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Spaceborne wind scatterometers provide useful measurements of ocean surface winds and are important to climatological studies and operational weather forecasting.
The Atmospheric Infrared Sounder (AIRS) is a hyperspectral infrared instrument on the EOS Aqua Spacecraft, launched on May 4, 2002. AIRS has 2378 infrared channels ranging from 3.7 μm to 15.4 μm and a 13.5 km footprint at nadir. The AIRS is a “facility” instrument developed by NASA as an experimental demonstration of advanced technology for remote sensing and the benefits of high resolution infrared spectra to science investigations. AIRS, in conjunction with the Advanced Microwave Sounding Unit (AMSU), produces temperature profiles with 1K/km accuracy on a global scale, as well as water vapor profiles and trace gas amounts for CO2, CO, SO2, O3 and CH4. AIRS data are used for weather forecasting, climate process studies and validating climate models. The AIRS instrument has far exceeded its required design life of 5 years, with over 10 years of operations as of September 2012. While the instrument has performed exceptionally well, with little signs of wear, the AIRS Project continues to monitor and maintain the health of AIRS, characterize its behavior and improve performance where possible. Radiometric stability has been monitored and trending shows better than 16 mK/year stability. Spectral calibration stability is better than 1 ppm/year, and a new gain table was recently uploaded to recover 100 significantly degraded or dead channels by switching to their redundant counterpart. At this time we expect the AIRS to continue to perform well for the next decade.
We have developed the HydroCube mission concept with a constellation of small satellites to remotely sense Snow Water Equivalent (SWE) and Root Zone Soil Moisture (RZSM). The HydroCube satellites would operate at sun-synchronous 3-day repeat polar orbits with a spatial resolution of about 1-3 Km. The mission goals would be to improve the estimation of terrestrial water storage and weather forecasts. Root-zone soil moisture and snow water storage in land are critical parameters of the water cycle. The HydroCube Signals of Opportunity (SoOp) concept utilizes passive receivers to detect the reflection of strong existing P-band radio signals from geostationary Mobile Use Objective System (MUOS) communication satellites. The SWE remote sensing measurement principle using the P-band SoOp is based on the propagation delay (or phase change) of radio signals through the snowpack. The time delay of the reflected signal due to the snowpack with respect to snow-free conditions is directly proportional to the snowpack SWE. To address the ionospheric delay at P-band frequencies, the signals from both MUOS bands (360-380 MHz and 250-270 MHz) would be used. We have conducted an analysis to trade off the spatial resolution for a space-based sensor and measurement accuracy. Through modeling analysis, we find that the dual-band MUOS signals would allow estimation of soil moisture and surface roughness together. From the two MUOS frequencies at 260 MHz and 370 MHz, we can retrieve the soil moisture from the reflectivity ratio scaled by wavenumbers using the two P-band frequencies for MUOS. A modeling analysis using layered stratified model has been completed to determine the sensitivity requirements of HydroCube measurements. For mission concept demonstration, a field campaign has been conducted at the Fraser Experimental Forest in Colorado since February 2016. The data acquired has provided support to the HydroCube concept.
Spaceborne infrared atmospheric sounders measure the spectrum of the upwelling radiance in the infrared with ultra-high spectral resolution. The resolution is sufficient to measure absorption features of atmospheric constituents enabling retrieval of atmospheric temperature and water vapor profiles, surface emission and atmospheric constituents. The Atmospheric Infrared Sounder (AIRS) on Aqua launched in May of 2002 was the first hyperspectral infrared sounder designed for this purpose and is still operational today. AIRS has been followed by the Infrared Atmospheric Sounding Interferometer (IASI) on MetOp A and B, and the Cross-track Infrared Sounder (CrIS) on Suomi NPP and JPSS. All instruments are operating well improving weather forecast and providing a wealth of information about the atmosphere. Additional CrIS and IASI instruments are expected to be launched providing data of this type into the late 2030’s. AIRS, CrIS and IASI are all Low Earth Orbit (LEO) instruments with nominal spatial resolutions of 14km. Future IR sounders must achieve higher spatial and temporal resolution to match improvements in forecast models and be less costly to match anticipated future budget pressures. Higher temporal resolution can be achieved in several ways including operation in Geostationary Earth Orbit (GEO) or in constellations of SmallSats and CubeSats. Higher spatial resolution can be achieved using larger format focal plane assemblies in the instruments and larger aperture telescopes. Grating spectrometers are well suited to large format FPAs by allowing a wide field of view in a compact package. Concepts for next generation grating spectrometer IR sounders that have been developed over the years at JPL are presented along with technology advancements made to enable these concepts to achieve their stated goals.
