1. A-J Punkka Weather Warning Service, FMI
Use of mesoscale observing network data in forecasting severe convection
A-J Punkka
Weather Warning Service, FMI

2. Outline Use of HTB data at FMI Weather Warning Service
Challenges and problems in severe thunderstorm forecasting
Examples of testbed data exploitation
Summary

3. Use of HTB data at FMI Weather Warning Service?
Only ’traditional’ observations available in the meteorological workstation (will be fixed soon )
Forecasters forced to use testbed.fmi.fi in order to see testbed data >> easy to forget if you’re busy >> use of testbed data occasional
However, denser observation network has clearly given extra value for nowcasting in some convection cases (e.g , )

4. Challenges and problems in severe thunderstorm forecasting
Focus of this presentation
State of boundary layer
Low-level humidity. Depth of moist layer and spatial distribution.
Low-level winds. Low-level jets, convergence lines, outflow boundaries (’ backed surface winds’).
Low-level temperature.
Moisture and temperature advection.
State of free atmosphere
Elevated inversions.
Location and ’strength’ of upper troughs.
Intensity of temperature advection.
Deep moist convection in NWP models
Unable to produce small-scale variations in low-level moisture, temperature and wind.
’Convective mode’ incorrect in hi-res models. Convective systems usually inadequately predicted.
Errors in timing and location of deep moist convection typical. For forecaster not as crucial as the previous one.

5. Examples – variations in sfc T, Td and winds
*Td=+19C
*Td=+12C
Wdir=260*
*Wdir=180
Primary tool : Surface mesonet
Benefits: Improved assessment of instability (CAPE), thunderstorm initiation and convective mode (storm-relative helicity)

6. Examples – mesolows and mesohighs
’cold pool high’
’pre-squall low’
Primary tool : Surface mesonet
Benefits: Improved assessment of cold pool intensity and MCS motion

7. Examples – outflow boundaries
Source: iastate.edu
Primary tools : Surface mesonet, radar
Benefits: Improved assessment of wind gust strength, initiation of new convective cells

8. Examples – low-level humidity profile
Deep moist layer
MLCAPE ~2000 J/kg
Knee-deep moist layer
MLCAPE ~700 J/kg
Primary tools : soundings, AMDARs (’aircraft soundings’)
Benefits: Improved assessment of instability (CAPE) and thunderstorm initiation

9. Examples – low-level wind profile
Gatzen (2004)
Primary tools : soundings, wind profilers
Benefits: Improved assessment of convective mode and tornado and downburst risks

10. Examples – mesovortices
Atkins et al. (2004)
Primary tool : doppler radar
Benefits: Improved assessment of severe straight-line wind and non-supercell tornado risks
Järvi et al. (2007)

11. Summary – challenges in severe convection forecasting
Improvements in forecasting
Instability
Convective mode
Ts initiation
Tornado and db risks
Problems
Moisture
Low-level moisture
Depth of moist layer
Winds
Low-level jets
Backed surface winds
Convergence lines
Outflow boundaries
Mesovortices
Moisture and temperature advection.
Observations
Mesonet of sfc observations
Wind profilers
Denser radar network
Soundings and AMDARs

12. Summary – challenges in severe convection forecasting
Improvements in forecasting
Instability
Convective mode
Ts initiation
Tornado and db risks
Problems
Moisture
Low-level moisture
Depth of moist layer
Winds
Low-level jets
Backed surface winds
Convergence lines
Outflow boundaries
Mesovortices
Moisture and temperature advection.
Observations
Mesonet of sfc observations
Wind profilers
Denser radar network
Soundings and AMDARs

13. Summary – challenges in severe convection forecasting
Improvements in forecasting
Instability
Convective mode
Ts initiation
Tornado and db risks
Problems
Moisture
Low-level moisture
Depth of moist layer
Winds
Low-level jets
Backed surface winds
Convergence lines
Outflow boundaries
Mesovortices
Moisture and temperature advection.
Observations
Mesonet of sfc observations
Wind profilers
Denser radar network
Soundings and AMDARs

14. Summary – challenges in severe convection forecasting
Improvements in forecasting
Instability
Convective mode
Ts initiation
Tornado and db risks
Problems
Moisture
Low-level moisture
Depth of moist layer
Winds
Low-level jets
Backed surface winds
Convergence lines
Outflow boundaries
Mesovortices
Moisture and temperature advection.
Observations
Mesonet of sfc observations
Wind profilers
Denser radar network
Soundings and AMDARs
Forecasters’ message to Testbed II developers:
More instruments for the detection of low-level profiles