Atmospheric motion vector (AMV) retrieval from water vapor measurements is important in climate research and weather forecasting. However, conventional feature tracking methods for AMV retrievals generate velocity fields with gaps and large errors. In this work, we test the optical flow algorithm by generating a nature run of a convective weather phenomenon, which provides water vapor variables and wind vector fields at various pressure levels. We show that our optical flow algorithm generates superior performance when compared with traditional feature tracking algorithms used in operational centers, generating dense AMVs with no gaps and significantly improving AMV accuracy. The optical flow algorithm performs well down to very low wind speeds and does not require a low-wind cutoff threshold. In our studies, we considered various measurement configurations, including water vapor retrievals at different temporal resolutions and found that the optical flow algorithm is not sensitive to the time interval between images.

NASA recently chose the Investigation into Convective Updrafts (InCUs) proposal as the next Earth Ventures program mission. INCUS will use a convoy of three identical Ka-band radars measuring radar reflectivity within their common swath to infer the characteristics of any convective updrafts that they observe. We summarize the theoretical basis for this approach, with justification from ground-based zenith profiler data as well as sensitivity analyses of convection-permitting simulations. We then describe and quantify the performance of the approach to detect updrafts from the radar observations. Finally, we illustrate the expected performance of retrievals of vertical transport, and evaluate their ability to meet the objectives of the INCUS mission. How this observation strategy can be adapted to miniaturized passive mm-wave radiometers is also discussed.

Recent advances in analyzing and predicting rapid intensity change in tropical cyclones suggest that the distribution and intensity of convective activity in the storm play an important role, particularly their occurrence with respect to the dynamically significant vortex structure. We developed a framework to detect and analyze these features from satellite observations of the condensed water, using it as a proxy for the distribution of the associated latent heating and, hence, the intensity of convective activity. Here we use passive microwave measurements of the condensed water from the GPM and TRMM constellations of conically-scanning radi-ometers and employ a low-wave-number decomposition of the 2D fields of columnar condensate to depict the radial distribution of the azimuthally-averaged fields (the magnitude of wave number 0 - WNO), as well as the radial distribution of the first order asymmetry that is captured by the magnitude and azimuthal orientation of the first harmonic in the Fourier decomposition (WN1). Our analyses of a number of hurricanes illustrate the potential predictive abilities of this satellite-based analysis framework, laying the ground for future investigations.

RainCube (Radar In a CubeSat), developed by the Jet Propulsion Laboratory (JPL) and launched in 2018, was a technology demonstration supported by NASA. RainCube’s radar is the first spaceborne profiling radar fitting on a platform as small as a 6U ($10\times 20\times 30\,\,\mathrm {cm^{3}}$) CubeSat. This article shows how, despite its smaller size compared to traditional spaceborne radars, RainCube was able to measure clouds and precipitation in the mid-latitude and intertropical regions. Moreover, since RainCube’s measurements are oversampled in the along-track (AT) direction, the horizontal resolution can be enhanced by a robust Wiener deconvolution algorithm. After more than two and a half years of operation, the RainCube mission came to an end on 24 December 2020. The collected record of Ka-band radar profiles compares favorably to collocated measurements from other ground-based and spaceborne radars both radiometrically and geophysically. The examples of multiradar collocations also provide some insights into the potential of constellations of spaceborne radars to study clouds and storms.

A new method to analyze the potential for rapid intensity change in tropical cyclones (TC) is presented. The method is based on satellite observations of precipitation derived from microwave (MW) radiometers. The approach is intended to condense the information in the environment and in the vortex using a low wavenumber representation of the rain index (RaIn, a multichannel nonlinear combination of passive MW observations), and train a deep-learning, multilayer neural network (NN) with the RaIn and the changes in the wind over the next 24 h. The resulting NN exhibits a near-perfect ability to identify rapid intensification (RI: changes in the hurricane wind speed in excess of 30 knots within a 24-h period). It is found that the spatial structure and amounts of the columnar water condensate within the extended environment is necessary to capture the most important information regarding the RI process. Analyses of the NN structure provide new insight into the physics of TC and can help improve model forecasting. Environmental conditions as far as 1050 km from the TC center might affect the process of RI by at least three physical processes: absolute angular momentum inflow, wind shear stabilization, and steering the outflow jets in the upper troposphere. The findings can be used to build a RI discriminant (RID) for real-time operations.

The paper discusses the hurricane intensity changes using machine learning technologies and data visualization.

