Remotely Sensing Temperature and Humidity Profiles in the PBL - Dave Turner NOAA / Global Systems Laboratory
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Remotely Sensing Temperature and Humidity Profiles in the PBL Dave Turner NOAA / Global Systems Laboratory 1
Thermodynamic Profiling: Primary Challenge is in the Lower Troposphere Published online Aug 2015 • Huge observational gaps exist in lower trop thermodynamic profiling • Closing these gaps is essential for progress in weather and climate research • Ground-based passive and active remote sensing systems can close these gaps • Marriage of these ground-based systems (and future networks of them) and satellite sensors enhance information content and utility 2
Caveat: Radio frequency interference becoming more relevant! Microwave Radiometer (MWR) • Measures downwelling microwave radiance in dozens of spectral channels • Over three decades of development • Hardened, automated instruments • Commercially available from multiple vendors • Need to calibrate with liquid nitrogen periodically Solheim et al. Radio Sci. 1998 Rose et al. Atmos. Res. 2005 3
Infrared Radiance Interferometer • Measures downwelling IR spectra emitted by atmosphere at high temporal and spectral resolution (30 s, 1 cm-1) • Invented by Univ Wisconsin – Madison in 1990s; matured as part of the DOE ARM Program • Hardened, automated instrument; self-calibrating • Commercially available from two vendors Wavelength [µm] AERI 25 µm 15 µm 10 µm 7.1 µm Radiance [mW / (m2 sr cm-1)] SGP, 8 Jul 2003 NSA, 1 Mar 2004 PWV: 48.0 mm PWV: 1.3 mm Knuteson et al. JTECH 2004 (two parts) Turner et al. AMS Monograph 2016 4 ASSIST
NCAR Water Vapor Differential Absorption Lidar (nDIAL) • Laser-based active remote sensor Still research based system • Developed at NCAR and Montana State University (NCAR now has 5 of these in their instrument pool) – Based upon prototype developed at MSU • Micropulse system using diode-based lasers • Automated instrument; self-calibrating (narrowband approach) • Deployed during FRAPPE, PECAN, Perdigao, CACTI, … • Lowest good data level: ~500 m AGL Spuler et al. AMT 2015 Weckwerth et al. JTECH 2016 5
Vaisala Water Vapor DIAL (vDIAL) • Laser-based active remote sensor • Developed by Vaisala; aim is to be commercially available by 2021 • Uses two separate systems; one optimized for near surface and one for “far range” • Uses broadband approach; a bit more challenging to calibrate • Demonstration deployments at ARM SGP in 2017, DWD Lindenberg, ECCC May-June 2017 at ARM SGP Site • Lowest good data level: ~20 m AGL vDIAL RLID AERI Newsom et al. JTECH 2020 6
Radio Acoustic Sounding System (RASS) • RASS can be paired with radar wind profiler or sodar • Radar/sodar tracks speed of sound wave, which is proportional to Tv • Higher frequency systems needed to profile near the surface; lower frequency systems profile to higher altitudes • Most groups typically only collect RASS Tv profiles once / hour (using 5 or 10 min integration) Martner et al. BAMS 1993 Bianco et al. AMT 2017 7
New Commercially Available Raman Lidar (Measures q and T) Similar WV performance as ARM Raman lidar at SGP This is the best temperature profiling system I have seen 8 Lange et al. GRL 2019 This shows 5-min resolution, but 30-s resolution also possible
Passive Remote Sensing • IR/MW radiometers measure radiation emitted from the atmosphere in channels sensitive to emission from different gases – Radiance contains info on T(z) and q(z) (and clouds, other trace gases, etc) – The channel selection should span a range of optical depth • Ill-defined problem; retrievals need to be constrained by either a priori data (e.g., climatology) or model background • Information content is key: what part of retrieved profile is from observation vs. from the a priori information • Calibration is absolutely key (both in obs and forward model) • No real information on how temperature and moisture covaries temporally / spatially / vertically, which hinders retrievals 9
