# Presentation: 681014

## Forecast-Related Research at NSSL:  Current and Future  

- Harold E. Brooks
- NOAA/National Severe Storms Laboratory
- Norman, Oklahoma
- Harold.Brooks@noaa.gov

## A researcher looks at forecasting

- Source of interesting problems on a regular basis
- Learn how the system *really* works

## Broad areas of concern

- Physical understanding
- Tools to model the atmosphere
- Interpretation
- Developing new approaches
- Evaluating forecasts

## How do fronts behave?

*How do fronts behave?*

- Cold

- Very
- Cold

**Notes:**
Researchers at NSSL consider basic weather features, including how fronts behave.  As an example of “unusual” frontal behavior, in a field project in Utah, scientists looked at a front in strong vertical wind shear moving into a region with dry air at the ground.  As precipitation fell from the upper levels ahead of the front, it evaporated and cooled the air.  Eventually, the ahead of the front became colder than the air the leading edge of the front.

## Slide 5

**Notes:**
Environmental conditions from balloon-borne measurements near thunderstorms have been analyzed to see differences between conditions in which strong tornadoes, severe nontornadic storms, and non-severe storms have been studied.  Using the difference in the environmental winds at the surface and 6 km aloft, and the convective available potential energy of the environmental, we can discriminate between non-severe storms and other kinds, but not between tornadic and non-tornadic stoms.

**Notes:**
Using the results of the discrimination study between severe and non-severe storms, we can estimate how often environmental conditions are favorable for severe storms anywhere in the world.  The locations with the frequent favorable conditions are the central US, southern South America east of the Andes, the Sahel, and the region near the Himalayas.

## Forecasters frequently examine point soundings from models to assess the likelihood of deep convection (thunderstorms)...

*Soundings from the Operational Eta model often reveal unrealistic structures that can be linked to other things going on in the model*

- Pressure (mb)

- Temperature (oC)

- BMX 00Z 12 May 2000

**Notes:**
NSSL scientists have collaborated with SPC forecasters to compare observed soundings and computer model forecast soundings and found systematic differences.

## 48 h Eta CAPE forecast

*48 h Eta CAPE forecast*

*Consider a real forecasting dilemma...*

**Notes:**
As an example of how the research into model and observed soundings can be applied to forecasting, consider a case where two different numerical models make very different forecasts.

## SPC forecasters identified the characteristic bias of the Eta model

*SPC forecasters identified the characteristic bias of the Eta model*

- SPC day 2 convective outlook valid 28 Mar 2002

**Notes:**
As a result of the research, SPC forecasters used the information they had learned about the systematic problems with one of the models and use it to improve the forecast they put out.

## New approaches to processing model data

**Notes:**
NSSL scientists are also looking at new ways to process computer forecasting data.  In one technique, information from the last several days of numerical forecasts is used to improve the current forecasts.

## Tomorrow’s Forecast Process?

- Forecaster interacts with models
- to create an ***adapted*** numerical forecast

- Better guidance to
- public

**Notes:**
New philosophical approaches are also being investigated. Currently, computer forecasts and observations are presented to forecasters to make them forecasts.  NSSL is studying having feedback so that forecasters interact with the models to make better numerical forecasts and, eventually, better information for the public.

## Ensemble design

**Forecaster chooses level, field, and time**

**A box is drawn around interest area**

**Deterministic forecast (Eta 12z)**

**Notes:**
Ensembles of large numbers of forecasts have been created by having people identify sensitive areas in the forecast and use those sensitivities to change the initial conditions of the models.

## Probability of severe storms

- Examples 5 and 3 May 2003:

*Probability of severe storms*

**Notes:**
The experiments produced contoured probabilities of forecast severe storms occurring in comparison with contoured probabilities of observed severe storms (filled contours on the left.)  The observed “probabilities” are based on the density of reported severe weather (on the right.)  Sometimes it works well (top), and sometimes not so well (bottom.)

