Alexander G. Fore

L-Band Passive and Active Microwave Geophysical Model Functions of Ocean Surface Winds and Applications to Aquarius Retrieval

The L-band passive and active microwave geophysical model functions (GMFs) of ocean surface winds from the Aquarius data are derived. The matchups of Aquarius data with the Special Sensor Microwave Imager (SSM/I) and National Centers for Environmental Prediction (NCEP) winds were performed and were binned as a function of wind speed and direction. The radar HH GMF is in good agreement with the PALSAR GMF. For wind speeds above 10 m·s-1, the L-band ocean backscatter shows positive upwind-crosswind (UC) asymmetry; however, the UC asymmetry becomes negative between about 3 and 8 m·s-1. The negative UC (NUC) asymmetry has not been observed in higher frequency (above C-band) GMFs for ASCAT or QuikSCAT. Unexpectedly, the NUC symmetry also appears in the L-band radiometer data. We find direction dependence in the Aquarius TBV, TBH, and third Stokes data with peak-to-peak modulations increasing from about a few tenths to 2 K in the range of 10-25- m·s-1 wind speed. The validity of the GMFs is tested through application to wind and salinity retrieval from Aquarius data using the combined active-passive algorithm. Error assessment using the triple collocation analyses of SSM/I, NCEP, and Aquarius winds indicates that the retrieved Aquarius wind speed accuracy is excellent, with a random error of about 0.75 m·s-1. The wind direction retrievals also appear reasonable and accurate above 10 m·s-1. The results of the error analysis indicate that the uncertainty of the GMFs for the wind speed correction of vertically polarized brightness temperatures is about 0.14 K for wind speed up to 10 m·s-1.

Point-Wise Wind Retrieval and Ambiguity Removal Improvements for the QuikSCAT Climatological Data Set

In this paper, we introduce a reprocessing of the entire SeaWinds on QuikSCAT mission. The goal of the reprocessing is to create a climate data record suitable for climate studies and to incorporate recent algorithm improvements. Three different levels of QuikSCAT data are produced at the Jet Propulsion Laboratory: L1B, geolocated, calibrated, backscatter measurements in chronological order by acquisition time; L2A, backscatter measurements binned into a geographical grid; and L2B, gridded ocean surface wind vectors. This reprocessing only changes the L2A and L2B data; we have not changed the L1B processing at all. We introduce new algorithms used in the L1B to L2A processing and in the L2A to L2B processing. After introducing our new algorithms, we show the validation studies performed to date, which include comparisons to numerical weather products, comparisons to buoy data sets, comparisons to other remote sensing instruments, and spectral considerations.

Passive and Active L-Band Microwave Observations and Modeling of Ocean Surface Winds

L-band microwave backscatter and brightness temperature of sea surfaces acquired using the Passive/Active L-band Sensor during the High Ocean Wind campaign are reported in terms of their dependence on ocean surface wind speed and direction. We find that the L-band VV, HH, and HV radar backscatter data increase by 6-7 dB from 5 to 25 m/s wind speed at a 45° incidence angle. The data suggest the validity of Phased Array type L-band Synthetic Aperture Radar (PALSAR) HH model function between 5 and 15 m/s wind speeds, but show that the extrapolation of PALSAR model at above 20 m/s wind speeds overpredicts A0 and a1 coefficients. There is wind direction dependence in the radar backscatter with about 4 dB differences between upwind and crosswind observations at 24 m/s wind speed for VV and HH. The passive brightness temperatures show about a 5-K change for TV and a 7-K change for TH for a wind speed increasing from 5 to 25 m/s. Circle flight data suggest a wind direction response of about 1-2 K in TV and TH at 14 and 24 m/s wind speeds. The L-band microwave data show excellent linear correlation with the surface wind speed with a correlation better than 0.95. The results support the use of L-band radar data for estimating the wind-driven excess brightness temperature of sea surfaces. The data also support the applications of L-band microwave signals for high-resolution (kilometer scale) observation of ocean surface winds under high wind conditions (10-28 m/s).

Validation of Aquarius sea surface salinity with in situ measurements from Argo floats and moored buoys

We validate sea surface salinity (SSS) retrieved from Aquarius instrument on SAC-D satellite with in situ measurements by Argo floats and moored buoy arrays. We assess the error structure of three Aquarius SSS products: the standard product processed by Aquarius Data Processing System (ADPS) and two data sets produced at the Jet Propulsion Laboratory (JPL): the Combined Active-Passive algorithm with and without rain correction, CAP and CAP_RC, respectively. We examine the effect of various filters to prevent unreliable point retrievals from entering Level 3 averaging, such as land or ice contamination, radio frequency interference (RFI), and cold water. Our analyses show that Aquarius SSS agrees well with Argo in a monthly average sense between 40°S and 40°N except in the Eastern Pacific Fresh Pool and Amazon River outflow. Buoy data within these regions show excellent agreement with Aquarius but have discrepancies with the Argo gridded products. Possible reasons include strong near-surface stratification and sampling problems in Argo in regions with significant western boundary currents. We observe large root-mean-square (RMS) difference and systematic negative bias between ADPS and Argo in the tropical Indian Ocean and along the Southern Pacific Convergence Zone. Excluding these regions removes the suspicious seasonal peak in the monthly RMS difference between the Aquarius SSS products and Argo. Between 40°S and 40°N, the RMS difference for CAP is less than 0.22 PSU for all 28 months, CAP_RC has essentially met the monthly 0.2 PSU accuracy requirement, while that for ADPS fluctuates between 0.22 and 0.3 PSU.

