Planetary Boundary Layer (PBL) Retrieval

from Space: Challenges and Imperatives

Joseph A. Santanello, Jr.

NASA-GSFC

Hydrological Sciences Laboratory

Earth Observation for Water Cycle Science 2015 ESA-ESRIN, Italy 20 October 2015

Outline

L-A Coupling

PBL Role in Water/Energy Cycle Science

Ground-based PBL Observing Systems

Satellite PBL Retrieval: Status and Outlook

Motivation

The Planetary Boundary Layer (PBL) is a critical component of Earth System Science:

• There is an established and growing need for routine PBL measurements over land for a range of applications:

– Hydrology

– Clouds & Convection

– Pollution & Chemistry

– Land-Atmosphere Interactions & Coupling

– PBL & LES Model Development

• Each is modulated by PBL structure, growth, and feedback processes such as entrainment.

3

Weather & Climate and Support of Extremes

PBL Structure and Evolution

PBL Height

12 UTC

20 UTC

Mixed Layer

Free Atmosphere

Residual Layer

17 14

Daytime profiles of potential temperature (q) at the ARM-SGP central facility

PBL Budgets

20

17 14

Entrainment

Advection

RFD

The connection of surface hydrology to clouds and precipitation relies on proper quantification of heat and moisture transport through the coupled system via the PBL.

PBL Heat Budget

Sensible Heat Flux

Observable PBL Diagnostics

• PBL Height (PBLH)

– Responds directly to the flux of heat into the CBL

• Atmospheric Stability (g)

– dq/dz from 12Z profile

– Incorporates influence of residual mixed layer

• Mixed-Layer Temperature and Humidity (Tml, qml)

– Represents balance between sfc and entrainment

• Change in 2-meter Potential Temperature (DT2m)

– Calculated from 12-20Z

– Sensitive to heat input and PBL height

g

PBLH

Ti Tf

DT2m

Tml,qml

SM (% vol) g (K m-1)

DT2m (K) Dq2m (g kg-1)

PBL Height (m)

PBL Height (m)

PBL Height (m)

PBL Height (m)

Atmos. Stability Soil water content

2m Pot. temp 2m Sp. humidity

Observed PBL Diagnostics

Strong relationships between PBL and land surface properties are observed….

Outline

L-A Coupling

PBL Role in Water Cycle Science

Ground-based PBL Observing Systems

Satellite PBL Retrieval: Status and Outlook

Complexity of L-A Interactions

Ek, M. B., and A. A. M. Holtslag, 2004: Influence of Soil Moisture on Boundary Layer Cloud Development. J Hydrometeorol., 5, 86-99.

Land Surface and Hydrological Community

NWP/GCM Community

Typical Land Surface/Hydrology Expertise

• Geological Sciences

• Geosciences/Geophysics

• Environmental Science

• Civil and Environmental Engineering

• Mathematics

• Meteorology

• Atmospheric Sciences

• Physical Geography

• Natural and Earth Resources

• Computer Engineering

• Hydrology and Water Resources

Fluxes

Often, atmospheric scientists end up

‘doing’ land surface & hydrology

(not vice-versa!)

PBL

Community Motivation: GEWEX-GLASS

GLACE

LoCo

Benchmarking

Land-Atmosphere Coupling (LAC)

Model Data Fusion (MDF)

Metrics

• LAC: Combines the global (GLACE) and local (LoCo) studies

• MDF: Incorporates data assimilation and parameter estimation/calibration studies

• Benchmarking: Standardized way to evaluate models and their ‘goodness’

• Metrics: Diagnostics and quantification at the heart of each component

Current themes of the GEWEX- GLASS Panel

Global Land-Atmosphere

Coupling Experiment (GLACE)

• ‘Omega’ diagnostic determined by ensembles of GCMs indicates the connection between soil moisture and precipitation

• Well-known “Hot Spots” of L-A coupling strength, in particular over the SGP of the U.S.

• These results have steered L-A investigations over the last decade more than any other study to date.

