# Presentation: 790349

## Event focused Ensemble data

- Richard H. Grumm and ER/SSD
- National Weather Service
- State College PA 16803
- Version 1.1 29 October 2006

## OBJECTIVES

**Objective 1: ***Outline key forecast problems and requisite data.*
**Objective 2: ***Improve access to full suite of ensemble data.*
**Objective 3: ***ER suggestions to improve the EPS system for forecasting.*

## Significant forecast problems

| Event type | Problems | Key Fields |
| --- | --- | --- |
| Winter Storms | Snow accumulation
Ice accumulations
Timing issues | QPF,Prob QPF amounts, PTYPE, thresholds of fields such as temperatures.
 |
| Heat/cold outbreaks | Temperatures and humidity | Key temperatures and anomalies along with PW. |
| Non-convective High winds | High winds and timing | Thermal gradients
Prob of Winds exceeding threshold values |
| Heavy rains | When, where, time of rapid accumulations | QPF,Prob QPF Prob thresholds |
| Severe weather | When where and type | Stability and shear parameters |

**Notes:**
This table summarizes the impact of initial conditions (ICs) and model errors on forecasts from the short-range to the range of climatic models. For the operational forecaster note how the BIG impact IC’s can have at both the short- and medium- range. 

In addition to the big impacts of IC’s on the forecast not that model errors too can be quite significant. 

There is an excellent paper by Zhang, Snyder, and Rotunno about the effects of moist convection on predictability in the Journal of Atmospheric Sciences published in 2003. In this study they showed how changing the convective parameterization scheme significantly impacted the forecasts.  After 30-hours time the convective scheme caused each version of the MM5 to produce markedly different forecasts. Thus the big effect on the Model Core and the model’s ability to introduce errors in the forecasts.

## Winter Storms

- Focus on timing, intensity, and type of precipitation.
- Key fields:
- PoPs by significant thresholds over specified times as related to Winter Storm Warning (WSW) and advisory criteria.
- Varies by WFO for thresholds
- Requires at least 3-hourly QPF to be
- Set to probabilities and thresholds
- Get rates when expected rapid accumulations
- Related to model Precipitation type forecasts (PTYPE) for conditional probabilities and accumulations.

## Winter Storms

- Character of the storm:
- MSLP and key thermal, height, moisture and wind fields.
**Significant parameters**:
- Temperatures : 2m, 925, 850, and 700 hPa
- Heights: 1000, 850, 500 hPa
- Winds: 925, 850, 700, and 250 hPa.
- Precipitable water
- Recent work with the MREF shows potential to show probabilities of outlier events.
- Bias correct and anomaly categories
- Enhancing our ability to define and categorize significant and historic weather events.

## Bias corrected MSLP and the chances of the pressure being in the upper (98%) and lower (2%) percentiles.

- This case, the MREF depicted a high probability of an anomalous cyclone over the Great Lakes with a late season snow storm.
- NRC recommendation for bias correction to improve forecasts. Need the need for these data.

## Bias corrected 850 hPa temperatures and of the the 850 hPa temperatures being in the upper (98%) and lower (2%) percentiles.

- This case, the MREF depicted a high probability of anomalous cold air behind the cycle.

## Conditional ProbabilityProbPTYPE*ProbPrecipitation

## Cold and heat events

- Duration and intensity of the event
- Key fields:
- MSLP, thermal fields, moisture, and wind fields.
- Heat wave focus more on thermal and moisture anomalies
- Cold events have thermal and critical wind criteria for wind chills during the colder months.
- Critical fields such as:
- MSLP
- 925 and 850 hPa temperatures and anomalies
- PW anomalies
- 10m, 925 and 850 hPa winds for wind chills in the winter.

## Heat Wave Example August 2006

- Gage the anomalies and relate to the potential for record or near record temperatures.
- Need for probabilities of exceedance of specified values related to heat episodes.

## Severe Weather

- Location and character of an event. Placing it in a historical context for large or potentially large outbreaks.
- Key fields:
- MSLP, thermal fields, moisture, stability, helicity, and wind fields.
- Uniquely Critical fields such as:
- CIN and CAPE (various formulations)
- Helicity
- Winds 925, 850,700,500 and 250 hPa derive shear and shear probabilities.
- Convective rainfall for joint probabilities.

## Parameters and probabilitiesSevere weather example here

## Heavy rain events

- Location and character of an event. Two key types in eastern United States:
- Maddox Frontal and Synoptic. The synoptic type interacts with tropical air streams to produce some of the record events in the Mid-Atlantic region.
- key fields:
- QPF probabilities and critical thresholds related to flooding thresholds.
- Uniquely Critical fields such as:
- MSLP
- Winds 925, 850 and 250 hPa critical for event type and then the U or V wind anomalies will indicate potential record events.
- PW and PW anomalies. The probabilities thereof would be quite useful as would moisture flux anomalies.

## Display concepts

- Vary by event type and problem.
- Probabilities of exceedance critical in most event
- Plume diagrams for more local applications.
- Need to develop and improve these concepts
- But
- Key to all of this is data and quality data
**Thus the need to improve our EPS******

## Plumes for frontal timing

## Plumes for timing precipitation and type

## Improve access to full suite of ensemble data

**There is a need for easy access to a diverse set of fields **
**based on forecast problems and user needs**
**NCEP Ensemble Prediction System data:**
*NOMAD sites not reliable.*
*NCEP site serves the world and lacks some EPS members*
*Not a full suite of GEFS (MREF) members are available (11/15)*
*NWS regions/offices should have access to these data*
*SREF data access suffers similar problems*
*Concatenated SREF files only for 15 of 21 members and lack some critical fields like helicity and 925 variables.*
**Critical need for better access to these data**

## Access and EPS issues

- CMC EPS data is time delayed by nearly 12 hours
- Can NWS access to these data outside of TPC,SPC, and NCEP be improved?
- We could all benefit to some access of full suite of EPS data run them and use them!

##  ER EPS Issues

- NRC (recommendation 3.3):
*Improve the SREF system by running the EPS at higher resolution.*
*Some SREF members are not as fine the GFS.*
- NRC 3.1.5
*Improving the ensemble through post processing *
*Bias correction, which is conducted now for MREF at 0000 UTC.*
- ER would like to see more focus on improvements to the quality of the EPS system rather than more product development.

## Summary

**We looked at key forecast problems in the eastern United States**
**Each has its own data requirements to facilitate the forecasting and identification of the events.**
**We did not cover every type in its entirety.**
***TO improve forecasts event types************ need better access to an improved full suite of ensemble data****.*
*Bias corrected and anomalies*
*Critical fields for problems and forecast issues*
*Relate to how we can convey uncertainty in weather forecasts....NRC report on conveying uncertainty in weather forecasts.*
**Comments/questions?**