scholarly journals Hurricane Juan (2003). Part II: Forecasting and Numerical Simulation

2006 ◽  
Vol 134 (7) ◽  
pp. 1748-1771 ◽  
Author(s):  
Ron McTaggart-Cowan ◽  
Lance F. Bosart ◽  
John R. Gyakum ◽  
Eyad H. Atallah
Keyword(s):  
Prediction Models ◽  
Weather Prediction ◽  
The United States ◽  
Mean Flow ◽  
Initial State ◽  
Near Surface ◽  
Steering Flow ◽  
Operational Forecasting ◽  
Model Set ◽  
Regional Forecasts

Abstract The landfall of Hurricane Juan (September 2003) in the Canadian Maritimes represents an ideal case in which to study the performance of operational forecasting of an intense, predominantly tropical feature entering the midlatitudes. A hybrid cyclone during its genesis phase, Juan underwent a tropical transition as it drifted slowly northward 1500 km from the east coast of the United States. Shortly after reaching its peak intensity as a category-2 hurricane, the storm accelerated rapidly northward and made landfall near Halifax, Nova Scotia, Canada, with maximum sustained winds of 44 m s−1. Although the forecasts and warnings produced by the U.S. National Hurricane Center and the Canadian Hurricane Centre were of high quality throughout Hurricane Juan’s life cycle, guidance from numerical weather prediction models became unreliable as the storm accelerated toward the coast. The short-range, near-surface forecasts from eight operational models during the crucial prelandfall portion of Juan’s track are investigated in this study. Despite continued improvements to operational numerical forecasting systems, it is shown that those systems not employing advanced tropical vortex initialization techniques were unable to provide forecasters with credible near-surface guidance in this case. A pair of regional forecasts, one successful and one from the failed model set, are compared in detail. Spurious asymmetries in the initial vortex of the deficient model are shown to hamper structural predictions and to cause nonnegligible track perturbations from the trajectory implied by the well-described deep-layer mean flow. The Canadian Mesoscale Compressible Community model is rerun with an improved representation of the hurricane’s vortex in the initial state. The hindcast produced following the tropical cyclone initialization contains reduced track, structure, and intensity errors compared with those generated by the model in real time. The enhanced initial intensity produces a direct improvement in the forecast storm strength throughout the period, and the symmetrization of the vortex eliminates the interactions that plague the operational system. The southeastward relocation of the implanted vortex to Juan’s observed location eliminates a significant northwestward track bias under the influence of a broad area of southerly steering flow. The study concludes that the initialization of Hurricane Juan’s structure and position adds value to numerical guidance even as the storm accelerates poleward at a latitude where the implantation of a quasi-symmetric vortex may not be generally valid.

scholarly journals Local and Mesoscale Impacts of Wind Farms as Parameterized in a Mesoscale NWP Model

2012 ◽  
Vol 140 (9) ◽  
pp. 3017-3038 ◽  
Author(s):  
Anna C. Fitch ◽  
Joseph B. Olson ◽  
Julie K. Lundquist ◽  
Jimy Dudhia ◽  
Alok K. Gupta ◽  
...  
Keyword(s):  
Boundary Layer ◽  
Wind Speed ◽  
Kinetic Energy ◽  
Wind Farm ◽  
Weather Prediction ◽  
Wind Farms ◽  
Mean Flow ◽  
Thrust Coefficient ◽  
Near Surface ◽  
Neutral Boundary Layer

