Global numerical weather models are starting to resolve atmospheric moist convection which comes with a critical need for observational constraints. One avenue for such constraints is spaceborne radar which tends to operate at three wavelengths, Ku, Ka, and W bands. Many studies of deep convection in the past have primarily leveraged the Ku band because it is less affected by attenuation and multiple scattering. However, future spaceborne radar missions might not contain a Ku-band radar, and thus, considering the view of convection from the Ka band or W band compared to the Ku band would be useful. This study examines a coincident dataset between the Global Precipitation Measurement (GPM) mission and CloudSat as well as the entire GPM record to compare convective characteristics across various wavelengths within deep convection. We find that W-band reflectivity Z tends to maximize near the Ku-band defined echo top, while the Ka band often maximizes 4-5 km below. The height of the maximum Z above the melting level for the W band does not linearly relate to the Ku-band maximum. However, using the full GPM record, the Ka-band 30-dBZ echo tops can be linearly related to the Ku-band 40-dBZ echo top with an R2 of 0.62 and a root-mean-squared error of about 1 km. The spatial distribution of echo tops from the Ka band corresponds well to the Ku-band echo tops, highlighting regions of relatively large ice water path. This paper suggests that Ka-band only missions, like NASA's Investigation of Convective Updrafts, should be able to characterize global convection in a similar manner to a Ku-band system. SIGNIFICANCE STATEMENT: There has been a long history of studying global storms using a Ku-band (2 cm, 13 GHz) spaceborne radar, most likely because of the least number of challenges (e.g., loss of signal) at the Ku band compared to the Ka band (8 mm, 35 GHz) and W band (3 mm, 89 GHz). However, each radar system offers different perspectives on hydrometeor profiles observed in deep convection that have remained largely unexplored, perspectives that provide insights into convective storm systems. Therefore, it is useful to know how storms measured at the Ka band and W band compare to storms measured at the Ku band. We find that many of the Ka-band convective properties can be linearly related to the Ku band, and thus, the Ka-band only mission designs should be suitable for studying convective storms.

There are many unknowns surrounding why convective storms occur exactly when and where they do, including environmental impacts on storm morphology and processes. One of primary science objectives of NASA's upcoming INvestigation of Convective UpdraftS (INCUS) satellite mission is to quantify relationships between a storm's environment and its convective mass flux. To do this, we seek to distill the somewhat nebulous concept of a “convective environment” into a small number of scalar variables. In this study, as a first step towards those goals, we leverage 25 years of spaceborne radar observations from TRMM and GPM to evaluate how the depth and width of precipitating convective storms are related to large-scale environmental variables obtained from ERA5 reanalysis. Using random forests, we identify eight variables that are useful for distinguishing between global convective storm modes. We then show that principal component analysis can collapse most of the information in these variables into just two dimensions. The top two principal components effectively separate the environments of the deepest convective features from the environments of other convective features (Fig. 1).

