Challenges to Satellite Sensors of Ocean Winds: Addressing Precipitation Effects (original) (raw)
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IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2020
Ocean surface rain rate information is crucial for quality control (QC) research in wind scatterometry. High-quality precipitation retrievals from microwave instruments are available from the Global Precipitation Mission (GPM). In addition, rain rates can be estimated with high spatiotemporal resolution from geostationary passive visible-infrared imagers such as the Spinning Enhanced Visible and Infrared Imager (SEVIRI) onboard the Meteosat Second Generation MSG satellites. The two products are complementary in observing time and regions. We compare them at different spatial scales, and show that the best consistency is obtained for grids with a size of 25 km or larger; where 25 km corresponds to a common scatterometer wind vector cell size. The results show that correlation coefficient of rain rates from GPM and MSG products for rain rates less than 5 mm/h is about 0.2 and ranges from 0.2 to about 0.5 for rain rates higher than 5 mm/h, while bias and root mean square deviation fluctuate about 2 and 3 mm/h, respectively. Also, QC indicator performances are analyzed with references to MSG and GPM rain rates respectively, for tropical regions. References to these QC indicators further indicate better consistency with both products at rain rates higher than 5 mm/h. The effectiveness of a newly proposed QC indicator has been confirmed as well. The three-way comparison of rain products and scatterometer QC indicators provides a reliable reference for research and for the application of corrections for rain effects on scatterometers. Index Terms-Comparison, global precipitation mission (GPM), Ku-band scatterometer quality control (QC), Meteosat Second Generation (MSG), rain rates. I. INTRODUCTION A MONG all remote sensing instruments for ocean-surfacerelated parameter retrieval, space-borne wind scatterometry takes an important role. It is the only technique to effectively obtain global ocean surface vector winds at high and Manuscript
Effects of rain-rate and wind magnitude on Sea Winds scatterometer wind speed errors
2001
Rain within the footprint of the Sea Wmds scatterometer on the QuikSCAT satellite causes more significant errors than existed with its predecessor, the NASA scatterometer (NSCAT) on Advanced Earth Obsenling Satellite-I (ADEOS-I). Empirical relations are developed that showhow-dle rain..indnced errors in the scatterometer wind magnitude depend on both the rain rate and on the wind magnitude. These relations are developed with collocated National Data Buoy Center (NDBC) buoy measurements (to provide accurate sea surface winds) and simultaneous Next Generation Weather Radar (NEXRAD) observations of rain reflectivity. An analysis, based on electromagnetic scattering theory, interprets the dependence of the scatterometer wind errors on volumetric rain rate over a range of wind and rain conditions. These results demonstrate that the satellite scatterometer responds to rain in a manner similar to that of meteorological radars, with a Z-R relationship. These observations and results indicate that the combined (wind and rain) normalized radar cross section will lead to erroneously large wind estimates when the rain-related radar cross section exceeds a particular level that depends on the rain rate and surface wind speed.
Journal of Geophysical Research, 2005
Rain can strongly modify the normalized radar cross section (NRCS) measured by Ku-band scatterometers and alter the wind vector retrieval. Part 1 of this paper presented a theoretical model of interaction between rain and scatterometer signal and used it to quantify the effect of rain on the backscatter and on wind vectors. Their results showed that the scatterometer data are strongly affected by rain, that they are extremely sensitive to the rain distribution within scatterometer resolution cells, and that the normalized radar cross section (NRCS) variability induced by rain could be a good indicator for rain flagging. The model is further tested and validated on a tropical cyclone case using colocated high resolution rain and Seawinds NRCS data. The model is used to compute attenuation and volume scattering from Tropical Rainfall Mapping Mission Precipitation Radar (TRMM PR) rain data. The comparison of the high-resolution (4 km) NRCS to synthetic NRCS computed from National Hurricane Center (NHC) winds and modeled rain terms shows a good qualitative agreement. The rain terms are used to correct the measured NRCS, to infer corrected winds which are significantly improved compared to NHC winds, especially for high winds. The wind correction using low-resolution rain data (such as Special Sensor Microwave Imager (SSM/I) ones) is also investigated using rain data averaged over wind scatterometer cells. This can also significantly improve the rain retrieval. A new rain flag based on the NRCS variability within wind vector cells is presented and shown to perform better than the operational one.