Abstract A new wind farm parameterization has been developed for the mesoscale numerical weather prediction model, the Weather Research and Forecasting model (WRF). The effects of wind turbines are represented by imposing a momentum sink on the mean flow; transferring kinetic energy into electricity and turbulent kinetic energy (TKE). The parameterization improves upon previous models, basing the atmospheric drag of turbines on the thrust coefficient of a modern commercial turbine. In addition, the source of TKE varies with wind speed, reflecting the amount of energy extracted from the atmosphere by the turbines that does not produce electrical energy. Analyses of idealized simulations of a large offshore wind farm are presented to highlight the perturbation induced by the wind farm and its interaction with the atmospheric boundary layer (BL). A wind speed deficit extended throughout the depth of the neutral boundary layer, above and downstream from the farm, with a long wake of 60-km e-folding distance. Within the farm the wind speed deficit reached a maximum reduction of 16%. A maximum increase of TKE, by nearly a factor of 7, was located within the farm. The increase in TKE extended to the top of the BL above the farm due to vertical transport and wind shear, significantly enhancing turbulent momentum fluxes. The TKE increased by a factor of 2 near the surface within the farm. Near-surface winds accelerated by up to 11%. These results are consistent with the few results available from observations and large-eddy simulations, indicating this parameterization provides a reasonable means of exploring potential downwind impacts of large wind farms.

LIMITED AREA WEATHER PREDICTION MODELS VIEWED AS DYNAMICAL SYSTEMS

1999 ◽  
Vol 09 (05) ◽  
pp. 831-842 ◽  
Author(s):  
F. CHOMÉ ◽  
C. NICOLIS
Keyword(s):  
Prediction Models ◽  
Weather Prediction ◽  
Dynamical Behavior ◽  
Limited Area ◽  
Initial State ◽  
Weather Forecasts ◽  
Fine Mesh ◽  
Area Of Interest ◽  
Variable Mesh ◽  
High Resolution Models

Different strategies for building high-resolution models providing a more detailed description of a limited area of interest as for example, in regional weather forecasts are developed. They are subsequently compared, on the basis of the dynamical behavior generated by the corresponding models. The statistical properties of the relevant fields are analyzed, and predictability experiments are performed on statistical ensembles of close lying trajectories whose mean distance represents the uncertainty in the initial state of the system. The results show that a global, variable-mesh model performs much better than a limited area fine mesh one embedded into a coarser global model.

scholarly journals Progress towards an improved parameterisation of small-scale orographic impacts on the atmospheric boundary layer

2021 ◽  
Author(s):  
Julian Quimbayo-Duarte ◽  
Juerg Schmidli
Keyword(s):  
Boundary Layer ◽  
Atmospheric Boundary Layer ◽  
Prediction Models ◽  
Weather Prediction ◽  
Vertical Flux ◽  
Initial Time ◽  
Small Scale ◽  
Mean Flow ◽  
Horizontal Momentum ◽  
The Impact

<p>An accurate representation of the momentum budget in numerical models is essential in the quest for reliable weather forecasting, from large scales (climate models) to small scales (numerical weather prediction models, NWP). It is well known that orographic waves play an important role in large-scale circulation. The vertical propagation of such waves is associated with a vertical flux of horizontal momentum, which may be transferred to the mean flow by wave-mean flow interaction and wave-breaking (Sandu et al., 2019). The orography scales inducing such phenomena are often smaller than the model resolution, even for NWP models, leading to the need for parameterisation schemes for orographic drag. Yet, such parameterization in current models is fairly limited (Vosper et al., 2020). The present work aims to contribute to an improved understanding and parameterization of the impact of small-scale orography on the lower atmosphere with a focus on the stable atmospheric boundary layer.</p><p>As a first step, an idealized set of experiments has been designed to explore the capabilities of the Icosahedral Nonhydrostatic model in its large eddy simulation mode (ICON-LES, Dipankar et al., 2015) to represent turbulence processes in the stably-stratified atmosphere. Initial experiments testing the model performance over flat terrain (GABLS experiment, Beare et al., 2006), orographic wave generation (shallow bell-shaped topography, Xue et al., 2000) and moderate complex terrain (U-shaped valley, Burns and Chemel 2014) have been conducted. The results demonstrate that ICON-LES adequately represents the boundary layer processes for the investigated cases in comparison to the literature.</p><p>In a second step, an idealized set of experiments of atmospheric flow over idealized sinusoidal and multiscale terrain has been designed to study the impact of the orographically-induced gravity waves on the total surface drag and the vertical flux of horizontal momentum. The influence of different atmospheric conditions is assessed by varying the background wind speed and the temperature stratification at the initial time.</p>

scholarly journals Improving WRF-Chem meteorological analyses and forecasts over aerosol polluted regions by incorporating NAAPS aerosol analyses

