# Boundary Layer Verification

## Presentation on theme: "Boundary Layer Verification"— Presentation transcript:

Boundary Layer Verification
Work on model evaluation, particularly the boundary layer and/or clouds. Using ground based (ARM) and satellite based (A-train) observations. Did PhD on stratocumulus eval using space-borne lidar obs. Will summarize steps for BL evaluation in the end, and point out as I go along. Examples will come from my own experience, convective, well mixed layers, marine cloudy BL. Apologize for being biased, but hope that principles will be clear and apply to other cases as well. ECMWF training course May 2012 Maike Ahlgrimm

What does the BL parameterization do?
Attempts to integrate effects of small scale turbulent motion on prognostic variables at grid resolution. Turbulence transports temperature, moisture and momentum (+tracers). As discussed in previous lectures, parameterization tries to represent effects of subgrid scale processes on prognostic variables on model grid. Transports momentum, temp, moisture (tracers) by turbulence. If the effect of small scale turbulence is integrated right, then hopefully, the model variables on levels are going to be correct. Ultimate goal: correct model output

Which aspect of the BL can we evaluate?
2m temperature/humidity Depth of BL Diurnal variability of BL height Structure of BL (temperature, moisture, velocity profiles) Turbulent transport within BL Boundaries: entrainment, surface fluxes, clouds etc. large scale Very simple: 2m temp/hum. Available as model output, available from SYNOP. Right/wrong, what’s good enough? No indication of WHY. Problem: point measurement in time/space/height. If it is correct, no guarantee that everything’s correct. If it’s wrong. 2m values in model interpolated anyway. Next step: look at the vertical: does BL param act over correct depth? This, we can check. Also, for convectively mixed layer, you can get away with a very simple parameterization just looking at mixed layer value of conserved variables and BL depth. Much on BL depth. Esp. overland, look at temporal resolution - does the BL grow properly? macroscopic quantities more important for assessing model smaller scale quantities and boundary conditions more important to understand processes, develop and test parameterizations, define empirical relationships (i.e. log wind profile) Need to know proper boundary conditions, or result won’t be correct Going down the list, go from macroscopic (model) to microscopic (processes) level, closer to the process level to understand the WHY of the bias, and suggest improvements for parameterization. Also: need observations small scale Chandra et al. 2010

Depth of the boundary layer
Part 1 Depth of the boundary layer

How to define the BL top? Heat and moisture well-mixed in BL (convective BL) Flow transitions from turbulent to laminar at BL top (any BL) normalized BL height Figure: Martin Köhler Three methods: Heffter (1980) (1) Liu and Liang Method (2010) (1+) Richardson number method (2) Must apply same method to observations and model data for equitable comparison!

Heffter method to determine PBL height
Potential temperature gradient Potential temperature gradient exceeds K/m Pot. temperature change across inversion layer exceeds 2K Note: Works on convective BL only May detect more than one layer Detection is subject to smoothing applied to data Potential temperature Sivaraman et al., 2012, ASR STM poster presentation

Liu and Liang method First, determine which type of BL
is present, based on Θ difference between two near-surface levels Liu and Liang, 2010

Liu and Liang method: convective BL
For convective and neutral cases: Lift parcel adiabatically from surface to neutral buoyancy (i.e. same environmental Θ as parcel), and Θ gradient exceeds minimum value (similar in concept to Heffter). Parameters δs, δ u and critical Θ gradient are empirical numbers, differing for ocean and land. Liu and Liang, 2010

Liu and Liang method: stable BL
Stable case: Search for a minimum in θ gradient (top of bulk stable layer). If wind profile indicates presence of a low-level jet, assign level of jet nose as PBL height if it is below# the bulk layer top. Advantage: Method can be applied to all profiles, not just convective cases. Liu and Liang, 2010

Turbulent kinetic energy equation
pressure correlation shear production dissipation buoyancy production/ consumption turbulent transport

