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GCRF African SWIFT
GCRF African-SWIFT is a programme of research and capability building, led by the National Centre for Atmospheric Science (NCAS), and funded by UK Research and Innovation Global Challenges Research Fund. The project aims to deliver a step change in African weather forecasting capability from hourly to seasonal timescales, and build research capability to continue forecasting improvements in Africa for the foreseeable future.
The GCRF African-SWIFT team works with forecast users across sectors from aviation to agriculture, energy, water and emergency response to understand how to ...
The Forecast Process: Using the Forecast Funnel
This lesson was developed by meteorologist, Dr. Mick Pope, with sponsorship from the Australian Bureau of Meteorology (BoM). The lesson is a somewhat broad-brush review of the overall forecast process, but with specific application of the forecast funnel approach as used by Australia's Bureau of Meteorology (BoM). The forecast process components include decision support and communication, use of numerical weather prediction, and applying the forecast funnel approach. The forecast funnel is described in detail, along with the forecaster time pyramid, and it is applied using a BoM forecast polic ...
CESM Distance Learning Course
The Community Earth System Model (CESM) is a fully-coupled, community, global climate model that provides state-of-the-art computer simulations of the Earth's past, present, and future climate states. The CESM Distance Learning Course is based on the CESM Tutorial held annually at the National Center for Atmospheric Research (NCAR) in Boulder, Colorado. This course consists of 12 lectures and 4 practical sessions on simulating the climate system and practical sessions on running Community Earth System Model (CESM), modifying components, and analyzing data. The course is targeted at the graduat ...
Convection-allowing Models (CAMs): Winter Applications
In this lesson, forecasters will practice using guidance from different convection-allowing models (CAMs) over the short term. As they review and analyze the model guidance they will encounter some of the advantages and limitations of using CAMs for winter weather.
What's New in NBM v3.2
The U.S. National Weather Service (NWS) National Blend of Models (NBM) is scheduled to be upgraded to version 3.2 in November 2019. It includes the first probabilistic blended guidance for temperature, precipitation, snow, and ice. There are more blended forecast products for aviation, marine, water resources, fire weather, winter weather, and tropical weather. Version 3.2 uses more model components to improve guidance, and introduces a new Guam domain. For a transcript, see What’s New in NBM v3.2. (https://www.meted.ucar.edu/nwp/blend_v32_video/NBM_v32_script.pdf)
Basic Satellite and NWP Integration
NWP is one of the most important forecasting tools in our toolbox. Yet identifying when/where it isn’t capturing reality is difficult. In the short-term forecasting range, it is important as a forecaster to identify when/where NWP output isn’t matching reality. Then you can make appropriate changes to the forecast output. To find those mismatches anywhere in the world, one of the best tools is satellite imagery. In this lesson, we will focus on a few cases using satellite imagery to help identify mismatched features/processes between the satellite imagery and the NWP. Anyone trying to add valu ...
NWP Comparisons: Total-column Variables
Another way to try to find mismatches between observed weather and NWP output is by using total-column variables. There are a few of them to choose from, and they make for a relatively simple comparison method for finding correctable mismatches. In this lesson, we'll address appropriate methods for making these comparisons and build to a point where we will focus on bigger picture atmospheric processes. This is the second in a series of video lessons that introduces three different methods for modifying NWP output to add human value to forecasts.
PV Modification
You know what PV is, yet aren't quite sure how to modify it to make a better forecast. In this short lesson, we will discuss how to modify the PV surface to match water vapour imagery and how those adjustments affect the surface sensible weather. This is the fifth in a series of video lessons that introduces three different methods for modifying NWP output to add human value to forecasts. Pre-requisite Knowledge: Satellite Water Vapour Interpretation -- Short Course
Interpreting and Communicating EPS Guidance: Germany Winter Event
This 45-minute lesson briefly introduces learners to the benefits of using probabilistic forecast information to assess weather and communicate forecast uncertainties. Learners will explore a winter weather event in Germany and practice synthesizing deterministic and probabilistic forecast guidance to better understand forecast uncertainties based on lead-time. Also, learners will decide how to best communicate the potential weather threats and impacts to local end users. The lesson is another component of the Forecast Uncertainty: EPS Products, Interpretation, and Communication distance learn ...
Introduction to Modifying NWP Output
Surface observations are usually the first place we go when trying to find mismatches between observed weather and NWP output. We'll talk in this lesson about appropriate methods for making those comparisons and build to a point where we will focus on bigger picture atmospheric processes. This is the first in a series of video lessons that introduces three different methods for modifying NWP output to add human value to forecasts.
