# Presentation: 149118

## Satellite-derived Rainfall Estimates 

- Satellite-derived Rainfall Estimates
- over the Western U.S.:
***Fact or Fiction?***
- John Janowiak
- Bob Joyce
- Pingping Xie
- Phil Arkin
- Mingyue Chen
- Yelena Yarosh

## OUTLINE

**OUTLINE**

- Brief review of IR & passive microwave info.
- Describe “CMORPH”
- Validation (US & Australia)
- Simple gauge vs. satellite sampling study
- A look at  western US precip
- Conclusions & on-going work

## Infrared

**Infrared**

**Geostationary**
**         ****& **
**      ****Polar**

## Passive Microwave “Emission”

- Surface

**Passive Microwave “Emission”**

- Detects thermal emission
- from raindrops

-  most physically direct
-  over ocean only
-  polar platform only

## Passive Microwave “Scattering”

- Freezing Level

**Passive Microwave “Scattering”**

** ****land & ocean      **
-  **polar platform only**

## Slide 6

## 3-hr mosaic:  good coverage

- <date/time>

- but time of obs. varies by 3 hrs

## CPC Morphing Technique 

- CPC Morphing Technique
-    “CMORPH”
**Spatial Grid:  **0.0728o lat/lon (8  km at equator)
**Temporal Res’n:** 30 minutes
**Domain:**  Global (60o N - 60o S)
**Period of record: **Dec. 2002 - present

**Bob Joyce!**

- http://www.cpc.ncep.noaa.gov/products/janowiak/MW-precip_index.html

- Paper (Joyce et al.) submitted to *J. Hydrometeor.*

## “CMORPH” is  NOT

- “CMORPH” is  **NOT**

-   a precipitation estimation technique but

- rather a technique that creates spatially & temporally

- complete information using existing precipitation products

- that are derived from passive microwave observations.

- `

- At  present, precipitation estimates are used from

- 3 sensor types on 7 platforms:

- SSM/I      (DMSP 13, 14, 15)

- TMI         (TRMM)

***Soon:***  AMSR (“ADEOS-II”)  & AMSR-E (“Aqua”)

## Slide 10

## Slide 11

## Slide 12

## http://www.cpc.ncep.noaa.gov/products/janowiak/us_web.html

## Slide 14

## http://www.bom.gov.au/bmrc/wefor/staff/eee/SatRainVal/dailyval_dev.html

## Remote Sensing Errors & Limitations

-  Indirect  estimates inferred radiometrically
-  Instrument  calibration
-  Conversion from retrieval to rain rate (algo.)
-  Temporal sampling

**ERRORS:**

**LIMITATIONS:**

-  Measurements not temporally continuous
-  Depending on instrument only convective (“scattering’) precip. may be sensed

## Raingauge Errors & Limitations

-  Wind & gauge exposure effects
-  Human element (time, accuracy)
- - Automated (calibration, maintenance)
-  Biological contamination

**ERRORS:**

**LIMITATIONS:**

-  Representativeness of area (point value)
-  Spatially incomplete
-  Available frequency (daily, 6-hr)

## 1o x1o box in s-central TN (July14, 2003)

- 1o x1o box in s-central TN (July14, 2003)

- Distribution of Rainfall by Amount

**Notes:**
Precip. (in.) for 26 stns in 1x1 degree grid box over south-central TN for July 14, 2003

## 4 gauge   0.00        0.01        0.97

- Radar      0.00        0.02        0.96
- Cmorph   0.08        0.04        0.91

**Notes:**
1 stn:  stn near center of gridbox
4 stn: 4 well-distributed stations used

## Slide 20

**Notes:**
Distribution  errors in TN (86-87W; 35-36N) during July 2003 for all possible single gauges, all possible pairs (2 gauges), etc. using “truth” as the mean of all gauges in the gridbox.  Percentages (red) denote % of cases that fall in the error range: +/- 4 mm/day.

## 40%

- Gauge

**Notes:**
Distribution of errors in daily preip. For rainy days only during July 2003 over 1 deg. Lat/lon box in TN (86-87W; 35-36N) for CMORPH (left) and for all single gauge reports (one of 28-31) in which the ~30 stn mean is used as “truth”.  For CMORPH, “truth” is the Higgins gauge analysis.
The percentages (red) represent the % of cases that fall in the range of error between -6 mm/day and + 6 mm/day.  [Note: y-axis #’s are a range: -4 mean –4 to –6 mm/day error].  Also, the y-axis numbers aren’t plotted exactly right.

