Ziad S. Haddad

Effects of Nonuniform Beam Filling on Rainfall Retrieval for the TRMM Precipitation Radar

Abstract The Tropical Rainfall Measuring Mission (TRMM) will carry the first spaceborne radar for rainfall observation. Because the TRMM Precipitation Radar (PR) footprint size of 4.3 km is greater than the scale of some convective rainfall events, there is concern that nonuniform filling of the PR antenna beam may bias the retrieved rain-rate profile. The authors investigate this effect theoretically and then observationally using data from the NASA Jet Propulsion Laboratory Airborne Rain Mapping Radar (ARMAR), acquired during Tropical Oceans Global Atmosphere Coupled Ocean–Atmosphere Response Experiment in early 1993. The authors’ observational approach is to simulate TRMM PR data using the ARMAR data and compare the radar observables and retrieved rain rate from the simulated PR data with those corresponding to the high-resolution radar measurements. The authors find that the path-integrated attenuation and the resulting path-averaged rain rate are underestimated. The reflectivity and rain rate near th...

Overview of Temporal Experiment for Storms and Tropical Systems (TEMPEST) CubeSat constellation mission

The proposed Temporal Experiment for Storms and Tropical Systems (TEMPEST) satellite mission addresses key science needs related to cloud and precipitation processes using a constellation of five CubeSats with identical five-frequency millimeter-wave radiometers spaced 5-10 minutes apart in orbit. This CubeSat constellation will directly observe the time evolution of clouds to study the conditions that control the transition of clouds to precipitation. The TEMPEST millimeter-wave radiometers will penetrate into the cloud to directly observe changes as the cloud begins to precipitate or ice accumulates inside the storm. TEMPEST provides observations at five millimeter-wave frequencies from 90 to 183 GHz using a single compact instrument that is well suited for a 6U CubeSat architecture and fits well within the NASA CubeSat Launch Initiative capabilities.

Impact of Non-Uniform Beam Filling on Spaceborne Cloud and Precipitation Radar Retrieval Algorithms

In this presentation we will discuss the performance of classification and retrieval algorithms for spaceborne cloud and precipitation radars such as the Global Precipitation Measurement mission [1] Dual-frequency Precipitation Radar (GPM/DPR) [2], and notional radar for the Aerosol/Clouds/Ecosystem (ACE) [1] mission and related concepts. Spaceborne radar measurements are simulated either from Airborne Precipitation Radar 2nd Generation (APR-2, [3]) observations, or from atmospheric model outputs via instrument simulators contained in the NASA Earth Observing Systems Simulators Suite (NEOS3). Both methods account for the three dimensional nature of the scattering field at resolutions smaller than that of the spaceborne radar under consideration. We will focus on the impact of nonhomogeneities of the field of hydrometeors within the beam. We will discuss also the performance of methods to identify and mitigate such conditions, and the resulting improvements in retrieval accuracy. The classification and retrieval algorithms analyzed in this study are those derived from APR-2’s Suite of Processing and Retrieval Algorithms (ASPRA); here generalized to operate on an arbitrary set of radar configuration parameters to study the expected performance of spaceborne cloud and precipitation radars. The presentation will highlight which findings extend to other algorithm families and which ones do not.

Comparison of Radar Rainfall Retrieval Algorithms in Convective Rain During TOGA COARE

Abstract The authors compare deterministic and stochastic rain-rate retrieval algorithms by applying them to 14-GHz nadir-looking airborne radar reflectivity profiles acquired in tropical convective rain during the Tropical Ocean Global Atmosphere Coupled Ocean–Atmosphere Response Experiment. The deterministic algorithms both use the path-integrated attenuation (PIA), measured by the surface reference technique, as a constraint. One deterministic algorithm corrects the k–R relation, while the second corrects the Z–R relation. The stochastic algorithms are based on applying an extended Kalman filter to the reflectivity profile. One employs radar reflectivity only;the other additionally uses the PIA. The authors find that the stochastic algorithm, which uses the PIA, is the most robust algorithm with regard to incorrect assumptions about the drop size distribution (DSD). The deterministic algorithm that uses the PIA to adjust the Z–R relation is also fairly robust and produces rain rates similar to the stoc...

