Assimilation of Pacific Lightning Data into a Mesoscale NWP Model. Antti Pessi, Steven Businger , and Tiziana Cherubini University of Hawaii K. Cummins, N. Demetriades, and T. Turner Vaisala Thunderstorm Group Inc. Tucson, AZ. Outline. Long-range lightning detection PacNet
Antti Pessi, Steven Businger, and Tiziana Cherubini
University of Hawaii
K. Cummins, N. Demetriades, and T. Turner
Vaisala Thunderstorm Group Inc. Tucson, AZ
(soundings, SYNOPs) over the Pacific
IR images - difficult to distinguish
between areas of active convection and anvil cloud.
Vertical electric field waveforms for cloud-to-ground return strokes at three different distances. Note the increased complexity and lower frequency content of the waveforms at longer distances.
The amplitude scale is not calibrated.
The time scale is in microseconds post digitizer trigger.
Pacific Lightning Detection Network (PacNet) Model Motivation and goals
Antti Pessi and installation at Dutch Harbor, AK
Currently 4 sensors installed at Dutch Harbor, Lihue, Kona and Kwajalein. Sensors in North-America and Japan contributing
Average number of lightning strokes observed at each hour over the N. Pacific (45 day average). 10 UTC is midnight HST.
Warm Season Normalized Rain-Yields
Warm season normalized CG flash density vs rainfall
Sloping black lines are contours of constant rain yield (kg/fl)
(Petersen and Rutledge 1998)
Lightning rates from Long-Range Network
Rainfall rate from AMSR-E and TMI sensors
Lightning strokes occurring within ±15 min of satellite overpass time are counted
Lightning count and average rainfall are computed over each square
Methodology to determine lightning vs rainfall ratio
Extratropical storm in the northeast Pacific
Aqua with its AMSR-E on top left
Composite analysis of 15 storms in the central Pacific. Blue line is fitted function where R is rainfall rate and L lightning rate.
Rainfall Assimilation into MM5
terrain-following, sigma-coordinate model
- Obs nudging radius of influence in horizontal and vertical
- Time window of influence
- Nudging coefficient G
The model equations are written in "flux" form, where the prognostic variables for horizontal wind, temperature, and mixing ratio are mass weighted by p*. p* = ps - pt where ps is surface pressure and pt is constant pressure at the top of the model
Experiment Design Model
00Z or 12Z
Model integration 60 h
Assimilation 8 h
- Go through each grid point over the storm
- Bin rainfall and corresponding moisture profile into
one of 7 categories
- Make a composite of all gridpoint values which results in 7 rainfall and moisture profile categories
3 strokes btw 0:00 and 0:30=> obs. time 0:15
5 strokes btw 0:15 and 0:45=> obs. time 0:30
2 strokes btw 0:30 and 1:00=> obs. time 0:45
Impact of PacNet Lightning Data
Lightning strikes between 5-7 UTC on 28 Feb. 2004.
Six-hour MM5 control forecast for rainband position was off by ~150 km at 06UTC, 28 February 2004.
Six-hour MM5 FDDA forecast improved surface pressure and wind forecasts for 06UTC, 28 February 2004.
Observed Sea-level Pressure (left) and ETA 24-hr SLP and rainfall forecasts valid at 12 UTC 19 December 2002 (middle), show a 11mb forecast error in storm central pressure (12 hr forecast shows 9mb error).
Reducing Forecast Error over the Eastern Pacific
Assimilation of lightning data results in a significantly improved forecast of storm central pressure.
(last assim. time 08Z)
Central sea-level pressure of simulated storm with lightning nudging is 10 mb deeper than control run.
8 hours of assimilation in the beginning of the model run
Acknowledgements ModelThe authors would like to thank ONR and NASA for support of PacNet.
MM5 Control/FDDA ModelNE Pacific Low 19 Dec. 2002