scholarly journals Fire-Modified Meteorology in a Coupled Fire–Atmosphere Model

2015 ◽  
Vol 54 (3) ◽  
pp. 704-720 ◽  
Author(s):  
Mika Peace ◽  
Trent Mattner ◽  
Graham Mills ◽  
Jeffrey Kepert ◽  
Lachlan McCaw
Keyword(s):  
Initial Conditions ◽  
Fire Behavior ◽  
Wrf Model ◽  
South Australia ◽  
Vertical Motion ◽  
Propagation Speed ◽  
Atmospheric Model ◽  
Energy Fluxes ◽  
Spread Model ◽  
Atmosphere Model

AbstractThe coupled fire–atmosphere model consisting of the Weather and Forecasting (WRF) Model coupled with the fire-spread model (SFIRE) module has been used to simulate a bushfire at D’Estrees Bay on Kangaroo Island, South Australia, in December 2007. Initial conditions for the simulations were provided by two global analyses: the GFS operational analysis and ERA-Interim. For each NWP initialization, the simulations were run with and without feedback from the fire to the atmospheric model. The focus of this study was examining how the energy fluxes from the simulated fire modified the local meteorological environment. With feedback enabled, the propagation speed of the sea-breeze frontal line was faster and vertical motion in the frontal zone was enhanced. For one of the initial conditions with feedback on, a vortex developed adjacent to the head fire and remained present for over 5 h of simulation time. The vortex was not present without fire–atmosphere feedback. The results show that the energy fluxes released by a fire can effect significant changes on the surrounding mesoscale atmosphere. This has implications for the appropriate use of weather parameters extracted from NWP and used in prediction for fire operations. These meteorological modifications also have implications for anticipating likely fire behavior.

scholarly journals Coupled Fire–Atmosphere Simulations of the Rocky River Fire Using WRF-SFIRE

2016 ◽  
Vol 55 (5) ◽  
pp. 1151-1168 ◽  
Author(s):  
Mika Peace ◽  
Trent Mattner ◽  
Graham Mills ◽  
Jeffrey Kepert ◽  
Lachlan McCaw
Keyword(s):  
Fire Behavior ◽  
South Australia ◽  
Simulation Models ◽  
Fire Spread ◽  
Fire Plume ◽  
Spread Model ◽  
Mesoscale Convective ◽  
Rayleigh Benard ◽  
Fire Spread Model ◽  
Extreme Fire

AbstractThe coupled atmosphere–fire spread model “WRF-SFIRE” has been used to simulate a fire where extreme fire behavior was observed. Tall flames and a dense convective smoke column were features of the fire as it burned rapidly up the Rocky River gully on Kangaroo Island, South Australia. WRF-SFIRE simulations of the event show a number of interesting dynamical processes resulting from fire–atmosphere feedback, including the following: fire spread was sensitive to small changes in mean wind direction; fire perimeter was affected by wind convergence resulting from interactions between the fire, atmosphere, and local topography; and the fire plume mixed high-momentum air from above a strong subsidence inversion. At 1-min intervals, output from the simulations showed fire spread exhibiting fast and slow pulses. These pulses occurred coincident with the passage of mesoscale convective (Rayleigh–Bénard) cells in the planetary boundary layer. Simulations show that feedback between the fire and atmosphere may have contributed to the observed extreme fire behavior. The findings raise questions as to the appropriate information to include in meteorological forecasts for fires as well as future use of coupled and uncoupled fire simulation models in both operational and research settings.

scholarly journals Application of Adjoint-Derived Forecast Sensitivities to the 24–25 January 2000 U.S. East Coast Snowstorm

2005 ◽  
Vol 133 (11) ◽  
pp. 3148-3175 ◽  
Author(s):  
Daryl T. Kleist ◽  
Michael C. Morgan
Keyword(s):  
Response Function ◽  
Initial Conditions ◽  
Forecast Error ◽  
Weather Prediction ◽  
Lower Troposphere ◽  
Vertical Motion ◽  
Initial Condition ◽  
Response Functions ◽  
Eastern United States ◽  
Surface Cyclone

