1. High Resolution Simulations of an Extreme Precipitation Event over Long Island on 13 August 2014
Nicholas Leonardo1, Brian A. Colle1, and
David Stark2
1. School of Marine and Atmospheric Sciences
Stony Brook University
2. NOAA/National Weather Service
New York, NY
Introduction: just say name and title (< 15 seconds)

2. Operational models underpredicted amounts by an order of magnitude!
KISP Rainfall Total
Over 12” of rain fell over central Long Island within 4 hours on August
Operational models underpredicted amounts by an order of magnitude!
1.76” of rain in 15 minutes!
Islip Airport 13.57”
Storm Total Rainfall Forecast Issued 1420 UTC 12 AUG.
KOKX Dual Pol Storm Total Accumulation

3. Motivational Questions
How well can a mesoscale model at high resolution simulate this flooding event?
What are uncertainties associated with the precipitation forecast?
What mesoscale features were important for the heavy precipitation?
What is the role of latent heating and evaporative cooling with the precipitation on mesoscale evolution?
Did the coastal front (Long Island) play any role in enhancing the rainfall event?

4. 0900 UTC 13 August RAP 500 mb Heights (m) and Water Vapor
What happened is that there was a deep negatively-tilted trough over the Eastern US bringing south-westerlies aloft and- at the time of heaviest rainfall, there was ample amounts of tropical moisture feeding in from the SE. In fact, the precipitable water at this point is estimated to be 50-60mm over this region.

5. 0600 UTC 13 Aug 2014 (2-hours before rain on LI)
WPC Surface Analysis:
0600 UTC 13 Aug 2014 (2-hours before rain on LI)
At the surface, 2-hours before the event, there was a low over NJ and trailing warm front, with south-easterlies blowing into LI.

6. 0900 UTC 13 Aug 2014 (during heaviest rain)
WPC Surface Analysis:
0900 UTC 13 Aug 2014 (during heaviest rain)
This low remains south during the peak rainfall over LI. However, there is actually a surface trough extending northward, along which the convection is propagating. While it is not drawn out in this surface analysis, I’ve annotated it’s position here and will later show it’s presence in surface observations and it’s importance in the evolution of this event.

7. Regional Radar Animation (21Z – 13Z)
Loop through animation once, describing organization of convection along the way (~20 seconds).

8. WRF Setup and Runs Used conventional nested domains: 27, 9, and 3-km.
Initialized 0000 UTC August 13 (18-h forecast).
Ran with GFS (0.5°-by-0.5°) and NAM (12-km) forecasts.
Tested Different model physics: PBL (ACM2, YSU, MYJ, MYNN 2.5/3) and MP (WSM6, Thompson, Morrison). KF and G3 convective parameterization (CP) on 27 and 9-km.
Used 2-way nesting.
3-km Storm Total: using GFS w/ G3, Thomp, YSU.
...Here I show the 3-km accumulations of one of the better runs from this ensemble in that you have higher amounts in a band over Central LI...

9. WRF Setup and Runs Used conventional nested domains: 27, 9, and 3-km.
Initialized 0000 UTC August 13 (18-h forecast).
Ran with GFS (0.5°-by-0.5°) and NAM (12-km) forecasts.
Tested Different model physics: PBL (ACM2, YSU, MYJ, MYNN 2.5/3) and MP (WSM6, Thompson, Morrison). KF and G3 convective parameterization (CP) on 27 and 9-km.
Used 2-way nesting.
OKX Storm Total
3-km Storm Total: using GFS w/ G3, Thomp, YSU.
However, the amounts peak at 160mm at most, whereas OKX Doppler-estimates show at least 280 mm over this region.

10. Model Accumulated Precipitation of 3-km (4-18 UTC)
G3, Thomp, YSU.
w/NAM: G3, Thomp, YSU.
G3, WSM6, MYJ.
KF, Thomp, MYJ.
KF, WSM6, ACM2.
KF, WSM6, MYJ.
While this ensemble showed great sensitivity to the physics and initial conditions, there was no member that produced over 200mm of rainfall over LI. Also, members using the NAM tended to do worse.

11. WRF Setup and Runs Tested the role of domain size.
Used same physics and initial/L.B. conditions.
“Control”: KF, WSM6, MYJ.
This motivated me to test the Ensemble Kalman Filter, for which I modified and reduced the WRF domains. While testing this setup, just running WRF for a few members to see what happens, I was surprised to find a very different solution from before...

12. WRF Setup and Runs Tested the role of domain size.
Used same physics and initial/L.B. conditions.
“Control”: KF, WSM6, MYJ.
OKX Storm Total
In this run, which I’ll call my “Control,” the 3-km has rainfall accumulations that resemble the radar estimates. So this motivated me to further test this by repeating the same experiment with these domains- using the same physics and initial conditions...

13. Model Accumulated Precipitation of 3-km (4-18 UTC)
OKX Storm Totals
“Control” w/NAM
G3, WSM6, MYJ
KF, WSM6, ACM2
KF, Thom, MYJ
“Control”: KF, WSM6, MYJ.
And I found that several other members also had +200mm of rain over LI, namely those using the Kain-Fritch scheme on the outer 9 and 27-km, though the position and amplitude varied and was even overpredicted in some. As before, the NAM tended to do noticeably worse. This time, though, members using the G3 produced lesser amounts.

