The high-latitude oceans are problematic for satellite estimations of precipitation due to the high frequency of occurrence of light drizzle and snowfall. Microwave radiometric observations are sensitive to integrated cloud water path but lack skill in distinguishing precipitation onset from cloud water and cloud ice due to radiation scattering. Precipitation radars to date have lacked sensitivity to drizzle and cloud radars have suffered from both the uncertainties inherent in Z-R relations and poor sampling due to nadir-only scans. This study optimally combines coincident active and passive microwave observations from CloudSat's Cloud Profiling Radar (CPR) and the Advanced Scanning Microwave Radiometer (AMSR2) to resolve cloud and hydrometeor distribution parameters and to force consistency between the two independent sets of coincident observations. The result is an estimation of drizzle frequency and intensity that are consistent with both the CPR and AMSR2 observations for the high-latitude oceans. This study finds that zonal means of retrieved high-latitude drizzle below 0.25 mm hr-1 from these combined observations (0.263 mm day-1) fall slightly above those of CloudSat estimates (0.244 mm day-1) provided by the 2C-RAIN-PROFILE and 2C-SNOW-PROFILE products (Lebsock, 2018; Wood & L'Ecuyer, 2018) and far below that of radiometer-only estimates (0.920 mm day-1) provided by GPROF (C. D. Kummerow et al., 2015).

After 5 years in orbit, the Global Precipitation Measurement (GPM) mission has produced enough quality-controlled data to allow the first validation of their precipitation estimates over Spain. High-quality gauge data from the meteorological network of the Spanish Meteorological Agency (AEMET) are used here to validate Integrated Multisatellite Retrievals for GPM (IMERG) level 3 estimates of surface precipitation. While aggregated values compare notably well, some differences are found in specific locations. The research investigates the sources of these discrepancies, which are found to be primarily related to the underestimation of orographic precipitation in the IMERG satellite products, as well as to the number of available gauges in the GPCC gauges used for calibrating IMERG. It is shown that IMERG provides suboptimal performance in poorly instrumented areas but that the estimate improves greatly when at least one rain gauge is available for the calibration process. A main, generally applicable conclusion from this research is that the IMERG satellite-derived estimates of precipitation are more useful (r(2) > 0.80) for hydrology than interpolated fields of rain gauge measurements when at least one gauge is available for calibrating the satellite product. If no rain gauges were used, the results are still useful but with decreased mean performance (r(2) approximate to 0.65). Such figures, however, are greatly improved if no coastal areas are included in the comparison. Removing them is a minor issue in terms of hydrologic impacts, as most rivers in Spain have their sources far from the coast.

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.

Current passive microwave precipitation retrievals assume that brightness temperature (TB) is sufficient to constrain rainfall. This information, however, often represents multiple rain states, resulting in rainfall estimate uncertainties. These uncertainties, while dominated by random variability, can also exhibit substantial regional biases which complicate the use of traditional ground validation techniques. This study aims to characterize the physical contributors to these biases for use in uncertainty quantification. Coincident GPROF Version 7, Global Precipitation Measurement (GPM) Microwave Imager (GMI), and GPM combined observations were examined over three tropical land regions, the Amazon, Congo, and Southeast Asia, which are known to exhibit distinct biases relative to one another when comparing GPROF with GPM Combined precipitation. The interrelation between rain intensity and ice-rain ratio (IRR) was identified as the primary descriptor of regional bias. Using this interrelation, the regional bias range is decreased from +/- 13% to +/- 7%. Including a third constraint based on the polarization-corrected 37-GHz TB further reduced this range to +/- 4% by accounting for second-order effects. Comparing the effects of these three parameters between GPROF Version 7 and the 1-D version of GPROF-NN showed similar improvements, indicating the utility of this uncertainty quantification and adjustment method across precipitation products. With these constraints, regional precipitation biases can be understood from a physical perspective, presenting an opportunity for uncertainty quantification which uses knowledge of the physical state of the atmosphere.