Adjusting NWP: Direct Comparison
If there were a way to make direct comparisons between satellite imagery and NWP output, that would appear to be the best possible way to find mismatches between the observed weather and NWP output. In this lesson, we'll address possible methods for making direct comparisons, starting with pseudo or synthetic satellite imagery and building to a point where we focus on a relatively unused NWP output. This is the third in a series of video lessons that introduces three different methods for modifying NWP output to add human value to forecasts. Pre-requisite Knowledge: Satellite Water Vapour Inte ...
Interpreting and Communicating EPS Guidance: Iberian Heat Wave
This 45-minute lesson briefly introduces learners to the benefits of using probabilistic forecast information to assess the weather and communicate forecast uncertainties. Learners will explore a heat wave event in Spain and practice interpreting EPS forecast products effectively to determine various forecast parameters based on lead-time. Also, learners will decide how to best communicate the potential weather threats and impacts information to local end users.
What's New in the National Blend of Models version 3.1
Intended for U.S. National Weather Service forecasters, this short video describes changes to the NWS National Blend of Models when it was updated to v3.1. These changes include: More global, mesoscale, and ensemble components; Increased spatial resolution of some components; New and improved weather elements for aviation, QPF, winter, fire, and marine weather forecasting; Significant wave height for offshore waters and the Great Lakes; Improved bias correction; MOS-like text products; Shortened NBM forecast projections delivered at 19 UTC. For an illustrated transcript, see What’s New in NBM ...
Mesoscale Model Components of the National Blend of Models Version 3.0
The National Weather Service National Blend of Models (NBM) was updated to version 3.0 on 27 July 2017. Changes include: Eight new components for the contiguous U.S. (CONUS) and Alaska, including four deterministic models, two ensemble systems, and two post-processed statistical components Five new components for Hawaii and Puerto Rico Expanded forecast domains for the CONUS and Alaska A “Time of Day” (ToD), rather than NWP model, initial time concept Hourly NBM forecasts, with short, day 2-4, and extended forecasts Updated NBM guidance available 50-60 minutes after hourly run time New weather ...
Unified Terrain in the National Blend of Models
This lesson discusses errors associated with the use of inconsistent terrain in the analyses in the Real-Time and the Un-Restricted Mesoscale Analyses (RTMA and URMA, respectively), and in downscaling numerical weather prediction model data to the resolution of the U.S. National Weather Service National Blend of Models (NBM). The sources of these inconsistencies are examined, and the errors that result are discussed. A solution is to use a unified, consistent terrain in the analyses and the NBM. This solution is only partial however, as resolution of small, meteorologically significant feature ...
National Water Model, Part 1: Science and Products
This lesson provides an introduction to the benefits, important input (forcing data), and key products of the National Water Model. Both official and evolving products are presented. The lesson uses the flooding associated with Hurricane Harvey in August 2017 to demonstrate key products.
Interpreting and Communicating EPS Guidance: British Columbia Winter Storm
This 45-minute lesson provides an opportunity to use ensemble prediction system products to evaluate uncertainty in the forecast and then communicate that information effectively to a public audience. The lesson places learners in the role of a Meteorological Service of Canada forecaster who must assess forecast uncertainty and then issue early warning notifications to decision-makers regarding the winter storm. In a subsequent work shift during the event, the learner must effectively deliver forecast information via social media and respond to questions from the general public. The lesson is ...
Statistical Methods in the NWS National Blend of Global Models Part 2
This lesson introduces users to the statistics used in generating the various weather element forecasts included in version 2 and 3 of the U.S. National Weather Service (NWS) National Blend of Global Models (NBM). This Level 3 lesson is intended for forecasters and users of NWS forecast products; some prior knowledge of numerical weather prediction and statistics is useful. Learners will be introduced to the analysis of record used to calibrate the NBM’s bias and error estimates. Learners will also explore the bias correction, weighting, and post-processing procedures used to produce the forec ...
Web-Based Ensemble Tools: Ensemble Situational Awareness Table
The National Weather Service (NWS) Western Region (WR) has developed a Ensemble Situational Awareness Table (ESAT), which uses probabilistic NWP to bring attention to the potential for extreme events, especially in middle-range forecasts. The lesson, which is the first of two on the ESAT, describes the ESAT and how its data can be used to support assessment of extreme weather event forecasts. Additionally, statistical methods, including employment of reanalysis and NWP model climatologies (R-Climate and M-Climate, respectively) are described in reference to the products available in the ESAT.
Introduction to EPS Theory
This lesson introduces the concepts needed to understand and use ensemble prediction system (EPS) products. It describes basic statistical quantities and methods used to develop EPS products, such as probability distribution functions (PDFs) and cumulative distribution functions (CDFs). From there, it discusses ways of using EPS products compared to deterministic products. The final section briefly introduces nine common EPS products. The lesson is a prerequisite for the EPS Products Reference Guide.
