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.

Active radar instruments provide vertically resolved clouds and precipitation measurements that cannot be provided by the passive instruments. These active measurements are not conventionally assimilated into the data assimilation systems because of the lack of fast forward radiative transfer models and also difficulties in the error modelling of the measurements. This paper describes the development, evaluation, and sensitivity analysis for a forward radar model implemented in the Community Radiative Transfer Model (CRTM). The scattering properties required by the forward model are provided by the hydrometeor lookup tables that were generated using the discrete dipole approximation. The model is able to calculate both the reflectivity and the attenuated reflectivity for any given radar instrument at any given zenith angles as long as CRTM instrument specific coefficients are available. The evaluation using CloudSat measurements shows a very good agreement between the simulations and measurements as long as the input profiles of hydrometeors are consistent with the measured reflectivity profiles. Major sources contributing to the differences between the measured and simulated reflectivities are input hydrometeor profiles, scattering lookup tables, lack of melting layer in the forward model, CRTM scattering solvers, and attenuation calculations. In addition to the forward model, both Tangent Linear and Adjoint of the model are also implemented and tested within CRTM. These components may be required by some data assimilation systems for the assimilation of radar measurements.

This paper presents an overview of the Hyperspectral Microwave Photonic Instrument (HyMPI), a 2021 NASA Instrument Incubation Proposal funded project aimed at developing the very first hyperspectral microwave sensor to augment thermodynamic sounding capability from space, with a focus on the Earth's Planetary Boundary Layer. This research responds to the recommendation expressed in the 2018 National Academies of Sciences decadal survey to accelerate the readiness of high-priority PBL observables not feasible for cost-effective spaceflight in 2017–2027. This paper provides an overview on HyMPI's design, configured as the objective instrument concept needed to fly in the future PBL mission and presents preliminary trade studies aim at demonstrating HyMPI's enhanced thermodynamic sounding skill in the Earth's Planetary Boundary Layer over conventional microwave sounders from the current Program of Record.

We present an overview of the Hyperspectral Microwave Photonic Instrument (HyMPI), a NASA Instrument Incubation Proposal funded research project aimed at developing a hyperspectral microwave instrument intended for enhanced remote sensing of atmospheric temperature and water vapor from space. This paper provides preliminary results on HyMPI's spectral and noise characteristics and a preliminary demonstration of its enhanced water vapor sensitivity and vertical resolution, with a particular focus on the Earth's Planetary Boundary Layer.

This paper evaluates the radiometric accuracy of observations from the Advanced Technology Microwave Sounder (ATMS) onboard Suomi National Polar-orbiting Partnership and Sondeur Atmospherique du Profil d' Humidité Intropicale par Radiométrie (SAPHIR) onboard Megha-Tropiques through intercalibration and validation versus in situ radiosonde and Global Positioning System Radio Occultation (GPS-RO) observations. SAPHIR and ATMS water vapor channels operate at slightly different frequencies. We calculated the bias due to radiometric errors as the difference between the observed and simulated differences between the two instruments. This difference, which is often referred to as double difference, ranges between 0.3 and 0.7 K, which shows good consistency between the instruments. We used a radiative transfer model to simulate the satellite brightness temperatures (Tbs) using radiosonde and GPS-RO profiles and then compared simulated and observed Tbs. The difference between radiosonde and ATMS Tbs for the middle and upper tropospheric temperature sounding channels was less than 0.5 K at most stations, but the difference between radiosonde and ATMS/SAPHIR Tbs for water vapor channels was between 0.5 and 2.0 K. The larger bias for the water vapor channels is mainly due to several errors in radiosonde humidity observations. The mean differences between the ATMS observations and the Tbs simulated using GPS-RO profiles were 0.2, 0.3, 0.4, 0.2, and -0.2 K for channels 10-14, respectively; and the uncertainty increases from 0.02 K for channel 10 to 0.07 K for channel 14.

A method is presented to calculate layer-averaged tropospheric humidity (LAH) from the observations of the Advanced Technology Microwave Sounder (ATMS) water vapor channels. The method is based on a linear relation between the satellite brightness temperatures (Tb) and natural logarithm of Jacobian weighted humidity. The empirical coefficients of this linear relation were calculated using different data sets, as well as a fast and a line-by-line radiative transfer (RT) model. It was found that the coefficients do not significantly depend on the data set or the RT model. This Tb to the LAH transformation method can be applied to either original or limb-corrected ATMS Tb's. The method was validated using both simulated and observed ATMS Tb's. The systematic difference between the estimated and calculated LAH values was less than 10% in most cases. We also tested the transformation method using a fixed Jacobian for each channel. The bias generally increases when fixed Jacobians are used, but there is still a satisfactory agreement between estimated and calculated LAH values. In addition, the spatial distribution of the bias was investigated using the European Center for Medium-Range Weather Forecasting (ECMWF) Interim Reanalysis (ERA-interim) and collocated ATMS observations. The bias did not indicate any significant regional dependence when actual Jacobians were used, but in the case of fixed Jacobians, the bias generally increased from middle latitude toward the poles.