Synergistic Remote Sensing • Combining active and passive observations into a retrieval can improve accuracy and information content of retrieved profiles • Consistent forward models and no systematic errors critical • Strength of one observing technology can be used to overcome the weakness of the other • Uncertainty analysis and information content is important • Retrievals performed using TROPoe algorithm (Turner and Löhnert 2014; Turner and Blumberg 2019; Turner and Löhnert 2021) – Physical-iterative method using optimal estimation framework – Able to combine different types of observations to retrieve T(z) and q(z) – Full error characterization and vertical resolution are standard output products 10
Physical-Iterative Retrieval (Optimal Estimation) Observation Forward Model # Mean Prior " = Jacobian of F ! Obs Uncertainty " State Vector (nth iter) # Prior Uncertainty " Iterative solution (n to n+1) "$% = # + #&% + "' !&% " &% ' &% " ! − " + " " − # ( = #&% + ' !&% &% Uncertainty in X A = #&% + ' !&% &% ' &% ! Averaging Kernal 11
Combining Observations within the Retrieval • Combine the observations into the obs vector Y = [ )"*+% , )"*+, , )"*+- ] • Use the appropriate forward model for each element of Y = [ )"*+% , )"*+, , )"*+- ] = [ /)"*+% , /)"*+, , /)"*+- ] • Combine the observational uncertainties )"*+% 0 0 ! = 0 )"*+, 0 0 0 )"*+- 12
Combining Observations within the Retrieval Example #1: XPIA on 17 Mar 2015 at 1830 z 13 Combined MWR+RASS retrieval described in Djalalova et al. AMT 2021 (in review)
Combining Observations within the Retrieval Example #1: XPIA on 17 Mar 2015 at 1830 z 14 Combined MWR+RASS retrieval described in Djalalova et al. AMT 2021 (in review)
Combining Observations within the Retrieval Example #1: XPIA on 17 Mar 2015 at 1830 z 15 Combined MWR+RASS retrieval described in Djalalova et al. AMT 2021 (in review)
Combining Observations within the Retrieval Example #1: XPIA on 17 Mar 2015 at 1830 z 16 Combined MWR+RASS retrieval described in Djalalova et al. AMT 2021 (in review)
Combining Observations within the Retrieval Example #2: PECAN on 20 Jun 2015 at 0248 z 18 Combined IR+DIAL retrieval described in Turner and Löhnert AMT 2021
Combining Observations within the Retrieval Example #2: PECAN on 20 Jun 2015 at 0248 z 19 Combined IR+DIAL retrieval described in Turner and Löhnert AMT 2021
Combining Observations within the Retrieval Example #2: PECAN on 20 Jun 2015 at 0248 z 20 Combined IR+DIAL retrieval described in Turner and Löhnert AMT 2021
So How Good are these Retrievals? • What is the inherent uncertainty? • How many independent pieces of info are in the retrieved T and q profiles? • How is this information distributed vertically? • What is the true vertical resolution of these profiles? • How should I use these data? ( = #&% + ' !&% &% Uncertainty in X A = #&% + ' !&% &% ' &% ! Averaging Kernal xkcd.com/1478 21
Example: Passive-Only Retrieval From Perdigao Field Campaign in Portugal, where an AERI, MWR, nDIAL and sondes were collocated 22 Combined IR+DIAL retrieval described in Turner and Löhnert AMT 2021
Example: Active + Passive Retrieval From Perdigao Field Campaign in Portugal, where an AERI, MWR, nDIAL and sondes were collocated 23 Combined IR+DIAL retrieval described in Turner and Löhnert AMT 2021
Bias and RMSE using Radiosondes as Truth AERI AERI MWRe MWRe 24 Blumberg et al. JAMC 2015
1-Sigma Uncertainty Profiles (Square root of the diagonal of Sx) 25 Turner and Löhnert AMT 2021
1-Sigma Uncertainty Profiles 26 Turner and Löhnert AMT 2021
1-Sigma Uncertainty Profiles 27 Turner and Löhnert AMT 2021
1-Sigma Uncertainty Profiles 28 Turner and Löhnert AMT 2021
Information Content and Vertical Resolution • Retrievals are performed on a discrete vertical grid – Often has 40 to 100 vertical layers • The amount of information in the observations is well less than the number of layers – E.g., the number of channels on a MWR is always less than 25 • Ill-posed problem • To constrain the retrieval, a priori data (e.g., radiosonde climatology) is used to provide guidance on the level-to-level correlation • So what is the true IC and Vres of the retrieved profiles? – Can be determined directly from the averaging kernel! 29
Cumulative Degrees of Freedom for Signal (Diagonal of the Averaging Kernel) 30 Turner and Löhnert AMT 2021
