Wenqing Tang

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.

Satellite and In Situ Salinity: Understanding Near-Surface Stratification and Subfootprint Variability

Remote sensing of salinity using satellite-mounted microwave radiometers provides new perspectives for studying ocean dynamics and the global hydrological cycle. Calibration and validation of these measurements is challenging because satellite and in situ methods measure salinity differently. Microwave radiometers measure the salinity in the top few centimeters of the ocean, whereas most in situ observations are reported below a depth of a few meters. Additionally, satellites measure salinity as a spatial average over an area of about 100 × 100 km 2 . In contrast, in situ sensors provide pointwise measurements at the location of the sensor. Thus, the presence of vertical gradients in, and horizontal variability of, sea surface salinity complicates comparison of satellite and in situ measurements. This paper synthesizes present knowledge of the magnitude and the processes that contribute to the formation and evolution of vertical and horizontal variability in near-surface salinity. Rainfall, freshwater plumes, and evaporation can generate vertical gradients of salinity, and in some cases these gradients can be large enough to affect validation of satellite measurements. Similarly, mesoscale to submesoscale processes can lead to horizontal variability that can also affect comparisons of satellite data to in situ data. Comparisons between satellite and in situ salinity measurements must take into account both vertical stratification and horizontal variability.

Aquarius geophysical model function and combined active passive algorithm for ocean surface salinity and wind retrieval

This paper describes the updated Combined Active-Passive (CAP) retrieval algorithm for simultaneous retrieval of surface salinity and wind from Aquarius brightness temperature and radar backscatter. Unlike the algorithm developed by Remote Sensing Systems (RSS), implemented in the Aquarius Data Processing System (ADPS) to produce Aquarius standard products, the Jet Propulsion Laboratorys CAP algorithm does not require monthly climatology SSS maps for the salinity retrieval. Furthermore, the ADPS-RSS algorithm fully uses the National Center for Environmental Predictions (NCEP) wind for data correction, while the CAP algorithm uses the NCEP wind only as a constraint. The major updates to the CAP algorithm include the galactic reflection correction, Faraday rotation, Antenna Pattern Correction, and geophysical model functions of wind or wave impacts. Recognizing the limitation of geometric optics scattering, we improve the modeling of the reflection of galactic radiation; the results are better salinity accuracy and significantly reduced ascending-descending bias. We assess the accuracy of CAPs salinity by comparison with ARGO monthly gridded salinity products provided by the Asia-Pacific Data-Research Center (APDRC) and Japan Agency for Marine-Earth Science and Technology (JAMSTEC). The RMS differences between Aquarius CAP and APDRCs or JAMSTECs ARGO salinities are less than 0.2 psu for most parts of the ocean, except for the regions in the Intertropical Convergence Zone, near the outflow of major rivers and at high latitudes.

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.

Uncertainty of Aquarius sea surface salinity retrieved under rainy conditions and its implication on the water cycle study

The uncertainty of Aquarius sea surface salinity (SSS) retrieved under rain is assessed. Rain not only has instantaneous impact on SSS but also interferes with the microwave remote sensing signals, making the task to retrieve SSS under rainy conditions difficult. A rain correction model is developed based on analysis of the L-band radiometer/scatterometer residual signals after accounting for roughness due to wind and flat surface emissivity. The combined active passive algorithm is used to retrieve SSS in parallel with (CAP_RC) or without rain correction (CAP). The CAP bias against individual ARGO floats increases with rain rate with slope of −0.14 PSU per mm h−1, which reduced to near zero in CAP_RC. On the global monthly basis, CAP_RC is about 0.03 PSU higher than CAP. RMSD against ARGO is slightly smaller for CAP_RC than CAP. Regional biases are examined in areas with frequent rain events. As expected, results show that ΔSSS (CAP_RC-CAP) is highly correlated with the seasonal precipitation pattern, reaching about 0.2–0.3 PSU under heavy rain. However, ΔSSS shows no correlation with the difference pattern between ARGO and CAP or CAP_RC. This, along with regional analyses, suggests that the difference between ARGO and Aquarius SSS is likely caused by the different spatial and temporal sampling, in addition to near surface stratification depicted by radiometer and ARGO at different depths. The effect of ΔSSS on water cycle in terms of mixed-layer salt storage tendency is about 10% in areas where evaporation-minus-precipitation is the dominant process driving the variability of near surface salinity.

Aquarius Wind Speed Products: Algorithms and Validation

This paper introduces and validates the Aquarius scatterometer-only wind speed algorithm and the combined active passive (CAP) wind speed products. The scatterometer-only algorithm uses the co-polarized radar cross-section to determine the ocean surface wind speed with a maximum-likelihood estimator approach while the CAP algorithm uses both the scatterometer and radiometer channels to achieve a simultaneous ocean vector wind and sea surface salinity retrieval. We discuss complications in the speed retrieval due to the shape of the scatterometer model function at L-band and develop mitigation strategies. We find the performance of the Aquarius scatterometer-only wind speed is better than 1.00 ms-1, with best performance for low wind speeds and increasing noise levels as the wind speed increases. The CAP wind speed product is significantly better than the scatterometer-only due to the inclusion of passive measurements and achieves 0.70 ms-1 root-mean-square error.