Courtesy of Koster et al. (2004)

LoCo Process Chain

- Diagnose the components of GLACE at the diurnal process level:

- Our focus: Evaluate the ‘links in the chain’ and their sensitivities to land and PBL

perturbations:

)(

)(

)(

)(

)(

)(

EFd

Pd

SMd

EFd

SMd

Pd

DSM → DEFsm → DPBL → DENT → DT2m,q2m ► DP/Clouds

(a) (b) (c) (d)

SM: Soil Moisture

EF: Evaporative Fraction

PBL: Mixed-layer quantities

ENT: Entrainment fluxes at PBL top

P/Cloud: Moist processes

‘LoCo Process-Chain’ defined by non-linear series of

interactions and feedbacks

Defining ‘L-A Coupling’

• The strength of coupling or degree with which changes in land states (e.g. soil moisture) affect surface fluxes ("near-surface local"

coupling), surface fluxes affect PBL evolution ("local" coupling), and coupled surface/PBL processes affect precipitation patterns ("non-

local/regional/larger scale" coupling).

• Land-Atmosphere coupling includes all interactions between the atmosphere and land, including radiative, momentum, heat, and

mass transfer. Mass includes water as well as chemical constituents. Interactions may include positive or negative feedback loops

operating on multiple space/time scales.

• The role of the local land surface (on a scale of up to 100 km) in modulating atmospheric processes, opposed to the role of the large-

scale circulations. More specifically; LoCo involves the influence of local land surface conditions such as soil moisture, land surface

roughness, albedo, etc. on surface fluxes of energy, moisture and momentum through atmospheric boundary layer dynamics, to cloud,

radiation and precipitation processes. The extent to which this coupling is local is defined by the co-variability of the atmospheric

processes (cloud, radiation, precipitation) with the surface conditions, as opposed to the co-variability of these processes with the large-

scale circulation.

• For me land atmosphere interactions reflect the two way coupling between the land and the atmosphere which modifies the surface

fluxes of heat, moisture and carbon at the surface and therefore the boundary condition for the surface hydrology and free troposphere

state (temperature, humidity, precipitation) across temporal scales (daily to interannual) and spatial scale (hundred of meters to hundred

of kilometers).

• The impact of the local (i.e. 100km) land surface state on the diurnal evolution of the PBL- from humidity to clouds and in extreme

cases precipitation- as distinguishable from large-scale advective forcing.

• To me, the definition is that the atmosphere is demonstrably sensitive to the land surface state (be it soil moisture, vegetation, other

surface properties), particularly in a way that impacts atmospheric predictability (temperature, precipitation, etc.). Implicit is that the land

surface is always sensitive to the atmosphere, so the key bit is to complete the feedback loop from land back to the atmosphere (the

ΔEFsm → ΔPBL leg of your process chain).

• L-A coupling describes the local influence of the land on the overlying atmosphere and PBL (and vice-versa), as described by the

processes and feedback involved with the transfer of water, heat, and momentum with the cumulative impact on cloud and precipitation

formation.

Motivation

•The GLASS LoCo WG has made substantial progress in developing

specific diagnostic approaches to quantify L-A interactions in coupled

models.

•GEWEX Newsletter (Nov. 2011): contributing parties, papers, models,

and diagnostics.

•We now have a substantial collection of assets to bring to bear to study

LoCo in terms of:

a) LoCo diagnostics and model applications

b) Dataset needs and production

15

LoCo Diagnostics

DSM → DEFsm → DPBL → DENT → DEFpbl ► DP/Clouds

Condensed list of LoCo metrics

Each attempts to quantify particular links in the process-chain

Range from simple correlations to multi-variate space to preconditioning assessment

Wide ranging input requirements (temporally, spatially) and model application (SCM, MM, GCM)

Ultimate impact of land/near-surface variables on the PBL, clouds, precip Courtesy C. Ferguson

Coupling Metrics Toolkit (CoMeT)

http://www.ahmedbtawfik.com/comet/

Outline

L-A Coupling

PBL Role in Water Cycle Science

Ground-based PBL Observing Systems

Satellite PBL Retrieval: Status and Outlook

Global/Operational Radiosonde Network

Radiosondes still only serve as a ‘synoptic’ network and provide snapshots of the PBL.

Often limited to 2x/day and insufficient daytime CBL sampling.

