We present an overview of the 2024 West-Coast Hyperspectral Microwave Sensor Intensive Experiment (WHyMSIE). WHyMSIE is a joint NASA-NOAA multi-sensor airborne experiment, embracing passive and active sensors from the Program of Record (PoR) along with novel technology funded through the NASA ESTO Instrument Incubation Program. At the core of this effort is the demonstration of the Conical Scanning Millimeter-wave Imaging Radiometer Hyperspectral (CoSMIR-H) instrument, a PBL DSI funded effort to develop hyperspectral sounding capability in the thermal microwave domain finalized to improved temperature and water vapor soundings in the Earth’s Planetary Boundary Layer (PBL). An overview of the field campaign design, instrument payload and validation plan is presented here.

We present a comprehensive Earth Planetary Boundary Layer temperature and water vapor retrieval improvement demonstration by the use of hyperspectral microwave measurements. Our results indicate that the use of a hyperspectral sampling in the oxygen and water vapor sounding lines alone provides significant improvements in the lower and free tropospheric thermodynamic fields (up to 40%), when compared against the program of record (i.e., the Advanced Technology Microwave Sounder, ATMS). Our experiments also demonstrate the essential role played by extending the coverage in the so called spectral window regions, leading to an overall PBL temperature and water vapor improvement of up to 50%.

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.

Dr. Gail Skofronick-Jackson (IEEE Fellow), 58, died suddenly September 7, 2021. Skofronick-Jackson was deployed with a joint NASA-ESA sub-orbital campaign in St. Croix, U.S. Virgin Islands. On a day off from experiments, she perished in a tragic accident while hiking with colleagues. Skofronick-Jackson's contributions to satellite remote sensing spanned 25 years of remote sensing research, NASA spaceflight mission leadership, professional volunteerism, and scientific program management.

Observations from the Special Sensor Microwave Imager/Sounder (SSMIS) onboard the Defense Meteorological Satellite Program F16, F17, F18, and F19 spacecrafts provide a significant portion of the microwave radiometer data within the Global Precipitation Measurement (GPM) mission constellation. In preparation for the GPM Version 7 (V07) data release, SSMIS corrections developed over a decade ago and incorporated in GPM Version 5 (V05) are reexamined and updated. The calibration updates presented here include pointing parameters affecting geolocation and viewing geometry, along-scan bias adjustments to account for scan edge falloffs and sun angle corrections to account for heating anomalies including an emissive reflector. To address errors in the V05 geolocation, the sensor roll, pitch, yaw, half cone angle, and timing offsets are reanalyzed and updated. The along-scan bias adjustment is updated in a manner consistent with recent Special Sensor Microwave Imager updates to account for variations in scene temperature. Finally, significant improvements to the SSMIS sun angle correction are made by using data from the entire mission, extending the corrections to include the higher frequency channels, and deriving a more consistent channel-to-channel correction. From V05 to V07, the geolocation adjustment is approximately 5–10 km, the earth incidence angle difference is 0–0.4°, and the average brightness temperature change is 0–2 K, but individual pixels may be up to several kelvin difference depending on sensor and channel. The result of these updates is a significant improvement in the quality and long-term consistency of the SSMIS data that are included in the GPM V07 dataset.

Striping has been observed in the MetOp-A microwave humidity sounder (MHS) data since its degradation in November 2018. However, accurate striping quantification and characterization remain challenging due to the large scene dynamics observed at W-/G-bands of MHS. Here, we have developed a set of novel algorithms for striping quantification, decomposition, characterization, and simulation. Our algorithm extracts striping from the warm-load and cold-space scenes that are relatively stable. We break down the striping into two parts of thermal and$1/f$noises, and quantify their absolute magnitude and relative ratio. We found a significant increase in striping at 157 GHz, which has more than quadrupled by October 2019 relative to its normal level. Regardless of the degradation, the ratio of thermal and$1/f$noises remains the same. Our simulation reproduces all the characteristics of striping against observation. It is shown that$1/f$noise generates sharp, nonperiodic stripes, while thermal noise also generates stripes but with smoother band features. The latter is due to the periodic calibration that has a chopping effect. The striping percentage, defined as the ratio of$1/f$to total noise, shows no dependence on the scene temperature. Striping is pronounced not only in 157 GHz but also in 89 and 190 GHz with the striping percentage over 50% while lower in 183 GHz of 20%. The results provide insights for quantifying and understanding striping. Our algorithm can be applied to other radiometers and to simulate striping for evaluating its impact on data assimilation and science products.

The Special Sensor Microwave Imager/Sounder (SSMIS) provides vital long-term microwave radiometer observations as a member of the Global Precipitation Measurement (GPM) mission constellation. There are four SSMIS sensors that have been launched with three still in operation. In preparation for the GPM Version 7 (V07) data release, previous SSMIS calibration corrections are reexamined and updated using the latest data and recent knowledge gained from calibrating similar microwave radiometers. These calibration updates are presented here and include pointing parameters that affect geolocation, along-scan bias adjustments to correct for fall-off at the edge of the scan, and thermal heating issues caused by an emissive reflector and solar intrusions. The new updates greatly improve the accuracy of the SSMIS observations that will be released as part of the GPM V07 dataset.