Measuring precipitation on a global scale is only possible from satellite platforms. Satellite precipitation estimates are based on geosynchronous infrared sensors on geostationary satellites, characterized by high sampling frequency, and polar-orbiting microwave sensors on low-Earth-orbiting satellites with less-frequent sampling. Assessing satellite product performance is fundamental to infer the reliability of such estimates and effectively use them in water resources management, extreme event characterization, disease control, or weather forecasting. Nevertheless, errors and uncertainties associated with satellite precipitation products are often masked due to temporal and spatial sampling, as well as bias-corrections against a reference dataset. Moreover, the verification of satellite precipitation products is easier over land areas, where rain gauges and ground radars are available as benchmark, but extremely limited over the oceans. Substantial work still remains to better quantify relative and absolute errors and uncertainties within satellite-based precipitation products over land and oceans.
We examine differences among reanalysis highcloud products in the tropics, assess the impacts of these differences on radiation budgets at the top of the atmosphere and within the tropical upper troposphere and lower stratosphere (UTLS), and discuss their possible origins in the context of the reanalysis models. We focus on the ERA5 (fifthgeneration European Centre for Medium-range Weather Forecasts – ECMWF – reanalysis), ERA-Interim (ECMWF Interim Reanalysis), JRA-55 (Japanese 55-year Reanalysis), MERRA-2 (Modern-Era Retrospective Analysis for Research and Applications, Version 2), and CFSR/CFSv2 (Climate Forecast System Reanalysis/Climate Forecast System Version 2) reanalyses. As a general rule, JRA-55 produces the smallest tropical high-cloud fractions and cloud water contents among the reanalyses, while MERRA-2 produces the largest. Accordingly, long-wave cloud radiative effects are relatively weak in JRA-55 and relatively strong in MERRA-2. Only MERRA-2 and ERA5 among the reanalyses produce tropical-mean values of outgoing long-wave radiation (OLR) close to those observed, but ERA5 tends to underestimate cloud effects, while MERRA-2 tends to overestimate variability. ERA5 also produces distributions of longwave, short-wave, and total cloud radiative effects at the top of the atmosphere that are very consistent with those observed. The other reanalyses all exhibit substantial biases in at least one of these metrics, although compensation between the long-wave and short-wave effects helps to constrain biases in the total cloud radiative effect for most reanalyses. The vertical distribution of cloud water content emerges as a key difference between ERA-Interim and other reanalyses. Whereas ERA-Interim shows a monotonic decrease of cloud water content with increasing height, the other reanalyses all produce distinct anvil layers. The latter is in better agreement with observations and yields very different profiles of radiative heating in the UTLS. For example, whereas the altitude of the level of zero net radiative heating tends to be lower in convective regions than in the rest of the tropics in ERAInterim, the opposite is true for the other four reanalyses. Differences in cloud water content also help to explain systematic differences in radiative heating in the tropical lower stratosphere among the reanalyses. We discuss several ways in which aspects of the cloud and convection schemes impact the tropical environment. Discrepancies in the vertical profiles of temperature and specific humidity in convective regions are particularly noteworthy, as these variables are directly constrained by data assimilation, are widely used, and feed back to convective behaviour through their relationships with thermodynamic stability.
New precipitation (P) datasets are released regularly, following innovations in weather forecasting models, satellite retrieval methods, and multi-source merging techniques. Using the conterminous US as a case study, we evaluated the performance of 26 gridded (sub-)daily P datasets to obtain insight into the merit of these innovations. The evaluation was performed at a daily timescale for the period 2008–2017 using the Kling–Gupta efficiency (KGE), a performance metric combining correlation, bias, and variability. As a reference, we used the high-resolution (4 km) Stage-IV gauge-radar P dataset. Among the three KGE components, the P datasets performed worst overall in terms of correlation (related to event identification). In terms of improving KGE scores for these datasets, improved P totals (affecting the bias score) and improved distribution of P intensity (affecting the variability score) are of secondary importance. Among the 11 gauge-corrected P datasets, the best overall performance was obtained by MSWEP V2.2, underscoring the importance of applying daily gauge corrections and accounting for gauge reporting times. Several uncorrected P datasets outperformed gauge-corrected ones. Among the 15 uncorrected P datasets, the best performance was obtained by the ERA5-HRES fourth-generation reanalysis, reflecting the significant advances in earth system modeling during the last decade. The (re)analyses generally performed better in winter than in summer, while the opposite was the case for the satellite-based datasets. IMERGHH V05 performed substantially better than TMPA-3B42RT V7, attributable to the many improvements implemented in the IMERG satellite P retrieval algorithm. IMERGHH V05 outperformed ERA5-HRES in regions dominated by convective storms, while the opposite was observed in regions of complex terrain. The ERA5-EDA ensemble average exhibited higher correlations than the ERA5-HRES deterministic run, highlighting the value of ensemble modeling. The WRF regional convection-permitting climate model showed considerably more accurate P totals over the mountainous west and performed best among the uncorrected datasets in terms of variability, suggesting there is merit in using high-resolution models to obtain climatological P statistics. Our findings provide some guidance to choose the most suitable P dataset for a particular application.