Abstract The 24–25 January 2000 eastern United States snowstorm was noteworthy as operational numerical weather prediction (NWP) guidance was poor for lead times as short as 36 h. Despite improvements in the forecast of the surface cyclone position and intensity at 1200 UTC 25 January 2000 with decreasing lead time, NWP guidance placed the westward extent of the midtropospheric, frontogenetically forced precipitation shield too far to the east. To assess the influence of initial condition uncertainties on the forecast of this event, an adjoint model is used to evaluate forecast sensitivities for 36- and 48-h forecasts valid at 1200 UTC 25 January 2000 using as response functions the energy-weighted forecast error, lower-tropospheric circulation about a box surrounding the surface cyclone, 750-hPa frontogenesis, and vertical motion. The sensitivities with respect to the initial conditions for these response functions are in general very similar: geographically isolated, maximized in the middle and lower troposphere, and possessing an upshear vertical tilt. The sensitivities are maximized in a region of enhanced low-level baroclinicity in the vicinity of the surface cyclone’s precursor upper trough. However, differences in the phase and structure of the gradients for the four response functions are evident, which suggests that perturbations could be constructed to alter one response function but not necessarily the others. Gradients of the forecast error response function with respect to the initial conditions are used in an iterative procedure to construct initial condition perturbations that reduce the forecast error. These initial condition perturbations were small in terms of both spatial scale and magnitude. Those initial condition perturbations that were confined primarily to the midtroposphere grew rapidly into much larger amplitude upper-and-lower tropospheric perturbations. The perturbed forecasts were not only characterized by reduced final time forecast error, but also had a synoptic evolution that more closely followed analyses and observations.

scholarly journals Climatological Increased Precipitation from July to August in the Western North Pacific Region Simulated by CMIP6 Models

Atmosphere ◽  
2021 ◽  
Vol 12 (6) ◽  
pp. 664
Author(s):  
Xiao Dong ◽  
Renping Lin
Keyword(s):  
North Pacific ◽  
Western North Pacific ◽  
Vertical Motion ◽  
Atmospheric Model ◽  
Coupled Models ◽  
Model Simulations ◽  
Observational Analysis ◽  
Budget Analysis ◽  
Circulation Change ◽  
Precipitation Increase

In this study, the climatological precipitation increase from July to August over the western North Pacific (WNP) region was investigated through observations and simulations in the Coupled Model Intercomparison Project Phase 6 (CMIP6), atmospheric model simulations and historical experiments. Firstly, observational analysis showed that the precipitation increase is associated with a decrease in the local sea surface temperature (SST), indicating that the precipitation increase is not driven by the change in SST. In addition, the pattern of precipitation increase is similar to the vertical motion change at 500-hPa, suggesting that the precipitation increase is related to the circulation change. Moisture budget analysis further confirmed this relation. In addition to the observational analysis, the outputs from 26 CMIP6 models were further evaluated. Compared with atmospheric model simulations, air–sea coupled models largely improve the simulation of the climatological precipitation increase from July to August. Furthermore, model simulations confirmed that the bias in the precipitation increase is intimately associated with the circulation change bias. Thus, two factors are responsible for the bias of the precipitation increase from July to August in climate models: air–sea coupling processes and the performance in vertical motion change.

scholarly journals Biogenic isoprene emissions driven by regional weather predictions using different initialization methods: case studies during the SEAC<sup>4</sup>RS and DISCOVER-AQ airborne campaigns

2017 ◽  
Vol 10 (8) ◽  
pp. 3085-3104 ◽  
Author(s):  
Min Huang ◽  
Gregory R. Carmichael ◽  
James H. Crawford ◽  
Armin Wisthaler ◽  
Xiwu Zhan ◽  
...  
Keyword(s):  
Case Studies ◽  
Air Temperature ◽  
Initial Conditions ◽  
Wrf Model ◽  
Surface Air Temperature ◽  
Heat Fluxes ◽  
Aircraft Observation ◽  
Near Surface ◽  
Boundary Layer Height ◽  
Small Scales

