# Presentation: 771800

## Passive Microwave Rain Retrievals:Lessons Learned from TRMMand Applications to GPM

**Kyle Hilburn and Frank Wentz**
*Remote Sensing Systems, Santa Rosa, CA, USA*
- Third NASA/JAXA International TRMM Science Conference
- Las Vegas, Nevada, USA
- 4-8 February 2008

## Unique Challenges of Satellite Data

- With *in situ* data: worry about the instrument in the environment
- No different when the instrument is in space!
- Especially important when merging multi-sensor observations
- Examples:
- Mis-specification of pointing geometry (SSM/I)
- Satellite attitude problems, orbital drift/decay
- Emissive antenna (TMI)
- Gradients in the hot load (AMSR-E)
- Moon reflection in cold mirror
- Radio frequency interference reflected off sea surface
- Sun glitter on the sea surface
- Real diurnal variability in geophysical parameters (LEO)

## Data Monitoring is Important

- Largest recorded X-ray solar flare (4 Nov 2003, 19:47 UTC)
- Originally classified as X28, now estimated (in JGR) to be X40
- Microwave radiation also emitted, reflected off the sea surface and saturated 11 GHz TMI channels
- Detected during routine data quality check

## Evolution of the UMORA Rain Algorithm

**Rain, Precipitation, and the Water Cycle**

**Intercalibration, Beamfilling, and Satellite Altitude**

**Time-of-Day Effects**

## Evolution of the UMORA Rain Algorithm

**Rain, Precipitation, and the Water Cycle**

**Intercalibration, Beamfilling, and Satellite Altitude**

**Time-of-Day Effects**

## What prompted this work?

**F13 (green),**** ****F14 (blue),**** ****F15 (purple),**** ****AMSR-E (orange),**** ****TMI (red),**
**Global Precipitation Climatology Project (black)**

## Wentz & Spencer Rain Algorithm

- Retrievals from all _SSM/I_, _TMI_, and _AMSR_ over the ocean
- Simultaneous retrievals of: sea surface temperature (_SST_), surface _wind _speed, columnar water _vapor_, columnar _cloud_ water, and _rain_ rate
- Passive microwave rain retrieval is inherently an *underdetermined* and *non-unique* problem
- Our approach is not Bayesian – no database of profiles
- Dual polarization measurements are used to accurately separate the scattering and emission signals
- Emission is related to surface rain rate through three components:
- Rain Column Height (a climatological function of SST)
- Cloud/Rain Partitioning (a global relationship)
- Beamfilling (a dual-frequency approach)
- Our new rain algorithm: **UMORA** (**U**nified **M**icrowave **O**cean **R**etrieval **A**lgorithm) is a modification of the Wentz and Spencer (1998) approach
- New version of SSM/I, TMI, and AMSR-E products in September 2006
- Upcoming paper describing algorithm in J. Appl. Meteor. Climatol.

## What changes did we make?

- _Rain column height_
- Old Wentz and Spencer (1998) rain rates were unrealistically high in the tropics due to inappropriate rain column heights
- Used NCEP and radiosondes to re-derive our climatological expression
- _Rain/Cloud partitioning_
- Old W&S algorithm had the same relationship for all sensors
- Possibly a function of resolution (larger footprint should have a lower threshold); but no changes were made because adjusting the partitioning has a small effect on average rain rate, but a large effect on rain fraction
- _Non-uniform beamfilling adjustment_
- Old W&S rain rates did not agree among the sensors because beamfilling is a function of sensor resolution
- Footprints: 32 km (SSM/I) vs 12 km (AMSR-E and TMI)
- Heavy rain causes radiometer saturation, which must be modeled when using a dual-frequency approach for the beamfilling correction
- _Calibration of brightness temperatures down to 0.05 K_
- Water vapor retrieval is robust, only requires calibration accuracies of 0.2 K
- New calibration performed in order to get accurate wind trends
- Old W&S footprint resampling caused a cross-track bias for rain when Yaw=0 deg; new optimal interpolation removes this problem

## Tropical Rain Time Series

_**OLD**_** W&S**
**Rain Rates**

_**NEW**_** UMORA**
**Rain Rates**

***No Adjustments Made for Time-of-Day Effects***

***TMI and AMSR-E***
***now consistent***
***with SSM/I***

***Averages in***
***tropics now***
***realistic***

## Evolution of the UMORA Rain Algorithm

**Rain, Precipitation, and the Water Cycle**

**Intercalibration, Beamfilling, and Satellite Altitude**

**Time-of-Day Effects**

## Using AMSR to Simulate and Modelthe Resolution Effect

**Using AMSR to Simulate and Model****the Resolution Effect**

| Resolution
(km) | Scene
Average
(non-dim) |
| --- | --- |
| 12 | 1.17 |
| 21 | 0.95 |
| 38 | 0.86 |
| 56 | 0.79 |

- 37 GHz Attenuation:

**Example storm south of the Aleutian Islands (15 Sep 2003)**

- AMSR has highest resolution – use AMSR brightness temperatures, artificially resampled to lower resolutions (using OI), to get at functional relationship between resolution and biasing.
- Lower resolution is not only smoothed, but the average attenuation values are biased low – this is “the beamfilling effect”.
- By modeling the resolution effect we  remove differences between SSM/I (~32 km), TMI and AMSR-E (~12 km).

