# Presentation: 102181

## Lab 2A: Relating Potential Threat to uncertainty

- Richard Grumm
- And
- Bill Bua

## Overview

- We will started using GFS data to define
*The key threat*
*The area of the threat, and *
*The timing of the threat*
- We will now use EPS data
-  from 16 & 17 January
- to forecast this event and identify
- Areas of uncertainty and
- use probabilities for things like QPF

## Goal

- Determine the value and limitations of our single deterministic model.
**Now we will integrate our EPS data into the process**
*Find/define uncertainty and the threat potential*
**Ultimately we will try to assign probabilities to the event based on uncertainties**

## Examine GEFS from 0000 UTC 16 January 2007

- Use the provided Graphical User interface
- Open in browser: /home/wrfuser/cases/view/Jan.html
- Examine the data
*Look at Precipitation probabilities by thresholds*
*Look at key features associated with heavy rainfall*
- Define:
*The potential threat**** what is it?*
*The timing and the location**** date/time and location.*
*And explain EPS products that aided in the forecast *

## Questions to ponder

**What is the risk of heavy precipitation in Mozambique?**
**When do you see a threat over Angola?**
**How do the ensemble forecasts show the threat of heavy rain? Time periods and locations focused on day 5?**** **
**What anomaly fields suggest it could be a big event?**

## Examine GEFS from 0000 UTC 17 January 2007

- Use the provided Graphical User interface
- Open in browser: /home/wrfuser/cases/view/Jan.html
- Examine the data
*Look at Precipitation probabilities by thresholds*
*Look at key features associated with heavy rainfall*
- Define:
*The potential threat**** what is it?*
*The timing and the location**** date/time and location.*
*And explain EPS products that aided in the forecast *

## Questions to ponder

**What is the risk of heavy precipitation in Mozambique?**
**When do you see a threat over Angola?**
**How do the ensemble forecasts show the threat of heavy rain? Time periods and locations focused on day 5?**** **
**What anomaly fields suggest it could be a big event?**
_***How has the new data impacted your thinking?***_

## Examine GEFS from 1200 UTC 17 January 2007

- Use the provided Graphical User interface
- Open in browser: /home/wrfuser/cases/view/Jan.html
- Examine the data
*Look at Precipitation probabilities by thresholds*
*Look at key features associated with heavy rainfall*
- Define:
*The potential threat**** what is it?*
*The timing and the location**** date/time and location.*
*And explain EPS products that aided in the forecast *

## Questions to ponder

**What is the risk of heavy precipitation in Mozambique?**
**When do you see a threat over Angola?**
**How do the ensemble forecasts show the threat of heavy rain? Time periods and locations focused on day 5?**** **
**What anomaly fields suggest it could be a big event?**
_***How has the new data impacted your thinking?***_

## Discussion and Summary

**We will look at some comparative images**
*Recall our Jumpiness and LAF lecture and lab?*
*We will show examples for this case of both concepts.*
**Then we will examine a few key EPS products**
*That define uncertainty*
*That facilitate using probabilities*.

## dProg/dt-MSLPthere are some differences

## LAF vaid 0000 UTC 21 Jansee the high spread in these forecast? The LAF product is more revealing than visual comparisons

- Error sources here:
- Initial conditions
- Convection likely played role here too.

## LAF vaid 1200 UTC 21 Jansee the high spread in these forecast? The LAF product is more revealing than visual comparisons

- Error sources here:
- Initial conditions
- Convection likely played role here too.

## GFS QPFareas of heavy rain were a moving target run-to-run

## LAF shows the uncertainty better

## Clues in QPFthis event had boundary and lots of convection

## Where did it rain

- Multi-day event
- Note Mozambique had several days of heavy rain
- Angola had primarily 1 big day on the 21st.
- Several areas of heavy rainfall
- Note the following images
- Limited here to 24 hours of data cover 6-days
- Anyone with finer resolution data should contact us to improve upon our knowledge
- And improve this case example.

## Slide 18

## Clues in QPF and it had the axisthis event had boundary and lots of convection

## Review

- We determined the value and limitations of our single deterministic model
- We then integrate our EPS data into the process
- Looking to define uncertainty
- And assign probabilities
- We want to leverage probabilistic and uncertainty information in the forecast process