Remote Sensing of Water Vapor Over Land Using the Advanced ...

Remote Sensing of Water Vapor Over Land Using the Advanced ...

Validation of Microwave Moisture Retrievals Over Land Presented by : Matthew J. Nielsen Cooperative Institute for Research in the Atmosphere Research Scope Attempt to estimate water vapor over land Created C1DOE retrieval (used

AMSU data) Examined analytical Jacobian Validated retrieval with radiosondes and GPS CSU/CIRA Matthew Nielsen Cooperative Research Program 2nd Annual Science Symposium/Satellite Calibration & Validation 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 CSU/CIRA Matthew Nielsen Cooperative Research Program 2nd Annual Science Symposium/Satellite Calibration & Validation Cost function The cost function used in the C1DOE is given by:

T 1 T 1

x x S x x y F x

S y y from F x the a a The first term isa a penalty for deviating 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. CSU/CIRA Matthew Nielsen Cooperative Research Program 2nd Annual Science Symposium/Satellite Calibration & Validation 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 CSU/CIRA Matthew Nielsen Cooperative Research Program 2nd Annual Science Symposium/Satellite Calibration & Validation Data flow CSU/CIRA Matthew Nielsen Cooperative Research Program 2nd Annual Science Symposium/Satellite Calibration & Validation

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) CSU/CIRA Matthew Nielsen Cooperative Research Program 2nd Annual Science Symposium/Satellite Calibration & Validation

AMSU-B Channelization Ch. # Center freq. of channel (GHz) No. of pass bands Bandwidth per passband (MHz)

NETTT Polarization angle 16 17 18 19 20 89.0 150.0 183.311.00

183.313.00 183.317.00 2 2 2 2 2 1000 1000 500 1000 2000

0.37 0.84 1.06 0.70 0.60 90- 90- 90- 90- 90- CSU/CIRA Matthew Nielsen

Cooperative Research Program 2nd Annual Science Symposium/Satellite Calibration & Validation 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 NET)

CSU/CIRA Matthew Nielsen Cooperative Research Program 2nd Annual Science Symposium/Satellite Calibration & Validation AMSU APC (cont.) CSU/CIRA Matthew Nielsen Cooperative Research Program 2nd Annual Science Symposium/Satellite Calibration & Validation 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 CSU/CIRA Matthew Nielsen Cooperative Research Program 2nd Annual Science Symposium/Satellite Calibration & Validation CSU/CIRA Matthew Nielsen Cooperative Research Program 2nd Annual Science Symposium/Satellite Calibration & Validation CSU/CIRA Matthew Nielsen Cooperative Research Program 2nd Annual Science Symposium/Satellite Calibration & Validation

CSU/CIRA Matthew Nielsen Cooperative Research Program 2nd Annual Science Symposium/Satellite Calibration & Validation CSU/CIRA Matthew Nielsen Cooperative Research Program 2nd Annual Science Symposium/Satellite Calibration & Validation CSU/CIRA Matthew Nielsen Cooperative Research Program 2nd Annual Science Symposium/Satellite Calibration & Validation CSU/CIRA Matthew Nielsen

Cooperative Research Program 2nd Annual Science Symposium/Satellite Calibration & Validation 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 CSU/CIRA Matthew Nielsen Cooperative Research Program 2nd Annual Science Symposium/Satellite Calibration & Validation 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 CSU/CIRA Matthew Nielsen Cooperative Research Program 2nd Annual Science Symposium/Satellite Calibration & Validation CSU/CIRA Matthew Nielsen Cooperative Research Program 2nd Annual Science Symposium/Satellite Calibration & Validation CSU/CIRA Matthew Nielsen

Cooperative Research Program 2nd Annual Science Symposium/Satellite Calibration & Validation CSU/CIRA Matthew Nielsen Cooperative Research Program 2nd Annual Science Symposium/Satellite Calibration & Validation CSU/CIRA Matthew Nielsen Cooperative Research Program 2nd Annual Science Symposium/Satellite Calibration & Validation CSU/CIRA Matthew Nielsen Cooperative Research Program 2nd Annual Science Symposium/Satellite Calibration & Validation

CSU/CIRA Matthew Nielsen Cooperative Research Program 2nd Annual Science Symposium/Satellite Calibration & Validation Conclusions Antenna pattern correction fixed a consistent

~3 K bias from observed Tbs 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 CSU/CIRA Matthew Nielsen Cooperative Research Program 2nd Annual Science Symposium/Satellite Calibration & Validation

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 CSU/CIRA Matthew Nielsen Cooperative Research Program 2nd Annual Science Symposium/Satellite Calibration & Validation

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