Spaceborne microwave radiometers represent an important component of the Global Precipitation Measurement (GPM) mission due to their frequent sampling of rain systems. Microwave radiometers measure microwave radiation (brightness temperatures Tb), which can be converted into precipitation estimates with appropriate assumptions. However, detecting shallow precipitation systems using spaceborne radiometers is challenging, especially over land, as their weak signals are hard to differentiate from those associated with dry conditions. This study uses a random forest (RF) model to classify microwave radiometer observations as dry, shallow, or nonshallow over the Netherlands }a region with varying surface conditions and frequent occurrence of shallow precipitation. The RF model is trained on fi ve years of data (2016 - 20) and tested with two independent years (2015 and 2021). The observations are classi fi ed using ground -based weather radar echo top heights. Various RF models are assessed, such as using only GPM Microwave Imager (GMI) Tb values as input features or including spatially aligned ERA5 2-m temperature and freezing level reanalysis and/or DualFrequency Precipitation Radar (DPR) observations. Independent of the input features, the model performs best in summer and worst in winter. The model classi fi es observations from high -frequency channels ( $85 GHz) with lower Tb values as nonshallow, higher values as dry, and those in between as shallow. Misclassi fi ed footprints exhibit radiometric characteristics corresponding to their assigned class. Case studies reveal dry observations misclassi fi ed as shallow are associated with lower Tb values, likely resulting from the presence of ice particles in nonprecipitating clouds. Shallow footprints misclassifi ed as dry are likely related to the absence of ice particles.

The Temporal Experiment for Storms and Tropical Systems Demonstration (TEMPEST-D) demonstrated the capability of CubeSat satellites to provide high-quality, stable microwave signals for estimating water vapor, clouds, and precipitation from space. Unlike the operational NOAA and MetOp series satellites, which combine microwave and hyperspectral infrared sensors on the same platforms to optimize retrievals, CubeSat radiometers such as TEMPEST do not carry additional sensors. In such cases, the high-temporal- and spatial-resolution and multi-channel measurements from the Advanced Baseline Imager (ABI) on the next-generation series of Geostationary Operational Environmental Satellites (GOES-R) are ideal for assisting these smaller, stand-alone radiometers. Based on sensitivity tests, the water vapor retrievals from TEMPEST are improved by adding water-vapor-sounding, window, and CO2 channels at 6.2, 6.9, 7.3, 8.4, 10.3, 11.2, 12.3, and 13.3 mu m from ABI, which help to increase the vertical resolution of soundings and reduce retrieval errors. Adding three ABI water-vapor-sounding channels, under clear-sky conditions, retrieval biases and root mean square errors improve by approximately 10 %, while under cloudy skies, biases remain unchanged, but root mean square errors still decrease by 5 %; meanwhile, retrieval biases and root mean square errors are substantially reduced by adding more information from eight ABI bands in both clear and cloudy skies. Humidity soundings are also validated using coastal radiosonde data from the Integrated Global Radiosonde Archive (IGRA) from 2019 to 2020. When ABI indicates clear skies, water vapor retrievals improve somewhat by decreasing the overall bias in the microwave-only estimate by roughly 10 %, although layer root mean square errors remain roughly unchanged at 1 gkg-1 when three or eight ABI channels are added. When ABI indicates cloudy conditions, there is little change in the results. The small number of matched radiosondes may limit the observed improvement.

Over the past decades, spaceborne radiometers have proven to be valuable input to realize a global coverage of precipitation estimates. However, retrieving accurate shallow precipitation estimates from radiometers remains challenging. The signal related to precipitation formed close to the Earth’s surface is difficult to distinguish from dry weather, especially over land. Despite the relatively low precipitation rates that are often associated with shallow precipitation, its persistent presence results in a significant contribution to the total amount of rainfall over the mid- and high latitudinal regions. Hence, correct identification is important.This study aimed to improve our understanding of the radiometric signatures of shallow precipitation from passive microwave observations by implementing a Random Forest (RF) model. RF is chosen because of its limited risk of overfitting and the ability to physically interpret the resulting model structure and parameters. The RF model is applied to brightness temperature observations from all channels onboard the Global Precipitation Measurement (GPM) Microwave Imager (GMI) during 2017-2020 over The Netherlands (52°N). A high-quality gauge-adjusted radar product is used as reference. The echo top height retrieved from the two radars in The Netherlands (Herwijnen and Den Helder) are used to classify the GMI footprints to either dry, shallow (<3km) or non-shallow (>3km) regime.Hyperparameter settings, such as the depth of the model, and choices such as the number of years the model is trained on or the threshold to classify footprint as dry, shallow, or non-shallow regime have a limited effect on the performance of the RF. In general, the model tends to wrongly classify dry footprints as wet (both shallow and non-shallow). The model showed a seasonal dependency, with the best performance in summer. Preliminary results also showed a strong seasonal effect when excluding all footprints within 40km distance of the coast. These results indicate that four different parameter sets representing each season are required. Furthermore, the specific years the model is trained or tested on are found to strongly affect its performance. Currently, additional variables (such as ERA5 freezing level, two-meter air temperature) and simultaneous observations from the GPM dual-frequency precipitation radar (DPR), are included to further improve and understand the performance of the RF model.

