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National Weather Service |
Combining all of the knowledge and skills described in the IST PDS Units #1-8, demonstrate proficiency in using AWIPS to solve forecasting and warning problems in the operational weather forecast environment.
To improve efficiency and accuracy when forecasting weather phenomena and issuing warnings for severe weather events, forecasters should know how to properly integrate the full suite of AWIPS operational datasets. As data capacities expand, the ability to manage extensive datasets and assimilate output from multiple data sources becomes a critical element in issuing timely and effective warnings and forecasts.
1. Understanding of how profilers can supplement or complement other observing systems.
2. Understanding of how lightning can supplement or complement other observing systems.
3. Understanding of how ASOS can supplement or complement other observing systems.
4. Understanding of how RAOB and other upper-air data can supplement or complement other observing system data.
5. Understanding of how radar can supplement or complement other observing systems.
6. Understanding of how satellite data can supplement or complement other observing system data.
7. Understanding of how multi-source data displays can improve the forecast process.
8. Ability to assess the mesoscale and synoptic-scale numerical model analyses using integrated datasets to determine timing and accuracy of key features.
Instructional Component 9.1: VISIT will develop short phenomena-based case studies (offered both as instructor-led teletraining sessions and standalone web-based modules) that illustrate the integrated sensor approach forecasting methodology. (all)
9.1.1 Low-level Thunderstorm Outflow (LTO). (Web module) A dynamic session on the integrated use of satellite, radar, and other datasets to monitor nocturnal convective outflow.
9.1.2 The Enhanced-V Cloud Top Signature. (Teletraining - Also available with instructor audio and annotations) A dynamic session on the integrated use of satellite, radar, and other datasets to monitor structure of severe thunderstorms.
9.1.3 The Midland TX Heavy Precipitation Event of 11 December 1998. (Web module) An interactive session provides an intermediate-level lesson on the integrated use of AWIPS data sets to diagnose the role of elevated mesoscale ascent which was responsible for the enhancement of precipitation rates across southwest Texas and southeast New Mexico on 11 December 1998.
9.1.4 Lake-Effect Snow I. (Available with instructor audio and annotations)) This session is designed to increase your theoretical understanding of lake-effect snow as well as improve lake-effect snow forecasting skills. The unique characteristics of the eastern and western Great Lakes are examined with case studies for both regions.
9.1.5 An Ingredients-Based Approach to Forecasting Winter Season Precipitation. (Web module) This intermediate-level lesson discusses the primary ingredients involved in winter season precipitation events (forcing, instability, moisture, precipitation efficiency, temperature), and diagnostics which are useful in evaluating these ingredients. Ingredients Analysis Tool scripts are employed using GEMPAK and AWIPS, for a variety of CONUS winter precipitation events.
9.1.6 An Application of Pattern Recognition to Medium Range Forecasting . (Web module) This lesson is designed to increase your understanding of medium range forecasting. The Objectives for the session are: Review the reliability of D-3, D+0, and D+3 500 mb mean charts as predictors; Investigate model errors based on particular mean patterns; Understand how a changing mean pattern leads to changes in the character of model errors and understand how diagnosis of model bias provides an additional tool in helping anticipate individual forecast shortwave errors.
9.1.7 HPC Medium Range Forecasting . (Web module) The lesson is designed to increase your understanding of medium range forecasting. The session covers the 12 step methodology of medium range forecasting.
9.1.8 Using GOES Rapid Scan Operations in AWIPS (Web module): This web session explains GOES RSO and how the datasets can be utilized within AWIPS.
9.1.9 Applying
Mesoscale Tools and Techniques to Predict and Detect Severe Thunderstorm
Development (Web module).
This teletraining session reviews strategic
goals for severe weather prediction and detection. Various
operationally-available AWIPS data sets are shown in a context to highlight
their complementary nature in analysis roles during severe weather operations.
(Developed by Peter Wolf, SOO, Wichita, KS.)
9.1.10 Mesoscale Analysis
of Convective Weather Using GOES RSO Imagery
(Teletraining - Also available with instructor audio and
annotations).
This teletraining session
is designed to increase the forecasters skill in incorporating satellite data in
the short-range forecast, nowcasting, and warning decision making processes.
9.1.11 Cyclogenesis:
Analysis utilizing Geostationary Satellite Imagery
(Teletraining - Also available with instructor audio and
annotations).
The objectives of this
teletraining session are to: examine various conceptual models of cyclogenesis
(basic, split flow, cold air, instant occlusion and in-stream) and learn to
utilize a blend of conceptual models, satellite imagery, and NWP output in
diagnosing cyclogenesis .
9.1.12 TROWAL
Identification (Teletraining - Also available with instructor audio and
annotations).
The objectives of this teletraining
session (TROWAL stands for TROugh of Warm air ALoft) are to: 1.Learn more about
extratropical cyclone structure 2.Learn how to use AWIPS to find TROWALs 3.Can
TROWAL identification help forecast accuracy? .