## Synthetic Data Sampling Study

***Question: ***Are there situations when an estimate from satellite is ‘better’ for assessing area-mean precipitation than a measurement  from gauge(s)?

## :

## Approach (overly?) simplistic:                         

**Approach **(overly?) **simplistic**:
- 	- ‘real-world’ nonzero rainfall
-              distribution characteristics not
-              modeled
- 	- on average, the % of locations with
-               rain over an area is < 50% used here
- 	- rainfall ‘generators’ exist that more
-              nearly duplicate the statistics of
-              actual rainfall over time-space
- 	- much work on aspects of this  topic
- 	   done in hydro. & satellite sampling 	     	   communities
-              (Bell et al. :1990, 1996, 2003)

## Samples of synthetic precipitation within a 

- Samples of synthetic precipitation within a
- 1o x 1o lat/lon box at satellite resolution
- Precip amounts of 0 to 1 chosen randomly;
- impose condition that 50% are = “0”

**Notes:**
Precipitation is randomly generated as follows:  at each of the 169 locations (13x13 array), “rainfall” is assigned a random value from 0 to 1, but 50% of the locations are set to “0” values by a random selection process.  1000 such fields are produced.

## 1o x 1o lat/lon box containing 169 satellite pixels  

- 1o x 1o lat/lon box containing 169 satellite pixels

**Notes:**
This slide & the following 7 slides show the various gauge locations for this validation exercise.  Note that this is a very forgiving procedure with regard to the gauges because:   1.  Gauge value is exactly equal to the true value
                2.  The point value is exactly equal to the area-average value
                3.  Gauges are extremely well-distributed within the grid box 

## 2 Gauges

## 3 Gauges

## 4 Gauges

## 5 Gauges

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- 9 Gauges

## X

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- 13 Gauges

## X

**X**

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- 25 Gauges

## Time series of absolute error (1st 100 days)

- Time series of absolute error (1st 100 days)

## “Perfect” Gauge

-  200% error

-  10%/ error
- (satellite)

-  100% error

- (90%)

- “Perfect” Gauge

## Time series of absolute error

## “Perfect” Gauge

- (90%)

-  0-10% error

-  0-50% error

-  0-100% error

-  0-200% error

- Light  blue: 9 gauges
- Dark blue: 5 gauges
- Green: 3 gauges
- Red: 1 gauge

- “Perfect” Gauge

##                      RMSE                         

-                      _RMSE                         _
- _  _
-   0.251    satellite (200% + bias)
-   0.103    1 gauge ~*10% of earth*
-   0.063    satellite (100% + bias)
-   0.063    satellite (0-200% random)
-   0.054    2 gauges
-   0.033    3 gauges
-   0.023    4 gauges
-   0.021    5 gauges
-   0.016    satellite (50% + bias)
-   0.016    satellite (0-100% random)
-   0.011    9 gauges
-   0.009   13 gauges
-   0.009   satellite (0-50% random)
-   0.004   25 gauges
-   0.001   satellite (10% + bias)

**Notes:**
RMS error using synthetic data.  169 locations (13 x 13) within a 1x1 degree box & 1000 comparisons.

## Number of HADS/RFC stations per 1⁄4 degree lat/lon grid box

- (9/10/2003)

## “CAMS”  - 1 or more gauges per 1o Grid

- 29% of land area ( “ )

- “CAMS”  - 1 or more gauges per 1o Grid
- 10% of earth (60N-60S); 29% of land

## http://www.cpc.ncep.noaa.gov/products/janowiak/us_web.html

## Crude RH Adjustment to CMORPH

- (Aug 2003)

- Scofield, 1987
- Rosenfeld and Mintz (1988)
- McCollum et al. (2000)

## CMORPH vs. gauge over ‘NAME’ zones

## CMORPH with RH adjustment  vs. gauge over ‘NAME’ zones

## Time series of statistics over 9 NAME Zones

##  Conclusions

-  Conclusions
*Fact or Fiction?*

- 1.	CMORPH estimates compare quite favorably to raadar estimates over the US and to gauge analyses over the US and Australia.

- 2.	Satellite estimates of rainfall can be useful over the western U.S. (and elsewhere) – perhaps better than gauge data in some situations

- 3.	Many satellite techniques overestimate rainfall considerably in semi-arid regions during the warm season, but an RH adjustment is promising.

##  Work in Progress

-  Work in Progress

## ‘0’ precip

- ‘0’ precip

- 0 < precip < 1

- Long-term Box Mean = 0.25
- So, 200% error = 0.500
-       100% error = 0.250
-         50% error = 0.125

## Correlation with MW availability