The U.S. SGP (DOE-ARM site) identified as testbed for LoCo studies and datasets:

•Based on feedback and through collaboration between LoCo, ARM, and the NASA NEWS program, a new data product

called ARM Best Estimate (ARMBE) - Land has been produced for the SGP Central Facility (Lamont, OK).

•General Description: The ARMBELAND is a subset of the ARM Best Estimate (ARMBE) products for supporting community

land-atmospheric research and land model developments. It is recommended to use with other ARMBE data products such as

ARMBECLDRAD (cloud and radiative fluxes) and ARMBEATM (surface precipitation, surface sensible and latent heat fluxes

and other atmospheric state variables) to get a more complete description of the land condition and its associated large-scale

environment. Currently, the ARMBE-Land contains the following quantities:

* Soil temperature measured from CO2FLX, EBBR, and SWATS

* Soil moisture content measured from CO2FLX, EBBR and SWATS

* Soil heat flux from CO2FLX, EBBR

* CO2 flux/density/PAR from CO2FLX

* Friction velocity from CO2FX

20

Data availability:

Quantities from CO2FLX : 2003-2012

Quantities from EBBR: 1994-2012

Quantities from SWATS: 1996-2012

ARM-SGP Testbed

Ground-based PBL Retrieval

21

• ARM-SGP Value Added Product (*PBLH) – SGP sonde data (4x daily) over period of record 1996-

present – 4 method intercomparison of PBLH detection

• Heffter (gradient), Liu-Liang (parcel), Bulk Richardson 0.5 and 0.25 (stability/shear)

• ARM-SGP PBLH Product (*PBLH) – AERI (IR) + Raman Lidar to construct temperature

profile in PBL – 2009-2012 hourly dataset

• Li-Sawyer Method (*PBLH) – Combines gradient/wavelet detection – Employs sonde, AERI, and MPL (lidar) @ SGP – To be released soon

• AERIoe Produce (*profs) – Profiles based on AERI for clear and cloudy – focused on

PBL • Ceilometer-derived (*PBLH)

– Ceilometer, 2011-present at SGP; 3 methods, 16s resolution

• Airborne HSRL (*PBLH) – Raman Lidar, Evaluated vs. WRF-Chem and Sondes – NASA Langley; Transects over California

SGP products highlight the diversity and difficulties in PBL profile and height estimation,

even with 4x/daily sonde launches.

Still site-specific and subject to

sonde/profiler limitations.

Motivation for satellite-derived PBL information (e.g. AIRS).

ARM-SGP PBL Height Retrieval

22

4 Methods to estimate PBL height from sonde profiles AERI + Raman Lidar

MPL Lidar

Model and Satellite-based PBL Retrieval

23

• ARM-SGP Mergesonde (*profs)

– Sonde + Met + ECMWF data

– 1996-present profiles @ hourly resolution

• ECMWF (ERA-Interim) Climatology (*PBLH)

– Teixeira and von Englen

– Monthly gridded PBLH @ 0, 6, 12, 18 UTC

– 5 gradient methods – RH most robust

MERGESONDE shows promise

for filling in temporal dimension and allowing for automation.

Outline

L-A Coupling

PBL Role in Water Cycle Science

Ground-based PBL Observing Systems

Satellite PBL Retrieval: Status and Outlook

Satellite Monitoring of Earth System

Global Monitoring of LoCo Variables from Satellite:

• Atmosphere

· Precipitation: TRMM, GPM

· Clouds & Aerosol: CALIPSO, EarthCARE

· Chemistry: Aura, GOES

· Temp/Water Vapor: AIRS/IASI

• Land Surface

· Soil Moisture: AMSR-E, SMOS, SMAP

· Groundwater: GRACE

· Snow: MODIS, AMSR-E

· Surface Water: SWOT

· Fluxes (ET): Landsat, MODIS

PBL Gap

Observational Requirements

Observational Requirements

Temperature Humidity

Current Satellite Capabilities

• Today’s spaceborne instruments have limited PBL sensitivity:

– Hyperspectral Sounders (e.g. AIRS/IASI) are the most capable in term spectral resolution but have not been tailored for PBL sounding and are confounded by surface emissivity.

