A 1-D variational system has been developed to process spaceborne measurements. It is an iterative physical inversion system that finds a consistent geophysical solution to fit all radiometric measurements simultaneously. One of the particularities of the system is its applicability in cloudy and precipitating conditions. Although valid, in principle, for all sensors for which the radiative transfer model applies, it has only been tested for passive microwave sensors to date. The Microwave Integrated Retrieval System (MiRS) inverts the radiative transfer equation by finding radiometrically appropriate profiles of temperature, moisture, liquid cloud, and hydrometeors, as well as the surface emissivity spectrum and skin temperature. The inclusion of the emissivity spectrum in the state vector makes the system applicable globally, with the only differences between land, ocean, sea ice, and snow backgrounds residing in the covariance matrix chosen to spectrally constrain the emissivity. Similarly, the inclusion of the cloud and hydrometeor parameters within the inverted state vector makes the assimilation/inversion of cloudy and rainy radiances possible, and therefore, it provides an all-weather capability to the system. Furthermore, MiRS is highly flexible, and it could be used as a retrieval tool (independent of numerical weather prediction) or as an assimilation system when combined with a forecast field used as a first guess and/or background. In the MiRS, the fundamental products are inverted first and then are interpreted into secondary or derived products such as sea ice concentration, snow water equivalent (based on the retrieved emissivity) rainfall rate, total precipitable water, integrated cloud liquid amount, and ice water path (based on the retrieved atmospheric and hydrometeor products). The MiRS system was implemented operationally at the U.S. National Oceanic and Atmospheric Administration (NOAA) in 2007 for the NOAA-18 satellite. Since then, it has been extended to run for NOAA-19, Metop-A, and DMSP-F16 and F18 SSMI/S. This paper gives an overview of the system and presents brief results of the assessment effort for all fundamental and derived products.

Artificial intelligence (AI) techniques have had significant recent successes in multiple fields. These fields and the fields of satellite remote sensing and NWP share the same fundamental underlying needs, including signal and image processing, quality control mechanisms, pattern recognition, data fusion, forward and inverse problems, and prediction. Thus, modern AI in general and machine learning (ML) in particular can be positively disruptive and transformational change agents in the fields of satellite remote sensing and NWP by augmenting, and in some cases replacing, elements of the traditional remote sensing, assimilation, and modeling tools. And change is needed to meet the increasing challenges of Big Data, advanced models and applications, and user demands. Future developments, for example, SmallSats and the Internet of Things, will continue the explosion of new environmental data. ML models are highly efficient and in some cases more accurate because of their flexibility to accommodate nonlinearity and/or non-Gaussianity. With that efficiency, ML can help to address the demands put on environmental products for higher accuracy, for higher resolution—spatial, temporal, and vertical, for enhanced conventional medium-range forecasts, for outlooks and predictions on subseasonal to seasonal time scales, and for improvements in the process of issuing advisories and warnings. Using examples from satellite remote sensing and NWP, it is illustrated how ML can accelerate the pace of improvement in environmental data exploitation and weather prediction—first, by complementing existing systems, and second, where appropriate, as an alternative to some components of the NWP processing chain from observations to forecasts.

Passive microwave (PMW) satellite-based precipitation over land algorithms rely on physical models to define the most appropriate channel combinations to use in the retrieval, yet typically require considerable empirical adaptation of the model for use with the satellite measurements. Although low-frequency channels are better suited to measure the emission due to liquid associated with rain, most techniques to date rely on high-frequency, scattering-based schemes since the low-frequency methods are limited to the highly variable land surface background, whose radiometric contribution is substantial and can vary more than the contribution of the rain signal. Thus, emission techniques are generally useless over the majority of the Earth's surface. As a first step toward advancing to globally useful physical retrieval schemes, an intercomparison project was organized to determine the accuracy and variability of several emissivity retrieval schemes. A three-year period (July 2004-June 2007) over different targets with varying surface characteristics was developed. The PMW radiometer data used includes the Special Sensor Microwave Imagers, SSMI Sounder, Advanced Microwave Scanning Radiometer (AMSR-E), Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI), Advanced Microwave Sounding Units, and Microwave Humidity Sounder, along with land surface model emissivity estimates. Results from three specific targets in North America were examined. While there are notable discrepancies among the estimates, similar seasonal trends and associated variability were noted. Because of differences in the treatment surface temperature in the various techniques, it was found that comparing the product of temperature and emissivity yielded more insight than when comparing the emissivity alone. This product is the major contribution to the overall signal measured by PMW sensors and, if it can be properly retrieved, will improve the utility of emission techniques for over land precipitation retrievals. As a more rigorous means of comparison, these emissivity time series were analyzed jointly with precipitation data sets, to examine the emissivity response immediately following rain events. The results demonstrate that while the emissivity structure can be fairly well characterized for certain surface types, there are other more complex surfaces where the underlying variability is more than can be captured with the PMW channels. The implications for Global Precipitation Measurement-era algorithms suggest that physical retrievals are feasible over vegetated land during the warm seasons.

Promising new opportunities to apply artificial intelligence (AI) to the Earth and environmental sciences are identified, informed by an overview of current efforts in the community. Community input was collected at the first National Oceanic and Atmospheric Administration (NOAA) workshop on “Leveraging AI in the Exploitation of Satellite Earth Observations and Numerical Weather Prediction” held in April 2019. This workshop brought together over 400 scientists, program managers, and leaders from the public, academic, and private sectors in order to enable experts involved in the development and adaptation of AI tools and applications to meet and exchange experiences with NOAA experts. Paths are described to actualize the potential of AI to better exploit the massive volumes of environmental data from satellite and in situ sources that are critical for numerical weather prediction (NWP) and other Earth and environmental science applications. The main lessons communicated from community input via active workshop discussions and polling are reported. Finally, recommendations are presented for both scientists and decision-makers to address some of the challenges facing the adoption of AI across all Earth science.