## SSM/I Altitudes

**F08**

**F10**

**F11**

**F13**

**F14**

**F15**

- Which one of these is not like the others? (same scale on each: 200 km)
- Theory says:  lower altitude = smaller footprint = larger rain rates (we are overcorrecting beamfilling)
- We find big differences with altitude correction for F10, small for others
- All have ~1%/decade decay in altitude
- Currently, UMORA rain algorithm has altitude “hard wired” (i.e., fixed) for each satellite (except TRMM, which has both pre- and post-boost values)

## F10/F11 Rain Rates

**Color: ****	****Prediction using beamfilling theory from paper**
**	****(assuming F11 has fixed diameter)**
**Solid:****	****Data (heavy line)**
**Solid:****	****Linear function altitude**
**Dotted:****	****Fractional number of observations**

**AMSR simulation:**
**only four points**
**(12, 21, 38, 56 km)**
**F10/F11 study: confirms linear behavior, also refinement**

## SSM/I Time Series

**SSM/I Time Series**

**Version 6 Rain Rates**
_***No Adjustment***_

**Version 6 Rain Rates**
_***Footprint size adjustment***_

**Huge F10 improvement**
**Other satellites: small changes**

## Evolution of the UMORA Rain Algorithm

**Rain, Precipitation, and the Water Cycle**

**Intercalibration, Beamfilling, and Satellite Altitude**

**Time-of-Day Effects**

## Indirect Validation using Hydrological Consistency

- Global evaporation balances global precipitation (_with a static, latitude-dependent adjustment to rain_)
- Average evaporation: 962 mm/year
- Average precipitation: 951 mm/year
- Imbalance on the order of 1%

- Trends in evaporation and precipitation have the same magnitude as trends in water vapor, in contrast with climate models
**Evaporation trend: + 1.3 % / decade**
**Precipitation trend: + 1.5 % / decade**
**Water Vapor trend: + 1.4 % / decade**

**Climate prediction models predict a muted response by precipitation see Wentz et al., 2007, *****Science*****.**

## High Latitude GPCP-UMORA Differences Are Seasonal

**Note that UMORA is much lower than GPCP in the winter hemisphere.**
**The patterns are similar to Petty (1995) snow observations.**

**GPCP / UMORA rain rate ratios for 1988-2005**

## Evaporation (E)
Precipitation (P)
Water Vapor (V)

Averaging over time (~month) at a particular location we have:

div Q = E – P

Water Vapor Transport (Q) (a vector)
Water Vapor Transport Divergence (div Q)

- Precipitation (**P**)
- Water Vapor (**V**)
- Averaging over time (~month) at a _particular location_ we have:
**div Q = E – P**
- Water Vapor Transport (**Q**) (a vector)
- Water Vapor Transport Divergence (**div Q**)

**The Hierarchical Context**

- The *Science* paper
- (one-dimension: time)

- The next step:
- add 2-D space

**Wentz et al., “How much more rain will global warming bring?”, *****Science*****, 13 July 2007.**

## Seasonal Cycle of V-Wind and Rain

_**Color:**_**  Meridional Wind (m/s)**
_**Contours:**_**  Rain Rate (4, 6, 8, 10 mm/hr)**

***going from thin to thick***

*using TMI UMORA rain rates and Atlas Level-2.5 TMI winds*

**Rain occurs where there is convergence**

***Atlas winds are based on our wind speeds***

## Seasonal Cycle in Precipitation

***Note that Evap-WVTD has the expected seasonal cycle***

_**Northern Hemisphere**_
- 1988-2006, Mid-Latitudes: 30 to 60 deg

_**Southern Hemisphere**_
- 1988-2006, Mid-Latitudes: -30 to -60 deg

***using water cycle balance with SSM/I data***

**- Evaporation based on our winds**
**- Water Vapor Transport Divergence (WVTD) based on our vapor and Atlas/Ardizzone winds (which are based on our winds)**

## Using Scattering Information inRain/Cloud Partitioning

**control  /  ****with scat info**

**Some evidence based on scatterometer rain impacts work (Hilburn et al, 2006) that the radiometer scattering signal provides rain detection information independent from emission based threshold already used**
**Working with Grant Petty to compare our similar algorithms.  His algorithm retrieves much more extratropical rain.**
**This example shows what would happen if we made our rain/cloud threshold a function of scattering information.  Changes are focused in the extratropics.**

## Evolution of the UMORA Rain Algorithm

**Rain, Precipitation, and the Water Cycle**

**Intercalibration, Beamfilling, and Satellite Altitude**

**Time-of-Day Effects**

## Time-of-day Effects are a Major Sourceof Intersatellite Rain Differences

- In general, early morning satellites (F08, F11, and F13) have adjustments that decrease the average, whereas, late morning satellites (F10, F14, and F15) have adjustments that increase the average.  F10 is an outlier with known instrument problems.  These scaling factors are not applied to the publicly available UMORA products.

## Latitudinal Structure of Diurnal Signal

**It is well known that rain over the ocean achieves a maximum in the local morning.**
**It appears that the max/min have an asymmetric structure with latitude.  What goes on in the extratropics?**
**The time-of-day issue is a major remaining source of uncertainty in (LEO) inter-satellite comparisons.**

## Conclusions

**Accurate TB intercalibration does not necessarily guarantee agreement among rain rates from different sensors/satellites**
- The big nonlinearity:  beamfilling
- Depends on resolution, which depends on sensor and satellite altitude
- Time-of-day effects
- Not an “error”, but we must account for it
**Measurement of precipitation, not just rain, becomes important in the extratropics**
- UMORA does not retrieve frozen precipitation
- Consistency in retrieved parameters is a useful constraint (water cycle)
- Scattering signal might be useful, will continue Grant Petty collaboration
**Despite remaining uncertainties, satellite rain rates can be carefully used with reasonable confidence for climate studies on time scales of years to decades**
- The rain gauge is half full

***Lessons learned from TRMM and applications to GPM***