Quantifying Uncertainties in Land Surface Microwave Emissivity Retrievals

Uncertainties in the retrievals of microwave land surface emissivities were quantified over two types of land surfaces: desert and tropical rainforest. Retrievals from satellite-based microwave imagers, including SSM/I, TMI and AMSR-E, were studied. Our results show that there are considerable differences between the retrievals from different sensors and from different groups over these two land surface types. In addition, the mean emissivity values show different spectral behavior across the frequencies. With the true emissivity assumed largely constant over both of the two sites throughout the study period, the differences are largely attributed to the systematic and random errors in the retrievals. Generally these retrievals tend to agree better at lower frequencies than at higher ones, with systematic differences ranging 1~4% (3~12 K) over desert and 1~7% (3~20 K) over rainforest. The random errors within each retrieval dataset are in the range of 0.5~2% (2~6 K). In particular, at 85.0/89.0 GHz, there are very large differences between the different retrieval datasets, and within each retrieval dataset itself. Further investigation reveals that these differences are mostly likely caused by rain/cloud contamination, which can lead to random errors up to 10~17 K under the most severe conditions

Dynamic Inversion of Global Surface Microwave Emissivity Using a 1DVAR Approach

A variational inversion scheme is used to extract microwave emissivity spectra from brightness temperatures over a multitude of surface types. The scheme is called the Microwave Integrated Retrieval System and has been implemented operationally since 2007 at NOAA. This study focuses on the Advance Microwave Sounding Unit (AMSU)/MHS pair onboard the NOAA-18 platform, but the algorithm is applied routinely to multiple microwave sensors, including the Advanced Technology Microwave Sounder (ATMS) on Suomi-National Polar-orbiting Partnership (SNPP), Special Sensor Microwave Imager/Sounder (SSMI/S) on the Defense Meteorological Satellite Program (DMSP) flight units, as well as to the Global Precipitation Mission (GPM) Microwave Imager (GMI), to name a few. The emissivity spectrum retrieval is entirely based on a physical approach. To optimize the use of information content from the measurements, the emissivity is extracted simultaneously with other parameters impacting the measurements, namely, the vertical profiles of temperature, moisture and cloud, as well as the skin temperature and hydrometeor parameters when rain or ice are present. The final solution is therefore a consistent set of parameters that fit the measured brightness temperatures within the instrument noise level. No ancillary data are needed to perform this dynamic emissivity inversion. By allowing the emissivity to be part of the retrieved state vector, it becomes easy to handle the pixel-to-pixel variation in the emissivity over non-oceanic surfaces. This is particularly important in highly variable surface backgrounds. The retrieved emissivity spectrum by itself is of value (as a wetness index for instance), but it is also post-processed to determine surface geophysical parameters. Among the parameters retrieved from the emissivity using this approach are snow cover, snow water equivalent and effective grain size over snow-covered surfaces, sea-ice concentration and age from ice-covered ocean surfaces and wind speed over ocean surfaces. It could also be used to retrieve soil moisture and vegetation information from land surfaces. Accounting for the surface emissivity in the state vector has the added advantage of allowing an extension of the retrieval of some parameters over non-ocean surfaces. An example shown here relates to extending the total precipitable water over non-ocean surfaces and to a certain extent, the amount of suspended cloud. The study presents the methodology and performance of the emissivity retrieval and highlights a few examples of some of the emissivity-based products

Toward the Next Generation of Microwave Sounders: Benefits of a Low-Earth Orbit Hyperspectral Microwave Instrument in All-Weather Conditions Using AI

This study presents scientific results that serve as arguments for advocating the development of a hyperspectral microwave sensor (HyMS). Through simulation experiments, the results of this study demonstrate the major benefits of HyMS sensor observations in low-Earth orbit (LEO), including; 1) increased information content over the microwave region, 2) improved temperature and moisture sounding in all-weather conditions, resulting from higher signal-to-noise ratios, finer vertical resolution, and a reduced dependence on background information due to the increased spectral resolution around oxygen and water vapor absorption features between 23 and 183 GHz, 3) improved profiling of hydrometeors, and 4) improved resilience to radio frequency interference, demonstrated at 23 GHz, associated with the redundant information provided by the HyMS. The deployment of HyMS instruments in LEO orbit is expected to provide an improved knowledge of the state of the atmosphere, particularly if deployed in the form of a constellation, due to the enhanced temporal, spatial, and spectral resolution capabilities that those sensors can provide with respect to present meteorological microwave sounders. This work takes advantage of artificial intelligence (AI), particularly its capability to rapidly and simultaneously process hundreds of channels and retrieve large sets of geophysical parameters, to assess the impact of HyMS in geophysical space. The results presented in this manuscript are expected to contribute to the design of the next generation of microwave sounders, but also to consider the usage of AI to fully exploit the information content provided by these sensors, particularly if deployed in the form of a constellation of satellites

Temperature and Moisture Sounding Performance of Current and Prospective Microwave Instruments under All-Sky Conditions

We provide consistent theoretical and empirical assessments of the major driving factors of the information content and retrieval performance for current and potential future microwave (MW) sounders. For the specific instrument concepts assessed, we find that instrument noise is a major driver, impacting vertical resolution as measured by the degrees of freedom for signal as much as 50%. We also observe diminished performance in the 118 GHz temperature sounding band as compared to the 50–60 GHz band, which is largely due to the increased sensor noise in the assessed 118 GHz sensor for comparable channels—a reduction in the performance gap between 118 GHz and 50 GHz bands can be obtained with a reduction of instrument noise in the 118 GHz temperature sounding channels. As expected, scene-type also significantly impacts the vertical resolution, emphasizing the importance of separating clear, cloudy, rainy, and icy conditions when evaluating instrument performance

The alternative of CubeSat-based advanced infrared and microwave sounders for high impact weather forecasting

The advanced infrared (IR) and microwave (MW) sounding systems have been providing atmospheric sounding information critical for nowcasting and improving weather forecasts through data assimilation in numerical weather prediction. In recent years, advanced IR and MW sounder systems are being proposed to be onboard CubeSats that are much more cost efficient than traditional satellite systems. An impact study using a regional Observing System Simulation Experiment on a local severe storm (LSS) was carried out to evaluate the alternative of using advanced MW and IR sounders for high-impact weather forecasting in mitigating the potential data gap of the Advanced Technology Microwave Sounder (ATMS) and the Cross-track Infrared Sounder (CrIS) on the Suomi-NPP (SNPP) or Joint Polar Satellite System (JPSS). It was found that either MicroMAS-2 or the CubeSat Infrared Atmospheric Sounder (CIRAS) on a single CubeSat was able to provide a positive impact on the LSS forecast, and more CubeSats with increased data coverage yielded larger positive impacts. MicroMAS-2 has the potential to mitigate the loss of ATMS, and CIRAS the loss of CrIS, on SNPP or JPSS, especially when multiple CubeSats are launched. There are several approximations and limitations to the present study, but these represent efficiencies appropriate to the principal goal of the study — gauging the relative values of these sensors