Simultaneous Wind and Rain Retrieval using Seawinds Data

The Sea Winds scatterometer is designed primarily to retrieve winds over the ocean. Since the deployment of Sea Winds on QuikSCAT in 1999, rain corruption in wind measurements has been recognized as one of the largest contributors to wind retrieval error. This paper presents a new estimation method that incorporates rain effects into Sea Winds wind retrieval. The new method simultaneously retrieves wind and rain, giving improved wind estimates in rain-corrupted areas and providing Sea Winds-derived estimates of the rain rate. The simultaneous wind/rain estimation method works especially well in the sweet spot of Sea Winds\u27 swath. On the outer-beam edges of the swath, rain estimation is not possible. This area, however, is only a small fraction of the total data. Wind speeds from simultaneous wind/rain retrieval are nearly unbiased, while the wind-only wind speeds become increasingly biased with rain rate. A synoptic example demonstrates that the new method has the capability of visually reducing the error due to rain while producing a consistent (yet somewhat noisy) estimate of the rain rate

An Improved Ocean Vector Winds Retrieval Approach Using C- And Ku-band Scatterometer And Multi-frequency Microwave Radiometer Measurements

This dissertation will specifically address the issue of improving the quality of satellite scatterometer retrieved ocean surface vector winds (OVW), especially in the presence of strong rain associated with tropical cyclones. A novel active/passive OVW retrieval algorithm is developed that corrects Ku-band scatterometer measurements for rain effects and then uses them to retrieve accurate OVW. The rain correction procedure makes use of independent information available from collocated multi-frequency passive microwave observations provided by a companion sensor and also from simultaneous C-band scatterometer measurements. The synergy of these active and passive measurements enables improved correction for rain effects, which enhances the utility of Ku-band scatterometer measurements in extreme wind events. The OVW retrieval algorithm is based on the next generation instrument conceptual design for future US scatterometers, i.e. the Dual Frequency Scatterometer (DFS) developed by NASA’s Jet Propulsion Laboratory. Under this dissertation research, an end-to-end computer simulation was developed to evaluate the performance of this active/passive technique for retrieving hurricane force winds in the presence of intense rain. High-resolution hurricane wind and precipitation fields were simulated for several scenes of Hurricane Isabel in 2003 using the Weather Research and Forecasting (WRF) Model. Using these numerical weather model environmental fields, active/passive measurements were simulated for instruments proposed for the Global Change Observation Mission- Water Cycle (GCOM-W2) satellite series planned by the Japanese Aerospace Exploration Agency. Further, the quality of the simulation was evaluated using actual hurricane measurements from the Advanced Microwave Scanning Radiometer and iv SeaWinds scatterometer onboard the Advanced Earth Observing Satellite-II (ADEOS-II). The analysis of these satellite data provided confidence in the capability of the simulation to generate realistic active/passive measurements at the top of the atmosphere. Results are very encouraging, and they show that the new algorithm can retrieve accurate ocean surface wind speeds in realistic hurricane conditions using the rain corrected Ku-band scatterometer measurements. They demonstrate the potential to improve wind measurements in extreme wind events for future wind scatterometry missions such as the proposed GCOM-W2

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

Insights on the OAFlux ocean surface vector wind analysis merged from scatterometers and passive microwave radiometers (1987 onward)