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.

Despite its long history, improving upon current precipitation estimation techniques remains an active area of research. While many methods exist to assess precipitation, the use of satellites has allowed for near-global observation. However, satellites do not directly sense precipitation, resulting in retrieval uncertainties. Analysis of these uncertainties is typically conducted through validation studies, which, while necessary, are sensitive to local conditions. As such, predicting retrieval uncertainties where there is no validation data remains a challenge. In this study, we propose a method by which validation statistics can be extended to other regions. Using a neural network–style retrieval, the Geostationary Operational Environmental Satellite–16 ( GOES-16 ) Precipitation Estimator using Convolutional Neural Networks (GPE-CNN), we show that, by exploiting the information content of both the satellite and ancillary meteorological data, one can predict large-scale retrieval behaviors over other regions without the need for that region’s validation data. By developing classes using satellite information content, we demonstrate bias prediction improvement of up to 83% relative to a simple extension of mean bias. Including relative humidity information improves the overall prediction by up to 98% relative to the original mean bias. Although limited in scope, this method presents a pathway toward characterizing uncertainties on a broader scale.

&lt;p&gt;The Global Precipitation Measurement (GPM) mission was launched in February 2014 as a joint mission between JAXA from Japan and NASA from the United States.&amp;#160; GPM carries a state of the art dual-frequency precipitation radar and a multi-channel passive microwave radiometer that acts not only to enhance the radar&amp;#8217;s retrieval capability, but also as a reference for a constellation of existing satellites carrying passive microwave sensors.&amp;#160; In April 2022, GPM approved V 7 of its precipitation products starting with GMI and continuing with the constellation of radiometers.&amp;#160; The precipitation products from these sensors are consistent by design and show relatively minor differences in the mean global sense.&amp;#160; Validation results will be shown for work done over the Continental United States using a Radar/Gauge composite as truth, and Kwajalein atoll to represent truth over tropical oceans. &amp;#160;The validation results are a necessary but not sufficient component to quantify the algorithm&amp;#8217;s uncertainties.&amp;#160; Good results for bias, MAR and RMSE are demonstrated.&amp;#160; Validation results, however, are only able to assess errors at their own sites, and systematic errors, in particular, are not actually systematic, but regime dependent errors that vary as a function of how well the algorithm assumptions are captured at the validation sites.&amp;#160;&amp;#160; This talk will explore ways of validating not by location, but by precipitation states that, the environment that the precipitation evolves in, as a way of obtaining robust statistics of individual precipitation states that are universal and can be applied with confidence to areas outside the validation domain.&lt;/p&gt;

