A NON-RAINING 1DVAR RETRIEVAL FOR GMI

DAVID DUNCAN

JCSDA COLLOQUIUM

7/30/15

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)

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’

From Imaoka et al. (2010)

Wind effect on Tb (RSS emissivity model)

NON-RAINING PARAMETERS AND GMI

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-xa)TSa-1(x-xa) + [y-f(x,b)]TSy

-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:

Sx = (KTSy-1K + Sa

-1)-1

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

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.

Mean10m Wind

Std DevWind

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

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 Sy matrix in which there are no channel error covariances

Sa determination is easier, since that can be taken from a model

Sy is necessarily different for every sensor, every forward model used!

How to get a ‘real’ Sy matrix?

The approach:

Run both the simplified forward model of the retrieval AND the fullest forward model possible, then analyze the difference:

(TbS, Simplified – TbO) - (TbS, Full – TbO) = TbS, Full – TbS, Simplified

3. CHANNEL ERRORS

Full

Simplified

3. CHANNEL ERRORS

A good retrieval needs a good Jacobian (δTb/δx), which stems from Sy

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 ECFull 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 Sy is formed!

RESULTSGLOBAL IMAGES—NON-RAINING PARAMETERS

LWP [mm]

TPW [mm] Wind [m/s]