UAVSAR Polarimetric Calibration

Uninhabited aerial vehicle synthetic aperture radar (UAVSAR) is a reconfigurable polarimetric L-band SAR that operates in quad-polarization mode and is specifically designed to acquire airborne repeat-track SAR data for interferometric measurements. In this paper, we present details of the UAVSAR radar performance, the radiometric calibration, and the polarimetric calibration. For the radiometric calibration, we employ an array of trihedral corner reflectors, as well as distributed targets. We show that UAVSAR is a well-calibrated SAR system for polarimetric applications, with absolute radiometric calibration bias better than 1 dB, residual root-mean-square (RMS) errors of ~0.7 dB, and RMS phase errors ~5.3°. For the polarimetric calibration, we have evaluated the methods of Quegan and Ainsworth et al. for crosstalk calibration and find that the method of Quegan gives crosstalk estimates that depend on target type, whereas the method of Ainsworth et al. gives more stable crosstalk estimates. We find that both methods estimate leakage of the copolarizations into the cross-polarizations to be on the order of -30 dB.

Combined Active/Passive Retrievals of Ocean Vector Wind and Sea Surface Salinity With SMAP

In this paper, we introduce the combined active/passive (CAP) data product for the Soil Moisture Active Passive mission. We develop the algorithms for a radiometer-only salinity product, a radar-only vector wind product, and a CAP vector wind and salinity product. We show that the performance of the radiometer-only salinity product nears but is still inferior to the Aquarius salinity accuracy performance when aggregated on a monthly timescale. Then, we show that the radar-only vector wind product has reasonable accuracy away from the nadir track while suffering from inadequate measurement geometry in the middle of the swath. Finally, we demonstrate that the CAP salinity and vector wind performance is superior to individual algorithms and provides wind vectors nearly as good as RapidScat for low-to-moderate winds and possibly superior to traditional scatterometers for wind speeds larger than 12.5 m/s.

Optimized Tropical Cyclone Winds From QuikSCAT: A Neural Network Approach

We have developed a neural network technique for retrieving accurate 12.5-km resolution wind speeds from Ku-band scatterometer measurements in tropical cyclone conditions including typical rain events in such storms. The method was shown to retrieve accurate wind speeds up to 40 m/s when compared with aircraft reconnaissance data, including GPS dropwindsondes and Stepped-Frequency Microwave Radiometer surface wind speed measurements, and when compared to global best track maximum wind speeds. Wind directions were unchanged from the current (version 3) Jet Propulsion Laboratory (JPL) global wind vector product. The technique removes positive biases with respect to best track winds in the developing phase of tropical cyclones that occurred in the nominal (version 2) JPL QuikSCAT product. The new technique also reduces negative biases with respect to best track wind speeds that occurred in the nominal product (both versions 2 and 3) during the most extreme period of the lifetime of intense storms. The wind regime with the most notable improvement is 20-40 m/s (40-80 kn), with more modest improvement for higher winds and the improvement at lower winds comparable to that achieved previously by the version 3 JPL global rain-corrected product. The net effect of all the wind speed improvements is a much better measurement of storm intensity over time in the new product than what has been previously available. When compared with speed data from aircraft flights in Atlantic hurricanes, the new product exhibited a 1-2-m/s positive overall bias and a 3-m/s mean absolute error. The random error and systematic positive bias in the new scatterometer wind product is similar to that of the Hurricane Research Division H*WIND analyses when aircraft data are available for assimilation. This similarity may be explained by the fact that H*WIND data are used as ground truth to fit the coefficients used by the new technique to map radar measurements to wind speed. The fact that H*WIND was designed to match maximum winds while preserving radial symmetry may explain the overall positive biases that we observe in both H*WIND and the new scatterometer wind product which compared to aircraft reconnaissance data. The new scatterometer product could also be inheriting systematic biases in the presence of rain from H*WIND. Under the most extreme rain conditions, the radar signal from the surface can be lost. In such cases, the technique makes use of measurements in the 87.5-km region comprising the 7

Estimation of Sea Surface Roughness Effects in Microwave Radiometric Measurements of Salinity Using Reflected Global Navigation Satellite System Signals

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Aquarius Wind Speed Products: Algorithms and Validation

7 neighboring cells around the target 12.5-km wind vector cell. In so doing, we sacrifice resolution in cases where the highest resolution region has no useful measurements. Even so, the most extreme rain conditions can result in reduced accuracy. The new technique has been used to retrieve wind fields for every tropical cyclone of tropical storm force or above that has been observed by QuikSCAT during the period of time from October 1999 to November 2009. The resulting data set has been made available online for use by the tropical cyclone research community.

SMAP L-Band Passive Microwave Observations of Ocean Surface Wind During Severe Storms

In February-March 2009, an airborne field campaign was conducted using the Passive Active L- and S-band (PALS) microwave sensor and the Ku-band Polarimetric Scatterometer to collect measurements of brightness temperature and near-surface wind speeds. Flights were conducted over a region of expected high-speed winds in the Atlantic Ocean, for the purposes of algorithm development for sea surface salinity (SSS) retrievals. Wind speeds encountered during the March 2, 2009, flight ranged from 5 to 25 m/s. The Global Positioning System (GPS) delay mapping receiver from the National Aeronautics and Space Administration (NASA) Langley Research Center was also flown to collect GPS signals reflected from the ocean surface and generate postcorrelation power-versus-delay measurements. These data were used to estimate ocean surface roughness. These estimates were found to be strongly correlated with PALS-measured brightness temperature. Initial results suggest that reflected GPS measurements made using small low-power instruments can be used to correct the roughness effects in radiometer brightness temperature measurements to retrieve accurate SSS.