Temporal Experiment for Storms and Tropical Systems – Demonstration (TEMPEST-D) is a nearly 3-year NASA mission to demonstrate global observations from a multi-frequency microwave sensor deployed on a 6U CubeSat platform. TEMPEST was proposed to Earth Venture Instrument-2 in 2013 to perform high temporal resolution observations of rapidly evolving storms using a constellation of five 6U CubeSats with identical microwave sensors in a single orbital plane, providing 7-minute temporal sampling of rapidly-developing convective activity over 30 minutes. To demonstrate necessary capability for TEMPEST constellation operation, NASA’s Earth Venture Technology program funded the TEMPEST-D mission, a multi-frequency microwave radiometer on a single 6U CubeSat, successfully delivered for launch less than 2 years after PDR. TEMPEST-D was deployed from the ISS into low Earth orbit on July 13, 2018, and observed the Earth’s atmosphere nearly continuously until it re-entered on June 21, 2021. TEMPEST-D performed the first global Earth observations from a multi-frequency microwave radiometer on a CubeSat. The TEMPEST-D mission substantially exceeded expectations of data quality, stability, consistency and mission duration. TEMPEST-D data were validated using the double-difference technique for cross-calibration with scientific and operational microwave sensors observing at similar frequencies, including 4 MHS sensors on NOAA-19, MetOp-A, -B and -C, as well as GPM/GMI. These validation results showed that TEMPEST had comparable or better performance to much larger operational sensors in terms of calibration accuracy, precision and stability throughout the nearly 3-year mission. TEMPEST-D performed detailed observations of the microphysics of hurricanes, typhoons and tropical cyclones during three consecutive hurricane seasons. Simultaneous observations by TEMPEST-D and JPL’s RainCube weather radar demonstrated physical consistency and well-correlated passive and active microwave measurements of severe weather from the two CubeSats. Quantitative precipitation estimates retrieved from TEMPEST-D data are highly correlated with standard ground radar precipitation products, such as NOAA/NWS MRMS. TEMPEST-D also periodically performed along-track scanning measurements to provide the first space-borne demonstration of “hyperspectral” microwave sounding observations to retrieve the height of the planetary boundary layer. The stability, accuracy and reliability of TEMPEST-D on a 6U CubeSat open a breadth of possibilities for future Earth observation and science missions on small satellites to enable rapid temporal observations of cloud and precipitation processes. Early in the development of the TEMPEST-D mission, a nearly identical microwave sensor, TEMPEST-D2, was produced alongside the original to reduce risk from the original manifest for launch. TEMPEST-D2 was delivered to the U.S. Space Force in 2021 for integration with the Compact Ocean Wind Vector Radiometer (COWVR), previously developed by NASA/Caltech JPL. On December 21, 2021, COWVR and TEMPEST-D2 were launched from KSC as part of the Space Test Program (STP-H8) mission for at least 3 years of operations on the ISS. These two passive microwave sensors provide a unique, synergistic opportunity for coordinated global observations of the Earth’s oceans and atmosphere using complementary small satellite instruments. Finally, the demonstrated success of TEMPEST-D and RainCube was essential in NASA’s selection in November 2021 of the Investigation of Convective Updrafts (INCUS) mission as Earth Venture Mission-3, to be launched in 2027.

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.

Temperature and humidity soundings form the bedrock of modern data assimilation due to their ability to directly constrain the atmospheric state variables. Because of their ability to penetrate clouds and work in all weather conditions, microwave sounders have very large impacts on constraining numerical weather prediction models. Recent advancements in integrated microwave assembly, space-grade high speed analog to digital converters, gigabit-per-second data interconnects, and field programmable gate arrays have enabled a transition from traditional analog detector-based demodulators to digitally channelized systems that allow for hyperspectral, or fine spectral resolution microwave sounders to be viable replacements to the current operational instruments. This article demonstrates that retrievals of temperature and moisture soundings can be improved by as much as 50% when 60–80 appropriately chosen pseudochannels are employed. While the current simulations were limited to cloud free oceans, perhaps even greater benefits can be realized over land and cloud conditions where additional channels can help constrain the surface and clouds. The article also demonstrated the advantages of hyperspectral sensors as a way to detect radio frequency interference in the few Kelvin range, as well as its ability to improve intercalibration efforts due to its ability to match frequency response functions of target sensors.

