A global planetary boundary layer (PBL) observing system is urgently needed to address fundamental PBL science questions and societal applications related to climate, weather, and air quality. Such a PBL observing system should optimally combine emerging yet technically viable space-based observations of the PBL thermodynamic structure with complementary surface-based and suborbital assets, while taking advantage of, and helping improve, climate and weather models as well as data assimilation systems. The Earth science community has expressed great interest in improving the characterization of the atmospheric PBL in the recent National Academies of Sciences, Engineering, and Medicine (NASEM) 2017-27 decadal survey for Earth Science and Applications from Space (ESAS). Specifically, higher spatial and temporal resolution observations of PBL temperature and water vapor profiles, and of PBL height, were selected as priorities by the decadal survey, which recommended a PBL mission in its incubation class. In response, NASA established the Decadal Survey Incubation program and a PBL Study Team focused on prioritizing PBL science and technology that would require advancement and development prior to implementation. In this paper, we summarize the key findings of the NASA PBL Study Team report. SIGNIFICANCE STATEMENT: The planetary boundary layer (PBL) is the atmospheric turbulent layer adjacent to Earth's surface. Humans live in the PBL, and the weather and climate that we experience have a tremendous impact on our health, safety, and economy. The science commu-nity has manifested an urgent need for a global PBL observing system focused on PBL profiles of temperature and water vapor, as well as PBL height, to address fundamental PBL science ques-tions and societal applications. The measurement requirements of such a system will likely not be satisfied using a single measurement technology, and this global PBL observing system should optimally combine a diverse set of space-based observations with suborbital and surface-based measurements.

Observing System Simulation Experiments (OSSEs) are essential for evaluating and designing remote sensing systems using synthetic observations. A key aspect of OSSEs is tuning observation errors, typically by comparing data assimilation (DA) statistics from real and synthetic observations. However, during instrument design, only limited specifications, such as channels’ frequency and spectral response functions, are available. This paper presents a method for generating pseudo-realistic infrared observations for proposed instruments using high-resolution measurements from the Infrared Atmospheric Sounding Interferometer (IASI) as a reference. The approach involves converting IASI measurements into infrared spectra using the Fast Fourier Transform (FFT) and inverse FFT (iFFT), then deriving channel-specific radiances for new instruments by convolving sensor response functions. Validation using IASI and AIRS simulations shows a mean difference below 0.1 K and a standard deviation under 0.5 K when comparing Fourier-derived AIRS brightness temperatures with those from a radiative transfer model. The accuracy of these conversions is confirmed by reconstructing IASI brightness temperatures via FFT, yielding negligible differences. An example of how these pseudo-observations can be used in OSSE experiments is presented, and the results are discussed.

The CRTM transmittance coefficient generation package is a high-performance computing workflow to generate spectral and transmittance coefficients for the JCSDA Community Radiative Transfer Model (CRTM), which is used as an observation operator in satellite data assimilation applications. The characteristics of new instruments, whose observations are modeled by the CRTM, are encapsulated in these aforementioned coefficient data structures. This approach is a key factor in achieving the necessary high computational speeds with the CRTM. Complex atmospheric transmittance spectra in particular are approximated using the ODPS and ODAS algorithms. The transmittance coefficient generation package for the first time provides a unified and reproducible workflow to CRTM generate coefficients.

The NASA Convective Processes Experiment - Cabo Verde (CPEX-CV) field campaign took place in September 2022 out of Sal Island, Cabo Verde. A unique payload aboard the NASA DC-8 aircraft equipped with advanced remote sensing and in situ instrumentation, in conjunction with radiosonde launches and satellite observations, allowed CPEX-CV to target the coupling between atmospheric dynamics, marine boundary layer properties, convection, and the dust-laden Saharan Air Layer in the data-sparse tropical East Atlantic region. CPEX-CV provided measurements of African Easterly Wave environments, diurnal cycle impacts on convective lifecycle, and several Saharan dust outbreaks, including the highest dust optical depth observed by the DC-8 interacting with what would become Tropical Storm Hermine. Preliminary results from CPEX-CV underscore the positive impact of dedicated tropical East Atlantic observations on downstream forecast skill, including sampling environmental forcings impacting the development of several non-developing and developing convective systems such as Hurricanes Fiona and Ian. Combined airborne radar, lidar, and radiometer measurements uniquely provide near-storm environments associated with convection on various spatiotemporal scales and, with in situ observations, insights into controls on Saharan dust properties with transport. The DC-8 also collaborated with the European Space Agency to perform coordinated validation flights under the Aeolus spaceborne wind lidar and over the Mindelo ground site, highlighting the enhanced sampling potential through partnership opportunities. CPEX-CV engaged in professional development through dedicated team building exercises that equipped the team with a cohesive approach for targeting CPEX-CV science objectives and promoted active participation of scientists across all career stages.

