# Presentation: 200373

## 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**

## 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**

## 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**

## 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.***

## Lessons Learned:  #0, #1 & #2

**0.****	****Temperature is much less variable than water vapor.**
**Water vapor is highly sporadic in space and time.**
**Maxima in water vapor are embedded in cloudy regions, where most of our questionable retrievals occur.**

## Lesson 1: The highly localized nature of water vapor (v3.1.9)

**1 January 2003**
**1000-700 mb **
**precipitable water vapor**
**Full scale = 30 mm**

**700-500 mb **
**precipitable water vapor**
**Full scale = 6 mm**

## 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.**

## 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***

## Lesson #3:  Highly variable water makes for nonstationary time series

**Precipitable Water Vapor, Lihue, Hawaii**
**1000-700 mb & 700-500 mb layers**
**Blue = Sonde****, ****Red = AIRS (diamond = nonzero rettyp)**

## Lesson #4:  Water vapor at ‘upper’ levels can be highly variable

**Lihue, 700-500 mb**
**dynamic range ~  0.1 to 10 mm precipitable water**
**Dave Tobin:  ‘UTWV is the last precipitable 0.1 mm’**

## Lesson #5: Locally, conclusions like L. McMillin’s in Val ReportCaution:  Standard deviation is not robust to outliers

**1000-700 mb**
**Lihue:                      ****2.7 ± 14.1 %  Bias ± Std. Dev.**
**McMillin Globally:  ****3.6 ± 11.6 %**
**700-500 mb**
**Lihue:                     ****-4.3 ± 44.3 % ****(15th & 85th percentiles:-19, +12%)**
**McMillin Globally :  ****0.0 ± 26    %**

**Lihue 1000-700 mb**
**20.5 mm mean**

**Lihue 700-500 mb**
**1.95 mm mean**

## 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.

## Lesson #7: Sippican sondes not consistent at upper levels

**UNBIASED:  ~few ± 15-45%**
**BIASED:  ~-15-30 ± 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 & Hilo****	****too 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. 1153.**

## 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  **

## Lesson # 8:The 1000-700 mb and 700-500 mb layers are statistically decoupled

**1-16 January 2003**
**1000-700 mb water (mm)**

**1-16 January 2003**
**700-500 mb water (mm)**

**Also apparent**
**in radiosonde**
**difference statistics**
** **

## Some Conclusions from this study

**Retrieval type (or RetQAFlag) bias our observations toward dry conditions**
**The results over the eastern Pacific is similar to Larry McMillin’s conclusions globally.**
***Higher variability => larger standard deviations at altitude***
**We have sensitivity in 2 km layers.**
**We are meeting 15% / 2 km RMS at ARM TWP up to 300 mb with v3.5.0.**

## Some Questions

**What is our true vertical resolution for water vapor?**
***Statistically independent 1000-700 and 700-500 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?**

## Next Steps

**Extend the analysis of dedicated sondes into others regions**
***Galapagos (Voemel)***
***Caribbean, Ascencion Is. (Schmidlin)***
***Chesapeake light platform (McMillan)***
**Extend the operational sondes westward over the Pacific**
** **
**3.****	****Look for regional variability in statistics**
***For example: is high variability (~45%) over Hawaii at 700-500 mb seen at Ascencion Is?***
**4.****	****Move over land...**

## The Latest: TWP Dedicated Sondes and Version 3.5.0 Looking GoodBlack = RMS, Red = Std Dev, Blue = Bias

**18 Nov 2002 to 31 Jan 2003**
**26 sondes, 45 matches with rettyp = 0 and landfrac = 0.**

**100-700 mb**
**700-500 mb**
**500-300 mb**

**Standard layers: 100-925 mb, 925-850, etc. **

**RMS = 8-15% in 2 km layers**
**up to 300 mb**

**Bias ~10%, RMS <35%up to 150 mb In 1 km layers**