Forecasting Aviation Convective Impacts with INSITE
The National Weather Service (NWS) has developed the INSITE tool (INtegrated Support for Impacted air-Traffic Environments) to improve NWS convective impact forecasts by providing functionality that enables forecasters to include more precise impact areas in aviation convective weather forecast products. The tool lets forecasters identify potential constraints to the National Airspace System by combining forecast weather and air-traffic data. Improved convective weather forecast products can reduce delays in air-traffic and increase efficiency in the National Airspace System (NAS). In this 45- ...
Communicating Forecast Uncertainty, European Case
This lesson is a follow-on to COMET’s Communicating Forecast Uncertainty lesson, which introduces research findings on the effective communication of uncertainty information and enables learners to apply them to a North American case. This lesson focuses on a European winter weather case and provides an additional opportunity to evaluate end-user needs and formulate effective responses to their questions based on the research findings. Learners are strongly encouraged to take Communicating Forecast Uncertainty before starting this lesson. The lesson is aimed at experienced forecasters with kno ...
Limitations of High-Resolution NWP Models
This scenario-based lesson examines how the limitations of high-resolution NWP forecasts affect their analyses and forecasts of winter and severe weather, and how best to use the output in light of the limitations. The lesson is structured around a case that occurred in Texas in December 2015 when winter weather and severe weather hit Amarillo and Dallas-Ft. Worth, respectively. As users go through the case, they learn how spin-up time, errors in initial conditions, and deficiencies in the modeling of mesoscale phenomena can impact high-resolution forecasts in the NAM nest and HRRR models.
EPS Products Reference Guide
The EPS Products Reference Guide provides information about nine commonly used ensemble prediction system (EPS) products. Each has a description, tips for interpreting and using it effectively, a list of its strengths and weaknesses, and practice exercises. The Guide is meant to be used as reference material and does not have a quiz.
Guidelines for Nowcasting Techniques
The purpose of the WMO nowcasting guidelines presented here is to help National Meteorological and Hydrological Services (NMHSs) by providing them with information and knowledge on how to implement a nowcasting system with the resources available to them and an understanding of the current state of science and technology.
Using NWP Lightning Products in Forecasting
This lesson introduces two numerical weather prediction (NWP) lightning hazard products that forecasters can use during a convective meteorological watch and to assess lightning risk at Day 2 and beyond. The first product is the Flash Rate Density, a derived, deterministic lightning product implemented in some NCEP high-resolution NWP models. The second product, the SPC Calibrated Thunderstorm Probability, combines forecasts of measurable precipitation and favorable lightning environments determined from the Cloud Physics Thunder Parameter. Information about these products is presented in the ...
Weather Forecast Uncertainty Information for Everyday Users - Presentation at 2015 Workshop on Communicating Uncertainty to Users of Weather Forecasts
Although previous research suggests that we are not very good at reasoning with uncertainty, the research described in this talk is far more encouraging. Unlike earlier work that compares peoples' decisions to a rational standard, these experiments compared decisions made by people with uncertainty information to decisions made by people without uncertainty information. The results suggest that including specific numeric uncertainty estimates in weather forecasts leads to better decisions. This talk was part of Meteorological Service of Canada's 2015 Workshop on Communicating Uncertainty to Us ...
Short-Range Ensemble Forecast Upgrade
The Short-Range Ensemble Forecast (SREF) system underwent a major upgrade in Fall 2015. The intended result of the upgrade was to improve the SREF ensemble spread and probabilistic skill, and to reduce a cool, wet bias in surface and near-surface temperatures and moisture. This 20-minute lesson addresses the changes to improve the SREF, including the increase in ensemble size, the increase in initial condition and model physics diversity, and drier land surface parameters to lessen the cool, wet bias. Each is introduced by comparing the old and new SREF forecasts for a potential winter storm f ...
SatFC-G: GOES-R Impacts on Satellite Data Assimilation
This five minute lesson presents a brief overview of how GOES-R observations are expected to support and potentially enhance NWP for various analysis and forecast applications. This lesson is a part of the NWS Satellite Foundation GOES-R Course.
HiresW HREF Upgrade
This 20-minute lesson presents upgraded versions of the two NWP models used as High Resolution Window (HiresW), the Weather Research and Forecasting-Advanced Research WRF (WRF-ARW) and the Non-Hydrostatic Multiscale Model on the B-grid (NMMB). Domains include the CONtinental US (CONUS), Alaska, Hawaii, Guam, and Puerto Rico. The CONUS runs of the NMMB and WRF-ARW became part of a new High Resolution Ensemble Forecast (HREF) system in 2015, the first of its kind produced at the National Centers for Environmental Prediction. To familiarize the operational forecaster with the HREF, products from ...