Small satellite constellations provide the potential to improve spatiotemporal resolution of microwave observations of precipitation from low-Earth orbit. Shorter revisit times are essential to improve understanding of the development and evolution of extreme precipitation systems, in turn improving numerical weather prediction and accuracy of parameterization of extreme weather events in global climate models. To this end, Temporal Experiment for Storms and Tropical Systems (TEMPEST) was proposed in 2013 as a constellation of 6U CubeSats in LEO to provide frequent observations of rapidly developing storms. TEMPEST-D, the resulting NASA Earth Venture Technology Mission, demonstrated the first global observations from a multi-frequency microwave radiometer on a CubeSat for nearly three years from 2018 to 2021. TEMPEST-D exceeded expectations for scientific data quality, instrument calibration, radiometer stability, and mission duration. TEMPEST-D brightness temperatures were validated using double-difference intercomparison with scientific and operational microwave sensors, including GPM/GMI and four Microwave Humidity Sounders (MHS), operating at similar frequencies to TEMPEST-D channels at 87, 164, 174, 178 and 181 GHz. TEMPEST-D performance was shown to be comparable to or better than much larger operational sensors, in calibration accuracy, precision, stability and instrument noise, during its nearly 3-year mission.A nearly identical TEMPEST flight spare was produced by JPL alongside TEMPEST-D for risk reduction. The TEMPEST flight spare was made available to the U.S. Space Force to demonstrate low-cost space technologies for improving global weather forecasting. TEMPEST was then integrated with the Compact Ocean Wind Vector Radiometer (COWVR) produced by NASA/JPL for the U.S. Air Force. COWVR and TEMPEST were launched together as the Space Test Program – Houston 8 (STP-H8) on December 21, 2021, and deployed on the ISS Japanese Experiment for at least 3 years of operations. COWVR and TEMPEST have performed complementary observations of Earth’s oceans and atmosphere from the ISS nearly continuously since January 8, 2022. Atmospheric retrievals of water vapor profiles, clouds, and precipitation from COWVR/TEMPEST-H8 are performed collaboratively by JPL and Colorado State University.Atmospheric inversion techniques have been developed to retrieve water vapor altitude profiles, as well as single-layer cloud liquid water and cloud ice water, from TEMPEST brightness temperatures, using ECMWF Reanalysis v5 (ERA5) data as an initial guess. These retrievals are enhanced through the inclusion of geostationary infrared data from GOES-16 ABI channels, increasing the number of levels and reducing the error of water vapor retrieval, particularly in the upper troposphere. The accuracy and precision of TEMPEST-D brightness temperatures have previously been validated using clear-sky oceanic observations. Recent studies have extended the validation of both TEMPEST-D and TEMPEST-H8 to include observations of tropical cyclones, hurricanes, and typhoons using GPM-GMI passive microwave brightness temperatures and GPM-DPR active microwave vertical cumulative reflectivity. These passive/active microwave intercomparisons employ techniques developed for quantitative evaluation of the cross correlation between TEMPEST-D and RainCube observations of tropical cyclones, hurricanes, and typhoons. Such passive/active microwave observations also provide the basis for the development of surface rain rate estimates and retrieval of the vertical structure of precipitation from combined TEMPEST and DPR observations.

AbstractIn this study, we leverage 25 years of observations from spaceborne radars, along with coincident reanalysis data, to determine how the depth and width of precipitating convective storms are related to the large‐scale environments in which they are observed. We find that the deepest convective features are observed in environments markedly different from the environments of other convective features, including organized convection. Deep storms co‐occur with relatively dry, unstable conditions, while wide storms are observed in moist, relatively stable environments. We identify eight large‐scale environmental variables that serve to distinguish between storm modes, and then show that principal component analysis can be used to condense this information into just two scalar variables. The methodology presented offers a succinct way to describe a storm's environment and will allow us to better relate a storm's initial environment to its dynamical characteristics.

A significant fraction of liquid clouds are not captured in existing CloudSat radar-based products because the clouds are masked by surface clutter or have insufficient reflectivities. To account for these missing clouds, we train a random forest regression model to predict cloud optical depth and cloud top effective radius from other CloudSat and CALIPSO observables that do not include the radar reflectivity profile. By assuming a subadiabatic cloud model, we are then able to retrieve a vertical profile of cloud microphysical properties for all liquid-phase oceanic clouds that are detected by CALIPSO’s lidar but missed by CloudSat’s radar. Daytime estimates of cloud optical depth, cloud top effective radius, and cloud liquid water path are robustly correlated with coincident estimates from the MODIS instrument onboard the Aqua satellite. This new algorithm offers a promising path forward for estimating the water contents of thin liquid clouds observed by CloudSat and CALIPSO at night, when MODIS observations that rely upon reflected sunlight are not available.

There are many sources of uncertainty in satellite precipitation retrievals of warm rain. In this paper, the second of a two-part study, we focus on uncertainties related to spatial heterogeneity and surface clutter. A cloud-resolving model simulation of warm, shallow clouds is used to simulate satellite observations from three theoretical satellite architec-tures}one similar to the Global Precipitation Measurement Core Observatory, one similar to CloudSat, and one similar to the planned Atmosphere Observing System (AOS). Rain rates are then retrieved using a common optimal estimation framework. For this case, retrieval biases due to nonuniform beamfilling are very large, with retrieved rain rates negatively (low) biased by as much as 40%-50% (depending on satellite architecture) at 5 km horizontal resolution. Surface clutter also acts to negatively bias retrieved rain rates. Combining all sources of uncertainty, the theoretical AOS satellite is found to outperform CloudSat in terms of retrieved surface rain rate, with a bias of 219% as compared with 228%, a reduced spread of retrieval errors, and an additional 17.5% of cases falling within desired uncertainty limits. The results speak to the need for additional high-resolution modeling simulations of warm rain so as to better characterize the uncertainties in satellite precipitation retrievals.