Power density of ocean surface wind from international scatterometer tandem missions

For 6 months between April and October 2003, two identical scatterometers flew in tandem. Their observations demonstrate the need for more than one scatterometer in the polar orbit to include sufficient temporal variability and reduce aliasing of ocean surface wind‐stress measurements required for applications such as estimating electricity generation potential and ocean–atmosphere gas exchange. The energy deficiency over a 12‐h period, evident in the data from one scatterometer, is eliminated with the additional scatterometer. The missions in tandem allow an improved understanding of the diurnal variability from coastal regions to the open ocean. The power density distributions were found to be very different at the different sampling times of the two satellites. Two scatterometers will be launched by India and China in the next few years and will fly in tandem with the scatterometers of the USA and Europe, which are already in operation. The potential improvement in the coverage of ocean wind stress by this constellation is analysed and discussed. The constellation is found to meet the 6‐hourly revisit requirement of operational weather forecasting over most of the ocean.

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

The L-band passive microwave data from the Soil Moisture Active Passive (SMAP) observatory are investigated for remote sensing of ocean surface winds during severe storms. The surface winds of Joaquin derived from the real-time analysis of the Center for Advanced Data Assimilation and Predictability Techniques at Penn State support the linear extrapolation of the Aquarius and SMAP geophysical model functions (GMFs) to hurricane force winds. We apply the SMAP and Aquarius GMFs to the retrieval of ocean surface wind vectors from the SMAP radiometer data to take advantage of SMAPs two-look geometry. The SMAP radiometer winds are compared with the winds from other satellites and numerical weather models for validation. The root-mean-square difference (RMSD) with WindSat or Special Sensor Microwave Imager/Sounder is 1.7 m/s below 20-m/s wind speeds. The RMSD with the European Center for Medium-Range Weather Forecasts direction is 18° for wind speeds between 12 and 30 m/s. We find that the correlation is sufficiently high between the maximum wind speeds retrieved by SMAP with a 60-km resolution and the best track peak winds estimated by the National Hurricane Center and the Joint Typhoon Warning Center to allow them to be estimated by SMAP with a correlation coefficient of 0.8 and an underestimation by 8%-18% on average, which is likely due to the effects of spatial averaging. There is also a good agreement with the airborne Stepped-Frequency Radiometer wind speeds with an RMSD of 4.6 m/s for wind speeds in the range of 20-40 m/s.

Rain-Induced Near Surface Salinity Stratification and Rain Roughness Correction for Aquarius SSS Retrieval

The effect of rain on surface salinity stratification is analyzed to develop a rain roughness correction scheme to reduce the uncertainty of Aquarius sea surface salinity (SSS) retrieved under rainy conditions. Rain freshwater inputs may cause large discrepancies in salinity measured by Aquarius at 1-2 cm within the surface and the calibration reference SSS from HYCOM (SSSHYCOM) a few meters below the surface. We used the rain impact model (RIM) to adjust SSSHYCOM to reflect near surface salinity stratification caused by freshwater inputs accumulated from rain events that occurred over the past 24 h before Aquarius measurements (SSSRIM). When calibrated with SSSRIM, the residuals, i.e., the difference between measured and model predicted brightness temperature TB, are considered as rain-induced roughness. It was found that rain-induced roughness is larger at lower wind speeds, and decreases as wind increases. The Combined Active Passive algorithm is used to retrieve SSS with (SSSCAP_RC) or without (SSSCAP) rain roughness correction. We find that the simultaneously retrieved wind speed with rain roughness correction has significantly improved agreement with the NCEP wind speed with the rain-dependent bias reduced, self-justifying our rain correction approach. SSS retrieved is validated with salinity measured by drifters at a depth of 45 cm. The difference between satellite retrieved and in situ salinity increases with rain rate. With rain-induced roughness accounted for, the difference between satellite retrieval and drifter increases with rain rate with slope of -0.184 psu (mm h-1)-1, representing the salinity stratification between the two depths (1-2 cm versus 45 cm).

Detection of diurnal cycle of ocean surface wind from space-based observations

We derive the diurnal cycle of ocean surface vector wind from three contemporary space-based wind sensors: OSCAT, WindSAT, and ASCAT, assuming the diurnal signal is embedded in the deviation from the daily mean as measured by ascending and descending passes of each sensor. A Monte Carlo simulation technique is used to estimate uncertainties. Strong diurnal signals are found in coastal regions and tropical oceans. Their geographical and seasonal variations are described.