Coupling X-band dual-polarized mini-radars and hydro-meteorological forecast models: the HYDRORAD project

Abstract. Hydro-meteorological hazards like convective outbreaks leading to torrential rain and floods are among the most critical environmental issues world-wide. In that context weather radar observations have proven to be very useful in providing information on the spatial distribution of rainfall that can support early warning of floods. However, quantitative precipitation estimation by radar is subjected to many limitations and uncertainties. The use of dual-polarization at high frequency (i.e. X-band) has proven particularly useful for mitigating some of the limitation of operational systems, by exploiting the benefit of easiness to transport and deploy and the high spatial and temporal resolution achievable at small antenna sizes. New developments on X-band dual-polarization technology in recent years have received the interest of scientific and operational communities in these systems. New enterprises are focusing on the advancement of cost-efficient mini-radar network technology, based on high-frequency (mainly X-band) and low-power weather radar systems for weather monitoring and hydro-meteorological forecasting. Within the above context, the main objective of the HYDRORAD project was the development of an innovative \\mbox{integrated} decision support tool for weather monitoring and hydro-meteorological applications. The integrated system tool is based on a polarimetric X-band mini-radar network which is the core of the decision support tool, a novel radar products generator and a hydro-meteorological forecast modelling system that ingests mini-radar rainfall products to forecast precipitation and floods. The radar products generator includes algorithms for attenuation correction, hydrometeor classification, a vertical profile reflectivity correction, a new polarimetric rainfall estimators developed for mini-radar observations, and short-term nowcasting of convective cells. The hydro-meteorological modelling system includes the Mesoscale Model 5 (MM5) and the Army Corps of Engineers Hydrologic Engineering Center hydrologic and hydraulic modelling chain. The characteristics of this tool make it ideal to support flood monitoring and forecasting within urban environment and small-scale basins. Preliminary results, carried out during a field campaign in Moldova, showed that the mini-radar based hydro-meteorological forecasting system can constitute a suitable solution for local flood warning and civil flood protection applications

Storm microphysics and kinematics at the ARM-SGP site using dual polarized radar observations at multiple frequencies

2014 Fall.Includes bibliographical references.This research utilizes observations from the Atmospheric Radiation Measurement (ARM) Climate Research Facility at the Southern Great Plains location to investigate the kinematic and microphysical processes present in various types of weather systems. The majority of the data used was collected during the Mid-latitude Continental Convective Cloud Experiment (MC3E), and utilizes the network of scanning radars to arrive at a multi-Doppler wind retrieval and is compared to vertical wind measurements from a centrally located profiling radar. Microphysical compositions of the storms are analyzed using a multi-wavelength hydrometeor identification algorithm utilizing the strengths of each of the radar wavelengths available (X, C, S). When available, a comparison is done between observational analysis and simulated model output from the Weather Research Forecasting model with Spectral-bin Microphysics (WRF-SBM) using bulk statistics to look at reflectivity, vertical motions, and microphysics

Analysis of the microphysical properties of snowfall using scanning polarimetric and vertically pointing multi-frequency Doppler radars

Radar dual-wavelength ratio (DWR) measurements from the Stony Brook Radar Observatory Ka-band scanning polarimetric radar (KASPR, 35 GHz), a W-band profiling radar (94 GHz), and a next-generation K-band (24 GHz) micro rain radar (MRRPro) were exploited for ice particle identification using triple-frequency approaches. The results indicated that two of the radar frequencies (K and Ka band) are not sufficiently separated; thus, the triple-frequency radar approaches had limited success. On the other hand, a joint analysis of DWR, mean Doppler velocity (MDV), and polarimetric radar variables indicated potential in identifying ice particle types and distinguishing among different ice growth processes and even in revealing additional microphysical details. We investigated all DWR pairs in conjunction with MDV from the KASPR profiling measurements and differential reflectivity (ZDR) and specific differential phase (KDP) from the KASPR quasi-vertical profiles. The DWR-versus-MDV diagrams coupled with the polarimetric observables exhibited distinct separations of particle populations attributed to different rime degrees and particle growth processes. In fallstreaks, the 35–94 GHz DWR pair increased with the magnitude of MDV corresponding to the scattering calculations for aggregates with lower degrees of riming. The DWR values further increased at lower altitudes while ZDR slightly decreased, indicating further aggregation. Particle populations with higher rime degrees had a similar increase in DWR but a 1–1.5 m s−1 larger magnitude of MDV and rapid decreases in KDP and ZDR. The analysis also depicted the early stage of riming where ZDR increased with the MDV magnitude collocated with small increases in DWR. This approach will improve quantitative estimations of snow amount and microphysical quantities such as rime mass fraction. The study suggests that triple-frequency measurements are not always necessary for in-depth ice microphysical studies and that dual-frequency polarimetric and Doppler measurements can successfully be used to gain insights into ice hydrometeor microphysics