Moderate Resolution Imaging Spectroradiometer (MODIS) on Aqua has been inoperation providing continuous global observations for science research and applications since2002. The long-term stability of thermal emissive bands (TEBs) of Aqua MODIS was monitoredthrough inter-comparisons with measurements by hyperspectral or multi-spectral infraredsensors or through vicarious monitoring over cold targets such as Dome-C and deep convectiveclouds. The radiative transfer modeling (RTM)-based simulation models are developed to per-form the long-term monitoring of the stability of Aqua MODIS TEBs. Long-term EuropeanCentre for Medium-Range Weather Forecasts global atmospheric reanalysis data are used asinputs to the RTM simulation. By confining ocean in low to middle latitudes as the area ofinterest, the long-term stabilities of Aqua MODIS TEBs are monitored through observationminus background (O-B) brightness temperature (BT) bias between MODIS measurements andsimulations from the RTM. In general, the RTM-based O-B analysis shows that the long-termstability of Aqua MODIS TEBs are maintained well. It is shown that Aqua MODIS surfacebands are all radiometrically stable with yearly BT bias drifts <0.005 K∕yearfor B20, B22,and B23 and∼0.01 K∕yearfor B31 and B32. The carbon dioxide (CO2) absorption channelsof Aqua MODIS, e.g., B33 to B36, are stable with a BT bias drift <0.005 K∕year. It is also foundthat after accounting for the long-term growth of greenhouse gasN2O, the O-B bias trends forB24 and B25 are quite stable with yearly BT bias drifts around 0.0002 and0.0055 K∕year,respectively. The stabilities of the moisture-sensitive channels B27 to B29 and the ozone channelB30 of Aqua MODIS are also evaluated and the remnant small variations in the O-B bias trend-ing of these channels are further discussed. The RTM-based inter-comparison of MODIS TEBmeasurements with global atmospheric and climate re-analysis data provides validation of thelong-term radiometric stability of Aqua MODIS TEBs. The analysis in this paper also demon-strates that the comparison of well-calibrated MODIS TEB measurements with RTM simulationhelps quantify the long-term impacts of global greenhouse gas concentration growth on the BTdecreases in the MODIS greenhouse gas-sensitive channels.
We present results from an evaluation of precipitable water vapor (W) over remote oceanic areas as derived from global reanalysis models and from satellites against observations from the Maritime Aerosol Network (MAN) for cloudless skies during the period of 2004–2017. They cover polar, mid latitude and tropical oceanic regions and represent a first effort to use MAN observations for such evaluation. The global reanalysis model products evaluated in this study are from the Modern-Era Retrospective analysis for Research and Applications Version 2 (MERRA-2), the European Centre for Medium-Range Weather Forecasts (ECMWF) Interim Reanalysis (ERA I), and the Climate Forecast System Reanalysis (CFSR) model. The satellite products evaluated are from the Moderate Resolution Imaging Spectroradiometer (MODIS), the Polarization and Directionality of the Earth's Reflectances (POLDER), the Global Ozone Monitoring Experiment (GOME-2), the Scanning Imaging Absorption Spectrometer for Atmospheric Chartography (SCIAMACHY), and the Atmospheric Infra-red Sounder (AIRS). Satellite retrievals of W are based on the attenuation of solar reflected light by water vapor absorption bands, except those from AIRS that rely on brightness temperature measurements. A very good agreement is observed between the model estimates and MAN, with mean differences of ~5% and standard deviations of ~15%. These results are within the uncertainties associated with the models and the measurements, indicating the skill of the reanalysis models to estimate W over oceans under clear sky conditions. Mean differences of W between the satellite and MAN products are ~11, 6.7, 12, −7, and 3% for MODIS, POLDER, GOME-2, SCIAMACHY and AIRS respectively, while their standard deviations are 31, 29, 28, 20 and 17%. These differences reveal the need to address inconsistencies among different satellite sensors and ground-based measurements to reduce the uncertainties associated with the retrievals.
