Development Of An Oceanic Rain Accumulation Product In Support Of Sea Surface Salinity Measurements From Aquarius/sac-d

Aquarius/SAC-D is a joint mission by National Aeronautics and Space Administration (NASA) and the Comision Nacional de Actividades Espaciales (CONAE), Argentine Space Agency. The satellite was launched in June 2011 and the prime remote sensing instrument is also named Aquarius (AQ). The main objective of this science program is to provide Sea Surface Salinity (SSS) maps of the global oceans every 7 days for understanding the Earth’s hydrologic cycle and for assessing long-term global climate change. The Aquarius instrument was built jointly by NASA’s Goddard Space Flight Center and the Jet Propulsion Laboratory. It is an active/passive L-band remote sensor that measures ocean brightness temperature (Tb) and radar backscatter, and these quantities are used to infer sea surface salinity. Other environmental parameters (e.g., sea surface temperature, wind speed and rain) also affect the microwave emitted radiance or brightness temperature. The SSS geophysical retrieval algorithm considers all these environmental parameters and makes the Tb corrections before retrieving SSS. Instantaneous rainfall can cause increase roughness that raises the ocean surface Tb. Further short term rain accumulation can produce a fresh water lens that floats on the ocean surface and dilutes the surface salinity. iv This thesis presents results of a study to develop an oceanic rain accumulation (RA) product that may be valuable to remote sensing engineers and algorithm developers and Aquarius scientists. The use of this RA product, along with in situ ocean salinity measurements from buoys, may be used to mitigate the effects of rain on the SSS retrieva

Microwave Radiometer Inter-Calibration: Algorithm Development and Application.

Microwave radiometer inter-calibration is an essential component of any effort to combine measurements from two or more radiometers into one dataset for scientific studies. One spaceborne instrument in low Earth orbit is not sufficient to perform long-term climate studies or to provide measurements more than twice per day at any given location on Earth. Measurements from several radiometers are necessary for analyses over extended temporal and spatial ranges. In order to combine the measurements, the radiometers need to be inter-calibrated due to the instruments having unique instrument designs and calibrations. Inter-calibration ensures that consistent scientific parameters are retrieved from the radiometers. The development of a cold end inter-calibration algorithm is presented. The algorithm makes use of vicarious cold calibration, along with the double difference method, to calculate calibration differences between radiometers. The performance of the algorithm is characterized using data from current conical scanning microwave radiometers. The vicarious cold calibration double difference is able to sufficiently account for design differences between two radiometers including frequency, earth incidence angle, and orbital characteristics. An estimate of the uncertainty in the inter-calibration algorithm is given as a result of potential errors in the geophysical inputs and improper accounting of seasonal and diurnal variability. The vicarious cold calibration double difference method is shown to be a valid and accurate inter-calibration algorithm. Results are compared with calibration differences calculated using alternate algorithms and sufficient agreement is attained. Inter-calibration is shown to be necessary for achieving consistency in retrieved scientific parameters by using the vicarious cold calibration double difference method to inter-calibrate two radiometers that are then used to derive rain accumulations. Inter-calibration results in a significant improvement in the rain accumulation agreement between the radiometers. This validates inter-calibration algorithm development and shows that it has a positive impact on achieving consistency in scientific parameter retrievals.PhDAtmospheric, Oceanic and Space SciencesUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/107078/1/rakro_1.pd

Oceanic rain rate estimates from the QuikSCAT Radiometer: A Global Precipitation Mission pathfinder

[1] The SeaWinds scatterometer, launched onboard the QuikSCAT satellite in 1999, measures global ocean vector winds. In addition to measuring radar backscatter, SeaWinds simultaneously measures the microwave brightness temperature of the atmosphere/surface, and this passive microwave measurement capability is known as the QuikSCAT Radiometer (QRad). This paper presents a QRad retrieval algorithm used to infer instantaneous oceanic rain rates. This statistical algorithm is trained using near-simultaneous observations of major rain events by QRad and the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI). Rain rate retrieval algorithm validation is presented through comparisons with independent rain measurements from the TMI 2A12 surface rain rates and the TRMM 3B42RT composite microwave and visible and infrared near-real time data product. Results demonstrate that QRad rain rate measurements are in good agreement with these independent microwave rain observations and superior to the visible/infrared rain estimates. Thus the QRad rain measurement time series is a valuable addition to the oceanic precipitation climatology that can be used to improve the diurnal estimation of the global rainfall, which is a goal for the future Global Precipitation Mission program. Moreover, the availability of QRad data will provide GPM users early access to learn to use less-precise rain measurements that will occur in the GPM era with the use of less-capable constellation satellites. Finally, these QRad rain estimates will be available in the planned data reprocessing (FY 2006) of QuikSCAT winds to improve the rain flagging of rain-contaminated oceanic wind vector retrievals

LMODEL: A satellite precipitation methodology using cloud development modeling. Part I: Algorithm construction and calibration

The Lagrangian Model (LMODEL) is a new multisensor satellite rainfall monitoring methodology based on the use of a conceptual cloud-development model that is driven by geostationary satellite imagery and is locally updated using microwave-based rainfall measurements from low earth-orbiting platforms. This paper describes the cloud development model and updating procedures; the companion paper presents model validation results. The model uses single-band thermal infrared geostationary satellite imagery to characterize cloud motion, growth, and dispersal at high spatial resolution (similar to 4 km). These inputs drive a simple, linear, semi-Lagrangian, conceptual cloud mass balance model, incorporating separate representations of convective and stratiform processes. The model is locally updated against microwave satellite data using a two-stage process that scales precipitable water fluxes into the model and then updates model states using a Kalman filter. Model calibration and updating employ an empirical rainfall collocation methodology designed to compensate for the effects of measurement time difference, geolocation error, cloud parallax, and rainfall shear