Search

Tropical cyclones (TCs) are among the most destructive natural phenomena with huge societal and economic impact. They form and evolve as the result of complex multiscale processes and nonlinear interactions. Even today the understanding and modeling of these processes is still lacking. A major goal of NASA is to bring the wealth of satellite and airborne observations to bear on addressing the unresolved scientific questions and improving our forecast models. Despite their significant amount, these observations are still underutilized in hurricane research and operations due to the complexity associated with finding and bringing together semicoincident and semicontemporaneous multiparameter data that are needed to describe the multiscale TC processes. Such data are traditionally archived in different formats, with different spatiotemporal resolution, across multiple databases, and hosted by various agencies. To address this shortcoming, NASA supported the development of the Jet Propulsion Laboratory (JPL) Tropical Cyclone Information System (TCIS)—a data analytic framework that integrates model forecasts with multiparameter satellite and airborne observations, providing interactive visualization and online analysis tools. TCIS supports interrogation of a large number of atmospheric and ocean variables, allowing for quick investigation of the structure of the tropical storms and their environments. This paper provides an overview of the TCIS’s components and features. It also summarizes recent pilot studies, providing examples of how the TCIS has inspired new research, helping to increase our understanding of TCs. The goal is to encourage more users to take full advantage of the novel capabilities. TCIS allows atmospheric scientists to focus on new ideas and concepts rather than painstakingly gathering data scattered over several agencies.

The soil moisture active passive (SMAP) mission was designed to acquire L-band radiometer measurements for the estimation of soil moisture (SM) with an average ubRMSD of not more than 0.04 $\text{m}^{3}/\text{m}^{3}$ volumetric accuracy in the top 5 cm for vegetation with a water content of less than 5 kg/ $\text{m}^{2}$ . Single-channel algorithm (SCA) and dual-channel algorithm (DCA) are implemented for the processing of SMAP radiometer data. The SCA using the vertically polarized brightness temperature (SCA-V) has been providing satisfactory SM retrievals. However, the DCA using prelaunch design and algorithm parameters for vertical and horizontal polarization data has a marginal performance. In this article, we show that with the updates of the roughness parameter $h$ and the polarization mixing parameters $Q$ , a modified DCA (MDCA) can achieve improved accuracy over DCA; it also allows for the retrieval of vegetation optical depth (VOD or $\tau$ ). The retrieval performance of MDCA is assessed and compared with SCA-V and DCA using four years (April 1, 2015 to March 31, 2019) of in situ data from core validation sites (CVSs) and sparse networks. The assessment shows that SCA-V still outperforms all the implemented algorithms.

More than half of the global land area undergoes seasonal freeze/thaw (FT) transitions in spring. Spatial patterns and timing of spring thawing influence eco-hydrological processes and landscape moisture availability over arctic and boreal ecosystems. The seasonal progression of spring thawing coincides with warmer temperatures, snowmelt, and a rapid increase in soil moisture, which initiates the growing season for ecosystem productivity. In this study, we utilize complementary satellite observations to determine the pattern and order of occurrence in landscape thawing, soil moisture increase, and ecosystem productivity that collectively define the eco-hydrological spring onset across Alaska and Northern Canada. Satellite data utilized include landscape FT status from SMAP and AMSR-2, OCO-2 derived solar-induced chlorophyll fluorescence (GOSIF), and gross primary production (GPP) and soil moisture from SMAP. The resulting spring onset maps showed spring thawing as the precursor to growing season onset, indicated by a rapid rise in available soil moisture and GPP. Our results indicated an average spring transition period of $3\pm 2$ (SD) weeks between initial landscape thawing and growing season onset. A rapid increase in soil moisture generally followed landscape thawing but occurred before the subsequent seasonal rise in GPP. Spring onset generally occurred earlier in boreal forest (DOY $102\pm 14$ ) than arctic tundra (DOY $124\pm 22$ ).

The Soil Moisture Active Passive (SMAP) mission is designed to acquire L -band radiometer measurements for the estimation of soil moisture with 0.04 m3/m3 volumetric accuracy in the top 5 cm for vegetation with water content of less than 5 kg/m2. In regions near the coast or near inland bodies of water, the signal measured by the SMAP radiometer contains emissions from land and water, resulting in errors in the soil moisture estimation. In this article, the effort to extract the brightness temperature (TB) according to the land fraction or water fraction (depending on the center of the footprint location) from the affected SMAP measurements was addressed. A single pixel correction algorithm was applied and its performance was evaluated over simulated data. A data-driven approach for the estimation of land and water TB for data correction was developed. The correction algorithm was then applied to real data and its performance was assessed over the SMAP soil moisture retrievals. We showed that the single pixel algorithm is an effective and computationally efficient algorithm for removing land or water TB contamination from the SMAP data.

