Water Vapor and Cloud Detection Validation for Aqua Using Raman Lidars and AERI David Whiteman, Belay Demoz NASA/Goddard Space Flight Center Zhien Wang,

Slides:



Advertisements
Similar presentations
Upper Tropospheric Humidity: A Comparison of Satellite, Radiosonde, Lidar and Aircraft Measurements Satellite Lidar Aircraft Radiosonde.
Advertisements

A thermodynamic model for estimating sea and lake ice thickness with optical satellite data Student presentation for GGS656 Sanmei Li April 17, 2012.
Calibration Scenarios for PICASSO-CENA J. A. REAGAN, X. WANG, H. FANG University of Arizona, ECE Dept., Bldg. 104, Tucson, AZ MARY T. OSBORN SAIC,
DIRECT TROPOSPHERIC OZONE RETRIEVALS FROM SATELLITE ULTRAVIOLET RADIANCES Alexander D. Frolov, University of Maryland Robert D. Hudson, University of.
Forschungszentrum Karlsruhe in der Helmholtz-Gemeinschaft NDACC H2O workshop, Bern, July 2006 Water vapour profiles by ground-based FTIR Spectroscopy:
ISSI Working Group on Atmospheric Water Vapor, 11 Feb 2008 Holger Vömel Cooperative Institute for Research in Environmental Sciences University of Colorado.
NDACC Working Group on Water Vapor NDACC Working Group on Water Vapor Bern, July 5 -7, 2006 Raman Lidar activities at Rome - Tor Vergata F.Congeduti, F.Cardillo,
Characterizing and comparison of uncertainty in the AVHRR Pathfinder SST field, Versions 5 & 6 Robert Evans Guilllermo Podesta’ RSMAS Nov 8, 2010 with.
6 July 2006First NDACC Water Vapor Working Group Workshop, Bern, Switzerland. 1 NDACC and Water Vapor Raman Lidars Thierry Leblanc JPL - Table Mountain.
Scanning Raman Lidar Error Characteristics and Calibration For IHOP David N. Whiteman/NASA-GSFC, Belay Demoz/UMBC Paolo Di Girolamo/Univ. of Basilicata,
Problems and measuring errors of radiosonde humidity measurements in the global aerological network and new possibilities of their correction and validation.
Cirrus Cloud Boundaries from the Moisture Profile Q-6: HS Sounder Constituent Profiling Capabilities W. Smith 1,2, B. Pierce 3, and Z. Chen 2 1 University.
Evaluation of Dropsonde Humidity and Temperature Sensors using IHOP and DYCOMS-II data Junhong (June) Wang Hal Cole NCAR/ATD Acknowledgement: Kate Young,
David N. Whiteman/NASA-GSFC, Belay Demoz/UMBC
LIDAR: Introduction to selected topics
Aircraft spiral on July 20, 2011 at 14 UTC Validation of GOES-R ABI Surface PM2.5 Concentrations using AIRNOW and Aircraft Data Shobha Kondragunta (NOAA),
GEWEX/GlobVapour Workshop, Mar 8-10, 2011, ESRIN, Frascati, Italy WATER VAPOUR RAMAN LIDARS IN THE UTLS: Where Are We Now? or “The JPL-Table Mountain Experience”
G O D D A R D S P A C E F L I G H T C E N T E R Goddard Lidar Observatory for Winds (GLOW) Wind Profiling from the Howard University Beltsville Research.
Penn State Colloquium 1/18/07 Atmospheric Physics at UMBC physics.umbc.edu Offering M.S. and Ph.D.
Diagnosing Climate Change from Satellite Sounding Measurements – From Filter Radiometers to Spectrometers William L. Smith Sr 1,2., Elisabeth Weisz 1,
High Spectral Resolution Infrared Land Surface Modeling & Retrieval for MURI 28 April 2004 MURI Workshop Madison, WI Bob Knuteson UW-Madison CIMSS.
Problems and Future Directions in Remote Sensing of the Ocean and Troposphere Dahai Jeong AMP.