The deconvolution of potentially cloud-affected passive microwave brightness temperatures is an important step for utilization in direct data assimilation in cloud-resolving numerical weather prediction (NWP) models for the purpose of improving model initial conditions. Geophysical retrieval algorithms, such as precipitation rate retrievals, also benefit from consistent resolution across channels. In this paper, we explore how to derive the posterior error estimates that are required for ingestion into data assimilation models or end-to-end error-quantified retrieval algorithms. To this end, we present a minimum variance, best linear-unbiased estimator approach that seeks an optimal estimate of the apparent (i.e., without the effects of antenna pattern convolution) brightness temperatures by iteratively minimizing a cost function measuring the lack of fit between observations and departures from a first guess. Both the observation and first-guess departure terms are weighed by a corresponding covariance term that estimates their relative uncertainty. The first-guess uncertainty, a Bayesian prior “belief” in the spread of the first-guess error, is estimated using geophysical fields from an NWP model in a radiative transfer model plus an antenna pattern forward operator, then iteratively improved using the posterior deconvolved brightness temperatures of actual special sensor microwave imager/sounder observations. The error for the posterior distribution, subject to the initial belief, is derived. The error-quantified results are shown to increase the spatial resolution of microwave observations.

This is the first of two papers that quantify the high added value of frequent 3-D radar observations of the atmosphere to capture the dynamics of weather systems. Recent advances in small-satellite and radar technologies, such as the “Radar in Cubesat” 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 such observations to the underlying cloud variables and processes, and to quantify the sensitivity to the different physical and instrument parameters. In this first part, a robust algorithm is proposed to estimate the horizontal advection from successive radar reflectivity measurements, and use it to compute total time derivatives$d_{t} Z$of the observed radar reflectivity factors$Z$. As illustrated using Next-Generation Radar measurements in a blizzard coupled with an atmospheric river in California, the maps of$d_{t} Z$reveal features about locations of sources and sinks of condensed water, which are, otherwise, not visible in the maps of$Z$alone. Using numerical simulations of the blizzard and a radiative-transfer model to forward calculate the corresponding reflectivity factors$Z$in the S-band, we show the robust correlation between$d_{t} Z$and the moistening of the troposphere.

This is the second of two papers that quantify the high added value of frequent 3-D radar observations of the atmosphere to capture the dynamics of weather systems. Recent advances in small-satellite and radar technologies, such as the “Radar in Cubesat” 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 such observations to the underlying cloud variables and processes, and to quantify the sensitivity to the different physical and instrument parameters. In this paper, we quantify the uncertainty in the relation between the measured radar reflectivities$Z$and their time derivatives$d_{t} Z$, on one hand, and the underlying rate of change of the condensed-water mass$M$, and fluxes of dry and moist air in convection, on the other hand. The uncertainties are due to the variability of the atmospheric parameters as well as the constraints of an observation strategy that would use pairs of spaceborne instruments. We specifically analyze the sensitivities for pairs of satellites, each carrying a Ka-band profiling radar. Our simulations show that, with a convoy of two spacecraft separated by ~90 s, each with a pointing accuracy of ~0.025° in rms error, a sensitivity of 17 dBZ and a precision of 1 dBZ, the proposed observation strategy would capture more than 70% of the tropical convection between 5 and 10 km of altitude and resolve the air-mass and condensed-water fluxes.

Recent evidence suggests that the tropics have expanded over the last few decades by a very rough 10per decade. Until now, understanding the mechanisms of that expansion has been confined to models and proxies because of the unavailability of systematic observations of the large-scale circulation. Scatterometer-derived ocean surface vector winds, provide for the first time, an accurate depiction of the large-scale circulation and allow the study of the Hadley cell evolution through analysis of its surface branch. In this study we determine the extent of the Hadley cell as defined by the subtropical zero-crossing of the zonally-averaged zonal wind component. We use scatterometer observations from a number of missions, covering ~13 years. Our analyses reveal seasonal and interannual variability, as well as a long-term trend for expansion of the Hadley cell width. More interestingly, our results show an apparent discontinuity in the signal when the data source changes from one observing system to another. This raises the question about the significance of the unresolved diurnal signal. Indeed, analyses of observations from tandem missions support this notion. Fortunately, the RapidScat mission makes it possible to resolve, for the first time, the details of the diurnal signal. Our preliminary analyses of the RapidScat observations show the presence of a clear semidiurnal signal in the width of the Hadley cell. This helps explain previously found discrepancies. More importantly, this points to a clear need to understand and resolve the diurnal signal before merging wind observations from different missions to form a consistent climate record.

Spaceborne scatterometer observations, especially at Ku-band, are affected by rain in several ways and these effects need to be corrected to avoid errors in wind retrievals. In this work we propose a model to derive the surface backscattering coefficient in presence of both wind and rain. Our approach consists in the development of an ocean surface wind wave spectrum accounting for two effects due to raindrops impact on the surface: the rain-induced wave damping and the generation of ring waves. The results show that this extended spectrum is able to model the ocean surface wave modifications due to rain so that it can be used for further study in physically representing the scatterometer observations in presence of both wind and rain.