Journal of Geophysical Research, 1982
On the SEASAT-A satellite, a microwave scatterometer was used to determine the vector wind over the world's oceans. The technique is based on the sensitivity of microwave radar backscatter to the centimeter length ocean waves created by the action of the surface wind. This paper describes the algorithm used to convert the scatterometer' s measurements of ocean normalized radar cross section, •, to the neutral stability vector wind at 19.5 m height and the comparison of these winds with high quality surface observations. The wind vector algorithm used an empirical • model function to describe the dependence of the ocean • on the 19.5-m neutral stability wind vector. Two model functions, developed from a limited base of aircraft and satellite o • measurements, were evaluated by using an independent set of in situ surface wind observations from the Joint Air Sea Interaction Experiment (JASIN). Although these model functions were found to have some weaknesses, the results of these comparisons produced better results than the SEASAT specifications of wind speed accuracy of +-2 m/s and wind direction accuracy of +-20 ø over the 0-16 m/s range of winds observed during JASIN. An improved model function was later developed by 'tuning' to these JASIN data so that the remaining biases between the observed surface winds and the scatterometer-derived winds were minimized. Results are presented for this model function compared against other surface wind observations from the Gulf of Alaska SEASAT Experiment and the SEASAT Storms (Hurricane) Experiment. INTRODUCTION On June 28, 1978, •he National Aeronautics and Space Administration (NASA) launched SEASAT, the first satellite dedicated to establishing the utility of microwave sensors for remote sensing of the earth's oceans [Born et al., 1981]. This concept had its beginning in the mid-1960's when a conference called 'On the Feasibility of Conducting Oceanographic Explorations from Aircraft, Manned Orbital and Lunar Laboratories' was held at Woods Hole Oceanographic Institute, Woods Hole, Mass., in August 1964 [Ewing, 1965]. At this conference, the rudiments of many of the remote sensing systems for measuring oceanographic parameters were described that eventually were orbited on Skylab, Geos-3, and SEASAT. A few years later, a second conference sponsored by the National Academy of Sciences at Woods Hole made a broader study of potential areas of activity for NASA, including the study of the oceans. The concepts of high precision radar altimetry and of using radar backscatter to measure the winds both received considerable attention [National Research Council, 1970]. A third conference at Williamstown, Mass. [Kaula, 1970] also investigated the general subject; and ocean and atmospheric scientists postulated that satellite technology could provide the mecha
Calibrating the quikscat/seawinds radar for measuring rainrate over the oceans
IEEE Transactions on Geoscience and Remote Sensing, 2003
This effort continues a study of the effects of rain, over the oceans, on the signal retrieved by the SeaWinds scatterometer. It is determined that the backscatter radar cross section can be used to estimate the volumetric rain rate, averaged horizontally, across the surface resolution cells of the scatterometer. The dual polarization of the radar has a key role in developing this capability. The relative magnitudes of the radar backscatter depends on the volumetric rain rate, the rain column height and surface wind velocity, the viewing angle, as well as the polarization (due to the oblateness of raindrops at the higher rain rates). The approach to calibrating the SeaWinds normalized radar cross section (NRCS) is to collect National Weather Service Next Generation Weather Radar (NEXRAD) radar-derived rain rate measurements (4-km spatial resolution and 6-min rotating cycles) colocated in space (offshore) and time with scatterometer observations. These calibration functions lead to a Z-R relationship, which is then used at mid-ocean locations to estimate the rain rate in 0.25 or larger resolution cells, which are compared with Tropical Rainfall Mapping Mission (TRMM) Microwave Imager (TMI) rain estimates. Experimental results to date are in general agreement with simplified theoretical models of backscatter from rain, for this frequency, 14 GHz. These comparisons show very good agreement on a cell-by-cell basis with the TMI estimates for both wide areas (1000 km) and smaller area rain events.
