Temporal Experiment For Storms Research Articles

Single-Point Calibration for Microwave Sounders: Application to TEMPEST-D

Abstract Passive microwave sounders are critical for accurate forecasts from numerical weather prediction models. These sensors are calibrated using a traditional two-point approach, with one source typically a free-space blackbody target and the second a clear view to the cosmic microwave background, commonly referred to as “cold space.” Occasionally, one or both of these calibration sources can become corrupted, either by solar/lunar intrusion in the cold space view or by thermal instability of the blackbody calibration source. A Temporal Experiment for Storms and Tropical Systems (TEMPEST) microwave sounder instrument is currently deployed on the International Space Station (ISS) for a 3-yr mission. TEMPEST is also calibrated using a blackbody target and cold space view; however, the cold space view will be routinely obstructed by objects present on the ISS. Here we test an alternative single-point calibration methodology that uses only the blackbody calibration target. We find the brightness temperature difference between this new approach and the traditional two-point calibration approach to be <0.1 K when applied to 3 years of the TEMPEST CubeSat Demonstration (TEMPEST-D) mission data from 2018 to 2020. This approach is applicable to other microwave radiometers that experience occasional degradation of calibration sources, such as thermal effects, intrusions, or instability of noise diodes. Significance Statement Cross-track microwave sounders have relied on two distinct calibration sources, often the cosmic microwave background using a clear view to cold space and an ambient blackbody target. We have tested an alternative approach that uses a single calibration target, making the sensor robust to occasional field-of-view intrusions of the space view or alternatively simplifies the spaceborne sensor design by eliminating the need for a clear view to space. We find that the performance difference between this new approach and the traditional two-calibration source approach is indistinguishable for both microwave temperature/water vapor profiling and precipitation-rate estimation. This calibration technique can be applied to past, current, and future microwave sounders to help diagnose systematic uncertainties in sensor calibration targets.

Correcting Calibration Drifts Using Solar and Lunar Intrusions for Miniaturized Microwave Radiometers

CubeSats with miniaturized microwave radiometers are now demonstrating the potential to provide science-quality weather measurements. For example, the Micro-Sized Microwave Atmospheric Satellite-2A (MicroMAS-2A) and Temporal Experiment for Storms and Tropical Systems–Demonstration (TEMPEST-D) CubeSats are both launched in 2018 and have demonstrated microwave atmospheric sounder data from orbit. The NASA Time-Resolved Observations of Precipitation structure and storm Intensity with a Constellation of Smallsats (TROPICS) mission is a follow-on constellation of six 3U CubeSats based on the MicroMAS-2 design that is scheduled for launch no sooner than 2021. The TROPICS sensors use internal noise diodes (NDs) for calibration. Although the NDs on TROPICS are similar to technology flown on GMI, they have not been tested on-orbit at TROPICS frequencies. In order to track and correct ND drift, we develop a novel method of calibration for CubeSat constellations, such as TROPICS, by incorporating periodic solar and lunar intrusions as an additional source of information to counter ND drift. These lunar intrusions also occur for existing satellites hosting microwave radiometers in sun-synchronous polar orbits but are much more infrequent than for the TROPICS constellation’s scanning payload. In this work, we develop a solar/lunar calibration correction algorithm and test it using advanced technology microwave sounder (ATMS) lunar intrusion data. The mean bias and standard deviation between the solar/lunar calibration correction algorithm and actual ATMS data fall within the expected ATMS error budget of 0.6–3.9 K, validating our model.

Rainfall Estimation From TEMPEST-D CubeSat Observations: A Machine-Learning Approach

In this study, a machine-learning model was used to produce surface rainfall estimates from Temporal Experiment for Storms and Tropical Systems – Demonstration (TEMPEST-D) microwave radiance observations from a CubeSat. The machine-learning model is based on an artificial neural network (ANN). The space-borne TEMPEST-D sensor performed brightness temperature (TB) observations at five frequencies (i.e., 87, 164, 174, 178, and 181 GHz) during its nearly three-year mission. The TEMPEST-D TBs were used as inputs, and the multiradar/multisensor system (MRMS) radar-only quantitative precipitation estimation product at the surface was used as the ground truth to train the ANN model. A total of 19 storms were identified that were simultaneously observed by TEMPEST-D and ground weather radar over the contiguous United States. The training dataset used 14 of the 19 storm cases. The other five storm cases, consisting of three continental storms and two land-falling hurricanes, were used for independent testing. A spatial alignment algorithm was developed to align the TEMPEST-D observed storm with the ground radar measurement of the storm. This study showed that the TEMPEST-D TBs captured storm features as well as current-generation satellite sensors, such as the global precipitation mission microwave imager. The results of this study demonstrated that the rainfall estimated from TEMPEST-D matches well with the MRMS surface rainfall product in terms of rainfall intensity, area, and precipitation system pattern. The average structural similarity index measure score for the five independent test cases is 0.78.

