Forecast Decision Training Branch...Featured Training

New Hydrology Training
from FDTB, COMET, and VISIT

*To receive credit, NWS employees must take training in the NWS Learning Center.*
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NWS Hydrologic Ensemble Forecast System

Description:

In this webcast, Dr. Richard Koehler, the National Hydrologic Sciences Training Coordinator for NOAA's NWS provides an introduction to the elements of the NWS Hydrologic Ensemble Forecast System (HEFS). The module starts with a background on Ensemble Streamflow Prediction (ESP), along with an historical perspective on its development. Details are then provided on the need for and characteristics of hydrologic ensemble forecasts. A comparison is made to show how hydrologic and meteorological ensembles differ. Dr. Koehler then looks at the relationship between probability, risk and uncertainty as well as the probabilistic information within hydrologic ensemble products. Also discussed is how errors and uncertainty arise from both meteorological and hydrologic data input as well as the uncertainty within the model itself.

Objectives:

Understand the general characteristics and need for hydrologic ensembles
Recognize the difference between meteorological and hydrologic ensembles
Understand the relationship between probability, risk and uncertainty
Recognize and identify sources of hydrologic uncertainty within ensembles
Understand the goals of Hydrologic Ensemble Forecast System processes
Comprehend probability information within hydrologic ensemble products

Distributed Hydrologic Models for Flow Forecasts - Part 1

Description:

Distributed Hydrologic Models for Flow Forecasts – Part 1 provides a basic description of distributed hydrologic models and how they work. This module is the first in a two-part series focused on the science of distributed models and their applicability in different situations. Presented by Dr. Dennis Johnson, the module begins with a review of hydrologic models, and then examines the differences between lumped and distributed models. It explains how lumped models may be distributed by subdividing the basin and suggests when distributed hydrologic models are most appropriate. Other topics covered include the advantages of physically-based versus conceptual approaches and some strengths and challenges associated with distributed modeling.

Objectives:

Describe distributed hydrologic models (DHMs) and how they work
Describe how lumped models may be distributed by subdividing the basin. (i.e. grids, sub-basins, and flow planes)
Explain when DHMs are most appropriate
Explain differences between lumped and distributed models:
− DHMs tend to rely on physically-based approaches
− Lumped models rely on conceptual approaches
Describe some of the challenges associated with DHMs

Precipitation Estimates, Part I: Measurement

Description:

This is part one of a two-module series on estimation of observed precipitation. Through use of rich illustrations, animations, and interactions, this module provides an overview of the science of precipitation estimation using various measuring platforms. First, we define quantitative precipitation estimation (QPE) and examine technologies for remote sensing of QPE, including radar and satellite and the strengths and limitations of each. That is followed by an examination of the use of rain gauges for precipitation estimation and important issues to consider with rain gauge measurement. Finally we provide an introduction to the strengths and limitations of using precipitation climatology for QPE including PRISM.

Objectives:

Define quantitative precipitation estimation (QPE)
List the tools used to measure precipitation
Explain a drop size distribution (DSD)
Explain a Z-R relationship and its limitations in radar-derived QPE
Explain how the radar’s ability to estimate snow QPE may differ from rain QPE
Understand the basics of radar-derived precipitation from dual-polarized radar
Illustrate what is meant by inconsistency in radar sampling and coverage
Be able to use radar climatology guidance
Describe the uses and limitations of satellite QPE
List some of the limitations of rain gauge measurements
Explain how wind, exposure, and turbulence can influence gauge catch for rain
Explain how the gauge performance for snow may differ from rain
Describe other ways to obtain snow water equivalent
Describe the general strengths and limitations of measurement from automated gauges
Explain how the strengths and limitations of manual gauge reports may differ from those of automated gauges
Describe how precipitation climatology may enhance QPE
Explain some key limitations of precipitation climatology
Describe weather situations that would likely result in useful estimates from each of the three measurement tools: radar, satellite, and rain gauges

An Introduction to Remote Sensing for Hydrology

Description:

This session is a component of the VISIT program's soon to be released SHyMet for Forecasters course. SHyMet for Forecasters is expected to be released in full during December 2009. This portion of the SHyMet course introduces a variety of ways that remote sensing data can be used for hydrologic applications. First, satellite products useful in hydrology are identified. Then examples of using remote sensing data for monitoring cumulative precipitation, surface thermal properties, surface moisture, vegetation, water supply, snow, ice, flooding, and land use are presented.

Objectives:

Identify satellite products useful in hydrology
Become familiar with remote sensing applications for hydrometeorology
Understand uses of remote sensing data for operational hydrology
Identify watershed characteristics from satellite data