# Presentation: 476698

## High Latitude Precipitation: AMSR, Cloudsat, AIRS

- Bob Adler (U. of Maryland/NASA Goddard)
- _Eric Nelkin_ (SSAI/NASA Goddard)
- _Dave Bolvin_ (SSAI/NASA Goddard)
- George Huffman (SSAI/NASA Goddard)

*and GPCP, too*

## Motivation

*TRMM Multi-satellite Precipitation Analysis (TMPA) limited to 50°N-50°S--*_***desire high time resolution global analyses***_* approaching and through the GPM era*

- TMPA

- • Take advantage of current and future developments by others in precipitation estimation applicable at high latitudes (e.g., high frequency microwave [150-200 GHz]--Ferraro, Jackson, Staelin, etc.)
- •  Develop empirical technique using AIRS (and/or other sounders) as “fall-back” when/where more physically-based microwave algorithms fail--especially over land
- •  Develop merging procedures to produce globally complete, ~3-hr analysis

- Approach

## Current AMSR, SSM/I, AMSU estimates leave large gaps at high latitudes (and not so high latitudes) over land

- January example 3B40RT, part of the TMPA

## Current Monthly and Daily Globally Complete Products--GPCP

- _Daily_ GPCP Product (1997-present)--Huffman et al. (2001)

**Uses SSM/I to Set Rain Frequency Statistically, but Daily Variations Determined by IR**
_**High latitude based on TOVS/AIRS**_** adjusted to match GPCP monthly which includes gauges corrected for wind loss**

- Current Monthly and _Daily_ Globally Complete Products--GPCP

- •  For daily precipitation TMPA now a better choice at lower latitudes
- •  Daily averaging hides many problems that become obvious when doing instantaneous to 3-hr estimates

## •  In current GPCP products TOVS/AIRS-based estimates are adjusted area-wise by comparison with SSM/I (GPROF) at lower latitudes where confident and extrapolated to higher latitudes, adjusting pdf of precipitation rate and constraining total by monthly GPCP (with gauges over land)

- •  Susskind et al. (1997) developed a calibrated cloud volume proxy from TOVS
*	**Precip*  =  *revised cloud depth*  *  *cloud fraction*  *  ƒ ( latitude, season )
- •   The calibration is TOVS _swath_ data vs. _daily_ station precip data
- •   Results show low precip rates, very high fractional occurrence
- 	•  done as a regression
- 	•  uses instantaneous data as a proxy for daily data
- 	•  has only one sample for the day

*cloud top ht.*  –  ( *scaled RH*  +  *scaled cloud fraction* )

- 0 = sat. sfc500 mb
- 9 = dry        “

- 0    = overcast
- 4.5 = clear

*•  **In current GPCP products TOVS/AIRS-based estimates are adjusted area-wise by comparison with SSM/I (GPROF) at lower latitudes where confident and extrapolated to higher latitudes, adjusting pdf of precipitation rate and constraining total by monthly GPCP (with gauges over land)*

## Comparison of GPCP Daily (1° lat./long.) and Finland (FMI) Gauges for August 2005 and February 2006

- Comparison of _GPCP Daily (1° lat./long.) and Finland (FMI) Gauges_ for August 2005 and February 2006
*As Starting Point for Evaluation of High Latitude Satellite Precipitation Estimates Now and For GPM*

- _GPCP daily product_ is research product using various satellite data sets as input and monthly GPCP product (which contains monthly gauges with wind loss) applied to restrain bias.
- _At latitude of Finland satellite information is from TOVS/AIRS._
- Daily analysis is globally complete and extends from 1997 through 2007 (updated about 2 months after real-time).
- Reference: Huffman et al., 2001:  Global Precipitation at One-Degree Daily Resolution from Multi-Satellite Observations.  *J. Hydrometeor.*
*	**	*

## Comparison of GPCP Daily,  2° Long./1°Lat. (~100 km) 

**Comparison of GPCP Daily,  2° Long./1°Lat. (~100 km) **
_**August**_** 2005**

- Scatterplot of all 8 Daily 2°x1° Precipitation Values for the month within the 4°x4° Green Box (previous slide)

- Time Series of Daily Precipitation
- for the 2°x1° Red Box

- % Bias Diff = -14%
- Corr = 0.6

- % Bias Diff = 7%
- Corr = 0.72

- _Summer results “good”, but with significant room for improvement_

## Comparison of GPCP Daily,  2° Long./1°Lat. (~100 km)

_**February**_** 2006**

- Scatterplot of all 8 Daily 2°x1° Precipitation Values for the month within the 4°x4° Green Box

- Time Series of Daily Precipitation
- for the 2°x1° Red Box

- _Winter results worse, with wind loss major factor in bias_

*Additional analysis continuing*

*Also looking at Canadian data set and BALTEX data*

## with the other satellites in the A-Train constellation.   

**COMPARISONS WITH CLOUDSAT**

- Launched in 2006, CloudSat, with its Profiling Radar, flies in formation
- with the other satellites in the A-Train constellation.

