In this paper, we will present results for tropical cyclone wind radii estimation (34-, 50-, 64-knot) using wind speed retrievals from ASCAT-B/C ultra-high-resolution (UHR) processing. ASCAT-UHR derived surface wind radii are compared to storms best tracks obtained from the International Best Track Archive for Climate Stewardship (IBTrACS) dataset. The results were developed using 275 ASCAT-B/C hurricane overpasses from 2022 and 2023, and spans wind speed range from 17-75 m/s. Results show that the root-mean-square-errors (RMSEs) of wind radii from ASCAT-UHR (25.05, 8.75, and 5.52 nautical miles for R34, R50, and R64 respectively) are comparable to the positional uncertainty of IBTrACS.

The Compact Ocean Wind Vector Radiometer (COWVR) and WindSat are two fully polarimetric microwave radiometers that are designed to measure ocean surface vector winds (OSVW). In this paper, a comparison between the COWVR and WindSat common fully polarimetric channels will be presented. The main focus is to analyze the sensitivity of COWVR measured Stokes parameters (3rdand 4thStokes at 18.7 and 34 GHz) to multiple surface and atmospheric parameters and compare them to their WindSat counterparts (3rdand 4thStokes 18.7 and 37 GHz). Approximately 2000 randomly selected orbits of COWVR and WindSat were used in the analyses along with modeled geophysical parameters from the Global Data Assimilation System (GDAS) 0.25° resolution product. Preliminary results show that COWVR Stokes measurements exhibits strong wind vector signature comparable to that of WindSat.

This paper describes the development and validation of a statistical algorithm to retrieve global all-weather sea surface wind speeds (GAWS) from microwave radiometers in operational environment. Measurements from the Advanced Microwave Scanning Radiometer-2 (AMSR2) are utilized to demonstrate the efficacy of the new all-weather wind speed data product. The GAWS algorithm exploits the linear combination of dual-polarized radiometer channels to significantly mitigate the effect of rain contamination while maintaining sensitivity to all wind speed regimes from global winds to tropical cyclone conditions. The GAWS algorithm was developed using ~1000 AMSR2 orbits from 2013 - 2021 covering all possible variations of brightness temperatures and wind speeds. The Global Data Assimilation System (GDAS) and the Hurricane Weather Research and Forecasting Model (HWRF) were used as the assumed surface truth for training and validation. Results from comprehensive quantitative and qualitative analyses show that GAWS retrievals are less susceptible to rain than standard microwave radiometer wind speeds and can reach hurricane force winds up to hurricane category 5 (> 70 m/s).

This paper describes the development of a new all-weather geophysical model function (AW-GMF) for the purpose of retrieving sea surface wind (SSW) under all weather conditions. The AW-GMF makes use of the Advanced Microwave Scanning Radiometer-2 (AMSR2) dual-polarized C-, X-, and K-band channels. We show how the linear combination of these channels significantly mitigate the effect of rain contamination while maintaining sensitivity to SSW regimes including tropical and extra-tropical cyclones. AW-GMF was trained using ~750 AMSR2 hurricane overpasses from 2013 - 2021. The Global Data Assimilation System (GDAS) 0.25° resolution product and the Hurricane Weather Research and Forecasting Model (HWRF) were used as the assumed surface truth. Preliminary results show that SSW retrievals using the AW-GMF are much less susceptible to rain and capable of retrieving hurricane force winds.

Microwave radiometers are designed to capture the Earth's electromagnetic radiation in the form of brightness temperatures. At low and medium frequencies, the relatively large footprint of microwave radiometers results in mixing land and water brightness temperatures in coastal areas and lakes. This mixing of signals, also known as land contamination, limits the usability of radiometer measurements and makes them unsuitable for geophysical retrievals up to ~ 100 km away from the coastline. In this paper, we present preliminary results of applying a land contamination correction on AMSR2 measurements to extract coastal information. The correction technique relies on calculating the land fraction within AMSR2 footprints using a high-resolution land mask and a representative antenna pattern.

The Advanced Microwave Scanning Radiometer-2 (AMSR2) on board the Global Change Observation Mission-Water (GCOM-W) launched in May 2012 by the Japan Aerospace Exploration Agency (JAXA) is acquiring electromagnetic radiation from the Earth for the purpose of monitoring its environmental and climate system. Among a suite of oceanic environmental data records (EDR), the Ocean Surface Winds Team of the Satellite Applications and Research (STAR) group at the National Oceanic and Atmospheric Administration (NOAA) has developed an all-weather wind speed (AWS) by exploiting AMSR2 brightness temperature (Tb) measurements. The new product provides wind speeds in normal and extreme weather conditions with minimal flagging and excellent accuracy. Validation results of this novel AMSR2 AWS product, represented in this paper, show a mean bias of 0 m/s and an rms error < 2 m/s when compared to numerical weather models under all weather conditions.