Richardson number-based approach
Richardson number defined as: flow is turbulent if Ri is negative flow is laminar if Ri above critical value calculate Ri for model/radiosonde profile and define BL height as level where Ri exceeds critical number buoyancy production/consumption shear production (usually negative) Ri= as an example: Richardson number approach, when is TKE generated? Was introduced first class Flux number: uses actually turbulent terms. Gradient number: substitute gradients of winds/temp as proxy for turbulent motion (K-theory). advantage of this approach: works for shear-driven layers and convective layers This is what’s used for the BLH variable in ECMWF model. Caution: not the same as internal BL depth, but tuned to be consistent. Problem: defined only in turbulent air! “Flux Richardson number”

Alternative: relate turbulent fluxes to vertical gradients (K-theory) flux Richardson number gradient Richardson number Remaining problem: We don’t have local vertical gradients in model

Bulk Richardson number
Solution: use discrete (bulk) gradients: Limitations: Values for critical Ri based on lab experiment, but we’re using bulk approximation (smoothing gradients), so critical Ri will be different from lab Subject to smoothing/resolution of profile Some versions give excess energy to buoyant parcel based on sensible heat flux – not reliable field, and often not available from observations Lab values determined for critical numbers for transitions from turbulent to laminar flow and back are based on knowledge of gradients throughout layer Have to use bulk approximation instead – averages out gradients – critical values shift in model, take values at each model layer, bottom is lowest surface layer This approach is used in the IFS for the diagnostic BLH in IFS. It is currently “tuned” to best agree with parameterization based BL height

How-to recipe Need T, u,v,q,z and some constants
Define conserved variable, e.g. virtual dry static energy: Apply smoothing in the vertical if necessary Starting at lowest model level, calculate Ri number, adding an excess to the dse to make up for missing surface fluxes Iterate, until Ri exceeds critical level (e.g. 0.25) Assign height of nearest layer as BL top height virtual dry static energy is conserved under dry adiabatic motion, equivalent to virtual potential temperature

Example: dry convective boundary layer NW Africa
2K excess red stars mark detected BL top in observations. smoothing of profiles helps not to get caught on kinks (how much?, don’t want to lose gradients) double inversions: previous residual layer. which one is correct? sensitive to fixed excess 1K excess Figures: Martin Köhler Theta [K] profiles shifted

Example: Inversion-topped BL
Inversion capped BLs dominate in the subtropical oceanic regions Identify height of jump across inversion EPIC, October 2001 southeast Pacific Will do stratocumulus-topped BL next day. shown are temperature profiles from Ron Brown, off the Chilean coast. Temperature inversion very strong and clear Relatively easy to write an algorithm that searches for large gradient in temperature/pot temp/humidity

Limitations of sonde measurements
Sonde measurements are limited to populated areas Depend on someone to launch them (cost) Model grid box averages are compared to point measurements (representativity error) Fundamental limitation of sonde measurements (synop as well, to a degree): don’t have sondes everywhere you want them Last point not really limitation of sonde, but of model. Model will never agree with sonde if it can’t resolve observed features

Took many years to compile this map
An example of a BL height map compiled from ship track missons. sparse samples from variety of measurement mission – mix ‘n match are these measurements representative in time/season/space? More missions since, measurements available, but still patchy coverage Neiburger et al. 1961

CALIPSO tracks Calipso tracks Arabic peninsula - daytime
Satellites open up new opportunities, particularly active instruments. Passive sensors tend to have little resolution in the BL, but active instruments good. Two example tracks to explain concept Majority of aerosols originate at surface, are lifted by turbulence. Concentration much less in free troposphere Direct measurement of height, vertical resolution good (30m in the lowest 8km) Accuracy depends on strength of signal gradient deep, dry convective BL over Arabian peninsula, probably dust over Pacific: probably salt aerosol, rhs shows cloud top in good agreement with aerosol gradient, more in sub-cloud layer on left side Can see, cloud tops give much stronger signal than clear air BL notice difference between day and night retrieval Global coverage – hurray! Arabic peninsula - daytime

BL from lidar how-to Easiest: use level 2 product (GLAS/CALIPSO)
Algorithm searches from the ground up for significant drop in backscatter signal Align model observations in time and space with satellite track and compare directly, or compare statistics Deciding what’s signal and what’s noise, and whether source is cloud or aerosol is not trivial. Easiest to use level 2 product, or cloudy boundary layer. (Later) molecular backscatter backscatter from BL aerosol surface return Figure: GLAS ATBD