The Soil Moisture Active/Passive (SMAP) microwave radiometer is a fully polarimetric L-band radiometer flown on the SMAP satellite in a 6 AM/6 PM sun-synchronous orbit at 685 km altitude. After the SMAP L1B_TB data product version 3 was released in 2016, the radiometer has been undergoing further calibration and validation with the goal of reducing both the bias in the cold-sky measurements and calibration drift in the global ocean measurements experienced during eclipse seasons in data product version 3. The post-launch calibration algorithm has been upgraded by using new estimates of the reflector emissivity as well as using multiple scenes to calibrate the radiometer internal reference sources and antenna gain simultaneously. In addition, a correction offset is applied to the ocean roughness model for horizontal polarization based on nadir observations. Test and validation results show that the goal is achieved (e.g., biases are removed and the calibration stability achieved for data release version 4 is 0.1 K (rms) over both the global ocean and CS).

Recent advances in L-band passive microwave remote sensing provide an unprecedented opportunity to monitor soil moisture at ~40 km spatial resolution around the globe. Nevertheless, retrieval of the accurate high spatial resolution soil moisture maps that are required to satisfy hydro-meteorological and agricultural applications remains a challenge. Currently, a variety of downscaling, otherwise known as disaggregation techniques have been proposed as the solution to disaggregate the coarse passive microwave soil moisture into high-to-medium resolutions. These techniques take advantage of the strengths of both the passive microwave observations of soil moisture having low spatial resolution and the spatially detailed information on land surface features that either influence or represent soil moisture variability. However, such techniques have typically been developed and tested individually under differing weather and climate conditions, meaning that there is no clear guidance on which technique performs the best. Consequently, this paper presents a quantitative assessment of the existing radar-, optical-, radiometer-, and oversampling-based downscaling techniques using a singular extensive data set collected specifically for that purpose, being the Soil Moisture Active Passive Experiment (SMAPEx)-4 and -5 airborne field campaigns, and the OzNet in situ stations, to determine the relative strengths and weaknesses of their performances. The oversampling-based soil moisture product best captured the temporal and spatial variability of the reference soil moisture overall, though the radar-based products had a better temporal agreement with airborne soil moisture during the short SMAPEx-4 period. Moreover, the difference between temporal analysis of products against in situ and airborne soil moisture reference data sets pointed to the fact that relying on in situ measurements alone is not appropriate for validation of spatially enhanced soil moisture maps., ARC Discovery Project (MoistureMonitor, DP140100572) and Infrastructure grant (LE0453434)

The NASA Soil Moisture Active Passive (SMAP) mission was launched on January 31st, 2015. The spacecraft was to provide high-resolution (3 km and 9 km) global soil moisture estimates at regular intervals by combining for the first time L-band radiometer and radar observations. On July 7th, 2015, a component of the SMAP radar failed and the radar ceased operation. However, before this occurred the mission was able to collect and process ~2.5 months of the SMAP high-resolution active-passive soil moisture data (L2SMAP) that coincided with the Northern Hemisphere's vegetation green-up and crop growth season. In this study, we evaluate the SMAP high-resolution soil moisture product derived from several alternative algorithms against in situ data from core calibration and validation sites (CVS), and sparse networks. The baseline algorithm had the best comparison statistics against the CVS and sparse networks. The overall unbiased root-mean-square-difference is close to the 0.04 m3/m3 the SMAP mission requirement. A 3 km spatial resolution soil moisture product was also examined. This product had an unbiased root-mean-square-difference of ~0.053 m3/m3. The SMAP L2SMAP product for ~2.5 months is now validated for use in geophysical applications and research and available to the public through the NASA Distributed Active Archive Center (DAAC) at the National Snow and Ice Data Center (NSIDC). The L2SMAP product is packaged with the geo-coordinates, acquisition times, and all requisite ancillary information. Although limited in duration, SMAP has clearly demonstrated the potential of using a combined L-band radar-radiometer for proving high spatial resolution and accurate global soil moisture.

