Keywords: data assimilation, microwave sounder, NWP, precipitation, temperature and moisture profiles, TROPICS, tropical cyclones

Table 1.

2.1. Space vehicles

Each SV in the six‐member TROPICS constellation is an identical 3U CubeSat consisting of a MIT Lincoln Laboratory (MIT LL) spectrometer payload integrated onto a commercially procured bus. The spectrometer payload consists of a rotating passive radio frequency (RF) antenna measuring spectral radiance as it rotates about the SV velocity vector. The payload is based upon a similar payload previously designed by MIT LL for the MicroMAS‐2 mission (Micro‐sized Microwave Atmospheric Satellite; Blackwell,2017). The engineering team will modify the design in order to meet TROPICS performance and mission reliability requirements. The redesign includes:

• Antenna modification to optimize ground profile while minimizing side lobes

• Noise reduction in analogue front end

• Higher‐dynamic‐range analogue‐to‐digital converter

• Modifications to spectrometer channel centre frequencies and bandwidths

• Higher‐reliability control electronics

• Higher‐reliability and lower‐power motor–scanner assembly

The redesign effort does not include any high‐risk modifications, and should simplify the build and calibration of the payload relative to the MicroMAS‐2 baseline design.

A notional SV including the bus and payload is shown in Figure4. The MicroMAS‐2 bus does not have sufficient pointing accuracy or power generation capability to meet TROPICS mission requirements. The TROPICS bus will match much of the functionality of the MicroMAS‐2 bus, but will take advantage of recent commercial advances in CubeSat reliability and bus technology. In particular, making use of available GPS receivers for position knowledge and star cameras for attitude knowledge will greatly enhance the data product geolocation accuracy.

The spacecraft bus will be procured from Blue Canyon Technologies and will provide power and power conditioning, communications, on‐board processing, thermal management, and Attitude Determination and Control System (ADCS) to the satellite. The flight software will provide command and control of the payload, and will interface with the bus communications system to manage payload commands and prepare payload telemetry for downlink.

2.2. Radiometer characteristics

TROPICS will continue a long history of microwave sounding/imaging missions targeted to the study of storms. For example, the Global Precipitation Measurement Mission (Houet al.2014) and the Megha‐Tropiques Mission (Desboiset al.2003) both observe storms and severe weather with very high measurement fidelity but very low temporal resolution. TROPICS will fly two total power radiometers that measure 12 channels spanning approximately 90–206 GHz. The “WF‐band” radiometer comprises eight channels from 90 to 119 GHz, and the “G‐band” radiometer comprises four channels from 183 to 206 GHz. The specific channel properties are shown in Table2. The full‐widths at half maximum antenna beamwidths are achieved using an offset parabolic reflector illuminated with two feed horns which are physically separated, and the beams are combined and colocated using a polarizing wire grid diplexer. Beam efficiencies for the temperature and water vapour sounding channels are designed to exceed 95%. Radiometer calibration is accomplished using weakly coupled noise diodes with known and stable noise output which are turned on and off against the cold space background. Satellite intercalibration is optimized using cross‐comparisons (Biswaset al.2013) and daily calculated numerical model residuals (Saunderset al.2013) to derive and implement any needed bias corrections.

The radiometer operates in an “integrate‐while‐scanning” mode which results in elongated footprints in the cross‐track direction. The spatial resolution is thus reported as the geometric mean of the minor and major axes of the ellipse projected on the Earth, also accounting for Earth curvature. As the constellation of six satellites scans the Earth, the footprints near the edge of the scan are revisited more often than the footprints near nadir. This effect is quantified by calculating an “effective” spatial resolution which weights the spatial resolution of each footprint by the relative frequency with which it is revisited. The nadir, mean‐across‐scan, and effective spatial resolutions are shown in Table3. The satellite pointing accuracy and sensor mounting requirements are set to ensure geolocation errors are smaller than approximately 10% of the footprint size.

