Ousmane O. Sy

Simulation of EarthCARE Spaceborne Doppler Radar Products Using Ground-Based and Airborne Data: Effects of Aliasing and Nonuniform Beam-Filling

This paper describes the expected performance of the Doppler cloud profiling radar being built for the Earth Cloud Aerosols Radiation Explorer (EarthCARE) mission of the Japanese Aerospace Exploration Agency and the European Space Agency. Spaceborne Doppler radar data are simulated starting from high-resolution Doppler measurements provided by ground-based and airborne Doppler radars, ranging from nonconvective to moderately convective scenarios. The method hinges upon spatial and spectral resampling to consider the specificities of the spaceborne configuration. An error analysis of the resulting Doppler product is conducted to address aliasing and nonuniform beam-filling (NUBF) problems. A perturbation analysis is applied to explore the latter problem and allow for a self-standing systematic correction of NUBF using merely the received reflectivity factor and mean Doppler velocities as measured by the instrument. The results of our simulations show that, at a horizontal integration of 1 km, after proper de-aliasing and NUBF correction, the radar will typically yield a velocity accuracy in the order of 1.3 m·s-1 over intertropical regions where the pulse-repetition frequency (PRF)=6.1 kHz, of 0.8 m·s-1 where the cloud-profiling radar (CPR) operates at PRF=7 kHz, and, of 0.7 m·s-1 over high latitudes where the CPR of EarthCARE will operate at PRF=7.5 kHz.

Raincube: A proposed constellation of precipitation 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 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.

Application of Matched Statistical Filters for EarthCARE Cloud Doppler Products

This paper presents a method for filtering the random noise that affects spaceborne Doppler measurements of atmospheric velocities. The proposed method hinges on adaptive low-pass filters that apply to the measured pulse-pair correlation function. The parameters of the filters are found by optimizing the statistics of the velocity residue of the filter. The method is illustrated by simulations of the cloud-profiling radar of the future Earth Cloud, Aerosol and Radiation Explorer (EarthCARE) mission of the European Space Agency and the Japanese Space Exploration Agency. These simulations, which do not include strong convection, show the higher performance of the filters when compared with the traditional increase of the along-track integration length. The results obtained with the filters show that velocity accuracies of 0.48, 0.42, and 0.39 m · s-1 are achievable at PRF = {6.1, 7, 7.5} kHz, respectively, while preserving the initial 500-m sampling of the measured EarthCARE data. These results also show the potential benefits of avoiding excessive alongtrack integration, for postprocessing tasks such as dealiasing or the retrieval of the vertical distribution of the atmospheric velocity (e.g., longer than 5 km for cases consistent with the climatologies represented in this data set).

High-Resolution Simulations of the 2010 Pakistan Flood Event: Sensitivity to Parameterizations and Initialization Time

AbstractEstimating the risk of flood-generating precipitation events in high-mountain regions with complex orography is a difficult but crucial task. Quantitative precipitation forecasts (QPFs) at fine resolution are an essential ingredient to address this issue. Along these lines, the ability of the Weather Research and Forecasting (WRF) Model, operated at 3.5-km grid spacing, to reproduce the extreme meteorological event that led to the 2010 Pakistan flood and produced heavy monsoonal rain in the Indus basin is explored. The model results are compared with Tropical Rainfall Measuring Mission (TRMM) rainfall estimates, the available ground measurements, and radar observations from the CloudSat mission. In particular, the sensitivity of the WRF simulations to the use of different convective closures (explicit and Kain–Fritsch) and microphysical parameterizations (WRF single-moment 6-class microphysics scheme and Thompson) is analyzed. The impact of using different initial conditions, associated with a dif...

Toward Improving Ice Water Content and Snow-Rate Retrievals from Radars. Part II: Results from Three Wavelength Radar–Collocated In Situ Measurements and CloudSat–GPM–TRMM Radar Data

AbstractTwo methods for deriving relationships between the equivalent radar reflectivity factor Ze and the snowfall rate S at three radar wavelengths are described. The first method uses collocations of in situ aircraft (microphysical observations) and overflying aircraft (radar observations) from two field programs to develop Ze–S relationships. In the second method, measurements of Ze at the top of the melting layer (ML), from radars on the Tropical Rainfall Measuring Mission (TRMM), Global Precipitation Measurement (GPM), and CloudSat satellites, are related to the retrieved rainfall rate R at the base of the ML, assuming that the mass flux through the ML is constant. Retrievals of R are likely to be more reliable than S because far fewer assumptions are involved in the retrieval and because supporting ground-based validation data are available. The Ze–S relationships developed here for the collocations and the mass-flux technique are compared with those derived from level 2 retrievals from the standar...

