National Aeronautics and Space Administration Jet Propulsion Laboratory California Institute of Technology Pasadena, California 1 Validating Water Vapor Retrievals: Lessons Learned and Some Results Eric J. Fetzer and Annmarie Eldering Jet Propulsion Laboratory AIRS Science Team Meeting 30 March-1 April 2003 Greenbelt, Maryland

National Aeronautics and Space Administration Jet Propulsion Laboratory California Institute of Technology Pasadena, California 2 First Order of Business: JGR Special Issue Our proposal for a Special Issue on AIRS validation was accepted by the JGR editor Have manuscripts ready for peer review in the October or November

National Aeronautics and Space Administration Jet Propulsion Laboratory California Institute of Technology Pasadena, California 3 Overview of Today’s Talk A Motivating Problem – Understanding water vapor in the subtropical eastern Pacific associated with temperature inversions Analyses (v3.1.9) – Lots of maps of retrieved quantities – Intercompare retrievals and sondes along the west coast and Hawaii. Increasing complexity: Intercompare matched profiles Compare time series of retrieved and sonde T & q Create statistical summaries Next steps First results with v3.5.0 and dedicated sondes

National Aeronautics and Space Administration Jet Propulsion Laboratory California Institute of Technology Pasadena, California 4 Motivating Problem What is the water vapor distribution associated with retrieved temperature inversions in the eastern Pacific? Inversion distribution present at October Sci. Tm. Meeting, Fall AGU. GRL manuscript in preparation – Should be straightforward: Over water Mostly high pressure Sonde stations on West Coast and Hawaii, with decent time match. – Not straightforward.

National Aeronautics and Space Administration Jet Propulsion Laboratory California Institute of Technology Pasadena, California 5 Lessons Learned: #0, #1 & #2 0.Temperature is much less variable than water vapor. 1. Water vapor is highly sporadic in space and time. 2. Maxima in water vapor are embedded in cloudy regions, where most of our questionable retrievals occur.

National Aeronautics and Space Administration Jet Propulsion Laboratory California Institute of Technology Pasadena, California 6 Lesson 1: The highly localized nature of water vapor (v3.1.9) 1 January mb precipitable water vapor Full scale = 30 mm mb precipitable water vapor Full scale = 6 mm

National Aeronautics and Space Administration Jet Propulsion Laboratory California Institute of Technology Pasadena, California 7 Lesson 2: Maxima in water vapor are embedded in cloudy regions with nonzero retrieval_type 1 January 2003 Retrieval type Gray = 0: Full IR Red = 10: 1st failed Blue = 20: MW & Strat IR Green = 30: MW & Strat IR Black = 100: Crash & burn X: |SST error| > 3 K. Error with forecast SST Black = -5 K Red = 5 K.

National Aeronautics and Space Administration Jet Propulsion Laboratory California Institute of Technology Pasadena, California 8 Intercomparing AIRS and Sondes Look at December 2002 and January 2003 Consider operational sondes at 6 locations: – Quillayute, Washington – Oakland, California – Vandenberg, California, – San Diego, California – Hilo, Hawaii – Lihue, Hawaii

National Aeronautics and Space Administration Jet Propulsion Laboratory California Institute of Technology Pasadena, California 9 Lesson #3: Highly variable water makes for nonstationary time series Precipitable Water Vapor, Lihue, Hawaii mb & mb layers Blue = Sonde, Red = AIRS (diamond = nonzero rettyp)

National Aeronautics and Space Administration Jet Propulsion Laboratory California Institute of Technology Pasadena, California 10 Lesson #4: Water vapor at ‘upper’ levels can be highly variable Lihue, mb dynamic range ~ 0.1 to 10 mm precipitable water Dave Tobin: ‘UTWV is the last precipitable 0.1 mm’