RaInCube: a proposed constellation of atmospheric profiling radars in cubesat

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 evo- lution of short time-scale weather phenomena and improve numerical weather prediction models. A constellation of cloud- and precipitation-profiling instruments in LEO would provide this essential capability, but the cost and timeframe of typical satellite platforms and instruments constitute a possibly prohibitive challenge. A new radar instrument architecture that is compatible with low-cost satellite platforms, such as CubeSats and SmallSats, has been designed at JPL. Its small size, moderate mass and low power requirement enable constellation missions, which will vastly expand our ability to observe weather systems and their dynamics and thermodynamics at sub-diurnal time scales down to the temporal resolutions required to observe developing convection. In turn, this expanded observational ability can revolutionize weather now-casting and medium-range forecasting, and enable crucial model improvements to improve climate predictions.

Principal‐component analysis for raindrops and its application to the remote sensing of rain

In the problem of inverting remote-sensing measurements of rain, current representations of the raindrop size distribution suffer crucially from the expedient but unjustified and empirically ill-fitting assumption that the distribution has a known closed-form shape, whether log-normal or Γ-distributed. This paper proposes an approach to avoid such unfounded a priori assumptions entirely. The resulting representation of the rain is then used to derive ‘forward’ formulae for rain remote-sensing algorithms. Copyright

Quantifying and monitoring convection intensity from mm-wave sounder observations

Few systematic attempts to interpret the measurements of mm-wave radiometers over clouds and precipitation have been made to date because the scattering signatures of hydrometeors at these frequencies are very difficult to model. The few algorithms that have been developed try to retrieve surface precipitation, to which the observations are partially correlated but not directly sensitive. In fact, over deep clouds, mm-wave radiometers are most sensitive to the scattering from solid hydrometeors within the upper levels of the cloud. In addition, mm-wave radiometers have a definite advantage over the lower-frequency window-channel radiometers in that they have finer resolution and can therefore explicitly resolve deep convection. Preliminary analyses (in particular of NOAAs MHS brightness temperatures, as well as Megha-Tropiquess SAPHIR observations) indicate that the measurements are indeed very sensitive to the depth and intensity of convection. The challenge is to derive a robust approach to make quantitative estimates of the convection, for example the height and depth of the condensed water, directly from the mm-wave observations, as a function of horizontal location. To avoid having to rely on a specific set of microphysical assumptions, this analysis exploits the substantial amount of nearly- simultaneous coincident observations by mm-wave radiometers and orbiting atmospheric profiling radars in order to enforce unbiased consistency between the calculated brightness temperatures and the radar and radiometer observations.

Time series analysis of retrieved soil moisture and vegetation water content using QuikSCAT data

Future water resources are a critical societal impact of climate change and hydrological cycles. Current climate models uncertainties result in disagreement on the amount of water. Soil moisture and vegetation water content are key environmental variables on evaporation and transpiration at the land-atmosphere boundary. Radar remote sensing helps to improve our estimate of water resources spatially and temporally. SMAP (Soil Moisture Active Passive) and SWOT (Surface Water Ocean Topography) are the two future NASA missions to monitor water resources and their variations at L-band and Ka-band, respectively. In this study, we show the potential of using already available QuikSCAT Ku-band backscattered power data over the land to quantify vegetation water content and soil moisture. Li et al. developed a physically based six-channel algorithm, which uses dual-polarization Windsat passive microwave data to retrieve soil moisture and vegetation water content. We use the retrieved soil moisture and vegetation water content using Windsat descending pass (around 6AM), and also simultaneous collocated QuikSCAT dual-polarization backscattered power to estimate different parameters of land surface using a proposed backscattered power formulation.

Instantaneous vertical profiling of precipitation using passive microwave radiometers

Several methods have been proposed to train microwave radiometers to retrieve precipitation rates estimated by a radar which observed the same location at the same time. These radar-trained passive-microwave algorithms differ in the quantities that are estimated: some estimate the vertically-integrated liquid water, while others estimate the near-surface precipitation. Since it is no more or less credible to estimate the rain rate at the surface than it is to estimate the rain rate at any discrete altitude, it is particularly interesting to quantify the accuracy with which vertical profiles of precipitation can be estimated from a passive microwave radiometer, what the obstacles are, and what vertical resolution would be achievable. To that end, we conducted several studies to 1) establish that the main impediment to the vertical profiling is the unknown signature of the sea surface in the non-precipitating portions of the field of view, and 2) use surfaceinsensitive principal components of the brightness temperatures to retrieve the vertical principal components of the precipitation. We report on the results of our studies in the case of mid-latitudes regions, in the case of the Atlantic Inter- Tropical Convergence Zone during May 2009 where we produced unique estimates that quantify the vertical structure of the convection in which flight AF447 disappeared, and in the case of polar precipitation where the dearth of instruments and the radiometrically cold frozen surface present additional challenges.