Single-layer nonprecipitating warm clouds are integral to Earth's climate, and accurate estimates of cloud liquid water content for these clouds are critical for constraining cloud models and understanding climate feedbacks. As the only cloud-sensitive radar currently in space, CloudSat provides very important cloud-profiling capabilities. However, a significant fraction of clouds is missed by CloudSat because they are either too thin or too close to the Earth's surface. We find that the CloudSat Radar-Visible Optical Depth Cloud Water Content Product, 2B-CWC-RVOD, misses about 73 % of nonprecipitating liquid cloudy pixels and about 63 % of total nonprecipitating liquid cloud water content compared to coincident Moderate Resolution Imaging Spectroradiometer (MODIS) observations. Those percentages increase to 84 % and 69 %, respectively, if MODIS “partly cloudy” pixels are included. We develop a method, based on adiabatic parcel theory but modified to account for the fact that observed clouds are often subadiabatic, to estimate profiles of cloud liquid water content based on MODIS observations of cloud-top effective radius and cloud optical depth combined with lidar observations of cloud-top height. We find that, for cloudy pixels that are detected by CloudSat, the resulting subadiabatic profiles of cloud water are similar to what is retrieved from CloudSat. For cloudy pixels that are not detected by CloudSat, the subadiabatic profiles can be used to supplement the CloudSat profiles, recovering much of the missing cloud water and generating realistic-looking merged profiles of cloud water. Adding this missing cloud water to the CWC-RVOD product increases the mean cloud liquid water path by 228 % for single-layer nonprecipitating warm clouds. This method will be included in a subsequent reprocessing of the 2B-CWC-RVOD algorithm.

A significant part of the uncertainty in satellite-based precipitation products stems from differing assumptions about drop size distributions (DSDs). Satellite radar-based retrieval algorithms rely on DSD assumptions that may be overly simplistic, whereas radiometers further struggle to distinguish cloud water from rain. We utilize the Ocean Rainfall and Ice-phase Precipitation Measurement Network (OceanRAIN), version 1.0, dataset to examine the impact of DSD variability on the ability of satellite measurements to accurately estimate rates of warm rainfall. We use the binned disdrometer counts and a simple model of the atmosphere to simulate observations for three satellite architectures. Two are similar to existing instrument combinations on the GPM Core Observatory and CloudSat, and the third is a theoretical triple-frequency radar-radiometer architecture. Using an optimal estimation framework, we find that the assumed DSD shape can have a large impact on retrieved rain rate. A three-parameter normalized gamma DSD model is sufficient for describing and retrieving the DSDs observed in the OceanRAIN dataset. Assuming simpler single-moment DSD models can lead to significant biases in retrieved rain rate, on the order of 100%. Differing DSD assumptions could thus plausibly explain a large portion of the disagreement in satellite-based precipitation estimates.

Satellite-based oceanic precipitation estimates, particularly those derived from the Global Precipitation Measurement (GPM) satellite and CloudSat, suffer from significant disagreement over regions of the globe where warm rain processes are dominant. GPM estimates of average rain rate tend to be lower than CloudSat estimates, due in part to GPM being less sensitive to shallow and/or light precipitation. Using coincident observations between GPM and CloudSat, we find that the GPM_2BCMB product misses about two-thirds of total accumulated warm rain compared to the CloudSat 2C-RAIN-PROFILE product. This difference becomes much smaller when products are compared at 1000 m above the surface (mitigating surface clutter issues) and when forcing the frequency of rain from CloudSat to match the frequency from GPM (mitigating sensitivity issues). However, even then a gap of about 25% remains. Using an optimal estimation retrieval algorithm on the underlying data, we retrieve a similar result, but find that the remaining difference between the GPM and CloudSat retrieved rain rates can be almost entirely accounted for by inconsistent assumptions about the shape of the drop size distribution (DSD) that are made in the two retrievals. We conclude that DSD assumptions contribute significantly to the relative underestimation of warm rain by GPM compared to CloudSat. Because the choice of DSD model has such a large effect on retrieved rain rates, more work is needed to determine whether the DSD models assumed by either the GPM_2BCMB or 2C-RAIN-PROFILE algorithms are actually appropriate for warm rain.