Infrared Interferometers and Microwave Radiometers Dr. David D. Turner Space Science and Engineering Center University of Wisconsin - Madison
William Crosson, Ashutosh Limaye, Charles Laymon National Space Science and Technology Center Huntsville, Alabama, USA Soil Moisture Retrievals Using C-
Measurement Example III Figure 6 presents the ozone and aerosol variations under a light-aerosol sky condition. The intensity and structure of aerosol.
Hyperspectral Data Applications: Convection & Turbulence Overview: Application Research for MURI Atmospheric Boundary Layer Turbulence Convective Initiation.
Measurement of cirrus cloud optical properties as validation for aircraft- and satellite-based cloud studies Daniel H. DeSlover (Dave Turner, Ping Yang)
Berechnung von Temperaturen aus Lidar-Daten Michael Gerding Leibniz-Institut für Atmosphärenphysik.
Wu Sponsors: National Aeronautics and Space Administration (NASA) Goddard Space Flight Center (GSFC) Goddard Institute for Space Studies (GISS) New York.
Evaluation of the WVSS-II Sensor Using Co-located In-situ and Remotely Sensed Observations Sarah Bedka, Ralph Petersen, Wayne Feltz, and Erik Olson CIMSS.
WATER VAPOR RETRIEVAL OVER CLOUD COVER AREA ON LAND Dabin Ji, Jiancheng Shi, Shenglei Zhang Institute for Remote Sensing Applications Chinese Academy of.
LASE Measurements During IHOP Edward V. Browell, Syed Ismail, Richard A. Ferrare, Susan A Kooi, Anthony Notari, and Carolyn F. Butler NASA Langley Research.
Characterization of Aerosols using Airborne Lidar, MODIS, and GOCART Data during the TRACE-P (2001) Mission Rich Ferrare 1, Ed Browell 1, Syed Ismail 1,
UV Aerosol Product Status and Outlook Omar Torres and Changwoo Ahn OMI Science Team Meeting Outline -Status -Product Assessment OMI-MODIS Comparison OMI-Aeronet.
Intercomparisons of Water Vapor Measurements during IHOP_2002 – Radiosonde and Dropsonde Junhong (June) Wang NCAR Atmospheric Technology Division Acknowledgement:
Tony Clough, Mark Shephard and Jennifer Delamere Atmospheric & Environmental Research, Inc. Colleagues University of Wisconsin International Radiation.
Retrieval of Methane Distributions from IASI
Transitioning unique NASA data and research technologies to the NWS AIRS Profile Assimilation - Case Study results Shih-Hung Chou, Brad Zavodsky Gary Jedlovec,
INFRARED-DERIVED ATMOSPHERIC PROPERTY VALIDATION W. Feltz, T. Schmit, J. Nelson, S. Wetzel-Seeman, J. Mecikalski and J. Hawkinson 3 rd Annual MURI Workshop.
Use of Solar Reflectance Hyperspectral Data for Cloud Base Retrieval Andrew Heidinger, NOAA/NESDIS/ORA Washington D.C, USA Outline " Physical basis for.
BBHRP Assessment Part 2: Cirrus Radiative Flux Study Using Radar/Lidar/AERI Derived Cloud Properties David Tobin, Lori Borg, David Turner, Robert Holz,
SRL/Reference sonde P. Di Girolamo, D. Whiteman, B. Demoz, J. Wang, K. Beierle, T. Weckwerth The Reference sonde (C34) consists of a Snow White (SW) chilled-mirror.
CLN QA/QC efforts CCNY – (Barry Gross) UMBC- (Ray Hoff) Hampton U. (Pat McCormick) UPRM- (Hamed Parsiani)
AIRS science team meeting Camp Springs, February 2003 Holger Vömel University of Colorado and NOAA/CMDL Upper tropospheric humidity validation measurements.