Operational weather analysis, forecasting, and warning utilize a wide variety of data products and tools, including satellite imagery and derived products. Satellite observations provide information where in-situ measurements are lacking or not readily available. Passive microwave satellite observations are routinely exploited by forecasters at the National Oceanic and Atmospheric Administration (NOAA), National Weather Service (NWS) in the United States (U.S.) to support their weather analysis and forecasts. In this paper, we present examples of hurricane Dorian observations from Advanced Scanning Radiometer -2 (AMSR-2) on the Global Change Observation Mission (GCOM), which is part of the Japanese Aerospace Exploration Agency (JAXA). We compare NOAA AMSR-2 ocean EDR products with storm finding documented within National Hurricane Center Dorian discussions from Aug 24th through September 9th, 2019. NOAA AMSR-2 products are part of NWS forecasting product suite and are regularly used in daily operations. While Microwave Imagery product has been used the most for hurricane forecasting we examine usefulness of other ocean products by following Hurricane Dorian from its formation on August 24th to its demise on September 6th.

Operational weather analysis, forecasting, and warning utilize a wide variety of data products and tools, including satellite imagery and derived products. Satellite observations provide information where in-situ measurements are lacking or not readily available. Passive microwave satellite observations are routinely exploited by forecasters at the National Oceanic and Atmospheric Administration (NOAA), National Weather Service (NWS) in the United States (U.S.) to support their weather analysis and forecasts. In this paper, we present examples of ocean measurements and derived products from Advanced Scanning Radiometer -2 (AMSR-2) on the Global Change Observation Mission (GCOM), which is part of the Japanese Aerospace Exploration Agency (JAXA) that supported critical forecasts.

The Advanced Microwave Scanning Radiometer-2 (AMSR2) on board the Global Change Observation Mission-Water (GCOM-W) launched in May 2012 by the Japanese Exploration Agency (JAXA) is acquiring earth electromagnetic radiation for the purpose of monitoring Earth's environmental and climate system. The ocean vector winds team part of the National Oceanic and atmospheric administration (NOAA), National Environmental Satellite, Data, and Information Service (NESDIS), Center for Satellite Applications and Research (STAR), has exploited AMSR2 observations of brightness temperature (Tb) to develop an environmental data record (EDR), which includes several oceanic parameters. The purpose of this paper is to show the validation results for these geophysical parameters when compared to other active and passive microwave sensors and numerical weather models.

Passive microwave radiometry is a special application of microwave communications technology for the purpose of collecting Earth's electromagnetic radiation. With the use of radiometers onboard earth orbiting satellites, scientists are able to monitor the Earth's environment and climate system on both short- and long-term temporal scales with near global coverage.

The Stepped Frequency Microwave Radiometer (SFMR) is an instrument flown on research and reconnaissance aircraft through tropical and extratropical cyclones providing rain rate and surface wind speed estimates. Errors have been observed with the retrievals from SFMR, especially in extratropical cyclones over cold water, when compared with other sensors. In this paper, some of the SFMR wind speed errors that manifest over cold water are characterized using comparisons with in situ measurements by dropwindsondes.

Airborne co-polarization and cross-polarization observations of ocean surface normalized radar cross section (NRCS) were conducted over the North Atlantic during January and February 2015. Observations were made using the University of Massachusetts' Imaging Wind and Rain Airborne Profiler (IWRAP) radar system and a prototype antenna for the next-generation European scatterometer aboard MetOp-SG. Both were installed on a National Oceanic and Atmospheric Administration (NOAA) WP-3D research aircraft to characterize the wind response of the ocean-surface cross-polarization NRCS. During the flights, numerous constant-roll-angle circle maneuvers were performed at several different angles to collect NRCS measurements over a range of incidence angles. Surface winds at speeds between 8 and 34 ms-1were observed at incidence angles from 20° to 60° at all polarization combinations. The majority of measurements fell between 8 and 20 ms-1. Wind-direction dependence similar to copolarized NRCS was observed in the cross-polarized (VH) NRCS. The amplitude of the VH NRCS with respect to direction is less than that of copolarized NRCS at all wind speeds. Incidence angle dependence was also observed in the VH NRCS at all wind speeds. As a function of wind speed, the mean VH NRCS (A0) has a similar shape to the VV NRCS. The VH NRCS appears to not saturate at most incidence angles, unlike the VV and HH NRCS. VH and HH geophysical model functions (GMFs) were developed as functions of wind speed, incidence angle, and wind-relative azimuth for the wind speeds and incidence angles observed.