Verification Methods in the NWS National Blend of Global Models
This lesson introduces learners to the methods used in verifying the various weather element forecasts included in Version 1.0 of the U.S. National Weather Service (NWS) National Blend of global Models (NBM). This Level 2 lesson is intended for forecasters and users of NWS forecast products; some prior knowledge of numerical weather prediction and statistics is useful. Learners will be introduced to the analysis of record used to verify the NBM. Learners will also explore single event, grid-to-observation, and grid-to-grid verification methods, as well as how to interpret the results using the ...
Statistical Methods in the NWS National Blend of Global Models
This lesson introduces users to the statistics used in generating the various weather element forecasts included in Version 1.0 of the U.S. National Weather Service (NWS) National Blend of global Models (NBM). This Level 3 lesson is intended for forecasters and users of NWS forecast products; some prior knowledge of numerical weather prediction and statistics is useful. Learners will be introduced to the analysis of record used to calibrate the NBM’s bias and error estimates. Learners will also explore the downscaling, bias correction, and weighting procedures applied to the model products, an ...
Communicating Forecast Uncertainty
This scenario-based lesson introduces the topic of communicating forecast uncertainty to decision-makers, such as emergency managers, related industry professionals, the public, and other end-users. In a case that spans the lesson, learners begin by developing a forecast discussion using deterministic data, refine it with probabilistic ensemble data, and evaluate how well it conveys uncertainty information. Then they assume several end-user roles, assessing how well the forecast discussion addresses their needs. From there, important research findings on communicating uncertainty are discussed ...
Introduction to the NWS National Blend of Global Models
The National Blend of Global Models was developed to utilize the best available science and provide a consistent National Weather Service forecast product across the U.S. This lesson describes the background and motivation for the National Blend and includes comparisons of Blend forecasts with current guidance. The lesson also offers a short summary of future plans and training related to the National Blend.
Assessing NWP with Water Vapour Imagery
You've seen it happen repeatedly. Forecasters have a tough forecast ahead of them. But how are they supposed to know which model data will be the best one to help them come to a conclusion about the situation? In situations like this, the first step should always be to assess the model data against a set of current observations that should show a 1-to-1 relationship with the model output. Which variable should be plotted? On which surface? Which current observations will make the most sense to assess against? If you know the answers to some, but not all of these questions, find these answers a ...
Operational Models Encyclopedia
The availability of numerical guidance from NWP models has been an important component of operational forecasting for decades. For many, the output from this numerical guidance was produced by a mysterious “black box”. Rules for using and adjusting the guidance for operational forecasters were often subjective “Rules of Thumb” based on experience rather than based on quantitative analysis. To open up this “black box”, we produced this web-based “Operational Models Encyclopedia” linking both generic information on how NWP models work, and specifics on physical parameterizations, dynamics, and d ...
NWP Essentials: NWP and Forecasting
This lesson introduces forecasters to the complex and multifaceted process for creating a forecast. It also discusses how NWP fits into that process. In addition, the lesson provides a broad overview of the basic components of NWP and how they combine to produce a model forecast.
NWP Essentials: Structure and Dynamics
This lesson is focused on how a model forecast and the interpretation of that forecast, is affected by the basic design of the model. Topics include how meteorological variables are represented in grid point and spectral models, fundamental differences between hydrostatic and nonhydrostatic models, horizontal resolution of orographic and free-atmosphere features, vertical coordinate systems and how they affect the vertical resolution of features in the model forecast.
Gridded Products in the NWS National Blend of Global Models
This lesson introduces users to the five different guidance products that will be included in Version 1.0 of the U.S. National Weather Service (NWS) National Blend of global Models (NBM). The primary audience for this lesson includes forecasters and users of NWS forecast products; some prior knowledge of numerical weather prediction is useful. Learners will explore how model guidance from the Global Forecast System, Global Ensemble Forecast System, Canadian Meteorological Centre Ensemble, Ensemble Kernel Density Model Output Statistics (MOS) and gridded GFS MOS is produced. The strengths and l ...
Introduction to Tropical Meteorology, 2nd Edition: Chapter 6 Vertical Transport
This chapter examines vertical transport of heat, moisture, momentum, trace gases, and aerosols, including the role of tropical deep convection and turbulence. Diurnal and seasonal variations in surface fluxes and boundary layer depth are examined. The boundary layer is compared over the ocean, humid, and dry tropics, including its role in dispersing chemicals and aerosols. Boundary layer clouds are examined in terms of their connection to sub-cloud layer properties. Comparisons are made between heat and moisture transport under a variety of convective modes such as mesoscale convective system ...
NWP Essentials: Model Physics
This lesson describes model parameterizations of surface, PBL, and free atmospheric processes. It specifically addresses how models treat these processes, how such processes can potentially interact with each other, and how they can influence forecasts of sensible weather elements. Topics covered include: soil moisture processes, radiative processes involving clouds, and turbulent processes in the PBL and free atmosphere.