Airborne/ground-based sensor intercomparison: SRL/LASE Paolo Di Girolamo, Domenico Sabatino, David Whiteman, Belay Demoz, Edward Browell, Richard Ferrare.
Radiative transfer in the thermal infrared and the surface source term
Bryan A. Baum, Richard Frey, Robert Holz Space Science and Engineering Center University of Wisconsin-Madison Paul Menzel NOAA Many other colleagues MODIS.
IHOP Radiosonde and Dropsonde Data: Highlights and Problems
A new method for first-principles calibration
2004 ARM Science Team Meeting, March 22-26, Albuquerque, New Mexico THE 2004 NORTH SLOPE OF ALASKA ARCTIC WINTER RADIOMETRIC EXPERIMENT Ed R. Westwater.
RADIANT SURFACE TEMPERATURE OF A DECIDUOUS FOREST – THE EFFECTIVENESS OF SATELLITE MEASUREMENT AND TOWER-BASED VALIDATION.
Cloud property retrieval from hyperspectral IR measurements Jun Li, Peng Zhang, Chian-Yi Liu, Xuebao Wu and CIMSS colleagues Cooperative Institute for.
National Aeronautics and Space Administration Jet Propulsion Laboratory California Institute of Technology Pasadena, California
AIRS science team meeting, Greenbelt, 31 March 2003 Holger Vömel University of Colorado and NOAA/CMDL Cryogenic Frost point Hygrometer (CFH) Measurements.
PRELIMINARY VALIDATION OF IAPP MOISTURE RETRIEVALS USING DOE ARM MEASUREMENTS Wayne Feltz, Thomas Achtor, Jun Li and Harold Woolf Cooperative Institute.
MODIS, AIRS, and Midlevel Cloud Phase Shaima Nasiri CIMSS/SSEC, UW-Madison Brian Kahn Jet Propulsion Laboratory MURI Hyperspectral Workshop 7-9 June, 2005.
MODIS Atmosphere Products: The Importance of Record Quality and Length in Quantifying Trends and Correlations S. Platnick 1, N. Amarasinghe 1,2, P. Hubanks.
A global, 2-hourly atmospheric precipitable water dataset from ground-based GPS measurements and its scientific applications Junhong (June) Wang NCAR/EOL/T.
International Ocean Color Science Meeting, Darmstadt, Germany, May 6-8, 2013 III. MODIS-Aqua normalized water leaving radiance nLw III.1. R2010 vs. R2012.
NCAR - Atmospheric Technology Division Reporting work by: Erik Miller (ATD) Junhong Wong (ATD) Ari Paukkunen et al. (Vaisala, OY) Funded by: ATD, Vaisala;
Shaima Nasiri University of Wisconsin-Madison Bryan Baum NASA - Langley Research Center Detection of Overlapping Clouds with MODIS: TX-2002 MODIS Atmospheres.
NASA, CGMS-44, 7 June 2016 Coordination Group for Meteorological Satellites - CGMS LIMB CORRECTION OF POLAR- ORBITING IMAGERY FOR THE IMPROVED INTERPRETATION.
DOE ARM Calibration / Validation Instrumentation Data essentially available in real-time –ARM (Atmospheric Radiation Measurement) –CART (Cloud And Radiation.
Characterizing and comparison of uncertainty in the AVHRR Pathfinder Versions 5 & 6 SST field to various reference fields Robert Evans Guilllermo Podesta’
Toward Continuous Cloud Microphysics and Cloud Radiative Forcing Using Continuous ARM Data: TWP Darwin Analysis Goal: Characterize the physical properties.
Comparison of lidar water vapor measurements at Fixed PISA 2
Summary Sondes RS 80 has dry bias. RS 92 virtually without bias
NPOESS Airborne Sounder Testbed (NAST)
LASE Measurements of Water Vapor During IHOP
Presentation transcript:

Water Vapor and Cloud Detection Validation for Aqua Using Raman Lidars and AERI David Whiteman, Belay Demoz NASA/Goddard Space Flight Center Zhien Wang, Chris Barnet, Wallace McMillan, Raymond Hoff, Igor Veselovskii, Felicita Russo University of Maryland, Baltimore County/JCET Gary J. Jedlovec NASA/Marshall Space Flight Center, GHCC Daniel H. DeSlover University of Wisconsin/CIMSS And thanks to John Braun, Chris Rocken, Teresa VanHove, Larry Miloshevich, June Wang – UCAR Barry Lesht – ANL Rich Ferrare – NASA/LaRC

Outline Overview of work performed to date NWS radiosonde conversion plans Raman Lidar introduction AFWEX water vapor measurements – 4 water vapor lidars, Vaisala radiosonde, SW Chilled mirror, in-situ sensors, GPS, MWR Raman Lidar calibration Lidar/retrieval comparisons – “To tune or not to tune” – Clear and cloudy water vapor and temperature profiles versus 5 retrieval schemes

Activity to Date and Outlook Sept – Nov, 2002 at GSFC – 26 nighttime clear and cloudy overpasses Scanning Raman Lidar water vapor, sonde T & P, GPS PW – Study various clear and cloudy retrievals of T & q Jan, 2003 and UMBC – 15 measurement sessions Scanning Raman Lidar, BBAERI, ALEX, ELF, sonde, GPS 15 Lidar/BBAERI measurements –7 AIRS overpasses –Others for study of cirrus particle size retrievals AERI vs lidar Dual sonde launches at GSFC Jan, 2004 deployment to UMBC planned MWR and AERI at GSFC to be running soon (Tsay, Code 913) – GPS vs MWR

NWS Radiosonde Conversion NWS announced that Sippican and Intermet will be supplying radiosonde packages for operational launches over next 3 years – Vaisala and Sippican used formerly – Previous comparisons have found significant differences Calibration Response at cold temperatures The new sondes will be phased in and transfer functions between the previous sondes (Vaisala or Sippican) implemented – Smooth through glitches Dual sonde (Intermet, Sippican) launches coming soon

Raman Lidar Laser transmitter (UV better) – excites Raman scattering in atmospheric species. Telescope receiver – wavelength selection optics separate the wavelengths Time gated data acquisition gives range information Measurements – Water vapor mixing ratio – Aerosol backscatter, extinction, depolarization – Liquid water mixing ratio – Many other things

Raman Lidar for water vapor measurements Advantages – Based on scattering physics-> direct measurement of mixing ratio – Purely vertical profiles – No time lag, sensor contamination issues – Stable calibration has been demonstrated – Temperature sensitivity of Raman scattering well known, is a small effect (<5%), and can be simulated (accepted Appl. Opt.) Disadvantages – UT measurements only at night – Raman cross section not well known Absolute calibration not difficult but will have uncertainties > 10%

Scanning Raman Lidar (SRL), Sippican Radiosonde (T+P), SuomiNet GPS GSFC site North-largely forested, some open fields South-suburban sprawl Patuxent River, Chesapeake Bay, Potomac River

ARM FIRE Water Vapor Experiment (2000) Goal: characterize UT water vapor technologies (4 WV lidar, sondes, passive, in-situ) Location: ARM CART SGP site northern OK

Example Water Vapor Profile Comparison CART Raman (CARL) and Scanning Raman (SRL) Lidar profiles correspond to 10 minute averages LASE profile corresponds to a 2 minute average centered over the SGP site. Courtesy of Richard Ferrare – NASA/LaRC

Summary of LASE Comparisons from AFWEX Courtesy of Richard Ferrare – NASA/LaRC

Vaisala RS-80H and RS-90 Time Lag Courtesy Larry Milosevich - UCAR

TPW Comparisons RS-80H and RS-90 vs MWR (SGP) RS-80H: ~2% dryer than MWR, ~5% standard deviation RS-90 : ~1% dryer than MWR, ~5% standard deviation Courtesy Barry Lesht, ANL

Assessment of Upper Troposphere Water Vapor Measurements using LASE LASE and Raman lidars in excellent average agreement (within ~2-3%) Corrections to Vaisala RS-80H radiosondes reduced sonde dry bias from 10% to less than 5% Chilled Mirror sondes about 8-10% drier than LASE and Raman lidars DC-8 diode laser hygrometer slightly (~3% drier) than LASE and Raman lidars DC-8 cryogenic frost point hygrometer 10-20% drier than LASE and Raman lidars Courtesy of Richard Ferrare – NASA/LaRC

Raman water vapor lidar calibration Comparison with respect to radiosonde profile – Normal radiosondes show variability and dry bias – Research radiosondes are promising Need large enough sample to study variability of calibration Comparison based on total column water – MWR scaling being performed at the SGP site – GPS provides a stable reference as well large volume average implies numerous comparisons required to assess calibration constant – Important to get the bottom part of the lidar profile correct ~5% of TPW can be in the bottom 250 meters SRL uses vertical and scanned lidar measurements along with 2 ground sensors for composite profile

SuomiNet GPS vs ARM CF MWR (WVIOP3) Plot courtesy John Braun, Chris Rocken, Teresa Van Hove, UCAR GPS Research Group

SuomiNet GPS vs FSL GPS (WVIOP3) Plot courtesy John Braun, Chris Rocken, Teresa Van Hove, UCAR GPS Research Group

SRL Calibration constant determined from GPS and Sippican SuomiNet GPS (PW) Sippican radiosonde (profile comparison ~1-2 km) Sippican profile calibration ~6% moister, PW calibration ~15% moister