NASA’s Earth-observing Aqua satellite was launched into space on May 4, 2002; and since that time, Aqua has been collecting and transmitting data on the Earth’s atmosphere, oceans, land, and ice. These data have contributed to over 20,000 scientific publications cited over 500,000 times in the scientific literature and have been used for numerous practical applications, from weather forecasting to air quality analyses to monitoring such phenomena as forest fires, oil spills, sea ice, dust storms, and volcanic ash plumes. The speaker has been Project Scientist for the Aqua mission since April 1993, starting nine years before the satellite launched. She will give an overview of the mission, a sampling of its results, an estimate of when the mission might end (based on fuel and power limitations), and an indication of the types of tasks that take place largely behind the scenes by the Project Scientist and others to enable such missions to continue and succeed.
Inland open water bodies often pose a systematic error source in the passive remote sensing retrievals of soil moisture. Water temperature is a necessary variable used to compute water emissions that is required to be subtracted from satellite observation to yield actual emissions from the land portion, which in turn generates accurate soil moisture retrievals. Therefore, overestimation of soil moisture can often be corrected using concurrent water temperature data in the overall mitigation procedure. In recent years, several data sets of lake water temperature have become available, but their specifications and accuracy have rarely been investigated in the context of passive soil moisture remote sensing on a global scale. For this reason, three lake temperature products were evaluated against in-situ measurements from 2007 to 2011. The data sets include the lake surface water temperature (LSWT) from Global Observatory of Lake Responses to Environmental Change (GloboLakes), the Copernicus Global Land Operations Cryosphere and Water (C-GLOPS), as well as the lake mix-layer temperature (LMLT) from the European Centers for Medium-Range Weather Forecast (ECMWF) ERA5 Land Reanalysis. GloboLakes, C-GLOPS, and ERA5 Land have overall comparable performance with Pearson correlations (R) of 0.87, 0.92 and 0.88 in comparison with in-situ measurements. LSWT products exhibit negative median biases of −0.27 K (GloboLakes) and −0.31 K (C-GLOPS), whereas the median bias of LMLT is 1.56 K. When mapped from their respective native resolutions to a common 9 km Equal-Area Scalable Earth (EASE) Grid 2.0 projection, similar relative performance was observed. LMLT and LSWT data are closer in performance over the 9 km grid cells that exhibit a small range of lake cover fractions (0.05–0.5). Despite comparable relative performance, ERA5 Land shows great advantages in spatial coverage and temporal resolution. In summary, an integrated evaluation on data accuracy, long-term availability, global coverage, temporal resolution, and regular forward processing with modest data latency led us to conclude that LMLT from the ERA5 Land Reanalysis product represents the most optimal path for use in the development of a long-term soil moisture product.