Over one-third of the global land area undergoes a seasonal transition between predominantly frozen and non-frozen conditions each year, with the land surface freeze/thaw (FT) state a significant control on hydrological and biospheric processes over northern land areas and at high elevations. The NASA Soil Moisture Active Passive (SMAP) mission produced a daily landscape FT product at 3-km spatial resolution derived from ascending and descending orbits of SMAP high-resolution L-band (1.4 GHz) radar measurements. Following the failure of the SMAP radar in July 2015, coarser (36-km) footprint SMAP radiometer inputs were used to develop an alternative daily passive microwave freeze/thaw product. In this study, in situ observations are used to examine differences in the sensitivity of the 3-km radar versus the 36-km radiometer measurements to the landscape freeze/thaw state during the period of overlapping instrument operation. Assessment of the retrievals at high-latitude SMAP core validation sites showed excellent agreement with in situ flags, exceeding the 80% SMAP mission accuracy requirement. Similar performance was found for the radar and radiometer products using both air temperature and soil temperature derived FT reference flags. There was a tendency for SMAP thaw retrievals to lead the surface flags due to the influence of wet snow cover conditions on both the radar and radiometer signal. Comparison with other satellite derived FT products showed those derived from passive measurements (SMAP radiometer; Aquarius radiometer; Advanced Microwave Scanning Radiometer - 2) retrieved less frozen area than the active products (SMAP radar; Aquarius radar).

The goal of this study is to assess the performance of the active–passive disaggregation algorithm for the National Aeronautics and Space Administration Soil Moisture Active Passive (SMAP) mission using airborne observations from the Soil Moisture Active Passive Validation Experiment 2012 (SMAPVEX12). This algorithm disaggregates the whole domain resolution (around 30 km) radiometer brightness temperature (TB) using the 1.6-km-resolution radar backscatter $(\sigma^{o})$ observations (both acquired by the aircraft-based Passive Active L- and S-band Sensor), to a medium 6.4-km resolution. The parameters of the disaggregation method are affected by changes in soil and vegetation. Different time windows are studied to assess the best representation of the campaign vegetation growth and senescence processes. The algorithm performance is evaluated by comparing disaggregated and observed TB at the medium resolution. A minimum performance algorithm is also applied where the radar data are withheld. The minimum performance algorithm serves as a benchmark to assess the value of the radar to the SMAP active–passive algorithm.

More than half of the global land area undergoes seasonal frozen and thawed conditions that constrain eco-hydrological processes. The freeze-thaw (FT) retrieval from satellite microwave remote sensing detects landscape changes between frozen and non-frozen conditions due to the strong dependence of surface microwave emissions on liquid water abundance. We conducted an assessment of the latest version (R16) of the NASA Soil Moisture Active Passive (SMAP) Level 3 FT (L3_FT) global product. The L3_FT product provides a global FT classification with 3-day mean temporal fidelity derived using SMAP L-band (1.4 GHz) microwave brightness temperature (Tb) retrievals. The R16 product uses both normalized polarization ratio (NPR) and single channel vertically-polarized Tb (FT-SCV) algorithms to obtain FT retrievals over land areas where frozen temperatures are a significant ecological constraint. The L3_FT product is generated in a standard global grid with similar grid cell resolution (36-km) as the SMAP radiometer footprint. An enhanced 9-km global grid L3_FT product is also produced from optimally interpolated SMAP Tb retrievals. The resulting L3_FT products span a larger domain and longer period (2015–present) than earlier product releases. The L3_FT 36-km results showed a respective global mean annual FT classification accuracy of approximately 78 and 90 percent for descending (AM) and ascending (PM) orbit observations in relation to independent surface air temperature-based FT estimates from ~5000 global weather stations. The FT accuracy was lower in areas with greater terrain complexity, open water and vegetation cover; where the combined land cover factors explained 29–53% of the variability in the SMAP FT global accuracy. The L3_FT 9-km product showed an apparent enhancement of FT spatial patterns, but with ~4% lower accuracy than the 36-km product; the lower 9-km accuracy was attributed to stronger degradation from land cover heterogeneity, particularly in coastal areas, and artifact noise introduced from the spatial interpolation of SMAP Tb retrievals. Selected regional applications indicated product utility in capturing anomalous frost events over Australia and seasonal thaw and spring onset patterns over Alaska. Overall, the L3_FT global accuracy meets or exceeds the FT product science requirements established by the mission, while enabling studies of dynamic FT and water mobility constraints influencing hydrological and ecosystem processes, and global water-carbon-energy cycle linkages.

