Snowfall retrieval algorithms for spaceborne passive microwave (PMW) sensors have been developed and refined in recent years, but many complicating issues still affect their accuracy and reliability. Previous work showed that the Global Precipitation Measurement (GPM) mission Goddard PROFiling (GPROF) algorithm snowfall retrieval performance strongly depends on the snowfall type. In particular, PMW-based detection of shallow cumuliform snowfall (SCS), which accounts for 36%-70% of global snowfall frequency, can be very challenging. The snowfall scattering signal can be contaminated by the background surface or supercooled cloud liquid water emission. A scattering index (SI) approach that exploits the GPM Microwave Imager (GMI) dual-polarization 166 GHz channels is developed to analyze its behavior in presence of SCS over ocean. Case studies show that, compared to the SI at 89 GHz, it can isolate the SCS snowfall scattering signal in extremely dry conditions. Some issues are still observed in presence of supercooled liquid water.

Dr. Gail Skofronick-Jackson (IEEE Fellow), 58, died suddenly September 7, 2021. Skofronick-Jackson was deployed with a joint NASA-ESA sub-orbital campaign in St. Croix, U.S. Virgin Islands. On a day off from experiments, she perished in a tragic accident while hiking with colleagues. Skofronick-Jackson's contributions to satellite remote sensing spanned 25 years of remote sensing research, NASA spaceflight mission leadership, professional volunteerism, and scientific program management.

Previous studies used the temperature-related variables from model outputs (e.g., 2-m temperature) for precipitation phase determination (i.e., rain-snow separation). This study presents a new idea for precipitation phase determination using brightness temperatures (TBs) from Advanced Technology Microwave Sounder. It is found that TB-based phase discrimination shows comparable determination performance to that from model outputs over land. In contrast, TB-based phase discrimination over ocean performs noticeably worse than that from model outputs. Further analyses reveal that the phase determination performance over land from TBs slightly depends on the satellite local zenith angle. However, the determination performance over ocean strongly depends on the satellite local zenith angle, with the skill score decreasing sharply from 0.74 near nadir to 0.55 near the edge. These results imply that over land TBs may be used directly for phase determination, which can be extended to other operational and future microwave sounders with similar channels available.

The uncertainty of passive microwave retrievals of snowfall is notoriously high where the high-frequency surface emissivity is significantly reduced and varies markedly in response to changes of snowpack physical properties. Using the dense media radiative transfer theory, this article studies the potential effects of terrestrial snow-cover depth, density, and grain size on high-frequency channels 89 and 166 GHz of the radiometer onboard the Global Precipitation Measurement (GPM) core satellite, which are commonly used to capture the snowfall scattering signals. Integrating the inference across all feasible grain sizes, ranges of snowpack density and depth are identified over which the snowfall scattering signatures can be time varying and potentially obscured. Using 10 years of reanalysis data, the seasonal vulnerability of snowfall retrievals to changes of snowpack emissivity in the Northern Hemisphere is mapped in a probabilistic sense and connections are made with uncertainties of the GPM passive microwave snowfall retrievals. It is found that among different snow classes, relatively light Arctic tundra snow in fall, with a density below 260 kg m-3, and shallow prairie snow during the winter, with a depth of less than 40 cm, can reduce the surface emissivity and obscure the snowfall passive microwave signatures. It is demonstrated that, during the winter, the highly vulnerable areas are over Kazakhstan, and Mongolia with taiga and prairie snow. In the fall, these areas are largely over tundra and taiga snow in north of Russia and the Arctic Archipelagos as well as prairies in Canada and the Great Plains in the United States.

