Roland B. Stull

Bio: Roland B. Stull is an academic researcher. The author has contributed to research in topics: Precipitation & Quantitative precipitation forecast. The author has an hindex of 1, co-authored 1 publications receiving 8472 citations.

An Introduction to Boundary Layer Meteorology

Abstract: 1 Mean Boundary Layer Characteristics.- 1.1 A boundary-layer definition.- 1.2 Wind and flow.- 1.3 Turbulent transport.- 1.4 Taylor's hypothesis.- 1.5 Virtual potential temperature.- 1.6 Boundaiy layer depth and structure.- 1.7 Micrometeorology.- 1.8 Significance of the boundary layer.- 1.9 General references.- 1.10 References for this chapter.- 1.11 Exercises.- 2 Some Mathematical and Conceptual Tools: Part 1. Statistics.- 2.1 The significance of turbulence and its spectrum.- 2.2 The spectral gap.- 2.3 Mean and turbulent parts.- 2.4 Some basic statistical methods.- 2.5 Turbulence kinetic energy.- 2.6 Kinematic flux.- 2.7 Eddy flux.- 2.8 Summation notation.- 2.9 Stress.- 2.10 Friction velocity.- 2.11 References.- 2.12 Exercises.- 3 Application of the Governing Equations to Turbulent Flow.- 3.1 Methodology.- 3.2 Basic governing equations.- 3.3 Simplifications, approximations, and scaling arguments.- 3.4 Equations for mean variables in a turbulent flow.- 3.5 Summary of equations, with simplifications.- 3.6 Case studies.- 3.7 References.- 3.8 Exercises.- 4 Prognostic Equations for Turbulent Fluxes and Variances.- 4.1 Prognostic equations for the turbulent departures.- 4.2 Free convection scaling variables.- 4.3 Prognostic equations for variances.- 4.4 Prognostic equations for turbulent fluxes.- 4.5 References.- 4.6 Exercises.- 5 Turbulence Kinetic Energy, Stability, and Scaling.- 5.1 The TKE budget derivation.- 5.2 Contributions to the TKE budget.- 5.3 TKE budget contributions as a function of eddy size.- 5.4 Mean kinetic energy and its interaction with turbulence.- 5.5 Stability concepts.- 5.6 The Richardson number.- 5.7 The Obukhov length.- 5.8 Dimensionless gradients.- 5.9 Miscellaneous scaling parameters.- 5.10 Combined stability tables.- 5.11 References.- 5.12 Exercises.- 6 Turbulence Closure Techniques.- 6.1 The closure problem.- 6.2 Parameterization rules.- 6.3 Local closure - zero and half order.- 6.4 Local closure - first order.- 6.5 Local closure - one-and-a-half order.- 6.6 Local closure - second order.- 6.7 Local closure - third order.- 6.8 Nonlocal closure - transilient turbulence theory.- 6.9 Nonlocal closure - spectral diffusivity theory.- 6.10 References.- 6.11 Exercises.- 7 Boundary Conditions and External Forcings.- 7.1 Effective surface turbulent flux.- 7.2 Heat budget at the surface.- 7.3 Radiation budget.- 7.4 Fluxes at interfaces.- 7.5 Partitioning of flux into sensible and latent portions.- 7.6 Flux to and from the ground.- 7.7 References.- 7.8 Exercises.- 8 Some Mathematical and Conceptual Tools: Part 2. Time Series.- 8.1 Time and space series.- 8.2 Autocorrelation.- 8.3 Structure function.- 8.4 Discrete Fourier transform.- 8.5 Fast Fourier Transform.- 8.6 Energy spectrum.- 8.7 Spectral characteristics.- 8.8 Spectra of two variables.- 8.9 Periodogram.- 8.10 Nonlocal spectra.- 8.11 Spectral decomposition of the TKE equation.- 8.12 References.- 8.13 Exercises.- 9 Similarity Theory.- 9.1 An overview.- 9.2 Buckingham Pi dimensional analysis methods.- 9.3 Scaling variables.- 9.4 Stable boundary layer similarity relationship lists.- 9.5 Neutral boundary layer similarity relationship lists.- 9.6 Convective boundary layer similarity relationship lists.- 9.7 The log wind profile.- 9.8 Rossby-number similarity and profile matching.- 9.9 Spectral similarity.- 9.10 Similarity scaling domains.- 9.11 References.- 9.12 Exercises.- 10 Measurement and Simulation Techniques.- 10.1 Sensor and measurement categories.- 10.2 Sensor lists.- 10.3 Active remote sensor observations of morphology.- 10.4 Instrument platforms.- 10.5 Field experiments.- 10.6 Simulation methods.- 10.7 Analysis methods.- 10.8 References.- 10.9 Exercises.- 11 Convective Mixed Layer.- 11.1 The unstable surface layer.- 11.2 The mixed layer.- 11.3 Entrainment zone.- 11.4 Entrainment velocity and its parameterization.- 11.5 Subsidence and advection.- 11.6 References.- 11.7 Exercises.- 12 Stable Boundary Layer.- 12.1 Mean Characteristics.- 12.2 Processes.- 12.3 Evolution.- 12.4 Other Depth Models.- 12.5 Low-level (nocturnal) jet.- 12.6 Buoyancy (gravity) waves.- 12.7 Terrain slope and drainage winds.- 12.8 References.- 12.9 Exercises.- 13 Boundary Layer Clouds.- 13.1 Thermodynamics.- 13.2 Radiation.- 13.3 Cloud entrainment mechanisms.- 13.4 Fair-weather cumulus.- 13.5 Stratocumulus.- 13.6 Fog.- 13.7 References.- 13.8 Exercises.- 14 Geographic Effects.- 14.1 Geographically generated local winds.- 14.2 Geographically modified flow.- 14.3 Urban heat island.- 14.4 References.- 14.5 Exercises.- Appendices.- A. Scaling variables and dimensionless groups.- B. Notation.- C. Useful constants parameters and conversion factors.- D. Derivation of virtual potential temperature.- Errata section.