In southern Africa, widespread agricultural fires produce substantial biomass burning (BB) emissions over the region. The seasonal smoke plumes associated with these emissions are then advected westward over the persistent stratocumulus cloud deck in the southeast Atlantic (SEA) Ocean, resulting in aerosol effects which vary with time and location. Much work has focused on the effects of these aerosol plumes, but previous studies have also described an elevated free tropospheric water vapor signal over the SEA. Water vapor influences climate in its own right, and it is especially important to consider atmospheric water vapor when quantifying aerosol–cloud interactions and aerosol radiative effects. Here we present airborne observations made during the NASA ORACLES (ObseRvations of Aerosols above CLouds and their intEractionS) campaign over the SEA Ocean. In observations collected from multiple independent instruments on the NASA P-3 aircraft (from near-surface to 6–7 km), we observe a strongly linear correlation between pollution indicators (carbon monoxide (CO) and aerosol loading) and atmospheric water vapor content, seen at all altitudes above the boundary layer. The focus of the current study is on the especially strong correlation observed during the ORACLES-2016 deployment (out of Walvis Bay, Namibia), but a similar relationship is also observed in the August 2017 and October 2018 ORACLES deployments. Using reanalyses from the European Centre for Medium-Range Weather Forecasts (ECMWF) and Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2), and specialized WRF-Chem simulations, we trace the plume–vapor relationship to an initial humid, smoky continental source region, where it mixes with clean, dry upper tropospheric air and then is subjected to conditions of strong westward advection, namely the southern African easterly jet (AEJ-S). Our analysis indicates that air masses likely left the continent with the same relationship between water vapor and carbon monoxide as was observed by aircraft. This linear relationship developed over the continent due to daytime convection within a deep continental boundary layer (up to ∼5–6 km) and mixing with higher-altitude air, which resulted in fairly consistent vertical gradients in CO and water vapor, decreasing with altitude and varying in time, but this water vapor does not originate as a product of the BB combustion itself. Due to a combination of conditions and mixing between the smoky, moist continental boundary layer and the dry and fairly clean upper-troposphere air above (∼6 km), the smoky, humid air is transported by strong zonal winds and then advected over the SEA (to the ORACLES flight region) following largely isentropic trajectories. Hybrid Single-Particle Lagrangian Integrated Trajectory model (HYSPLIT) back trajectories support this interpretation. This work thus gives insights into the conditions and processes which cause water vapor to covary with plume strength. Better understanding of this relationship, including how it varies spatially and temporally, is important to accurately quantify direct, semi-direct, and indirect aerosol effects over this region.
The potential of microwave observations in improving the weather forecasts is limited by the accuracy of all-sky radiative transfer calculations. We introduce a novel Bayesian MonteCarlo technique to improve the assimilation of microwave observations over the rainbands of tropical cyclones. The BMCI technique eliminates the need for a forward model in the data assimilation system. The technique includes three steps, (i) generating a comprehensive dataset using in-situ cloud measurements and atmospheric profiles, (ii) generating synthetic ATMS observations from the training dataset, and (iii) using real observations to estimate the geophysical variables over the rainbands of tropical cyclones. The retrieved profiles of temperature, relative humidity, and cloud water content as well assurface information such as SST were then assimilated into the model. The results show that assimilating the BMCI retrievals can influence the dynamical features of the cyclone, including a stronger warm core, a symmetric eye, and vertically aligned wind columns.
Within the planetary boundary layer (PBL), surface forcing response, drag, turbulence, and vertical mixing are important processes and play a more critical role here than in the overlying “free atmosphere”. The PBL Height (PBLH) is an important parameter in climate models, weather forecasts, and air quality prediction. The NASA Goddard Earth Sciences Data and Information Services Center (GES DISC) provides data processing, archiving, and distribution services for numerous Earth science products. PBLH is a parameter in three products served by GES DISC, which are from the Atmospheric Infrared Sounder (AIRS), the Global Positioning System (GPS) radio occultation (RO) experiment, and the NASA reanalysis product Modern-Era Retrospective analysis for Research and Applications – 2 (MERRA-2). These products have different spatial and temporal resolutions and coverages, and their PBLH definitions are also different. To better serve the PBL research community, we have summarized the specifications of these products. A ten-year seasonal mean intercomparison is also conducted to provide further guidance to users. The intercomparison results show that MERRA-2 has a much shallower PBL than AIRS and GPS RO. An experimental study indicates the different PBLH definition in MERRA-2 caused smaller values of PBLH. The improvement of the water vapor retrieval in AIRS version 7 over version 6 results in the version 7 PBLH agreeing better with GPS RO and MERRA-2 than version 6, especially near the equator and low latitudes.
NASA’s vision for Advanced Air Mobility (AAM) is to help emerging aviation markets to safely develop an air transportation system that moves people and cargo between places previously not served or underserved by aviation using revolutionary new aircraft types. Urban Air Mobility (UAM), the concept of expanding transportation networks by serving short flights to transport people and goods around metropolitan areas, is part of a larger paradigm shift toward AAM, in which new technologies and business models are enabling transformational applications of aviation. In NASA’s Concept of Operations for UAM, Providers of Services for UAM (PSU) play a key role to enable safe and efficient UAM operations by sharing operational data among UAM operators. PSUs provide a pre-departure strategic conflict management service that establishes operational plans for new flights and coordinates them with all relevant operations in shared airspace. The strategic conflict management aims to minimize the need for tactical separation provision, while considering anticipated traffic demand, vertiport capacity and availability, forecasted weather, and airspace restrictions. NASA is currently preparing a new lab evaluation activity with aviation industry partners, called the X4 simulation, to demonstrate their airspace services and capabilities and test new information exchange requirements for UAM operations. This simulation activity will support NASA’s National Campaign (NC) flight tests which are planned over the next several years to guide the collective community and stakeholders through a series of scenario-based test activities that involve vehicles and airspace management services operating in a live test environment. This presentation will provide an overview of the X4 simulation, including objectives, scenarios, assumptions, system architecture, and test schedule. The X4 simulation will evaluate the interconnectivity and operational intent sharing between PSUs for multiple operators, as well as the performance of strategic conflict management.