Abstract. Land and atmospheric initial conditions of the Weather Research and Forecasting (WRF) model are often interpolated from a different model output. We perform case studies during NASA's SEAC4RS and DISCOVER-AQ Houston airborne campaigns, demonstrating that using land initial conditions directly downscaled from a coarser resolution dataset led to significant positive biases in the coupled NASA-Unified WRF (NUWRF, version 7) surface and near-surface air temperature and planetary boundary layer height (PBLH) around the Missouri Ozarks and Houston, Texas, as well as poorly partitioned latent and sensible heat fluxes. Replacing land initial conditions with the output from a long-term offline Land Information System (LIS) simulation can effectively reduce the positive biases in NUWRF surface air temperature by ∼ 2 °C. We also show that the LIS land initialization can modify surface air temperature errors almost 10 times as effectively as applying a different atmospheric initialization method. The LIS-NUWRF-based isoprene emission calculations by the Model of Emissions of Gases and Aerosols from Nature (MEGAN, version 2.1) are at least 20 % lower than those computed using the coarser resolution data-initialized NUWRF run, and are closer to aircraft-observation-derived emissions. Higher resolution MEGAN calculations are prone to amplified discrepancies with aircraft-observation-derived emissions on small scales. This is possibly a result of some limitations of MEGAN's parameterization and uncertainty in its inputs on small scales, as well as the representation error and the neglect of horizontal transport in deriving emissions from aircraft data. This study emphasizes the importance of proper land initialization to the coupled atmospheric weather modeling and the follow-on emission modeling. We anticipate it to also be critical to accurately representing other processes included in air quality modeling and chemical data assimilation. Having more confidence in the weather inputs is also beneficial for determining and quantifying the other sources of uncertainties (e.g., parameterization, other input data) of the models that they drive.

scholarly journals Assessing the Potential of a Long-Term Climate Forecast for Cuba Using the WRF Model

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Lourdes Álvarez-Escudero ◽  
Yandy G. Mayor ◽  
Israel Borrajero-Montejo ◽  
Arnoldo Bezanilla-Morlot
Keyword(s):  
Mesoscale Model ◽  
Initial Conditions ◽  
Wrf Model ◽  
Reanalysis Data ◽  
Weather Research And Forecasting ◽  
Tropical Storms ◽  
Precipitation Regime ◽  
Wide Debate ◽  
One Year ◽  
Cumulative Precipitation

Seasonal climatic prediction studies are a matter of wide debate all over the world. Cuba, a mainly agricultural nation, should greatly benefit from the knowledge, which is available months in advance of the precipitation regime and allows for the proper management of water resources. In this work, a series of six experiments were made with a mesoscale model WRF (Weather Research and Forecasting Model) that produced a 15-month forecast for each month of cumulative precipitation starting at two dates, and for three non-consecutive years with different meteorological characteristics: one dry year (2004), one year that started dry and turned rainy (2005), and one year where several tropical storms occurred (2008). ERA-Interim reanalysis data were used for the initial and border conditions and experiments started 1 month before the beginning of the rainy and the dry seasons, respectively. In a general sense, the experience of using WRF indicated that it was a valid resource for seasonal forecast, since the results obtained were in the same range as those reported by the literature for similar cases. Several limitations were revealed by the results: the forecasts underestimated the monthly cumulative precipitation figures, tropical storms entering through the borders sometimes followed courses different from the real courses inside the working domain, storms that developed inside the domain were not reproduced by WRF, and differences in initial conditions led to significantly different forecasts for the corresponding time steps (nonlinearity). Changing the model parameterizations and initial conditions of the ensemble forecast experiments was recommended.

scholarly journals On the improvement of wave and storm surge hindcasts by downscaled atmospheric forcing: application to historical storms

2018 ◽  
Vol 18 (4) ◽  
pp. 997-1012 ◽  
Author(s):  
Émilie Bresson ◽  
Philippe Arbogast ◽  
Lotfi Aouf ◽  
Denis Paradis ◽  
Anna Kortcheva ◽  
...  
Keyword(s):  
Storm Surge ◽  
Initial Conditions ◽  
Storm Surges ◽  
Atmospheric Model ◽  
Small Scale ◽  
Severe Damage ◽  
Weather Forecasts ◽  
Whole Process ◽  
Medium Range ◽  
Using Data

