Global,

0000Z, 30Nov 2001

Microwave-based, Precipitation Analyses from Satellite at ½ Hr & 8 km Scales

0000Z, 30Nov 2001 AMSU

Bob Joyce

: RSIS, Inc.

John Janowiak

: Climate Prediction Center/NWS

Phil Arkin

: ESSIC/Univ. Maryland

Pingping Xie

: Climate Prediction Center/NWS 0030Z, 30Nov 2001 TRMM 0100Z, 30Nov 2001 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!” 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) Microwave & IR data Combined statistically

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) and Kummerow (CSU: TRMM) but method is algorithm independent.

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

“Advection vectors” are computed from IR for each 2.5

o box gridbox and

all microwave pixels

contained in that grid

are propagated in the direction of that vector

2.5

o 2.5

o

2.5

o IR Spatial Correlation Domain for Computation of “Advection Vectors” 2.5

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

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

t+0 Actual Microwave Observations Interpolated “observations” t+2 hrs Time interpolation weights 0.75

0.25

IR 0.50

0.50

0.25

0.75

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

“Validation”

Microwave estimates propagated byIR.

Microwave data from overpasses between the “initial” and “next” overpasses were withheld in this test to assess the performance of the technique. Validating analysis is in the lower-center frame.

Initial microwave pass Valid time is 5 hours after the “initial” pass and 2.5 hours before the “next” pass Next microwave pass Validating analysis, ie. the microwave pass between the “initial” and “next” microwave passes that was withheld.

Satellite Radar

Microwave-Advected GPCP “1DD” GPI (IR)

Potential Applications

 Real-time

quantitative

 Disaster mitigation global precipitation monitoring  Provide timely updates for U.S. interests abroad  Numerical model initialization & validation  Improve diurnal cycle in the models  Diagnostic studies: diurnal cycle in particular

Continuing Work

 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

Finis

mm/day Figure 5

Another test – this one is over The South Atlantic Convergence Zone (SACZ) Initial microwave pass Microwave propagated by IR Valid time is 1.5 hours after the “initial” pass and 6 hours before the “next” pass Next microwave 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 2.5

o x 2.5

o 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 within 5 o x 5 directions from the “target” 5 2.5

o x 2.5

o .

o gridboxes that are displaced from1, 2, …, up to 12 pixels in the X and Y o x 5 o IR brightness temperatures gridbox. The X and Y vector fields are then interpolated to 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” 2.5

direction of the advection vector.

o x 2.5

o gridbox are then propagated in the 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.

Spatial Lag Correlation of IR nearby 5 o x 5 o pixel temperature among grid boxes to determine propagation direction (t + 1/2 hr) (t + 0 hr) 5 o + 12 pixels 5 o 5 o 5 o + 12 pixels