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.

Cross-track microwave sounders make up a significant percentage of the radiometers included in the Global Precipitation Measurement (GPM) constellation. Therefore, it is important to properly assess the calibration of each sounder instrument and to understand the impact of the calibration on the derived precipitation rates. This ensures an accurate precipitation product is produced for the entire constellation. This paper will use data from past and current microwave sounders to show how offsets in the calibration can impact the precipitation using the GPM Level 2 GPROF algorithm. Potential improvements to the instrument calibration will be assessed by analyzing how they would positively impact the precipitation trends and agreement among the constellation sensors.

The airborne Conical Scanning Millimeter-wave Imaging Radiometer (CoSMIR) has participated in the Global Precipitation Measurement (GPM) Olympic Mountains Experiment (OLYMPEX) from November to December, 2015, with great success. With similar channels as that of the GPM Microwave Imager (GMI) at 89-183 GHz, CoSMIR served as a proxy for GMI by flying onboard the DC-8 Aircraft for a total of 17 science flights, collecting over 72 h of observations. The high-quality, calibrated brightness temperature data set is the result of several improvements made to CoSMIR prior to OLYMPEX to make the instrument more reliable. This paper describes these improvements and gives a detailed summary of the CoSMIR measurements obtained from OLYMPEX. CoSMIR experienced minor performance issues during the campaign, most of them were not excessive and only resulted in a loss of approximately 4 h of data for the entire campaign. Performance issues are discussed and shown how they were mitigated to achieve a quality data set. Comparisons of CoSMIR and GMI observations are presented to show that the CoSMIR measurements agree well with GMI. The CoSMIR data set is publicly available as a part of the OLYMPEX data suite and can reliably be used in the GPM algorithm development and related studies.

Spaceborne microwave radiometers with high frequency channels (150 - 183 GHz) are important for retrieving many geophysical parameters such as snowfall, ice water path, and atmospheric water vapor profiles. In order to obtain accurate retrievals from the brightness temperature measurements, the radiometers must be properly calibrated. Several methods have been developed to analyze the on-orbit calibration, including comparisons with radiative transfer models, comparisons with radiosonde profiles, and cross-calibration with similar radiometers. This paper introduces a new method to calibrate high frequency channels that utilizes TB histograms. This new method gives an independent approach that can be used by itself to analyze radiometer calibration or in conjunction with other methods to corroborate results.

The Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) dataset released by the Precipitation Processing System (PPS) will be updated to a final version within the next year. These updates are based on increased knowledge in recent years of radiometer calibration and sensor performance issues. In particular, the Global Precipitation Measurement (GPM) Microwave Imager (GMI) is used as a model for many of the TMI version updates. This paper discusses four aspects of the TMI data product that will be improved: spacecraft attitude, calibration and quality control, along-scan bias corrections, and sensor pointing accuracy. These updates will be incorporated into the final TMI data version, improving the quality of the data product and ensuring accurate geophysical parameters can be derived from TMI.

Vicarious cold calibration (VCC) for spaceborne microwave radiometers is analyzed and modified for application to conical scanning microwave imagers at frequencies from 6 to 90 GHz. The details of the algorithm are modified to account for additional frequencies and polarizations that were not included in the development of the original algorithm. The modified algorithm is shown to produce a more stable cold reference brightness temperature (TB) than the original algorithm. An analysis is performed of this updated algorithm to show the global regions that contribute to the derivation of the cold reference TB and to show which geophysical parameters contribute to the coldest TBs. The analysis suggests that water vapor variability has the largest impact on the TBs in the VCC algorithm. The modified VCC algorithm is applied to microwave imager data and is used as an intercalibration method. It is shown to agree well with other intercalibration methods, demonstrating that it is a valid and accurate method for calibration of microwave imagers.

A constellation of disparate radiometers is inherent to the Global Precipitation Measurement (GPM) mission concept. The task of the Intersatellite Calibration Working group is to generate adjustments to make the measurements of all these radiometers physically consistent. A key role of the GPM Microwave Imager (GMI) on the GPM Core satellite is to serve as a transfer standard among the constellation radiometers. The TRMM Microwave Imager (TMI) has served this role during the development phase and for interim corrections early in the GPM mission. The stability of GMI appears to be very good and a physically based calibration has been generated that appears to be accurate at the 1K level or better.

The effect of inter-calibration on a Level 3 rainfall product for the Global Precipitation Measurement (GPM) mission is examined using two spaceborne microwave radiometers that are currently used to derive rain measurements, the Tropical Rainfall Measuring Mission Microwave Imager (TMI) and the Special Sensor Microwave/Imager (SSM/I). It is found that inter-calibrating the microwave radiometer brightness temperatures from the two instruments improves the agreement of the derived rain accumulations between the two radiometers. The average difference between TMI and F13 derived rain accumulations is 0.60 mm/day before inter-calibration is applied. This difference decreases to 0.08 mm/day when F13 is inter-calibrated to TMI.