The challenges of representing vertical motion in numerical models

Even though vertical motion is resolved within convection-permitting models, recent studies have demonstrated significant departures in predicted storm updrafts and downdrafts when compared with Doppler observations of the same events. Several previous studies have attributed these departures to shortfalls in the representation of microphysical processes, in particular those pertaining to ice processes. Others have suggested that our inabilities to properly represent processes such as entrainment are responsible. Wrapped up in these issues are aspects such as the model grid resolution, as well as accuracy of models to correctly simulate the environmental conditions. Four primary terms comprise the vertical momentum equation: advection, pressure gradient forcing, thermodynamics and turbulence. Microphysical processes including their impacts on latent heating and their contributions to condensate loading strongly impact the thermodynamic term. The focus of this study is on the thermodynamic contributions to vertical motion, the shortfalls that arise when modeling this term, and the observations that might be made to improve the representation of those thermodynamical processes driving convective updrafts and downdrafts.

Evaluation of Triple‐Frequency Radar Retrieval of Snowfall Properties Using Coincident Airborne In Situ Observations During OLYMPEX

Scattering models of precipitation-size ice particles have shown that aggregates and spheroidal particles occupy distinct regions of the Ku-Ka-W-band dual-frequency ratio (DFR) plane. Furthermore, past ground-based observations suggest that particle bulk density and characteristic size can be retrieved from the DFR plane. This study, for the first time, evaluates airborne DFR observations with coincident airborne microphysical measurements. Over 2 hr of microphysical data collected aboard the University of North Dakota Citation from the Olympic Mountains Experiment are matched with Airborne Precipitation and cloud Radar Third Generation triple-frequency radar observations. Across all flights, 31% (63%) of collocated data points show nonspheroidal (spheroidal) particle scattering characteristics. DFR observations compared with in situ observations of effective density and particle characteristic size reveal relationships that could potentially be used to develop quantitative dualand triple-frequency DFR ice property retrievals. Plain Language Summary Currently, remote sensing retrievals of ice clouds require assumptions since particle shape and size vary greatly in the atmosphere. Additionally, particle shape and size constrain relationships of mass and fall velocity of ice within a cloud, which affect remote sensing retrievals. Modeling studies have shown that the scattering characteristics of complex ice particles (e.g., aggregates) have a distinct signature compared to spherical representations of the same particles when using three frequencies under the following conditions: (1) at least one radar with its wavelength close to the size of the particle and (2) particles have low effective densities. Thus, there is potential to retrieve information about particle shape using triple-frequency radar observations to constrain the assumptions of particle shape in the ice cloud retrieval. This paper is the first study to use airborne triple-frequency radar observations coincident with airborne in situ microphysical measurements to evaluate both the scattering signal discussed and retrievals of characteristic size and effective density. We found that 31% (63%) of the observations from the Olympic Mountains Experiment show nonspheroidal (spheroidal) scattering characteristics. Furthermore, the triple-frequency observations confirm the relationships with observed particle size and effective density outlined in a previous study supporting future use of triple-frequency missions.

Retrieval of snowflake microphysical properties from multi-frequency radar observations

We have developed an algorithm that retrieves the size, number concentration and density of falling snow from multifrequency radar observations. This work builds on previous studies that have indicated that three-frequency radars can provide information on snow density, potentially improving the accuracy of snow parameter estimates. The algorithm is based on a Bayesian framework, using lookup tables mapping the measurement space to the state space, which allows fast and robust retrieval. In the forward model, we calculate the radar reflectivities using recently published snow scattering databases. We demonstrate the algorithm using multifrequency airborne radar observations from the OLYMPEX– RADEX field campaign, comparing the retrieval results to hydrometeor identification using ground-based polarimetric radar and also to collocated in situ observations made using another aircraft. Using these data, we examine how the availability of multiple frequencies affects the retrieval accuracy, and we test the sensitivity of the algorithm to the prior assumptions. The results suggest that multifrequency radars are substantially better than single-frequency radars at retrieving snow microphysical properties. Meanwhile, triplefrequency radars can retrieve wider ranges of snow density than dual-frequency radars and better locate regions of highdensity snow such as graupel, although these benefits are relatively modest compared to the difference in retrieval performance between dualand single-frequency radars. We also examine the sensitivity of the retrieval results to the fixed a priori assumptions in the algorithm, showing that the multifrequency method can reliably retrieve snowflake size, while the retrieved number concentration and density are affected significantly by the assumptions.