14. Impact of Domain Size (1-hr precip, 925-mb PV, 10-m winds)
3 km
3 km
3 km
Small 9 and 3km
08:00
10:00
10:00
9 km
9 km
9 km
Large 3km
3 km
3 km
3 km
In a similar fashion, I examined this sensitivity to domain size, comparing the control with the same run, but using the larger nests. This time, the large 3-km also has a band south of LI by 8 UTC, but also has these bands of convection upstream, which perturb this line in the following hours, such that by 10 UTC, the line in this run is gone. In comparison, the control has very little convection upstream, such that the CAPE is some 700 J higher.
08:00
10:00
10:00
J/kg

15. Accumulated Precipitation and Horizontal Resolution
27-km
9-km
Overestimation of rainfall at 3 and 1-km may be due to:
Stronger LLJ.
Too much precipitable water
*No feedback included.
95 mm
288 mm
3-km
1-km
451 mm
627 mm

16. Evolution of 3-km and Observed Convection
This boundary developed insitu with the convection as it finally organized into a band ~8:00 UTC in the model, resembling observations.
06:00 UTC

17. Evolution of 3-km and Observed Convection
This boundary developed insitu with the convection as it finally organized into a band ~8:00 UTC in the model, resembling observations.
08:00 UTC

18. Evolution of 3-km and Observed Convection
This boundary developed insitu with the convection as it finally organized into a band ~8:00 UTC in the model, resembling observations.
09:00 UTC

19. Evolution of 3-km and Observed Convection
12:00 UTC

20. 3-km WRF and Surface Observations
Control 2-m T, SLP, 10-m wind at 0600 UTC
MesoWest Surface Analysis at 0600 UTC C°
Control 2-m T, SLP, 10-m wind at 0900 UTC
MesoWest Surface Analysis at 0900 UTC L C°

21. Structure of the 1-km (at 09:00 UTC)
KOKX Reflectivity
Structure of the 1-km (at 09:00 UTC) 6 5 4 3 2 1
Height (km)
1-km Reflectivity, 925mb PV, 10m winds
OKX
1-km Reflectivity, θE*
Reflectivity and θE* A B
Pressure (hPa)
A Distance (km) B

22. Structure and Trajectories of the 1-km (at 09:30 UTC)
Reflectivity and trajectories
Reflectivity, CAPE, θE*
102.4 hPa/s (vert)
32.2 m/s (hori) A
Pressure (hPa) B
Divergence, RH, θE, trajectories
5-min output. Time step=50s.
This is how I estimated the loss of water; given within those 5 minutes, the parcel’s mixing ratio decreased from 12.8 to 6.6 g/kg as it rose from 851 to 550 hPa (2.5 to 5 km) and cooled from 288 to 272K.
At each point, I calculated the pressure of dry air (Pd) in the parcel by subtracting-out the vapor pressure of water given by the Clausius Clapeyron equation:
Pwv = *exp((L/R)*((1/T)-(1/373)); Pd = P – Pwv
I calculated the density of dry air in the parcel by the ideal gas law: rho=Pd/Rd*T
I multiplied q*rho to get the grams of water per m^3 of dry air. Plugging in the numbers, the water content decreased from to kg/m^3.
Now suppose this parcel is representative of the column of air above it; that is, the whole 2.5-km thick layer (like a conveyor belt of parcels) was displaced upward in those 5 minutes and lost kg/m^3 of water. Hence, multiplying this change by the height, you get a column loss of 19.2 kg/m^2.
Dividing by the density of water and converting to cm (*100/1000), you get 1.92 cm of water.
While the legend’s vectors in the top right imply a peak vertical velocity of 53.5 hPa/s versus the ~100 hPa/s rise of the parcel, I took this cross section through the middle of the swarm of trajectories, where the values for omega just a couple of points northwest are ~100 hPa/s where parcel #3 rose.
On average, the parcels rose ~9.9 m/s.
q decreased from 14.4 to 8.3 g/kg = ~2.6 cm/m2 in 5 minutes.
Pressure (hPa)
A Distance (km) B

23. The Role of Latent Heating
Difference in 1-km (Control – No LH): 925-mb PV, SLP, and 10-m Winds at 09:00 UTC.
No LH run Storm totals -1 -2 -2 -3 -1 -2 -1 -1 -2 -3 -4 -3 -2
Turning off latent heating by 06:00 UTC does not allow the development of a surface trough: convection does not organize.

24. The Role of Evaporative Cooling
Difference in 1-km (Control – No EC): 2-m Temp., SLP, and 10-m Winds at 09:00 UTC.
No EC run Storm totals 1 1 2 3 1 2 1 1 1 C°
Turning off evaporative cooling at 06:00 UTC weakens/shifts the ridge to the northeast slightly, but still has the trough and heavy rain band.
Maximum rainfall of mm (a 9.6% increase).

25. The Role of Coastal Front (Long Island)
Difference in 3-km (Control – No LI): 2-m Temp., SLP, and 10-m Winds at 09:00 UTC.
No LI run Storm totals 1 1 1 C°
Removing Long Island at hour 0 does not affect the trough/ridge.
Maximum rainfall of mm (only an 8.3% decrease).

26. Conclusions and Future Work
The WRF is capable of simulating the amplitude of the heavy rainfall for this Long Island flood event at 6-9 h lead time, but there are uncertainties in physics, initial conditions, and domain size.
A larger 3-km domain resulted in more amplified convection over the ocean, which in turn perturbed the flow and did not allow the band to strengthen over LI.
The heavy precipitation developed along a low-level trough and wind shift boundary across Long Island, with J/kg of CAPE to the south, and a strong kt LLJ. This surface trough/boundary developed as a result of latent heating, while there was less impact from evaporative cooling and the coastal front.
Future: Can the WRF realistically simulate this case using larger explicit domains, but applying mesoscale data assimilation?