Understanding the vertical distribution of ice clouds is crucial for climate modeling and weather forecasting. Since its launch to space in 2006, the Cloud-Aerosol Lidar with Orthogonal Polarization instrument (CALIOP) onboard the CALIPSO spacecraft has been providing unprecedented high-quality profiles of ice clouds, especially optically thin cirrus clouds, during both day and night, on a global scale. Recently the lidar science working group at the NASA Langley Research Center delivered a level 3 (L3) ice cloud product which reports monthly statistics of ice cloud extinction coefficient and ice water content on a uniform 3-dimensional spatial grid. This presentation compares the ice cloud climatology derived from this product with two other ice cloud products, namely the raDAR/liDAR(DARDAR) ice cloud product and the CloudSat-CALIPSO Ice Cloud Property Product (2C-ICE). Using processing similar to the CALIPSO L3 product, granules of DARDAR and 2C-ICE data have been processed into monthly statistics for a better comparison. Similarities and differences will be presented and discussed. The findings illustrate the strengths of the different products and help to identify which product might be most appropriate for a given research topic.
Atmospheric aerosols have high spatiotemporal variations due to their diverse sources including windblown dust, wildfires, volcanic eruptions, and anthropogenic emissions. These aerosols also have strong diurnal emission characteristics that influence long-range transport, air quality, Earth’s radiation budget, biogeochemical cycles, and weather forecasting. While current sensors can measure the steady-state of global 3D cloud and aerosol properties, we lack the observational datasets to discover the diurnal variability of aerosols, even at seasonal and regional scales, because these missions utilize sun synchronous orbits. An Aerosols and Clouds, Convection and Precipitation (ACCP) observing system has been established as part of NASA’s new Earth System Observatory (ESO) to fulfill the science needs presented in the 2017 Earth Science Decadal Survey. The selected ACCP architecture encompasses sensors in two orbit planes, one of which utilizes an inclined orbit (nominally ~55 degrees at a 407 km altitude) to advance our understanding of aerosol and cloud properties and target the dynamics of the cloud and aerosol processes on sub-daily time scales. The anticipated instrumentation suite for the ACCP Inclined Orbit Project is a Stereo Camera System, Polarimeter, Microwave Radiometer, Two-Frequency Radar, and Backscatter Lidar. The polarimeter is expected to have multiple wavelength bands and viewing angles to enable accurate retrievals of aerosol microphysical and optical properties. The backscatter lidar will provide vertical profiles of attenuated backscatter and depolarization ratio at two wavelengths, with better daytime signal-to-noise ratio than previous space-based lidar systems to improve aerosol detection/typing and estimates of aerosol optical properties. The stereo camera system, while designed primarily for clouds, will also provide aerosol plume heights and air motions at the plume heights. This invited presentation will provide (1) an overview of the ACCP Inclined Orbit Project, (2) a description of the new sensor capabilities and orbital characteristics, and (3) a preview of the potential for new synergistic aerosol inversion techniques.
A multisensor snowfall observational suite has been deployed at the Marquette, Michigan, National Weather Service Weather Forecast Office (KMQT) since 2014. Micro Rain Radar (MRR; profiling radar), Precipitation Imaging Package (PIP; snow particle imager), and ancillary ground-based meteorological observations illustrate the unique capabilities of these combined instruments to document radar and concomitant microphysical properties associated with northern Great Lakes snowfall regimes. Lake-effect, lake-orographic, and transition event case studies are presented that illustrate the variety of snowfall events that occur at KMQT. Case studies and multiyear analyses reveal the ubiquity of snowfall produced by shallow events. These shallow snowfall features and their distinctive microphysical fingerprints are often difficult to discern with conventional remote sensing instruments, thus highlighting the scientific and potential operational value of MRR and PIP observations. The importance of near-surface lake-orographic snowfall enhancement processes in extreme snowfall events and regime-dependent snow particle microphysical variability controlled by regime and environmental factors are also highlighted.