2021 ◽  
Author(s):  
Brittany N. Carson-Marquis ◽  
Jianglong Zhang ◽  
Peng Xian ◽  
Jeffrey S. Reid ◽  
Jared Marquis
Keyword(s):  
Boundary Conditions ◽  
Surface Temperature ◽  
Great Plains ◽  
Transport Model ◽  
Weather Prediction ◽  
The United States ◽  
Northern Great Plains ◽  
Chemical Transport Model ◽  
Near Surface ◽  
Initial And Boundary Conditions

AbstractWhen unaccounted for in numerical weather prediction (NWP) models, heavy aerosol events can cause significant unrealized biases in forecasted meteorological parameters such as surface temperature. To improve near-surface forecasting accuracies during heavy aerosol loadings, we demonstrate the feasibility of incorporating aerosol fields from a global chemical transport model as initial and boundary conditions into a higher resolution NWP model with aerosol-meteorological coupling. This concept is tested for a major biomass burning smoke event over the Northern Great Plains region of the United States that occurred during summer of 2015. Aerosol analyses from the global Navy Aerosol Analysis and Prediction System (NAAPS) are used as initial and boundary conditions for Weather Research and Forecasting with Chemistry (WRF-Chem) simulations. Through incorporating more realistic aerosol direct effects into the WRF-Chem simulations, errors in WRF-Chem simulated surface downward shortwave radiative fluxes and near-surface temperature are reduced compared with surface-based observations. This study confirms the ability to decrease biases induced by the aerosol direct effect for regional NWP forecasts during high-impact aerosol episodes through the incorporation of analyses and forecasts from a global aerosol transport model.

scholarly journals A Dynamical-Statistical-Analog Ensemble Forecast Model: Theory and an Application to Heavy Rainfall Forecasts of Landfalling Tropical Cyclones

2020 ◽  
Vol 148 (4) ◽  
pp. 1503-1517 ◽  
Author(s):  
Fumin Ren ◽  
Chenchen Ding ◽  
Da-Lin Zhang ◽  
Deliang Chen ◽  
Hong-li Ren ◽  
...  
Keyword(s):  
Prediction Models ◽  
Weather Prediction ◽  
Forecast Model ◽  
The United States ◽  
Ensemble Forecast ◽  
Initial Value ◽  
Forecast Performance ◽  
Typhoon Track ◽  
Perfect Model ◽  
Accumulated Rainfall

Abstract Combining dynamical models with statistical algorithms is an important way to improve weather and climate prediction. In this study, a concept of a perfect model, whose solutions are from observations, is introduced, and a dynamical-statistical-analog ensemble forecast (DSAEF) model is developed as an initial-value problem of the perfect model. This new analog-based forecast model consists of the following three steps: (i) construct generalized initial value (GIV), (ii) identify analogs from historical observations, and (iii) produce an ensemble of predictands. The first step includes all appropriate variables, not only at an instant state but also during their temporal evolution, that play an important role in determining the accuracy of each predictand. An application of the DSAEF model is illustrated through the prediction of accumulated rainfall associated with 21 landfalling typhoons occurring over South China during the years of 2012–16. Assuming a reliable forecast of landfalling typhoon track, two different experiments are conducted, in which the GIV is constructed by including (i) typhoon track only; and (ii) both typhoon track and landfall season. Results show overall better performance of the second experiment than the first one in predicting heavy accumulated rainfall in the training sample tests. In addition, the forecast performance of both experiments is comparable to the operational numerical weather prediction models currently used in China, the United States, and Europe. Some limitations and future improvements as well as comparisons with some existing analog ensemble models are also discussed.