Passive microwave temperature and water vapor sounding of the Earth’s atmosphere provides one of the most valuable quantitative contributions to weather prediction and is a key factor in initializing and validating climate models. Recent advances in the capabilities and robustness of small satellite components and systems provide an opportunity for NOAA, EUMETSAT and other operational agencies to explore the value of launching passive microwave sounder/imagers and complementary instruments on small spacecraft, including CubeSats, for relatively small investments. This provides the potential for deployment of microwave sounder constellations in low-Earth orbit (LEO) to substantially shorten revisit times. In this context, the first CubeSat-based multi-frequency microwave sounder to provide global data over a substantial period is the Temporal Experiment for Storms and Tropical Systems Demonstration (TEMPEST-D) mission. This mission was designed to demonstrate on-orbit capabilities of a five-frequency millimeter-wave radiometer to enable a future constellations of 6U CubeSats with low-mass, low-power millimeter-wave sensors to observe changes in convection and water vapor vertical profiles with revisit times on the order of minutes instead of hours. TEMPEST millimeter-wave radiometers provide observations at five frequencies from 87-181 GHz, with spatial resolution ranging from 12.5-25 km. To demonstrate technology necessary for deployment and operation of a CubeSat constellation of microwave sounders, the TEMPEST-D satellite was launched on May 21, 2018 from NASA Wallops to the ISS and successfully deployed into a 404-km orbit at 51.6° inclination on July 13, 2018. Now more than two years and nine months into its mission, the TEMPEST-D radiometer continues to provide science-quality data. The TEMPEST-D mission met all of its Level-1 requirements within the first 90 days of operations and achieved TRL 9 for both instrument and spacecraft systems. Validation of the TEMPEST-D brightness temperatures was performed over 50 days during a 13-month period through comparisons with GPM/GMI and MHS on NOAA-19, MetOp-A, MetOp-B and MetOp-C satellites. Results demonstrated calibration accuracy of TEMPEST-D within 1 K and stability within 0.6 K, as well as no evidence of any significant changes over time or with instrument temperature. TEMPEST-D brightness temperatures have been used to demonstrate data assimilation into NOAA numerical weather prediction models as well as atmospheric science parameter retrievals. In summary, on-orbit results show that TEMPEST-D is a very well-calibrated, highly stable radiometer, indistinguishable in performance from larger, more expensive operational sensors. Over its mission lifetime of nearly three years, TEMPEST-D has demonstrated the feasibility of deployment of a constellation of microwave sounders on CubeSats for relatively low cost and short timeline for implementation. A recently-completed CSU study, funded by NOAA, showed the potential for a CubeSat constellation of TEMPEST-based microwave sounders to perform temperature and moisture profiling with shorter refresh times. The InP HEMT low-noise amplifier technology developed for TEMPEST-D receivers for moisture profiling using 87-181 GHz frequencies can be enhanced by adding receivers with temperature profiling frequencies from 114-118 GHz range. The NOAA study demonstrated that a TEMPEST-based constellation of less than 12 CubeSats has the potential to greatly improve revisit times of current polar-orbiting operational microwave sensors.

This Conference Presentation, “Millimeter-wave sounder/imager on a CubeSat providing global atmospheric science observations for more than two years on orbit: Temporal Experiment for Storms and Tropical Systems-Demonstration (TEMPEST-D) Mission,” was recorded at SPIE Optics + Photonics 2021 held in San Diego, California, United States.