NASA's Soil Moisture Active Passive (SMAP) mission objective is global mapping of surface volumetric soil moisture at 10-km resolution every two to three days and with accuracy of 0.04 cm3 cm−3 (one sigma). In order to achieve this resolution and accuracy, the SMAP utilizes L-band radar and L-band radiometer measurements. The instruments share a rotating 6-m mesh reflector antenna that scans across a 1000-km swath in order to meet the required data refresh rate. The Level-2 Active–Passive soil moisture product (L2_SM_AP) at 9 km is retrieved from the disaggregated/downscaled brightness temperature obtained by merging of active and passive L-band observations. The baseline L2_SM_AP algorithm disaggregates the coarse-resolution (∼36 km) brightness temperatures of the SMAP L-band radiometer using the high-resolution (∼3 km) backscatter data from the SMAP L-band radar with unfocused synthetic aperture processing. The inversion of brightness temperature to estimate surface soil moisture is more mature when compared with inversions of radar backscatter. This is the primary driver of the brightness temperature disaggregation approach to the combined active–passive surface soil moisture product. Furthermore, this approach allows some consistency with the coarse-resolution radiometer-only surface soil moisture product since the disaggregated brightness temperatures sums to the radiometer measurement. The disaggregated brightness temperature contains instrument errors (∼0.7 dB for co-pol backscatter and ∼1.0 dB for cross-pol backscatter, and ∼1.3 K in brightness temperature) inherent in the radar and radiometer. Furthermore, the algorithm has two critical parameters that add uncertainty. Finally, correction of the land brightness temperature (used in the inversion) for water body contributions is a source of uncertainty. In this paper, we introduce analytical expressions for the SMAP downscaled brightness temperature due to all these sources of uncertainty. The expressions allow estimation of uncertainty (in kelvin) for each data granule of the SMAP L2_SM_AP product. Since the uncertainties depend on the given ground conditions, e.g., existing water body fraction and local algorithm parameters that depend on vegetation cover and landscape heterogeneity, it is necessary to evaluate the uncertainty for each data granule. In this paper, we show that the uncertainty expressions closely match Monte Carlo simulations with an overall difference of only ∼0.1 K. Whereas Monte Carlo estimates of uncertainty can only be afforded for a nominal case (such as those typically reported in Algorithm Theoretical Basis Documents as uncertainty tables), the analytical expressions allow uncertainty estimates for every data granule. The expressions are now used to provide uncertainty standard deviation of downscaled brightness temperature at 9 km in the SMAP L2_SM_AP product. These standard deviations are useful for the following: 1) guidance on the expected level of error in the estimate brightness temperature due to the downscaling process and 2) observation error in direct radiance data assimilation.

The NASA Soil Moisture Active Passive (SMAP) mission has been successfully operated for almost three years. The SMAP freeze/thaw algorithm is based on a seasonal threshold approach. It's important to have a stable and self-consistent freeze and thaw reference that can be applied for multi-year dataset. The three-year long radiometer datasets allow us to reassess the criteria of reference setup and evaluate its stability. In this paper, we first refined the freeze reference requirements and compare three different methods of setting up the thaw reference to minimize the false flags. The original freeze/thaw products is in the polar grid and only cover the region north of 45° N latitude. The limitation is due to lack of enough freezing days in the lower latitude, where the freezing reference cannot be generated. To extend the freeze/thaw product to global region, we combine the single channel algorithm in the lower latitude and southern atmosphere. The global results have been validated through WMO air temperature.

The SMAP microwave radiometer will see its third anniversary of operations on March 31, 2018. Instrument behavior is stable over 33 months of operation to date. The physical temperature of the internal calibration sources varies 0.5°C. The bias current of the noise source drifted by less than 0.1 %. The avalanche breakdown voltage of the noise diode shows 0.01% seasonal variation. The average $\mathrm{NE\Delta T}$ of the radiometer has maintained a stable 1-K value over the period. This stable behavior of the hardware is critical for the consistent calibration. The reflector emissivity was re-estimated using on-orbit data. Use of the new value nearly eliminates bias caused by solar eclipse during the southern hemisphere winter. The radiometer data were recalibrated using, as earlier, global ocean and cold sky views with additional ocean and land views at nadir incidence. The Version 4 recalibrated data exhibit 0.1-K RMS stability over average global ocean and monthly cold-sky views.

