MICROWAVE RADIOMETER (MWR) OCEANIC INTEGRATED RAIN RATE ALGORITHM FOR AQUARIUS/SAC-D

Spencer Farrar1, Linwood Jones1, Sergio Masuelli2, and Juan-Cruz Gallio2

Central Florida Remote Sensing Laboratory (CFRSL)1

University of Central Florida Orlando, FL 32816-2450

Comision Nacionel De Actividades Espaciales (CONAE)2 Bonus Aires, Argentina

ABSTRACT

The Microwave Radiometer (MWR) flying on the Aquarius/SAC-D mission is a Dicke radiometer operating at 23.8 and 36.5 GHz that is developed by the Argentina Space Agency CONAE. This instrument will complement Aquarius (NASA's L-band radiometer /scatterometer) by providing simultaneous spatially collocated environmental measurements such as oceanic wind speed and rain rate. This paper describes the development of the pre-launch MWR rain rate algorithm using simulated MWR brightness temperatures from actual WindSat radiometer observations. WindSat provides high spatial resolution brightness temperatures that are spatially averaged to simulate the resolution of MWR. Also WindSat provides retrieved environmental parameters (EDR’s), which includes rain rate for developing the statistical regression algorithm. Examples of simulated MWR rain retrievals are presented.

Index Terms— MWR, WindSat, Integrated Rain Rate, rain rate algorithm, radiometer

1. INTRODUCTION

The Aquarius/SAC-D joint international science mission, between the National Aeronautics and Space Administration (NASA) and Argentina Space Agency, Comision Nacional De Actividades Espaciales (CONAE), will be launched on a polar orbiting sun-synchronous satellite in late 2010. The Aquarius/SAC-D mission is to provide measurements of the global sea surface salinity (SSS), which contributes to understanding climatic changes in the global water cycle and how these variations influence the general ocean circulation [1]. NASA’s instrument, Aquarius, is an L-band 3-beam pushbroom radiometer/scatterometer providing global observations of SSS every 7 days with 150 km resolution. Another instrument operating on Aquarius/SAC-D is CONAE’s Microwave Radiometer (MWR), a pushbroom three channel

Dicke radiometer operating at 23.8 GHz (H-Pol) and 36.5 GHz (V- & H-Pol). MWR is to complement Aquarius by providing simultaneous spatially collocated environmental measurements such as water vapor, cloud liquid water, surface wind speed, sea ice concentration, and rain rate. Flagging rain is important for both salinity retrieval and scientific reasons.

This paper describes the development of the pre-launch MWR rain rate algorithm using simulated MWR brightness temperatures from actual WindSat radiometer observations. WindSat is selected because it flies in a similar orbit and the MWR channels are a sub-set of the WindSat channels. The higher spatial resolution of WindSat is an advantage because the 23.8 and 36.5 GHz brightness temperatures (Tb) may be spatially averaged to simulate the larger instantaneous field of view (IFOV) of MWR pushbroom beams. Also since WindSat provides retrieved environmental parameter (EDR) rain rates that are associated with the simulated MWR Tb, these may be used for developing the empirical regression geophysical model functions (GMF) upon which the statistical retrieval algorithm is based. The current GMF is developed at the WindSat earth incident angle (EIA) of 52° over the latitude range of +10 to -30° for January and February, 2007.

2. SATELLITE INSTRUMENTS 2.1. Microwave Radiometer (MWR) The Microwave Radiometer (MWR) is a pushbroom three channel Dicke radiometer, 23.8 GHz (H-Pol) and 36.5 GHz (V- & H-Pol), with a swath of 380 km, approximately the same as Aquarius [2]. MWR has 16 beams, 8 forward-looking IFOVs, 36.5 GHz (V- & H-Pol), and 8 aft-looking 23.8 GHz (H-Pol). An illustration of MWR and Aquarius IFOVs is shown in Fig. 1. The 3 center beams viewing cross-track to the right are Aquarius’s IFOVs. The MWR looks forward (37 GHz dual pol beams) and aft (24 GHz H-pol beams). Because of mechanical constraints, the

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Fig. 1 MWR and Aquarius IFOV and measurement swaths. pushbroom beams are stacked in the elevation plane, which results in two incident angles of 52° and 58° for both forward and aft-looks. 2.2. WindSat

WindSat is a conical scanning polarimetric radiometer that was developed by the Naval Research Laboratory (NRL) and operates on the USAF Coriolis polar-orbiting satellite. WindSat comprises 22 channels operating at 5 frequencies: 6.8, 10.7, 18.7, 23.8, 37.0 GHz, of which the 10.7-, 18.7-, and 37.0-GHz channels are fully polarimetric (V/H, ±45, and left- & right-hand circular polarized). This conical scanning instrument has a swath of ~950 km with channels at EIA’s between 50°and 55° [3].