The rapid development of miniaturized satellite instrument technology has created a new opportunity to deploy constellations of passive microwave (PMW) radiometers to permit retrievals of the same Earth scene with very high temporal resolution to monitor cloud evolution and processes. For such a concept to be feasible, it must be shown that it is possible to distinguish actual changes in the atmospheric state from the variability induced by making observations at different Earth incidence angles (EIAs). To this end, we present a flexible and physical optimal estimation-based algorithm designed to retrieve profiles of atmospheric water vapor, cloud liquid water path, and cloud ice water path from cross-track PMW sounders. The algorithm is able to explicitly account for the dependence of forward model errors on EIA and atmospheric regime. When the algorithm is applied to data from the Temporal Experiment for Storms and Tropical Systems Technology Demonstration (TEMPEST-D) CubeSat mission, its retrieved products are generally in agreement with those obtained from the similar but larger Microwave Humidity Sounder instrument. More importantly, when forward model brightness temperature offsets and assumed error covariances are allowed to change with EIA and sea surface temperature, view-angle-related biases are greatly reduced. This finding is confirmed in two ways: through a comparison with reanalysis data and by making use of brief periods in early 2019 during which the TEMPEST-D spacecraft was rotated such that its scan pattern was along track, allowing dozens of separate observations of any given atmospheric feature along the satellite’s ground track.

Passive microwave radiometer systems have provided both temperature and water vapor sounding of the Earth’s atmosphere for several decades, including MSU, AMSU, MHS, ATMS, etc. Due to its ability to penetrate clouds, dust, and aerosols, among global datasets, microwave atmospheric sounding provides the most valuable quantitative contribution to weather prediction. Long-term, well-calibrated sounding records can be indispensable for climate measurement and model initialization/validation. Hence, passive microwave sounders are deployed on large, operational satellites and operated by NOAA, EUMETSAT and other similar national/international organizations. In the past five years or so, advances in CubeSats and other small satellites have enabled highly affordable space technology, providing access to space to private industries, universities and smaller nations. This provides a valuable opportunity for organizations such as NOAA and EUMETSAT to explore the added value of acquiring data from passive microwave sounders on small, low-cost spacecraft for relatively small investments, both for sensor and spacecraft acquisition and launch. This provides the potential for deployment of constellations of low-Earth orbiting microwave sounders to provide much more frequent revisit times than are currently available. For passive microwave sounding data to be valuable for weather prediction and climate monitoring, each sensor needs to be calibrated and validated to acceptable accuracy and stability. In this context, the first CubeSat-based multi-frequency microwave sounder to provide global data over a substantial period is the Temporal Experiment for Storms and Tropical Systems Demonstration (TEMPEST-D) mission. This mission was designed to demonstrate on-orbit capabilities of a new, five-frequency millimeter-wave radiometer to enable a complete TEMPEST mission using a closely-spaced train of eight 6U CubeSats with identical low-mass, low-power millimeter-wave sensors to sample rapid changes in convection and surrounding water vapor every 3-4 minutes for up to 30 minutes. TEMPEST millimeter-wave radiometers scan across track and observe at five frequencies from 87 to 181 GHz, with spatial resolution ranging from 25 km to 13 km, respectively. The TEMPEST-D satellite was launched on May 21, 2018 from NASA Wallops to the ISS and was successfully deployed on July 13, 2018, into a 400-km orbit at 51.6° inclination. The TEMPEST-D sensor has been operating nearly continuously since its first light data on September 5, 2018. With more than 16 months of operations to date, TEMPEST-D met all of its Level-1 mission objectives within the first 90 days of operations and has successfully achieved TRL 9 for both instrument and spacecraft systems. Validation of observed TEMPEST-D brightness temperatures is performed by comparing to coincident observations by well-calibrated on-orbit instruments, including GPM/GMI and MHS on NOAA-19, MetOp-A and MetOp-B satellites. Absolute calibration accuracy is within 0.9 K for all except the 164 GHz channel, well within the required 4 K for all channels. Calibration stability is within 0.5 K for all channels, also well within the 2 K requirement. TEMPEST-D has NEDTs similar to or lower than MHS. Therefore, although the TEMPEST-D radiometer is substantially smaller, lower power, and lower cost than operational radiometers, it has comparable performance, i.e. instrument noise, calibration accuracy and calibration stability.