We have developed a neural network technique for retrieving accurate 12.5-km resolution wind speeds from Ku-band scatterometer measurements in tropical cyclone conditions including typical rain events in such storms. The method was shown to retrieve accurate wind speeds up to 40 m/s when compared with aircraft reconnaissance data, including GPS dropwindsondes and Stepped-Frequency Microwave Radiometer surface wind speed measurements, and when compared to global best track maximum wind speeds. Wind directions were unchanged from the current (version 3) Jet Propulsion Laboratory (JPL) global wind vector product. The technique removes positive biases with respect to best track winds in the developing phase of tropical cyclones that occurred in the nominal (version 2) JPL QuikSCAT product. The new technique also reduces negative biases with respect to best track wind speeds that occurred in the nominal product (both versions 2 and 3) during the most extreme period of the lifetime of intense storms. The wind regime with the most notable improvement is 20-40 m/s (40-80 kn), with more modest improvement for higher winds and the improvement at lower winds comparable to that achieved previously by the version 3 JPL global rain-corrected product. The net effect of all the wind speed improvements is a much better measurement of storm intensity over time in the new product than what has been previously available. When compared with speed data from aircraft flights in Atlantic hurricanes, the new product exhibited a 1-2-m/s positive overall bias and a 3-m/s mean absolute error. The random error and systematic positive bias in the new scatterometer wind product is similar to that of the Hurricane Research Division H*WIND analyses when aircraft data are available for assimilation. This similarity may be explained by the fact that H*WIND data are used as ground truth to fit the coefficients used by the new technique to map radar measurements to wind speed. The fact that H*WIND was designed to match maximum winds while preserving radial symmetry may explain the overall positive biases that we observe in both H*WIND and the new scatterometer wind product which compared to aircraft reconnaissance data. The new scatterometer product could also be inheriting systematic biases in the presence of rain from H*WIND. Under the most extreme rain conditions, the radar signal from the surface can be lost. In such cases, the technique makes use of measurements in the 87.5-km region comprising the 7 $\times$ 7 neighboring cells around the target 12.5-km wind vector cell. In so doing, we sacrifice resolution in cases where the highest resolution region has no useful measurements. Even so, the most extreme rain conditions can result in reduced accuracy. The new technique has been used to retrieve wind fields for every tropical cyclone of tropical storm force or above that has been observed by QuikSCAT during the period of time from October 1999 to November 2009. The resulting data set has been made available online for use by the tropical cyclone research community.

Microwave scatterometers are the standard for satellite ocean vector winds (OVW) measurements, and they provide the major source of global ocean surface winds observations for scientific and operational applications. A major challenge for Ku-band scatterometry missions is to provide reliable retrievals in the presence of precipitation, particularly in extreme ocean wind events that are usually associated with intense rain. This paper explores the advantages of combining dual frequency (C- and Ku-band) scatterometer measurements and passive microwave observations to improve high wind speed retrievals. For this study, a conceptual design proposed by the Jet Propulsion Laboratory for a Dual Frequency Scatterometer (DFS) to fly onboard the future Japan Aerospace Exploration Agency (JAXA) GCOM-W2 mission with the Advanced Microwave Scanning Radiometer (AMSR) was adopted. A computer simulation that combines the DFS and AMSR measurements was used to develop an artificial neural network OVW retrieval algorithm. The Weather Research and Forecasting (WRF) numerical weather model of Hurricane Katrina (2005) was used as the nature run (surface truth), and simulated OVW retrievals demonstrate that this new technique offers a robust option to extend the useful wind speed measurements range beyond the current operating scatterometers for future satellite missions.

We describe a method for retrieving winds from colocated Ku- and C-band ocean wind scatterometers. The method utilizes an artificial neural network technique to optimize the weighting of the information from the two frequencies and to use the extra degrees of freedom to account for rain contamination in the measurements. A high-fidelity scatterometer simulation is used to evaluate the efficacy of the technique for retrieving hurricane force winds in the presence of heavy precipitation. Realistic hurricane wind and precipitation fields were simulated for three Atlantic hurricanes, Katrina and Rita in 2005 and Helene in 2006, using the Weather Research and Forecasting model. These fields were then input into a radar simulation previously used to evaluate the Extreme Ocean Vector Wind Mission dual-frequency scatterometer mission concept. The simulation produced high-resolution dual-frequency normalized radar cross-section (NRCS) measurements. The simulated NRCS measurements were binned into 5 x 5 km wind cells. Wind speeds in each cell were estimated using an artificial neural network technique. The method was shown to retrieve accurate winds up to 50 m/s even in intense rain.