Quantifying uncertainty in radar rainfall estimates using an X-band dual polarisation weather radar

Weather radars have been used to quantitatively estimate precipitation since their development in the 1940s, yet these estimates are still prone to large uncertainties which dissuade the hydrological community in the UK from adopting these estimates as their primary rainfall data source. Recently dual polarisation radars have become more common, with the national networks in the USA, UK and across Europe being upgraded, and the benefits of dual polarisation radars are beginning to be realised for improving quantitative precipitation estimates (QPE). The National Centre for Atmospheric Science (NCAS) mobile Doppler X-band dual polarisation weather radar is the first radar of its kind in the UK, and since its acquisition in 2012 has been deployed on several field campaigns in both the UK and abroad. The first of these campaigns was the Convective Precipitation Experiment (COPE) where the radar was deployed in Cornwall (UK) through the summer of 2013. This thesis has used the data acquired during the COPE field campaign to develop a processing chain for the X-band radar which leverages its dual polarisation capabilities. The processing chain developed includes the removal of spurious echoes including second trip, ground clutter and insects through the use of dual polarisation texture fields, logical decision thresholds and fuzzy logic classification. The radar data is then corrected for the effects of attenuation and partial beam blockage (PBB) by using the differential phase shift to constrain the total path integrated attenuation and calibrate the radar azimuthally. A new smoothing technique has been developed to account for backscatter differential phase in the smoothing of differential phase shift which incorporates a long and a short averaging window in conjunction with weighting smoothing using the copolar correlation coefficient. During the correction process it is shown that the calculation of PBB is insensitive to the variation in the ratio between specific attenuation and specific differential phase shift provided a consistent value is used. It is also shown that the uncertainty in attenuation correction is lower when using a constrained correction such as the ZPHI approach rather than a direct linear correction using differential phase shift and is the preferred method of correction where possible. Finally the quality controlled, corrected radar moments are used to develop a rainfall estimation for the COPE field campaign. Results show that the quality control and correction process increases the agreement between radar rainfall estimates and rain gauges when using horizontal reflectivity from a regression correlation of -0.01 to 0.34, with a reduction in the mean absolute percentage difference (MAPD) from 86% to 31%. Using dual polarisation moments to directly estimate rainfall shows that rainfall estimates based on the theoretical conversion of specific attenuation to reflectivity produce the closest agreement to rain gauges for the field campaign with a MAPD of 24%. Finally it is demonstrated that merging multiple dual polarisation rainfall estimates together improves the performance of the rainfall estimates in high intensity rainfall events while maintaining the overall accuracy of the rainfall estimates when compared to rain gauges

Quantitative precipitation estimates from dual-polarization weather radar in lazio region