Automatic input variable selection for analog methods using genetic algorithms

2021 ◽  
Author(s):  
Pascal Horton ◽  
Olivia Martius
Keyword(s):  
Genetic Algorithms ◽  
Climate Models ◽  
Prediction Models ◽  
Weather Prediction ◽  
Precipitation Prediction ◽  
Great Consistency ◽  
Operational Forecasting ◽  
Target Date ◽  
New Variables ◽  
Selection Of

<p>Analog methods (AMs) are statistical downscaling methods often used for precipitation prediction in different contexts, such as operational forecasting, past climate reconstruction of climate change impact studies. It usually relies on predictors describing the atmospheric circulation and the moisture content of the atmosphere to sample similar meteorological situations in the past and establish a probabilistic forecast for a target date. AMs can be based on outputs from numerical weather prediction models in the context of operational forecasting or outputs from climate models in climatic applications.</p><p>AMs can be constituted of multiple predictors organized in different subsequent levels of analogy that refines the selection of similar situations. The development of such methods is usually a manual process where some predictors are assessed in different structures. As most AMs use multiple predictors, a comprehensive assessment of all combinations becomes quickly impossible. The selection of predictors in the application of the AM often builds on previous work and does not evolve much. However, the climate models providing the predictors evolve continuously and new variables might become relevant to be considered in AMs. Moreover, the best predictors might change from one region to another or for another predictand of interest. There is a need for a method to automatically explore potential variables for AMs and to extract the ones that are relevant for a predictand of interest.</p><p>We propose using genetic algorithms (GAs) to proceed to an automatic selection of the predictor variables along with all other parameters of the AM. We even let the GAs automatically pick the best analogy criteria, i.e. the metric that quantifies the analogy between two situations. The first test consisted of letting the GAs select the single best variable to predict daily precipitation for each of 25 selected catchments in Switzerland. The results showed great consistency in terms of spatial patterns and the underlying meteorological processes. Then, different structures were assessed by varying the number of levels of analogy and the number of variables per level. Finally, multiple optimizations were conducted on the 25 catchments to identify the 12 variables that provide the best prediction when considered together.</p>

Global Air–Sea Fluxes of Heat, Fresh Water, and Momentum: Energy Budget Closure and Unanswered Questions

2019 ◽  
Vol 11 (1) ◽  
pp. 227-248 ◽  
Author(s):  
Lisan Yu
Keyword(s):  
Surface Energy ◽  
Fresh Water ◽  
Prediction Models ◽  
Weather Prediction ◽  
Ocean Surface ◽  
Surface Radiation ◽  
Global Ocean ◽  
Personal Perspective ◽  
Near Surface ◽  
Water Budgets

The ocean interacts with the atmosphere via interfacial exchanges of momentum, heat (via radiation and convection), and fresh water (via evaporation and precipitation). These fluxes, or exchanges, constitute the ocean-surface energy and water budgets and define the ocean's role in Earth's climate and its variability on both short and long timescales. However, direct flux measurements are available only at limited locations. Air–sea fluxes are commonly estimated from bulk flux parameterization using flux-related near-surface meteorological variables (winds, sea and air temperatures, and humidity) that are available from buoys, ships, satellite remote sensing, numerical weather prediction models, and/or a combination of any of these sources. Uncertainties in parameterization-based flux estimates are large, and when they are integrated over the ocean basins, they cause a large imbalance in the global-ocean budgets. Despite the significant progress that has been made in quantifying surface fluxes in the past 30 years, achieving a global closure of ocean-surface energy and water budgets remains a challenge for flux products constructed from all data sources. This review provides a personal perspective on three questions: First, to what extent can time-series measurements from air–sea buoys be used as benchmarks for accuracy and reliability in the context of the budget closures? Second, what is the dominant source of uncertainties for surface flux products, the flux-related variables or the bulk flux algorithms? And third, given the coupling between the energy and water cycles, precipitation and surface radiation can act as twin budget constraints—are the community-standard precipitation and surface radiation products pairwise compatible?

scholarly journals Intercomparison of In Situ Sensors for Ground-Based Land Surface Temperature Measurements

