Remotely Sensing Temperature and Humidity Profiles in the PBL
Transcript of Remotely Sensing Temperature and Humidity Profiles in the PBL
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Remotely Sensing Temperature and Humidity Profiles in the PBL
Dave TurnerNOAA / Global Systems Laboratory
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Thermodynamic Profiling: Primary Challenge is in the Lower Troposphere
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ā¢ Huge observational gaps exist in lower trop thermodynamic profilingā¢ Closing these gaps is essential for progress in weather and climate researchā¢ Ground-based passive and active remote sensing systems can close these gapsā¢ Marriage of these ground-based systems (and future networks of them) and satellite sensors
enhance information content and utility
Published online Aug 2015
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Microwave Radiometer (MWR)
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ā¢ Measures downwelling microwave radiance in dozens of spectral channelsā¢ Over three decades of developmentā¢ Hardened, automated instruments ā¢ Commercially available from multiple vendorsā¢ Need to calibrate with liquid nitrogen periodically
Solheim et al. Radio Sci. 1998Rose et al. Atmos. Res. 2005
Caveat: Radio frequency interference becoming more relevant!
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Infrared Radiance Interferometer
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25 Āµm 7.1 Āµm10 Āµm15 Āµm
Rad
ianc
e [m
W/ (
m2
srcm
-1)]
Wavelength [Āµm]
SGP, 8 Jul 2003PWV: 48.0 mmNSA, 1 Mar 2004
PWV: 1.3 mm
Knuteson et al. JTECH 2004 (two parts)Turner et al. AMS Monograph 2016
ā¢ Measures downwelling IR spectra emitted by atmosphere at high temporal and spectral resolution (30 s, 1 cm-1)
ā¢ Invented by Univ Wisconsin ā Madison in 1990s; matured as part of the DOE ARM Program
ā¢ Hardened, automated instrument; self-calibratingā¢ Commercially available from two vendors
AERI
ASSIST
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NCAR Water Vapor Differential Absorption Lidar (nDIAL)
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Spuler et al. AMT 2015Weckwerth et al. JTECH 2016
ā¢ Laser-based active remote sensor ā¢ Developed at NCAR and Montana State University
ā Based upon prototype developed at MSUā¢ Micropulse system using diode-based lasersā¢ Automated instrument; self-calibrating (narrowband approach)ā¢ Deployed during FRAPPE, PECAN, Perdigao, CACTI, ā¦ā¢ Lowest good data level: ~500 m AGL
Still research based system(NCAR now has 5 of these in their instrument pool)
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ā¢ Laser-based active remote sensor ā¢ Developed by Vaisala; aim is to be commercially available by 2021ā¢ Uses two separate systems; one optimized for near surface and one for āfar rangeāā¢ Uses broadband approach; a bit more challenging to calibrate
Vaisala Water Vapor DIAL (vDIAL)
6Newsom et al. JTECH 2020
vDIALRLIDAERI
May-June 2017 at ARM SGP Site
ā¢ Demonstration deployments at ARM SGP in 2017, DWD Lindenberg, ECCC
ā¢ Lowest good data level: ~20 m AGL
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Radio Acoustic Sounding System (RASS)
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Martner et al. BAMS 1993Bianco et al. AMT 2017
ā¢ RASS can be paired with radar wind profiler or sodarā¢ Radar/sodar tracks speed of sound wave, which is proportional to Tvā¢ Higher frequency systems needed to profile near the surface; lower
frequency systems profile to higher altitudesā¢ Most groups typically only collect RASS Tv profiles once / hour (using
5 or 10 min integration)
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New Commercially Available Raman Lidar
(Measures q and T)
8This shows 5-min resolution, but 30-s resolution also possible
Similar WV performance as ARM Raman lidar at SGP
This is the best temperature profiling system I have seen
Lange et al. GRL 2019
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Passive Remote Sensing
ā¢ IR/MW radiometers measure radiation emitted from the atmosphere in channels sensitive to emission from different gasesā Radiance contains info on T(z) and q(z) (and clouds, other trace gases, etc)ā The channel selection should span a range of optical depth
ā¢ Ill-defined problem; retrievals need to be constrained by either a prioridata (e.g., climatology) or model background
ā¢ Information content is key: what part of retrieved profile is from observation vs. from the a priori information
ā¢ Calibration is absolutely key (both in obs and forward model) ā¢ No real information on how temperature and moisture covaries temporally
/ spatially / vertically, which hinders retrievals9
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Synergistic Remote Sensing
ā¢ Combining active and passive observations into a retrieval can improve accuracy and information content of retrieved profiles
ā¢ Consistent forward models and no systematic errors criticalā¢ Strength of one observing technology can be used to overcome the
weakness of the otherā¢ Uncertainty analysis and information content is importantā¢ Retrievals performed using TROPoe algorithm
ā Physical-iterative method using optimal estimation frameworkā Able to combine different types of observations to retrieve T(z) and q(z) ā Full error characterization and vertical resolution are standard output products
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(Turner and Lƶhnert 2014; Turner and Blumberg 2019; Turner and Lƶhnert 2021)
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Physical-Iterative Retrieval(Optimal Estimation)
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šš!