Chris Barnet’s retrieval “tweaks” Tune: T, E(n,m)=0 NoTune: T=0, E(n,m)=0 Covar: T=0, E(n,n)=(Obs-Calc(n)) 2 Covar_New: T=0, E(n,m) =(Obs-Calc(n)*Obs-Calc(m)) B50: T=0, E(n,n)=1/4 of Covar

Retrieval Tweaks.. Two versions of “NoTune” for Sept 13 “clear” case 2/3/03 2/21/03

More “clear” examples “NoTune” vs “NoTune2”

More “clear” examples “NoTune” vs “Notune2” - II

Some “Clear” Water Vapor Examples

Some “Cloudy” Water Vapor Examples

(a) (b)

The September 14 Case Cirrus cloud scattering ratio Quick look plot - uncalibrated A sharp increase in water vapor beneath cirrus cloud decks is common and can persist

SRL vs Aqua water vapor retrieval (mean of 17) ret dry ret wet

SRL vs Aqua water vapor retrieval (9 clear cases) ret dry ret wet

SRL vs Aqua water vapor retrieval (8 cloudy cases) ret dry ret wet

Sippican vs Aqua Temperature Retrieval (17 Cases) ret warm ret cold

Sippican vs Aqua Temperature Retrieval (9 clear cases) ret warm ret cold

Sippican vs Aqua Temperature Retrieval (8 cloudy cases) ret warm ret cold

(a) (b)

Summary of PW comparisons Total precipitable water comparisons Ret/SRL – All: 0.97 – 1.02, Clear: 0.91 – 0.95, Cloudy: 1.01 – 1.07 All retrievals show the following general trends – increase in moisture below mb More rapid for cloudy cases than clear – clear conditions ~5 - 15% dry bias with respect to SRL above 900 mb – cloudy conditions generally good agreement (+2% to - 7%) with SRL above 900 mb wet bias (25 – 30%) between mb due to missed structure below cloud layers “Tune” and “Covar_New” – performed similarly in water vapor comparisons Similar results can be obtained from damping as with tuning “No_Tune” – shows significant oscillations in clear profiles (+/- 30 – 50%), large positive bias near the surface in cloudy profiles (40-50%) “Covar” – showed the least positive bias (~25%) in the UT (270 – 460 mb) – Generally dryer than “Tune” by 1-7%

Summary of Temperature Comparisons Overall agreement of all retrievals within +/- 1K above 900 mb – NoTune2 up to 2K cold ~950 mb All retrievals show temperatures decreasing between 850 – 950 mb (~1K), then abrupt increase below 950 mb (~3K) Under both clear and cloudy conditions, warm bias between 250 – 600 mb of ~0.3 – 1.0K Significant difference between clear and cloudy retrievals between 600 – 900 mb – Clear retrievals ~0.5 – 1.0K colder “Tune” and “Covar_New” – similar performance with the smallest overall biases “NoTune” – Showed the largest deviations under most conditions “Covar” – Showed largest warm bias in UT (~0.7 – 1.1 K)

TPW and Temperature comparison results-II TPW: (retrieval/SRL) – 1) entire profile 2) UT (460 – 270 mb), 3) above 1 st km, 4) within 1 st km, Temp: mean (Sonde – Retrieval) for same layers Retrieval PW/SRL (986 – 51, , 904–51, 986–900 mb) Sonde-Retrieval (K) (986 – 51, , 904–51, 986–900 mb) All (Above 986, UT, Above 1 km, 1 st km) ClearCloudyAllClearCloudy Tune0.99, 1.16, 0.94, , 0.90, 0.88, , 1.32, 0.98, , -0.2, -0.1, , -0.3, + 0.1, , -0.2, -0.2, -0.1 NoTune0.97, 1.17, 0.89, , 0.95, 0.84, , 1.29, 0.93, , -0.6, -0.1, ,-0.8, + 0.0, , -0.5, -0.2, +1.4 Covar0.98, 1.10, 0.94, , 0.84, 0.87, , 1.25, 1.00, , -0.9, , -1.1, -0.1, , -0.7, -0.4, -0.1 CovarNew0.97, 1.14, 0.92, , 0.89, 0.86, , 1.29, 0.97, , -0.2, , -0.4, +0.1, , -0.2, -0.2, +0.2 B501.02, 1.15, 0.97, , 0.95, 0.90, , 1.28, 1.02, , , , -1.2, -0.1, , -0.6, -0.3, +0.2