NWP Essentials: Precipitation and Clouds
Both the processes of convection and of rainfall formation are typically subgrid scale, and require parameterisation. This lesson examines two types of precipitation parameterisation used by models: Convective parameterisation Microphysics The lesson also discusses how to identify when these parameterisations are not performing well and steps to address the issues that arise.
HYSPLIT Applications for Emergency Decision Support, 2nd Edition
This module helps forecasters provide decision support services during hazardous materials emergencies. Topics covered include: Types of weather data inputs required for short-range dispersion models typically used by emergency managers Types of inputs required to run the web version of the HYSPLIT model with the ALOHA source term, which is now available to NWS forecasters The types and scales of events that are appropriate and inappropriate for modeling by HYSPLIT Key uncertainties that can cause misleading dispersion model forecasts The processes and limitations of CAMEO/ALOHA and HYSPLIT Ho ...
NWP Essentials: Data Assimilation
This lesson introduces the processes of model data assimilation. It also discusses the impacts of errors in the data assimilation on model forecasts and how a human forecaster can compensate for them.
Ensemble Applications in Winter
This lesson provides an introduction to ensemble forecast systems using an operational case study of the Blizzard of 2013 in Southern Ontario. The module uses models available to forecasters in the Meteorological Service of Canada, including Canadian and U.S. global and regional ensembles. After briefly discussing the rationale for ensemble forecasting, the module presents small lessons on probabilistic ensemble products useful in winter weather forecasting, immediately followed by forecast applications to a southern Ontario case. The learner makes forecasts for the Ontario Storm Prediction Ce ...
Introduction to Aircraft Meteorological Data Relay (AMDAR)
Introduction to Aircraft Meteorological Data Relay (AMDAR) provides national meteorological services worldwide, airlines, and aviation organizations with information about the World Meteorological Organization (WMO) aircraft-based observing system. The audience includes meteorological service managers and providers, observational development groups, the aviation industry, and others interested in benefiting from an aircraft-based observing system in their region. The content includes interviews with several experts to provide examples of AMDAR use for both meteorological and aviation applicati ...
How Satellite Observations Impact NWP
Satellite observations have a huge impact on numerical weather prediction (NWP) model analyses and forecasts, with sounding data from polar orbiting and GPS-radio occultation satellites reducing model forecast error by almost half. All of this despite the fact that NWP models only assimilate 5% of all satellite observations! This lesson discusses the use of satellite observations in NWP and how model limitations prevent more of the data from being assimilated. The lesson begins by briefly describing the history of satellite observations in NWP and their impact on NWP model forecast skill. The ...
Introduction to Ensembles: Forecasting Hurricane Sandy
This module provides an introduction to ensemble forecast systems with an operational case study of Hurricane Sandy. The module concentrates on models from NCEP and FNMOC available to forecasters in the U.S. Navy, including NAEFS (North American Ensemble Forecast System), and NUOPC (National Unified Operational Prediction Capability). Probabilistic forecasts of winds and waves developed from these ensemble forecast systems are applied to a ship transit and coastal resource protection. Lessons integrated in the case study provide information on ensemble statistics, products, bias correction and ...
WRF-EMS Aviation Products
This lesson illustrates how numerical guidance from the Weather Research and Forecasting Model - Environmental Modeling System (WRF-EMS) can be added to surface observations, satellite graphics, and conceptual models of important aviation phenomena, to produce TAFs. Specifically, the lesson describes how visibility, cloud ceilings, and the flight categories variables provide values for aviation forecasts in Africa.
Gridded Forecast Verification and Bias Correction
To become a better forecaster, it is not enough to simply know that a forecast did not verify. One must determine what happened and identify methods for improvement through forecast verification. The forecast verification process helps answer questions like: Is there a particular method that has been more effective in the past in similar circumstances? Is there guidance that is more accurate? Are there persistent biases in our forecasts? Do our forecasts perform better in certain regimes than others? In the era of gridded forecasts, grid-based verification provides more information about the s ...
Introduction to Climate Models
This module explains how climate models work. Because the modeling of both weather and climate share many similarities, the content throughout this module draws frequent comparisons and highlights the differences. We explain not only how, but why climate models differ from weather models. To do so, we explore the difference between weather and climate, then show how models are built to simulate climate and generate the statistics that describe it. We conclude with a discussion of models are tuned and tested. Understanding how climate responds to changes in atmospheric composition and other fac ...
Optimizing the Use of Model Data Products
Each model forecast tells a story about the weather events to unfold. As a forecaster, you employ diagnostics to understand and interpret that story, in order to modify it, blend it with other stories, and generate your own forecast. This lesson will help you sift through the abundance of model data so you can understand and interpret the model’s story. Other lessons cover evaluating the model’s story against observations and against your conceptual models of the evolving situation, blending the stories, and adjusting the forecast to add value over an objective forecast. The diagnostic approac ...