A continuation of the National Aeronautics and Space Administration’s (NASA) truncated Convective Processes Experiment – Aerosols and Winds (CPEX-AW) field program flown out of St. Croix, USVI, in the summer of 2021, CPEX – Cabo Verde (CPEX-CV) deployed NASA’s DC-8 from Sal Island, Cabo Verde in September 2022, equipped with a unique and comprehensive suite of active and passive remote sensing and in-situ capabilities that, in combination with the availability of similar spaceborne observations, allowed for the measurements of tropospheric aerosols, winds, temperature, water vapor, and clouds and precipitation. The tropical northern East Atlantic Ocean is a data sparse region that, in boreal summer, offers a unique location to study convective lifecycles and processes in a variety of thermodynamic, dynamic, and aerosol environments, such as within persistent (e.g., Intertropical Convergence Zone, ITCZ) and periodic (e.g., African easterly waves and tropical cyclones) large-scale forcing, local terrain effects (e.g., land-ocean transition off western Africa), and aerosol-cloud interactions (e.g., Saharan air layer). In addition to observing the interaction between large-scale environmental forcing and convective systems, the payload is uniquely capable of observing the smaller-scale processes within the near-environment of convection, including those within the marine boundary layer (e.g., cold pools), the inflow/outflow of the storm, and dust-cloud interactions, that affect convective initiation and lifecycle, as well as other poorly resolved/understood properties of these systems.CPEX-AW and -CV were a part of a joint observing effort with the European Space Agency (ESA) and their partner laboratories and universities called the Joint Aeolus Tropical Atlantic Campaign (JATAC) out of Cabo Verde to validate ESA’s Aeolus satellite. As part of the CPEX-CV – JATAC collaboration in September 2022, the NASA DC-8 carried out coincident underpasses of the Aeolus satellite on four of the thirteen CPEX-CV research flights, performed four overpasses of the ASKOS ground site at Mindelo, and two coordinated flights with the Slovenian WT-10 in situ aircraft that also encompassed a coincident flight under Aeolus and over the Mindelo ground site. Here, we summarize the CPEX-CV science objectives, mission architecture, scientific targets observed on flights flown during the September 2022 campaign, and highlights of the data collected planned to be released to the community in early April 2023 with a focus on collaborative efforts between CPEX-CV and JATAC.

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 (RT) models and also difficulties in the error modeling of the measurements. This article describes the development, evaluation, and sensitivity analysis for a forward radar model implemented in the community RT 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 (DDA). 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 (TL) and adjoint (AD) 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.

In this article, we present a comprehensive sensitivity analysis and geophysical retrieval product demonstration to assess the enhanced information content in atmospheric temperature and water vapor, harnessed in hyperspectral microwave measurements. A particular focus of this study is devoted to quantifying and comparing the impact on retrieval performance resulting from novel spectral bands of the microwave thermal spectrum, by means of data addition and data denial trade studies. Various spectral configurations are assessed, each reflecting specific technology solutions intended to maximize geophysical product performance within feasible size, weight, power, and cost constraints. 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 $\sim$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 window regions, leading to an overall improvement of up to $\sim$50% in the Earth's planetary boundary layer thermodynamic fields. This work concludes with an overview on the state of the art in hyperspectral microwave technology and a discussion on future applications of interest to numerical weather prediction and climate science. The work presented in this study focuses on ocean, clear-sky demonstrations. All-sky, all-surface investigations will be the focus of a follow-up study, as we advance our capability to simulate more complex scenarios and improve scene variability.