Soil moisture impacts exchanges of water, energy and carbon fluxes between the land surface and the atmosphere. Passive microwave remote sensing at L-band can capture spatial and temporal patterns of soil moisture in the landscape. Both ESA and NASA have launched L-band radiometers, in the form of the SMOS and SMAP satellites respectively, to monitor soil moisture globally, every 3-day at about 40 km resolution. However, their coarse scale restricts the range of applications. While SMAP included an L-band radar to downscale the radiometer soil moisture to 9 km, the radar failed after 3 months and this initial approach is not applicable to developing a consistent long term soil moisture product across the two missions anymore. Existing optical-, radiometer-, and oversampling-based downscaling methods could be an alternative to the radar-based approach for delivering such data. Nevertheless, retrieval of a consistent high resolution soil moisture product remains a challenge, and there has been no comprehensive intercomparison of the alternate approaches. This research undertakes an assessment of the different downscaling approaches using the SMAPEx-4 field campaign data.

The baseline science objective of the NASA Soil Moisture Active Passive (SMAP) mission is to produce a daily landscape freeze/thaw state for the region north of 45° N latitude with a mean spatial classification accuracy of 80% and 2–3 day average intervals separated by AM and PM overpasses [1]. Following the loss of the SMAP radar in July 2015, radiometer inputs were used to develop a standard freeze/thaw product (L3-FT-P) with relaxed spatial resolution from 3km to 36km. A 9km gridded product (L3-FT-P-E) has been developed by applying enhanced resolution radiometer inputs to the same algorithm. This paper provides an overview of the algorithm development as well as the validation and calibration using in situ observations from both selected core sites and sparse ground station networks.

Many previous studies have shown the sensitivity of radar backscatter to surface soil moisture content, particularly at L-band. Moreover, the estimation of soil moisture from radar for bare soil surfaces is well-documented, but estimation underneath a vegetation canopy remains unsolved. Vegetation significantly increases the complexity of modeling the electromagnetic scattering in the observed scene, and can even obstruct the contributions from the underlying soil surface. Existing approaches to estimating soil moisture under vegetation using radar typically rely on a forward model to describe the backscattered signal and often require that the vegetation characteristics of the observed scene be provided by an ancillary data source. However, such information may not be reliable or available during the radar overpass of the observed scene (e.g., due to cloud coverage if derived from an optical sensor). Thus, the approach described herein is an extension of a change-detection method for soil moisture estimation, which does not require ancillary vegetation information, nor does it make use of a complicated forward scattering model. Novel modifications to the original algorithm include extension to multiple polarizations and a new technique for bounding the radar-derived soil moisture product using radiometer-based soil moisture estimates. Soil moisture estimates are generated using data from the Soil Moisture Active/Passive (SMAP) satellite-borne radar and radiometer data, and are compared with up-scaled data from a selection of in situ networks used in SMAP validation activities. These results show that the new algorithm can consistently achieve rms errors less than 0.07 m3/m3 over a variety land cover types.

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.

