Joshua P. Hacker

Long-range transport of Asian dust to the Lower Fraser Valley, British Columbia, Canada

For the first time, long-range transport of “Kosa” mineral aerosol from western China to southwestern British Columbia is documented. This late April 1998 event coincided with an episode of photochemical smog and reduced dispersion in the Lower Fraser Valley (LFV). Filter samples in the region show a massive injection of crustal elements (Si, Fe, Al, and Ca) with concentrations of Si approximately double those previously recorded. Ratios of these elements to Fe are shown to be statistically similar to ratios observed in mineral aerosol events in Hawaii and China. On the basis of the difference between observed and expected elemental concentrations and reconstructed soil mass in the episode, it is estimated that Asian dust contributed 38–55% to observed PM10 in the LFV, the remainder being attributed to local sources. Comparison of the April 1998 event with two spring meteorological analogs is consistent with this estimate. Mesoscale model simulations suggest that mineral dust was incorporated into the planetary boundary layer as a result of strong subsidence over the interior of southern British Columbia and Washington State which permitted interception of lower tropospheric elevated aerosol layers by surface-based mixing processes over mountainous terrain. Surface easterly (“outflow”) winds then transported this material into the Lower Fraser Valley where it contributed significantly to total particulate loadings and an intense haze. This mechanism is consistent with the observed spatial and temporal distribution of PM10.

Model Uncertainty in a Mesoscale Ensemble Prediction System: Stochastic versus Multiphysics Representations

AbstractA multiphysics and a stochastic kinetic-energy backscatter scheme are employed to represent model uncertainty in a mesoscale ensemble prediction system using the Weather Research and Forecasting model. Both model-error schemes lead to significant improvements over the control ensemble system that is simply a downscaled global ensemble forecast with the same physics for each ensemble member. The improvements are evident in verification against both observations and analyses, but different in some details. Overall the stochastic kinetic-energy backscatter scheme outperforms the multiphysics scheme, except near the surface. Best results are obtained when both schemes are used simultaneously, indicating that the model error can best be captured by a combination of multiple schemes.

Explicit Numerical Diffusion in the WRF Model

Abstract Diffusion that is implicit in the odd-ordered advection schemes in early versions of the Advanced Research core of the Weather Research and Forecasting (WRF) model is sometimes insufficient to remove noise from kinematical fields. The problem is worst when grid-relative wind speeds are low and when stratification is nearly neutral or unstable, such as in weakly forced daytime boundary layers, where noise can grow until it competes with the physical phenomena being simulated. One solution to this problem is an explicit, sixth-order numerical diffusion scheme that preserves the WRF model’s high effective resolution and uses a flux limiter to ensure monotonicity. The scheme, and how it was added to the WRF model, are explained. The scheme is then demonstrated in an idealized framework and in simulations of salt breezes and lake breezes in northwestern Utah.

Ensemble Kalman Filter Assimilation of Fixed Screen-Height Observations in a Parameterized PBL

Abstract In situ surface layer observations are a rich data source that could be more effectively utilized in NWP applications. If properly assimilated, data from existing mesonets could improve initial conditions and lower boundary conditions, leading to the possibility of improved simulation and short-range forecasts of slope flows, sea breezes, convective initiation, and other PBL circulations. A variance–covariance climatology is constructed by extracting a representative column from real-time mesoscale forecasts over the Southern Great Plains, and used to explore the potential for estimating the state of the PBL by assimilating surface observations. A parameterized 1D PBL model and an ensemble Kalman filter (EnKF) approach to assimilation are used to test this potential. Analysis focuses on understanding how effectively the EnKF can spread the surface observations vertically to constrain the state of the PBL model. Results confirm that assimilating surface observations can substantially improve the s...

The U.S. Air Force Weather Agency's mesoscale ensemble: scientific description and performance results

This work evaluates several techniques to account for mesoscale initial-condition (IC) and model uncertainty in a short-range ensemble prediction system based on the Weather Research and Forecast (WRF) model. A scientific description and verification of several candidate methods for implementation in the U.S. Air Force Weather Agency mesoscale ensemble is presented. Model perturbation methods tested include multiple parametrization suites, landsurface property perturbations, perturbations to parameters within physics schemes and stochastic ‘backscatter’ streamfunction perturbations. IC perturbations considered include perturbed observations in 10 independent WRF-3DVar cycles and the ensemble-transform Kalman filter (ETKF). A hybrid of ETKF (for IC perturbations) and WRF-3DVar (to update the ensemble mean) is also tested. Results show that all of the model and IC perturbation methods examined are more skilful than direct dynamical downscaling of the global ensemble. IC perturbations are most helpful during the first 12 h of the forecasts. Physical parametrization diversity appears critical for boundary-layer forecasts. In an effort to reduce system complexity by reducing the number of suites of physical parametrizations, a smaller set of parametrization suites was combined with perturbed parameters and stochastic backscatter, resulting in the most skilful and statistically consistent ensemble predictions.