3. DATA DESCRIPTIONS

To simulate MWR rain rate retrievals, several data products were used: the WindSat’s Environmental Data Record (EDR) and Intermediate Data Record (IDR); National Center for Environmental Prediction’s (NCEP) Global Tropospheric Analysis; and Tropical Rain Measuring Mission (TRMM) Microwave Imager (TMI) 3A11 monthly rain rate product.

Two data products provided by WindSat were used for the simulation of MWR measurements, namely: 1) the EDR provided rain rate over oceans at 14 km resolution [4] and 2) the intermediate data record (IDR) provided the Tb’s at the native resolution of WindSat.

The radiative transfer model used was the NASA Global Precipitation Mission (GPM) inter-satellite calibration working group (X-Cal) consensus radiative transfer model (RTM) that utilized the NCEP Global Tropospheric Analysis data product, a gridded 1x1° at six hour intervals, with air temperature and relative humidity profiles, sea surface temperature, water vapor, and wind speed [5]. The TMI 3A11 product provided the atmospheric 0 C isotherm height over the oceans and was used to convert

EDR surface rain rate into integrated rain rate along the antenna’s path length [6].

4. MWR IRR SIMULATION

WindSat provides environmental (EDR) and brightness temperature (IDR) data products, which is used to derive the rain rate algorithm GMF, which relates MWR excess brightness temperature to integrated rain rate relationship. Since EDR and IDR resolutions are smaller than the MWR IFOV, we collocate and averaging WindSat data into the MWR beam footprints to simulate a MWR measurement pixel. For simplicity, the MWR IFOVs are approximated to be rectangular instead of elliptical, which does not significantly affect the average value of the MWR TB spatial average [7]. Figure 2 depicts simulated MWR rectangular footprint at EIA of 52° made up of smaller IDR TB footprints for MWR footprints 27.8x50.8 and 26.7x45.2 km for 37- & 23 GHz, respectively. This paper provides retrievals for an MWR footprint size at 52° but at the actual WindSat EIA of 53°.

5. EXCESS TB-to-IRR RELATIONSHIP

5.1. Excess TB-to-IRR Relationship

The pre-launch MWR rain rate algorithm is a statistical based retrieval that uses an empirical passive brightness temperature (TB) to rain rate relationship to derive the integrated rain rate over oceans. The observed brightness temperature (integrated along the slant path) is directly proportional to the path integrated rain rate (IRR) [8]. In order, to calculate the path IRR the path length of the rain must be determined by knowing the EIA of the satellite and the freezing height of the rain cell. The conversion of surface rain rate (SRR) (mm/hr) to IRR (km*mm/hr) is to multiply SRR by secant of the earth incidence angle and freezing height of the rain column.

Brightness temperatures over the calm oceans vary slightly for given EIA, frequency, polarization, SST, and constant atmospheric constituents; however, for disturbances over the oceans such as wind or rain can increase the brightness temperature. Utilizing this relationship of an excess brightness temperature due to rain is used to determine the rain rate, i.e., the difference between a rain free ocean TB and rainy ocean TB is an Excess TB ( TB).

TB = (Rainy TB) – (rain free TB) (1) Rain free TB is the brightness temperature used due to clear-air atmospheric absorption (water vapor and oxygen), surface wind speed, and sea surface temperature. Rainy TB is increased because of rain and columnar cloud liquid water (CLW) absorption within the atmosphere.

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Fig. 2 Simulated rectangular MWR footprints made up of WindSat’s native resolution brightness temperatures with actual -3 dB elliptical footprints superimposed. 5.2. Development of IRR Retrieval

The preliminary GMF for MWR is a statistical regression model using WindSat environmental (EDR) and brightness temperature (IDR) data. WindSat provides oceanic surface rain rate (SRR) (mm/hr). In order, to utilize the direct relationship between brightness temperature along the slant path and path integrated rain rate, SRR was converted to IRR using TMI 3A11freezing level data product. Fig. 3 shows the 5x5° 3A11 map interpolated into a higher resolution and was used to properly compute IRR. The boxed region is the +10 to -30°latitude region where the GMF is developed. As previously stated rain increases the TB over the oceans and so the X-Cal RTM was used to model rain free TB to obtain TB. After binning and averaging two months of IRR and TB, excess TB-to-IRR relationship for latitude bands +10° to-30° was created over the IRR range 0 to 250 km*mm/hr, and the GMF is shown in Fig. 4. Note that an

TB threshold of ~6 Kelvin has been applied to minimize false rain retrievals. Also note that each channel TB saturates and then decreases, which needs to be accommodated in future versions of the retrieval algorithm.