Soil Moisture Active Passive (SMAP) mission of NASA was launched in January 2015. Currently, SMAP has an L-band radiometer and a defunct L-band radar with a rotating 6-m mesh reflector antenna. On July 7th, 2015, the SMAP radar malfunctioned and became inoperable. Consequently, the production of high-resolution active-passive soil moisture product got hampered, and only ~2.5 months (April 15th, 2015 to July 7th, 2015) of data remain available. Therefore, during the SMAP post-radar phase, many ways were examined to restart the high-resolution soil moisture product generation of the SMAP mission. One of the feasible approaches was to substitute the SMAP radar with other available SAR data. Sentinel-1A/Sentinel-1B SAR data was found most suitable for combining with the SMAP radiometer data because of its nearly similar orbit configuration that allows overlapping of their swaths with a minimal time difference, a key feature/requirement for the SMAP active-passive algorithm. The Sentinel interferometric wide swath (IW) mode acquisition also provides the co-polarized and cross-polarized observations required for the SMAP active-passive algorithm. However, some differences do exist between the SMAP and Sentinel SAR data. They are mainly: 1) Sentinel has a C-band SAR whereas SMAP operates at L-band; 2) Sentinel has multiple incidence angles within its swath, and SMAP has one single incidence angle; and 3) Sentinel 1A/B Interferometric Wide (IW) swath width is ~250 km as compared to SMAP with 1000 km swath width. On any given day, the narrow swath width of the Sentinel observations significantly reduces the overlap spatial coverage between SMAP and Sentinel as compared to the original SMAP radar and radiometer swath coverage. Hence, the temporal resolution (revisit interval) suffers due to narrow overlapped swath width and degrades from 3 days to 12 days. One advantage of using very high-resolution resolution Sentinel-1A/Sentinel-1B data in the SMAP active-passive algorithm is the potential of obtaining the disaggregated brightness temperature and thus soil moisture at a much finer spatial resolution of 3 km and 1 km at global extent. The assessment of high-resolution product at 3 km and 1 km using the soil moisture calibration and validations sites shows reasonable accuracy of ~0.05 m3/m3. The SMAP-Sentinel1 active-passive high-resolution product is now available to the public (new version released in October 2018) through NSIDC (NASA DAAC). The duration of this product is from April 2015 to current date.

The NASA Soil Moisture Active Passive (SMAP) mission produced a daily landscape freeze/thaw product (L3_FT_A) at 3-km spatial resolution derived from ascending and descending orbits of SMAP high-resolution L-band (1.4 GHz) radar measurements. Following the loss of the SMAP radar in July 2015, coarser (36-km) footprint passive microwave retrievals from the SMAP radiometer were used to derive an alternative daily freeze/thaw product (L3_FT_P). This presentation will provide an overview of the development of both L3_FT products. Validation using in situ observations from core validation sites is used to illustrate differences in the sensitivity of the 3 km radar versus the 36 km radiometer measurements to the landscape freeze/thaw state.

We describe a method for retrieving winds from colocated Ku- and C-band ocean wind scatterometers. The method utilizes an artificial neural network technique to optimize the weighting of the information from the two frequencies and to use the extra degrees of freedom to account for rain contamination in the measurements. A high-fidelity scatterometer simulation is used to evaluate the efficacy of the technique for retrieving hurricane force winds in the presence of heavy precipitation. Realistic hurricane wind and precipitation fields were simulated for three Atlantic hurricanes, Katrina and Rita in 2005 and Helene in 2006, using the Weather Research and Forecasting model. These fields were then input into a radar simulation previously used to evaluate the Extreme Ocean Vector Wind Mission dual-frequency scatterometer mission concept. The simulation produced high-resolution dual-frequency normalized radar cross-section (NRCS) measurements. The simulated NRCS measurements were binned into 5 x 5 km wind cells. Wind speeds in each cell were estimated using an artificial neural network technique. The method was shown to retrieve accurate winds up to 50 m/s even in intense rain.

We exhibit a technique for improving wind accuracy in Ku-band ocean wind scatterometers in the presence of rain. The technique is autonomous in that it only makes use of measurements made by the scatterometer itself, so that no colocation of an external data set (e.g., rain radiometers) is required to perform the correction. The only inputs to the technique are the normalized radar cross-section measurements for each wind vector cell, the cross-track distance of the cell as a proxy for measurement geometry, and the nominal retrieved wind vector for the cell without rain correction. This last input is used to avoid modifying winds not contaminated by rain. The technique was applied to QuikSCAT data for the month of January 2008, resulting in a marked improvement to rainy data. For data that were determined to be rain contaminated by the Jet Propulsion Laboratory rain flag, the rms speed error with respect to National Data Buoy Center buoy winds improved from 8.9 to 3.5 m/s for colocations within 25 km. The rms speed error in rain also improved when compared with the European Centre Medium-Range Weather Forecast winds from 7 to 3 m/s. Data that were not flagged as rain contaminated were not significantly changed, despite the fact that the technique does not make use of the rain flag. The technique was able to distinguish between rain-contaminated wind cells and rain-free wind cells and to substantially improve the wind speed accuracy of the former using QuikSCAT data alone without recourse to any external information about the extent of the rain.

