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)
Advection Rates for 00Z 30 Nov 2001
ZONAL
MERIDIONAL
|………………. EAST …………|………… WEST ………………| (pixels/hour)
|………………. NORTH ……...|…………SOUTH ………………| (pixels/hour)
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
Finis
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
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