1. A NON-RAINING 1DVAR RETRIEVAL FOR GMI DAVID DUNCAN JCSDA COLLOQUIUM 7/30/15

2.  The GPM satellite is in a non- sun-synchronous LEO at 65° inclination  Together, GMI and the Dual- frequency Precipitation Radar provide an active-passive combination designed for measuring light to heavy precipitation, rain and snow  GMI is nearly identical to TMI (17 functional years—don’t change it!) but with additional high frequency channels  GMI is the calibration standard for the GPM constellation GPM MICROWAVE IMAGER (GMI)

3. NON-RAINING PARAMETERS AND GMI  Why develop a non-raining retrieval for GMI? Isn’t GPM tailor-made to sense rain and snow?  Other imager algorithms are sensitive to assumption of water vapor distribution—so I try to solve for it  Using GMI as an ‘ideal’ sensor to develop code and methods that can be applied to other sensors (AMSR2, SSMIS, etc.)  GMI is one of the best absolute-calibrated sensors in orbit (according to X-Cal), thus a good test bed for a new approach  ‘Non-raining parameters’ means total precipitable water (TPW), wind speed, and cloud liquid water path (LWP) over ocean—also called ‘Ocean Suite’

4. From Imaoka et al. (2010) Wind effect on Tb (RSS emissivity model) NON-RAINING PARAMETERS AND GMI

5. OPTIMAL ESTIMATION / 1DVAR  Through iteration, the cost function is minimized to find an optimal solution to the inversion, given the measurement vector (Tbs) and a priori knowledge of the environment and the measurement: Φ = (x-x a ) T S a -1 (x-x a ) + [y-f(x,b)] T S y -1 [y-f(x,b)].  Iterate to find state vector that minimizes the difference between observed Tbs and simulated Tbs  One huge advantage to 1DVAR is output error diagnostics (posteriori errors) that come out of the formalism: S x = (K T S y -1 K + S a -1 ) -1

6. To execute a 1DVAR retrieval, you need: 1.Prior knowledge of the environment 2.A good forward model—a method of modeling the atmosphere and surface to simulate what the satellite sees  Radiative transfer model  Surface emissivity model  Assumptions about the atmospheric profiles of water vapor, cloud water, etc. 3.Knowledge of channel errors and their covariances

7. 1. PRIOR KNOWLEDGE OF THE ENVIRONMENT From analysis of ECMWF Interim Reanalysis 6-hourly data, LUTs consist of means/variances/covariances of  10m winds  EOFs of water vapor, broken up by SST. Mean 10m Wind Std Dev Wind

8. 2. FORWARD MODEL  NOAA’s Community Radiative Transfer Model (CRTM v2.1.3)  Emission/absorption only—not a bad assumption in microwave unless rain or lots of ice present  User has option of FASTEM5 (or FASTEM4) or RSS ocean emissivity model  16 vertical layers defined by pressure  Liquid water cloud set at 850-750mb  Ice cloud may be added as well, and scattering turned on, but no skill in retrieving IWP currently  Reynolds OI SST used as base temperature, though retrieval shows some skill at retrieving SST if it’s allowed to vary  SST used as index for climatological mean water vapor profile from ERA-Int  10m wind, CLWP, SST and 3 EOFs of water vapor are retrieved parameters

9. 3. CHANNEL ERRORS  Determining forward model error is an omnipresent issue for satellite retrievals  Most retrievals make up numbers, or at best assume a diagonal S y matrix in which there are no channel error covariances  S a determination is easier, since that can be taken from a model  S y is necessarily different for every sensor, every forward model used!  How to get a ‘real’ S y matrix? The approach: Run both the simplified forward model of the retrieval AND the fullest forward model possible, then analyze the difference: (Tb S, Simplified – Tb O ) - (Tb S, Full – Tb O ) = Tb S, Full – Tb S, Simplified

10. 3. CHANNEL ERRORS Full Simplified

11. 3. CHANNEL ERRORS  A good retrieval needs a good Jacobian (δT b /δx), which stems from S y  The approach takes into account all simplifying assumptions: no scattering, no ice, fewer levels, etc.  Attempted to screen out rain, sea ice, RFI-contaminated pixels  Even EC Full has trouble, especially at middle frequencies, though other channels in this analysis largely matched Xcal results  What about channel biases?  Success of retrieval depends heavily upon how S y is formed!

12. RESULTS GLOBAL IMAGES—NON-RAINING PARAMETERS LWP [mm] TPW [mm]Wind [m/s]