# Presentation: 220766

## Use of Ensembles in Central Region

**Pete Browning**
**Central Region SSD**
**May 13, 2008**

## Office Feedback

**Asked 4 questions:**
***What ensemble information is used?***
***How is this information used?***
***Describe notable problems or issues***
***Describe notable successes ***
**12 offices responded (32%)**

## What is used

**AWIPS:**
***SREF***
***GFS Ensembles members and mean***
***Ensemble MOS (MEN)***
***Create your own super ensemble (model comparison)***

## What is used

**WEB:**
***SPC (SREF)***
***FSU (level of uncertainty)***
***PSU /Grumm (SREF/MREF plumes & anomalies)***
***HPC (hazard graphics)***
***NCEP/CPC***
***CAP’s ARPS (severe weather)***
***Canadian Ensembles***

## How Used?

**See spread, ascertain where operational run fits**
***Are current and recent runs outliers or near mean***
***Determine the likelihood of the deterministic model outcomes***
***Help to avoid extreme shifts in the forecast***
**Use GFS ensemble mean as another forecast solution**
**Capture the hazard threat of the weather system and its probability of occurring – gage uncertainty (watch issuance)**

## How Used?

**Extended forecast – assess deviations from climo and spread**
**Ensemble MOS mean PoP used to increase confidence (nwa 2006)**
**SREF used for aviation forecasts – probability for ceiling and visibility categories**

## Notable Problems/Issues

**Not in AWIPS**
***Not used if not in AWIPS/D2D***
***12 of 21 SREF members available***
***Unable to calibrate***
**Increase amount of derived threat parameters (e.g., prob. of blizzard occurrence via HPC) that are calibrated (like SPC’s calibrated ens data: Road Snow, Cloud Thunder, Severe Weather).**
**Need scientifically documented manner for using ensembles consistently to improve forecast**

## Notable problems/Issues

**Like to see ECMWF ensemble output**
**Like to see expansion of plume output (winter events).**
**More training needed**
**Insufficient spread – especially during significant events – solution outside of ensemble envelope.**
**Equal weighting of SREF members in various prob fields problematic since certain config/members are better performers overall.**
**Timing of ensemble runs (09Z SREF vs 12Z Ops run)**
**Operational runs outperform ensembles (HPC low track verif outperforms everything else – uses only NAM/GFS 2-model ensemble)**

## Notable Successes

**Derived SREF probabilities extremely helpful in diagnosing threat likelihood (Prob 0-6km bulk shear >40 kts, Prob of IFR, Prob of FZRA > 3hrs,etc.)**
**Plumes - Useful in winter season – give plausible outcomes **
***ICT remembers 2 cases when operational was forecasting snow, but majority of ensembles indicated ice storm – forecast was altered in right direction.***
***ARX case showing precip type outcome 24hrs prior – predictability high for prolonged IP event (see plume diagram next slide)***
***EAX also mentioned successes using plumes.***

## Notable Successes

## Notable Successes

**Use of Ensemble MOS mean PoP**
***Study showed that higher MEN POP provides confidence for forecasters to forecast higher PoPs (Gagan, NWA 2006).  Results in better forecast of “wet” periods.***
**Eased forecast collaboration by identifying forecast problems which can’t justify a lot of effort because they are inherently intractable (Its hard to be sexy saying “I don’t know”)**

## Last Slide

**Summary**
***Offices are all over the map***
**Variability in how/if used (2/3rds not responding)**
***Successes seen***
***Strong use of web – need to get ensemble done right in AWIPS***
***Most notable successes with precip type plumes from PSU/Grumm past winter.***
***Need to train on forecast process that utilizes ensembles***