Cross Validation of TEMPEST-D and RainCube Observations Over Precipitation Systems

This paper presents cross-validation of nearly simultaneous observations between the Temporal Experiment for Storms and Tropical Systems<b>&#x00A0;-</b>Demonstration<b>&#x00A0;(</b>TEMPEST-D) and RainCube (Radar in a CubeSat) satellite microwave sensors over precipitation systems. RainCube senses the atmosphere using a Ka-band nadir-pointing radar, and TEMPEST-D senses using multi-frequency millimeter-wave radiometers. Nine precipitation systems were used in this cross-validation study. Occurring over nearly a two-year period, these storms were scattered over many regions of the world from the Tropics to the mid-latitudes. A shift correction algorithm was developed to remove the uncertainty between the two sensors&#x0027; observations due to the time difference and the storm motion between the two instrument overpasses. Correlation coefficients were calculated between self-normalized, inverted RainCube cumulative reflectivity and self-normalized TEMPEST-D brightness temperatures. As expected, these correlation coefficients are consistently higher for the four TEMPEST-D high-frequency channels than for the single low-frequency channel. The shift correction algorithm improved the average correlation coefficient by 19&#x0025; for the four high-frequency channels and by 58&#x0025; for the single low-frequency channel. The average correlation coefficient after shift correction is 0.76 for TEMPEST-D&#x0027;s four high-frequency channels and 0.54 for the single low-frequency channel. The comparisons demonstrated high consistency between the TEMPEST-D and RainCube observations, even though the two microwave sensors&#x0027; fundamental physics is different; one is passive, and the other is active.

Derived Observations From Frequently Sampled Microwave Measurements of Precipitation—Part III: Convoys of mm-Wave Radiometers

This is the third of three papers that quantify the high added value of frequent satellite microwave observations of the atmosphere (with a “refresh” time on the order of one minute) to capture the dynamics of weather systems. Recent advances in small-satellite and microwave miniaturization, such as the “Temporal Experiment for Storms and Tropical systems” (TempEST) millimeter-wave radiometer developed at the Jet Propulsion Laboratory, are paving the way for the design of convoys of spaceborne radars that can directly observe the evolution of severe weather at very fine temporal scales. The analyses presented here are to establish the relation between passive microwave observations and their change in time and the underlying cloud variables and processes, and to evaluate the sensitivity to the different physical and instrument parameters. In this third part, simulations are used to demonstrate and quantify the direct sensitivity of a time sequence of mm-wave radiances to the vertical structure of the vertical updrafts in convective storms. It is demonstrated that the brightness temperatures from a pair of low-Earth-orbit radiometers maintaining a separation of one or two minutes can indeed be used to retrieve the column-maximum magnitude of the vertical wind as well as the height of the maximum. While such passive retrievals do not provide the vertical detail that a pair of radars would produce, the radiometers have a vast swath and therefore enable the observation of an entire storm. The application is therefore quite different from the case of a convoy of radars: rather than compiling radar statistics over multiple years, the radiometer-convoy measurements can be used to analyze every storm that is observed.

TEMPEST-D Radiometer: Instrument Description and Prelaunch Calibration

The Temporal Experiment for Storms and Tropical Systems Technology Demonstration (TEMPEST-D) instrument is a five-frequency millimeter-wave radiometer operating from 87 to 181 GHz. The cross-track scanning radiometer has been operating on a 6U CubeSat in low Earth orbit since September 5, 2018. The direct-detection architecture of the radiometer reduces its mass and power consumption by eliminating the need for a local oscillator and mixer, also reducing system complexity. The instrument includes a scanning reflector and ambient calibration target. The reflector rotates continuously to scan the antenna beams in the cross-track direction, first across the blackbody calibration target, then toward the Earth over the full range of incidence angles, and finally to cosmic microwave background radiation at 2.73 K. This enables precision end-to-end calibration of the millimeter-wave receivers during every 2-s scan period. The TEMPEST-D millimeter-wave radiometers are based on 35-nm indium phosphide (InP) high-electron-mobility transistor (HEMT) low-noise amplifiers. This article describes the instrument and its characterization prior to launch.

Calibration and Validation of the TEMPEST-D CubeSat Radiometer

Temporal Experiment for Storms and Tropical Systems-Demonstration (TEMPEST-D) is a 6U CubeSat satellite with a cross-track scanning millimeter-wave radiometer measuring at five frequencies from 87 to 181 GHz. It employs a direct-detection architecture with InP HEMT monolithic microwave integrated circuit (MMIC) low-noise amplifiers and related new technologies. An end-to-end two-point external calibration is performed every 2-s rotation of the scanning mirror, based on observations of the cosmic microwave background and an internal blackbody calibration target, with three thermistors to monitor the target physical temperature. Corrections for antenna pattern effects and cross-scan biases based on prelaunch measured values were updated using data from an on-orbit calibration pitch maneuver. Validation of the observed brightness temperatures ( T <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">B</sub> ) is performed by comparing to coincident nonprecipitating ocean observations from five well-calibrated on-orbit instruments, including Global Precipitation Measurement (GPM) mission Microwave Imager (GMI) and four Microwave Humidity Sounder (MHS) sensors on board NOAA-19, MetOp-A, MetOp-B, and MetOp-C satellites. Absolute calibration accuracy is within 1 K for all channels, well within the 4-K requirement. Calibration precision, or stability over time, is within 0.6 K for all channels, also well within the 2-K requirement. The intrinsic noise of TEMPEST-D is lower than MHS, resulting in similar on-orbit noise equivalent differential temperatures (NEDTs), even though TEMPEST-D has a much shorter integration time of 5 ms as compared to 18 ms for MHS. As a result, although the TEMPEST-D radiometer is substantially smaller, lower power, and lower cost than similar current operational radiometers, it has comparable or better performance in terms of instrument noise, calibration accuracy, and calibration stability or precision.

A Passive Microwave Retrieval Algorithm with Minimal View-Angle Bias: Application to the TEMPEST-D CubeSat Mission

AbstractThe 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.