Preparing to Evaluate NWP Models
This lesson prepares the forecaster to evaluate NWP analyses and forecasts using physically based conceptual models of the atmosphere, and the "Vertical Phenomenon Analysis Funnel". This funnel divides the atmosphere into three sections: lower stratosphere and tropopause, mid-to-upper troposphere, and lower troposphere. We discuss tools to use and atmospheric features to assess for each section of the atmosphere, using interactive case examples, and summarize the methodology with a comprehensive example. Finally, we compare model capabilities and the time and space scales of assessment tools u ...
Adding Value to NWP Guidance
The purpose of this module is to train operational meteorologists at NWS WFOs and elsewhere how to maximize opportunities to add value to NWP forecasts. The training includes use of the methods and tools from earlier modules in Course 2 of Effective Use of NWP in the Forecast Process. Included in the module are two case examples for the short- and medium-range. Additionally, a WES "caselet" is available from the NWS Warning Decision Training Branch that further illustrates how to add value to NWP guidance.
Determining Plausible Forecast Outcomes
The content of this lesson will assist the forecaster with the third step of the forecast process, namely, determining plausible forecast outcomes forward in time. The lesson will highlight the role of probabilistic forecast tools to assess the degree of uncertainty in a forecast, as well as suggest an approach for evaluating past and present model performance.
How NWP Fits into the Forecast Process
This introductory module presents the basis for the other modules in the new NWP Series: Effective Use of NWP in the Forecast Process. We present the four steps in the forecast process, as determined by best practices in U.S. National Weather Service (NWS) Weather Forecast Offices (WFOs). Then we show the module topics and summarize how to navigate through the course.
Analysis, Diagnosis, and Short-Range Forecast Tools
This lesson is divided into three sections. The first section discusses the importance of analysis and diagnosis in evaluating NWP in the forecast process. In section two, we discuss a methodology for dealing with discrepancies between both the official forecast and NWP compared to analysis and diagnosis. The third section shows a representative example of the methodology.
Bias Correction of NWP Model Data
The lesson "Bias Correction of NWP Model Data" first describes what affects bias in NWP models: regime continuity, timing of features that affect sensible weather, and existence (or not) of those features in the models. After discussing examples of each of these, three bias correction methods are presented: Model Output Statistics (MOS), decaying average, and a SmartInit tool developed at the Boise ID WFO called BOIVerify. Situations where each perform well and each perform poorly are discussed. Finally, after a comprehensive review question and feedback, a summary and series of points to reme ...
Downscaling of NWP Data
Forecasters utilize downscaled NWP products when producing forecasts of predictable features, such as terrain-related and coastal features, at finer resolution than provided by most NWP models directly. This lesson is designed to help the forecaster determine which downscaled products are most appropriate for a given forecast situation and the types of further corrections the forecaster will have to create. This module engages the learner through interactive case examples illustrating and comparing the major capabilities and limitations of some commonly-used downscaled products for 2-m tempera ...
Forecasting Dust Storms - Version 2
Forecasting Dust Storms Version 2 provides background and operational information about dust storms. The first part of the module describes dust source regions, the life cycle of a dust storm, and the major types of dust storms, particularly those found in the Middle East. The second part presents a process for forecasting dust storms and applies it to a case in the Middle East. Although the process refers to U.S. Department of Defense models and tools, it can easily be adapted to other forecast requirements and data sources. Note that this module is an updated version of the original one publ ...
Introduction to Tropical Meteorology, 2nd Edition, Chapter 9: Observations, Analysis, and Prediction
The chapter describes the challenges of tropical weather forecasting. We examine types of observations and weather analysis techniques used by tropical forecasters. Those analysis tools are applied to examples of tropical synoptic weather systems as well as mesoscale analysis and nowcasting. The last three sections focus on numerical weather prediction (NWP) including: the fundamentals, data assimilation, comparisons of statistical and dynamical models, ensemble techniques, cumulus convection in NWP, tropical cyclone prediction, and methods of forecast verification and validation. We have spec ...
Understanding the Role of Deterministic versus Probabilistic NWP Information
Understanding the Role of Deterministic versus Probabilistic NWP Information is part of the "NWP Training Series: Effective Use of NWP in the Forecast Process." This lesson first covers deterministic (single) NWP model forecasts and explains advantages and limitations through a case example. Then it discusses overcoming the limitations in deterministic forecasts through the use of ensemble forecast systems, and the use of deterministic and probabilistic forecasts together, through case examples.