Latent heating (LH) is an important factor in both weather forecasting and climate analysis, being the essential factor affecting both the intensity and structure of convective systems. Yet, inferring LH rates from our current observing systems is challenging at best. For climate studies, LH has been retrieved from the precipitation radar on the Tropical Rainfall Measuring Mission (TRMM) using model simulations in a lookup table (LUT) that relates instantaneous radar data to corresponding heating profiles. These radars, first on TRMM and then the Global Precipitation Measurement Mission (GPM), provide a continuous record of LH. However, the temporal resolution is too coarse to have significant impacts on forecast models. In operational forecast models such as High-Resolution Rapid Refresh (HRRR), convection is initiated from LH derived from ground-based radars. Despite the high spatial and temporal resolution of ground-based radars, their data are only available over well-observed land areas. This study develops a method to derive LH from the Geostationary Operational Environmental Satellite-16 (GOES-16) in near-real time. Even though the visible and infrared channels on the Advanced Baseline Imager (ABI) provide mostly cloud top information, rapid changes in cloud top visible and infrared properties, when formulated as an LUT similar to those used by the TRMM and GPM radars, can successfully be used to derive LH profiles for convective regions based on model simulations with a convective classification scheme and channel 14 (11.2 µm) brightness temperatures. Convective regions detected by GOES-16 are assigned LH profiles from a predefined LUT, and they are compared with LH used by the HRRR model and one of the dual-frequency precipitation radar (DPR) products, the Goddard convective–stratiform heating (CSH). LH obtained from GOES-16 shows similar magnitude to LH derived from the Next Generation Weather Radar (NEXRAD) and CSH, and the vertical distribution of LH is also very similar with CSH. A three-month analysis of total LH from convective clouds from GOES-16 and NEXRAD shows good correlation between the two products. Finally, LH profiles from GOES-16 and NEXRAD are applied to WRF simulations for convective initiation, and their results are compared to investigate their impacts on precipitation forecasts. Results show that LH from GOES-16 has similar impacts to NEXRAD in terms of improving the forecast. While only a proof of concept, this study demonstrates the potential of using LH derived from GOES-16 for convective initialization.

Precipitation processes play a critical role in the longevity and spatial distribution of stratocumulus clouds through their interaction with the vertical profiles of humidity and temperature within the atmospheric boundary layer. One of the difficulties in understanding these processes is the limited amount of observational data. In this study, robust relations among liquid water path ( LWP ), cloud droplet number concentration ( Nd ) and cloud base rain rate ( R cb ) from three subtropical stratocumulus decks are obtained from A-Train satellite observations in order to obtain a broad perspective on warm rain processes. R cb has a positive correlation with LWP/Nd and the increase of R cb becomes larger as LWP/Nd increases. However, the increase of R cb with respect to LWP/Nd becomes more gradual in regions with larger Nd , which indicates the relation is moderated by Nd . These results are consistent with our theoretical understanding of warm rain processes and suggest that satellite observations are capable of elucidating the average manner of how precipitation processes are modulated by LWP and Nd . The sensitivity of the auto-conversion rate to Nd is investigated by examining pixels with small LWP in which the accretion process is assumed to have little influence on R cb . The upper limit of the dependency of auto-conversion rate on Nd is assessed from the relation between R cb and Nd , since the sensitivity is exaggerated by the accretion process, and was found to be a cloud droplet number concentration to the power of −1.44 ± 0.12.

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.

Abstract. Latent heating (LH) is an important quantity in both weather forecasting and climate analysis, being the essential factor driving convective systems. Yet, inferring LH rates from our current observing systems is challenging at best. For climate studies, LH has been retrieved from the Precipitation Radar (PR) on the Tropical Rainfall Measuring Mission (TRMM) using model simulations in the look-up table (LUT) that relates instantaneous radar profiles to corresponding heating profiles. These radars, first on TRMM and then Global Precipitation Measurement (GPM), provide a continuous record of LH. However, with observations approximately 3 days apart, its temporal resolution is too coarse to be used to initiate convection in forecast models. In operational forecast models such as High-Resolution Rapid Refresh (HRRR), convection is initiated from LH derived from ground based radar. Despite the high spatial and temporal resolution of ground-based radars, one disadvantage of using it is that its data are only available over well observed land areas. This study suggests a method to derive LH from the Geostationary Operational-Environmental Satellite-16 (GOES-16) in near-real time. Even though the visible and infrared channels on the Advanced Baseline Imager (ABI) provide mostly cloud top information, rapid changes in cloud top visible and infrared properties, when coupled to a LUT similar to those used by the TRMM and GPM radars, can equally be used to derive LH profiles for convective regions using model simulations coupled to a convective classification scheme and channel 14 (11.2 μm) brightness temperature. Convective regions detected by GOES-16 are assigned LH from the LUT, and they are compared with LH from NEXRAD and one of Dual-frequency Precipitation Radar (DPR) products, Goddard Convective-Stratiform Heating (CSH). LH obtained from GOES-16 show similar magnitude with NEXRAD and CSH, and vertical distribution of LH is also very similar with CSH. Overall, GOES LH appear to have the ability to mimic LH from radars, although the area identified as convective is roughly 25 % smaller than the current HRRR model, while the heating is correspondingly higher.