The overall accuracies of vector wind measurements from the NASA scatterometer (NSCAT) are quantified by comparisons with collocated data from operational U.S. National Data Buoy Center ocean buoys. A vector correlation statistic is used to examine the geographical distribution of full-mission NSCAT-buoy differences. The dependences of the vector correlation on collocation radius and scatterometer errors such as wind speed offset and random component errors are quantified. For purposes of NSCAT validation, 30 ocean moored buoys having high full-mission sample correlations are considered. When applied to this set of ocean buoys, a nonlinear analysis shows that the 25-km resolution NSCAT wind speeds have unity gain, an offset of −0.3 m s−1, an rms error of 1.3 m s−1, and component standard deviations of ∼1.3 m s−1. For wind speeds greater than 6 m s−1, fewer than 3% of all collocated solutions have significant ambiguity removal errors. The rms directional difference for the remaining vectors is less than 17°. At lower wind speeds the frequency of ambiguity removal errors increases and the directional accuracy of the “correct” vectors decreases with decreasing wind speed, consistent with the assumptions of the random error model underlying the nonlinear wind speed regression approach. While the primary emphasis of this paper is on determining the quantitative accuracy of the NSCAT wind velocity measurements, the analysis techniques and interpretations have more general application to validation studies involving vector quantities.

This paper presents a designs for a scatterometer satisfying the performance requirements set by the National Research Council Decadal Review in its description of the Extended Ocean Vector Winds Mission (XOVWM). Our design consists of a Ku and C-band pencil-beam scatterometer system coupled with an X-band poilarimetric radiometer. Multiple frequencies allow us to achieve all winds and all-weather capabilities, and we achieve high spatial resolution thanks to an onboard SAR processor. We present examples of significant improvements in performance that can be achieved by this next-generation system compared with current ocean vector winds instruments.

From September 15 to 25, 1996, NASA's scatterometer (NSCAT) monitored the evolution of twin typhoons-Violet and Tom-as they moved north from the western tropical Pacific, acquiring features of mid-latitude storms. The typhoons developed frontal structures, increased asymmetry, and dry air was introduced into their cores. Violet hit Japan, causing death and destruction, and Tom merged with a mid-latitude trough and evolved into a large extratropical storm with gale-force winds. We understand relatively little about the extratropical transition of tropical cyclones because of the complex thermodynamics involved, but we do know that the mid-latitude storms resulting from tropical cyclones usually generate strong winds and heavy precipitation. Since the transition usually occurs over the ocean, few measurements have been made. The transition is a fascinating science problem, but it also has important economic consequences. The transition occurs over the busiest trans-ocean shipping lanes, and when the resulting storms hit land, they usually devastate populated areas. NSCAT was successfully launched into a near-polar, sunsynchronous orbit on the Japanese Advanced Earth Observing Satellite (ADEOS) in August 1996 from Tanegashima Space Center in Japan. NSCAT's six antennas send microwave pulses at a frequency of 14 GHz to the Earth's surface and measure the backscatter.

One year of global surface wind products from multiple operational numerical weather prediction (NWP) forecast/analysis systems are used as comparison data to derive an empirical wind speed model function for the Geosat altimeter. The resulting model function is nearly identical to the modified Chelton-Wentz model for wind speeds from 4.5 to 15 m/s. Highly skewed distributions at low wind speeds are consistent with specular reflections and antenna mispointing errors hypothesized by others on the basis of extremely limited data. Mesoscale variability in the wind field and synoptic scale errors in the NWP products are shown to account for about 30 percent of the observed scatter of sigma(0) at each wind speed. The remaining scatter is largest at low winds, and decreases to a nearly constant value of about 12 percent at speeds greater than 7 m/s. Model function uncertainty expressed more traditionally in units of wind speed is examined for historical model functions as well as the present NWP-based model. The historical models have significant biases at high wind speeds owing to the lack of comparison in situ data used in their construction. The present study demonstrates that modern operational NWP surface wind products are sufficiently accurate to allow development of fully empirical model functions and associated error analyses.

Thermal emission models indicate that Charon contributes a significant amount of the infrared radiation detected by IRAS during the observation of mutual eclipse events. The IRAS observations also show that the most probable diameters for Pluto and Charon are 2200 and 1300 (+ or - 150) km. These results are consistent with there being some atmosphere on Pluto.

The present 'first order' analytical and numerical models of light curves due to mutual events between close planetary binaries, the effects of shadowing are included. Attention is given to the case of the Pluto-Charon system. The results of the analytical and numerical approaches agree to well within the expected light curve measurement error. The model predicts that the current mutual eclipse event series will end by November 1990.