## Forecast evaluation

- How good (or bad) are forecasts?
- Measures that match how people evaluate forecasts

## NWS Tornado Warning History

*NWS Tornado Warning History*

**Notes:**
New techniques for evaluating forecasts are also being evaluated.  Using an approach based on signal detection theory to look at tornado warning performance, we can see periods of time when performance was relatively consistent.  Changes occur associated with the deployment of the NWS WSR-88D radars, and with the development of tornado warning guidance based on results of the VORTEX project.

## Slide 16

**Notes:**
Traditional scores for precipitation forecasts from computer forecasters often show little difference in performance between different models.

## Slide 17

**Notes:**
New techniques (e.g., the extreme dependency score) can show big differences.

## Forecast #1: smooth

- OBSERVED

- FCST #1: smooth

- FCST #2: detailed

- OBSERVED

**Notes:**
Evaluating forecasts of fields of precipitation can be very difficult.  In most traditional approaches, a smooth forecast, even if its aligned very badly (top), will get better scores than a detailed forecast, where the details are in the wrong places (bottom).  Subjective evaluation may give a very different answer.

- Fcst_1

- NCAR WRF 4km

- Stage II radar ppt

- Attributes

- Area=70000 km2
- mean(ppt)=0.97
- ppt= 1.26
- corr(50)=1.17
- corr(100)=0.99
- corr(150)=0.84
- =173°
- =173°
- =173°
- lat = 40.2°N
- lon = 92.5°W

- Fcst_2

- Obs_1

- Obs_2

- Area=70000 km2
- mean(ppt)=0.60
- ppt= 0.67
- corr(50)=0.36
- corr(100)=0.52
- corr(150)=0.49
- =85°
- =75°
- =65°
- lat = 44.9°N
- lon =84.5°W

- Area=135000
- mean(ppt)=0.45
- ppt= 0.57
- corr(50)=0.37
- corr(100)=0.54
- corr(150)=0.58
- =171°
- =11°
- =11°
- lat = 39.9°N
- lon = 91.2°W

- Area=285000
- mean(ppt)=0.32
- ppt= 0.44
- corr(50)=0.27
- corr(100)=0.42
- corr(150)=0.48
- =95°
- =85°
- =85°
- lat = 47.3°N
- lon = 84.7°W

- Obs_2

- Obs_1

**Notes:**
NSSL scientists are working on object-oriented techniques, where forecast and observed precipitation is described as a series of objects, and a wide variety of parameters describing those objects can be compared to each other.

## In 1997, the SPC began full-time operations in the NSSL/CIMMS facility; close proximity and complementary interests have catalyzed organized interactions:

*In 1997, the SPC began full-time operations in the NSSL/CIMMS facility; *_*close proximity*_* and *_*complementary interests*_* have catalyzed organized interactions:*

- Daily Map Discussions

- Individual Research Projects

- Annual “Spring Programs”

**Notes:**
Daily map discussions between SPC forecasters, NSSL and CIMMS scientists, students, and others, have led to many research problems and to improved forecasts.

## NSSL/CIMMS/SPC Spring Programs

*NSSL/CIMMS/SPC Spring Programs*

- General Theme:  *Investigate problems of *_*mutual and compelling interest*_* to local forecasters and research scientists.*

-   with recent participation by:

**NOAA Agencies:**
***• ******OAR/FSL***
***• ******NWS/NCEP/EMC***
***• ******NWS/OST***
***• ******NWS/WFOs***

**Academic Partners:**
***• ******Univ. of Oklahoma***
***• ******Univ. of Arizona***
***• ******Univ. of Washington***
***• ******Iowa State Univ.***
***• ******MIT***

**International Collaborators:**
***• ******UK Met. Office***
***• ******Met. Service of Canada***
***• ******Finnish Met. Institute***

***In Summary:****  The close working relationship between researchers and forecasters at NSSL/CIMMS/SPC has fostered:*

*- An increased appreciation by Research Meteorologists of operational constraints and requirements...and the insight of operational forecasters*

*Education of Operational Meteorologists with regard to NWP models, other research and diagnostic tools, and scientific knowledge*

*- Migration of useful new products and diagnostic tools from research to operations*

*- Improved severe weather forecasts!*