Abstract. Winds, waves and storm surges can inflict severe damage in coastal areas. In order to improve preparedness for such events, a better understanding of storm-induced coastal flooding episodes is necessary. To this end, this paper highlights the use of atmospheric downscaling techniques in order to improve wave and storm surge hindcasts. The downscaling techniques used here are based on existing European Centre for Medium-Range Weather Forecasts reanalyses (ERA-20C, ERA-40 and ERA-Interim). The results show that the 10 km resolution data forcing provided by a downscaled atmospheric model gives a better wave and surge hindcast compared to using data directly from the reanalysis. Furthermore, the analysis of the most extreme mid-latitude cyclones indicates that a four-dimensional blending approach improves the whole process, as it assimilates more small-scale processes in the initial conditions. Our approach has been successfully applied to ERA-20C (the 20th century reanalysis).

scholarly journals A Heuristic Approach for Precipitation Data Assimilation: Characterization Using OSSEs

2019 ◽  
Vol 147 (9) ◽  
pp. 3445-3466 ◽  
Author(s):  
Andrés A. Pérez Hortal ◽  
Isztar Zawadzki ◽  
M. K. Yau
Keyword(s):  
Data Assimilation ◽  
Initial Conditions ◽  
Grid Point ◽  
Wrf Model ◽  
Ensemble Member ◽  
Absolute Difference ◽  
Time Interval ◽  
State Variables ◽  
Vertical Column ◽  
A New Technique

Abstract We introduce a new technique for the assimilation of precipitation observations, the localized ensemble mosaic assimilation (LEMA). The method constructs an analysis by selecting, for each vertical column in the model, the ensemble member with precipitation at the ground that is locally closest to the observed values. The proximity between the modeled and observed precipitation is determined by the mean absolute difference of precipitation intensity, converted to reflectivity and measured over a spatiotemporal window centered at each grid point of the model. The underlying hypothesis of the approach is that the ensemble members that are locally closer to the observed precipitation are more probable to be closer to the “truth” in the state variables than the other members. The initial conditions for the new forecast are obtained by nudging the background states toward the mosaic of the closest ensemble members (analysis) over a 30 min time interval, reducing the impacts of the imbalances at the boundaries between the different selected members. The potential of the method is studied using observing system simulation experiments (OSSEs) employing a small ensemble of 20 members. The ensemble is produced by the WRF Model, run at a horizontal grid spacing of 20 km. The experiments lend support to the validity of the hypothesis and allow the determination of the optimal parameters for the approach. In the context of OSSE, this new data assimilation technique is able to produce forecasts with considerable and long-lived error reductions in the fields of precipitation, temperature, humidity, and wind.

scholarly journals A Two-Season Impact Study of Radiative Forced Tropospheric Response to Stratospheric Initial Conditions Inferred From Satellite Radiance Assimilation

Climate ◽  
2019 ◽  
Vol 7 (9) ◽  
pp. 114
Author(s):  
Min Shao ◽  
Yansong Bao ◽  
George P. Petropoulos ◽  
Hongfang Zhang
Keyword(s):  
Weather Forecasting ◽  
Initial Conditions ◽  
Stratospheric Ozone ◽  
Wrf Model ◽  
Lower Troposphere ◽  
Short Wave ◽  
Advanced Technology ◽  
Short Term ◽  
Radiative Processes ◽  
Stratospheric Temperature

This study investigated the impacts of stratospheric temperatures and their variations on tropospheric short-term weather forecasting using the Advanced Research Weather Research and Forecasting (WRF-ARW) system with real satellite data assimilation. Satellite-borne microwave stratospheric temperature measurements up to 1 mb, from the Advanced Microwave Sounding Unit-A (AMSU-A), the Advanced Technology Microwave Sounder (ATMS), and the Special Sensor microwave Imager/Sounder (SSMI/S), were assimilated into the WRF model over the continental U.S. during winter and summer 2015 using the community Gridpoint Statistical Interpolation (GSI) system. Adjusted stratospheric temperature related to upper stratospheric ozone absorption of short-wave (SW) radiation further lead to vibration in downward SW radiation in winter predictions and overall reduced with a maximum of 5.5% reduction of downward SW radiation in summer predictions. Stratospheric signals in winter need 48- to 72-h to propagate to the lower troposphere while near-instant tropospheric response to the stratospheric initial conditions are observed in summer predictions. A schematic plot illustrated the physical processes of the coupled stratosphere and troposphere related to radiative processes. Our results suggest that the inclusion of the entire stratosphere and better representation of the upper stratosphere are important in regional NWP systems in short-term forecasts.