National Aeronautics and Space Administration Jet Propulsion Laboratory California Institute of Technology Pasadena, California 11 Lesson #5: Locally, conclusions like L. McMillin’s in Val Report Caution: Standard deviation is not robust to outliers mb Lihue: 2.7 ± 14.1 % Bias ± Std. Dev. McMillin Globally: 3.6 ± 11.6 % mb Lihue: -4.3 ± 44.3 % (15th & 85th percentiles:-19, +12%) McMillin Globally : 0.0 ± 26 % Lihue mb 20.5 mm mean Lihue mb 1.95 mm mean

National Aeronautics and Space Administration Jet Propulsion Laboratory California Institute of Technology Pasadena, California 12 Lesson #6 Trade wind cumulus regions are challenging – In Hawaii, a moist boundary layer is overlain by very dry, descending air This explains the large standard deviation at Lihue. – Need to revisit this problem with dedicated sondes Andros, Ascencion, others.

National Aeronautics and Space Administration Jet Propulsion Laboratory California Institute of Technology Pasadena, California 13 Lesson #7: Sippican sondes not consistent at upper levels UNBIASED: ~few ± 15-45% BIASED: ~ ± 15-20% The Goldilocks Effect? Vandenberg may have a combination of high relative humidity and temperature => Sippican sensitivity & low bias Oakland too cold? San Diego & Hilotoo dry? Vandenberg just right? SEE da Silviera, R. B., G. Fisch, L. A. T. Machado, A. M. D’all Antonia, L. F. Sapucci, D. Fernandes and J. Nash, (2003) Executive summary of the WMO intercomparison of GPS radiosondes, WMO Instruments and Observing Methods Report No. 76, WMO/TD No

National Aeronautics and Space Administration Jet Propulsion Laboratory California Institute of Technology Pasadena, California 14 Lesson #7 Nonzero retrieval types contain potentially useful information about water vapor THIS IS REFLECTED IN TIME AVERAGED FIELDS OAKLAND Blue crosses: Sonde Water Red Cross: AIRS Water, ret. type = 0; Red Diamond: AIRS, ret. type NE 0

National Aeronautics and Space Administration Jet Propulsion Laboratory California Institute of Technology Pasadena, California 15 Lesson # 8: The mb and mb layers are statistically decoupled 1-16 January mb water (mm) 1-16 January mb water (mm) Also apparent in radiosonde difference statistics

National Aeronautics and Space Administration Jet Propulsion Laboratory California Institute of Technology Pasadena, California 16 Some Conclusions from this study 1. Retrieval type (or RetQAFlag) bias our observations toward dry conditions 2. The results over the eastern Pacific is similar to Larry McMillin’s conclusions globally. Higher variability => larger standard deviations at altitude 3. We have sensitivity in 2 km layers. 4. We are meeting 15% / 2 km RMS at ARM TWP up to 300 mb with v3.5.0.

National Aeronautics and Space Administration Jet Propulsion Laboratory California Institute of Technology Pasadena, California 17 Some Questions What is our true vertical resolution for water vapor? – Statistically independent and mb layers imply we can do better E. G., can we resolve the abrupt transition from moist to dry over trade wind cumulus? What is our true RMS uncertainty? – How strongly does this depend on the vertical distribution of q? What is the information content of non-zero retrieval types?

National Aeronautics and Space Administration Jet Propulsion Laboratory California Institute of Technology Pasadena, California 18 Next Steps 1. Extend the analysis of dedicated sondes into others regions Galapagos (Voemel) Caribbean, Ascencion Is. (Schmidlin) Chesapeake light platform (McMillan) 2. Extend the operational sondes westward over the Pacific 3.Look for regional variability in statistics For example: is high variability (~45%) over Hawaii at mb seen at Ascencion Is? 4.Move over land…

National Aeronautics and Space Administration Jet Propulsion Laboratory California Institute of Technology Pasadena, California 19 The Latest: TWP Dedicated Sondes and Version Looking Good Black = RMS, Red = Std Dev, Blue = Bias 18 Nov 2002 to 31 Jan sondes, 45 matches with rettyp = 0 and landfrac = mb mb mb Standard layers: mb, , etc. RMS = 8-15% in 2 km layers up to 300 mb Bias ~10%, RMS <35%up to 150 mb In 1 km layers