A hybrid analog-ensemble, convolutional-neural-network method for post-processing precipitation forecasts

Abstract: An ensemble precipitation forecast post-processing method is proposed by hybridizing the Analog Ensemble (AnEn), Minimum Divergence Schaake Shuffle (MDSS), and Convolutional Neural Network (CNN) methods. This AnEn-CNN hybrid takes the ensemble mean of Global Ensemble Forecast System (GEFS) 3-hourly precipitation forecasts as input and produces bias-corrected, probabilistically calibrated, and physically realistic gridded precipitation forecast sequences out to 7-days. The AnEn-CNN hybrid post-processing is trained on the European Centre for Medium-Range Weather Forecasts Reanalysis version 5 (ERA5), and verified against station observations across British Columbia (BC), Canada, from 2017 to 2019. The AnEn-CNN hybrid produces more skillful forecasts than a quantile-mapped GEFS baseline and other conventional AnEn methods, with a roughly 10% increase in Continuous Ranked Probability Skill Score. Further, it outperforms other AnEn methods by 0-60% in terms of Brier Skill Score (BSS) for heavy precipitation periods across disparate hydrological regions. Longer forecast lead times exhibit larger performance gains. Verification against 7-day accumulated precipitation totals for heavy precipitation periods also demonstrates that precipitation sequences are realistically reconstructed. Case studies further show that the AnEn-CNN hybrid scheme produces more realistic spatial precipitation patterns and precipitation intensity spectra. This work pioneers the combination of conventional statistical post-processing and neural networks, and is one of only a few studies pertaining to precipitation ensemble post-processing in BC.