6. RESULTS

Using the 37 GHz H-Pol GMF to retrieve IRR, comparisons between the MWR IRR retrieval and WindSat IRR (considered as truth) are shown in Fig. 5 as a scatter plot with the green points representing bin-averaged IRR with respect to the truth. The results indicate good agreement up to ~95 km*mm/hr, after which the retrieval progressively underestimates the higher rain rates (red line is the perfect agreement). The corresponding results for 23 GHz H-Pol

Fig. 3 TMI 3A11 0°C Isotherm level/freezing height used to convert SRR to IRR

Fig. 4 Excess TB vs. IRR for all MWR channels and 37 GHz V-Pol are shown in Fig. 6, and the retrieval error (retrieved minus truth) is shown in Fig. 7 for all channels. Note in Fig. 7, for low rain rates, 0 to 50 km*mm/hr, the 37 GHz V & H channels perform better than the 23GHz H-Pol channel. However, for greater rain rates both frequencies at H-Pol performs significantly better than V-Pol as shown in Fig. 6. An image of a rain event is shown in Fig 8 for the 37GHz H-Pol retrieved IRR for two orbits. Applying a retrieval cutoff and a Gaussian fit to retrieval error, the mean and standard deviation are calculated in Table 1, where the cutoff for the V- & H-Pol channels were set at 50 and 95 km*mm/hr, respectively. Also Table 1 shows the mean for the latitude sub bands, ±10° and -10° to -30°.

7. CONCLUSION The MWR rain retrieval algorithm presents unique challenges due to the small number of radiometric channels and poor spatial footprint size relative to rain cell dimensions. Preliminary results of the MWR rain retrieval simulation indicate that for lower integrated rain rates

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Fig. 5 Preliminary MWR 37 GHz H-pol IRR retrieval performance.

Fig. 6 Bin averaged Retrieved IRR compared to EDR IRR (truth) for all MWR channels (0 to 50 km*mm/hr), all three channels are in reasonable agreement with WindSat IRR, and both H-Pol channels perform well up to 90 km*mm/hr. Thus it appears that MWR will be able to flag rain over the ocean, which is important to support Aquarius’s salinity measurements. Further work is required to complete the pre-launch retrieval algorithm, namely: 1) improve IRR retrievals for higher rain rates, 2) extend the GMFs to mid-latitudes, and 3) retrieve IRR for MWR footprint at EIA of 58°. However, these preliminary results are encouraging and we are confident that there will be a pre-launch rain rate algorithm available at the time of the Aquarius/SAC-D launch.

Fig. 7 Retrieval Error to make the retrieval agree with the Truth

Fig. 8 Comparison of WindSat IRR and MWR IRR Retrieval for 37 GHz H-Pol

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TABLE 1: Retrieval Error Mean/ Standard Deviation Latitude

Bands 23 H-Pol 37 H-Pol 37 V-Pol

10 to -30° -2.46 /7.75 -0.76 /3.73 -1.11 /4.20 10 to -10° -1.52 /7.65 -0.39 /4.18 -0.46 /4.51 -10 to -30° -3.47 /7.73 -1.09 /3.21 -1.70 /3.80

7. REFERENCES

[1] http://aquarius.gsfc.nasa.gov/ [2] http://www.conae.gov.ar/AQ_SAC-D_5thScienceMeet/3_G_ Lagerloef.pdf [3] P.W. Gaiser, "The WindSat Space Borne Polarimetric Microwave Radiometer: Sensor Description and Early Orbit Performance", IEEE Trans. on Geosci. and Remote Sensing, Vol. 42, no. 11, pp. 2347-2361, 2004 [4] M.H. Bettenhausen, "A Nonlinear Optimization Algorithm for WindSat Wind Vector Retrievals," IEEE Trans. on Geosci. and Remote Sensing, Vol. 44, no. 3, pp. 597-610, 2006.

[5] Kaushik Gopalan, “A Time-Varying Radiometric Bias Correction for the TRMM Microwave Imager”, in Electrical Engineering and Computer Science. Vol. PhD Orlando: UCF, 2008 [6] http://disc.sci.gsfc.nasa.gov/precipitation/documentation/ TRMM_README/TRMM_3A11_readme.shtml [7] Salman Khan, “Simulation of Brightness Temperatures for The Microwave Radiometer On The Aquarius/Sac-D Mission,” in Electrical Engineering and Computer Science. Vol. PhD Orlando: UCF, 2009 [8] Khalil Ali Ahmad, “Estimation of Oceanic Rainfall Using Passive and Active Measurements from SeaWinds Spaceborne Microwave Sensor,” in Electrical Engineering and Computer Science. Vol. PhD Orlando: UCF, 2007 Thanks to Peter Gaiser (NRL) for providing the WindSat data.

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