Characterizing the complex three-dimensional (3D) thermodynamic structure of the Planetary Boundary Layer (PBL) from a global perspective remains a challenge. As identified by the 2017 Decadal Survey and the NASA PBL Incubation Study Team Report (STR), enhanced horizontal and vertical resolution in PBL thermodynamic structure and PBL height from space-based sensors will facilitate major advances in Earth System science across a wide array of disciplines. Current Program of Record (POR) space-borne passive sounders (infrared, microwave) were not designed with a specific PBL focus. Consequently, current operational retrieval methods have limitations that preclude them from profiling PBL temperature and water vapor with the requirements expressed in the NASA PBL Incubation Study Team Report (STR). To that end, the report highlights the need for investing in optimal combinations of different remote sensing approaches and technologies spanning the active and passive field. In this framework, the study lists hyperspectral microwave sensors as an "Essential Component" of the future global PBL observing system, to provide accurate PBL and free tropospheric 3D temperature and water vapor structure context to active measurements (e.g., lidars and radars) and in combination with other passive sensors (e.g., infrared and radio occultation).

We have developed a process for evaluating future satellite-based hyperspectral microwave sensor designs using deep neural networks (DNN). Our approach combines a sophisticated simulated data product with a hierarchical deep neural network capable of comparing the relative performance of a variety of different microwave sounder configurations. These configurations include both spectral band coverage and resolution which allows for a thorough investigation of the solution space. The relative performance between these configurations as tested on the prediction of the planetary boundary layer height (PBLH) is used to perform the evaluation. We plan to extend this method to the prediction of entire temperature and water profiles to further refine this process.

The Conical Scanning Millimeter-wave Imaging Radiometer (CoSMIR) is participating as one of the primary remote sensing airborne instruments in the IMPACTS campaign. IMPACTS (Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms) is a 3-year long campaign in Jan-Feb 2020, 2022, and 2023, focusing on studying snowfall and winter storms on the United States East Coast. CoSMIR is a microwave radiometer with frequencies from 50 to 183 GHz that is flying on the high-altitude NASA ER-2 aircraft, complementing the other remote sensing instruments onboard. This presentation will detail CoSMIR's performance in the first two years of the IMPACTS campaign and show initial observations and analysis.

Snowfall retrieval algorithms for spaceborne passive microwave (PMW) sensors have been developed and refined in recent years, but many complicating issues still affect their accuracy and reliability. Previous work showed that the Global Precipitation Measurement (GPM) mission Goddard PROFiling (GPROF) algorithm snowfall retrieval performance strongly depends on the snowfall type. In particular, PMW-based detection of shallow cumuliform snowfall (SCS), which accounts for 36%-70% of global snowfall frequency, can be very challenging. The snowfall scattering signal can be contaminated by the background surface or supercooled cloud liquid water emission. A scattering index (SI) approach that exploits the GPM Microwave Imager (GMI) dual-polarization 166 GHz channels is developed to analyze its behavior in presence of SCS over ocean. Case studies show that, compared to the SI at 89 GHz, it can isolate the SCS snowfall scattering signal in extremely dry conditions. Some issues are still observed in presence of supercooled liquid water.

We highlight recent progress in and discuss future plans for the OpenSSP particle and scattering property database. Ongoing work has focused on expanding the types of particles to include polycrystals and melting snowflakes. Future expansion will include rimed particles, hail, and aligned snowflakes.

To address the acute challenge posed by the melting layer to accurate surface precipitation retrievals from space, we ensure the compositional consistency in ice, liquid, and total masses of synthetic melting hydrometeors with a method of stochastic compensation. The method is applied to simulated melting hydrometeors prior to calculating their scattering properties using the discrete dipole approximation (DDA). We investigate the impact of this stochastic compensation to calculated scattering properties by contrasting it with a naïve approach and report our findings.

Using a three-dimensional radiative transfer model combined with cloud-resolving model output, we simulate active and passive sensor observations of clouds and precipitaiton. This combination of tools allows us to diagnose the contributions of various hydrometeor types. Radar multiple scattering is most closely associated with the presence of graupel. At W-band, massive amounts multiple scattering in deep convection can decorrelate the reflectivity profile from the vertical structure, but for less intense events, multiple scattering could be a useful indicator of riming. For passive sensors, polarization differences at 166 GHz indicate the presence of horizontally-aligned frozen particles with pronounced aspect ratios, while high concentrations of more isotropic aggregates and graupel dampen the polarization difference while also contributing to the lowest brightness temperature depressions. The insights into remote sensing measurements will facilitate the development of improved algorithms and advanced sensors.

An optimal estimation scheme is employed to demonstrate the utility of using multi-band radar observations for estimating supercooled liquid profiles. Qualitative comparisons with microphysical probe images show that the retrievals are capable of producing supercooled liquid consistent with in situ data. Finally, a path forward for quantifying performance and extending the study to a more robust measurement suite is given.

A three-dimensional radiative transfer model is utilized to determine the feasibility of a forward-viewing passive sensor for remotely detecting hazardous icing conditions. W-band ground-based radar simulations show no obvious ability to discriminate a cloud-top supercooled layer; however, the spectra for a forward-viewing passive sensor show a strong signal at two stand-off distances when compared with the clear sky spectrum. Such an instrument would be critical for manned and unmanned aircraft, particularly when size, weight, and power requirements restrict the installation of deicing equipment.