Sensors ◽  
2020 ◽  
Vol 20 (18) ◽  
pp. 5268
Author(s):  
Praveena Krishnan ◽  
Tilden P. Meyers ◽  
Simon J. Hook ◽  
Mark Heuer ◽  
David Senn ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Field Experiments ◽  
Prediction Models ◽  
Weather Prediction ◽  
Surface Air Temperature ◽  
Near Surface ◽  
Oak Ridge

Land surface temperature (LST) is a key variable in the determination of land surface energy exchange processes from local to global scales. Accurate ground measurements of LST are necessary for a number of applications including validation of satellite LST products or improvement of both climate and numerical weather prediction models. With the objective of assessing the quality of in situ measurements of LST and to evaluate the quantitative uncertainties in the ground-based LST measurements, intensive field experiments were conducted at NOAA’s Air Resources Laboratory (ARL)’s Atmospheric Turbulence and Diffusion Division (ATDD) in Oak Ridge, Tennessee, USA, from October 2015 to January 2016. The results of the comparison of LSTs retrieved by three narrow angle broadband infrared temperature sensors (IRT), hemispherical longwave radiation (LWR) measurements by pyrgeometers, forward looking infrared camera with direct LSTs by multiple thermocouples (TC), and near surface air temperature (AT) are presented here. The brightness temperature (BT) measurements by the IRTs agreed well with a bias of <0.23 °C, and root mean square error (RMSE) of <0.36 °C. The daytime LST(TC) and LST(IRT) showed better agreement (bias = 0.26 °C and RMSE = 0.67 °C) than with LST(LWR) (bias > 1.1 and RMSE > 1.46 °C). In contrast, the difference between nighttime LSTs by IRTs, TCs, and LWR were <0.47 °C, whereas nighttime AT explained >81% of the variance in LST(IRT) with a bias of 2.64 °C and RMSE of 3.6 °C. To evaluate the annual and seasonal differences in LST(IRT), LST(LWR) and AT, the analysis was extended to four grassland sites in the USA. For the annual dataset of LST, the bias between LST (IRT) and LST (LWR) was <0.7 °C, except at the semiarid grassland (1.5 °C), whereas the absolute bias between AT and LST at the four sites were <2 °C. The monthly difference between LST (IRT) and LST (LWR) (or AT) reached up to 2 °C (5 °C), whereas half-hourly differences between LSTs and AT were several degrees in magnitude depending on the site characteristics, time of the day and the season.

scholarly journals Downscaling surface wind predictions from numerical weather prediction models in complex terrain with WindNinja

2016 ◽  
Author(s):  
N. S. Wagenbrenner ◽  
J. M. Forthofer ◽  
B. K. Lamb ◽  
K. S. Shannon ◽  
B. W. Butler
Keyword(s):  
Numerical Weather Prediction ◽  
Complex Terrain ◽  
Prediction Models ◽  
Wildland Fire ◽  
Weather Prediction ◽  
Surface Wind ◽  
Atmospheric Stability ◽  
Wind Model ◽  
Near Surface ◽  
Numerical Weather

Abstract. Wind predictions in complex terrain are important for a number of applications. Dynamic downscaling of numerical weather prediction (NWP) model winds with a high resolution wind model is one way to obtain a wind forecast that accounts for local terrain effects, such as wind speed-up over ridges, flow channeling in valleys, flow separation around terrain obstacles, and flows induced by local surface heating and cooling. In this paper we investigate the ability of a mass-consistent wind model for downscaling near-surface wind predictions from four NWP models in complex terrain. Model predictions are compared with surface observations from a tall, isolated mountain. Downscaling improved near-surface wind forecasts under high-wind (near-neutral atmospheric stability) conditions. Results were mixed during upslope and downslope (non-neutral atmospheric stability) flow periods, although wind direction predictions generally improved with downscaling. This work constitutes evaluation of a diagnostic wind model at unprecedented high spatial resolution in terrain with topographical ruggedness approaching that of typical landscapes in the western US susceptible to wildland fire.

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.

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