality simulated Advanced Baseline Imager (ABI) radiances and derived products in real time over the continental United States. These data are mainly used for testing data-handling systems, evaluating ABI-derived products, and providing training material for forecasters participating in GOES-R Proving Ground test bed activities. The modeling system used to generate these datasets consists of advanced regional and global numerical weather prediction models in addition to state-of-the-art radiative transfer models, retrieval algorithms, and land surface datasets. The system and its generated products are evaluated for the 2014 Pacific Northwest wildfires; the 2013 Moore, Oklahoma, tornado; and Hurricane Sandy. Simulated aerosol optical depth over the Front Range of Colorado during the Pacific Northwest wildfires was validated using high-density Aerosol Robotic Network (AERONET) measurements. The aerosol, cloud, and meteorological modeling system used to generate ABI radiances was found to capture the transport of smoke from the Pacific wildfires into the Front Range of Colorado and true-color imagery created from these simulated radiances provided visualization of the smoke plumes. Evaluation of selected simulated ABI-derived products for the Moore tornado and Hurricane Sandy cases was done using real-time GOES sounder/imager products produced at CIMSS. Results show that simulated ABI moisture and atmospheric stability products, cloud products, and red–green–blue (RGB) airmass composite imagery are well suited as proxy ABI data for user preparedness.

Evaluation of Low-Level Winds from WRF Model that Driven by Different Background Field Data with Applications to Wind Energy Forecasting

2012 ◽  
Vol 608-609 ◽  
pp. 692-697 ◽  
Author(s):  
Xiao Lin Liu ◽  
Zhao Ming Yang ◽  
Shuang Long Jing ◽  
Zhi Qiang Wang ◽  
Shi Gong Wang
Keyword(s):  
Wind Power ◽  
Large Scale ◽  
Prediction Models ◽  
Rapid Development ◽  
Weather Prediction ◽  
Wrf Model ◽  
Northwest China ◽  
Low Level ◽  
Background Data ◽  
The Impact

With the large-scale and rapid development of wind power in China, the accuracy of wind power prediction is asked for higher. So how to improve the accuracy of numerical weather prediction models which forecast wind has become an important and critical issue. That the accuracy of numerical prediction models as well as the bias of background data is main cause why generate simulated error. This paper attempted to employ the advanced WRF model to simulate the low-level wind in arid region of northwest China, and then evaluated the impact size that using FNL and GFS background data. The results show that using FNL and GFS data simulated wind is very close. It is found that simulation results driven by the FNL assimilated data are worse sometimes. Consequently, we can conclude that FNL assimilated data as well as GFS forecast data are close and the assimilation of FNL data is still need to improvement in northwest China.

scholarly journals An ethic of care? Academic administration and pandemic policy

2021 ◽  
Vol 20 (1-2) ◽  
pp. 632-638
Author(s):  
Stephanie A Bryson
Keyword(s):  
Social Work ◽  
Political Theory ◽  
Pacific Northwest ◽  
Ethic Of Care ◽  
The Political ◽  
Academic Administration ◽  
The Public ◽  
Political Landscape ◽  
The Pacific ◽  
The Impact

This reflexive essay examines the adoption of an intentional ‘ethic of care’ by social work administrators in a large social work school located in the Pacific Northwest. An ethic of care foregrounds networks of human interdependence that collapse the public/private divide. Moreover, rooted in the political theory of recognition, a care ethic responds to crisis by attending to individuals’ uniqueness and ‘whole particularity.’ Foremost, it rejects indifference. Through the personal recollections of one academic administrator, the impact of rejecting indifference in spring term 2020 is described. The essay concludes by linking the rejection of indifference to the national political landscape.

scholarly journals Health Impact Assessment of PM2.5 attributable mortality from the September 2020 Washington State Wildfire Smoke Episode

2020 ◽  
Author(s):  
Yisi Liu ◽  
Elena Austin ◽  
Jianbang Xiang ◽  
Tim Gould ◽  
Tim Larson ◽  
...  
Keyword(s):  
Pacific Northwest ◽  
Excess Mortality ◽  
Health Impact Assessment ◽  
Health Impact ◽  
Attributable Mortality ◽  
Short Term ◽  
Wildfire Smoke ◽  
The Pacific ◽  
All Cause Mortality ◽  
Short Term Mortality