Many phenomena (such as attenuation and range degradation) can influence the accuracy of rainfall radar estimates. They introduce errors that increase as the distance from the radar increases, thereby decreasing the reliability of radar estimates for applications that require quantitative precipitation estimation. The aim of the present work is to develop a range dependent error model called adjustment factor, that can be used as a range error pattern for allowing to correct the mean error which affects long-term quantitative precipitation estimates. A range dependent gauge adjustment technique was applied in combination with other processing of radar data in order to correct the range dependent error affecting radar measurements. Issues like beam blocking, path attenuation, vertical structure of precipitation related error, bright band, and incorrect Z-R relationship are implicitly treated with this type of method. In order to develop the adjustment factor, radar error was determined with respect to rain gauges measurements through a comparison between the two devices, based on the assumption that gauge rain was real. Therefore, the G/R ratio between the yearly rainfall amount measured in each rain gauge position during 2008 and the corresponding radar rainfall amount was calculated against the distance from radar. Trend of the G/R ratio shows two behaviors: a concave part due to the melting layer effect close to the radar location, and an almost linear increasing trend at greater distance. Then, a linear best fitting was used to find an adjustment factor, which estimates the radar error at a given range. The effectiveness of the methodology was verified by comparing pairs of rainfall time series that were observed simultaneously by collocated rain gauges and radar. Furthermore, the variability of the adjustment factor was investigated at the scale of event, both for convective and stratiform events. The main result is that there is not an univocal range error pattern, as it is also a function of the event characteristics. On the other hand, the adjustment factor tends to stabilize over long periods of observation as in the case of a whole year of measures

ASSIMILATION OF ATTENUATED DATA FROM X-BAND NETWORK RADARS USING ENSEMBLE KALMAN FILTER

To use reflectivity data from X-band radars for quantitative precipitation estimation and storm-scale data assimilation, the effect of attenuation must be properly accounted for. Traditional approaches try to make correction to the attenuated reflectivity first before using the data. An alternative, theoretically more attractive approach builds the attenuation effect into the reflectivity observation operator of a data assimilation system, such as an ensemble Kalman filter (EnKF), allowing direct assimilation of the attenuated reflectivity and taking advantage of microphysical state estimation using EnKF methods for a potentially more accurate solution.This study first tests the approach for the CASA (Center for Collaborative Adaptive Sensing of the Atmosphere) X-band radar network configuration through observing system simulation experiments (OSSE) for a quasi-linear convective system (QLCS) that has more significant attenuation than isolated storms. To avoid the problem of potentially giving too much weight to fully attenuated reflectivity, an analytical, echo-intensity-dependent model for the observation error (AEM) is developed and is found to improve the performance of the filter. By building the attenuation into the forward observation operator and combining it with the application of AEM, the assimilation of attenuated CASA observations is able to produce a reasonably accurate analysis of the QLCS inside CASA radar network coverage. Compared with foregoing assimilation of radar data with weak radar reflectivity or assimilating only radial velocity data, our method can suppress the growth of spurious echoes while obtaining a more accurate analysis in the terms of root-mean-square (RMS) error. Sensitivity experiments are designed to examine the effectiveness of AEM by introducing multiple sources of observation errors into the simulated observations. The performance of such an approach in the presence of resolution-induced model error is also evaluated and good results are obtained.The same EnKF framework with attenuation correction is used to test different possible configurations of 2 hypothetical radars added to the existing network of 4 CASA radars through OSSEs. Though plans to expand the CASA radar network did not materialize, such experiments can provide guidance in the site selection of future X-band or other short-wavelength radar networks, as well as examining the benefit of X-band radar networks that consist of a much larger number of radars. Two QLCSs with different propagation speeds are generated and serve as the truth for our OSSEs. Assimilation and forecast results are compared among the OSSEs, assimilating only X-band or short-wavelength radar data. Overall, radar networks with larger downstream spatial coverage tend to provide overall the best analyses and 1-hour forecasts. The best analyses and forecasts of convective scale structure, however, are obtained when Dual- or Multi-Doppler coverage is preferred, even at the expense of minor loss in spatial coverage.Built-in attenuation correction is then applied, for the first time, to a real case (the 24 May 2011 tornadic storm near Chickasha, Oklahoma), using data from the X-band CASA radars. The attenuation correction procedure is found to be very effective--the analyses obtained using attenuated data are better than those obtained using pre-corrected data when all the values of reflectivity observations are assimilated. The effectiveness of the procedure is further examined by comparing the deterministic and ensemble forecasts started from the analysis of each experiment. The deterministic forecast experiment results indicate that assimilating un-corrected observations directly actually retains some information that might be lost in the pre-corrected CASA observations by forecasting a longer-lasting trailing line, similar to that observed in WSR-88D data. In the ensemble forecasts, assimilating un-corrected observations directly, using our attenuation-correcting EnKF, results in a forecast with a more intense tornado track than the experiment that assimilates all values of pre-corrected CASA data.This work is the first to assimilate attenuated observations from a radar network in OSSEs, as well as the first attempt to directly assimilate real, uncorrected CASA data into a numerical weather prediction (NWP) model using EnKF