The Advanced Scatterometer (ASCAT) on the MetOp-A satellite is a radar instrument designed specifically to retrieve the ocean surface wind speed and direction. The ASCAT wind vector products are produced and utilized operationally in support of the National Oceanic and Atmospheric Administration (NOAA)'s weather forecasting and warning mission. The standard ASCAT winds at NOAA are produced using the ASCAT wind data processor developed at the Royal Netherlands Meteorological Institute (KNMI) utilizing the CMOD5.n geophysical model function (GMF). Recent validation of the ASCAT wind retrievals revealed a low bias at high wind speeds when compared to both the QuikSCAT winds and the National Centers for Environmental Prediction numerical weather prediction (NWP) model winds. The goal of this paper is to investigate the ASCAT high-wind-speed performance and to modify, as appropriate, the high-wind-speed portion of CMOD5.n GMF. This effort would potentially improve the utility of ASCAT wind retrievals in supporting wind warning and analysis and thus better mitigate the loss of QuikSCAT data products. Traditionally, the GMF is developed empirically by collocating scatterometer measurements and other truth data such as buoy and NWP model winds. However, NWP models are known to underestimate the intensity of higher wind speeds, and data sources such as ship-based or buoy-based observations provide an inadequate quantity of measurements for empirical GMF development. In this paper, a method utilizing aircraft-based scatterometer measurements in the high-wind-speed regimes is used in conjunction with satellite scatterometer measurements to refine the satellite GMF. As a result of this paper, a high wind C-band satellite GMF, CMOD5.h, was developed and implemented in NOAA's ASCAT processor. The validation comparison of the high wind and standard ASCAT wind products revealed 0.6-m/s reduction in the wind speed bias for winds greater than 15 m/s with respect to QuikSCAT, WindSat, and Step Frequency Microwave Radiometer high wind measurements.

This paper presents a conceptual conical-scanning radiometer/scatterometer (RadScat) instrument design for the purpose of improving satellite ocean vector wind retrievals under rain-free conditions. This technique combines the wind vector signature in the passive linearly polarized ocean brightness temperatures with the anisotropic signature of multiazimuthal radar cross-sectional measurements to retrieve oceanic surface wind vectors. The performance of the RadScat is evaluated using a Monte Carlo simulation based on actual measurements from the SeaWinds scatterometer and the Advanced Microwave Scanning Radiometer onboard the Advanced Earth Observing Satellite II. The results demonstrate significant improvements in wind vector retrievals, particularly in the near-subtrack swath, where the performance of conical-scanning scatterometers degrades.

Microwave scatterometers are the standard for satellite ocean vector winds (OVW) measurements, and they provide the major source of global ocean surface winds observations for scientific and operational applications. A major challenge for Ku-band scatterometry missions is to provide reliable retrievals in the presence of precipitation, particularly in extreme ocean wind events that are usually associated with intense rain. This paper explores the advantages of combining dual frequency (C- and Ku-band) scatterometer measurements and passive microwave observations to improve high wind speed retrievals. For this study, a conceptual design proposed by the Jet Propulsion Laboratory for a Dual Frequency Scatterometer (DFS) to fly onboard the future Japan Aerospace Exploration Agency (JAXA) GCOM-W2 mission with the Advanced Microwave Scanning Radiometer (AMSR) was adopted. A computer simulation that combines the DFS and AMSR measurements was used to develop an artificial neural network OVW retrieval algorithm. The Weather Research and Forecasting (WRF) numerical weather model of Hurricane Katrina (2005) was used as the nature run (surface truth), and simulated OVW retrievals demonstrate that this new technique offers a robust option to extend the useful wind speed measurements range beyond the current operating scatterometers for future satellite missions.

This work investigates the design of an innovative conical scanning Ku-band (13.4 GHz) scatterometer/radiometer for measuring ocean vector winds. The sensor design is based upon actual measurements obtained by the SeaWinds scatterometer and the Advanced Microwave Scanning Radiometer (AMSR), which operated simultaneously on JAXA's Advanced Earth Observing Satellite-II (ADEOS-II) during 2003. This new design combines the conventional forward and aft-looking two-beam microwave scatterometer (SeaWinds) measurements with simultaneous linearly polarized passive microwave brightness temperatures. The unique aspect of this remote sensing technique is that it operates at a single microwave frequency, and it combines vertical and horizontal polarized microwave brightness temperatures with the scatterometer normalized cross sections to retrieve unambiguous ocean wind vectors. This technique has the potential to significantly improve the Ocean Vector Winds retrievals for future conical-scanning microwave scatterometers.

There is a strong national interest in the observation of ocean surface winds with high spatial and temporal resolution for understanding tropical cyclones and their effects on weather and climate and in forecasting storms making landfall. Current satellite and aircraft based remote sensing capability is limited in wind speed dynamic range and in the ability to retrieve wind information in the presence of rain, or in temporal and spatial coverage, respectively. The hurricane imaging radiometer (HIRAD) is capable to capture all the hurricane features and dynamics from a high altitude aircraft preserving high resolution measurements. A detailed description of the methods used in simulating the HIRAD instrument surface sampling of wind speed, in intense rain, from various aircraft platforms with realistic operational flight patterns through a time evolving hurricane will be provided in this paper. A noise model used to simulate the effects of rain for various observation path lengths over the swath will also be described. Results will demonstrate the extent of spatial and temporal coverage available from currently available aircraft platforms.