Optimizing Analog Ensembles for Sub-Daily Precipitation Forecasts

Abstract: This study systematically explores existing and new optimization techniques for analog ensemble (AnEn) post-processing of hourly to daily precipitation forecasts over the complex terrain of southwest British Columbia, Canada. An AnEn bias-corrects a target model forecast by searching for past dates with similar model forecasts (i.e., analogs), and using the verifying observations as ensemble members. The weather variables (i.e., predictors) that select the best past analogs vary among stations and seasons. First, different predictor selection techniques are evaluated and we propose an adjustment in the forward selection procedure that considerably improves computational efficiency while preserving optimization skill. Second, temporal trends of predictors are used to further enhance predictive skill, especially at shorter accumulation windows and longer forecast horizons. Finally, this study introduces a modification in the analog search that allows for selection of analogs within a time window surrounding the target lead time. These supplemental lead times effectively expand the training sample size, which significantly improves all performance metrics—even more than the predictor weighting and temporal-trend optimization steps combined. This study optimizes AnEns for moderate precipitation intensities but also shows good performance for the ensemble median and heavier precipitation rates. Precipitation is most challenging to predict at finer temporal resolutions and longer lead times, yet those forecasts see the largest enhancement in predictive skill from AnEn post-processing. This study shows that optimization of AnEn post-processing, including new techniques developed herein, can significantly improve computational efficiency and forecast performance.

An Introduction To The Boundary Element Method Bem

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Improved Analog Ensemble Formulation for 3-hourly Precipitation Forecasts

Abstract: Analog ensembles (AnEns) traditionally use a single numerical weather prediction (NWP) model to make a forecast, then search an archive to find a number of past similar forecasts (analogs) from that same model, and finally retrieve the actual observations corresponding to those past forecasts to serve as members of an ensemble forecast. This study investigates new statistical methods to combine analogs into ensemble forecasts and validates them for 3-hourly precipitation over the complex terrain of British Columbia, Canada. Applying the past analog error to the target forecast (instead of using the observations directly) reduces the AnEn dry bias and makes prediction of heavy-precipitation events probabilistically more reliable—typically the most impactful forecasts for society. Two variants of this new technique enable AnEn members to obtain values outside the distribution of the finite archived observational dataset—that is, they are theoretically capable of forecasting record events, whereas traditional analog methods cannot. While both variants similarly improve heavier precipitation events, one variant predicts measurable precipitation more often, which enhances accuracy during winter. A multi-model AnEn further improves predictive skill, albeit at higher computational cost. AnEn performance shows larger sensitivity to the grid spacing of the NWP than to the physics configuration. The final AnEn prediction system improves the skill and reliability of point forecasts across all precipitation intensities.

Atmospheric Modeling, Data Assimilation and Predictability

Abstract: This comprehensive text and reference work on numerical weather prediction, first published in 2002, covers not only methods for numerical modeling, but also the important related areas of data assimilation and predictability. It incorporates all aspects of environmental computer modeling including an historical overview of the subject, equations of motion and their approximations, a modern and clear description of numerical methods, and the determination of initial conditions using weather observations (an important science known as data assimilation). Finally, this book provides a clear discussion of the problems of predictability and chaos in dynamical systems and how they can be applied to atmospheric and oceanic systems. Professors and students in meteorology, atmospheric science, oceanography, hydrology and environmental science will find much to interest them in this book, which can also form the basis of one or more graduate-level courses.

Review of the Governing Equations, Computational Algorithms, and Other Components of the Models-3 Community Multiscale Air Quality (CMAQ) Modeling System