Observation
Obs Uncertainty
š¹š"
Forward Model
State Vector (nth iter)
š#š#
Mean Prior
Prior Uncertaintyš¾" =
šš¹šš"
Jacobian of F
š"$% = š# + š#&% +š¾"'š!&%š¾" &%š¾"'š!&% š ā š¹ š" +š¾" š" āš#
Iterative solution (n to n+1)
š( = š#&% +š¾'š!&%š¾ &% Uncertainty in X
A= š#&% +š¾'š!&%š¾ &%š¾'š!&%š¾ Averaging Kernal
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Combining Observations within the Retrieval
ā¢ Combine the observations into the obs vector Y
ā¢ Use the appropriate forward model for each element of Y
ā¢ Combine the observational uncertainties
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š = [š)"*+%, š)"*+,, š)"*+-]
š¹ š = [š¹)"*+% š , š¹)"*+, š , š¹)"*+- š ]
š¹ š = [/š)"*+%, /š)"*+,, /š)"*+-]
š! =š)"*+%
š)"*+,š)"*+-
0 0
00
0 0
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Combining Observations within the Retrieval
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Example #1: XPIA on 17 Mar 2015 at 1830 z
Combined MWR+RASS retrieval described in Djalalova et al. AMT 2021 (in review)
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Combining Observations within the Retrieval
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Example #1: XPIA on 17 Mar 2015 at 1830 z
Combined MWR+RASS retrieval described in Djalalova et al. AMT 2021 (in review)
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Combining Observations within the RetrievalExample #1: XPIA on 17 Mar 2015 at 1830 z
Combined MWR+RASS retrieval described in Djalalova et al. AMT 2021 (in review)
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Combining Observations within the Retrieval
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Example #1: XPIA on 17 Mar 2015 at 1830 z
Combined MWR+RASS retrieval described in Djalalova et al. AMT 2021 (in review)
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Combining Observations within the Retrieval
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Example #2: PECAN on 20 Jun 2015 at 0248 z
Combined IR+DIAL retrieval described in Turner and Lƶhnert AMT 2021
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Combining Observations within the Retrieval
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Example #2: PECAN on 20 Jun 2015 at 0248 z
Combined IR+DIAL retrieval described in Turner and Lƶhnert AMT 2021
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Combining Observations within the Retrieval
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Example #2: PECAN on 20 Jun 2015 at 0248 z
Combined IR+DIAL retrieval described in Turner and Lƶhnert AMT 2021
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So How Good are these Retrievals?
21xkcd.com/1478
š( = š#&% +š¾'š!&%š¾ &% Uncertainty in X
A= š#&% +š¾'š!&%š¾ &%š¾'š!&%š¾ Averaging Kernal
ā¢ What is the inherent uncertainty?ā¢ How many independent pieces of info are in
the retrieved T and q profiles?ā¢ How is this information distributed vertically?ā¢ What is the true vertical resolution of these
profiles?ā¢ How should I use these data?
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Example: Passive-Only Retrieval
22From Perdigao Field Campaign in Portugal, where an AERI, MWR, nDIAL and sondes were collocatedCombined IR+DIAL retrieval described in Turner and Lƶhnert AMT 2021
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Example: Active + Passive Retrieval
23From Perdigao Field Campaign in Portugal, where an AERI, MWR, nDIAL and sondes were collocatedCombined IR+DIAL retrieval described in Turner and Lƶhnert AMT 2021
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Bias and RMSE using Radiosondes as Truth
24Blumberg et al. JAMC 2015
AERIMWRe
AERIMWRe
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1-Sigma Uncertainty Profiles
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(Square root of the diagonal of Sx)
Turner and Lƶhnert AMT 2021
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1-Sigma Uncertainty Profiles
26Turner and Lƶhnert AMT 2021
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1-Sigma Uncertainty Profiles
27Turner and Lƶhnert AMT 2021
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1-Sigma Uncertainty Profiles
28Turner and Lƶhnert AMT 2021
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Information Content and Vertical Resolution
ā¢ Retrievals are performed on a discrete vertical gridā Often has 40 to 100 vertical layers
ā¢ The amount of information in the observations is well less than the number of layersā E.g., the number of channels on a MWR is always less than 25
ā¢ Ill-posed problemā¢ To constrain the retrieval, a priori data (e.g., radiosonde climatology)
is used to provide guidance on the level-to-level correlationā¢ So what is the true IC and Vres of the retrieved profiles?ā Can be determined directly from the averaging kernel!