9.1.13 Use of GOES/RSO Imagery with other Remote Sensor Data for Diagnosing Severe Weather across the CONUS (RSO 3) (Teletraining - Also available with instructor audio and annotations). This is the third in a series of VISIT teletraining sessions on GOES Rapid Scan Operations (RSO) Imagery. Objectives include identifying different air masses, analysis of storm scale features and how to most effectively use GOES RSO imagery with other datasets. Case studies are presented that encompass a variety of regions across the CONUS.
9.1.14 Mesoscale Convective Vortices (Teletraining - Also available with instructor audio and annotations). Objectives include: Remind you of the satellite presentation of MCV's, give hints on how to anticipate MCV genesis/decay, and discuss model performance of MCV's.
9.1.15 Interactive Cloud Height Algorithm and GOES Sounder Point Retrievals in AWIPS (Available with instructor audio and annotations). The first part of this lesson deals with the Cloud Height Algorithm in AWIPS. Objectives include: How the algorithm works, what the numbers in AWIPS really mean and knowing when the cloud height estimate may be in error. The second part of this lesson deals with GOES Sounder Point Retrievals in AWIPS. Objectives include: Understanding how the soundings are generated, how to plot the soundings in AWIPS, and limitations to the sounder point retrievals.
9.1.16 Utilizing GOES Imagery within AWIPS to Forecast Winter Storms (Teletraining - Also available with instructor audio and annotations). The objectives of this lesson are to blend GOES Imagery with other available AWIPS products to help improve forecast skill in precipitation associated with winter storms. This is accomplished by analyzing case studies various parts of the CONUS. Topics include: Conveyor belt identification, synoptic features (dry slot, short wave, jet streak, etc.), model divergence, mesoscale banding, topographic effects, shear zones, as well as precipitation type.
9.1.17 Predicting Supercell Motion in Operations (Available with instructor audio and annotations). Course objective is to increase operational awareness of forecasting supercell motion - leads to better nowcasts & pathcasts of severe convective weather, as well as better derived parameters. Topics include 1. Discuss primary mechanisms controlling supercell (and thunderstorm) motion, 2. Explain the B2K method for predicting supercell motion which includes stengths and limitations of the B2K method and vertical wind shear perspective. 3. Provide examples of predicting supercell motion in operations. 4. Summarize and provide recommendations.
9.1.18 Monitoring Gulf Moisture return with GOES Imagery (Teletraining - Also available with instructor audio and annotations). Objectives are to identify and track low-level moisture utilizing GOES imagery with other datasets. This includes nighttime, daytime and cloudy or clear clear conditions.
Instructional Component 9.2 A set of training sessions developed by the Warning Decision Training Branch, in coordination with CIRA, CIMSS, NSSL, and SPC.
9.2.1 Using AWIPS to Detect Surface Boundaries - Session 1: Detection of Surface Boundaries. This lesson is an interactive VISITview session of approximately 2 hours in length. The objective is to familiarize forecasters on basic applications of integrated sensors to the analysis of surface-based boundaries.
9.2.2 Using AWIPS to Detect Surface Boundaries - Session 2: Diagnosing the Potential for Surface Boundaries to Initiate Convection.. This lesson is an interactive VISITview session of approximately 2 hours in length. The objectives are: 1) Using AWIPS tools, be able to objectively analyze and assess the most relevant boundary characteristics which are presently considered influential in initiating deep moist convection; and 2) Through the process of 1) above, incorporate successful forecasts of convection initiation in the short-term forecast/nowcast (NOW).
Instructional Component 9.3: WDTB's Warning Decision-Making Workshops. (all)
Instructional Component 9.4: Training providers/WFOs will develop and maintain a library of case studies that can be played back on AWIPS workstations to illustrate forecast and warning methodology. (Use AWIPS 2D2 LINUX)
Instructional Component 9.5: COMET workshops. (Propose to hold workshops on IST)
Instructional Component 9.6: Using Near-Storm Environment Data in the Warning Decision Making Process The goal of this training is to illustrate the utility of integrating near storm environment (NSE) data sets in AWIPS such as RUC, LAPS, MSAS in the warning decision making process. Objectively analyzed fields of CAPE and CIN (for assessing a storm�s updraft capabilities) equivalent potential temperature (for assessing areas of potential convective instability), and vertical wind shear parameters such as bulk shear and storm relative helicity (for modulating storm organization and development) are evaluated for a number of cases.
Instructional Component 9.7: Bibliography: The training providers will provide an on-line (web-based) bibliography that identifies some of the key subject matter-related references for this particular unit. The NWSTC will assist WFOs in obtaining articles, if needed.
Evaluation 9: After each teletraining session, the SOO or training officer is sent an e-mail notice that describes the process for completing the evaluation form.
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