<p>Established in 2017 as a pilot project, the NASA Commercial Smallsat Data Acquisition (CSDA) Program evaluates and acquires commercial datasets that compliment NASA Earth Science research and application goals. The success of the pilot and recognition of the value commercial data provide to the scientific community led to establishment of a sustained program within NASA’s Earth Science Division (ESD) with objectives of providing continuous on-ramp of new commercial vendors to evaluate the potential to advance NASA’s Earth science research and application activities, enable sustained use of the purchased data by the scientific community, ensure long-term preservation of purchased data for scientific reproducibility, and coordinate with other U.S. Government agencies and international partners on the evaluation and use of commercial data. This presentation will focus on data made available for scientific use through the CSDA Program, especially those datasets added since the conclusion of the original pilot project, describe the process for end users to access of CSDA managed datasets, and provide a status overview of ongoing and upcoming vendor evaluation activities will be given. Recent scientific research results from CSDA subject matter experts utilizing commercial data will also be provided.</p>

A new instrument has been proposed for measuring surface air pressure over the marine surface with a combined active/passive scanning multichannel differential absorption radar to provide an estimate of the total atmospheric column oxygen content. A demonstrator instrument, the Microwave Barometric Radar and Sounder (MBARS), has been funded by the National Aeronautics and Space Administration for airborne test missions. Here, a proof-of-concept study to evaluate the potential impact of spaceborne surface pressure data on numerical weather prediction is performed using the Goddard Modeling and Assimilation Office global observing system simulation experiment (OSSE) framework. This OSSE framework employs the Goddard Earth Observing System model and the hybrid 4D ensemble variational Gridpoint Statistical Interpolation data assimilation system. Multiple flight and scanning configurations of potential spaceborne orbits are examined. Swath width and observation spacing for the surface pressure data are varied to explore a range of sampling strategies. For wider swaths, the addition of surface pressures reduces the root-mean-square surface pressure analysis error by as much as 20% over some ocean regions. The forecast sensitivity observation impact tool estimates impacts on the Pacific Ocean basin boundary layer 24-h forecast temperatures for spaceborne surface pressures that are on par with rawinsondes and aircraft and estimates greater impacts than the current network of ships and buoys. The largest forecast impacts are found in the Southern Hemisphere extratropics.

Hyperspectral sounder brightness temperatures assimilated in the Global Earth Observing System Atmospheric Data Assimilation System (GEOS-ADAS) were previously limited to assimilating temperature and moisture. The ozone-sensitive 9.6 mu m region is sensed by several hyperspectral sounders including AIRS (Atmospheric InfraRed Sounder), IASI (Infrared Atmospheric Sounding Interferometer), and CrIS (Cross-track Infrared Sounder). Direct assimilation of brightness temperatures in the 9.6 mu m region have been operational at ECMWF for several years. With this study, similar improvements using the GEOS-ADAS are presented. Channels were selected from available operational subsets evaluating information content and minimizing inter-channel correlation. Additionally, information such as channel selections made by other studies, and vertical sensitivities of ozone and temperature were considered. The analyses produced show improvements verified against ozonesondes taken from SHADOZ (Southern Hemisphere Additional Ozonesondes) and WOUDC (World Ozone and Ultraviolet Data Center). This work will be added to the GEOS-ADAS and will provide an improved source of ozone data in NASA Global Modeling and Assimilation Office products.

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.

Directly assimilating microwave radiances over land, snow, and sea ice remains a significant challenge for data assimilation systems. These data assimilation systems are critical to the success of global numerical weather prediction systems including the Global Earth Observing System-Atmospheric Data Assimilation System (GEOS-ADAS). Extending more surface sensitive microwave channels over land, snow, and ice could provide a needed source of data for numerical weather prediction particularly in the planetary boundary layer (PBL). Unfortunately, the accuracy of emissivity models currently available within the GEOS-ADAS along with other data assimilation systems are insufficient to simulate and assimilate radiances. Recently, Munchak et al. published a 5-yr climatological database for retrieved microwave emissivity from the Global Precipitation Measurement (GPM) Microwave Imager (GMI) aboard the GPM mission. In this work the database is utilized by modifying the GEOS-ADAS to use this emissivity database in place of the default emissivity value available in the Community Radiative Transfer Model (CRTM), which is the fast radiative transfer model used by the GEOS-ADAS. As a first step, the GEOS-ADAS is run in a so-called stand-alone mode to simulate radiances from GMI using the default CRTM emissivity, and replacing the default CRTM emissivity models with values from Munchak et al. The simulated GMI observations using Munchak et al. agree more closely with observations from GMI. These results are presented along with a discussion of the implication for GMI observations within the GEOS-ADAS.

