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.

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.

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 recent successful space-borne demonstration of a miniaturized CubeSat precipitation radar is highlighted. The low cost of such a radar, together with the availability of small satellite, platforms to carry it, now make it feasible to consider employing a more distributed approach to observe important atmospheric processes that relate to precipitation. An approach to quantify the transport of water and air by deep convection is described based on a clustering of small radar satellites providing measurements seconds apart. This strategy now adds time as a new dimension for observing such processes. A mission concept, referred to as D-train, comprised of a train of three satellites 30, 90, and 120 s apart is described, and the expected performance of it for providing measures of convective transport is examined based on a large ensemble of simulations of convection with an advanced cloud-resolving model.

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.

The absence of systematic estimates of vertical transport of water by convective storms is one of the main causes of uncertainties in weather forecasting in the tropics, in severe weather forecasting, and in the climate-scale analysis and prediction of global circulation. The difficulty of providing such estimates lies in that the only instruments capable of providing such estimates at present, radiometers onboard satellites, operate at short wavelengths, and that makes difficult the retrievals. This paper describes a method capable of providing robust estimates of the maximum height of precipitation and the 3-D structure of condensed water in convective systems using a constellation of radiometers. Using hurricane Winston and the Global Precipitation Measurement (GPM) mission Core Observatory satellite dual precipitation radar as a reference, it is shown that the method is suitable to improve the parameterization of convection in numerical models.

An accurate representation of the electromagnetic (EM) behavior of precipitation particles requires modeling of realistic complex geometry and a numerically efficient technique to calculate averaged scattering properties over multiple random target orientations. The discrete dipole approximation is commonly used to compute scattering and absorption by snow particles, because of its geometry flexibility and numerical low cost. However, this method becomes inefficient when the scattering quantities need to be calculated for a large number of orientations. To overcome this limitation, we apply, in this paper, a direct solver-based method, known as the characteristic basis function method (CBFM), to the modeling of scattering by randomly oriented and complex-shaped snow particles. This domain decomposition technique is based on the generation of a new set of basis functions adapted to the geometry of the scatterer in order to significantly reduce the numerical size of the EM problem. This enables us to use a direct solver for the resolution of the final compressed system of linear equations, which is better adapted for multiple excitation problems. When applied to numerically large snow particles, our CBFM-based model, named Numerically Efficient Scattering by Complex Particles, has been shown to yield good results, which compare well with those obtained with discrete dipole scattering, while providing a dramatic reduction in the CPU time.

A direct-solver based domain decomposition method is applied to efficiently compute orientationally averaged scattering efficiencies from complex-shaped ice pristine and aggregation. This numerically powerful method, known as the characteristic basis function method (CBFM), is based on the generation of a new set of basis function adapted to the geometry of the scatterer, in order to significantly reduce the numerical size of the EM problem. Being better adapted to multiple excitation problems, the CBFM enables us to achieve a significant reduction in CPU time in comparison to DDScat, a widely used implementation of the discrete dipole approximation (DDA), while maintaining a satisfactory level of accuracy.

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.

Surface winds from the QuikSCAT scatterometer over a region parallel to southern peninsular (SP) India are analyzed to monitor the daily evolution of the monsoon. For this purpose, the daily flow direction (DFD) and the corresponding optimal angle are derived from scatterometer winds for the period 2003-2009. These are correlated with the modern era retrospective analysis for research and application reanalysis land surface temperatures averaged over the SP. In the time series for DFD, positive DFD period is wet monsoon with a well-defined onset, followed by a short wet-to-dry transition where DFD is mostly negative and decreasing, then negative DFD period is dry monsoon, and followed by a dry-to-wet transition during which the sign and amplitude of DFD alternates daily between positive and negative. The correlation of DFD with averaged land surface temperatures shows that maximum temperature is achieved during the dry-to-wet transition season and is more pronounced in the years 2004-2007. Though 2005 and 2006 have the longest period of precursor cooling, in 2008 the land surface temperature continues to increase right up to the onset of monsoon. The hypotheses derived from these are: every year, the land surface cooling begins weeks before the monsoon onset over Kerala (MOK); the potential for convection over land is most favorable where DFD is positive, and is inhibited when negative. This study also reveals that one can extract the onset dates with a standard deviation of 3-4 days from these time series data sets, and the latter are used to objectively define the MOK.

