1. Merging of microwave rainfall retrieval swaths in preparation for GPM A presentation, describing the Merging of microwave rainfall retrieval swaths in preparation for GPM was presented to the Atmospheric Sciences Department of the University of Washington on Nov. 14. A procedure that derived rainfall distributions at 0.125 degree spatial and hourly temporal resolution from composites of SSM/I, AMSU and AMSR-E microwave measurements was described. The algorithm does not average rainfall distributions from successive swaths, instead it advects the rain patches in accord with mid-tropospheric winds. Although water vapor drift winds from geostationary satellite IR imagery are employed in the algorithm, the procedure does not rely on IR cloud top brightness temperatures to derive rainfall. We previously developed the AMIR automatic morphing algorithm, but the present algorithm does not require completely overlapping images as was the case with that earlier algorithm. The algorithm provides translation and rotation of varying amounts throughout the image, see Fig. 1. Figures 2, 3 and 4 show a sequence of hourly composite images of rainfall in a N. Pacific frontal derived from NOAA-16 AMSU and DMSP SSM/I retrievals. (These can be shown in fast succession. Motion can be observed with respect to the Aleutian Islands.) More work is needed to remove artifacts caused by the displacement of rain patch imagery. Calibrate the rainfall products obtained from various sensors to provide consistent distributions. The AQUA AMSR-E rainfall retrievals will be folded into our composites. With the newly available NOAA-17 AMSU, that will provide seven low polar orbiting microwave sensors that can provide concatenated rainfall distributions. The TRMM TMI will be used to provide bench mark calibration where orbits intersect. Beside its applicability to GPM, there is considerable local interest in this product to aid forecasts of winter storms that impinge on the US west coast. Several times during this past in a winter season, the operational forecasts have predicted surface pressure with ~10s of mb errors and the lows were misplaced by ~ 100 km. Much of the N. Pacific is cloud covered so information from IR imagery is limited. The availability of these hourly composites of rainfall has thus aroused considerable interest. This work will be described at the 83rd Annual Meeting of the AMS in Feb 2003.

2. Moving Rain, t 1

3. Moving Rain, t 3

4. Feature Matching for Automatic Morphing Hatha et al., 1996

5. Moving Rain, t 2

6. Quantifying the Uncertainty in Passive Microwave Snow Water Equivalent Observations 1 James Foster, 1,3 Chaojiao Sun, 2 Jeffrey P Walker, 1,3 Richard Kelly, and 1 Alfred Chang 1. Hydrological Sciences Branch, NASA Goddard Space Flight Center, Greenbelt, Maryland, 20771 USA 2. Department of Civil and Environmental Engineering, The University of Melbourne, Parkville, Victoria, 3010 Australia 3. Goddard Earth Sciences and Technology Center Because snow cover and snow mass often display variability at spatial and temporal scales below those resolved by climate models and the errors associated with model physics, the problem of accurately forecasting snow in regional and global atmospheric and hydrologic models is challenging. Therefore, any Land Surface Model (LSM) snow initialization based on model spin-up will be affected by these errors. By assimilating snow observation products into the LSM, the effects of these errors may be offset. Moreover, accurate observations of the spatial variation in snow water equivalent (SWE) are vital for other applications such as water resource managers and flood forecasters. Passive microwave (PM) observations of brightness temperature from remote sensing satellites may be used to estimate the snow water equivalent (or snow depth with knowledge of the snow density), and hence snow cover extent. However, in most every publication dealing with PM snow algorithms, assumptions directly, such as the degree to which the forest canopy conceals the underlying snow cover, or which vary considerably over time and space, such as snow density and snow crystal size. Algorithm sensitivity to differences in snow crystal size and in the fractional forest cover can be quite severe (Figure 1). In order for the remotely sensed SWE observations to be useful for climate modeling, for water resource managers and for flood forecasters, it is necessary to have a quantitative, rather than qualitative, estimate of the uncertainty. In data assimilation, the error estimate of the observational data is required so that the correct weighting between observations and model estimates may be applied. For most PM algorithms, the effects of snow grain size and the effects of forest cover are the main source of error in estimating SWE. The standard PM algorithm takes a form similar to the following: SWE = FC (18GHz - 37GHz) Where F is the forest cover percentage and C is a coefficient related to crystal size. In this investigation, we have better defined the systematic errors and the estimate of uncertainty associated with differences in forest cover and crystal size in PM algorithms. This is shown in Table 1 (for errors related to forest cover) and Table 2 (errors related to differences in snow crystal size from one area to another).

7. Table 1 FOREST COVER FRACTION SYSTEMATIC^ F FACTOR * Variance of F Percent ERROR (SE) % F=1+e o (  e) 2 e o >80 50  5 1.50 0.007 70-79 40  15 1.40 0.04 60-69 30  20 1.30 0.1 50-59 25  20 1.25 0.1 40-49 20  20 1.20 0.1 30-39 15  15 1.15 0.04 20-29 10  10 1.10 0.02 <20 5  5 1.05 0.003 ^ systematic error based on value of c as a constant *Fractional Forest Factor note - negative value denotes underestimation of snow water equivalent (swe) Quantifying the Uncertainty in Passive Microwave Snow Water Equivalent Observations

8. ALPINE NOV - 10  5 5.28 0.07 ALPINE DEC 05  5 4.56 0.05 ALPINE JAN 10  5 4.32 0.05 ALPINE FEB 15  10 3.60 0.29 ALPINE APR 20  10 3.60 0.29 ALPINE MAY 20  10 3.60 0.29 MARITIME NOV - 20  5 5.76 0.19 MARITIME DEC - 05  5 5.04 0.02 MARITIME JAN 10  5 4.32 0.05 MARITIME FEB 15  5 4.08 0.09 MARITIME MAR 15  5 4.08 0.09 MARITIME APR 15  5 4.08 0.04 EPHEMERAL DEC - 20  5 5.76 0.19 EPHEMERAL JAN - 20  5 5.76 0.19 EPHEMERAL FEB - 20  5 5.76 0.19 *systematic error based on value of c as a constant ^adj swe coef. is adjusted snow water equivalent coefficient note – negative value denotes underestimation of swe Table 2 (Cont’d) SNOW CLASS MONTH SYSTEMATIC* ADJ. SWE COEF.^ VARIANCE OF C ERROR (SE)% SWE= C (18-37) Quantifying the Uncertainty in Passive Microwave Snow Water Equivalent Observations

9. Figure 1 caption- Example of a large (2 mm in diameter) depth hoar crystal. These large crystals from where temperature and vapor gradients are quite large and are especially effective scatterers of microwave energy at 37 GHz. Quantifying the Uncertainty in Passive Microwave Snow Water Equivalent Observations