Author Posting. © American Geophysical Union, 2014. This article is posted here by permission of American Geophysical Union for personal use, not for redistribution. The definitive version was published in Journal of Geophysical Research: Oceans 119 (2014): 5244–5269, doi:10.1002/2013JC009648.A high-resolution global daily analysis of ocean surface vector winds (1987 onward) was developed by the Objectively Analyzed air-sea Fluxes (OAFlux) project. This study addressed the issues related to the development of the time series through objective synthesis of 12 satellite sensors (two scatterometers and 10 passive microwave radiometers) using a least-variance linear statistical estimation. The issues include the rationale that supports the multisensor synthesis, the methodology and strategy that were developed, the challenges that were encountered, and the comparison of the synthesized daily mean fields with reference to scatterometers and atmospheric reanalyses. The synthesis was established on the bases that the low and moderate winds (<15 m s−1) constitute 98% of global daily wind fields, and they are the range of winds that are retrieved with best quality and consistency by both scatterometers and radiometers. Yet, challenges are presented in situations of synoptic weather systems due mainly to three factors: (i) the lack of radiometer retrievals in rain conditions, (ii) the inability to fill in the data voids caused by eliminating rain-flagged QuikSCAT wind vector cells, and (iii) the persistent differences between QuikSCAT and ASCAT high winds. The study showed that the daily mean surface winds can be confidently constructed from merging scatterometers with radiometers over the global oceans, except for the regions influenced by synoptic weather storms. The uncertainties in present scatterometer and radiometer observations under high winds and rain conditions lead to uncertainties in the synthesized synoptic structures.The project is sponsored by the NASA Ocean Vector Wind Science Team (OVWST) activities under grant NNA10AO86G.2015-02-1

Assessment and Analysis of QuikSCAT and COAMPS Model Vector Wind Products for the Gulf of Mexico: A Long-Term and Hurricane Perspective

Global weather changes have become a matter of grave concern in hurricane prone areas as intensities of hurricanes are observed to be increasing every year, necessitating improved monitoring capabilities. NASA’s QuikSCAT satellite sensor has provided significant support in analyzing and forecasting winds for the past 8 years. In this study, the performance of QuikSCAT products, including JPL’s latest L2B 12.5km swath winds, was evaluated against buoy-measured winds in the Gulf of Mexico. The long-term study period was 1/2005 – 2/2007. The Coupled Ocean/Atmospheric Mesoscale Prediction System (COAMPS) was also assessed. The regression analyses showed very good results for QuikSCAT products, with the best results obtained from L2B winds. R2 values for moderate wind speeds were 0.75 and 0.89, 0.88 and 0.93, 0.66 and 0.77 for speed and direction and for L3, L2B and COAMPS respectively. The National Weather Product (NWP) model winds provided in the L2B dataset were also studied. Hurricanes that took place from 2002 to 2006 were studied individually to obtain regressions of QuikSCAT and COAMPS versus buoys for those events. The correlations were very high indicating that QuikSCAT is at par with buoys during hurricanes. These measurements were compared with the NHC best track analyses to determine the accuracy and found to be almost half those obtained by NHC, possibly due to rain contamination. Sea Surface Height Anomaly (SSHA) measurements by Jason-1 and sea surface temperature (SST) measurements by the Moderate Resolution Imaging Spectroradiometer (MODIS) Aqua and GOES-12 (Geostationary) were compared with wind fields during hurricanes to study the effects of the Loop Current and Warm Core Rings on the intensification of the hurricanes. A preliminary study was conducted in which the regions of enhanced wind speeds were observed by studying the longitudinal and latitudinal transects across the hurricane for two hurricanes, namely Hurricanes Ivan and Katrina. This study would act as a precursor to further analysis of the radius of maximum wind and critical wind radii

Can GNSS reflectometry detect precipitation over oceans?

For the first time, a rain signature in Global Navigation Satellite System Reflectometry (GNSS‐R) observations is demonstrated. Based on the argument that the forward quasi‐specular scattering relies upon surface gravity waves with lengths larger than several wavelengths of the reflected signal, a commonly made conclusion is that the scatterometric GNSS‐R measurements are not sensitive to the surface small‐scale roughness generated by raindrops impinging on the ocean surface. On the contrary, this study presents an evidence that the bistatic radar cross section σ0 derived from TechDemoSat‐1 data is reduced due to rain at weak winds, lower than ≈ 6 m/s. The decrease is as large as ≈ 0.7 dB at the wind speed of 3 m/s due to a precipitation of 0–2 mm/hr. The simulations based on the recently published scattering theory provide a plausible explanation for this phenomenon which potentially enables the GNSS‐R technique to detect precipitation over oceans at low winds