Bob Joyce : RSIS, Inc. John Janowiak : Climate Prediction Center/NWS

Bob Joyce : RSIS, Inc.John Janowiak : Climate Prediction Center/NWSPhil Arkin : ESSIC/Univ. MarylandPingping Xie: Climate Prediction Center/NWS

0000Z, 30Nov 2001

Global, Microwave-based, Precipitation Analyses

from Satellite at ½ Hr & 8 km Scales

0030Z, 30Nov 2001

0100Z, 30Nov 2001

0000Z, 30Nov 2001

TRMM

AMSU

SSM/I

Two primary types of precipitation algorithms:

Infrared (GPI, convective-stratiform, OPI)

(-) indirect - can only sense cloud-top temperature

(+) very good sampling characteristics (time & space)

Passive Microwave (SSM/I, MSU, AMSU):

(+) considerably better estimate than IR – “sees” thru cloud and can directly sense information from hydrometeors

(-) poor temporal sampling (polar orbit platforms)

Pessimist: “The food here is terrible!”

Optimist: “Ah yes, but such huge portions!”

Microwave & IR dataCombined statistically

Pragmatist: Meld together the IR & microwave data to take advantage of the strengths of each

Vicente (U. Wisc.)

Turk (NRL, Monterey)

Adler and Huffman (NASA/GSFC)

Kuligowski (NOAA/NESDIS)

Our Approach

Use the IR and microwave data but do NOT mix them

Use the IR only as a transport and “morphing”mechanism

Here we use precipitation algorithms developed by Ferraro (NESDIS: AMSU-B & SSM/I) andKummerow (CSU: TRMM) but method is algorithm independent.

Enables the generation of spatially and temporally complete precipitation fields while maintaininga pure, albeit manipulated, microwave-based analysis

2.5o

2.5o

“Advection vectors” are computed from IR for each 2.5ogridbox andall microwave pixels contained in that grid box are propagated in the direction of that vector

precip

2.5o

2.5o

IR Spatial Correlation Domain for Computation of “Advection Vectors”

IR (t+0) IR (t+1/2 hr)

Actual Microwave Observations

t+0 t+2 hrst+1/2 hr t+1 hr t+1.5 hr

t+ 1/2 hr t+1 hr t+1.5 hr

IR

0.75 0.50 0.250.25 0.50 0.75

Time interpolation weights

Interpolated “observations”

t+1/2 hr t+1 hr t+1.5 hr

“Validation”

Initial microwave pass Next microwave pass

Valid time is 5 hours afterthe “initial” pass and 2.5 hours before the “next”pass

Microwave estimates propagated byIR.Microwave data from overpassesbetween the “initial” and “next”overpasses were withheld in this testto assess the performance of the technique. Validating analysis is inthe lower-center frame.

Validating analysis, ie. themicrowave pass between the “initial” and “next” microwavepasses that was withheld.

Satellite Radar

Microwave-Advected

GPCP “1DD”

GPI (IR)

Potential Applications

Real-time quantitative global precipitation monitoringDisaster mitigationProvide timely updates for U.S. interests abroad

Numerical model initialization & validationImprove diurnal cycle in the models

Diagnostic studies: diurnal cycle in particular

Account for precipitation that forms and dissipates

between microwave overpasses

Refine advection vector computation

Continue validation effort

Test/Include new precipitation products as they

become available – method is not restricted to

particular algorithms or sensors

Continuing Work

mm/day

Figure 5

Initial microwave pass Next microwave pass

Microwave propagated by IRAnother test – this one is over The South Atlantic ConvergenceZone (SACZ)

Valid time is 1.5 hours afterthe “initial” pass and 6 hours before the “next”pass

Validating microwave data

Methodology

AMSU-B, SSM/I and, TMI derived rainfall is mapped to global, half-hourly rectilinear grids, equivalent to 8-km at the equator. Between overpasses by a microwave sensor, features within each non-overlapping 2.5o x 2.5o lat/lon grid box are propagated by using IR data until the next microwave overpass occurs, as follows:

1. We compute the spatial lag correlations of 8-km pixel IR brightness temperatures within 5o x 5o gridboxes that are displaced from1, 2, …, up to 12 pixels in the X and Y directions from the “target” 5o x 5o gridbox. The X and Y vector fields are then interpolated to 2.5o x 2.5o.

2. “Advection vectors” are computed which are oriented in the direction of highest spatial lag correlation in the X-Y plane.

3. All microwave estimates in the “target” 2.5o x 2.5o gridbox are then propagated in the direction of the advection vector.

4. Once a new microwave overpass occurs, the same process is repeated, but backward in time from the most current data. This process allows features to “morph” with time by linearly interpolating both propagations using each pixel’s temporal “distance” from analysis time as the interpolation weights.

(t + 0 hr)

5o + 12 pixels

5o + 12 pixels

Spatial Lag Correlation of IR pixel temperature amongnearby 5o x 5o grid boxes to determine propagation direction

(t + 1/2 hr)

5o

5o

Bob Joyce : RSIS, Inc. John Janowiak : Climate Prediction Center/NWS

Documents

Transcript of Bob Joyce : RSIS, Inc. John Janowiak : Climate Prediction Center/NWS