October 2, 2002 a “clear” case with a sharp increase

Use of MODIS to look for clouds and PW variability

Summary and Future Work Continue to work interactively in algorithm research – Clear and cloudy cases – Focus on UT and mb bias Assess AIRS cloud products – Lidar, MODIS Expand use of MODIS to study scene variability UMBC deployments – Analyze data from 2003 – Deploy in 2004 GSFC-based measurements – Dual (or more?) sonde launches – Compare GPS and MWR at GSFC – Further lidar measurements as needed

Revised Work Plan – July 2001 L+3 to L+5 – Perform validation measurements from mid-Atlantic region during Aqua overpasses Raman Lidar water vapor/aerosols, radiosonde, GPS 26 successful measurements out of 40 possible –Nighttime, angles > 40 degrees Years 2 and 3 – Deploy SRL to UMBC correlated measurements of SRL, BBAERI, ALEX, radiosonde, GPS Validation under presence of clouds Study particle size retrievals from ground based sensors

Temperature sensitivity of water vapor Raman scattering (accepted by Appl. Opt.) Simulated Raman water vapor spectrum (Avila et. al., 1999) Simulated temperature correction curves for SRL and CARL water vapor measurements

AFWEX (2000) Examples - I

Vaisala RS-80 H vs RS-90 Courtesy June Wang - NCAR

Vaisala RS-80H and RS-90 – RH Corrections Courtesy Larry Milosevich - UCAR Estimate of the residual dry bias in RS-90s is 5-8% RH.

Sippican PW versus SuomiNet GPS Both slope of line and offset indicate Sippican moister – Sippican mean is 15% moister than GPS

September 13

September 20

November 3

SA02 vs GOES (GSFC)

SA02 vs MODIS – Near IR (GSFC)

SA02 vs CIMEL (GSFC)

CIMEL vs MWR at SGP

Good Bad “UT bad, mid OK” “mid trop bad” “bad” “mid trop a little bad”

LASE vs. Vaisala RS-80H Radiosondes Uncorrected Vaisala sondes showed ~8-10% dry bias in upper troposphere Scaling Vaisala sondes to match MWR PWV did not reduce dry bias in upper troposphere Vaisala corrections for calibration and temp. dependent calibration reduced bias by 4-5% Additional correction (Miloshevich) for time lag of Vaisala sondes further reduced bias by 2-3%  Total of corrections to Vaisala radiosonde measurements reduced dry bias to less than 5% Courtesy of Rich Ferrare – NASA/LaRC

LASE vs. DC-8 In situ sensors, Chilled Mirror and Sippican sondes In upper troposphere… Chilled Mirror sondes about 8-10% drier than LASE and Raman lidars DC-8 diode laser hygrometer slightly (~3% drier) than LASE and Raman lidars DC-8 cryogenic frost point hygrometer 10-20% drier than LASE and Raman lidars Courtesy of Rich Ferrare – NASA/LaRC

LASE vs. CART Raman Lidar (CARL) Initial (uncorrected) CARL profiles were 5-7% wetter than LASE in upper trop. Correction for altitude dependence of CARL overlap reduced CARL values in upper trop. by ~4%  LASE and CARL measurements generally within 5% on average for all altitudes Courtesy of Rich Ferrare – NASA/LaRC

LASE vs. Scanning Raman Lidar (SRL) SRL profiles were ~3% wetter than LASE in upper trop. SRL slightly (~3-4%) drier than LASE below 5 km  LASE and SRL measurements generally within 5% on average for all altitudes Courtesy of Rich Ferrare – NASA/LaRC

AFWEX (2000) Examples - III

AFWEX(2000) Examples – IV

AFWEX – Profile Summaries 1)SRL/CARL agreement within +/- 5% from 1-10 km (no low channel in SRL). SRL tends toward being moister than CARL above 10km and below 2 km. 2)SRL/LASE shows moist bias of LASE in lowest 2 km, dry bias above 6 km. 3)SRL/Vaisala show increasing dry bias of sonde above 5 km

SRL vs Sippican Mean Comparison

SRL vs Sippican (Sept. 5)

SRL vs Sippican (Sept 6)

SRL vs Sippican (Sept 7)