This article studies changes in microwave signals of oceanic snowfall in response to the formation of snow-covered sea ice using active and passive coincident data from the radar and radiometer onboard the CloudSat and the global precipitation measurement satellites. Using reanalysis data of liquid and ice water path as well as satellite retrievals of sea ice snow-cover depth, spectral regions are determined over which the snowfall signatures are likely to be obscured or falsely detected. Relying on ana prioridatabase populated with the active–passive coincidences, a Bayesian snowfall retrieval algorithm is presented that links a$k$-nearest neighbor matching with the inverse Gaussian estimator used in the Goddard profiling algorithm. Without relying on any ancillary data of air temperature, the results demonstrate that over open oceans (sea ice), we can passively retrieve the CloudSat active snowfalls with a true positive rate of 92 (85%) and the root mean squared error of 0.24 (0.15) mmh−1.

A microwave emissivity retrieval is applied to five years of global precipitation measurement (GPM) microwave imager (GMI) observations over land and sea ice. The emissivities are colocated with GPM's dual-frequency precipitation radar (DPR) surface backscatter measurements in clear-sky conditions. The emissivity-backscatter database is used to characterize surfaces within the GPM orbit for precipitation retrieval algorithms and other applications. The full 10-166-GHz emissivity vector is retrieved using optimal estimation. Since GMI includes water vapor sounding channels, retrieval of the atmospheric and surface states are performed simultaneously. Using the MERRA2 reanalysis as the a priori atmospheric state and with proper characterization of its error, we are able to effectively screen for cloud- and precipitation-affected emissivities. Comparisons with colocated CloudSat data show that this GMI-based screen is able to detect precipitation that DPR alone does not; however, about 10% of precipitation occurrence from CloudSat is still undetected by GMI. The unsupervised Kohonen classification technique was then applied to multiyear monthly 0.25° gridded mean retrieved emissivities and backscatter distinctly for snow-free, snow-covered, and sea ice surfaces in order to classify surfaces based on both active and passive microwave characteristics. The classes correspond to vegetation coverage and type, inundation zones, soil composition, and terrain roughness. Snow and sea ice surfaces show clear seasonal cycles representing the increase in snow and ice spatial extent and reduction in the spring. Applications toward GPM precipitation retrieval algorithms and sensitivity to accumulated rain and snowfall are also explored.

A physically based Bayesian passive microwave precipitation retrieval requires an accurate forward radiative transfer model along with realistic database representation of hydrometeors, atmospheric properties, and surface emission. NASA's Global Precipitation Measurement (GPM) Mission provides an unprecedented opportunity for the development of such databases, matching a well-calibrated radiometer with dual-frequency radar. Early versions of passive microwave products from GPM utilized a physically constructed database in a Bayesian retrieval scheme, assumed ice particles to be spheres, and used Mie radiative transfer. A large body of recent work demonstrates that this is insufficient for retrieval at the GPM radiometer frequencies. In this paper, the retrieval is updated to use nonspherical particles. Simulated brightness temperature (Tb) agreement with observations is shown to be significantly improved across the high frequencies, decreasing biases significantly and increasing correlations to observed Tb. This is compared with a second identical retrieval performed with the assumption of spherical ice particles, and retrieval results are compared globally, seasonally, and instantaneously for a case study at the rain rate level. While not at the high level of improvement shown in Tb space, the precipitation retrieval is improved as compared to one using observed Tb in correlation, bias, and root-mean-square error. Reported improvements, while modest in magnitude, advance the retrieval to more physical consistency which allows for deeper insight into ice particle properties associated with precipitation.

Retrievals of falling snow from space represent an important data set for understanding the Earth's atmospheric, hydrological, and energy cycles, especially during climate change. Estimates of falling snow must be captured to obtain the true global precipitation water cycle, snowfall accumulations are required for hydrological studies, and without knowledge of the frozen particles in clouds one cannot adequately understand the energy and radiation budgets. While satellite-based remote sensing provides global coverage of falling snow events, the science is relatively new and retrievals are still undergoing development with challenges remaining (e.g., [1], [2], [3]). This work reports on the development and testing of retrieval algorithms for the Global Precipitation Measurement (GPM) mission Core Satellite [4-5], launched February 2014, with a specific focus on meeting GPM Mission requirements for falling snow.

