Validation of Microwave Moisture Retrievals Over Land

1. Validation of Microwave Moisture Retrievals Over Land Presented by : Matthew J. Nielsen Cooperative Institute for Research in the Atmosphere

2. Research Scope • Attempt to estimate water vapor over land • Created C1DOE retrieval (used AMSU data) • Examined analytical Jacobian • Validated retrieval with radiosondes and GPS

3. C1DOE retrieval • Uses Optimal Estimation to produce layer T, skin T, emissivity, TPW, and layer q • Layer information calculated at 100, 200, 300, 500, 700, 850, and 1000 mb. • Emissivities calculated at 23, 31, 50, 89, and 150 GHz

4. Cost function • The cost function used in the C1DOE is given by: • The first term is a penalty for deviating from the first guess (first guess and a priori are equivalent in this retrieval). This limits the outcome to only physical solutions. • The second term is a penalty for deviations of the simulated radiances from the forward model output. This is a way to constrain the forward model and observational errors.

5. First guess data • AGRMET: surface temperature first guess from three hour average data • MEM: emissivity first guess at all five frequencies • Radiosondes: temperature and moisture profile first guess

6. Data flow

7. AMSU • Data came from the Advanced Microwave Sounding Unit (AMSU) • 20 channel microwave radiometer • Ch. 1-15 used for temperature • (AMSU-A) • Ch. 16-20 used for water vapor (AMSU-B)

8. Ch. # Center freq. of channel (GHz) No. of pass bands Bandwidth per passband (MHz) NEΔT Polarization angle 16 89.0 2 1000 0.37 90-θ 17 150.0 2 1000 0.84 90-θ 18 183.31±1.00 2 500 1.06 90-θ 19 183.31±3.00 2 1000 0.70 90-θ 20 183.31±7.00 2 2000 0.60 90-θ AMSU-B Channelization

9. AMSU-B Antenna Pattern Correction • AMSU-B mainbeam only receives ~95% of total power • 5% comes from Earth, cold space, and satellite • Sidelobe contamination can cause bias up to 3 or 4 K in retrieved brightness temperatures (corresponds to values up to 4x the NET)

10. AMSU APC (cont.)

11. Analytical Jacobian • Defined as a derivative of the forward model with respect to the state vector parameters • Important because it provides information on sensitivity of forward model to changes in state vector • Shows performance of each channel, along with denoting which channels have signal and which do not • Good for channelization and retrieval setup

18. Retrieval configuration • Retrieval was run with highly accurate first guess in order to detect bias • Data was from September 21-September 30, 2003 • Radiosonde match-up dataset created (50 km and two-hour window) with 555 data points • GPS match-up dataset created (30 km and 30 min window) with 26 data points

19. Validation • GPS calculations of TPW considered highly accurate (within 1mm) • TPW calculated from a “tropospheric wet delay” • Ground receivers are sent signals from satellites to calculate delay

26. Conclusions • Antenna pattern correction fixed a consistent ~3 K bias from observed Tb’s • Jacobian illustrated where retrieval did well and where it provided little information. Also highlighted the channels that were best suited to retrieve water vapor • Retrieval bias detection showed issues near coastlines due to poor first guess and ocean contamination • GPS validation yet to be satisfactory due to dataset constraints and coastline issues

27. Future work • Cloud liquid and ice module to be added • Need improved emissivity first guess Add soil moisture module • Explore better covariance matrix options • Provide water vapor and temperature first guess from GDAS (better spatial coverage and able to be performed in real time) • Validate TPW with increased # of GPS stations • Will be used in CloudSat project