Effective Use of High-Resolution Models
High-resolution models have transitioned from research into forecast operations, helping forecasters utilize additional mesoscale information after accounting for the inherent unpredictability of many small-scale phenomena. This module covers the major capabilities and limitations of models run without a convective parameterization using grid spacings of around 4 km or less. Model forecast interpretation issues are discussed, including introducing convective mode diagnostics such as updraft helicity and interpreting the forecast as an event prediction rather than as a precise point forecast. M ...
Introduction to the North American Ensemble Forecast System (NAEFS)
This webcast introduces the forecaster to the new multiple-forecast-center North American Ensemble Forecast System (NAEFS). Beginning with a brief review of the theory behind ensemble prediction, this presentation then introduces the elements of the NAEFS. These include the U.S. National Centers for Environmental Prediction’s Global Ensemble Forecast System (GEFS) and the Canadian Meteorological Center’s Ensemble Forecast System (CEFS). A description of each separate ensemble system is followed by a discussion of how the NAEFS improves the ensemble forecast over either the GEFS or CEFS alone. ...
How Models Produce Precipitation and Clouds - version 2
This module, part of the "NWP Training Series: Effective Use of NWP in the Forecast Process", explores how NWP models handle both grid-scale microphysical (precipitation) and convective processes through parameterizations and/or explicit methods, with an emphasis on how model treatment (and errors in the triggering) of these processes affects forecast depiction of precipitation and related forecast variables. Back in 2000, the subject matter expert for this module was Dr. Ralph Petersen of the National Centers for Environmental Prediction, Environmental Modeling Center (NCEP/EMC). Revisions to ...
Model Fundamentals - version 2
Model Fundamentals, part of the Numerical Weather Prediction Professional Development Series and the "NWP Training Series: Effective Use of NWP in the Forecast Process", describes the components of an NWP model and how they fit into the forecast development process. It also explores why parameterization of many physical processes is necessary in NWP models. The module covers background concepts and terminology necessary for learning from the other modules in this series on NWP. Back in 2000, the subject matter expert for this module was Dr. Ralph Petersen of the National Centers for Environmen ...
Intelligent Use of Model-Derived Products - version 2
This module, part of the "NWP Training Series: Effective Use of NWP in the Forecast Process", discusses three aspects of forecast guidance developed from raw NWP model data: Post-processing Statistical guidance Model assessment tools Post-processing methods, including a new section of downscaling of coarser resolution data, bias correction, and post-processing of ensemble forecast system data, are introduced. Interpolation of raw model data to produce the data seen by operational meteorologists is also described. Next, we present information on statistical guidance methods and techniques, incl ...
Influence of Model Physics on NWP Forecasts - version 2
This module, part of the "NWP Training Series: Effective Use of NWP in the Forecast Process", describes model parameterizations of surface, PBL, and free atmospheric processes, such as surface snow processes, soil thermal and moisture processes, surface vegetation effects such as evapotranspiration, radiative processes involving clouds and trace gases, and turbulent processes in the PBL and free atmosphere. It specifically addresses how models treat these processes, how such processes can potentially interact with each other, and how they can influence forecasts of sensible weather elements. B ...
Impact of Model Structure and Dynamics - version 2
Impact of Model Structure & Dynamics, part of the "NWP Training Series: Effective Use of NWP in the Forecast Process", explains how a model forecast, and thus interpretation of that forecast, is affected by the basic design of the model. Topics include how meteorological variables are represented in grid point and spectral models, fundamental differences between hydrostatic and nonhydrostatic models, horizontal resolution of orographic and free-atmosphere features, vertical coordinate systems and how they affect the vertical resolution of features in the model forecast, and the forecast impact ...
Effective Use of NWP in the Forecast Process: Introduction
This lesson, part of the "NWP Training Series: Effective Use of NWP in the Forecast Process", introduces the student to the full series. Motivation for the series is presented by Mr. LeRoy Spayd, Chief of the National Weather Service Training Division; this includes a demonstration of the value added by human forecasters to NWP forecasts through recent precipitation verification. Contributors to the series are acknowledged as well. Then Dr. Bill Bua, a member of the NWP Training Team, expands on points raised by Mr. Spayd by posing and answering a question on the role of NWP in the forecast pr ...
Understanding Assimilation Systems: How Models Create Their Initial Conditions - version 2
Understanding Assimilation Systems: How Models Create Their Initial Conditions, is part of the "NWP Training Series: Effective Use of NWP in the Forecast Process." This module explains the data assimilation process, including the role of the model itself as well as the observations. It provides learners an appreciation for how models use data as a function of model resolution and data type, how data influence the analysis, the limitations of data assimilation systems, the importance of initial conditions on the quality of NWP guidance, as well as the challenges of assessing the quality of NWP ...