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Cumulative Degrees of Freedom for Signal
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(Diagonal of the Averaging Kernel)
Turner and Lƶhnert AMT 2021
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Cumulative Degrees of Freedom for Signal
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ā¢ DFS provides a measure of the number of independent pieces of information
ā¢ In this example, the T(z) retrieved from theā MWR has DFS = 2.3 below 3 kmā AERI has DFS = 5.4 below 3 km
ā¢ But below 1 km, theā MWR has DFS = 1.7ā AERI has DFS = 4.4
ā¢ If you wanted to assimilate these profiles, you would want to use levels that are separated by at least 1 DFS
ā¢ In this example, if we take the first level at 100 m AGL then we would assimilateā 2 levels from the MWRā 4 levels from the AERI
Interpreting the cumulative DFS profile
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Cloud Impacts on the Retrieval: MW vs IR
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ā¢ Clouds are much more opaque in the IR than MWā Cloud is opaque in IR when LWP > 60 g m-2
ā MWR remains very transparent until LWP is very high (>1000 g m-2)
ā¢ IR has no ability to profile above cloud base in these situations, but MW can
ā¢ In this example (assume LWP > 60 g m-2)ā DFS below cloud base:
ā¢ AERI 4.3, MWR 1.7ā DFS above cloud base:
ā¢ AERI 0.0, MWR 0.6ā¢ True information content above/below
cloud depends on CBH and LWPā¢ The points one would assimilate are
determined the same way as before
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Comparing Similar Instrumentsā¢ 3 ASSISTs operated several hours in clear sky
conditions at Quebec City on 12 July 2021ā¢ How well do they compare?
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Examples of How these Retrievals are Being Used
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This is just a subset.There are many I could have chosen to highlight
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Composite Radar Image: 0425 UTC on 10 Aug 2014
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Composite Radar Image: 0525 UTC on 10 Aug 2014
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Composite Radar Image: 0625 UTC on 10 Aug 2014
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Composite Radar Image: 0725 UTC on 10 Aug 2014
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Composite Radar Image: 0825 UTC on 10 Aug 2014
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Composite Radar Image: 0925 UTC on 10 Aug 2014
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Composite Radar Image: 1025 UTC on 10 Aug 2014
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Thermodynamic Profiles and LWP from the AERI
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Toms et al. MWR 2017Haghi et al. BAMS 2019
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Cross-comparisons with UAS Observations
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Koch et al. JTECH 2018
de Boer et al. BAMS 2018
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DA Impact on a Tornado Forecast
49Storm of interest
MRMS Composite Reflectivity (dBZ)
2330 UTC
Storm-scale Domain (3 km)
Hu et al. WAF 2019
ā¢ On 13 July 2015, a tornado formed near Nickerson, KSā¢ Near surface air was very warm (>35 C) and dryā¢ Environmental storm-relative helicity was very smallā¢ Neither a tornado or severe thunderstorm watch was issuedā¢ After the supercell formed, it moved towards the SW
ā¢ Event occurred during PECAN, when there were 6 fixed sites with ground-based AERIs and wind profilers
ā¢ Used the NSSL Warn-on-Forecast ensemble system (NEWS-E) using GSI-EnKF
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Nickerson Storm (After 2-h DA)Forecast initialized at 2000 z
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MRMS CREF at 2330 UTC
Probability of CREF > 25 dBZ (3 km neighborhood)
CNTL
%
CIAERI_DL
1-h Fcst valid 2100 UTC
1.5-h Fcst valid 2130 UTC
2-h Fcst valid 2200 UTC
3-h Fcst valid 2300 UTC
3.5-h Fcst valid 2330 UTC
Black is 25 dBZ contour from MRMS composite reflectivity
Hu et al. WAF 2019
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Many important quantities depend on spatial gradients of the kinematic and thermodynamic fields:
ā¢ Divergenceā¢ Advection
We can calculate this at SGP using Greenās Theorem:
The right-hand side of this equation looks a lot like the definitions of these quantities.
If we can use observations to approximate the left-hand side, we can get advection and similar properties.
Quantifying WV and Temperature Advection
51
!š šš„ + š šš¦ =)šššš„ ā
šššš¦ šš“
ARM SGP Site
Wagner et al. JTECH 2021 (in review)
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Quantifying Land-Atmosphere Interactionsā¢ Mixing diagram approach to study
energy and moisture budget evolution in the CBL pioneered by Betts (1992)
ā¢ Santanello et al. (2009) showed utility of approach to evaluate models
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Wakefield et al. J Hydro Met 2021 (in preparation)
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Summaryā¢ Ground-based thermodynamic (TD) profiles in the PBL very useful for a range
of operational and research objectivesā¢ Only passive TD profilers are currently commercially available
ā Active lidar-based systems coming in next few yearsā¢ Understanding the information content, vertical distribution of this
information, and resolution is critical to use the obs correctlyā Variational methods like the TROPoe retrieval provide this infoā Combining observations from different instruments can increase the information
content and improve the retrievalsā The retrieval method matters! Working to make TROPoe available to all via Docker
ā¢ Utility of TD profilers demonstrated in several different applicationsā¢ Continuing to use these data for both process-study research and in limited-
area data assimilation experiments53