2011
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 NA...
Physically based modeling of QuikSCAT SeaWinds passive microwave measurements for rain detection
Journal of Geophysical Research, 2002
1] We present a method for detecting rain-contaminated wind vector cells in QuikSCAT SeaWinds scatterometer observations. This rain detection method uses passive measurements of microwave brightness temperature obtained as a signal processing byproduct from the standard SeaWinds active scatterometer measurements. The rain flag is developed theoretically first by calibrating the SeaWinds brightness temperatures using Special Sensor Microwave Imager (SSM/I) observations and then by using physically based simulations including the effects of both rain and ice precipitation. Rain retrievals are validated by comparison to SSM/I-observed rain rates and to other independently produced SeaWinds rain flags and produce rain maps that agree well with the SSM/I estimates. The rain detection method may be used to complement existing rain flags in the current operational QuikSCAT data product. In addition, an atmospheric correction algorithm was developed to dynamically adjust the backscatter coefficient measurements for variations in water vapor and cloud liquid water; results are not significantly different from the climatological correction currently implemented.
Impact of rain cell on scatterometer data: 1. Theory and modeling
Journal of Geophysical Research, 2003
The two scatterometers currently in operation, the Ku-band NASA Seawinds on the QuikScat satellite and the C-band AMI-Wind on the ERS-2 satellite, are designed to infer the ocean wind vectors from sea surface radar backscatter measurements. They provide excellent coverage of the ocean, and their wind products are of great value for ocean and meteorological communities. However, the presence of rain within scatterometer cells can significantly modify the sea surface backscatter coefficient and hence alter the wind vector retrieval. These perturbations can hamper the analysis of wind fields within atmospheric low-pressure systems or tropical cyclones. Rain perturbations result from volume scattering and attenuation by raindrops in the atmosphere as well as changes of sea surface roughness by impinging drops. For scatterometers operating at Ku-Band, attenuation and volume scattering are strong and one order of magnitude larger than at C-band. The wind retrieval will thus be less affected for the C-band AMI-Wind instrument than for the Ku-band Seawinds. A theoretical model, based on radiative transfer formulation including rain attenuation and scattering, has been developed to quantify the modification by rain of the measured backscatter and of the retrieved wind vectors. Changes in surface roughness, a complex phenomenon not yet fully understood and parameterized, is not considered here although it could be of importance for high rain rates. As a scatterometer cell covers several hundred square kilometers, inhomogeneities of rain within the cell will further modify the measured backscatter, particularly in case of small, intense precipitating rain cells. Using analytical rain cell models and constant wind fields, the effects of partial beam filling by rain is investigated. The model results show that Ku-band scatterometer data are greatly affected by rain and are extremely sensitive to the distribution of rain within scatterometer cells, i.e., to the distance between the rain cell center and the scatterometer resolution cell center. When the scatter from the sea surface is low, the additional volume scattering from rain will have a marked effect leading to an overestimation of the low wind speed actually present. Conversely, when the backscatter is already high (at high winds), attenuation by rain will reduce the signal causing an underestimation of the wind speed. The wind direction is modified in a complex manner and mainly depends on the rain distribution within the scatterometer cell. These results show that, especially at low and moderate wind speed, rain data such as the Special Sensor Microwave/Imager (SSM/I) rain fields are too coarse for correction of Normalized Radar Cross Section (NRCS) and that high-resolution rain data (such as the Tropical Rainfall Mapping Mission (TRMM) ones) are necessary. They also show that a good rain flagging is still an important issue for the operational use of Ku-band scatterometer data. A succeeding paper will present an example of application of the model for the correction of QuikScat data using TRMM rain data within a tropical cyclone.