The Temporal Experiment for Storms and Tropical Systems Demonstration (TEMPEST-D) mission is the first CubeSat-based multi-frequency microwave sounder to provide global data over a sustained period. The mission was designed to demonstrate on-orbit capabilities of a five-frequency millimeter-wave radiometer for a complete TEMPEST mission using a closely-spaced train of eight 6U CubeSats with identical low-mass, low-power millimeter-wave sensors to sample rapid changes in convection and surrounding water vapor every 3-4 minutes for up to 30 minutes. The TEMPEST-D satellite was launched on May 21, 2018 from NASA Wallops to the ISS and was successfully deployed on July 13, 2018, into a 400-km orbit at 51.6° inclination. The TEMPEST-D sensor has been operating nearly continuously since its first light data on September 5, 2018. On-orbit results indicate that TEMPEST-D is a very well-calibrated, highly stable radiometer, indistinguishable in performance from larger operational sensors.

Unlike large, expensive, and high-risk operational satellites, small/cube satellites (SmallSats) are a small, inexpensive, and a low-risk type of satellite. As a NOAA Cooperative Institute with specialties in satellite data processing and data assimilation, CIRA is funded by a Technology Maturation Program (TMP) research project to help NOAA exploit upcoming constellation of SmallSats to be considered for use in operations. In this research, a CSU-led technology demonstration mission entitled “the Temporal Experiment for Storms and Tropical System - Demonstration (TEMPTEST-D)” is used as an example to explore quick and agile methodologies to entrain SmallSats into the NOAA processing stream. Specifically, a workflow that enables TEMPEST-D data for assimilation into the NCEP Global Forecast System (GFS) with Finite-Volume Cube-Sphered (FV3) dycore (FV3GFS) under the Gridpoint Statistical Interpolation (GSI) based hybrid 4DEnVar system is established. One objective of this TMP research project is to assess the impact of SmallSat data on NOAA modeling and assimilation systems used in operations. We begin by asking whether the use of TEMPEST-D data is as good as the use of those obtained from well-established operational satellite sensors. Since the radiometric specification of TEMPEST-D is similar to the Microwave Humidity Sounder (MHS), we address the above question by directly comparing the following three cycled FV3GFS data assimilation and forecasting experiments: 1) the control experiment, which includes all routinely assimilated observations, but only assimilates MHS from the NOAA-19 and MetOp-B platforms, 2) the AddMHS experiment, which is the control plus MHS from the MetOp-A platform, and 3) the AddTEMPEST experiment, which is the control plus TEMPEST-D. By differentiating the AddMHS and AddTEMPEST experiments against the control experiment, we will be able to demonstrate that a cost-effective TEMPEST-D is as beneficial as a well-established operational satellite like MHS, in terms of aiding operational global weather forecasting. In addition, results from this research offers implications of the utility of a constellation of SmallSats microwave radiometers for global weather forecasting.

To improve understanding of rapid, dynamic evolution of convective cloud and precipitation processes as well as the surrounding water vapor environment, we require fine time-resolution multi-frequency microwave sounding observations capable of penetrating inside the storm where the microphysical processes leading to precipitation occur. To address this critical observational need, the Temporal Experiment for Storms and Tropical Systems (TEMPEST) mission deploys a train of 6U CubeSats carrying identical low-mass, low-power millimeter-wave radiometers to sample rapid changes in convection and water vapor every 3-4 minutes for up to 30 minutes. These millimeter-wave radiometers observe at five frequencies from 87 to 181 GHz. By rapidly sampling the life cycle of convection, TEMPEST fills a critical observational gap and complements existing and future satellite missions. To demonstrate global, well-calibrated radiometric measurements to meet the needs of TEMPEST, the TEMPEST Technology Demonstration (TEMPEST-D) mission satellite was launched on May 21, 2018 on Orbital ATK’s CRS-9 mission to the ISS and deployed into a 400-km altitude and 51.6° inclination orbit by NanoRacks on July 13, 2018. TEMPEST-D has met all mission requirements on schedule and within budget. After achieving first light on September 5, 2018, the TEMPEST-D mission has achieved TRL 7 for both the instrument and spacecraft systems. TEMPEST-D brightness temperatures have been cross-calibrated with those of four NASA, NOAA and EUMETSAT reference sensors observing at similar frequencies. Results demonstrate that the TEMPEST-D on-orbit instrument is a very well-calibrated and stable radiometer with very low noise, rivaling that of much larger, more expensive operational instruments.