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.

The “non-uniform beam filling” (NUBF) problem has received much attention in the context of retrieving instantaneous rain amounts from radar measurements because of the non-linearity of the relation between the amount of rain and the signature it leaves in the received echo signals. The non-linearities convert additive white uncertainty into biases in the measured quantities, and the accuracy of any estimate of the biases depends on how detailed a description one can use of the underlying non-uniformity. In this paper, we describe a method that we developed to characterize this non-uniformity in a very similar way to the treatment of Hydrometeor-Size-Distribution variability, by parametrizing it. This should be particularly useful to allow retrieval algorithms to account for NUBF in a more realistic way.

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.

Numerical climate and weather models depend on measurements from space-borne satellites to complete model validation and improvements. Precipitation profiling capabilities are currently limited to a few instruments deployed in Low Earth Orbit (LEO), which cannot provide the temporal resolution necessary to observe the evolution of short time-scale weather phenomena and improve numerical weather prediction models. A constellation of precipitation profiling instruments in LEO would provide this essential capability, but the cost and timeframe of typical satellite platforms and instruments make this solution prohibitive. A new radar instrument architecture that is compatible with low-cost satellite platforms, such as CubeSats and SmallSats, has been designed at JPL that enables constellation missions, which could revolutionize climate science and weather forecasting.

Estimating the amount of above ground biomass in forested areas and the measurement of carbon flux through the quantification of disturbance and regrowth are critical to develop a better understanding of ecosystem processes. Well-resolved and globally consistent inventories of forest carbon must rely on remote sensing measurements, particularly from polarimetric radars. While a wide variety of studies conducted over the past three decades have shown how radar polarimetric measurements can be used to estimate above ground carbon for regions with less than 100 Mg of biomass per hectare, there is no established methodology for assessing biomass estimation accuracy based on a priori instrument and mission parameters. In this paper, a framework for assessing biomass estimation accuracy is presented that is a blend of the basic imaging physics and empirically derived parameters that describe various relationships between biomass and radar polarimetric observable quantities. The implications of this error model on the design and performance of a polarimetric radar are explored using instrument, mission, and science parameters from a notional Earth observing mission.

The joint National Aeronautics and Space Administration and Japanese Aerospace Exploration Agency (JAXA) Global Precipitation Measurement (GPM) mission will provide considerably more observations over complex and dynamically changing land backgrounds. A physically based precipitation retrieval using GPM's satellite constellation of passive microwave (PMW) observations has to accommodate the spatially and temporally varying radiometric signature of the land surface to constrain the set of candidate rainfall solutions. The challenge for retrieval algorithms is to identify and isolate precipitation profiles whose simulated observations agree with the satellite observations and are also representative of the surface conditions. Microwave emissivity modeling results are presented from a physically based land algorithm that retrieves soil moisture, vegetation water content, and surface temperature, along with the emissivity using polarized 10, 18, and 37 GHz channel measurements from the WindSat sensor onboard the Coriolis satellite, and results from the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI). The emissivity mean, coefficient of variation, covariance, and correlation slope are examined for the range of clear-scene surface properties observed by WindSat and TRMM between 2003-2012 and 2002-2011, respectively, under a range of seasons, time of day, rain events, etc. These joint data provide a means to examine the extent to which the surface geophysical properties control the microwave land surface emissivity covariability, to better utilize these lower frequency observations in overland PMW-based precipitation retrievals.

Soil Moisture Active Passive (SMAP), a proposed mission in support of the Earth Science Decadal Survey, conducted afield campaign in June 2010 to support algorithm development. As part of the experiment in situ soil moisture measurements were made over a two week period in which multiple UAVSAR flights were conducted. Repeat-pass polarimetric-interferometric data generated from these flights were analyzed to see if phase changes could be correlated with soil moisture changes. Also, we compared the data to that predicted by simple surface scattering models and showed moderate agreement with the Oh model [4].

