severe convection punkka

Use of mesoscale observing network data in forecasting severe convection: 

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

Outline: 

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

Use of HTB data at FMI Weather Warning Service: 

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. 9.8.2005, 26.8.2005) ?

Challenges and problems in severe thunderstorm forecasting: 

Challenges and problems in severe thunderstorm forecasting 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.

Examples – variations in sfc T, Td and winds: 

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

Examples – mesolows and mesohighs: 

Examples – mesolows and mesohighs Primary tool : Surface mesonet Benefits: Improved assessment of cold pool intensity and MCS motion

Examples – outflow boundaries: 

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

Examples – low-level humidity profile: 

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

Examples – low-level wind profile: 

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

Examples – mesovortices: 

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

Summary – challenges in severe convection forecasting: 

Summary – challenges in severe convection forecasting Observations Mesonet of sfc observations Wind profilers Denser radar network Soundings and AMDARs 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. Improvements in forecasting Instability Convective mode Ts initiation Tornado and db risks

Summary – challenges in severe convection forecasting: 

Summary – challenges in severe convection forecasting Observations Mesonet of sfc observations Wind profilers Denser radar network Soundings and AMDARs 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. Improvements in forecasting Instability Convective mode Ts initiation Tornado and db risks

Summary – challenges in severe convection forecasting: 

Summary – challenges in severe convection forecasting Observations Mesonet of sfc observations Wind profilers Denser radar network Soundings and AMDARs 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. Improvements in forecasting Instability Convective mode Ts initiation Tornado and db risks

Summary – challenges in severe convection forecasting: 

Summary – challenges in severe convection forecasting Observations Mesonet of sfc observations Wind profilers Denser radar network Soundings and AMDARs 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. Improvements in forecasting Instability Convective mode Ts initiation Tornado and db risks Forecasters’ message to Testbed II developers: More instruments for the detection of low-level profiles