WRF-Solar: Description and Clear-Sky Assessment of an Augmented NWP Model for Solar Power Prediction

AbstractWRF-Solar is a specific configuration and augmentation of the Weather Research and Forecasting (WRF) Model designed for solar energy applications. Recent upgrades to the WRF Model contribute to making the model appropriate for solar power forecasting and comprise 1) developments to diagnose internally relevant atmospheric parameters required by the solar industry, 2) improved representation of aerosol–radiation feedback, 3) incorporation of cloud–aerosol interactions, and 4) improved cloud–radiation feedback. The WRF-Solar developments are presented together with a comprehensive characterization of the model performance for forecasting during clear skies. Performance is evaluated with numerical experiments using a range of different external and internal treatment of the atmospheric aerosols, including both a model-derived climatology of aerosol optical depth and temporally evolving aerosol optical properties from reanalysis products. The necessity of incorporating the influence of atmospheric aer...

Increasing the Skill of Probabilistic Forecasts: Understanding Performance Improvements from Model-Error Representations

Four model-error schemes for probabilistic forecasts over the contiguous United States with the WRFARW mesoscale ensemble system are evaluated in regard to performance. Including a model-error representation leads to significant increases in forecast skill near the surface as measured by the Brier score. Combining multiple model-error schemes results in the best-performing ensemble systems, indicating that current model error is still too complex to be represented by a single scheme alone. To understand the reasons for the improved performance, it is examined whether model-error representations increase skill merely by increasing the reliability and reducing the bias—which could also be achieved by postprocessing—or if they have additional benefits. Removing the bias results overall in the largest skill improvement. Forecasts with model-error schemes continue to have better skill than without, indicating that their benefit goes beyond bias reduction. Decomposing theBrier scoreintoits components revealsthat, in addition tothe spread-sensitivereliability, the resolution component is significantly improved. This indicates that the benefits of including a model-error representation go beyond increasing reliability. This is further substantiated when all forecasts are calibrated to have similar spread. The calibrated ensembles with model-error schemes consistently outperform the calibrated control ensemble. Including a model-error representation remains beneficial even if the ensemble systems are calibrated and/ or debiased. This suggests that the merits of model-error representations go beyond increasing spread and removing the mean error and can account for certain aspects of structural model uncertainty.

Linear and non-linear response to parameter variations in a mesoscale model

Parameter uncertainty in atmospheric model forcing and closure schemes has motivated both parameter estimation with data assimilation and use of pre-specified distributions to simulate model uncertainty in short-range ensemble prediction. This work assesses the potential for parameter estimation and ensemble prediction by analysing 2 months of mesoscale ensemble predictions in which each member uses distinct, and fixed, settings for four model parameters. A space-filling parameter selection design leads to a unique parameter set for each ensemble member. An experiment to test linear scaling between parameter distribution width and ensemble spread shows the lack of a general linear response to parameters. Individual member near-surface spatial means, spatial variances and skill show that perturbed models are typically indistinguishable. Parameter—state rank correlation fields are not statistically significant, although the presence of other sources of noise may mask true correlations. Results suggest that ensemble prediction using perturbed parameters may be a simple complement to more complex model-error simulation methods, but that parameter estimation may prove difficult or costly for real mesoscale numerical weather prediction applications.

PBL State Estimation with Surface Observations, a Column Model, and an Ensemble Filter

Abstract Following recent results showing the potential for using surface observations of temperature, water vapor mixing ratio, and winds to determine PBL profiles, this paper reports on experiments with real observations. A 1D column model with soil, surface-layer, and PBL parameterization schemes that are the same as in the Weather Research and Forecasting model is used to estimate PBL profiles with an ensemble filter. Surface observations over the southern Great Plains are assimilated during the spring and early summer period of 2003. To strictly quantify the utility of the observations for determining PBL profiles in the ensemble filter framework, only climatological information is provided for initialization and forcing. The analysis skill, measured against rawinsondes for an independent verification, is compared against climatology to quantify the influence of the observations. Sensitivity to changing parameterization schemes, and to prescribed values of observation error variance, is examined. Tem...

Improved Vertical Covariance Estimates for Ensemble-Filter Assimilation of Near-Surface Observations

Strategies to improve covariance estimates for ensemble-based assimilation of near-surface observations in atmospheric models are explored. It is known that localization of covariance estimates can improve conditioning of covariance matrices in the assimilation process by removing spurious elements and increasing the rank of the matrix. Vertical covariance localization is the focus of this work, and two basic approaches are compared: 1) a recently proposed hierarchical filter approach based on sampling theory and 2) a more commonly used fifth-order piecewise rational function. The hierarchical filter allows for dynamic estimates of localization functions and does not place any restrictions on their form. The rational function is optimized for every analysis time of day and for every possible observation and state variable combination. The methods are tested with a column model containing PBL and land surface parameterization schemes that are available in current mesoscale modeling systems. The results are expected to provide context for assimilation of near-surface observations in mesoscale models, which will benefit short-range mesoscale NWP applications. Results show that both the hierarchical and rational function approaches effectively improve covariance estimates from small ensembles. The hierarchical approach provides localization functions that are irregular and more closely related to PBL structure. Analysis of eigenvalue spectra show that both approaches improve the rank of the covariance matrices, but the amount of improvement is not always directly related to the assimilation performance. Results also show that specifying different localization functions for different observation and state variable combinations is more important than including time dependence.