Maximizing the utility of limited Earth observing satellite resources is a difficult ongoing problem. Dynamic Targeting is an approach to this challenge that intelligently plans and executes primary sensor observations based on information from a look-ahead sensor. However, current implementations have failed to account for realistic satellite operational constraints and have used static utility for repeat observations of the same target. To address these limitations, we implement a more general Dynamic Targeting framework that comprises a physics-based slew model, a dynamic model of observation utility, and an algorithm for gathering high-utility observations. To demonstrate this framework, we also supply complex dynamic utility models that are applicable to many missions and new algorithms for intelligently scheduling observations with slewing restrictions and changing utility, including a greedy algorithm and a depth-first search algorithm. To evaluate these algorithms, we test their performance across simulated runs through two datasets and compare to the performance of an algorithm representative of most scheduling algorithms aboard Earth science missions today as well as an intractable upper bound. We show that our algorithms have great potential to improve science return from Earth science missions.

Dynamic targeting (DT) is an emerging concept for improving science yield on Earth-observing missions limited by power-constrained sensors. DT uses a lookahead sensor together with on-board decision making to save resources for valuable observations in the future. Previous work has focused on developing DT mission use cases, such as storm hunting and cloud avoidance, that have relatively straightforward observation goals (i.e., look for storms, avoid clouds). However, DT has the potential to improve the science return of more complex missions and studies. To demonstrate this, we present and develop a new DT mission scenario to study the Planetary Boundary Layer (PBL). This paper describes the elements of our PBL mission scenario, which not only only involves multiple spacecraft, but also more sophisticated instruments, science models, and on-board decision making.

Different retrieval algorithms have been developed to process top-of-atmosphere (TOA) spectral radiance data provided by hyperspectral infrared sounder missions. Those algorithms are either optimal estimation method (OEM) based schemes with radiative transfer calculation involved in the retrieval process, or machine learning based methods that allow ultra-efficient data procession but lack of radiometric consistency validation based on the directly measured information. Combining both approaches leverages their respective technical advantages, leading to more accurate results. This study introduces a hyperspectral sounder fingerprinting algorithm to explore this hybrid approach. This approach involves the use of a spectral information-based classification method to identify an reference geophysical state and the corresponding radiative kernel. This enables the efficient retrieval of geophysical variables of interest through a radiative kernel-based linear inversion procedure. The fingerprinting method has been applied to analyze a decadelong hyperspectral sounder data record.

The Smart Ice Cloud Sensing (SMICES) is an active/passive sensor. SMICES is sponsored by NASA Earth Science Technology Office (ESTO) under Instrument Incubator Program 19 (IIP-19) awarded to Northrop Grumman Corporation (NGC) and Jet Propulsion Laboratory (JPL). The instrument is designed to measure upper tropospheric and lower stratospheric cloud ice and water vapor. SMICES uses a suite of passive radiometers that are constantly conically scanning to locate ice clouds. The ice clouds are located using an artificial intelligence controller that identifies key labels related to the ice cloud. Once an ice cloud is identified, the artificial intelligence controller activates and targets the on-board radar. While the SMICES instrument is currently being developed for an airborne demonstration, the final goal is to deploy it as a small satellite (SmallSat) instrument in low-Earth orbit (LEO). The onboard AI controller will significantly reduce DC power consumption of the satellite mission. This will enable the SMICES system to be hosted on a smaller platform with fewer solar cells and significantly drive down mission costs while maintaining the quality of scientific data. This work presents the latest development on the SMICES microwave radiometer.

The Smart Ice Cloud Sensing (SMICES) instrument aims at providing onboard smart autonomous observation of upper tropospheric water vapor and ice particle size distribution in clouds at various local times. SMICES is an active/passive combined sensor with sounding channels at 380 GHz, radiometric channels at 250, 310 and 670 GHz, and a radar instrument operating at 239 GHz. A low-noise, low-power radiometer command and data handling (C&DH) subsystem has been designed to acquire the 24 analog radiometer channels and 8 analog thermistor data. A radiometric power regulation system provides the required power supplies for the other radiometric subsystems of the SMICES instrument. An on-board FPGA provides command and control of other instrument subsystems, performs synchronous data acquisition. The radiometer electronics are designed to fit into less than 2U horizontal dimensions of a CubeSat instrument. An AI controller unit directly interfacing with radar and radiometer C&DH subsystems performs on-board artificial intelligence operations for full system autonomy. The AI unit will control the radar instrument depending on the system health conditions, including the battery level, and based on the observed scene through the radiometer instrument.

We have developed a$k$-coefficient retrieval approach for Atmospheric Infrared Sounder (AIRS) observations, using AIRS cloud-cleared radiances (ACCRs) as input. This new approach takes advantage of the available ACCR, reduces computational expense, offers an efficient and accurate cirrus cloud retrieval alternative for hyperspectral infrared (IR) observations, and is potentially applicable to the compilation of a long-term cirrus cloud climatology from hyperspectral IR observations. The retrieval combines a lookup-table method coupled to a residual minimization scheme using observed cloudy and cloud-cleared AIRS radiances as input. Six AIRS channels between 766 and 832$\hbox{cm}^{-1}$with minimal water vapor absorption/emission have been selected, and their spectral radiances have been demonstrated to be sensitive to both cirrus cloud optical depth$(\tau_{c})$and ice crystal effective particle size$(D_{e})$. The capability of the$k$-coefficient approach is demonstrated by comparison with a more accurate retrieval program, which combines the delta-four stream (D4S) approximation with the currently operational Stand-alone AIRS Radiative Transfer Algorithm (SARTA). The distribution patterns and the range of retrieved cloud parameters from the$k$-coefficient approach are nearly identical to those from SARTA$+$D4S retrievals, with minor differences traced to uncertainties in parameterized cloudy radiances in the$k$-coefficient approach and in the ACCR. The$k$-coefficient approach has also been applied to four AIRS granules over North Central China, Mongolia, and Siberia containing a significant presence of cirrus clouds, and its results are quantitatively compared to simultaneous Moderate Resolution Imaging Spectroradiometer/Aqua cirrus cloud retrievals. Finally, AIRS retrieved$\tau_{c}$and$D_{e}$are consistent with the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) and CloudSat derived values for semitransparent cirrus clouds, with more significant differences in thicker cirrus and multilayer clouds.