Operational Use of Wave Watch III
In this webcast, Dr. Hendrik Tolman (NOAA Marine Analysis Branch) discusses the operational use of NOAA WAVEWATCH III. The NOAA WAVEWATCH III is a forecast system that predicts wind-generated ocean waves. Dr. Tolman discusses what WAVEWATCH III can and cannot predict along with the model physics, numerics, and forecast products. Numerous examples illustrate the practical effects of several recent model improvements including high-resolution hurricane winds, surf zone physics, wave partitioning, and use of a multi-grid mosaic. The webcast concludes with a discussion of future improvements plann ...
Wave Ensembles in the Marine Forecast Process
The NCEP Marine Modeling and Analysis Branch (MMAB) Ensemble Global Ocean Wave Forecast System (EGOWaFS) provides five-day forecasts of global winds, wind wave and swell conditions in probabilistic terms. This product became available early in 2007 both through an NCEP non-operational web page and, for raw data, through FTP for use by marine forecasters at NWS WFOs and other locations. The data from the EGOWaFS can be used in a number of ways, including:* As input to probabilistic marine forecasts for wind waves and swell* As input to a local wave ensemble, such as Simulated Waves Nearshore (S ...
Real-Time Mesoscale Analysis (RTMA): What is the NCEP RTMA and how can it be used?
The NCEP Real-Time Mesoscale Analysis (RTMA), provides current conditions in digital form on the NWS National Digital Forecast Database (NDFD) 5-km grid. This product was upgraded in early July 2007 to the point where its use by forecast offices is now encouraged for situational awareness, creating short-term forecast grids, and evaluating recent forecast grids and forecast bias. Unique to the RTMA is an uncertainty or error estimate for some of its analysis parameters. These uncertainty estimates perhaps could be used to determine when a forecast is “good enough”. This Webcast discusses why t ...
Using the WRF Mesoscale Model
This module provides insights on how to best use WRF mesoscale model guidance in the forecast process. Using two cases in southwest Asia where AFWA WRF is currently in use, it examines improvements offered by the WRF for forecasting fronts, topographic impacts, precipitation type, and hazards to aviation. The module also discusses some mesoscale model limitations, and offers strategies for transitioning between using mesoscale and global NWP guidance for medium-range forecasts, even when the models differ significantly.
Advances in Microwave Remote Sensing: Ocean Wind Speed and Direction
This Webcast covers the ocean surface wind retrieval process, the basics of microwave polarization as it relates to wind retrievals, and several operational examples. Information on the development of microwave sensors used to retrieve ocean surface wind speed and the ocean surface wind vector (speed and direction) is also included.
Introduction to Ensemble Prediction
This webcast is a shorter companion to the Ensemble Prediction Explained module, focusing more directly on immediate operational needs. Introductory content includes the role of ensemble forecasts, presentation of basic ensemble forecasting terms, and discussion of how ensemble prediction systems (EPSs) are created. The largest section is focused on common ensemble forecast products, including how they differ from traditional NWP products, how we interpret ensemble forecast products, the advantages and limitations of each product, how EPS products are verified, and how to use ensemble products ...
Ensemble Forecasting Explained
This module, the latest in our series on Numerical Weather Prediction, covers the theory and use of ensemble prediction systems (EPSs). The module will help forecasters develop an understanding of the basis for EPSs, the skills to interpret ensemble products, and strategies for their use in the forecast process. It contains six sections: an Introduction that briefly presents background theory; Generation, which describes how ensemble systems are constructed; Statistical Concepts, which provides a brief refresher on knowledge required for ensemble product interpretation; Summarizing Data, which ...
The Balancing Act of Geostrophic Adjustment
This 7-page module provides a primer on geostrophic adjustment concepts. It discusses their application for understanding and forecasting real weather features, interpreting model forecasts, and recognizing the type and duration of impact that observations exert on the model forecast. The module also includes an interactive Exercises section.
Ten Common NWP Misconceptions
This lesson introduces forecasters to ten of the most commonly encountered or significant misconceptions about NWP models. This list of ten misconceptions includes issues surrounding data assimilation, model resolution, physical parameterizations, and post-processing of model forecast output.
Freezing and Melting, Precipitation Type, and Numerical Weather Prediction
This Webcast is based on a COMET classroom presentation by Dr. Gary Lackmann at the 2nd MSC Winter Weather Course held in Boulder, Colorado on 22 February 2002. Dr. Lackmann reviews the basic thermodynamics of freezing and melting and how operational models represent these processes. He also touches upon the biases that occur in the models by looking at examples of melting snow aloft, melting snow at the surface, freezing aloft (ice pellets), and freezing rain. Dr. Lackmann is a faculty member in the Department of Marine, Earth, and Atmospheric Sciences at North Carolina State University.
How Mesoscale Models Work
The goal of this training module is to help you increase your understanding of how mesoscale models work. Such understanding, in turn, can help you more efficiently and accurately evaluate model-generated forecast products.
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