Temperature weighting functions for all 12 TROPICS channels are shown in Figure5. Channel passbands are designed to span altitudes from the surface up to 20 km for temperature and 10 km for water vapour. Multiple temperature channels probe the upper troposphere to observe TC warm core anomalies.

2.3. Ground station and data processing

The TROPICS SVs will interface with a ground station network to allow for SV command and control and downlink of bus and payload telemetry for each member of the constellation. Stored mission data will be downlinked at S‐band. The data products will be made available to the data processing centre via a secured connection. University of Wisconsin (UW) Space Science and Engineering Center (SSEC) as the data processing lead will archive the data to an Earth Observing System Data and Information System (EOSDIS) Distributed Active Archive Center (DAAC) in a format approved by NASA Earth Science Data Systems (ESDS).

3.1. What are the relationships between upper‐level warm‐core evolution and storm intensity and structure change?

The formation of an upper‐level warm core is a key indicator of TC development and the processes that lead to warm core formation are thus key to understanding how it is linked with the onset and continuation of intensification. The TC warm core has been the subject of numerous studies spanning decades of research (e.g. Velden,1989; Veldenet al.1991; Brueske and Velden,2003; Demuthet al.2006; Halversonet al.2006; Besshoet al.2010; Stern and Nolan,2012; Kieuet al.2016). However, mainly due to observing limitations, our understanding of how it evolves and relates to TC structure change is still incomplete. In order to improve TC intensity analyses and forecasts, the factors controlling warm‐core evolution need to be better observed and understood.

PMW sounders operating in the 53–55 GHz and 115–118 GHz frequency bands are sufficiently transparent to cloud cover to allow a coarse mapping of the TC warm core in the ∼100–400 hPa layer (Figure6) and coincident horizontal precipitation structure (Figure7, from the Advanced Technology Microwave Sounder (ATMS) and having horizontal resolution comparable to that of the TROPICS mission). A robust relationship exists between these thermal anomalies and TC intensity as measured by reconnaissance aircraft. Since 1998, the Advanced Microwave Sounding Unit (AMSU) flown aboard the Polar‐orbiting Operational Environmental Satellite (POES) series of spacecraft has been used to develop intensity estimation techniques which exploit a microwave sounder's ability to map upper‐level temperatures in TCs and monitor the evolution of the warm‐core anomaly. Since then, similar microwave sounders have become available (e.g. Special Sensor Microwave Imager/ Sounder – SSMIS – on Defense Meteorological Satellite Program – DMSP – satellites; ATMS on the Suomi National Polar‐orbiting Partnership satellite – NPP).

The primary limitation of current PMW sounders for monitoring TCs is that they reside on polar‐orbiting satellites. Views over weather systems in the Tropics are not continuous, and there can be gaps in coverage over a particular TC of up to 8–12 hr, even with the current constellation. Since rapid intensity changes can take place on the order of a few hours, this coverage is sub‐optimal for monitoring the evolution of TC characteristics (i.e. warm‐core anomaly, precipitation structure). TROPICS will bridge this temporal gap by providing more rapid refresh of these critical observations.

3.2. What is the role of rapidly evolving storm structure in TC formation and intensity change?

Many TC observational and modelling studies have examined the role of precipitation in rapid intensification (RI) with an emphasis on the role of convective bursts (CBs), i.e. focused locations of deep, vigorous convection characterized by cold and expanding cloud tops, ice scattering in PMW imagery, lightning, and towers of high reflectivity (Cecilet al.2002) which are indicative of strong updraughts. Kelleyet al.(2005) identified CBs from the TRMM Precipitation Radar (PR) and found that storms exhibiting deep (>14.5 km altitude) convective towers in the eyewall intensified 71% of the time while storms with echo tops <10 km weakened 87% of the time. The role that CBs play in intensification has been attributed to localized vortex stretching and aggregation in the boundary layer (Hendrickset al.2004; Montgomeryet al.2006; Braunet al.2010), warming from upper‐level subsidence around the periphery of the bursts (e.g. Heymsfieldet al.2001; Zhang and Chen,2012) and diabatic heating in a region of elevated inertial stability inside the radius of maximum wind (RMW; e.g. Vigh and Schubert,2009; Rogerset al.2013).