scholarly journals Microphysical Process Comparison of Three Microphysics Parameterization Schemes in the WRF Model for an Idealized Squall-Line Case Study

2019 ◽  
Vol 147 (9) ◽  
pp. 3093-3120 ◽  
Author(s):  
J.-W. Bao ◽  
S. A. Michelson ◽  
E. D. Grell
Keyword(s):  
Wrf Model ◽  
Propagation Speed ◽  
Front Propagation ◽  
Particle Growth ◽  
Population Characteristics ◽  
Squall Line ◽  
Microphysical Process ◽  
Process Comparison ◽  
Freezing Level ◽  
Squall Lines

Abstract Three bulk microphysics schemes with different complexities in the Weather Research and Forecasting Model are compared in terms of the individual microphysical process terms of the hydrometeor mass and number mixing ratio tendency equations in an idealized 2D squall-line case. Through evaluation of these process terms and of hydrometeor size distributions, it is shown that the differences in the simulated population characteristics of snow, graupel, and rainwater are the prominent factors contributing to the differences in the development of the simulated squall lines using these schemes. In this particular case, the gust front propagation speed produced by the Thompson scheme is faster than in the other two schemes during the first 2 h of the simulation because it has a larger dominant graupel size. After 2 h into the simulation, the initially less intense squall lines in the runs using the WSM6 and Morrison schemes start to catch up in intensity and development to the run using the Thompson scheme. Because the dominant size of graupel particles in the runs using the WSM6 and Morrison schemes is smaller, these particles take more time to fall below the freezing level and enhance the rainwater production and its evaporative cooling. In the run using the Thompson scheme, the graupel production slows down at later times while the snow particle growth increases, leading to more snow falling below the freezing level to melt and surpass graupel particle melting in the production of rainwater.

scholarly journals Development of a High-Resolution Wind Forecast System Based on the WRF Model and a Hybrid Kalman-Bayesian Filter

Energies ◽  
2019 ◽  
Vol 12 (16) ◽  
pp. 3050 ◽  
Author(s):  
Carlos Otero-Casal ◽  
Platon Patlakas ◽  
Miguel A. Prósper ◽  
George Galanis ◽  
Gonzalo Miguez-Macho
Keyword(s):  
Wind Speed ◽  
Wind Farm ◽  
Wrf Model ◽  
Statistical Error ◽  
Atmospheric Model ◽  
Horizontal Resolution ◽  
Integrated System ◽  
Combined System ◽  
Local Flow ◽  
Bayesian Filter

Regional microscale meteorological models have become a critical tool for wind farm production forecasting due to their capacity for resolving local flow dynamics. The high demand for reliable forecasting tools in the energy industry is the motivation for the development of an integrated system that combines the Weather Research and Forecasting (WRF) atmospheric model with an optimization obtained by the conjunction of a Kalman filter and a Bayesian model. This study focuses on the development and validation of this combined system in a very dense wind farm cluster located in Galicia (Northwest of Spain). A period of one year is simulated at 333 m horizontal resolution, with a daily operational forecasting set-up. The Kalman-Bayesian filter was tested both directly on wind speed and on the U-V (zonal and meridional) components for nowcasting periods from 10 min to 6 h periods, all of them with important applications in the wind industry. The results are quite promising, as the main statistical error indices are significantly improved in a 6 h forecasting horizon and even more in shorter horizon cases. The Mean Annual Error (MAE) for 1 h nowcasting horizon is 1.03 m/s for wind speed and 12.16 ° for wind direction. Moreover, the successful utilization of the integrated system in test cases with different characteristics demonstrates the potential utility that this tool may have for a variety of applications in wind farm operations and energy markets.

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