- CloudSat views on a nadir track, providing a “curtain” of cloud/precip data at all latitudes.

## High-latitude Southern Hemisphere

- winter case, with precipitation
- near the center of the swath

- 9-10 July 2006, 22:30-01:30 UTC

- Original AIRS precipitation is too
- widespread in both high latitudes
- and the tropics.
- Calibrating to AMSR-E greatly
- reduces spatial coverage; rates
- are boosted closer to AMSR-E.
- Mapping via histogram-matching
- can be extreme, due to limited
- range of AIRS rain rates
- (max. ~1.2 mm/hr).

**CALIBRATION WITH AMSR-E ***Assuming AMSR precipitation is correct--over ocean*** **

## High-latitude Northern Hemisphere

- summer case, with precipitation
- near the center of the swath

- 26 July 2006, 04:27-05:10 UTC

- Original AIRS precipitation is too
- light and probably too widespread.
- Calibrating to AMSR-E reduces
- spatial coverage, but overestimates
- in the heaviest rain areas.
- Histogram-matching cannot “create”
- precipitation where it did not exist
- (e.g., the AMSR-E “tail”).

**CALIBRATION WITH AMSR-E (cont.)**

## Application of CALIBRATION WITH AMSR-E

- Example of AIRS filling in a feature over snow where AMSR cannot reliably estimate

**AMSR-E**

**Calibrated**
**AIRS**

- Land precip feature

## CloudSat Profiling Radar

- (41.0°S, 16.8°E)

- (61.7°S, 7.3°E)

- 10 July 2006, ~00:00-00:06 UTC

- The A-train formation allows for
- nearly-coincident space/time match-ups
- between AIRS relative humidity profiles
- and CloudSat radar reflectivities.

- Good correspondence between
- high/saturated deep-layer AIRS RH
- and high/deep CloudSat reflectivities
- in the heart of the storm system.

**COMPARISONS WITH CLOUDSAT**** **

## CloudSat Profiling Radar

- )

- (76.1°N, 5.1°W)

- (56.7°N, 30.8°W)

- 26 July 2006, ~04:24-04:30 UTC

- Excellent agreement between
- AIRS humidity profile and nearly-
- coincident CloudSat reflectivities,
- both in precipitating and non-
- precipitating clouds.

**COMPARISONS WITH CLOUDSAT (cont.)**

- Quality flags indicate a
- fall-back to the microwave scheme in
- deep clouds, where CloudSat indicates
- precipitation.

## Ocean Precipitation Frequency from Various Satellite Estimates

- _Ocean_ Precipitation Frequency from Various Satellite Estimates

- • All estimates (except GPCP Daily) are instantaneous; different footprint sizes and precipitation rate thresholds also complicate comparison
- • Preliminary results indicate Cloudsat estimates reasonable and useful for adjustment of other estimates--*e.g.* AIRS; but large difference among Cloudsat estimates
- • SSM/I and AMSR estimates fall off rapidly at high latitudes; HOAPS (SSM/I) better, but questionable at low latitude

## Seasons

- Winter

- Winter

- Summer

- Summer

- • Cloudsat shows higher oceanic frequencies in winter (50-70° lat.); HOAPS agrees, other estimates do not
- • Cloudsat “snow possible” bigger in winter, especially in N.H.
- • HOAPS tropical features very different from AMSR and SSM/I
- • AMSR may have ice “problem” at higher latitudes in winter

*Currently working on time-space scale issues so we can better use Cloudsat and AMSR to determine “correct” fraction *

## Conclusions and Future

- Research in progress--developing comparisons of various high latitude precipitation estimates and empirical AIRS/TOVS technique to _merge multiple estimates for globally-complete_, high-time resolution precipitation analysis
- Cloudsat, AMSR and AIRS “simultaneity” key to much of analysis
- Cloudsat precipitation estimates (from L’Ecuyer, Haynes--CSU) very useful--but need to develop techniques to compare with other estimates--with larger footprints (and 2-d)--also which “certain”, “probable” or “possible” versions
- High latitude (especially land) precipitation still challenging--need many approaches and comparisons