Cumulative Degrees of Freedom for Signal Interpreting the cumulative DFS profile • DFS provides a measure of the number of independent pieces of information • In this example, the T(z) retrieved from the – MWR has DFS = 2.3 below 3 km – AERI has DFS = 5.4 below 3 km • But below 1 km, the – MWR has DFS = 1.7 – AERI has DFS = 4.4 • If you wanted to assimilate these profiles, you would want to use levels that are separated by at least 1 DFS • In this example, if we take the first level at 100 m AGL then we would assimilate – 2 levels from the MWR – 4 levels from the AERI 31
Cloud Impacts on the Retrieval: MW vs IR • Clouds are much more opaque in the IR than MW – Cloud is opaque in IR when LWP > 60 g m-2 – MWR remains very transparent until LWP is very high (>1000 g m-2) • IR has no ability to profile above cloud base in these situations, but MW can • In this example (assume LWP > 60 g m-2) – DFS below cloud base: • AERI 4.3, MWR 1.7 – DFS above cloud base: • AERI 0.0, MWR 0.6 • True information content above/below cloud depends on CBH and LWP • The points one would assimilate are determined the same way as before 32
Comparing Similar Instruments • 3 ASSISTs operated several hours in clear sky conditions at Quebec City on 12 July 2021 • How well do they compare? 37
Examples of How these Retrievals are Being Used This is just a subset. There are many I could have chosen to highlight 38
Composite Radar Image: 0425 UTC on 10 Aug 2014
Composite Radar Image: 0525 UTC on 10 Aug 2014
Composite Radar Image: 0625 UTC on 10 Aug 2014
Composite Radar Image: 0725 UTC on 10 Aug 2014
Composite Radar Image: 0825 UTC on 10 Aug 2014
Composite Radar Image: 0925 UTC on 10 Aug 2014
Composite Radar Image: 1025 UTC on 10 Aug 2014
Thermodynamic Profiles and LWP from the AERI Toms et al. MWR 2017 Haghi et al. BAMS 2019 46
47
Cross-comparisons with UAS Observations de Boer et al. BAMS 2018 Koch et al. JTECH 2018 48
DA Impact on a Tornado Forecast MRMS Composite Reflectivity (dBZ) Storm-scale Domain (3 km) • On 13 July 2015, a tornado formed near Nickerson, KS • Near surface air was very warm (>35 C) and dry • Environmental storm-relative helicity was very small • Neither a tornado or severe thunderstorm watch was issued • After the supercell formed, it moved towards the SW • Event occurred during PECAN, when there were 6 fixed sites with ground-based AERIs and wind profilers • Used the NSSL Warn-on-Forecast ensemble system (NEWS-E) using GSI-EnKF 2330 UTC Storm of interest Hu et al. WAF 2019 49
MRMS CREF at Nickerson Storm (After 2-h DA) 2330 UTC Forecast initialized at 2000 z Black is 25 dBZ contour from MRMS composite reflectivity 1-h Fcst valid 1.5-h Fcst valid 2-h Fcst valid 3-h Fcst valid 3.5-h Fcst valid 2100 UTC 2130 UTC 2200 UTC 2300 UTC 2330 UTC CNTL 50 AERI_DL CI Hu et al. WAF 2019 % Probability of CREF > 25 dBZ (3 km neighborhood)
Quantifying WV and Temperature Advection Many important quantities depend on spatial gradients of the kinematic and thermodynamic fields: • Divergence • Advection We can calculate this at SGP using Green’s 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. Wagner et al. JTECH 2021 (in review) 51 ARM SGP Site
Quantifying Land-Atmosphere Interactions • Mixing diagram approach to study energy and moisture budget evolution in the CBL pioneered by Betts (1992) • Santanello et al. (2009) showed utility of approach to evaluate models Wakefield et al. J Hydro Met 2021 (in preparation) 52
Summary • Ground-based thermodynamic (TD) profiles in the PBL very useful for a range of operational and research objectives • Only passive TD profilers are currently commercially available – Active lidar-based systems coming in next few years • Understanding the information content, vertical distribution of this information, and resolution is critical to use the obs correctly – Variational methods like the TROPoe retrieval provide this info – Combining observations from different instruments can increase the information content and improve the retrievals – The retrieval method matters! Working to make TROPoe available to all via Docker • Utility of TD profilers demonstrated in several different applications • Continuing to use these data for both process-study research and in limited- area data assimilation experiments 53
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