AbstractMajor wildfires that started in the summer of 2020 along the west coast of the U.S. have made PM2.5 concentrations in cities in this region rank among the highest in the world. Regions of Washington were impacted by active wildfires in the state, and by aged wood smoke transported from fires in Oregon and California. This study aims to assess the population health impact of increased PM2.5 concentrations attributable to the wildfire. Average daily PM2.5 concentrations for each county before and during the 2020 Washington wildfire episode were obtained from the Washington Department of Ecology. Utilizing previously established associations of short-term mortality for PM2.5, we estimated excess mortality for Washington attributable to the increased PM2.5 levels. On average, PM2.5 concentrations increased 91.7 μg/m3 during the wildfire episode. Each week of wildfire smoke exposures was estimated to result in 87.6 (95% CI: 70.9, 103.1) cases of increased all-cause mortality, 19.1 (95% CI: 10.0, 28.2) increased cardiovascular disease deaths, and 9.4 (95% CI: 5.1, 13.5) increased respiratory disease deaths. Because wildfire smoke episodes are likely to continue impacting the Pacific Northwest in future years, continued preparedness and mitigations to reduce exposures to wildfire smoke are necessary to avoid this excess health burden.

scholarly journals A Transformed Lagged Ensemble Forecasting Technique for Increasing Ensemble Size

2007 ◽  
Vol 135 (4) ◽  
pp. 1424-1438 ◽  
Author(s):  
Andrew R. Lawrence ◽  
James A. Hansen
Keyword(s):  
Prediction Models ◽  
Weather Prediction ◽  
Ensemble Forecast ◽  
Ensemble Forecasting ◽  
Limited Area ◽  
Ensemble Size ◽  
Probabilistic Forecasting ◽  
Ensemble Forecasts ◽  
Chaotic Model ◽  
The Impact

Abstract An ensemble-based data assimilation approach is used to transform old ensemble forecast perturbations with more recent observations for the purpose of inexpensively increasing ensemble size. The impact of the transformations are propagated forward in time over the ensemble’s forecast period without rerunning any models, and these transformed ensemble forecast perturbations can be combined with the most recent ensemble forecast to sensibly increase forecast ensemble sizes. Because the transform takes place in perturbation space, the transformed perturbations must be centered on the ensemble mean from the most recent forecasts. Thus, the benefit of the approach is in terms of improved ensemble statistics rather than improvements in the mean. Larger ensemble forecasts can be used for numerous purposes, including probabilistic forecasting, targeted observations, and to provide boundary conditions to limited-area models. This transformed lagged ensemble forecasting approach is explored and is shown to give positive results in the context of a simple chaotic model. By incorporating a suitable perturbation inflation factor, the technique was found to generate forecast ensembles whose skill were statistically comparable to those produced by adding nonlinear model integrations. Implications for ensemble forecasts generated by numerical weather prediction models are briefly discussed, including multimodel ensemble forecasting.

Role of initial condition and parametric uncertainty in a severe hailstorm forecast

2021 ◽  
Author(s):  
Patrick Kuntze ◽  
Annette Miltenberger ◽  
Corinna Hoose ◽  
Michael Kunz
Keyword(s):  
Initial Conditions ◽  
Weather Prediction ◽  
Parametric Uncertainty ◽  
Radar Data ◽  
Cloud Microphysics ◽  
Initial Condition ◽  
Relative Contribution ◽  
Cloud Properties ◽  
Set Up ◽  
The Impact

<p>Forecasting high impact weather events is a major challenge for numerical weather prediction. Initial condition uncertainty plays a major role but so potentially do uncertainties arising from the representation of physical processes, e.g. cloud microphysics. In this project, we investigate the impact of these uncertainties for the forecast of cloud properties, precipitation and hail of a selected severe convective storm over South-Eastern Germany.<br>To investigate the joint impact of initial condition and parametric uncertainty a large ensemble including perturbed initial conditions and systematic variations in several cloud microphysical parameters is conducted with the ICON model (at 1 km grid-spacing). The comparison of the baseline, unperturbed simulation to satellite, radiosonde, and radar data shows that the model reproduces the key features of the storm and its evolution. In particular also substantial hail precipitation at the surface is predicted. Here, we will present first results including the simulation set-up, the evaluation of the baseline simulation, and the variability of hail forecasts from the ensemble simulation.<br>In a later stage of the project we aim to assess the relative contribution of the introduced model variations to changes in the microphysical evolution of the storm and to the fore- cast uncertainty in larger-scale meteorological conditions.</p>

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