Effects of various SST sources on estimates of the Height of the Stratocumulus Topped Boundary Layer LCDR Mike Cooper

Overview McBride’s Motivation McBride Method / NPS Model Why do we need this capability? McBride Method / NPS Model The theory behind the madness The Purpose of this Study What am I adding to the cause? Data Collection The agony! Analysis and Results What SST source produced the best results Conclusions and Recommendations Do I think it really works

Motivation Most areas of the world have at least a 40% chance of ducting. The top of a stratocumulus topped boundary layer marks the axis of a duct. Ducts have significant effects on RADAR operation, but this is a separate topic.

McBride/NPS Method Physically Based Method Strong Subsidence TCT Inversion Depth Marine Stratocumulus LCL Dry Adiabatic Lapse Rate TS DT = TCT - TS Ocean

McBride/NPS Method Assumptions Adiabatic Lapse Rates Unsaturated air follows the dry adiabatic lapse rate (Γd) Saturated air follows a case dependent pseudo or moist adiabatic lapse rate (Γm) The Boundary layer is coupled No inversion below top The surface air temperature is equal to the sea surface temperature TA=TS May not always be true The cloud top temperature is equal to the 11m brightness temperature TCT=TB May not always be true…..unless

McBride Method Assumptions-Cont. The atmosphere has a negligible amount of water vapor above the layer or the water vapor content is known and corrected for Assuming no vapor has some error Since the structure of the STBL is unknown you must assume a certain percentage of cloud Must Assume that the STBL is optically thick That is no photon from below in the cloud top temp estimate.

Procedure of the NPS Model Step 1: Using the measured ∆T and the dry adiabatic lapse rate, estimate the boundary layer depth with no clouds (all temperatures in °C, lapse rates in °C/m, heights in meters). Zdry = ∆T / Γdry (3) Step 2: Assuming a vertical cloud fraction of 1/3 (meaning 2/3 cloud-free), estimate the height of the cloud base for a 2/3 cloud-free boundary layer. ZCloudBase = 2/3 * Zdry (4) Step 3: Using the dry adiabatic lapse rate, Γdry, and the sea-surface temperature, TS, estimate the cloud-base temperature for a 2/3 cloud-free boundary layer. TCloudBase = Γdry* ZCloudBase + TS (5) Step 4: Using the measured cloud-top temperature, TCT, the estimated cloud-base temperature, TCloudBase, and the pseudo-adiabatic lapse rate, Γmoist, estimate the cloud depth (m). CloudDepth = ( TCT - TCloudBase ) * Γmoist (6) Step 5: Compute the cloud-top height, ZCloudTop, using the estimates of cloud-base height and cloud depth. ZCloudTop = ZCloudBase + CloudDepth (7) Step 6: If the cloud-top height is less than 400 meters, recompute the cloud-top height using an assumption of 2/3 vertical cloud fraction (meaning 1/3 cloud-free). Use Equation 4 with a cloudfree ratio of 1/3 vice 2/3. Using the new estimate of cloud-base height, use Equations 5-7 to generate a new, higher estimate of cloud-top height. The rationale for this step is that shallow STBLs typically have a higher vertical cloud fraction. The 400m break point was determined from observation by McBride (2000).

This Study? First Second Looks at both 2 temperature fields compared to ground truth Ground truth being the physical SST measurement on the boat Which field is closer to ground truth? Which would you predict to do a better job at height prediction? Second Looks at the STBL HT predictions from both SST fields using the cloud top temp from one souce and compares them to ground truth Ground truth being the STBL HT as indicated in the Sonde Profile (Hght of the inversion)

How did I get TCT ? GOES (Geostationary Operational Environmental Satellite) http://noaasis.noaa.gov/NOAASIS/ml/imager.html

A little RS theory review L is the spectral radiance B is the planks blackbody emittance as a function of ya know

GOES Data field

How did I get SST NCOM (NRL Coastal Ocean Model) 1/8º Resolution (~15-16km grid) Shorter 5-7 day forecasts with a nowcast Forced by NOGAPS Derives mesoscale information from the 3-D MODAS T and S fields which are derived from the NRL Layered Ocean Model (NLOM) and MODAS SST analysis Forced into a 4km grid template in Terascan so data is taken from the same central pixel as the GOES data

NCOM Data Field

How did I get SST NAVO K10 MCSST field Multi-Channel Sea Surface Temperature field Created from multiple AVHRR channels Daytime SST=1.0346T11+2.5779(T11-T12)-283.21 Nighttime includes T3.7 which is contaminated during the day 1/10º resolution (~11-12km) Forced into a 4km grid template in Terascan so data is taken from the same central pixel as the GOES data

NCEP Data Field

UDAS SST Comparison Clearly the mean would be zero, but the boom temp extremes seem to be a little more “extreme” For the first part of this study the locker temp was used as the reference

K10 Leg 1 Within a degree….not bad

NCOM Leg 1 Within a degree..but K10 wins

K10 Leg 2 Still within a degree….

NCOM Leg 2 Out to lunch…K10 wins again

K10 Leg 3 Now above the one degree mark

NCOM Leg 3 Still out to lunch…K10 wins

Now lets look at the height predictions

326m 302m 326m

432m 431m 388m

363m 321m 336m

396m 577m

Results Anyone want to guess which turned out better?

Ground Truth????? What do you think this will look like? What???

Conclusions and Recommendations Surprisingly, yet clearly, the NCOM SST field produced better height results in this limited study The method may need some tweaking if the K10 fields are going to continue to be the source Recommendations Conduct a more thorough study using NCOM data and K10 data Conduct a more thorough study into why the field that was further from ground truth (NCOM) produced a height field closer to ground truth