To study the effects of radio-frequency interference (RFI) on remote sensing data, we examined five years of WindSat ocean observations from regions affected by reflected X-band emissions from geostationary communication satellites. We compared measured brightness temperatures to modeled brightness temperatures obtained using mitigated retrievals as input to the WindSat parameterized radiative transfer model, demonstrating a considerable contribution to the radiometric measurements from interference. Comparisons of potentially contaminated retrievals of sea surface temperature (SST), wind speed, and wind direction with surface reanalyses confirmed that the presence of RFI can bias retrievals while also increasing retrieval uncertainty. Mitigation removed most of the biases. The quality of mitigated SST and wind speed retrievals was comparable to uncontaminated retrievals; however, the performance of first-rank wind directions was noticeably degraded. Recommendations are given on the use of contaminated and mitigated data, from radiance assimilation to climate studies.

Ocean retrievals using passive microwave radiometers are sensitive to small fluctuations in ocean brightness temperatures. As such, the signals emanating from geostationary satellites that reflect off the ocean surface can result in large errors in ocean retrievals. Since geostationary communication satellites maintain fixed positions above the Earth and constantly transmit to predetermined regions while most other error sources, e.g., precipitation, are transient, time-averaged retrieval error statistics can be used to identify regions of measurements contaminated with radio-frequency interference (RFI). This letter describes a new method of identifying regions of ocean where ocean retrievals are affected by geostationary communication (television) satellites by using geophysical retrieval chi-square probability (goodness-of-fit) estimates. A three-month time-averaged collection of retrieval chi-square estimates is used to identify regions of the ocean where RFI may be present. This information is combined with information on geostationary satellite bandwidths, locations, and antenna contours to identify the source of the RFI. A mask derived from the analysis is used, in conjunction with satellite geometry calculations, to flag individual channels for RFI. These channels can then be ignored in the geophysical retrieval processing in order to produce uncontaminated ocean retrievals.

The ability to accurately measure ocean surface wind vectors from space in all weather conditions is important in many scientific and operational usages. One highly desirable application of satellite-based wind vector retrievals is to provide realistic estimates of tropical cyclone intensity for hurricane monitoring. Historically, the extreme environmental conditions in tropical cyclones (TCs) have been a challenge to traditional space-based wind vector sensing provided by microwave scatterometers. With the advent of passive microwave polarimetry, an alternate tool for estimating surface wind conditions in the TC has become available. This paper evaluates the WindSat polarimetric radiometer's ability to accurately sense winds within TCs. Three anecdotal cases studies are presented from the 2003 Atlantic Hurricane season. Independent surface wind estimates from aircraft flights and other platforms are used to provide surface wind fields for comparison to WindSat retrievals. Results of a subjective comparison of wind flow patterns are presented as well as quantitative statistics for point location comparisons of wind speed and direction.

WindSat is the world's first microwave polarimetric radiometer, designed to measure ocean vector winds. In late 2004, the first preliminary oceanic wind vector results were released, and this paper presents the first evaluation of this product for several Atlantic hurricanes during the 2003 season. Both wind speed and wind direction comparisons will be made with surface wind analysis (H*Wind) developed by the NOAA Hurricane Research Division (HRD) and provided by the NOAA National Hurricane Center (NHC). Examples are presented where HRD aircraft flights were conducted within several hours of the WindSat overpass. These H*Wind surface wind analyses provide the most complete independent surface winds comparison data set available. Both WindSat retrieved wind speeds and wind directions are evaluated (against H*Wind) as a function of storm quadrant. To complement the analysis, rain rates were derived using WindSat brightness temperatures with a modified version of the TMI 2A12 heritage rain algorithm. Effects of rain on the derived wind speeds and directions are discussed.

NASA's Sea Winds scatterometer, on Japan's ADEOS-II satellite, is a special purpose radar remote sensor used to measure ocean surface wind vector. This paper presents the novel use of SeaWinds as a radiometer (SRad), to measure the ocean's brightness temperature simultaneously with the radar backscattered power. The calibration/validation of the SRad ocean Tb data processing algorithm uses the Advanced Microwave Scanning Radiometer (AMSR) that also operates on ADEOS-II as a brightness temperature standard. Empirical on-orbit comparisons are presented for SRad and independent, simultaneous Tb measurements from AMSR. © 2005 IEEE.

Hurricanes Isabel and Fabian offer an unprecedented look at tropical cyclones. With the availability of two identical instruments, temporal sampling was as frequent as 6-12 hours. Utilizing the SeaWinds scatterometers' ability to make simultaneous active and passive measurements, high quality wind and rain retrievals were performed for 8 storm passes. Passive rain estimates are used to quantify both the attenuating and scattering effects of precipitation, which can then be used to correct the active wind measurements. The high resolution of the retrievals allows for feature identification, while a binary rain mask removes pixels too contaminated for wind speed retrieval. Wind results compare well with surface models produced by the Hurricane Research Division of NOAA, and rain rates show great spatial similarity to SSM/I F13 and F15 data sets.

Because of their high wind gradient structures, tropical cyclones (TCs) present a major challenge to space-borne scatterometer measurements of ocean surface wind vectors. Frequently spiral bands of strong rains accompany the high winds, and this precipitation attenuates the ocean backscatter measured by the scatterometer. Furthermore, traditional geophysical model functions (GMF), which relate wind speed and direction with radar backscatter (sigma-0), have not been tuned for the high wind conditions of TC's. The SeaWinds scatterometer has the ability to measure simultaneously the ocean backscatter and brightness temperature. By using this combined active/passive approach, simultaneous wind and rain estimates are made in TC's. Rain rate, determined passively, is used to model both the attenuative and scattering effects of rain. These parameters are used to correct the measured ocean sigma-0 at 12.5 km resolution. Wind speed retrievals are performed using a special TC-GMF developed using airborne scatterometer measurements in hurricanes. SeaWinds wind speed results for several hurricanes occurring between 1999 and 2002 compare well with high-resolution surface wind fields available from NOAA's Hurricane Research Division aircraft flights.