In contrast to emphasizing the role of CBs, other studies have used PMW sensors to emphasize the importance of the azimuthal coverage of rainfall on RI. Using 85.5 GHz imagery, Harnos and Nesbitt (2011) showed that a symmetric ring of convection appeared in a climatological composite of RI events. A PMW‐aided study by Jiang and Ramirez (2013) showed that RI is accompanied by greater spatial coverage of rain in the inner core. A TRMM PR study by Zagrodnik and Jiang (2014) showed that precipitation frequency and the coverage of rainfall increase in the inner core and become more symmetric over time in rapidly intensifying TCs.

In order to determine whether or not CBs play a critical role in RI, rapid‐refresh microwave observations which allow for tracking the evolution of CBs (movement around the eye, azimuthal asymmetries about the centre, intermittency or tendency to form in clusters) will be necessary, as illustrated in Figure8.

In the figure, Hovmöller diagrams illustrate the evolution of CBs in a simulated storm in a WRF model nature run (Nolanet al.2013) with data degraded to 30 km resolution. The abscissa represents azimuth (direction relative to the storm centre) while the ordinate is time (in days). Tracking of CBs within TCs rapidly degrades when gaps in sampling are consistently >2 h. TROPICS will provide a viable configuration for viewing TCs with median refresh rates (∼40 min) commensurate with the time‐scales of evolving convection and gaps >2h occurring about 55% of the time. The TROPICS frequencies will provide a wealth of information on scattering by precipitation‐sized ice particles in CBs, including information in the expanding upper‐cloud shield (in the unique 205 GHz channel sensitive to small precipitation‐ice particles). The data will be used to track the macrostructure of CBs in TCs across the globe for entire TC life cycles and will provide an unprecedented opportunity to investigate the importance of CBs in RI.

3.3. How does environmental moisture impact TC structure, size, and intensity?

High relative humidity in the middle troposphere has long been recognized as an important factor in determining where TCs form (Gray,1975;1979;1998; McBride,1998). DeMariaet al. (2001) showed that their formulation of a TC genesis parameter, of which mid‐level moisture is a part, can provide useful information on the probability of storm formation. Environmental humidity also exerts a significant impact on storms after development. Hill and Lackmann (2009) and Kimball (2006) examined the impact of dry environmental air in numerical simulations and found that higher environmental humidities led to increased outer rainband production, larger storms, and broader storm‐force wind distributions, but not necessarily to stronger storms. Braunet al. (2012) showed that asymmetric ingestion of dry air (e.g. caused by dry air being located preferentially on one side of the storm) can lead to significant precipitation asymmetries, but found that, in the absence of significant wind shear, dry air had difficulty penetrating the core of storms, thereby limiting impacts on intensity.

In contrast, other studies have suggested that significant relationships exist between environmental humidity and storm intensity. (Kaplan and DeMaria,2003) showed that high values of 700–850 hPa relative humidity generally favour RI of TCs. Shu and Wu (2009) suggested that, in weakening storms, dry air penetrates to smaller storm radius than in intensifying storms, usually in the quadrants to the left of the storm motion. Wuet al. (2012) found more moist conditions within 200–400 km of the storm centre in intensifying and RI storms compared to weakening storms. A major unknown is whether the dry air acts to potentially weaken TCs through modification of precipitation structure or overall convective activity (Braunet al.2012). TROPICS will provide coupled measurements of the more slowly varying environmental humidity around a TC and previously unresolvable short‐term variations in vortex‐scale horizontal precipitation structure over the lifetime of storms which will enhance our ability to determine the extent of environmental humidity control on TC precipitation and intensity.