– Lidar (e.g. CALIPSO) can obtain high vertical resolution, but is limited in return time and spatial sampling and does not provide thermodynamic state information.

– Geostationary (e.g. GOES-R) have frequent temporal sampling, coarse spectral bands and PBL resolution.

– GPS-RO (e.g. COSMIC) shows promise for PBL retrieval, but is limited by sampling and confounding issues related to humidity/topography.

• Thus, each of these sensors has some advantages, but also considerable limitations that make them impractical for PBL studies.

28

– Hyperspectral Sounders (e.g. AIRS/IASI) are the most capable in terms of spectral resolution but have not been tailored for PBL sounding and are confounded by surface emissivity.

– Lidar (e.g. CALIPSO) can obtain high vertical resolution, but is limited in return time and spatial sampling and does not provide thermodynamic state information.

– Geostationary (e.g. GOES-R) have frequent temporal sampling, coarse spectral bands and PBL resolution.

– GPS-RO (e.g. COSMIC) shows promise for PBL retrieval, but is limited by sampling and confounding issues related to humidity/topography.

AIRS (NASA - Aqua)

• 2002-present

• 2x daily retrievals

• 1:30am/pm

• 2378 IR channels

• 45km spatial resolution

• 1-2km vertical resolution

– 1K temperature

– 20% humidity

200

300

400

500

600

700

800

900

290 300 310 320 330 340 350

P (m

b)

Pot. Temp (K)

28 Jul 2003

OBSV5.20-DayV5.20-Night

Mixed

Layer

PBL Ht.

IASI (ESA - Metop)

• 2006-present

• 2x daily retrievals

• 10:30am/pm

• 8641 IR channels

• 25km spatial resolution

• 1-2km vertical resolution

– 1K temperature

– 10% humidity

CALIPSO (NASA)

• 2006-present

• 14-day revisit time

• 10:30am/pm

• Lidar

– Backscatter gradient

– Aerosol density

– PBL height

• 333m vertical resolution

– 1K temperature

– 10% humidity

5

2 km

km5

2 km

km

2

4

He

ight (k

m)

Distance (km)500 1000 1500 2000 2500 3000 3500

5

2 km

km5

2 km

km

2

4

He

ight (k

m)

Distance (km)500 1000 1500 2000 2500 3000 3500

GOES (NOAA)

• Hourly profiles

• 19-channel IR retrieval

• 10-km spatial resolution

• AVN first guess

GPS-RO (COSMIC)

• 2006-present

• Atmospheric refractivity

– Sensitive to topography

– Confounded by WV

• 200km spatial sampling

– Irregular in time and space

• 200m vertical resolution

0

1

2

3

4

5

6

7

8

9

10

1

20

39

58

77

96

115

134

153

172

191

210

229

248

267

286

305

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362

381

400

419

438

457

476

495

514

533

552

571

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628

647

666

685

704

723

742

761

780

799

818

837

856

875

894

913

932

951

970

989

1008

1027

1046

1065

1084

1103

1122

Height(km)

Profile#

LowestHeightofCOSMIS-RORetrieval@SGP(+/-2deg)

Outline

L-A Coupling

PBL Role in Water Cycle Science

Ground-based PBL Observing Systems

Satellite PBL Retrieval: Making the Best of it

Exploiting Satellite Observations of the PBL for Land-Atmosphere Interaction Studies

Joseph A. Santanello, Jr.1, and Toshihisa Matsui2

1Code 617, NASA/GSFC; 2Code 612, NASA/GSFC and ESSIC/UMCP

The Planetary Boundary Layer (PBL; <4km above the surface) is a critical reservoir and integrator of land

surface process, water and energy cycling, and land-atmosphere (L-A) interactions.

Observations of PBL height, temperature, and humidity are therefore necessary for evaluation and

development of L-A coupling in weather and climate models.

Current satellite-based indirect estimates of PBL structure (such as from AIRS; Fig. 1) are limited by

biases in the PBL and/or insufficient spatial, vertical, or temporal coverage.

By converting profiles into radiances (brightness temperature; Tb) measured by the satellite (Fig. 2),

models and in-situ observations can be directly compared against the full spectrum of instrument channels

sensitive to the land surface and PBL.