Example: Lidar-derived BL depth from GLAS
Only 50 days of data yield a much more comprehensive picture than Neiburger’s map. Before CALIPSO, there was GLAS… Compare with Neiburger’s map - took several years worth of observational data to get map of rather small area 2 months of GLAS, delivers relatively reliable obs for whole Pacific But exactly what is shown here? Mix of cloudy BL tops and clear tops Ahlgrimm & Randall, 2006

GLAS - ECMWF BLH comparison
m shallow in model, patterns good Palm et al. 2005

Limitations to this method
Definition of BL top is tied to aerosol concentration - will pick residual layer Does not work well for cloudy conditions (excluding BL clouds), or when elevated aerosol layers are present Overpasses only twice daily, same local time Difficult to monitor given location orbit of satellite dictates times an places of observation - great coverage, but not so good for process studies in one place

The case of marine stratocumulus
Well mixed convective layer underneath strong inversion Are clouds part of the BL? As Sc transition to trade cumulus, where is the BL top? Again, Martin will do this in detail Serves to illustrate the issue - how to define BL layer transition from Sc to trade cu not smooth, can see evidence of subcloud layer inversion in the previous figures

Stratocumulus cloud top height
Model underestimates Sc top height SEP EPIC obs IFS Left: Lidar detected cloud top height of Sc clouds, October 2001 in the south east Pacific. Right: From EPIC campaign - comparable cloud top height (also see profiles), different October, short period of time. Lucky, Sc clouds persistent in time, cover large areas, homogeneous. Hannay et al. 2009 Köhler et al. 2011

Diurnal cycle of boundary layer height
Part 2 Diurnal cycle of boundary layer height parts overlap some, smooth transition

Diurnal cycle of convective BL from radiosonde
Example: stratocumulus-topped marine BL in the south-east Pacific: East Pacific Investigation of Climate (EPIC), 2001 Clear diurnal cycle of ~200m with minimum in early afternoon, maximum during early morning. Problem as previously: need lots of radiosondes. Cost! But makes for a nice case study. Check out maximum and minimum - max at night. This BL driven by cloud top cooling - greatest at night. Bretherton et al. 2004, BAMS

Diurnal cycle from CALIPSO
second example: diurnal cycle by lidar. Lucky for us: local overpass times correspond to max/min of diurnal cycle of Sc Note: cloud top height shown. as discussed, it only corresponds to BL height in well mixed cases near coast hatching indicates low sample number (less than 35 samples per 2x2 deg bin) same picture: deeper clouds at night, by about 200m

Part 3 Turbulent transport
This is where things get difficult to observe: internal profiles of the BL, transports

Flux towers: measuring BL fluxes in-situ
Example: Cabauw, 213m mast obtain measurements of roughness length, drag coefficients etc. Another example: flux towers Get log wind profile, for example With proper instruments, can measure small wind, T, Q perturbations, calculate fluxes Not my area of expertise Limited the lowest layers/surface layers KNMI webpage

Stevens et al. 2001 Here’s an example: Barbados oceanographic and meteorological experiment This is an LES study, where obs provide rough boundary conditions, LES provides transports. Big if: Is the LES correct? Two step process - first model case, then compare weather LES model agrees with parameterization output (SCM?) Limited to test cases. Primarily trade cumulus area. lower row gives some constraints or measurements on fluxes throughout the BL But, some observations available! Model fluxes via LES, constrain LES results with observations

Bomex - DualM Dual Mass Flux parameterization - example of statistical scheme mixing K-diffusion and mass flux approach Updraft and environmental properties are described by PDFs, based on LES Need to evaluate PDFs! This is an example for specific parameterization that has been tested in ECMWF model. Idea is to explicitly model large updrafts with mass flux approach, rest of turbulence by K-diffusion (like EDMF). Difference: clear/cloudy updrafts integrate clouds in BL scheme. Need to describe PDFs of total specific humidity, vertical velocity, potential temperature. Width of curve ultimately decides cloud fraction. Neggers et al. 2009