3.4. Can TC intensity forecasts be improved through utilization of rapid‐update microwave information?

TROPICS data offer great potential for improving forecasts from numerical weather prediction (NWP) models. Data impact studies will utilize two state‐of‐the‐art numerical models, the NOAA Hurricane WRF (HWRF) model and the NASA Goddard Earth Observing System (GEOS‐5) model. Figure9 shows a mesoscale Observing System Simulation Experiment (OSSE) performed with HWRF to quantify the potential improvement in the intensity forecast of a TC from a WRF HNR (Nolanet al.2013) when TROPICS temperature and moisture observations are added to a control that includes all routine operational observations. The data were spatially blurred to 30 km to approximate TROPICS resolution (23.3 km mean across scan for moisture and 34.9 km mean across scan for temperature), and included measurements at 30 min intervals throughout the HNR life cycle. Because the forecast system does not use a vortex initialization technique to correct for errors in the initial analysis, without TROPICS data the analyzed initial intensity becomes poorly represented (Figure9a) as the storm intensifies. Simulated TROPICS data lead to significant improvements in the initial mean sea‐level pressure (not shown) and maximum wind speed (Figure9b) which are maintained through the first 27 hr of simulation. HWRF will be used to conduct pre‐launch OSSEs and post‐launch Observing System Experiments (OSEs) to evaluate the impacts of TROPICS data. Furthermore, simulations will be used to evaluate the relative impact of environmental moisture on TC structure and intensity, particularly in the context of dry air–shear–vortex interactions.

Once real TROPICS data become available, these data will be ingested by the GEOS‐5 data assimilation system (DAS). It is expected that the GEOS‐5 DAS will already have a 4D variational assimilation system implemented by then to better exploit the temporally frequent observations provided by TROPICS. Data impacts will be examined in the context of a diminished observing system, such as a future atmosphere‐observing satellite gap. A variety of metrics will be considered, including general NWP scores and more specialized metrics concerning TC analysis and forecasts.

Statistical and dynamical models provide intensity forecasts of comparable accuracy. However, statistical models are still superior for anticipating rapid intensity changes (Kaplanet al.2015). Parameters derived from infrared imagery from geostationary satellites, and PMW data from LEO satellites, are important components of statistical RI models (Rozoffet al.2015). Rozoffet al. (2015) found that the relative skill improvements from including PMW‐based predictors range from 10 to 45%. However, the low time resolution of current microwave data is a limitation of their use in RI prediction. In addition, the resolution of current microwave sounder data is fairly coarse relative to the size of the TC warm core, which introduces sensitivity to the satellite footprint location relative to the storm centre. The higher time resolution of TROPICS data makes it much more likely to obtain a satellite pass where the sounder footprint is aligned with the cyclone centre. A number of predictors from the TROPICS data will be evaluated as input to the statistical RI models, including eyewall organizational parameters and temperature and moisture parameters in the near‐storm environment.

Statistical models for RI rely on classification methods such as linear discriminant analysis and logistic regression to separate RI and non‐RI cases using predictors from the storm environment and inner core. Experimental versions of these models can be developed to assess the potential for improvement through new data sources, provided that a developmental data source is available for model training. Ideally, these models would be trained on TROPICS data. This would not be possible until near the end of the experiment, where one season of global TC cases might be marginally sufficient. However, new satellite data types have been successfully evaluated using statistical models trained on proxy data. For example, the Cooperative Institute for Research in the Atmosphere (CIRA) TC intensity and structure algorithm was developed from AMSU temperature retrievals, but implemented directly with ATMS retrievals when those first became available. The algorithm worked well and the bias that was introduced was correctable with only a short overlap period between ATMS and AMSU. This method does not capture all of the information available in new data sources since the statistical relationships with the proxy data usually are weaker than they would be with more accurate input, but this method can provide a lower‐bound estimate of what can be expected from the new observations. For the TROPICS experiment, we will utilize ATMS data and possibly AMSU for the training, and then run the algorithm with the TROPICS input. When an overlap period is obtained, a bias correction will be investigated, followed by training with TROPICS data near the end of the experiment.

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