Vicarious cold calibration in the frequency range of 85-92 GHz is analyzed. Vicarious cold calibration cannot be applied at these frequencies as easily as at lower frequencies due to greater sensitivity to water vapor and hydrometeor scattering. The effects of that sensitivity are mitigated by selective filtering of the high-frequency brightness temperatures (TBs) to remove those data where large amounts of water vapor and/or hydrometeor scattering are present. Potential filtering algorithms are presented, and the performance of each with respect to vicarious cold calibration TB stability is characterized. A scattering-based precipitation filter that utilizes a combination of both the lower frequencies from 19 to 37 GHz and the frequencies from 85 to 92 GHz is shown to be the most effective and easily implemented filter. For horizontal polarization, the theoretical minimum TB at the higher frequencies occurs at an unphysically high sea surface temperature (SST), which makes the vicarious cold statistic more sensitive to the population of actual SST values as well as the higher amounts of water vapor associated with warm SSTs. The statistic is stabilized in this case by considering the difference between observed and simulated vicarious cold TBs. Intercalibration between two radiometers using the vicarious cold calibration double difference method at high frequencies is shown to be greatly improved when using the precipitation filter.

A method for estimating the pitch and roll errors of a satellite with an onboard conical scanning microwave radiometer is described. The method makes use of the vicarious cold calibration algorithm which derives a stable cold brightness temperature (TB) over ocean. This cold TB is sensitive to the Earth Incidence Angle (EIA) of the radiometer. Given no pitch or roll errors, the EIA can be modeled as a function of the Earth radius and altitude of the satellite. Deviation from this EIA can then be used to estimate the pitch and roll errors. The pitch/roll algorithm is applied to the current spaceborne microwave radiometer WindSat to show its performance, and the results are compared to the derived pitch and roll of WindSat that are found using a different attitude analysis method.

The robustness of the double difference method used for inter-calibration of microwave radiometers in the Global Precipitation Measurement (GPM) mission is analyzed. The double difference provides a way to compare two different radiometers and is more accurate than just a direct comparison. This is due to the double difference being able to remove geophysical variability from a radiometer's data, as well as frequency and incidence angle dissimilarity between radiometers that would otherwise get included in a direct comparison. These variations are removed by incorporating radiometer modeled brightness temperatures into the inter-calibration process using a vicarious calibration technique. This analysis shows how well the modeled radiometer data is able to remove these variations as well as how well the double difference method performs compared to a direct radiometer comparison.

The Global Precipitation Measurment (GPM) mission requires a high degree of consistency among the microwave radiometers in the constellation which, in turn, demands a standard against which all the sensors can be compared. Ultimately this standard will be the GPM Microwave Imager, but for the present the TRMM Microwave Imager (TMI) fills this need. Since its calibration leaves much to be desired, a refinement using Windsat has been developed. This article defines the Consensus Calibration 1.1 which is applied to the TMI. In turn the TMI serves as a transfer standard to other satellite radiometers.

The vicarious cold calibration statistic has been analyzed to determine its stability. Modeled top of atmosphere brightness temperatures representing microwave radiometer observations are computed for the month of July 2005 using a radiative transfer model. The vicarious cold calibration algorithm is applied to the population of brightness temperatures along with two other statistics for comparison. The stability is assessed by perturbing the sea surface temperatures and atmospheric water vapor in the model to simulate a global warming event. The results show that the vicarious cold calibration statistic is the most stable since it has the least variation for the simulated warming event.

The WindSat polarimetric microwave radiometer measures top-of-atmosphere brightness temperature, useful for retrieving surface wind vector over the ocean. This procedure was previously documented in low to moderate wind and light precipitation. An atmospheric clearing algorithm designed to remove the emissive and absorptive effects of stronger precipitation and extract the emissivity of the wind-driven ocean surface worked well in moderate rain but had limited success with strong rainfall and high winds. This paper presents results using an improved forward model including Mie scattering from rain. We consider three 2005 WindSat hurricane overpasses for proper atmospheric conditions (Dennis, Katrina and Rita). The improved atmospheric clearing algorithm extracts ocean surface emissivity in and near the hurricane rain bands and eyewall. The emissivity is compared to NOAA H*Wind analysis of the near-surface wind field. Results show a monotonic dependence of emissivity on wind speed up to category 3 hurricane-force winds.

Analysis of detectability of the kurtosis algorithm for pulsed-sinusoidal radio frequency interference (RFI) has already been performed in detail. The detectability for wide-band spread-spectrum RFI is investigated here. A commercial RF communications product XBee is used for generating the spread-spectrum signal which is fed to the Agile Digital Detector (ADD) through a bench-top radiometer. ADD measures the probability distribution function of the incoming signal. The performance of the detection algorithm for spread-spectrum RFI is characterized and compared to pulsed-sinusoidal RFI. The sensitivity of the kurtosis algorithm with respect to the spectral properties of the wide-band signal is also investigated.