The Community Radiative Transfer Model (CRTM) is a fast model that requires bulk optical properties of hydrometeors in the form of lookup tables to simulate all‐sky satellite radiances. Current cloud scattering lookup tables of CRTM were generated using the Mie‐Lorenz theory thus assuming spherical shapes for all frozen habits, while actual clouds contain frozen hydrometeors with different shapes. The Discrete Dipole Approximation (DDA) technique is an effective technique for simulating the optical properties of non‐spherical hydrometeors in the microwave region. This paper discusses the implementation and validation of a comprehensive DDA cloud scattering database into CRTM for the microwave frequencies. The original DDA database assumes total random orientation in the calculation of single scattering properties. The mass scattering parameters required by CRTM were then computed from single scattering properties and water content dependent particle size distributions. The new lookup tables eliminate the requirement for providing the effective radius as input to CRTM by using the cloud water content for the mass dimension. A collocated dataset of short‐term forecasts from Integrated Forecast System of the European Center for Medium‐Range Weather Forecasts and satellite microwave data was used for the evaluation of results. The results overall showed that the DDA lookup tables, in comparison with the Mie tables, greatly reduce the differences among simulated and observed values. The Mie lookup tables especially introduce excessive scattering for the channels operating below 90 GHz and low scattering for the channels above 90 GHz.

An observing system simulation experiment (OSSE) was performed to assess the impact of assimilating hyperspectral infrared (IR) radiances from geostationary orbit on numerical weather prediction, with a focus on the proposed sounder on board the Geostationary Extended Observations (GeoXO) program's central satellite. Infrared sounders on a geostationary platform would fill several gaps left by IR sounders on polar-orbiting satellites, and the increased temporal resolution would allow the observation of weather phenomena evolution. The framework for this OSSE was the Global Modeling and Assimilation Office (GMAO) OSSE system, which includes a full suite of meteorological observations. The experiment additionally assimilated four identical IR sounders from geostationary orbit to create a "ring " of vertical profiling observations. Based on the experimentation, assimilation of the IR sounders provided a beneficial impact on the analyzed mass and wind fields, particularly in the tropics, and produced an error reduction in the initial 24-48 h of the subsequent forecasts. Specific attention was paid to the impact of the GeoXO Sounder (GXS) over the contiguous United States (CONUS) as this is a region that is well-observed and as such difficult to improve. The forecast sensitivity to observation impact (FSOI) metric, computed across all four synoptic times over the CONUS, reveals that the GXS had the largest impact on the 24-h forecast error of the assimilated hyperspectral infrared satellite radiances as measured using a moist energy error norm. Based on this analysis, the proposed GXS has the potential to improve numerical weather prediction globally and over the CONUS.Significance StatementThe purpose of this study is to understand the impact of the proposed geostationary hyperspectral infrared sounder as part of the Geostationary Extended Observations (GeoXO) program on numerical weather prediction. The evaluation was done using a simulated environment, and showed a beneficial impact on the tropical mass and wind fields and an error reduction in the initial 24-48 h forecasts. Over the contiguous United States, the GeoXO Sounder (GXS) performed well and had the largest impact of the assimilated infrared satellite radiances on the 24 h forecast as measured by a moist energy error norm. Based on the results of this study, the proposed GXS has the potential to improve numerical weather prediction.

