Radar Rainfall Estimation in Morocco: Quality Control and Gauge Adjustment

This study focused on investigating the impact of gauge adjustment on the rainfall estimate from a Moroccan C-band weather radar located in Khouribga City. The radar reflectivity underwent a quality check before deployment to retrieve the rainfall amount. The process consisted of clutter identification and the correction of signal attenuation. Thereafter, the radar reflectivity was converted into rainfall depth over a period of 24 h. An assessment of the accuracy of the radar rainfall estimate over the study area showed an overall underestimation when compared to the rain gauges (bias = −6.4 mm and root mean square error [RMSE] = 8.9 mm). The adjustment model was applied, and a validation of the adjusted rainfall versus the rain gauges showed a positive impact (bias = −0.96 mm and RMSE = 6.7 mm). The case study conducted on December 16, 2016 revealed substantial improvements in the precipitation structure and intensity with reference to African Rainfall Climatology version 2 (ARC2) precipitations.

X-band dual-polarized radar quantitative precipitation estimate analyses in the Midwestern United States

Over the past decade, polarized weather radars have been at the forefront of the search for a replacement of estimating precipitation over the spatially, and temporally inferior tipping buckets. However, many radar-coverage gaps exist within the Continental US (CONUS), proposing a dilemma in that radar rainfall estimate quality degrades with range. One possible solution is that of X-band weather radars. However, the literature as to their long-term performance is lacking. Therefore, the overarching objective of the current study was to analyze two year’s worth of radar data from the X-band dualpolarimetric MZZU radar in central Missouri at four separate ranges from the radar, utilizing tippingbuckets as ground-truth precipitation data. The conventional R(Z)-Convective equation, in addition to several other polarized algorithms, consisting of some combinations of reflectivity (Z), differential reflectivity (ZDR), and the specific differential phase shift (KDP) were used to estimate rainfall. Results indicated that the performance of the algorithms containing ZDR were superior in terms of the normalized standard error (NSE), missed and false precipitation amounts, and the overall precipitation errors. Furthermore, the R(Z,ZDR) and R(ZDR,KDP) algorithms were the only ones which reported NSE values below 100 %, whereas R(Z) and R(KDP) equations resulted in false precipitation amounts equal to or greater than 65 % of the total gauge recorded rainfall amounts. The results show promise in the utilization of the smaller, more cost-effective X-band radars in terms of quantitative precipitation estimation at ranges from 30 to 80 km from the radar.

Comparison of Rain Gauge Measurements in the Mid-Atlantic Region

A comparative study of daily and monthly rainfall between research and operational gauges was conducted at the mid-Atlantic region. Fifty research tipping-bucket gauges were deployed to 20 sites where each site had dual or triple gauges. The gauges were in place to validate the National Aeronautics and Space Administration’s newly developed polarimetric radar rainfall estimate. For logistic purposes, these research gauges were collocated with operational gauges and were operated over a year at each site. Therefore, this is an experimental study, which involves a mixture of one to five sites of seven operational gauge networks. A very good to excellent agreement between the two collocated research gauges at daily time scale raised the authors’ confidence to consider them as a reference before comparing with the operational gauges. Among operational networks, the National Weather Service’s (NWS) Automated Surface Observing Systems (ASOS) weighing bucket and the Climate Reference Network and Forest Services tipping-bucket gauges demonstrated high performance for both daily and monthly rainfall, while the Federal Aviation Administration’s Automated Weather Observing Systems (AWOS) tipping-bucket gauges performed poorly. Among the other networks, the ASOS tipping-bucket and Cooperative observer program’s stick gauges seemed to be reliable for monthly rainfall, but not always for daily rainfall. The Virginia Agricultural Experimental Station (VAES) tipping-bucket gauges, on the other hand, had a mixture of high and low performance for daily and monthly rainfall. Unlike other gauge networks, VAES gauges were in place for long-term research applications.