Earth Sciences Division – Hydrospheric and Biospheric Sciences

(1)

(2)

‘window’ region

sensitive to

surface and PBL

WRF PBL Ht.

Sonde PBL Ht.

AIRS PBL

Ht.

Land-atmosphere coupling from satellite remote sensing as a drought-monitoring tool

Joshua K. Roundy1 and Joseph A. Santanello, Jr.2

1Code 617, NASA/GSFC and ORAU; 2Code 617, NASA/GSFC

The Coupling Drought Index

(CDI) summarizes the impact of

land-atmosphere feedbacks on

drought:

(+) is drought degradation or

Dry Coupling

(-) is drought improvement or

Wet Coupling

NASA’s AQUA satellite (AIRS

and AMSR-E) provides the

needed variables to calculate the

CDI based on remote sensing.

The CDI from AQUA compares

well with MERRA and CFSR

reanalysis (Fig. 1).

The ability of the AQUA-based

CDI to capture the development of

the 2012 Midwest drought (Fig. 2)

illustrates its potential use as a

drought monitoring tool.

Earth Sciences Division – Hydrospheric and Biospheric Sciences

Change in the 2012 Drought (May 8 - July 31)

US Drought Monitor

U.S. Drought Monitor Class Change

5 Class Degradation

4 Class Degradation

3 Class Degradation

2 Class Degradation

1 Class Degradation

No Change

1 Class Improvement

2 Class Improvement

3 Class Improvement

4 Class Improvement

5 Class Improvement

July 31, 2012

July 3, 2012

compared to

http://droughtmonitor.unl.edu

1 Month

U.S. Drought Monitor Class Change

5 Class Degradation

4 Class Degradation

3 Class Degradation

2 Class Degradation

1 Class Degradation

No Change

1 Class Improvement

2 Class Improvement

3 Class Improvement

4 Class Improvement

5 Class Improvement

July 31, 2012

July 3, 2012

compared to

http://droughtmonitor.unl.edu

1 MonthU.S. Drought Monitor Class Change

5 Class Degradation

4 Class Degradation

3 Class Degradation

2 Class Degradation

1 Class Degradation

No Change

1 Class Improvement

2 Class Improvement

3 Class Improvement

4 Class Improvement

5 Class Improvement

July 31, 2012

July 3, 2012

compared to

http://droughtmonitor.unl.edu

1 Month

U.S. Drought Monitor Class Change

5 Class Degradation

4 Class Degradation

3 Class Degradation

2 Class Degradation

1 Class Degradation

No Change

1 Class Improvement

2 Class Improvement

3 Class Improvement

4 Class Improvement

5 Class Improvement

July 31, 2012

May 8, 2012

compared to

http://droughtmonitor.unl.edu

3 Months

AQUA CDI

Drought

Improvement

Drought

Degradation

Average US June-Aug CDI (1)

(2)

Land-Atmosphere Coupling in Modern Reanalysis Products

Joseph A. Santanello, Jr.1, Josh Roundy1, and Paul Dirmeyer2

1Hydrological Sciences Laboratory, NASA/GSFC; 2COLA/George Mason University

An integrated assessment of land-

atmosphere (L-A) interactions in current

reanalysis products highlights important

differences and varying accuracies in

each, and suggests that updates to

MERRA-2 (vs. MERRA) have led to

improvements in the representation of L-A

coupling.

Relationship of Evaporation to PBL Height Reanalysis Errors over the U.S. SGP (1) (2)

Earth Sciences Division – Hydrospheric and Biospheric Sciences

Sonde IOP (Summer 2015)

•15 June to 31 August 2015 at ARM-SGP Central Facility

•12 IOP days chosen by PI’s

– Sample range of dry/wet regimes

– Consecutive day dry-downs

– Tropical systems

•14-sounding supplement to standard 6-hourly sondes

– Hourly launches (daytime)

– 10-min lagged launch every 3-hours

Sonde IOP (Summer 2015)

3-hourly sonde 10-min lagged sonde 1-hourly sonde

Space-based Extensions

Closing Comments on PBL Retrieval from Space

Are there Challenges? Yes, but..

Is this an Imperative? Let’s see..