Turbulent characteristics: humidity
Raman lidar provides high resolution (in time and space) water vapor observations Or use ground-based remote sensing: Raman Lidar for water Vapor variablilty For example, ECMWF model uses statistical BL scheme, can get means and moments of water vapor distribution. On place, but long-term record. Plot: Franz Berger (DWD)

Turbulent characteristics: vertical motion
Observations from mm-wavelength cloud radar at ARM SGP, using insects as scatterers. reflectivity Other example: ARM site MMCR from insect returns. Again, can get means and moments of vertical velocity distributions. Can also get dimensions of up- and downdrafts. doppler velocity reflectivity Chandra et al. 2010 local time red dots: ceilometer cloud base

Turbulent characteristics: vertical motion
Variance and skewness statistics in the convective BL (cloud free) from four summer seasons at ARM SGP Example from long term measurements, vs. individual cases Same can be done for in-cloud vertical velocity, using cloud droplets as scatterers Chandra et al. 2010

Characterizing the boundary layer
Skewness of vertical velocity distribution from doppler lidar distinguishes surface-driven vs. cloud-top driven turbulence Hogan et al. 2009

Part 4 Stable Boundary Layer

10m wind biases compared to synop observations
OLD NEW Vegetation type Vegetation type No snow No snow Bias+st dev U10m Bias+st dev U10m Vegetation type Vegetation type Irina Sandu

10m wind biases compared to synop observations
OLD NEW Irina Sandu

T2m (new-old) 00 UTC absolute error T2m (new-old) Irina Sandu

Part 5 Boundaries

Forcing BL turbulence driven through surface fluxes, or radiative cooling at cloud top. Check: albedo, soil moisture, roughness length, clouds BL top entrainment rate: important but elusive quantity … at least convectively driven turbulence. Even if BL scheme perfect, needs the correct forcing. Check surface properties to see if surface fluxes are correct. Can observe, but difficult to extrapolate from point to area. BL grows by capturing and incorporating free tropospheric air in the BL layer. This rate is parameterized one way or another, and critically determines BL growth/stability in subsidence

Entrainment rate - DYCOMS II
Example: DYCOMS II - estimate entrainment velocity mixed layer concept: Dynamics and chemistry of marine stratocumulus Goal: estimate of entrainment velocity take example of mixed layer model: Martin does details Simple budget: mass must be conserved in the model, you want to know: mean properties of BL (T,q,u,v) know surface forcing (fluxes), need fluxes across BL top, need estimate of entrainment velocity Stevens et al. 2003

Summary & Considerations
What parameter do you want to verify? What observations are most suitable? Define parameter in model and observations in as equitable and objective a manner as possible. Compare! Are your results representative? How do model errors relate to parameterization?

References (in no particular order)
Neiburger et al.,1961: The Inversion Over the Eastern North Pacific Ocean Bretherton et al., 2004: The EPIC Stratocumulus Study, BAMS Stevens et al., 2001: Simulations of trade wind cumuli under a strong inversion, J. Atmos. Sci. Stevens et al., 2003: Dynamics and Chemistry of Marine Stratocumulus - DYCOMS II, BAMS Chandra, A., P. Kollias, S. Giangrande, and S. Klein: Long-term Observations of the Convective Boundary Layer Using Insect Radar Returns at the SGP ARM Climate Research Facility, J. Climate, 23, 5699–5714. Hannay et al., 2009: Evaluation of forecasted southeast Pacific stratocumulus in the NCAR, GFDL, and ECMWF models. J. Climate Hogan et al, 2009: Vertical velocity variance and skewness in clear and cloud-topped boundary layers as revealed by Doppler lidar, QJRMS, 135, 635–643. Köhler et al. 2011: Unified treatment of dry convective and stratocumulus-topped boundary layers in the ECMWF model, QJRMS,137, 43–57. Ahlgrimm & Randall, 2006: Diagnosing monthly mean boundary layer properties from reanalysis data using a bulk boundary layer model. JAS Neggers, 2009: A dual mass flux framework for boundary layer convection. Part II: Clouds. JAS