Retrievals of falling snow from space represent an important data set for understanding the Earth's atmospheric, hydrological, and energy cycles, especially during climate change. Estimates of falling snow must be captured to obtain the true global precipitation water cycle, snowfall accumulations are required for hydrological studies, and without knowledge of the frozen particles in clouds one cannot adequately understand the energy and radiation budgets. While satellite-based remote sensing provides global coverage of falling snow events, the science is relatively new and retrievals are still undergoing development with challenges remaining (e.g., [1], [2], [3]). This work reports on the development and testing of retrieval algorithms for the Global Precipitation Measurement (GPM) mission Core Satellite [4–5], launched February 2014.

Better estimation of land surface microwave emissivity promises to improve over-land precipitation retrievals in the GPM era. Forward models of land microwave emissivity are available but have suffered from poor parameter specification and limited testing. Here, forward models are calibrated and the accompanying change in predictive power is evaluated. With inputs (e.g., soil moisture) from the Noah land surface model and applying MODIS LAI data, two microwave emissivity models are tested, the Community Radiative Transfer Model (CRTM) and Community Microwave Emission Model (CMEM). The calibration is conducted with the NASA Land Information System (LIS) parameter estimation subsystem using AMSR-E based emissivity retrievals for the calibration dataset. The extent of agreement between the modeled and retrieved estimates is evaluated using the AMSR-E retrievals for a separate 7-year validation period. Results indicate that calibration can significantly improve the agreement, simulating emissivity with an across-channel average root-mean-square-difference (RMSD) of about 0.013, or about 20% lower than if relying on daily estimates based on climatology. The results also indicate that calibration of the microwave emissivity model alone, as was done in prior studies, results in as much as 12% higher across-channel average RMSD, as compared to joint calibration of the land surface and microwave emissivity models. It remains as future work to assess the extent to which the improvements in emissivity estimation translate into improvements in precipitation retrieval accuracy.

In an effort to better simulate land surface microwave emissivity, a semi-empirical technique is developed and tested over the U.S. Southern Great Plains (SGP) area. A physical model is used to calculate emissivity at the 10-GHz frequency, combining contributions from the underlying soil and vegetation layers, including the dielectric and roughness effects of each medium. Adjustments are added for post-precipitation surface water effects on emissivity of the soil and water-coated vegetation emissivity. A five-year data set of retrieved emissivities from the Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) during clear-sky conditions is employed for calculation of a robust set of channel covariances. These covariances, combined with the modeled 10-GHz emissivities, provide emissivity values for each AMSR-E channel, which are then used to compute top of the atmosphere brightness temperatures Tbs. Results comparing these calculated Tbs to observed AMSR-E values show correlations of 0.85-0.93 and biases generally less than 1 K, with the largest bias appearing in the highest AMSR-E frequency. Such a modeling system could be easily implemented for the emissivity calculation required for atmospheric retrievals over similar land surfaces.

An accurate understanding of land surface emissivity in terms of associated surface properties is necessary for improved passive microwave remote sensing of the atmosphere, including water vapor, clouds, and precipitation, over land. In an effort to advance this understanding, emissivities are calculated for a 5°latitude by 5°longitude region in the U.S. Southern Great Plains using a combination of land surface model and physical emissivity model. Results are compared to retrieved values from the Advanced Microwave Scanning Radiometer-Earth Observing System passive microwave observations for cloud-free scenes over a six-year period. The resulting emissivities are compared in the context of surface properties including surface temperature, leaf area index (LAI), soil moisture, and precipitation. The comparison confirms that lower frequency channels respond most directly to the surface soil and its dielectric properties. Differences between retrieved and modeled emissivities are generally lower than 2%-3% and appear to be a function of soil moisture and LAI at frequencies less than 37 GHz. Agreement is better for the vertical polarization channels. At 89 GHz, a large difference is present between retrieved and modeled emissivities in both mean and magnitude of variability, particularly in the summer months. Problems are likely present at higher microwave frequencies in both the retrieved and modeled products, including the inability of the emissivity model to represent liquid water in the form of dew or precipitation interception on the vegetation canopy.

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.