In this paper, the overall quality of the water vapor profiles of global operational radiosonde data for the period 2000-2009 is investigated using upper tropospheric humidity (UTH) retrieved from microwave satellite data. Overall, the nighttime radiosonde data showed a dry bias (- 5% to -15%) over Europe, Australia, and New Zealand and systematically moist bias (greater than 30%) over China and the former Soviet Union. The nighttime sonde data from the U.S. and Canada showed a bias between -10% and 20%. Most stations indicated a daytime radiation dry bias, except for a few stations from the U.S. and the former Soviet Union. A sensorwise comparison showed a large nighttime wet bias for the Russian (MRZ-3A and MARS) and Chinese GZZ-2 sensors, a relatively small nighttime wet bias for the U.S. Sippican and VIZ-B2 sensors, and a nighttime dry bias for the Chinese GTS1, Vaisala (RS80-A, RS80-H, RS90, RS92K, and RS92-SGP), and the U.S. VIZ-MKII sensors. All sensors had a daytime radiation dry bias, except for the Russian MRZ-3A sensor that had a daytime radiation wet bias that could be because of the daytime radiation bias correction. Because of the large differences between different radiosonde sensors, it is essential for UTH studies to only use the data measured using a single type of sensor at any given station.

Microwave (MW) satellite data are widely used as input in numerical weather prediction models and also in other applications such as climate monitoring and re-analysis. MW satellite data are prone to different problems, including geolocation errors. These data do not have a fine spatial resolution like visible and infrared data; therefore, the accuracy of their geolocation cannot be easily determined using the normal methods such as superimposing coastlines on the satellite images. Currently, no geolocation correction is performed on data from MW instruments aboard the satellites in the National Oceanic and Atmospheric Administration (NOAA) Polar Operational Environmental Satellite program. However, geolocation error can be a significant source of bias in the satellite measurements. In this paper, we investigated and corrected the geolocation errors of the observations from the Advanced Microwave Sounding Unit (AMSU)-A aboard NOAA-15 to NOAA-19, AMSU-B aboard NOAA-15 to NOAA-17, and Microwave Humidity Sounder (MHS) aboard NOAA-18 and NOAA-19. We used the difference between ascending and descending observations along the coastlines to quantify the geolocation errors in terms of the satellite attitudes (Euler angles), i.e., pitch, roll, and yaw. Then, new geographical coordinates and scan/local zenith angles were calculated using new attitudes. The results show that NOAA-15 AMSU-A2 instrument has a mounting error of about 1.2$^{\circ}$cross-track, and$-0.5^{\circ}$along-track, NOAA-16 AMSU-A1 and -A2 instruments have a mounting error of about$-0.5^{\circ}$along-track, and NOAA-18 AMSU-A2 instrument has a mounting error of more than$-1^{\circ}$along-track.

More than one decade of observations from the Advanced Microwave Sounding Unit-A (AMSU-A) onboard the polar-orbiting satellites NOAA-15 to NOAA-19 and European Meteorological Operational satellite program-A (MetOp-A) provide global information on atmospheric temperature profile, water vapor, cloud, precipitation, etc. However, a pronounced asymmetric cross-scan bias of the AMSU-A window channels was discovered, and it severely impacted water cycle product generation. Several approaches, including vicarious cold and hot reference calibration techniques, are applied to characterize the cross-scan bias. The bias pattern appears to be stable through several years of data examined from the same satellite but is quite different among those onboard the different NOAA (NOAA-15, NOAA-16, NOAA-17, NOAA-18, and NOAA-19) and EUMETSAT (MetOp-A) satellites. The scan bias may be caused by sensor polarization misalignment or cross-polarization, even after the radiance/brightness temperature data have been geocorrected with regard to geolocation and view angles. Based upon the characterization information, two-point and three-point correction approaches are proposed; both approaches provide promising results for AMSU-A window channels at brightness temperature level and product level and outperform the current operational correction approach, which is essentially a one-point correction. This serves as the first step toward a more stable fundamental and thematic climate data record to be used in hydrological and meteorological applications.