The objective of this study is to compare microwave brightness temperature observations performed by the Temporal Experiment for Storms and Tropical Systems (TEMPEST) Space Test Program-Houston 8 (TEMPEST- H8) with those from the Global Precipitation Measurement (GPM) Microwave Imager (GMI). This study utilizes TEMPEST-H8 and GMI observations over tropical cyclones (TCs). Brightness temperature (TB) observations from the TEMPEST-H8 165 GHz channel and the GMI 166 GHz horizontal polarization channel were used in the cross-comparison study. Three tropical cyclones, including Tropical Cyclone Batsirai, Typhoon Mawar and Hurricane Hilary, occurring over a variety of oceanic regions, were analyzed. The cross-comparison results from all three cases showed that TC size and rain band structures appear similar in TEMPEST-H8 and GMI observations. The average correlation coefficient (r) values between the observations from the two instruments are 0.85 for TC Batsirai, 0.88 for Typhoon Mawar and 0.93 for Hurricane Hilary. These results demonstrate that the TEMPEST-H8 small-satellite sensor, which is nearly identical to TEMPEST-D, performs similarly to that of a traditional science mission sensor, i.e. GPM GMI.

The principal objective of this study is to develop machine learning (ML) models, in particular a random forest (RF) classifier and an artificial neural network (ANN) regression model, for estimating surface rain rates on a global basis using brightness temperature (TB) observations from the Temporal Experiment for Storms and Tropical Systems Demonstration (TEMPEST-D) CubeSat. The accuracy of the models is assessed by comparing the estimated rain rates with the Integrated Multi-satellitE Retrievals for GPM (IMERG) final run rain rate product, which also serves as the ground truth for ML model development. The ML models are developed using a dataset consisting of TEMPEST-D observations of 12 tropical cyclones (TC) and the corresponding IMERG products from various locations around the globe, including the Atlantic, eastern/western Pacific and Indian Oceans. To evaluate the performance of the ML models, independent validation is conducted using Hurricane Isaac and Typhoon Hagibis. The structural similarity index measure (SSIM) is used to assess the similarity between the ML estimated rain rates and the IMERG products. For Hurricane Isaac, an SSIM score of 0.8 is achieved, indicating a strong resemblance to the IMERG product. Similarly, for Typhoon Hagibis, the SSIM score is 0.73, indicating quite good agreement with the IMERG rain rate.

The objective of this study is to cross-validate observations of the Temporal Experiment for Storms and Tropical Systems Demonstration (TEMPEST-D) CubeSat mission with those observed by the Global Precipitation Measurement (GPM) Microwave Imager (GMI) [1] over precipitating systems. The purpose of this paper is twofold: first, to show consistency between TEMPEST-D and GPM/GMI, and second, if the measurements are consistent, to demonstrate the potential to enhance temporal sampling when the TEMPEST-D and GPM/GMI observations are merged. This paper demonstrates both objectives and shows good agreement between TEMPEST-D and GPM/GMI observations.

This paper presents some of the first nearly simultaneous observations between TEMPEST-D and RainCube, two CubeSat missions supported by NASA for on-orbit validation of technology for studying the Earth's atmosphere. This paper presents simultaneous observations by a Ka-band radar and multi-frequency millimeter-wave radiometers over precipitation systems, in three widely dispersed locations over the globe. The first storm was near Mexico's Pacific coast, whereas the second storm was over the South Pacific Ocean near the Solomon Islands, and the third storm was near Houston, Texas, USA. The comparisons showed good physical consistency between the TEMPEST-D and RainCube observations.

This paper presents a machine learning model to estimate surface rainfall from TEMPEST-D observations. An artificial neural network (ANN) was chosen to build the rainfall estimation model from TEMPEST-D measurements. TEMPEST-D brightness temperature (TB) observations performed at five frequencies (i.e. 87, 164, 174, 178 and 181 GHz) were used as inputs, and the Multi-Radar/Multi-Sensor System (MRMS) radar-only rain rate product at the surface was used as ground truth and target to train the ANN model. A spatial alignment algorithm was developed to align the TEMPEST-D observed storm with the storm measurement from ground radar. The training data set was generated from 14 storm events observed simultaneously by the ground radar network and TEMPEST-D over the continental U.S. Two storm events were used for independent testing. The testing showed that estimated rainfall matched well with the MRMS surface rainfall product in terms of rainfall intensity, area, and precipitation system pattern. The structural similarity index measure scores for the two independent test cases are 0.72 and 0.81.

The Temporal Experiment for Storms and Tropical Systems (TEMPEST) mission concept consists of a constellation of five identical 6U-Class nanosatellites observing at five millimeter-wave frequencies with five-minute temporal sampling to observe the time evolution of clouds and their transition to precipitation. The TEMPEST concept is intended to improve understanding of cloud processes, by providing critical information on the temporal development of cloud and precipitation microphysics and by improving our understanding of some of the largest sources of uncertainty in cloud process models. TEMPEST millimeter-wave radiometers are able to perform observations inside the cloud to observe changes as the cloud begins to precipitate or ice accumulates inside the storm. The TEMPEST Technology Demonstration (TEMPEST-D) mission is intended to reduce risk and demonstrate measurement capabilities for 6U-Class satellite constellations for Earth Science. The capabilities to be demonstrated include differential drag maneuvers to provide desired time separation in a 6U-Class satellites constellation. In addition, TEMPEST-D millimeter-wave radiometers will be cross-calibrated with space-borne radiometers with similar frequency channels. TEMPEST-D will provide radiometric observations at five millimeterwave frequencies from 89 to 183 GHz using a low-power, compact instrument that is highly suitable for deployment on 6U-Class satellites.