Due to the sheer volume of data, leveraging satellite instrument observations effectively in a data assimilation context for numerical weather prediction or for remote sensing requires a radiative transfer model as an observation operator that is both fast and accurate at the same time. Physics-based line-by-line radiative transfer (RT) models fulfil the requirement for accuracy, but are too slow and too costly in computational terms for operational applications. Therefore, fast methods were developed to be able to perform fast RT calculations using techniques such as spectral sampling or pre-computed look-up tables. The operational fast models currently calculate the absorption and scattering coefficients from the pre-computed regression coefficients and atmospheric state and cloud profiles. As a novel solution to this problem, this work investigates a deep learning approach to replace the regression coefficients in the fast RT models. A selection of hidden-layer neural network configurations is trained against atmospheric transmittance profile data computed by an accurate line-by-line model and their performance is evaluated and their advantages and disadvantages are discussed.

Atmospheric winds are a key physical phenomenon impacting natural hazards, energy transport, ocean currents, large-scale circulation, and ecosystem fluxes. Observing winds is a complex process and presents a large gap in NASA's Earth Observation System. Atmospheric motion vectors (AMVs) aim to fill this gap by making numerical estimates of cloud movement between sequences of multi-spectral satellite images, tracking clouds and water vapor. Recent imaging hardware and software advancements have enabled the use of numerical optical flow techniques to produce accurate and dense vector fields outperforming traditional methods. This work presents WindFlow as the first machine learning based system for feature tracking atmospheric motion using optical flow. Due to the lack of large-scale satellite-based observations, we leverage high-resolution numerical simulations from NASA's GEOS-5 Nature Run to perform supervised learning and transfer to satellite images. We demonstrate that our approach using deep learning based optical flow scales to ultra-high-resolution images of size 2881x5760 with less than 1 m/s bias and 2.5 m/s average error. Four network and learning architectures are compared and it is found that recurrent all-pairs field transforms (RAFT) produces the lowest errors on all metrics for wind speed and direction. Results on held out numerical outputs show RAFT's good performance in each of the spatial, temporal, and physical dimensions. A comparison between WindFlow and an operational AMV product against rawinsonde observations shows that RAFT transfers across simulations and thermal infrared satellite observations. This work shows that machine learning based optical flow is an efficient approach to generating robust feature tracking for AMVs consistently over large regions.

Observing System Simulation Experiments (OSSEs) for numerical weather prediction rely on simulated observations that should include simulated observation errors in order to realistically represent the behaviour of real data. Real observations include many types of error, such as instrument error, representativeness error, and observation operator error, with some portion of this error being correlated in time and space or possibly between data types. Data assimilation systems are designed to account for random, uncorrelated errors, but are not yet adept at handling correlated errors; as a result, the correlated errors are more readily incorporated into the analysis increment by the data assimilation system than uncorrelated errors. In this work, the role of correlated observation errors in modifying the behaviour of the National Aeronautics and Space Administration Global Modeling and Assimilation Office (NASA/GMAO) OSSE framework is investigated. The effects on analysis increment, analysis error, forecast errors and observation impacts of including or neglecting correlated simulated errors is explored. The use of correlated observations for calibration and validation of the OSSE is also discussed.

Under its Commercial Smallsat Data Acquisition (CSDA) Program, NASA has evaluated data from and entered into a sustained purchase agreement with Spire Global, Inc. The primary goal of this agreement is to make Spire datasets available to the research community to enhance the scientific objectives of the Earth Science Division (ESD), though the applicability to the NASA Heliophysics Division of these data is noted as well. The purpose of this talk is to present the data holdings from Spire and to present scientific results that have utilized these commercially acquired data.

In the recent years, the Earth observation (EO) capacity from space has grown with the multiplication of both institutional and commercial missions; in particular the domain of high-resolution optical sensors and SAR (Synthetic Aperture Radar) has dramatically increased. The “NewSpace players” are considered in full in the evolution of the EO international strategy. In this context, ESA and NASA put in place several activities that aim at assessing the data coming from these new missions, like the ESA’ s Earthnet Data Assessment Pilot (EDAP) project and the NASA's Commercial Smallsat Data Acquisition (CSDA) Program. Given the initial success of both of these activities and the multiplication of new missions in the context of the NewSpace, ESA and NASA are proposing to extend these activities with a coordinated approach and the definition of joint guidelines for data quality assessment.