What can we learn from the cloudsat radiometric mode observations of snowfall over the ice-free ocean?

The quantification of global snowfall by the current observing system remains challenging, with the CloudSat 94 GHz Cloud Profiling Radar (CPR) providing the current state-of-the-art snow climatology, especially at high latitudes. This work explores the potential of the novel Level-2 CloudSat 94 GHz Brightness Temperature Product (2B-TB94), developed in recent years by processing the noise floor data contained in the 1B-CPR product; the focus of the study is on the characterization of snow systems over the ice-free ocean, which has well constrained emissivity and backscattering properties. When used in combination with the path integrated attenuation (PIA), the radiometric mode can provide crucial information on the presence/amount of supercooled layers and on the contribution of the ice to the total attenuation. Radiative transfer simulations show that the location of the supercooled layers and the snow density are important factors affecting the warming caused by supercooled emission and the cooling induced by ice scattering. Over the ice-free ocean, the inclusion of the 2B-TB94 observations to the standard CPR observables (reflectivity profile and PIA) is recommended, should more sophisticated attenuation corrections be implemented in the snow CloudSat product to mitigate its well-known underestimation at large snowfall rates. Similar approaches will also be applicable to the upcoming EarthCARE mission. The findings of this paper are relevant for the design of future missions targeting precipitation in the polar regions

Radar multi-sensor (RAMS) quantitative precipitation estimation (QPE)

Includes bibliographical references.2015 Summer.Quantitative precipitation estimation (QPE) continues to be one of the principal objectives for weather researchers and forecasters. The ability of radar to measure over broad spatial areas in short temporal successions encourages its application in the pursuit of accurate rainfall estimation, where radar reflectivity-rainfall (Z-R) relations have been traditionally used to derive quantitative precipitation estimation. The purpose of this research is to present the development of a regional dual polarization QPE process known as the RAdar Multi-Sensor QPE (RAMS QPE). This scheme applies the dual polarization radar rain rate estimation algorithms developed at Colorado State University into an adaptable QPE system. The methodologies used to combine individual radar scans, and then merge them into a mosaic are described. The implementation and evaluation is performed over a domain that occurs over a complex terrain environment, such that local radar coverage is compromised by blockage. This area of interest is concentrated around the Pigeon River Basin near Asheville, NC. In this mountainous locale, beam blockage, beam overshooting, orographic enhancement, and the unique climactic conditions complicate the development of reliable QPE's from radar. The QPE precipitation fields evaluated in this analysis will stem from the dual polarization radar data obtained from the local NWS WSR-88DP radars as well as the NASA NPOL research radar

Global Precipitation Measurement (GPM): Unified Precipitation Estimation From Space

Global Precipitation Measurement (GPM) is an international satellite mission that uses measurements from an advanced radar/radiometer system on a Core Observatory as reference standards to unify and advance precipitation estimates through a constellation of research and operational microwave sensors. GPM is a science mission focusing on a key component of the Earth's water and energy cycle, delivering near real-time observations of precipitation for monitoring severe weather events, freshwater resources, and other societal applications. This work presents the GPM mission design, together with descriptions of sensor characteristics, inter-satellite calibration, retrieval methodologies, ground validation activities, and societal applications