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.

Precipitation is the fundamental source of freshwater in the water cycle. It is critical for everyone, from subsistence farmers in Africa to weather forecasters around the world, to know when, where, and how much rain and snow is falling. The Global Precipitation Measurement (GPM) Core Observatory spacecraft, launched in February 2014, has the most advanced instruments to measure precipitation from space and, together with other satellite information, provides high-quality merged data on rain and snow worldwide every 30 min. Data from GPM and the predecessor Tropical Rainfall Measuring Mission (TRMM) have been fundamental to a broad range of applications and end-user groups and are among the most widely downloaded Earth science data products across NASA. End-user applications have rapidly become an integral component in translating satellite data into actionable information and knowledge used to inform policy and enhance decision-making at local to global scales. In this article, we present NASA precipitation data, capabilities, and opportunities from the perspective of end users. We outline some key examples of how TRMM and GPM data are being applied across a broad range of sectors, including numerical weather prediction, disaster modeling, agricultural monitoring, and public health research. This work provides a discussion of some of the current needs of the community as well as future plans to better support end-user communities across the globe to utilize this data for their own applications.

We present in this study the results obtained when applying a physical algorithm based on a variational methodology to data from the Advanced Technology Microwave Sounder (ATMS) onboard the Suomi National Polar‐Orbiting Partnership (SNPP) for a consistent retrieval of geophysical data in all weather conditions. The algorithm, which runs operationally at the U.S. National Oceanic and Atmospheric Administration, is applied routinely to a number of sounders from the Polar‐Orbiting Operational Environmental Satellites, the Defense Meteorological Satellite Program, and the European Meteorological Operational satellite constellations. The one‐dimension variational (1DVAR) methodology, which relies on a forward operator, the Community Radiative Transfer Model, allows for solving the inversion of the radiometric measurements into geophysical parameters which have a direct impact on the brightness temperatures. The parameters that are produced by this Microwave Integrated Retrieval System algorithm include the atmospheric temperature T(p), moisture Q(p), and vertically integrated total precipitable water; and the surface skin temperature and emissivity as well as the hydrometeor products of nonprecipitating cloud liquid water and rain‐ and ice‐water paths. In this algorithm, a simple postprocessing is applied to the 1DVAR‐generated emissivity to derive cryospheric products (snow water equivalent and sea‐ice concentration) when the data are measured over these surfaces. The postprocessing is also applied to the hydrometeors products to generate a surface rainfall rate. This comprehensive set of sounding, surface, hydrometeor, and cryospheric products generated from SNPP/ATMS is therefore radiometrically consistent, meaning that when input to the forward operator, it will allow the simulation of the actual brightness temperatures measurements within noise levels. The geophysical consistency between the products, also critical, is satisfied due to the physical approach adopted and the geophysical constraints introduced through the correlation matrix used in the variational system. The results shown in this paper confirm that the performances of all products are within the expected accuracy and precision figures and comparable to performances usually obtained with single‐parameter‐dedicated algorithms, with the added value that the inverted products are both radiometrically and geophysically consistent.

The empirical cumulative density function (ECDF) approach can be used to combine multiple, diverse assessment metrics into summary assessment metrics (SAMs) to analyze the results of impact experiments and preoperational implementation testing with numerical weather prediction (NWP) models. The main advantages of the ECDF approach are that it is amenable to statistical significance testing and produces results that are easy to interpret because the SAMs for various subsets tend to vary smoothly and in a consistent manner. In addition, the ECDF approach can be applied in various contexts thanks to the flexibility allowed in the definition of the reference sample. The interpretations of the examples presented here of the impact of potential future data gaps are consistent with previously reported conclusions. An interesting finding is that the impact of observations decreases with increasing forecast time. This is interpreted as being caused by the masking effect of NWP model errors increasing to become the dominant source of forecast error.

This study presents the scientific results that serve as arguments for advocating the development of a hyperspectral microwave sensor: a sensor with a high spectral resolution in imaging and sounding microwave bands. Some of the major benefits that will be discussed include the (1) Higher vertical resolution sounding in all-weather conditions, (2) Increased precision and accuracy of the products due to decreased cross-talk and to a better isolation of signals, (3) Reduced sensitivity to background information due to increased information content, (4) Extended spatial coverage, (5) Hydrometeors profiling in both liquid and frozen phases, (6) Numerical Weather Prediction (NWP) model forecast improvement, (7) Reduction in the uncertainties of the spectroscopic parameters in the microwave spectrum (line coupling, temperature dependence, line strengths and widths) and (8) Better physical modeling of absorption and scattering by atmospheric and surface parameters. The major application of such a sensor would be improving the forecast skills at both short-term and medium-range scales, especially in extreme weather.