Airbornein situfrozen particle size distribution data from the TRMM field campaigns is used to develop mass and mean size dual-frequency radar relations

The NASA/JPL Airborne Rain MApping Radar (ARMAR) was operated on the NASA DC-8 aircraft during TOGA COARE in early 1993. On 12 flights ARMAR observed stratiform precipitation associated with mesoscale convective systems. The statistics of 16 melting layer parameters, including maximum reflectivity, cooling rate, Doppler velocity, LDR, and the HH-VV correlation coefficient are presented and discussed.

This paper revisits the problem of finding a parametric form for the rain drop size distribution (DSD) which (1) is an appropriate model for tropical rainfall, and (2) involves statistically independent parameters. Using TOGA/COARE data, the authors derive a parametrization which meets these criteria. This new parametrization is an improvement on the one that was derived by Z. S. Haddad et al. (1996) using TRMM ground truth data from Darwin, Australia. The new COARE data allows the authors to verify that the spatial variability of the two "shape" parameters is relatively small, thus confirming that this parametrization should be particularly useful for remote sensing applications. They also derive new DSD-based radar-reflectivity-rain-rate power laws, whose coefficients are directly related to the shape parameters of the DSD. Perhaps most important, since the coefficients are independent of the rain-rate itself, and vary little spatially, the relations are ideally suited for rain retrieval algorithms. It should also prove straightforward to extend this method to the problems of estimating cloud hydrometeors from remote-sensing measurements.

Given measurements m/sub 1/,m/sub 2/,...,m/sub J/ representing radar cross-sections of a given resolution element at different polarizations and/or different frequency bands, the authors consider the problem of making an "optimal" estimate of the actual dielectric constant /spl epsiv/ and the rms surface height h that gave rise to the particular {m/sub j/} observed. To obtain such an algorithm, the authors start with a data catalog consisting of careful measurements of the soil parameters /spl epsiv/ and h, and the corresponding remote sensing data {m/sub j/}. They also assume that they have used these data to write down, for each j, an average formula which associates an approximate value of m/sub j/ to a given pair (/spl epsiv/;h). Instead of deterministically inverting these average formulas, they propose to use the data catalog more fully and quantify the spread of the measurements about the average formula, then incorporate this information into the inversion algorithm. This paper describes how they accomplish this using a Bayesian approach. In fact, their method allows them to (1) make an estimate of /spl epsiv/ and h that is optimal according to the authors' criteria; (2) place a quantitatively honest error bar on each estimate, as a function of the actual values of the remote sensing measurements; (3) fine-tune the initial formulas expressing the dependence of the remote sensing data on the soil parameters; (4) take into account as many (or as few) remote sensing measurements as they like in making their estimates of /spl epsiv/ and h, in each case producing error bars to quantify the benefits of using a particular combination of measurements.

Consider the problem of detecting and localizing a faint object moving in an "essentially stationary" background, using a sequence of 2D low S/N ratio images of the scene. A natural approach consists of "digitizing" each snapshot into a discrete set of observations, sufficiently (perhaps not exactly) matched to the object in question, then tracking the object using an appropriate stochastic filter. The tracking would be expected to make up for the low S/N ratio, thus allowing one to "coherently" process successive images in order to beat down the noise and localize the object. The problem then becomes one of choosing the appropriate image representation as well as the optimal (and necessarily nonlinear) filter. We propose exact and approximate solutions using wavelets and the Zakai equation. The smoothness of the wavelets used is required in the derivation of the evolution equation for the conditional density giving the filter, and their orthogonality makes it possible to carry out actual computations of the Ito- and change-of-gauge-terms in the algorithm effectively.>

The purpose of this note is to outline a numerically efficient procedure for finding complete solutions to wave propagation problems in elastic media whose parameters (density, sound speed, etc.) depend on depth only, but where one of the interfaces (water/ air, or deep water/solid ocean bottom) is allowed to vary (under some restrictions) with range. This procedure is a straight forward modification to the method proposed in [1],[2] (which allows no range variation at all); the latter is summarized in the next section, and the former is explained in the following one. In the last section, this procedure is extended to allow the rough interface to move (again, under some restrictions) with time, thus allowing a rapid complete solution of the wave equation.

We use a "transform method" based on S. Rice's original ideas to derive the output spectrum of an arbitrary (non-linear) system when its input consists of the sum of a few (practically up to three) narrow-band components and, possibly, a broadband "noise" term. The procedure is then illustrated on three different problems.