Abstract: This article describes the governing equations, computational algorithms, and other components entering into the Community Multiscale Air Quality (CMAQ) modeling system. This system has been designed to approach air quality as a whole by including state-ofthe-science capabilities for modeling multiple air quality issues, including tropospheric ozone, fine particles, acid deposition, and visibility degradation. CMAQ was also designed to have multiscale capabilities so that separate models were not needed for urban and regional scale air quality modeling. By making CMAQ a modeling system that addresses multiple pollutants and different spatial scales, it has a “one-atmosphere” perspective that combines the efforts of the scientific community. To implement multiscale capabilities in CMAQ, several issues (such as scalable atmospheric dynamics and generalized coordinates), which depend on the desired model resolution, are addressed. A set of governing equations for compressible nonhydrostatic atmospheres is available to better resolve atmospheric dynamics at smaller scales. Because CMAQ is designed to handle scale-dependent meteorological formulations and a large amount of flexibility, its governing equations are expressed in a generalized coordinate system. This approach ensures consistency between CMAQ and the meteorological modeling system. The generalized coordinate system determines the necessary grid and coordinate transformations, and it can accommodate various vertical coordinates and map projections. The CMAQ modeling system simulates various chemical and physical processes that are thought to be important for understanding atmospheric trace gas transformations and distributions. The modeling system contains three types of modeling components (Models-3): a meteorological modeling system for the description of atmospheric states and motions, emission models for man-made and natural emissions that are injected into the atmosphere, and a chemistry-transport modeling system for simulation of the chemical transformation and fate. The chemical transport model includes the following process modules: horizontal advection, vertical advection, mass conservation adjustments for advection processes, horizontal diffusion, vertical diffusion, gas-phase chemical reactions and solvers, photolytic rate computation, aqueous-phase reactions and cloud mixing, aerosol dynamics, size distributions and chemistry, plume chemistry effects, and gas and aerosol deposition velocity estimation. This paper describes the Models-3 CMAQ system, its governing equations, important science algorithms, and a few application examples. This review article cites 114 references. DOI: 10.1115/1.2128636

Estimates of the annual net carbon and water exchange of forests: the EUROFLUX methodology

Abstract: Publisher Summary The chapter has described the measurement system and the procedure followed for the computation of the fluxes and the procedure of flux summation, including data gap filling strategy, night flux corrections and error estimation. It begins with the introduction of estimates of the annual net carbon and water exchange of forests using the EUROFLUX methodology. The chapter then provides us with the theory and moves on to discuss the eddy covariance system and its sonic anemometer, temperature fluctuation measurements, infrared gas analyser, air transport system, and tower instrumentation. Additional measurements are also given in the chapter. Data acquisition and its computation and correction is discussed next in the chapter by giving its general procedure, half-hourly means (co-)variances and uncorrected fluxes, intercomparison of software, and correction for frequency response losses. The chapter has also discussed about quality control and four criteria are investigated here for the same. Spatial representativeness of measured fluxes and summation procedure are reviewed. The chapter then moves on to the discussion of data gap filling through interpolation and parameterization and neural networks. Corrections to night-time data and error estimation are also explored in the chapter. Finally, the chapter closes with conclusions.

Technical note: The Lagrangian particle dispersion model FLEXPART version 6.2

Abstract: The Lagrangian particle dispersion model FLEXPART was originally (about 8 years ago) designed for calculating the long-range and mesoscale dispersion of air pollutants from point sources, such as after an accident in a nuclear power plant In the meantime FLEXPART has evolved into a comprehensive tool for atmospheric transport modeling and analysis Its application fields were extended from air pollution studies to other topics where atmospheric transport plays a role (eg, exchange between the stratosphere and troposphere, or the global water cycle) It has evolved into a true community model that is now being used by at least 25 groups from 14 different countries and is seeing both operational and research applications A user manual has been kept actual over the years and was distributed over an internet page along with the model's source code In this note we provide a citeable technical description of FLEXPART's latest version (62)

The Surface Energy Balance System (SEBS) for estimation of turbulent heat fluxes

Abstract: A Surface Energy Balance System (SEBS) is proposed for the estimation of atmospheric turbulent fluxes and evaporative fraction using satellite earth observation data, in combination with meteorological information at proper scales SEBS consists of: a set of tools for the determination of the land surface physical parameters, such as albedo, emissivity, temperature, vegetation coverage etc, from spectral reflectance and radiance measurements; a model for the determination of the roughness length for heat transfer; and a new formulation for the determination of the evaporative fraction on the basis of energy balance at limiting cases Four experimental data sets are used to assess the reliabilities of SEBS Based on these case studies, SEBS has proven to be capable to estimate turbulent heat fluxes and evaporative fraction at various scales with acceptable accuracy The uncertainties in the estimated heat fluxes are comparable to in-situ measurement uncertainties Keywords: Surface energy balance, turbulent heat flux, evaporation, remote sensing